From ef86f63da366033c5121609ba34802350a4bf70e Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 5 Jun 2026 11:05:19 -0500
Subject: [PATCH 01/42] Add out-of-core StreamingDataset variational inference
 example

Minibatch ADVI fed from Parquet shards on disk (flat memory), shown
equivalent to in-RAM pm.Minibatch, with ELBO / posterior / memory
figures and the shuffling caveat. Paired .ipynb + .myst.md.
---
 .../streaming_dataset.ipynb                   | 500 ++++++++++++++++++
 .../streaming_dataset.myst.md                 | 232 ++++++++
 2 files changed, 732 insertions(+)
 create mode 100644 examples/variational_inference/streaming_dataset.ipynb
 create mode 100644 examples/variational_inference/streaming_dataset.myst.md

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
new file mode 100644
index 000000000..0db53ca9d
--- /dev/null
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6cb44821",
+   "metadata": {},
+   "source": [
+    "(streaming_dataset)=\n",
+    "\n",
+    "# Out-of-core minibatch variational inference with StreamingDataset\n",
+    "\n",
+    ":::{post} June 2026\n",
+    ":tags: variational inference, minibatch, out-of-core, ADVI\n",
+    ":category: intermediate, how-to\n",
+    ":author: Yicheng (Ethan) Yang\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24dc00a0",
+   "metadata": {},
+   "source": [
+    "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
+    "minibatch variational inference is capped at datasets that fit in memory -- the\n",
+    "very regime where minibatching is meant to help. `StreamingDataset` feeds\n",
+    "minibatches from an arbitrary source (a generator, a directory of Parquet shards,\n",
+    "...) into a small `pytensor.shared` buffer, so peak memory is bounded by the\n",
+    "buffer and is independent of the dataset size `N`.\n",
+    "\n",
+    "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass\n",
+    "`total_size=N` to the observed distribution and PyMC scales the minibatch\n",
+    "log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a\n",
+    "streaming source is only as well mixed as the order it yields rows in -- which we\n",
+    "handle with `shuffle_buffer`.\n",
+    "\n",
+    "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n",
+    "are easy to compare; the streaming machinery is identical at any size, and the\n",
+    "final section shows why it matters at scale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd6b36bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:34:18.647210Z",
+     "iopub.status.busy": "2026-06-05T13:34:18.647038Z",
+     "iopub.status.idle": "2026-06-05T13:34:21.397969Z",
+     "shell.execute_reply": "2026-06-05T13:34:21.396818Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import tempfile\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import pymc as pm\n",
+    "\n",
+    "from pymc.variational.streaming import StreamingDataset, shuffle_buffer\n",
+    "\n",
+    "RANDOM_SEED = 8927\n",
+    "rng = np.random.default_rng(RANDOM_SEED)\n",
+    "az.style.use(\"arviz-variat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd000ab7",
+   "metadata": {},
+   "source": [
+    "## Put a dataset on disk and forget the array\n",
+    "\n",
+    "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n",
+    "delete the in-memory copy. From here on the data only exists on disk -- exactly\n",
+    "the situation `StreamingDataset` is for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f1bfe614",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:34:21.401339Z",
+     "iopub.status.busy": "2026-06-05T13:34:21.400723Z",
+     "iopub.status.idle": "2026-06-05T13:34:21.433205Z",
+     "shell.execute_reply": "2026-06-05T13:34:21.432412Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 shards written\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 30_000\n",
+    "b_true = np.array([0.4, -1.2, 0.8, -0.5])  # intercept + 3 slopes\n",
+    "\n",
+    "X = rng.normal(size=(N, 3))\n",
+    "p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))\n",
+    "y = (rng.random(N) < p).astype(\"float64\")\n",
+    "table = np.column_stack([X, y]).astype(\"float64\")  # 3 features + 1 observed column\n",
+    "\n",
+    "shard_dir = tempfile.mkdtemp(prefix=\"streaming_demo_\")\n",
+    "for i, s in enumerate(range(0, N, 5_000)):\n",
+    "    block = table[s : s + 5_000]\n",
+    "    pq.write_table(\n",
+    "        pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n",
+    "        f\"{shard_dir}/part_{i:03d}.parquet\",\n",
+    "    )\n",
+    "del table  # the data now lives only on disk\n",
+    "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "317be48a",
+   "metadata": {},
+   "source": [
+    "## Stream minibatches off disk and fit with ADVI\n",
+    "\n",
+    "`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows\n",
+    "into a buffer, shuffles them, and yields fixed-size batches (carrying over any\n",
+    "remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`\n",
+    "buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`\n",
+    "rescaling and a `fit_callback()` advances the buffer each step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "052c1279",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:34:21.436023Z",
+     "iopub.status.busy": "2026-06-05T13:34:21.435858Z",
+     "iopub.status.idle": "2026-06-05T13:34:57.543965Z",
+     "shell.execute_reply": "2026-06-05T13:34:57.543226Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Finished [100%]: Average Loss = 531.45\n"
+     ]
+    }
+   ],
+   "source": [
+    "def chunks():\n",
+    "    for path in sorted(glob.glob(f\"{shard_dir}/*.parquet\")):\n",
+    "        t = pq.read_table(path)\n",
+    "        yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])\n",
+    "\n",
+    "\n",
+    "batch_size = 1024\n",
+    "ds = StreamingDataset(\n",
+    "    shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),\n",
+    "    batch_size=batch_size,\n",
+    "    sample_shape=(4,),  # 3 features + 1 observed column, streamed together\n",
+    "    total_size=N,\n",
+    ")\n",
+    "ds.advance()  # seed the buffer\n",
+    "\n",
+    "with pm.Model():\n",
+    "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
+    "    buf = ds.as_tensor()\n",
+    "    logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]\n",
+    "    pm.Bernoulli(\"y\", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)\n",
+    "    approx = pm.fit(\n",
+    "        30_000,\n",
+    "        method=\"advi\",\n",
+    "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
+    "        callbacks=[ds.fit_callback()],\n",
+    "        progressbar=False,\n",
+    "    )\n",
+    "    idata_stream = approx.sample(1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c4823cb",
+   "metadata": {},
+   "source": [
+    "The negative-ELBO trace shows the fit converging while only ever holding a\n",
+    "`batch_size` buffer in memory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9d8d99e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:34:57.545936Z",
+     "iopub.status.busy": "2026-06-05T13:34:57.545794Z",
+     "iopub.status.idle": "2026-06-05T13:34:57.783063Z",
+     "shell.execute_reply": "2026-06-05T13:34:57.782229Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 3))\n",
+    "ax.plot(approx.hist, alpha=0.6)\n",
+    "ax.set(xlabel=\"iteration\", ylabel=\"negative ELBO\", title=\"Streaming ADVI convergence\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b600054",
+   "metadata": {},
+   "source": [
+    "## The same posterior as in-RAM `pm.Minibatch`\n",
+    "\n",
+    "For comparison, the status-quo fit that keeps the whole dataset in memory.\n",
+    "Streaming changes *where the data lives*, not the inference: the two posteriors\n",
+    "land on top of each other (both show ADVI's characteristic mild bias relative to\n",
+    "the dashed ground truth, but they agree with each other)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6ab4c56a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:34:57.784640Z",
+     "iopub.status.busy": "2026-06-05T13:34:57.784513Z",
+     "iopub.status.idle": "2026-06-05T13:35:04.185598Z",
+     "shell.execute_reply": "2026-06-05T13:35:04.184893Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Finished [100%]: Average Loss = 531.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model():\n",
+    "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
+    "    xb, zb, sb, yb = pm.Minibatch(\n",
+    "        X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size\n",
+    "    )\n",
+    "    pm.Bernoulli(\n",
+    "        \"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N\n",
+    "    )\n",
+    "    approx_inram = pm.fit(\n",
+    "        30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n",
+    "    )\n",
+    "    idata_inram = approx_inram.sample(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ab687535",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:35:04.187668Z",
+     "iopub.status.busy": "2026-06-05T13:35:04.187515Z",
+     "iopub.status.idle": "2026-06-05T13:35:04.490699Z",
+     "shell.execute_reply": "2026-06-05T13:35:04.490137Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/claude-501/ipykernel_20806/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "  fig.tight_layout();\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/X3zxxWn27NnZ3yMu8KUvfSk12o477pjFFbbaaqs+/z0WY++8885ZMuDxxx9fEeuIxMFof/zpT3+64eOisQqbIFit3mpb1c8vZdI3wxlnnJGef/753veJjOLIwJ01a1a2Yz322GPpzjvvTEcccUQ2+fGd73wnW/07mAMPPLCm94/s34suuihbGRDvGyL7OMYVJe6h23TS8SL2w/nz5/e2omj2ibWTPjsA7XfPPfc0vTpsTGo3o1VSu0Xg7OCDD86SBEvWW2+9bFVuq8/PkEcbbbRRVgkwAk99LZKL6ln/+7//mw466KB0wQUXpJNPPrk3GTfaEEdgPKqzUD/H/s7g2F+M+9Y5c+ZkbSTL21fWu8o/KgVGkkgtolLIhRdemN773vf2tkiKlf3RhiiOixRXp+0X1113XTr88MOzuHYtCVNxLo8F8vvss0/6whe+UFerrE777HSObtwvzAkBRddJ96uddp5p5TnA/SrNYP/uW8Q5o5VvSeQKjB07tuHb/+1vf3tNz4vj40knnZQlUUbiYkm0K3//+9+fxT3oXIVNEFxxxRUrHj/33HN1V+0q19cK3UaJpJ/+MnBjB//a176WtUoKN910U5o2bVo20REVBRtlzTXXzDJ9o0d4yW9/+9t03HHHFXLCFvJwvIhy3+edd17v41gN0Oj9sfqzV3+WwVRvq2YeKwFovSOPPLJ3ZVqzTJ8+Pb3xjW9MRRJJSocccki6/fbbe78Xq3e///3v13SDXH0+rff83Mp7mSLrpKBUN4qV67WI5MGorBJtc8qTX2LB3fXXX5+tfqU+jv3t4difv3v2ZakeuPvuuzc0nteXqLYaiSRRdbAkqqpGJUGLCIqjk/eLq666KrsWjgqWJa95zWuyahTbbLNNFguPSeio+hvn6h/+8IfZopq4ho6Yd1xDR1Jrf9dIYln0p5v3i2VhTgjIo3berxbpGqTec4D7VVrB/t23yBWKNuEhWodHEl4nOOqoo9INN9yQFixYkD2OOPqMGTNqXuBIexQ2QbD6RFXvzWCnnKTjYuOrX/1qdnNYCiredtttWdJQrSsBarXDDjukzTffPN11113Z47hZjYTEt7zlLQ19H+g0nXi8iFWiEcwvVUGJ/fNtb3tb6rTAWfXzV1555YaMCwDyKlb/RlWIP/7xjxXXBhFcjAn7ZTk/l6qN5+1eJu8kSeVLJN3MnDkz/eY3v+n9Xiy0kyBIXgz12F/9/CId+zvxnr3W/5MrrrhiSO2Fl9Xee++dVRiI6oEhWg7HIsRYkEwxdOp+8fDDD2ddcMqToD760Y9mSfzlFYGjBVdMbsVXtML7xCc+kW655Zbe2PcxxxxTUSWjmbGsIh0vu1037xfLypwQQPfOp9VzDhCrpOg6df+OCtRXXnll9vdY8BftwEeM6IwUr7h2jcUuUU2w5MYbb5Qg2OFe3qenIKqz2xcvXlzXz1eXeV911VVTO8UkY/mBKcrLRwJRo0WLpnKliwIosk48Xvz0pz/NAj8hSvRGAKgZqsc61M+uAg4A3S5W9F1++eUVN8rf/e5308SJE1t2fq5+frvvZaBVPvShD1U8vvnmm5ty3wzNEAHe8rhPtKqvpyJFp8Wxin7PXos//OEP6fHHH+99HJNv0c6rFaJK4eTJkyu+FwmCFEen7hfRVqrU3jpES+2oaFmeBFVt1KhR6cwzz8yqAZdEwn+06G5FLKtIx8tu1837xVCYEwLo3vm0Ws8BYpUUXSfu3xEX+spXvtL7ONqC19pxpVW23377isdyizpfZ6SXNsEGG2xQ8fjBBx+s6+cfeuihisfjx49P7RStyLbbbrusIkKp13jsYI1e+Ttu3LghHfwgjzrxePG9732vosJABIyixUQ9qxOqxxUlw6tXQFR/9uqfGawaQvkFTwSuammbCEB+fPvb3657tVy9yic88u5b3/pWuvDCC3sfDx8+PJ1++ukvC7YNZo011siCCEuXLu2t+hPn3Ug2rEX1tUy772WgVSLxJtp5lxJy4s9ozTZ27Fj/CXVw7G+fOF6XB1Pj/myjjTZK3X7s78R79lpceumlFY/33HPP1EpifMXWqfvF7373u4rHtXbAiZjSfvvtl52DSn7961/3uchmKLGsvp5f/XrkVzfvF0PhfAHkSTvvV4s4n1brOUCsklawf1eK675S+96obL3vvvsOmivw6KOPvizJsPxnYjHhOuus07D/M9eR+VPYBMGYFIiTVelkGzvDM88887LkmP5U71ybbLJJarcNN9yw4vH8+fMbniC4wgorVDxu9kUWdIJOPF6UV4q46KKLsq96ve9976t4HKtOq0uDb7zxxi87rtSq+rnVrwVA/o0ZM6bdQ8iNqAoRlQJLouT/CSec0GdbjlrEefXvf/97b9viuN6o9RqjE+9l8kiSVP7EfrfuuutWVOyK4LYEwfo49rdPHK/LEwTjnqvWBMEiH/s78Z59MDHW66+/vmLRwB577JFaSYyv2Dpxv3jqqad6J7BCLG75n//5n5p/ftttt614fPvtt/f5vOqx1hPLim0Ui2/KEwBigp5i6Ob9YiicL4A8aef9ahHn0+o5B4hV0mz27/5zBSLRb++9916mhSrli1Uit+hXv/pVapTogliunk4YtEdhWwyHzTbbrPfv0Vaonpunv/3tb/2+VrtU9xOP1QaNVr0yIG6qoRsU7XhRq1e+8pUDfpaBVLe02HTTTRs2LgDIk1/84hfpxBNPrPjeUUcdNaRKQdXn6HpaSeX52qTTglLrr79+U7+qA7EMXfU2FZgiTxp5f1a0Y3/e7tmjDWRPT0/v4x122KHlkx1ifMXXaftFqfp1eVw5kmNrFZ0vaqlgUz3Weo6VsQAntlWJWFbxdOt+MRTOFwDdO59WzzlArJIiK+L+3QquI/On0AmC1T2vb7311pp+LkrPl6/qioz49dZbL7Xbww8/XPE4VsM1WqlKScnaa6/d8PeATlS040WtXvva12ZlicuPAbUmH1dvo5jwAIBu89vf/jYdffTRWZW/kkMPPTTtv//+Q3rd6rbEtV6bxErf8kmwlVdeOW299dZDGgvkicAUeVZ97P/LX/5S88+Wnyeimma97e07Xd7u2S+77LK2thcOYnzF12n7RVx3lovKbfWofn55vKq6jVV5p52o+lYdN++PWFbxdet+MRTOFwDdO59WzzlArJIiK+L+3QquI/On0AmCO++8c8Xjyy+/vKafq35e9eu0Q6x2u/nmmyu+t8EGGzT0PaIN04033ljxvW222aah7wGdqtOOF3Ex8c9//rOur9e//vUVr3HNNddU/HtfLQ6jrUX5hUqUKL766qtrSj74/e9/3/t45MiR6Y1vfOOQPzcA5MkNN9yQPv/5z6cXX3yx93sf+chH0sEHHzzk195pp50qKojHeXegNh8lcR6P83nJjjvumJ3voRs88cQT6b777qtIktIulzyJloPli0EjDlRL0sttt91W8bu/1VZbpXXWWScVSafdsw8k2kTfcccdvY9XW221Pu/Hm+nuu+9Od955Z+/jqFY1adKklo6B7tsvVllllYpJtSeffDI98MADNf/8P/7xjwErpw005lrbZHVi3J/G6ub9YlmYEwKoXdHm0+o9B4hVUmSduH8fcMABdecKXHDBBRWvsddee1X8eyPbC4crr7yy4vHkyZMb+vo0XqETBLfYYou0+eabVwTHrrvuugF/JtoPXXTRRRXfe8c73pHa7Ze//GV65JFHKsqcxmrJRvrOd75TMZm47rrrZn3IoRsU6XhRr913373i8Q9/+MOKKkj9tVJcsmRJxY3Bqquu2rQxAkCn+etf/5o+9alPVbQPfO9735sOO+ywhrx+JBNEcl954tMll1wy4M/E+TvO43m/NoGhVPQsbxv46le/OpuUhbwYNmxYmjp1au/jSECvDu725bzzziv8sT9P9+yXXnppxeO3ve1tLU3Wj+uBk08+ueK+PqohxLUFxdKJ+0X1pHLEtGtV/dyBklrf/va3VzyOzxSfbSCxbe65556K64SitWOnu/eLZWFOCKB759PqPQeIVVJ0Rdq/WyFaK8+YMaPie29605vaNh5qU+gEwfDJT36y4vHxxx+fTa7159RTT60oJR8rfCNYMNANWtx0lr7e//73DzieH//4x+mxxx6r6zPMmjUrfe1rX6v4Xkw+9uff//53+sMf/lDXe8QB7kc/+lHF9z72sY/V9RqQd512vGiVXXfdtSJwFtUnBpqEitYtp59+ekVlluptBwB5Eufk8nP0YBM2sdourpXLg2gRQDj22GMbOq5PfOITFY/j/Ft+7dHXNf3f/va33sdbbrllevOb39zQMUGniuv2733vexXfi6AcNOrY3yoHHnhgRTJZHNurW7aUixXtkRxbElUzB4oZ5Vke7tkjqbO6ClWs2F9WcV6vp9V0JEmfdNJJaebMmRXfP+igg5Z5DHS2TtsvIsZUbvr06dnE0WDiWFfdKn2g69jXvOY1acqUKb2P4zOddtpp/T4/tslxxx1X8T2xrOLqxv3CnBBAsebTopJt+bmmustgu84BYpUUWSfu363ys5/9LC1durTm58+bNy87HpQv1I6FidWtyOk8w7phR44WLSXz589P+++/fzapVy5Ku8eNYvlOvvzyy6dDDz204TtX3MQdddRR6c9//vOAvctjReNXv/rV9MEPfrBi8jGy9/fdd99+fy4qDcZJfM8990znn3/+gJOIUaI+DlQnnHBCxfcnTpyY3vWud9X9+SDPOu140SpxwfKFL3wh+7Pk61//ejr77LNf1sowEpbf9773ZdugfEXtQEEzABiKWIEXN9t9fZV74YUX+n1eLa15axXX1tFGuHyCaeutt06f/vSns1ZR/Y2hr68Y80Bi4rO8Okpsi/32269ikijE54vEqBNPPLH3e3Fej/bH5ed3yEuSVLTmqN7HBxL7RtzXPvjgg73fi1ZuH/jAB5o0Qrrt2F8Sr1nr+z300EN9Pq98ZXlfojVwtJEpiUq1H/7wh1/W2iY+e8SYqu9DDznkkLTCCiukIsrDPfuf/vSntHDhwt7HG2+8cRYkX1ZR+SrO/XEfHv/fjz76aJ/Pi6oGcb8eMcTqipK77LJLR7Qwozv2i4hHb7DBBhWV2eL3MhbN93Vcjv3lK1/5ysti07vttlt2XTKQGHt1QnXE0qsntqLddmyT8nNIVHRrdetvWqcb9wtzQgDdO5/WynOAWCVF1on7d6ucddZZWQ5T3E/Nnj2733mLmBOJ7fHud7+7ovtp3JcdeeSRLRwxy2pEKrjYgb/1rW9lJ7XSL+ldd92V3vnOd2YVNcaPH58ef/zxbMXWU089VfGzsQNEK99Gi2S/n//859nXiBEj0qabbpoFgEeNGpVl2Uaw+F//+ld6+OGHX/azcSN5zjnnZD83mDjJx1ccuNZaa63sfaL8b9zkxnvEDXEErPt6jzgIRI906CadeLxolZgsiJuCKClemlyIlddxM7HVVlulFVdcMUtajmNTubjQiYAVADRLTPSdccYZgz4vrp37q6YQEz7bbrttQ8YTK/rKJ/5L1X2qq0LU4pprrknrr7/+gM+JCas4/8Y1SYjr9wg+xGrGSDqIe4vbb789LV68+GUJIttvv33dY6KzxH3bYMlE5UlSfYlqYnEP2EgRFKveD8r/rVxf95wh7n/jqy/XXntt+uxnP5tN3Ed1zvhdjoS/arHgLiqnffvb384mfst95jOfSWussUYdn4pO0mnH/vLV47Umnsaxui9x3xUt6gcSx/A4tt94443Z4zgOxMrsDTfcMDv+xz4/d+7cl8WNIkAbX0WVh3v2yy67rGHVA8vF4oD4+vKXv5y1/oprgGhbFLG7+Mzx+9BXx5JYxBDthimuTtsv4ncyqmjEAoTSgvf4M6r3nXLKKdmkclybRPJzXLtE8l5U3iy30UYbpaOPPnrQ94pF9PG6hx9+eO/3LrzwwqzNd0xyRxz8vvvuS3fccUfFz8U+9M1vfrNhn5nO0837hTkhgO6dT2vVOUCskiLr1P27FeL6OO6n4iuOG3FNHNeoq6yySrbAJYoj9HWdOnz48KyTQdyD0fkKnyAYIvnu3HPPzap6RJnd0s4cwdb4qha/8BFY2GOPPZo+tgjqxgm5evVaX2KFWNwErrnmmnW/T6ww7m+VcbmY2Iyb4LhggG7UyceLZosJpwhERRJyqSRwTDD88Y9/7PP5sRI3gm19TdYCAI2x8sorZ+fmqGIRiSklMcFVShosN2zYsGzV8MEHH+y/oAC6OUkqrkuvvPLK7Ct+rzfZZJNsQr+0sC7ub+P6/JlnnnnZz0aFGNUDybNYFBr3WrFyvbxV1H/+85/sqy+RGHjMMcekouvke/aoHFBe6TGOXZGM0mhRLbW8Ymp/ovvIEUcc0fAkcTpPp+0XMXH2gx/8IH3uc5+rqGATCVGDtc963etelyVM1ZrkH0m4keAVE1KlTj1RQbCUYF0tJspjYUFMdFFs3bxflJgTAuje+bRmngPEKim6Tt+/WyEWgPd1zVwtYrVxnRoV2smHrkgQDLHCOlYPnnnmmVnLpEWLFr3sOZEZv+OOO2aTb4O1MFhWkVUf7ZIiSBFJgYO1FYuTbLQCiUBvrTtWBDpiUvCmm27KVgoM1MY4RKZzfO6YQJk8eXJdnweKqFOOF+1YXRuVVt7whjdkE9G33HJLFjirNm7cuLTPPvtk7RVjVQAA0Fxxox3tp2JC6aKLLuqzVUicxyMJLJKuXNNTNBGMmzdvXvY1kGirGhO7kRQDeRfJsNG2JdrKRrJwf7//sUL7wAMPzGJH3aJT79kjobm8iup2222Xxo4dO6TXjKSQaDkdySNRoWCwOGKs7I/fhfiZqK5G9+i0/SImyX79619n164XX3xxVslvIFHtMtppR3JWJNfWIxYGRNw8qgJef/31fe4nUUVn7733Th//+MezWDjdoZv2C3NCAN07n9aOc4BYJUXWSft3q0SxgZkzZ2bthas7FfW1feK6OT57tDZ3f5Uvr3ipr9/mgosgQfxyR7n2yJ6PJLwI2MUNWitbEEWlgwjuxTiiPVM8jhu9GM/o0aOzHStahtQbFCkX2c1333139h5RVSJWVMbnj4BhBJujCsOECRNqalkM3ahTjhftEKWCY3VAHDuidPDaa6+dtbSK4FSc/AGA1ovbt2hpHBWkol1WJERFdYxoSxXBOYol2lnUUkFwIINVEIxWZxHoKolWNDGBPpBIVBlqhb6BKghGUPtXv/pVNq7+WieXi+vzCEhFtcK4ZoUiiomeiO/E/VkEnuPYHwlgETfqZt10zx7JhxFHjIUCEUeMqlPR2idaDUfFj2j/E8kwQ4kjUgyduF+UYkzxuxsV/iKuFLHp9dZbL7uOjVh4I0QCWFQ6jvZ5EQePxMD1118/TZo0Sfy7y3XTfmFOCKB759PacQ4Qq6ToOmX/bpXoWBAVuOPPaDscsYioth3HkIhFxefWjTS/ujJBEAAAAOh8TzzxRJYQE0GpmPSPhXWRHBVBqZjM3XLLLbMVuwAAAAAAQN8kCAIAAAAAAAAAAEAB6TkBAAAAAAAAAAAABSRBEAAAAAAAAAAAAApIgiAAAAAAAAAAAAAUkARBAAAAAAAAAAAAKCAJggAAAAAAAAAAAFBAEgQBAAAAAAAAAACggCQIAgAAAAAAAAAAQAFJEAQAAAAAAAAAAIACkiAIAAAAAAAAAAAABSRBEAAAAAAAAAAAAApIgiAAAAAAAAAAAAAUkARBAAAAAAAAAAAAKCAJggAAAAAAAAAAAFBAEgQBAAAAAAAAAACggCQIAgAAAAAAAAAAQAFJEAQAAAAAAAAAAIACkiAIAAAAAAAAAAAABSRBEAAAAAAAAAAAAApIgiAAAAAAAAAAAAAUkARBAAAAAAAAAAAAKCAJggAAAAAAAAAAAFBAEgQBAAAAAAAAAACggCQIAgAAAAAAAAAAQAFJEAQAAAAAAAAAAIACkiAIAAAAAAAAAAAABSRBEAAAAAAAAAAAAFLx/H83GwR2pKxBjAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bs_stream = idata_stream.posterior[\"b\"].values.reshape(-1, 4)\n",
+    "bs_inram = idata_inram.posterior[\"b\"].values.reshape(-1, 4)\n",
+    "names = [\"intercept\", \"slope x1\", \"slope x2\", \"slope x3\"]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(13, 3))\n",
+    "for k, ax in enumerate(axes):\n",
+    "    ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label=\"streaming\")\n",
+    "    ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label=\"in-RAM\")\n",
+    "    ax.axvline(b_true[k], color=\"k\", ls=\"--\", lw=1)\n",
+    "    ax.set(title=names[k], yticks=[])\n",
+    "axes[0].legend(fontsize=8)\n",
+    "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04)\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d845037",
+   "metadata": {},
+   "source": [
+    "## Why bother: memory\n",
+    "\n",
+    "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
+    "rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`\n",
+    "only ever holds one `batch_size` buffer. The dense `float64` design matrix is the\n",
+    "dominant cost, so its footprint is a faithful proxy for the gap:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c9ae88bf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:35:04.492436Z",
+     "iopub.status.busy": "2026-06-05T13:35:04.492289Z",
+     "iopub.status.idle": "2026-06-05T13:35:05.138046Z",
+     "shell.execute_reply": "2026-06-05T13:35:05.137532Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Sdiue6HwS7RkfTlUrehQ9Bp62GNN+6PifvhyFrTuUrFYVBItWg2hVkju6M01nNYQLbwcm6BHV7nbY6pHjvraxp+oUb1UrvlqVeWO10lRphaO2G/JG30u1BVMw6Y5hbzMNBvNzRQ82CMZBF+FMZ9tYoc8rrY7ua7DmSgO2gQiW9X721s6qPz/j6mwlbStrlc44GcjWnH157vpad/0OpRUrffl87qk9MwAMBAbaAwDgRVZWlunf7G5nuu7QdrT70tJ2d18kdIe4DofuK21hom0OvNEhjxrk6JG+ut569LFjgPv69evdXkePhtWjN7X9QyAD2z21S9F2ADp4Xndu/e1vf/M6TNP1SFzd2XfVVVeZbZ2dnW1aC2jpv7ce4XrUmu4A8BYc+TrqUgMFbR1x+OGHm8HI2itct4Xe3952LOi66ewaPbK6r1JSUsy20wG+eh9ov2oNhnT+hevjSIM+DXbc0f79+kVVB9y7o8v0diS3LjuceDvSWR8r+rw7+eSTzQDbpKQk81jXL+u6E1K/cOvj3krP9kC4tvrT2/bWekXDK087iT3Nv+gLfRzo64AOWx41apQZTq+vCTpXRx/n+lql20aP0tf5E/p411ZJ7ujOa209pxVgrvT/4/r41x2x3mYZBas1YjjR566+5mkLSX3d0sefPkd16LE3+trVl0HlwXDGGWeYI9v1tcZda6mLL744LNoEaqWIVn14qm7Qyr3ewbA+933ttHPMXen9vuctXNEh1Xl5edIX+vqllYg6Y0Cf+xpc6ucHXy2H9DGjMyzc0RY82srL23uWzorR9zp93mqbSX3/1fdZHVTtLXjXI9C1NZS+hkTSc8Xbe4yuq34e0SBN/9/6mqmfJfRzjX5G0O2m19dWsN4CGv3s5/oe5O0giGHDhsncuXO9Donvi6ioKJk4caI56fNawz59/Xe3HXReibcqE/08op81dXi7ts7V55C2m9XPIfq5z1PllD7W9HVl8uTJXtdVt7/j/Uufc3obNpvNvH/pwU66fG1fpwdg6H2g71/e3nu0SjoYFZj6uNWqY30O6broa4U+77x9znD9nHvcccfJNddcYz4j6+Pm73//u9f5RjqDRm/DarVcsGiVo85F1NdZ/X/r53Kdz+TtICFf666vd94qBB2fY3R7zZgxw/ys97FWYuk2G+iWaQDQG+EKAAA+6E5Bd+GK7sTVqg/9wuGpT73O+tAvin2lX2S87TjRdfzhD394wI4i/UKuO3G09N7TOurONW0BojvbrNLb0KNm9Qu80p03uj7aukuHknuj66zzHU455RTn73Snk2MgqqedF/pFS3dMuds5rUdeepsv4thm2rLEsc6O9dagQ7/M6c5kTzvo9Ig+Dbv0C2dfaC9yPYLc3WNEt53uEHfQ2TQaVrnbcaE7GfUxqstzR3eeeXr86M6+YASAweStFYX2kNd2Fe7oTjjHDir9/+pRz3rkqqcBtH3hehSu7qTyFq7ozqRQHLWrAZ1WHehrUkxMjNfL6k5i3V76HNPtqY8BT0eq6utc73BFh07ryfV1xNsOrnBohxVMurNPXwM0OHPQ1yLd9tq+0VvLkr4eSR0M+rqrR1W7q2DRoE1ff3vf5wNBd7CmpaWZHdvuaJDiLlzxxTF3Zfz48X6HK33dyalBsG7XY445psdj5ogjjjA7D70NavbW+lGrOPft2+fx70cddZQJSTIyMnr8XoODadOmmZ3k2gLMHQ1idU6M7nCPpOeKt/eYX/ziF3Lqqae6/duECRN67DjXcELX391r50033dTjvLdwRUPw/nqN1OfQz372M7chlR4Y4LqTW8Mub481XU8NxXqHbXp/6owknd2mO8Q9VS3oZ9k//elPHkMbfZxqqKWBljd6expS6GNXb0/bmnqa0dOXeXuuwYoGR/oe66Dvf9q2VecT+TM778EHH+zxeVMPDtIQwlu4qY//gQxX9LVD7y/XAykcn5N1uLyGjYGuu2Mmjjd6G/p8cZ17qfe5Ppb1M55+Hg5GyzoACBbaggEA4IMe6adfMNzRLwiejtjWL1HBOFpbj9DT2Qee6Jd9d8GKg/5ee7brDg5PNFyxSr8Qa3jj+qXRQY9S9UVDHddgxUGDBXe/d+Vp7oqv/8+YMWPMF1t366y0KkVbcXnTl23mCAm02sRT+KZHbLpWouiXTV0vbzvYPAUo3r686xdV3XEQTrztWPH3CGq9b3XHqe4g9lTxE2l054MeHesrWHF3PW/tjj7++GOOFO1FBxS77ix2pUfoe+PPvKhQ0Oe+7qxyR0M63fE30PSxPHXqVI9/dxekuAtINKDxtdPV17yVvs5O09ci12AlkMeM7sT0FPZrtaUneqS5Vv70DlZcnXnmmV7fqzWg9mfmy2B6rgTjPUYfm0cffbT5fBWus6n086E+lz1V/+jnD/085M9jSd9T9SAab9tHd7p7mlXoaPnpKcTXz5JakeorWHH33PZW9aNVOlpZ0xcaoLgGKw66vp4+R7qun6fPyFoVFq7vFRqoaBjmrkJb7ytfn5E9rbtWvPi6P3784x/3CFZcaRjdl3ZrANAfCFcAAPCDp5BE+6A//vjjbv+mR2zpDvK+0qPGve3Y0C84vlp6aSsFTzt1HDuZrPZ21iM8PbWj8rRDxd9B6q5f+t3xdDSzr17jV1xxhTni0BtP1REOetSz1Z7UGthde+21AV/PU2WK0i+rb7zxxgG/14oCb33rw2mQvYO2+/LkoYce6pdKlEijrxna+kh3rOqOCD3Kdvr06eZ1QNvgjR07tsfJWwCnrw1WBttGqkMOOcRjiyal29PbDjcNzMOFp+oUPSpYh9uHA29HbmvLS9f3Lq101FkHrvS+0JZYvkIYb1UruiNddxxbpa2YtCWSJxpwe2sRqMG5u/c7fQ/S8NMTfZxqyylfvIWr+ni1WkESrs8VTztulbZKdW3LOZhp2yp/25fqe4a3GXzaqkvfO/ryWNLHsb9z/rT9lL4G6YEoWiWhFcVaHaOfn3q/f/kaoO6tssufijNPld0aTvlq7anVPJ7CTV+fcQfyvUI/M3j7v/Wu+vN33fVziTf6vUkrkrzRz63BaMsLAMFCWzAAAPygX460VUTvHYz6ZdRdz/pgzhjQFlfeaCuJvraT0B1p2lJBv6QGSo+U98S1rZWnllSeqoKUp9DGtaWFu9/5GnSprVB80ZZqeqSi9ob3tJNAd/LrkYuB0seGr6MdPR2Fqkdxe9oJqLMxdAeEqyVLlnhcnv7/+rLDsL9oCLBgwQK3f9PHqc6I0ceGhnf6f9Ce4Pqz4xToka+RRFvEafs4beXRl4G07naUeNshOZT4qqjTx58e2esp/HX3ujWQwYWn1xQdSqyBrlbNDSRvFSP63qVtjBzvIx988MEBByPo+5oeBKDtrbzNXfEWrujR0nqf9mU7+3pd0vdLbXXpiT5ueu+k1fcgby1DNTT1Fpz6S8MV3bkeKc8VfZ/0NBNO53jo/Lfc3Fyzo1ffY/TzgP6s7y/aGsvXwRnhQJ/Xgcyr0fdWbzvzNci08hnR3WNJt6872vZUW5xqqOJrJkcg+hJSaFtPrQDzFox6C2+8Vd55qygb6PcKX89dX5/vPa27t1kt/lYIarCir0daCQUA4SD8PxUAABAGdAeAHinlqUrF3ZH3Vna6uxPMHaT9cTveqlO0YsYb3WnhrYWRrx0Y7tqk+Pp/6JGG/u4sPPTQQz2GK/7clid9CTS0jYqnnYCrV682rW0clR+6083bjjUNKayEPP1Nj3bWFmjajsYTHSKrO8H01Pu5qjtC9Qu6HnnprpVHpCotLTWPD1/hohXeBjcPNf7sYNSdcZ52GHvbGT5Q1Svuqip0qPVjjz1m5jUMJH2P0ddsfXx7ag3mCFfcvTbq0e66k1mrR1x3svaeu+ItXOlrSzB/HzOBvt+FqnWb1dsJ1+eKBvj6PuGtClLbSemp9+NCd+zq400DM61o8KcyaCB4O/DFnYF+LOlzc/78+SYgDaf3L18V6L4+52oY5+0AI28G8r3CVzDn6//tad31s5s3/lb86/sC4QqAcEFbMAAA/KR9pP09WlF3VPlq1RUp4Yq3o3l9fXHUnV3BVlVVFbTbdNenv6/bTFtM+NvT3R1tl6Dhg6edb3q0uYMGD56O/tTHsrbaCFe6Q1fbfwRKdwjrEem//e1vTTuaG2+80eNO2UiiR/Bffvnl/RKsKE/zHoYiXxV1ajAc2e6gcyM8HQzw0ksvyRdffCEDzVu44To7xV1AojuZ9f3YXWtMx3V9zVvx1upoIB8z4f75IFyfK3pQgbZMtBK+62utth791a9+ZZ43d999t88dxgPB0+eEcHws6fvLdddd1y/BimP5Vvn6HOjrc66v64crX89dX/9vTzwFqQ7+Vgj2pZIQAIKNcAUAAD9p72FvvcNdj8I8//zzB912bWlpsXS9vrRgGortm/r6RVt3CnmbB/PCCy+YgMFXSzBt+aBtT8J5Oz333HPmqPqcnBzLy3nttddMMBrMFiPhSFseeauyQvD4OmJXBStcD5Wbb77ZbRWbtunRndADzVu4oSGyrqe2oek9f0T/T1OmTPHYnscRxnirWnG0oBnKjxnHe0ok/b+1Guqf//ynaX1n9X1ZW9D97W9/M8F2OM1S6q+DV/rrsaTbUNuOhaO+fk4drJ9zfT13rVY9+9oe+lren69JANAfwvcTHAAAYcjdUNzetJe0P0dr+kv7OcN/vvpAB7IDxNcRdlbuG6tH+7nSdlc2m83jUaH/+c9/zBdUb221tCVYuNMv4domRNud/eEPfzA7wbT/eaBHLGo/9J/+9KcSqbT9hlYYeKPVUnqEtT42dI6TtkNyPZ1zzjkhW1+EH22Npa343NHHzM6dO2WgK1c87czTSgINVRwhi6vDDjvM+Z7gLlxxzF3xFq5oW7FgvG73Bz4f9I2+l9xxxx3y5ptvmmpHnYems820wjQQOkfi4YcflnASaEXQQD6W9KAQb/QztQ62/9e//mWe55988kmP968bbrghZOu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",
+      "text/plain": [
+       "<Figure size 1600x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ncols = 4  # 3 features + observed\n",
+    "n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows\n",
+    "inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident\n",
+    "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+    "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch  (O(N))\", lw=2)\n",
+    "ax.loglog(n_grid, stream_gb, label=\"StreamingDataset  (O(batch))\", lw=2)\n",
+    "ax.axhline(24, color=\"0.4\", ls=\"--\", lw=1)\n",
+    "ax.text(1.2e5, 26, \"24 GiB RAM\", color=\"0.4\")\n",
+    "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix footprint (GB)\",\n",
+    "       title=\"Memory is flat in N when streaming\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a5dd95",
+   "metadata": {},
+   "source": [
+    "On a real 122 GB order-book dataset (121.8 M rows) this is not just the array\n",
+    "size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to\n",
+    "out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed\n",
+    "flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.\n",
+    "\n",
+    "## When to reach for it\n",
+    "\n",
+    "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n",
+    "  index gives perfectly i.i.d. minibatches for free.\n",
+    "* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by\n",
+    "  streaming from disk. The cost is a `fit_callback` to advance the buffer and the\n",
+    "  responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use\n",
+    "  `shuffle_buffer`); a bounded buffer over strongly ordered data only\n",
+    "  block-shuffles it and biases the posterior."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1cd4da",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "\n",
+    "* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code\n",
+    "  project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4b437a",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic\n",
+    "  variational inference. *Journal of Machine Learning Research*, 14(1).\n",
+    "* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).\n",
+    "  Automatic differentiation variational inference. *JMLR*, 18(1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80f4917e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-05T13:35:05.139909Z",
+     "iopub.status.busy": "2026-06-05T13:35:05.139765Z",
+     "iopub.status.idle": "2026-06-05T13:35:05.155326Z",
+     "shell.execute_reply": "2026-06-05T13:35:05.154721Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Fri, 05 Jun 2026\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.12\n",
+      "IPython version      : 9.13.0\n",
+      "\n",
+      "pytensor: 3.0.3\n",
+      "pyarrow : 24.0.0\n",
+      "\n",
+      "arviz     : 1.1.0\n",
+      "matplotlib: 3.10.9\n",
+      "numpy     : 2.4.6\n",
+      "platform  : 1.0.8\n",
+      "pyarrow   : 24.0.0\n",
+      "pymc      : 6.0.1\n",
+      "\n",
+      "Watermark: 2.6.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor,pyarrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "138db362",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
new file mode 100644
index 000000000..4a35cfe48
--- /dev/null
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -0,0 +1,232 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+(streaming_dataset)=
+
+# Out-of-core minibatch variational inference with StreamingDataset
+
+:::{post} June 2026
+:tags: variational inference, minibatch, out-of-core, ADVI
+:category: intermediate, how-to
+:author: Yicheng (Ethan) Yang
+:::
+
++++
+
+`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so
+minibatch variational inference is capped at datasets that fit in memory -- the
+very regime where minibatching is meant to help. `StreamingDataset` feeds
+minibatches from an arbitrary source (a generator, a directory of Parquet shards,
+...) into a small `pytensor.shared` buffer, so peak memory is bounded by the
+buffer and is independent of the dataset size `N`.
+
+The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass
+`total_size=N` to the observed distribution and PyMC scales the minibatch
+log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a
+streaming source is only as well mixed as the order it yields rows in -- which we
+handle with `shuffle_buffer`.
+
+We use a modest `N` here so the notebook runs in seconds and the two posteriors
+are easy to compare; the streaming machinery is identical at any size, and the
+final section shows why it matters at scale.
+
+```{code-cell} ipython3
+import glob
+import tempfile
+
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pymc as pm
+
+from pymc.variational.streaming import StreamingDataset, shuffle_buffer
+
+RANDOM_SEED = 8927
+rng = np.random.default_rng(RANDOM_SEED)
+az.style.use("arviz-variat")
+```
+
+## Put a dataset on disk and forget the array
+
+We synthesise a logistic-regression dataset, write it to Parquet shards, and
+delete the in-memory copy. From here on the data only exists on disk -- exactly
+the situation `StreamingDataset` is for.
+
+```{code-cell} ipython3
+N = 30_000
+b_true = np.array([0.4, -1.2, 0.8, -0.5])  # intercept + 3 slopes
+
+X = rng.normal(size=(N, 3))
+p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))
+y = (rng.random(N) < p).astype("float64")
+table = np.column_stack([X, y]).astype("float64")  # 3 features + 1 observed column
+
+shard_dir = tempfile.mkdtemp(prefix="streaming_demo_")
+for i, s in enumerate(range(0, N, 5_000)):
+    block = table[s : s + 5_000]
+    pq.write_table(
+        pa.table({f"c{j}": block[:, j] for j in range(4)}),
+        f"{shard_dir}/part_{i:03d}.parquet",
+    )
+del table  # the data now lives only on disk
+print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
+```
+
+## Stream minibatches off disk and fit with ADVI
+
+`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows
+into a buffer, shuffles them, and yields fixed-size batches (carrying over any
+remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`
+buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`
+rescaling and a `fit_callback()` advances the buffer each step.
+
+```{code-cell} ipython3
+def chunks():
+    for path in sorted(glob.glob(f"{shard_dir}/*.parquet")):
+        t = pq.read_table(path)
+        yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])
+
+
+batch_size = 1024
+ds = StreamingDataset(
+    shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),
+    batch_size=batch_size,
+    sample_shape=(4,),  # 3 features + 1 observed column, streamed together
+    total_size=N,
+)
+ds.advance()  # seed the buffer
+
+with pm.Model():
+    b = pm.Normal("b", 0.0, 3.0, shape=4)
+    buf = ds.as_tensor()
+    logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]
+    pm.Bernoulli("y", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)
+    approx = pm.fit(
+        30_000,
+        method="advi",
+        obj_optimizer=pm.adam(learning_rate=0.008),
+        callbacks=[ds.fit_callback()],
+        progressbar=False,
+    )
+    idata_stream = approx.sample(1000)
+```
+
+The negative-ELBO trace shows the fit converging while only ever holding a
+`batch_size` buffer in memory:
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(9, 3))
+ax.plot(approx.hist, alpha=0.6)
+ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI convergence");
+```
+
+## The same posterior as in-RAM `pm.Minibatch`
+
+For comparison, the status-quo fit that keeps the whole dataset in memory.
+Streaming changes *where the data lives*, not the inference: the two posteriors
+land on top of each other (both show ADVI's characteristic mild bias relative to
+the dashed ground truth, but they agree with each other).
+
+```{code-cell} ipython3
+with pm.Model():
+    b = pm.Normal("b", 0.0, 3.0, shape=4)
+    xb, zb, sb, yb = pm.Minibatch(
+        X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size
+    )
+    pm.Bernoulli(
+        "y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N
+    )
+    approx_inram = pm.fit(
+        30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False
+    )
+    idata_inram = approx_inram.sample(1000)
+```
+
+```{code-cell} ipython3
+bs_stream = idata_stream.posterior["b"].values.reshape(-1, 4)
+bs_inram = idata_inram.posterior["b"].values.reshape(-1, 4)
+names = ["intercept", "slope x1", "slope x2", "slope x3"]
+
+fig, axes = plt.subplots(1, 4, figsize=(13, 3))
+for k, ax in enumerate(axes):
+    ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label="streaming")
+    ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label="in-RAM")
+    ax.axvline(b_true[k], color="k", ls="--", lw=1)
+    ax.set(title=names[k], yticks=[])
+axes[0].legend(fontsize=8)
+fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04)
+fig.tight_layout();
+```
+
+## Why bother: memory
+
+Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
+rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`
+only ever holds one `batch_size` buffer. The dense `float64` design matrix is the
+dominant cost, so its footprint is a faithful proxy for the gap:
+
+```{code-cell} ipython3
+ncols = 4  # 3 features + observed
+n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows
+inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident
+stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
+
+fig, ax = plt.subplots(figsize=(8, 4.5))
+ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch  (O(N))", lw=2)
+ax.loglog(n_grid, stream_gb, label="StreamingDataset  (O(batch))", lw=2)
+ax.axhline(24, color="0.4", ls="--", lw=1)
+ax.text(1.2e5, 26, "24 GiB RAM", color="0.4")
+ax.set(xlabel="dataset size N", ylabel="design-matrix footprint (GB)",
+       title="Memory is flat in N when streaming")
+ax.legend();
+```
+
+On a real 122 GB order-book dataset (121.8 M rows) this is not just the array
+size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to
+out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed
+flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.
+
+## When to reach for it
+
+* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random
+  index gives perfectly i.i.d. minibatches for free.
+* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by
+  streaming from disk. The cost is a `fit_callback` to advance the buffer and the
+  responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use
+  `shuffle_buffer`); a bounded buffer over strongly ordered data only
+  block-shuffles it and biases the posterior.
+
++++
+
+## Authors
+
+* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code
+  project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS).
+
++++
+
+## References
+
+* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic
+  variational inference. *Journal of Machine Learning Research*, 14(1).
+* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).
+  Automatic differentiation variational inference. *JMLR*, 18(1).
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor,pyarrow
+```
+
+:::{include} ../page_footer.md
+:::

From 43f9ccddf808030f89305be7529d69c8aa7e57f8 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 8 Jun 2026 22:41:16 -0500
Subject: [PATCH 02/42] Rewrite streaming example to the DataLoader/Trainer API

Update the out-of-core variational inference example to the reworked
streaming API: a DataLoader over parquet_source, a pm.Data placeholder, and
the callback-free Trainer (replacing the removed StreamingDataset and
fit_callback). Replace the non-reproducible private-data memory note with
measured peak-RSS figures on the public Criteo benchmark. The notebook
executes end-to-end; outputs and figures regenerated.
---
 .../streaming_dataset.ipynb                   | 249 ++++++++++--------
 .../streaming_dataset.myst.md                 | 132 ++++++----
 2 files changed, 208 insertions(+), 173 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 0db53ca9d..ee7be9257 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6cb44821",
+   "id": "6f9305b8",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
     "\n",
-    "# Out-of-core minibatch variational inference with StreamingDataset\n",
+    "# Out-of-core minibatch variational inference with DataLoader and Trainer\n",
     "\n",
     ":::{post} June 2026\n",
     ":tags: variational inference, minibatch, out-of-core, ADVI\n",
@@ -18,21 +18,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24dc00a0",
+   "id": "c97c0132",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
     "minibatch variational inference is capped at datasets that fit in memory -- the\n",
-    "very regime where minibatching is meant to help. `StreamingDataset` feeds\n",
-    "minibatches from an arbitrary source (a generator, a directory of Parquet shards,\n",
-    "...) into a small `pytensor.shared` buffer, so peak memory is bounded by the\n",
-    "buffer and is independent of the dataset size `N`.\n",
+    "very regime where minibatching is meant to help. This notebook uses the streaming\n",
+    "API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:\n",
     "\n",
-    "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass\n",
-    "`total_size=N` to the observed distribution and PyMC scales the minibatch\n",
-    "log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a\n",
-    "streaming source is only as well mixed as the order it yields rows in -- which we\n",
-    "handle with `shuffle_buffer`.\n",
+    "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n",
+    "  shuffles) an out-of-core source -- here a directory of Parquet shards read by\n",
+    "  {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size\n",
+    "  minibatches, holding only one batch in memory at a time;\n",
+    "* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;\n",
+    "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each\n",
+    "  minibatch into that placeholder with `set_data` every step -- **no callbacks**.\n",
+    "\n",
+    "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the\n",
+    "`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`\n",
+    "to the observed distribution and PyMC scales the minibatch log-likelihood by\n",
+    "`N / batch_size`. The one extra obligation is shuffling -- a streaming source is\n",
+    "only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`\n",
+    "handles with a bounded buffer.\n",
     "\n",
     "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n",
     "are easy to compare; the streaming machinery is identical at any size, and the\n",
@@ -42,13 +49,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fd6b36bb",
+   "id": "416e522b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:34:18.647210Z",
-     "iopub.status.busy": "2026-06-05T13:34:18.647038Z",
-     "iopub.status.idle": "2026-06-05T13:34:21.397969Z",
-     "shell.execute_reply": "2026-06-05T13:34:21.396818Z"
+     "iopub.execute_input": "2026-06-09T02:46:45.421431Z",
+     "iopub.status.busy": "2026-06-09T02:46:45.421338Z",
+     "iopub.status.idle": "2026-06-09T02:46:47.072976Z",
+     "shell.execute_reply": "2026-06-09T02:46:47.072225Z"
     }
    },
    "outputs": [],
@@ -63,7 +70,7 @@
     "import pyarrow.parquet as pq\n",
     "import pymc as pm\n",
     "\n",
-    "from pymc.variational.streaming import StreamingDataset, shuffle_buffer\n",
+    "from pymc.variational.streaming import DataLoader, Trainer, parquet_source\n",
     "\n",
     "RANDOM_SEED = 8927\n",
     "rng = np.random.default_rng(RANDOM_SEED)\n",
@@ -72,26 +79,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd000ab7",
+   "id": "770008ea",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
     "\n",
     "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n",
-    "delete the in-memory copy. From here on the data only exists on disk -- exactly\n",
-    "the situation `StreamingDataset` is for."
+    "delete the in-memory table. From here on the features only exist on disk -- exactly\n",
+    "the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to\n",
+    "build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches\n",
+    "them.)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f1bfe614",
+   "id": "3e94e139",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:34:21.401339Z",
-     "iopub.status.busy": "2026-06-05T13:34:21.400723Z",
-     "iopub.status.idle": "2026-06-05T13:34:21.433205Z",
-     "shell.execute_reply": "2026-06-05T13:34:21.432412Z"
+     "iopub.execute_input": "2026-06-09T02:46:47.074733Z",
+     "iopub.status.busy": "2026-06-09T02:46:47.074453Z",
+     "iopub.status.idle": "2026-06-09T02:46:47.104025Z",
+     "shell.execute_reply": "2026-06-09T02:46:47.102969Z"
     }
    },
    "outputs": [
@@ -119,34 +128,36 @@
     "        pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n",
     "        f\"{shard_dir}/part_{i:03d}.parquet\",\n",
     "    )\n",
-    "del table  # the data now lives only on disk\n",
+    "del table  # the design matrix now lives only on disk\n",
     "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "317be48a",
+   "id": "77cfc727",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
     "\n",
-    "`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows\n",
-    "into a buffer, shuffles them, and yields fixed-size batches (carrying over any\n",
-    "remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`\n",
-    "buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`\n",
-    "rescaling and a `fit_callback()` advances the buffer each step."
+    "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:\n",
+    "it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data\n",
+    "scan), so `total_size=\"auto\"` resolves `N` for free. The `DataLoader` batches and\n",
+    "shuffles it into fixed-size minibatches. The model reads a `pm.Data(\"batch\", ...)`\n",
+    "*placeholder* -- the only data ever resident -- and the `Trainer` streams each\n",
+    "minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`\n",
+    "triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "052c1279",
+   "id": "7ea8d824",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:34:21.436023Z",
-     "iopub.status.busy": "2026-06-05T13:34:21.435858Z",
-     "iopub.status.idle": "2026-06-05T13:34:57.543965Z",
-     "shell.execute_reply": "2026-06-05T13:34:57.543226Z"
+     "iopub.execute_input": "2026-06-09T02:46:47.105597Z",
+     "iopub.status.busy": "2026-06-09T02:46:47.105502Z",
+     "iopub.status.idle": "2026-06-09T02:47:07.034618Z",
+     "shell.execute_reply": "2026-06-09T02:47:07.034120Z"
     }
    },
    "outputs": [
@@ -154,44 +165,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.45\n"
+      "Finished [100%]: Average Loss = 531.47\n"
      ]
     }
    ],
    "source": [
-    "def chunks():\n",
-    "    for path in sorted(glob.glob(f\"{shard_dir}/*.parquet\")):\n",
-    "        t = pq.read_table(path)\n",
-    "        yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])\n",
-    "\n",
-    "\n",
     "batch_size = 1024\n",
-    "ds = StreamingDataset(\n",
-    "    shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),\n",
+    "loader = DataLoader(\n",
+    "    parquet_source(shard_dir),  # an IterableDataset over the shards\n",
     "    batch_size=batch_size,\n",
     "    sample_shape=(4,),  # 3 features + 1 observed column, streamed together\n",
-    "    total_size=N,\n",
+    "    shuffle=True,  # bounded-buffer shuffle (the shards are written in order)\n",
+    "    buffer_size=15_000,\n",
+    "    seed=0,\n",
+    "    total_size=\"auto\",  # read N from Parquet metadata; len(loader) == N\n",
     ")\n",
-    "ds.advance()  # seed the buffer\n",
     "\n",
-    "with pm.Model():\n",
+    "with pm.Model() as model:\n",
     "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
-    "    buf = ds.as_tensor()\n",
-    "    logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]\n",
-    "    pm.Bernoulli(\"y\", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)\n",
-    "    approx = pm.fit(\n",
-    "        30_000,\n",
+    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder -- the ONLY data in RAM\n",
+    "    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n",
+    "    pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n",
+    "\n",
+    "    # No callbacks: the Trainer streams each minibatch into \"batch\" with set_data.\n",
+    "    approx = Trainer(\n",
     "        method=\"advi\",\n",
+    "        dataloader=loader,\n",
+    "        data_name=\"batch\",\n",
     "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
-    "        callbacks=[ds.fit_callback()],\n",
-    "        progressbar=False,\n",
-    "    )\n",
+    "    ).fit(30_000, random_seed=RANDOM_SEED)\n",
     "    idata_stream = approx.sample(1000)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4c4823cb",
+   "id": "32d12782",
    "metadata": {},
    "source": [
     "The negative-ELBO trace shows the fit converging while only ever holding a\n",
@@ -201,19 +209,19 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9d8d99e1",
+   "id": "2f3356ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:34:57.545936Z",
-     "iopub.status.busy": "2026-06-05T13:34:57.545794Z",
-     "iopub.status.idle": "2026-06-05T13:34:57.783063Z",
-     "shell.execute_reply": "2026-06-05T13:34:57.782229Z"
+     "iopub.execute_input": "2026-06-09T02:47:07.035832Z",
+     "iopub.status.busy": "2026-06-09T02:47:07.035753Z",
+     "iopub.status.idle": "2026-06-09T02:47:07.179976Z",
+     "shell.execute_reply": "2026-06-09T02:47:07.179360Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9P9Vn7jRUWiWiGZ/IT+kuz/7gowY6lYYxLF0Dwwy+Z6q9rbSMv/vd7zxtDt3W5+IEVXQs77//vg3QaL8Yxy9/+ctQK6Y0MKpgqHvgeciQIebGG2/0DHRokEqrwtROcWivIE3ACjNAhdwMVtStW9cccsgh9pzU7/yrDdUefO2112w70b2iUPsaaSW0Ahf5Uga0CkvpHx0KgqhN7V49qba8AiBa6eDuM+h3+++/f1a0Z5B7MlGe/cFHrRj86U9/GvoY9Rphylku9Ydpb6My1PY86qijzKGHHmon4mhv03jjVSoHTz/9tHnqqac8QUil+1Rmj0ToP+dvea5d3QcAoHppLwz3QIj2ctCAh3sgxEktcdNNN9nKzr0RtwZIgHynchP2J0zgUQMAWungzoyuARLNYnYPBDidIzUs3A2Q119/3TZCknnhhRc8Ha0BAwaYv//9756OlpNDXumY3Kll1OF0d6Di0SCFZre6B4HUWNVzOR0tUedVKXweeOABz+NVx6iuAaLQXgdKC6ZrlgbGRo0aZdMpVWVfJwflM3r5VP3h7jgpuKNBWne9JWp7qA3Srl272O/Gjh0baYIHck86y7Of2rtRrulhV/1kor2tvST9q5dU9vQ5uVdz6dqrQSZdi3XtdShYoUGlZLQ6zB100XMpnaR/hrVWc6hNoZVgjjlz5tjgEPKP2n9q53344Yd2X0ENuAelOdU5qUE5nScqD+6An87ZfCkDWjXpDjzqs1B735+2VfWjgqbulRFOOyVb2jPIPZkoz35ajRvl+hx2kksu9Ydpb6MyVK9rRa4m8R1zzDEJx6tUDq699lobOHfT+Ry077GD/nN+l2eCj0Ce07J3N+WU1/LteK6++mpPpaZZo5MmTUrrMQL5RoOUU6ZM8XSCEqWdUedIKZbcjTv/4IufGjx/+9vfPPvEaJalf7DB/Xftn+PuWCrdUrJBGnXI3I0rNZoSpcA58sgjPSns1DlloBJRXHbZZeabb76x540a6JqZr/Rk7s59VVA+o5VPpYRz722jNoRWc8SjWeTuVSuSrD5D7kp3ec6l9rauyUoN516JlWg/KqWTc6/y1KCRu10QbwBVaSMdahOovKqNEERtCrUt3H9XuyDMvjXILTpvbr311tD7BGq/Iq10cnvjjTfypgwooOOmdr4CKvHofbiDnErD9sEHH1R7ewa5KRPlORNyqT9MexuVpTZ12JTjjuOPP96udnYoLeuXX34Z9/70n4/O6/JM8BHIY+psuPeQ0P4TBx98cMLHaIa38uFnW8MRyCX+PSHVQUk20KpZpe4ZpZ988oknvY2fZretXLkydvuAAw6wKeAS0Uw3bSruUCdKDcl4du7caVPRONSoDbN3h1I9uVHHIArtTeFeyZBqlM9o5dP/eakNoZUaiagt0qVLl9htBV38+00hP6S7POdSe9tf1vzX0iBqX7iDIlpp5V4F5adrvjtoolR1yfaU6dmzpyf9owZptV8e8kvbtm0jP0aDm+7VxbNnz/YEF3O1DKxZs8aMHj06dlsTFX7+858nfA31E/wpo6PWGenobyA3ZaI8Z0Iu9YdpbyPTBg8e7Lnt30vZjf7z+Xldngk+AnnMPZNCtHdFGP77Kd0GgNTQgMann34au609rRLN2nZohqZmSTq0T4Ty6oct/+oQhuG/X6Ly/91333k6dJodF2aGrGZeK1WVe+aXZogB1Y3yGb18Vrauoa2BXJGJ9rZWY2mlk3tfOfcqqHh0TVY6RYcGgseNG5f28pxNqaCQvdS27dy5s+d38a43uVQG1H7X3m/utK7xVmK5qb/gDu6oPxFvhWWm2jNAZcpzpuRSf5j2NjLNn8FDqx+D0H82eV+eCT4CeUwzvdz8e0jEU1pa6kkFNWvWLLNo0aKUHx+Qj9RBcTfclJIl7J4V/jLsL+OpKP9KUeU+nq+++soOPAT5/PPPPbe1eXlY7vtqBrr/uYDqQPmMVj4XLFhgZ7Y7lDKuTZs2Ka/PgHxvb1flehu2rOlar2u+Q22BRGnjEr0G13SE5U8F5w7K5WoZ8P8tbJ2h4I77eNavX2/Gjx9fre0ZoDLlOVNypT9MexvVwd8m1argIPSfy+VzeSb4COQx9xJspZvp27dv6Mf6Z5JOmzYtpccG5Ct/WVLnprLlMl6aBQ02LFy4MHZb6aLCps9RR6t3796emWxz586ttvcCZBLlM1r59P8+Sh2g+7rTv1EHoKbKRHs7E3XTnDlzPIOraguEDVZo4MSdmnL+/Pl2fz0gEQ3O6Vxxa9GiRc6XAf97CRvgzLY6A6hsec6EXOoP095GpulaOGrUqFDBe/rP5fK5PNfsDTQAVJr2knCnf1DDz52mJRn/hveajX3QQQfxjSAv3X777ebbb7+1HRjNMtYm9NqnSgOMSq2iDeMbNWoU6rlUltw6dOhQpXIZRDOp3PvZRHkN53XcKan0OtrDqjreC5BJlM9o5bMqn5faJBoI0t5YTio87StVVFQU+XsDwhozZozdI2Xy5MlmxYoVNljYrFkzuwJx4MCB5rDDDosUCMhUezsTdVNVXsN5HWd/L7VB1Bbp1atXpOdAfhk7dqxZvXq1p/zECw7kUhlwv44GEbPtvdBGR7rLc5B3333X7p2oQIauq/Xq1TPFxcU2xbJSJysdcFB/NJ5c6g/T3kYmacXyrbfe6lmdd+ihh3rSBLvRfy6Xz+WZlY9AnvLPzFJqpyj8y77jzfQC8sGzzz5r95lZtWqVnQWmf2fMmGH+/e9/m//3//6fHax8/PHH7Ybzyfg36g6bYsFJt6ROmGPt2rX2WPz85TXKa4h/n4p45d//XqLUM9QxyEaUz2jlM9V1jVadAOn09ddf273YlPJIq5KclRH6/aOPPmpOPvlkc/7555upU6dmVXs7ldfbeOWM8ozqaF+7HXzwwZ4Z/blYBhRU0URGhyYzhl1dWZU6Ix39DaCy5TnIxx9/bEaPHm0WL15sVxpu2LDBrqRUWsEHHnjADB061FxxxRWhtwPKpf4w12ekm9rDWkX3j3/8w5x44onmxRdf9KRbvfnmm+M+lv5zuXwuzwQfgTzl7tQ4HZso3B0O0WwKAME0y/O+++4zF154oV0FkYi/LEUtm/77+8t6Ol4jqPzv2LHDs5dMw4YNTWFhYUpfA8g0yme08pmJugbINO3Rcuqpp9oVGNnS3vb/3v+4RPzXZ127gyZLUZ6RSV988YVd6eRQkOKcc86Je/9cKQNVrTOq6/oc1N8AKlueK0NlVq+hwMiXX36Z9P651B/m+oxUO+CAA0zPnj1jP3vuuac59thjzS233OKZgKf0w5pYkGgVM/3ncvlcnkm7CuQpzRTzz2CMwt9ocjeqgHzRrVs3c8ghh5g+ffrYlC9Kt7pp0ya7SkKp20aOHOkJNmrA8vLLLzdPPvmkqVs3+BLsL0tVLZv+sp6O1wgq//7fRZm1HXRM1DHIBpTPaOUzE3UNkAoaENRAy09+8hPTvXt323HXdVqreX744Qfz3nvv2eu6Q9f6a665xjRp0sQMHjy42tvbqShr7v3n9Hxq06T6NRI9H+BQubvuuus8H8hJJ53k2WMtV8tAptrPmWjPAJUtz25Kfa5043vvvbftezsTC/S8Snv61ltv2QxEDv3+sssusyu0EqX2pjzHL89cn5GMtiBQJhBtL1SnTp2E96X/XC6f+88EH4E8pUGTVHZs/M8H5LIDDzzQnHXWWXZPxyDq6CjVqgKNt912m3nttddif9PgpVK3KS1MEH9ZitrY8JfloLKZifJf1UYTwUdkI8pntPKZic8LqGrQUamitFJC//fr0qWL2Wuvvew1XxOIFHDUXpDOioarrrrKBiY1OFqd7e1Ul7WgwEtVX4O+A8JwypVSK7pTjvmDF7laBjLVfub6jGwuz6J9y+6++25zzDHHxL127rPPPjaz0BtvvGGv5c75rn/VD1eGAu0NGSSX+sOUZ2TaxIkTzXPPPWfP0SOOOCLhfek/l8vn8kzaVQBWlHz7Qfd3b9YN5DqlnIgXeHTToIU6Taeddprn908//XTovVEyUTZrwmsA2agmlJ1sKp+0NZBttMJx2LBhgYFHv/3339+mlioqKor9TtkNnnjiiawrAzWhrNF3QBBN2lOg36HAwf33329XGUeRK2WA6zPytTzvtttu5oQTTggVFDzuuOPM448/7gk0at+0V155JfSx0t7+EddnvPzyy3YPdP188MEHNqPXgw8+aM444wzTqFEje458++23Zvjw4XaCgfZhzaWyxvU5tQg+AnmqQYMGnttbtmyJ9Hh3WhoJM2gD5Ksbb7zRtGvXzpOaKN5eUf6y6S9ryfjLclDZzET59/8u6vugjkE2onxGK5+Z+LyATNJg6LXXXuv5nQY34w1SZKq9neqypoGlVL8XyjOS+ctf/mJTJTpq165t/vjHP9p0i8nkShnIVPuZ6zOyuTxXxsCBA80FF1zg+d1LL70U9/651B+mPCPVSktLTfv27e1Phw4dzO67725TrGrPRwUkDz300Nh9lfpYmUHiof9cLp/LM8FHIE/5KyAGEID00YxNpWxzc88CTefARtDgSSbKv/99RJkNF3T/bGg0AZTPaOWTYAVykfaqatq0aey20rBOmTKlWtvbmShrlGek07/+9S/zpz/9yfO7m266yaZcDCNXykBV28/VVWcE9TeQv6panivrvPPOs0FOh/ZsXr16deB9c6k/zPUZmaR9Vx9++GGz3377xX737rvv2iBkEPrP5fK5PBN8BPKUfw+LsCkgHStXrvTcdqegAhCcrs1t6tSpgR+TvyxVtWz6y3qmyn/dunU9DSfltI8ya4s6BtmI8hmtfKb686KtgWygtG5aYeEWL/iYqfZ2Vcqa9oJx7wejQQr34G0qXkMoz4hn1KhRdjWF25VXXmnOPPPM0B9arpSBqr6G//6Zuj4H9TeQn1JRnquSSr1nz56x2zt37jTTp08PvG8u9Ye5PiPTdG5rQoHbU089FXhf+s/l8rk8E3wE8lTHjh09txctWhTp8e5Nw0VL8QHE5067mqgR4S+b/rKWbDaVu7Gh/TQ0My3d5d//fPHqhSjvxX9M1DHIBpTPaOWzKp9XlLoGqCnX9HS1t1N5vY1XzjLVdkB+GT16tE1lrCCBQ6kTL7300kjPkytlQMETd1BEK6ujrJbKxPU5bH8D+SdV5TmV1+d4Kx9zqT9MexvVtRVBjx49YrcnTpxo90L3o/9cLp/LM8FHIE81a9bMdm4cy5cv98z4TGb+/Pme2127dk3p8QG5pn79+p7b8WY9dunSxXN73rx5oV/Df1//c7l/795U21+ek/HfP97r+OuFKO+FOgbZiPIZrXxWpQ5Qm0SDro6WLVvaAU4gGxQWFoZKiZSp9nYmrrf+31el7aA2SLy2A/LHN998Yy6//HKzbdu22O9OOeUU89vf/jbyc+VSGXD/XvvJRnmdsO8lE+0Z5JdUludUXp8T9blzpT9MexvVpVOnTp7r1YIFCyrch/5zuXwuzwQfgTzWrVu32P81O00zVcIaN25c3OcCUJF/VYQGJIN07949YVlLZPz48RVmowVROqm2bdvGbi9btswsXLgw9GznSZMmedLPxZtNlcr3Qh2DbED5jFY+/b+PUgdMmDDBM3M+Xn0GZPM1PVPt7UzUTbrW65rv3ksr7IosDUYp8OpemeIfIEZ+0flzySWXeILxQ4YMMbfeemulni+XyoD/vfiPr6bUGcgfqS7Pmbg+51J/mPY2qjP9qlvQNZH+c7l8Ls8EH4E85t+DbuzYsaGXi7tntGgmi7vhBiC4IeBWUlIS+DENGDDAsym0Hhd2YMNfhgcPHpzy8q9Gk/t4Bg0a5BmIcXNvQu7MiA3LfTyalep/LqA6UD6jlU8NrrpnxGrG55IlS1JenwHZek3PVHvb/xqVvd4mKmsFBQWevS7VFvB/DmFf44ADDgh9fMg9M2fONBdeeKFZt25d7HcHHXSQueeeewL3Wgwjl8qA/5oats7QCmz35IZGjRqZ/v37V2t7BrkvHeW5sjTo/v3336f9+pxt/WHa26gu/n5dixYtKtyH/nO5fC7PBB+BPHbYYYd5br/xxhuhHue/n/95AFQ0atQoz2334IV/YMPdSNDG9B988EGoAYf33nsvdlsdIHX84vGX29dffz3U1+a/X6Lyv+eee3rSzY0ZMyZUw+m7774zc+fOjd3u27evad26dajjA9KJ8hm9fPrriH//+9+hPmvaGshWM2bMMJMnT47drlOnjtlrr72qtb2tMtinT5/Y7Tlz5oSaKa1rsq7N7vRM8YIVqWw7HH744aEeh9yjlUXaA869Z6DaxA899FDcwfswcqkMHHzwwZ7VJGrfx0vt7Kb+gvoNjgMPPNC2W6qzPYPclq7yXFmff/65Z49HrXpMlD0nl/rDtLeRaevXr/dMwNE2Q0HnKP1nk/flmeAjkMd69uzp2SBYgymffPJJwscoZ/4///lPz++OO+64tB0jkAs0O/Ltt9/2/O6QQw6Je/+hQ4d6bo8YMcLm0E/k5ZdfNmvXrvUMXBQVFSWc6enuCH322Wdm6tSpCV9D6WjeeustTwPzqKOOint/zXY95phjYrd37NhhnnnmGZPMU0895blNHYNsQvmMVj6PPfZYz221IeLtv+NQW0Qz6R29e/cm9TKygq7FWs3hviZrRnfTpk2rvb3tr5v819Igal+40zOpvLr3wPLTNd8dzHjzzTc9qSSDTJkyxQ4Iu4M7ZDPITwpQKFChVb3uAfW//vWvKUnDmytlQPWJAoeONWvWmFdeeSXha6hO0nupSp2Rjv4Gcle6y3NUWol4//33e36nwHii1Ze51B+mvY1Me+KJJzx7vOq6Fm/CC/3np/K6PBN8BPLcr371K8/t2267zXZw4rnvvvs8KaCOOOIIW6kB+eLFF1+0s7zCmj59uhk+fLhnYEMDlYkGHdSBcQ9UauZjok6KUjE88MADsdsaNPGXbT91Cn/xi1/Ebuv4brrpprgpl/T33//+9573fvrpp5tWrVolfJ2LL77Y0wjVwEaiFFWadf3OO+/Ebuv5TzvttISvAWQS5TNa+dxjjz3MoYceGrutNoR/cMhNbRD/HkHJ6jOgMrQqKkr6M10H7777bvPRRx95fn/ppZdmRXv7jDPOsEENd8aFRCuZNDHKHazwtwuCaEb7qaeeGrutNoHaBu42jn+VlNoW7r/72wXIDzpXLrroIjNr1izPPlB///vfTePGjVPyGrlUBtR3cFM7310n+Ol9uFd6ahVoshXGmWjPIDeluzyHCQK6qf963XXXeVKuKuinfSjzpT9MexuV9eSTT5oNGzZEeowm1j/22GOe3yU6R+k/v5PX5ZngI5DndBFQKgjHvHnzzFlnnWVnaLoph78GStwdEs3y+s1vfpPR4wWqm2ZzqjN/++23m2+//dZs3749bgNADbJTTjnFLF26NPZ7dTxuuOGGhK+hzvy1117rmXl955132ufzp136+uuvzbBhwzz7bGgmVZhJAWeeeabp0KGDZ9BBe3aoHvDP8LzyyivN+++/70ljk6xD5wzSnHfeebHbmh2nWbL+wSB9jgrs+uuUX//619UyexY1mwbIgn7cs/Vl1apVgffTOR8P5TN6+VS59g+6qA71T+RQKku1QfQdOPbZZx8beEH+Sld51gpEXQd1DdX1J97qJa0E0rX23HPPrbAS4cgjjwyVcjAT7e0GDRqYyy+/3PM7Xbtfeuklu9LC/X50Pde12N2GOf/885MOoIqu/e6VnkrBeNVVV1X4/PQeNTjtDohoDxsFiJBfNJB/2WWXeQIDxcXF9lzXgGe8Mh70k2iANJfKgAYe3SsfVN+pvvLv56R+wV/+8hdz1113edop11xzTcIVnJnsbyC3ZKI8q499/PHH26CdUpDHm6yjcq0JQepvu1cjyjnnnBNq1U8u9Ydpb6MyHn30UTu+9Yc//MGe//HGt0TlXtcNlQX3dVVZvRKlH6b/nN/luVZZsrwKAHKecs6ffPLJngCJLg6aMamGmPLma2aov3GotFNqFAL5RI0q98xjDQpqpqcGKzTTU+kQtP+FGgHuBpmzL9S9997rSb2SyMMPP2z3zHBTahiltNEAi9IqTJs2zfN3DQK88MILpmHDhqFeQ7NKNfPKvUeM9pnp16+fadOmjR1IUSPUPQNUe3gozcagQYNCvYYasJpV6k455QzAaMa1/v7DDz9U2P9CHUk1sIColOawKvbdd1/z7LPPJrwP5TNa+Rw5cqSdle6mOlN1jQZxta+NeyBLSktL7aBxmMFg5K50ledXX33VXH/99RXOuS5dutg0grrWqQ2s65N7PyuH9oXTQICux9nU3tZ70ntz0/Vc7QO1Q3Tdd+8jJUrxqICD/h7GF198YYMq7gEqtYf0mWjlmdLw6b2420Eq7xpU3W233UK/F+QGDYilap9PBcdOOumkvCgDqgu0qsq/AkxtZ9VTartPnDjRTrzwD1gqOBRWJtozyB2ZKM86H3VeunXs2NH+6PqsVY0rVqyw7UZ3QNyh49PjE6VczdX+MO1tRKVAlbsc6VqmwL36XypvCpZrAoAmzAW1h1VONDkvzKpn+s+n5GV5JvgIINbguuKKKzypM+LRxUgVoGaJAfkefAxLjQAFHtW4C0vzg/70pz+Zxx9/PG46JzetqnjwwQcTbkYfRLOZNWPbPSAaT5MmTcwdd9xhV3tEoRnbmiX38ccfh7q/Olq/+93vbMcOyMbgI+Uzevl87rnnbNrKeOms3DQ4++c//zlr9qpAfgQfw9LKJT1WbeJsa2+rfOn66Q++xKO0TgpwRt237d1337WZHIIGfv3ULlG6qChtIOSOTAcfc6kMKIipYKICH8k4aSajZibKVHsGuaG6go9haPLAL3/5Sxt8DzuRIBf7w7S3UZXgY1iaQKcJMjqnGzVqFOox9J9/l5flmeAjgJhNmzaZRx55xHbUNJPMT5WkZoWqQ1PVgSCgptIsIqV3UToY/0zjoAaZyooaZSeccELo1RF+X331le2A6d+ghAXt2rWzr6EUMVE7Wg69Fw08KGVNUONTqSGUuuHqq682bdu2NZWlGd9aKaK9MINoFpdS7ETtzAGZDj46KJ/RaFW46prRo0cHpvXRihENRGnwqLJ1JnJLusrznDlz7MqdMWPG2FU9idJMOTONdW1S6rRevXplfXtbqSAVTIi3r5QGJvReNLhZWc4eNEodp8wPfgrmKHWk3ovS8iE/ZTr4mGtlQHWT9uT65z//GTgBUv0Nrb7S/k4DBw40lZWJ9gxqvkyUZ63kUZ9R5+Ls2bOTBsVVtoYOHWpTrWp1ZGXlUn+Y9jbC0kr+//znP+bLL7+0WxKEKW9Dhgyxq4Ur2x6m/5xf5ZngI4AKVJkpsKKGpVJMaBaL0k1olqNSsAD4cTayVi/oX6VL0/4oWqmg2ZCaDazUS+49YapK6VyVXknpWDTAUVJSYlO16HWS7esSlt6D9pPRaymtho5fqzb33nvvUKk0wpo0aZJt3Oq9aABDn5car0ojBdRElM9oFHTRSo7Fixfb1HLqNLVv397stddeNtUVkEm69ikAqYF97euk1GtKl6jAga6DSq+utGhhU7hlU3tb7RQNWuh6q/ek662CLlUJoPpp/5lvvvnGtoeUmkvHr4FZzaaPujoUSLVcKQMKCGr/SE2c0OosBUL0XrQ/pNrqNak9A4SlNqKuzzovda10UqPq+qwgiMpx165dU/qB5lJ/mPY2ol7LlKVDbVOd+5owp3NT5U3XNaXcrkqA34/+c36UZ4KPAAAAAAAAAAAAAFIidVM3AQAAAAAAAAAAAOQ1go8AAAAAAAAAAAAAUoLgIwAAAAAAAAAAAICUIPgIAAAAAAAAAAAAICUIPgIAAAAAAAAAAABICYKPAAAAAAAAAAAAAFKC4CMAAAAAAAAAAACAlCD4CAAAAAAAAAAAACAlCD4CAAAAAAAAAAAASAmCjwAAAAAAAAAAAABSguAjAAAAAAAAAAAAgJQg+AgAAAAAAAAAAAA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jAAAAAAAAAAAAgJQg+AgAAAAAAAAAAAAgJQg+AgAAAAAAAAAAAEgJgo8AAAAAAAAAAAAAUoLgIwAAAAAAAAAAAICUIPgIAAAAAAAAAAAAICUIPgIAAAAAAAAAAABICYKPAAAAAAAAAAAAAEwq/H9ihcyeVmqUogAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1800x600 with 1 Axes>"
       ]
@@ -230,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b600054",
+   "id": "f1605433",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -244,13 +252,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6ab4c56a",
+   "id": "6425b460",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:34:57.784640Z",
-     "iopub.status.busy": "2026-06-05T13:34:57.784513Z",
-     "iopub.status.idle": "2026-06-05T13:35:04.185598Z",
-     "shell.execute_reply": "2026-06-05T13:35:04.184893Z"
+     "iopub.execute_input": "2026-06-09T02:47:07.181731Z",
+     "iopub.status.busy": "2026-06-09T02:47:07.181613Z",
+     "iopub.status.idle": "2026-06-09T02:47:10.642405Z",
+     "shell.execute_reply": "2026-06-09T02:47:10.641996Z"
     }
    },
    "outputs": [
@@ -258,7 +266,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.03\n"
+      "Finished [100%]: Average Loss = 531.43\n"
      ]
     }
    ],
@@ -280,13 +288,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ab687535",
+   "id": "ad162375",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:35:04.187668Z",
-     "iopub.status.busy": "2026-06-05T13:35:04.187515Z",
-     "iopub.status.idle": "2026-06-05T13:35:04.490699Z",
-     "shell.execute_reply": "2026-06-05T13:35:04.490137Z"
+     "iopub.execute_input": "2026-06-09T02:47:10.643730Z",
+     "iopub.status.busy": "2026-06-09T02:47:10.643661Z",
+     "iopub.status.idle": "2026-06-09T02:47:10.881067Z",
+     "shell.execute_reply": "2026-06-09T02:47:10.880696Z"
     }
    },
    "outputs": [
@@ -294,13 +302,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_20806/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_77321/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -327,33 +335,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d845037",
+   "id": "2b38e94b",
    "metadata": {},
    "source": [
     "## Why bother: memory\n",
     "\n",
     "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
-    "rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`\n",
-    "only ever holds one `batch_size` buffer. The dense `float64` design matrix is the\n",
-    "dominant cost, so its footprint is a faithful proxy for the gap:"
+    "rows resident, so the array it must hold grows linearly in `N`; the streaming\n",
+    "`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design\n",
+    "matrix is the dominant cost; the line below is its *theoretical lower bound*\n",
+    "(`N * ncols * 8` bytes), not a measurement:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c9ae88bf",
+   "id": "ddbbded0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:35:04.492436Z",
-     "iopub.status.busy": "2026-06-05T13:35:04.492289Z",
-     "iopub.status.idle": "2026-06-05T13:35:05.138046Z",
-     "shell.execute_reply": "2026-06-05T13:35:05.137532Z"
+     "iopub.execute_input": "2026-06-09T02:47:10.882401Z",
+     "iopub.status.busy": "2026-06-09T02:47:10.882316Z",
+     "iopub.status.idle": "2026-06-09T02:47:11.132510Z",
+     "shell.execute_reply": "2026-06-09T02:47:11.132189Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x900 with 1 Axes>"
       ]
@@ -365,43 +374,50 @@
    "source": [
     "ncols = 4  # 3 features + observed\n",
     "n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows\n",
-    "inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident\n",
+    "inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident (array lower bound)\n",
     "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
-    "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch  (O(N))\", lw=2)\n",
-    "ax.loglog(n_grid, stream_gb, label=\"StreamingDataset  (O(batch))\", lw=2)\n",
-    "ax.axhline(24, color=\"0.4\", ls=\"--\", lw=1)\n",
-    "ax.text(1.2e5, 26, \"24 GiB RAM\", color=\"0.4\")\n",
-    "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix footprint (GB)\",\n",
-    "       title=\"Memory is flat in N when streaming\")\n",
+    "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch  (O(N), array lower bound)\", lw=2)\n",
+    "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader  (O(batch))\", lw=2)\n",
+    "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n",
+    "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n",
+    "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n",
+    "       title=\"Array footprint is flat in N when streaming (theoretical lower bound)\")\n",
     "ax.legend();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a5a5dd95",
+   "id": "64cdea8e",
    "metadata": {},
    "source": [
-    "On a real 122 GB order-book dataset (121.8 M rows) this is not just the array\n",
-    "size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to\n",
-    "out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed\n",
-    "flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.\n",
+    "That line is only the bare array; actual peak RSS is higher (the framework plus\n",
+    "PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real\n",
+    "number on public, reproducible data, I measured peak memory on the\n",
+    "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),\n",
+    "the standard out-of-core learning benchmark, with the same logistic model (13\n",
+    "numeric features + the click label). Streaming through the `DataLoader` stayed flat\n",
+    "at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline\n",
+    "rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to\n",
+    "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n",
+    "within ADVI noise (correlation **0.999** across all 14 coefficients). The point of\n",
+    "using Criteo rather than a private dataset is that anyone can rerun it.\n",
     "\n",
     "## When to reach for it\n",
     "\n",
     "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n",
     "  index gives perfectly i.i.d. minibatches for free.\n",
-    "* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by\n",
-    "  streaming from disk. The cost is a `fit_callback` to advance the buffer and the\n",
-    "  responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use\n",
-    "  `shuffle_buffer`); a bounded buffer over strongly ordered data only\n",
-    "  block-shuffles it and biases the posterior."
+    "* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory\n",
+    "  flat in `N` by streaming from disk, with no callbacks to wire up. The remaining\n",
+    "  obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave\n",
+    "  shards for strongly ordered data, since a bounded buffer over strongly ordered\n",
+    "  data only block-shuffles it and biases the posterior."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fe1cd4da",
+   "id": "9dbff83e",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -412,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d4b437a",
+   "id": "26996637",
    "metadata": {},
    "source": [
     "## References\n",
@@ -426,13 +442,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "80f4917e",
+   "id": "5f48f79a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-05T13:35:05.139909Z",
-     "iopub.status.busy": "2026-06-05T13:35:05.139765Z",
-     "iopub.status.idle": "2026-06-05T13:35:05.155326Z",
-     "shell.execute_reply": "2026-06-05T13:35:05.154721Z"
+     "iopub.execute_input": "2026-06-09T02:47:11.134055Z",
+     "iopub.status.busy": "2026-06-09T02:47:11.133970Z",
+     "iopub.status.idle": "2026-06-09T02:47:11.143141Z",
+     "shell.execute_reply": "2026-06-09T02:47:11.142770Z"
     }
    },
    "outputs": [
@@ -440,7 +456,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Fri, 05 Jun 2026\n",
+      "Last updated: Mon, 08 Jun 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -468,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "138db362",
+   "id": "b5d5ae89",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
@@ -477,6 +493,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 4a35cfe48..fcf60a86c 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -12,7 +12,7 @@ kernelspec:
 
 (streaming_dataset)=
 
-# Out-of-core minibatch variational inference with StreamingDataset
+# Out-of-core minibatch variational inference with DataLoader and Trainer
 
 :::{post} June 2026
 :tags: variational inference, minibatch, out-of-core, ADVI
@@ -24,16 +24,23 @@ kernelspec:
 
 `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so
 minibatch variational inference is capped at datasets that fit in memory -- the
-very regime where minibatching is meant to help. `StreamingDataset` feeds
-minibatches from an arbitrary source (a generator, a directory of Parquet shards,
-...) into a small `pytensor.shared` buffer, so peak memory is bounded by the
-buffer and is independent of the dataset size `N`.
-
-The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass
-`total_size=N` to the observed distribution and PyMC scales the minibatch
-log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a
-streaming source is only as well mixed as the order it yields rows in -- which we
-handle with `shuffle_buffer`.
+very regime where minibatching is meant to help. This notebook uses the streaming
+API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:
+
+* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally
+  shuffles) an out-of-core source -- here a directory of Parquet shards read by
+  {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size
+  minibatches, holding only one batch in memory at a time;
+* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;
+* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each
+  minibatch into that placeholder with `set_data` every step -- **no callbacks**.
+
+The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the
+`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`
+to the observed distribution and PyMC scales the minibatch log-likelihood by
+`N / batch_size`. The one extra obligation is shuffling -- a streaming source is
+only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`
+handles with a bounded buffer.
 
 We use a modest `N` here so the notebook runs in seconds and the two posteriors
 are easy to compare; the streaming machinery is identical at any size, and the
@@ -50,7 +57,7 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 import pymc as pm
 
-from pymc.variational.streaming import StreamingDataset, shuffle_buffer
+from pymc.variational.streaming import DataLoader, Trainer, parquet_source
 
 RANDOM_SEED = 8927
 rng = np.random.default_rng(RANDOM_SEED)
@@ -60,8 +67,10 @@ az.style.use("arviz-variat")
 ## Put a dataset on disk and forget the array
 
 We synthesise a logistic-regression dataset, write it to Parquet shards, and
-delete the in-memory copy. From here on the data only exists on disk -- exactly
-the situation `StreamingDataset` is for.
+delete the in-memory table. From here on the features only exist on disk -- exactly
+the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to
+build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches
+them.)
 
 ```{code-cell} ipython3
 N = 30_000
@@ -79,46 +88,45 @@ for i, s in enumerate(range(0, N, 5_000)):
         pa.table({f"c{j}": block[:, j] for j in range(4)}),
         f"{shard_dir}/part_{i:03d}.parquet",
     )
-del table  # the data now lives only on disk
+del table  # the design matrix now lives only on disk
 print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 ```
 
 ## Stream minibatches off disk and fit with ADVI
 
-`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows
-into a buffer, shuffles them, and yields fixed-size batches (carrying over any
-remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`
-buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`
-rescaling and a `fit_callback()` advances the buffer each step.
+`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:
+it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data
+scan), so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and
+shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)`
+*placeholder* -- the only data ever resident -- and the `Trainer` streams each
+minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`
+triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop.
 
 ```{code-cell} ipython3
-def chunks():
-    for path in sorted(glob.glob(f"{shard_dir}/*.parquet")):
-        t = pq.read_table(path)
-        yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])
-
-
 batch_size = 1024
-ds = StreamingDataset(
-    shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),
+loader = DataLoader(
+    parquet_source(shard_dir),  # an IterableDataset over the shards
     batch_size=batch_size,
     sample_shape=(4,),  # 3 features + 1 observed column, streamed together
-    total_size=N,
+    shuffle=True,  # bounded-buffer shuffle (the shards are written in order)
+    buffer_size=15_000,
+    seed=0,
+    total_size="auto",  # read N from Parquet metadata; len(loader) == N
 )
-ds.advance()  # seed the buffer
 
-with pm.Model():
+with pm.Model() as model:
     b = pm.Normal("b", 0.0, 3.0, shape=4)
-    buf = ds.as_tensor()
-    logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]
-    pm.Bernoulli("y", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)
-    approx = pm.fit(
-        30_000,
+    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder -- the ONLY data in RAM
+    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]
+    pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader))
+
+    # No callbacks: the Trainer streams each minibatch into "batch" with set_data.
+    approx = Trainer(
         method="advi",
+        dataloader=loader,
+        data_name="batch",
         obj_optimizer=pm.adam(learning_rate=0.008),
-        callbacks=[ds.fit_callback()],
-        progressbar=False,
-    )
+    ).fit(30_000, random_seed=RANDOM_SEED)
     idata_stream = approx.sample(1000)
 ```
 
@@ -172,40 +180,48 @@ fig.tight_layout();
 ## Why bother: memory
 
 Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
-rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`
-only ever holds one `batch_size` buffer. The dense `float64` design matrix is the
-dominant cost, so its footprint is a faithful proxy for the gap:
+rows resident, so the array it must hold grows linearly in `N`; the streaming
+`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design
+matrix is the dominant cost; the line below is its *theoretical lower bound*
+(`N * ncols * 8` bytes), not a measurement:
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
 n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows
-inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident
+inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident (array lower bound)
 stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
 
 fig, ax = plt.subplots(figsize=(8, 4.5))
-ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch  (O(N))", lw=2)
-ax.loglog(n_grid, stream_gb, label="StreamingDataset  (O(batch))", lw=2)
-ax.axhline(24, color="0.4", ls="--", lw=1)
-ax.text(1.2e5, 26, "24 GiB RAM", color="0.4")
-ax.set(xlabel="dataset size N", ylabel="design-matrix footprint (GB)",
-       title="Memory is flat in N when streaming")
+ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch  (O(N), array lower bound)", lw=2)
+ax.loglog(n_grid, stream_gb, label="streaming DataLoader  (O(batch))", lw=2)
+ax.axhline(26, color="0.4", ls="--", lw=1)
+ax.text(1.2e5, 28, "26 GB RAM", color="0.4")
+ax.set(xlabel="dataset size N", ylabel="design-matrix size (GB) = N·ncols·8 bytes",
+       title="Array footprint is flat in N when streaming (theoretical lower bound)")
 ax.legend();
 ```
 
-On a real 122 GB order-book dataset (121.8 M rows) this is not just the array
-size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to
-out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed
-flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.
+That line is only the bare array; actual peak RSS is higher (the framework plus
+PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real
+number on public, reproducible data, I measured peak memory on the
+[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),
+the standard out-of-core learning benchmark, with the same logistic model (13
+numeric features + the click label). Streaming through the `DataLoader` stayed flat
+at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline
+rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to
+out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to
+within ADVI noise (correlation **0.999** across all 14 coefficients). The point of
+using Criteo rather than a private dataset is that anyone can rerun it.
 
 ## When to reach for it
 
 * Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random
   index gives perfectly i.i.d. minibatches for free.
-* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by
-  streaming from disk. The cost is a `fit_callback` to advance the buffer and the
-  responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use
-  `shuffle_buffer`); a bounded buffer over strongly ordered data only
-  block-shuffles it and biases the posterior.
+* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory
+  flat in `N` by streaming from disk, with no callbacks to wire up. The remaining
+  obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave
+  shards for strongly ordered data, since a bounded buffer over strongly ordered
+  data only block-shuffles it and biases the posterior.
 
 +++
 

From aa8be2759bf1eaf45c3878017161f05d02b0b346 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 8 Jun 2026 22:49:09 -0500
Subject: [PATCH 03/42] Apply pre-commit formatting (black line-length 100,
 jupytext sync)

---
 .../streaming_dataset.ipynb                      | 15 ++++++++-------
 .../streaming_dataset.myst.md                    | 16 +++++++++-------
 2 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index ee7be9257..da2026d85 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -276,9 +276,7 @@
     "    xb, zb, sb, yb = pm.Minibatch(\n",
     "        X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size\n",
     "    )\n",
-    "    pm.Bernoulli(\n",
-    "        \"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N\n",
-    "    )\n",
+    "    pm.Bernoulli(\"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)\n",
     "    approx_inram = pm.fit(\n",
     "        30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n",
     "    )\n",
@@ -373,8 +371,8 @@
    ],
    "source": [
     "ncols = 4  # 3 features + observed\n",
-    "n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows\n",
-    "inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident (array lower bound)\n",
+    "n_grid = np.logspace(5, 9, 50)  # 1e5 .. 1e9 rows\n",
+    "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
     "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
@@ -382,8 +380,11 @@
     "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader  (O(batch))\", lw=2)\n",
     "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n",
     "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n",
-    "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n",
-    "       title=\"Array footprint is flat in N when streaming (theoretical lower bound)\")\n",
+    "ax.set(\n",
+    "    xlabel=\"dataset size N\",\n",
+    "    ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n",
+    "    title=\"Array footprint is flat in N when streaming (theoretical lower bound)\",\n",
+    ")\n",
     "ax.legend();"
    ]
   },
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index fcf60a86c..87698f9b0 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -1,5 +1,6 @@
 ---
 jupytext:
+  default_lexer: ipython3
   text_representation:
     extension: .md
     format_name: myst
@@ -152,9 +153,7 @@ with pm.Model():
     xb, zb, sb, yb = pm.Minibatch(
         X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size
     )
-    pm.Bernoulli(
-        "y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N
-    )
+    pm.Bernoulli("y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)
     approx_inram = pm.fit(
         30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False
     )
@@ -187,8 +186,8 @@ matrix is the dominant cost; the line below is its *theoretical lower bound*
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
-n_grid = np.logspace(5, 9, 50)            # 1e5 .. 1e9 rows
-inram_gb = n_grid * ncols * 8 / 1e9       # whole dataset resident (array lower bound)
+n_grid = np.logspace(5, 9, 50)  # 1e5 .. 1e9 rows
+inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
 stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
 
 fig, ax = plt.subplots(figsize=(8, 4.5))
@@ -196,8 +195,11 @@ ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch  (O(N), array lower bound
 ax.loglog(n_grid, stream_gb, label="streaming DataLoader  (O(batch))", lw=2)
 ax.axhline(26, color="0.4", ls="--", lw=1)
 ax.text(1.2e5, 28, "26 GB RAM", color="0.4")
-ax.set(xlabel="dataset size N", ylabel="design-matrix size (GB) = N·ncols·8 bytes",
-       title="Array footprint is flat in N when streaming (theoretical lower bound)")
+ax.set(
+    xlabel="dataset size N",
+    ylabel="design-matrix size (GB) = N·ncols·8 bytes",
+    title="Array footprint is flat in N when streaming (theoretical lower bound)",
+)
 ax.legend();
 ```
 

From 505382a43693987f7751c679713505c65b2aae61 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 8 Jun 2026 23:08:22 -0500
Subject: [PATCH 04/42] Clean up memory figure layout (title/legend/annotation
 overlap)

---
 .../streaming_dataset.ipynb                   | 141 +++++++++---------
 .../streaming_dataset.myst.md                 |  21 ++-
 2 files changed, 84 insertions(+), 78 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index da2026d85..842e1b7ac 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6f9305b8",
+   "id": "f258e12b",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c97c0132",
+   "id": "f4f61327",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
@@ -49,13 +49,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "416e522b",
+   "id": "3828b233",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:46:45.421431Z",
-     "iopub.status.busy": "2026-06-09T02:46:45.421338Z",
-     "iopub.status.idle": "2026-06-09T02:46:47.072976Z",
-     "shell.execute_reply": "2026-06-09T02:46:47.072225Z"
+     "iopub.execute_input": "2026-06-09T04:07:15.994900Z",
+     "iopub.status.busy": "2026-06-09T04:07:15.994790Z",
+     "iopub.status.idle": "2026-06-09T04:07:17.733770Z",
+     "shell.execute_reply": "2026-06-09T04:07:17.733191Z"
     }
    },
    "outputs": [],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "770008ea",
+   "id": "ade8f410",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
@@ -94,13 +94,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3e94e139",
+   "id": "4eb91e27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:46:47.074733Z",
-     "iopub.status.busy": "2026-06-09T02:46:47.074453Z",
-     "iopub.status.idle": "2026-06-09T02:46:47.104025Z",
-     "shell.execute_reply": "2026-06-09T02:46:47.102969Z"
+     "iopub.execute_input": "2026-06-09T04:07:17.735172Z",
+     "iopub.status.busy": "2026-06-09T04:07:17.734922Z",
+     "iopub.status.idle": "2026-06-09T04:07:17.756154Z",
+     "shell.execute_reply": "2026-06-09T04:07:17.755779Z"
     }
    },
    "outputs": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77cfc727",
+   "id": "9f8bcf9f",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
@@ -151,13 +151,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7ea8d824",
+   "id": "cd4d4c9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:46:47.105597Z",
-     "iopub.status.busy": "2026-06-09T02:46:47.105502Z",
-     "iopub.status.idle": "2026-06-09T02:47:07.034618Z",
-     "shell.execute_reply": "2026-06-09T02:47:07.034120Z"
+     "iopub.execute_input": "2026-06-09T04:07:17.757225Z",
+     "iopub.status.busy": "2026-06-09T04:07:17.757170Z",
+     "iopub.status.idle": "2026-06-09T04:07:36.098596Z",
+     "shell.execute_reply": "2026-06-09T04:07:36.098132Z"
     }
    },
    "outputs": [
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32d12782",
+   "id": "fa093535",
    "metadata": {},
    "source": [
     "The negative-ELBO trace shows the fit converging while only ever holding a\n",
@@ -209,13 +209,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "2f3356ef",
+   "id": "efccd50a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:47:07.035832Z",
-     "iopub.status.busy": "2026-06-09T02:47:07.035753Z",
-     "iopub.status.idle": "2026-06-09T02:47:07.179976Z",
-     "shell.execute_reply": "2026-06-09T02:47:07.179360Z"
+     "iopub.execute_input": "2026-06-09T04:07:36.099763Z",
+     "iopub.status.busy": "2026-06-09T04:07:36.099686Z",
+     "iopub.status.idle": "2026-06-09T04:07:36.211882Z",
+     "shell.execute_reply": "2026-06-09T04:07:36.211538Z"
     }
    },
    "outputs": [
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1605433",
+   "id": "0a7cb7b8",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -252,13 +252,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6425b460",
+   "id": "7f44b970",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:47:07.181731Z",
-     "iopub.status.busy": "2026-06-09T02:47:07.181613Z",
-     "iopub.status.idle": "2026-06-09T02:47:10.642405Z",
-     "shell.execute_reply": "2026-06-09T02:47:10.641996Z"
+     "iopub.execute_input": "2026-06-09T04:07:36.212996Z",
+     "iopub.status.busy": "2026-06-09T04:07:36.212919Z",
+     "iopub.status.idle": "2026-06-09T04:07:39.535782Z",
+     "shell.execute_reply": "2026-06-09T04:07:39.535456Z"
     }
    },
    "outputs": [
@@ -266,7 +266,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.43\n"
+      "Finished [100%]: Average Loss = 531.55\n"
      ]
     }
    ],
@@ -286,13 +286,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ad162375",
+   "id": "377a3b12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:47:10.643730Z",
-     "iopub.status.busy": "2026-06-09T02:47:10.643661Z",
-     "iopub.status.idle": "2026-06-09T02:47:10.881067Z",
-     "shell.execute_reply": "2026-06-09T02:47:10.880696Z"
+     "iopub.execute_input": "2026-06-09T04:07:39.537071Z",
+     "iopub.status.busy": "2026-06-09T04:07:39.536998Z",
+     "iopub.status.idle": "2026-06-09T04:07:39.769680Z",
+     "shell.execute_reply": "2026-06-09T04:07:39.769277Z"
     }
    },
    "outputs": [
@@ -300,13 +300,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_77321/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_16604/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b38e94b",
+   "id": "bdee0324",
    "metadata": {},
    "source": [
     "## Why bother: memory\n",
@@ -348,21 +348,29 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "ddbbded0",
+   "id": "9a2a3b7d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:47:10.882401Z",
-     "iopub.status.busy": "2026-06-09T02:47:10.882316Z",
-     "iopub.status.idle": "2026-06-09T02:47:11.132510Z",
-     "shell.execute_reply": "2026-06-09T02:47:11.132189Z"
+     "iopub.execute_input": "2026-06-09T04:07:39.770819Z",
+     "iopub.status.busy": "2026-06-09T04:07:39.770747Z",
+     "iopub.status.idle": "2026-06-09T04:07:39.968826Z",
+     "shell.execute_reply": "2026-06-09T04:07:39.968516Z"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/claude-501/ipykernel_16604/1052516414.py:15: UserWarning: The figure layout has changed to tight\n",
+      "  fig.tight_layout();\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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+8If09a9/PftvazruuONWmAXxv//9r1XXAADQ1lRAAABAQf7617+mI444Ims7lLftttumHXfcMbvK/KWXXspa4YS77747nX322TW9RrQwyuvYsWPaaaedslZGG2ywQRbEj3ZHL774YhaEDvHf8847L2vHFFUX9VQZYI33vP3226dOnTpllQIR6I7ESSRqoi1VSQSH47HNBcBHjRqVJV/y4n3vvPPOWTuheL0lS5ZkrxF7kH+N5owcOTJLKlSuP567c+fOWXuraGH0zDPPZO2Ywquvvpq12vntb3+bnYfVRT33qa3dc889WYujSC7kRWusqC6K9xmtmJ566qmsHVOIRN9JJ52Unbc+ffq0yjqjuicqRuLvPDz33HPpjjvuyJIhAAAfFhIQAABQgEgMRKuifPIhEg9nnXXWCr3fI1FxxhlnZPMhon1MzESoRbSS+c53vpO1TYre/Y21cXrjjTey9jRRiVAKlo8YMSLts88+WW/8ShFAj/tF0PZHP/pR+ffDhw9PBxxwQJPtbRrzsY99LAvyRvA1WtFEsLtSBLynTp2aJRxKlQbRtimuam8qQBv3+/Wvf10+jkBz7Hm8VmPzCyLxElf/33nnndltZeK188mHmBvwwx/+MGtHFO8n780338zWfeONN2bH//znP7MKjilTpjTYk0i6lILPcS6uvfba8r/95je/yf69MbV+Hlpzn6oR7cWihVK1Iik1bNiwbFZIScwIaap6KPY6n3yIv4UhQ4ZksyzyIulw3XXXZecqXiMSXFF5tOuuu2azWFqrCiISJqXEXLSnir+nNWl+CABAS0hAAABAAX7/+983aHUTwdBo57P55puvcN8IfkZLpAhw33///dlw5mpFBUMENZsLYMZV7nHF92c/+9ksuBsiaBuB75NPPnmF+5eC4ZVzIGLWQS298z/96U9n7ykqM1YmqjFihkLMkTj00EPLSYjYl6YSEDHzIgLJJT//+c/Tt771rSZfIyoWIgESt3xSpVIkjU4//fTycSQcImHQ1NyMOKfnnHNOVhVRmnUQwfNIQOQHD6+77rrlvatMKsR+12smQb32qVrxXqtNokRgPj6n+eTD5z//+awtV2OirVSpqiH84he/yFqMNSaSLUceeWT61Kc+lY466qgs0RKPjUqXqBBpDZEgjM/zH//4x+w4qk0i8dZaVRgAAG3NZRcAAFCAuNI675e//GWjyYd8cDqG7NZ6tXsErmu5ejquto6ZBiUxvLqeNt5442aTD5XJmHywOWZCNNXTP9of5dUyuLqpao3SuXvvvffKx1G10tzQ7hDrjgRPSb7CoS3Va5/q4YILLmgwmyHaWMUA8MYqNR599NFsmHM+GddU8iHvS1/6UlaVkR/MXblH9RSDuiPJUxLvr1SVBADQ3klAAABAC0VLpZgLUNKzZ8/sKu7mxNyGes9kCNEKqSQqHKKF0Ookv75SEqIalbM2VkVcgR8Dp/MB8N69e1f12Gh99d3vfrdBe6D4LKxuitineohqnHyrqEiujR8/foWWVyVRUZRP4DVVJdGYAQMGlH+OSojp06en1hKzKfIVKDEHptS+CwCgvZOAAACAFspflR1qGTJbbbC7mhkUMYsghiJHBUH+VlkxMX/+/NTaItAf7W9i6HHl+iqvBm9qfZV9+y+66KLyoO1VNW/evAYJmabmXTQl2hblzZo1K7W1euxT0WLWRLRPKomkwxVXXNHkXIzw5z//ufzz5z73uZVWGFWKuSdbb711m52nY445psEslF/96lfZjAoAgPbODAgAAGihOXPmNDjOt+Vpzo477pi1vckP1a1GBC/vu+++NG3atPTss8+mBQsWVB1krmXmREvMnDkz630fCZoI9C9btqyqx/3jH/9o9PcxzDtmUixevDg7jueeO3duNkviq1/96irNVKgMRG+22WZNtoBqTGUQuXKGRluoxz4VKWalxNyHSJqFCMzHLI2Yl9CUqCzJV3JEMqGW8xSi3Vnp/LT2eYo979u3b7mKY9GiRemGG25I3/ve91p1HQAArU0CAgAAWuitt95qcLzttttW/djoDR/ByRhOW61IPMScglUNoubnHdRDvJcY6vz4448Xur4NNtgge94YlBwVFaVqiREjRmS3CEpH+6uoSojh1pWVAI2JipG8GNjcEk0lT1pTPfapKJFIOProo8vJqGhjFWv6whe+UNN5uvnmm7PbmnSehgwZkm666aZysvHyyy/PZljEQHYAgPZKAgIAAFqosqJgww03rOnxTfW8b8yUKVPSaaedVg4sr4rSlef1qgY54ogjWhTgXdl7i5ZV66+/fjrzzDNXCEpHQiZuU6dOzY532GGH7KrzQw89tMkgb9GB6GgztTooep+KStQNHDgwvfPOO+XfnXjiienAAw9s9rFFn6clS5ak1rbVVltlM1+uvvrq7Dhaf02cODENGjSo1dcCANBaJCAAAGANEUOO4wr9fIA+WjhFALdHjx7Zle0x2Hq99dbLbiWPPfZYq7R6iXZEcdV9PlgcLY1iAG9cbf/JT34ybbHFFllgPNpO5a2s/U6lXr16pS9/+cvp9ttvz9oLzZgxo9HAf7R9Ovfcc9M111yTRo4cmXbfffcV7lM5f6KlWpIYKlqR+9RS8boRaF+4cGH5d/37988SEtUo+jy1lcGDB6dJkyaVz0MM4e7Xr1/NSUsAgDWFBAQAALRQ9JavbCEUiYBqxfDoaowfPz69//775eMjjzwyDR8+PGtjszpc7R2B7kiSlER7nxi221xwdVVaQkUCo0+fPtktgtPPPfdcNs8hBhU//PDDDQLt0W//qKOOSpMnT07dunVr8DwbbbRRg+MYhLzPPvuk9qKofWqJeN3jjz8+PfPMM+XfxSyKqOSpVuV5inWefPLJaU0T3wuRDLzsssuy46gGmTBhQho6dGhbLw0AoC7Wrs/TAgDAh0dc5Z/30ksvVf3YGBxd7TDd+++/v/xz165dswBsc8mHUquX1pBf39prr53OO++8qq7sbun61l133Wzw9+GHH57Gjh2bVXycf/75qXPnzg2SHKNGjWr23JUGN7dHLdmnloiqnfxnIyosLrroouwzUq32dJ4icZhvuxYtmfJtqQAA2hMJCAAAaKFddtmlwfHs2bOrfuxf/vKX8lDalYkr1d94443ycbTWqTaA++STT1a9nmoSGk1ZsGBB+eftt98+denSpfD1VSPaT0Xbp7iyPD/TIILgkfDJ23XXXRscP/3006keWrKv9VLLPq2qMWPGZBUV+cTZuHHjsjZctYhWYzFcu97nqTVENUfMScknfa688so2XRMAQL1IQAAAQAvttttuK7Qiqlb05l+VNk3V9oxfunRpuuuuu6peT352RMi3fGpOvpVSLT3tb7311lQP2223XYNzE0mcyivNoyIgv9a77747m2VRtJbsa71Vs0+r4sYbb0yjR49uUMUQgfZa2pOVdOjQIe25554NEndxW1NFFcomm2xSPr7uuuuyId0AAO2NBAQAABQQwP3MZz5TPp45c2bW3qY5b7/9drr++utXac5EftbCysSQ2/xQ6ObkW8OEfNVFLY+Naojly5c3+5iYRfDQQw+leqlMhEQgu7ItUd++fcvHr776arr22mvrvo7WaotV1D7Vavr06en0008vH0f1wuWXX5622WabVX7OQw45pMHxhRdeuFoN/a51v/MDuCNRWJoLAQDQnkhAAABAAfr379/g+Kc//elKg8wxmPfUU09N7777blXPHy1y8sHbe++9t9kkRNwnhkDXIl4jgvIl1SRSSnbaaacGyZWpU6eu9P6RpIg5FtUGke+44440b968qtcT+//II4+UjzfffPMVEjmlgcb5FkQXX3xx1ZUpJXEep02bttIkVV4t+1qreu1TtZ599tk0bNiw7DMe4vN0ySWXZNUmLbHffvs1aHd23333pXPOOaemdlGxpqi4Ka2trb8ztthii/LxPffc06brAQCoBwkIAAAoQJ8+fbLhuiULFy5M/fr1S48++ugK943EwQ9+8IMsgBqqDfYecMAB5Z+jTVAMs3388ccbbdcUAd9jjz02C7TmW71U0yqoR48eDSoUIpkSAepYdwzMLt0qqyPy6wtnnHFG1oanMkAc7YduueWWdNhhh2UVB9WuL/brm9/8Zvr+97+fJk2alF5//fUm7xv7Em1u8m2hDjzwwEbvG0Hgs88+u3wc6z3hhBPSSSedlObOndvka0SrokjyRCJpn332yapNmhLB9/zcg/Hjx2fJoZh/EUPL8/tabVKqtfepWoMGDcr2Jp/giZkg+ffY3C0SWI254IILGlRrRLVKBPIfeOCBJhMR8TcQ+xyPjSRGnNeiZly0RHwehgwZ0tbLAACoq/+/tAkAAFhlMRB6xIgRWVB98eLF5Sv8I7i77bbbZtUBcSV4JCbmzJlTfly0/4kAdAT6mxNJiylTppT787/yyitZ8LVbt27ZkN511lknvfbaa9mA3tKMgQjun3LKKenHP/5x1e9lwIABadasWeXjeM24Vdprr73SxIkTy8cRhI8+/TNmzMiOly1bln7yk5+kkSNHZi2qOnbsmK091lcKsse+xVXs1QZio1oikiGlK/a32mqr7P3HYN9oGxTtpp5//vlsH/K23nrrdMwxxzT5vBF0j8D3pZdeWq7IiCvl4xYJiu7du6eNN944C1xHgifuG+etmjZTIYLmkaS64YYbynsTSaK4VYrE0dChQ1NL1GufqlGZmIrWQrW2FzrooIPSueeeu8LvI5ER5yj2p5TkeOKJJ7J2RvH5igqJmDERf2uRVInkS1SD1GOuRxEOPvjgLHEVf8sAAO2RBAQAABQk2uxMmDAhu+I7334pAtVxq7TvvvtmVQKRWKhGBFbHjh2bBg8e3OCK9fnz52e3StFKJ/ruL1mypKb30bt37/TUU0+lq6++OtUqAuqRwMivJwLSUSlQKQLhZ511VurVq1daVRFArwyiV4rkT+xD5XyLSpEEiSD9aaed1qAKIdZfzSyM5ipZhg8fnl588cVygqY1FblPbW3vvffOKjuOO+64bD9L4nNeTSIvkkFrrbVWWh1ExVF87uIzBwDQHmnBBAAABdp5553TbbfdllU25Fvu5EVFRAQcx40blwUga7HHHntkbY0iedFUEDWu1I/KiLh6Pz8cuxbRVmjy5MnZ80T7oHjOagYTR9IjHhdJiKbefzzP/vvvn1VVfPvb3656TdEW6Wc/+1n6yle+ssLQ5KYC6nH/m2++OXXp0qWq14g2UpEsieB2NQOT43njXF911VXpiiuuWOl94wr9aBk0ZsyYrEXSDjvskAX78zM3itAa+9TWouInPt/nn39+9vmMSprmkkPxmTvvvPPSgw8+WPPfXT1FtUfXrl3behkAAHWx1v+qnfgGAADUJKoUYgbEokWL0tKlS9OWW26ZVUnEjIUirsCOK9pnzpyZzVGIPvfRKqhz586pZ8+eVSULWuP9x/qiFVW0y4l2UNEKKGZlRCuglojWR1FlEXMp4v2XqjwiyN+pU6csERTthFoqWuPMnj07m0kQVRGRLIigfjx3JBBiv1dnrbVPbS1aSkUrpqhUiRZo8b+5cZ7iby6qWiLAHy3KAABoXRIQAAAAAABA4bRgAgAAAAAACicBAQAAAAAAFE4CAgAAAAAAKJwEBAAAAAAAUDgJCAAAAAAAoHASEAAAAAAAQOEkIAAAAAAAgMJJQAAAAAAAAIWTgAAAAAAAAAonAQEAAAAAABROAgIAAAAAACicBAQAAAAAAFA4CQgAAAAAAKBwEhAAAAAAAEDhJCAAAAAAAIDCSUAAAAAAAACFk4AAAAAAAAAKJwEBAAAAAAAUTgICAAAAAAAonAQEAAAAAABQOAkIAAAAAACgcBIQAAAAAABA4SQgAAAAAACAwklAAAAAAAAAhZOAAAAAAAAACicBAQAAAAAApKL9H3VOHNrlqtvFAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1600x900 with 1 Axes>"
+       "<Figure size 1600x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -375,22 +383,21 @@
     "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
     "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
-    "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch  (O(N), array lower bound)\", lw=2)\n",
-    "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader  (O(batch))\", lw=2)\n",
-    "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n",
-    "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n",
-    "ax.set(\n",
-    "    xlabel=\"dataset size N\",\n",
-    "    ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n",
-    "    title=\"Array footprint is flat in N when streaming (theoretical lower bound)\",\n",
-    ")\n",
-    "ax.legend();"
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch  (O(N))\")\n",
+    "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader  (O(batch))\")\n",
+    "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n",
+    "ax.text(n_grid[-1], 30, \"26 GB RAM\", color=\"0.5\", ha=\"right\", va=\"bottom\")\n",
+    "ax.set_xlabel(\"dataset size N\")\n",
+    "ax.set_ylabel(\"array footprint (GB, lower bound)\")\n",
+    "ax.set_title(\"Memory is flat in N when streaming\")\n",
+    "ax.legend(loc=\"lower right\", framealpha=0.95)\n",
+    "fig.tight_layout();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "64cdea8e",
+   "id": "1209a446",
    "metadata": {},
    "source": [
     "That line is only the bare array; actual peak RSS is higher (the framework plus\n",
@@ -418,7 +425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9dbff83e",
+   "id": "b5388542",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -429,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26996637",
+   "id": "6dc3ce89",
    "metadata": {},
    "source": [
     "## References\n",
@@ -443,13 +450,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "5f48f79a",
+   "id": "ad8a11ba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T02:47:11.134055Z",
-     "iopub.status.busy": "2026-06-09T02:47:11.133970Z",
-     "iopub.status.idle": "2026-06-09T02:47:11.143141Z",
-     "shell.execute_reply": "2026-06-09T02:47:11.142770Z"
+     "iopub.execute_input": "2026-06-09T04:07:39.969974Z",
+     "iopub.status.busy": "2026-06-09T04:07:39.969907Z",
+     "iopub.status.idle": "2026-06-09T04:07:39.980436Z",
+     "shell.execute_reply": "2026-06-09T04:07:39.980077Z"
     }
    },
    "outputs": [
@@ -485,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5d5ae89",
+   "id": "845a0b24",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 87698f9b0..acbcd544e 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -190,17 +190,16 @@ n_grid = np.logspace(5, 9, 50)  # 1e5 .. 1e9 rows
 inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
 stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
 
-fig, ax = plt.subplots(figsize=(8, 4.5))
-ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch  (O(N), array lower bound)", lw=2)
-ax.loglog(n_grid, stream_gb, label="streaming DataLoader  (O(batch))", lw=2)
-ax.axhline(26, color="0.4", ls="--", lw=1)
-ax.text(1.2e5, 28, "26 GB RAM", color="0.4")
-ax.set(
-    xlabel="dataset size N",
-    ylabel="design-matrix size (GB) = N·ncols·8 bytes",
-    title="Array footprint is flat in N when streaming (theoretical lower bound)",
-)
-ax.legend();
+fig, ax = plt.subplots(figsize=(8, 5))
+ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch  (O(N))")
+ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader  (O(batch))")
+ax.axhline(26, color="0.5", ls="--", lw=1)
+ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom")
+ax.set_xlabel("dataset size N")
+ax.set_ylabel("array footprint (GB, lower bound)")
+ax.set_title("Memory is flat in N when streaming")
+ax.legend(loc="lower right", framealpha=0.95)
+fig.tight_layout();
 ```
 
 That line is only the bare array; actual peak RSS is higher (the framework plus

From 26ea08857be754f8f710e147931ba3fe30929f34 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 8 Jun 2026 23:15:20 -0500
Subject: [PATCH 05/42] Tighten example prose

---
 .../streaming_dataset.ipynb                   | 106 +++++++++--------
 .../streaming_dataset.myst.md                 | 110 +++++++++---------
 2 files changed, 106 insertions(+), 110 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 842e1b7ac..3d033611c 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -22,28 +22,27 @@
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
-    "minibatch variational inference is capped at datasets that fit in memory -- the\n",
-    "very regime where minibatching is meant to help. This notebook uses the streaming\n",
-    "API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:\n",
+    "minibatch variational inference only works on data that already fits in memory.\n",
+    "This notebook uses the streaming API in `pymc.variational.streaming`, which follows\n",
+    "the same structure as PyTorch's `torch.utils.data`:\n",
     "\n",
     "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n",
-    "  shuffles) an out-of-core source -- here a directory of Parquet shards read by\n",
-    "  {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size\n",
-    "  minibatches, holding only one batch in memory at a time;\n",
-    "* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;\n",
-    "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each\n",
-    "  minibatch into that placeholder with `set_data` every step -- **no callbacks**.\n",
+    "  shuffles) an out-of-core source into fixed-size minibatches, holding only one\n",
+    "  batch in memory at a time. Here the source is a directory of Parquet shards read\n",
+    "  by {func}`~pymc.variational.streaming.parquet_source`.\n",
+    "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n",
+    "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n",
+    "  minibatch into that placeholder with `set_data` every step. There are no callbacks.\n",
     "\n",
-    "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the\n",
-    "`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`\n",
-    "to the observed distribution and PyMC scales the minibatch log-likelihood by\n",
-    "`N / batch_size`. The one extra obligation is shuffling -- a streaming source is\n",
-    "only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`\n",
-    "handles with a bounded buffer.\n",
+    "The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`\n",
+    "is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed\n",
+    "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The\n",
+    "one extra requirement is shuffling. A streaming source is only as well mixed as the\n",
+    "order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a\n",
+    "bounded buffer.\n",
     "\n",
-    "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n",
-    "are easy to compare; the streaming machinery is identical at any size, and the\n",
-    "final section shows why it matters at scale."
+    "`N` is small here so the notebook runs in seconds. The streaming code is the same at\n",
+    "any size, and the last section shows what changes at scale."
    ]
   },
   {
@@ -84,11 +83,10 @@
    "source": [
     "## Put a dataset on disk and forget the array\n",
     "\n",
-    "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n",
-    "delete the in-memory table. From here on the features only exist on disk -- exactly\n",
-    "the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to\n",
-    "build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches\n",
-    "them.)"
+    "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n",
+    "in-memory table. From here the features only exist on disk. We keep `X` and `y`\n",
+    "around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit\n",
+    "never reads them."
    ]
   },
   {
@@ -139,13 +137,14 @@
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
     "\n",
-    "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:\n",
-    "it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data\n",
-    "scan), so `total_size=\"auto\"` resolves `N` for free. The `DataLoader` batches and\n",
-    "shuffles it into fixed-size minibatches. The model reads a `pm.Data(\"batch\", ...)`\n",
-    "*placeholder* -- the only data ever resident -- and the `Trainer` streams each\n",
-    "minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`\n",
-    "triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop."
+    "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n",
+    "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n",
+    "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n",
+    "batches and shuffles it into fixed-size minibatches. The model reads a\n",
+    "`pm.Data(\"batch\", ...)` placeholder, the only data resident at any point, and the\n",
+    "`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:\n",
+    "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n",
+    "runs the loop."
    ]
   },
   {
@@ -183,7 +182,7 @@
     "\n",
     "with pm.Model() as model:\n",
     "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
-    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder -- the ONLY data in RAM\n",
+    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder, the only data in RAM\n",
     "    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n",
     "    pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n",
     "\n",
@@ -243,10 +242,10 @@
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
     "\n",
-    "For comparison, the status-quo fit that keeps the whole dataset in memory.\n",
-    "Streaming changes *where the data lives*, not the inference: the two posteriors\n",
-    "land on top of each other (both show ADVI's characteristic mild bias relative to\n",
-    "the dashed ground truth, but they agree with each other)."
+    "For comparison, here is the usual fit that keeps the whole dataset in memory.\n",
+    "Streaming changes where the data lives, not the inference, so the two posteriors\n",
+    "land on top of each other. Both show ADVI's usual mild bias relative to the dashed\n",
+    "ground truth, but they agree with each other."
    ]
   },
   {
@@ -336,13 +335,12 @@
    "id": "bdee0324",
    "metadata": {},
    "source": [
-    "## Why bother: memory\n",
+    "## Memory usage\n",
     "\n",
     "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
-    "rows resident, so the array it must hold grows linearly in `N`; the streaming\n",
-    "`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design\n",
-    "matrix is the dominant cost; the line below is its *theoretical lower bound*\n",
-    "(`N * ncols * 8` bytes), not a measurement:"
+    "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n",
+    "only holds one `batch_size` buffer. The dense `float64` design matrix dominates the\n",
+    "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:"
    ]
   },
   {
@@ -400,25 +398,25 @@
    "id": "1209a446",
    "metadata": {},
    "source": [
-    "That line is only the bare array; actual peak RSS is higher (the framework plus\n",
-    "PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real\n",
-    "number on public, reproducible data, I measured peak memory on the\n",
-    "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),\n",
-    "the standard out-of-core learning benchmark, with the same logistic model (13\n",
-    "numeric features + the click label). Streaming through the `DataLoader` stayed flat\n",
-    "at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline\n",
-    "rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to\n",
+    "That line is only the bare array. Actual peak RSS is higher, because of the\n",
+    "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n",
+    "the real number on public data, I measured peak memory on the\n",
+    "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n",
+    "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n",
+    "features plus the click label). Streaming through the `DataLoader` stayed flat at\n",
+    "**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n",
+    "rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to\n",
     "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n",
-    "within ADVI noise (correlation **0.999** across all 14 coefficients). The point of\n",
-    "using Criteo rather than a private dataset is that anyone can rerun it.\n",
+    "within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is\n",
+    "public, so anyone can rerun this.\n",
     "\n",
-    "## When to reach for it\n",
+    "## When to use it\n",
     "\n",
     "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n",
     "  index gives perfectly i.i.d. minibatches for free.\n",
-    "* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory\n",
-    "  flat in `N` by streaming from disk, with no callbacks to wire up. The remaining\n",
-    "  obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave\n",
+    "* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory\n",
+    "  flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to\n",
+    "  watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave\n",
     "  shards for strongly ordered data, since a bounded buffer over strongly ordered\n",
     "  data only block-shuffles it and biases the posterior."
    ]
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index acbcd544e..67a355294 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -24,28 +24,27 @@ kernelspec:
 +++
 
 `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so
-minibatch variational inference is capped at datasets that fit in memory -- the
-very regime where minibatching is meant to help. This notebook uses the streaming
-API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:
+minibatch variational inference only works on data that already fits in memory.
+This notebook uses the streaming API in `pymc.variational.streaming`, which follows
+the same structure as PyTorch's `torch.utils.data`:
 
 * a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally
-  shuffles) an out-of-core source -- here a directory of Parquet shards read by
-  {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size
-  minibatches, holding only one batch in memory at a time;
-* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;
-* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each
-  minibatch into that placeholder with `set_data` every step -- **no callbacks**.
-
-The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the
-`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`
-to the observed distribution and PyMC scales the minibatch log-likelihood by
-`N / batch_size`. The one extra obligation is shuffling -- a streaming source is
-only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`
-handles with a bounded buffer.
-
-We use a modest `N` here so the notebook runs in seconds and the two posteriors
-are easy to compare; the streaming machinery is identical at any size, and the
-final section shows why it matters at scale.
+  shuffles) an out-of-core source into fixed-size minibatches, holding only one
+  batch in memory at a time. Here the source is a directory of Parquet shards read
+  by {func}`~pymc.variational.streaming.parquet_source`.
+* the model observes a `pm.Data` placeholder of one batch, not the whole array.
+* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each
+  minibatch into that placeholder with `set_data` every step. There are no callbacks.
+
+The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`
+is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed
+distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The
+one extra requirement is shuffling. A streaming source is only as well mixed as the
+order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a
+bounded buffer.
+
+`N` is small here so the notebook runs in seconds. The streaming code is the same at
+any size, and the last section shows what changes at scale.
 
 ```{code-cell} ipython3
 import glob
@@ -67,11 +66,10 @@ az.style.use("arviz-variat")
 
 ## Put a dataset on disk and forget the array
 
-We synthesise a logistic-regression dataset, write it to Parquet shards, and
-delete the in-memory table. From here on the features only exist on disk -- exactly
-the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to
-build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches
-them.)
+We build a logistic-regression dataset, write it to Parquet shards, and delete the
+in-memory table. From here the features only exist on disk. We keep `X` and `y`
+around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit
+never reads them.
 
 ```{code-cell} ipython3
 N = 30_000
@@ -95,13 +93,14 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 
 ## Stream minibatches off disk and fit with ADVI
 
-`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:
-it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data
-scan), so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and
-shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)`
-*placeholder* -- the only data ever resident -- and the `Trainer` streams each
-minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`
-triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop.
+`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.
+It reads one shard at a time and gets `n_rows` from the Parquet metadata without
+scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader`
+batches and shuffles it into fixed-size minibatches. The model reads a
+`pm.Data("batch", ...)` placeholder, the only data resident at any point, and the
+`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:
+`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`
+runs the loop.
 
 ```{code-cell} ipython3
 batch_size = 1024
@@ -117,7 +116,7 @@ loader = DataLoader(
 
 with pm.Model() as model:
     b = pm.Normal("b", 0.0, 3.0, shape=4)
-    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder -- the ONLY data in RAM
+    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder, the only data in RAM
     logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]
     pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader))
 
@@ -142,10 +141,10 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg
 
 ## The same posterior as in-RAM `pm.Minibatch`
 
-For comparison, the status-quo fit that keeps the whole dataset in memory.
-Streaming changes *where the data lives*, not the inference: the two posteriors
-land on top of each other (both show ADVI's characteristic mild bias relative to
-the dashed ground truth, but they agree with each other).
+For comparison, here is the usual fit that keeps the whole dataset in memory.
+Streaming changes where the data lives, not the inference, so the two posteriors
+land on top of each other. Both show ADVI's usual mild bias relative to the dashed
+ground truth, but they agree with each other.
 
 ```{code-cell} ipython3
 with pm.Model():
@@ -176,13 +175,12 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.
 fig.tight_layout();
 ```
 
-## Why bother: memory
+## Memory usage
 
 Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
-rows resident, so the array it must hold grows linearly in `N`; the streaming
-`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design
-matrix is the dominant cost; the line below is its *theoretical lower bound*
-(`N * ncols * 8` bytes), not a measurement:
+rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`
+only holds one `batch_size` buffer. The dense `float64` design matrix dominates the
+cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
@@ -202,25 +200,25 @@ ax.legend(loc="lower right", framealpha=0.95)
 fig.tight_layout();
 ```
 
-That line is only the bare array; actual peak RSS is higher (the framework plus
-PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real
-number on public, reproducible data, I measured peak memory on the
-[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),
-the standard out-of-core learning benchmark, with the same logistic model (13
-numeric features + the click label). Streaming through the `DataLoader` stayed flat
-at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline
-rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to
+That line is only the bare array. Actual peak RSS is higher, because of the
+framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get
+the real number on public data, I measured peak memory on the
+[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a
+standard out-of-core learning benchmark, with the same logistic model (13 numeric
+features plus the click label). Streaming through the `DataLoader` stayed flat at
+**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline
+rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to
 out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to
-within ADVI noise (correlation **0.999** across all 14 coefficients). The point of
-using Criteo rather than a private dataset is that anyone can rerun it.
+within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is
+public, so anyone can rerun this.
 
-## When to reach for it
+## When to use it
 
 * Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random
   index gives perfectly i.i.d. minibatches for free.
-* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory
-  flat in `N` by streaming from disk, with no callbacks to wire up. The remaining
-  obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave
+* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory
+  flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to
+  watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave
   shards for strongly ordered data, since a bounded buffer over strongly ordered
   data only block-shuffles it and biases the posterior.
 

From 9d49f19f479b89d49292fb1f24676db3548ae463 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 06:33:32 -0500
Subject: [PATCH 06/42] Trim code comments and prose emphasis

---
 .../streaming_dataset.ipynb                   | 135 +++++++++---------
 .../streaming_dataset.myst.md                 |  23 ++-
 2 files changed, 78 insertions(+), 80 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 3d033611c..2872da191 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f258e12b",
+   "id": "3bc2b2ad",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4f61327",
+   "id": "ab54cc68",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
@@ -48,13 +48,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3828b233",
+   "id": "571a608f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:15.994900Z",
-     "iopub.status.busy": "2026-06-09T04:07:15.994790Z",
-     "iopub.status.idle": "2026-06-09T04:07:17.733770Z",
-     "shell.execute_reply": "2026-06-09T04:07:17.733191Z"
+     "iopub.execute_input": "2026-06-10T11:32:49.788523Z",
+     "iopub.status.busy": "2026-06-10T11:32:49.788448Z",
+     "iopub.status.idle": "2026-06-10T11:32:52.092871Z",
+     "shell.execute_reply": "2026-06-10T11:32:52.090513Z"
     }
    },
    "outputs": [],
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ade8f410",
+   "id": "d1832538",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
@@ -92,13 +92,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4eb91e27",
+   "id": "78c39ff1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:17.735172Z",
-     "iopub.status.busy": "2026-06-09T04:07:17.734922Z",
-     "iopub.status.idle": "2026-06-09T04:07:17.756154Z",
-     "shell.execute_reply": "2026-06-09T04:07:17.755779Z"
+     "iopub.execute_input": "2026-06-10T11:32:52.098068Z",
+     "iopub.status.busy": "2026-06-10T11:32:52.097199Z",
+     "iopub.status.idle": "2026-06-10T11:32:52.140323Z",
+     "shell.execute_reply": "2026-06-10T11:32:52.139939Z"
     }
    },
    "outputs": [
@@ -117,7 +117,7 @@
     "X = rng.normal(size=(N, 3))\n",
     "p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))\n",
     "y = (rng.random(N) < p).astype(\"float64\")\n",
-    "table = np.column_stack([X, y]).astype(\"float64\")  # 3 features + 1 observed column\n",
+    "table = np.column_stack([X, y]).astype(\"float64\")\n",
     "\n",
     "shard_dir = tempfile.mkdtemp(prefix=\"streaming_demo_\")\n",
     "for i, s in enumerate(range(0, N, 5_000)):\n",
@@ -126,13 +126,13 @@
     "        pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n",
     "        f\"{shard_dir}/part_{i:03d}.parquet\",\n",
     "    )\n",
-    "del table  # the design matrix now lives only on disk\n",
+    "del table\n",
     "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9f8bcf9f",
+   "id": "8807e3db",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
@@ -150,13 +150,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cd4d4c9b",
+   "id": "ef67327d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:17.757225Z",
-     "iopub.status.busy": "2026-06-09T04:07:17.757170Z",
-     "iopub.status.idle": "2026-06-09T04:07:36.098596Z",
-     "shell.execute_reply": "2026-06-09T04:07:36.098132Z"
+     "iopub.execute_input": "2026-06-10T11:32:52.141370Z",
+     "iopub.status.busy": "2026-06-10T11:32:52.141295Z",
+     "iopub.status.idle": "2026-06-10T11:33:13.067413Z",
+     "shell.execute_reply": "2026-06-10T11:33:13.066448Z"
     }
    },
    "outputs": [
@@ -171,13 +171,13 @@
    "source": [
     "batch_size = 1024\n",
     "loader = DataLoader(\n",
-    "    parquet_source(shard_dir),  # an IterableDataset over the shards\n",
+    "    parquet_source(shard_dir),\n",
     "    batch_size=batch_size,\n",
-    "    sample_shape=(4,),  # 3 features + 1 observed column, streamed together\n",
-    "    shuffle=True,  # bounded-buffer shuffle (the shards are written in order)\n",
+    "    sample_shape=(4,),  # 3 features + 1 observed column\n",
+    "    shuffle=True,  # the shards were written in order\n",
     "    buffer_size=15_000,\n",
     "    seed=0,\n",
-    "    total_size=\"auto\",  # read N from Parquet metadata; len(loader) == N\n",
+    "    total_size=\"auto\",  # N from the Parquet metadata\n",
     ")\n",
     "\n",
     "with pm.Model() as model:\n",
@@ -186,7 +186,6 @@
     "    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n",
     "    pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n",
     "\n",
-    "    # No callbacks: the Trainer streams each minibatch into \"batch\" with set_data.\n",
     "    approx = Trainer(\n",
     "        method=\"advi\",\n",
     "        dataloader=loader,\n",
@@ -198,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa093535",
+   "id": "93a0f3c8",
    "metadata": {},
    "source": [
     "The negative-ELBO trace shows the fit converging while only ever holding a\n",
@@ -208,13 +207,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "efccd50a",
+   "id": "14202643",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:36.099763Z",
-     "iopub.status.busy": "2026-06-09T04:07:36.099686Z",
-     "iopub.status.idle": "2026-06-09T04:07:36.211882Z",
-     "shell.execute_reply": "2026-06-09T04:07:36.211538Z"
+     "iopub.execute_input": "2026-06-10T11:33:13.070274Z",
+     "iopub.status.busy": "2026-06-10T11:33:13.070129Z",
+     "iopub.status.idle": "2026-06-10T11:33:13.382429Z",
+     "shell.execute_reply": "2026-06-10T11:33:13.381618Z"
     }
    },
    "outputs": [
@@ -237,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a7cb7b8",
+   "id": "fd3600f2",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -251,13 +250,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7f44b970",
+   "id": "0448355b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:36.212996Z",
-     "iopub.status.busy": "2026-06-09T04:07:36.212919Z",
-     "iopub.status.idle": "2026-06-09T04:07:39.535782Z",
-     "shell.execute_reply": "2026-06-09T04:07:39.535456Z"
+     "iopub.execute_input": "2026-06-10T11:33:13.384387Z",
+     "iopub.status.busy": "2026-06-10T11:33:13.384018Z",
+     "iopub.status.idle": "2026-06-10T11:33:16.995799Z",
+     "shell.execute_reply": "2026-06-10T11:33:16.995376Z"
     }
    },
    "outputs": [
@@ -265,7 +264,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.55\n"
+      "Finished [100%]: Average Loss = 531.98\n"
      ]
     }
    ],
@@ -285,13 +284,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "377a3b12",
+   "id": "39864bac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:39.537071Z",
-     "iopub.status.busy": "2026-06-09T04:07:39.536998Z",
-     "iopub.status.idle": "2026-06-09T04:07:39.769680Z",
-     "shell.execute_reply": "2026-06-09T04:07:39.769277Z"
+     "iopub.execute_input": "2026-06-10T11:33:16.996992Z",
+     "iopub.status.busy": "2026-06-10T11:33:16.996925Z",
+     "iopub.status.idle": "2026-06-10T11:33:17.307953Z",
+     "shell.execute_reply": "2026-06-10T11:33:17.307475Z"
     }
    },
    "outputs": [
@@ -299,13 +298,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_16604/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_88126/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -332,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdee0324",
+   "id": "ed4b5c3f",
    "metadata": {},
    "source": [
     "## Memory usage\n",
@@ -346,13 +345,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9a2a3b7d",
+   "id": "dea82a3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:39.770819Z",
-     "iopub.status.busy": "2026-06-09T04:07:39.770747Z",
-     "iopub.status.idle": "2026-06-09T04:07:39.968826Z",
-     "shell.execute_reply": "2026-06-09T04:07:39.968516Z"
+     "iopub.execute_input": "2026-06-10T11:33:17.309098Z",
+     "iopub.status.busy": "2026-06-10T11:33:17.309011Z",
+     "iopub.status.idle": "2026-06-10T11:33:17.527207Z",
+     "shell.execute_reply": "2026-06-10T11:33:17.526802Z"
     }
    },
    "outputs": [
@@ -360,7 +359,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_16604/1052516414.py:15: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_88126/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
@@ -377,7 +376,7 @@
    ],
    "source": [
     "ncols = 4  # 3 features + observed\n",
-    "n_grid = np.logspace(5, 9, 50)  # 1e5 .. 1e9 rows\n",
+    "n_grid = np.logspace(5, 9, 50)\n",
     "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
     "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
     "\n",
@@ -395,7 +394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1209a446",
+   "id": "e38def80",
    "metadata": {},
    "source": [
     "That line is only the bare array. Actual peak RSS is higher, because of the\n",
@@ -404,10 +403,10 @@
     "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n",
     "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n",
     "features plus the click label). Streaming through the `DataLoader` stayed flat at\n",
-    "**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n",
-    "rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to\n",
-    "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n",
-    "within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is\n",
+    "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n",
+    "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n",
+    "out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to\n",
+    "within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is\n",
     "public, so anyone can rerun this.\n",
     "\n",
     "## When to use it\n",
@@ -423,7 +422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5388542",
+   "id": "fc44cb1d",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -434,7 +433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dc3ce89",
+   "id": "3b3b6311",
    "metadata": {},
    "source": [
     "## References\n",
@@ -448,13 +447,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ad8a11ba",
+   "id": "3f78084b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-09T04:07:39.969974Z",
-     "iopub.status.busy": "2026-06-09T04:07:39.969907Z",
-     "iopub.status.idle": "2026-06-09T04:07:39.980436Z",
-     "shell.execute_reply": "2026-06-09T04:07:39.980077Z"
+     "iopub.execute_input": "2026-06-10T11:33:17.528393Z",
+     "iopub.status.busy": "2026-06-10T11:33:17.528322Z",
+     "iopub.status.idle": "2026-06-10T11:33:17.539045Z",
+     "shell.execute_reply": "2026-06-10T11:33:17.538653Z"
     }
    },
    "outputs": [
@@ -462,7 +461,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Mon, 08 Jun 2026\n",
+      "Last updated: Wed, 10 Jun 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -490,7 +489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "845a0b24",
+   "id": "70321b5e",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 67a355294..b6744e5d0 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -78,7 +78,7 @@ b_true = np.array([0.4, -1.2, 0.8, -0.5])  # intercept + 3 slopes
 X = rng.normal(size=(N, 3))
 p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))
 y = (rng.random(N) < p).astype("float64")
-table = np.column_stack([X, y]).astype("float64")  # 3 features + 1 observed column
+table = np.column_stack([X, y]).astype("float64")
 
 shard_dir = tempfile.mkdtemp(prefix="streaming_demo_")
 for i, s in enumerate(range(0, N, 5_000)):
@@ -87,7 +87,7 @@ for i, s in enumerate(range(0, N, 5_000)):
         pa.table({f"c{j}": block[:, j] for j in range(4)}),
         f"{shard_dir}/part_{i:03d}.parquet",
     )
-del table  # the design matrix now lives only on disk
+del table
 print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 ```
 
@@ -105,13 +105,13 @@ runs the loop.
 ```{code-cell} ipython3
 batch_size = 1024
 loader = DataLoader(
-    parquet_source(shard_dir),  # an IterableDataset over the shards
+    parquet_source(shard_dir),
     batch_size=batch_size,
-    sample_shape=(4,),  # 3 features + 1 observed column, streamed together
-    shuffle=True,  # bounded-buffer shuffle (the shards are written in order)
+    sample_shape=(4,),  # 3 features + 1 observed column
+    shuffle=True,  # the shards were written in order
     buffer_size=15_000,
     seed=0,
-    total_size="auto",  # read N from Parquet metadata; len(loader) == N
+    total_size="auto",  # N from the Parquet metadata
 )
 
 with pm.Model() as model:
@@ -120,7 +120,6 @@ with pm.Model() as model:
     logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]
     pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader))
 
-    # No callbacks: the Trainer streams each minibatch into "batch" with set_data.
     approx = Trainer(
         method="advi",
         dataloader=loader,
@@ -184,7 +183,7 @@ cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measureme
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
-n_grid = np.logspace(5, 9, 50)  # 1e5 .. 1e9 rows
+n_grid = np.logspace(5, 9, 50)
 inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
 stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
 
@@ -206,10 +205,10 @@ the real number on public data, I measured peak memory on the
 [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a
 standard out-of-core learning benchmark, with the same logistic model (13 numeric
 features plus the click label). Streaming through the `DataLoader` stayed flat at
-**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline
-rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to
-out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to
-within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is
+about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline
+rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to
+out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to
+within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is
 public, so anyone can rerun this.
 
 ## When to use it

From 2e79e44908e967426664fcf840177b41f318b4bb Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 06:40:03 -0500
Subject: [PATCH 07/42] Use the bibliography directive and standard watermark
 section

---
 examples/references.bib                       |   8 ++
 .../streaming_dataset.ipynb                   | 127 +++++++++---------
 .../streaming_dataset.myst.md                 |  17 ++-
 3 files changed, 83 insertions(+), 69 deletions(-)

diff --git a/examples/references.bib b/examples/references.bib
index bf45cc5d1..994c98ff0 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -373,6 +373,14 @@ @book{hernan2020whatif
   year          = {2020},
   publisher     = {Chapman \& Hall/CRC}
 }
+@article{hoffman2013stochastic,
+  title         = {Stochastic Variational Inference},
+  author        = {Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John},
+  year          = {2013},
+  journal       = {Journal of Machine Learning Research},
+  volume        = {14},
+  pages         = {1303--1347}
+}
 @article{hoffman2014nuts,
   title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
   author        = {Hoffman, Matthew and Gelman, Andrew},
diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 2872da191..e06644d9d 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3bc2b2ad",
+   "id": "4b2f1f62",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
@@ -18,11 +18,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab54cc68",
+   "id": "260cd177",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
-    "minibatch variational inference only works on data that already fits in memory.\n",
+    "minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic`\n",
+    "only works on data that already fits in memory.\n",
     "This notebook uses the streaming API in `pymc.variational.streaming`, which follows\n",
     "the same structure as PyTorch's `torch.utils.data`:\n",
     "\n",
@@ -48,13 +49,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "571a608f",
+   "id": "09a82a7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:32:49.788523Z",
-     "iopub.status.busy": "2026-06-10T11:32:49.788448Z",
-     "iopub.status.idle": "2026-06-10T11:32:52.092871Z",
-     "shell.execute_reply": "2026-06-10T11:32:52.090513Z"
+     "iopub.execute_input": "2026-06-10T11:39:34.361751Z",
+     "iopub.status.busy": "2026-06-10T11:39:34.361654Z",
+     "iopub.status.idle": "2026-06-10T11:39:35.984364Z",
+     "shell.execute_reply": "2026-06-10T11:39:35.983837Z"
     }
    },
    "outputs": [],
@@ -78,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1832538",
+   "id": "5edda1f3",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
@@ -92,13 +93,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "78c39ff1",
+   "id": "a48d4744",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:32:52.098068Z",
-     "iopub.status.busy": "2026-06-10T11:32:52.097199Z",
-     "iopub.status.idle": "2026-06-10T11:32:52.140323Z",
-     "shell.execute_reply": "2026-06-10T11:32:52.139939Z"
+     "iopub.execute_input": "2026-06-10T11:39:35.986097Z",
+     "iopub.status.busy": "2026-06-10T11:39:35.985811Z",
+     "iopub.status.idle": "2026-06-10T11:39:36.007332Z",
+     "shell.execute_reply": "2026-06-10T11:39:36.006869Z"
     }
    },
    "outputs": [
@@ -132,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8807e3db",
+   "id": "f4c3da32",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
@@ -150,13 +151,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ef67327d",
+   "id": "b016b139",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:32:52.141370Z",
-     "iopub.status.busy": "2026-06-10T11:32:52.141295Z",
-     "iopub.status.idle": "2026-06-10T11:33:13.067413Z",
-     "shell.execute_reply": "2026-06-10T11:33:13.066448Z"
+     "iopub.execute_input": "2026-06-10T11:39:36.008377Z",
+     "iopub.status.busy": "2026-06-10T11:39:36.008303Z",
+     "iopub.status.idle": "2026-06-10T11:39:55.655782Z",
+     "shell.execute_reply": "2026-06-10T11:39:55.655387Z"
     }
    },
    "outputs": [
@@ -197,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93a0f3c8",
+   "id": "dbf19682",
    "metadata": {},
    "source": [
     "The negative-ELBO trace shows the fit converging while only ever holding a\n",
@@ -207,13 +208,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "14202643",
+   "id": "670b64e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:33:13.070274Z",
-     "iopub.status.busy": "2026-06-10T11:33:13.070129Z",
-     "iopub.status.idle": "2026-06-10T11:33:13.382429Z",
-     "shell.execute_reply": "2026-06-10T11:33:13.381618Z"
+     "iopub.execute_input": "2026-06-10T11:39:55.656948Z",
+     "iopub.status.busy": "2026-06-10T11:39:55.656886Z",
+     "iopub.status.idle": "2026-06-10T11:39:55.774489Z",
+     "shell.execute_reply": "2026-06-10T11:39:55.774093Z"
     }
    },
    "outputs": [
@@ -236,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd3600f2",
+   "id": "0d4b92eb",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -250,13 +251,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0448355b",
+   "id": "87575de0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:33:13.384387Z",
-     "iopub.status.busy": "2026-06-10T11:33:13.384018Z",
-     "iopub.status.idle": "2026-06-10T11:33:16.995799Z",
-     "shell.execute_reply": "2026-06-10T11:33:16.995376Z"
+     "iopub.execute_input": "2026-06-10T11:39:55.775563Z",
+     "iopub.status.busy": "2026-06-10T11:39:55.775481Z",
+     "iopub.status.idle": "2026-06-10T11:39:59.242341Z",
+     "shell.execute_reply": "2026-06-10T11:39:59.241833Z"
     }
    },
    "outputs": [
@@ -264,7 +265,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.98\n"
+      "Finished [100%]: Average Loss = 531.48\n"
      ]
     }
    ],
@@ -284,13 +285,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "39864bac",
+   "id": "e4db8d22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:33:16.996992Z",
-     "iopub.status.busy": "2026-06-10T11:33:16.996925Z",
-     "iopub.status.idle": "2026-06-10T11:33:17.307953Z",
-     "shell.execute_reply": "2026-06-10T11:33:17.307475Z"
+     "iopub.execute_input": "2026-06-10T11:39:59.243475Z",
+     "iopub.status.busy": "2026-06-10T11:39:59.243401Z",
+     "iopub.status.idle": "2026-06-10T11:39:59.495750Z",
+     "shell.execute_reply": "2026-06-10T11:39:59.495272Z"
     }
    },
    "outputs": [
@@ -298,13 +299,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_88126/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_93000/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -331,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed4b5c3f",
+   "id": "9a73c466",
    "metadata": {},
    "source": [
     "## Memory usage\n",
@@ -345,13 +346,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "dea82a3a",
+   "id": "e8de6035",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:33:17.309098Z",
-     "iopub.status.busy": "2026-06-10T11:33:17.309011Z",
-     "iopub.status.idle": "2026-06-10T11:33:17.527207Z",
-     "shell.execute_reply": "2026-06-10T11:33:17.526802Z"
+     "iopub.execute_input": "2026-06-10T11:39:59.496810Z",
+     "iopub.status.busy": "2026-06-10T11:39:59.496737Z",
+     "iopub.status.idle": "2026-06-10T11:39:59.718260Z",
+     "shell.execute_reply": "2026-06-10T11:39:59.717855Z"
     }
    },
    "outputs": [
@@ -359,7 +360,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_88126/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_93000/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
@@ -394,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e38def80",
+   "id": "1e807647",
    "metadata": {},
    "source": [
     "That line is only the bare array. Actual peak RSS is higher, because of the\n",
@@ -422,38 +423,40 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc44cb1d",
+   "id": "ab62416d",
    "metadata": {},
    "source": [
     "## Authors\n",
     "\n",
-    "* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code\n",
-    "  project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS)."
+    "* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026\n",
+    "  for the Google Summer of Code project Streaming Variational Inference for Large\n",
+    "  Datasets (PyMC / NumFOCUS)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3b3b6311",
+   "id": "f52db625",
    "metadata": {},
    "source": [
     "## References\n",
     "\n",
-    "* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic\n",
-    "  variational inference. *Journal of Machine Learning Research*, 14(1).\n",
-    "* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).\n",
-    "  Automatic differentiation variational inference. *JMLR*, 18(1)."
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::\n",
+    "\n",
+    "## Watermark"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3f78084b",
+   "id": "ee70f944",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:33:17.528393Z",
-     "iopub.status.busy": "2026-06-10T11:33:17.528322Z",
-     "iopub.status.idle": "2026-06-10T11:33:17.539045Z",
-     "shell.execute_reply": "2026-06-10T11:33:17.538653Z"
+     "iopub.execute_input": "2026-06-10T11:39:59.719505Z",
+     "iopub.status.busy": "2026-06-10T11:39:59.719423Z",
+     "iopub.status.idle": "2026-06-10T11:39:59.729582Z",
+     "shell.execute_reply": "2026-06-10T11:39:59.729189Z"
     }
    },
    "outputs": [
@@ -489,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70321b5e",
+   "id": "6750e1c1",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index b6744e5d0..8806eb6eb 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -24,7 +24,8 @@ kernelspec:
 +++
 
 `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so
-minibatch variational inference only works on data that already fits in memory.
+minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic`
+only works on data that already fits in memory.
 This notebook uses the streaming API in `pymc.variational.streaming`, which follows
 the same structure as PyTorch's `torch.utils.data`:
 
@@ -225,17 +226,19 @@ public, so anyone can rerun this.
 
 ## Authors
 
-* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code
-  project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS).
+* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026
+  for the Google Summer of Code project Streaming Variational Inference for Large
+  Datasets (PyMC / NumFOCUS).
 
 +++
 
 ## References
 
-* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic
-  variational inference. *Journal of Machine Learning Research*, 14(1).
-* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).
-  Automatic differentiation variational inference. *JMLR*, 18(1).
+:::{bibliography}
+:filter: docname in docnames
+:::
+
+## Watermark
 
 ```{code-cell} ipython3
 %load_ext watermark

From 52d15cd0cb450b5adff6a7fa78ddecb3835e0b86 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 06:41:56 -0500
Subject: [PATCH 08/42] Drop the redundant ADVI tag

---
 examples/variational_inference/streaming_dataset.ipynb   | 2 +-
 examples/variational_inference/streaming_dataset.myst.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index e06644d9d..5159e1281 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -10,7 +10,7 @@
     "# Out-of-core minibatch variational inference with DataLoader and Trainer\n",
     "\n",
     ":::{post} June 2026\n",
-    ":tags: variational inference, minibatch, out-of-core, ADVI\n",
+    ":tags: variational inference, minibatch, out-of-core\n",
     ":category: intermediate, how-to\n",
     ":author: Yicheng (Ethan) Yang\n",
     ":::"
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 8806eb6eb..e4014eb70 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -16,7 +16,7 @@ kernelspec:
 # Out-of-core minibatch variational inference with DataLoader and Trainer
 
 :::{post} June 2026
-:tags: variational inference, minibatch, out-of-core, ADVI
+:tags: variational inference, minibatch, out-of-core
 :category: intermediate, how-to
 :author: Yicheng (Ethan) Yang
 :::

From 015cda332475c26fa61481fd03a2cb6933d4aa30 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 22:45:32 -0500
Subject: [PATCH 09/42] State the memory behavior precisely

---
 .../streaming_dataset.ipynb                   | 118 +++++++++---------
 .../streaming_dataset.myst.md                 |   8 +-
 2 files changed, 63 insertions(+), 63 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 5159e1281..fe3418a5a 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4b2f1f62",
+   "id": "69dca719",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "260cd177",
+   "id": "6118e68b",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
@@ -49,13 +49,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "09a82a7f",
+   "id": "07118d45",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:34.361751Z",
-     "iopub.status.busy": "2026-06-10T11:39:34.361654Z",
-     "iopub.status.idle": "2026-06-10T11:39:35.984364Z",
-     "shell.execute_reply": "2026-06-10T11:39:35.983837Z"
+     "iopub.execute_input": "2026-06-11T03:45:03.295353Z",
+     "iopub.status.busy": "2026-06-11T03:45:03.295157Z",
+     "iopub.status.idle": "2026-06-11T03:45:04.943356Z",
+     "shell.execute_reply": "2026-06-11T03:45:04.942801Z"
     }
    },
    "outputs": [],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5edda1f3",
+   "id": "06d7ccca",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
@@ -93,13 +93,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a48d4744",
+   "id": "5776870b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:35.986097Z",
-     "iopub.status.busy": "2026-06-10T11:39:35.985811Z",
-     "iopub.status.idle": "2026-06-10T11:39:36.007332Z",
-     "shell.execute_reply": "2026-06-10T11:39:36.006869Z"
+     "iopub.execute_input": "2026-06-11T03:45:04.945137Z",
+     "iopub.status.busy": "2026-06-11T03:45:04.944792Z",
+     "iopub.status.idle": "2026-06-11T03:45:04.964769Z",
+     "shell.execute_reply": "2026-06-11T03:45:04.964475Z"
     }
    },
    "outputs": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4c3da32",
+   "id": "982a35b7",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
@@ -142,8 +142,8 @@
     "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n",
     "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n",
     "batches and shuffles it into fixed-size minibatches. The model reads a\n",
-    "`pm.Data(\"batch\", ...)` placeholder, the only data resident at any point, and the\n",
-    "`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:\n",
+    "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n",
+    "writes each minibatch into it with `set_data`. There are no callbacks:\n",
     "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n",
     "runs the loop."
    ]
@@ -151,13 +151,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b016b139",
+   "id": "38fb2079",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:36.008377Z",
-     "iopub.status.busy": "2026-06-10T11:39:36.008303Z",
-     "iopub.status.idle": "2026-06-10T11:39:55.655782Z",
-     "shell.execute_reply": "2026-06-10T11:39:55.655387Z"
+     "iopub.execute_input": "2026-06-11T03:45:04.965956Z",
+     "iopub.status.busy": "2026-06-11T03:45:04.965893Z",
+     "iopub.status.idle": "2026-06-11T03:45:24.342515Z",
+     "shell.execute_reply": "2026-06-11T03:45:24.341968Z"
     }
    },
    "outputs": [
@@ -183,7 +183,7 @@
     "\n",
     "with pm.Model() as model:\n",
     "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
-    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder, the only data in RAM\n",
+    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder for one minibatch\n",
     "    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n",
     "    pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n",
     "\n",
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbf19682",
+   "id": "d575798c",
    "metadata": {},
    "source": [
     "The negative-ELBO trace shows the fit converging while only ever holding a\n",
@@ -208,13 +208,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "670b64e1",
+   "id": "3208e4e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:55.656948Z",
-     "iopub.status.busy": "2026-06-10T11:39:55.656886Z",
-     "iopub.status.idle": "2026-06-10T11:39:55.774489Z",
-     "shell.execute_reply": "2026-06-10T11:39:55.774093Z"
+     "iopub.execute_input": "2026-06-11T03:45:24.343821Z",
+     "iopub.status.busy": "2026-06-11T03:45:24.343736Z",
+     "iopub.status.idle": "2026-06-11T03:45:24.456998Z",
+     "shell.execute_reply": "2026-06-11T03:45:24.456629Z"
     }
    },
    "outputs": [
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d4b92eb",
+   "id": "b4326fea",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -251,13 +251,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "87575de0",
+   "id": "a72964f1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:55.775563Z",
-     "iopub.status.busy": "2026-06-10T11:39:55.775481Z",
-     "iopub.status.idle": "2026-06-10T11:39:59.242341Z",
-     "shell.execute_reply": "2026-06-10T11:39:59.241833Z"
+     "iopub.execute_input": "2026-06-11T03:45:24.458117Z",
+     "iopub.status.busy": "2026-06-11T03:45:24.458036Z",
+     "iopub.status.idle": "2026-06-11T03:45:27.836757Z",
+     "shell.execute_reply": "2026-06-11T03:45:27.836192Z"
     }
    },
    "outputs": [
@@ -265,7 +265,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.48\n"
+      "Finished [100%]: Average Loss = 531.53\n"
      ]
     }
    ],
@@ -285,13 +285,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e4db8d22",
+   "id": "9834da2a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:59.243475Z",
-     "iopub.status.busy": "2026-06-10T11:39:59.243401Z",
-     "iopub.status.idle": "2026-06-10T11:39:59.495750Z",
-     "shell.execute_reply": "2026-06-10T11:39:59.495272Z"
+     "iopub.execute_input": "2026-06-11T03:45:27.837837Z",
+     "iopub.status.busy": "2026-06-11T03:45:27.837765Z",
+     "iopub.status.idle": "2026-06-11T03:45:28.077191Z",
+     "shell.execute_reply": "2026-06-11T03:45:28.076718Z"
     }
    },
    "outputs": [
@@ -299,13 +299,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_93000/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_71399/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -332,27 +332,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a73c466",
+   "id": "c04c6f42",
    "metadata": {},
    "source": [
     "## Memory usage\n",
     "\n",
     "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
     "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n",
-    "only holds one `batch_size` buffer. The dense `float64` design matrix dominates the\n",
+    "holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the\n",
     "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e8de6035",
+   "id": "118f6849",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:59.496810Z",
-     "iopub.status.busy": "2026-06-10T11:39:59.496737Z",
-     "iopub.status.idle": "2026-06-10T11:39:59.718260Z",
-     "shell.execute_reply": "2026-06-10T11:39:59.717855Z"
+     "iopub.execute_input": "2026-06-11T03:45:28.078399Z",
+     "iopub.status.busy": "2026-06-11T03:45:28.078326Z",
+     "iopub.status.idle": "2026-06-11T03:45:28.334354Z",
+     "shell.execute_reply": "2026-06-11T03:45:28.333943Z"
     }
    },
    "outputs": [
@@ -360,7 +360,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/claude-501/ipykernel_93000/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
+      "/tmp/claude-501/ipykernel_71399/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
       "  fig.tight_layout();\n"
      ]
     },
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e807647",
+   "id": "624c0133",
    "metadata": {},
    "source": [
     "That line is only the bare array. Actual peak RSS is higher, because of the\n",
@@ -423,7 +423,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab62416d",
+   "id": "7fdf59e4",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -435,7 +435,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f52db625",
+   "id": "2e983b8d",
    "metadata": {},
    "source": [
     "## References\n",
@@ -450,13 +450,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ee70f944",
+   "id": "0c4c4ddb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-10T11:39:59.719505Z",
-     "iopub.status.busy": "2026-06-10T11:39:59.719423Z",
-     "iopub.status.idle": "2026-06-10T11:39:59.729582Z",
-     "shell.execute_reply": "2026-06-10T11:39:59.729189Z"
+     "iopub.execute_input": "2026-06-11T03:45:28.335556Z",
+     "iopub.status.busy": "2026-06-11T03:45:28.335482Z",
+     "iopub.status.idle": "2026-06-11T03:45:28.345025Z",
+     "shell.execute_reply": "2026-06-11T03:45:28.344643Z"
     }
    },
    "outputs": [
@@ -492,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6750e1c1",
+   "id": "7e62e534",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index e4014eb70..010211c1e 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -98,8 +98,8 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 It reads one shard at a time and gets `n_rows` from the Parquet metadata without
 scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader`
 batches and shuffles it into fixed-size minibatches. The model reads a
-`pm.Data("batch", ...)` placeholder, the only data resident at any point, and the
-`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:
+`pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer`
+writes each minibatch into it with `set_data`. There are no callbacks:
 `total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`
 runs the loop.
 
@@ -117,7 +117,7 @@ loader = DataLoader(
 
 with pm.Model() as model:
     b = pm.Normal("b", 0.0, 3.0, shape=4)
-    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder, the only data in RAM
+    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder for one minibatch
     logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]
     pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader))
 
@@ -179,7 +179,7 @@ fig.tight_layout();
 
 Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
 rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`
-only holds one `batch_size` buffer. The dense `float64` design matrix dominates the
+holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the
 cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:
 
 ```{code-cell} ipython3

From e413b2ecb12fe17e25612822a821d8c6bb339e03 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 22:46:41 -0500
Subject: [PATCH 10/42] Attribute the agreement numbers to the right comparison

---
 examples/variational_inference/streaming_dataset.ipynb   | 6 +++---
 examples/variational_inference/streaming_dataset.myst.md | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index fe3418a5a..018ae014d 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -400,14 +400,14 @@
    "source": [
     "That line is only the bare array. Actual peak RSS is higher, because of the\n",
     "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n",
-    "the real number on public data, I measured peak memory on the\n",
+    "the real number on public data, we measured peak memory on the\n",
     "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n",
     "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n",
     "features plus the click label). Streaming through the `DataLoader` stayed flat at\n",
     "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n",
     "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n",
-    "out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to\n",
-    "within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is\n",
+    "out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the\n",
+    "largest gap is about 0.1, on the intercept. Criteo is\n",
     "public, so anyone can rerun this.\n",
     "\n",
     "## When to use it\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 010211c1e..5b71d4ee5 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -202,14 +202,14 @@ fig.tight_layout();
 
 That line is only the bare array. Actual peak RSS is higher, because of the
 framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get
-the real number on public data, I measured peak memory on the
+the real number on public data, we measured peak memory on the
 [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a
 standard out-of-core learning benchmark, with the same logistic model (13 numeric
 features plus the click label). Streaming through the `DataLoader` stayed flat at
 about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline
 rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to
-out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to
-within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is
+out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the
+largest gap is about 0.1, on the intercept. Criteo is
 public, so anyone can rerun this.
 
 ## When to use it

From 2d9771e835c2ec49f3e7a17f38d816d6cf1df108 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Wed, 10 Jun 2026 22:55:54 -0500
Subject: [PATCH 11/42] State the loader memory behavior precisely in the intro

---
 examples/variational_inference/streaming_dataset.ipynb   | 6 +++---
 examples/variational_inference/streaming_dataset.myst.md | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 018ae014d..2833d339f 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -28,9 +28,9 @@
     "the same structure as PyTorch's `torch.utils.data`:\n",
     "\n",
     "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n",
-    "  shuffles) an out-of-core source into fixed-size minibatches, holding only one\n",
-    "  batch in memory at a time. Here the source is a directory of Parquet shards read\n",
-    "  by {func}`~pymc.variational.streaming.parquet_source`.\n",
+    "  shuffles) an out-of-core source into fixed-size minibatches without loading\n",
+    "  the whole dataset into memory. Here the source is a directory of Parquet shards\n",
+    "  read by {func}`~pymc.variational.streaming.parquet_source`.\n",
     "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n",
     "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n",
     "  minibatch into that placeholder with `set_data` every step. There are no callbacks.\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 5b71d4ee5..cad94466f 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -30,9 +30,9 @@ This notebook uses the streaming API in `pymc.variational.streaming`, which foll
 the same structure as PyTorch's `torch.utils.data`:
 
 * a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally
-  shuffles) an out-of-core source into fixed-size minibatches, holding only one
-  batch in memory at a time. Here the source is a directory of Parquet shards read
-  by {func}`~pymc.variational.streaming.parquet_source`.
+  shuffles) an out-of-core source into fixed-size minibatches without loading
+  the whole dataset into memory. Here the source is a directory of Parquet shards
+  read by {func}`~pymc.variational.streaming.parquet_source`.
 * the model observes a `pm.Data` placeholder of one batch, not the whole array.
 * a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each
   minibatch into that placeholder with `set_data` every step. There are no callbacks.

From 5d13f6e11361e8de05837af7829e7f64a9833f06 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Thu, 11 Jun 2026 00:03:42 -0500
Subject: [PATCH 12/42] Fix figure layout warnings and tighten the scale-test
 prose

Set layout=tight at figure creation instead of calling tight_layout on
a constrained-layout figure, which emitted a warning into the committed
outputs. Declare pyarrow via extra_dependencies and the standard
install note. State that the Criteo numbers come from a run outside the
notebook, fix the out-of-memory extrapolation to follow from the stated
measurements, and correct two sentences that overstated what stays in
memory.
---
 .../streaming_dataset.ipynb                   | 177 +++++++++---------
 .../streaming_dataset.myst.md                 |  47 ++---
 2 files changed, 112 insertions(+), 112 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 2833d339f..2b69da89b 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "69dca719",
+   "id": "3a1d0597",
    "metadata": {},
    "source": [
     "(streaming_dataset)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6118e68b",
+   "id": "928fd0f3",
    "metadata": {},
    "source": [
     "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
@@ -46,16 +46,25 @@
     "any size, and the last section shows what changes at scale."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0d67f52e",
+   "metadata": {},
+   "source": [
+    ":::{include} ../extra_installs.md\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "07118d45",
+   "id": "c2f6955f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:03.295353Z",
-     "iopub.status.busy": "2026-06-11T03:45:03.295157Z",
-     "iopub.status.idle": "2026-06-11T03:45:04.943356Z",
-     "shell.execute_reply": "2026-06-11T03:45:04.942801Z"
+     "iopub.execute_input": "2026-06-11T05:01:07.808492Z",
+     "iopub.status.busy": "2026-06-11T05:01:07.808295Z",
+     "iopub.status.idle": "2026-06-11T05:01:09.181327Z",
+     "shell.execute_reply": "2026-06-11T05:01:09.180785Z"
     }
    },
    "outputs": [],
@@ -79,27 +88,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06d7ccca",
+   "id": "90fd8ed5",
    "metadata": {},
    "source": [
     "## Put a dataset on disk and forget the array\n",
     "\n",
     "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n",
-    "in-memory table. From here the features only exist on disk. We keep `X` and `y`\n",
-    "around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit\n",
-    "never reads them."
+    "in-memory table. From here the streaming path reads only the disk copy. We keep `X`\n",
+    "and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming\n",
+    "fit never reads them."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5776870b",
+   "id": "03ccf873",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:04.945137Z",
-     "iopub.status.busy": "2026-06-11T03:45:04.944792Z",
-     "iopub.status.idle": "2026-06-11T03:45:04.964769Z",
-     "shell.execute_reply": "2026-06-11T03:45:04.964475Z"
+     "iopub.execute_input": "2026-06-11T05:01:09.182814Z",
+     "iopub.status.busy": "2026-06-11T05:01:09.182552Z",
+     "iopub.status.idle": "2026-06-11T05:01:09.201682Z",
+     "shell.execute_reply": "2026-06-11T05:01:09.201315Z"
     }
    },
    "outputs": [
@@ -133,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "982a35b7",
+   "id": "8bd75c32",
    "metadata": {},
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
@@ -151,13 +160,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "38fb2079",
+   "id": "477343ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:04.965956Z",
-     "iopub.status.busy": "2026-06-11T03:45:04.965893Z",
-     "iopub.status.idle": "2026-06-11T03:45:24.342515Z",
-     "shell.execute_reply": "2026-06-11T03:45:24.341968Z"
+     "iopub.execute_input": "2026-06-11T05:01:09.202716Z",
+     "iopub.status.busy": "2026-06-11T05:01:09.202653Z",
+     "iopub.status.idle": "2026-06-11T05:01:27.550831Z",
+     "shell.execute_reply": "2026-06-11T05:01:27.550360Z"
     }
    },
    "outputs": [
@@ -198,23 +207,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d575798c",
+   "id": "2a1d41c8",
    "metadata": {},
    "source": [
-    "The negative-ELBO trace shows the fit converging while only ever holding a\n",
-    "`batch_size` buffer in memory:"
+    "The negative-ELBO trace shows the fit converging on minibatches read off disk:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "3208e4e3",
+   "id": "a3ff7cb9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:24.343821Z",
-     "iopub.status.busy": "2026-06-11T03:45:24.343736Z",
-     "iopub.status.idle": "2026-06-11T03:45:24.456998Z",
-     "shell.execute_reply": "2026-06-11T03:45:24.456629Z"
+     "iopub.execute_input": "2026-06-11T05:01:27.552089Z",
+     "iopub.status.busy": "2026-06-11T05:01:27.552002Z",
+     "iopub.status.idle": "2026-06-11T05:01:27.665910Z",
+     "shell.execute_reply": "2026-06-11T05:01:27.665530Z"
     }
    },
    "outputs": [
@@ -237,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4326fea",
+   "id": "9035a137",
    "metadata": {},
    "source": [
     "## The same posterior as in-RAM `pm.Minibatch`\n",
@@ -251,13 +259,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "a72964f1",
+   "id": "b3b5d9fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:24.458117Z",
-     "iopub.status.busy": "2026-06-11T03:45:24.458036Z",
-     "iopub.status.idle": "2026-06-11T03:45:27.836757Z",
-     "shell.execute_reply": "2026-06-11T03:45:27.836192Z"
+     "iopub.execute_input": "2026-06-11T05:01:27.667067Z",
+     "iopub.status.busy": "2026-06-11T05:01:27.666979Z",
+     "iopub.status.idle": "2026-06-11T05:01:30.969240Z",
+     "shell.execute_reply": "2026-06-11T05:01:30.968822Z"
     }
    },
    "outputs": [
@@ -265,7 +273,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.53\n"
+      "Finished [100%]: Average Loss = 531.87\n"
      ]
     }
    ],
@@ -285,27 +293,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9834da2a",
+   "id": "6731e9d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:27.837837Z",
-     "iopub.status.busy": "2026-06-11T03:45:27.837765Z",
-     "iopub.status.idle": "2026-06-11T03:45:28.077191Z",
-     "shell.execute_reply": "2026-06-11T03:45:28.076718Z"
+     "iopub.execute_input": "2026-06-11T05:01:30.970410Z",
+     "iopub.status.busy": "2026-06-11T05:01:30.970349Z",
+     "iopub.status.idle": "2026-06-11T05:01:31.236510Z",
+     "shell.execute_reply": "2026-06-11T05:01:31.236118Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/claude-501/ipykernel_71399/503280259.py:13: UserWarning: The figure layout has changed to tight\n",
-      "  fig.tight_layout();\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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tLABQHcl/ANANYmZ3kRjki46fokHoSLg7+uijs878GICqVHQkRYdS3KJz5Te/+U1aYIEFKn5+bFsMiMXzq3nfEI+PWYNxu/7667O/xcDaIYcckppBDOKdfPLJJRPryolO4egwjVsMVu6///5dMns/BuujImR0zDZKo465apISf/7znxcO6nSl+P2ecMIJWWJsuWSTUqJjMpJ34hYJsrvvvns2kN9bxO/nwAMPLEziikHt448/PksgiqUBa0m4bRYxYHPiiScWfuZyA4lxaxvgjwoprbBkfCRzRGd0VL+pRQxaR+d+dOi3VeGotA2K3+Rll12WJd/tt99+2cBdpSJpP9q9WkVbEMswxu2II47IBpxiAKDe4twVHf5RuSNvH0bS2ZZbbpklokVb097ZZ5+d/vjHP+YmhkWyWyw/Gcs1RlXjogS2Rojkx/gcRcuhxzk8Hhfn/zPPPDNLeKhGDAjutdde6Zlnnik8BuIYjKTVAw44IEtibpQY9Irk8RtvvLHsY2J/VJP8F4NDeYl/MUC81lprlbyvN7aFlYrBsN/+9reFbWbb8RWD+JG0UO+E0e5WSQJjJZMkyslLQg6RrB0Dw6uttlqWOJX3Ol2V/LfJJptk57qOIjkjJihtvvnmJZ8XicjlYvRIGIhJFs0gEr6iynM5kXwV1atqFd9bXLMUtSuxPHLEn/H4iN/rVSk4rtmKzgt5Inkh4o64xXkprpUjCbAazdq2xuvEPo/PmCcS5SIRJhL44/wcVa+qFX0OcRxEAkGliY+R6BAJFnGLfoE99tij5pit0cdhPUR7UjSJrSvEZJXurjIXsUzE/EXJ5ZFsF99TXFf86U9/6tSE2kb1eUQybEyQqaaKbsTUDz74YHaLpK+I/3fZZZdOtdXNXjku2tBoh/Kq1LYlqMVvONqrSBSMaqLViElmMUmqaFJ1nDvje4tj79hjj836H6lfX0NvF8d5JHVGrNjVx3ybmGBQLvkvzsdRaXvo0KE1vTYA9Ha195oBABWr9IK4qDM8Os2jE/qmm26qOgGvvejUjlnLkUxRaYdfzEqPzqbOvG97RZ0K3SEGTSMhLW7VJP51FIkdMfMxOo7rKTqgY5CvkYl/jTrmKhXfW+yjRib+xQzxSOiIwbZqEv9KfZZf//rXWYJQXjJFTxGD2/F5qxmcjEGPaIuio7wVRQJYVG+oZUC2WdvRItF+xXdWa2d8tINR5SD2XTWJf+1F2xVtWVTNaNSxE+f3GNyJhLFKB6IrEW1ftK15iX8d90UMasfgZZvDDz88q0hQ6SBvbH885+KLL07NIn4LsX/jOClK/OtY4SASIstVHS0lqkDGc6pJ8Ig2PRImo5pCI5WrwNcmqjqXq1BULlkwTySzl6oQ0xvbwkpFpb/tt9++qjYzjt9oJ6PCVyurJM6M6je1nksieTlPW6LqmmuuWZiwk5fA1hkR05arpBIJfqVE7BmV1EqJCkFRfbZZRPXwvO85qt7VKgahK5lQ0rE9jwlpRZW5GiGScyLJ9Ljjjqv4Oc3atkbCUFSuLOrr6JhoFdWR3nzzzareKxISIt6L81mt8Vbs+4gpYtJIuepHPf04jESvSG7r7luc/7pTW1XVaqrKxuSiODajD6i7dLbPIyYcRsLewQcfXFXiX0cRI8bvObYlkoB6mjifRtXRSLSrpv2L68uIw6p5TiQ+xfFe6WoqbRODd9xxx6ydoz59Db1dTPKPpOK4Ru3qY75j8l+eopgdAChP8h8AdINKl/uJpX7yBgOjY7KapYPyxCB/DN7HrPQiMfO1kQloXSEG4mPWctHAdaWigz9mm0ZVjnqIgbH4viPZqVEaecxV+h1G52x0gjZKDLRvuumm2UBwvUSlxOgIrnawqZXEZ/zDH/5Qc6d8dHo3MuGzFlFFJaqS9SZRXS06k2tNio3fdlRBuvvuu+uyPZE8GIMD5WaZd4eoABcV5xrdBkYHf3TaR1WdWpOyoy2vZsCqK0VVvaKqXuXEZ4iKopWIJIRY7q3WJTSjUky5JJ3uEJVCYrnocuK8U2mVpxhki2qS1SYb9sa2sFJxDP/+97+vaSAtvrtIVmlEpaZ6qWTgdu6556650nDevomEuzXWWCP776hWPeWUU+a+Xq3tTZFYHrxc9c1IVi51/oprj3KxekxOaqZl04quJ+eZZ56aXjcqp0WF6FrEOTQGsJs1cSCSUOKarEiztq1RSSgqCtUizrVRXb9SUalr77337tRkrPZi4mNUS660Te7Jx2FPFJX3I8GzlgmOkTATVbe7I2Gzs30ekbAXE4UiIbZeIgE+YrxmuQ6ol0iQrPW689FHH83aoErEBKJ4rziOqhXtUUwW7c7k057a18B/KhZHldyuPuarjfd62vgDAHQnyX8A0A0qqQ7Rr1+/skv+RgfUYYcd1gVbltJZZ52VLr300tzOxnKVJlpVdPBGxbEnnniiSxIQhg8f3unXieSAqJbXKI085qoZDItlnRolZs1Hp21XdDY+/PDD2WBET9XZJNnY57EkYitVe6q1Y7SVRXJYNZU02ovBkPh91buqRCR/NDqxOo6FSOLorNi3tQ66RTWcaGNqHaRu+x1GxdNmUJSEVsk5N6oMFe2zSK6qpnJRKfWadFDrcnqRUFuP7YuqI3kD5vPNN1+af/75f/D33tgWVqqzcV8sR33zzTenVlU04D7BBBPUvLRxUdwRrzto0KDvlhZuSwQsJ5Za7qpJGlFZtJzzzz//B3+74IILSj42Pke5ZYIbpejaa6655qr6NeM8GNVrOyPa/6hK2KwiQb0onmrWtrWzEzji+ZX0pUTieq0Ti4rePyoqFukNx2FPEr+nSCztTDXuiINjdYSu1pk+j7juiQmq9Zyo2CaSEeNarV4TRXvC9UT0cxXFBvGdRD9GLYl/7ZcmjkkNvV1n+hqozzm6kmO+3OpIMeEl75qmM78RAOjN+jR6AwCgp4vklJgFXmTOOecsW1EulqWNDp4iU089dVatYuDAgdkM30hCqyTB4ZBDDsmqwZS6+L7vvvsKlyCNJbgWXnjhNO2002YDc3GRHkuQRXJUVOdqZGW2UqJ6wi233FJRh0QsPzX99NNnA2jRqR/7Iy/ZKzo+ouJADED07du35m1s5Kz/Rh9zlWp0ZYRI9KxkGdJY8nDppZfOPmssRxnLc8Ys2aLBhkisGDp0aOHyc60uKtIstdRS2e8sfj8xEz4G+Yr2Twwgx285b5C8WcSgSNFys9F2Rjs600wzZcdMDAxEOxrtZ7SjUXGsXsuud5fO/Eaj4mwlg73R9kQ7Pc0002T7JwbHIoEmrxM6krdiCdaLLrqoqm0af/zxs+N0hhlmyN43jt1I2o+2MtrNOOc9/fTThee8OLaj8l69KgC2JXXF8jkRS8SxEx35sSxZno5tdZ8+fbLf4iyzzJKd56KKUFRUKEoUiwGs2DfNIiZTLLPMMlmbG991fI6iSn3xHUZyUCyxmNcmRwxQiUhgWXDBBbPjI76HqNbVLFUpYjnEWLKt3Dk+ks8j6TaO9TxFS46VqvrXW9vCWkTcGW3bHHPMkV1LxLFXSXwVlaqKEteatSJw0bLFsSxvXpX0vAHEWFaz6LU7LlmdVxU12pSoorTaaquleotExGjLSy3nHu/Z/vcZiULlKiGvuOKK2fmqmZZmzKuSFb/9vMqk5URV8Ura13j9iMdjn0SMENvy2GOPdSr5p5LzUXxXcYvr5YgbYjva2rU33ngjjRgxonC59fh8UdVvzz33LHl/q7StcW0cccbMM8+c9S9ErFLJEqTRrsWSsOXEZ4hKWEXfZcRKcW6Oc3Tsg/gdx3577bXXcp8XCbZRkTNii1Y8DvmhiMEr6aOKqrBxLp599tmz7ygm70Sc1J0T0DpzPRWTdCqJXeMaYNFFF02zzjpr9t/R7xWxa/Rd5In9EUm3sZxwTxPxRvzmp5pqqqzPJ6qjFf3Go02JhNCOMUXH9iT6O4pEe7XQQgtl7VW0ndFOxXfSk1eHaLX+wJ6mq475cqK9iTiolLhOjTg479wPAJQm+Q8AulB0oEdHcCXLgaywwgol/x4DT0Udk9EZFBV8YvnR6CRqLyqARGd43qBCdCD95S9/KVnprSh5IGb7xmzi6CQsJ2ZjPvjgg9ktOhDKVSqK5Qfbd3zHbOy8weXoiI2ZhkUdtu3FQEMkXeSJzxKVoWJZ4I7VGGNwJWaY5lWui0G2GKSI5VXqJQYoIskuBgpi0CA6umI/dkX1wkYfc7WafPLJswHTSPiIbYrqCZEIFMk49Rad/nfeeWfh4+IYiGTQ+P7aizZhr732Kjmw214skxUDy+2P40gsbf872W677XIH/NZdd92y+7njd9fdIrHxoIMOyr679uK4/tWvflU4GBfH6hZbbNHwz1GkaEA12v9jjz32B/uhY9sVSaOR2BbL/MRAYSmxPFn7BKZIzopE1TxFSXb12r/RkRztWCQNx6BZW4J4JGa0F+1buUpG7QfUI4Fvgw02+MH5Jzqr4zPnLW/V9hsud+5t+9wxUB6/wRgQi+VpipK647cZg2wxCJZXpSMS0GPgvTNJ4m1iecpI5ooBovZtbJyfK10SKjrf4zVmm2227/4WSQHbbLNNbkJOnBNjAKtZOuajTYllU9snCMXniHY4lnfLEzFKXvJfJcspTjHFFNlvebnllvvBfoqk96KEue4Qv78f/ehHZSdBxDEc2xnLaJUT33ne+St+n5E81ci2sJVFWxOJCe1/j/G9RNwUidF5YhJG7ONIHmwmMYjXPokgzgHxu4j2P6rXFFXmi2Mi4oJaFC3RG+3wqquu+r2/RYJQJGzlJc7FNndF8l/YaqutSp6747uNxPWYpBPyEhTjNZpJnJvzJpRFjFBtvBFxYiXL1cVkmoiF26o7tonzWxxXr776aqqHiNdj4HyVVVbJ4oZI4iz6LcZvI+KViBvyJhVdf/31ZZP/WqFtjaS/qGAY56A2EQcdeuihhRMlixIb49q+KEEhzstxzTHjjDN+7+/RtkYlzwMPPDCLF8r505/+VDb5r9mOQ/JF31Ql8dgCCyyQJbZFsmp7UUUvruOLksq7SqV9HnE8V1IRNCYPHXnkkVk/U8f9FDH1jTfeWFg9e8cddyycNNJKoh8wVgpp3xcYE6JiidRoJ4uuJ8olQkU7XaqCb0dxzEV19nnnnfd7f49zRFz/VtIP1VV6Ql9Do3VHv3ezHPNF31eeiMGbpY8BAFqJ5D8AqONAVohOhg8++CA98sgjWcdO0YVy24X66quvXvL1K1lSL5aYKTXIGmLwIToIYwAob1AgBsZiQCGqGbWX1wkeg+t77LFHYWdNDIbH4FjcomMjOn9iH3XUsXJQUQdGDKZEIlw1onOyqFpbDEyU67yIaoDRaR8DJHlLbUSHTj2S/yKpJQbgI8ErqkV0FFWN6rmMSzMcc9WKJIPogP/JT35SMgk1OsSLqldV6/TTTy98TGxPdJiXEjO4I5Fk/fXXz52xHN9tJGi0H5Tu+DspGlSM31G1v5PuEANgkdxQavtjsCX2z3rrrZebRBqDZFE1ZPnll0/NLK8dDTHglzcgG+L3H5VC4hbJyZGYXWqJyPgNtP8d5CVmt+nq4yN+45EsHIlZpc4XMWjavgpZtEF5lSbiM8VvMDrC85Lhtt1226wzOq+dzkv+i3NBx8TdIvH54juK7Yvfbbk2MM4hMWjYPmGvFvF+kSTc8XWindh1110rSv6LNvS00077waDfJJNMklX1K0ogiUHrZuiYj6o8pdqU+Bzx92hz8ioA5iU5RtxSNMgbCURxTHUcqGuLHeJ4iu+9O5aIKxJV+fIqIMdAXl7yX9HSwPFbj/3eyLawVcXg+dlnn51VCev4W48BuLi+uOuuu8o+PxJgInaIKkXN5NRTT81utYh2OJ4bbXu14rqsKMkjzgP9+/cveRxH21hOfA+RHBHXOfUWEzei3SrVZsW1zO67755dz0Rl01Li+8+rUtYI5aq7tIkKvtW6+OKLCyumLbLIItl5slSyfSxNHjFHUbxZqbj2rzZuiJjmxz/+cXYMxuB7Xswb33mp30Gzt60RI8RvqWO8Gd9JJORF0nJeckYkNkWF5VL7Nn4jcRzkiSqYkTxdqm8h2ta2qqLbb7992deIZMhIQiwVezbbcUhxpdmi6mmRJBrn4ojfSk2YiftiAlLRRNl6qrbPIyarFlW+jpg1rllK9TPFuS0S0GIyUV6yWSTxxvEb/Xw9QVz3xHVrR5G0e8IJJ2TXG3krZORdT0TcUHTMRFsd/SClzonR/p988snZNlbSz9wVekJfQ6N1R793sxzznYn7iuJGAKC05poKDAAtJmY4Rmd2+1tUCVpppZWyWXOVdshsvPHG36vu0b6TuajDLjroyyVhtYlO6kgyyhMX87F0VEd5y2u1LRVUrdhPUXWmEYpm5UdVgEpmLUbSY56oypdX9akS0XEVVQZisLdUh2zb9xPHXL00wzFXjegEi873qP5WruMxZpSWSsaoVQwW3H///bmPie+lrSpLOdGhXkkVm7xB/lYV31VU2MhLXIxqBnlVuNp09hjqDkXLFBZVOCwllsaLQadmFwksUaUo2tVyieIxwBYDaZW20xtttFHZxL/2x1i0nXmi+l9epZhqB/Dbi0S6okSVWjvK24sEw3LngBhMrqSyYCSqd0z8axP7uWg/NMOSS1GlIBIIyrUpMWDasRpfR5HEU25go5Il06ISbt65Jo7/iBvrUe2xsyLZadpppy17fywvW65KRyRTXXPNNbmvHwPTpfTmtrBS++yzzw8S/9qLJKEikQDQU8w333xZAkOtsW4kQBdN+ikX0xZdD0TCQ9FvoTNtWpzrSok4Pd73wgsvLJtgHnFxsyk18au9Ukk2RWIJxCIxoJ3X7lYab1aiM3FDVAosUi5uaPa2NaoalktciKSH6DvJE8d5uYlccR0Qv8W8eDDig6LkiogRimLLcglQzXYc1kNUlotK9d19q6QqWmdVEtNFxei8Nil+c/GY7lJLn0cl1eHiuCzXzxTidxPX7EW/n57SXxHXbtGPm5esFJMUa43BKum32HnnnXOToiJxrFSiVm9VS18D3XfM5yk18aaauBEAKE3yHwA0WHSsl0skq6RzKBIHK7HJJpsUPqbU+8VyReXEAPCWW26ZVXBrhVl5kbSVVwUqVLpcQSRKFFVQ6GxSUiQSVDLIW0/NcMxVI5ac7mzlrGpF1Z2ipaWikmepqkcdrbHGGoUDhZUu29lKYumvGOQqEgmkRZUN4/vIs+GGGxYONHX1MkXRXkTyUTmxfE9UXYhlNKNd7UliwKjjcll54lxSVM200na6qBpdDBZXWtUmBp4jWTCqLUTVjWgHo5JMDI5Gh3hU8+x4K0qKi2SzzspLxI7BoaLldIr2Z/z+in4fsXRno0XiQPulBEup5DgsV3EnqhIVKZeo015sY1ESYneI77Voe8tV94skh7xkqrnnnrvsb683t4WVDsBFdY2uOo5j4kKptqqSW1Ss6k6RCBWV76LKXakJUpUqWk44jseoClruWC5676LX74xI8iiXcBFVlmLflBtMLZqA0whFsXP7peYqEZXgIobLM8ccc2SJ8PWIN2u57owYPqrmxLV+JNLF5LdYkjC2qeNvLBJdi5SLG5q5bY0ltIsmYXWmXStKOoqKe5Vcc1QSN5a6Jmv245AfxvNFE3Sjj6coIbX9agfdodo+j6igHtctRb+7ooTXtvYlrt3zxKSR7qyC2FXiN5iXDFlJe5V3XVTUbxG//6gAWklb1ZnYqDf3NdC9x3yeomqGUTEfAKieZX8BoIFiUD6WdCi3XFR00BeppMOurYMoEgHyljgp1XEdnYxR7r/cbPuobhdVSkJ0GkTnYNstZldG8uA888xT9YBOV4hOyS+++CL3Mfvuu292q4fOVP6Ljr+8pYe6SjMcc5WKpLnNN988NeM+qqR6R1uHVyQO5VUgiI70cktdtapKj6FIhIi2JJY6KydvmbBmEb+DWJr4xhtvLFtF9Q9/+EN2i2oOMUgZM9Tjs0dnayzfF4OG3TXAVC+x/ZHgWo1K2oS8ZfFqOS/kiQpLsZTr3//+98KqqNWqx9JuMaieJ46ZvMG4aIPiHJ2nKJG5GTrmoyJtkUqqSpX7LEXHSSRdRKJQJSIR4bbbbkuNFgmssQxiucph1113XRbfdaxUVLTkbywpXE5vbQsrFcnERYkfRVUymuU32VnR3kYbGUtplqvkUiQGI2+++ebcx0SCR96gZyRHn3jiiWXvHzFiRHbeiuStrpggFttXaqnwF154oezzIrE3LxGsUYqW2ay2KmpMFChKZIv2tl7xZqXeeuutdMopp2TVGSN+7464oZnb1krOz51p14rixpjkUa/fZ6lYoFmPQ0qLVSuKElUitq5kKdNos+I6vqur3tXS5xHHaiQA1uN6vK1vo2hCYvSRxHmrldXjeiIv0b2o3yLa5VjCthLRrrz44oupN6ulr4HuPearWf64o6K+ewCgNMl/ANAgMfP/0EMPza1UU1QVKAar8paEaC86MKNzJK+DqFRZ/ag48ctf/jJbqq6STvno9OuYHBUdo5EIFtVMYiZrJBM2Qj2qLFWjM8sUxCBMuWUYu1IzHHOVisoZjUiIq+Q4mmWWWSp+vXhs0fJD8Z49KfmvmtnZ8di8QbAY0IylOisZpGmkqNhw66235i5NFuKzxPJspZZoi8ohce5Yd911uyTRoN6iMl61SRvd3U7nvV8MckW1nnok6ZXS2aSAaI+Lqt0VdapHG12UbFS01Fck5zRa/DaKVDIJodxn+eijjwr3Y6XH+kwzzZSaQSz7G8kid9xxR9nzcwxot69+E4M7eclUcUwWVSzpjW1hpfKqbVdaJaNZfpOdFbFjLBV6ww03ZBVXa1kS9vrrry9MfsirnlpJ8l9b9b/f/va3qStEhfVSyX/lRHvejEv+VnI+KmoTqm2Xq21vi+LNSkRydFwzFx13XRE3NGvbWsn5uTPtWncuCxjXHJHE2v5YbsbjkPIq+b4iAatS8d12dfJfLX0eXdFfUY/3bHb1up4oJdqOoiSpavtIerta+hrovmO+s5NCKokNAIAfUkseALpRdExE510MIp122mmFg/ZFndmVLCvaXtHAWbn3iyou2267bapVDELEsiPHHHNM1kFz9NFHd9mgSE9J/qtk6aeu0CzHXCvvo1DNIHUlj+3Oga3uUM3+qeSYq2Qgpxk6VqPiSrWVbTpWCzjjjDOyQdmf/exnWXWZZla0zFszt9N333132nnnnbss8a8eCTqV/DaKjrdq2/RmVUnVoM4kCBdVialmULaZ9nlelb5SVf4iCSlv4DKqbxR9vt7YFtbzOO7MfmtFsVTxLrvsUjhAWMuSvJNNNlnhMtwxuF4Ub0aFt65aSjWWWqwkKbRNJG41YvJQJYqWlau2wksl5+dq2tvOts2xDHMkgXblNW5e3NCsbWtXn58bHTc223FYL1EVOI7l7r7V0tZXo5Lvq9liulr6PPRX1Cbigq5qr3pqW9FqfQ103zFfpCjuK4obAYDSmrs8BgC0qKiSE50xcYslfWMpuFgSJErqVzOTuJnEUrgxAHXcccdVtOxpXiLgWWedlS1XFcvN9eRBzGorWLQ3YMCAum5LT2Qf0WpVjlZfffWsqmckQEeViM5sd1R3iaXOLrzwwqyCVzNqhd9oqXY6KutE5Z7OtOHNUEWpu16jGVRSGaCowmGeiFXyjoeopFSpZjquhg4dmqaaaqr0zjvvlLw/lieOxMe2hO2rr766U8mEvbUtrOdx3KoVTnbdddesMllb9aw33ngjq656zjnnFCYYPfTQQ+n444+vqrpeVDWLiUdFEwdiqdPOGj16dJYwHol3XVX978ADD6zosVtttVVqVkUxQVGSdTOLY/jwww9veDzajG1rV5+fu1szncO70oMPPpi23nrrbn/fmCh7/vnnd9nrVxL3VhPTdXWyYqtcT3VGpe1UdywB2tPaq56uK38bzXRc9tRjvighdvLJJ++S9wWAnk7yHwB0wpFHHpk23HDDhnVmxOBZNYoGVYreLwaJ4/b444+nW265JT3wwANpxIgRNXV43HnnnVnH7nbbbZe6SyRitop+/fo15H2b7ZhrxX1U7QBmJY/taZ3+1eyfSo65SmYsN4sYlD399NPT66+/nv75z39mVY0effTR9OGHH1b9WqNGjcqWRTz11FNTM6rlN9oM7fSwYcOyxJQ8kQwVSyvGOTEqQ8X/d1weN5ZLje+I1heDD3kV76r5/TbTsmhxzEYcW64NiSo8sezqJptskiUI3nvvvWVfK6qTLbzwwhW/d29qC/m+mBwVx0vc4tiKyn6RaJInkgSjsuSQIUPqUvWv3uL9uir5L6qw/fGPfyyscjzbbLOlZZddNjWr6aabLvf+aqvMVVJRrprrlmqvcdr729/+lrskb5hyyimzZKqoNhnVGaO6WMcB9Xostdvb2taIG4titq7UTMchqS7XjNX8VrqjOn8t11Ot1F9RaUJtV1Zj7w7aivrryv7A3nJcNtLbb7+de//000/fbdsCAD2J5D8AaGJFSRCff/55dsFctHxw2wzmkSNH1qXDLgbe2gbfYkmY6HB/5ZVXsiobMdDw0ksvpccee6xwgDsqADZb8t95552XllxyydRbNesx10wqOY7i91DpAPXLL79c+JhW3E9F+6dej43B01asIBqdmdH+tbWB7733XnYsvPrqq9lga3zuJ598Mvv/PFGZK9rcWWedNfUElfy+Ivm8KzuDY2nTooGGSy65JEu0yGMAt+cYNGhQbnJBxD5xfqxkeaJnnnkmNZNIvvrrX/9atsJFLP0bj7n22mtzlzattOpfR9rC3i0SAf/85z9nCW55g4BxvXHEEUekiy++uPA141ju7uS/OC+1r5JZT9GubLTRRtl1U55ISG9mkfw24YQTlp00Ft9/fHeVVresJImnKIaqNTbt6Oabb869P66bYlng2AfdFTP0lrY1rpHyzs8bbLBBOuqoo7rs/ZvpOKTYwIEDszYmr6pXrFBRqWaL6artr6hUV/ZXVDKROCajFPUrNbuoOhnXkHmTierZR0Ln9JbjspGKJn0MHjy427YFAHoSyX8A0MSiIkbM2C9aCmuttdYqfK0nnniicFmSeL9qRcWCGFzomIgRg3QxYLz//vuXXTollsmKJYRred9azDTTTLmDTiEqj/Tm5L9WOOYarZJtjn0UA9mVdBjGwFtRp1cMjvckRUvxtXn33XcLO1Rb8RgqNxgVt8UWW+x7f49Kqz//+c+z9rKce+65p2kHZatVyfcZ7XRXJv8VDfrFsnpFiX/RmV1UpYnWEcnc8VssJ2KeWMJ0lVVWyX2dGGyOJKFmEr+lqBYWy5aWO59FokjEdOVEbLXeeuvVZXu0hV0n4ttYxrMZK2vG9ULbssDlPPLII9lSpFFVtegc0d1VVyOei6qxP/nJT7rk9SOx79xzzy2bgBtJh5Hk1Mwi4SYq28UEsVLimiESqaMqXiUi7onqpXlJyXHM1CvezGv/ixJjIjk6L/EvdPVvs6e2rXFMPfXUU7ntQTVJpdVqluOQykTy1RxzzJH1QZUT1+YxibUogS7aq2ZN/ouq5LGcZ5ybyon4rlLDhw+v+RquaGJMJZUWo4Jp3m+sVcSxV+4cGGIydSRqR1tdpNJ2hdIcl4334osvlr0vzqs9pZ8PALrb99cXAACayjLLLFP4mKgiUInLLrusLu9XTVJgDEIttdRSuY/LG5yL2bF58jozS4kO0I4DHh1deumlubNxKxGVP55//vnUilr5mOsusaxhUWfhjTfeWFEFj1hOsehxrbiPisTSkW+++Wbh46JyTwyq5ilaZvKKK67IBgbzbjF408xJR7FMXFe1o7W0pV0pkl1jwCpPLBlfdFxUMsAaAyylxKBLURW4erR/tI5FF1208DGxLGmRqJ7XjO1NXtW+SJr4wx/+kDvAHQmxlSxn1si2kOa26qqrVrRsdFQJLHLllVemRujK940k3VhmvpyoDNiVy9/Vy/zzz597f15CTqnKz0XL5MbrjRgxoi7xZt6yn0VJKc0cN7R621q01HWccyNpuDPiPFgu0aZZjkPqF9PFxNW41iiSVzW50aLfq+hzRtJyJUl98fuPa/eiSbblJmYVVcTNSwBqU0nV31ZQFOfE7/+aa64pfJ1I2q6mQmVXarW+hmY8Luvd790KYpJkXsXvmGhZSUV9AOCHJP8BQBNbcMEFs2oYeaLSTFSayBOd1UUDUn369CmZZBSzno8//viySRJFxo4dm3v/p59+Wva+6EzPEx1elSzH0N6PfvSjwmpNBxxwQE0zi2NJn8MPPzx7j0jqakXNcMw1u+icK0pqjRnskSyRp+23VWSFFVZIPU0Mqhx66KG5AyaRHHjaaacVvlarHEMnnXRSRQMspRRV0MxrRyupGhlVOpvJiiuumHt/VHg57rjjanrtSGDad999sySKchUvInk9z6OPPlo4IHP66afXtH00p2iHi2KSqC6Ul5gUx95hhx2WmlFUUstLTik651ez5G+j2kKa36677lr4mGj/85ZYjeW3YwJGI0Ts25XL8G255ZZlz1nNvuRvm0UWWST3/qeffrqq11t66aULHxPXZuWq0Ldd+1USb5ZTSUW5vEpPISrCdnap6t7atsZ1QN++fXMf8/vf/76mvoyYEPj3v/89q3j/61//uqmPQyoXExaKnHHGGVmfRznxe41Jo82skj6EiEvz+tMiGe2QQw4p7BvLe6+pppqqsN/kvvvuK3v/TTfd1HRVs2tVSb/FKaeckpsUFe31EUcckZpFK/Y1NNtx2RX93s2uKCG+kglBAEBpkv8AoIlFctS2225b+Ljf/va3Wcd0qUSa6JDYeeedCzvsokpfqYHf6Fw69dRTs4ocm266aTr55JOzwa2iQYLo3D777LOzJTry5C2nUtQhEwMRe+65Z7Y9UXUhZkS2v5XaxqiMUbSMRlTmiQoIRUtpRGJjdFrE/tlwww2zfXTeeec19QBJKxxzrWDHHXcsfMzf/va3LMGt1PEQFSBiPxdVv5tlllnSyiuvnHqiOE722muvkkujxnJLsX+Klk2dccYZCyt+NIurr746bbbZZmm11VZLRx55ZLrjjjsqqg4ZS3EWVRQbMGBA2fuKlpkLBx54YLr99tuzJd+iY7ljW9rd4rsvmgEfyXWxTF1RlaDY/mjLI9E2BvtiadKoBpl3Ditqlx544IH0l7/85QevEe1hfM+x/T2tg763i4GtOM9XknwR54f4fcfvKSoBR9Lfn/70pyyGqmRps0aIxIlalwydffbZK6qM2Oi2kOa33HLLVTTYF9cieYOxRXF4xGaRpF3t7aKLLircts4mcOWJBKMzzzwzO/+1v0WFqoiHWkHEbHkJ9g8//HBVrxfLLBcl30WifywpXaqqb1zHbbPNNoXxZlG7U5R8FsumR6JQx6pucS0Z7dovf/nLTld8661ta/QlbLzxxrmPeeedd7Ik9UsuuSRLEM4T12axLyMZOX5zMSmwqAJUMxyHXbVMfHffKqm411kxia9oOcmI8Xfaaacs8S0m/UT8FhP3YqLHb37zm6wvpFmr/rXZZJNNCid1RkJ9xK2lqlJH/9qvfvWr7BoxT7R/cfyWs8ACCxRu6+9+97uSv7O4ZstLvG01yy+/fFblPk8ca3EtWSoZPirXRzvSTEv+tmJfQ7Mdl13R793sipYd74kToAGgu/TptncCAGoSSWiRUBadjeXExX50TMdgWHTURif4mDFjstn/sZxHkeiwiySKSpa7iduJJ56YJYnF0h6xPGMMCMRsxVheJDofotM8Hpe3zSE6ySO5qZz55puvcNtjGZ9yS/lEdYlIwGovtnOXXXbJZt8XdUbEQP10002XLVEVHTKRjBKDKNExH8ufRNJJK3a0tMox18wi0SE6pO68887cx11wwQXZQHAMHk077bTZIF8MbMQgQiWDfLvvvnsab7zxUk8Vibbx+41BmGhP4rhq2z+VDKjEsVpUpa3ZRFWgGGRtG2ideuqps3Yw2phon2J5k0icjc79SIKMiqJFZp111rL3zTHHHNnvLa8Ka3RqR8Ju3sBQtPndJX4rm2++eeFgdCSQxi3OQ/POO2+W2B2fNdqiaKdj37300ku5lVbK/b6LKsSccMIJ2SBynGfiu4v3i6TAZl4aj87ZbrvtssGeosSiOC8UnRuadZA4Kt1UO5gdz2uFtpDWEAk322+/fe5jIlEmqv+tssoqVSffRUz14x//OLtmqVa093Gc5lXkiaShSOSqpBpcrQmSrSyuGWOZ2XIVdOP6MeKVomS69ktNRrXg2267LfdxEWtGhdOIx2eYYYaq48088V1H0mrEAOVEW7b//vtnbWwkHES8Em1bTJKLZIh66o1t6y9+8Yvst5+X2BcJE5GActRRR2XX9rFPYn9E30XEcJGUF8n68bhqNcNxSHXiGruo2mxcP1x44YXZrVUnruywww6FqxFE2xVJw4sttlj2W4/zZCQDxlK/lUxmism1cTznXVfF9Xpe30dcP8UErTjHxe8pruViu0olJbay2A9Rxffoo4/OfVxcv8ako4UWWihbVjyuw0eOHJl9J822BGwr9jU023HZFf3ezS4vZorjqZKKugBAaZL/AKDJRaf0MccckyWsFSUwxHIxUVmgWtERHklu1YhticGFzixvFQMlkeRRTgwOTTrppFnlnHqKpbGiykFURCjyxhtvZLfepFmPuWYTlQAi6aFo0C4SRqMSTbXWWWedtOaaa6aeLpbUKteRmScGT6PCSauLJIK8RIIiE044YW51yEhajgTdaPNabVAuqmvEYECRzp6LOorfXdGy5W3tX7nlUGMAI24qAPYccc6KJaMjgaMzJptssqaq7tMmBrWirchb4qqjSKBaf/31W6ItpLWq/xVVtYkltuP7bp9kF8dPDIznieSGWquYxXtFwmFeIkgMFMeAZvyWKC2q8JZL/ovkraj+F5NCKrXffvtlSXQRT+aJ83FRclZn4oa8gexK4pVI1KmkUl+1ekPbGpWnoqLn3nvvXZhEF8dJfFeVfF/VaIbjkMpFEvi6666bJWz3xJiuTVSQu+uuuwpX44g+n4j/qokB26o/RyX/PJGAHJMmiyoIRvJYb/htbLXVVln/WSQb54mktJhYW+ty7t2lVfsamum47Kp+72YVsU65OLCtQmbRUsgAQHmtVSYDAHqpuPjt7GBzXiWbWAanu8Usy1hGpGiwoWgZn1rEbOZYgi9m/dN7jrl6i8TVWPK5X79+dX/tmL17xBFHpJ4qqgt0RuzzmDHf3TPEm1HMoo8B46KE51YTHb6nnXZaVhGyu8VAwBJLLNGp14jKT0VLzNN6IuG71kp3IZb6iuT3ZlXtuXnVVVctXFKumdpCWkMsaVcklsOL6n/txWB6UVXlSPjojEqe35VL//YEMbklr6p1x++1SFSc2meffTq1TdGOLb744jU/P65XI4G6Mw4++ODUjFqlbY3jqpHLgzbDcUh1Ih6rpOJW3vVCVCpvZlFBK5Llu6J6Z6z8EH0hkbRUSTvSmWr9gwYN6nQb20zfSfRjRF9nrdomIzSLVuxraKbjsqv6vZtVVMjPW0GnXhPLAKC3kvwHAC0iKlxFwlq9Ot+j0ykq2Pz2t79N3S06q+J9K+ncjgHAruisjOShWNp2gw02qPtr9xQ96ZjrKpFAGkts5C1fXa04Js8666xsFnVPFR3EP/vZz2r+7Z5++ulpttlmS71dJFHmLaHTJpYYi0HRVhMd6rG0bgyudbdjjz02qwhQi1j+aqeddqr7NtEcorrQpptuWvXzIpY5//zzK6o6VsuSpPUQiU3VVEX7yU9+klqpLaQ1LLvsstkkiCKR0NC+yldRxda4/uhs8l9cu0Slpzw33HBDYfWv3izO7THJqJxbbrml6iVQoy3aY489atqeSFzp7GSDuMY58cQTa75miuvdtddeOzWbVmtbd9xxx6x6faOSFRt9HFL9Po/lsRdccMGqd12cS04++eTCtqoZruf79++f9VcMHTq0rpXKLr300tzlftuLc/r2229f03tNM8002fdU63VZM5p77rnTKaecUlMCYMQyBx10UFNVZG3VvoZmOi67qt+7GeVN8ogk+Hq2VQDQG0n+A4AWssYaa2TVJGKWZ2dmKMaSVxdddFG2DEiR6LDMW5q3WtGhHR1dlbx3W/WnCy64IK244op124b2r33UUUelE044oS6JRNERF/s2KjdsvfXWqSdoxDHXip23V1xxRdpmm206VQVwxhlnTH/4wx+yY3KiiSZKPV0Mjh122GFVdXrPPPPMWSdrHE+tJmaFt1+esDNiUDOqm0TblVc9p70jjzwy7bDDDi1XLTGSBCLZ8/e//31dlgqPzx9JBzE4HEsP5nXoR6LWXHPNVfFrRxsZg+SHH354p7eT5hW/4zjPx++vkgGfOC4iWTAGSaPy34cfflhRNZVGiJiv0moLMUBVS4WiRreFtIZdd9218DGxZN5NN92U/ffjjz+eXnrppcIJG9G2d/YcUjQoGYl///znPzv1Pr1h2cNy3njjjfTggw9W/ZoxqSRihWpi8WjHIimmluSfUtcDEaNGO19N0uABBxxQ0fFeRNv6H+utt1527RqJi/U4L0Q/SMTP0X/RCsch1SfGRV9TVOyu5Jo04o5Y4vmkk07KYqYPPvigKeO5Ugk1cQxHtcPOJCvF/vrFL36R9etUm6ga8VqsAFGNZZZZJouf55hjjtTTxGc788wzq9qPkbAaMW9M0m02rdrX0CzHZVf2ezfbkr+33npr2fvjmrkZkqYBoJW1VjQGAGSza2OW8csvv5wuvPDCdM8992SDXUWzjmMgYskll8w6imKmbqWiw/L222/P3uPee+9NjzzySHryySfTq6++Wri0VvuOjOggiRnSa621VtUdQrF04l//+tdskO+6665LTzzxRPb5P/7442yArdrqEB1FAkjc/vWvf2VJXA899FB66623Cp8Xg9eRjBKJSEsttVRWqaQnzUhu1DHXimKA53e/+136+c9/nlUqiw6tp556Ko0dO7awI37hhRfOqv3F76MzCZatKJbPjN9NDEZce+21ZSvlxDEY1dRiZnardgaeccYZ6d13381+Pw8//HDWjj733HO5S560F+1mHCsxsz72RVHloVIDzHvvvXeWpHr11VdnbXm8fyQiffrpp+mbb75JzSzakViSNGaKX3PNNWn48OHpvffeK3xe/KYisTaW8V166aWztrrSgbgYSI+O/RiQjQH9N998s+TjYnA5juMYDFtooYWq/my0pogbYoAm2vuo9PX888+nd955J3355ZfZ4GgM5sfxFokI7SujxLmhSCOXjI7f2dlnn13R41qxLaS1qv9FW19U/S/ip6Kqf23LVNdDvF/R0r5xv2XLyltuueWyQfNoN0uJ67E4b9cSK0RyZlThiwTMuFYsJa7fogp1TNaqZ1y5wAILZN/9ueeem10zlUsMipgsjqNI+qtXJWtt6//EOTeOgVGjRmXXZXfffXd69tln09dff11Rcldct8b5O+LG+E6rTVhv9HFIdWLfxzV8xDURz0X10ddeey2NHj066++IiUhzzjlnVol83XXX/d4ytyNGjGjaeK6jOI7jeIvPGX1qce0d14ORjFNUjTp+B9FmxbV79O3V+v6xAkT8NiKBLWLAcv1JkQj705/+NJuI2pNFP2J8F9F+x3kv2qxy7VLEFLvsskuacsopUzNq1b6GZjouu7rfuxlcf/316fPPPy97DDX7UuoA0ArG+bYnRA0A0MvFwEIMJr///vvpo48+yjrwonJYJBbFLSoRdLbSRUfR+RCdU1GdIQa7o0On7SI+3js6BaeaaqpsOdRIAmu1iiyR/BeDBLFvo7Ml9ml0DMfnikH9GFSIgf1aOz9bXSOOuVbzxRdfZEkN8fuITs84juJ3EIkKsY+iit3ss89etwpIrS4SPx577LH04osvZvsrfm/RhsTvbN555009USSHRhsat2hz4hiJ4yYGJ+P3FLdIVIsktDheGrUUaLOKgbkXXngha4PGjBmTnYeiakcMkEQ7HfsszkH12m/xXvGbjnYvErzifSKxMAYC4jcNRWLga80110yvvPJKbpWhmHTRm2gLofeJJLkYcC83qebOO+/8XpJNLXFlDOJHjBUJxxFvR1w5zzzzVFXVt1YxSe7pp5/OBvHjuiliu/g8EZdE3NAd15Da1v+Jfoq4do3rsogZ4xZDIvE9RDwXE/jimqPeE/kafRzSdUaOHJlVmMxLaooJIFFtvFlFOxUJWnFNFddTcQ0ev4u4jopbxKTzzTdflySnxvVU/DbafpNxvRYJsTGRqrf2I0WyV1wjROJptB0DBgzIEsTjnBGJUXQ9x2XX+slPfpIeffTRkvdtuOGGWQVJAKBzJP8BAAAAVCAGeWud0BAV9WJp9zyx3FEsKwzQm5Ohf/Ob32QVnwGaLaaLBLmo+B1VAvMcf/zxWTsH0NvFRN9yleMjuTWqAravlg8A1KZ3rSsGAAAAUKNYcuvQQw/NqgNXU1nlrLPOqqj6SyxnC9DTRcLNbrvtVvb+Cy64oGmXCQRaX1TjjKVsTzvttKz6XKWiUnskJxcl/kW1vFjiHICUzjnnnLK7Iar+SfwDgPpQ+Q8AAACgAptttlkaPnx49t8xSLHiiium+eefP80555xp4MCB2fKBMaAcg8Mvv/xytrTR1VdfnS2pViSWOrvkkkt8D0CvENWzNtpoo2w51lKOPfbYtO6663b7dgE9X8RqsaRtiOWYF1hggbTssstmSzLPPvvsabLJJsuWIP/iiy+y5XCff/75dN9996Vhw4Zl/19kxx13THvttVc3fBKA5l8mffXVV8/a3Y6inb3hhhvS1FNP3ZBtA4CeRvIfAAAAQJXJf/UUA8+R+Lfgggv6HoBeI9rTzTffPEsE7GjmmWfOEm1qXWodoJLkv3qbcsops2SWmBAC0Nvtu+++6Yorrih5369//eu00047dfs2AUBPZdlfAAAAgAY66KCDJP4Bvc4iiyyS1llnnZL3vfLKK1nlVIBWEQl/f/nLXyT+AaSUXn311XTVVVeV3BczzTRT2nbbbe0nAKgjlf8AAAAAGlD5Lypa7bPPPmnrrbe2/wEAWrTyX//+/dMpp5ySFltssbq+LgAAQCVU/gMAAADoZnPNNVe21K/EPwCA1rXqqqtmy5RL/AMAABqlT8PeGQAAAKDFKv/16dMnPfzww+mbb76p+vnjjDNOWmqppdJGG22UVl999dS3b98u2U4AAMpXXv75z3+ebr755vTcc8/VtJsmnHDC9OMf/zj93//9X1piiSXsagAAoKEs+wsAAABQhY8++ig9+uij6ZFHHkkvvPBCev3119M777yTPv/88/TFF19kA8KTTDJJmnTSSdNUU02V5plnnjT//POnRRddNE0zzTT2NQBAE3jjjTfS8OHD02OPPZZeeeWVLKZ7//33s5gulgfu169fFs9FXDf99NNnywXHbfHFF8/+BgAA0Awk/wEAAAAAAAAAAECLGbfRGwAAAAAAAAAAAABUR/IfAAAAAAAAAAAAtBjJfwAAAAAAAAAAANBiJP8BAAAAAAAAAABAi5H8BwAAAAAAAAAAAC1G8h8AAAAAAAAAAAC0GMl/AAAAAAAAAAAA0GIk/wEAAAAAAAAAAECLkfwHAAAAAAAAAAAALUbyHwAAAAAAAAAAALQYyX8AAAAAAAAAAADQYiT/AQAAAAAAAAAAQIuR/AcAAAAAAAAAAAAtRvIfAAAAAAAAAAAAtBjJfwAAAAAAAAAAANBiJP8BAAAAAAAAAABAi5H8BwAAAAAAAAAAAC1G8h8AAAAAAAAAAAC0GMl/AAAAAAAAAAAA0GIk/wEAAAAAAAAAAECLkfwHAAAAAAAAAAAALUbyHwAAAAAAAAAAALQYyX8AAAAAAAAAAADQYiT/AQAAAAAAAAAAQIuR/AcAAAAAAAAAAAAtRvIfAAAAAAAAAAAAtBjJfwAAAAAAAAAAANBiJP8BAAAAAAAAAABAi5H8BwAAAAA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c3Jl//etf2bVlWzsLANROgiAANIGYIV4kBgKjc6hooDqS8o455piswz8GqaoVnU3R6RS36ID51a9+lRZZZJGqnx/bFoNm8fxa3jfE42P2Ydyuv/767G8x+HbooYemZhADfaeeemqnyXeVRMdxdKrGLQY0f/vb3/ZIFYAY0I/KktF52yiNOuZqSVz86U9/Wjjw05Pi93vSSSdlybOVElI6E52XkeATt0ii3XPPPbPB/r4ifj8HHXRQYaJXDHyfeOKJWZJRLEPYlaTcZhGDOieffHLhZ6402Bi3tiSAqLTSCsvTR8JHdFhHFZ2uiIHtGACITv+2ah7VtkHxm7zsssuyBL0DDjggG9yrViT2R7vXVdEWxJKPcTvyyCOzQakYJKi3OHfFoEBUAMnbh5GYts0222TJatHWtHfOOeek3//+97nJY5EQF0tdxtKQUR25KMmtESJBMj5H0dLrcQ6Px8X5/6yzzsqSImoRg4b77LNPeuqppwqPgTgGI7H1wAMPzBKdGyUGxiLB/B//+EfFx8T+qCVBMAaQ8pIDYxB5/fXX7/S+vtgWVisGzH79618Xtpltx1cM9EdiQ72TSntbNUmO1UykqCQvUTlEQncMHq+99tpZclXe6/RUguDmm2+enes6igSOmMS01VZbdfq8SFauFKNHUkFMxGgGkRQW1aIriQStqILVVfG9xTVLUbsSSzFH/BmPj/i9XhWH45qt6LyQJxIcIu6IW5yX4lo5EgVr0axta7xO7PP4jHkimS6SZSLJP87PUT2rVtHnEMdBJBlUmxwZyRCRhBG36BfYa6+9uhyzNfo4rIdoT4omuvWEmNDS29XqIpaJmL8oAT0S8uJ7iuuKP/zhD92adNuoPo9ImI1JNLVU442Y+v77789ukRgW8f9uu+3Wrba62SvQRRsa7VBetdu2JLb4DUd7FcmEUZW0FjERLSZSFU28jnNnfG9x7B133HFZ/yP162vo6+I4j8TPiBV7+phvE5MQKiUIxvk4KnYPGTKkS68NAKTU9Z4zAKBuqr1oLuowj4716Ki+8cYba07Say86vmP2cyRcVNspGLPbo0OqO+/bXlHHQ2+IgdVIWotbLcmBHUXyR8ygjM7leopO6hgIbGRyYKOOuWrF9xb7qJHJgTHTPJI+YkCuluTAzj7LL3/5yyyJKC/hoixiADw+by0DmDEwEm1RdKa3okgSiyoQXRm0bdZ2tEi0X/GddbXDPtrBqJYQ+66W5MD2ou2KtiyqbzTq2InzewwARVJZtYPV1Yi2L9rWvOTAjvsiBr5jgLPNEUcckVU2qHYgOLY/nnPxxRenZhG/hdi/cZwUJQd2rJQQSZOVqpd2JqpJxnNqSQKJNj2SKqMqQyNVquTXJqpDV6p0VCmhME8kvHdWaaYvtoXVioqBO+20U01tZhy/0U5GpbBWVk2cGVV0unouiQTnPG3JrOutt15hUk9eklt3RExbqSJLJAF2JmLPqMjWmag0FFVsm0VUIc/7nqN6XlfFQHU1k046tucxaa2owlcjRAJPJKKecMIJVT+nWdvWSCqKCphFfR0dk7GiytLrr79e03tF0kLEe3E+62q8Ffs+YoqYWFKpilLZj8NIBosEuN6+xfmvN7VVZ62lOm1MQIpjM/qAekt3+zxiUmIk9R1yyCE1JQd2FDFi/J5jWyJRqGzifBrVSyMZr5b2L64vIw6r5TmRHBXHe7WrsrRNHh46dGjWzlGfvoa+LgoBROJxXKP29DHfMUEwT1HMDgDkkyAIAE2g2qWFYlmhvAHD6LysZZmiPJEIEAP8Mbu9SMygbWSSWk+IwfqY/Vw0uF2tGASIWatR3aMeYvAsvu9IiGqURh5z1X6H0YEbHaWNEoPxW2yxRTZYXC9RcTE6i2sdkGol8RmPP/74LnfcR8d4I5NCuyKqsUR1s74kqrRFh3NXE2fjtx3VlO688866bE8kGMYAQqXZ6r0hKslF5bpGt4ExCBAd+1Gdp6uJ29GW1zKo1ZOiOl9RdbBK4jNEZdJqRKJCLC3X1eU6o+JMpUSe3hAVR2Jp6krivFNttagYiIuqlLUmJPbFtrBacQz/7ne/69JgW3x3kdDSiIpP9VLN4O4CCyzQ5YrFefsmkvLWXXfd7L+j6vV0002X+3pdbW+KxFLklap4RkJzZ+evuPaoFKvHBKZmWqKt6HpywQUX7NLrRgW2qDTdFXEOjUHuZk0uiESVuCYr0qxta1QkispEXRHn2qjSX62o+LXvvvt2a8JWezE5MqouV9sml/k4LKOo4B9JoF2ZBBlJNVG9uzeSOrvb5xFJfTGZKJJm6yWS5CPGa5brgHqJJMquXnc+/PDDWRtUjZhkFO8Vx1Gtoj2KCaW9maBa1r4G/lv5OKrt9vQxX2u8V7bxBwDobRIEAaAJVFNlYsCAARWXF45OqsMPP7wHtiyls88+O1166aW5HZKVKla0qugEjspljz32WI8kKYwYMaLbrxMJBFF1r1EaeczVMmAWS0g1Ssy+j47dnuiQfPDBB7MBi7LqbiJt7PNYfrGVqkZ1tfO0lUUCWS0VOdqLAZP4fdW7OkUkiDQ6+TqOhUj06K7Yt10dmIuqOtHGdHUgu+13GJVTm0FRolo159yoVlS0zyIBq5YKSJ2p18SEri7dF0m39di+qF6SN6i+0EILpYUXXvg7f++LbWG1uhv3xdLXN910U2pVRYPyE000UZeXUS6KO+J1p5122m+WMW5LFqwklnXuqYkcUaG0kgsuuOA7f/vLX/7S6WPjc1RakrhRiq695p9//ppfM86DUQW3O6L9j+qGzSqS2IviqWZtW7s7ySOeX01fSiS3d3XyUdH7R2XGIn3hOCyT+D1F8ml3qnpHHByrLPS07vR5xHVPTGKt52TGNpGwGNdq9ZpMWobriejnKooN4juJfoyuJAe2XwY5Jj70dd3pa6A+5+hqjvlKqyzFpJi8a5ru/EYAoK/r1+gNAIC+LhJYYjZ5kfnmm69iZbpYAjc6gYrMMMMMWdWLaaaZJpspHIlq1SRBHHrooVlVmc4u0O+5557C5U5jua8lllgizTTTTNngXVzIx3JnkUAVVb4aWeGtM1GF4eabb66q0yKWupplllmyQbbo+I/9kZcQFp0jUbkgBin69+/f5W1sZPWARh9z1Wp0hYVIBq1mydNYXnGFFVbIPmssfRlLgcZs26IBiUi+GDJkSOFSd60uKtssv/zy2e8sfj8xoz4GAov2Twwyx285byC9WcTASdHSttF2Rjs6++yzZ8dMDB5EOxrtZ7SjUbmsXku895bu/Eajcm01A8LR9kQ7PeOMM2b7JwbQIskmr6M6ErxiudeLLrqopm2acMIJs+N01llnzd43jt1I7I+2MtrNOOc9+eSThee8OLajgl+9Kgm2JX7FUj0RS8SxE539sQRano5tdb9+/bLf4pxzzpmd56IaUVRmKEomi0Gu2DfNIiZcrLjiilmbG991fI6iin/xHUYCUSznmNcmRwxQjUhyWWyxxbLjI76HqPrVLNUtYunFWB6u0jk+EtQjMTeO9TxFy5t1Vj2wr7aFXRFxZ7Rt8847b3YtEcdeNfFVVLwqSm5r1srCRUskxxLAedXW8wYZYwnPotfuuDx2XnXVaFOiGtPaa6+d6i2SFaMt72zp+HjP9r/PSCaqVFF5tdVWy85XzbQMZF61rfjt51U4rSSqk1fTvsbrRzwe+yRihNiWRx55pFsJQtWcj+K7iltcL0fcENvR1q699tpraeTIkYVLu8fni+qAe++9d6f3t0rbGtfGEWfMMcccWf9CxCrVLHca7VosP1tJfIaoqFX0XUasFOfmOEfHPojfcey3V155Jfd5kYQblT0jtmjF45Dvihi8mj6qqC4b5+J55pkn+45igk/ESb05Sa0711Mxkaea2DWuAZZaaqk011xzZf8d/V4Ru0bfRZ7YH5GYG0sXl03EG/Gbn3766bM+n6iyVvQbjzYlkkY7xhQd25Po7ygS7dXiiy+etVfRdkY7Fd9JmVeZaLX+wLLpqWO+kmhvIg7qTFynRhycd+4HACqTIAgADRSd7NFZXM3SI6uuumqnf4/BqaLOy+gwikpAsdRpdCS1F5VEosM8b+AhOpn+9Kc/dVoxrijBIGYNx6zk6EisJGZ13n///dktOhkqVTyKpQ7bd47HrO68AejorI0Zi0Wduu3FYEQkZuSJzxIVpmIJ4o5VHWMAJmaq5lXAi4G4GMiIpVzqJQYxIhEvBhNiYCE6w2I/9kQVxEYfc1015ZRTZoOqkRQS2xRVGCJZKBJ26i0GBm6//fbCx8UxEAmj8f21F23CPvvs0+ngb3uxJFcMPrc/jiP5tP3vZMcdd8wdFNxwww0r7ueO311vi+THgw8+OPvu2ovj+he/+EXhgF0cq1tvvXXDP0eRokHXaP+PO+647+yHjm1XJJZG8lssKRSDiZ2JpdDaJzlFAlcks+YpSsSr1/6NzuZoxyKxOAbW2pLII3mjvWjfKlVEaj/oHkl+G2+88XfOP9GhHZ85bymttt9wpXNv2+eOwfT4DcagWSyFU5T4Hb/NGIiLgbK8ah+RpB6D891JJG8TS2FGwlcMIrVvY+P8XO3yU9FBH68x99xzf/O3SBzYfvvtc5N24pwYg1zN0nkfbUos0do+iSg+R7TDsZRcnohR8hIEq1m6ceqpp85+yyuvvPJ39lMkxhcl1fWG+P1973vfqzhRIo7h2M5YsquS+M7zzl/x+4wEq0a2ha0s2ppIXmj/e4zvJeKmSJ7OExM1Yh9HgmEziYG+9okGcQ6I30W0/1EFp6jCXxwTERd0RdFywNEOr7XWWt/6WyQRRVJXXnJdbHNPJAiGbbfdttNzd3y3kdweE3lCXhJjvEYziXNz3qSziBFqjTciTqxmabyYcBOxcFuVyDZxfovj6uWXX071EPF6DK6vueaaWdwQiZ5Fv8X4bUS8EnFD3sSj66+/vmKCYCu0rZEYGJUQ4xzUJuKgww47rHAyZVHyY1zbFyUxxHk5rjlmm222b/092taoCHrQQQdl8UIlf/jDHyomCDbbcUi+6JuqJh5bZJFFsuS3SGhtL6rxxXV8UeJ5T6m2zyOO52oqi8YEo6OOOirrZ+q4nyKm/sc//lFYhXvo0KGFE0taSfQDxooj7fsCY9JULMca7WTR9USlZKlopzurBNxRHHNR5X3w4MHf+nucI+L6t5p+qJ5Shr6GRuuNfu9mOeaLvq88EYM3Sx8DALQaCYIA0IuDXSE6It5777300EMPZZ0/RRfTbRfz66yzTqevX83yfbGcTWcDsSEGKKITMQaJ8gYOYvAsBh2iKlJ7eR3lMQC/1157FXboxIB5DKDFLTo/ooMo9lFHHSsQFXVyxIBLJMvVIjowi6q+xeBFpQ6OqCoYHfsxiJK3rEd0+tQjQTASX2KQPpLAoupER1EdqZ5LxjTDMVerSESITvof/ehHnSaqRqd5URWsWg0bNqzwMbE90anemZgJHskmG220Ue7M5/huI4mj/cB1x99J0cBj/I5q/Z30hhgkiwSIzrY/BmRi//zwhz/MTTSNgbSoPrLKKqukZpbXjoYYFMwbtA3x+4+KI3GLBOZI3u5sOcr4DbT/HeQlb7fp6eMjfuORUBzJW52dL2JgtX01s2iD8ipWxGeK32B0luclzO2www5Zh3VeO52XIBjngo7JvUXi88V3FNsXv9tKbWCcQ2JgsX1SX1fE+0UiccfXiXZi9913rypBMNrQM8444zsDg5NNNllWHbAoySQGtpuh8z6q+3TWpsTniL9Hm5NXSTAvETLilqKB4EgyimOq42BeW+wQx1N8772xHF2RqO6XV0k5BvvyEgSLliGO33rs90a2ha0qBtjPOeecrNpYx996DNLF9cUdd9xR8fmRJBOxQ1Q7aiann356duuKaIfjudG21yquy4oSQeI8MHDgwE6P42gbK4nvIRIo4jqn3mJyR7RbnbVZcS2z5557ZtczUSG1M/H951U7a4RKVWLaRCXgWl188cWFldeWXHLJ7DzZWUJ+LIMeMUdRvFmtuPavNW6ImOb73/9+dgzGAH1ezBvfeWe/g2ZvWyNGiN9Sx3gzvpNI2ovE5rwEjkh+ikrNne3b+I3EcZAnqmlGgnVnfQvRtrZVJ91pp50qvkYkTEaiYmexZ7MdhxRXrC2qwhaJpHEujvits0k1cV9MUiqaTFtPtfZ5xITWograEbPGNUtn/UxxbosktZhwlJeQFom+cfxGP18ZxHVPXLd2FIm9J510Una9kbfSRt71RMQNRcdMtNXRD9LZOTHa/1NPPTXbxmr6mXtCGfoaGq03+r2b5ZjvTtxXFDcCAJU115RhACiZmCkZHd7tb1FtaPXVV89m31XbabPZZpt9q0pI+47ook696MSvlKjVJjqyIxEpT1zwxzJVHeUt5dW2LFGtYj9F9ZpGKJrdH9UFqpn9GImReaK6X171qGpE51ZUK4gB4c46bdu+nzjm6qUZjrlaREdZdNBHFblKnZMxM7WzhI2uigGFe++9N/cx8b20VXepJDrdq6mGk5cI0Kriu4pKHXnJjVEVIa+aV5vuHkO9oWhJxKJKiZ2JZfhiYKrZRZJLVDuKdrVSMnkMwsVgW7Xt9KabbloxObD9MRZtZ56oIphXcabWQf72ItmuKJmlq53p7UUSYqVzQAw4V1OhMJLZOyYHton9XLQfmmF5p6h2EEkGldqUGFTtWNWvo0j0qTT4Uc3ybFFRN+9cE8d/xI31qBrZXZEQNdNMM1W8P5ayrVTtIxKurrnmmtzXj8HrzvTltrBa++2333eSA9uLRKIikSRQFgsttFCW5NDVWDeSpIsmBlWKaYuuByIpoui30J02Lc51nYk4Pd73wgsvrJiEHnFxs+lsclh7nSXiFInlFovEoHdeu1ttvFmN7sQNUXGwSKW4odnb1qiOWCm5IRIjou8kTxznlSZ7xXVA/Bbz4sGID4oSMCJGKIotKyVJNdtxWA9RoS4q3vf2rZrqat1VTUwXlafz2qT4zcVjektX+jyqqTIXx2WlfqYQv5u4Zi/6/ZSlvyKu3aIfNy+hKSYydjUGq6bfYtddd81NnIrkss6SufqqrvQ10HvHfJ7OJufUEjcCAJVJEASAJhed75WSzarpQIrkwmpsvvnmhY/p7P1iaaRKYpB4m222ySrBtcLsvkjsyqsmFapdGiGSKYoqMXQ3cSmSDaoZCK6nZjjmahHLW3e3AletonpP0TJWURG0s+pJHa277rqFg4nVLhHaSmKZsRgIKxJJpkUVEuP7yLPJJpsUDkb19JJI0V5EglIlsVRQVG+IJTujXS2TGFTquDRXnjiXFFVFrbadLqpqFwPK1VbHicHpSCiMqg1RvSPawahIEwOo0WkeVUE73ooS5yIhrbvykrVjAKlo6Z6i/Rm/v6LfRywT2miRXNB+2cLOVHMcVqrcE9WNilRK5mkvtrEoUbE3xPdatL2VqgRGIkRewtUCCyxQ8bfXl9vCagfpokpHTx3HMbmhs7aqmltUvupNkSwVFfSiWl5nk6iqVbR0cRyPUV200rFc9N5Fr98dkQhSKSkjqjXFvqk04Fo0SacRimLn9svaVSMqykUMl2feeefNkuXrEW925bozYviovhPX+pFsFxPkYvnD2KaOv7FIhi1SKW5o5rY1lusumqjVnXatKDEpKvdVc81RTdzY2TVZsx+HfDeeL5rEG308RUmr7VdN6A219nlEJfa4bin63RUlxba1L3HtnicmlvRmNcWeEr/BvITJatqrvOuion6L+P1HJdFq2qruxEZ9ua+B3j3m8xRVRYzK+wBA11hiGACaWAzcx/IRlZamik78ItV06rV1IkWyQN5yKp11bkdHZCwtUGnWflTJi2onIToWogOx7RazNCPBcMEFF6x50KcnRMflZ599lvuY/fffP7vVQ3cqCEbnYN4yRz2lGY65akVi3VZbbZWacR9VUwWkrVMskovyKhlEZ3ulZbVaVbXHUCRLRFsSy6pVkrckWbOI30Esg/yPf/yjYjXW448/PrtFVYgYyIyZ7vHZo0M2lgqMgcXeGoSql9j+SIKtRTVtQt4SfF05L+SJSk2xbOzf/va3wuqqtarHMnIx8J4njpm8Abtog+Icnaco2bkZOu+jsm2RaqpTVfosRcdJJGZEMlE1Ilnh1ltvreqxPSmSXGPJxUoVyK677rosvutY8ahoeeFYvriSvtoWVisSjouSQ4qqbTTLb7K7or2NNjKW7axUEaZIDFjedNNNuY+JJJC8gdFIoD755JMr3j9y5MjsvBUJXj0xiSy2r7NlyZ977rmKz4vk37xksUYpWtKz1uqqMZmgKNkt2tt6xZvVeuONN9Jpp52WVXmM+L034oZmblurOT93p10rihtjIki9fp+dxQLNehzSuVj9oiiZJWLrapZNjTYrruN7unpeV/o84liNJMF6XI+39W0UTVqMPpI4b7WyelxP5CXDF/VbRLscy+VWI9qV559/PvVlXelroHeP+VqWWu6oqO8eAKhMgiAANKmoIHDYYYflVrwpqi4UA1p5y0+0F52c0YGS14nUWQn/qFzx85//PFsWr5qO++gY7JhAFZ2nkSwWVVFiRmwkHDZCPao11aI7SyLEQE2lJR97UjMcc9WKChyNSJqr5jiac845q369eGzRUkfxnmVKEKxllnc8Nm+gLAY9Y1nQagZyGikqP9xyyy25y6CF+CyxFFxny8FFBZI4d2y44YY9koxQb1Fhr9bEjt5up/PeLwbCoupPPRL5OtPdxIFoj4uq5hV1vEcbXZSQVLSsWCTwNFr8NopUM1Gh0mf54IMPCvdjtcf67LPPnppBLDEcCSW33XZbxfNzDHq3r6ITA0B5CVdxTBZVPumLbWG18qp2V1tto1l+k90VsWMsS3rDDTdklVu7svzs9ddfX5ggkVeFtZoEwbYqgr/+9a9TT4hK7Z0lCFYS7XkzLi9czfmoqE2otV2utb0tijerEQnUcc1cdNz1RNzQrG1rNefn7rRrvbkEYVxzRKJr+2O5GY9DKqvm+4okrWrFd9vTCYJd6fPoif6Kerxns6vX9URnou0oSqSqtY+kr+tKXwO9d8x3d+JINbEBANA5dekBoIlE50V08MVA0xlnnFE4sF/U4V3NEqbtFQ2uVXq/qAazww47pK6KgYpY4uTYY4/NOnGOOeaYHhs4KUuCYDXLTPWEZjnmWnkfhVoGsqt5bG8OfvWGWvZPNcdcNYM9zdD5GpVbaq2Q07HqwJlnnpkN3P7kJz/JqtQ0s6Il5Zq5nb7zzjvTrrvu2mPJgfVI4qnmt1F0vNXapjeraqoPdSeJuKjaTC0Dt820z/Oq/XVWLTASlfIGN6OKR9Hn64ttYT2P4+7st1YUyyLvtttuhYOIXVn+d4oppihc8jsG4IvizagU11PLtsayjtUkjraJ5K5GTDCqRtESdrVWiqnm/FxLe9vdtjmWfI5E0Z68xs2LG5q1be3p83Oj48ZmOw7rJaoLx7Hc27eutPW1qOb7araYrit9Hvoruibigp5qr8raVrRaXwO9d8wXKYr7iuJGAKCy5i6jAQAlFdV2osMmbrF8cCw7F8uPRPn+WmYkN5NYdjcGqU444YSqlljNSxY8++yzs6WxYmm7Mg901loJo72pppqqrttSRvYRrVYtaZ111smqg0aSdFSb6M52R5WYWFbtwgsvzCqBNaNW+I121k5HhZ6oANSdNrwZqjH11ms0g2oqDBRVSswTsUre8RAVmarVTMfVkCFD0vTTT5/eeuutTu+PpZAjObItqfvqq6/uVsJhX20L63kct2qllN133z2rcNZWheu1117LqrSee+65hUlIDzzwQDrxxBNrqtIX1dFiclLR5IJYVrW7xowZkyWVR3JeT1URPOigg6p67LbbbpuaVVFMUJSI3cziGD7iiCMaHo82Y9va0+fn3tZM5/CedP/996ftttuu1983JtNecMEFPfb61cS9tcR0PZ3Q2CrXU91RbTvVG8uNlq29Krue/G0003FZ1mO+KGl2yimn7JH3BYC+QIIgAPSgo446Km2yySYN6/CIAbZaFA28FL1fDCTH7dFHH00333xzuu+++9LIkSO71Cly++23Z52/O+64Y+otkazZKgYMGNCQ9222Y64V91Gtg5zVPLZsAwO17J9qjrlqZj43ixi4HTZsWHr11VfTP//5z6w60sMPP5zef//9ml9r9OjR2RKMp59+empGXfmNNkM7PXz48Cx5JU8kTMUyjnFOjApT8f8dl+KNpVnjO6L1xQBFXuW8Wn6/zbQEWxyzEcdWakOimk8s8br55ptnSYR33313xdeKKmdLLLFE1e/dl9pCvi0mUMXxErc4tqJCYCSj5IlEwqhQueiii9alemC9xfv1VIJgVHP7/e9/X1gtee65504rrbRSalYzzzxz7v21VqurpjJdLdcttV7jtPfXv/41d/nfMN1002UJV1G1Mqo8RpWyjoPu9VjWt6+1rRE3FsVsPamZjkNSXa4Za/mt9EaV/65cT7VSf0W1Sbc9WdW9N2gr6q8n+wP7ynHZSG+++Wbu/bPMMkuvbQsAlI0EQQBoYUWJEp9++ml2UV20VHHbTOhRo0bVpVMvBufaBuhi+ZnolH/ppZeyah0xGPHCCy+kRx55pHAQPCoJNluC4Pnnn5+WW2651Fc16zHXTKo5juL3UO0g9osvvlj4mFbcT0X7p16PjQHWVqxEGh2e0f61tYHvvPNOdiy8/PLL2YBsfO7HH388+/88UeEr2ty55porlUE1v69IUO/JDuNYRrVoMOKSSy7JkjHyGOQtj2mnnTY3ASFinzg/VrMU0lNPPZWaSSRo/fnPf65YKSOWGY7HXHvttbnLqFZbPbAjbWHfFsmCf/zjH7MkuLyBwrjeOPLII9PFF19c+JpxLPd2gmCcl9pX26ynaFc23XTT7LopTyStN7NIkJt44okrTiyL7z++u2qrZFaT6FMUQ3U1Nu3opptuyr0/rptiCeLYB70VM/SVtjWukfLOzxtvvHE6+uije+z9m+k4pNg000yTtTF51cFipYtqNVtMV2t/RbV6sr+imsnGMWGlqF+p2UX1yriGzJtwVM8+ErqnrxyXjVQ0MWTQoEG9ti0AUDYSBAGghUVljZj5X7Ts1vrrr1/4Wo899ljhEijxfrWKygcxANExWSMG8mJQ+be//W3FZVpiSa5Yrrgr79sVs88+e+7AVIgKJn05QbAVjrlGq2abYx/FYHc1nYoxOFfUMRYD6GVStOxfm7fffruw07UVj6FKA1ZxW3rppb/196jY+tOf/jRrLyu56667mnbgtlbVfJ/RTvdkgmDRwGAs4VeUHBgd3kXVnmgdkfAdv8VKIuaJ5VLXXHPN3NeJAelIJGom8VuKqmOxRGql81kkk0RMV0nEVj/84Q/rsj3awp4T8W0sGdqMFTrjeqFtCeJKHnrooWzZ06jOWnSO6O3qrRHPRfXZH/3oRz3y+pH8d95551VM0o3ExEiEamaRlBMV8mISWWfimiGSraO6XjUi7okqqHmJy3HM1CvezGv/i5JnIoE6Lzkw9PRvs6xtaxxTTzzxRG57UEviaa2a5TikOpGgNe+882Z9UJXEtXlMdC1Ksov2qlkTBKO6eSwdGuemSiK+q9aIESO6fA1XNHmmmoqNUQk17zfWKuLYq3QODDHhOpK5o60uUm27Quccl433/PPPV7wvzqtl6ecDgEb49loFAEBLWXHFFQsfE9UIqnHZZZfV5f1qSRyMgarll18+93F5A3gxyzZPXodnZ6KTtOOgSEeXXnpp7qzeakQFkWeffTa1olY+5npLLKFY1KH4j3/8o6pKILF0Y9HjWnEfFYllKl9//fXCx0UFoBh4zVO0pOUVV1yRDR7m3WKAp5kTk2JJup5qR7vSlvakSIiNQa08sTx90XFRzSBsDMJ0JgZmiqrJ1aP9o3UstdRShY+JJVCLRBW+Zmxv8qr/RWLF8ccfnzsIHkmz1Syd1si2kOa21lprVbVEdVQbLHLllVemRujJ941E3ljSvpKoMNiTS+3Vy8ILL5x7f17STmcVpIuW5I3XGzlyZF3izbwlRosSV5o5bmj1trVoWe0450ZicXfEebBSMk6zHIfUL6aLya1xrVEkr/pyo0W/V9HnjMTmahL/4vcf1+5FE3ErTd4qqqyblyTUpprqwa2gKM6J3/8111xT+DqR2F1Lpcue1Gp9Dc14XNa737sVxETKvMrhMRmzmsr8AEDnJAgCQAtbbLHFsqoaeaJiTVSsyBMd2kWDVv369es0ESlmT5944okVEymKjBs3Lvf+jz/+uOJ90eGeJzrFqln6ob3vfe97hVWfDjzwwC7NUI7lg4444ojsPSLxqxU1wzHX7KIDryjxNWbCR0JFnrbfVpFVV101lU0MvBx22GG5gyqRQHjGGWcUvlarHEOnnHJKVYMwnSmqxJnXjlZTfTKqfTaT1VZbLff+qBRzwgkndOm1I8lp//33zxItKlXOiAT3PA8//HDhoM2wYcO6tH00p2iHi2KSqFKUl7wUx97hhx+emlFUZMtLYCk659eyvHCj2kKa3+677174mGj/85ZzjaW+Y5JGI0Ts25NL/m2zzTYVz1nNvrxwmyWXXDL3/ieffLKm11thhRUKHxPXZpWq2bdd+1UTb1ZSTWW6vIpRISrLdndZ7L7atsZ1QP/+/XMf87vf/a5LfRkxafBvf/tbVjn/l7/8ZVMfh1QvJjUUOfPMM7M+j0ri9xoTS5tZNX0IEZfm9adFwtqhhx5a2DeW917TTz99Yb/JPffcU/H+G2+8semqb3dVNf0Wp512Wm7iVLTXRx55ZGoWrdjX0GzHZU/0eze7oqT5aiYNAQCVSRAEgBYWCVQ77LBD4eN+/etfZ53XnSXbRKfFrrvuWtipF9X+Ohscjg6o008/PavsscUWW6RTTz01GwArGkiIDvBzzjknWw4kT97SLUWdNjFYsffee2fbE9UbYmZl+1tn2xgVNoqW7IgKP1FJoWjZjkh+jI6N2D+bbLJJto/OP//8ph5EaYVjrhUMHTq08DF//etfsyS4zo6HqCQR+7moit6cc86Z1lhjjVRGcZzss88+nS7DGks7xf4pWqJ1ttlmK6wc0iyuvvrqtOWWW6a11147HXXUUem2226rqspkLPtZVJlsqqmmqnhf0ZJ24aCDDkr/+te/suXlovO5Y1va2+K7L5pJHwl4sSReUbWh2P5oyyMZNwYEYxnUqCqZdw4rapfuu+++9Kc//ek7rxHtYXzPsf1l68Tv62LwK87z1SRoxPkhft/xe4qKwpEY+Ic//CGLoapZRq0RIrmiq8uTzjPPPFVVWGx0W0jzW3nllasaEIxrkbwB26I4PGKzSOSu9XbRRRcVblt3k7zyRBLSWWedlZ3/2t+i0lXEQ60gYra8JPwHH3ywpteLJZ2LEvRiMkAsX91ZdeC4jtt+++0L482idqcoQS2WaI9koo7V4eJaMtq1n//8592uHNdX29boS9hss81yH/PWW29lieyXXHJJlkScJ67NYl9GwnL85mLiYFElqWY4DntqSfrevlVTua+7YqJf0dKVEePvsssuWXJcTAyK+C0m98VkkF/96ldZX0izVg9ss/nmmxdO/Iyk+4hbO6tuHf1rv/jFL7JrxDzR/sXxW8kiiyxSuK2/+c1vOv2dxTVbXnJuq1lllVWyavl54liLa8nOEuajAn60I820vHAr9jU023HZE/3eza5oifMyTpIGgN7Ur1ffDQCou0hUi6Sz6JCsJDoEovM6BsyiMzc6yseOHZtVEYilQ4pEp14kWlSztE7cTj755CyRLJYRiaUgY9AgZj3GUibRQREd6/G4vG0O0ZEeCVCVLLTQQoXbHksGVVo2KKpURJJWe7Gdu+22WzaLv6jDIgbzZ5555mw5rOi0iYSVGGiJzvtYaiUSU1qxM6ZVjrlmFskQ0Wl1++235z7uL3/5SzZYHANMM800UzYQGIMfMdBQzUDgnnvumSaYYIJUVpGMG7/fGKiJ9iSOq7b9U82gSxyrRdXemk1UF4qB2LbB2BlmmCFrB6ONifYpllKJ5NoYAIhEyahMWmSuueaqeN+8886b/d7yqrlGx3ck9eYNHkWb31vit7LVVlsVDlhHkmnc4jw0ePDgLPk7Pmu0RdFOx7574YUXciu2VPp9F1WaOemkk7KB5jjPxHcX7xeJg828DB/ds+OOO2YDQkXJR3FeKDo3NOtAclTMqXXAO57XCm0hrSGScnbaaafcx0QyTVQRXHPNNWtO0IuY6vvf/352zVKraO/jOM2r7BOJRZHsVU1Vua4mUbayuGaMJW0rVeKN68eIV4oS7tovaxlVh2+99dbcx0WsGZVSIx6fddZZa44388R3HYmtEQNUEm3Zb3/726yNjaSEiFeibYuJdJEwUU99sW392c9+lv3285L/IqkiklSOPvro7No+9knsj+i7iBguEvcioT8eV6tmOA6pTVxjF1WtjeuHCy+8MLu16uSWnXfeuXBVg2i7IrF46aWXzn7rcZ6MhMFYVriaCU8xATeO57zrqrhez+v7iOunmMQV57j4PcW1XGxXZ4mLrSz2Q1QDPuaYY3IfF9evMTFp8cUXz5Ywj+vwUaNGZd9Jsy0324p9Dc12XPZEv3ezy4uZ4niqpjIvAFCZBEEAaHHRcX3sscdmSW1FSQ6xNE1UKKhVdJZHIlwtYltiAKI7S2nFYEokglQSA0iTTz55VoGnnmIZrqiWEJUVirz22mvZrS9p1mOu2URFgUiMKBrYi6TSqGhTqx/84AdpvfXWS2UXy3dV6uzMEwOsUSml1UWiQV6yQZGJJ544t8pkJDZHEm+0ea02cBdVOmLAoEh3z0Udxe+uaIn0tvav0tKrMcgRN5UEyyPOWbE8dSR5dMcUU0zRVFWC2sTAV7QVectpdRRJVhtttFFLtIW0VhXBouo4sZx3fN/tE/Hi+InB8zyRANHVamjxXpGUmJcsEoPJMegZvyU6F9V8KyUIRoJXVBGMiSPVOuCAA7JEu4gn88T5uCiBqztxQ95gdzXxSiTzVFPxr1Z9oW2NClZRGXTfffctTLSL4yS+q2q+r1o0w3FI9SJRfMMNN8ySussY07WJSnR33HFH4aoe0ecT8V8tMWBbFelYESBPJCnHxMqiSoSRYNYXfhvbbrtt1n8WCcl5InEtJt92den43tKqfQ3NdFz2VL93s4pYp1Ic2FZps2jZZQAgX2uV0wAAKl4gd3dAOq8iTiy509titmYsWVI0IFG0ZFBXxKzoWO4vqgfQd465eovk1lheesCAAXV/7ZgFfOSRR6ayiioF3RH7PGbe9/ZM82YUs/FjULkoKbrVRKfwGWeckVWW7G0xWLDssst26zWiglTRcva0nkgK72rFvBDLikWCfLOq9dy81lprFS5f10xtIa0hls8rEkvvRRXB9mLAvag6cySFdEc1z+/JZYbLICbA5FXH7vi9FonKVfvtt1+3tinasWWWWabLz4/r1Uiy7o5DDjkkNaNWaVvjuGrkUqTNcBxSm4jHqqnclXe9EBXPm1lU4oqE+p6oAhorSERfSCQ2VdOOdKfq/7TTTtvtNraZvpPox4i+zq5qm7DQLFqxr6GZjsue6vduVlFpP28lnnpNPgOAvkyCIACURFTKiqS2enXQR8dUVML59a9/nXpbdGjF+1bTAR6DhD3RoRkJRrGM7sYbb1z31y6LMh1zPSWSTGM5j7ylsmsVx+TZZ5+dzcYuq+hE/slPftLl3+6wYcPS3HPPnfq6SLTMW66nTSxnFgOnrSY63WMZ3xiA623HHXdcVlmgK2KprV122aXu20RziCpFW2yxRc3Pi1jmggsuqKp6WVeWP62HSH6qpbraj370o9RKbSGtYaWVVsomShSJpIf21cKKKr/G9Ud3EwTj2iUqRuW54YYbCquI9WVxbo+JSJXcfPPNNS+3Gm3RXnvt1aXtieSW7k5IiGuck08+ucvXTHG9u8EGG6Rm02pt69ChQ7Mq+I1KaGz0cUjt+zyW4l5sscVq3nVxLjn11FML26pmuJ4fOHBg1l8xZMiQulY8u/TSS3OXFm4vzuk77bRTl95rxhlnzL6nrl6XNaMFFlggnXbaaV1KEoxY5uCDD26qyq6t2tfQTMdlT/V7N6O8iSCRKF/PtgoA+ioJggBQIuuuu25WlSJmi3ZnpmMsr3XRRRdlS44UiU7NvGWAaxWd3tEZVs17t1WR+stf/pJWW221um1D+9c++uij00knnVSXZKPorIt9GxUgtttuu1QGjTjmWrGD94orrkjbb799t6oJzjbbbOn444/PjslJJpkklV0MoB1++OE1dYzPMcccWUdsHE+tJmaXt18KsTti4DOqpETblVeFp72jjjoq7bzzzi1XdTESCSIh9He/+11dliWPzx+JCTGAHMsc5nX6RzLX/PPPX/VrRxsZA+lHHHFEt7eT5hW/4zjPx++vmkGhOC4ioTAGUqOC4Pvvv19VVZZGiJiv2qoNMYjVlUpHjW4LaQ2777574WNieb4bb7wx++9HH300vfDCC4WTOqJt7+45pGjgMpID//nPf3brffrCEouVvPbaa+n++++v+TVj4knECrXE4tGOReJMVxKEOrseiBg12vlaEgsPPPDAqo73ItrW//rhD3+YXbtGcmM9zgvRDxLxc/RftMJxSO3Jc9HXFJW/q7kmjbgjlpM+5ZRTspjpvffea8p4rrOkmziGo2pidxKaYn/97Gc/y/p1ak1mjXgtVpKoxYorrpjFz/POO28qm/hsZ511Vk37MZJaI+aNibzNplX7GprluOzJfu9mW174lltuqXh/XDM3Q2I1ALS61orIAIBCMUs3Ziu/+OKL6cILL0x33XVXNiBWNHs5BiuWW265rDMpZvxWKzo1//Wvf2Xvcffdd6eHHnooPf744+nll18uXMarfWdHdKLETOv111+/5k6jWKbxz3/+czYQeN1116XHHnss+/wffvhhNghXa5WJjiJJJG7//ve/s0SvBx54IL3xxhuFz4sB7khYiWSl5ZdfPqt4UqaZzY065lpRDAL95je/ST/96U+zimfR6fXEE0+kcePGFXbWL7HEElnVwPh9dCcJsxXFUp3xu4kBi2uvvbZixZ04BqMqW8zwbtUOwzPPPDO9/fbb2e/nwQcfzNrRZ555Jnd5lfai3YxjJWbox74oqmDU2SD0vvvumyWyXn311VlbHu8fyUoff/xx+uqrr1Izi3Yklj+NGefXXHNNGjFiRHrnnXcKnxe/qUi+jSWDV1hhhaytrnawLgbbo/M/Bm1j0P/111/v9HExAB3HcQyYLb744jV/NlpTxA0xiBPtfVQMe/bZZ9Nbb72VPv/882wANQb843iLZIX2FVbi3FCkkctTx+/snHPOqepxrdgW0lpVBKOtL6oiGPFTUfXAtiWx6yHer2gZ4bjfEmmVrbzyytnAerSbnYnrsThvdyVWiATOqOYXSZpxrdiZuH6LatYxoaueceUiiyySfffnnXdeds1UKXkoYrI4jiIxsF4VsbWt/xPn3DgGRo8enV2X3Xnnnenpp59OX375ZVUJYHHdGufviBvjO601qb3RxyG1iX0f1/AR10Q8F1VMX3nllTRmzJisvyMmK80333xZRfMNN9zwW0vqjhw5smnjuY7iOI7jLT5n9KnFtXdcD0bCTlFV6/gdRJsV1+7Rt9fV94+VJOK3EUluEQNW6k+KZNkf//jH2WTVMot+xPguov2O8160WZXapYgpdttttzTddNOlZtSqfQ3NdFz2dL93M7j++uvTp59+WvEYavZl2wGgVYz3dRkiBwAgVww+xIDzu+++mz744IOsky8qkEXyUdyiokF3K2Z0FB0U0YEVVR5iQDw6fdou9OO9o+Nw+umnz5ZejUSxVqvsEgmCMZAQ+zY6ZGKfRudxfK4Y+I+Bhxj872oHaatrxDHXaj777LMs8SF+H9ExGsdR/A4imSH2UVTDm2eeeepWSanVRXLII488kp5//vlsf8XvLdqQ+J0NHjw4lVEkkEYbGrdoc+IYieMmBjDj9xS3SGaLRLU4Xhq17GizisG75557LmuDxo4dm52HovpHDKJEOx37LM5B9dpv8V7xm452L5LA4n0i+TAGC+I3DUVicGy99dZLL730Um61opiY0ZdoC6HviUS6GJSvNPHm9ttv/1YiTlfiyhjojxgrkpIj3o64csEFF6ypOnBXxUS6J598Mhvoj+umiO3i80RcEnFDb1xDalv/J/op4to1rssiZoxbDJnE9xDxXEzyi2uOek/2a/RxSM8ZNWpUVqkyL/EpJolE1fJmFe1UJHHFNVVcT8U1ePwu4joqbhGTLrTQQj2SwBrXU/HbaPtNxvVaJM3GZKu+2o8UCWFxjRDJqdF2TDXVVFkSeZwzInmKnue47Fk/+tGP0sMPP9zpfZtssklWiRIA6D4JggAAAAB1EAPBXZ30EJX5Yhn5PLG0UixhDNCXE6Z/9atfZZWjAZotposkuqgcHtUG85x44olZOwfQ18Vk4EoV6CMBNqoLtq+6DwB0Xd9aowwAAACgh8TyXocddlhWZbiWCi1nn312VVVkYulcgLKLpJw99tij4v1/+ctfmnZJQqD1RVXPWDb3jDPOyKrYVSsqvkcCc1FyYFTdi+XUAUjp3HPPrbgbonqg5EAAqB8VBAEAAADqYMstt0wjRozI/jsGMlZbbbW08MILp/nmmy9NM8002VKFMegcA8gvvvhitozS1VdfnS3fViSWVbvkkkt8T0CfEFW4Nt1002zp184cd9xxacMNN+z17QLKL2K1WD43xNLPiyyySFpppZWy5Z/nmWeeNMUUU2TLnX/22WfZ0rvPPvtsuueee9Lw4cOz/y8ydOjQtM8++/TCJwFo/iXZ11lnnazd7Sja2RtuuCHNMMMMDdk2ACgjCYIAAAAAdU4QrKcYnI7kwMUWW6zurw3QrKI93WqrrbJkwY7mmGOOLBmnq8u6A1STIFhv0003XZbwEpNGAPq6/fffP11xxRWd3vfLX/4y7bLLLr2+TQBQZpYYBgAAAGhiBx98sORAoM9Zcskl0w9+8INO73vppZeyCqwArSKSAv/0pz9JDgRIKb388svpqquu6nRfzD777GmHHXawnwCgzlQQBAAAAGjCCoJRGWu//fZL2223Xd1eEwCA3q0gOHDgwHTaaaelpZde2q4HAAAaQgVBAAAAgCYz//zzZ8sKSw4EAGhda621VrYkuuRAAACgkfo19N0BAAAASlRBsF+/funBBx9MX331Vc3PH2+88dLyyy+fNt1007TOOuuk/v3798h2AgBQuYLzT3/603TTTTelZ555pku7aeKJJ07f//730//93/+lZZdd1q4GAAAazhLDAAAAAHX0wQcfpIcffjg99NBD6bnnnkuvvvpqeuutt9Knn36aPvvss2zQeLLJJkuTTz55mn766dOCCy6YFl544bTUUkulGWec0XcBANAEXnvttTRixIj0yCOPpJdeeimL6d59990spouliAcMGJDFcxHXzTLLLNnSxHFbZpllsr8BAAA0CwmCAAAAAAAAAAAAUELjN3oDAAAAAAAAAAAAgPqTIAgAAAAAAAAAAAAlJEEQAAAAAAAAAAAASkiCIAAAAAAAAAAAAJSQBEEAAAAAAAAAAAAoIQmCAAAAAAAAAAAAUEISBAEAAAAAAAAAAKCEJAgCAAAAAAAAAABACUkQBAAAAAAAAAAAgBKSIAgAAAAAAAAAAAAlJEEQAAAAAAAAAAAASkiCIAAAAAAAAAAAAJSQBEEAAAAAAAAAAAAoIQmCAAAAAAAAAAAAUEISBAEAAAAAAAAAAKCEJAgCAAAAAAAAAABACUkQBAAAAAAAAAAAgBKSIAgAAAAAAAAAAAAlJEEQAAAAAAAAAAAASkiCIAAAAAAAAAAAAJSQBEEAAAAAAAAAAAAoIQmCAAAAAAAAAAAAUEISBAEAAAAAAAAAAKCEJAgCAAAAAAAAAABACUkQBAAAAAAAAAAAgBKSIAgAAAAAAAAAAAAlJEEQAAAAAAA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KuP2j/mMAq16R5rc8a8Vqq61W97ap53Nj0LoVOnEf6E900EVWn5JoMMUM28G48cYbswHN8uUFIzAvlpKNDrjybX/IIYf01FFkRjn22GPTN7/5zQE/I47tz33ucxXBgRH0d+KJJ1bs25H15YQTTki/+c1vehpdBx54YJo6dWrdGYzqUeTtX8qws9tuu2XLSfaWuSA6XuN8Up6FJ4JgonN27733ruuzojM3llCvVbfflAPEdXW99darKSNHM5UG8d73vvdl5+/qNlZkY5syZUo6//zze2ZuRoB4tPu+973vdX17n3zoxEDckljGO5aUBCiyTry3js8oDzaKtln0d0QQUqxcUC7aaxHgF5OvS0477bQ0adKkLNtff+IaEING1dn0I1PyyiuvXPG66FuJdlZ45JFHstdE5kFopSIfr60aDwGgtVox1jVURR2Hh246TqtFn3kpYHGo47GdHi9BcRQ+QDBS1kf0cUks9xM3pDG7s1zciMYFNk5WF198cc8gRdwkl5/MGuG6666rafnGKNNnPvOZtOWWW2aBGi+++GL2/C9/+cusg2mttdaq+TPjpDaYpY4GEjMNr7766ooyR8dX9Wctt9xyWRBRlKMU/BINlMhSFcspNUsRt3+892C2dTQay8Ugc2SBq0cz9rE87gP9iQbQk08+mf3/7W9/e1q4cGHWWKpX6fgqn/UQgQCRMahaBPTFcRvpkkvLX0cWu1hutrzjsDcREBYz5Utif40sdOWNvTB27NjsZiSCAuMYKAXDRZrpoSynNpCibv/IShsN77gpjWUe+3tdBIfEYMdll13W83x85w996EMVgx0DGey5B6BbRNsrAvBKP6973euy4LyhLCcyFHEtiywae+65Z9bG7u910ekS5Tz00EN72gYRHB7XmIGy+XZ6e59865RA3L4mR0THYan93Einnnpq2mKLLWp+vQFtoNk67d462hgxAFou+hv233//Xl8fGY+jryIGp0r32JGtLCZQxATH/kRfRvmywtGvEllvq0X/SdTJrrvummbPnp099+c//zkbHNphhx0G9T1hMIp8vLZiPASA1mrVWNdQFXUcHrrpOK1OoFLKdhjjqAcccEDDghS1QWmnvqMCCqL6ZjaCIKpvhst96UtfyjIulWcLiMjlRqolOKz6RPn+97+/4rlp06alTkkVW+6LX/xivye9uBCUD3TE8hulYKFmsP1rE9nfYl8vFx2aedCJ+0BfotOr1FCP7GpHHXXUkDoDY5ny8sC9WKK8L5HtL47fkmjEffe73+33M6LTMGZXlEQDKrIEVgcHlv++tFxJSaSNjuyCzVLE7f/Od74zu2Hccccd+w0OLBefVX7ujkZ5LEEJQEorrrhiuuWWW7L2d0yoiKV23/zmN1dcz9oRrBjXqJhM0l9wYLnIMrjLLrtUPFdLxrZOb++TL7FvxwSHCGr94Q9/mAVWXHPNNe0uVhaAF1ngY8nsOCZuuummbGb0pptu2pTPi+Uo4zir9afWNh9AXu6to7+jPDtaTEqMNtpA/bGRHa1cnMv7c9ddd1X0wcZ3Ovjgg/u9XkT2i3ID9a1AoxX1eAUgn1ox1tXJ9MvRDbrxOI37thjnDnvssUeWDADyoNC9xHEiKk+5GynoY1nX/kRQxu67717x3FVXXZXabdttt614XJ6xq11iubIYGCmJjobIONWfmJFYHS3erPq1/WsXy76Wp82NrDj1LNnQqbppH4jZ6NFhV5pdETN9yjvn6lU94B/HXRx//YmlaMuX9YgAsf4yovzxj3/syXYX/ud//idbsqQ/0Qm488479zyOxlc0HJuhqNs/stnUG4i+zDLLZLN1yk2fPn1Qnw+QN5FRecyYMamTRHBif4N8ffnoRz9a17m+09v75EcnBuKWRAab2267LctaE52X73jHO9Jqq63W7mIBtEwn3ls//PDDFY+jPyLuawcSGSliSbaSmLC4ePHimvtW4jtFdpb+RN1E9tuSCLQqLVsFzVbk4xWAfGrFWFen0i9Ht+i24/SKK65It956a88k3fJgReh2hQ4QrM6yF1kzalH9ulh+q93KT5ChUWugD0XM2ov0/iXvete7+swcVi4GVKLjoeTmm2+uWKqjUWz/+i6E5aoDhbpVN+0D5557bnrwwQez/2+44YbZMhqDFcdTHFclkVkojruBxPEb2edKlixZ0u/s3Or6jWWpa1H9umbVb1G3/2BVp+5+/PHHW14GADrrXN/p7X3yoxMDccsnuAAUWSfeW1cHCZUHEQ1kzTXXrHi8YMGChvd7dGLfMsVQ5OMVgPxp1VhXp9IvRzfotuM0Et+cdNJJPY8PP/zwtPLKKzf9c6FVCh0gGNmtym211VY133iWZ26KoI1HHnkktVP153fCIEVkeBhM/cYJPzLUlSxcuDDNnDmz4eWz/Wsze/bsiuwxw4cPr8jw1s26ZR+4//77swCxkliGN7bDYN15552vyAhZy8zc3uqoug4bUb/jx4+vKE8srRsNv0Yr6vYfrOqsg+UBIQDkJwirnnN9p7f3AYDm68R768jyUO7ZZ5+t+W/LXxtZLaonZZf3l/373//ueRxLutca2FRP3wo0UlGPVwDyqVVjXZ1KvxzdoNuO05NPPjnNnz8/+3+sXlLrhBroFoUOECxfvmHYsGF1rR2+xRZbVDy+9957UyelZq315r6ZquukfBCwE+rX9q/NL3/5y55lTcPb3/72NGrUqJQH3bAPRN0fe+yxPQFykb1x4sSJQ3rP6rJGQN5gv3dfy+BEY++///1vRWffWmutVdNnRMNw0003rZhd8tBDD6VGK+r2H6zqbVDdgQtA96s+1w+Usa3T2/sAQPN14r11lKF8Yl0s41trsNEDDzzQ8ziWAi7Petxff0g9fSvx2vIltSwxTKsU9XgFIJ9aMdbVyfTL0Q266Tj905/+lMVFhGifxvgs5E1lOqACeeqpp7IUoeWDX/XcQMas0HIxa+5tb3tbaoezzjqrZx30sPHGG2cRzfW45557svXT77rrrvTEE0+kF198MY0ePTqb+frGN74xbbfddmnrrbeu6z1Ly2GG6PQaN27ckOq3kWz/2l155ZUVjz/wgQ8Mut6/9rWvpRkzZmSBY5EpZqWVVkqrrLJK1hEU+9d73vOetOKKK6ZW6JZ94Gc/+1lPBseRI0emww47bMjvWX08NePYjFn05YGl9XxG6XP++te/VnzOBhtskBqlyNt/sK677rqKx/V0Ioc45o855ph0xx13pMceeywLIo3vFHUfjfy3vOUtWVrxWmcOAdD+c30nt/cBgObr1HvrWAJqp512Sr/61a+yx3FfHQNTG220Ub9/99Of/jQ999xzPY/7yxYxlL6VqKOYdDdnzpzscfTFPv3005auoqmKfLy2ajwEgNZqxVhXoxRtHB762rc6dT+NdmWs4Fby6U9/Oq2//vpN+axOipegeAobIFidHSPS5NejesmIZmS36svixYvT448/ngVZXHrppdm/5eu2R+rT8lmotYiZedWz8x599NHsJ1K/nn/++VlGkFhnvZbshNHZECe0kjip1RP00ez6tf1rExen8uVSouNoKB0/F198ccXjefPmZT+xhGp0An3zm99M++yzT3bRjVmszdQN+8DcuXPTt771rZ7HX/rSl7JjaahmzZpV8bjWJXBKSwLGTUtst7BgwYLs//Fcf/VRz2eE1Vdfvd/3G6oib//B+Oc//5kt9VwS15jtt9++7o7oyy67rOK5uBGOn3j/yy+/PKvXAw44IH34wx9uWNkBqL0TJALTy0Xm6G5t70OeRZvqzDPPzDpGY9mTGNyPLO8xoabUkVhv+xYgb/fWhxxySLYEVdxzvvTSS+kLX/hC+sEPfvCK/obyJdpOOeWUnsexCsInP/nJPt+/Ef0epQDB8J///KfuiXhQjyIfr60YDwGg9Vox1tUoRRuHh247Ts8+++yemIgIYtx3331Ts3RSvATFU9gAwfKLZqg36KL6xBOzPJvlIx/5SEUmrf6iqE899dS02WabNaUcf/vb39Jee+2VDj300LT33ns3tX6rX9/o+rX9a1NKo1uyyy67pKWXbt5pIwa3vv3tb2cpfE8//fQsu1izdMM+cOKJJ2ZBVWHLLbfMzgWNUF3WwRyfpcZYqS4Hqg/ngM7Z/vWKTJAnnHBCRUbIyPS39tprN/yz4mb4qKOOyrLifuMb35BNEKCFzjnnnPTII4/0PB47dmzacccdu7a9D3l29dVXVzxesmRJ1kkaHfrTpk3L7qki83vcuzfzngqgk/tWVltttSzA6HOf+1w2KBWDLZFhbPfdd09vfetbs8CjF154IQvMu/baa9PUqVOzTC6lCbLRNopMDn3p9H4PqFbk47UV4yEAtF4rxrpaKU/j8NBNx2lkr44g3ZKjjz46LbfccqldWhkvQfEUNkBw0aJFr4hArkf1SSGWSmyX9dZbL33qU59KH/zgB+sOpojXR4aBuNHeZJNNsuUtoi5icCEyOv3+97/PBhhKgSFxIx5BG3HDvdtuu/X5vtX1UW+5qrdHo+vX9q8ti8w111xT8dyuu+46qPrecMMNs2xjm2++eVp33XWz/ScyYUbq3Ntuuy1dccUVPYFQpVmgBx54YLrggguaFpDY6fvAzTffnH79619n/19qqaWytMb1ZgbtS3VZh/rdq+uyGZ9RtHNAM7d/vaZMmdKzzHGprmJmd62i3HHsR4r8CGCPWd2RGjsa8dHpG8d7LGUe54SS+O7xvWOWDADN95e//CWdd955Fc/Fub6/62Ont/ehyCJgMJbdi47Es846K732ta9td5GAnOr0e+uNN944y8BwySWXpF/84hdZRohzzz03++lN3IfGBInDDjusz8xlfZW10747VCvy8dqK8RAAWq8VY11DVdRxeOiW4zSOvWOOOSbrSwvvfve7s/HMZujEeAmKp7B7UnkgQiMunNXv10qxrNCPf/zjNHz48CxLQK2pRmO5yshIFUsR9Wb8+PHZMo9///vf00EHHVSRXjiCVSZMmNDn2utDPdk3u2Fi+w/s+uuvzxqoJTGoVO/A0rbbbpv23HPPPpdIifeLpevi4hYZysozFsaFMNL5fv7zn0/N0Mn7wLPPPpsdYyVRh5tuumnD3r+6rPUen9V11dt37+T6Lfr2r0dpdkq5uB685jWvqenv3/e+96WvfvWrWSB7b6IRHEvgxdIx0aEbgZEl0SkcN84f+tCHhvgtAOjP448/np3bowOyZNKkSdl9RX86vb0PeRSZPeP43GKLLbLlhGMGcWTPieX47rjjjqz9VFoOJTz88MPZciQ///nP6xo4B8jDvXVJLFdaS9miP/XjH/94lq2llnNmK/pWoJGKfLy2YjwEgNbr9PZYkcfhoVuO08suuyzNmDEj+/8KK6yQvvKVr6RG6+R4CYqnsAGC1erNzFT9+vKlFxvtzDPPTM8//3zP50RkdEQSR0anOEE8+eST6Z577klHHHFENiDw3e9+N40YMWLA9/3MZz5T0+dHAMell16azVSIzw0RRR3liiWNW1G/zVbE7V/v8sKRobJeO++8c02viwj5k08+OWsUxIW45MILL0yf+MQnWpLSu5P2gdjmkVmttMRGsy/6rfjunVS/tXxekbZ/X2I5lwjcKy3ZEmJAOjLW1ipubmsRy8FEpsLPfvaz6cYbb+x5PjLexJIy9XZaA8XywAMPNGWGb7kIdG7GklXtFp1/++23XxYkWBKZXmOmcr06vb0P3SwmZ0S7KDoLe5sQuNFGG6W3vOUtad99900XXXRR+ta3vtUT9DtnzpxsUCKy8QA0WyfdW4cbbrghHX744VnfWS2BSXEOjUnYsaxpLO1Wz7JSnfbdod59sAjHayvHQwCKopP65Trt2mYcnk7hOO1d9JnFUr4lMR67xhprNLz+uylegvwrbIDg8ssv/4rlVOvN8FQuIoqbJQJE+ookjhPV17/+9Wz5oHDrrbemyZMnZ53/kVGwUSJ4IyKW99lnn57nrr322iwzVG+Nsur6qK6vdtev7T/wBbE8k1ekrY0gnWY76qijss+dPXt29jhuKqZOnZrNCG20Tt0HIqX4D37wg57HMVOh0QEJ1d+93uOzuq56++6dWr+dXr5WbP9aPPbYY9m1pDyVdcxmi07QZgV0xGB3LCm8ww479HQoxLkgUmhHym2Avhx55JE9s/yaJYKY3/a2t+VqI0TwUASC33XXXT3PxYzm73//+zV1NnR6e5/8diQWUWQNqLU9FZl0YsmiCAosicllN910UzZjGaAI99bhd7/73Ssmvb3+9a/PMjdstdVWWX9rBPxEttU4R/7whz/MJk1EGyn6VaONdP755/d5bWpF3wo0UpGP11aMhwAUVTv75fLUHsvbODydxXHau4ixKa2mGEuARxBeJ2hlvATFU9gAweoLXb03xJ3SaIjGz9e+9rXsBrmU8e3OO+/MAkxqnZlQq2222SZtvPHG6V//+lf2OG7YIyDxHe94R6/lKlfKgNcp9Wv79++qq66q6JCJgaRonDZbZAmLjp+Iji+J4KBmXPA6cR+I2a/HHHNMT7aPOOZ23HHH1GkdgtWvX3HFFbuifju9fK3a/gOJoMBYiq7U8AwbbrhhOu+885p+rYuAlMhWWp7hRoAgQOPFjOjIzlGetTXO8dHhGsuW1qLT2/s0lkDc7rLLLrukadOmpV//+tc9z8WkQgGCQKN14r11adJbrLRR3rcV97kRPF2eiTX6oWIgKH5i+bf9998/3X777T39q8cee2xFRolm9q1oC9FsRT5eWzEeAkDrtWKsq5XyNA4PnX6cRvbqa665Jvt/JEaJZb0jYVInaGW8BMXzyrVpCqI62n7evHl1/X11qvuVV145tVMM8JWfYCPFfgSbNFosW1Su1EipVl0f9dZv9esbXb+2f/+uuOKKIS8vPFhvfetba9rH8rgP/OQnP8k6tEKkD46OrWYY6vFZ/d17m73UifXb6eVr1fYfaKnJWOa3/Lhbe+210wUXXNCy1NW1XmcAGNrsyJgQUt7pcPbZZ2fZYmvV6e19KLpPfepTFY9vu+22pvQRAMXWiffWpSWYnn766Z7H2223XbYEaW/LtJeMGDEiW849srCWRKD1zJkzW9K3oi1EsxX5eB0K/VQAnasVY12tlpdxeOjk4zTGQo8//viex7vttlvNq3fkLV6C4umMMNg2ePWrX13x+JFHHqnr7x999NGKx+PGjUvtFIEbb37zm7MsAaUlYuNEEUsRN1IEitRyEl9llVWyE/TChQuzx3Pnzs1mL8TgYy2qt0ej69f279vdd99dcZGJpeYmTZqUWqXWfSyP+8D3vve9iqDM6AiLpTPqmTlRXa7I/Fg9O6P6u1f/TX/iOC5vjEWHXG+BY42u3+r3G6oib//+tu0BBxyQ7rjjjp7nxo4dm2WkXX311VOrrLPOOi05BwD58Z3vfKfumYf1Kh946nZnnHFGuvjii3seL7XUUum00057RQfkQDq9vQ9Ft/nmm2f3cvPnz88ex7+xFN8aa6zR7qIBOdKJ99bhN7/5TcXjWldZiT6Oj33sY1n7suTKK6/sdRLFUPpWWtHvAdWKfLx2Q181QLdqZ79cK8a6Wi0v4/B0FsdppWgzllZRi+yZe+yxx4DjsU888cQrggzL/2b48OENHUvVBqVZChsgGB3lcfEsXfzjoF68eHHNgRTVJ4n1118/tdu6665b8XjWrFkNDxBcbrnlKh731+hbb7310t/+9reeZcyizmqtp2bXr+3ft9JS1SU777xzzQ3KRojMaeWeffbZpnxOJ+4D5d/10ksvzX7qVZ1eOGbTVqcfj2Oz+lxRq+rXVr9X+fORkjmO/TBQw6pa9ev7+pzBKvL2702kqo+lW/74xz/2PDdy5Mh0/vnnv+LakpdzAJAfEcxM7dk5IlNgSVyrTzzxxEEvkdXJ7X0aSyBu94nje8011+wJECwNLAgQBPJ+b71o0aKewZ4QfVpveMMbav77rbfeuuLxXXfd1evrqstaT99K1FEM4pYPesegNDRTkY/XVo2HABRRO/vlWjHW1Wp5GYenszhOU5/jjhHot+uuuw5qkkv5RJeIyfnVr36VGsVYKc1S2CWGw4Ybbtjz/1hqp54byL/+9a99vle7VK+LHjMFGq16pkJ0LPRlo402qnhcT4r/VtSv7f9KL7zwQrYcQ7kPfOADqZXq2ceGKm/7QK2qj83q79Kf6uN4gw026PV1MeNirbXW6nkcWU3/+9//1vQZce76xz/+UTHrohkz6Yu6/avFjeORRx6Zfvvb31Zsv/POOy9tsskmLS9P9TmgE2btAeTBz372s3TSSSdVPHfUUUcNqa3X6e19GtuRGFl+m/lT3QnO0FXXqYkXQDN02r11KYtKeb9SZEyuVWTiryVrS3VZ6+lbiYHc8mXf++pbgUYr6vHaLX3VAHTeWFer5WkcHvJ6nLaCNijNUugAweq1u//85z/XnHa3fGZbROiXB8K0y2OPPVbxOGYENlppJkLJaqut1udrq5cpq7V+YzZEeefEiiuumLbYYovUaLb/K914440VM5ijQdjoZRkauY8NVd72gVptueWWWQBYeZ3XGlBcXUfbbLNNw+s3Gnzl5YnZwBEk2GhF3f7VInPUFVdcUTFbO7JLxX7SDq08BwAUxbXXXpuOOeaYnsy+4aCDDkp77rnnkN6309v7UHQ6E4FW6LR762hXlIsMafWofn15/0n1kk/lGfcjC0t132wj+lagkYp6vA6FfiqAztWqsa5WytM4POT1OG0FbVCapdABgm9/+9srHl911VU1/V3166rfpx1ixt9tt91W8VyjM27F0kS33HJLxXNbbbVVn6/fbrvtKrIaRnaqWlLwX3fddVk615Jtt922KUvc2v4DLy/c6uyB4Zprrql4PHHixKZ9VqftA9HQ+ec//1nXz5ve9KaK97j++usrft/bkoFxPJU3ouJ4i+NuIHH8lmeZi6C9t73tbX2+vrperrzyygE/o7fXNescW9TtX+673/1uuuiii3oexzn7tNNOe8WNZTvPAf1dZwAY2M0335wOOeSQbDn5kn322Sftt99+Q66+Tm/vQ5E99dRT6aGHHqpYctiS7EAR7q1XWmmligGop59+uuYVDUL5iga9ZSjrr8y1LinViX3LFEORj9dWjIcA0FqtGutqlbyNw0OnHqd777133eOx5WOp4YMf/GDF7xu5vHCr4yUolkIHCMbSiRtvvHHP4/vvvz/dcMMN/f5NLMlz6aWXVjz33ve+N7XbL37xi/T4449XpGuNmayNFIEk5Q2GNddcM1tPvS8jR47MGhXlAxQ///nP+/2MyGrywx/+sCX1a/tXiu3z+9//vufxsGHD0vve977USpE5burUqRXPbb/99k37vDztA/XaZZddKh7HcVeeVaivpQkXLFhQcfOx8sor9zsruTyT6R//+Mf0r3/9q9/PiKWIr7766p7Hyy67bHrXu96VmqHI2z9EY/bMM8+sGDT+xje+MWBQYTP95je/SXfccUdFmWI/A2Bw4px64IEHpiVLlvQ899GPfjQddthhDanSTm/vQ9Ezh5YvX7nppptmg/AARbi3rh5IjX7TWlW/dsKECX2+duedd654HN9poOXco24eeOCBivOzJd1olSIfr60YDwEgn2NdrZK3cXjI43Gax3gJiqXQAYLhgAMOqHh8wgknZBfQvnz729+uSKcfgRTRkdPfTWrceJd+PvGJT/Rbnh/96EfpySefrOs7TJ8+PX3961+veC4G/vry4IMPpj/84Q91fUacqC+55JKK5z772c8O+Hf7779/xePITFVef719Tvna85tvvnnaYYcdUrMUcfv3JYKyygePI7hr9dVXT4N1+eWXp4ULF9b8+vvuuy/bX8oHsSLtcLMzmXXaPtAqEXRX3iF45513vmL2Q7lYKieO3/LAreq6q7bccsulyZMn9zyObXv00Uf3mTo6fn/88cdX7De77757UzOdFHX7R7bQWFq4XCw92cig4Dif1ZomvJRB8Ygjjqh4bscdd6xYsgmgyOIaUn5NGWjgLGYuRnu9vGMxOmOOO+64hpar09v7UETRnv3e975X8ZxJF0Azddq9dfVEwylTpmSDLAOJdkr5MlbR99FfO+X1r399mjRpUs/j+E6nnnpqn6+POvnqV79a8dxAfSvQaEU8Xls5HgJA/sa6Stlzy69v1av6lTMOD51/nLZKt8RLUByFDxCME9Ib3vCGngqZNWtW2nPPPbMBtXKR3j5ulstPVpHZ6qCDDmr4SSJuZI866qj0pz/9qd/gipht+rWvfS3ttddeFQN/MZtgjz326PPvItNg3MzG8rEXXnhhvwN4kaY/TrjVgSTjx49Pu+2224DfJzrKymfTRqT3xz72sVesGR9pYmMA46STTqo42cdyaPFvsxRx+9e6vHCkxh2Kc845J/suUcYZM2akF154odfXRQfUueeemz784Q9XZMGMlMNHHnlkarZO2wdaJY6rQw89tOL4iuxxsS2qU5BHEOrHP/7xrA7KZxT11xlYEsf7uHHjKhp9saxh1HN15sAvfvGL6Xe/+13Pc6NGjWp6x1sRt3/MDI9jq3x2TmzfSM0dje5afx599NF+PycGPeIcEB3JcS0pb8yWe+yxx9I3v/nNLKX3okWLKrb/wQcf3MBvDtA80cbt63xZLtpDfb2uliVAahXt+7jelg/0bbHFFunzn/98tmRXPef7vtpw3dLeh2YG4rZCLKdSfS7pTxyDcQ//yCOP9DwXS/d98pOfbFIJATrv3jr6PF/96ldXZECL/rOYyNZbmyv6JGLCYnX/53ve857setCfKHv5kmwRVBR9YdWDQPfcc09WJ+Xn9Mic1s4s/hRTEY/XVo6HAJDPsa56GIeHzj9OW6Vb4iUojv9bmL6g4kR0xhlnZDd3pYMtlsB8//vfn2WziMCW+fPnZ7PWygMXQhzIsZRvo0Ww109/+tPsZ+mll04bbLBBlsltxIgRWYBFdPjfe++9WVBFtbiZPu+887K/G0jc7MZPnIBXXXXV7HMiHXHc6MdnRKdAbwEg8RlxMou13msRHQlR3tLSovGecWKPSPH11lsv+7533XVXmjdvXsXffeELX8iy2DVTkbd/dbBheSaXSNH7zne+c8jfJeru4osvzn5iv4r6imxwsbRVdPbEAHV0kL744osVf7fUUkulk08+Oet4abZO3AdaJQLCosMr0paHCBiLme7RUfa6170uLb/88tm+EftbuWiERUdcLSKL4Nlnn51ltSwFst5+++1Zp11s3zXWWCM98cQTWeBgeUBsnF++853vpDFjxqRmKuL2v+aaa17RAI1O1/ipRyxjX74seW+iTuOcFD8xMB0dtaNHj04rrrhitj9EJ3TsX9WpxGO/OeussyqCSwE6WQwEly/b3pdov/WV1SIG3rbeeuuGlCdmR8bAWblo61Vn56jF9ddfn9ZZZ52ube+TH3EfVL60SF9Kgbi9iXuRuC9ppOjIrD7eyn9Xrq8JFnGvFz+9ifZWTJqIAJLIAhrHTLSrqkVbOpYVjjZ09WScmIizyiqr1PGtALr73jr6FCLjRAR+l/oi4t+YyHbKKadkExzimhAracQ1o7e+qde85jVZpv2BxETdeN/DDz+857noB7viiiuyfo/oa33ooYfS3//+94q/iyXjTj/99IZ9Z6hVkY/XVo2HAJC/sa7BMA4PnX+ctkI3xEtQHIUPEAwRfHX++ednGTUi7W/ppBSDWPFTLQ7c6PRp5FKM/Q1uxI1p9Qy+3kTATdwIDyagJgJ04mcgMagYHQFx41yrCASJ4JCYXRhBQCXR8VAaRCw3bNiwbEbffvvtl1rB9n9l9sBY1rMZA2e9HU/VooM0On9iFnWrdPI+0GyRpjg62OIYLWV4i2Wub7zxxl5fHzOMoxOxt0HJvkRwQLx/DGyWOh7j3BYzJXoTg6MxS7dRQRIDKfL2b6Xo3L3jjjtq2l/ipqAbgy8BiqrT2/vkQ6cF4pbEPl9rdr4InO1NdJAeeOCBff5dtNdjkkf8xPGz/vrrZ/dNpUlkcS8f7dbFixe/4m8jI5DsgUAR761jkOmCCy5IX/rSlyqyhcW96UBLTb3xjW/M+qZqDa6OVTgikCoGb0qTHyOD4C233NLr6yMoKQK6Y1AI2qHIx2srxkMAyOdY11AUeRweuuU4bYVOjpegGAq/xHB5QELM7Jw8eXKfAXYxQyzWLo/MbrFsVjNE9o0oQ8zWqyULXFz0IzV+ZH6KE2QtwYHRCRUX/lhmrHwJjL5ExHY0SC655JIsqnswN8NxEosyxk1+ZJ3qa/bim9/85mzQptXLdhZp+1eLC/CVV15Z8Vy851BFwzIG5iJb2EBi28eM6+OOOy4b9GrHxa5T9oFWi7qPjCIx4BqDpX0t8RfHbRy/sa9FJ2K9Jk6cmO1nu+++e5ahsjeRNS6yovzqV79qSAbLehR1+zdTpAvfZptt+tze1bNgoqEfjd1Yyk9wIED36fT2PuRF3L/dd9996aabbkpXX311dv8Uy69UBwdG2zrur44++ui2lRUonk67t477zOiLiPvT8iVM+xJ9pRHkF32ga621Vl2fFQHZ8Z0mTZrUZ59eZCz7zGc+k37+85+nDTfcsK73h0Yr0vHajvEQAPI51lUL4/DQ+cdpq3RbvAT596qXq9f1oyezVaSsj2j+CMKKZTDjJrWVy/JEB3+kUY1yxJJF8Tii+qM8cRKJpRpjya54brAiSvv+++/PPiMyLcRs1/j+kdI0shFEZoLNNtus7iVr+xO7XCxx9p///CfLJhYDF3Fyj+UCYmCx3Yq0/VvlkUceyWajxr+RRjei42Pmaexjse2jc6aTOlo6ZR9oh0hjHDMX4nwQaY1XW221tO6662bbqK+GWr1i+//5z3/OPitmhsS2j2M/ZvzGuafdirz9myHO+VGXcQ6I/eqpp57K9oGY8RPngOjEjfTYUc8A5EOnt/fpTjE4W0sGwf4MlEEwlra7/fbbex7H8nO77rprv+8ZWW2GmqGvvwyCt956azaBJsrV19LJ5aLdGhO+IlthtOUB2qUT761LfR7RzxYZ/qKfo3RfGu2UWgZtajF37twse0ssVRp9rREYuM4666QJEyY0tI8VGqVIx2s7xkMAyOdYVy2Mw0PnH6et0m3xEuSTAEEAAACADhcTLWISWXQkRvBJTCKLTMzRkRiD95GJvq8MngAAAAAAFJcAQQAAAAAAAAAAAMihzl+bFAAAAAAAAAAAAKibAEEAAAAAAAAAAADIIQGCAAAAAAAAAAAAkEMCBAEAAAAAAAAAACCHBAgCAAAAAAAAAABADgkQBAAAAAAAAAAAgBwSIAgAAAAAAAAAAAA5JEAQAAAAAAAAAAAAckiAIAAAAAAAAAAAAOSQAEEAAAAAAAAAAADIIQGCAAAAAAAAAAAAkEMCBAEAAAAAAAAAACCHBAgCAAAAAAAAAABADgkQBAAAAAAAAAAAgBwSIAgAAAAAAAAAAAA5JEAQAAAAAAAAAAAAckiAIAAAAAAAAAAAAOSQAEEAAAAAAAAAAADIIQGCAAAAAAAAAAAAkEMCBAEAAAAAAAAAACCHBAgCAAAAAAAAAABADgkQBAAAAAAAAAAAgBwSIAgAAAAAAAAAAAA5JEAQAAAAAAAAAAAAckiAIAAAAAAAAAAAAOSQAEEAAAAAAAAAAABI+fP/AMHKqa/NHSfdAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -319,20 +319,19 @@
     "bs_inram = idata_inram.posterior[\"b\"].values.reshape(-1, 4)\n",
     "names = [\"intercept\", \"slope x1\", \"slope x2\", \"slope x3\"]\n",
     "\n",
-    "fig, axes = plt.subplots(1, 4, figsize=(13, 3))\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout=\"tight\")\n",
     "for k, ax in enumerate(axes):\n",
     "    ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label=\"streaming\")\n",
     "    ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label=\"in-RAM\")\n",
     "    ax.axvline(b_true[k], color=\"k\", ls=\"--\", lw=1)\n",
     "    ax.set(title=names[k], yticks=[])\n",
     "axes[0].legend(fontsize=8)\n",
-    "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04)\n",
-    "fig.tight_layout();"
+    "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04);"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c04c6f42",
+   "id": "04dc1901",
    "metadata": {},
    "source": [
     "## Memory usage\n",
@@ -346,24 +345,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "118f6849",
+   "id": "6f25aa9a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:28.078399Z",
-     "iopub.status.busy": "2026-06-11T03:45:28.078326Z",
-     "iopub.status.idle": "2026-06-11T03:45:28.334354Z",
-     "shell.execute_reply": "2026-06-11T03:45:28.333943Z"
+     "iopub.execute_input": "2026-06-11T05:01:31.237556Z",
+     "iopub.status.busy": "2026-06-11T05:01:31.237493Z",
+     "iopub.status.idle": "2026-06-11T05:01:31.470524Z",
+     "shell.execute_reply": "2026-06-11T05:01:31.470161Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/claude-501/ipykernel_71399/2826343238.py:15: UserWarning: The figure layout has changed to tight\n",
-      "  fig.tight_layout();\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -379,9 +370,9 @@
     "ncols = 4  # 3 features + observed\n",
     "n_grid = np.logspace(5, 9, 50)\n",
     "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
-    "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer\n",
+    "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # one resident batch\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "fig, ax = plt.subplots(figsize=(8, 5), layout=\"tight\")\n",
     "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch  (O(N))\")\n",
     "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader  (O(batch))\")\n",
     "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n",
@@ -389,26 +380,25 @@
     "ax.set_xlabel(\"dataset size N\")\n",
     "ax.set_ylabel(\"array footprint (GB, lower bound)\")\n",
     "ax.set_title(\"Memory is flat in N when streaming\")\n",
-    "ax.legend(loc=\"lower right\", framealpha=0.95)\n",
-    "fig.tight_layout();"
+    "ax.legend(loc=\"lower right\", framealpha=0.95);"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "624c0133",
+   "id": "d304c6c7",
    "metadata": {},
    "source": [
     "That line is only the bare array. Actual peak RSS is higher, because of the\n",
-    "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n",
-    "the real number on public data, we measured peak memory on the\n",
+    "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a\n",
+    "real-data check, outside this notebook, we ran the same logistic model (13 numeric\n",
+    "features plus the click label) on the\n",
     "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n",
-    "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n",
-    "features plus the click label). Streaming through the `DataLoader` stayed flat at\n",
-    "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n",
-    "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n",
-    "out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the\n",
-    "largest gap is about 0.1, on the intercept. Criteo is\n",
-    "public, so anyone can rerun this.\n",
+    "standard, publicly available out-of-core learning benchmark. Peak memory for the\n",
+    "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n",
+    "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n",
+    "rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB\n",
+    "machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;\n",
+    "the largest gap was about 0.1, on the intercept.\n",
     "\n",
     "## When to use it\n",
     "\n",
@@ -423,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fdf59e4",
+   "id": "918da33a",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -435,7 +425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e983b8d",
+   "id": "1a8d9f52",
    "metadata": {},
    "source": [
     "## References\n",
@@ -450,13 +440,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "0c4c4ddb",
+   "id": "91539613",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T03:45:28.335556Z",
-     "iopub.status.busy": "2026-06-11T03:45:28.335482Z",
-     "iopub.status.idle": "2026-06-11T03:45:28.345025Z",
-     "shell.execute_reply": "2026-06-11T03:45:28.344643Z"
+     "iopub.execute_input": "2026-06-11T05:01:31.471814Z",
+     "iopub.status.busy": "2026-06-11T05:01:31.471732Z",
+     "iopub.status.idle": "2026-06-11T05:01:31.481894Z",
+     "shell.execute_reply": "2026-06-11T05:01:31.481532Z"
     }
    },
    "outputs": [
@@ -464,7 +454,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Wed, 10 Jun 2026\n",
+      "Last updated: Thu, 11 Jun 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -492,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e62e534",
+   "id": "5d51af15",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
@@ -520,6 +510,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.12.12"
+  },
+  "myst": {
+   "substitutions": {
+    "extra_dependencies": "pyarrow"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index cad94466f..62613be17 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -9,6 +9,9 @@ kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
   name: python3
+myst:
+  substitutions:
+    extra_dependencies: pyarrow
 ---
 
 (streaming_dataset)=
@@ -47,6 +50,11 @@ bounded buffer.
 `N` is small here so the notebook runs in seconds. The streaming code is the same at
 any size, and the last section shows what changes at scale.
 
++++
+
+:::{include} ../extra_installs.md
+:::
+
 ```{code-cell} ipython3
 import glob
 import tempfile
@@ -68,9 +76,9 @@ az.style.use("arviz-variat")
 ## Put a dataset on disk and forget the array
 
 We build a logistic-regression dataset, write it to Parquet shards, and delete the
-in-memory table. From here the features only exist on disk. We keep `X` and `y`
-around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit
-never reads them.
+in-memory table. From here the streaming path reads only the disk copy. We keep `X`
+and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming
+fit never reads them.
 
 ```{code-cell} ipython3
 N = 30_000
@@ -130,8 +138,7 @@ with pm.Model() as model:
     idata_stream = approx.sample(1000)
 ```
 
-The negative-ELBO trace shows the fit converging while only ever holding a
-`batch_size` buffer in memory:
+The negative-ELBO trace shows the fit converging on minibatches read off disk:
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(9, 3))
@@ -164,15 +171,14 @@ bs_stream = idata_stream.posterior["b"].values.reshape(-1, 4)
 bs_inram = idata_inram.posterior["b"].values.reshape(-1, 4)
 names = ["intercept", "slope x1", "slope x2", "slope x3"]
 
-fig, axes = plt.subplots(1, 4, figsize=(13, 3))
+fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout="tight")
 for k, ax in enumerate(axes):
     ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label="streaming")
     ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label="in-RAM")
     ax.axvline(b_true[k], color="k", ls="--", lw=1)
     ax.set(title=names[k], yticks=[])
 axes[0].legend(fontsize=8)
-fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04)
-fig.tight_layout();
+fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04);
 ```
 
 ## Memory usage
@@ -186,9 +192,9 @@ cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measureme
 ncols = 4  # 3 features + observed
 n_grid = np.logspace(5, 9, 50)
 inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
-stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # just the buffer
+stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # one resident batch
 
-fig, ax = plt.subplots(figsize=(8, 5))
+fig, ax = plt.subplots(figsize=(8, 5), layout="tight")
 ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch  (O(N))")
 ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader  (O(batch))")
 ax.axhline(26, color="0.5", ls="--", lw=1)
@@ -196,21 +202,20 @@ ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom")
 ax.set_xlabel("dataset size N")
 ax.set_ylabel("array footprint (GB, lower bound)")
 ax.set_title("Memory is flat in N when streaming")
-ax.legend(loc="lower right", framealpha=0.95)
-fig.tight_layout();
+ax.legend(loc="lower right", framealpha=0.95);
 ```
 
 That line is only the bare array. Actual peak RSS is higher, because of the
-framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get
-the real number on public data, we measured peak memory on the
+framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a
+real-data check, outside this notebook, we ran the same logistic model (13 numeric
+features plus the click label) on the
 [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a
-standard out-of-core learning benchmark, with the same logistic model (13 numeric
-features plus the click label). Streaming through the `DataLoader` stayed flat at
-about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline
-rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to
-out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the
-largest gap is about 0.1, on the intercept. Criteo is
-public, so anyone can rerun this.
+standard, publicly available out-of-core learning benchmark. Peak memory for the
+streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M
+rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M
+rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB
+machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;
+the largest gap was about 0.1, on the intercept.
 
 ## When to use it
 

From 79fdc5c4f6d1db2c6b15c1c1b181be6a81562261 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Thu, 11 Jun 2026 10:50:09 -0500
Subject: [PATCH 13/42] Seed the in-RAM baseline and tighten the memory-figure
 accounting

- pass random_seed to the in-RAM pm.fit and to both posterior sample
  calls so the comparison is reproducible
- the streaming memory line now includes the shuffle buffer and the
  current source chunk, matching the surrounding prose
- say 'no callbacks to write' (the Trainer uses pm.fit's hook
  internally) and 'can bias' for the bounded-buffer caveat
- note that the dropped end-of-pass remainder is re-drawn each epoch
  under shuffle=True
---
 .../streaming_dataset.ipynb                   | 113 ++++++++++--------
 .../streaming_dataset.myst.md                 |  43 ++++---
 2 files changed, 87 insertions(+), 69 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 2b69da89b..226c9c493 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -33,14 +33,17 @@
     "  read by {func}`~pymc.variational.streaming.parquet_source`.\n",
     "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n",
     "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n",
-    "  minibatch into that placeholder with `set_data` every step. There are no callbacks.\n",
+    "  minibatch into that placeholder with `set_data` every step. There are no\n",
+    "  callbacks to write.\n",
     "\n",
     "The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`\n",
     "is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed\n",
-    "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The\n",
-    "one extra requirement is shuffling. A streaming source is only as well mixed as the\n",
-    "order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a\n",
-    "bounded buffer.\n",
+    "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`.\n",
+    "Batches are exact-size, so each pass drops the rows that do not fill a final batch;\n",
+    "with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed\n",
+    "tail. The one extra requirement is shuffling itself. A streaming source is only as\n",
+    "well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to\n",
+    "shuffle through a bounded buffer.\n",
     "\n",
     "`N` is small here so the notebook runs in seconds. The streaming code is the same at\n",
     "any size, and the last section shows what changes at scale."
@@ -61,10 +64,10 @@
    "id": "c2f6955f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:07.808492Z",
-     "iopub.status.busy": "2026-06-11T05:01:07.808295Z",
-     "iopub.status.idle": "2026-06-11T05:01:09.181327Z",
-     "shell.execute_reply": "2026-06-11T05:01:09.180785Z"
+     "iopub.execute_input": "2026-06-11T15:48:31.179963Z",
+     "iopub.status.busy": "2026-06-11T15:48:31.179761Z",
+     "iopub.status.idle": "2026-06-11T15:48:32.724808Z",
+     "shell.execute_reply": "2026-06-11T15:48:32.724317Z"
     }
    },
    "outputs": [],
@@ -105,10 +108,10 @@
    "id": "03ccf873",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:09.182814Z",
-     "iopub.status.busy": "2026-06-11T05:01:09.182552Z",
-     "iopub.status.idle": "2026-06-11T05:01:09.201682Z",
-     "shell.execute_reply": "2026-06-11T05:01:09.201315Z"
+     "iopub.execute_input": "2026-06-11T15:48:32.726747Z",
+     "iopub.status.busy": "2026-06-11T15:48:32.726448Z",
+     "iopub.status.idle": "2026-06-11T15:48:32.752265Z",
+     "shell.execute_reply": "2026-06-11T15:48:32.751894Z"
     }
    },
    "outputs": [
@@ -152,7 +155,7 @@
     "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n",
     "batches and shuffles it into fixed-size minibatches. The model reads a\n",
     "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n",
-    "writes each minibatch into it with `set_data`. There are no callbacks:\n",
+    "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n",
     "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n",
     "runs the loop."
    ]
@@ -163,10 +166,10 @@
    "id": "477343ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:09.202716Z",
-     "iopub.status.busy": "2026-06-11T05:01:09.202653Z",
-     "iopub.status.idle": "2026-06-11T05:01:27.550831Z",
-     "shell.execute_reply": "2026-06-11T05:01:27.550360Z"
+     "iopub.execute_input": "2026-06-11T15:48:32.753347Z",
+     "iopub.status.busy": "2026-06-11T15:48:32.753280Z",
+     "iopub.status.idle": "2026-06-11T15:48:50.379526Z",
+     "shell.execute_reply": "2026-06-11T15:48:50.379109Z"
     }
    },
    "outputs": [
@@ -202,7 +205,7 @@
     "        data_name=\"batch\",\n",
     "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
     "    ).fit(30_000, random_seed=RANDOM_SEED)\n",
-    "    idata_stream = approx.sample(1000)"
+    "    idata_stream = approx.sample(1000, random_seed=RANDOM_SEED)"
    ]
   },
   {
@@ -219,10 +222,10 @@
    "id": "a3ff7cb9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:27.552089Z",
-     "iopub.status.busy": "2026-06-11T05:01:27.552002Z",
-     "iopub.status.idle": "2026-06-11T05:01:27.665910Z",
-     "shell.execute_reply": "2026-06-11T05:01:27.665530Z"
+     "iopub.execute_input": "2026-06-11T15:48:50.380772Z",
+     "iopub.status.busy": "2026-06-11T15:48:50.380702Z",
+     "iopub.status.idle": "2026-06-11T15:48:50.538325Z",
+     "shell.execute_reply": "2026-06-11T15:48:50.537986Z"
     }
    },
    "outputs": [
@@ -252,8 +255,8 @@
     "\n",
     "For comparison, here is the usual fit that keeps the whole dataset in memory.\n",
     "Streaming changes where the data lives, not the inference, so the two posteriors\n",
-    "land on top of each other. Both show ADVI's usual mild bias relative to the dashed\n",
-    "ground truth, but they agree with each other."
+    "should agree, and here they land on top of each other. Both show ADVI's usual mild\n",
+    "bias relative to the dashed ground truth, but they agree with each other."
    ]
   },
   {
@@ -262,10 +265,10 @@
    "id": "b3b5d9fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:27.667067Z",
-     "iopub.status.busy": "2026-06-11T05:01:27.666979Z",
-     "iopub.status.idle": "2026-06-11T05:01:30.969240Z",
-     "shell.execute_reply": "2026-06-11T05:01:30.968822Z"
+     "iopub.execute_input": "2026-06-11T15:48:50.539450Z",
+     "iopub.status.busy": "2026-06-11T15:48:50.539367Z",
+     "iopub.status.idle": "2026-06-11T15:48:53.984891Z",
+     "shell.execute_reply": "2026-06-11T15:48:53.984550Z"
     }
    },
    "outputs": [
@@ -273,7 +276,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finished [100%]: Average Loss = 531.87\n"
+      "Finished [100%]: Average Loss = 531.25\n"
      ]
     }
    ],
@@ -285,9 +288,13 @@
     "    )\n",
     "    pm.Bernoulli(\"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)\n",
     "    approx_inram = pm.fit(\n",
-    "        30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n",
+    "        30_000,\n",
+    "        method=\"advi\",\n",
+    "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
+    "        progressbar=False,\n",
+    "        random_seed=RANDOM_SEED,\n",
     "    )\n",
-    "    idata_inram = approx_inram.sample(1000)"
+    "    idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED)"
    ]
   },
   {
@@ -296,16 +303,16 @@
    "id": "6731e9d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:30.970410Z",
-     "iopub.status.busy": "2026-06-11T05:01:30.970349Z",
-     "iopub.status.idle": "2026-06-11T05:01:31.236510Z",
-     "shell.execute_reply": "2026-06-11T05:01:31.236118Z"
+     "iopub.execute_input": "2026-06-11T15:48:53.986146Z",
+     "iopub.status.busy": "2026-06-11T15:48:53.986070Z",
+     "iopub.status.idle": "2026-06-11T15:48:54.193017Z",
+     "shell.execute_reply": "2026-06-11T15:48:54.192668Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2600x600 with 4 Axes>"
       ]
@@ -338,8 +345,9 @@
     "\n",
     "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
     "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n",
-    "holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the\n",
-    "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:"
+    "holds one batch plus its bounded shuffle buffer and the current source chunk. The\n",
+    "dense `float64` design matrix dominates the cost. The lines below are array lower\n",
+    "bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:"
    ]
   },
   {
@@ -348,16 +356,16 @@
    "id": "6f25aa9a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:31.237556Z",
-     "iopub.status.busy": "2026-06-11T05:01:31.237493Z",
-     "iopub.status.idle": "2026-06-11T05:01:31.470524Z",
-     "shell.execute_reply": "2026-06-11T05:01:31.470161Z"
+     "iopub.execute_input": "2026-06-11T15:48:54.194247Z",
+     "iopub.status.busy": "2026-06-11T15:48:54.194176Z",
+     "iopub.status.idle": "2026-06-11T15:48:54.488206Z",
+     "shell.execute_reply": "2026-06-11T15:48:54.487819Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Hp3uKzirQ2S3WaIeT5o4PFPaC9zoy11qnpo5AdrSNbc1ysDf7wVFaKHvcnbGodKacpnaxR79LNd2NJ2nnqb0c68H8WdGAvCcGJgQyrbXhDv1c6SzbgQafetMglD+Cr/o+20sd5M1zXK31oik83aU1F/yZBnEgn11HbddrKJ35MJDzc1upcAHAgiLUAICgkpKSYvLJWutw1k5fS2olnUZs7WRbO421oOtAaboInVJujxZm02CHjhjVdusoVkvR5TVr1lhdRkdV6ihAnWrvSpFlW6kpdOq1FovWDqBnn33WbgG83iM6tUNs/vz5Zlunpqaaadw6zdpezmId/aQXyfaCK45G72mnu07TP+igg0wxU81drNtC3297F9/aNq2loSN0B2rIkCFm22nRTX0PNH+uBk80H3/v/UiDYRr8sEbzievFnBaltkbXaW9EsK47kNgbMav7in7uTjzxRFN0Mi4uzuzrekGrHXV6Uar7vTs5pF3RO62avra9NBca4LHVkWorH/9A6H6gxwEtkDpq1ChTUFqPCVrnQ/dzPVbpttHR3poPX/d3TUtjjXbwapovnUnUm/4/vfd/7ay0V1vFU2noAol+dvWYp+n69Lil+59+RrVQqT167BpIcWFPmDlzphkhrccaa2l8LrnkkoBIyaYzDnT2gK1R8joDrH/wVD/7jjq2LHUg+n/v2QtAaGHZrKwsGQg9fumMNs15rp99De7p+YOj9C66z2hOfWs03YmmTbL3naW1K/S7Tj+3mtJPv3/1e1aLy9oLTutIZk3D46gofbB9Vux9x2hb9XxEg036f+sxU88l9LxGzxF0u+nymnbTXhBDz/16fwfZGyiQkZEhp556qt3CzgMRFhYmkyZNMjf9XGtATI//1raD1k+wN1tBz0f0XFMLLmuaUv0MaWpPPQ/R8z5bM3B0X9PjyiGHHGK3rbr9Ld9f+pnT10hLSzPfXzogSNevqcJ0kIK+B/r9Ze+7R2fbemImn+63OntVP0PaFj1W6OfO3nlG7/NcLQh+1VVXmXNk3W+ee+45u/VWtCaGvoa7s648RWfLaZ02Pc7q/63n5Vovxt5AGkdt1+OdowLteh6j2+uEE04w9/U91hk9us38nZ4KQHAgAAEACDracWYtAKEdnTp7QE/KbeXN1toDejE1UHqyb69zQdv461//+oDOFL1o1Y4OneZsq43aAaXpFrRDyl36Gjr6Ui9ylXZwaHs0TZIWErZH26z55n/wgx/0/E47ZixFDG1d4OvFiHbeWOvA1RF89uodWLaZpoewtNnSbg0G6AWPdrja6sTSkWEaENKLsoHQ3Mg6EtnaPqLbTjuNLbRWhgZ0rF3ca0ec7qO6Pmu0g8nW/qMdYp4IknmSvWn/mtPaVmFu7aiydOLo/6ujZ3UEpK2ikQPRezSnduTYC0Boh4svRn9qEEtHr+sxKSIiwu5ztSNVt5d+xnR76j5ga8SjHuf6ByC0UGzvouV6HLHXCRQIqYc8STvE9BigwSULPRbpttdUefbSQwx0RK4n6HFXR+damwmhwSg9/vZ/z/1BOyETExNN5681GmywFoBwxFIHYsKECU4HIAbaEajBUt2uhx12WJ995uCDDzYdbPaKq9pLs6ezAauqqmz+ferUqSaQkJyc3Of32rl+/PHHm45kTbdkjQYrtW6FdkqH0mfF3nfMH//4RznttNOs/m3ixIl9Ope1A1/bb+3Yef311/d5bC8AoYFibx0j9TP0hz/8wWogR4PnvTuCNSBkb1/TdmrgqH9ASt9PrdmitaS009jW6Hc9l/3Xv/5lM7Ch+6kGfjToY4++nnbk676rr6cpJG3VDBlI/a/ewQcNruh3rIV+/2mKTK2X4kwtr/vuu6/P+aYOoNGOensBQN3//RmA0GOHvl+9BxtYzpO1ILQG5Fxtu6VGhz36Gvp56V2HT99z3Zf1HE/Phz1ZZBxAaCIFEwAg6OiIMT0Jt0ZPom2N/NULDU+M+tWRXpqL3Ra9ILYWfLDQ32sOae0EsEUDEO7Si0YNcPS+sLLQ0Y6OaOCjd/DBQjvfrf2+N1t1IBz9P/n5+ebiz1qblc5u0LRH9gxkm1k60nXWgq0AlY786z2jQS/ItF32OqFsBRnsXeDqxZxeXAcSe50Pzo7E1fdWOxe1E9XWzJFQoxfoOsrSUfDB2nL2Usts2LCBEYf9aFHR3h2qvelIb3ucqV/jC/rZ1w4dazSQpZ1j/qb78pFHHmnz79aCDdaCCBrEcNQx6aj+w0BrOemxqHfwwZV9Rjv6bAXEddaeLTpiWWeQ9A8+9HbOOefY/a7WIO5AitEH4mfFE98xum8eeuih5vwqUGvl6PmhfpZtzSLR8w89H3JmX9LvVB1oYm/7aMe0rdpplvSKtgLdei6pMxsdBR+sfbbtzR7R2R46Q2MgNMjQO/hgoe21dR7Zu322zpF1dlGgfldo0EEDRtZm+up75egc2VbbdeaEo/fjd7/7XZ/gQ28asB1IaisAgwcBCABAULIVSNC8zI888ojVv+nIH+1EHigdfWzv4l8vAhylT9Jp67Y6PiwdMe7mmtWRgrZS/9jqdHC2+HHvC2NrbI2KdZT7eO7cuWbkmj22Rtlb6OhZd3PkalDr6quvdnk5WzMclF7QffTRRwf8Xkem28ujHUjFpy00tZIt999/v1dmNIQaPWZomhntfNSLdR2tOW3aNHMc0JRjBQUFfW72glR6bHCnGGWoGjt2rM10OEq3p71OKQ0qBwpbsxx0dKkWpA4E9kYAa3rB3t9dOmNOc6/3pu+Fph9yFKiwN/tBO5u1c9VdmvZG08/YokFge+nYNLhs7ftOv4M0QGiL7qea3scRewFI3V/dnYkQqJ8VW52bStNS9k6BGMw0RZCzqSL1O8NeTTBNi6TfHQPZl3Q/drbumKb60WOQDtbQ0fY6M1VnWej5U//vL0dFj+3NEHJm5pKtGcIawHGURlFnhdgKADo6x/Xnd4WeM9j73/rPHnO27XpeYo9eN+nMFnv0vNUTKVABhDZSMAEAgpJeQOi0/P6dcHrBZi2Htidznms6IXt02v5Ap+5rZ5NOX9cLOVfpiGtbeqcQspX+x9bsEmUrsNE7fYC13zkqTqdpJxzR9FU64k1zVdu6kNaOcB0B5yrdNxyNmrM1mlFHA9vqKNNc/XqR3tvKlSttrk//v4F0qnmLdpRr0XVrdD/VmhW6b2iAS/8HzVGs9y03V0dQhhJNx6WpujRtwkCKSFrrTLDXaTeYOJqZpfufjhC1FSC1dtzyZ+e+rWOKFhLVoKfOvvInezMP9LtLU8ZYvke++eabAwL2+r2mgXJNJWSvDoS9AISOutX3dCDb2dFxSb8v7RV81/2mf0emfgfZS8+ogUV7wUVnaQBCO6BD5bOi35O2alRpXQGtR5Wenm46Q/U7Rs8H9L5+v2gaIkcDGAKBfq5dqZ+h3632Orw12OfOOaK1fUm3rzWaYlLTSWrgwVGNAFcMpCNfUyjqTCJ7wUN7AQ57M7jszUzy93eFo8+uo/N7W223VzvC2ZlmGnzQ45HOqAEAWwL/mxoAABsXyTrixtZsB2sjuN3pmLbGk52I3ngde7McdOaFPXphby9djKOLfGspKRz9HzpizdkOtXHjxtkMQDjzWrYMpNNfU1bY6ij74IMPTBoRywwC7Ziy1/mkHfnuBEK8TUfNaropTf1hixZ+1I4ivfX/rGpnoV7E6gg+a2kTQpUWG9f9w1EAzh32iq0ONs50wmmHla1OVXsdxv6aBWFtdL4Won344YdN/nh/0u8YPWbr/m0rDZMlAGHt2KijprUjVmch9O6I7F8Hwl4AYqDpl5zdZ1z9vvNVmix3XydQPysa5NbvCXuz6TR1j9767xfa+an7mwaVdGS8MzNM/MHe4BBr/L0v6WdzwYIFJogYSN9fjmYyOzrP1YCVvUE49vjzu8JR8MrR/22r7faKditnZ47r9wIBCAD2kIIJABC0NK+ts6PetDPHUVqkUAlA2BsV6ujiSjuEPG3v3r0ee01recMHus10Or+zOaat0anp2kFvq4NKRy1baOe8rVGEui9rWoNApZ2emmrBVdppqiObtbC5pv746U9/arPjMpToSPDLL7/cK8EHZSv//GDkaGaWCoYR0haax95WwHzFihWybds28Td7AYDetRysBRG0I1a/j62lIbQs66j+g720Mv7cZwL9/CBQPysaeNf0dO4EqPVYq2ke//znP5vPzW233eawU9UfbJ0nBOK+pN8v11xzjVeCD5b1u8vReaCj81xHywcqR59dR/+3LbaCjRbOzjQbyIw0AIMDAQgAQNDSXKj2chn3Hs13wQUXSLBpbW11a7mBpLsZjKlyBnoxqh0n9upTvPDCC6YT3lH6JZ1erykmAnk7aWFtHZ09bNgwt9fz5ptvmuChJ9M5BCJNL2Nvtg48x9HIT+WpALSv3HDDDVZnQ2lKFO2o9Td7AQANtGo7NeVH/3oI+j8dfvjhNlOhWAIW9mY/WNJ9DOZ9xvKdEkr/t86qefHFF02aMXe/lzXd17PPPmuCv4FU28VbAzy8tS/pNtQUT4FooOepwXqe6+iz6+7sWUfbQ4/l3jwmARg8AvesCgAAJ1grZNmf5rZ1ZtSfszS/LJznKC+tK50EjkZqufPeuDtqrDdNLZSWlmZzdOGrr75qLuLspTDS9EuBTi9UNSWDppb6xz/+YTqKNB+zqyPfND/zHXfcIaFKUx3oSHV7dNaNjtTVfUPrymjqmd63WbNm+ay9CDyahkjTnlmj+8zWrVvF3zMgbHV46Yh0DTxYAhG9jR8/vuc7wVoAwlIHwl4AQlM4eeK47Q2cHwyMfpfcfPPN8vHHH5tZc1qfSWst6UxFV2he+wceeEACiaszS/y5L+nACXv0nFqLUb/00kvmc15UVNTn++u6667zWVsxMI7qXjg7E6d/TT4A6C945iIDAGCjI0JzGusFz0CCFJ68KHzssccOKDw8mDnaXjpKVjuks7KyHK7LUTobf12wa8e8dpT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+arf7ySeftLf/+OMPu4Jj9OjRSftEQRdv8lnvxUMPPZT4f48++qj9/6mEPR6KuZ+CUHkxlVAKSkGpc845x/YK8ahHSLrVQ9rXbvBBn4UePXrYXhYuBR0eeeQR+17pbyjApZVHrVq1sr1YirUKQgETLzCn8lT6PFVS/xAAAIB8EIAAAAAAIvD0008nlbrRZKjK+ay//vqr3FeTnyqJpAnuSZMm2ebMQWkFgyY1s01gKstdGd//+c9/7OSuaNJWE98XXnjhKvf3JsP9fSDU6yBM7fytt97aviatzMhEqzHUQ0F9JI455phEEEL7JV0AQj0vNJHsueKKK8xhhx2W9m9oxYICIPpxgyp+ChpdfvnlidsKOChgkK5vht7T6667zq6K8HodaPJcAQi38XDNmjUT+84fVND+LlRPgkLtp6D0WoMGUTQxr+PUDT7suuuutixXKior5a1qkGuuucaWGEtFwZZTTjnFbLXVVua0006zgRY9VitdtEKkGBQg1PH8/PPP29tabaLAW7FWYQAAAJQaaRcAAABABJRp7br22mtTBh/cyWk12Q2b7a6J6zDZ08q2Vk8Dj5pXF1K9evWyBh/8wRh3slk9IdLV9Ff5I1eYxtXpVmt4792ff/6ZuK1VK9madou2WwEej7vCoZQKtZ8K4YYbbkjqzaAyVmoAnmqlxttvv22bObvBuHTBB9fuu+9uV2W4jbn9+6iQ1KhbQR6PXp+3KgkAACDuCEAAAAAAeVJJJfUF8LRu3dpmcWejvg2F7skgKoXk0QoHlRAqJ+72eUGIIPy9NnKhDHw1nHYnwDt06BDosSp9dfzxxyeVB9KxUG6i2E+FoNU4bqkoBdfuueeeVUpeebSiyA3gpVslkUq3bt0Sv2slxBtvvGGKRb0p3BUo6gPjle8CAACIOwIQAAAAQJ7crGwJ02Q26GR3kB4U6kWgpshaQeD++FdMzJw50xSbJvpV/kZNj/3b588GT7d9/rr9N954Y6LRdq5mzJiRFJBJ1+8iHZUtcn3wwQem1Aqxn6KmXhMqn+RR0OHuu+9O2xdD3n333cTvO+64Y8YVRn7qe7LxxhuX7H3q2bNnUi+UO++80/aoAAAAiDt6QAAAAAB5+vTTT5Nuu2V5stliiy1s2Ru3qW4Qmrx87bXXzMSJE81nn31mvv/++8CTzGF6TuRj6tSptva9AjSa6F+6dGmgx/3+++8p/13NvNWTYsGCBfa2nvuLL76wvST222+/nHoq+Cei69evn7YEVCr+SWR/D41SKMR+ipJ6pajvg4Jmool59dJQv4R0tLLEXcmhYEKY90lU7sx7f4r9Pmmfd+nSJbGKY+7cuWbkyJHmhBNOKOp2AAAAFBsBCAAAACBP8+fPT7rdpEmTwI9VbXhNTqo5bVAKPKhPQa6TqG6/g0LQa1FT5/fffz/S7VtjjTXs86pRslZUeKslBgwYYH80Ka3yV1qVoObW/pUAqWjFiEsNm/ORLnhSTIXYT1FRIKF79+6JYJTKWGmbdtttt1Dv09ixY+1PJb1PPXr0MGPGjEkEG++66y7bw0IN2QEAAOKKAAQAAACQJ/+Kgrp164Z6fLqa96mMHj3aXHrppYmJ5Vx4meeFWg1y8skn5zXBm+m1qWTV6quvbq688spVJqUVkNHPM888Y283b97cZp0fc8wxaSd5o56IVpmpchD1fooqUHf66aebhQsXJv7t/PPPN506dcr62Kjfp8WLF5ti22CDDWzPlwceeMDeVumvhx9+2JxxxhlF3xYAAIBiIQABAAAAVAg1OVaGvjtBrxJOmsDdbrvtbGa7GlvXrl3b/njeeeedopR6UTkiZd27k8UqaaQGvMq233TTTU2DBg3sxLjKTrkyld/xa9++vdljjz3M+PHjbXmh9957L+XEv8o+DRw40Dz44INmyJAhZocddljlPv7+E/nKJzAUtSj3U770dzXRPnv27MS/HXfccTYgEUTU71OpnHnmmeaJJ55IvA9qwt21a9fQQUsAAIBKQQACAAAAyJNqy/tLCCkQEJSaRwdxzz33mH/++Sdx+5RTTjEXXXSRLWNTDtnemuhWkMSj8j5qtpttcjWXklAKYHTu3Nn+aHL6888/t/0c1Kj4rbfeSppoV7390047zYwaNco0a9Ys6XnWWWedpNtqhNy2bVsTF1Htp3zo75577rlm+vTpiX9TLwqt5AnK/z5pOy+88EJTaTQuKBg4bNgwe1urQYYPH2569epV6k0DAAAoiOqFeVoAAACg6lCWv2vWrFmBH6vG0UGb6U6aNCnx+2abbWYnYLMFH7xSL8Xgbl/16tXN9ddfHyizO9/tq1mzpm38feKJJ5rbb7/drvgYNGiQ2WijjZKCHLfeemvW985r3BxH+eynfGjVjntsaIXFjTfeaI+RoOL0Pilw6JZdU0kmtywVAABAnBCAAAAAAPK0zTbbJN3+5JNPAj/266+/TjSlzUSZ6r/88kvitkrrBJ3A/eijjwJvT5CARjrff/994vfNN9/cNGrUKPLtC0Llp1T2SZnlbk8DTYIr4ONq1apV0u2PP/7YFEI++7VQwuynXA0dOtSuqHADZ3fccYctwxWGSo2puXah36di0GoO9Ulxgz733ntvSbcJAACgUAhAAAAAAHnafvvtVylFFJRq8+dSpilozfglS5aYl156KfD2uL0jxC35lI1bSilMTftnn33WFELTpk2T3hsFcfyZ5loR4G7ryy+/bHtZRC2f/VpoQfZTLp588klz2223Ja1i0ER7mPJknlq1apmdd945KXCnn0qlVSjrrrtu4vYjjzxim3QDAADEDQEIAAAAIIIJ3G233TZxe+rUqba8TTa//fabGTFiRE59JtxeC5moya3bFDobtzSMuKsuwjxWqyFWrFiR9THqRfDmm2+aQvEHQjSR7S9L1KVLl8Ttn376yTz00EMF345ilcWKaj+F9cYbb5jLL788cVurF+666y7TuHHjnJ/z6KOPTro9ePDgsmr6HXZ/uw24FSj0+kIAAADECQEIAAAAIALHHXdc0u1LLrkk4ySzGvP269fPLFq0KNDzq0SOO3n76quvZg1C6D5qAh2G/oYm5T1BAimeFi1aJAVXnnnmmYz3V5BCfSyCTiJPmDDBzJgxI/D2aP9PmTIlcXv99ddfJZDjNTR2SxDddNNNgVemePQ+Tpw4MWOQyhVmv4ZVqP0U1GeffWbOOecce4yLjqebb77ZrjbJx7777ptU7uy1114z1113XahyUdomrbjxtq3UY0aDBg0St1955ZWSbg8AAEAhEIAAAAAAItC5c2fbXNcze/Zs07VrV/P222+vcl8FDk499VQ7gSpBJ3sPPPDAxO8qE6Rmtu+//37Kck2a8D377LPtRKtb6iVIqaDtttsuaYWCgimaoNZ2q2G29+NfHeFun/Tv39+W4fFPEKv80FNPPWWOPfZYu+Ig6PZpfx1yyCHmpJNOMk888YSZN29e2vtqv6jMjVsWqlOnTinvq0ngq6++OnFb29unTx9zwQUXmC+++CLt31CpIgV5FEhq27atXW2Sjibf3b4H99xzjw0Oqf+Fmpa7+zVoUKrY+ymoM844w+4bN8CjniDua8z2owBWKjfccEPSag2tVtFE/uuvv542EKHPgPazHqsght7XqHpc5EPHQ48ePUq9GQAAAAX1f6lNAAAAAHKmhtADBgywk+oLFixIZPhrcrdJkyZ2dYAywRWY+PTTTxOPU/kfTUBroj8bBS1Gjx6dqM//ww8/2MnXZs2a2Sa9NWrUMD///LNt0Ov1GNDkft++fc3//ve/wK+lW7du5oMPPkjc1t/Uj98uu+xiHn744cRtTcKrTv97771nby9dutRcfPHFZsiQIbZE1Zprrmm3XdvnTbJrvymLPehErFZLKBjiZexvsMEG9vWrsa/KBqnc1Jdffmn3g2vjjTc2PXv2TPu8mnTXxPctt9ySWJGhTHn9KECx5ZZbmnr16tmJawV4dF+9b0HKTIkmzRWkGjlyZGLfKEikHz8Fjnr16mXyUaj9FIQ/MKXSQmHLCx1++OFm4MCBq/y7Ahl6j7R/vCDHhx9+aMsZ6fjSCgn1mNBnTUEVBV+0GqQQfT2icNRRR9nAlT7LAAAAcUQAAgAAAIiIyuwMHz7cZny75Zc0Ua0fv3bt2tlVAgosBKGJ1dtvv92ceeaZSRnrM2fOtD9+KqWjuvuLFy8O9To6dOhgpk2bZh544AETlibUFcBwt0cT0lop4KeJ8Kuuusq0b9/e5EoT6P5JdD8Ff7Qf/P0t/BQE0ST9pZdemrQKQdsfpBdGtpUsF110kfnmm28SAZpiinI/ldqee+5pV3b07t3b7k+PjvMggTwFg6pVq2bKgVYc6bjTMQcAABBHlGACAAAAItSyZUszbtw4u7LBLbnj0ooITTjecccddgIyjJ122smWNVLwIt0kqjL1tTJC2ftuc+wwVFZo1KhR9nlUPkjPGaQxsYIeepyCEOlev55n//33t6sqjjjiiMDbpLJIl112mdlrr71WaZqcbkJd9x87dqxp1KhRoL+hMlIKlmhyO0jDZD2v3uv777/f3H333Rnvqwx9lQwaOnSoLZHUvHlzO9nv9tyIQjH2U6lpxY+O70GDBtnjUytpsgWHdMxdf/31ZvLkyaE/d4Wk1R6bbbZZqTcDAACgIKqtDNrxDQAAAEAoWqWgHhBz5841S5YsMQ0bNrSrJNRjIYoMbGW0T5061fZRUJ17lQraaKONTOvWrQMFC4rx+rV9KkWlcjkqB6VSQOqVoVJA+VDpI62yUF8KvX5vlYcm+TfccEMbCFI5oXypNM4nn3xiexJoVYSCBZrU13MrgKD9Xc6KtZ9KTSWlVIpJK1VUAk2XuXqf9JnTqhZN8KtEGQAAAIqLAAQAAAAAAAAAAIgcJZgAAAAAAAAAAEDkCEAAAAAAAAAAAIDIEYAAAAAAAAAAAACRIwABAAAAAAAAAAAiRwACAAAAAAAAAABEjgAEAAAAAAAAAACIHAEIAAAAAAAAAAAQOQIQAAAAAAAAAAAgcgQgAAAAAAAAAABA5AhAAAAAAAAAAACAyBGAAAAAAAAAAAAAkSMAAQAAAAAAAAAAIkcAAgAAAAAAAAAARI4ABAAAAAAAAAAAiBwBCAAAAAAAAAAAEDkCEAAAAAAAAAAAIHIEIAAAAAAAAAAAQOQIQAAAAAAAAAAAgMgRgAAAAAAAAAAAAJEjAAEAAAAAAAAAACJHAAIAAAAAAAAAAESOAAQAAAAAAAAAAIgcAQgAAAAAAAAAABA5AhAAAAAAAAAAACByBCAAAAAAAAAAAEDkCEAAAAAAAAAAAAATtf8HVATGDT7TsfcAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1600x1000 with 1 Axes>"
       ]
@@ -370,11 +378,12 @@
     "ncols = 4  # 3 features + observed\n",
     "n_grid = np.logspace(5, 9, 50)\n",
     "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
-    "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # one resident batch\n",
+    "stream_rows = batch_size + 15_000 + 5_000  # one batch + shuffle buffer + one shard\n",
+    "stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9)\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 5), layout=\"tight\")\n",
     "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch  (O(N))\")\n",
-    "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader  (O(batch))\")\n",
+    "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader  (O(batch + buffer + chunk))\")\n",
     "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n",
     "ax.text(n_grid[-1], 30, \"26 GB RAM\", color=\"0.5\", ha=\"right\", va=\"bottom\")\n",
     "ax.set_xlabel(\"dataset size N\")\n",
@@ -388,7 +397,7 @@
    "id": "d304c6c7",
    "metadata": {},
    "source": [
-    "That line is only the bare array. Actual peak RSS is higher, because of the\n",
+    "Those lines are only the bare arrays. Actual peak RSS is higher, because of the\n",
     "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a\n",
     "real-data check, outside this notebook, we ran the same logistic model (13 numeric\n",
     "features plus the click label) on the\n",
@@ -408,7 +417,7 @@
     "  flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to\n",
     "  watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave\n",
     "  shards for strongly ordered data, since a bounded buffer over strongly ordered\n",
-    "  data only block-shuffles it and biases the posterior."
+    "  data only block-shuffles it and can bias the posterior."
    ]
   },
   {
@@ -443,10 +452,10 @@
    "id": "91539613",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-11T05:01:31.471814Z",
-     "iopub.status.busy": "2026-06-11T05:01:31.471732Z",
-     "iopub.status.idle": "2026-06-11T05:01:31.481894Z",
-     "shell.execute_reply": "2026-06-11T05:01:31.481532Z"
+     "iopub.execute_input": "2026-06-11T15:48:54.489384Z",
+     "iopub.status.busy": "2026-06-11T15:48:54.489297Z",
+     "iopub.status.idle": "2026-06-11T15:48:54.498664Z",
+     "shell.execute_reply": "2026-06-11T15:48:54.498267Z"
     }
    },
    "outputs": [
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 62613be17..081a718e0 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -38,14 +38,17 @@ the same structure as PyTorch's `torch.utils.data`:
   read by {func}`~pymc.variational.streaming.parquet_source`.
 * the model observes a `pm.Data` placeholder of one batch, not the whole array.
 * a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each
-  minibatch into that placeholder with `set_data` every step. There are no callbacks.
+  minibatch into that placeholder with `set_data` every step. There are no
+  callbacks to write.
 
 The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`
 is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed
-distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The
-one extra requirement is shuffling. A streaming source is only as well mixed as the
-order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a
-bounded buffer.
+distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`.
+Batches are exact-size, so each pass drops the rows that do not fill a final batch;
+with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed
+tail. The one extra requirement is shuffling itself. A streaming source is only as
+well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to
+shuffle through a bounded buffer.
 
 `N` is small here so the notebook runs in seconds. The streaming code is the same at
 any size, and the last section shows what changes at scale.
@@ -107,7 +110,7 @@ It reads one shard at a time and gets `n_rows` from the Parquet metadata without
 scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader`
 batches and shuffles it into fixed-size minibatches. The model reads a
 `pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer`
-writes each minibatch into it with `set_data`. There are no callbacks:
+writes each minibatch into it with `set_data`. There are no callbacks to wire up:
 `total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`
 runs the loop.
 
@@ -135,7 +138,7 @@ with pm.Model() as model:
         data_name="batch",
         obj_optimizer=pm.adam(learning_rate=0.008),
     ).fit(30_000, random_seed=RANDOM_SEED)
-    idata_stream = approx.sample(1000)
+    idata_stream = approx.sample(1000, random_seed=RANDOM_SEED)
 ```
 
 The negative-ELBO trace shows the fit converging on minibatches read off disk:
@@ -150,8 +153,8 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg
 
 For comparison, here is the usual fit that keeps the whole dataset in memory.
 Streaming changes where the data lives, not the inference, so the two posteriors
-land on top of each other. Both show ADVI's usual mild bias relative to the dashed
-ground truth, but they agree with each other.
+should agree, and here they land on top of each other. Both show ADVI's usual mild
+bias relative to the dashed ground truth, but they agree with each other.
 
 ```{code-cell} ipython3
 with pm.Model():
@@ -161,9 +164,13 @@ with pm.Model():
     )
     pm.Bernoulli("y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)
     approx_inram = pm.fit(
-        30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False
+        30_000,
+        method="advi",
+        obj_optimizer=pm.adam(learning_rate=0.008),
+        progressbar=False,
+        random_seed=RANDOM_SEED,
     )
-    idata_inram = approx_inram.sample(1000)
+    idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED)
 ```
 
 ```{code-cell} ipython3
@@ -185,18 +192,20 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.
 
 Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
 rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`
-holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the
-cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:
+holds one batch plus its bounded shuffle buffer and the current source chunk. The
+dense `float64` design matrix dominates the cost. The lines below are array lower
+bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
 n_grid = np.logspace(5, 9, 50)
 inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
-stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9)  # one resident batch
+stream_rows = batch_size + 15_000 + 5_000  # one batch + shuffle buffer + one shard
+stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9)
 
 fig, ax = plt.subplots(figsize=(8, 5), layout="tight")
 ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch  (O(N))")
-ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader  (O(batch))")
+ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader  (O(batch + buffer + chunk))")
 ax.axhline(26, color="0.5", ls="--", lw=1)
 ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom")
 ax.set_xlabel("dataset size N")
@@ -205,7 +214,7 @@ ax.set_title("Memory is flat in N when streaming")
 ax.legend(loc="lower right", framealpha=0.95);
 ```
 
-That line is only the bare array. Actual peak RSS is higher, because of the
+Those lines are only the bare arrays. Actual peak RSS is higher, because of the
 framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a
 real-data check, outside this notebook, we ran the same logistic model (13 numeric
 features plus the click label) on the
@@ -225,7 +234,7 @@ the largest gap was about 0.1, on the intercept.
   flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to
   watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave
   shards for strongly ordered data, since a bounded buffer over strongly ordered
-  data only block-shuffles it and biases the posterior.
+  data only block-shuffles it and can bias the posterior.
 
 +++
 

From 5865dea05320077bd9cb8c1cb4e81e08bfddc8ad Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Thu, 11 Jun 2026 23:52:30 -0500
Subject: [PATCH 14/42] Correct the Criteo paragraph against the benchmark logs

The largest streaming vs in-RAM posterior gap was 0.12 on a feature
coefficient, not ~0.1 on the intercept (the intercept gap was 0.01),
and the comparison ran on a 1M-row slice; the OOM extrapolation from
the fitted slope is ~240M rows, not 250M.
---
 examples/variational_inference/streaming_dataset.ipynb   | 7 ++++---
 examples/variational_inference/streaming_dataset.myst.md | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 226c9c493..470b3da56 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -405,9 +405,10 @@
     "standard, publicly available out-of-core learning benchmark. Peak memory for the\n",
     "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n",
     "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n",
-    "rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB\n",
-    "machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;\n",
-    "the largest gap was about 0.1, on the intercept.\n",
+    "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n",
+    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and\n",
+    "in-RAM posterior means agreed coefficient for coefficient (correlation 0.998\n",
+    "across the 14 coefficients); the largest gap on any single coefficient was 0.12.\n",
     "\n",
     "## When to use it\n",
     "\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 081a718e0..248f396a6 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -222,9 +222,10 @@ features plus the click label) on the
 standard, publicly available out-of-core learning benchmark. Peak memory for the
 streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M
 rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M
-rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB
-machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;
-the largest gap was about 0.1, on the intercept.
+rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB
+machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and
+in-RAM posterior means agreed coefficient for coefficient (correlation 0.998
+across the 14 coefficients); the largest gap on any single coefficient was 0.12.
 
 ## When to use it
 

From 90d83fd58d9d588671c0982fde6352af1d886f21 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 12 Jun 2026 07:35:40 -0500
Subject: [PATCH 15/42] Temper the Criteo equivalence sentence and fix the read
 granularity

The artifact shows two near-zero coefficients flipping sign between the
two stochastic fits, so 'agreed coefficient for coefficient' overstated
it; state the max gap against the coefficient scale and disclose the
sign flips. parquet_source reads row groups, not whole shards.
---
 .../variational_inference/streaming_dataset.ipynb    | 12 +++++++-----
 .../variational_inference/streaming_dataset.myst.md  | 12 +++++++-----
 2 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 470b3da56..7a21d6625 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -151,8 +151,9 @@
     "## Stream minibatches off disk and fit with ADVI\n",
     "\n",
     "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n",
-    "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n",
-    "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n",
+    "It reads one row group at a time (one or more per shard) and gets `n_rows` from\n",
+    "the Parquet metadata without scanning the data, so `total_size=\"auto\"` resolves\n",
+    "`N` for free. The `DataLoader`\n",
     "batches and shuffles it into fixed-size minibatches. The model reads a\n",
     "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n",
     "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n",
@@ -406,9 +407,10 @@
     "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n",
     "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n",
     "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n",
-    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and\n",
-    "in-RAM posterior means agreed coefficient for coefficient (correlation 0.998\n",
-    "across the 14 coefficients); the largest gap on any single coefficient was 0.12.\n",
+    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap\n",
+    "between the streaming and in-RAM posterior means on any single coefficient was\n",
+    "0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients\n",
+    "differed in sign between the two stochastic fits.\n",
     "\n",
     "## When to use it\n",
     "\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 248f396a6..637d16552 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -106,8 +106,9 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 ## Stream minibatches off disk and fit with ADVI
 
 `parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.
-It reads one shard at a time and gets `n_rows` from the Parquet metadata without
-scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader`
+It reads one row group at a time (one or more per shard) and gets `n_rows` from
+the Parquet metadata without scanning the data, so `total_size="auto"` resolves
+`N` for free. The `DataLoader`
 batches and shuffles it into fixed-size minibatches. The model reads a
 `pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer`
 writes each minibatch into it with `set_data`. There are no callbacks to wire up:
@@ -223,9 +224,10 @@ standard, publicly available out-of-core learning benchmark. Peak memory for the
 streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M
 rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M
 rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB
-machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and
-in-RAM posterior means agreed coefficient for coefficient (correlation 0.998
-across the 14 coefficients); the largest gap on any single coefficient was 0.12.
+machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap
+between the streaming and in-RAM posterior means on any single coefficient was
+0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients
+differed in sign between the two stochastic fits.
 
 ## When to use it
 

From 712ed93fb2c329867e0b6fe38854ba10f1dd9749 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sat, 13 Jun 2026 13:13:03 -0500
Subject: [PATCH 16/42] Match the example's prose to the pymc-examples house
 style

Plainer section headers (Write the dataset to disk; Compare with in-RAM
pm.Minibatch), walk-through connectors, and a tighter stream-and-fit
lead-in that no longer repeats the intro. Markdown only; outputs
unchanged.
---
 .../streaming_dataset.ipynb                   | 30 ++++++++-----------
 .../streaming_dataset.myst.md                 | 30 ++++++++-----------
 2 files changed, 26 insertions(+), 34 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 7a21d6625..3d5ba68b5 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -94,12 +94,12 @@
    "id": "90fd8ed5",
    "metadata": {},
    "source": [
-    "## Put a dataset on disk and forget the array\n",
+    "## Write the dataset to disk\n",
     "\n",
-    "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n",
-    "in-memory table. From here the streaming path reads only the disk copy. We keep `X`\n",
-    "and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming\n",
-    "fit never reads them."
+    "First, we build a logistic-regression dataset, write it to Parquet shards, and\n",
+    "delete the in-memory table. From here the streaming path reads only the disk copy.\n",
+    "We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later;\n",
+    "the streaming fit never reads them."
    ]
   },
   {
@@ -150,15 +150,12 @@
    "source": [
     "## Stream minibatches off disk and fit with ADVI\n",
     "\n",
-    "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n",
-    "It reads one row group at a time (one or more per shard) and gets `n_rows` from\n",
-    "the Parquet metadata without scanning the data, so `total_size=\"auto\"` resolves\n",
-    "`N` for free. The `DataLoader`\n",
-    "batches and shuffles it into fixed-size minibatches. The model reads a\n",
-    "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n",
-    "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n",
-    "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n",
-    "runs the loop."
+    "Next, the source is `parquet_source`, an out-of-core\n",
+    "{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a\n",
+    "time and gets `n_rows` from the Parquet metadata, so `total_size=\"auto\"` resolves\n",
+    "`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and\n",
+    "passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer`\n",
+    "streams each minibatch into the placeholder."
    ]
   },
   {
@@ -252,12 +249,11 @@
    "id": "9035a137",
    "metadata": {},
    "source": [
-    "## The same posterior as in-RAM `pm.Minibatch`\n",
+    "## Compare with in-RAM `pm.Minibatch`\n",
     "\n",
     "For comparison, here is the usual fit that keeps the whole dataset in memory.\n",
     "Streaming changes where the data lives, not the inference, so the two posteriors\n",
-    "should agree, and here they land on top of each other. Both show ADVI's usual mild\n",
-    "bias relative to the dashed ground truth, but they agree with each other."
+    "match; both show ADVI's usual mild bias relative to the dashed ground truth."
    ]
   },
   {
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index 637d16552..dad709b67 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -76,12 +76,12 @@ rng = np.random.default_rng(RANDOM_SEED)
 az.style.use("arviz-variat")
 ```
 
-## Put a dataset on disk and forget the array
+## Write the dataset to disk
 
-We build a logistic-regression dataset, write it to Parquet shards, and delete the
-in-memory table. From here the streaming path reads only the disk copy. We keep `X`
-and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming
-fit never reads them.
+First, we build a logistic-regression dataset, write it to Parquet shards, and
+delete the in-memory table. From here the streaming path reads only the disk copy.
+We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later;
+the streaming fit never reads them.
 
 ```{code-cell} ipython3
 N = 30_000
@@ -105,15 +105,12 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
 
 ## Stream minibatches off disk and fit with ADVI
 
-`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.
-It reads one row group at a time (one or more per shard) and gets `n_rows` from
-the Parquet metadata without scanning the data, so `total_size="auto"` resolves
-`N` for free. The `DataLoader`
-batches and shuffles it into fixed-size minibatches. The model reads a
-`pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer`
-writes each minibatch into it with `set_data`. There are no callbacks to wire up:
-`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`
-runs the loop.
+Next, the source is `parquet_source`, an out-of-core
+{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a
+time and gets `n_rows` from the Parquet metadata, so `total_size="auto"` resolves
+`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and
+passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer`
+streams each minibatch into the placeholder.
 
 ```{code-cell} ipython3
 batch_size = 1024
@@ -150,12 +147,11 @@ ax.plot(approx.hist, alpha=0.6)
 ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI convergence");
 ```
 
-## The same posterior as in-RAM `pm.Minibatch`
+## Compare with in-RAM `pm.Minibatch`
 
 For comparison, here is the usual fit that keeps the whole dataset in memory.
 Streaming changes where the data lives, not the inference, so the two posteriors
-should agree, and here they land on top of each other. Both show ADVI's usual mild
-bias relative to the dashed ground truth, but they agree with each other.
+match; both show ADVI's usual mild bias relative to the dashed ground truth.
 
 ```{code-cell} ipython3
 with pm.Model():

From a981520db6c7f555fd637cab06c947296ebf16e8 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sat, 13 Jun 2026 13:42:11 -0500
Subject: [PATCH 17/42] Report the Criteo equivalence honestly and soften the
 memory wording

On the 1M-row slice two near-zero slopes flip sign and the streaming
estimates sit ~5 sd from zero, so 'two near-zero coefficients differed
in sign' understated it; say plainly that the weak slopes disagree. The
memory paragraph now notes the figure ignores the np.concatenate copy,
and the comparison paragraph states the fits share everything but the
minibatch source.
---
 .../streaming_dataset.ipynb                   | 27 +++++++++++--------
 .../streaming_dataset.myst.md                 | 27 +++++++++++--------
 2 files changed, 32 insertions(+), 22 deletions(-)

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
index 3d5ba68b5..eb26e4d64 100644
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ b/examples/variational_inference/streaming_dataset.ipynb
@@ -251,9 +251,10 @@
    "source": [
     "## Compare with in-RAM `pm.Minibatch`\n",
     "\n",
-    "For comparison, here is the usual fit that keeps the whole dataset in memory.\n",
-    "Streaming changes where the data lives, not the inference, so the two posteriors\n",
-    "match; both show ADVI's usual mild bias relative to the dashed ground truth."
+    "For comparison, here is the usual fit that keeps the whole dataset in memory. The\n",
+    "prior, optimizer, iteration count, and seeds match the streaming fit; only the\n",
+    "minibatch source differs. On this toy problem the two posteriors land on top of\n",
+    "each other, with ADVI's usual mild bias relative to the dashed ground truth."
    ]
   },
   {
@@ -341,10 +342,12 @@
     "## Memory usage\n",
     "\n",
     "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
-    "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n",
-    "holds one batch plus its bounded shuffle buffer and the current source chunk. The\n",
-    "dense `float64` design matrix dominates the cost. The lines below are array lower\n",
-    "bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:"
+    "rows resident, so its array grows linearly in `N`. The streaming path keeps only a\n",
+    "bounded number of rows resident — the model batch, a shuffle-buffer fill, and the\n",
+    "source chunks used to build it — independent of `N`. The dense `float64` design\n",
+    "matrix dominates the cost. The lines below are array lower bounds, not measurements;\n",
+    "they also ignore framework overhead, PyTensor's resident copy, and the transient\n",
+    "copy `np.concatenate` makes while filling the shuffle buffer:"
    ]
   },
   {
@@ -403,10 +406,12 @@
     "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n",
     "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n",
     "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n",
-    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap\n",
-    "between the streaming and in-RAM posterior means on any single coefficient was\n",
-    "0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients\n",
-    "differed in sign between the two stochastic fits.\n",
+    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors\n",
+    "agreed on the large coefficients (the intercept near -3.6 and the strong slopes),\n",
+    "but the weakest slopes were noisier: the largest gap in posterior means was 0.12,\n",
+    "and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming,\n",
+    "flipping sign. On coefficients near the noise floor the two stochastic fits are not\n",
+    "interchangeable at this slice size.\n",
     "\n",
     "## When to use it\n",
     "\n",
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
index dad709b67..034023b72 100644
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ b/examples/variational_inference/streaming_dataset.myst.md
@@ -149,9 +149,10 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg
 
 ## Compare with in-RAM `pm.Minibatch`
 
-For comparison, here is the usual fit that keeps the whole dataset in memory.
-Streaming changes where the data lives, not the inference, so the two posteriors
-match; both show ADVI's usual mild bias relative to the dashed ground truth.
+For comparison, here is the usual fit that keeps the whole dataset in memory. The
+prior, optimizer, iteration count, and seeds match the streaming fit; only the
+minibatch source differs. On this toy problem the two posteriors land on top of
+each other, with ADVI's usual mild bias relative to the dashed ground truth.
 
 ```{code-cell} ipython3
 with pm.Model():
@@ -188,10 +189,12 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.
 ## Memory usage
 
 Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
-rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`
-holds one batch plus its bounded shuffle buffer and the current source chunk. The
-dense `float64` design matrix dominates the cost. The lines below are array lower
-bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:
+rows resident, so its array grows linearly in `N`. The streaming path keeps only a
+bounded number of rows resident — the model batch, a shuffle-buffer fill, and the
+source chunks used to build it — independent of `N`. The dense `float64` design
+matrix dominates the cost. The lines below are array lower bounds, not measurements;
+they also ignore framework overhead, PyTensor's resident copy, and the transient
+copy `np.concatenate` makes while filling the shuffle buffer:
 
 ```{code-cell} ipython3
 ncols = 4  # 3 features + observed
@@ -220,10 +223,12 @@ standard, publicly available out-of-core learning benchmark. Peak memory for the
 streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M
 rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M
 rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB
-machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap
-between the streaming and in-RAM posterior means on any single coefficient was
-0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients
-differed in sign between the two stochastic fits.
+machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors
+agreed on the large coefficients (the intercept near -3.6 and the strong slopes),
+but the weakest slopes were noisier: the largest gap in posterior means was 0.12,
+and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming,
+flipping sign. On coefficients near the noise floor the two stochastic fits are not
+interchangeable at this slice size.
 
 ## When to use it
 

From b75ee95f98338ab8355eb245e3d14dffbb6869a3 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 18:57:49 -0500
Subject: [PATCH 18/42] Draft: streaming variational inference on
 high-frequency tick data

Applied capstone for the streaming stack: hierarchical hurdle-Student-t
next-event returns on the Binance public archive, with the executed path
on synthetic data and full-scale results (491.6M rows, 38 symbols)
inlined from published run artifacts. Companion to streaming_dataset.
---
 .../streaming_tick_data.ipynb                 | 2772 +++++++++++++++++
 .../streaming_tick_data.myst.md               | 1172 +++++++
 .../streaming_tick_data_results.json          | 1273 ++++++++
 3 files changed, 5217 insertions(+)
 create mode 100644 examples/variational_inference/streaming_tick_data.ipynb
 create mode 100644 examples/variational_inference/streaming_tick_data.myst.md
 create mode 100644 examples/variational_inference/streaming_tick_data_results.json

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
new file mode 100644
index 000000000..728dcd069
--- /dev/null
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -0,0 +1,2772 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8449bee",
+   "metadata": {},
+   "source": [
+    "(streaming_tick_data)=\n",
+    "\n",
+    "# Streaming variational inference on high-frequency tick data\n",
+    "\n",
+    ":::{post} August 2026\n",
+    ":tags: variational inference, minibatch, out-of-core, hierarchical model, time series\n",
+    ":category: advanced, tutorial\n",
+    ":author: Yicheng Yang\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9926a13",
+   "metadata": {},
+   "source": [
+    "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
+    "trades for a single liquid pair is roughly 28 million rows, and a modest\n",
+    "38-symbol universe over six months already exceeds 450 million rows — far more\n",
+    "than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a\n",
+    "hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of\n",
+    "that scale by streaming minibatches from disk, using the\n",
+    "{class}`~pymc_extras.variational.dataloader.DataLoader` introduced in the\n",
+    "{ref}`streaming_dataset` companion notebook.\n",
+    "\n",
+    "The companion covers the mechanics of the streaming API. This notebook is the\n",
+    "applied piece, and it tries to teach three things that only show up on real\n",
+    "data:\n",
+    "\n",
+    "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
+    "   time-series models fail — and a model class that passes both.\n",
+    "2. **The full workflow at scale**: causal feature engineering, an on-disk\n",
+    "   global shuffle, streaming ADVI with `total_size` rescaling, and an online\n",
+    "   stopping rule.\n",
+    "3. **Honest reporting.** Mean-field variational inference on half a billion\n",
+    "   rows produces some numbers you should trust and some you should not; we\n",
+    "   measure which are which instead of guessing.\n",
+    "\n",
+    "The notebook itself runs in about a minute on synthetic data with known ground\n",
+    "truth — the streaming code is identical at any scale. Full-scale results on the\n",
+    "real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus` from runs\n",
+    "of the exact scripts shown here, with every number reproducible from public\n",
+    "data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93f21b44",
+   "metadata": {},
+   "source": [
+    "## When is minibatch VI valid? Two gates\n",
+    "\n",
+    "Minibatch variational inference rests on one identity: if the likelihood\n",
+    "factors over rows given the parameters, then the batch log-likelihood scaled by\n",
+    "$N/B$ is an unbiased estimator of the full-data log-likelihood\n",
+    "{cite:p}`hoffman2013stochastic`. For a streaming showcase to be honest, two\n",
+    "conditions must hold. They became the acceptance gates for this notebook's\n",
+    "model, and they are worth internalizing before reaching for `total_size` on\n",
+    "your own data:\n",
+    "\n",
+    "**Gate 1 — the likelihood must factor over rows.** No latent path coupling\n",
+    "observations, and no label shared across rows. This is exactly why the classic\n",
+    "stochastic volatility model of the {ref}`stochastic_volatility` notebook\n",
+    "*cannot* be minibatched: its latent volatility path ties every observation to\n",
+    "its neighbours, so a random subset of rows does not carry $B/N$ of the\n",
+    "log-likelihood. Any model whose rows share one outcome (for example, every tick\n",
+    "of a match sharing the final result) fails the same gate through\n",
+    "pseudo-replication — the effective sample size is the number of outcomes, not\n",
+    "the number of rows.\n",
+    "\n",
+    "**Gate 2 — no low-dimensional sufficient statistics.** A Normal likelihood\n",
+    "with per-cell variances collapses to per-cell $(\\sum y^2, n)$: one linear scan\n",
+    "computes those, and \"streaming inference\" degenerates into a glorified\n",
+    "`groupby`. What provably breaks the collapse: a Student-t likelihood, a mixture\n",
+    "(the hurdle below), and row-level continuous covariates through nonlinear\n",
+    "links.\n",
+    "\n",
+    "The model below passes both gates, and each ingredient that makes it pass is\n",
+    "also substantively motivated by the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03e62ad1",
+   "metadata": {},
+   "source": [
+    ":::{include} ../extra_installs.md\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ed91a6db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:46.731924Z",
+     "iopub.status.busy": "2026-07-31T23:51:46.731757Z",
+     "iopub.status.idle": "2026-07-31T23:51:48.734115Z",
+     "shell.execute_reply": "2026-07-31T23:51:48.733712Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import logging\n",
+    "import os\n",
+    "import tempfile\n",
+    "import warnings\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import pymc as pm\n",
+    "import pytensor.tensor as pt\n",
+    "\n",
+    "from pymc_extras.variational.dataloader import DataLoader, parquet_source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "38df3017",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:48.735284Z",
+     "iopub.status.busy": "2026-07-31T23:51:48.735154Z",
+     "iopub.status.idle": "2026-07-31T23:51:48.737898Z",
+     "shell.execute_reply": "2026-07-31T23:51:48.737576Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "RANDOM_SEED = 20260731\n",
+    "rng = np.random.default_rng(RANDOM_SEED)\n",
+    "az.style.use(\"arviz-variat\")\n",
+    "# the loss figures carry the fit story; keep the fit logger's lines out of the output\n",
+    "logging.getLogger(\"pymc\").setLevel(logging.ERROR)\n",
+    "# pytensor's predictive path emits two benign internals warnings (a 0/0 inside its\n",
+    "# own allclose diagnostics; numba falling back to object mode for the python\n",
+    "# `random`); neither is actionable here, so they are filtered by origin\n",
+    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning, module=r\"pytensor\\.tensor\\.type\")\n",
+    "warnings.filterwarnings(\"ignore\", message=r\"Numba will use object mode\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb89fcc5",
+   "metadata": {},
+   "source": [
+    "## The model: hierarchical hurdle–Student-t next-event returns\n",
+    "\n",
+    "One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:\n",
+    "the return $y_i = 10^4 \\, (\\log p_{i+1} - \\log p_i)$ in basis points, and the\n",
+    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. On real per-trade data roughly a\n",
+    "third of all transitions are *exactly* zero — the price simply does not move —\n",
+    "so a continuous likelihood alone would put zero probability mass on the single\n",
+    "most common outcome. The fix is a hurdle: model *whether* the price moves\n",
+    "separately from *how far* it moves given that it does.\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "m_i &\\sim \\text{Bernoulli}(\\pi_i) \\\\\n",
+    "y_i \\mid m_i = 1 &\\sim \\text{StudentT}(\\nu,\\ \\mu_i,\\ \\sigma_i) \\\\\n",
+    "\\operatorname{logit} \\pi_i &= \\kappa_0 + b^{(\\kappa)}_s\n",
+    "  + B(h)^\\top\\!\\left(c + b^{(\\pi h)}_s\\right)\n",
+    "  + \\lambda_a a_i + \\lambda_q q_i \\\\\n",
+    "\\log \\sigma_i &= \\alpha_0 + b^{(\\alpha)}_s\n",
+    "  + B(h)^\\top\\!\\left(g + b^{(\\sigma h)}_s\\right)\n",
+    "  + (\\beta_a + b^{(\\beta a)}_s)\\, a_i + \\beta_q q_i \\\\\n",
+    "\\mu_i &= \\theta_d\\, d_i + \\theta_r\\, \\text{ylag}_i\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "with covariates computed *causally* (from information available at trade $i$):\n",
+    "the trade sign $d_i$, standardized log notional $q_i$, standardized trailing\n",
+    "60-second activity $a_i$, and the most recent previous nonzero return\n",
+    "$\\text{ylag}_i$. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
+    "the standard way to encode intraday periodicity smoothly\n",
+    "{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
+    "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
+    "$b_s \\sim \\mathcal{N}(0, \\tau)$ are partially pooled {cite:p}`gelman2006data`,\n",
+    "so a thin symbol borrows the global intraday shape where its own data run out —\n",
+    "that is the hierarchical payoff we will measure. They are parameterized\n",
+    "*centered*, deliberately: the familiar non-centered trick exists for data-poor\n",
+    "groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich —\n",
+    "{ref}`tick-real-corpus` shows what happened when we tried it the other way.\n",
+    "The degrees of freedom are\n",
+    "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
+    "the floor of 1 (rather than the more comfortable 2) is not a stylistic choice,\n",
+    "and {ref}`tick-real-corpus` shows what the data did to force it.\n",
+    "\n",
+    "The reported estimand is **event-return dispersion on the event clock**:\n",
+    "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
+    "$\\sigma_{s,h} \\cdot t^{-1}_{0.95}(\\nu)$. Quantile-based dispersion stays finite\n",
+    "for any $\\nu > 0$; the familiar moment-based alternative does not survive this\n",
+    "dataset, as we will see."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eb53b7e",
+   "metadata": {},
+   "source": [
+    "## Synthetic data with known truth\n",
+    "\n",
+    "Notebooks in this collection do not download data at build time, so the\n",
+    "executed path uses a seeded synthetic generator that mirrors the real corpus:\n",
+    "same schema, same hurdle structure, same heavy tails, twelve symbols with a\n",
+    "deliberately thin tail so shrinkage is visible. Because the truth is known,\n",
+    "recovery is checkable — and the streaming code is byte-for-byte the code used\n",
+    "on the real corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2d494699",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:48.738639Z",
+     "iopub.status.busy": "2026-07-31T23:51:48.738586Z",
+     "iopub.status.idle": "2026-07-31T23:51:48.740735Z",
+     "shell.execute_reply": "2026-07-31T23:51:48.740424Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n_symbols = 12\n",
+    "counts = np.array(\n",
+    "    [90_700, 55_000, 40_000, 30_000, 24_000, 19_000, 15_000, 11_000, 8_000, 4_600, 1_800, 900]\n",
+    ")\n",
+    "thin = [10, 11]  # the two symbols with the least data\n",
+    "\n",
+    "truth = {\n",
+    "    \"kappa0\": np.log(0.7 / 0.3),  # ~30% zero-move share at the baseline\n",
+    "    \"c\": np.array([0.25, -0.15, 0.10, 0.05]),\n",
+    "    \"lambda_a\": 0.35,\n",
+    "    \"lambda_q\": 0.20,\n",
+    "    \"alpha0\": np.log(0.05),  # baseline sigma ~ 0.05 bp, the real-data scale\n",
+    "    \"g\": np.array([0.20, -0.12, 0.08, 0.04]),\n",
+    "    \"beta_a\": 0.18,\n",
+    "    \"beta_q\": 0.12,\n",
+    "    \"theta_d\": 0.02,\n",
+    "    \"theta_r\": 0.25,\n",
+    "    \"nu\": 3.5,\n",
+    "}\n",
+    "\n",
+    "def standardize(x, axis=0):\n",
+    "    return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)\n",
+    "\n",
+    "z_truth = {\n",
+    "    \"z_k\": standardize(rng.standard_normal(n_symbols)),\n",
+    "    \"z_ph\": standardize(rng.standard_normal((n_symbols, 4))),\n",
+    "    \"z_al\": standardize(rng.standard_normal(n_symbols)),\n",
+    "    \"z_sh\": standardize(rng.standard_normal((n_symbols, 4))),\n",
+    "    \"z_ba\": standardize(rng.standard_normal(n_symbols)),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "36d3b89f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:48.741599Z",
+     "iopub.status.busy": "2026-07-31T23:51:48.741544Z",
+     "iopub.status.idle": "2026-07-31T23:51:48.896109Z",
+     "shell.execute_reply": "2026-07-31T23:51:48.895708Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "300,000 rows, zero-move share 32.1%, median nonzero |y| 0.041 bp\n"
+     ]
+    }
+   ],
+   "source": [
+    "def hour_basis(hour):\n",
+    "    w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0\n",
+    "    return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])\n",
+    "\n",
+    "sym = np.repeat(np.arange(n_symbols), counts)\n",
+    "n = len(sym)\n",
+    "hour = rng.integers(0, 24, size=n)\n",
+    "d = rng.choice([-1.0, 1.0], size=n)\n",
+    "a = standardize(rng.standard_normal(n))  # trailing activity (already standardized)\n",
+    "q = standardize(0.3 * a + np.sqrt(1 - 0.3**2) * rng.standard_normal(n))\n",
+    "\n",
+    "B = hour_basis(hour)\n",
+    "logit_pi = (\n",
+    "    truth[\"kappa0\"]\n",
+    "    + 0.30 * z_truth[\"z_k\"][sym]\n",
+    "    + B @ truth[\"c\"]\n",
+    "    + (B * (0.15 * z_truth[\"z_ph\"][sym])).sum(1)\n",
+    "    + truth[\"lambda_a\"] * a\n",
+    "    + truth[\"lambda_q\"] * q\n",
+    ")\n",
+    "log_sigma = (\n",
+    "    truth[\"alpha0\"]\n",
+    "    + 0.35 * z_truth[\"z_al\"][sym]\n",
+    "    + B @ truth[\"g\"]\n",
+    "    + (B * (0.12 * z_truth[\"z_sh\"][sym])).sum(1)\n",
+    "    + (truth[\"beta_a\"] + 0.10 * z_truth[\"z_ba\"][sym]) * a\n",
+    "    + truth[\"beta_q\"] * q\n",
+    ")\n",
+    "\n",
+    "m = (rng.random(n) < 1 / (1 + np.exp(-logit_pi))).astype(np.int8)\n",
+    "t_draw = rng.standard_t(truth[\"nu\"], size=n)\n",
+    "\n",
+    "# sequential generation per symbol so ylag feeds back causally\n",
+    "y = np.zeros(n)\n",
+    "ylag = np.zeros(n)\n",
+    "sigma = np.exp(log_sigma)\n",
+    "for s in range(n_symbols):\n",
+    "    idx = np.flatnonzero(sym == s)\n",
+    "    last = 0.0\n",
+    "    for i in idx:\n",
+    "        ylag[i] = last\n",
+    "        if m[i]:\n",
+    "            y[i] = truth[\"theta_d\"] * d[i] + truth[\"theta_r\"] * last + sigma[i] * t_draw[i]\n",
+    "            last = y[i]\n",
+    "\n",
+    "print(f\"{n:,} rows, zero-move share {(m == 0).mean():.1%}, \"\n",
+    "      f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e2e9891",
+   "metadata": {},
+   "source": [
+    "The zero spike is the single most prominent feature of per-trade return data —\n",
+    "here is the synthetic version of the picture that forced the hurdle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b238a53a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:48.897039Z",
+     "iopub.status.busy": "2026-07-31T23:51:48.896965Z",
+     "iopub.status.idle": "2026-07-31T23:51:49.279473Z",
+     "shell.execute_reply": "2026-07-31T23:51:49.279153Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 723,
+       "width": 1750
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "moves = y[m == 1]\n",
+    "ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color=\"C0\",\n",
+    "        label=\"nonzero moves\")\n",
+    "ax.bar([0.0], [(m == 0).sum()], width=0.02, color=\"C1\",\n",
+    "       label=f\"exactly zero ({(m == 0).mean():.0%} of rows)\")\n",
+    "ax.set_xlabel(\"next-event return (bp)\")\n",
+    "ax.set_ylabel(\"count (log scale)\")\n",
+    "ax.set_title(\"A continuous density puts zero mass on the most common outcome\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08af15d5",
+   "metadata": {},
+   "source": [
+    "## The on-disk global shuffle\n",
+    "\n",
+    "The trap in streaming ordered data is subtle enough to deserve its own\n",
+    "section. A bounded runtime shuffle buffer only *block*-shuffles a strongly\n",
+    "ordered stream: with tick data sorted by symbol and time, early optimization\n",
+    "steps would only ever see early dates and the first symbols, and an early\n",
+    "stopping decision would be biased by construction. The fix is to shuffle\n",
+    "**once, globally, on disk at ETL time**: every row gets a deterministic hash\n",
+    "key, rows are scattered across shards by that key, and each shard is sorted by\n",
+    "it. After that, sequential reads *are* a uniform permutation, and the loader\n",
+    "can run with `shuffle=False` at full speed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "14730572",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:49.280501Z",
+     "iopub.status.busy": "2026-07-31T23:51:49.280425Z",
+     "iopub.status.idle": "2026-07-31T23:51:49.434279Z",
+     "shell.execute_reply": "2026-07-31T23:51:49.433905Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first 10k rows of one shard cover 24 hours and 12 symbols\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dir = tempfile.mkdtemp(prefix=\"ticks_\")\n",
+    "table = pa.table(\n",
+    "    {\n",
+    "        \"y_bp\": y.astype(np.float32),\n",
+    "        \"m\": m,\n",
+    "        \"d\": d.astype(np.int8),\n",
+    "        \"q_std\": q.astype(np.float32),\n",
+    "        \"a_std\": a.astype(np.float32),\n",
+    "        \"ylag_bp\": ylag.astype(np.float32),\n",
+    "        \"hour\": hour.astype(np.int8),\n",
+    "        \"sym\": sym.astype(np.int16),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "def splitmix64(x):\n",
+    "    x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    return x ^ (x >> np.uint64(31))\n",
+    "\n",
+    "key = splitmix64(np.arange(n, dtype=np.uint64))\n",
+    "order = np.argsort(key, kind=\"stable\")\n",
+    "n_shards = 8\n",
+    "for i in range(n_shards):\n",
+    "    part = table.take(order[i::n_shards])\n",
+    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"))\n",
+    "\n",
+    "head = pq.read_table(os.path.join(data_dir, \"shard_000.parquet\")).slice(0, 10_000)\n",
+    "print(f\"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours \"\n",
+    "      f\"and {len(np.unique(head['sym']))} symbols\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3200deb",
+   "metadata": {},
+   "source": [
+    "On the real corpus the same check is part of the ETL script's validation: the\n",
+    "head of any shard must already mix all 24 hours and all symbols, *verified, not\n",
+    "assumed*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8bf25",
+   "metadata": {},
+   "source": [
+    "## Streaming the model\n",
+    "\n",
+    "The `DataLoader` yields `(batch_size, 8)` blocks in a frozen column order and\n",
+    "knows the dataset size from the Parquet metadata, so `total_size` is simply\n",
+    "`len(loader)`. The model reads one `pm.Data` placeholder; everything derived —\n",
+    "the Fourier basis, the integer symbol index — is computed inside the graph, so\n",
+    "advancing the stream is a single `set_value` per step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "13c1ae1b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:49.435185Z",
+     "iopub.status.busy": "2026-07-31T23:51:49.435121Z",
+     "iopub.status.idle": "2026-07-31T23:51:49.438118Z",
+     "shell.execute_reply": "2026-07-31T23:51:49.437766Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N = 300,000 rows -> 292 steps per epoch\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns = [\"y_bp\", \"m\", \"d\", \"q_std\", \"a_std\", \"ylag_bp\", \"hour\", \"sym\"]\n",
+    "loader = DataLoader(\n",
+    "    parquet_source(data_dir, columns=columns),\n",
+    "    batch_size=1024,\n",
+    "    shuffle=False,  # the shards are already globally shuffled on disk\n",
+    "    sample_shape=(len(columns),),\n",
+    "    total_size=\"auto\",\n",
+    ")\n",
+    "print(f\"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "937133fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:49.438973Z",
+     "iopub.status.busy": "2026-07-31T23:51:49.438909Z",
+     "iopub.status.idle": "2026-07-31T23:51:49.443236Z",
+     "shell.execute_reply": "2026-07-31T23:51:49.442972Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def build_model(symbols, batch_init, total_size):\n",
+    "    coords = {\"symbol\": list(symbols), \"harmonic\": [\"sin1\", \"cos1\", \"sin2\", \"cos2\"]}\n",
+    "    with pm.Model(coords=coords) as model:\n",
+    "        batch = pm.Data(\"batch\", batch_init)\n",
+    "        y_ = batch[:, 0]\n",
+    "        m_ = batch[:, 1]\n",
+    "        d_ = batch[:, 2]\n",
+    "        q_ = batch[:, 3]\n",
+    "        a_ = batch[:, 4]\n",
+    "        ylag_ = batch[:, 5]\n",
+    "        w = 2.0 * np.pi * batch[:, 6] / 24.0\n",
+    "        B_ = pt.stack([pt.sin(w), pt.cos(w), pt.sin(2 * w), pt.cos(2 * w)], axis=1)\n",
+    "        sym_ = pt.cast(batch[:, 7], \"int32\")\n",
+    "\n",
+    "        kappa0 = pm.Normal(\"kappa0\", 0.0, 5.0)\n",
+    "        c = pm.Normal(\"c\", 0.0, 0.5, dims=\"harmonic\")\n",
+    "        lambda_a = pm.Normal(\"lambda_a\", 0.0, 0.5)\n",
+    "        lambda_q = pm.Normal(\"lambda_q\", 0.0, 0.5)\n",
+    "\n",
+    "        alpha0 = pm.Normal(\"alpha0\", 0.0, 5.0)\n",
+    "        g = pm.Normal(\"g\", 0.0, 0.3, dims=\"harmonic\")\n",
+    "        beta_a = pm.Normal(\"beta_a\", 0.0, 0.3)\n",
+    "        beta_q = pm.Normal(\"beta_q\", 0.0, 0.3)\n",
+    "\n",
+    "        theta_d = pm.Normal(\"theta_d\", 0.0, 0.25)\n",
+    "        theta_r = pm.Normal(\"theta_r\", 0.0, 0.25)\n",
+    "\n",
+    "        tau_k, tau_ph, tau_al, tau_sh, tau_ba = (\n",
+    "            pm.LogNormal(name, 0.0, 1.5)\n",
+    "            for name in [\"tau_k\", \"tau_ph\", \"tau_al\", \"tau_sh\", \"tau_ba\"]\n",
+    "        )\n",
+    "        b_k = pm.Normal(\"b_k\", 0.0, tau_k, dims=\"symbol\")\n",
+    "        b_ph = pm.Normal(\"b_ph\", 0.0, tau_ph, dims=(\"symbol\", \"harmonic\"))\n",
+    "        b_al = pm.Normal(\"b_al\", 0.0, tau_al, dims=\"symbol\")\n",
+    "        b_sh = pm.Normal(\"b_sh\", 0.0, tau_sh, dims=(\"symbol\", \"harmonic\"))\n",
+    "        b_ba = pm.Normal(\"b_ba\", 0.0, tau_ba, dims=\"symbol\")\n",
+    "\n",
+    "        eta = pm.Normal(\"eta\", 5.0, 1.0)\n",
+    "        nu = pm.Deterministic(\"nu\", 1.0 + pt.softplus(eta))\n",
+    "\n",
+    "        logit_pi = (\n",
+    "            kappa0\n",
+    "            + b_k[sym_]\n",
+    "            + (B_ * (c + b_ph[sym_])).sum(axis=-1)\n",
+    "            + lambda_a * a_\n",
+    "            + lambda_q * q_\n",
+    "        )\n",
+    "        log_sigma = (\n",
+    "            alpha0\n",
+    "            + b_al[sym_]\n",
+    "            + (B_ * (g + b_sh[sym_])).sum(axis=-1)\n",
+    "            + (beta_a + b_ba[sym_]) * a_\n",
+    "            + beta_q * q_\n",
+    "        )\n",
+    "        mu = theta_d * d_ + theta_r * ylag_\n",
+    "\n",
+    "        def hurdle_logp(value, logit_pi, mu, log_sigma, nu, m):\n",
+    "            sigma = pt.exp(log_sigma)\n",
+    "            t_ll = (\n",
+    "                pt.gammaln((nu + 1.0) / 2.0)\n",
+    "                - pt.gammaln(nu / 2.0)\n",
+    "                - 0.5 * pt.log(nu * np.pi)\n",
+    "                - log_sigma\n",
+    "                - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)\n",
+    "            )\n",
+    "            return pt.where(m > 0, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))\n",
+    "\n",
+    "        def hurdle_random(logit_pi, mu, log_sigma, nu, m, rng=None, size=None):\n",
+    "            # simulate the full hurdle: first whether the price moves, then how far\n",
+    "            pi = 1.0 / (1.0 + np.exp(-logit_pi))\n",
+    "            move = rng.random(size=size) < pi\n",
+    "            draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)\n",
+    "            return np.where(move, draw, 0.0)\n",
+    "\n",
+    "        pm.CustomDist(\n",
+    "            \"y_obs\", logit_pi, mu, log_sigma, nu, m_,\n",
+    "            logp=hurdle_logp, random=hurdle_random,\n",
+    "            observed=y_, total_size=total_size,\n",
+    "        )\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9166bbe",
+   "metadata": {},
+   "source": [
+    "Two implementation notes worth pausing on. The likelihood is a\n",
+    "{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`\n",
+    "gives the term *observed-RV semantics*, which is what makes PyMC's own\n",
+    "`total_size` minibatch rescaling apply {cite:p}`kucukelbir2015automatic`. And\n",
+    "the hurdle's two logs are written with `softplus` —\n",
+    "$\\log \\pi = -\\operatorname{softplus}(-x)$,\n",
+    "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$ — which is exact and stable at both\n",
+    "tails."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aa3113cb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:49.444069Z",
+     "iopub.status.busy": "2026-07-31T23:51:49.444015Z",
+     "iopub.status.idle": "2026-07-31T23:51:49.679257Z",
+     "shell.execute_reply": "2026-07-31T23:51:49.678778Z"
+    }
+   },
+   "outputs": [
+    {
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+       "<!-- batch -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>batch</title>\n",
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+       "</g>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"48.97\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
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+       "</g>\n",
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+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
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+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
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+       "<title>tau_ba</title>\n",
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+       "<title>b_ba</title>\n",
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+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.65,-395.53C1157.31,-385.58 1162.39,-374.73 1167.24,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.27,-366.16 1171.34,-355.62 1163.93,-363.19 1170.27,-366.16\"/>\n",
+       "</g>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M173.75,-279.44C181.83,-265.98 192.88,-252.03 206.97,-243.82 286.44,-197.49 536.51,-188.32 691.31,-187.27\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.25,-190.77 701.23,-187.22 691.22,-183.77 691.25,-190.77\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"256.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M273.82,-279.57C281.92,-266.13 292.95,-252.15 306.97,-243.82 369.88,-206.4 561.75,-194.08 691.89,-190.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.7,-193.56 701.59,-189.77 691.49,-186.57 691.7,-193.56\"/>\n",
+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"356.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.93,-279.75C382.04,-266.34 393.06,-252.34 406.97,-243.82 453.69,-215.2 589.88,-200.95 693,-194.09\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"693.18,-197.59 702.94,-193.45 692.73,-190.6 693.18,-197.59\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.5,-393.28C1511.82,-385.5 1511.11,-377.25 1510.41,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.91,-369.08 1509.56,-359.42 1506.94,-369.69 1513.91,-369.08\"/>\n",
+       "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.18,-393.93C1273.16,-385.49 1275.27,-376.46 1277.33,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.67,-368.78 1279.54,-358.24 1273.85,-367.18 1280.67,-368.78\"/>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"456.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.35,-279.68C482.46,-266.42 493.37,-252.54 506.97,-243.82 538.18,-223.78 623.06,-209.41 696.42,-200.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"696.61,-203.8 706.11,-199.12 695.76,-196.85 696.61,-203.8\"/>\n",
+       "</g>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.44,-394.25C1627.64,-385.52 1624.63,-376.15 1621.72,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.08,-366.09 1618.69,-357.64 1618.41,-368.23 1625.08,-366.09\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"660.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"706.09,-345.73 615.84,-345.73 615.84,-288.23 706.09,-288.23 706.09,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M660.97,-393.28C660.97,-381.58 660.97,-368.79 660.97,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"664.47,-357.45 660.97,-347.45 657.47,-357.45 664.47,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"556.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M574.72,-280.23C582.89,-267.05 593.73,-253.08 606.97,-243.82 634.06,-224.85 667.2,-212.34 699.15,-204.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"699.58,-207.6 708.46,-201.82 697.92,-200.8 699.58,-207.6\"/>\n",
+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1388.97,-393.28C1388.97,-385.5 1388.97,-377.25 1388.97,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.47,-369.43 1388.97,-359.43 1385.47,-369.43 1392.47,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M677.73,-287.94C687.33,-273.51 700.37,-256.36 714.97,-243.82 722.55,-237.3 731.03,-231.24 739.78,-225.71\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"741.32,-228.87 748.06,-220.7 737.69,-222.88 741.32,-228.87\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"764.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M780.59,-279.01C786,-266.2 792.15,-251.62 797.93,-237.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"801,-239.69 801.66,-229.12 794.55,-236.97 801,-239.69\"/>\n",
+       "</g>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"873.97\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M857.76,-278.32C852.36,-265.77 846.26,-251.58 840.52,-238.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"843.78,-236.96 836.61,-229.16 837.35,-239.73 843.78,-236.96\"/>\n",
+       "</g>\n",
+       "<!-- g -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>g</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"982.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- g&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>g&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M963.21,-280.82C954.79,-268.03 944.1,-254.18 931.97,-243.82 923.48,-236.58 913.9,-230.01 903.98,-224.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"905.73,-221.11 895.3,-219.25 902.29,-227.21 905.73,-221.11\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1082.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1065.23,-280.21C1057.06,-267.03 1046.21,-253.07 1032.97,-243.82 1005.4,-224.56 971.64,-211.97 939.15,-203.73\"/>\n",
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+       "</g>\n",
+       "<!-- b_k&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>b_k&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1372.12,-279.56C1364.03,-266.11 1352.99,-252.14 1338.97,-243.82 1274.77,-205.71 1078.46,-193.59 946.26,-189.8\"/>\n",
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+       "</g>\n",
+       "<!-- b_al&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>b_al&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1272.01,-279.74C1263.9,-266.32 1252.88,-252.32 1238.97,-243.82 1191.01,-214.52 1050.66,-200.37 945.3,-193.72\"/>\n",
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+       "</g>\n",
+       "<!-- b_ba&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>b_ba&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.61,-280.39C1161.89,-266.9 1150.23,-252.67 1135.97,-243.82 1103.74,-223.81 1016.52,-209.37 941.71,-200.22\"/>\n",
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+       "</g>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1589.25,-279.33C1581.18,-265.86 1570.12,-251.92 1555.97,-243.82 1504.46,-214.32 1142.39,-198.39 945.6,-191.82\"/>\n",
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+       "</g>\n",
+       "<!-- b_sh&#45;&gt;y_obs -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>b_sh&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1484.47,-281.94C1473.57,-267.66 1459.01,-252.41 1441.97,-243.82 1358.06,-201.49 1102.79,-190.96 946.26,-188.57\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"946.73,-185.08 936.68,-188.44 946.63,-192.08 946.73,-185.08\"/>\n",
+       "</g>\n",
+       "<!-- y_obs&#45;&gt;batch -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>y_obs&#45;&gt;batch</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M818.97,-146.27C818.97,-134.38 818.97,-121.37 818.97,-109.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"822.47,-109.67 818.97,-99.67 815.47,-109.67 822.47,-109.67\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x11d49f140>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = build_model(\n",
+    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), len(loader)\n",
+    ")\n",
+    "pm.model_to_graphviz(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "742f5cc4",
+   "metadata": {},
+   "source": [
+    "The plate diagram makes the two-link structure visible at a glance: five\n",
+    "partially pooled effect families on the symbol plate feeding two linear\n",
+    "predictors, one shared tail parameter, and a single observed node whose batch\n",
+    "dimension is whatever the placeholder currently holds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1c77b843",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:49.680288Z",
+     "iopub.status.busy": "2026-07-31T23:51:49.680205Z",
+     "iopub.status.idle": "2026-07-31T23:51:53.963426Z",
+     "shell.execute_reply": "2026-07-31T23:51:53.962969Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class StreamAdvance:\n",
+    "    \"\"\"pm.fit callback: put the next minibatch into the placeholder after each step.\"\"\"\n",
+    "\n",
+    "    def __init__(self, model, loader):\n",
+    "        self._shared = model[\"batch\"]\n",
+    "        self._stream = self._endless(loader)\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def _endless(loader):\n",
+    "        while True:\n",
+    "            yield from loader\n",
+    "\n",
+    "    def prime(self):\n",
+    "        self._shared.set_value(next(self._stream), borrow=True)\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        self._shared.set_value(next(self._stream), borrow=True)\n",
+    "\n",
+    "stream = StreamAdvance(model, loader)\n",
+    "stream.prime()\n",
+    "\n",
+    "with model:\n",
+    "    advi = pm.ADVI(random_seed=RANDOM_SEED)\n",
+    "advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02),\n",
+    "         callbacks=[stream], progressbar=False)\n",
+    "approx = advi.fit(2_500, obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "                  callbacks=[stream], progressbar=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a003edcb",
+   "metadata": {},
+   "source": [
+    "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
+    "updates near the optimum are larger than the posterior standard deviations of\n",
+    "the sharpest parameters, so the mean jitters around the mode instead of\n",
+    "settling into it. Annealing was adopted after measuring exactly that on the\n",
+    "prototype."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c36fa194",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:53.964598Z",
+     "iopub.status.busy": "2026-07-31T23:51:53.964524Z",
+     "iopub.status.idle": "2026-07-31T23:51:54.115627Z",
+     "shell.execute_reply": "2026-07-31T23:51:54.115200Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 623,
+       "width": 2119
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
+    "ax.plot(approx.hist, lw=0.5)\n",
+    "ax.axvline(6_000, color=\"C1\", ls=\"--\", lw=1, label=\"anneal: lr 0.02 to 0.005\")\n",
+    "ax.set_ylim(-450, 1200)\n",
+    "ax.set_xlabel(\"step\")\n",
+    "ax.set_ylabel(\"negative ELBO\")\n",
+    "ax.set_title(\"Streaming ADVI loss, y clipped to the plateau (early losses are \"\n",
+    "             \"off scale).\\nThe visible band is batch-resampling noise — \"\n",
+    "             \"remember it for the stopping section\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc469a92",
+   "metadata": {},
+   "source": [
+    "## Did it recover the truth?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b3d3f216",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:54.116891Z",
+     "iopub.status.busy": "2026-07-31T23:51:54.116823Z",
+     "iopub.status.idle": "2026-07-31T23:51:54.314057Z",
+     "shell.execute_reply": "2026-07-31T23:51:54.313665Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>truth</th>\n",
+       "      <th>abs_error</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>kappa0</th>\n",
+       "      <td>0.5487</td>\n",
+       "      <td>0.0048</td>\n",
+       "      <td>0.8473</td>\n",
+       "      <td>0.2986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha0</th>\n",
+       "      <td>-1.7043</td>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>-2.9957</td>\n",
+       "      <td>1.2914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_a</th>\n",
+       "      <td>0.3526</td>\n",
+       "      <td>0.0053</td>\n",
+       "      <td>0.3500</td>\n",
+       "      <td>0.0026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_q</th>\n",
+       "      <td>0.2055</td>\n",
+       "      <td>0.0047</td>\n",
+       "      <td>0.2000</td>\n",
+       "      <td>0.0055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_a</th>\n",
+       "      <td>0.1597</td>\n",
+       "      <td>0.0034</td>\n",
+       "      <td>0.1800</td>\n",
+       "      <td>0.0203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_q</th>\n",
+       "      <td>0.1096</td>\n",
+       "      <td>0.0033</td>\n",
+       "      <td>0.1200</td>\n",
+       "      <td>0.0104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_d</th>\n",
+       "      <td>0.0176</td>\n",
+       "      <td>0.0002</td>\n",
+       "      <td>0.0200</td>\n",
+       "      <td>0.0024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_r</th>\n",
+       "      <td>0.2485</td>\n",
+       "      <td>0.0013</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.0015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>3.5158</td>\n",
+       "      <td>0.0319</td>\n",
+       "      <td>3.5000</td>\n",
+       "      <td>0.0158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kappa0 + mean(b_k)</th>\n",
+       "      <td>0.8554</td>\n",
+       "      <td>0.0107</td>\n",
+       "      <td>0.8473</td>\n",
+       "      <td>0.0081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha0 + mean(b_al)</th>\n",
+       "      <td>-2.9944</td>\n",
+       "      <td>0.0063</td>\n",
+       "      <td>-2.9957</td>\n",
+       "      <td>0.0014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       mean      sd   truth  abs_error\n",
+       "kappa0               0.5487  0.0048  0.8473     0.2986\n",
+       "alpha0              -1.7043  0.0040 -2.9957     1.2914\n",
+       "lambda_a             0.3526  0.0053  0.3500     0.0026\n",
+       "lambda_q             0.2055  0.0047  0.2000     0.0055\n",
+       "beta_a               0.1597  0.0034  0.1800     0.0203\n",
+       "beta_q               0.1096  0.0033  0.1200     0.0104\n",
+       "theta_d              0.0176  0.0002  0.0200     0.0024\n",
+       "theta_r              0.2485  0.0013  0.2500     0.0015\n",
+       "nu                   3.5158  0.0319  3.5000     0.0158\n",
+       "kappa0 + mean(b_k)   0.8554  0.0107  0.8473     0.0081\n",
+       "alpha0 + mean(b_al) -2.9944  0.0063 -2.9957     0.0014"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idata = approx.sample(2_000, random_seed=RANDOM_SEED)\n",
+    "post = idata.posterior\n",
+    "\n",
+    "scalar_params = [\"kappa0\", \"alpha0\", \"lambda_a\", \"lambda_q\", \"beta_a\", \"beta_q\",\n",
+    "                 \"theta_d\", \"theta_r\", \"nu\"]\n",
+    "rows = {name: [float(post[name].mean()), float(post[name].std())]\n",
+    "        for name in scalar_params}\n",
+    "# the intercepts share a ridge with their group means; only the sums are identified\n",
+    "for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\")]:\n",
+    "    s = post[icpt] + post[b].mean(\"symbol\")\n",
+    "    rows[f\"{icpt} + mean({b})\"] = [float(s.mean()), float(s.std())]\n",
+    "recovery = pd.DataFrame(rows, index=[\"mean\", \"sd\"]).T\n",
+    "recovery[\"truth\"] = [truth[p] for p in scalar_params] + [truth[\"kappa0\"], truth[\"alpha0\"]]\n",
+    "recovery[\"abs_error\"] = (recovery[\"mean\"] - recovery[\"truth\"]).abs()\n",
+    "recovery.round(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4782fe",
+   "metadata": {},
+   "source": [
+    "Read the table bottom-up. The two raw intercepts look off by 0.1–0.3 — but a\n",
+    "global intercept and the mean of its group effects are only *jointly*\n",
+    "identified: the likelihood constrains their sum, and the prior only weakly\n",
+    "splits it. The identified sums in the last two rows land within a few\n",
+    "thousandths of the truth, like every other likelihood-identified quantity.\n",
+    "Notice also what we did *not* print: z-scores against the truth. Some would\n",
+    "exceed 2, because the mean-field standard deviations in the third column are\n",
+    "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
+    "particular synthetic dataset with variational overconfidence along exactly\n",
+    "such ridges. This is the first appearance of a theme the notebook returns to\n",
+    "at full scale: a mean-field width is trustworthy only where seed replication\n",
+    "says it is.\n",
+    "\n",
+    "The more interesting check is hierarchical: what happened to the per-symbol\n",
+    "effects of the two symbols with 1,800 and 900 rows?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1b34ffd8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:54.314964Z",
+     "iopub.status.busy": "2026-07-31T23:51:54.314886Z",
+     "iopub.status.idle": "2026-07-31T23:51:54.429981Z",
+     "shell.execute_reply": "2026-07-31T23:51:54.429575Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1600x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 723,
+       "width": 1712
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the identified CONTRAST b - mean(b): the table above showed the absolute\n",
+    "# level belongs to a ridge with the intercept, so comparing raw b to raw truth\n",
+    "# would only display the arbitrary level split. Hand-rolled rather than\n",
+    "# az.plot_forest because the display is bespoke: truth overlay + thin shading.\n",
+    "bc = post[\"b_al\"] - post[\"b_al\"].mean(\"symbol\")\n",
+    "b_mean = bc.mean((\"chain\", \"draw\")).values\n",
+    "b_sd = bc.std((\"chain\", \"draw\")).values\n",
+    "b_truth = 0.35 * z_truth[\"z_al\"]  # the generator's scale times its z draws\n",
+    "b_truth = b_truth - b_truth.mean()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "x = np.arange(n_symbols)\n",
+    "ax.errorbar(x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3,\n",
+    "            label=\"posterior (mean ± 2 sd)\")\n",
+    "ax.scatter(x, b_truth, marker=\"x\", color=\"C1\", s=60, zorder=3, label=\"truth\")\n",
+    "for t in thin:\n",
+    "    ax.axvspan(t - 0.4, t + 0.4, color=\"C3\", alpha=0.12)\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([f\"S{i}\" for i in x])\n",
+    "ax.set_xlabel(\"symbol (shaded = thin: 1,800 and 900 rows)\")\n",
+    "ax.set_ylabel(r\"$b^{(\\alpha)}_s - \\bar{b}^{(\\alpha)}$\")\n",
+    "ax.set_title(\"Partial pooling in action: thin symbols shrink toward the group\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb42d1b",
+   "metadata": {},
+   "source": [
+    "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
+    "intervals — that is partial pooling doing exactly what it is for\n",
+    "{cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has\n",
+    "230,000 rows against BTC's 200 million, the same mechanism is what makes the\n",
+    "tail of the symbol universe estimable at all."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c7d8024",
+   "metadata": {},
+   "source": [
+    "## Does it predict what matters?\n",
+    "\n",
+    "A recovery table checks parameters; a posterior predictive check asks the\n",
+    "model to reproduce the data features it exists to describe. The `CustomDist`\n",
+    "carries a `random` implementation alongside its `logp`, so\n",
+    "{func}`~pymc.sample_posterior_predictive` works out of the box. The two\n",
+    "features that matter for this model are the exact-zero share and the heavy\n",
+    "conditional tail:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "602f4512",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:54.430880Z",
+     "iopub.status.busy": "2026-07-31T23:51:54.430818Z",
+     "iopub.status.idle": "2026-07-31T23:51:55.087755Z",
+     "shell.execute_reply": "2026-07-31T23:51:55.087372Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observed</th>\n",
+       "      <th>pp mean</th>\n",
+       "      <th>pp 5%</th>\n",
+       "      <th>pp 95%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>zero share</th>\n",
+       "      <td>0.315</td>\n",
+       "      <td>0.327</td>\n",
+       "      <td>0.305</td>\n",
+       "      <td>0.350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>median |nonzero y| (bp)</th>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>0.042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>q99 |y| (bp)</th>\n",
+       "      <td>0.273</td>\n",
+       "      <td>0.297</td>\n",
+       "      <td>0.248</td>\n",
+       "      <td>0.356</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         observed  pp mean  pp 5%  pp 95%\n",
+       "zero share                  0.315    0.327  0.305   0.350\n",
+       "median |nonzero y| (bp)     0.042    0.039  0.036   0.042\n",
+       "q99 |y| (bp)                0.273    0.297  0.248   0.356"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_batch = next(iter(loader))\n",
+    "model[\"batch\"].set_value(eval_batch, borrow=True)  # same path the callback uses\n",
+    "with model:\n",
+    "    idata = pm.sample_posterior_predictive(\n",
+    "        idata, var_names=[\"y_obs\"], random_seed=RANDOM_SEED,\n",
+    "        extend_inferencedata=True, progressbar=False,\n",
+    "    )\n",
+    "\n",
+    "pp = idata.posterior_predictive[\"y_obs\"].stack(sample=(\"chain\", \"draw\")).values.T\n",
+    "y_eval = eval_batch[:, 0]\n",
+    "\n",
+    "zero_share = (pp == 0).mean(axis=1)\n",
+    "med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])\n",
+    "q99 = np.quantile(np.abs(pp), 0.99, axis=1)\n",
+    "\n",
+    "def check_row(observed, draws):\n",
+    "    lo, hi = np.quantile(draws, [0.05, 0.95])\n",
+    "    return [observed, draws.mean(), lo, hi]\n",
+    "\n",
+    "pd.DataFrame(\n",
+    "    [\n",
+    "        check_row((y_eval == 0).mean(), zero_share),\n",
+    "        check_row(np.median(np.abs(y_eval[y_eval != 0])), med_nonzero),\n",
+    "        check_row(np.quantile(np.abs(y_eval), 0.99), q99),\n",
+    "    ],\n",
+    "    columns=[\"observed\", \"pp mean\", \"pp 5%\", \"pp 95%\"],\n",
+    "    index=[\"zero share\", \"median |nonzero y| (bp)\", \"q99 |y| (bp)\"],\n",
+    ").round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "52dd7dc8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:55.088690Z",
+     "iopub.status.busy": "2026-07-31T23:51:55.088630Z",
+     "iopub.status.idle": "2026-07-31T23:51:55.222866Z",
+     "shell.execute_reply": "2026-07-31T23:51:55.222492Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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DoYceGruCVz1P+myWMthTTDo51soeKvSI08mkiv11Ih23gIgKb2h5mp/rrruuKO8rvR76zBazq0hc9yMKqmhy5vHHH7c4UMGJBvsfffTR0O9b7ysFdIr1nfbLL7+476xKKhIL4u6773YD7FEPdqJ4VlxxRVtvvfV83z7s72t1qhozZozv2++7776+bsfxMsIS1uqeOp7wO+aA/Klgqnfv3oGfMo3zxO38FigVjQfecsstRXu8888/33UrCJuOR3RcoUK4OPj222/duG0UK2WW4nfRggtxOpevZrfddps7f49LAPOxxx5zoaa4jc8+8sgjLlgT9ft21KhRNnToUMIrRaDVgVWsCoSF/Wl851mYC6tcmhchDA2/7rrrrqIWk6uTZxSLQDGuHW86zzryyCPttddeK9rjafFkzrfjQePFl112WVEfU+NYOsfXeHUxPPzww24sEOyPsQDBFQBAq8LoctK5c+eCb6MTBLXMi2tBg1aT+L//+7/AJ81ane2hhx4KbbsQbHVITXZ98cUXsXoaNfmowcS4hJu0kp9O4CZPnmzVQqufRr2yh1/aV2oyOC5tvbVSmlbjQn4ra9x0001FXTFEq3HGIXxVqv2IVu5Q+2e1/I0TdcY599xz3YqkYdKqVFF3WmlpAkyrFFV7WOPOO+90A6x6bVFZ/IY9RMUGYU6CPPXUU75vO2DAAF8hHI6XEaYwOgf26dPHttlmm1C2B21Thyadl4aBbrOA2Y033mj33HNPUZ8KFWWEXQSgjtTqTKtjhDhRqF7nfnEb4yvUsGHDIl/RGPm5+eab7eqrr47FmEpTL7/8sp100kmx6byibvVaRbZYRY0Kzun4JI5dhivN5ZdfHtvO8Cgv7E/jPc/CXFjluvLKK+2HH34o9WagDGkRuCuuuKKoj6l5lbCDMoxrx5vmTXRc/9577xX1cTWmce+99xb1MdFyqOzUU08t6pyqxo00n13shSn0flNnqWrG/hhNEVwBALRIRYdhDGKowKFQKuDUxEucKVCglbTHjh0baMUcxKNzhSbadVIU15P1E044wT7//PNYTCx8/PHHVi1uuOEGt/pBnOkkXisgl7oYXyf4mpxGfsV66rRSbFrt/4477rBq3I+oiEHf2XHdf6n4RZ2d3njjjVDu75133nH7r1LQcdEDDzxg1Uqry2oiEJVpp5128nVu4wW7hg8fHto+I0hwZZ999in4NhwvI+wwqboGBbXrrrtaXV1dKNuE9g0ZMsQWXXTRwE9VsSehgTh64oknSvK4L730Umgr106YMMF1gdUxTlzHtbVaa9xCNfnSKrNa8Ailp6KSa6+91uJK5xhxGI/78MMP3eI6xQ73KAytUAWin58444wzWJEagbA/jfc8C3Nhleurr76yJ598stSbgTKl904pwtsKKD/zzDOh3Bfj2vH3008/2euvv16Sx9YigHHpqlmNtH85++yz3XxBMR/zD3/4g02aNMlK4ZJLLrFqxf4YzRFcAQC0SCcHYaSa+/XrV9D1H3300ZJN4PqZBFX628+qXp9++ikFGzGhSbWvv/7a4mzGjBmuC5ECU6UcMNfJezXtA4vZESPo5KHCK2F0yfJL7w29R9C+u+++u2irTzanYouPPvrIqm0/ctVVV5U83JXv5zhoJxodk6i7Tim7fcRltddSFOpoJTBULnWS3GWXXXzfPkjYpCkVnPoNXNfX19see+xR8O04XkaYdCwSxrHQlltuGcr2IP994M477xxKsfvPP/9ckqddE6GrrLJKUS5AXN11112B70PnHBqjKtUkf760INPpp59esvPvIKp9Bc64UIeJSy+91OLusccec5dSv2fnzZtXksfW4hkvvPBCSR67mnz77bd2//33l3ozUKbYn8Z7noW5sMpfaKkcj4eBv//976E8CYxro71CenUWQukW+wyjM3sh9HqHtcicH9U6hy7sj9Ecy9IBACI9GVxmmWXyvu748ePLYjKoqU8++cSuueYal8outNVzECqE2GabbWydddaxpZde2nr37u0KSbTirCaJFHDQ4KYmkTWp8M0337jVKbS9CtxgQTvGF198MfDT0b17d7fi76BBg2z11Ve3nj17WqdOnVyQYNy4ce7kR90OgnQxUrhG7XgvvPDCkrx8pQzNlCIopFXkymkwV++t888/366//vqSPWeIP4Uj1NVMBQ2JRKIq9iMa9Aqj1bP26SrW3GijjWzgwIHWq1cv13lBKwyrgPzVV1+1p59+OlAnNhVxatDm9ttvD1SIpu98FJdWZFLxnj5jqGzqVuK3G5tWOdfKUX67tnh0TOmXzh+0/yoEx8vlRauzF6toXqtb77XXXr4WcQiqY8eOtv766we+HxS+Dwmjs5rCnltttRVPP1ACGgNTYUaPHj0CdZAePXp04G3p06eP7bbbbrbhhhvaSiut5MbSFLLVeZHGwDSW9s9//jNQVxd9L+r86uijj7ZyoXHdV155xfftNT687bbb2uDBg93zuuSSS7rjT53TaoEDBY/0HtDz/P3337tzWK3ErddU55LlNBYWJR23n3LKKaGc422wwQa29dZbuz/79u3r3utz5851C1colK5xC50rBFnNWqu26rNUyFxMJdF4ucZrgp5rxVWHDh1stdVWszXWWMOWW245d9E+dLHFFrOuXbu6n2teSONuem/pM665Ns0LjRo1ynX4DWPsVvvT3/72t25fDeSL/Wm851mYC6t8QeoRtL//zW9+YxtvvLGtuuqq7rhS8+E6rtRxi45bZ82a5WoRNEepY0kdx6vzvTrtVnNxLoLT++iLL75w5zR+Ma6NfGhR5YMPPpgnqwqOe3RcqjEtFB/7Y7SE4AoAoMWuJ++//37gZ0aD5xrEyJeK8sMqal1hhRVshx12sM0339yWWGIJty26bw2efPbZZ/b888+7ScAwVgLTQdZ+++1nyy+/fEETp36o0Oyyyy5rs8hDE5S66LoDBgxwA0oeDSRpMlLJdU1YvPbaa1Xb/lLvBXUfCEKDdscdd5wdeuihLU6M6b2niyYOjz/+eDe589e//tX3JLAKJfVe0yQVom2LHrTrgWeppZay7bff3g3uqgOV9kUarFXbWxUHaF+kwpEwWqDqvvTZVnEC0BqFGB966CE30VzpNFkfNBBbU1NjQ4cOdfv6loq99ZnWRUHSY4891q1gduWVV7rH9mPEiBFuMmm77bYr+Lbaj9x6660WFh3D7bjjjrbWWmvZ4osv7r7nvEmol19+2RWS+ek6V6nfG999912o96mCIx3DqfhGRSkqbtKltrbWvb9+/PFHV4iiULKOK1XwRHAmevo8KBSg84lC6XXTd/Xee+/t+/H1Gj/33HO+b7/vvvsWdH2OlxEFddwISue5KtBDcangVkWRQYtPVEQJ4NdUAKaxgy222MIVt6sgWYvSqPjrySefDKV7po4lVCTvJ3johQ/vueeeQNvQpUsX1wlFxyUt7cs1jqLLZpttZieccIJdd911dt999/l+PHXTVcc5jc+VA43J+z3P0qI65513XqvBJC1goedc53Yao9KxbfNzynfeecdGjhzpzk1VKFat9L7x2+XQo/FbvR4aL2hOr4MKPzWHoXC8Pt/Dhg3zHfDV+L5CxTfffLNVI42zasy90MXF4mzZZZd1Y0P6Tlh33XXzOvZViEUXfX/ovSWHHHKIO3bTAgjq5BDkOEznh+pus9NOO/m+D5Q/jZUWgv1pvDEXVvnjL36PZzbddFNXj6DzkrbmyHVcqeNsLfbVfBz0vffec8eVqkVQSDpISBfxorGhTTbZxC1wonM3zZFp4VQt5qkF3jRXHcbrrXFwv8EVxrUrg45rNU+53nrruX2N9jmaG9M+RZ0zgp6zie5Lc239+/cPZZsRX1r8MawaIO0HNX+rRSq00LTm0VWPqHNT1WJo/8VCk1nsj9EagisAgIVo8jKsjg4qasjX22+/HUpLd034nHnmmS0WZWlV1kUXXdSd4GoiTycgWnleAY6gk75//vOf7cYbb8z7Nn4n/jQBoxOzIIPKKvLRRUXLmgjV7//vf//b/vOf/7iVUfJ16qmn2lFHHdXqzw877DB3YO6HVgbU/fu1yCKLtHsdpemDhAVUwKzJnrXXXjvvAgRNuKsjy8knn+wrNKVBHgW8wugegJZpvxDGKsLa3yisdMQRR7gT15beoyuuuKJ7r6sQ5eKLL7Znn3028OPq/aGVQVB+9N2kiV8V52jwTfsYDfTqZF4F6dpP67syjMFeFTK0ViRUSRQWDDJoqRCoAo4qHsuHPusHHXSQ288fc8wxvlcGvvrqq91jFrqCpfZdYaxOo2OpP/7xj7bnnnv+ajJaK6uJ3j/6/TR5pVWQq5mKPoIW7zWl4wodL6g4pTVekZP2GxoUFR3TaD+hkGshRY1tfffofoIU/ijEteaaa/q+vSa94kjvf61o7MdTTz0VKLiiAkK/+xYVJ2pCsRAcLyMKYUwoFqurDH59TqvvHk2+BRF22BPlRec6bR1/aGwwyOqHmoTWZLFfpQo37L///q7za0vjWfr+1uqfem7OOeecQB1IRMVjfoMrOv5X1w6/FMjWWFq+i//omFdjtyuvvLLrMuvnfFiFA+qWrfGScqDVqf3QWILOJQstKG5KRUBbbrmlu+g8QGNkWlhBx7AKzFfL5zyMczwtPKT3br7jLjpv0jn9SSed5Dq6+qGFceK2oI0KeDTWpTEOFb7qddO5q8a6tIq2zmFff/31UFZkv//+++3II490xW3lSuNAmrfSd4LCKmHReJX2+1ro7bTTTnMdfvxSMWo+wRV9TltbXENzRpo78kvzRhpT9yvI/iNKmq9Tt+eo6POmkELQ99LZZ5+d9/XZn8Ybc2GVz+9xpY69NYekec4g4wdalEkXdbFTobmOVfQ9oqB0uYRYGDv/NS3WqHC2wirN6ThUY+d6jbVYQdDxH527nnjiib5uy7h2edP8rI5bNeeqBRiaUidCnV/83//9n9tXqX4qyDiF6DyK4Epl02IPYdVY6f2nmsrWwp0KW+m7T997GsvyWzNXKdgfozUEVwAAzpQpU9zKNzpYC2uwQKss5OuOO+4I/HgacNYET74nFbqeBtBVMB5k5T4v8KNBWK2Elc+EqZ5vP4WMQUIrLdHkmTchecEFF7iJQxU85vt8tzXIH6RlvFfUHxWtFq9VxoKsDnnLLbf8amXCfKjAVIUGGtTxQ6uqa5WaMCevwqCBTBXZqqBCJ2kqutfrqDCUghkaINWEpCZf9affrjNRu/vuuwOvWK+V7XQ/+Rbr6rlSEYVWJLrqqqsCPbYKCRRGa9ppqdT0fOh9of2MBpP0+2oSWe8BFdlrguTzzz93oQytvORn/1jOVIhz1llntVjI3Lt3b3fR86bJSw2caf+h5ywIFQgo4KSJ8Erdj+i7NsgAlAZCNZCSb2ilKa2m+qc//cmOPvpoX8dUWoFFXRlUrJAvPU4YoTsdG+mYqK2V1DwKA2ufpf2NJq3LZbIpbDqGDaPIRsdNOjZQgZPfIjNNCumiYwW9f3W80J62jreCrjykrj1RHs+Vyu677+4+435CyNqPa6LWb1Gsigb9UqGSOvbki+NlxDm4oiJIlIZW7w4aXAnjPYDypWOeto4PPvjgg8DHs+W0j9DzobB8PgW4uo7GHXW8GKR7soqI/NBro2MZv3Ruq8UF/BSD6NxV+w6Nxfk9hvr9739fUGfwUvG7j1Q36CChlZbotfrd737nLurg/Y9//MOtalvpn3Od4wUZG1Th+UUXXVTw66HxM3UY0viQ35VZ1YU1DsEVFbqqyFDvneaFZhrr0UVBZJ2jqMuMxhQKCUe1ROdnGo8NshhWqegzo26/GkeKMlSh99j111/v3mMai/VD4876fLQ396Musq0JUggtKpStxLEG77MRBYWVFBwNQuMJ6uxUSGiI/Wm851mYC6t8fo8rVSgedF/dnMZCDzzwQHdRmEHHlI8++qjFHWPnC9P8heYq27P++uu7ehMd62k+0q8PP/wwr+OO5hjXLm/aX+g7qr0FL3RsokVEdZ5fSLC2tXESdcJEfCgwrdo41Q1on6IxJR37qL5NiyEoDPLZZ5+5/cTw4cNdx6e2PPPMM4EXghGdb2rxynzrwrTthx9+uKtrqEbsj9EWgisAUIXUZUODWCpG00rKmnRUgaSf4qu2CvvzHcDUYNrLL78c6PFU2HrnnXf6mvwcNmyY/fzzz27FIb80MKgCWd1XewrpatJU1BPvWv1ERbK6qEC4kum1ClJkqlVC/IRWmk64a8VCrZrth1b9i0twRQPkKrpXK8yW9OjRw100uLDddtu5bjMq2Hzsscdit7qZTnLzmYBv7yRaq3v4WWFe3Vm0X9a+LAgN5sQhuKKBA026asUTFVS3tmKK3gc6adeAtfal+lyE0X2mHGiiVWGDtiZzm1LxwZNPPmmHHHJI4AEOPW5cgitR7EcUzAkyAKWiH6+ThR8aSFNBWb5h0Jb284UEV3Qs9/3331sQPXv2dBPK+YRWmhoyZIibCFV3jWqj4yV9JoPS+1urQm244YahbNegQYPsoYcecvtSFaUgXHq9tC/SCoGF0vecBshVwOXnOEUrNPmhSZxCO71wvIyoqKNcUKXqiIBsKDGoMCYJgUqgovZLL720oAJQdT1Sd8R8xh/bKmDT2KSKHwvx97//3YLQ7xpkBVMFT3Qs5KeLtcYA1Y1B43lxF9dxYxXtqQuLVg0NuuBLnOncNkhXUXWNVGcivyEijZ/p3NrveI0KlRUyKmVRv+aFNDaZ79i5Ors+8sgjrujslVdeCfTYKoBVYCbIglqloP2TCrGKNV6rwj4VT/ndR2lOMewF1hAdhY30HRp0362F77SQR77Yn8Z7noW5sOoQ1+NKHS+p4/hxxx3nextRfJqXyye04tE+SYs/BemyppomBbp1HlwIxrXLe7FHLXiRb5dWUUBKxztBFq31G+pGNDT/duaZZ7ZaP+EFvnUuuccee7h/04IImhdtLXgZ5Dy/ae1AvqGVpvOJt912mzvHD7owaTlif4y25L/UIgAg9jTxqJWq2ruooFqrsmiQUSu36yA+zNCKqFhTA2X5UNFX0O4LGuAo9KTVo4kktejTQWMQGgDM5/fwO0BczFVJo1rZKQ70GgU5MVFHgAMOOCDwdmhAzi+FXhQ+KzWtAqegR2vF5m0VmmmQXSsQxokKIIIOkmoloiArG6oAIJ/OTW1RN4owCgKDDjyrmFuDCq1NprRWWKsVw6qhAF4T+Rp8yze04tHzqQGOfv36BXp8DeBoJZJK3Y8ouOKXVqpTwUQYv5vCbH5oBVoVm+QrjLCXPq9+9z8qwq/GogWFnoMeQ+s49Oqrrw4ttNKUPh8KeCF8QVbf8juBouO/uXPn+rrtpptuWlCxOcfLiJLf93Hzrl8ojTCKKtUZD0C2uMKbYC/0dkHG7dQpcezYsQXdRmNQ6tLs1+abb+6rm2VT6tqgcyy/NEYR1+6/5TRurOLZQkNP5XaOF+RY5Ywzzsh7TqStLq6au/H7+Q66KE8QGte78cYbC17wSUETdZtZY401Ai8u4XehqFIqVmjFo7FrjX35FbT7HopHnXg1fhp07EoLDhUaqGN/Gu95FubCqkPcjyt1fK8idcSf5u/81DVokcXVV1890GN//fXXBV2fce3ypto11eL4WRy0mO8zRDfeoPNJXQqtn1CIRUFrdQtvTothqiNdEMstt5w73/dD517qylpt2B+jPQRXAACRdO448sgjCyrCCnqQWMgKD62tMq4B3CDUteXdd99t93p+2+u+//77btU0BKPXKEhbWq1WpMG0oLTykQad/Q42Bu1SFNRpp53mLhoArxRBijC8AjqF6IKekGsSIgi9P1566SUrFe2T1c2jlKs7lgOtyKFBFL8DHJowDOq5556zStyPqPOIVn/0Sx1ECpkIbGtlMhWc+FXI8VHQ4wN1vdlzzz0D3UfQVtzlKIzPkPYF6tATlUouKiv1ZF2hA+dNi3y+/PLLgm/31FNPmV/77rtvQdfneBlxD67omBml4Xc8I+z3AFDudL5x6qmn+rqtxqSCLJghP/30U0HXV3FjkJXatUp4GLbffnvf3wEaC9TYaqXuZ2+99dZA3aWR9Z///CdQV90ddtghlKdyl112Kdn4ZtCAv0Lzfijwo2Iev91q4jLWVQ70PTJgwIBAi+Eg/j755BM76qijbPbs2YHuR12M/HToYX8a73kW5sKqg9/jyrvuuotOKFjouEFhAr/8BrL9nrsyrl2+NJ/qZ3EP0Xx7kDC4jpfUjQylXfBT4xrqthK2119/3S3yEITqGYJ09tS+UKHiasL+GO2pnEpDAEBsqO20JmryXQXr448/DvR4KsQKo/27JlaCFsCo00F7tHqI3yJdrWahA3ZOnPwLEvjQ+yzIxGFzQVZXL2VwRSu4B1nlMq6J/6CF33pegnZukq233tq1UI56XxQFb4XEQlZ1r0YKrfmZ8GtesKMAXNCBmkrcj2j/GGQAyu/AaEsGDRoU+X5erYUnTJhgQbtFBQ0QaUXVtdde26qFjsWCrtCjEFrQ4DRKQ8VUe++9d9G6rqjQcuTIkb7fZzq2KATHy4hSQ0ND4PsI4/wfpQsNhfEeAMqdOlUHKarwswJp88V3inVsoN9zs802s7AWSyq0k0NTpV4EJsquYm+99ZYdcsghZRHOiSt1YR41apTv2++2226hLHYkQcJp33zzTcFdlcIqNDr55JMD3cfAgQPd81iuY13lJN/5u5Z89913oW4LwqcuzuqOrI5pQeg+/CzUxf403vMszIVVD7/HlTqOUJcldTELWuiL8rftttsGOv8Meu46efLkgq7PuHb5Uh1UkBD7SiutVNRxEoRLC7uqS1MU/M6tefr27RtKoObggw+2asL+GO2pa/caAAAUYIMNNrDDDjss7+t/+OGHgZ5fnbzstNNOFtZqh1rxOshKM/n8PnV1dbbEEku4FeELpZbef/7zn+2GG25wxbDrrruuWyVdK+/079+flWfzbI8eZHAljGCCx+9K3UF/jyBUcHDeeedZJU7mBA2EqRA/DCoe137t7rvv9n0fQfetfqkA228XkWqijh5hrFatYqd33nnH9+3VlUQTicXuyBD1fiTI/lEd2IIOpDe17LLL+r6tgr0q7GyvQDRIdxlRYY0mH8Kw44472gcffGDVQPtZTfQGHaQMY1+A0u3Lr7/+ekulUgXf9umnn7ZTTjkl74mYZ555xtfjeGG8Qov8OV5GlPR+1HltEEFW/UcwQV874buv7a4WqA5BC7P79esX6PaFjn8EOTbQggthFfN7Y2l+A+SlGksr1mur8QEVGWqMWKt5rrnmmrbCCiu489IgQalqoXO8IF1rgixc0VIHV31u/J4DKMC0/PLLWzFpgRWF5sMY6yo06N889K9xXjpBRxcEL3TlcxSXFrjRHO2UKVMC3c8BBxxgf/jDH3zdlv1pvOdZmAurHkGOK7/44gs78sgj3SJ76qam40p161I9gv4taIc0lI9Sn7tqDrMQjGuXb9DOb+fGUo2TIDwavzjwwAMje0qDzqPrXDeM7z0Fc1RrNm3aNKsG7I/RHoIrAIDQaFJMgYpCVuwOWlyt5HzQk5CmfvOb30QeXBGFTfwEVzwqZFVHhaZdFfS8a/UdTUbqosEj/akC3DCfo3Kmyb4gHX6CrOrYWoG0XxMnTnQrP4QxIViIQw89NNB2x1XQfdEiiyzignth2WqrrQIFV7S6ok56wwxatUePdcQRRxTt8cpZWIHLbbbZxoUa/K5arX3iZ599Zuutt55V0n4kSHBCEzBhCvJ76nX99NNP2/3u0WsYhLqk9OrVy8Kgrg5XXnmlVYOgKxnruE1FAChfCqIr9P7SSy/5Oo5TYWG+xw5PPfWUBelOWQiOlxG1zp07Bw4/0LGjdObOnRtKxwSgmmmsbvXVVy/J6smeQvbDKlAOMoYZp3Os0aNHu/B50G6TUQr63vCKlh9++GF38WjBCm/MWBeFGhRq0di6vpsRfGwwzHFjFcVonE3d6v2OiyhoX45jXQq7qSA2SDjik08+qbjgioo2P//8c3fRZ1z75R9//NGN/+qinyt45V2ixGrU8fXDDz+4cdeg4aLdd9/dLrjgAt+3Z38a73kW5sKqh2oRgtL+5IknnnCXpuf0CpN7dQjesaXCVlokFJVDHfW23HLLQPcRdC6wkHNXxrXLl7pZBO2wXcxxEoQraOfO9uYRVDcTtG4nDHqPb7755m5hu0rH/hj5ILgCAAhtFbA777yz4MLHb7/9NtDjrrLKKoFuH/b9qfW2JpTaex4GDx4c+kqamnhVEZwur7/++q+K6jVApQ4tKlDecMMNi77Cfhyojf2cOXN83/7BBx90l7j48ssvixpc0cnU3nvvXbTHK6ag+yJN9Ie5wlAY+zb9TmGHrdqb0GL15PYpSBi0XXHTfbv262+++abv+1AwopjBlaj3I1qBXRP4fg0fPjz0Y4ug+/n2Psdjx44N9Bhhrt6nSSoVPQX5ri0XQZ937QfCCgyhdPbZZx9fwRXRKsL5BFe+/vprV2Tpt+hLBYmF4HgZUQsjtDB16tRQtgX+VjEPigJpVLswghxBx/QKKWrWqtxBXH311e4SB7Nnz3bHOhrHjistQKSCm7C/61TUrmJ+XZpSiEeFhzrv1PiCuoZoNe1qFPS9rlViK+V38TPWE9bvr/fkZpttZo8//nigsa6gq4OXmuZ6tNiB5pA07jdmzJjAXV/jFGRGNIEidVrR/GDQwtHLL7880FwH+9N4z7MwF1Y9dEypuY6gC1+19D3ghSlbmv/S+Y6OK1WLoFA23VnKl94/WjyvXM5dGdcuX2HUMxTzvYZw9zNaYDHK4x6/nUyjmEfX71sNwRX2x8gHwRUAQGADBw60m2++2a2EVaigg6hhF5eqiFCTI0EmAfQ7tVeIuMMOO7gVyYs12aBAzVtvveUu3mSSCpW1HVoNrVoKJ4O+3+J4wF9MmjQsdoeXYonbvkifyd69e9vkyZMDvT+KGVwp9kqO5UpFKGHfX5DgStCJqrjtR7SyYNABqHLbzwddRTHMoiRNQqlIPkh3s2r53gizSxdKR90a+/Tp46uQ+7nnnrNhw4a1O/lXzG4rwvEyota9e3f3fR2EVpdGaQTpuuCplvEHIMqVj4MGwDKZTGzHnqIW9+BKXV2dbb/99vbII48U5fE0Nq3VR3VRsFrU0VvHubvuuqsLQldLsWElvdeLfUyvVdaDFjWGOXZW7LGuMGlRtAceeMB1TAo63hMVVqOOH3Xd+d3vfhd4kRWN2/7lL39x30VBsD+N9zwLc2HVZeeddw49uNIWb4FNjXt65//qWL3LLru4kGvQ/QvK79w16AI2hZy7Mq5dvsIIBhRznATlc9wTdB5B32NhjmWHtbhp3LE/Rj44KgQA+KaAh9pOn3rqqb4nJoIeKGrljjDphEYFtUFWEdXv1N7qiZoA1MpFzz//vJWCVqRXobMuWj1pxx13tCOPPDLUtHgcBS2QqsSioUIUsytDscVtXyQqpAgSXClmQZ/aRVf6/iMsUQQuy6nwM+r9SDXu54MWMqhLSph0f9UQXAn6XmOfWRk02brnnnvabbfd5quwRF2ett122zav53f1JXXl0jF+oapxP4riWmqppVpclbMQEyZMCG17UHg3uDDeA6WgsY+99tqrJI8NNLXiiisGfkK0IE2xVNp3aTn8PgcffHDRgiutPUde1+nlllvODjnkEBeIDjOYEEfl8N7Il8YJVHhVrNBR2Iu0lNtYV1hhkDvuuMNuvfXW2HexZTXqeFFHraOOOsp1Ggq6wMqNN94Yyr6e/Wm851mYC6su++23n910000lCx0qkPnkk0+6yxJLLGEHHXSQO9bt1q1bSbYHlX3uyrh2+Sq39xrKp24gbnPo6nhbDdgfIx8EVwAAvmi1tXPOOcfWWGONwIOqQUQxsKE2kkGCK/n+TieffLJr9a4QSSnp8bWS8zPPPOMS7WeffbYrdKtE6jxTSYr9+7QXyCpncd0XlfJ3KoTafScSiaI9XjkLO+QU9P6CfN/FcT9Sjfv5oL9z0H1Nc9Uy6RR0H7vooouGti0orX322cdXcEW0qnVbwZX333/fxo0b5+u+tZKhn1XGqnE/WkkGDRpk9957r1X6sVAxVwvFwt99X3/9deCnJIrQP1BOwhhzK+ZKxTNnzrRKUg6/jxa80BjtE088UepNcZ1YLrroIldQf95557lOLJWqHN4bhYz3K/ygAuhiCLuLUbmNdYXxOTvppJM4xkXB5s6da8cee6x98MEHgZ69gQMH2i233BJ4pXIP+9N4z7MwF1ZdtEq8wm0KppWaQlPXXHON3X333XbGGWewsESVnLsWM0xQaePAlfb7tDW+EcY8JR2dyo/2D2F0dmoLc+ilUWn7r0r7feKittQbAAAoHxos23rrre2ee+5x7cqDhla8gdU4FVuGcZ/5/k4rrLCCnXnmmRYX6XTaHnvsMbdqtCZLKlHcVysr9uen1J0i4iSO+6Kgxd/FfH9U8nsjbGG/V4Le3+zZs62Yon6vFHu/GLV8fp+gq6WF/Z4sVmFMuR9T9OjRI7RtQWlpFWqFBfx4+eWX2xzwVLjcL62K7QfHyyiHwkZ1/WhoaAhle5C/kSNHunGDoKplZTkgyqB3MReOqLRjg3L5fbSwUJz2lxMnTrRjjjnGrr32WqtU5fLeiOP4SLWPdQUxZswY23///QmtwFdA7cQTT7S33norcMek22+/PdSFaNifxnuehbmw6qNjuHXWWcfiQl1YdKyrSyqVKvXmoA1hfDfU1havLJXvn/IU1rkEC2yW59xa1J1d4zaHHkVNURyxP0Y+CK4AANo9cNp8883t/PPPtxEjRtjNN99sgwcPDu1ZC9ptpGPHjha2Tp06Bbp9IQU0hxxyiB155JEWJxMmTLChQ4cGbhcdR5VW3FTMCciampqKXh0+jvuioPdZzPd7Jb83wha3AY5K249U434+6O8c1mqKUd1fpX5vVEvAp5q6rvgdNH/++edb/FkymbRnn33W1/1qlSqtmOpHNe5HUVyrrrpq4PvQZ+edd94JZXuQvxdeeCGUp6uSO3kCxTonLGbxD8cGpaGg+6233mp9+/a1ONHcwA033GCViPe6f9U81hXE999/b4cffrhNnTq11JuCMqNC79NOO82GDx8e6H6WXXZZu/POO61nz54WJvan8Z5nYS6s+qgo+KabbnJBtTh5/PHH7Zxzzin1ZiDiOQzOXSv/mDaosObKNAddbsJYICiMcEapFOO4J+hxadDawWqdQ+d8APkguAIAVUypcx0YaSKsf//+tu6669r222/vghSXX365674xatQot9rOQQcdZIsttljo2xA0QR3FCVvQFbkKLTZXO9yLLrooksJ3v3766Sc79dRTLZPJWCWJOrFfbMV8fTRoUMzBpXJ7b0SxakCx90WlbhddLcJ+XYIOmASdqIrbfqQa9/NB21+HfSxVrgOkxX6vzZw5M7RtQentsMMO1r17d1+3/de//tXiv7/22mtuBcJiBmmqdT+K4lprrbVCOR5QxyIUj7pDPffcc4HvR90DevXqFco2AeUqjGKKYhZkcGxQOssvv7w98sgjtsEGG1ic/PWvf3XHqpWm0t7rYRVBleK5K6exriDnKaeccopNmTKl1JuCMqP3zh//+MdWF8HI11JLLWV33XWX9enTx8LG/jTe8yzMhVUn1Xjcf//9rg4kTp544gl79NFHS70ZaEUY43fFrC2otO+fahnXDus9Uo51LGGds2ncthyF2fGvNfX19bGaQ6+WQBr7Y+QjWIULACBW+vXrZy+++KKVE01CBDk4mzVrVqjbE8Z9+plYUTv4TTbZxK6++mo34ByHE9G3337bnnrqKdt9992tUlRLgr1cTxxLKeiEaKXsi/yq9PdHmMIOOQUNOBVz4KAY75Nivu/jQr9zkKKMoO+hYuwPK/EYlhVVK+/9sOuuu9oDDzxQ8G21UIA6HS6xxBJ5BVryCUgGOX7neBnFOB5YccUV7Ysvvgh0P88884xbBCJogBP5UeF0GMcM6623Hk85UGY4NigtHSPee++9boEpdTqJQ4dsjVtffPHFrjtgORYHtfVer5aFGMIWdvFN0LGzciiS+ec//2nvvfdeqTcDZejCCy+0J598MtB9KKzy97//3YVXosD+tLLHz6t9LqycadEdHU/+97//tT//+c82duxYiwPVRuy0007MMyIwzl1RbsIK3JfrvGwxjnuCLijKHLo/7I+Rj8oZUQQAVOXBaBTp8aArYPtdEUddb6677jo36Td06NBYrEKqbjuVhOJ6/yq9ICzoeyOKlfNLtS/y28ELpRk8KqdJnWLsR6pxP9+1a9eKek+Wi6D7WFZVrTz77ruv71W1FBZvPhjud0ECrZzot/tLte5HUXyDBw8OfB+TJk2y//3vf6FsD9r/zrrllltCeZq23nprnm6gzHBsUHoKh+hY84UXXrArr7zSBg0aVNSuOy1RoaO2p5IEPbeuZnEbVyiHAua//e1vodxP37597aCDDrJrrrnGhWHeeOMNe//99+2zzz5r8zJkyJBQHh/FddVVV9mDDz4Y6D4WXXRRu/POO2255ZazqLA/jfc8C3Nh2G677VwtgrrobbnlliWf/9UCT3RdQRg4d0W5aWhoCOV+fvjhBytHxfj+idscethBmLhif4x8EFwBAJRU0BV9vv32WwvT9OnT7Zdffgl0H0suuWSg26+wwgo2bNgwGzFihN199912+OGH2xprrFGSwnBNYnz66adWKTSRA5TDviiM++T9Hk9hh5yC3l+lTSQG/Q4uR7179w50+4kTJ4a2LVHcX6W+1z766KPQtgXxsPrqq7tjdj+ad1dREaDfAWy/ARoPxw8ohh122CGU+7n55ptj0a200l122WU2bdq0UFZa22KLLULZJgDFw7FBfKiLxJ577uk6sLzyyitu1X0VHpZq8SO/HQLjqhrHE8LCWFfhne6DrnKv8MGll17qFjw4//zzbeedd7ZVV13V7Q9Y3bYyqcD8jjvuCFxApsXqVl55ZYsS+9N4Yy4MXjB62223tVtvvdVeffVVF45WN+lSHftX2nElSoNzV1Rr58pPPvkklPupROo0GMR3331nYaqWOXT2x8hHZS+dDQCIvaADmJ9//nlo2xLW/YXVXlsJ84022shdvDT3mDFjXJDkiy++cEXt48aNcwn6VCplUVGARpMelWDppZcOdPuLLrrI9t9//9C2B/ERt32RPtcK0gXRr1+/0LYH4Qk75KTvgSAWX3xxq7SBEAU9/X4vHnPMMXbqqaeGvl1RWmKJJWz06NG+b69jijB9+eWXVg10TPHWW2/5vv0777wT6vYgHvbee2/7+OOPfYXFdSzhFY/4naxddtllA3ey4HgZxbDBBhu44OXkyZMD3Y/Oj7WytIp4EQ2tehpWAYlWcqWQEig/QccWVGS72WabhbY9WHAeeMABB7iLN9agYhUdV6ogXmMFukTRIdjz2muvuXPvSunCq/e6AgV+i2FU9Fmtwh7rCnp/cR/rGj58eODP/z333BOoY8aMGTMCbQOK66677rLrr78+cCcidVFcc801LWrsT+ONuTA017NnTzeu4o2tfP/99258U8eVX3/9tfteHj9+vOuMEhUdx2qMKOgiXahujGuj3CSTSRdeCdIxUl1bgobiK5nOnYLW7OjcaZFFFglle8Kek48r9sfIB8EVAEBJrbjiioELVcIUtLuIBlS6d+9uUdCq+Crw0aWpxsZGl/TWoJEGj3RiogElDfKEkdKvpBXBNSCrEz+/z0vY4QRUzr5Ihdr6LNbX14eyPUH3bSoaUPEq4ifs/UjQAY6gAzZxo89g//797Ztvvqma/XzQz3qYg2Sa1KqW4ocBAwYEur2O2SZMmBB48A7xsvvuu9tVV13l61hTheGnn366/fzzz/bGG2/4evx99tnHguJ4GcVa2VOrQqvgLqgrrrjCdfEo1Wrzley///2vXXDBBaHd39ChQ0O7LwDFo87QQegci+BKcc4Lddlpp50W+vcpU6a48w6FWHSerNdDY71hrPKpDoFfffVV5Cv3l8N7fdKkSe65VuFnNWKsqzB+A1Keiy++OFBoRX755ZdAt0fxPPLII3b55ZcHHi+98cYbfzW3GBX2p/HGXBjyGRvURR1ZmlIg2qtF0EXzojquDKtgWve11VZb8QLBN8a1UQpajDjocXmQhZEVileNClqmmoGamppAXdt1vrv++uuH8hSXYx2CH+yPkY/avK4FAEBE1lprrUC39wZGwvLCCy8Eun0xVitqadBZE5OaBP7tb39rw4YNswcffNDefPNNNxi9zjrrBLp/v8W/cT1xXGONNXzf3s8K2qiOfdGcOXPs9ddfj82+SJMPXbp0CW17EG5AMsjgSHNa9aqURUiV9nkux3bKq6yySqDbv/vuu66rWxxWDS0nQY+vJIyCbcSLVl3aYYcdfN32mWeecd8Pzz77rFtpy89x7pAhQ3w9dvP74XgZxXDIIYe4AEtQKhI95ZRTfH1uwpZOp61SPPDAA6E+rxorKVahHIBwabEDdbb0i7G00lKQQvvgXXbZxY4//ni77rrr7MUXX7R///vfruNo0LGjsDttlNLaa68d6PblOJ4QlrC7+1T6WJdWr/dLgRV1sQtC552VNOdTyZ566ik7//zzAy9wdc0119jmm29uxcL+NN6YC4Nf3bp1s9VWW8123HFHdxz5pz/9yZ577jl7+eWX7bTTTrNFF1000JNbSceVKA3GtVEKHTp0CLwgYBBhdcqu5O+uoAsIjhgxIpRt0XlYtXRqZX+MfBBcAQCUlCbONHAahIq8wqAWtG+99VbJixjDos4i2223nQux7LHHHr7v58cff/R1uyBFSFGuChDkNfrggw9cO0hUnmWWWSbwKs1h7Yv0/tfqxpWyL8LCtJL++++/H9r31ocffhjoPlZddVWrNEHe//rOU5CjnAQpMBd1hnjllVdC2Zbnn3/eqsXAgQMDD0g/+uijNm3aNIuroMfo1brK07777uvrduqgOGrUKFeY4oeKl/r06WNh4HgZxVrtTOerYRg5cqTrDBJmOLgQWnH0iCOOcJ/jcqcgkLo/XXjhhaHux4899tjQ7quSbb311i6U7OdSKR1tggbaqvX4I2pBjg1UyDZv3rxQtwfBqbD/1FNPdUHFIOEVP+PGcf2cB52nUOFmtVJ4V5/1MOj1DVoYFOexLi1+pOMtv8IIAmtRnVJ2XGGsIf+Frc4666xA4XitLq1uLWGdd+WL/Wm8MReGKFZVP/roo+2JJ54INDbptx6hVPg+iyfGtVFsXbt2DXT7IIs0a0z6pZdeCvT41WD11VcPdPv//Oc/oWzHe++9V3bfdUGwP0Z7CK4AAEqecA7aVk9Ff2GsFH7fffdZKpUKdB+/+c1vLG40GXjuuedax44dfd1+9uzZvm4XpJgzyiLOICuSaZKAVQsqV9DPr05awwg2Pfnkk4E/A0FX3kO0tLJpWCGBIJOX2k+vtNJKVmmCvv/1GSwnAwYMsN69ewc+lgpjhVV1e6sWOq7adNNNA92HjrEuueQSi6ugwZzp06dbNdpwww3dKrh+3HTTTS4oXczATEs4XkaxqLBBxVRh0HfZeeedF/icvhAqxL7llltst912K/vV0lREeccdd9hOO+3kO0DXmo033ti23XbbUO8TlYvjj3gKcmygLgxBu8oiOlo1O0jwzc+4cVw/50HnKRRcqeaQVlhjXepoHfQ1DrrAR5SCzmMttthisXmt/Aq6D4jzAiBhee2110LpfqhuLUEWtfOL/Wn8MReGKCy11FJ24okn+r59WF3hiyWux7TVjnFtFFvQ4/Mg86qXXXaZNTQ0BHr8arDRRhsFuv1XX31l77zzTuDtCGMuvpywP0Z7CK4AAEpu++23D3T7SZMm2a233hroPiZOnGh33nln4BVj47qa1yKLLOIKW/3wW/gTZLW+L774wqIsJgxS4KtinlKtSKbJz7vuuouVEyISdOUxFXupNXYQKui49tprA92HPnubbbZZoPtAtP75z38GnmTVvvmhhx4KdB8qyPAbaowztfxVNwy/Hn/8cfv666+tFPS66vEfe+yxvG+jgl8VhAYtDBk+fHig+7j66qsDT6iXmx133DHwfSgQ+8gjj4SyPS355ptvfN82yLGcfP7551at9t57b1+3e+ONN3zdbokllrAtttjCwsLxMopF39d+Py8t0f708MMPd13poqSVwPVdvcMOO9hf/vIXdx5QjnTcoZXeLrroIle4dNVVVwVaAbwldXV1NmzYsFDvE5Ut6EqV1Xz8EaVtttnG6uvrfd/++uuvL1kxxYwZM9zjf/TRRyV5/HKw7rrr+r6tn3PAOH/Og5zjqfhQgdZSGTNmjPtOLxV1cg1y/um59957A3cS0LhQXAXtGBS0qFfjkffff7+VUtCxhijnjuLg7bfftuOPPz7w9+aZZ55pBx54oJUK+9N4Yy4McTyuLOZCJGFg7DyeGNdGsfXt2zfQ7V988UVX71YodbnSbdG+oIsQisbNg3a9LLeFM4Nif4z2EFwBAJTczjvvHHhVCoVORo4c6TsM8Ic//CHwimi777573tcdPXq0ffzxx1ZMfotJO3fu7Ot2vXr1Mr/UteL999+3qDrQaEVcv6ZOnWpXXHGFFZMKodQRSIPJau0edjERshT2CNq14Omnn/bdlUedM7RKdNBCO4UBO3XqFOg+EC3tR2688cZA9/GPf/wjcLHGJptsYpVqzz33DFREoBUJizlRosfUgNUuu+xiZ599tn333XdFD1BceumlvgNVWkFZHYCqjb6Xu3fvHvh+VFykwFLY76nbb7/dfvvb3/q+jyDHcl4nskwmY9Vor732csXaxTJkyBBLJBKh3R/Hyyim0047LZR9adNV6jTGoBBL2N/lOob7+9//7vb/f/zjH+3777+3uFPBm4q2dWyhc2ytsK0C7uOOO86tNnfAAQe44kX9blHQaqt+F9FAderZs2eg26vjAcLXo0cP22qrrXzfXsXsN998sxWT9mt6TIVu/vrXv/ruKF0KGlcqZigyyAIEfgr24vw5V+exIPMUt912m3355ZdWTJpjOOmkk9w5wcsvv2ylonPQoMU86mA3YsSIih7rCrqAjAqegrjyyivdwkmlpPkmv3NOXkhq7ty5Vok0b3jMMccE/g5Q8OWII46wUmJ/Gm/MhVUPHTdFdb4f9nFlkO+GUmDsPJ4Y10axLb/88oFur+O+008/vaD9p+ZkWSgof8suu2zgBag1ru53AQDVIaruQLVA1YT9MdpDcAUAEIv2ibvuumvgYoz/+7//s3fffbfgg8STTz7ZRo0aFejxNaF10EEHFbQCmgraDjvsMHdiEfUK5eoQMnbs2KIOvARdXSCMAv7WDB06NFAxobolaGXdqCnAc80117jVby+++GL78ccfI3/MaqbPcRirkKno/L///W9Bt1FR3YUXXmjPPPNM4MfXfgXx98ADD/jucKFuIEH3QerSoaLOSqXvWBVX+fXWW2+5UGvUg0gKIqqwRcVUejy/39XqtLDooosG2hYVkx177LEFT/6rPbKKjquRVgpWwW8YBT767lABSdDiC71nNSGpQLW6gAUJZqugLEhRjdpn6zim2gZjRUFYHb8Vg/bn++yzT+j3y/FyedL31yqrrFL0i1YY90vnm2FPtCmIee6557qOKPfcc0+gjpkq8Hj22WddQaiKe7SIQdwCK1phr7XXZs0117QNNtjAFZzvv//+bvxDBdxakS/I65YPPV8qvAOKOZakoucHH3yQJz0CQccaFCIJ2jU03/NljaFpv6eOtkG7nZaCulnqWFLH0uoSHrVCx9ODhlDi/DnXcckee+wRaI5CxeLjxo2zKGkcUd/lhxxyiBv/0EIScVg04H//+589+uijvm6reYALLrgg8DZoQZA401hVkA5W6pY3fvx4X7fVa1NIh98oqWuoX9qvq/CrVJ28oqJOMtp/BA0W6fta5y6lxv403pgLqx533323Oy6+5JJLihKuDXJcGTQIUmyMnccX49ooppVXXjnwfWiBZh2/tRc01Pyh5hG1UFDU9WWVppBFqNtaANJPDZDm0KNatDnu2B+jLcVb/hEAgDb87ne/c2GAICuiqk26DnwOPfRQO+GEE9pd8U1FPSpmCaOFvVY089Op4Y033nCXPn36uINlrUKkwpIoJjz9DuQvt9xyJVld4LPPPnMryOuy3nrr2TLLLOMKRNtb9W7JJZds97Xv16+fm8TSe86vW265xX7++WdXaNqtWzcLi1ZV0Op4WnldE7HVWOxZSgqgqYOT9id+aaBA+yBNHJ955pntTuJrpTwV63300UcWRnHYaqutFvh+ED29T37/+9+70IKKCfOl1bKPPPLIwKtkrb/++rb00ktbpdL3hTpN3HTTTb7v46mnnnKTxeqGsfjii4e2bQopvP76624/r6IO/XdQ+m7ce++97Y477gg8saR9l7p7rb322m1eV8ds2l/ecMMNgbvWlTMdd6orWhgrOOv51PvuqKOOsn333beg1YsnTJjgCqtVhBLGsa0XiNBKREG6O+l4SQVVKh7XikYqTlFXsLa6gyhcrMctdwqTKKAetcGDB1v//v1Dv1+Ol1FMKhBVp5SwC+lU1KcJJYVNdE6p45+BAwe6z4y+27Wf1XeozpU18afzOy0eoNt98sknbgVz/ck5mb99iAKU+i4BCqHPpsZYghRtqvBZga5tt93WVlppJbdgjlbv1Up/rdHxid63aJ32oTp3ffvtt309TSqq12uj8J9Wgg/afbspBfFUPKDvEQXrK4HO+f/2t7/Zrbfe6jpIaNEnLXgQZpcyr1A6SKDIz/hv3D/nmqfQffstBNKxxMEHH2yXXXaZG6cLkxYHUEcejSf89NNPFkcKFOj11TxHIUGEo48+2ncgw6PXd8MNN7Q407mwttPvebvGYvT+17l2IQtzaXVgFS3HhfYdQcYu1LVY30d6n62++uq21FJLuX1Ae8/JCiusEMvj02+//dYOP/zwwOO9m266qe23335uXxEVBa80V5cP9qfxxlxY9dC49b333usu6667rqtH2H777X3VNbRFxyY6dvXLbz1CqTB2Hl+Ma6OYNNeuxRl0HhiE5ot13q+5Xp3n6n2s8yrtWzX/p5+rk/akSZNC2/Zqono+dSEPsnigzsVU26HjdgWN2lv4T4FRdW3/4IMPrFqxP0ZbCK4AAGJBE0wq0Au68p4mlFS0qVXXttxySzc5pBMFDb4oEKADeRXfaSVqTcyFVRwbdAUjbZe2WxcdvGm7NSmpiRZNvPmlExkVn6qQ0a/2CldbE7TdosyYMcOtBFbISm0qAtfK8+1Rsl2T2kEKTf/xj3/YiBEjXLcfnUj6DbBoUk6FWgqsKKwStBU8LNCKPuo48Oc//znw06hJZBUga8JIK2VqAk0hNe2n9NnUKqB6D3744YehTXwqKIPyof2PVsfUJJ7CTiqiaKvIR/tCraQSdOU9UdCz0qn4X8UmQVZFf+mll1zQUauFK9Dhd9UvdcxSYPaVV15x+3p9v0URoNCq8kGDMJrcVheRjTfe2BUArLXWWq6wSMc7WgFVz6e6BalDVNCikkqgY0ytbqTPZhh0TKgCJxX7aiJxo402cgUd6qijIKT29RpY1fW0+rK6+GnFVb/detqjMGSQ4IromLuQ4249p6+99pqVOx2PKqgTdcc8nUNFheNlFJM6biokov1a2DSppC6rQTutIj8657nrrrvKbrVUxIfGk/yGIzyaFC5kYlhjX4888kigx6wG55xzjgvn+l14SOe1CmP85z//cWNpKpjz2+FPBQAaS1NIWn+GsSBAHCk8qbFCXVQsrLFijRnrfE2fFb8dpfVaaHxcCzX4HRvVGIa6e1Xa51yF7Qqe6LvML50DqHOCxovV/cDvQlU699P5nha+0hiixhLjTvsHdXjTmLk6y7bXDVe/l7pQh1GApQVM4hhKaOm9GCS0of2BxiE0dtDegkl6L2pRMwWe4kRjDRpzC0IFfJoLKoTG4YN0lo2KvhvD+AxoLCXqDtuav9R3bz7Yn8Ybc2HVSccVuqhD4TrrrOPmL3VcqWOVIMFyHatogb4g46B+6xFKibHz+GJcG8Wk/ajmo4PS8aCOC3VB+Mc9Gs/SQoRBz3dvv/12d36lOXQtMKLjY42He3O3muNQyEjnxJU6VlUI9sdoDcEVAEBsaEJDExVa5TQoTbjpYFCXqCm0EuaqJCpCfPjhh91FdKCrVWFVtKjVRlT8rsfTpIRWkfIGknQgrBUGdXt1cNAgkQpkgx4Mb7755r5upwEvTajG9WBchYRKxCvYE4QG4bSSnApWFZbSwJpeLwWmFllkERdm0SSzXh8VKmu1Bd1GE+zqKjN69OjAKzAgXJpQ1qptYaxMpve/itR1iZomZ/0WDKB0NMChSdYHHnjADW5ookD7J+3jFVBRyEldMJ5//vnQVtPUd4kKhCqdVlFXYZVCQUHou1UhgmuvvdYFSxUm8FZr1z5e+3pR1xG9ZtrHa7+ufYj28yrA1cqJUdP7Rivl3X333YHvS99bmuyuhPBAMSiApqCiBiPDou8PhZ10KSUVxgXpUFfNFDLSKk5RTjIo0BTl/pzjZRSTil816aNj2rA6R6H4tF/Sghz5rsIMtCRIVw9ES6vaaz+twHwQKr4//fTTXUH7Vltt5Qrl1lhjDReY1/mVQvM6V9bCLjof0/mVuo82HUsLY/y43HjdO3XxvjtVLKexII0b6xxVxRIaN1ZnFo0Za2xWncX0XOpcddy4cW6MQau1Bj1P1bmC3wLwuH/OFQpQsCfomO3TTz/tLnqNNN6jsQQt4KX3vvcaaSzBK3DR46kYX+9zXdT9La5j6+1RByT97gr0a8xcXdL13tS8jX5X/W6au9F7Mgx6Pvfff38rBxpbCnqereCAzgX33HNP9xzrPabjML1ftOiI5od0HY0nxnGRrLh3xik2hQkrFfvTeGMurHpp/F/HhLqoo7qOGVdeeWV3bKn5I3XD1rigCn31/eIdV2phPn2v6LtGi1opBKP5T33vBKGgWzl2wGTsPL4Y10Yx7bDDDqEEVxAtdfnUwrNBFjb2qGZD8/FhzMlXOvbHaA3BFQBAbKhI96qrrrIjjzyybAZqFerQCudRUhBFl1IYMGCA7xVOFKrRKuFKkseVXruRI0cGXt2r2GEpREsDsNdcc41bwVyTx+VAK1Wq6APla9asWW51jmKsgHjKKadYbW2tVYPtttvOFVbdf//9ge9Lk//6vgjjOyPKyWAVhlRjEVcpaZXj6667zrXwVmFdJVGBkfYXmsxE4bSC0y233BLZuc1uu+0WaDXEfHC8jGJSMePf//53F8QM0jENpaHilltvvdUVuABBbL311qwuGWNnnHGGvfPOO67oPKhp06a5hUN0gf9uHLqUgrqSVurnXItUaGxQixSEERzxgijVRuOqWqhMl6ipg7bfbujFpoVrFNDTWGAQGn9QkDBomLAUFF5TUDGKjsSIF/an8cZcGDw63tHxfRjH+MU+riwlxs7jjXFtFIsWKdAiEmF00EO0AQqdN/7lL3/haS4y9sdoSXVUKwEAyoZWmwq6MnqxLL300i5oUw7t5/066qijAt1eK37FmV47vYZalRBoSqvUXXDBBWXxpGi1I3WC8LvKJaqvXfGOO+5o1eSss86y9ddf36qBJv3PPffcUm9GVdLK8ldffbVbea6SaNXrTTbZpNSbUba06vXgwYMju3+FbKPG8TKKTR1GFTjVSp8oH4MGDbJHHnmE0ApCoQVUFIRCfAscr7/++lC7T6P86DOqhSIq+XO+3nrr2R//+MdSbwbyXKVdIaNyodDKAQccUPIOoaX+Ltl5551Lug0oHvan8cZcGOIwx7nffvtZOWLsPN4Y10Yxj20PPvjgkj3h5RLgj4PDDz/cdRNGcbE/RksIrgAAYkfBFa2OHmdKzGslWLXIrVTrrLOO7bHHHoHuQ8XRcW/t2717d9fCUQWnQPMVfrSSaJx16dLFrWpM+Kq8OjOUcuDq4osvtmocMNTnZK211rJqsNNOO5VsokeTTOoAUa20wppW6inl5zwK6oaI+IVLtE9TcUExcLyMYtM55IMPPugWtkC8KbCpjm933nmn9ejRo9SbgwrC8Uf8F/PRuKi6Z6M6KdARNLRfDp9zdYE77bTTSr0ZsVfKc2A99hVXXFF2i0gcffTRJduH6lwyDovaqGis1AEaFA/703hjLgyldPLJJ5f1eEI5HNNWM8a1USxDhw51YbZiUwhD+1HkXzOgeVzV2JTqfVKt2B+jOYIrAIBY0mrhcT1oUxHNXXfdVdFBBxWe6oA9aDcZTRyVw8p0agt5zz33FK34D+U14HjmmWfGsrOSPqe33XabW6US5UMFD507dy7JYyu0otX/q5FCO3fccUeknQ/idhxV7C4z+s6/7rrrqn6l9e23395uvPFGt4JqJXVq2mabbUq9GWX9ntB3djl2W2mK42WU4rv7lltucaGISgsEVoqBAwfaY4895hYfKbdiUZRH8dwaa6xR6s1AG9QZSwvBqFMWqouKzRXar5bPuQIGZ599ttXWMqXemuOOO84WW2wxK4Xf//73ZTk2qnPEUnT71oJsGrNQwVapaSGmuC9gh3CxP4035sJQCjvssIMdeOCBZf3kM3Yef4xroxg0J1jsuijVq2nhxkqajyzWecjVV19d9BC9FunS2EI1Y3+MphhlAwDEkorEhw0b5gov41SkokmQRx55xAYMGGCVnHS+/fbbQ+uUsu2225bFBMSSSy5pDz30kBskA5o64ogj7Prrry/ZygstWW655dz7dYMNNij1pqBAKsq46qqril7w8H//93+28847WzXT95tWAy+H76SgVIBw8803u2KyYlCxqgKvG220UVEeL+622more/jhhysqxKPgW9y76MX58xh2JyIdk5Rin87xMopNYwEKRegcfKWVVuIFiNHk3pVXXuleFxZ/QFQ0efznP/+5rFferQbaByjAxthE9dhzzz3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DDSngjcfpuuuuC7vuumuyOnkxxaLp3/zmN+GMM84ouBC2GB599NEk1BMLwRvq448/Dvvuu2/RjxH/4+9//3tyfBsSImns1xvKL4YE4zXjlVdeKcvzxw5fMWj32WefNXhbsXPNXnvtFT744IPQGGVda2NXrxhaKJcYfjv77LPDySef3OD5eAxYxM4+8br01VdfhVKLoZPY6ej666+vsXNJIeLnnBj0POaYY5JQXylkdY5yvwYAAADyIrgCAAAAAFScadOmhYMPPrjoXRli14DYGSGvorB77rknWak7zwK6uKJ57CDz5ptv1uvfL7PMMuHPf/5zsup2miuuuCK89957oRgFiieeeGJmceI222yTHDMKLxKOhbUfffRRbofuu+++S16bf/7zn/X691ndTGKR/aeffhryFPf/5ZdfTh2PgZMY5qqP2FUlBkvieyWGyvLywAMPhEMPPTS5JpayADy+b4u5in0M8R111FHhrbfeKto2CeHqq69OisAbWjRdDdcbytsZL3ZmKmZYtxCx+0+c1xSzw8v333+fdI6JHbUam6zQQa9evUK5xJBavLfEsF0xxU5lMbQU/7eUoZX99tsvtbNNfT355JNJ16IpU6aEcgZXXnvttbKEZgEAAIDqJ7gCAAAAAFSUWCj1+9//vigr/aeFV+KK8cUWV1w+99xzcy1i/++i+SOOOKLex2irrbZKiuHTxL/h+OOPb3Dh3F//+tfw9ttvp44vt9xyubwW1W7IkCHJqtylCDPEcyGujP70008X/G9j56FWrVqVrevKI488koSn6hOsyTJ58uTk/fPCCy+EUohhjxi4mzlzZu7PFYMysQA8DzEIc9xxxxW1W0dTFl+rq666KvfnaSzXG8oXkjjzzDOLGhopRAwP3nnnnblsO4Zujz322KKG+ErxHho2bFi9wgp5+8tf/hIef/zx3IJvMfARu5WV4nNKDGJ+8cUXuWw/dp6Kn4Py/jyx4YYbps7RYghN0BQAAADIQ8tctgoAAAAAUE8xyPDqq6/WONatW7ew5ZZbJsVWSyyxRFhyySWTYskffvghjBw5Mikkf+WVV2pdJfj+++9Pui1ssskmRXmd7rjjjnD55ZfX+rjmzZuHjTfeOFnxOv7vsssuGzp16hQ6dOiQFMPHFb7jitHx74jFoPF3tXUwiCudxy4qhTrhhBOSUElaZ5XRo0eH008/PVx55ZWhPuLrcOONN6aOx04XAwcOTP5+Cut8cOqpp9apSHjddddNilTjed61a9fkWHfs2DEJPsX3TFxJ/sUXX0zOt3jupYndHGIxeVwlfY011qjzvnbu3DkJScV9TguWxNXX69v1pDaDBw9OHVtnnXXCqquuWvA247UlhsZiAK428Xj37t07eb+vvfbayfGIv2vZsmVyvOPP66+/nrzXY4FoXI0+TXyvxuLwSy+9NOQlFsuec845NY61b98+bLbZZqFPnz5h+eWXT66/8Xdx1flYMByv2c8991wYN25c5nPEx8a/4aKLLsrpr2gaRowYEc4777zU+0x872+99dZhlVVWSV6reO7F93F8vWKno1jYPnTo0Kq63lB68XWNgd20jj89evQIP//5z5PXMc7X4nm4IBASz4fY1aEhAcAYgrjuuutqHIvnX7xexetWnGvF90GbNm2S4NzYsWOTblzxueN7Ikuck910003JfKsxiHO6rJDZRhttFMohXm9uvvnmGscWWWSR5DyJr1W8L8fXLl7HJk2alIRD3njjjeRalDUnXnB/iZ3QYqgvvtZ5iYHBtFDHUkstFfr27ZvM8bt37x4WXXTR5P0R3yvxXIpz43gsauuSFecG8Rp//vnn5/RX/M9xX2uttVK7xsT9jHMYAAAAgGJqNj/rv4QAAAAAABTZgQcemBSh1SQWUH344YcL/X6DDTZIClnrUnAXAxexKLq2FdvXX3/9cN9994WGikVosRtCVmeHuKJx7O7w61//Oilkq4tYoBe7uNx1112Zq33HovgYnGnWrFnB+z5q1Khkv7KKAc8666zwy1/+sqDtxuLB/v37ZxYnx44uscCwvn7xi18kr3VN4uu/++67h2rzySefhH333TcJLaWJxZ7bb799Eq7o2bNnnbYbz6/bb789CRplddlZccUVk7BJ69at67zPsdg0rhyeJhaybrHFFqHY/vOf/yTnYDHP6+gPf/hDZiAmisGOuPL7XnvtlRSG1sUHH3wQLrnkkqRYtbZg3x577BHqI4bcTjvttBrHFl988dC2bduFVquPBcS/+93vwn777Vfr6x5Xh4/n0TXXXFPrOfroo48moYr6imGKtI498Zp28cUXh8YsXpu32WabzHPsp69VvAfE617sEFGXMGO8Psdi6hguqJbrTV3nGjGEcPTRR4dyvHbPPvtsEuwptrz+3rTzI+08jOLff8oppySvYW3iORDDCTH4VFNA4Oqrr67x38W5VDx/YzDqp/sU3wMxnBzPzywx4BGDL7fddltmd4t27dqF559/vlEEbeM9NS3gGEMVMbCTh6z7y4LQ0E/PlTg3Puigg5LrR23HNl6H4nw4vl5Z16QF74Uzzjgj5HF/WXPNNZPw30+DJ/EeGue18Rqc1WkuigHP2JEwHrPayjTi+b/tttuGvMS5UNpnofiZ65577sntuQEAAICmKfsbOwAAAACAEvppaCV2YoireceV1+u6SnSXLl2SQq9YOJ4lrjCc1m2kruKq4bE4Myu0ssIKKyRFYXHV5LqGVqK4Un3cdiwaiyuVp4mFqg8++GCoj1g8e8EFF2Q+5s9//nP4+OOP67zNuDJ/DBllhVbiCs6xUJHCOn3E7iRZBZtxNftYtHrFFVfUuYg8igWl8fWIhZpZhcZffvllEkooROy4Egs609QWAqmvtKLTKBaV7rTTTgVv8x//+Eet+7vLLruExx57LCmcrWtoJYodWWIALRZcZ4nhllisXWyx68BPi4pjx44YPDr44IPrFB6IxzWG82699dakG0vWNSL+rdTfT1+rWJAeO4nFYFNdO3DF+0paKKGxXm8o73kY3/cxkHTttdfWKbQSxY4UNYVWahO77P00tLLnnnuGJ554Igkt1hZaWbC/J510UnIOZ4UN4vsgvr8agxiCTLP66quHcoj3rJ+eK/H6E+8DcZ5bl0BQDA/F60ac79Z2bsWAS126otU3FPvT0Mp6660XhgwZEvbZZ59aQytRvEbHa3W8vsW/K0v8DPTT87yYsoJpH330UebnGwAAAID6EFwBAAAAACpSLPqOAZS44nt9xM4I/fr1y3xMQ4vm46r+WQGNDTfcMCnOjZ1k6isWtMfgS1Yxcly5OasrS5btttsus/NE3G4spo8rk9dF7BLz6quvpo7HYsW4Gnhdikr5/2644YZkle80sZAzns+bb755vQ9bDFnFkFhWIWNcmT521KmrWMS58847p47HYESxizJjUWns6JHVrafQlfPHjx9fa8grFuD+5S9/qbUQNUvsbhJDbllhuXgu5C2uGh+LamNReaFih6zLL7888zH//Oc/k3AEDbfccsuFu+++u17F/9V2vaF84rXib3/7WxJWLIff/va3yTU6BqMK1bdv33D66adnPiYGExp7cCXrvVZKMcwaQytxjlyo2KnrzjvvzAyCxy4mMRhSCrF7Yzzv6xoY/GlnoptuuinznI3XvxgGy0vWOTFjxozw2Wef5fbcAAAAQNPkvwwCAAAAABUpFhHGAvOGOO2000KHDh1Sx59++ul6bzt2IckKvsTC3rjqeH0Kv2vqjDJw4MCkA01aYdsDDzxQ7+2feuqpYc0118xc+T6u+lybt956KwkbpYlhldgxIquDDAuL4ahbbrkls/NBLH6sT+FkTQWlcfX5tPDFzJkzk44ahYYg0sTtPf7446GYXnrppcyuJAMGDCh4m7FwNIZG0hxyyCHhsMMOC8Ww9957h7322it1PAbZ8ui6ssDGG2+cFIC3bNmy3tuIxevbb7996vjkyZOTblE0TAyGxXOzkG5e1X69oTzi6xg7T5RDDEced9xxDdpG7JaRFf6KBfxffPFFqGSTJk0KX3/9der4aqutFsqtWbNmybkSAyj1tfTSSyfz66yuZu+++25miLoY4lw2BjyzOozV5X579tlnZz4mBnXiPTMPtYWZ3n///VyeFwAAAGi6BFcAAAAAgIoTu4DE4u2GisVtWUXzMfARQxn1EQMacVXnmsSC7zgei3KLZZNNNkmK47OK2eurdevWmcXDC1Ybf/jhh1PHJ0yYEE444YQwd+7c1MccccQRDVqhv6mKReJx5es0MQzUrVu3oj3fyiuvHE466aTU8XgeFNIto2fPnmH11VfPrfNRIdtbYoklwhZbbFFwIf+9996bOh5XjY8dnorpjDPOCF26dEkt5i/2MVugY8eOReuIFDsgZHnzzTcb/BxNXbymZr23muL1htLr379/6NOnT1kOfQz21iVYW5t4zTvyyCMb9TVrxIgRmeNp95RSnys/+9nPGrydVVddtdawaOyEkqcTTzyxKEHsPfbYI5njp5k+fXq4//77Qx5iuD7e9+t7TgEAAAAUSnAFAAAAAKgosXjwlFNOKdr2dtlll8zx9957r+Btjhs3Ljz33HOp4zF0k8fK1r/61a9CmzZtahwbPnx4+PDDD+u97dghprbiz/PPP7/GIrYY4IldW+JxySruP/roo+u9f01VDClkBYZiZ4tCgxh1ETt+LLXUUjWOxc4jL7zwQkHbywqQvf3225mrxBe64vzzzz+feT0otJNIPP7xdUhbPT52dipG0OO/xZXkDz744NTxvIIr8TmXX375omwrdnHKWlW/Ptde/r/Y8eQ3v/lNUQ9JtVxvKJ04Jyl2cK8QRx11VGZnvUJsueWWmUX8lX7N+uabbzLHi9ElqSFiODoGnIvl8MMPD8suu2zq+Msvv5wET/Ow9tpr16t7W1aXyax5RF73/CjrGI4ePTq35wUAAACaJsEVAAAAAKCibLPNNmGFFVYo2vbWXXfd0Llz59Txzz//vOBtxsLetM4irVq1Sgop8xBXdt55551Tx1988cUGbX/XXXfNDBjEVZ+PP/748OOPPy60qnVWYXGnTp3CZZddVnBggBCeeuqpMHny5NRDEV+PPMQuPPvtt1/RzrWswEgMPhWrKPOxxx7L7M5Qn0LTBx98MHXsF7/4RXKNyUMs5m/fvn1qUG3s2LElfc3rIwYd0ljJvWHiuRxfs2KqlusNpbPttttmzrHyFMNOO+20U1HPw80226zRXrNqC65kBRRKYc8990w6IRZL27Ztw6GHHpo6HufpTzzxRMhD7CgWg6vFEoOeW2+9der4p59+mtz385D1mtR2TgEAAAAUSnAFAAAAAKgosbCt2FZfffXUsfp0enjmmWdSx3r37h2WWGKJkJc+ffqkjr366qsN3v5ZZ50VVl111dTxTz75JFxwwQX/+///+9//TkIpWeLjl1tuuQbvW1OUda7F1ykWO+Ylq7NCoedaDF1lnbsxuBIDLA2VFYCJx2qNNdYoaHuxWHTkyJH17ujU0NXpN9poo9TxYcOGFfX5fv7znxf92tWjR4/UsW+//XahEBx1lxUybOrXGxr3nK2u4vW32OGtrGvWqFGjQiXL2r/FFlss6eRVTnncL/v165fZqSR2XSm22JUnducp9fHJ42+JdFwBAAAASklwBQAAAACoGHH14g033LDo211ttdVSx77//vuCtvXDDz+EDz/8MHV8xx13DHnaeOONU8c++uijBhf/xxWsL7/88swCx/vuuy88/vjjYcqUKckK/LNnz0597IEHHhj69u3boH1qqubMmZMZTsj7XOvZs2cSnqjJ6NGjw4QJEwraXla3k1hw+9Zbb4WGiN2T3nvvvXo9f5qXXnop872StUJ63u/3rOtQsZ8rj9DggusphVtppZVC9+7di3roqu16Q/7iNbBXr15lO9SlvmaNHz++KAHLvMQwYJrFF188lFO3bt1y6U4Wu4VknYNxXpHWIbG+tttuu6IHphZ0cEu7BkZvvPFGyENWYDWe81lzfAAAAIBCCa4AAAAAABUjBkziSsbF1rlz59SxSZMmFbStd999N7NwMY9Cyp8W6XXq1KnGsalTpxZlRfBYuHn66adnPubMM88Mxx13XObzrbXWWuGUU05p8P40VZ999lkSDirXudayZcukQD7Nxx9/XHBRZtq5W1u3lLoYMmRI6lirVq3qtdr7O++8kzq29tprJ4Xbecoqoi70+Ncmj9Bg1rW3Ptdf/v+1tdiq7XpD/mIHqxYtWpTtUG+wwQZF32ZWwCMGILLeI+U2ffr01LFyd1vZbLPNctt27HSYJr5eWV3Tiv18DRFfo6z78AcffJDL87Zp06be5xUAAABAoQRXAAAAAICKkVW02hDt27dPHfvxxx8L2lbsapJVdNalS5eQt6zi/7gyfTHsvffeYaeddsosBnz55Zczj3ns3JLHqtRNRW0dNVZZZZWynmvffPNNQduK50K/fv1Sx5944okwc+bMUB/z5s3LDK5sueWWtYYoCn2/r7zyyiFviy22WNGOfzmuv1nX3vpcf8kvuFJt1xsa53lYyLlSn2t6bTp06NBor1kzZsyo2OBKDDmVa9uffvppSZ+vIXr06JE69t133+XSeaq2c6O+8zIAAACAmrSs8bcAAAAAAGWQVaSdV/H0rFmzCl6VPs3yyy9fkuLarC4PY8eOLdrznHfeeckKz/VZrfrcc88N3bt3L9q+NEVZ51q7du2SAtpidNjJkhU8qs+5NmDAgHDPPfekdgx6+umn69UZ5fXXXw9jxozJfN5CxVXGs97PsWg67+OfVSQdi1hjYKd584avURY70tQWMqmP2rZZ6PWX/5HHtbUarzfkq5z3+KyQU1O9ZmUFDGrrqlHNwZVidlyJx3HFFVcM5fxbsroC5RFc0XEFAAAAKCbBFQAAAACgYnTs2DGX7RajsLsuxbOff/552GabbUI5TZo0qagrj1922WVh3333DbNnz67zv9tjjz3qFT6oVHPmzCl60XTLli3Dsssum/mYrOeMhYSN8Vxbd911k84NI0aMqHF88ODB9Tp3Bg0alDoWizy33nrrgreZFYSJrrvuuuSnXOJ7ctq0aWHRRRdttNfe+fPn5/K81a4Yr3lTuN7Q+M7Dcl+zmjVr1mivWVkdV8odXMmro2K0zDLLJOG6tIDFt99+W9SwVosWLUJeauvkFgOrxVbbuaHjCgAAAFBMgisAAAAAQMWobdXfSlDMArhSd2ioj7XXXjuccsop4YILLqjT41ddddVw5plnhmoSC7qLXbTdpUuX8NxzzzXJc61///5h4MCBNY4NGzYsjBs3LilErasY3oidWtLsvPPOSUeRQlX68V/wGhSjeLwxXHvJNzDQ1O5tNO7gimtWzYHYNHPnzg3llFfQ6L/PxbTgyg8//FDU58lTbdv//vvvcwln1/e8AgAAAChU8ZYZBAAAAABoAqZOnRoq2axZs4q+zYMOOij07du3ToWkl19+eWjbtm3R96EpqtZzbbfddkvtxDFv3rzwj3/8o6DtPfXUU6kFq9GAAQNCfcRATKVTzN80tW/fvujbrNbrDY3rPKT+suZe5eyaEfcrzy4lCzoE1qcTTTGfpxTbL+bfUtdzw5weAAAAKCbBFQAAAACAApSz+K8u5s+fn8s2a1uROfrFL34RVl999aI/f1NVreda7KbSu3fv1PHBgwcXtL1BgwaljsXzca211grVePzzer9T+dKCXw1R6ee7c71pnIdUX3Al77BHbc9RzIBnuYMreYRVa9tmu3btiv6cAAAAQNPlG0UAAAAAgALMnTu3yR2v2267Lbzwwgu1Pu7xxx8PQ4cOLck+NQXVfK7tvvvuqWOfffZZeP/99+u0ndGjR4c33ngjdbx///6hvuoS1oJqUc3XG2jqwZVydudq1qxZWUNUxQy95f231Lb9PAJ8tYWaYjdFAAAAgGIRXAEAAAAAKECbNm2a1PH697//HQYOHFinx8aCuj/84Q/hu+++y32/moJqPtf69u0bFl100QZ3XRkyZEhqIWeLFi3CrrvuWu99rObjDz/lfIfGrVOnTqljU6ZMCeUyderUsj5HMa9t06ZNC+U8Vnlcp2s7dlmBKAAAAIBCtSz4XwAAAAAANGFx5eEZM2bUOLbuuuuGBx54IFSLWMx2wgknhNmzZ9f53/zwww/h5JNPDrfeemvmCtjULmuV6yWWWCIMGzas0R7GWAy54447hvvvv7/G8UcffTSceuqpoVWrVrUGV9L06dMnLLXUUvXex9qKNc8+++yw//7713v7UEmq+XoDTcHyyy+fOTebN29eWeZlcc6c93NnhS+K2TEk7xBObcGYPLqfjBs3LnVsueWWK0nHHAAAAKDpEFwBAAAAAChALOCdMGFCjWOTJk2qqmN5xhlnhK+//rrgf/fqq6+GG264Ifz2t78N1aBr167hk08+Kcu5lmby5MmhsRswYEBqcGXixInhxRdfTDqzpHn33XfDl19+mbn9hujcuXPmeLW932naqv16U+3SArU0HV26dEkdmzNnTtINb5lllgmlFruixUBGVpe1PAMlWde2Yj5PKbZfzL+lLsGVOP8FAAAAKCbL3QEAAAAAFCCuPpwmFttXi/vuuy88/vjjqePt27fPXIX5qquuCm+//XZOe9c0ZJ1rsQtO3gWUedtwww3DiiuumDo+aNCgzH+fNb7YYouFbbbZJrfV66vt/Q7Vfr0pldq6E8Qi/jwI0pEVXKktoJC3kSNH5rbt8ePHhylTpqSON6Tz2k999dVXub2H63Kcivm3LDB27Nh6n1MAAAAAhRJcAQAAAAAoQFahfVyVvhpWPR8+fHi48MILMx9zySWXhMMOOyx1fO7cueHEE09U3J/TuVbuItRi6d+/f+pY7LgSC1JrMmvWrMxgVb9+/ULr1q0btG9xZfMOHTpU9fGHpnS9KYWWLVtmjsfOE3nQFYcVVlih3gGFvH388ce5bbu2jni1HZdCxPfvqFGjQrmOUzH/lgVz9e+//z51XMcVAAAAoNgEVwAAAAAACrD22munjsVVmN95551GfTxj8Ob4448PM2fOTH3MAQccEPr27Zs8bv3110993JgxY8If//jHnPa0aZ9rUTV0tNltt91C8+bNU7s8PPbYYzWOPfPMM5mF2gMGDChK54S11lqrqo8/NKXrTSm0adOm5MGVGPDTAYru3bsn3fDSjBgxoiqDK7Vte7XVVivp8+W17U6dOoWll166qM/35Zdfhjlz5qSO9+zZs6jPBwAAACC4AgAAAABQgKygRvT666836uN5/vnnh88++yx1fM011wx/+MMf/ndl+YEDB4aOHTumPv7ZZ58Nd955Zy77Wu1iaKJVq1ZVe65Fyy+/fNh0001TxwcNGlTj7wcPHpz6b1ZeeeWw3nrrFWX/Nthgg9Sxb7/9NnzxxRdFeR4KDxVRXE3helMKiy66aGoYL5owYULRn/Pdd98t+jZpfGoLW9bWmSRPb775Zm7bfuONN1LHFllkkWROUKrna4gYIMl6L+cRIqntnKgt0AgAAABQKMEVAAAAAIACrLjiislPmhdeeKHRHs9HHnkkPPTQQ6nj7dq1C5dffnlo3br1//6ua9euSdglyyWXXBI++uijou5rDM2k+fHHH0M1iMe7V69eqePDhg0Ls2bNCo1d//79U8c+/PDDhYJU33//fXjllVdy7baywFZbbZU5/uKLLxbtuai7Fi1aVP37v9Sq7XpTrntEPDc7d+6cOj58+PCiP+dbb71V9G3SOGUFDcoZXImdRPLo+DJp0qQwdOjQ1PENN9wwM5BXH48//niYN29eKLaXX345s3NS1vW5vrLOiW7duiVdXgAAAACKSXAFAAAAAKBA2223XWYRWGMsZh85cmQ4++yzMx8Tx1daaaWFfr/DDjuE/fbbL/XfxWLn448/PkybNi0US5s2bVLHZs6cGZrCuTZ+/PjwwAMPhMZu++23D+3bt69z15UYsIork9ckdjrYbbfditphaemll04d/9vf/taoivmrRdb7f8aMGSXdl2pSTdebct4jVlhhhZKFB+L1J14TIVpnnXUy53nlDPb94x//KPo2n3zyyTB79uzU8d69exf9Ob/77rvw2muvFX27tb2PN99886I/Z9b1SLcVAAAAIA+CKwAAAAAABdprr72SAvk01157baM6pnUJlsSuGFmdMU477bTQo0eP1PEvv/wynHvuuaFYFltssdSx2JGjWuy8885JJ4Q0N998c6MPTrRt2zYJr2QVu86dOzc1yPLTItVlllmmaPsW3+fx/Z5m7NixmV2KyEfW+/+HH35w2Oupmq435bxHrLzyyqljseA9q9C+UI8++mhSSA/RxhtvnHog4n30P//5T9kOVAy+TZ06tWjbiwHWO+64I/MxO+64Y8jDLbfcUtTtff311+Hpp59OHe/SpUtYd911Q7FldUPMo8MLAAAAgOAKAAAAAECBunXrFrbaaqvU8X/9619JgW9jcemll4YPP/wwdTx2WamtG0tc3f7yyy/PLHoeMmRIZuigEEsuuWTq2FdffRWqRYcOHcIee+yROv7NN9+Eiy66KDR2u+++e+rYt99+G4YNG5b837HoNmuF8KxwVX3tu+++oXXr1qnj8byPwSxKp6m8/0utmq435TxHYqemNBMnTgxDhw4tyvPEEMA111xTlG1RHWKHsDXXXDN1/I033gjlEkOFxTxf77333vDpp5+mjm+00Uaha9euIQ8vv/xyeP7554u2vUsuuSSzG86uu+4aii3OW8aNG5c6vuWWWxb9OQEAAAAEVwAAAAAA6uG4447L7LoycODA8MILL5Ts2P53R4pCPPfcc5krVseC/SuuuCIzkLLAKqusEs4888zMx5x33nnh888/Dw3VvXv31LF33nknzJs3L1SLI488MvP433PPPUkBZ6Wfa7WtEp9VYDp48ODkf7OCT7Hoftttt82lEPiAAw5IHZ80aVL4zW9+EyZPnhwa82vQmGS9/2Mw4LPPPivp/lSTarneZJ0jw4cPD1OmTAl5qa1TwY033hjmz5/f4OeJgdJRo0Y1eDtUl6zAweuvvx7K6c4770zefw0VAxdXX3115mMOOeSQkKeLL744TJ8+vcHbeemll8JTTz2VOt6qVauw//77h2LLOhdWXXXVpMsLAAAAQLEJrgAAAAAA1MMaa6wR9t5779TxGJyI4ZYFBfd5+f7775NgyWGHHVbwvx0zZkw47bTTMh9z6qmnJn9rIZ0zdtlll9TxWOR3/PHHh1mzZoWGyFpRPB6TYq1oXwmWWmqp8Nvf/jbzMX/605+SLj/FKIZOEwu943Pk0dWkWbNmmdt95plnkkDCo48+mvqYHXfcMSyyyCIhD/H4Z3Vw+OKLL8JBBx0URo4cGfL09ttvh9///vdJeKApy3r/Rw8//HDJ9qXaVMv1JuscmT17dvjHP/4R8rLiiiuG1VZbLXX83XffDQ8++GCDniPe97OuhzRdWcGVGOyN53+5xOeO97DYfaW+4jzy6KOPTuYEWcGLvn37hjzFjiWnnHJKg4KkMch98skn1zqvjgHaUgZXdFsBAAAA8iK4AgAAAABQT7FgLWtV9xkzZoQ//OEP4Y9//GPSlaFYYrHwv//973DGGWeEn//85+G6664LEyZMKGgbsdDuxBNPzCz8i90rfvnLXxa8f+eee25SuJvm448/DhdddFFoaIeOGHZIc84554RPPvkkVIsYTNpwww1Tx+fMmRMuvfTSpPNHXIm8mOLq6HFl8a222ip5jhh4ykMsUE97TWfOnBlOP/30zGLXAQMGhLx07NgxOWezzrn//Oc/SYFpLIgvZkF/LNJ97LHHwj777JOsuh5DPE2940rnzp2TDk9pYhepJ598sqT7VE2q4XoTgyvt27fPDH68+eabIS+1BW7ifbI+Xdl+/PHHZE4R7/tQk/XXXz906tQp9X4S54/l9NVXX4UDDzwwfP311wX/2/HjxyfXp/feey/zcXHundUVsViefvrpJKRen84rH374YRJ4zZqHx05yxx57bCi2OEd54403Use33nrroj8nAAAAQNTSYQAAAAAAqJ9YFBuLX2O4I6to7aGHHgqPP/542HPPPZMitRVWWKHg54odSuIq7bFILv6MHTu2QS/blVdemXRvSNOlS5dwwQUX1Pu4XH755UmhfVpnldgxYrPNNgvbbbddvZ4jdr/YYIMNktXDa/LNN9+EPfbYI/Tp0yf5iUXuiy++eGjXrl1mMeOyyy4bWrasvK/OW7RoEQYOHJh0+fnuu+9SHxcLobfZZpuk680hhxwSevToUfBzxVBELKiM59lTTz2VrCpeCvF9EQNJacXkMbCRJgbINtpooxz37n9WID/yyCPD9ddfn/qYqVOnJqunx6LyQw89NOy666716gITi3NfeumlJHzxyiuvJMXqLBysGzFiROqq/sccc0xYd911k1X34/sgrlgf3/9Z7+94jcgKOzQV1XC9ia/zL37xi/DII4/UOD558uSkeH7TTTdNirRjh4Z4X4nnSPz7szrStGnTptbnj8fu2muvDdOmTUs9R4866qikCD8GgNq2bVvrcYqhuL/+9a8LhXkWXXTRJMyVd8cnGod47u+0007h7rvvTr2X5n2//G8LQjT/HdCI1+4Y9DzhhBPCXnvtVeu8KwYt4jw6Bki//fbbzMf269cvt44hca7x0/dZvG599tln4cwzzwy9e/eudRsxiPu3v/0tuT7U1n3wpJNOCksssUQothheSru2L7fccqFXr15Ff04AAACAqPL+6xsAAAAAQCPSs2fPJATy29/+NilETRODLbELQPyJ3Uhi0WAMXsQi2NjNYbHFFku6OcTC91hQO2XKlCScEruTxE4On3/+eeb2C/Hqq6+GG2+8MXU8FhDGouW4Tw05LrGAPyv8EjvGrLXWWklIpj7222+/1OBKFI/X888/n/zU1bPPPhu6du0aKtHyyy8fbrrppnDAAQck50nW3/3www8nP7EAMZ5rsXtC/L/juRaLSONrHM+zuJ14rsVC0NihJp5rn376admCErFrSn26INTW3aBYjj/++KTDxKBBgzIfF9+vsYj1/PPPD+uss04SyFljjTWS91T8iYXmsXg1Hvv4E1+L+G8WvAYNDaY1BTEYcPPNNyfdP7KKcwvpLhCLomMxNdVxvYn3iLTgyoJi+Ndeey35qat4D49hl9rEvz0GUuK9NOvYxSDc/fffn3RP23zzzZPwZAyhxGMSO0zFa8GwYcPCyy+/nNqZIV5nYhhUcIUFdtttt9TgSgxaxI4kpRLDgLFrSOxS+N/iNSF2x7vhhhuS8FsMMy8IGcf58IL74uuvvx4effTR8MUXX9TpuhW7GeUlXts22WST8OCDD/6f38f9jGHVOKeNoaF4HYwhl3ivj6Gz+F6O3aTi+zhek7K6rCwQrwnxGpaHrI5kMXCb1V0OAAAAoCEEVwAAAAAAGmiLLbZIOizEFf6zOq8sEFeUjz+xE0upxeK5GCiZN29e6mNigWEM1TRU7C4TC4JjGKQmkyZNCieeeGK466676tXlJBYH3nbbbclq/U3FmmuumRROH3HEEeH777+v9fFxZf5Y8Bl/GoPtt98+KcKeMWNGnf9NLLAsVXAluvDCC0Pr1q3DfffdV+tj42rqsbNRVncj6icG3mK3q9tvv90hzEljv97E4vHYcSerW1OeYheaJ554otZ7VOywFOcD9ZkTxHDMjjvumARXYIH11lsvrLTSSjWGPUaNGhU++uijJGBcyiDN0KFDawySxetGDFNnBarrGpCJc/EYGsvTaaedlnRArKnjV3yvF2NOuvrqq4e//OUvIS8xvJT1WgEAAADkpXluWwYAAAAAaGLhlRjAiCssV6q4unxc8fq7775LfUyfPn3C4YcfXtQi/7jyfppY/PfXv/61Xttu0aJFspp9XNG/KYkrev/9738Pa6+9dqg2HTp0CNttt11B/yZ2P4irrJdK8+bNw3nnnZesWN+qVauSPS8LO+GEE5ICbfLT2K83MQjXrVu3sjx3DLjF+1vsrJaH/fffP+kCBTXJCnRmddzIS5wPxu5jeWjXrl249tprk85mpZinxC4xSy65ZC7bj10ZY7er+Dx5iJ2uvv766xrH4nU+dr0BAAAAyIvgCgAAAABAEQt8Bw0aFPbZZ5+kuL1UYseJuhTrxUK4l19+OXU8FuH9+c9/TrZXLDFUEleNjiGTrP165ZVX6rX9uKL4vffem6xO3ZSssMIKSTF5XG0/FkeXugtDngYMGJDr44vlV7/6VfIalKJQ9r8tuuiioWvXriV9zkq1yCKLhFtvvbXgsBNN53rTuXPncPfdd+dWMF+XYxfP0WWWWaZo24z36Nit7Oyzzy7aNqk+e+65Z+r7NXZFimHmUor7Eud7W221VVG3G4Nhsfvez372s1DK93XsclTsUFwMYsbtLrvssiEv//jHPzLDcAAAAAB5ElwBAAAAACii9u3bJ90YYoBl6623LmoIpKZVmY877rjw3HPPJYGTLLV1NolBm0svvTSXFaRjwfDRRx9dayeY77//vl7bX3nllZPjfe655zapAEvs9hFX2//nP/8Zdt5551y7f8QiyiOOOCJ5rjvvvDPkKRaf1rWDSlxlvZyhhbg6eTz3Lrjggly7OsTgV+zqdNlllyUhr759++b2XI1NXJX+qquuCjfffHPo3bt3SUODTUljvt4svfTSSUe0yy+/PKy//vqh1OJ96cEHH0zeww0Vu7rdfvvtyfGBLHE+F9+rNRk1alQYNmxYyQ/ggs4oxx57bFGuIT//+c/DkCFDytJ5K74X77///rDTTjs1eFstW7ZMuh3GkN0SSywR8jJr1qwwePDg1PNll112ye25AQAAAKKWDgMAAAAAQPHFLgw33HBDGDlyZHjggQfC008/Hb788ssGF/xttNFGSXF2/Klrp4fJkycnq7PPmTMn9TGxYC5uMy9HHnlkeP3118Orr75a43gMrZx88snhlltuqVfheSz623f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+      "text/plain": [
+       "<Figure size 1600x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 723,
+       "width": 1623
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# hand-rolled ECDF: the bespoke feature is the annotated discrete jump at zero,\n",
+    "# which the predictive must reproduce in both location and height\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "grid = np.linspace(-0.5, 0.5, 801)\n",
+    "for draw in pp[:60]:\n",
+    "    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size,\n",
+    "            color=\"C0\", alpha=0.08, lw=1)\n",
+    "ax.plot(grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size,\n",
+    "        color=\"k\", lw=1.6, label=\"observed\")\n",
+    "ax.plot([], [], color=\"C0\", label=\"posterior predictive (60 draws)\")\n",
+    "zero_jump = (y_eval == 0).mean()\n",
+    "below = (y_eval < 0).mean()\n",
+    "ax.annotate(f\"vertical step at exactly 0:\\nthe zero share ({zero_jump:.0%})\",\n",
+    "            (0.03, below + zero_jump / 2), fontsize=9, ha=\"left\", va=\"center\")\n",
+    "ax.set_xlabel(\"next-event return (bp)\")\n",
+    "ax.set_ylabel(\"ECDF\")\n",
+    "ax.set_title(\"Posterior predictive ECDF: the step at zero is the hurdle\")\n",
+    "ax.legend(loc=\"lower right\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "639ba490",
+   "metadata": {},
+   "source": [
+    "The predictive distribution reproduces the zero step, the bulk scale, and the\n",
+    "tail quantile within its own 90% bands. On real data one caveat would surface\n",
+    "here that the synthetic generator cannot show: real price changes are integer\n",
+    "multiples of the tick size, so the continuous Student-t's residual misfit\n",
+    "concentrates at |y| near one tick — visible as small stair-steps in the\n",
+    "observed ECDF that the smooth predictive cannot follow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dfe4506",
+   "metadata": {},
+   "source": [
+    "## Knowing when to stop — and how a stop rule can lie to you\n",
+    "\n",
+    "A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo\n",
+    "estimate — and eyeballing it does not scale to overnight runs. The stopping\n",
+    "rule proposed for PyMC in [pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)\n",
+    "standardizes each loss *improvement* by a robust running scale estimate and\n",
+    "feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming\n",
+    "itself only after `min_steps` — which on streaming fits should be at least\n",
+    "one full pass, since stopping before the model has seen every row once is\n",
+    "never evidence of convergence.\n",
+    "\n",
+    ":::{admonition} The minibatch trap\n",
+    ":class: warning\n",
+    "Successive losses are evaluated on *different batches*, so the per-step noise\n",
+    "dwarfs the per-step improvement: on our full-scale run the median one-step\n",
+    "change is about 106 loss units while the true improvement is 0.04 units per\n",
+    "step. A rule that standardizes *raw* increments concludes \"improvement is\n",
+    "indistinguishable from zero\" immediately upon arming, and fires a fixed\n",
+    "~$h/\\kappa$ steps later regardless of whether the fit converged.\n",
+    ":::\n",
+    "\n",
+    "The fix is as small as the trap is subtle: apply the identical rule to\n",
+    "*window means* of the loss, where averaging beats the batch-resampling noise\n",
+    "down by $\\sqrt{k}$ and real improvement becomes visible again. We run both\n",
+    "against the same fit, as non-fatal shadow observers, and let them disagree:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4d8099e4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:55.224032Z",
+     "iopub.status.busy": "2026-07-31T23:51:55.223961Z",
+     "iopub.status.idle": "2026-07-31T23:51:55.226405Z",
+     "shell.execute_reply": "2026-07-31T23:51:55.226161Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class CheckLossConvergence:\n",
+    "    \"\"\"One-sided CUSUM on robustly standardized loss improvements (pymc#8384).\"\"\"\n",
+    "\n",
+    "    def __init__(self, kappa=0.25, h=20.0, halflife=200.0, min_steps=1000, z_clip=4.0):\n",
+    "        self.kappa, self.h, self.halflife = kappa, h, halflife\n",
+    "        self.min_steps, self.z_clip = min_steps, z_clip\n",
+    "        self._lam = float(np.exp(np.log(0.5) / halflife))\n",
+    "        self._prev_loss = self._prev_delta = self._scale = None\n",
+    "        self._S = 0.0\n",
+    "\n",
+    "    def __call__(self, approx, loss, i):\n",
+    "        current = float(loss[-1])\n",
+    "        if not np.isfinite(current):\n",
+    "            return\n",
+    "        if self._prev_loss is None:\n",
+    "            self._prev_loss = current\n",
+    "            return\n",
+    "        delta, self._prev_loss = self._prev_loss - current, current\n",
+    "        if self._prev_delta is None:\n",
+    "            self._prev_delta = delta\n",
+    "            return\n",
+    "        abs_diff, self._prev_delta = abs(delta - self._prev_delta), delta\n",
+    "        if self._scale is None:\n",
+    "            self._scale = abs_diff\n",
+    "            return\n",
+    "        sigma = self._scale * np.sqrt(np.pi) / 2.0 + 1e-12\n",
+    "        self._scale = self._lam * self._scale + (1.0 - self._lam) * abs_diff\n",
+    "        z = float(np.clip(delta / sigma, -self.z_clip, self.z_clip))\n",
+    "        if i >= self.min_steps:\n",
+    "            self._S = max(0.0, self._S + (self.kappa - z))\n",
+    "        if self._S > self.h:\n",
+    "            raise StopIteration(f\"converged at step {i} (S={self._S:.1f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e0955b70",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:55.227410Z",
+     "iopub.status.busy": "2026-07-31T23:51:55.227343Z",
+     "iopub.status.idle": "2026-07-31T23:51:55.229258Z",
+     "shell.execute_reply": "2026-07-31T23:51:55.228977Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class WindowMean:\n",
+    "    \"\"\"Feed a stop rule the mean of every k losses instead of raw increments.\"\"\"\n",
+    "\n",
+    "    def __init__(self, monitor, k):\n",
+    "        self.monitor, self.k = monitor, k\n",
+    "        self._means = []\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        if len(losses) % self.k:\n",
+    "            return\n",
+    "        self._means.append(np.mean(losses[-self.k:]))\n",
+    "        self.monitor(approx, self._means, len(self._means))\n",
+    "\n",
+    "\n",
+    "class Shadow:\n",
+    "    \"\"\"Record where a stop rule WOULD fire, without actually stopping the fit.\"\"\"\n",
+    "\n",
+    "    def __init__(self, callback):\n",
+    "        self.callback, self.stopped_at = callback, None\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        if self.stopped_at is None:\n",
+    "            try:\n",
+    "                self.callback(approx, losses, i)\n",
+    "            except StopIteration:\n",
+    "                self.stopped_at = len(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3292e06e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:51:55.230050Z",
+     "iopub.status.busy": "2026-07-31T23:51:55.230000Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.412537Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.411984Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw increments: would stop at step 371 (loss there -28)\n",
+      "window means (k=50): would stop at step 4,700 (loss there -344, final -341)\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader2 = DataLoader(\n",
+    "    parquet_source(data_dir, columns=columns),\n",
+    "    batch_size=1024, shuffle=False, sample_shape=(len(columns),), total_size=\"auto\",\n",
+    ")\n",
+    "model2 = build_model(\n",
+    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), len(loader2)\n",
+    ")\n",
+    "stream2 = StreamAdvance(model2, loader2)\n",
+    "stream2.prime()\n",
+    "steps_per_epoch = len(loader2) // loader2.batch_size\n",
+    "\n",
+    "k = 50\n",
+    "raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))\n",
+    "win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=steps_per_epoch // k), k))\n",
+    "\n",
+    "with model2:\n",
+    "    advi2 = pm.ADVI(random_seed=RANDOM_SEED)\n",
+    "approx2 = advi2.fit(20_000, obj_optimizer=pm.adam(learning_rate=0.02),\n",
+    "                    callbacks=[stream2, raw_shadow, win_shadow], progressbar=False)\n",
+    "assert raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None, (\n",
+    "    \"a shadow rule never fired within the 20k-step budget; raise the budget\"\n",
+    ")\n",
+    "win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules\n",
+    "print(f\"raw increments: would stop at step {raw_shadow.stopped_at:,} \"\n",
+    "      f\"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})\")\n",
+    "print(f\"window means (k={k}): would stop at step {win_stop:,} \"\n",
+    "      f\"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3a56a0ac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.414289Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.414181Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.597795Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.597453Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZOHcpZv7x6KOP/hyXXpt/MNZgPE+7yb3PWIO1GmLPmMtW8fook/H87WGkn0aUeaKWtPjii2eT/FgMcli4Tm1UCdQd0UZSPWhwb7nllpAHLnEadjYuWcRmwFdbxC7q8ud7ZOFz6aWXzjqNvIJ9mRAsscQSoaruvPPO3BMjWJy74YYbss1nBnZ5/WYsJDDY+93vfheWXHLJLOkstmMnqOnaa68t5J7j2mWh47rrrssW7hrBIOb3v/992GyzzbKEp3bzyCOPhA022CD6+RdccEF2n/aEQeD1118fLrvssmwy0SiCH9Zbb72w2mqrVSLYn0X2G2+8MdsUTekjul9f88wzT9YmrbzyytkiRNHWX3/9bNAeY7755gsXXnhh7osJt99+e7jjjjvCww8/nE0g8kSbVFt8p72qbaiyQF82NvxuvfXW7BqiP03p48EG8VJLLRVWWGGFbALe7phExtp2223DdtttN8KFD+7tiy66KNvwaQSLWPSB6667btYPFrWodc0114Q999yzKWOLlHHx4MGDw+GHH165z8yC0NVXXx0uv/zyLOCkUbQhHHix7LLLZmOhZuAz0L9eccUV2WJdI1jAo92g/+/tvkq551J++xFhDMfchjEfwUlsMOeJJCMSlQlO6NpfsNjZClL696L11gaX0c7T39L3XnLJJVkQTqP9L2OGIUOGZPc9C5tVWEhkXseGL5+LOXnqtU+QSG1M0SrXfKwi+xfGtcw7edTbvzAPOOWUU+r6twRfsx7DOJLNOOa5rG/UAn/yxrVAsCVjHMYDRVwbKW3X0UcfHVZaaaXov33MMceEM844o6Hn8F2cfPLJ0X+T34/2JAZtEW1Y1bXaPVaF8U6R62FFro8edthh2XpN0dqt36vCNVcW5pH0HzGYszDmTwkWJVmh0UMGaeNp62MddNBB2dpCjC222CLsvPPOlV7bcx25d6yFX3XVVVk/xd5ZI7jWaZOZc7DHUsQaTRHruj1hrs7cnfVe1nprCe6pSCBZZJFFwnLLLRcWXnjh3ANM6ItjE+/ZvzznnHOi/zbJonyuRvD5uSdTDoBkLE1QT4yzzz47a2erLHWs09s6Awcs8puzp9HIfIx1p3XWWSd7xBz8x3rGTTfdFM4///xsXtgIEkVYy2Svk72SKuB75Du8++67o/YQexpnEVPAQRokm5WJ4En235nz8eC+Zs5OIkkR6DdIrGH/ms/MWmbRipw/sEdD30Ef+sQTTzT82swh11577fCHP/yh9IOiCKplf6l2yEvecTMkdHDPEvdDQi7r1SRXVTGpqhFFjmfZiz3zzDPDX//617rHIanzgK5IyuFvM3/l8MPagYexCLblXl9mmWWye7CZvz3JcsQccRgp/RAJF3liHsi1zjVPG8f1TmJBI31mypy7aL3F1XTSuLvM/oWxE/cjYycO96injabdfeyxx0JejA9JU9S+YV7xwUVKWXtlbEU/xPVPQjlJ/XkiJqZ2qDnjUJJAaLNZq+4kXDu1+K/HH388ea+OJEXWrGl/iSPMC7EktLOx/fZ+++2XxUMVZa211sra6Bj0q/SveeJ+qf2urLv0VkyhHhyKQlvDWK6MfrVd5y5Vi3XtHpfP6zNOZn240XHXGmuska0TjegAFeKVt9566+j3xz3MXliMTojnT/mMRRvcQnuCVqJT7lgIYPOj0Qaiq9TgfHAiwk477ZQt+Ba10NvbYJPgGx4kltBh0CFyupj6xuCXzZSLL744erBZz98geI/HueeemwV1MZjfe++9K/ETEShxwgknZIHysZMBJjls4vBgI2Lfffet5GmGVcJ3TWLCiSeemAX5xyJZ989//nMW2Lj//vtni3VlI1iLNojBbuyGcl/XFydQ8DjqqKPC8ssvHzbffPNsQtHuCAJmo5lrJa9F3RH1hSzq8yDorutgnQQJFgtSgm/r+fssYBO4n+eCKzi99KyzzsoeBAFusskm0QFqnYrf5ogjjmg4CamGRYXaPcxGzl577ZUFm6uaWOBivEK/ktLucAouwY28DkGStCVlod8477zzwmmnnRa9+crGGoF9PGj/dtttt0qPbWjrTj/99DB06NDCEkVqfT4BLjxuu+22n/97gm5Y4GNhiYUYtRY2j2nnCWRKmVNwz7EIv8MOO2QJqM1Y4GbRnqBs7l3GUXle+13Ho6uuumrYdNNNK1V1sxUw5zzyyCOT1nBGhPk1GxqcblgmPgsJTTxYjGaDZ6ONNsr1hHeqrsRuepBckTKO5/mNYqOC8V/swQkxf7Prd9XJirzHVE32e62PdReSBViPiZmzcMgNhwzGIIGp0QQ6sG6TEjybcqhFp7bz7bCOzNiAMfrxxx8fdd2BuS4BOjxITNpnn32yE8hbCWNVAtYJfChiL5F+gYAGHrQvBJcQxJVXZa2Ue5CED+ZVJGqXNUZkzZk2K3YtmL1f7psYzA06df2T65xg1tgDbOkTCX4hkJt5HgGS9WLOfuCBB2brVjFYC2Wvi3uUZKNddtmlKVXpCejlfXA4GHvLeSIIkMdJJ50UFl100SyYKs/g057Qbp966qlZ0BztQFnoN0hC5EEfSrIJidisX1bhUNJG3HzzzdkhOSkB7CRg8BrsORIAt8oqq4SiETfAemGjCa2Nok8lzoMH904tjoiDNRkrb7zxxlar+/9xSCttNO1cHnFSjSKOgPadazrPfW8CcXlNHuzjMP7hEOIy23ACyLneScQoUm0eyIP9oNq4gwQNrnc+e1USwZup1cfdZWGMzryKPrpsxoeomX0he5nEtxW5js46CNVVeXQ9+I+YUeImqIJNX9WuSK4iFpd9uzzHHLTv7APzYHzPOI89sNQ9aRIeWe8kqTIGa15FJdFxDRF7Eyv20MqekAjJvUNyep4xKay/sL/Fg4PyiTNYc801S523OXcpBmOmv/zlL9k8LbYt4DUYZzOXZQ2Dg/a6V51nrSGlTaeYU5UYz9+eOrueogqTWtkij4UTFmVYmC87ga47SkiTJEAS03HHHZd75Yl2wyIaC+W77rprYQl0I+rkqpKZTQIhCX1UaMnrFCwCqgkqJXBOIz4lgFNCDjjggKTAh67YOPnjH/+Y3ftlFn5lAklSG4mTRSTQ9TRo5f7hlEwW9PI+Dagq+A3ZRCAIl2SWIhPo+rqu2OymjSgKAcdUGCTZJu8Eup6CLbbaaqss2ZzTY9T3AiLf14477hidQNcdBw6wGHjssceWumGu+hBQwOY5fUle7Q73GqfbkiRbBgJt+HsEBKSeXlrDaUiMlwj4qBqCarifGNOyqFhkAl1fYxsq6+ZV4Vvl4D4nsZkAopQEuu6vSSAGi5d53YP14G8RkMsGB9WU8kyg6455Nid+MQamrWn0tLROxO/DmILrrahNSdZkyk6g6442mNMJV1999VzHTynByinjaxLrGbvFbHalBF6YXFHNe0zVYr/XXlLa+ZTE49g+IuVvpiSkEAjdW/WxdtUO68is3/L3WMuNTaDrjgA0gqRiqxo2o93m4BKSgQg2KmMvkd+ZA0D4m6zt5vFbc3hnbOUq5oop887Ytie1nYz93jjhudWCqPNa62eNKo/1IeZTVMbgcK6+1rtYHyN5jgDK2AS6rgjsYi2TuV2jVTNTcL0RWEY1PJKe806g6/63OFCRQM/tt9++7irWMej7WSNu9n4A3yfJY/QfVPZrBfSbHOLEYc55VYBhPMGBcVRH594pavzCPtw222xTeAJdb/vJJIewx8r70X/7Na7/lADWWLQxf/rTn7L9J4LMi9z3Zl2YPRP22K+88srCx7vcp7SjBHsXnUA3IvSTJNWRrJASZN8O2mXcXTTeI/spjAOakUBnfIiahT1/+gcOWGjWOjr9BvdASqX2qo83OJCEg/FJpCtyzMH4nnEt1QhT5v41KUmNtYrbRcVlxvYtVD3jmk/FfI7flDgc4q2LjEnh8GjWAVhbILGtaM5dikMbwPyD8X8ebQHzV+JAmFNwOFC7Mp6/fZlEp9ylZhBjvPHGC+2GRUFOdKNUZTMmvFXHxgsDOxacy9z8qBIGs5zgSNJKUScvUe2JE7f0vwjA594satOAe5+giqKxycKCMA8mMGUj6ZOFb5LpOOGknbBxwMbSoYce2rTkuTJwDW233XZZOe3YAKqUwFw2wDmBrhUWspvh5ZdfzhabeisbHovJMafE0BebeFAdbCZxUmIRG8qM2embOO2+6EVZTtIqoo9l443qWmyEVQXjLTZmuZ+alTyn1kVQGWNSki+LcO+992bVuMpIpCN5ioR8Fk3LHDuxUMtYgoVakpA14g1BNp6KGFNUGUGjec1VqGo3xhhjRI/pYgPuCTaL7V9iE+EYJ6YcyNOJFYo69R7rZPZ77ed3v/tdU5KlY4NLCFCJHePVqpXGJqSQSNdJ2mEdmbEI83ROHs8b4xSCagjIrvKcmMrfzFcIkGvGqcaMFTgAj/kZp5e3auJv7Bgx5W+mHO6Q0ra3Ktb92NfIe8+RZFn2Mkd0nxMnwPVN4kDea/3sIbNeSltWRoIJ63wkNuWVLNVIUCbtVFUOgC0a698c/kRyd1FJZHlgnMfv0rVySZ5IpGKPN+8+lEBJ+v6UNlT5IwCZ2I133nmn1K+Xdpl2nOBZEhfKTN774IMPsvucz53XQQ7dsSbMXmps1Rrlq93G3UVhz5akE/ZT8jpovV7Gh6hZuNY5pIL1g7wOKNL/Iu6H75jkXPaWysRaJQc4cMhryvh+vvnmyyq7xiKGsQgph9RwIGz3il2NYK5wwgknZON7xvll4vBs1hioEF9UvIFzl+IwHmPeXURxjNqhcxR6aSfG87c/k+iUO07RSZ3UTT/99KFdsbi/xhpruEjZBZu1LJyUPbCrEgaWDFLI8i8Sp9ZvueWWuZz62C7OO++8LHmz6OBeKmMQUF8UTlCh2mAVFoRZ4OA00sMPP7z0U/OKwISeCgKcPNTO2PwjqLiZExomH1SPoT1s52TF2FOECMQt8vRZPPDAA6UlWKj3TUxOZTz44IMLDzo75phjwtChQwt5bYIJOJWXCopFourzfvvt1/QE3FqAUNmL0GoPjz/+eHZSXNGnMBPsS/JpUfcLr8upwpw0X3aAWVf0lwTVXXbZZU17D1VFW8WYosjT86uMMQ5zFfqOFP369cs278pOsEhJzIh9LtXNP/3006jnUhVlwIABoZN0+j3Waez32heJFiONNFL0uC52PSy2rWY/hr8bw2qjnbWOTP/EODmv6rwjQkA2B3VVMRGCpCLm7s2cr3QNKmZNP/Xgw2Yk0Q0bNiw6EJnrMPYQr5QEvE473OGkk07K1v2Kmv+zj0ByWU/7Q7QzKQdx9IW/wZoj1VSLwuEr7F03M+mI/oYqbVS3bnbFuLIQ5EriYhX3JwiUZb2r6CBvqhFSxTEvxF8Q6FrF77TTE+hImCw7qYjrgGrEHHrw1VdfhWahOhyHoOVdoY1qM9ynVL5T87XjuLsIjNVIRihqr7Y3xoeomdc941z38IrDoSO0e838jvmdWUtjfpiStEMyXqwbb7wx9zEPayEpYxgOl4/17rvvZlUFTz755KbOEfleObQg77gG5y7F+ctf/pLFoBUZU8W9tuOOO2aVEduB8fydwSQ65S6PU/Nnm2220M5Y9CZBwJPxQzj//POz76Lo4OqqXw+bbbZZlrhQ1t9jwFLlk2DLQpUKTj0pK9GLk0BiA1r6mkRwcgyTpSo599xzKxss0YhDDjkkW1htZyzKsqlQxGkjsdc0G6WeOPVfVJbccMMNS/s+GJ9w79pPNA+nznMKUFk4gYxTo/LerOQ6IoG/DJdffnnWXjcLi02cEkmigdQoAss4fTe1onsjCa6pCUQ9IeiDBD0Cfpud1Ar6Mdo3Agf1X4zLaZuLTtZsBVQxpSpCq1UpSgkajn2u1T7q5z3WWez32tt4440XfdAf69wx8wKSmlKCC2PbeRNSOmcdmaQngqbLmneQBMA+RFUOWeN97L333oUmFcXgsAKSgVKCZueff/4w8shxW/4cBBTzfaQc7kCgV8z1Tfv6wgsvRP1Nqki3+95zVwRKnnjiiYX/HRJmr7jiil/sf5dRJY5+s6ckvrzafObOVTnoj9gLkqCamfBSJtqlzTffvFJ7iwSJUn2xrPfEfZVH4CF7SxwA4F5PtTz77LNh1113Lb3aFHMNgq6LqEYcgz1p9j7zOmibMa4Jo9XQzuPuooLaqXheNuND1EyMt6t2L7YTktWpCJV3/EfKAa+MQWIPDKd6bv/+/aOTYPJO6Elps6eddtrotQkOJKJwC3E4VcBcgwOC+X3zej3nLsU4++yzw+mnnx7KwByHCqN33XVXaGXG83cOk+iUq4ceeihcd911Sa8x+uijh4UXXji0OxpaAgxjT/NuBwSHH3rooaUvEFYJi9Ys5pVdsYSNxgsvvDB0MoLtqXhVJq51Tg/MM3CA5K4qbeb1dJIeg+NWvc8JGG3304+uv/76LPGjShuSYKLNQnbsqcTtguAQAg/KCnDqurBGFTSVjwpOnDpfJvoQxmR5IeGTyrdl902Mbcr+7mqYA3VyVWXFY8Gbdr7sQChOqMuz+gTzGsakVaiK3B2Bg6eeemqz30YlHHnkkU09Rb9qCIRPOS0ypZJFTNAx4/WU9/v+++9HJVCmBEh3WrUP77HOYb/XGcqu7JTaR8f8zc8//zyrfB9jrLHGCrPMMkvoBO2wjkzi71ZbbZWNB8p05513ZsGzVbD//vuHq666KlS1X+EU/tj51DjjjBNmnHHGqOey5hiT+JuSgBs7xuQAmth7Yt55582qSXeCJ598stR1XdYUawc8ch2XeYAsybrsQeWJ9QPa/CoF/ePuu+/OqlZ1SkU67ndOya/Kd7/77ruXnhTOvZW6P7bPPvt0/B5b1TD+3nbbbUvfO6GdporLv//971AlrIkTK5PHYba0Ge+8804u70tp2nncXUTlWyr2lc34EDUTiTIcHKTiYrfZd2bMUSUk9HGQeky12NFGGy0MGTIkqdp13hWqY8V+DuJ8qe5ateIKxAVxUHAeY0znLsXNZzlYtky1RLqXXnoptCLj+TuLSXTKdZK19dZbJydKLL/88tngpxNQ3jfPgOVWwglqVVn8biYmByQqNAMVIqqaeFWGm266qWmB0nn9bSZJBD8QBFFlLEK2ajIOJ3+1MzaZ99prr8ptCNc899xzHbUx3JNLLrkkvPrqq0352ySQ3nbbbU35252MU22b1R7kcco97QlBBWUH5tU0o7+hjXKxXylBpc3YyCBA4ZRTTsnt9bjvq5xIetxxxyUf+NPqHnnkkXDRRRc1+21UbhGck79jT2Sfbrrpwvjjjx/1XBIWOAGz0UMmUiu8xgQ7xwZIjzTSSFlVlE7hPdZZ7Pc6Q0rF0TLb2xpOIW60n0hNSBlllFFCJ2iHdWT2oZpVOZ0q2M0O8uFgDZIhq4y91V122SX6AIOUcVfM30xts2ISh1OSjTvpcAeqz5dZdYr1BdbFWEtuRkA6B5Lltb/B4VysH1TVvffeG/bbb7/QKVjDyCOxJhXXdTP2qFhfTzkQ94EHHvAgpQo65JBDshihMnGoN0HXKVWvi0SfRWJhyliVCqidvvZbFZ0w7s4z8aAZ/brxIWo25g5WyS0Ge0jEblftAPUaxkB//OMfG94bwzrrrBNGHjku1YHCFnkl8zCeij045le/+lVYZZVVGn7esGHDsgPoyz54vZGD4TfbbLOktT/nLsXggAn2spoRE8qBMIcffnhoRcbzdxaT6BSFwSwTOk6zO++888LgwYOzKjapCTm//vWvs5OGOi358Nlnnw2dhMEkp6iquVgwveaaa/wZmuDcc89Nfg3a2x133LFlEiFJBMq7RHrRCGwtu0pkmd58880saLjqCWps+ucZ5N9qmv37/PnPf+7oqrmdhnF9KsYWBHR00j1D4lDZG99SXnNR5vV5jG2bFVjcaJ/Wqiee5VV1raoHJzT71NOLL7649CQxgjVIXGhEHoEdjQY7c8/EthNUQxl33HFDp/Ae6xz2e51jnnnmia5a1Ggbn0dCCns2ja5jmZDSGevIJCCQ3NKp61scYtgqa4vcxwT0fvzxx5WunkkwCePoFDEHRKS0kymJ0erbtddem42Hm4E5Sx5rkVT9Jrml6q6++urs0Sk4kLfsCnBVSySMDTInKVTVwgEJtJdlo+rWK6+8EqqMQ+Z22GGHLDE79gDvTm4rqqJTxt15Hnqex/5MI4wPUbNxzVelEmS7IaaHvrTqMYTE4R100EENP2+yySYLiy66aPTfZayQh1tvvTX6ubz/8cYbr6HnkBDJ79qsA6wbSdai8ljseMy5S3HVgZuZfFnVhN4qM56/fHE7gGpbZMtPP/30Tfv7JNBNOumkpf29scceO8w666xhhhlmCL/97W+zAReniPP4zW9+E0YdddQsKIqNHAaZDObZGGKRifLHVOtIPR2DILbTTz+9Y6pX8D0yuPNUkWpgoXa99dZr9tvoOCTOUsp62mmnTdo4qvqCd3f77rtvFoA00UQThVZw++23Jz2f/nSJJZYIc8wxRxg0aFCYYIIJskqrBGBRPZCNAPoVJrv0La+99lq2gUL1NU5qKRJBuzvttFMulW8Ijl166aXDfPPNl31OAmVp4z/88MMsGILTzFhISAmWYRF5oYUWCnPNNVfy+1VjuEaPP/74jjrhtpPddddd2aS8f//+Uc/n1LB2r+BZRH8x55xzZoumzEuYC9FfcLgI/QXzEPqLDz74IOsv6Ct40FfQZ1R9IVzVxnjklltuCeuuu27SuPaYY45Jfi9c70suuWTW388+++zZfTDWWGNlYxUW3tmA50CGlANouJ923nnnLNiM0/46TTMqHvaFNRfGkjPPPHO2JsNjwgknzNZkxhhjjOx/59qgHeT3o49io5+xMwG0rMvk8bmojsJ9EHNdEKwce1gISXG///3vS0uuiHmNlOSKTgtUruI9pvzZ73WWMcccMxujxxywxPiddbupppqq7n/POD+Pdr6RRJqUvqWTqjq18joy63GtkBRSZP/MIWKtFFTN/IcDQBrdM2TdnfFszN5bo4c15HG4A++Tg1rrHTOy5sRaSAyC1Jq5990J2G/g0Swc6tXI3Kqn64v9kjz2rrkXF1988ew/J5lkkmy/hPksfT1xBXfeeWe2Z5JyyM3BBx+cVYSdfPLJQ5F4/dlmmy0bz0w55ZRhwIAB2f00zjjjZGuXzNn5zpizs0bJIWNvvPFGdq/ed999uYxteC0OMFtsscVCJ2I9mKoMjQYMs+bH9xaLvUzW6Tg4iDEIvz1jY+JnuNcJhmSNhn3At99+O7z66qtZJTAqI/O7t1K/2+7rBlQzZN8nj7iqFVdcMdsLnmmmmbK2jeuBBCTue5KZhw4dmo1jYjF/olrFAQccUOoeDeMn7jHmN8SNcb3zefl8tNXcT1TS4H7k83GN814Zp5PI3ezDUKuik8bdrdomGB+iKrj77ruTxtyMT4lNYm+dGN+JJ5745/hexieMu4lvYexN/0Sb/cILL2RtNuOWdrbPPvvkUnV2iimmCMstt1w2DuT/ps8ndprvlO+SMeYNN9wQnfgOqscyf1t++eUbet76668fPa7hYFmSvLhWUrCvHmvIkCENP4fYG8bYqdh7XWmllbJ5JON7flfGQNwXjGsYy/EdpSRcsZ/Ifuvmm2/e0POcuxSDdQceaj3G85fLJDpVBgtxjXaiMRhIs3i9yCKLZJsmDPT6wqIgDxI/WDjBVlttlS0OsvBE1Y6UCS4L5pS0ZXDf7k488cTcK3UwSWJRi802gu4Y6PGgjDMTJL5bAu1YaGfAlkfyYxkIHFx22WWzpBGuDa5BPguDYzaE8pj88Fp8N0wu9V8EaS6wwAJZ8hOB7EwkSGhign3jjTeGRx55JJcKDiQVxQY/cC3ndUoKCwq0hyw00CbyeQlYrS0Es9HFe2UDJFUtuaJVyjXHBoyyiXjooYf2uqnHBhQP/u0000zzP4FHXF9sNnGtERjMBlneSRKcPk2CW2rbS2Ik1093bKDSZrGowqbKtttumy1C85liF3U5jYigd9p2/betYrOKtorkRcYoLC5w77744ovZfUuQVR4uv/zysOmmm4aBAwf61TcR7TUbedxzBGAwTmARmEUtFhpT72kwPmJcutpqq0U9nzExC6h5XeOM7Ri3165xFmM57ZzkMa7xPAIxmtlfMP466qijsvnJiIw++ujZg9+bfpoEoxo2I/kuaFtJMOI/Pc2pvZBYxEYFgUrMB9iQJzCFDYLbbrsta/dTcS/FJtExZiGoIXVuxViB5Lae+hnGSjwIzthss82yZCX+ZuwiPhs8VB3baKONkt6z4jE+XGqppbL+jPavng0r5ic8aAtryQgbbLBBdk8QoMOBCymVMBg/3XHHHdn9VmYCQSPBx4yH86iSzXoMawn1jutMrpD+H/u9zkQ7H9v+Uo2u3iS6PBKlG30dAkFjE1JYP0w5HKxdtMI6MoFNrBPlgb009ipYR2ZdkHk6YxTm6QQQ8z45Ubxq+1F5rVPwG/PZWZthLMXvzXiUz8/aOafpM1djDTwVr8X100jVY9YOOJAkJsGtdsgbY/Uy2yzea71JdOzrxSZp8Tfq2Qtud6wtrLLKKmG66abL1haZyzM3YC2QQMAig96Z1xOwRxvC36b9rO0/EaiXEoBZC8ZlP5j10xhUzUndc2UNh4PoOFSxO+a9JGQwLlh99dWzdVyCXGPbTPaLqPx36qmnhjzRjtRiKFiDZA7eF/aCeJBYRztJ8mBtbZlgZd5j6gFkVCWoYhId7Qqft2u/yG8zbNiwbN2OAOM8kmvoXxtNoiNJOXadmHU6ruURHbQ3yiijZNc0e4D0hyRadkU/yDiYSrjc43ntUylE9e/HHXdc0ldHcDVxURtuuGH2m3fHmjUPArI5MJ3A6ZNPPjm6T2E/cs0118za1Hpxz8W24QsuuGC2p0/f1Nt3wGfnc84yyyz/87/R9zBf5HpnP5/YmzzG/62ok8bdrcr4ELXynjqxVcQbrbrqqiOc2zEX4MG4lHE3sTRdERtJe83j/vvvb2p1prwx7mT8mYKx/957753tk/UUk0VcAw/iu4n/ov9MqcrGfIZ2njlIvViDm3rqqbN4ukbxezMvWWGFFUIsxhyxMTn0ZT3F1vWG+eIFF1wQUvD9kjy4xhpr9LgfS5/Lg/kfvysHrFMNOxZza9YdGomBd+6SP8ajxx57bO5rBVwn/Las8XBPMTajTWVdqZ3a1N4Yz99+TKJTJSyzzDLh6KOPLmwTgwHy2muvnS1M55ksxOIhAwgGWFtuuWV0IC8TfgaWLD71hk6otxPOCfpKqf5B4HNKhai+BkBswqUO7rpiQ5Dvv7dBZm1jgk1mOnOwmMKGEItweQSc543J3y677BLWWWedbCG6K07pZxKx9dZbZxsPLEKmnu7IopBJdP/FYhwbA0xeumPhjEkFC/9MMFKTQRlEbrfddtFV6PLY0ORkF0o391QBlAVJHnPPPXdWPfKSSy7JqowQYJOCRA/aOq7lqovd2OG+TKmYRl9IYh0PgtrZ6CKZjnaLhY/U34BgntQT1diwpN/uacNkRBv055xzTrYxzIkZMQjs4vqJTe5pJ3z/3Lsj2tShLaN/ZBGS7zwlqLw2Tjn77LOtRtdEa621VnaCI1WZelospKos40AWNlMXJ+ifYu4zAgQ4XCIPjHVIkhnRNc4hA5wOzSYZC+YszjQLnztmg5bgknPPPTdpDMYCNpvJPDbZZJOsfyAwgfkCwUN5JFiVgXFNb8FSjZ5C1xX9aEqVNQ7maAYC2th8oPJJdyQd8Z1wuAZj0pQqUWCTPzbQjEC3p556KukaPvDAA7Mxdr347IwjSe6ODY5gAX/w4MHRVTfVOIJNCMKiP+stcbhRBF7SZ7GmxBw65UQ9At1jkuhox5k3xVyPrEfQVtMn9IWk6TwCQ2pBz0Un0fGbE0wotRP7vc5NomPsEIM2tN5xTh5VncDYjHWkepLUSUiJDezuhADDdlhHJmDizDPPDHlgDEdFO4KVesIhCQRY85lZm8jjQLZUBMixpp2KsRpB4cxBGH92x1oN3wuBZBx2xEFgsZWKu+IgukbXUWmzYtsT2qx6k+jyarMamc96uEM8DiRhLXnllVf+xf9Gwhd73KwtctBt3hVRmPf/8Y9/zNqw7pW/SUTgvuHe2mKLLbKDwmKxrtFIZcO8965J+ODAwXqrGrDeQ/u0/fbbZwG8MVgXzSvon9+Cqg70bY0EsfaFNcuTTjopXHnllUmHMNWCnIn3qAoShqgIyOG+3TEXJsCbBHPWsFOT2RkHNIq/HYOgyNSYHfYN2X/msfvuu2f9MQHLJPbHHuCgOMQNpawlERzLwVXE5NSD9WX2JjkMbccdd4zan2D8yhiokb2m2OudNXjibepZl+vtMzP+4sH9zpo97TPrjIyLG0mo6238yBoi91PKHkxPew316imWpNPH3a3G+JD2Q3zniMZW7JunHCTJoZfcZ7F6izuNjcHifudQjhS1JDDWa4izpJ0mBosHlVVbFWt77CmnoK9n3F5vzDDxwSRbkaTDWCEG1ykxSI2ud7Hvz95yDIoVpCTRpSQNMj/oHgPcF37XlJhg5ir8PsTr1YP4aua1jJGIwYk5GICYD66LRoorOHfJH+NRDhbOY02He45xLmtLPWGtif2IM844I7veWqGwTbvE89Nfs+Y2IowFYmPpllxyyez1Y/UU21hVJtGpqZgEs5DCifJFVJYh6JHkNhLoYk+fqweDDToCMuljT8ujLG5fSXRsMIxokxIpAYyg4aXSR1EY/OZx4hrfAxvUbErELt4yKeLBJh0LiHmc6J4HJhrnn39+nwNY7hcWkAYMGBD23HPPpL/JxJAE007HNVVPkDVJZSRgEqzJCW6xnn766Wzg2H3jsC9sMMRsmHTHpmS9gx02S/hu2IDkP1MWEJhkkTRw5JFHhiqjLY/5nCwupCTQ9YSN19qGE5vdJMpwDaacMpaSZMMGIEl4jZa5Z7GbYBsW0mMDLehrCXrv5JOD2SQhWaYenK5FoCdjrdjN+Bo2D5gQjmhirGLQR3BSaD0L1fwbgp0YH6VUr2RcEOOmm27K5XQh+ib6qHpwQAL98sYbb5ydbNwMscGQVCrI+xAD7k8SDHmwOc5vktJflKWvDdjU+WBv86cqYhOIRfG++lnmDWySsaFMsn0sxqNswhP80+iYjn45BYnejSTQ1TBXIpid9i4mGIOK8oyH6m1rOgHXE4fT8OD0dE5RIziN75fviyBoFtupeknwar3fO/0YgXgEY6Yc2FNP+8f4dMiQIdHBadxHMfMzEChz1VVXNfw8/h6BnvUEPeYVqAx+Qzbs+sJBUbFzXuZFeQZftrqi7jGVx36vc5FcwAZpzJp/I213XlWdaD9Yc6wnkTmlb0mpxNoOWmUdmfFNzOnc3bG/RvBMPXt4fGbWokgM4XCXZmKfJTVwg3Emr1Nv0DHB5gQLkZCSuv7NPgC/YSP3G+v3VAGJQZtQzz4Ra+Z5VRVqJPHXJLo4jDkJau9rbYQ9DdbuuXfzwho+65ocetIbxsenn356FniVUpGONc2YJDr2rlPaCg77IYiz0T0L2hcCT5nHxh7Sy7pMShIdCdKsxXav0pE31n4+/PDD6JPwCT6jwlNKwGueCFJjr6Sv35yDMmtVtVLazbfffjtbg25kDTX24CliVfLef2MNnD0tHoxLWD+55pprcv0b7YDvnfEDe8Hc1xzUwNoBcxHW+qnwxXXEOi77fX2tf1Htc+jQodHvhzUd2ubulQbr3bfhUAXGzDGI3yF2p96DuGKvdwJgUxLoRrT+84c//CF7cN9yrZNIXI/e+urUCm/EFBW5T9KJ4+6i8X1wiCptAoHZfF7aBA7a5tAD4i1oBxjD00f2FddifEj7YQw9IqltG31QUW1GzL4674dDGvNEAgRjYB70WcTskmCVxxpK2aioGTufAFX7mFfEHFhBYg3V2Ugej0HSPDHjjHfqxd5W7EEBJKXQfsbGaKQk0THva3S9hPcba4IJJsgqBMd8Vg5EZXzFWDAGh2dQrIHxRz2cu+Qvj8MNWIcmGYyxSF9Y16sVwiF2K69DYauiqvH8xF70Fn8RE/PQ/fCKTpB/1pJUZyA9i+FsphFMVUQCHYNoEg3IqC0yga6GoOW+kuB6w+Q2NQO5yjgJiApCqagWQAJObAJdd0yIWDBggYUBZDNxogMD2HpPgAAb8D2dHtmI1BPw2gGnoTVSpYQByFFHHZX0N9kcjpnI5lHNkQ2rmNMCGBwxeU5tUzkRLCVwpAyx1d6KTEQG3z0LRCwmsMHaKBKZU0qvs2BLe9loAl3XxSjundiBOsGlVFnqVBwMUG8CXQ0LTgSVs4mQgqQsKiGq3E1TglcaOemNyrt77bVX0t9lkSqmDWRcn4qxdKNJLYwNOVm/WVV1q9pfcGgEC5xs0u62226F/i3lZ+GFF85OZqu3n6VfZaMgtaJazHyADaWUzSSC6FKqBM4wwwzZYmzK4nEeB7y0OubABPtRwY0T1Dl1nu+W9RTGa7QlVCzjJHISNgku5EAP/i2nSvfl4osvzpIli0ygq+G+SVmQpj2PrVSfEmBRbzBwatXJmL9poHL17zGVx36vc9G/kBQUO7ci6LkvJNPmuT5bRt9SpeDCsrXSOnIe83T6rnoT6LoG7JE4ExN0nReCNVID9NnH5PTgmKod9OuNruH1hACNsg4yqLftaLSqSl/Vw+oZg5NY9cwzz0T9DcZazVozajbWDFjLrzfohXl6T1W1YhE011cCXdcg4NR935i+lIDzlLaSpCYOQopNOmI+QHWgWATNx67NkDzC/njRCXRdE89SDtLK43DRPLC/SpXZen9z2mT28nqqqFTk9V3VNWvaIw6KZJ8vdc+qnXAfMm4hwZBqLPz/xIsQO8LaAQfFsfdD0i7fH4HJ7NcRdM6/6Qn7yClrn1znKWM59iZY647VSDxEVa932jzWr6kEklLRqeo6ddxd5BoEn4l1KKpC0e/QXnI9Mc9i3585JusUJGsec8wx2aFcxPBweGdPjA9RlcS02czpijxkmzaIBHAqKLXC4bTdceBq6pwxpeIz+x+MVWKwJtpoLDExa8TKxmAto97k9u5IFozdQ+TQmkZigUFsdArijFLWQpjPM/6MQb/D/nCrj+Vade5CZWTGBiloc1nPrieBrivmD4zpUpK3qsZ4/vZnEp2agskvFRKK3LhgAsnic5lSTkBjs+iVV14J7Yry16knadNBH3300Q1XSagHi45XX311aCZON6EkcqNYREnRztddPTiZcquttooKFkkNamv0uydgglNkUrColpJgwYA3dSGSk8iqnowTe1pa7AkpMThxLGZTlQlTShJXahA0i7z1VL4YkU49oZL2hoWKGCwkkZTRvaR4o6hqpfKw+EeV45jnxbQPXRcPSVhtBO1KaoUcgnR23XXXqOdy+iILtM3Qrv2FyscGBRuejQbV8PtSpbXs+QAVSlM2gvfYY4+QioCv2ARCTmNmXNTJOMmWuQVBLY2MEQgAY/O+niTGsg+q4aRu+oRYzz//fNTzYqod1NTTf9I3x1aK7cnrr78e3nvvvUKTK1K+k3ZRxj2m8tjvdbaUCi/1tKX0BXklpNReL6/ElRElOhDA1IlabR359ttvT/qbjLVJ7I45BJPgTtaiUpMGYhGsHBuE03W+kXL/cyI7h2+moNLMZ599Vve/J0glNvGXgDDmSWVVzmykzaJ6c+z6Sycn/bIGz5i0ESkJD92DzRo9KCv1wNCYtQ32rukTY7GW2UjVhBFVvW30d6ph/BC7X5Iyd47BnIjYkJQqQc1G4CuJ5TH3w2KLLVbq9V31NWvWBtm36nSMsajWRsJbo2NFxhi0QT0lwlKpKiVBmHgVKhGnihk31zCOpdpWO1zvtH8jSnZsB5067i4CMRQkk3AwZiOxjrQlBLmPKDHf+BBVSUybTfW6PNfN2mlPnQM1nn766ejnsw/BoX8pGNOlFB2Jmc+st9560YmV/L2YgwaYO5ZVhY4xEIVjYjGvX3TRRUPq+IXCOLHozxiTtsNYrtXmLlw79X73va3PLLfcclHPZX5CNbp2YTx/+zOJTk3xxBNPhK233jqbyJF53i4V2KaZZpqkAPV//etfoV2llDSuYcOFsq9FaeaAh02SmEB5MKFJCU6kulC7ldGtF/crg51YsRtbNfUELnZf4OL3SkEFutjTaLueGJmaSJUaxFE0ErFjsKFf5YDslORFJoYEgDY76ZyT1wgE6jRUVkmp3MspQX2V+q4nqKUTv/tmYHMkpmJorW9L2WyK6Z84ySh18XqXXXZJOpGIPrnRk5DyEFuZk8WrF154Iff3o9auaBGbEFb2mJTDUe65557ov8eJqCmnj3dtK1M2AVI2IFodv8Hpp59e+sFDRaMPZF2m7DUZ5uOxJzI+9dRTfW7Y/fvf/w6ffPJJXX1SvZu89QQ/xwZIM9+kCkona9d7rFPZ7yklAaOeJOh6DyQZMGBAXf/u8ccf77NvYX/GhJT2XkdmjTI1mJXA55Sq1yQNpFaWatZYn0NOUhPaGZulVobnPqWibVltVj3tUb1jxHrbrHqSja2c2TjW2NiHb1Rep6tz/zS6lj3zzDMnVUBotJ1M3S+hunS9lfaK3C9ppbWN6aefPvq5zItTAwFTcUBp7Pp12eOA2D1OKhmlVC9TY2PLI488sqEKx/ViPvD+++9HP5/qVqmHcoLE/tg1YMZAd999d6HXO5V7UsfL6uxxd544aPSSSy6pu4JwI4wPUZXEtNmMg2Krh7W71EP4N9hgg1zex4orrhj9XA75avQgeBKnY+OHGSPVO8bII+6Z/bJGk5FIUI9dt8X6668f8rD00ktHx8LwPbM2WQ/nLvmicl4Krllit1JQoIE1k1ZnPH9nMIlOTfXBBx9kVSM4Da8dEsjYEEg5lYLTM9oRCVqp1Uk4ES9mw6dVsCGdUn48NmCv5sMPPwydaMkll4yq/leT8txaG1jmQJdTzlI247oOmFMqiYE2IbU6ZZH4rmITlrif2WiqYnJqzGJADSdlpgTOdC9XH7vZyIZGSuBEK5pxxhl7PE2y7MWSr776qu6FDqUhyCwlQT61f2p0XPDwww8n/b1JJpkkLLXUUiEVp46Vbdxxx416Hn0g9+Tll19e6f5Q5eB+TwkuTQlCihmTPvLII0kHO8QeHtKTlP6xmZvizUT1GCq8x258VF3KonzKmkxs5TXupWeffTaXQGWqhtd7T/T1mm+++WZ4++23Q2yQVEpifKtr93usE9nviaD+2PWQPJOWN9poo7qCWelb+qrWklJJqlOrjbbaOnLqPJ0AFk4kb8V5OkkWqYecLb/88rmsgy6++OLJh9FRFaOsJLq+2gbWu+vdy91ss83q+nck9fZ10KptVuMWWmihMGjQoIafN9VUU4VUBMPHJJexR5ry9z/99NOGAg3ZZ0i5tlZaaaVckkyQciDaa6+9Fl599dXQClLWu1k/bbQvzBMJpikH/ZY9DohNSGXvjWBq94GKt8kmm2TtSNX2glnPySOWoGbeeect/HPEXu+0nRweS/xFWRWO2k2nj7vzwn13/PHH130IRaOMD1GVxLbZVAM+6qijwkcffZT7e2plKfc36515Je5ONtlk0W0YfXDM50hZb2o0KZP9smeeeSbqb1GNu9GiHim/K3Mu1gPy8Jvf/Cbbd6zqWM65S8/3U2o8JcmTE088cdJrcM03WoGxiozn7wwm0akSXnzxxexEozvvvDO0upQgmZhT8loBpaNTT4dj8Bt78kDVMRhccMEFk15j4MCBSc+vYrJPGVIXp1MraDR6uhlVClKDPfIK5EtdQGczs69g0Wbq169f9KSAzbxjjjkmC1jYdNNNw0knnZT1by+//HJTq3gxsU/ZZMwjiauG9jxl0tVpG3ixZdJ7SrhOqc6C2MUhtVb/1Oi4gBPCUhdiUg4TqKHdzSvZt5EEwNiAGYJ79ttvvywIlcMiSMCmutfrr7/uib8dhnY+JfCKPpWxS1n3PEGOsTikICV4oqfTBmNxCt7w4cNDJ6GtPfTQQ5MrU1dZSvJWyppMkcHK9R5KNM8889R9f/X1N1OCSVO+i1bXCfdYJ7Lf+y+qKMc+Dj/88NDKGL/Erom88sorvQb4sDb4/PPP13240QwzzFDXv+1rszy2naed69QkulZbR06dpxNwk0dFVQKyUhMHGsUabOp+B8G8ebUfqet67K81ehhXbABSX+NOKtv0lfBWWyvh8JR6Dqnj2u5tn4D19Nh9EdZCU5J2WhmB5DFSA6VAolHsGgfBlynBYo20ldxbKRW38twvIeExZV2oVfZLUg9baWYcRbPHAY32aylxA1RSJrGIPWXWrgky5r9rZhJju2Gfbvvtt6/kHJZxW557LBw0VPTnSLneqXJJ4j991x577BEuvvji7CAdKuKYWNe3Th9354X9wXrn+o0yPkRVE9tmE3N61llnZWsVxI8ee+yxWWUwDnnhIOhORFxaX4doFXWQR959fsx8ZuGFF86qeMYggf6dd96p+9/fcsstIVZMIlHKWI5DLvM67KUVxnLOXf4XcUaplZbzOlAjpUJlFRjP3znio6yknHFCKmXaSTZYYoklSvt+qbTBZj4LJATQcdI2wXQEtX722WfZYJtFdBI+UhPB6nkv7Sh18Z4Fj7XXXju0KyqvpG4cxG6K1nRiBRSC2n7/+983pfpMzPfOZjFtVQqCbfLCQh6b4Y1MLHtaiKQiWVURcBBbfaH2m3FiWddTy2jPOIGHgG8eTOr5TzYlUpNhi07CTDlhZkT3z7Bhw5ryWVpNnuMiXuull16Kfn47VA6uOtqEmWaaqaX6J048rkL/xHiKBdMbb7wxl9er92+y6Z3SRzMPItm664EiJETRL7AwSV9Re9BfpJ6mqepJ3dwlmJnghtj5ZKNJ/imbyVNOOWUuwbh5tXeMKYoeg1UJG4x5JjHmgcV8DlbiUat+RqAKazI8+N9Zk6k9qromQwAlG1P1BBX3FKzcW6WOehMd+G3rDRImyIWkjvHGGy/pb/akk5PoqniPKZ39nmpt2+233x71ZdCmjqgiD8EE9fRvzAGYK9LG1HMoVW99S0pCCvOREfUd7azV1pFRpXVkknkY65UlNfh1rLHGyg4nyAvf5fnnnx/9fNZcGBfXG9DO/JBguL/+9a8N/62+xoiNHO7A98geQj1BfbzuiNaeaa9iD6br1KRfxFbMSm2rwNpcrLHHHru0tjK1rchzv6S2rhNbWYP7ZPDgwSFvzG8JwqNPoWIT1duZs3/88cdZDMXnn3+e3Z/EUDCeKTrhpJlxFKlrd6n3VqPtYOr+Alijufzyy7NH14oCtf1NHqzzUUGSpOXRRhst+W92im222aaw6vXctymH2BaxFxyL2C3u+/HHH7/P/fxUJOlee+212aNr9ZXa/kzXa57xTZ7r262s08fdeeBvcShzUYwPUdXQZtc7rxxRP8c6W9f9C8bStXWzrnFYjE9ox+s53KUVMddupBJ3d7POOmtl+vyYtorffd111w2HHHJI1HV09dVXZ2OyepCwGYPrsNF+jjFJSpxilX5XDmgn1r6ve9C5S35S4/mYo+S1x0sbzIFBsXGhzWY8f+cwiU6VQsf5pz/9KVx22WVh+umnL+RvfP3111m52HvvvTc7RahKDTXvrR2xsJ7aqbbz5nwei5GNln7uruhgxCqijUldoC7ze2ehOGUCjLzbVV4vJYmOzbcqI9jgrrvuyr2f47fk8eCDD/5ioZhFoznnnDNLLiQwKvUa6x4EkWKNNdYIVZH6WVoJmzRsQuYltR1I7dNVzsJWmf0TbXlMwkBXeZ6wyDVeZhJdLXEiNUCxp9+A75YHp6F1X6yceeaZs/6C08R4FLXpruKRdDPLLLPkct/HBhM1OsZM6Yd5blFz/RidNKYAJ4k3G+NhTgZknM2aDNV3ij6wqIw1Gcby3Msxm31U9BjRRhLBGxz01BeSr+kXCPLnkJ1PPvmk139PcCOb1VSDzTOJjr+dR/BSq6rCPab82e8pNRGjtyS6etvbWsAFa0XnnXden/+evnZEfQuBjrEHqnVqonSrrSNzUAuHEqTIc8xe9vg/dc2Z/SiCsar0+flMjewjca/GJNExRqT9IFAkpsplT21WPUl0vO4mm2yS9Dd70qltFoloHIAYg7aO9eiUuVFK9cnUJJxG2srU9YAFFlggtOPaBntXt912W3jooYeyOWPqKfZ5ataBsARlp1Zp5NpmXBa7/tHo2h33YT1rA43ieqBd796289kIUqevYm2C9XIOntMvEfNClb+ikOyaUpHn0ksvzR5VwaGgfSXRca0z3sp7j4a+sHbwV3ccysZeHtc74x2Cv/McP7YKx93pVl555fDrX/86FMX4EFUxBuvCCy/M9TWZx7IGwqP7/JH9EvqIOeaYI4vBYoySWqSgKlLv75122il7VAH7YMzl2OdqxGqrrZZVJWQdrFEk0W211VZ9JnhxsETsAQUxVehSf9ejjz46e1QBvwtjUxKpeuPcJT9cr6lroqmFYLrHgVUpN6MRxvN3DpPo9ItFwHo2YWuLZJRmZzGVzPEHHngguSGudaAk0l133XW5lpdlgYXPdtNNN0UNnsrQrtXAuEZS5Hn6UBXlETieurFU9OmAVZRHYF+Z3zsTixQsDqSU2R7RROaee+6Jfn7qZyoaAU5HHHFEaUG9nNrJok5tYYeJCQs5vA9OuUxNJq76990IFsBI2slznFBVU089da6nY6VuXKYGYan9+qfUa4K2Lc/DEljYKdvyyy+f+4J/bzjtuWulU35vFv3pKwh08xTU1sLptXlskhLkVgY2MTgFr1200/ioL2wM5llRpFGcqH/JJZdkJ5hX9RpKXZMhSDcmiY4TjgnI6WltoN7kCsYPteB85hD1HAbCa/eURMdBKbHre2yCd2LQUBXuMRXDfk9d5+bsncT0YSSkjEijSXT8J+1sX3M2qsMQRNrT/NKElMZ12jydayzPwPeyg+hT96PyTvpjzYNqwR988EHSvKXRJLpYJM30lERHIDl7wfWoVeblP+upBsKhElzjPY0jYw93YN2YtZJOlHogW0oSHevYnLYfKzVhucw9typJbffYa7njjjuy+7V2P1ZRsw4jzuugGMYCZSUlEoDMfP+KK64o5e+xj0rwM4+hQ4dm/92AAQPCoosuGlZcccXsELhOXSvoboUVVij0QLzU9qBq6m2r2aPJO4muN7UDc2uVaRjvUQWW35ck60aTAFqV4+50RVSSbdfxTifFh7Qzqlazj028cRmID37iiSeyx7nnnpvNV5hbM+dljBJ7+EgVtNP9zb3NPU6SeiO4lmhHL7744qg+jFj3viqp33LLLSEGbdWqq67a0b8r6kmic+6Snyodqgb2vVlraEXG83eO9qxXq2gE7LMhXM+DQSULAFSmOeCAA8Ltt98ezj777Fw2xAgeohpdHjixe/fdd88Gv1deeWVlE+jauRpYSqWqvKuTVBH3U6o8TwHoFHl872UugKbeR2xW5L1B0ddEp9WTcfjORnTqbhk4WZLqHAceeGC28E6CeUrp7ZSS71XsL+upyNEOpphiikq9HgE/VakW065arX9KTYTI+xrPO2G8HiQrUBmuWThZlqT2PfbYI1vo3X///XM53EStc8+XOR/gnk+tPlkl7TQ+6gunIzcjgIPEtFNOOSUsvvji4cQTT6xsAl0eazIpVYoIVm7kvx9RoHL3/zvmb5pc0Vr3mIplv6c82nnWcnoKDqKP/Oc//1nXa9TadhJ26z04ZESJJ7EJKbRx7X7Y3Yh02jydhC8OZMtL3vP+otecGw3aquI6OklMrG3HGFEb8eSTT9ZVCWncccf9eZ+43qQJDpXoKfCdv8ffjUGVaKpFd6LU6y0luYP2o6xDflK103oA7X5s4tuDDz6YVcHZfvvts8T/qibQoVnrUa22dlez3nrrhWbfY1Q0W3fddcOyyy6bBTd/++23odOxl1DleIJWbavXXHPNQqt51XN4GIfE//GPf8zWQE877bTSEkSayXF3GuZcRcfCtdN4p5PiQ9oZcwXa7GYhzoU55lFHHZUdiLflllvWvQdTNe10f6d8HsaasbGQxJH3pZYw3yhiNjiQrVGd+rs6d8lHygFeyDuxuJUTlY3n7xwm0Sk3DEgWWmihcO2112YJa6lOPvnkujZkekMwKe+FBYsqL/q2u9RT1dqllPaINs9rp8Wnvo4ak8cGapEnxeV9HxVRlSb12i3rxMUUO+64YyWSVOkPb7jhhuwUnb322iurWteodlusj/kOWlHe9y4Lkyl9BhvVVT6QoB3k0T+V2W6l3ot5jIO6alYVtt122y1UAfcnwQlUpWMDwMCE6ssrqK+s+77d+t92+zx9JfiUjRPIOXjp+OOPzxJ+OyEQKjZgJzXRoWtSQ71JdCR19HQPxCZXpFY9aXXNuMdUvHbrJ9rt85Qtto0jQKenanRUL61nvN49ca7edr6n9pz1JU7fjm3nOrXqdautI1dtns74qMx1CtfRi0n8rTewsOu4lKos9R682lObRaJx7Dyik8elqfdwSnWPvNuPIrXTfgn9e6P3Cs85+OCDw8Ybbxxeeuml0AqaFevRamt3XasIFF3hqJH1IQ4MJZnu7rvvDp2s6LWDdpvz1ft5GHOQwFaVxLJjjz02O6j3mmuuCe3McXeamWaaqfCqau003mnHNq5Tbb755pWIA2W97m9/+1uWhLX11ltX+gDITri/Yz8PiS4UgYlx1113hQ8//HCE//sbb7wRnn322ajXHjJkSNTzOvV3de5Sjertea/ptNIaUVfG83cWk+iUOzYDDz/88ORTSRmkkAQXiwUJToz45JNPkt6H0qUGy/Xv379tf4a8BguWrG9cHkEflHrv1IFuHt9hKwTSTjXVVJVJjKgt5Fx99dVZ2Xc2ndrt++7kz1PmvZv6mpyWr2r3T2WOC1Kvh3ZZiCE4baONNgpVQSDMWWedFdZee+1eF3/VfHkFIpc1Lk0dk1ZNu32evjYgyvT888+HtdZaq8eKEu2KAPE555wz6rk9JVdwevjw4cPrOtSq6xocARj19IcjSuqIrURHtROqnnSqsu8xlaPd+ol2+zxlS0nI6Ck5pN6k5e6VnOrdd+kp4eWZZ54xIaUD1pFTD1MpYl6dZ2W7TlxHj/lMsUl0HKD1+OOP53K4Q0//fxHtZN5Voltd6jWc0l6Vea+narf9hUbaCfoJAoUvvPDCQt9Tu2i1tbuu9txzzzD55JOHqmCNY4sttgjHHXdc6ES0kZNNNlmhf6OT2zaurTnmmCNUBdXpuAd5NKuSZtEcd1d/LbHd2oR2+zydiurlhxxySHT1sCLceeedWXXm2ErozdBu90PK54mtYkY8BcVi8q5CR3I/lQ5jdPLv6tyl+WOzvA/Qa9UD+Yzn7ywm0akQnKRFpZzUAe/1118f9TxOsNp7772zgCA1X2pFwVbadGnWZ6vS5LKTvvsyv/fU+yi2IkJfFa2a+ZnKssEGG4TNNtssVMmwYcPC+uuvn51mV692q0bUKYlcqfdZT0YbbbSk57fbtdSOE/Iy+6fU6yHvazz1+k5B0nUeFbnzxOloJPe126ll7SSv+UBZgTjt1gd0UjJBmSd7vv3229lp9p14qFFsgsX7778fXn311ahkNqoTdT18iGT6epP5uv+NDz74oOHDOmo6udoHqnB6br3yWi/thDmZ/Z7yShbuKaGt3qpO3SvP1VuJjqDN7pVlrDbaGevIqW1XEfPqMufqVVxHT33NmN80ZWzWvX3iO6V6ZpFtVk9tYmybxfdNlehO1cy1sWb+7UZ18jiPNcx777230PfTTlp5L5+1gjPOOCNMMskkoUpOPfXUcOKJJ4ZOU8a6QSe3bRz2fsopp4TpppsuVAmHvxO71o4cd6exTWhcJ6xFdooll1wy7LvvvpWKdfz444/DJptsklVnbwXt1uen3N+LLrpoGDRoUNRzr7rqqhH+b7fcckvUa5KQGVuFupPHcs5dmi/veJNWLQrTymsAapxJdCrMzDPPnHzST8zp15RX3n333U2gqxAWrFK0c9BvXoOPZpxe1+ry+M7KHOyl3kdFnFbyxRdfNPUzlWnXXXcNBx54YCFBFLHo73beeefw008/td33XY9OSZQvYjE4NWEgdsFH5U2kyxwXpF4PeSewNDMhhnHBUUcdFbbbbrtKLQi9+OKL4YADDmj221CbzAfabTxR7ziqHYw11lilfac77bRTtuHYifIMVq43aLinwOT55psv6m/GVqHr9GofZd5jVZpLff7556Hd2e8pr7aOCnBd5yoEG9Z7unX3Kk4TTjhh3cl83fuS2IQUkiKqVNWhbK22jtyvX7/KzavLnKtXcR39yy+/THp+zJr0xBNPHKaaaqqov9e9rfjnP/9Z1/fCCdMzzjhjVBLdhx9+GF5++eU+K+LVgwS6Kq3jl62ZQTqttFfZbuO8esf4l112WXQwaKfK67pu1nrxlFNOGa644oq6K4OW5eSTTw4PPPBA6CRlVGLo9LXb8ccfP1x88cVh6aWXDlVClZsrr7wytBvH3dVfS2y3NqFT4kM6xbrrrhtOOumk/zkgsNmIgdt+++1bImHT+/t/x+tcTzE43LKn9dI33ngjPPfcc1GvOWTIkBCr08dyzl3SpK6Fpa5ftsseYqvF7yiNv5IKlXoKNaeWv/nmmw0959hjj+3I086rLLXCiL+nlH6CZ2rCWxGv2UqnkmKttdYKN910U1h22WUrc1oEQa833HBDW37fqu69W0R1PLWuqi3EFHHPNLoQsu2222aniy6wwAKhKoYOHRr+8Y9/NPttqA3YB7SuMgJ1cP3114cnnngidKpZZpklOgAiNomup0C4eoPjqFjatS9OqVDU6Ul0Zd1jecirKnw7H3pVY7+nvPY7uifNdW9/e6tUPtNMM/3iv683KaVru56SkDL33HO3XRBHO0ttu/Kep5c9V0/9/O20FhfbZpE01zVosN7KmSSvdQ8QmWiiicIUU0zRcJtFOxn7vXV6hWTVpxP3Sz799NMsjkKdh8TqCy+8MBx88MHZ/12VgNqDDjqooxIiylg36MS2rbuxxx47q3RIYgaB2FVx9NFHt906iuPuNLYJ0n8r0nHAw5prrlmZg5xff/31cM4554Sqs8//ZeJa7HfCgRPdxR48MuussyZVxfV3de7SzLFZ3mPVZsduSfUwiU6FqndjpDcvvPBC3f/2nXfeyQK28jDDDDOEbbbZJpxyyinh1ltvDQ8//HC2ccT76e0xcODAXP5+O0k9QadTT7CX8lxEK+J0h9TBcysFGdZMNtlk4fjjjw8333xzWH/99cN4443X7LcUzjrrrLr+HYFXaj15Tyq//fbbpKBZEkgNJFWebUve13gRwX6xc4lzzz03XHXVVdmibRX6vDPPPLPZb0FtoArXsqp94vlpp52Wy+tMMskkYZ111skC/Fjneeihh7Lkg77WZAYPHhya/T3Xm9jQW9AwlTg4AbMePf09NurqGbN9//33/5P0GJtEN/XUU1cmEK9ZqlSFtp45QR7efffd0O7s99Td/PPPH32wUtcklHrb2znnnLPH9qXeZOmuf4fTk2PX8jo9UbrVVG2ezjpUXgncZbTdRQQ3p75m7B5bSuLvU089lUuF5Ng2K+VwB5PoVI9O3C+hCl0eB9dyqNpiiy0W9ttvvywx6957780O73r++ed7na9fcMEFuXwOxSHJeY011gh33HFHOOKII7IK9s0+MJR1D95Pp0itFlwP57D/z1JLLZXt51P18Pe//30p339vaH/brRqd4+7qryV24nhHrYcqoiTWMybYcsstw6STTtrst5SNW/NaQy+Kff7/oqLhyiuvHPVd3nbbbeGzzz7LJYkupQod/F3/y7lLfHtapUI3H3zwQa6vJxXBJDoVKo/kgkYaZyo+cKJqiumnnz4bDBOkRYnmJZZYIjuhaNxxx/W01UgDBgxI+k1IXpQ6Xep9NHz48CxAMu8TeJr5mZppqqmmCvvss0+47777wvnnnx823njjMPPMMzclcJLNx3/9619t/X13Mu7dKr0eY7tmbzSpWiaccMKk57/11luhyvdMKpIYDj300PDggw+GM844I0sImWaaaZoSoECfZYVnpaKKQCsliqhcJAfUm/g1IuOMM0445JBDwl133RX+/Oc/h+WXXz5LTGYM0ionMMYmGtCHcThUI0HDv/3tb3vsi6kWNPvss9f1Go8++mj2nx999FF46aWXQgwDlVvLV199lfwajCnyHsdVkf2eumOPgH4pRte2vd6qTiNKSCHouR7vvffez+t3JqR0jgkmmCA5wCHPYLGy5+mpAXepa95FvCYHTMSgreheGa5etTaDCkH1VrEcUbJcvW1WTLJxTwmHrNNLfenE/RIO+0q11lprhTvvvDM7QGfdddfN7m8OVCHYM7a9UblYL1h11VWzBMh77rknHHDAAVmyUbMODR06dGhT/m67ih0ztCvaJaocsTdz//33ZwmkK664YtO+p3a73h13V18njnfUumibd9ppp2xviMpgW221VXa4VDMq1LFXwr56ldnn/9J6660X9V1+/fXX/9NHs4bDASGN4mBLxhkp/F3/l3OXxqQedvrvf/875KmR4klSsxgBq0KxuZKqkepJnHSWepIsi76jjz56pao9tbpBgwb9HJQVg9PrpE6XugjJ6bGvvfZaFriflxdffDHp+VU4QSgVyUQEydYCZTmpmck0SW1MLphcv/HGG1kwbGqSd29YwOkriCulUiqbDFSosAJZ+VLvs7wnvZ1eYUT5XxO0j4yfUysXF7WwkxdOaOa0Ux749NNPs0oQ9BckLNT6C4Jcf/rpp0LeA8n0JPORkCLFYrOKhIK333476vlsHhxzzDH+AG0qdU2GPoVDjUgMa+U1mZSEMtZOOC2z3uSK3qp6kHjxyCOP9Pkatb/Ff8b2QSbRtZY8KgzFbCK3Ivs9jajNi7kHqOrE+hwHEqQmpLCmxjpPPclJJKJMMcUU0evznOQ800wzRT1XrTlPZw3zlVdeiU4YbfY8PTVQNO+1ONY9up9o3qjYdV3u3xlnnDE8++yzDT+3Nkakvaunkh7rxhwklFKJju/qzTffzD5v7L4gCT0e/KJ6cJ3VO+/qjoNMSMZoJRx4w9pjCirPkTjXyvN1/XLMsPbaa2cPsEbNmjXBjrVrhkcRVVprHnjggWzsYdudX1xOigMPPDBLlm3XA1FIIOUB1rcZI3G9M/bl+mccUuRBhNxfHFiReuhFVTjurj7jQ9SKOIiWAwJrhwR+88032X56LQaLuDfGJxzwVmTVe2KwKLzRjvc3br311qyoSDuZbrrpstjvevbFuqNabC0JL7YKHQdTpMa8pP6uZ599dlhooYVCO3LuUvxcIO810XoKQkjNZhKdCvXxxx+Xdjoyg+aUimUMYo4++uikBDoCU1M3w9pRatIOC1bDhg1L7uilVkZwDJUXUk6MZ2E2ryQ6gitTT4ygmlu7GWOMMbKggO6BASzesIjD4jttGptPLMzzm3CqTap6+r+U75ukeJI8ZplllujXUHyAK9fNZJNNlstXmDrpJfBO6oprk8XslMQvrsu55547ly8274WdohDIRvBt96QDTvvnnmfsyyYAm7fPPPNM1ufmsRFAf2ESnVIxpohNomuVe1RxYgMQaw466KCkBLraCaFV2KgjGIagmJjvkCS6eitvjKhCUSPByk8//XS2phZb7YMDN+qtLqJ8D3SJrTafx33CicCdwn5P3XGQ0jnnnNPwF8OaHmN7Ek3q2UPgII7ZZput13a+3iS61VZbre7Eve4IPrGyTGsZe+yxsyDhlP05xu15JdGVPQeYeuqpk57PGijz77xOu09NPCepIGU9jnWHmCS6J598Mhtr1DtGJMBxRN8Ze3sEWdczjyThl8Td2L1WD3dQvVL2S95///2sjaWt7ZT5+nLLLZeUQJdX3IiKRX/Dg9+7+2/HejUB66xZ07ezzpxHtdkvv/wyvPzyy9laitLR3zLfiN177qS1W74rHlSq64qk0dp+Pg/Ghlzv7O3ngddabLHFQjtw3F19xoeoHbA+1jWproYkfOaYjFForxmjMPdl7S2PQ+TYN6my1Hg7+vx2S6LD+uuvH5VER7IPvzlrsbFJdEOGDAlV+F3bNYmuO+cuvzT99NMnfafsHTI3Sx3jgfaZWCep6kyiU6FoVFORNFIPFu1ig0dAMCkn6qfIa+Gk3cwxxxzJr8Fp9HvttVcu70dqRWyWs3mcUpnxzjvvzAIy88D7SA286y0QqN0QQFCbwHWdsLKJwQlGZ511VhaYEIsFob50X1RqFAl/JtE1xz333PPzqUepuN6aOelW+xlzzDGzYCg2FVOuyzyS6Ejka7VToLsbddRRs0UpHrWqdbWTmunHzzzzzGzTtsj+QqpnDMcp0bFzZgLI653nq7WkLIaTPNe13YvtB6rSzpFgceONN0YFNhIwXG/QUm+JcnPOOWc2D+krCZsEbqojxSbRzTzzzFmwvsofM8Sug1L5loNSYpNiCFC4+eabQ6ew31NPCcz1tK8jaue5f+vBOk5v/5b3cf311/f5OrTv9CtUw47t09R6WL95+OGHk+bpea0jp65Flb3mzHyFKu6pY9OaO+64I+n5rA+kHP5JUhlrzzFJDawH53G4Q+1/Hzp0aF3tZErFI5PoVK889ksWXHDBlvnCU4PXNtxww6a/BzUPCaM8ulcc5Te97rrrwoUXXpj1G7EIfDeJLr8Dd1iniY0niEm8b8c9Lyr58ugeCHzDDTdkFV5SqtVxvbcLx93VZ3yI2j2GjhgFHl3Xrliv43CW8847L9x7771t217T38euT9bmM8sss0xoN4svvnhWpIBD7mOq0bHXFVM9q/t1mFJtbZJJJgnvvPNO1PMdy3X23IXE2JQDNUAS6bbbbpv8XtgjTzmIXSpL3E65VKeHHnool6o+9YgZ/HSVR+BubCBhXlJPhC2qzDVJF/UGB/Q2UI3d6JfaRWpCKok4KROBrlID5gicNhknZJMXSrpfeumlYZVVVon+Pt99990+/83kk08exh9//Oi/ceutt0Y/V2liTzrqaaxEgHQKknmlvK+L2267LZcv9YknnqirPWxFVM1eddVVwzXXXBMWWGCB6Ndpxe8nZY5T1Pym05GYk/KbpAaRqpoINk45Vb7eqmm9YWOrCpXoELtZxmFUt99+e5bg1BdOy2Zjrre5Rr2HYJCoHVtp3OSK5qh3vXREiZMpQQCsB8RUWmxV9nv/xRpO7GOPPfYI7YRklthgQZJD6q0E01ff2FfCSg0VQupJthsR2/nOnKf/7W9/y/qLVMxBUw4Oi8Ea6HjjjZf0GjfddFMu74X5D2O7Zu4JsPcZW1WPwMN6g/DzarP4m7GHO0w44YRhmmmmiXquOg8BdQTedsp+SUocBW1IHgfmkqCs9kKljJ133jlccsklSQnfrbhmXWUp9yt7iLFB2+2OdbjNN988XHvttdmYo8zrPaW/KnKfxHF39Rkf0pmq2maUhbErh11wMO2WW24Z/Toc7pJHRbsiK/TNMMMMHTOfaeT6/8Mf/hC9FkQsRozBgweHkUYaKTR7LHf33XeHb775Jpf30W46Ye7C9Z+6386hERykmYIDQK+++uqk11A646zqYxKdCsPGGCWS81iMqEfqwDUlsaAqA8zURDVOOy9q4J56Gh+JPwcffHBu70lqRYsuumhyYOvll1+e/D4ITGXQnII2gdPw9P8Grvvuu2/WXsaoNzlykUUWSUqMr/JksJ09/vjjdVcj6SshPeWkFxYc8whwV/tJDaokYSCl0mrXa7zd0U/QX8TKK5m+VeY4HsJRjPnmmy+pkhynvKn9VGFNJq+DB/KQUgHjjDPOqOvf1TMuqzdY+Yorrqgrca8nVvtojtR75pFHHom+14866qjQSez3lGfbx/y+3iS6vtpwqrjWG7x52WWXhRgTTTRRVgVLnTdPpxp6HvtdqWtRzVpH57CfPALImfukzktTK+Ixd4tNCOf3q+egDNYM+/ob9a4pDhs2LPrA0vnnnz/qeercSkMpB+zSRrZScGLKnH2cccZJDgjl1P/Yg1tUfVTsWn/99TtqzbrKUsYOrA3VUzm2k1HdZrvttiu1Pa5qHBgcd1ef8SGdJ7XNaKe91R122CGr6tWuY5SUPv+1114r/dCjsqyxxhpRsXf00VScjYn3W2211UIVfleSPz3MtrPnLqkx+rQN7BmnuPjii8Orr76a9BpKZ5xVfUyiUyE4ofLwww/P5bXYDK73bzYz4IvkAqpftOop1MgjQH9Ell122eTXYMEutZPuaxAgVRkbzamn6J588snJ1RlOOOGE5MVWqq/pl1WGYk/KrfcUkJS2mI2Tv/zlL6FZ3njjjbDbbruFTsR3f8QRRyS9BgmQ5557btJrzD777MljDbWn1IUYHHnkkcnVhzolMYdTsgheiZF6alQzpJwE9u9//zvX96L/F4SZsvF6//33h4cffrhpXyebBzGbICr2dNLUNRk2dlmUrwoqxE022WSFrk3UkyBXbxJd7KYPC/ApgaeKl7L5D9bXYpIaOOCq0w5Xsd9Tnkl0rKfVsy5Xb8WXott5E6VbF9dGbPWxmuOOOy5p3+39999v2rg7de2Zw+hSk8YJXOI7TJ0PL7TQQqFZSZX1jkupfkwV5L7WMiaYYIK6Xs82S2VJ2S+hTz/99NNDszz//PPhwAMPLGXOnkcFjlNPPbUpSdVqjQreVCpQvuPAevvcnjB+S40liEVy8nnnnZdVRW7X6z1mjyZlj6ToODDH3dVnfEjnSW0z2mlvleQmYlxiVX1fPTUW95hjjgnNwppRUXsN4447blhxxRVLG5eyfkqSfV6WWGKJpDU9YjpT4+hTDuXi7//zn/8MVdbOcxfGZqmH4Bx//PHh7bffjnouyXMnnnhi0t9XPoyzqo9JdCpkYYMg9zwSykgWqTfoqK8NmnoCb1M21A499NDQbAwCq1pJjw567LHHTn4dNgRiSyf3tnFw1llnhXXXXTfX15WKmOCvtNJKyROWPfbYI3pQf9dddyUns5KEs+SSS4aqo5rqs88+W+rfjP1d6q0Gs8ACC9R9SnlPSFAhabxMBGvss88+YbnllgvXX3996FQkHFx11VXRSXj7779/Nl5pdkK82tMUU0wRZphhhqTX4KSz2OQH5h9//vOfoyvopKKSS9knKcUu2qdUD2uWlAMEnnrqqVyqB+iXVl111aSvJY9+qREEapE8N2TIkLDNNtu01SZgVcRWVM5jTQYcOECgcjtVgOlLPdU85pprrjDKKKMU9h5I8Ehdj1OcKaecMumrY67LZmajhx7kvSbXKuz31B0BOEWOrWeaaaa6NjmLrhZvEl3r4vpJOfgCw4cPD8cee2zUc5mfs5fTrNOZSTxLCR7HjTfeGF2Jhc+/3377hQ8++CDpPSy99NK5jLWqMC5t5N/Fss1So9hzSDmZ+8wzzwwvvfRSqV884/jtt98+DB48ONx99911Py+lLaEtf/PNN5PWTm+66abo5ysOfViZa28pwaSpwf7KN57gk08+ye3A9HpxrV500UVZTNFhhx1WVyXc7nFOvO9WuN5j5pGphyxTZbmoRGbH3dVnfEjnoZ1JWbO65557wtdffx2Kiv0p+5DNlMMkqr6vPu2004aZZ545+vmPPvpodAxSLJLm2M+jz7/wwguTD+gckZRKY41i7zlP/fv3D4sttljSfcYhJmViHMbfJAGQ4g6Nrgc6d8kPuRaph58yFt94440bPliDuJxNNtkki01W86XMId555522rVbanUl0yg2T7nvvvTdbtL3llltyec3555+/7n87/vjjJ/2tm2++OXqxg82wIk/vKesUagL0L7300lAEkmbWXnvt5Ndh8LznnntmA+rUSRsbmSyorbzyytmpogRgS1XHRI/F7xQseuy+++4NnzxC8tSOO+6YfNoOE8gxxxwzVB0neVJyfaONNsqCros+TYTJR2wSRr0DXzaEUxOGt9tuu/D000+HorFgs/XWW2cb2VdeeWXlT3MpA0lCJLI26oADDoh6Xlf9+vULK6ywQtJrqL0xnkp1yCGHhNtvv72h59An7bLLLk2dwD/44INh+eWXD9tuu23WVxZ9qjLzjtiFp9TN1lab49B37LXXXpVLrGkHLN7XWzW+J4x5ttxyy8I24rqels4cl0Rwkuc4JEHFYFMn5WREDmKKDcpjrHj11VeHqikyiJc1sKmnnrrPf8e8KzXRvTcGKjfPdNNNl/wabGpyImRfc3w2SzbffPOOruJpv6fu6POKrMRZb6JJvZXoYtnOt7Y85unnnHNOuOCCCxp+HodOEqzbLKyB/uEPf0h+HfaiYtYpWIvLI2GEdek8zDbbbIUmKNTbFhXZZk0++eRh4MCBhb2+2hPrZKusskr089lj23TTTcMbb7wRikS7wvr+BhtskO1Z/fWvf214/TF1TTD2gEOCZHfeeefKV/FoR0cffXRYdNFFs4R4EuOL9vjjjzftoGj1HE/Avl7KPf+Xv/yl8K+W9QauUa7Vgw46KLoazfnnn5/N26loU0Zyc8r1HtMec4+kHGD28ssvZ99zEQdROu6uPuNDOtPEE08c/dxPP/00i0spopLVW2+9FTbccMMsZoy+pug4TWJMYytisfbXCnFtqesGHHpaRgVYDgMhTpEkK9aaij7sYcYZZyx07bbr3mhqVdYiflf2fi677LJQtFdeeSUbwzEOO+6447L2I4Zzl3zlEaNPLAdtdb3tA/kirFfQzqsaUnNJ9svhgLhWED9rVlti8MgEuh5kjBOMOGzYsCwQ7b777st9Aa6RYG0qYKQgC//000/PAusa+b723nvv6NMo8zbRRBNlA/iUIFEGx9dee21WJYoTKwjM4mSN3pJ2OL2uns0pJkKcIJXH6aMMqG+44Ybwxz/+MayxxhoNbQByzZI0SZAdv7vUSjgxgiBgruHUk3Sp9sCGfl+BOQQfU6qZU2BSF1dZaKAtaCUkRPCgghvBJyR1zTrrrLn/HSaFsQtRjQSxE0BC8GNsAgbP4+QQFs5SKyN2xziCa5P2+fXXX8/1tdsBySAk6bBgssMOO/S5YcL3ue+++4YHHngg+W8vs8wyLZl8o/JwkAbVTFISYgim4NrmVCNOVO7rGmczlAQpqo01G/0jgXU86KupVkJ/Pc000+T6d/iOUk6BTUl6amalHQ77iEUbSBtG/03g4KBBg7K5Q1/JPvyOKaeRt7uRRhopm4sxH47FaZP0aVzTeV6b3I+PPfZYNq/l0JRmVb/oNFQ7Y10gdo5N+8Z6BOsyjQT5UMWUAJkqouIH90oRydWNVPGYb775CquwbXJF8+SxAcy1edJJJ2XjF9bWOJm6FmDx/vvvZ2vEbHzdeeedpVYwqCL7PY2oDUwZp+aRaML6/TjjjFNI1QXGZ6kbrWquxRdfPGvXY4ORux54w2FjJJSNPfbYfQZCsxbFgZvNts4662T7SKxtp67FEQSy22679Rnkz3r7PvvsEx2k172qB0FfeWD+S7vC4Xp5Y++w3nFJkUl0jksVi70O1g9iD/Gj3VtvvfWy5GHu2zwxHicGgWrQ7733XtJrpa67sKfE2lo9h7l0ff+sHXVC0FVVMUY87bTTwhlnnJHN91ZcccUscLmv/rxR//73v5OCdFMrreuXWKMj1io2ARas0X344YfZGDDPJALWF6ikSdvGfCqvxC7WgIll4DHnnHNme/pU9U2tTtwd7TH3VJntMWsSxMOlHOzO70lCNnslHLjFPIE4L9Z0R4Q12nri8Bx3V5/xIZ2HvjUlHvK6667L9tkY/80000xh0kknzWJH+9q7mWqqqbI2qy/EGTPHZm+H/orDatnzSD1Qvqe2j3Xu2INaemsjq4LvjhiN2EMqiX3mIPVdd901l0P9u2IewD4t1e5YVyob87R//OMfhf4NxtdFxBOwzsI9wX0Yu/fDnuvbb7+dxcLn+R4/++yzbE+JuL48v1/nLvm3C6kH/pAQx6HIjK0ZQ1IQidhZkke5DlhzrlWe9yDj6kmdZ7/wwgtZzBuPueaaK+sXKaTUV3syYMCAlqo2bxKdfjHhpxGtAhpcThxqJIGMG5DOPxadB4s0W221VZ8Df6rwMJivQtBuVyx4xA6gavhMjXyu2WefPVxxxRV9/jsWqBh4U0UuD0x02JSgihydNQFqNP4EDrCZyWSGQG7+HYkEDMg53T620pNUFX/605+yILbUU3lIPqAqGSfYs4jMf9L2MpBhUZzNPxKk2VzPK/iYBDqC11sRbQkbhTzYfGBDlE0nNv9TqqHS9x522GFJiZG0w/WijSQ5hQCYWCTScR2y+bLZZptlE6V6FsNGdPITQfRU+ysquLedEGDONcjmORMVNjy5p7h3+T65ntiwZDGKE2HyqvBD5QmpNyRZrr766tmBCanX+FlnnZVd4yzMc43T5nKN18Z1zz33XBbQTR/FdV81LFJT1YUHmwW1/oLFzpQABZKLOe2HNrOM/qIq8qigxCI9m+eNoF9uJDCoExFISpW3lEVR5mcENJA8y6lozOljfPzxx+Hvf/971i4wTmYsq/LRxqRszBI4w5oB8/y+ApRZmOcQiqocatQT5ggkN6QEuOSRRMe/Pffcc3N/DyyUF3G4iOpDEBNjpDwONGMjpKrJqFViv6eyEjYaSUhhLYZ2njWVvJmQ0vrY0ObQijz2YwhyrgXdcgAjwb/M05nDsxZFogRrUfybqhxiwToFgR7HHHNMLp+fQx0XXHDBbN+SAEI+P0k3fH5O3iaAiH3DPLC3RUBh3vd0EUl0zNnrDaxn/6GoxF/2B6UYrN0RXHneeedFf4HMT6lIRwAl7W7sPIm1T9ZJONSRNoW2JS8cLJWCtp3PRpvKQS29oW28/PLLs0oEBNap+Yh/Yc2FRy2xmvVq+gba8diKZQTn0v8feOCB0f0/STzTTz991HPVu1122SVrS1LGZgS9s9669dZbZ21cbDIdeyYE2JI8x3VY9EE9tKU8qIwyxxxzZGM4rnfa55QgctpnDkxIOaQido+GwxVS1xjZP+ZRL+K76jmo1XF39Rkf0nloM1Kri1Gg4Mwzz2zoOcyJG6mcyViRfUYetDnsqdNeM79LOdiJaljs06fELbTKnjrjOBLe6atjEW/BvhyJMKyj/P73v49KIGTMSWwufT5rlfTFRVRBrRdxkHkcLtUbKnUVhYNsib+JrerNWJ0DNW677bbs+uD7iK1sS3wpvyvrfvxnkTE6zl3Scf9yKFhea4u1sbU6L87q888/D1deeWX2qBdjh0UWWSS0CpPoVFkMyvqqDtAdg+lGbtiecAIzQVgMQlgEZjNwrLHGyqq7EbT7+OOPZ4tNLO4UcZp4qpRTCMqwwQYbZJuNBD/nhYHZo48+mj2kTkCg3BZbbJEl/uaBBdciAju7Y5GDJOV2QKAim4A8ar/JLLPMkiXy0m8QSMEiD8G/nMhUW5BnE5SFIJ7PycQsthO8kDrBXHjhhRv69yRPcnpUatIaGyc8OG2CATDfAYNwPjd9J5+dZE82QwhuJzGTz06QJg/+flUCa1oNY5LaiYpFq51KKNWTbElgWR73NUFo559/fvZoZQS78Ljgggt+Pi201l/wf5MwxAYj7SYBAyxcsjBIf0G7SZtJW/nggw9mj5SFZoJx2SxuNUWeVK80XFNUpuUU09gT28FYoXYqNhtjBI1zn3CPkHjKmIK/xX1BFQnaBzY8OByF8QRjKhbvqzg/7zSsyaSccA02YNjIoaIn40sCqdjkZ7xMQiy/N//mr3/9a0tUxmLDt4i5ViNtI+tERVTEY80sNtBO+Y3TqbCjctjvqaeApCKSQUgyaeTwDfoEk+g0IhxUQWJIHgFDXOtd10NbAQkfrIGS5JeK8SiB3zyKxtpt3gkFRSXGNnK4A2NSTi5mPJ8nXtckOqXgMBcSgdi/SHHjjTdmD+5f1uBY2+BgFU5pp29nn4g1kNpBYfw9gpO77pcUFYzIYbQchJJSnZP1GPb52Y+iYgiBxSQUs5750UcfZZ+FSqQcfsZhYKomrrHaWjNYk2Zcy3XLmvVkk02W/a7scdauW2J2vv3222wdhjEFFQ2ImeEgq9TfmrFsbDCvekfQ+A477JAd5JqC35w1YA5mIKiee5/2jX1/1m1JrKvtaRBkSdvGc1ivpW3jALbU9jUW74trlQfJFFzLzLe45tnPZ/2Z74k9GuZ2teudtW6ud9YiSQAkaJgxIOuSqYnbxBTE3iup665FctxdfcaHdJZW3FulzWX+zgOMR+hvOPCUNpuDpTm4kEctBqu2d0j/Q8Uk+h4Oo2WMkrp/1GgMVjNxKDEPPncKCm4Q00chEw4QIvmcPpPvnHEhB/IzJqzFMdC/U+yEPbBan1+lQzTYv2Jd7Pjjjy/k9YmhmnnmmUNRqAJJ202cSQpiVTgkn3npYostlv2uvG9+Z8ZyzBNJ1OOe4ffjd63dT7XftVmH1zp3ibfKKqtka7hFV2NUdXGYCHObKh5MXyVGOqiSWKRba621ohr/1CQ6sPD3l7/8JbSixRdfPAtArCoGqAxOOYmhSgNnqRUTjUnAouJGK+CUC06ojD2hrupIcsijAkCMaaaZpuFTkPg9jj322KwtZkEpj34ztfqUqokFyD322KPZb0Mtgs1G+qdWHUcXjeQFKjSlVGlKTW5hMbTVsJHNBkkeQY/KHyeZ77zzzuHII4/MJaihaxCPWg+bdKlBeWCtgE2h1I2hKiBYOe+EcDYrGwmqJgioiIp4Bio3H+ugJtGVy35PXRGoM//882eJ3c0MdCoiMKr22dT6CHDi1GEqkXQigupYA11jjTWyxJVWQBAWwU15Y/zIAT4EuuWp0TaIgxjyTqLjsxH8LsVi34q2ggSxPIKLaklxVUKiFIdwcABa6vomiXI81B5qFRCbVVmAit8qzoYbbpglE6RWIwKHJ5Iky6NV0caTsJx6yGwzrncSGJmnNbOiT28cd1ef8SGdhcNOSI7JIxaoWTh0gv4rjz6sUeypsN/VSg455JCsChzJT6k4POOKK67IHq2O+PNTTz01S/5rpSp0NbvuumuWBJXH2IUKjV0TVVuRc5fGUJWZ67QVDqVVMfGe7OVTHEMjNnIv/5vUtJuXwNtGq9DVNmvIlm+mmHLGeSKRghM4qoyKRUcffXTUbyzpf5PSSFhoBQRqNHIqrer3xz/+MTopgYD3Zvdbqraddtopq6wo1WvjjTfOTsVS+/QXVUkSUHVtuummYfnll2/221AFkEDHqY7NVLWxLetUeVdro3oHQTONvo+8FVXNRI0F2Tezymy7HpLTF/s9FZ1Q3Oj6GSdC530/1qrsqT2suOKK2QninYoEq/333z+0Au674447rpCKPEVVa2u0zSpij8BxqfKaZ+21115tv25LW9AsVZuvq/mIaVlqqaWa/TbaGvc8e8FUGVTzx3lrrrlm9PM5IHGBBRYIVea4u/qMD+kcJLa6bxdv/fXXb7lKuRyac8IJJ2SHKen/oYpeEfcCMc8rrbRSKfcyvytVotVcrTh34aDqfffdt2nzkHXWWacpf1v/z6qrrurX0QeT6FQpDDDo+KmqE4tTNZs5iJ5kkklCs2222Wah6jgpiWTJvIPJpE5CAt25556bTYarbJNNNsmCzFRM6eWUxAKqlx5xxBENB+GqPCy8NCtwjQArTqmUGl3IY4zXrAVaxuP6JRaHOe29VZGUQ3KOqotgjMUWW6zZb0MVsPnmmzdtfkKFqGWXXTZUCUkNs8wyS66vGRN4nHewMhuP0003Xa6vqfhDL5oxn6OiT6ttGObJfk81RSSkNJr4TBtA4H+erDbafg477LCmVSZfcsklm35AE1U/OL27ylhHOeOMMwoNcs/73iYYp9EKcBy8lPf83iQ65YUAr3au3MkcrllVv4gB2XHHHZvyt1VdJK566HLxxh577HD++ednB12reWgD+/fv3/ZxYI67q8/4kM7BAQoeYtC4QYMGtWx8G4VHqLpGFWr9P+utt14ha11l7YVyTbZCbGi7a9W5C5XomhFDte2221Zuz74T8RsMHDiw2W+j0oyYVqUCi04++eSwyCKLJL0OwaHrrrtuKBubT3vssUeoysLEzDPPHKpu6aWXDieddJIBsVLiRvV5551X2Yp0LC7svvvuzX4bbYnEKhJVUk8OJUmLqoatdpJSJ50IR19Z9mR82mmnzQJDpRgEfFF1uOyF+YUWWijsueeepf7NVjDZZJOFAw44ILQyNpe33377Zr8N1XEgzgorrOD31OEYozaj0seEE06YjZlI5q6avIOVY6rK5V2Jjs/UzAoG+t+N8bIrQBJs0+pji1T2e6qZaqqpcl2TYy5FonKz23kTUtoPiU7sv4022milV6M46qijKjFuIOiYQzir8F56GkOfeeaZYfbZZy/07+R9b8cc1MBaUZ6Jv/TJRVS3U2cfDMP6YrsePEg72IzEZtYJim7j1HqB/Ry6rHIwZ7nggguysZnKt8wyy4Q//OEPuYzlllhiiVB1jrurz/iQzsAaUzPiZ1sZ+0tUhy977STvvRsO6GnWQd1V3UPhgPq8E5PKPhCFQxGafUhVp2r1ucvee+8dVlxxxVK/L5Lo1HwUOCIBVCPWnqt/ajkkfF1xxRW5dTYks+W9cdwbNnxOOeWUylRVYxOKhIjUk4zKQLWCyy+/PEsSkBRnhhlmCFdeeWWlkmfZvD7wwAObWh203U8NPOuss3I7LYIKRRdffHElqqnqlxjTkJhQVlA4Cy8E7rTCOELVxUben//859KC02r3iSfq/fJ+5mQy+o1Wt8EGG1jprEUqUTarKpKqdaoZgYdlJtCxeVTVg0XyDFZm8zZm3sd39Nvf/ja392FyRbWwDkpVlzKQLO2Y67/s91REmxi7p5HnXogJKe2Lqr0Eg5V1UBOHv5199tlNq1Q/ogPf6Meq9J4Yo1122WWlJIKxD5dnwFUV2iyC4axcr7xttNFG4fTTT2+L9bTuCKY98cQTS20HSUpcffXVS/t7qr5VV13V/eMmGDBgQDbmIKFL5VlwwQVzPbT0oIMOaolqEp0+7m4Fxod0BqoszzLLLM1+Gy2z1ss4mTlmq5t//vmzGEIOzlb+1egYU9G/l43DEK6++mr7sZK1w9yFmC0OOSvjQM4tt9zy50JEP/74Y9JrGfuVX+VMk+pHzKgmNRWnqpLpysCNDbU8B7annXZamHvuuUPRFlhggSyRoWqbNJwowgZlzMm1ZWPQft1114UNN9zQYEspEgGjLPoxGG32IJITUGjX11prraa+j3Y+5fzSSy/NffGG17vhhhtKPzGnEZ0ckE+1h1NPPbXw8QbjsUsuuSRb+JFS0Q8cfvjhhR80wSIlJ6pVbTzebHPOOWc2NqASXTugDzj22GNNpGsBjEe59qq8OdPJY4oyN2eZ4xeNoBES6PJcUyri4KXf/OY3ubwWp2XGBr7nGaycd3U9paGqOGOhPBMlR3R6JNWGy66SXXWd0O998803Sc8fd9xxQzvLM4kuNpiPgKi8Tsmmr2nlE7fVu0UXXTRLDCk6mHXGGWcMF154YZbIXzVLL710uP7660vZP6xn3eTaa6/N9vPKkuc4LnZ8mWfgsoc7qCiLLLJIuPHGG7N1+XYb4zFuKGMtlb3KfffdN0tKlGrBm9tss0047LDDXJtrEsaAJDaxb1PlCjXtsna75pprZnFrea0Lgviv8847r/A1oDx0+ri7FRgf0v5of8qoet7qWLvgUFrWTNrF5JNPniVc/fGPf6xMUZBm9vkc/pnXGhVJVc0aq4w33nhZdeHdd9+90uu3zY5bzUO7zV34DAcccEDYZ599CjnAn3kGsTwctlzz+eefJ71mla/xVqxGyNxEv9T6d7datmoSDfKdd96ZBVUV0XGOOeaY2eLBaqutForqKBloVjGBruuEl+S0pZZaKlQdHSkJlVdddVVLl7+VmolBLoNR7qNmnHrCYvuf/vSnbCJOoITyRaDiJptskn2/00wzTSFfLyerHnrooVlVujIruvb1ubmemczdd999oZMttNBC2e9PomoRGC+QoGkCnfLEIuI555wTJppookK+2M022yxb/K/SaZbNxndB8goBi1WtypSyUEYFcE6v8jevNjbkrrnmmuy08QkmmCBUAQEGa6yxRhYotv/++zf77XQE5vgEzBWVcLPwwgtn11mVE+hq8zQS6fKQEnCc1/h+0KBBbZOg3U7YAL7ooouyJPoi5okE2dH/tsNmYRHavd/7xz/+kXRtkmjYzqpQiY6+luS3PJgo3f5YZ2P9h4O6iloHILm4ygc8EkxGv3nwwQeHSSaZpClVAQl6OvDAA0uf2+bVZjEmjP3u2LvMK5jcJDoViXU1DrZjLYrYhirg3uEU8SOOOCIbf6aMOS6//PLCkjDoA1gTzrPig1ob+1r0vdtvv73zygoYPHhwuPnmm8P666+fHcxTBYxNt9hiiyzOgfFkK+PQr5NPPjmrGldEkDJjWb4nAmGrvk7TyePuVmF8SPsj6Yb7kDbWw9F+GftLXPHQoUPbsroXfXwthq8qh4PQb7Ffxl4DceOTTjppKX+Xaz+PQgBcM80+mJ44e2IHOSSfMVNVEtZmmmmmsMMOO4Sbbrqp5e+ndp67MP5nHJnnb7TYYotl7ShVbrv69NNPk17XJLr80E4wN2EtiXGB/p/qppmr7QZlTE4J/ubUhrIWmlmQIBt8iSWWyJIChg8fnsvrzjzzzGG//fbLbWO6SAQsn3TSSeHpp5/OOvc77rgjfPHFF6Gq+G4J7qi939tuuy189dVXhf5NBjsEGa2wwgqF/h2pzIkJm1OPPvpotij4t7/9LXz//feFLgSzSEvp37HGGiu0ixVXXDFbzH3ssceyxz//+c/k09djsKDLxJdJcFnBqkyWaIMffvjhrCrZXXfdFb777rtQFgbsvAf6bxZzWLzVf3FCHpUe6StJ5M/jmiTIkwTc1Vdf3a9ZhZh//vmzRZMjjzwyO+Dhxx9/TH5NkolJzKhCgOemm26aBYARXEx/8dxzzxXa7/aWzE5/zCElVQneLmrsTiUc+kb6KK6t1157rdlvSyOYj3Pa+Nprr50txtJ//etf/yrtu2Ijg+QqqseTKM7Yot0WulsBAXPzzTdflsCRkoTRPSGDjb9VVlkl+51bAcG9Dz74YPLrpCTC5ZVEZ6BydXFvsAbAXIEqQ99++23S63F/0d/uuuuulU6EqIp27vceeuih6Oeyht6/f//Q7gH2zNVfffXV5PW1lMARfvOU36rGdr4zsEdH8gVJ0hzAkse6H4cyEQTF6d6tgDaSZOOVV145a7NJJnnxxRcLbae5v1hDJwGmWfJaR0kZW9JnkoD+yCOPJAfTWFFBZajtUxDkSRIyc7s81jcbGWswr6btoEJeXkkAVFJmrTbPvoDALNYnd9xxx0pXueoE559/frZPzHo16zF5xcnExH6wlktAZVUCfPVfzPM5+JyDwzlclSpd7733XmlfD8HsVMbkkKxlllkm6QDZY4455ufr/e9//3vT9gtITGZ/huD6opMTickgEJb7i7WgW2+9NXz88cehijp13N1qjA9pb8zBdt5557DOOutkbQZJLu+8805TrjPGnbTVjE+eeOKJ8OWXXzbl+6DvoQ+cfvrpQyesAXE4yLPPPpt9/3/9619L/d7ps4jLZU7FunWz4hhIomPfJGXew1pIVQ6Z5H2QELPVVltlvyuV1D/55JPS/j5rMuQDkEDF78phS7Gcu5SLdo/xP2scFCliHB0zvqNQDmPfEe0nfPbZZ0nvc9xxx016vn6JPV+qRTMmZ2/gXyXuYVaVSXRKQmPI4gYPFmxZDOXBYi4d9RRTTJENxFjwaGZp4NqiMgs/LG4///zzDb8Gi3okATKh4LVGFIRA9aXYwV6Rp6czaCFwmQAaJiE8XnrppfDmm2+G999/PxtE8b+VmSRRz/sl0ILEv3vvvTc88MADuQ32CEhggE5QN5sf7RxorM7FphoP7vFbbrkl3H333dnAN4/7nMAe2kLaVwK02jEgmQkfC/c8QBtJgu8zzzwTXnjhhWxx9+WXXy4k0ZeNCwIamGzyHTfrdA3eAw8W3UlqphIcAVn/+c9/cl3IZrww99xzZycO8SjqJOx2wWmznHhD0huLnJwcFTP5JLiJAE+C26ngKxWJBQ4Ot9hggw2yBFAWZ2P6IxLFOR2JzbZmzi+6ItGXtrq2Gcei81NPPfU//cUrr7xSyDib+5hFqdp8p5NOEOSa2mabbbIH3y8bLizyELz87rvvho8++ijro/neywxsUs/9Fn0ND5JMb7/99nD//fdn90ievw2BCSS01sYTPNo9aL+VTuwj6fWee+7JDmpgbv/DDz9E9QGMXegDRjQ+Zi2qioG0eQQrp1YZYh2EeVxq4FwVEtjVewDAtttum80V2PwiKLbRICrWeFdaaaVs86vqlR6rqB37vdjELIJR2JDrBIzJU5PoUk9/ZQ0wFfc/6/LqDIyndt9992zPiwrvnGAdE0RFsDABqgTGtuIJwbSntXabfTPWQBm3su6bin1F7ikOFyV5gEocVTh4kz1b9geb3WalJtGxnlxEdRdpRPsYtfU/Ao5Z22S/hPWor7/+Orcvjb02kttq4zuuc+ZxRfcFtIHsNTB/iNmLJz6EYCz6FPZ7esIeRMp83dPSG8MhDzxqFTfefvvt8Pjjj2frp6xXs27Nf5c3+j4S56h8S9/HmpCqjdguEiuoHEKSMIfzMod9/fXXc/07tBOsa9X2g5nP5pVoxpoXbVCtgt2HH36YJWcQF8b1zoO4qJ9++ink3TfQZhNHxvy3GYews6fO4WUkRLLuwOdmnEci4QcffJCtCdFPcfBj3p+/UZ027m5Vxoe0Nw4R32233bJD0xgTkHxMG0mbQSI1e6u0GeytFtFmMH+rxbGBfSISuzjQvLan/u9//zvXeKSuiVz8XdoJDv/pxIO8GaMdfvjh2UHFtVhc+n5+9zwxfyEmt9bnMx6sQkwh6yH01ySbxWp2FboRrcvxmzKvIy6UvpWxXN6Jshzi2HUvgj3bvOJ1nLs0BzHzPBj3Mweg0ALt8FtvvfWLPoA9aq417mf2QohNYh7RG9r1FCmJmep9D4j9Xx7Dhw/PYsnZyyTmijgr5lLsERAn3QlxViP91OxZktQENPwMFkiGYELOoOHzzz/PbnwG7LWEQBZ56aQZ1JFs1U4VlloRjTLBCEzk6GSHDRuWNdxM5Pj9qMbDg4E3i/48xhhjjJ+TOnnQmbNA31cnLrUrgsm5hxj8MPBlg4Q2sLYYwoOFChYxeXAPMZEkSJ/FRhKFmVhX5WSVKrRLb7zxRtY28V3SJvF91gaVfN+177W2QM0kkskF3y19DUkALO4z+CdAke+X77qqVTX4DPSdtbaYAXWtLabSKe0w/SlDzFqiPf0nwXx8VhbmatcTn5cH34Xi8Z0zmWXTng0Sfp+ekur4DdhMYUGDCS2nJHnyp5qFfqe2gMdGJm1p9yQzxnS0jwR3cb1y3bbqpjttJ30F8xD6idqD9pNNzFp/wX9yT9O/1PoLAky4f0mwrvUXfA/0F7SnUqtiDsf9z5iCjX3uBx5s7nM/cC/QLtBXcS8wNuVe4MEhKFz/PJjj0U4wtrBfa50+gHHLk08++fOchLawNp9nbEjbR3vH70tQDcFXtnlS3Bikdho7AQkEjdHOsgHCeIP7jYAB1j9rm1+sgRqQnr9W7feYW3JNNLpZxnoHp1p7cJnUOghSI4CqlvhLEB1tU3es7bGeV1tfYqxW1XXMFKx10neyjl47rIUH7WJtL4rPTXtNAjXtHt8ND9ppgomYt7uvKLU39kKY19YSk2r7RBxu2XW/BLUxXm2/hKSw2n4JMQm0razf06Y0c/7A3KEWRFU7jLc2f+C91fYNawF0BNtTTco1mdaco3Ddss9XW6uuPfjfuq5X1/q9rtcxfR/BtKzfcA2zh8zDPb/2wHXAHJa94K5jIRJtuTZo27ruexNjxfpC7bqoHebEegNjI9q7ZqIdqx0yXtvP58GY79NPP/2f/XweXO98tq7tNp+Lz8R8nMPkud6tupnOcXf1GR+isjE2qcXzdt1Tr61r18Yn/GdPfRFxSYyxGaMwvmZ+zn86Xv0l4rq6xuLST9Iu19Y/6O/5rvl3fM/sGzDWq8V/EX9bi1+gzye5vMoHnJLEzWGdMdi35JDQZs7XGsFvyW/Kb0uMSm0sx7inNk8lPrT2u3YtoFOLE2Xcw73DWK7Z6/zOXcrD9cH9T3vL9cE9z3i40TaUQ4i4DmPw91ibkIpmEp0kSZLUhoFPbNKz0cOCBws6Bu2oyljgZiGG6xa1BAoDuCVJkqTmoZIe1Q0bxanGgwcPLuQ9SSoHyRIE1jBP5/8mmIa1pbwqhkiSJEmSJEllYs2aA0MaRYXnAw88sJD3JLUbEjY5fC0Wh/Wcf/75ub4nqSf51NOUJEmSVBkkH/GQWgUnGHH6Mg9JkiRJ1fDggw82/JyFFlrIBDqpDVAhmFPFeUiSJEmSJEmtjgOjYgwZMiT39yK1qyuuuCLp+XPNNVdu70Xqzci9/q+SJEmSJEmSJEnqOA899FBD/56K0gcddFBh70eSJEmSJEmSpEY9/fTTYfjw4Q0/b9pppw2zzz67X7hUh6+++ipcfPHFSd8VleikMphEJ0mSJEmSJEmSpJ+9++674dVXX23oG9lll13CpJNO6rcoSZIkSZIkSaqM6667Lup5VqFTK4tJHE1xwgknhI8//jj6+RNOOGGYd955c31P0oiYRCdJkiRJkiRJkqSfPfjggw19G/PMM09YZ511/AYlSZIkSZIkSZXx/vvvh6uvvrrh5/36178Oq666aiHvSSrDDjvsEHbbbbfwzjvvFP63br755nDOOeckvcbyyy8fRh7Z1CaVwytNkiRJkiRJkiRJP3vooYcaCiY4+OCDw0gjjeQ3KEmSJEmSJEmqjD//+c/h66+/jkroGXfccQt5T1IZfvzxx3D99deHpZZaKhxwwAGFVaa75ZZbwp577pn0Gv369Qsbbrhhbu9J6otJdJIkSZIkSZIkSYpKott2223DlFNO6bcnSZIkSZIkSaqEr776Kuy1117hzjvvjHr+xhtvnPt7kprh22+/DZdcckmWTLfNNtuEe++9N3z//ffJr/vWW2+FXXfdNey4445RiapdrbzyymHgwIHJ70mqV7+6/6UkSZIkSZIkSZLa2ssvvxzee++9uv7tzDPPHDbddNPC35MkSZIkSZIkST356aefsqS5jz76KLz66qvh4Ycfzipwvf/++1Ff2GKLLRamn356v2y1lR9++CHccccd2YMqi4svvnhYaKGFwtxzzx0mnnjiul7jk08+CY888ki4+eabswTV7777Lvl9jTXWWGGnnXZKfh2pESbRSZIkSZIkSZIkqaEqdP369QuHHnpoGGWUUfzmJEmSJEmSJEmFO/HEE8NJJ51U2OuPNNJIYYcddijs9aUq+Pjjj8PVV1+dPTD++OOHqaeeOgwYMCBLsPvNb34TRh555PDNN9+ETz/9NEtIfeWVV8KwYcOypNU87bbbbmGiiSbK9TWlvphEJ0mSJEmSJEmSpMyDDz5Y1zex2WabhRlmmMFvTZIkSZIkSZLUFgYPHhxmnHHGZr8NqVQffvhh9ijbqquuGtZcc83S/640sl+BJEmSJEmSJEmSfvjhh/Doo4/2+UVwIuk222zjFyZJkiRJkiRJagtU4PrTn/7U7LchdYS55547HHTQQc1+G+pQJtFJkiRJkiRJkiQpPPvss+Hzzz/vfWNp5JHDIYccEkYddVS/MUmSJEmSJElSWzjwwAPD+OOP3+y3IbW93/3ud+HMM890n0lN0695f1qSJEmSJEmSJElVMdtss4UXXnih2W9DkiRJkiRJkqTSbLLJJmHppZf2G5cKNnjw4HDAAQeEX//6137XahqT6CRJkiRJkiRJkiRJkiRJkiRJUkdZccUVw6677trstyG1tTHGGCPss88+YbXVVmv2W5FMopMkSZIkSZIkSZIkSZIkSZIkSZ1jvfXWC3vttVcYeeSRm/1WpLY00kgjhVVWWSX86U9/ChNOOGGz346UsRKdJEmSJEmSJEmSJEmSJEmSJElqewMHDgwHHHBAWHjhhZv9VqRCTDLJJOHZZ59t2rf7q1/9Kqy00kph0003DdNMM03T3ofUE5PoJEmSJEmSJEmSJEmSJEmSJElS25phhhnCmmuuGQYPHhxGH330Zr8dqTCnnHJKeOutt8K9996bPR5++OHwxRdfFPqNjzLKKGHuuecOyy23XFh22WXDeOONV+jfk2KZRCdJkiRJkiRJkiRJkiRJkiRJklrayCOPHMYYY4ww9thjh/79+4fpppsuzDbbbFlyD0l0UqeYdNJJw9prr509fvjhh/Dyyy+HZ555Jns899xz4bXXXgsff/xx9OtPNNFEWZW5mWaaKcw777zZPTbWWGPl+hmkIoz0008//VTIK0uSJEmSJEmSJEmSJEmSJEmSJEmqlK+//jq8/fbb2eOjjz4KX331Vfbf8fj2229Dv379wqijjpo9SJCbYIIJwvjjjx8GDhwYxhxzzGa/fSmKSXSSJEmSJEmSJEmSJEmSJEmSJEmSpLY1crPfgCRJkiRJkiRJkiRJkiRJkiRJkiRJRTGJTpIkSZIkSZIkSZIkSZIkSZIkSZLUtkyikyRJkiRJkiRJkiRJkiRJkiRJkiS1LZPoJEmSJEmSJEmSJEmSJEmSJEmSJEltyyQ6SZIkSZIkSZIkSZIkSZIkSZIkSVLbMolOkiRJkiRJkiRJkiRJkiRJkiRJktS2TKKTJEmSJEmSJEmSJEmSJEmSJEmSJLUtk+gkSZIkSZIkSZIkSZIkSZIkSZIkSW3LJDpJkiRJkiRJkiRJkiRJkiRJkiRJUtsyiU6SJEmSJEmSJEmSJEmSJEmSJEmS1LZMopMkSZIkSZIkSZIkSZIkSZIkSZIktS2T6CRJkiRJkiRJkiRJkiRJkiRJkiRJbatfs9+AJEmSJEmdbssttwx/+9vfGnrOJZdcEuaee+7C3pPU1SOPPBI22GCD6C/lggsuCPPPP3/Uc9dff/3w6KOPRj13vvnmCxdeeGHUcyWVK+VeHzhwYLjrrrtyf0+Sqjs+kMq2xx57hGuvvTb6+S+88EKu70eSJKkejtlVluuvvz7stttuDa8F7bPPPoW9J0mSJEmSemIlOkmSJEmSmuj+++9vOIFuwQUXNIFOkiRJkiRJkiQ13YorrhimnHLKhp5z6aWXhpdeeqmw9yRJkiRJUk9MopMkSZIkqUm+//77cOihhzb8vO22266Q9yNJkiRJkiRJktSIUUYZJWyzzTYN748cdthhftGSJEmSpFKZRCdJkiRJUpNcdNFF4eWXX27oOQsttFCYc845C3tPkiRJkiRJkiRJjVhhhRXC1FNP3dBz7r///nDnnXf6RUuSJEmSSmMSnSRJkiRJTfDRRx+Fk08+ueHnWYVOkiRJkiRJkiRVycgjj9xwNTocccQR4dtvvy3kPUmSJEmS1F2/X/w3kiRJkiSpcCeddFL47LPPGnrO7373uzDHHHMU9p4kSVL5rrnmmjB8+PCo5w4cODCsttpqub8nNd+wYcPCtddeG/38wYMHh0GDBuX6niRJUuc68cQTo58733zzhfnnnz/X9yNJqqblllsunHDCCeG1116r+zmvv/56uOiii8Imm2xS6HuTJEmSJAkm0UmSJEmSVLK33norXHHFFQ0/b6ONNirk/UiSpOYhUerRRx+NDkg2ia49kVjJoQuxuDZMopMkSXlJGZdsu+22JtFJUgdVo9tggw3CgQce2NDzzjzzzLD22muH0UcfvbD3JkmSJEkSRvZrkCRJkiSpXKecckr47rvvGnrOb3/727DooosW9p4kSZIkSZIkSZJSUBl97LHHbug5H330Ubjwwgv94iVJkiRJhTOJTpIkSZKkEr355ptZxZlGcXrrSCONVMh7kiRJkiRJkiRJSkU1uTXXXLPh551zzjnhP//5jz+AJEmSJKlQJtFJkiRJklSik08+OXz//fcNPad///7Z6a2SJEmSJEmSJElVtt5664V+/fo19JxPPvkknHfeeYW9J0mSJEmSYBKdJEmSJEklef3118PQoUMbft5qq62Wnd4qSZIkSZIkSZJUZQMGDAhLLLFEw88jie7zzz8v5D1JkiRJkgST6CRJkiRJKsn5558ffvjhh4afN2TIkELejyRJkiRJkiRJUt5i9jVIoLvqqqv8MSRJkiRJhTGJTpIkSZKkEnz22Wfh2muvbfh5s846a5h22mkLeU+SJEmSJEmSJEl5W2ihhcKEE07Y8PMuuuii8OOPP/qDSJIkSZIK0a+Yl5UkSZIkSV1deeWV4csvv2z4Sxk8eLBfpDrahRde2Oy3IEmSJEmSJLWc+eefP7zwwgvNfhvqUKOMMkpYeeWVw9lnn93Q84YNGxbuuOOOsPTSSxf23iRJkiRJnctKdJIkSZIkFeyHH34IF198ccPPG3XUUcOKK65YyHuSJEmSJEmSJEkqypAhQ6Ked/755+f+XiRJkiRJgkl0kiRJkiQV7M477wzDhw9v+HmLLbZY6N+/fyHvSZIkSZIkSZIkqShTTz11mGWWWRp+3mOPPRaee+65Qt6TJEmSJKmzmUQnSZIkSVLBrrnmmqjnLbnkkrm/F0mSJEmSJEmSpDLE7nPE7qtIkiRJktSbfr3+r5IkSZIkKclHH30U7rvvvoaf96tf/Sosuuiifvv62X/+85/w9ddfh2+++SZ8//334Te/+U0YffTRw2ijjRb69XOJR5LUOr744ousT+Px3XffhV//+tfZY4wxxsj+U5Ik9e6rr77KHswPedB/MjdkjjjqqKP69UmS2o7zyNa1xBJLhOOOO67h5918881hjz32cO1bkiRJkpQrI6wkSZIkSSrQTTfdlCU8NWreeecNY489diHvSdX17bffhn/+85/h+eefD//617/CK6+8Et59993w3nvvZf/biIw55phh8sknD5NNNln2mHHGGcOcc84ZBg4cWOr7V5qffvopS5YkKIhAWAJgxxprrCxhshNwjdc+/0gjjZQl09AOjjLKKKGVPgP37nPPPRdeeOGF8Pbbb2f38AcffPBzwlAtCZYg53HGGSdMOumkYdCgQWGmmWYKs802W5huuunCyCOPHKrqjTfeyJLDn3rqqfDyyy+Hd955J/vNSITimqU9oh2aZpppwlxzzRUWWmih7HOqMw0bNiw8+eST4dlnnw0vvvhiGD58eHZPfPnllyN8Tv/+/cPEE0/8830x88wzZ33auOOOW+p7V3W8+eab4f7778/GSLQ7b7311s8BtCSK0F8MGDAgTDHFFGH22WcP//d//xemnXbaUFX0Ay+99FJ2X9Bn8HloS2t9BUkxteRS+gvuCfoKxnUzzDBD1lcw1uPQDf0SbQz91OOPP56NpWmHuo6tuF74LumnZp111vD73/8+TDTRRG35VbbzuITDavhcfD7GJvQvH374Yfj000/D559/nn127iPGkXw+HrVkbX5v+pnaf9bGLfzfVZwf0H/W2ot///vf2e/I/JDrekRoG/kda3NEPh99KW1js3/Pdr4uU9FO/f3vfw8PP/xw1k+8+uqr4ZNPPvn5t+Y7GX/88bM2bPrppw9zzz13WHDBBbPvSZ3h448/Dvfcc082JqqNrZlD8/jhhx+y+4L5GO3Z5ZdfnrV5eWMcz5oV93CtTWIcw3ur3cM//vjjz/fweOONl12z3MOzzDJLdg9POeWU2Zy/qvhuGXs+88wz2djz/fffz75jPhffKd/xb3/72zDVVFOFeeaZJ7sP+e+UH+eRvaOfZJ794IMPZtcr/QVjI/oL2gLuP/pP7j36zNraTDP3GngfzNdef/31hp7H+I770cMGJUmSJEl5GuknVt8lSZIkSVIh1lhjjfD00083/Lz99tsvrLvuutF/lyAWTnhl4zxG6t/vy1prrZVt9seYf/75wwUXXBDaBUGet9xyS7jrrrvCI4880mtiQaMInCKwbrHFFsseJGTF4H1tsMEG0e+D34vfLcb6668fHn300ajnzjfffOHCCy8MVUVA5AMPPJAFg9SSJgk4647EAAJf+Q4J9CbwpB0QGEoAIIGiBN+RREBQWlckCBBgR7IAAT+LLLJI5RKyCOi59dZbs8/CvdLTb9gIEoUWX3zxsMwyy4SFF164EgHCBDsPHTo0XHLJJVkQdyMIXl9ggQWyNoTPM6JgyZR7ncAw2tCqOvHEE8NJJ50UqqiIdpL27Prrr8/uCYJO88B9QHIU98Yqq6xSyUSHGATAV1U9fXeR4wPGQzfccEO49NJLs0DtRpEost5664XVVlutEhWZCLz+61//mt0X9P38/ykI4GZMQF/BmL/TE+oYP9x2223h4osvbrgvoV8iuYh+aOmllx5hlQsqYFx77bXR75FkoTK087iE/uXGG2/MkiT5PvPe4mauNPXUU2dJESRw0++QuFr2/UVg/N133x1uv/32rL0geSPPz8jnYky91FJLZclpZWiH67LIeSkJUYyzWRcgkboRJCnxPWy22WbJCeQpn7Fo2267bdhuu+1Cu47r+vp8tAVnn312lmBZ7zof8+y8EmZI1qvdwySpk6ScgrE8bdByyy2XJaFVAclHV155ZZZ8yNpMI+gnSPDZcMMNs0PRRmTw4MHZukcMkqBOPfXUllvT68R5ZMqYsa+1Db4X5kd8T5999lnD1ynzB/oLxr7NcMQRR4Rzzjmn4ectv/zy4dhjjy3kPUmSJEmSOpOV6CRJkiRJKggnq8Yk0IGEpxQk3Sy55JJZsG6Mq666qrAkOgJGqGAUa8iQIaEdEPhJEBS/UWoQ4Yhwov/NN9+cPQiWoDILwRwEh6p5QUacZn7eeedlQWj1BJ+RFMuDgOGjjz46O7mdAMcVV1yxsIDRooJ++LwEPxNEWk8yFv+eU7V5ECREIgSBdptuumnTk08IHuRzEOCcGkTYFSf4X3311dmDU7q5Dtdcc82mJYHccccdWaATVV5iEOTJtcuDa5ckbSr/qL2QyECbRtsWmyTfV4LME088kT2OP/74bJy0+eabZ9eU2gttBu0fvzNViWKRnP3nP/85nHHGGWH//ffPEkaagf7r/PPPz8ZieR6UQKB3bYxHNS3G7SQNdmIVFJIEDj300KwiTmz7RZ/Og8T9fffdN6so02raeVzCWOS0007LEo2KPtyEPozHNddck/13VK+j+tkcc8wRNt5440KD70k0ox/lb6e0f319Rg7w4MF9Q1WorbfeOkvGLUI7X5d5oHLQUUcdFe68887o1yDp7rrrrsvmSnwHu+66a/ThOaoeEmb23nvvbAzcDCQzkaDOXK77gTepa1UXXXRR9uDAHNrXlVZaqSmJ2owDSJ5LGXvSvtHO8WDdjTkvCdndUS2Vw5RipB7AUFXOI+u/Z4477risvY+9F7lOGVPx4OAIxrxlV2NmHh+TRMcaI/dAJ851JEmSJEnFaP4xxpIkSZIktam//e1vUc8bNGhQLifCE0gbiyBUEn2KQNJYbNUAgsE4Zb2Vvfbaa2GHHXbITkAm0K2oBLqegiXuvffeLFBPzUEVCYIaSQSluk5sICnJubXXITC1VTz44IPZdU+CXqPVzLpWROO+WXXVVcM+++yTBaGVjdPRSd75wx/+kCUv5BkQ3FMy9kEHHRRWWGGFLNCpTCRo7L777mGbbbaJTqDr6doluJcTxPMMwlRzURGD9mjHHXcsJIGupwo9BKhS7Xf77bcPb775ZuF/U+Xgt1xnnXWygM68EkionPLHP/4xCzrNu2pVX5/lT3/6U9bvcThFngl03b333ntZu8oYmSDwMj9nM9H/HnnkkdnBArEJdN0xTt5kk03CXnvtFb755pvQCtp5XMK8id+XsUjRCXQjwnVA8sq5556bvZ8iEBTOtUwiG4m/RSXQ9YQ5P/143tr5uswD7TQH6pA0lJJA1/01qaDFmCyvCk5qLqoTUrmsGQl0jz32WFh77bWz+5hEuiLnblQc3m233bLqwSTGl4kxFAf05Dn2fOihh8LKK6+ce7XvduQ8sj4kznGYFEn2ed2LVHBmXe0f//hHKBOHEnBAQaNYOy9ivCJJkiRJ6lwm0UmSJEmSVBASlmLMN998ufx9Xme66aaLfj5BuEW45ZZbop+7/PLLh9/85jehFRH0T1AkwTRU6+mUAGf9F4FnVI4bOnRobl8JAeMky5500kmVTkgiaPbggw/OTpfPK6CTz0sbRdAPAX5lfY4TTjghC0zl9ywTSWwEkO+yyy6lnEBPMCFBzwRr5Y3fjmoyBCu262n6nYKkICp8bbTRRtGJsXkk5tOvEjSu1kbiAwlnRSVinnrqqeGAAw4IRWN8R1UVEiNImC+zfyb4mwRzksBI3G9n9B+bbbZZloRSxJia6lZrrbVWVvWjqtp9XMJn4rM9+uijoZ1RWYU5LtcyFcVaXbtfl3lVA6T9InGyiORCkgpJSKciq1rXMccck41byk7o5iAVKvlS4bbs5D2S6UicPuyww7LDc4r2wgsvZG3VAw88UNgaCIcfFZlE3KqcR9aH+4CDjXhwbxZRAZd5wyOPPBLKQsXY2GrysfsskiRJkiT1xCQ6SZIkSZIKQABc7AnK8847b27vgwCUWDfeeGPugXxvvfVWeOqpp6Kfv/rqq4dW9NFHH2VJBgRCtUpVC+WH5MktttgifPLJJ7l/rT/88EM48cQTw0477VRKoFmjqBS3wQYbFHYKO20K9xYVLopEED0n8Z988slZQmyz0C5zQn9RlVBq3ymflaDCoisTklj52WefFfp3VFyVLSrBXXrppU1PCicIc7/99gs777xzJdtB9e3iiy8O2223XSHBoV1xvZLEWxSSNqh6R0WkZibD1Cq/poy5q4x+g7FF0dUoCObnsIK33347VE27j0tIviIZqsqJUKn43Wgrttpqq0onazai3a/LvL4jEtzuv//+Qv8O804OrGAdQq3niCOOyNYQysaBNxxSc9lll4VmYV5x3nnnZfcJCT5FoUI6iYIcHlMkDlFircZEuv/HeWRjCddFHGzUvcLblltuGV555ZVQlth9j/vuuy/39yJJkiRJ6lwm0UmSJEmSVABOcY0N5M4ziY4qGP379496LkGLeSemUIEt1rTTTht9Wm0zcQI8p1vHJlWqtR1//PFZ8mTRSSbcWwThVik46+OPP84S3B5//PFC/w6fmYoT11xzTWFB9CQLPfPMM6EKqC5Blbh//vOfhSQ9kgAyfPjwUAaCFwkMM7m4tXDtUaHppZdeClVy0003ZYk1Jma2FgKlDzzwwNIqtlEhqYh+iXaT5JGqBHcSeL7hhhuWXgmqaMyvSK4qq/olla04lKRKiSjtPi7h9do94YB5NpVfqFrZLtr9uswDyTok5r744oulJexRvUithQS2c845pykJ+Izv6feqgHuXcRUJV0UkCzLnJUmpDLfffnvYbbfdmn7wSBU4j2xsnFBWhTgOpdlxxx1LG3vNN9980Qc+VW0NQpIkSZLUukyikyRJkiSpALEni0844YRhsskmy+19jDbaaFkCV6wrr7wy5OmWW26Jfm7K52gWgnMIYGajX53nrLPOCqecckqp7c5ee+1VieAsKvCQHPXcc8+V8vdIvth3331zTxagGhuJgFWrEEIwP0FVJOnmheuGSl5lByVRKWnPPfcs9W8qHoHf3NtFVoZI8cQTT4TNN9+8qVXAVD+Snw8//PBSvzIquOadtEcfQaJVnm1yHrgPSDgrumJbmfjtHn300VL/JgH8VEqsQlJXJ4xL9tlnn8KrUjYTn41+tKzA+DJ0wnWZx0EVvJeyE5Tuvffe8Ne//rXUv6m0A0aoUFm2hx56KGyxxRalJZXVi/uFAzLyrBZHchKHD1GtsUwcUEaVzk7mPLL+AyO4RmkPyu7LL7zwwlL+1hxzzBFGHjkuVLHoSq6SJEmSpM5hEp0kSZIkSQUlBcSYfvrpc38v66yzTvTmNMHoeSVUUKEjNgjgV7/6VVhllVVCK3n77bezBLqqJhqoWHfeeWdWga5sQ4cODWeffXZoJpKxqHpQdiWK77//PksCy+u0+mHDhoVNN9209AC7elFtiyDsvAKWzzzzzKYFJFFBrOiKhUrHtUYAeFXvia5jF6oYVSGhWL277rrrmvI7UTGJdievxAiSR8qq4NkoEr+23Xbb0iofFR2AnvcBH/V67LHHkipq56ETxiUPPPBA6UmSZd+PW265ZdZPtYtOuC7zSk5oVjLfscce65ioBVCZmzk0c9qyK4NtvfXWWeJOFXEgFVXjSH7LA4naVKpshpNOOinXhMBW4jyyfu+//37TxkKnnXZaVpWuaBz4N/nkk5e63yJJkiRJUncm0UmSJEmSVEBwHEFSMaabbrrcfw8q2y266KLRz7/iiityeR8pgae8//HGGy+0CgKQtt9++yz4QZ3p/PPPz7XKTSOOO+64LBiumYFuzap4QHAdiXSpwYfcwzvssEPl7+F33nkn/OlPf0q+1l599dVwwgknhGYqO2BUjeEa41qr+j1R87e//S2rBiqNyLnnnpvLl7PHHnuEV155pdJfNJVl6NNauULjxx9/HA444ICO7ac6ZVxy6aWXhnZ25JFHtlWSYKdcl62Ocf7dd9/d7LehPlx22WWljyc4CIB7uIykmRT/+te/cqnQd9ttt4VbbrklNAsHR1Shqm3ZnEe2DtoEKoWXIXb/o+zDuiRJkiRJ7atfs9+AJEmSJEnthhPGOUW6KpXosP7664e77ror6rnXX399FnQ26qijJr2HlGCVIUOGhFbyl7/8JbrqnpSKwKx99903XH311WGUUUbpuC+Ue++CCy7IKmal3MN5BOdMOOGEYaWVVgrzzjtvmHbaacO4446bVdakQiVBkvfee2/WxqZU7yAYm2ShzTffPPo1DjnkkI4M6FP9uMZaLfCfhOKFF144zDDDDM1+K6qgZ599Nhuz0zbHuvDCC6PH112NPfbYYcUVVwzzzTdfmGmmmbK+4je/+U2WOPbGG29kfQWVZkkEiUWfc/jhhzc9ES2lklJVK12VoRPGJcyf77nnnqSqJksuuWSYf/75s882YMCAMOaYY2b30g8//JAlfBEczmelYjiJRS+//HL2vb722muFJ1jRVjA+bSedcF22i2uvvTYstthizX4b6iPhvWxUZcujku4UU0wRlltuuaz95f/mHh5ppJHCBx98kB0wRtt+ww03JCXzU73497//fVh++eWjns/fPvTQQ6P/vuI5j2y9/mK99dYrJYmOxNZGMTdiPNe/f/9C3pckSZIkqXOYRCdJkiRJUs5SKkAVUYkOCyywQJh66qmzQMFGEax2++23hxVWWCH67w8bNiz6eyGobpFFFgmtglOy8w6QHGOMMbLA6v/7v/8Ls802WxaUxIPA0K+//jr7jQgG5W8/9dRT4YEHHsiCDtW5nn/++ew0+3XXXTd0opNOOikLxqX9aMY9PProo2fJx2ussUaPCciTTjpp9lhooYXCtttuG44//vhw0UUXRf+9U045Jayyyiph4oknjgoqvu+++0JeSABZdtllsz6H759EznfffTcLdqJC4ZNPPpmdwq/Wwe936qmn5vJa/fr1C7/73e+ya2TWWWcNE0wwQZZA9NFHH4X33nsvPPzww1nlWhKc8qgaRYIoiU5qLVwnjF2XWGKJrK2kLfnss8+yduTGG28MjzzySC7tCNdabBIdlZdI1ExBkshWW20VNtxww2xM1x1tOg8SS7bZZpssCPnkk0+OTva5/PLLw5prrhlmnnnm0ErefPPNcNVVV+X2er/97W+zYH8SbCeaaKIsyYrfk/nKnXfembVDJF1VRaeMSxgfkOgWgyTU/fbbb4QB1YxF+NzcZwMHDszmU90rGf/jH//IfnvGRCTY5onkjYMPPjj3dnL22WfP5ofzzDNP1k4yP+Q74GAEPhPzQ+b/zMOZH5IsmJdOuS7LwGdkrYW2udYm8ds9/vjjWeJQHvN6kpi4Dkk2lWrXREwCS1fjjz9+2HvvvbM+deSRR/7F/z7ZZJNlDxKcuYdJYmPsFeuwww4Liy66aNZ2xFQ65b7KAwmCtL/LLLNMNqbgvmXcwFzmxRdfzD4j/ynnkXnjnmMePddcc2V9GeMa5uoktFNBLo+kWF6LsTf3bpFSDhHkPS644IK5vh9JkiRJUucxiU6SJEmSpAKSV2JNOeWUoSgk0xx44IFRz73iiiuSkuhSAmVWXXXVlqqmRcB+XoG344wzTth0002z345Eup7w3/MgIJQASk4MJrCcxBiCKG666SYrTFUIwfoEmREMStDLeOONlyUGELxNoCQVGx977LFckgNIehlRUGozkCzA5yboh0Qrgn0JgCPYjIC2v/3tb1lb8dZbbyX/rS+++CJLNthzzz0bfi7BfSn3MIF0p512Wt3tOQlEVA4kifrPf/5z1G9PUCxVeqgy1KjTTz895IGkOdq/Oeec8xf/2yyzzJL958YbbxxeeumlsNdee2UJv52CNry3qgm77757dKI5iWhHHHFE9HurJ5iaoPUvv/wypCLYlCSCng4MmGSSSbIHiQ1UiaEt5N9SwSIFfSFJMSRjVdHNN988wv+Na4JrIxbXBddHSlB/Myy99NJZMkxPSdBUOKFfI9mFxIvU/oKEme222y66AhNJKrHo/+kruC/qQVIFAej0nzvuuGNU1Wv6F/qJVkssPfPMM3MZWzPuYi5EAH5v7TVjEq5Bql9VQaeMSxgfxGBsefTRR2dJDbEIQqfCEQ/aXYLHOUSGyknPPfdcSHX22WfnEtheG0+vttpqYcsttxxhO02CHf077Sj96uDBg7P/noQ6krKuvPLKrNJlik65LovEXJ/PxDpL9+uXhDoSj3bYYYfs/V5yySVJf4uDd0hUJemyr7FDb9XCYquA1drXlANeSBJVPjhoIrUqG+MXDq4hgazePpg5Bfcg934M1g1oTxsdu5Ggfc4554Q8cG/y3Y2o0vVSSy2VHXzAGJP7mwMgOpnzyHzQp++yyy5hnXXW+cX6+IwzzpiNbbfeeutsDZADN1LHzRxYUnQS3VRTTZW072ISnSRJkiQplUl0kiRJkiTljCoGMTg1vsiTwUlGiw34ZQM95STalCS6IUOGhFZBYBoB+3lYfPHFw1FHHdVjZZK+EIRHoDkPAi3OOOOMrCoZVQnUHAR+7rHHHj1ez1Ri4kHwC4GF3G+c6M49l4LEvGuvvTastdZaodnmmGOOLNiMRKsRnVBPtcWdd945S+oisC71eiX5lwo/BKjWi8Quvv9Y/I4k78W0lfxOBFjHJrUR6E2g7YABA+p+DhVJ7r///pBqscUWCyeccEJdCZvTTDNNVglg//33z36jTkCyDI8RSen7eW5P91VeaEeGDh2a/DpUhjzyyCN7rFLRE5KNSQwnUDA1+JSg2aom0fX226UmENIWFHltFIHEpXoC7Oeee+6sqhqJJFyjsZ5++umsryEppRHvvPNO0n1BAjltffdqWPWODxkj8F3FYJz6xBNP9JjwXEXMW66//vrk1+G7JvCe8Vg99w6JewQh07c1UyeNS2KTzKjkmJJA1xO+r0022SR7kHhGJUQOJ4lBEnpeiasknFFNLTbwnD6BuSEJHozBeK2YZLpOui6LQjLgueeem30Xvfn1r3+dJQ0yjiRhKQUJ6H0l0RWZQE8SXKuNS6qC64C5Mom+JK9xPVCRinH1559/nh0qQOUz+neqaVKZqjdUB0+pTEkbxBpTI/Psmp122ilbM6WycAza080226yh+RNJ0SnjxRrGnRzyUc8hX1TeZqzIesRDDz0UOpHzyHyQgHr++ef3mXROe0D/Tv8Wc5hU9/5i9dVXD0VKqQobu+8iSZIkSVJX9e1YS5IkSZKkusUGAKZsINeDamUEfcTgpHdOrI/d3I6tsDPXXHMVWp0vb3mdbk3AJoG7MQl03XEy+D777JNVpFtggQVyeX9qDFUgrr766roTQkl+pEoEp5ynSq1akAeqK5A0VU/QJIkMVNoheD21gh5By3yPjSCYNQWV2FJO7Caol2qFsRUFLr744oaek0dyFIkYxx13XEO/F4GHJNFVNbFJ/08eCdgLL7xwVkWl3gS6rm0n/SpBwimo7vnss88mvYaKR3W1RirUML7hsIEUVCaJCSIngJs2NxZV9GIS6LomkXBfxbrgggtCqyDQnwpKKah4RcJNPQl0XRGITIXnZuqkcQlVhGMMGjQoFInxK9XpSEypVdZtBMl3n3zySfL7IPmJxLeUyi1dK1tusMEG4Y477siu8UarvnfSdVkEEtVIIuwrga4r5kfzzjtv0t8lyUqthbEziSxUVeaaWX/99bPxA+0eSWQk13Ed8d/x77i3qKLKNb7yyiuPcOx93nnnRb8n2guqycUk0NVQFTa2suCnn37a8Bw/Ngm6K+at9SbQ1fAbsa4X03e0A+eR6Ri7cu83si7Ouj/3f9X7i7HGGis7WCRGXtV9JUmSJEmdzSQ6SZIkSZJy9vbbb1cyiQ4EycWe1E/gSUzA8C233BI6oQrdZ599Fu66667k11lxxRWzQM1Gkw36MsUUU2QBj32dPq98ERRC0Mvkk0/e0PNIoCSRbODAgUl//1//+ld44YUXQrNwvR1xxBENX8+156VqJEmMU/wJJo5FQsOiiy4aUhCUt/nmm0c/n4DCH3/8se5/f/PNN4cUJD2SHEUwdsxnJRCR4ClVV2yViK5tIAG9/fr1i3o+QfZUqGz251CxqNZBpY5G0VfMNNNMSX/7lVdeaejf08amVEaj+tDaa68dUsV8X10rstDntYLUfqoWrN9bNdDeMCZPSfZJ0WnjktiE7bICqTksgENpGtVosseIEkGpQtZoImg984199903bLfddnU/p9OuyyJQmbfRtR/WcDbeeOOkv/vqq68mPV/l6t+/f7jooouycTQHSzSCas4cNNDToUxU16QSb6w11lgjzDDDDCEFbSlVRGM1khRHm5VaCY7fotEEuq6flfly7FyolTmPTEf1aeYOjdp0001LnR/F4lCUGFTflCRJkiQplUl0kiRJkiTl6MMPPwxfffVVZZPoSKRaZJFFop77/vvvh7vvvrvh5916663RgffLLbdcaBUEE6ZW6+F0YQJ9ixQT/Kl4W2yxRXSQGdWX9thjj+SvP/YezCPY+KCDDsoSrWIsv/zyYbHFFkt6D1SfevPNN+v6tyTBptzDVAbIw9JLLx1dhY92+sknn6w78Cg1mJZEEAK7Y5HUsOWWWya9BxWHE+hjqnR1D+BLHd9wmn5qohQVPFRNBCQTIBortcrue++919C/f/zxx7O2NtYf/vCHqCDs7uaee+6smlEM+rqYMX3ZqBRIJckUjCOo8BuL/niXXXYJzdBp4xIqKsU444wzkipDFimlInvX7+X444/PpUJ5HvPDTrsu88b8JraiHAnnKf1Ho/2dmod1AKre0tcXUeE1BVUs88DhUbFoV9999926/i0JdD/88ENIQSXI2GR8UDmT8V8ncR6Zbo455girrLJK1HNZg2yk2ml3X375ZfjPf/4TijbJJJNEPc8kOkmSJElSHkyikyRJkiQpRylBteOOO24pvwXV6GJdeeWVDf17kleeeeaZqL+17LLLtlTC1z333JP8GgTpttJnVu8Ibk+tGECAaGrw3IMPPhiagSDZ1Ootu+22W3KywQMPPFDXv0tJKCBAaaGFFgp5oKrbbLPNFv38ej9H6on8qf1JzVprrRWdaKli3XfffUnPpwIlv2+qPF7n9ddfD2+88Ubye1H+llxyyagKCzUpz8UHH3zQ0L9P6Sto61ZYYYWQl9hEDLRCEh0Ji19//XXSa+SRsLPMMss0XAUoD502LhlnnHGiXv/RRx/NkjqalRRV9PxwzTXXTK76lKdOuy7ztvXWW0c/l3WClCrln376afKhPyoHlcumn376Ql475dqfeeaZw9RTT53L+2CdYMCAAVHP/emnn+r+HA8//HBIMdpoo4XVV189VGHe3EqcR4Zcqk5ThTQleTP1kMCixe6DlJXkJ0mSJElqbybRSZIkSZKUIzZyUwLByrDwwgtHVw4iEOKdd96p+9/fcsstIdaQIUNCK3niiSeSns9vssQSS+T2ftR8gwcPjq6q0T14NgUntX/xxRehbHmctj7VVFOFeeaZJ+k1HnnkkcLvYRId86gsVDP55JNHP7fez5FaGYWA7pQqdDVjjTVWbgHVytdTTz2VnOCTV+IJSSz9+vVr6udRMVZaaaWk56ckFKDR/jGlryDhr3///qGV+opmSu2nSMpKqULXNZGXNqhsnTYuSbmX//GPf2TJ1iTl7rffftnBL/x3jSbJ5i31PqPf22ijjUKVdNp1mSeSolKTGmKTjmqJR82YE6oxJGwtssgihXxt33zzTXjuueein59Hn5rXPVxv4nTK561VtB199NFDKubNs8wyS+gUziND8hh2wQUXbFp/gTKS1FL2QVL2XyRJkiRJgkl0kiRJkiTl6Kuvvqp8Eh0n2a677rpRz/3hhx/C1VdfXfe/v/XWW6MDTFITZ8pEYuG7776b9Bprr712FqSr9rHccsvl8jokV4466qjRz+e+feGFF0KZCBBLrUKX1/dYT+Dce++9F95+++3ovzHrrLOGPKVUJqX6548//tjnv0u9JhZddNGQl8UXXzy311J1ElgIOs3znph99tmTXuPpp5/O7f0oHwQl//73v096jdRKzgSUN9KfPvvss9F/K6VqUt6fffjw4aVUmEiR2k9xcEhq8m2z+qlOHJfMNNNMIRVV0C+//PKwzz77hHXWWScLQJ9rrrmygy123HHHcOyxx4brrrsu6w9S5u1lJRFwDQ8aNChURSdel1WbG5bZ56l8JJluv/32hb0+8+KUaoRVuofraV9JHE0dS6SOU4uaG1Wd88g0Sy21VFbBut37i5RDx8oYx0mSJEmS2ls+u0eSJEmSJKllkuiw2mqrZUGEMSe3kkS31VZb9ZnwRRBjbKBxq1Whe/XVV5NfI+9TvdVcVNNIrTTQtVLXnHPOWXdFtZ7861//yoKIy5JnglVq4Nobb7yRtXW9nSD/8ssvJ/2No48+OntUAZ/1rbfe6jPoOrXdohJdnpU5VC3ffvttUqB8Eb8rr0dloVivv/56ru9H+fymKUniGGOMMZKe//3339f9b2lbU8b6l156afaoipdeeimMP/74oao6uZ/qxHEJlRqpvPLJJ5/k+vepvEXiSPdDFZjLUgWJ5FbG2fPNN1+YZpppcvu7JKoMGzYs6TV+97vfhSrpxOsyT3kkUpfZ56l8zHsnnnjiyt7DO+20U/aogtdeey27nntLlv/oo4+Sq1XNOOOMIS95jkuqzHlkuk7pL1L2QUyikyRJkiSl8nhzSZIkSZJylLKJm3ICa6PGHHPM7ET+2MoVDzzwQJ//7pZbbok+fXvVVVcNrSQ1QJLfo1MCajoFgcBVer2yk0fyDDafdNJJs3skFlUmSKTrDUG07aSvz0Ng28cff5z0N/JKEsXUU0+dVUlVta4hqjdUKekktR1st/u8HeQRmJx6CEUj1zlj4HZS9Xsitcpznv3UBBNMkFzRo51+myI+D4kQSy+9dCgL40OSMIYOHRoOOOCAsMIKK2SHQOy///7hscceS+4DSURPrXQ299xzhyrpxOuyan1eb4eC1CP1ulaxYtfoOvEepjpwX+OE1HEElcCmmmqqkJdOWfNzHlmNa6UV+guT6CRJkiRJzWQSnSRJkiRJOWqloKR11103Omnhyiuv7PPf3HrrrVGvvfDCC4eJJpootJJ33nknOci3r8p+ai15J4+kBoKnBpA1O4mw6M+fWnGravr6PO+9917y36B6S15IkqxyNaROlNqvERCX92+aWi2m7HZQ9SXQpiLAuVXui6qpct9HcPyHH35YmX6qiNdr1d+myM+z3nrrhWa/T6pFMk9edtllw8UXX5wdPBD7Wu2WcNGp12UeqLI43njjJb9Ob1W31PqKrhzfaffw+++/n5xAn+c4c5JJJgmdwHlkNeZI7d5fpB5UIEmSJEmS0VmSJEmSJOVotNFGi37u119/Xfqm/AILLBD13LvuuqvXwFaqPj377LNRrz1kyJDQar744ovkoDq1l4EDB1bq9VIDyNr98//nP/8J7aSvz5P6eQkmHHXUUUOeUqoNqnr9WhG/5xhjjJH0/C+//DK396J8jDXWWC2VRPf555+HdlLlz8P9mhocm9pmNLOf6rRxSddDKIquxFQvqtQdeOCBWTLd3XffXXo/yvVbZvtWj069LvMw9thj5/I6VbsmlJ8BAwZkSVtF6rR7OHWNNe9xBElNqRWUW4HzyPTrJI9rrxX6i5R7NLXSniRJkiRJJtFJkiRJkpSjlICIspPoUk77/+6778K1116bexU6TmdfbLHFQqv56quvkp5vEl37yTvgqpWSR0iuyjtgp+jPn3oPV01fn+ebb76p1PUNg6CqpYrXSGoCy/fffx9dUUjFyCMpqcxKvu3WVzRj7lFWG1REO1RmP9Vu11ojn2fPPfcstepfX4YPHx622GKLcNxxx5V6f1VxftjJ12VVknCtXt++8q5k35NOu4dT2+Eikuc74eAY55HVGL+OMsoooepS2qSUQwwlSZIkSYJJdJIkSZIk5ShlEzePYNFGLbroomHQoEFRz73qqqtG+L/dcsstUa+58sort8RpuT0lFaYweaT9VC2JrsxA+SKSZ4r+/O2WWFP05y0iYMkgqGpJvUZ+/etfh7zlUbmh3e71VpfH+KfMhIJ2u36qnESXx3edd79SZj/Vydda//79wxlnnBEmmWSSUCWnnnpqOPHEE0v7Das4P+zk6zJVXr/nSCONlMvrqHrKSJxtt3u46DXUIu63TkiEdR4Z2q7/r+I97PqRJEmSJClV+6/SSJIkSZJUopRN3GacCk0Ax7rrrhv13FdffTX8/e9//8V//8Ybb4Tnnnsu6jWHDBkSWhGVt1J8/vnnub0XVUPeCSSpySOpiZ7NTp5Jfc2+Pn/qPVw1P/30U6//e79+/SoXdFzlZI5OlHpPFDGmyaOiZhHtk5obSFxmMHKn9RXNlNpPFdGvlHngSadfa1NOOWW44oorwjzzzBOq5OSTTw4PPPBAx84PO/26rEJf1QqVhRRnrLHGKvyra7d7+Mcffyx03J/H3KO7//znP6HdOY9M00n9RcpY3SQ6SZIkSVIqk+gkSZIkScrRBBNMEP3cjz/+uCm/BYlrsZvPBDfmVYVu1llnDdNNN11oRamb959++mlu70XVkHcCSWoAV5kBc0UkQ6V+n319/k4LwElNyiwioPCLL77I/TUVL/WeKOL3TA06JSmnFavdqjo6ra9opjwqT+bdV5XZT3mthTDxxBOHCy+8MBx88MHZ/12VpKuDDjqoz8SNdp0fel1KrVXNvdPv4arNZ+g7iphHV03Vvnc4j6ymjz76KOp5JMiWkXgsSZIkSWpvJtFJkiRJkpSjiSaaKDpA+913323Kb9G/f/+w8sorRz33tttuC5999lkuSXStWoUOY445ZtLzm5VAqeLkHfiT+np5BKM3M+ip6M+feg93WpAm1Xi+//77kKdOCChsJan3RBFVFlLbgU67z5U/r6FyA7BHGmmkthqLNcJr7f9VY1ljjTXCHXfcEY444ogw33zzJV8XqajGzvvpS2pwNwdINKNSfW+8LqVqV2DtS6fdw+ONN16l1uk++OCD0AmcR6rofZABAwY0fTwoSZIkSWp9JtFJkiRJkpQjNnEnnXTSqOe+9957Tfst1ltvveiKU0OHDv35/3/99dfD888/H5XgsuKKK4ZWFfub17zwwgvh22+/ze39qPnyTiBJfb0yTrav+e6777Ikqyp9/r4CuSaZZJLQSVKqpta89dZbIS9cL50SVNgqCExLQZ/29ttvhzy99tprTf1MUqf1Fc3EoSTjjDNOZfopDB8+PJTFa+2XFYVXXXXVrDLdPffcEw444ICw1FJLJSdIxOo6/y2yz3nmmWdClXhdSq2t0+7h1CqmJM/nOZ9h3a8TOI9U0Ul0gwYN8kuWJEmSJCUr/kgrSZIkSZI6DAlVJJO1SiU6TDfddGH++ecPjzzySMPPvfLKK39OwoutQkcQZOpp/c2UuoFPAsk///nPMPfcc+f2ntRcMW1Ab954443kKpllItll+umnb5nPP3DgwKTXP/vss8NCCy0UWsXoo4+etbmff/559Gu8+OKLYfLJJ8/l/bz88svhxx9/zOW1lF+QLRWAUn4XrpE8E9d4vWYmvEup470DDzwwrLXWWn6RDQS/p1SBoc34v//7v1y+708//bTUA086bVzS6HWx9tprZ4/amPu5557LkhOoEseYkUcRFVFrHnjggfDDDz+EUUYZpdd+lMpSKZV7H3vssTDvvPOGqvC6lFpb6j186//H3l1Ay1WdfwPecScGhCCB4CEkQHGH4O5e3ApFihWKa6EtFC3urkEKFLfgbikaLECAkAQS4vat9/S7+V+/d+T686x1VjInM+fsOXNsJvu338ceS/37909NaeCYGLArBv9qDN9nPv7449QS+B5Jbe+t8z02fa8HAACgGFSiAwAAgEbSwTb+A3ny5MkN9nnsueeeeXcEef/99wsK0e2www6pKVtsscUKXsbLL79clLbQOBQa9ijvs88+a9BR2Bvy/UeIJ0JWdfn+F1100Ub1edeHhRdeuEH3yaa+/Zq76PhfaEgyQg3FlE+l22Ie5xCdqKMzdr6c65rudaqYy6qNlnhfUsh+stlmm6U//elP6eKLL073339/euutt9Krr76a7r333vTPf/4zHXHEEWnTTTctOEBSYtKkSTXem0bArtCwSWP7fmi/hKatpR3DMSDIEkssUdAy3njjjaIGsFsC3yOpjR9++CHvDVWs+zkAAABaNiE6AAAAKLJCqi998cUXqaEMGTIk79FcoxpdVJ7KZ2TlCB2uttpqqSnr1atXwR1977rrrjRt2rSitYmGFcfC7Nmzi7a8qK7RlMIjhYZdSotzSyGjx9emE1eE7GLE9HwNHz48NTWFVgocNmxY0dpSzGVRPIMHDy7o9U8//XTR2jJq1KiCQ3mFvh+I68nAgQNb1LWiKV+nitlZ/YUXXkj1qSXelxRbz54906BBg9IWW2yR/vjHP2YBu2eeeSYb9OXggw/OqvLWddXp5ZdfvqB1vP76642qcpH9Epq2uIdp165doxkgoz4ss8wyBVffK4bRo0en1157LbUUvkdSl///MWDAABsYAACAggnRAQAAQJEtu+yyTXJk5xgtf7fddsvrtY888kgaOnRoXq/dbrvtUqtWrVJTt8IKKxTcqebBBx8sWntoWGPGjEnvvvtuUZb1888/z6n2mK+ll1461afopNxYlhUBwvbt29dpR+fnnnsuTZ06NTUlhQRBwjvvvJN++umngtsR2+3ZZ59NLVlUScjX9OnTU10ptPP/Bx98kEaOHFmUtjz66KMFB5OXW2651JTEfVkh6nLfaMkKOS7ee++9gqoutDSFXqe+//77bJsXw+OPP57qW0u7L6kvcV949NFHp9tvv72gIN2PP/5Y598Pw7XXXpsaE/tly9ZY71mpnQ4dOhT0u0CxAmX1aZVVVino9fFd5s033yy4HTHw2KxZs1JL4XskdTlQWCH/7wIAAAAlhOgAAACgyGJE1KhU0dRCdGGnnXbKOtbkauLEiem6667LqxPW9ttvn5qDqORXqAsvvLBWHTJpGqLSRbE6bhfS4SoCZEsssUSqT19++WXRKmcU2lmvtp3w11133bzX8dtvv6WnnnoqNSWFVgCNffK+++4rSjgqriEtWW1CnlX59ddfU10p5JgocdNNNxW8jKjSeueddxZ8bxYVdFrKfhHGjx9ftLZQnOMizpsPPfSQzVlLK620Ut7fqUp3Wi9UVAOLqrj1raXdl9S3uC7sueeeeb9+0qRJNT5nvfXWKzgQHYPVFLOqYqHsly1bY71npX6O4bgWFmugoPqyxhprFDxo1z/+8Y+CBvOI3/iuueaa1JL4Hkld/f9H3759U+/evW1gAAAACiZEBwAAAEXWsWPHtNhiizXJEF3Pnj3TlltumddrZ8yYkfNrVl999TT//POn5tJJpJBqBiXVy4455pg0c+bMVBemTJkipFePorJgoZ0FY18oNDyy4oor5hWObQzhmaggE9WsChHnmdrYYIMNUrt27fJezyWXXJKFfRrChAkTsvXnsq3iOlVoqCiqo4wdO7agc9JFF12UWrpCrh3ffPNNnVUbWnDBBQuuBHXHHXekESNGFHwuifdZiA033DA1NYXeUzT0PWVztfLKK6e5554779fHoBOFnDcLEeeKG2+8sclU/+zatWsaPHhwQcuIStmfffZZ3q+PTvN///vfU0NoafclDaGQSnG1+e4bnbzjnFGICN8ee+yxdfod7uuvv671c+2XLVsh9yaFnIspnk033bSg119wwQWpoYwePTqdffbZOZ0Pe/XqlYXyCxHBwfhOk+/vOaeddlqtgtfNie+R1NV31UGDBtm4AAAAFIUQHQAAANSB5ZZbLq/XffLJJ6mhFTIif6522GGH1JzCk1tssUXBy3njjTfSEUccUfRONm+//XbabrvtGn1n1ubkl19+SZdddllBy7j33nsLDkLE6OsN4YEHHkjDhw8vaBnnnntuQa+PUedr+/67d++e1l9//YJG5r/iiitSfe9jsc7o0Pyvf/0r5/PGJptsUtD6o9LNmWeemffI/P/85z/TDz/8kFq66NxZSCf+uqw2tM022xT0+mjfcccdl+0r+Xj//ffT5ZdfXlAbourt1ltvnVrSfhGeeOKJgqpmUPX+tNVWWxV03j7vvPPqdfNOnjw53XrrrWmjjTbKrqvjxo1LTUWh16mSzuv5hsluvvnmBrt3bon3JVGpMfbX+pLPIDC5homK8X03grf77rtvwYHy8r7//vt08MEHZ+eH2mqJ+yXFuTeJwVHc9ze8qFBfyCAZUZ01fqOoTxGa+9vf/pbdx9xyyy1p+vTpOb1+2223LbgNEd578sknc3pN3IefeuqpTWbwgmLzPZKqxG8D3377bV4bSIgOAACAYhGiAwAAgDqQb2glRlbO9z+Si2XAgAFZ5aq6Fh3wohNMcxKdGyO0U6gIROy+++5ZJ8NCRQWgqG632267pS+++KLg5ZGb22+/Pb3wwgt5bbb4vCJkVIjYHzfffPPUEKJyxgknnJDGjx+fd7Wed955p6A2RPWPeeaZp9bP32effQpaX3TQLbRyYG33jbPOOivrxByV3PKteFiMYNF//vOfvAIhN9xwQ1GqFTYH8803X0Gvj06luVSRyUV0/o9qUIWIMO0hhxySc5AuBhY48MADC+7sHsdJv379UlMT1YELqSIa1/8LL7wwOxdT/AEn2rZtW1Cl2kKv77URYYXYB9Zbb73smlGXlazqSlTIbtOmTUHLeOutt7JKXrkeC4899li9Bx5b+n3J+eefn+2vsd9+9913qa7FICOFnKNrI+7D+/btmwoV5/Sddtop7+8VpUWQNrbxZpttlp577rmcX9/S9kuKc88aodUTTzwx74EVKJ5Cj+HTTz+9XoJh8R3i+OOPzwKw119/fd4h6zjXxe+PhYby//SnP2XXqaimXpORI0dm27m+A4eNie+RVCV+58t3sJe11lrLhgUAAKAo8v9fPgAAAKBKa665ZtbhMzpa5OrNN99MCy64YINu3d///vdZh9O67hTbvn371JwstthiaeONN06PP/54wcv66KOPsk6XMXrzQQcdlPr371/r10bgIDpYDh06NA0bNkwH+gYUnQWPPPLIdM0116SVVlopp8oQBxxwQFY5oRARiG3I80lU0fvDH/6Qrr766pyCOLHv/uMf/6j30c9je8XnFOfhfERHoOhUOGrUqPTHP/6xqOe4CCPG6Pf33Xdf0c7PMYp3TIVW2bnxxhuzENcZZ5yR+vTpU+1zowPzX//616xSIf+Ty/m9MhGMiUDkhhtumFZdddW08MILp27dutUYwIqA6VxzzVXtc+K4jRB2nMMKEVUr4rp/yimnZB1hqxMVo6KjbFSLybd6VGlxLm2KIgQdn2Uh1Uivuuqq9Mwzz2TVvJZeeuns+IzKudWFkiIcFuulagsssEBWfTjCcIV8NmPGjEl/+ctfCg6qlhYdzCMUE9fRF198scnfA84999zZoBsRaCtE3JvHABXnnHNOdr9enegcf/HFF2fXtobefi3tviTEve+VV16Z3TvGwDRx7YjrRk3Xq1x99tlnBQW5anvtjnNqBMKjcm8xtk0sa5VVVkmHHXZY9mdtB3CJ3yUiNBj3X48++mhBAfWWuF/yf/t9XFvy9dJLL2X3JBFqGjx4cPY9Nao6tmvXrtrXLbTQQs3ut6OGFL8zXXLJJVnQKx9RCe7www/Pqk3HwAJRpbdYfv755+yaH+Gz+E2sGLp06ZK187LLLiv4t534ThTn0U033TS7NsU+HPcqcY796aefsvv2aH/cf0+dOjW1ZL5HUpV87x/i94sY+A8AAACKQYgOAAAA6kB08ltuueXyGt3+jTfeSNtuu22Dfi4RBIuO1nVZsSJGJW6OovJWBNjyHSW7tOiIE52gY1pkkUXS6quvnnU269WrV1b9IDqcRcAgOgtG6Orzzz9PH374YTaqb3RsonGITqp77bVX2m+//bIOrxFgqK5T6T333JNVlirGKP3RWayhRQfW6AAdnWWjukh1xo4dmwWs/v3vfxe83jhOYr25Oumkk9KOO+6YVwi65DOMzt9PPPFEOvTQQ7Pzab7VpOKYfu2117JOePFnXRzX0Rn7iCOOKHg5UY0g2hidCaNTYYQUopNTdKqMKqtRWTM6Ncd2UQmkrGJ0BIvQx8MPP5xNtRXHZATkahJB2Pvvvz/r1FqI6Cwfx0R0hI7jYtlll03zzjtvFviLYz86nr766qvp6aefLto+Ep20f/e736WmvG8UEqIrCYrEVFvRETg6uVO9qPIb57RCgijRQTwGO4jjIq5X+YbpohN8nH8jPBfhhmLcgzYmcZ0qNEQX4v44wvXrrrtudm6IYGmcg+IaHeefqAYe19uosBrXrcaipd2XlIgAY+zPMUXAJqoLR6guvg/FZ5dvNcjYHrE/Ragt3+M37uWXWmqpWj9/1113TXfffXf6+OOPUzFEMD2+W/Tu3TutttpqWaAt9uUePXpk1ZYi6DFx4sSsGmXcf0U1p+iwnm916Mq01P2ypYtjr1BxP3nLLbfk9JoIftYUgKb24vwZIf449vIVx1l8b3/kkUey7wpxbc2ncmyc6yMsF8fuU089lV2r6yLAvvfee6fbbrstq8RZqLhHiH041/24JfI9kqruY/Kx9tpr13rwAAAAAKiJEB0AAADUkXXWWSfvEF1j6FQTnf2iCkNddb4aOHBgao7mn3/+LJASIahiig6QMd1xxx1FXS71IzqYxqjlt99+exYyimqVEVSNMGSE5aLzdpwvolJK/L0YIngZHVIbgwjPHHzwwVmbImAVx38ErDp16pR1Qot/jxBWdJQuRuWpkgBhdYHFqiyzzDJpjz32SDfffHNB6//iiy/Ssccem3VmXn/99bOKb/G+S0JDMSJ+7BcRdohOzdHRuSQM+8knn2SB2KhSVNeiwk+0KzpYFyo6o0cAshghyJYkQmV1HVwvRAR7otLE8ccfX5TlReDnuuuuS3UtjrE///nPqSmL0Egh1c6oO3HMRqXZc889t6DlxHF/2mmnZfeN0QE9BuGIgOl8882XXSvi+IvO5BGUnTBhQnatiNeUvlbEvOYstkdU2ozO9YWKTv+xnGIsq760tPuSqj63l19+OZtC3N9FyDiCbFEZK66jcV8ZIeAYzCYqVkXwLu4p4/3EMfPNN99k99oR1I7quYWem3MJfEWwJEJ7UQ0xAm7FEp9HBFhiqm/2y5Yp9n2ah/hNIqY4JxbivffeS4ccckh2LYkBc+LaEufnCPnG+bhk4Ke4j4kAW1xb4rt/DBJRcm0pZsC3KtGWGADh5JNPrvN18X98j6S8OBd88MEHeYfoAAAAoFiE6AAAAKCOROfEiy66KOfXRae+6OgXnXMb0i677JKuuOKKogV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+      "text/plain": [
+       "<Figure size 1600x640 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 663,
+       "width": 1768
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
+    "ax.plot(approx2.hist, lw=0.5, label=\"loss\")\n",
+    "ax.axvline(steps_per_epoch, color=\"C2\", ls=\":\", label=\"one full pass (rule arms)\")\n",
+    "ax.axvline(raw_shadow.stopped_at, color=\"C3\", ls=\"--\",\n",
+    "           label=f\"raw increments: false stop at {raw_shadow.stopped_at:,}\")\n",
+    "ax.axvline(win_stop, color=\"C1\", ls=\"--\",\n",
+    "           label=f\"window means: stop at {win_stop:,}\")\n",
+    "ax.set_ylim(-550, 900)\n",
+    "ax.set_xlabel(\"step\")\n",
+    "ax.set_ylabel(\"negative ELBO\")\n",
+    "ax.set_title(\"Batch noise blinds the raw rule; window means restore its eyesight\\n\"\n",
+    "             \"(y clipped to the descent region)\")\n",
+    "ax.legend(fontsize=9, loc=\"upper right\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b83c5b6",
+   "metadata": {},
+   "source": [
+    "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
+    "its floor — a false stop that a fixed-budget run would silently avoid. The\n",
+    "windowed rule stops once per-window improvement falls below a quarter of the\n",
+    "window-noise scale, here capturing roughly 95% of the total loss descent at\n",
+    "under a quarter of the step budget. On the full-scale corpus the same replay\n",
+    "shows the windowed rule stopping shortly after one pass, at a loss within\n",
+    "noise of the two-pass reference — the model converges in about one epoch, and\n",
+    "the rule proves it rather than assumes it.\n",
+    "\n",
+    "(tick-real-corpus)=\n",
+    "\n",
+    "## The real corpus: 38 symbols, half a billion transitions\n",
+    "\n",
+    "Everything above runs identically on real data; what changes is only the ETL\n",
+    "in front of it. The full-scale corpus behind the numbers in this section:\n",
+    "\n",
+    "- **Source**: Binance's public aggregated-trades archive\n",
+    "  (`data.binance.vision`) — no API key, published SHA-256 checksums, monthly\n",
+    "  CSV dumps. The corpus is 491,559,069 transition rows: four majors\n",
+    "  (BTC/ETH/SOL/XRP × 6 months of 2026) for row bulk plus 34 alt pairs\n",
+    "  (June 2026) for hierarchy breadth, the thinnest at 67,000 rows.\n",
+    "- **ETL**: per symbol, *chronologically*: next-event returns, move indicator,\n",
+    "  trade sign, standardized log notional, trailing 60-second activity, last\n",
+    "  nonzero return, hour — then the splitmix64 global shuffle into 64 shards\n",
+    "  (~9 GB of Parquet), with the shard-head mixing check asserted. The complete\n",
+    "  script is published with the notebook's companion repository.\n",
+    "- **Hardware**: one Apple M4 laptop, 24 GB RAM. No cluster anywhere.\n",
+    "\n",
+    "Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes\n",
+    "about four minutes at roughly 1,000 optimization steps per second (4 million\n",
+    "rows per second through the model), with peak resident memory of 1.1 GB — the\n",
+    "loader's flat footprint, independent of N. The subsections below are the\n",
+    "measured results; each figure's numbers are inlined verbatim from the run\n",
+    "logs so the plots execute without any data access."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7d0a4b5",
+   "metadata": {},
+   "source": [
+    "### The memory wall, measured\n",
+    "\n",
+    "Every number in the cells below is inlined verbatim from the run artifacts of\n",
+    "the published scripts, so these figures execute without touching the data.\n",
+    "First, the reason streaming exists at all. Each point is a fresh subprocess's\n",
+    "peak resident memory (`ru_maxrss`) after fitting this exact model at that many\n",
+    "rows — `pm.Minibatch` holding the rows in RAM versus the loader streaming the\n",
+    "shards:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "87532fe4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.599133Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.599052Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.750840Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.750434Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 823,
+       "width": 2370
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with open(\"streaming_tick_data_results.json\") as f:\n",
+    "    results = json.load(f)\n",
+    "\n",
+    "mb = np.array(results[\"memwall\"][\"minibatch\"], dtype=float)\n",
+    "st = np.array(results[\"memwall\"][\"stream\"], dtype=float)\n",
+    "bpr = results[\"memwall\"][\"bytes_per_row\"]\n",
+    "icpt = results[\"memwall\"][\"intercept_gb\"]\n",
+    "cross = results[\"memwall\"][\"crossing_rows_12gb\"]\n",
+    "n_full = results[\"corpus\"][\"n_rows\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4), layout=\"constrained\")\n",
+    "ax.plot(mb[:, 0] / 1e6, mb[:, 1], \"o-\", color=\"C1\", label=\"pm.Minibatch (rows in RAM)\")\n",
+    "grid = np.linspace(0, n_full, 200)\n",
+    "ax.plot(grid / 1e6, icpt + bpr * grid / 1e9, ls=\":\", color=\"C1\", lw=1,\n",
+    "        label=f\"fit: {icpt:.1f} GB + {bpr:.0f} B/row\")\n",
+    "ax.plot(st[:, 0] / 1e6, st[:, 1], \"s-\", color=\"C0\", label=\"streaming DataLoader\")\n",
+    "ax.axhline(12, color=\"C3\", ls=\"--\", lw=1, label=\"12 GB (16 GB laptop budget)\")\n",
+    "ax.axvline(cross / 1e6, color=\"C3\", ls=\":\", lw=1)\n",
+    "ax.annotate(f\"wall: {cross / 1e6:,.0f}M rows\", (cross / 1e6 + 8, 10.6), color=\"C3\",\n",
+    "            fontsize=9, ha=\"left\", va=\"top\")\n",
+    "ax.axvline(n_full / 1e6, color=\"C2\", ls=\":\", lw=1)\n",
+    "ax.set_ylim(0, 42)\n",
+    "ax.annotate(f\"this corpus: {n_full / 1e6:,.0f}M rows\", (n_full / 1e6 - 6, 40.5),\n",
+    "            color=\"C2\", fontsize=9, ha=\"right\", va=\"top\")\n",
+    "ax.set_xlabel(\"dataset size (millions of rows)\")\n",
+    "ax.set_ylabel(\"peak RSS (GB)\")\n",
+    "ax.set_title(\"The in-memory path crosses a 16 GB laptop's budget at \"\n",
+    "             f\"{cross / 1e6:,.0f}M rows; streaming does not care\")\n",
+    "ax.legend(loc=\"upper left\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da23d0b7",
+   "metadata": {},
+   "source": [
+    "The corpus behind this notebook sits at roughly three times the wall.\n",
+    "\n",
+    ":::{warning}\n",
+    "Two caveats belong next to this figure, not in a footnote. The in-memory arm\n",
+    "was measured up to 64M rows and the crossing is a *projection* from the fitted\n",
+    "line — macOS does not enforce `RLIMIT_AS`, so a demonstrated hard failure\n",
+    "needs a memory-capped Linux subprocess. And the two samplers differ\n",
+    "semantically: `pm.Minibatch` draws with replacement, the loader makes\n",
+    "without-replacement epoch passes.\n",
+    ":::\n",
+    "\n",
+    "And speed? Steady-state, compile time subtracted, same model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a0c7fe23",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.752084Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.752018Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.828453Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.828063Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x520 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 543,
+       "width": 1922
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp = results[\"memwall\"][\"throughput\"]\n",
+    "labels = {\n",
+    "    \"minibatch\": \"pm.Minibatch (rows in RAM)\",\n",
+    "    \"stream\": \"DataLoader, pre-shuffled shards\",\n",
+    "    \"stream_shuffled\": \"DataLoader, runtime shuffle buffer\",\n",
+    "}\n",
+    "fig, ax = plt.subplots(figsize=(8, 2.6), layout=\"constrained\")\n",
+    "names = [labels[k] for k in labels]\n",
+    "vals = [tp[k] for k in labels]\n",
+    "ax.barh(names, vals, color=[\"C1\", \"C0\", \"C0\"])\n",
+    "for y, v in enumerate(vals):\n",
+    "    ax.annotate(f\"{v:,.0f} steps/s\", (v, y), xytext=(4, 0),\n",
+    "                textcoords=\"offset points\", va=\"center\", fontsize=9)\n",
+    "ax.set_xlabel(\"optimization steps per second (batch 4096, steady state)\")\n",
+    "ax.set_title(\"With a 500-parameter gradient, the loader is not the bottleneck\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65c7052e",
+   "metadata": {},
+   "source": [
+    "The ordering surprised us, so it is worth stating carefully. On this model the\n",
+    "gradient dominates the step, and the pre-shuffled streaming path *outpaces*\n",
+    "in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than\n",
+    "Minibatch's per-step random indexing. That ordering is model-dependent: on a\n",
+    "light model the loader overhead shows and `pm.Minibatch` wins the races it can\n",
+    "enter (we measured exactly that on a small probe model). The one consistently\n",
+    "expensive path is the runtime shuffle buffer — which is why the ETL-time\n",
+    "global shuffle matters twice: it is the *statistically* honest option, and it\n",
+    "is 3× the throughput.\n",
+    "\n",
+    "### What the data did to the model\n",
+    "\n",
+    "Three findings from the first full-scale fits are worth more than any smooth\n",
+    "success story:\n",
+    "\n",
+    "**Non-centered effects collapsed under mean-field.** The first version of this\n",
+    "model used the textbook non-centered parameterization\n",
+    "$b_s = \\tau \\cdot z_s,\\ z_s \\sim \\mathcal{N}(0,1)$. On the real corpus the\n",
+    "$z_s$ posteriors blew out to $\\pm 18$ while every $\\tau$ pinned near its prior:\n",
+    "real cross-symbol volatility levels span two orders of magnitude, the original\n",
+    "$\\tau$ prior could not reach them, and a mean-field approximation of the\n",
+    "$\\tau$–$z$ funnel collapses into its small-$\\tau$/huge-$z$ corner. Both fixes\n",
+    "are principled: weakly informative scale priors, and *centered* effects —\n",
+    "non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per\n",
+    "symbol these groups are anything but.\n",
+    "\n",
+    "**The variance does not exist.** With the conventional\n",
+    "$\\nu = 2 + \\operatorname{softplus}(\\eta)$ parameterization, 450 million rows\n",
+    "pinned $\\nu$ at the floor of exactly 2 with a posterior standard deviation of\n",
+    "$2 \\times 10^{-6}$ — the data demanding tails heavier than any\n",
+    "finite-variance Student-t. The parameterization was moved to a floor of 1 and\n",
+    "the estimand to quantile-based dispersion, which is finite for all $\\nu > 0$.\n",
+    "With the floor out of the way, three independent seeds put $\\nu$ at 1.895\n",
+    "with a seed spread of $8 \\times 10^{-5}$ — an *interior* optimum below 2.\n",
+    "Per-trade crypto returns, as far as this half-billion-row likelihood is\n",
+    "concerned, have no variance; they do have a mean, and quantiles behave.\n",
+    "\n",
+    "**The robust location disagrees with least squares about a sign.** Ordinary\n",
+    "least squares of $y$ on $\\text{ylag}$ gives a *negative* coefficient\n",
+    "(bid-ask bounce, −0.29); the fitted $\\theta_r$ is *positive*\n",
+    "(+0.29, seed spread $8 \\times 10^{-6}$). Direct likelihood profiling on a\n",
+    "million real rows\n",
+    "confirms the likelihood genuinely peaks at the positive value: small moves\n",
+    "(88% of rows) show continuation, large moves show reversal, and a heavy-tailed\n",
+    "likelihood follows the bulk while a quadratic loss follows the outliers.\n",
+    "Neither number is wrong — they answer different questions, and a robust\n",
+    "likelihood answers the one about typical transitions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b3427d9",
+   "metadata": {},
+   "source": [
+    "### The stopping rule at scale, and a pre-registration that failed\n",
+    "\n",
+    "The stopping-rule comparison was pre-registered before any full-scale run:\n",
+    "fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical\n",
+    "batch order and seeds, flat learning rate; savings to be claimed only if the\n",
+    "stopped fit's held-out log score landed within max(2 × ref seed spread,\n",
+    "2·10⁻⁴) nats/row of the 2-pass reference. Because a flat learning rate makes\n",
+    "every budget a prefix of the same deterministic trajectory, all five arms per\n",
+    "seed come from one run with mid-flight snapshots — and the windowed rule ran\n",
+    "as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated\n",
+    "one-pass floor plus its designed $h/\\kappa$-window detection delay, 46% below\n",
+    "the reference budget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6c5cf692",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.829454Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.829378Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.837561Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.837175Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>passes</th>\n",
+       "      <th>seed 0</th>\n",
+       "      <th>seed 1</th>\n",
+       "      <th>seed 2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>arm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>e0.25</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.117919</td>\n",
+       "      <td>0.117978</td>\n",
+       "      <td>0.117858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e0.5</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.117867</td>\n",
+       "      <td>0.117869</td>\n",
+       "      <td>0.117858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e1.0</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.117009</td>\n",
+       "      <td>0.117014</td>\n",
+       "      <td>0.117002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e2.0</th>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.117014</td>\n",
+       "      <td>0.117003</td>\n",
+       "      <td>0.117013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monitor</th>\n",
+       "      <td>1.07</td>\n",
+       "      <td>0.116262</td>\n",
+       "      <td>0.116299</td>\n",
+       "      <td>0.116323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         passes    seed 0    seed 1    seed 2\n",
+       "arm                                          \n",
+       "e0.25      0.25  0.117919  0.117978  0.117858\n",
+       "e0.5       0.50  0.117867  0.117869  0.117858\n",
+       "e1.0       1.00  0.117009  0.117014  0.117002\n",
+       "e2.0       2.00  0.117014  0.117003  0.117013\n",
+       "monitor    1.07  0.116262  0.116299  0.116323"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conv = pd.DataFrame(\n",
+    "    [(arm, passes, *scores) for arm, passes, scores in results[\"converge_table\"]],\n",
+    "    columns=[\"arm\", \"passes\", \"seed 0\", \"seed 1\", \"seed 2\"],\n",
+    ").set_index(\"arm\")\n",
+    "conv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c10b4aa",
+   "metadata": {},
+   "source": [
+    "**The equivalence test failed** — and not in the direction anyone would\n",
+    "guess: the ¼-pass arm *out-scored* the 2-pass reference, the monitor arm\n",
+    "under-scored it, and every gap dwarfed the seed spread (∼10⁻⁵). The loss\n",
+    "trace explains it: this fit converges within a quarter pass, so all flat-rate\n",
+    "arms sit on the same plateau, where the held-out score of a *point* estimate\n",
+    "is dominated by Adam's stationary jitter at learning rate 0.02 — position\n",
+    "along the batch cycle, not posterior quality. The proof is the polish\n",
+    "experiment: re-running to the monitor's stop and annealing for two thousand\n",
+    "further steps at a quarter of the learning rate moved the held-out score from\n",
+    "the worst of the table to the best — above an annealed two-pass reference run\n",
+    "on the same split — at 55% of its step budget.\n",
+    "\n",
+    "One more layer of honesty on the metric itself: even among fully annealed,\n",
+    "fully converged runs, path-to-path differences are about $5 \\times 10^{-4}$\n",
+    "nats/row. The pre-registered $2 \\times 10^{-4}$ equivalence floor was\n",
+    "therefore unachievable by *any* stopping protocol on this problem — a\n",
+    "pre-registration mistake worth admitting, because the comparison it pushed us\n",
+    "toward is the right one: flat-rate arms scatter over $1.7 \\times 10^{-3}$,\n",
+    "annealed endpoints over $5 \\times 10^{-4}$, and the seed spread within one\n",
+    "protocol is $10^{-5}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0d1d3007",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.838419Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.838346Z",
+     "iopub.status.idle": "2026-07-31T23:52:00.938391Z",
+     "shell.execute_reply": "2026-07-31T23:52:00.937960Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 623,
+       "width": 2457
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pol = results[\"polish\"]\n",
+    "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
+    "names = list(pol.keys())\n",
+    "scores = [pol[n] for n in names]\n",
+    "ax.barh(names, scores,\n",
+    "        color=[\"C1\" if \"anneal\" in n and \"stop\" in n else \"C0\" for n in names])\n",
+    "for y, v in enumerate(scores):\n",
+    "    ax.annotate(f\"{v:+.6f}\", (v, y), xytext=(4, 0), textcoords=\"offset points\",\n",
+    "                va=\"center\", fontsize=8)\n",
+    "lo = min(scores)\n",
+    "ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)\n",
+    "ax.set_xticks([0.116, 0.117, 0.118])\n",
+    "ax.set_xlabel(\"held-out log score (nats/row)\")\n",
+    "ax.set_title(\"Stop rule alone inherits Adam jitter; \"\n",
+    "             \"stop + short anneal beats the 2-pass reference at 55% cost\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2be140dc",
+   "metadata": {},
+   "source": [
+    "The honest protocol that comes out of this ablation: **a stop rule on a\n",
+    "streaming fit must be paired with an anneal-on-stop polish phase**, and\n",
+    "equivalence must be judged after the polish, not at the raw stopping point.\n",
+    "That finding — not the 46% headline — is what we would want a reader to take\n",
+    "away, and it fed directly back into the design discussion of the upstream\n",
+    "pull requests.\n",
+    "\n",
+    "### The estimand: dispersion on the event clock\n",
+    "\n",
+    "What the model is *for*: per symbol and hour, the probability that a trade\n",
+    "moves the price at all, and how far it typically goes when it does. Both come\n",
+    "from the posterior of the fitted links at reference covariates, so the\n",
+    "intraday shape below is pooled across the hierarchy — BTC's curve is almost\n",
+    "entirely its own data, the thin symbols' curves borrow the global harmonics\n",
+    "exactly as the model intends:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "97410d8a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:00.939961Z",
+     "iopub.status.busy": "2026-07-31T23:52:00.939863Z",
+     "iopub.status.idle": "2026-07-31T23:52:01.167615Z",
+     "shell.execute_reply": "2026-07-31T23:52:01.167275Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 743,
+       "width": 1944
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hours = np.arange(24)\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.6), sharex=True, layout=\"constrained\")\n",
+    "for sym_name, _ in results[\"forest_symbols\"]:\n",
+    "    disp = results[\"dispersion_full\"][sym_name]\n",
+    "    axs[0].plot(hours, disp[\"pi_mean\"], marker=\".\", label=sym_name)\n",
+    "    axs[1].plot(hours, disp[\"hw_mean\"], marker=\".\")\n",
+    "    axs[1].fill_between(hours,\n",
+    "                        np.array(disp[\"hw_mean\"]) - 2 * np.array(disp[\"hw_sd\"]),\n",
+    "                        np.array(disp[\"hw_mean\"]) + 2 * np.array(disp[\"hw_sd\"]),\n",
+    "                        alpha=0.2)\n",
+    "axs[0].set_title(r\"move probability $\\pi_{s,h}$\")\n",
+    "axs[1].set_title(\"conditional-move 90% half-width (bp)\")\n",
+    "axs[1].set_yscale(\"log\")\n",
+    "for ax in axs:\n",
+    "    ax.set_xlabel(\"UTC hour\")\n",
+    "    ax.set_xticks(range(0, 24, 6))\n",
+    "axs[0].legend(fontsize=8, loc=\"center left\")\n",
+    "fig.suptitle(\"Event-clock dispersion by hour: shared harmonics, per-symbol levels\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c579a489",
+   "metadata": {},
+   "source": [
+    "Read it as event time, not clock time: these are per-*transition* quantities,\n",
+    "and the number of transitions per hour itself varies enormously — the two\n",
+    "clocks tell different stories and this notebook only claims the first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d23321a",
+   "metadata": {},
+   "source": [
+    "### What full N buys: thin cells\n",
+    "\n",
+    "A uniform 1% subsample (4.6M rows, drawn by taking the hash-ordered head of\n",
+    "every shard) can estimate every *global* parameter of this model — and did,\n",
+    "matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more\n",
+    "data? Because the estimand lives in symbol-by-hour cells, and the thin end of\n",
+    "the universe is where the two fits part ways. Below, the posterior of the\n",
+    "conditional-move 90% half-width $\\sigma_{s,h}\\, t^{-1}_{0.95}(\\nu)$ at\n",
+    "reference covariates, full fit versus 1% subsample, for the thinnest and the\n",
+    "fattest symbols in the corpus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "96533f42",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:01.168840Z",
+     "iopub.status.busy": "2026-07-31T23:52:01.168772Z",
+     "iopub.status.idle": "2026-07-31T23:52:01.366601Z",
+     "shell.execute_reply": "2026-07-31T23:52:01.366237Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x840 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 863,
+       "width": 1955
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disp_full = results[\"dispersion_full\"]\n",
+    "disp_sub = results[\"dispersion_sub1pct\"]\n",
+    "show_syms = results[\"forest_symbols\"]  # thinnest three + BTC, with row counts\n",
+    "show_hours = [3, 14, 20]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4.2), layout=\"constrained\")\n",
+    "ypos = 0\n",
+    "labels, ticks = [], []\n",
+    "for sym_name, n_rows in show_syms:\n",
+    "    for arm, color, off in [(disp_full, \"C0\", 0.22), (disp_sub, \"C1\", -0.22)]:\n",
+    "        hw = arm[sym_name]\n",
+    "        for hi, h in enumerate(show_hours):\n",
+    "            hw_m, hw_s = hw[\"hw_mean\"][h], hw[\"hw_sd\"][h]\n",
+    "            ax.errorbar([hw_m], [ypos + off + 0.5 * (hi - 1)], xerr=[[2 * hw_s], [2 * hw_s]],\n",
+    "                        fmt=\"o\", ms=4, color=color, capsize=2)\n",
+    "    labels.append(f\"{sym_name}\\n({n_rows / 1e6:.2f}M rows)\")\n",
+    "    ticks.append(ypos)\n",
+    "    ypos += 2.2\n",
+    "ax.set_yticks(ticks)\n",
+    "ax.set_yticklabels(labels, fontsize=8)\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_xlabel(\"conditional-move 90% half-width (bp), posterior mean ± 2 sd\")\n",
+    "ax.set_title(\"Full fit (C0) vs 1% subsample (C1) at three hours of day\\n\"\n",
+    "             \"Global parameters agree; thin-cell uncertainty is where full N earns its keep\")\n",
+    "ax.plot([], [], \"o\", color=\"C0\", label=\"full corpus\")\n",
+    "ax.plot([], [], \"o\", color=\"C1\", label=\"1% subsample\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe7a5b9",
+   "metadata": {},
+   "source": [
+    "This is a capacity-and-estimand demonstration, not a claim of method\n",
+    "superiority: the subsample answers global questions at 1% of the cost.\n",
+    "\n",
+    ":::{warning}\n",
+    "No stratified oversampling of thin symbols, however tempting: unequal\n",
+    "inclusion probabilities break the plain `total_size` rescaling. The subsample\n",
+    "is uniform or it is invalid.\n",
+    ":::\n",
+    "\n",
+    "### What we do not sweep under the rug\n",
+    "\n",
+    "The single most useful diagnostic this notebook can leave you with costs\n",
+    "three fits and one table — the reported mean-field standard deviation next to\n",
+    "the across-seed spread of the posterior mean:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1ffabb4e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:01.367729Z",
+     "iopub.status.busy": "2026-07-31T23:52:01.367651Z",
+     "iopub.status.idle": "2026-07-31T23:52:01.373810Z",
+     "shell.execute_reply": "2026-07-31T23:52:01.373396Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>posterior_mean</th>\n",
+       "      <th>reported_sd</th>\n",
+       "      <th>seed_spread</th>\n",
+       "      <th>ratio</th>\n",
+       "      <th>verdict</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alpha0</th>\n",
+       "      <td>-1.570000</td>\n",
+       "      <td>0.000064</td>\n",
+       "      <td>0.028130</td>\n",
+       "      <td>442.1</td>\n",
+       "      <td>ridge — do not trust sd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_a</th>\n",
+       "      <td>0.053680</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.005310</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>ridge — do not trust sd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kappa0</th>\n",
+       "      <td>0.336200</td>\n",
+       "      <td>0.000151</td>\n",
+       "      <td>0.008867</td>\n",
+       "      <td>58.7</td>\n",
+       "      <td>ridge — do not trust sd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_d</th>\n",
+       "      <td>0.025290</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>eta</th>\n",
+       "      <td>0.369200</td>\n",
+       "      <td>0.000237</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>1.895000</td>\n",
+       "      <td>0.000140</td>\n",
+       "      <td>0.000079</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_a</th>\n",
+       "      <td>0.056380</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_q</th>\n",
+       "      <td>-0.392800</td>\n",
+       "      <td>0.000113</td>\n",
+       "      <td>0.000023</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_q</th>\n",
+       "      <td>-0.001442</td>\n",
+       "      <td>0.000108</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_r</th>\n",
+       "      <td>0.294700</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tau_k</th>\n",
+       "      <td>1.039000</td>\n",
+       "      <td>0.116900</td>\n",
+       "      <td>0.014510</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tau_al</th>\n",
+       "      <td>3.210000</td>\n",
+       "      <td>0.366800</td>\n",
+       "      <td>0.041400</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>ok</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          posterior_mean  reported_sd  seed_spread  ratio  \\\n",
+       "alpha0         -1.570000     0.000064     0.028130  442.1   \n",
+       "beta_a          0.053680     0.000077     0.005310   69.0   \n",
+       "kappa0          0.336200     0.000151     0.008867   58.7   \n",
+       "theta_d         0.025290     0.000001     0.000008    6.1   \n",
+       "eta             0.369200     0.000237     0.000134    0.6   \n",
+       "nu              1.895000     0.000140     0.000079    0.6   \n",
+       "lambda_a        0.056380     0.000139     0.000022    0.2   \n",
+       "lambda_q       -0.392800     0.000113     0.000023    0.2   \n",
+       "beta_q         -0.001442     0.000108     0.000010    0.1   \n",
+       "theta_r         0.294700     0.000076     0.000008    0.1   \n",
+       "tau_k           1.039000     0.116900     0.014510    0.1   \n",
+       "tau_al          3.210000     0.366800     0.041400    0.1   \n",
+       "\n",
+       "                          verdict  \n",
+       "alpha0    ridge — do not trust sd  \n",
+       "beta_a    ridge — do not trust sd  \n",
+       "kappa0    ridge — do not trust sd  \n",
+       "theta_d                        ok  \n",
+       "eta                            ok  \n",
+       "nu                             ok  \n",
+       "lambda_a                       ok  \n",
+       "lambda_q                       ok  \n",
+       "beta_q                         ok  \n",
+       "theta_r                        ok  \n",
+       "tau_k                          ok  \n",
+       "tau_al                         ok  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spread = pd.DataFrame(results[\"seed_spread\"]).T\n",
+    "spread[\"ratio\"] = (spread[\"seed_spread\"] / spread[\"reported_sd\"]).round(1)\n",
+    "spread[\"verdict\"] = np.where(spread[\"ratio\"] > 10, \"ridge — do not trust sd\", \"ok\")\n",
+    "spread.sort_values(\"ratio\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "468b6e2e",
+   "metadata": {},
+   "source": [
+    "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
+    "  share a ridge with their pooled group means (only the sums are identified),\n",
+    "  and across repeated seeds the spread of $\\alpha_0$ is roughly 440× its\n",
+    "  reported posterior standard deviation — $\\kappa_0$ and $\\beta_a$ show the\n",
+    "  same signature at 60–70×. The parameters *without* a ridge ($\\theta_r$,\n",
+    "  $\\nu$, $\\lambda_a$, the $\\tau$'s) have ratios at or below one. Report\n",
+    "  mean-field standard deviations only for parameters you have seed-checked.\n",
+    "- **The speed ordering is model-dependent.** Here the heavy gradient hides\n",
+    "  the loader entirely and pre-shuffled streaming outpaces in-RAM\n",
+    "  `pm.Minibatch`; on light models the ordering reverses. Streaming's claim\n",
+    "  is memory and scale — treat any steps-per-second comparison as specific to\n",
+    "  the model it was measured on.\n",
+    "- **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the\n",
+    "  loader makes without-replacement epoch passes.\n",
+    "- **Discreteness at the smallest moves.** Price changes are integer multiples\n",
+    "  of the tick size; the continuous Student-t is a working approximation whose\n",
+    "  residual misfit concentrates at |y| near one tick.\n",
+    "- **Event time is not clock time.** All dispersion statements are per\n",
+    "  transition, not per minute; activity itself varies by hour, and conflating\n",
+    "  the two clocks changes the intraday story."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b54b7ba5",
+   "metadata": {},
+   "source": [
+    "## Where the pieces live\n",
+    "\n",
+    ":::{seealso}\n",
+    "The `DataLoader`/`parquet_source` used here are in\n",
+    "[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698); a\n",
+    "callback-free `Trainer` wrapper\n",
+    "([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the\n",
+    "convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))\n",
+    "are under review, and this notebook's callback pattern is exactly what those\n",
+    "APIs wrap. The ETL and full-scale run scripts are published alongside the\n",
+    "notebook, and the {ref}`streaming_dataset` companion covers the API mechanics.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aac4475",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "\n",
+    "* Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August\n",
+    "  2026 for the Google Summer of Code project *Streaming Variational Inference\n",
+    "  for Large Datasets* (PyMC / NumFOCUS)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3bc8f67",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca2ff5fb",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9eba6c48",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-31T23:52:01.374622Z",
+     "iopub.status.busy": "2026-07-31T23:52:01.374563Z",
+     "iopub.status.idle": "2026-07-31T23:52:01.386341Z",
+     "shell.execute_reply": "2026-07-31T23:52:01.386058Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Fri, 31 Jul 2026\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.12\n",
+      "IPython version      : 9.16.0\n",
+      "\n",
+      "pytensor   : 3.1.3\n",
+      "pymc_extras: 0.13.1.dev4+gf8c50e5c6\n",
+      "\n",
+      "arviz      : 1.2.0\n",
+      "json       : 2.0.9\n",
+      "logging    : 0.5.1.2\n",
+      "matplotlib : 3.10.9\n",
+      "numpy      : 2.4.6\n",
+      "pandas     : 3.0.3\n",
+      "pyarrow    : 25.0.0\n",
+      "pymc       : 6.1.0\n",
+      "pymc_extras: 0.13.1.dev4+gf8c50e5c6\n",
+      "pytensor   : 3.1.3\n",
+      "\n",
+      "Watermark: 2.6.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor,pymc_extras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8009d53d",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  },
+  "myst": {
+   "substitutions": {
+    "extra_dependencies": "pymc-extras pyarrow"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
new file mode 100644
index 000000000..a5ad755e0
--- /dev/null
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -0,0 +1,1172 @@
+---
+jupytext:
+  default_lexer: ipython3
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+myst:
+  substitutions:
+    extra_dependencies: pymc-extras pyarrow
+---
+
+(streaming_tick_data)=
+
+# Streaming variational inference on high-frequency tick data
+
+:::{post} August 2026
+:tags: variational inference, minibatch, out-of-core, hierarchical model, time series
+:category: advanced, tutorial
+:author: Yicheng Yang
+:::
+
++++
+
+Financial tick data outgrows memory quickly: one month of Binance aggregated
+trades for a single liquid pair is roughly 28 million rows, and a modest
+38-symbol universe over six months already exceeds 450 million rows — far more
+than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a
+hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of
+that scale by streaming minibatches from disk, using the
+{class}`~pymc_extras.variational.dataloader.DataLoader` introduced in the
+{ref}`streaming_dataset` companion notebook.
+
+The companion covers the mechanics of the streaming API. This notebook is the
+applied piece, and it tries to teach three things that only show up on real
+data:
+
+1. **When minibatch VI is even valid.** Two acceptance gates that most
+   time-series models fail — and a model class that passes both.
+2. **The full workflow at scale**: causal feature engineering, an on-disk
+   global shuffle, streaming ADVI with `total_size` rescaling, and an online
+   stopping rule.
+3. **Honest reporting.** Mean-field variational inference on half a billion
+   rows produces some numbers you should trust and some you should not; we
+   measure which are which instead of guessing.
+
+The notebook itself runs in about a minute on synthetic data with known ground
+truth — the streaming code is identical at any scale. Full-scale results on the
+real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus` from runs
+of the exact scripts shown here, with every number reproducible from public
+data.
+
++++
+
+## When is minibatch VI valid? Two gates
+
+Minibatch variational inference rests on one identity: if the likelihood
+factors over rows given the parameters, then the batch log-likelihood scaled by
+$N/B$ is an unbiased estimator of the full-data log-likelihood
+{cite:p}`hoffman2013stochastic`. For a streaming showcase to be honest, two
+conditions must hold. They became the acceptance gates for this notebook's
+model, and they are worth internalizing before reaching for `total_size` on
+your own data:
+
+**Gate 1 — the likelihood must factor over rows.** No latent path coupling
+observations, and no label shared across rows. This is exactly why the classic
+stochastic volatility model of the {ref}`stochastic_volatility` notebook
+*cannot* be minibatched: its latent volatility path ties every observation to
+its neighbours, so a random subset of rows does not carry $B/N$ of the
+log-likelihood. Any model whose rows share one outcome (for example, every tick
+of a match sharing the final result) fails the same gate through
+pseudo-replication — the effective sample size is the number of outcomes, not
+the number of rows.
+
+**Gate 2 — no low-dimensional sufficient statistics.** A Normal likelihood
+with per-cell variances collapses to per-cell $(\sum y^2, n)$: one linear scan
+computes those, and "streaming inference" degenerates into a glorified
+`groupby`. What provably breaks the collapse: a Student-t likelihood, a mixture
+(the hurdle below), and row-level continuous covariates through nonlinear
+links.
+
+The model below passes both gates, and each ingredient that makes it pass is
+also substantively motivated by the data.
+
++++
+
+:::{include} ../extra_installs.md
+:::
+
+```{code-cell} ipython3
+import json
+import logging
+import os
+import tempfile
+import warnings
+
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pymc as pm
+import pytensor.tensor as pt
+
+from pymc_extras.variational.dataloader import DataLoader, parquet_source
+```
+
+```{code-cell} ipython3
+%config InlineBackend.figure_format = 'retina'
+RANDOM_SEED = 20260731
+rng = np.random.default_rng(RANDOM_SEED)
+az.style.use("arviz-variat")
+# the loss figures carry the fit story; keep the fit logger's lines out of the output
+logging.getLogger("pymc").setLevel(logging.ERROR)
+# pytensor's predictive path emits two benign internals warnings (a 0/0 inside its
+# own allclose diagnostics; numba falling back to object mode for the python
+# `random`); neither is actionable here, so they are filtered by origin
+warnings.filterwarnings("ignore", category=RuntimeWarning, module=r"pytensor\.tensor\.type")
+warnings.filterwarnings("ignore", message=r"Numba will use object mode")
+```
+
+## The model: hierarchical hurdle–Student-t next-event returns
+
+One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:
+the return $y_i = 10^4 \, (\log p_{i+1} - \log p_i)$ in basis points, and the
+move indicator $m_i = \mathbb{1}[y_i \neq 0]$. On real per-trade data roughly a
+third of all transitions are *exactly* zero — the price simply does not move —
+so a continuous likelihood alone would put zero probability mass on the single
+most common outcome. The fix is a hurdle: model *whether* the price moves
+separately from *how far* it moves given that it does.
+
+$$
+\begin{aligned}
+m_i &\sim \text{Bernoulli}(\pi_i) \\
+y_i \mid m_i = 1 &\sim \text{StudentT}(\nu,\ \mu_i,\ \sigma_i) \\
+\operatorname{logit} \pi_i &= \kappa_0 + b^{(\kappa)}_s
+  + B(h)^\top\!\left(c + b^{(\pi h)}_s\right)
+  + \lambda_a a_i + \lambda_q q_i \\
+\log \sigma_i &= \alpha_0 + b^{(\alpha)}_s
+  + B(h)^\top\!\left(g + b^{(\sigma h)}_s\right)
+  + (\beta_a + b^{(\beta a)}_s)\, a_i + \beta_q q_i \\
+\mu_i &= \theta_d\, d_i + \theta_r\, \text{ylag}_i
+\end{aligned}
+$$
+
+with covariates computed *causally* (from information available at trade $i$):
+the trade sign $d_i$, standardized log notional $q_i$, standardized trailing
+60-second activity $a_i$, and the most recent previous nonzero return
+$\text{ylag}_i$. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
+the standard way to encode intraday periodicity smoothly
+{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
+back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
+$b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
+so a thin symbol borrows the global intraday shape where its own data run out —
+that is the hierarchical payoff we will measure. They are parameterized
+*centered*, deliberately: the familiar non-centered trick exists for data-poor
+groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich —
+{ref}`tick-real-corpus` shows what happened when we tried it the other way.
+The degrees of freedom are
+shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
+the floor of 1 (rather than the more comfortable 2) is not a stylistic choice,
+and {ref}`tick-real-corpus` shows what the data did to force it.
+
+The reported estimand is **event-return dispersion on the event clock**:
+$\pi_{s,h}$ together with the conditional-move 90% half-width
+$\sigma_{s,h} \cdot t^{-1}_{0.95}(\nu)$. Quantile-based dispersion stays finite
+for any $\nu > 0$; the familiar moment-based alternative does not survive this
+dataset, as we will see.
+
++++
+
+## Synthetic data with known truth
+
+Notebooks in this collection do not download data at build time, so the
+executed path uses a seeded synthetic generator that mirrors the real corpus:
+same schema, same hurdle structure, same heavy tails, twelve symbols with a
+deliberately thin tail so shrinkage is visible. Because the truth is known,
+recovery is checkable — and the streaming code is byte-for-byte the code used
+on the real corpus.
+
+```{code-cell} ipython3
+n_symbols = 12
+counts = np.array(
+    [90_700, 55_000, 40_000, 30_000, 24_000, 19_000, 15_000, 11_000, 8_000, 4_600, 1_800, 900]
+)
+thin = [10, 11]  # the two symbols with the least data
+
+truth = {
+    "kappa0": np.log(0.7 / 0.3),  # ~30% zero-move share at the baseline
+    "c": np.array([0.25, -0.15, 0.10, 0.05]),
+    "lambda_a": 0.35,
+    "lambda_q": 0.20,
+    "alpha0": np.log(0.05),  # baseline sigma ~ 0.05 bp, the real-data scale
+    "g": np.array([0.20, -0.12, 0.08, 0.04]),
+    "beta_a": 0.18,
+    "beta_q": 0.12,
+    "theta_d": 0.02,
+    "theta_r": 0.25,
+    "nu": 3.5,
+}
+
+def standardize(x, axis=0):
+    return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)
+
+z_truth = {
+    "z_k": standardize(rng.standard_normal(n_symbols)),
+    "z_ph": standardize(rng.standard_normal((n_symbols, 4))),
+    "z_al": standardize(rng.standard_normal(n_symbols)),
+    "z_sh": standardize(rng.standard_normal((n_symbols, 4))),
+    "z_ba": standardize(rng.standard_normal(n_symbols)),
+}
+```
+
+```{code-cell} ipython3
+def hour_basis(hour):
+    w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0
+    return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])
+
+sym = np.repeat(np.arange(n_symbols), counts)
+n = len(sym)
+hour = rng.integers(0, 24, size=n)
+d = rng.choice([-1.0, 1.0], size=n)
+a = standardize(rng.standard_normal(n))  # trailing activity (already standardized)
+q = standardize(0.3 * a + np.sqrt(1 - 0.3**2) * rng.standard_normal(n))
+
+B = hour_basis(hour)
+logit_pi = (
+    truth["kappa0"]
+    + 0.30 * z_truth["z_k"][sym]
+    + B @ truth["c"]
+    + (B * (0.15 * z_truth["z_ph"][sym])).sum(1)
+    + truth["lambda_a"] * a
+    + truth["lambda_q"] * q
+)
+log_sigma = (
+    truth["alpha0"]
+    + 0.35 * z_truth["z_al"][sym]
+    + B @ truth["g"]
+    + (B * (0.12 * z_truth["z_sh"][sym])).sum(1)
+    + (truth["beta_a"] + 0.10 * z_truth["z_ba"][sym]) * a
+    + truth["beta_q"] * q
+)
+
+m = (rng.random(n) < 1 / (1 + np.exp(-logit_pi))).astype(np.int8)
+t_draw = rng.standard_t(truth["nu"], size=n)
+
+# sequential generation per symbol so ylag feeds back causally
+y = np.zeros(n)
+ylag = np.zeros(n)
+sigma = np.exp(log_sigma)
+for s in range(n_symbols):
+    idx = np.flatnonzero(sym == s)
+    last = 0.0
+    for i in idx:
+        ylag[i] = last
+        if m[i]:
+            y[i] = truth["theta_d"] * d[i] + truth["theta_r"] * last + sigma[i] * t_draw[i]
+            last = y[i]
+
+print(f"{n:,} rows, zero-move share {(m == 0).mean():.1%}, "
+      f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp")
+```
+
+The zero spike is the single most prominent feature of per-trade return data —
+here is the synthetic version of the picture that forced the hurdle:
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+moves = y[m == 1]
+ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color="C0",
+        label="nonzero moves")
+ax.bar([0.0], [(m == 0).sum()], width=0.02, color="C1",
+       label=f"exactly zero ({(m == 0).mean():.0%} of rows)")
+ax.set_xlabel("next-event return (bp)")
+ax.set_ylabel("count (log scale)")
+ax.set_title("A continuous density puts zero mass on the most common outcome")
+ax.legend();
+```
+
+## The on-disk global shuffle
+
+The trap in streaming ordered data is subtle enough to deserve its own
+section. A bounded runtime shuffle buffer only *block*-shuffles a strongly
+ordered stream: with tick data sorted by symbol and time, early optimization
+steps would only ever see early dates and the first symbols, and an early
+stopping decision would be biased by construction. The fix is to shuffle
+**once, globally, on disk at ETL time**: every row gets a deterministic hash
+key, rows are scattered across shards by that key, and each shard is sorted by
+it. After that, sequential reads *are* a uniform permutation, and the loader
+can run with `shuffle=False` at full speed.
+
+```{code-cell} ipython3
+data_dir = tempfile.mkdtemp(prefix="ticks_")
+table = pa.table(
+    {
+        "y_bp": y.astype(np.float32),
+        "m": m,
+        "d": d.astype(np.int8),
+        "q_std": q.astype(np.float32),
+        "a_std": a.astype(np.float32),
+        "ylag_bp": ylag.astype(np.float32),
+        "hour": hour.astype(np.int8),
+        "sym": sym.astype(np.int16),
+    }
+)
+
+def splitmix64(x):
+    x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    return x ^ (x >> np.uint64(31))
+
+key = splitmix64(np.arange(n, dtype=np.uint64))
+order = np.argsort(key, kind="stable")
+n_shards = 8
+for i in range(n_shards):
+    part = table.take(order[i::n_shards])
+    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"))
+
+head = pq.read_table(os.path.join(data_dir, "shard_000.parquet")).slice(0, 10_000)
+print(f"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours "
+      f"and {len(np.unique(head['sym']))} symbols")
+```
+
+On the real corpus the same check is part of the ETL script's validation: the
+head of any shard must already mix all 24 hours and all symbols, *verified, not
+assumed*.
+
++++
+
+## Streaming the model
+
+The `DataLoader` yields `(batch_size, 8)` blocks in a frozen column order and
+knows the dataset size from the Parquet metadata, so `total_size` is simply
+`len(loader)`. The model reads one `pm.Data` placeholder; everything derived —
+the Fourier basis, the integer symbol index — is computed inside the graph, so
+advancing the stream is a single `set_value` per step.
+
+```{code-cell} ipython3
+columns = ["y_bp", "m", "d", "q_std", "a_std", "ylag_bp", "hour", "sym"]
+loader = DataLoader(
+    parquet_source(data_dir, columns=columns),
+    batch_size=1024,
+    shuffle=False,  # the shards are already globally shuffled on disk
+    sample_shape=(len(columns),),
+    total_size="auto",
+)
+print(f"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch")
+```
+
+```{code-cell} ipython3
+def build_model(symbols, batch_init, total_size):
+    coords = {"symbol": list(symbols), "harmonic": ["sin1", "cos1", "sin2", "cos2"]}
+    with pm.Model(coords=coords) as model:
+        batch = pm.Data("batch", batch_init)
+        y_ = batch[:, 0]
+        m_ = batch[:, 1]
+        d_ = batch[:, 2]
+        q_ = batch[:, 3]
+        a_ = batch[:, 4]
+        ylag_ = batch[:, 5]
+        w = 2.0 * np.pi * batch[:, 6] / 24.0
+        B_ = pt.stack([pt.sin(w), pt.cos(w), pt.sin(2 * w), pt.cos(2 * w)], axis=1)
+        sym_ = pt.cast(batch[:, 7], "int32")
+
+        kappa0 = pm.Normal("kappa0", 0.0, 5.0)
+        c = pm.Normal("c", 0.0, 0.5, dims="harmonic")
+        lambda_a = pm.Normal("lambda_a", 0.0, 0.5)
+        lambda_q = pm.Normal("lambda_q", 0.0, 0.5)
+
+        alpha0 = pm.Normal("alpha0", 0.0, 5.0)
+        g = pm.Normal("g", 0.0, 0.3, dims="harmonic")
+        beta_a = pm.Normal("beta_a", 0.0, 0.3)
+        beta_q = pm.Normal("beta_q", 0.0, 0.3)
+
+        theta_d = pm.Normal("theta_d", 0.0, 0.25)
+        theta_r = pm.Normal("theta_r", 0.0, 0.25)
+
+        tau_k, tau_ph, tau_al, tau_sh, tau_ba = (
+            pm.LogNormal(name, 0.0, 1.5)
+            for name in ["tau_k", "tau_ph", "tau_al", "tau_sh", "tau_ba"]
+        )
+        b_k = pm.Normal("b_k", 0.0, tau_k, dims="symbol")
+        b_ph = pm.Normal("b_ph", 0.0, tau_ph, dims=("symbol", "harmonic"))
+        b_al = pm.Normal("b_al", 0.0, tau_al, dims="symbol")
+        b_sh = pm.Normal("b_sh", 0.0, tau_sh, dims=("symbol", "harmonic"))
+        b_ba = pm.Normal("b_ba", 0.0, tau_ba, dims="symbol")
+
+        eta = pm.Normal("eta", 5.0, 1.0)
+        nu = pm.Deterministic("nu", 1.0 + pt.softplus(eta))
+
+        logit_pi = (
+            kappa0
+            + b_k[sym_]
+            + (B_ * (c + b_ph[sym_])).sum(axis=-1)
+            + lambda_a * a_
+            + lambda_q * q_
+        )
+        log_sigma = (
+            alpha0
+            + b_al[sym_]
+            + (B_ * (g + b_sh[sym_])).sum(axis=-1)
+            + (beta_a + b_ba[sym_]) * a_
+            + beta_q * q_
+        )
+        mu = theta_d * d_ + theta_r * ylag_
+
+        def hurdle_logp(value, logit_pi, mu, log_sigma, nu, m):
+            sigma = pt.exp(log_sigma)
+            t_ll = (
+                pt.gammaln((nu + 1.0) / 2.0)
+                - pt.gammaln(nu / 2.0)
+                - 0.5 * pt.log(nu * np.pi)
+                - log_sigma
+                - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)
+            )
+            return pt.where(m > 0, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))
+
+        def hurdle_random(logit_pi, mu, log_sigma, nu, m, rng=None, size=None):
+            # simulate the full hurdle: first whether the price moves, then how far
+            pi = 1.0 / (1.0 + np.exp(-logit_pi))
+            move = rng.random(size=size) < pi
+            draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)
+            return np.where(move, draw, 0.0)
+
+        pm.CustomDist(
+            "y_obs", logit_pi, mu, log_sigma, nu, m_,
+            logp=hurdle_logp, random=hurdle_random,
+            observed=y_, total_size=total_size,
+        )
+    return model
+```
+
+Two implementation notes worth pausing on. The likelihood is a
+{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`
+gives the term *observed-RV semantics*, which is what makes PyMC's own
+`total_size` minibatch rescaling apply {cite:p}`kucukelbir2015automatic`. And
+the hurdle's two logs are written with `softplus` —
+$\log \pi = -\operatorname{softplus}(-x)$,
+$\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at both
+tails.
+
+```{code-cell} ipython3
+model = build_model(
+    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), len(loader)
+)
+pm.model_to_graphviz(model)
+```
+
+The plate diagram makes the two-link structure visible at a glance: five
+partially pooled effect families on the symbol plate feeding two linear
+predictors, one shared tail parameter, and a single observed node whose batch
+dimension is whatever the placeholder currently holds.
+
+```{code-cell} ipython3
+class StreamAdvance:
+    """pm.fit callback: put the next minibatch into the placeholder after each step."""
+
+    def __init__(self, model, loader):
+        self._shared = model["batch"]
+        self._stream = self._endless(loader)
+
+    @staticmethod
+    def _endless(loader):
+        while True:
+            yield from loader
+
+    def prime(self):
+        self._shared.set_value(next(self._stream), borrow=True)
+
+    def __call__(self, approx, losses, i):
+        self._shared.set_value(next(self._stream), borrow=True)
+
+stream = StreamAdvance(model, loader)
+stream.prime()
+
+with model:
+    advi = pm.ADVI(random_seed=RANDOM_SEED)
+advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02),
+         callbacks=[stream], progressbar=False)
+approx = advi.fit(2_500, obj_optimizer=pm.adam(learning_rate=0.005),
+                  callbacks=[stream], progressbar=False)
+```
+
+The two-stage learning rate is not decoration: at a constant 0.02 the Adam
+updates near the optimum are larger than the posterior standard deviations of
+the sharpest parameters, so the mean jitters around the mode instead of
+settling into it. Annealing was adopted after measuring exactly that on the
+prototype.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
+ax.plot(approx.hist, lw=0.5)
+ax.axvline(6_000, color="C1", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
+ax.set_ylim(-450, 1200)
+ax.set_xlabel("step")
+ax.set_ylabel("negative ELBO")
+ax.set_title("Streaming ADVI loss, y clipped to the plateau (early losses are "
+             "off scale).\nThe visible band is batch-resampling noise — "
+             "remember it for the stopping section")
+ax.legend();
+```
+
+## Did it recover the truth?
+
+```{code-cell} ipython3
+idata = approx.sample(2_000, random_seed=RANDOM_SEED)
+post = idata.posterior
+
+scalar_params = ["kappa0", "alpha0", "lambda_a", "lambda_q", "beta_a", "beta_q",
+                 "theta_d", "theta_r", "nu"]
+rows = {name: [float(post[name].mean()), float(post[name].std())]
+        for name in scalar_params}
+# the intercepts share a ridge with their group means; only the sums are identified
+for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al")]:
+    s = post[icpt] + post[b].mean("symbol")
+    rows[f"{icpt} + mean({b})"] = [float(s.mean()), float(s.std())]
+recovery = pd.DataFrame(rows, index=["mean", "sd"]).T
+recovery["truth"] = [truth[p] for p in scalar_params] + [truth["kappa0"], truth["alpha0"]]
+recovery["abs_error"] = (recovery["mean"] - recovery["truth"]).abs()
+recovery.round(4)
+```
+
+Read the table bottom-up. The two raw intercepts look off by 0.1–0.3 — but a
+global intercept and the mean of its group effects are only *jointly*
+identified: the likelihood constrains their sum, and the prior only weakly
+splits it. The identified sums in the last two rows land within a few
+thousandths of the truth, like every other likelihood-identified quantity.
+Notice also what we did *not* print: z-scores against the truth. Some would
+exceed 2, because the mean-field standard deviations in the third column are
+razor-thin — a z-score against a point truth mixes the sampling noise of this
+particular synthetic dataset with variational overconfidence along exactly
+such ridges. This is the first appearance of a theme the notebook returns to
+at full scale: a mean-field width is trustworthy only where seed replication
+says it is.
+
+The more interesting check is hierarchical: what happened to the per-symbol
+effects of the two symbols with 1,800 and 900 rows?
+
+```{code-cell} ipython3
+# plot the identified CONTRAST b - mean(b): the table above showed the absolute
+# level belongs to a ridge with the intercept, so comparing raw b to raw truth
+# would only display the arbitrary level split. Hand-rolled rather than
+# az.plot_forest because the display is bespoke: truth overlay + thin shading.
+bc = post["b_al"] - post["b_al"].mean("symbol")
+b_mean = bc.mean(("chain", "draw")).values
+b_sd = bc.std(("chain", "draw")).values
+b_truth = 0.35 * z_truth["z_al"]  # the generator's scale times its z draws
+b_truth = b_truth - b_truth.mean()
+
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+x = np.arange(n_symbols)
+ax.errorbar(x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3,
+            label="posterior (mean ± 2 sd)")
+ax.scatter(x, b_truth, marker="x", color="C1", s=60, zorder=3, label="truth")
+for t in thin:
+    ax.axvspan(t - 0.4, t + 0.4, color="C3", alpha=0.12)
+ax.set_xticks(x)
+ax.set_xticklabels([f"S{i}" for i in x])
+ax.set_xlabel("symbol (shaded = thin: 1,800 and 900 rows)")
+ax.set_ylabel(r"$b^{(\alpha)}_s - \bar{b}^{(\alpha)}$")
+ax.set_title("Partial pooling in action: thin symbols shrink toward the group")
+ax.legend();
+```
+
+The thin symbols' estimates are pulled toward zero and carry visibly wider
+intervals — that is partial pooling doing exactly what it is for
+{cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has
+230,000 rows against BTC's 200 million, the same mechanism is what makes the
+tail of the symbol universe estimable at all.
+
++++
+
+## Does it predict what matters?
+
+A recovery table checks parameters; a posterior predictive check asks the
+model to reproduce the data features it exists to describe. The `CustomDist`
+carries a `random` implementation alongside its `logp`, so
+{func}`~pymc.sample_posterior_predictive` works out of the box. The two
+features that matter for this model are the exact-zero share and the heavy
+conditional tail:
+
+```{code-cell} ipython3
+eval_batch = next(iter(loader))
+model["batch"].set_value(eval_batch, borrow=True)  # same path the callback uses
+with model:
+    idata = pm.sample_posterior_predictive(
+        idata, var_names=["y_obs"], random_seed=RANDOM_SEED,
+        extend_inferencedata=True, progressbar=False,
+    )
+
+pp = idata.posterior_predictive["y_obs"].stack(sample=("chain", "draw")).values.T
+y_eval = eval_batch[:, 0]
+
+zero_share = (pp == 0).mean(axis=1)
+med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])
+q99 = np.quantile(np.abs(pp), 0.99, axis=1)
+
+def check_row(observed, draws):
+    lo, hi = np.quantile(draws, [0.05, 0.95])
+    return [observed, draws.mean(), lo, hi]
+
+pd.DataFrame(
+    [
+        check_row((y_eval == 0).mean(), zero_share),
+        check_row(np.median(np.abs(y_eval[y_eval != 0])), med_nonzero),
+        check_row(np.quantile(np.abs(y_eval), 0.99), q99),
+    ],
+    columns=["observed", "pp mean", "pp 5%", "pp 95%"],
+    index=["zero share", "median |nonzero y| (bp)", "q99 |y| (bp)"],
+).round(3)
+```
+
+```{code-cell} ipython3
+# hand-rolled ECDF: the bespoke feature is the annotated discrete jump at zero,
+# which the predictive must reproduce in both location and height
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+grid = np.linspace(-0.5, 0.5, 801)
+for draw in pp[:60]:
+    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size,
+            color="C0", alpha=0.08, lw=1)
+ax.plot(grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size,
+        color="k", lw=1.6, label="observed")
+ax.plot([], [], color="C0", label="posterior predictive (60 draws)")
+zero_jump = (y_eval == 0).mean()
+below = (y_eval < 0).mean()
+ax.annotate(f"vertical step at exactly 0:\nthe zero share ({zero_jump:.0%})",
+            (0.03, below + zero_jump / 2), fontsize=9, ha="left", va="center")
+ax.set_xlabel("next-event return (bp)")
+ax.set_ylabel("ECDF")
+ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
+ax.legend(loc="lower right");
+```
+
+The predictive distribution reproduces the zero step, the bulk scale, and the
+tail quantile within its own 90% bands. On real data one caveat would surface
+here that the synthetic generator cannot show: real price changes are integer
+multiples of the tick size, so the continuous Student-t's residual misfit
+concentrates at |y| near one tick — visible as small stair-steps in the
+observed ECDF that the smooth predictive cannot follow.
+
++++
+
+## Knowing when to stop — and how a stop rule can lie to you
+
+A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo
+estimate — and eyeballing it does not scale to overnight runs. The stopping
+rule proposed for PyMC in [pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)
+standardizes each loss *improvement* by a robust running scale estimate and
+feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming
+itself only after `min_steps` — which on streaming fits should be at least
+one full pass, since stopping before the model has seen every row once is
+never evidence of convergence.
+
+:::{admonition} The minibatch trap
+:class: warning
+Successive losses are evaluated on *different batches*, so the per-step noise
+dwarfs the per-step improvement: on our full-scale run the median one-step
+change is about 106 loss units while the true improvement is 0.04 units per
+step. A rule that standardizes *raw* increments concludes "improvement is
+indistinguishable from zero" immediately upon arming, and fires a fixed
+~$h/\kappa$ steps later regardless of whether the fit converged.
+:::
+
+The fix is as small as the trap is subtle: apply the identical rule to
+*window means* of the loss, where averaging beats the batch-resampling noise
+down by $\sqrt{k}$ and real improvement becomes visible again. We run both
+against the same fit, as non-fatal shadow observers, and let them disagree:
+
+```{code-cell} ipython3
+class CheckLossConvergence:
+    """One-sided CUSUM on robustly standardized loss improvements (pymc#8384)."""
+
+    def __init__(self, kappa=0.25, h=20.0, halflife=200.0, min_steps=1000, z_clip=4.0):
+        self.kappa, self.h, self.halflife = kappa, h, halflife
+        self.min_steps, self.z_clip = min_steps, z_clip
+        self._lam = float(np.exp(np.log(0.5) / halflife))
+        self._prev_loss = self._prev_delta = self._scale = None
+        self._S = 0.0
+
+    def __call__(self, approx, loss, i):
+        current = float(loss[-1])
+        if not np.isfinite(current):
+            return
+        if self._prev_loss is None:
+            self._prev_loss = current
+            return
+        delta, self._prev_loss = self._prev_loss - current, current
+        if self._prev_delta is None:
+            self._prev_delta = delta
+            return
+        abs_diff, self._prev_delta = abs(delta - self._prev_delta), delta
+        if self._scale is None:
+            self._scale = abs_diff
+            return
+        sigma = self._scale * np.sqrt(np.pi) / 2.0 + 1e-12
+        self._scale = self._lam * self._scale + (1.0 - self._lam) * abs_diff
+        z = float(np.clip(delta / sigma, -self.z_clip, self.z_clip))
+        if i >= self.min_steps:
+            self._S = max(0.0, self._S + (self.kappa - z))
+        if self._S > self.h:
+            raise StopIteration(f"converged at step {i} (S={self._S:.1f})")
+```
+
+```{code-cell} ipython3
+class WindowMean:
+    """Feed a stop rule the mean of every k losses instead of raw increments."""
+
+    def __init__(self, monitor, k):
+        self.monitor, self.k = monitor, k
+        self._means = []
+
+    def __call__(self, approx, losses, i):
+        if len(losses) % self.k:
+            return
+        self._means.append(np.mean(losses[-self.k:]))
+        self.monitor(approx, self._means, len(self._means))
+
+
+class Shadow:
+    """Record where a stop rule WOULD fire, without actually stopping the fit."""
+
+    def __init__(self, callback):
+        self.callback, self.stopped_at = callback, None
+
+    def __call__(self, approx, losses, i):
+        if self.stopped_at is None:
+            try:
+                self.callback(approx, losses, i)
+            except StopIteration:
+                self.stopped_at = len(losses)
+```
+
+```{code-cell} ipython3
+loader2 = DataLoader(
+    parquet_source(data_dir, columns=columns),
+    batch_size=1024, shuffle=False, sample_shape=(len(columns),), total_size="auto",
+)
+model2 = build_model(
+    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), len(loader2)
+)
+stream2 = StreamAdvance(model2, loader2)
+stream2.prime()
+steps_per_epoch = len(loader2) // loader2.batch_size
+
+k = 50
+raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))
+win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=steps_per_epoch // k), k))
+
+with model2:
+    advi2 = pm.ADVI(random_seed=RANDOM_SEED)
+approx2 = advi2.fit(20_000, obj_optimizer=pm.adam(learning_rate=0.02),
+                    callbacks=[stream2, raw_shadow, win_shadow], progressbar=False)
+assert raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None, (
+    "a shadow rule never fired within the 20k-step budget; raise the budget"
+)
+win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules
+print(f"raw increments: would stop at step {raw_shadow.stopped_at:,} "
+      f"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})")
+print(f"window means (k={k}): would stop at step {win_stop:,} "
+      f"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})")
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
+ax.plot(approx2.hist, lw=0.5, label="loss")
+ax.axvline(steps_per_epoch, color="C2", ls=":", label="one full pass (rule arms)")
+ax.axvline(raw_shadow.stopped_at, color="C3", ls="--",
+           label=f"raw increments: false stop at {raw_shadow.stopped_at:,}")
+ax.axvline(win_stop, color="C1", ls="--",
+           label=f"window means: stop at {win_stop:,}")
+ax.set_ylim(-550, 900)
+ax.set_xlabel("step")
+ax.set_ylabel("negative ELBO")
+ax.set_title("Batch noise blinds the raw rule; window means restore its eyesight\n"
+             "(y clipped to the descent region)")
+ax.legend(fontsize=9, loc="upper right");
+```
+
+The raw rule fires a fixed ~80 steps after arming with the loss still far from
+its floor — a false stop that a fixed-budget run would silently avoid. The
+windowed rule stops once per-window improvement falls below a quarter of the
+window-noise scale, here capturing roughly 95% of the total loss descent at
+under a quarter of the step budget. On the full-scale corpus the same replay
+shows the windowed rule stopping shortly after one pass, at a loss within
+noise of the two-pass reference — the model converges in about one epoch, and
+the rule proves it rather than assumes it.
+
+(tick-real-corpus)=
+
+## The real corpus: 38 symbols, half a billion transitions
+
+Everything above runs identically on real data; what changes is only the ETL
+in front of it. The full-scale corpus behind the numbers in this section:
+
+- **Source**: Binance's public aggregated-trades archive
+  (`data.binance.vision`) — no API key, published SHA-256 checksums, monthly
+  CSV dumps. The corpus is 491,559,069 transition rows: four majors
+  (BTC/ETH/SOL/XRP × 6 months of 2026) for row bulk plus 34 alt pairs
+  (June 2026) for hierarchy breadth, the thinnest at 67,000 rows.
+- **ETL**: per symbol, *chronologically*: next-event returns, move indicator,
+  trade sign, standardized log notional, trailing 60-second activity, last
+  nonzero return, hour — then the splitmix64 global shuffle into 64 shards
+  (~9 GB of Parquet), with the shard-head mixing check asserted. The complete
+  script is published with the notebook's companion repository.
+- **Hardware**: one Apple M4 laptop, 24 GB RAM. No cluster anywhere.
+
+Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes
+about four minutes at roughly 1,000 optimization steps per second (4 million
+rows per second through the model), with peak resident memory of 1.1 GB — the
+loader's flat footprint, independent of N. The subsections below are the
+measured results; each figure's numbers are inlined verbatim from the run
+logs so the plots execute without any data access.
+
++++
+
+### The memory wall, measured
+
+Every number in the cells below is inlined verbatim from the run artifacts of
+the published scripts, so these figures execute without touching the data.
+First, the reason streaming exists at all. Each point is a fresh subprocess's
+peak resident memory (`ru_maxrss`) after fitting this exact model at that many
+rows — `pm.Minibatch` holding the rows in RAM versus the loader streaming the
+shards:
+
+```{code-cell} ipython3
+with open("streaming_tick_data_results.json") as f:
+    results = json.load(f)
+
+mb = np.array(results["memwall"]["minibatch"], dtype=float)
+st = np.array(results["memwall"]["stream"], dtype=float)
+bpr = results["memwall"]["bytes_per_row"]
+icpt = results["memwall"]["intercept_gb"]
+cross = results["memwall"]["crossing_rows_12gb"]
+n_full = results["corpus"]["n_rows"]
+
+fig, ax = plt.subplots(figsize=(8, 4), layout="constrained")
+ax.plot(mb[:, 0] / 1e6, mb[:, 1], "o-", color="C1", label="pm.Minibatch (rows in RAM)")
+grid = np.linspace(0, n_full, 200)
+ax.plot(grid / 1e6, icpt + bpr * grid / 1e9, ls=":", color="C1", lw=1,
+        label=f"fit: {icpt:.1f} GB + {bpr:.0f} B/row")
+ax.plot(st[:, 0] / 1e6, st[:, 1], "s-", color="C0", label="streaming DataLoader")
+ax.axhline(12, color="C3", ls="--", lw=1, label="12 GB (16 GB laptop budget)")
+ax.axvline(cross / 1e6, color="C3", ls=":", lw=1)
+ax.annotate(f"wall: {cross / 1e6:,.0f}M rows", (cross / 1e6 + 8, 10.6), color="C3",
+            fontsize=9, ha="left", va="top")
+ax.axvline(n_full / 1e6, color="C2", ls=":", lw=1)
+ax.set_ylim(0, 42)
+ax.annotate(f"this corpus: {n_full / 1e6:,.0f}M rows", (n_full / 1e6 - 6, 40.5),
+            color="C2", fontsize=9, ha="right", va="top")
+ax.set_xlabel("dataset size (millions of rows)")
+ax.set_ylabel("peak RSS (GB)")
+ax.set_title("The in-memory path crosses a 16 GB laptop's budget at "
+             f"{cross / 1e6:,.0f}M rows; streaming does not care")
+ax.legend(loc="upper left");
+```
+
+The corpus behind this notebook sits at roughly three times the wall.
+
+:::{warning}
+Two caveats belong next to this figure, not in a footnote. The in-memory arm
+was measured up to 64M rows and the crossing is a *projection* from the fitted
+line — macOS does not enforce `RLIMIT_AS`, so a demonstrated hard failure
+needs a memory-capped Linux subprocess. And the two samplers differ
+semantically: `pm.Minibatch` draws with replacement, the loader makes
+without-replacement epoch passes.
+:::
+
+And speed? Steady-state, compile time subtracted, same model:
+
+```{code-cell} ipython3
+tp = results["memwall"]["throughput"]
+labels = {
+    "minibatch": "pm.Minibatch (rows in RAM)",
+    "stream": "DataLoader, pre-shuffled shards",
+    "stream_shuffled": "DataLoader, runtime shuffle buffer",
+}
+fig, ax = plt.subplots(figsize=(8, 2.6), layout="constrained")
+names = [labels[k] for k in labels]
+vals = [tp[k] for k in labels]
+ax.barh(names, vals, color=["C1", "C0", "C0"])
+for y, v in enumerate(vals):
+    ax.annotate(f"{v:,.0f} steps/s", (v, y), xytext=(4, 0),
+                textcoords="offset points", va="center", fontsize=9)
+ax.set_xlabel("optimization steps per second (batch 4096, steady state)")
+ax.set_title("With a 500-parameter gradient, the loader is not the bottleneck");
+```
+
+The ordering surprised us, so it is worth stating carefully. On this model the
+gradient dominates the step, and the pre-shuffled streaming path *outpaces*
+in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than
+Minibatch's per-step random indexing. That ordering is model-dependent: on a
+light model the loader overhead shows and `pm.Minibatch` wins the races it can
+enter (we measured exactly that on a small probe model). The one consistently
+expensive path is the runtime shuffle buffer — which is why the ETL-time
+global shuffle matters twice: it is the *statistically* honest option, and it
+is 3× the throughput.
+
+### What the data did to the model
+
+Three findings from the first full-scale fits are worth more than any smooth
+success story:
+
+**Non-centered effects collapsed under mean-field.** The first version of this
+model used the textbook non-centered parameterization
+$b_s = \tau \cdot z_s,\ z_s \sim \mathcal{N}(0,1)$. On the real corpus the
+$z_s$ posteriors blew out to $\pm 18$ while every $\tau$ pinned near its prior:
+real cross-symbol volatility levels span two orders of magnitude, the original
+$\tau$ prior could not reach them, and a mean-field approximation of the
+$\tau$–$z$ funnel collapses into its small-$\tau$/huge-$z$ corner. Both fixes
+are principled: weakly informative scale priors, and *centered* effects —
+non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per
+symbol these groups are anything but.
+
+**The variance does not exist.** With the conventional
+$\nu = 2 + \operatorname{softplus}(\eta)$ parameterization, 450 million rows
+pinned $\nu$ at the floor of exactly 2 with a posterior standard deviation of
+$2 \times 10^{-6}$ — the data demanding tails heavier than any
+finite-variance Student-t. The parameterization was moved to a floor of 1 and
+the estimand to quantile-based dispersion, which is finite for all $\nu > 0$.
+With the floor out of the way, three independent seeds put $\nu$ at 1.895
+with a seed spread of $8 \times 10^{-5}$ — an *interior* optimum below 2.
+Per-trade crypto returns, as far as this half-billion-row likelihood is
+concerned, have no variance; they do have a mean, and quantiles behave.
+
+**The robust location disagrees with least squares about a sign.** Ordinary
+least squares of $y$ on $\text{ylag}$ gives a *negative* coefficient
+(bid-ask bounce, −0.29); the fitted $\theta_r$ is *positive*
+(+0.29, seed spread $8 \times 10^{-6}$). Direct likelihood profiling on a
+million real rows
+confirms the likelihood genuinely peaks at the positive value: small moves
+(88% of rows) show continuation, large moves show reversal, and a heavy-tailed
+likelihood follows the bulk while a quadratic loss follows the outliers.
+Neither number is wrong — they answer different questions, and a robust
+likelihood answers the one about typical transitions.
+
++++
+
+### The stopping rule at scale, and a pre-registration that failed
+
+The stopping-rule comparison was pre-registered before any full-scale run:
+fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical
+batch order and seeds, flat learning rate; savings to be claimed only if the
+stopped fit's held-out log score landed within max(2 × ref seed spread,
+2·10⁻⁴) nats/row of the 2-pass reference. Because a flat learning rate makes
+every budget a prefix of the same deterministic trajectory, all five arms per
+seed come from one run with mid-flight snapshots — and the windowed rule ran
+as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated
+one-pass floor plus its designed $h/\kappa$-window detection delay, 46% below
+the reference budget.
+
+```{code-cell} ipython3
+conv = pd.DataFrame(
+    [(arm, passes, *scores) for arm, passes, scores in results["converge_table"]],
+    columns=["arm", "passes", "seed 0", "seed 1", "seed 2"],
+).set_index("arm")
+conv
+```
+
+**The equivalence test failed** — and not in the direction anyone would
+guess: the ¼-pass arm *out-scored* the 2-pass reference, the monitor arm
+under-scored it, and every gap dwarfed the seed spread (∼10⁻⁵). The loss
+trace explains it: this fit converges within a quarter pass, so all flat-rate
+arms sit on the same plateau, where the held-out score of a *point* estimate
+is dominated by Adam's stationary jitter at learning rate 0.02 — position
+along the batch cycle, not posterior quality. The proof is the polish
+experiment: re-running to the monitor's stop and annealing for two thousand
+further steps at a quarter of the learning rate moved the held-out score from
+the worst of the table to the best — above an annealed two-pass reference run
+on the same split — at 55% of its step budget.
+
+One more layer of honesty on the metric itself: even among fully annealed,
+fully converged runs, path-to-path differences are about $5 \times 10^{-4}$
+nats/row. The pre-registered $2 \times 10^{-4}$ equivalence floor was
+therefore unachievable by *any* stopping protocol on this problem — a
+pre-registration mistake worth admitting, because the comparison it pushed us
+toward is the right one: flat-rate arms scatter over $1.7 \times 10^{-3}$,
+annealed endpoints over $5 \times 10^{-4}$, and the seed spread within one
+protocol is $10^{-5}$.
+
+```{code-cell} ipython3
+pol = results["polish"]
+fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
+names = list(pol.keys())
+scores = [pol[n] for n in names]
+ax.barh(names, scores,
+        color=["C1" if "anneal" in n and "stop" in n else "C0" for n in names])
+for y, v in enumerate(scores):
+    ax.annotate(f"{v:+.6f}", (v, y), xytext=(4, 0), textcoords="offset points",
+                va="center", fontsize=8)
+lo = min(scores)
+ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)
+ax.set_xticks([0.116, 0.117, 0.118])
+ax.set_xlabel("held-out log score (nats/row)")
+ax.set_title("Stop rule alone inherits Adam jitter; "
+             "stop + short anneal beats the 2-pass reference at 55% cost");
+```
+
+The honest protocol that comes out of this ablation: **a stop rule on a
+streaming fit must be paired with an anneal-on-stop polish phase**, and
+equivalence must be judged after the polish, not at the raw stopping point.
+That finding — not the 46% headline — is what we would want a reader to take
+away, and it fed directly back into the design discussion of the upstream
+pull requests.
+
+### The estimand: dispersion on the event clock
+
+What the model is *for*: per symbol and hour, the probability that a trade
+moves the price at all, and how far it typically goes when it does. Both come
+from the posterior of the fitted links at reference covariates, so the
+intraday shape below is pooled across the hierarchy — BTC's curve is almost
+entirely its own data, the thin symbols' curves borrow the global harmonics
+exactly as the model intends:
+
+```{code-cell} ipython3
+hours = np.arange(24)
+fig, axs = plt.subplots(1, 2, figsize=(9, 3.6), sharex=True, layout="constrained")
+for sym_name, _ in results["forest_symbols"]:
+    disp = results["dispersion_full"][sym_name]
+    axs[0].plot(hours, disp["pi_mean"], marker=".", label=sym_name)
+    axs[1].plot(hours, disp["hw_mean"], marker=".")
+    axs[1].fill_between(hours,
+                        np.array(disp["hw_mean"]) - 2 * np.array(disp["hw_sd"]),
+                        np.array(disp["hw_mean"]) + 2 * np.array(disp["hw_sd"]),
+                        alpha=0.2)
+axs[0].set_title(r"move probability $\pi_{s,h}$")
+axs[1].set_title("conditional-move 90% half-width (bp)")
+axs[1].set_yscale("log")
+for ax in axs:
+    ax.set_xlabel("UTC hour")
+    ax.set_xticks(range(0, 24, 6))
+axs[0].legend(fontsize=8, loc="center left")
+fig.suptitle("Event-clock dispersion by hour: shared harmonics, per-symbol levels");
+```
+
+Read it as event time, not clock time: these are per-*transition* quantities,
+and the number of transitions per hour itself varies enormously — the two
+clocks tell different stories and this notebook only claims the first.
+
++++
+
+### What full N buys: thin cells
+
+A uniform 1% subsample (4.6M rows, drawn by taking the hash-ordered head of
+every shard) can estimate every *global* parameter of this model — and did,
+matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more
+data? Because the estimand lives in symbol-by-hour cells, and the thin end of
+the universe is where the two fits part ways. Below, the posterior of the
+conditional-move 90% half-width $\sigma_{s,h}\, t^{-1}_{0.95}(\nu)$ at
+reference covariates, full fit versus 1% subsample, for the thinnest and the
+fattest symbols in the corpus:
+
+```{code-cell} ipython3
+disp_full = results["dispersion_full"]
+disp_sub = results["dispersion_sub1pct"]
+show_syms = results["forest_symbols"]  # thinnest three + BTC, with row counts
+show_hours = [3, 14, 20]
+
+fig, ax = plt.subplots(figsize=(8, 4.2), layout="constrained")
+ypos = 0
+labels, ticks = [], []
+for sym_name, n_rows in show_syms:
+    for arm, color, off in [(disp_full, "C0", 0.22), (disp_sub, "C1", -0.22)]:
+        hw = arm[sym_name]
+        for hi, h in enumerate(show_hours):
+            hw_m, hw_s = hw["hw_mean"][h], hw["hw_sd"][h]
+            ax.errorbar([hw_m], [ypos + off + 0.5 * (hi - 1)], xerr=[[2 * hw_s], [2 * hw_s]],
+                        fmt="o", ms=4, color=color, capsize=2)
+    labels.append(f"{sym_name}\n({n_rows / 1e6:.2f}M rows)")
+    ticks.append(ypos)
+    ypos += 2.2
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels, fontsize=8)
+ax.set_xscale("log")
+ax.set_xlabel("conditional-move 90% half-width (bp), posterior mean ± 2 sd")
+ax.set_title("Full fit (C0) vs 1% subsample (C1) at three hours of day\n"
+             "Global parameters agree; thin-cell uncertainty is where full N earns its keep")
+ax.plot([], [], "o", color="C0", label="full corpus")
+ax.plot([], [], "o", color="C1", label="1% subsample")
+ax.legend();
+```
+
+This is a capacity-and-estimand demonstration, not a claim of method
+superiority: the subsample answers global questions at 1% of the cost.
+
+:::{warning}
+No stratified oversampling of thin symbols, however tempting: unequal
+inclusion probabilities break the plain `total_size` rescaling. The subsample
+is uniform or it is invalid.
+:::
+
+### What we do not sweep under the rug
+
+The single most useful diagnostic this notebook can leave you with costs
+three fits and one table — the reported mean-field standard deviation next to
+the across-seed spread of the posterior mean:
+
+```{code-cell} ipython3
+spread = pd.DataFrame(results["seed_spread"]).T
+spread["ratio"] = (spread["seed_spread"] / spread["reported_sd"]).round(1)
+spread["verdict"] = np.where(spread["ratio"] > 10, "ridge — do not trust sd", "ok")
+spread.sort_values("ratio", ascending=False)
+```
+
+- **Mean-field understates uncertainty along soft ridges.** The intercepts
+  share a ridge with their pooled group means (only the sums are identified),
+  and across repeated seeds the spread of $\alpha_0$ is roughly 440× its
+  reported posterior standard deviation — $\kappa_0$ and $\beta_a$ show the
+  same signature at 60–70×. The parameters *without* a ridge ($\theta_r$,
+  $\nu$, $\lambda_a$, the $\tau$'s) have ratios at or below one. Report
+  mean-field standard deviations only for parameters you have seed-checked.
+- **The speed ordering is model-dependent.** Here the heavy gradient hides
+  the loader entirely and pre-shuffled streaming outpaces in-RAM
+  `pm.Minibatch`; on light models the ordering reverses. Streaming's claim
+  is memory and scale — treat any steps-per-second comparison as specific to
+  the model it was measured on.
+- **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the
+  loader makes without-replacement epoch passes.
+- **Discreteness at the smallest moves.** Price changes are integer multiples
+  of the tick size; the continuous Student-t is a working approximation whose
+  residual misfit concentrates at |y| near one tick.
+- **Event time is not clock time.** All dispersion statements are per
+  transition, not per minute; activity itself varies by hour, and conflating
+  the two clocks changes the intraday story.
+
++++
+
+## Where the pieces live
+
+:::{seealso}
+The `DataLoader`/`parquet_source` used here are in
+[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698); a
+callback-free `Trainer` wrapper
+([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the
+convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))
+are under review, and this notebook's callback pattern is exactly what those
+APIs wrap. The ETL and full-scale run scripts are published alongside the
+notebook, and the {ref}`streaming_dataset` companion covers the API mechanics.
+:::
+
++++
+
+## Authors
+
+* Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August
+  2026 for the Google Summer of Code project *Streaming Variational Inference
+  for Large Datasets* (PyMC / NumFOCUS).
+
++++
+
+## References
+
+:::{bibliography}
+:filter: docname in docnames
+:::
+
++++
+
+## Watermark
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor,pymc_extras
+```
+
+:::{include} ../page_footer.md
+:::
diff --git a/examples/variational_inference/streaming_tick_data_results.json b/examples/variational_inference/streaming_tick_data_results.json
new file mode 100644
index 000000000..e78d5e9b7
--- /dev/null
+++ b/examples/variational_inference/streaming_tick_data_results.json
@@ -0,0 +1,1273 @@
+{
+ "corpus": {
+  "n_rows": 491559069,
+  "n_symbols": 38,
+  "thinnest": [
+   [
+    "ZILUSDT",
+    67193
+   ],
+   [
+    "KSMUSDT",
+    89574
+   ],
+   [
+    "MANAUSDT",
+    111036
+   ]
+  ],
+  "fattest": [
+   [
+    "ETHUSDT",
+    178792634
+   ],
+   [
+    "BTCUSDT",
+    202839771
+   ]
+  ]
+ },
+ "seed_spread": {
+  "kappa0": {
+   "posterior_mean": 0.3362,
+   "reported_sd": 0.0001511,
+   "seed_spread": 0.008867
+  },
+  "alpha0": {
+   "posterior_mean": -1.57,
+   "reported_sd": 6.363e-05,
+   "seed_spread": 0.02813
+  },
+  "lambda_a": {
+   "posterior_mean": 0.05638,
+   "reported_sd": 0.0001387,
+   "seed_spread": 2.183e-05
+  },
+  "lambda_q": {
+   "posterior_mean": -0.3928,
+   "reported_sd": 0.0001135,
+   "seed_spread": 2.296e-05
+  },
+  "beta_a": {
+   "posterior_mean": 0.05368,
+   "reported_sd": 7.692e-05,
+   "seed_spread": 0.00531
+  },
+  "beta_q": {
+   "posterior_mean": -0.001442,
+   "reported_sd": 0.0001083,
+   "seed_spread": 9.631e-06
+  },
+  "theta_d": {
+   "posterior_mean": 0.02529,
+   "reported_sd": 1.348e-06,
+   "seed_spread": 8.24e-06
+  },
+  "theta_r": {
+   "posterior_mean": 0.2947,
+   "reported_sd": 7.647e-05,
+   "seed_spread": 7.992e-06
+  },
+  "tau_k": {
+   "posterior_mean": 1.039,
+   "reported_sd": 0.1169,
+   "seed_spread": 0.01451
+  },
+  "tau_al": {
+   "posterior_mean": 3.21,
+   "reported_sd": 0.3668,
+   "seed_spread": 0.0414
+  },
+  "eta": {
+   "posterior_mean": 0.3692,
+   "reported_sd": 0.0002372,
+   "seed_spread": 0.0001343
+  },
+  "nu": {
+   "posterior_mean": 1.895,
+   "reported_sd": 0.0001402,
+   "seed_spread": 7.94e-05
+  }
+ },
+ "memwall": {
+  "minibatch": [
+   [
+    2000000,
+    0.879
+   ],
+   [
+    4000000,
+    1.133
+   ],
+   [
+    8000000,
+    1.564
+   ],
+   [
+    16000000,
+    2.441
+   ],
+   [
+    32000000,
+    3.527
+   ],
+   [
+    64000000,
+    5.74
+   ]
+  ],
+  "stream": [
+   [
+    61452475,
+    0.819
+   ],
+   [
+    230405487,
+    0.824
+   ],
+   [
+    491559069,
+    0.481
+   ]
+  ],
+  "bytes_per_row": 77.2,
+  "intercept_gb": 0.9,
+  "crossing_rows_12gb": 143503428,
+  "throughput": {
+   "minibatch": 992.0,
+   "stream": 1439.0,
+   "stream_shuffled": 434.0
+  }
+ },
+ "converge_table": [
+  [
+   "e0.25",
+   0.25,
+   [
+    0.117919,
+    0.117978,
+    0.117858
+   ]
+  ],
+  [
+   "e0.5",
+   0.5,
+   [
+    0.117867,
+    0.117869,
+    0.117858
+   ]
+  ],
+  [
+   "e1.0",
+   1.0,
+   [
+    0.117009,
+    0.117014,
+    0.117002
+   ]
+  ],
+  [
+   "e2.0",
+   2.0,
+   [
+    0.117014,
+    0.117003,
+    0.117013
+   ]
+  ],
+  [
+   "monitor",
+   1.07,
+   [
+    0.116262,
+    0.116299,
+    0.116323
+   ]
+  ]
+ ],
+ "polish": {
+  "monitor stop, raw (1.07 passes)": 0.116295,
+  "flat 2-pass reference": 0.11701,
+  "stop + 2k-step anneal (1.09 passes)": 0.118446,
+  "annealed 2-pass reference": 0.117855
+ },
+ "forest_symbols": [
+  [
+   "ZILUSDT",
+   67193
+  ],
+  [
+   "KSMUSDT",
+   89574
+  ],
+  [
+   "MANAUSDT",
+   111036
+  ],
+  [
+   "BTCUSDT",
+   202839771
+  ]
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+    0.00118,
+    0.00116,
+    0.00114,
+    0.00113,
+    0.00114,
+    0.00116,
+    0.00117,
+    0.00117,
+    0.00117,
+    0.00116,
+    0.00116,
+    0.00116,
+    0.00114,
+    0.00112,
+    0.00108,
+    0.00105,
+    0.00103,
+    0.00103,
+    0.00103,
+    0.00105,
+    0.00108,
+    0.00111,
+    0.00115
+   ],
+   "hw_mean": [
+    0.08459,
+    0.08301,
+    0.08161,
+    0.08055,
+    0.07988,
+    0.07961,
+    0.07964,
+    0.07986,
+    0.08014,
+    0.0804,
+    0.0806,
+    0.08078,
+    0.08104,
+    0.08147,
+    0.08215,
+    0.08312,
+    0.08434,
+    0.08566,
+    0.08691,
+    0.08786,
+    0.08831,
+    0.08815,
+    0.08738,
+    0.08613
+   ],
+   "hw_sd": [
+    0.000299,
+    0.000309,
+    0.000322,
+    0.000315,
+    0.000281,
+    0.000235,
+    0.000213,
+    0.00024,
+    0.000286,
+    0.000314,
+    0.000314,
+    0.000297,
+    0.000287,
+    0.0003,
+    0.00032,
+    0.000323,
+    0.000298,
+    0.000258,
+    0.000237,
+    0.000263,
+    0.000309,
+    0.000336,
+    0.000331,
+    0.00031
+   ]
+  }
+ }
+}
\ No newline at end of file

From 2fae62234f186f2d6e126a9121c6e81f4514628b Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 19:53:00 -0500
Subject: [PATCH 19/42] Address external referee review: coherent hurdle
 distribution, calibrated claims

---
 .../streaming_tick_data.ipynb                 | 1135 +++++++++--------
 .../streaming_tick_data.myst.md               |  181 ++-
 .../streaming_tick_data_results.json          |    9 +
 3 files changed, 709 insertions(+), 616 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 728dcd069..87a261f79 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b8449bee",
+   "id": "9b92ec9d",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9926a13",
+   "id": "de21b971",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -45,28 +45,30 @@
     "\n",
     "The notebook itself runs in about a minute on synthetic data with known ground\n",
     "truth — the streaming code is identical at any scale. Full-scale results on the\n",
-    "real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus` from runs\n",
-    "of the exact scripts shown here, with every number reproducible from public\n",
-    "data."
+    "real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus`: the data\n",
+    "are public, and every quoted number traces to the run artifacts of the ETL and\n",
+    "fit scripts that accompany the submission."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "93f21b44",
+   "id": "8686eb0b",
    "metadata": {},
    "source": [
-    "## When is minibatch VI valid? Two gates\n",
+    "## Two gates: when minibatch VI is valid, and when it is worth it\n",
     "\n",
     "Minibatch variational inference rests on one identity: if the likelihood\n",
     "factors over rows given the parameters, then the batch log-likelihood scaled by\n",
     "$N/B$ is an unbiased estimator of the full-data log-likelihood\n",
-    "{cite:p}`hoffman2013stochastic`. For a streaming showcase to be honest, two\n",
-    "conditions must hold. They became the acceptance gates for this notebook's\n",
-    "model, and they are worth internalizing before reaching for `total_size` on\n",
-    "your own data:\n",
-    "\n",
-    "**Gate 1 — the likelihood must factor over rows.** No latent path coupling\n",
-    "observations, and no label shared across rows. This is exactly why the classic\n",
+    "{cite:p}`hoffman2013stochastic`. Two gates shaped this notebook's model — the\n",
+    "first is about *validity*, the second about whether streaming is doing any\n",
+    "real work — and they are worth internalizing before reaching for `total_size`\n",
+    "on your own data:\n",
+    "\n",
+    "**Gate 1 — validity: the likelihood must factor over rows, sampled uniformly.**\n",
+    "No latent path coupling observations, no label shared across rows, and every\n",
+    "row given the same inclusion probability by the batching scheme (unequal\n",
+    "inclusion breaks the plain $N/B$ rescaling). This is exactly why the classic\n",
     "stochastic volatility model of the {ref}`stochastic_volatility` notebook\n",
     "*cannot* be minibatched: its latent volatility path ties every observation to\n",
     "its neighbours, so a random subset of rows does not carry $B/N$ of the\n",
@@ -75,12 +77,14 @@
     "pseudo-replication — the effective sample size is the number of outcomes, not\n",
     "the number of rows.\n",
     "\n",
-    "**Gate 2 — no low-dimensional sufficient statistics.** A Normal likelihood\n",
-    "with per-cell variances collapses to per-cell $(\\sum y^2, n)$: one linear scan\n",
-    "computes those, and \"streaming inference\" degenerates into a glorified\n",
-    "`groupby`. What provably breaks the collapse: a Student-t likelihood, a mixture\n",
-    "(the hurdle below), and row-level continuous covariates through nonlinear\n",
-    "links.\n",
+    "**Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This\n",
+    "one is not about validity but about honesty of the *showcase*: a Normal\n",
+    "likelihood with per-cell means and variances collapses to per-cell\n",
+    "$(\\sum y, \\sum y^2, n)$, so one linear scan computes the exact posterior\n",
+    "inputs and \"streaming inference\" degenerates into a glorified `groupby` —\n",
+    "valid, but theater. What provably breaks the collapse: a Student-t likelihood,\n",
+    "a mixture (the hurdle below), and row-level continuous covariates through\n",
+    "nonlinear links.\n",
     "\n",
     "The model below passes both gates, and each ingredient that makes it pass is\n",
     "also substantively motivated by the data."
@@ -88,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03e62ad1",
+   "id": "73b79cc6",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -98,13 +102,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "ed91a6db",
+   "id": "3187c3f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:46.731924Z",
-     "iopub.status.busy": "2026-07-31T23:51:46.731757Z",
-     "iopub.status.idle": "2026-07-31T23:51:48.734115Z",
-     "shell.execute_reply": "2026-07-31T23:51:48.733712Z"
+     "iopub.execute_input": "2026-08-01T00:51:49.896400Z",
+     "iopub.status.busy": "2026-08-01T00:51:49.896177Z",
+     "iopub.status.idle": "2026-08-01T00:51:51.966968Z",
+     "shell.execute_reply": "2026-08-01T00:51:51.966527Z"
     }
    },
    "outputs": [],
@@ -130,13 +134,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "38df3017",
+   "id": "35e1e906",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:48.735284Z",
-     "iopub.status.busy": "2026-07-31T23:51:48.735154Z",
-     "iopub.status.idle": "2026-07-31T23:51:48.737898Z",
-     "shell.execute_reply": "2026-07-31T23:51:48.737576Z"
+     "iopub.execute_input": "2026-08-01T00:51:51.968147Z",
+     "iopub.status.busy": "2026-08-01T00:51:51.968005Z",
+     "iopub.status.idle": "2026-08-01T00:51:51.970710Z",
+     "shell.execute_reply": "2026-08-01T00:51:51.970477Z"
     }
    },
    "outputs": [],
@@ -156,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb89fcc5",
+   "id": "0b649fb6",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -210,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2eb53b7e",
+   "id": "0f2a3f6f",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -226,13 +230,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "2d494699",
+   "id": "27c43795",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:48.738639Z",
-     "iopub.status.busy": "2026-07-31T23:51:48.738586Z",
-     "iopub.status.idle": "2026-07-31T23:51:48.740735Z",
-     "shell.execute_reply": "2026-07-31T23:51:48.740424Z"
+     "iopub.execute_input": "2026-08-01T00:51:51.971601Z",
+     "iopub.status.busy": "2026-08-01T00:51:51.971527Z",
+     "iopub.status.idle": "2026-08-01T00:51:51.973835Z",
+     "shell.execute_reply": "2026-08-01T00:51:51.973494Z"
     }
    },
    "outputs": [],
@@ -272,13 +276,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "36d3b89f",
+   "id": "2bcfa9ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:48.741599Z",
-     "iopub.status.busy": "2026-07-31T23:51:48.741544Z",
-     "iopub.status.idle": "2026-07-31T23:51:48.896109Z",
-     "shell.execute_reply": "2026-07-31T23:51:48.895708Z"
+     "iopub.execute_input": "2026-08-01T00:51:51.974539Z",
+     "iopub.status.busy": "2026-08-01T00:51:51.974482Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.129595Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.129179Z"
     }
    },
    "outputs": [
@@ -342,7 +346,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e2e9891",
+   "id": "b319db8f",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -352,13 +356,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b238a53a",
+   "id": "d0a2623d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:48.897039Z",
-     "iopub.status.busy": "2026-07-31T23:51:48.896965Z",
-     "iopub.status.idle": "2026-07-31T23:51:49.279473Z",
-     "shell.execute_reply": "2026-07-31T23:51:49.279153Z"
+     "iopub.execute_input": "2026-08-01T00:51:52.130350Z",
+     "iopub.status.busy": "2026-08-01T00:51:52.130292Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.514212Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.513829Z"
     }
    },
    "outputs": [
@@ -393,7 +397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08af15d5",
+   "id": "5ef86566",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -412,13 +416,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "14730572",
+   "id": "887b0175",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:49.280501Z",
-     "iopub.status.busy": "2026-07-31T23:51:49.280425Z",
-     "iopub.status.idle": "2026-07-31T23:51:49.434279Z",
-     "shell.execute_reply": "2026-07-31T23:51:49.433905Z"
+     "iopub.execute_input": "2026-08-01T00:51:52.515133Z",
+     "iopub.status.busy": "2026-08-01T00:51:52.515066Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.717678Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.717261Z"
     }
    },
    "outputs": [
@@ -465,17 +469,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3200deb",
+   "id": "39c2bf3b",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
     "head of any shard must already mix all 24 hours and all symbols, *verified, not\n",
-    "assumed*."
+    "assumed*. Note that the cell above is the pedagogical miniature — it sorts the\n",
+    "whole table in memory, which at corpus scale would need several gigabytes for\n",
+    "the keys and permutation alone. The published ETL is genuinely out-of-core:\n",
+    "pass one hash-scatters rows into shards with bounded row-group appends, pass\n",
+    "two sorts each shard independently, and no step ever holds the corpus."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f7b8bf25",
+   "id": "eef98fc7",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -490,13 +498,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "13c1ae1b",
+   "id": "b42de1a7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:49.435185Z",
-     "iopub.status.busy": "2026-07-31T23:51:49.435121Z",
-     "iopub.status.idle": "2026-07-31T23:51:49.438118Z",
-     "shell.execute_reply": "2026-07-31T23:51:49.437766Z"
+     "iopub.execute_input": "2026-08-01T00:51:52.718590Z",
+     "iopub.status.busy": "2026-08-01T00:51:52.718513Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.721339Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.721008Z"
     }
    },
    "outputs": [
@@ -520,16 +528,29 @@
     "print(f\"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "745ab125",
+   "metadata": {},
+   "source": [
+    "One deliberate loader semantics to know about: only full batches are yielded,\n",
+    "so a trailing partial batch — here 992 of 300,000 rows, and with a fixed\n",
+    "replay order the *same* 992 every pass — is never seen, while `total_size`\n",
+    "still rescales by the full $N$. At 0.3% of the data this is a knowably tiny,\n",
+    "stated bias, not a hidden one; shuffling shard order between epochs would\n",
+    "rotate the tail if it ever mattered."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "937133fd",
+   "id": "f9f4a721",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:49.438973Z",
-     "iopub.status.busy": "2026-07-31T23:51:49.438909Z",
-     "iopub.status.idle": "2026-07-31T23:51:49.443236Z",
-     "shell.execute_reply": "2026-07-31T23:51:49.442972Z"
+     "iopub.execute_input": "2026-08-01T00:51:52.722146Z",
+     "iopub.status.busy": "2026-08-01T00:51:52.722089Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.726531Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.726197Z"
     }
    },
    "outputs": [],
@@ -539,7 +560,6 @@
     "    with pm.Model(coords=coords) as model:\n",
     "        batch = pm.Data(\"batch\", batch_init)\n",
     "        y_ = batch[:, 0]\n",
-    "        m_ = batch[:, 1]\n",
     "        d_ = batch[:, 2]\n",
     "        q_ = batch[:, 3]\n",
     "        a_ = batch[:, 4]\n",
@@ -590,7 +610,10 @@
     "        )\n",
     "        mu = theta_d * d_ + theta_r * ylag_\n",
     "\n",
-    "        def hurdle_logp(value, logit_pi, mu, log_sigma, nu, m):\n",
+    "        def hurdle_logp(value, logit_pi, mu, log_sigma, nu):\n",
+    "            # one coherent mixed distribution: an atom at exactly zero plus a\n",
+    "            # Student-t density off zero — the move indicator is value != 0,\n",
+    "            # never a separate parameter, so logp and random describe the SAME law\n",
     "            sigma = pt.exp(log_sigma)\n",
     "            t_ll = (\n",
     "                pt.gammaln((nu + 1.0) / 2.0)\n",
@@ -599,17 +622,18 @@
     "                - log_sigma\n",
     "                - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)\n",
     "            )\n",
-    "            return pt.where(m > 0, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))\n",
+    "            moved = pt.neq(value, 0.0)\n",
+    "            return pt.where(moved, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))\n",
     "\n",
-    "        def hurdle_random(logit_pi, mu, log_sigma, nu, m, rng=None, size=None):\n",
-    "            # simulate the full hurdle: first whether the price moves, then how far\n",
+    "        def hurdle_random(logit_pi, mu, log_sigma, nu, rng=None, size=None):\n",
+    "            # simulate the same law: first whether the price moves, then how far\n",
     "            pi = 1.0 / (1.0 + np.exp(-logit_pi))\n",
     "            move = rng.random(size=size) < pi\n",
     "            draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)\n",
     "            return np.where(move, draw, 0.0)\n",
     "\n",
     "        pm.CustomDist(\n",
-    "            \"y_obs\", logit_pi, mu, log_sigma, nu, m_,\n",
+    "            \"y_obs\", logit_pi, mu, log_sigma, nu,\n",
     "            logp=hurdle_logp, random=hurdle_random,\n",
     "            observed=y_, total_size=total_size,\n",
     "        )\n",
@@ -618,7 +642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9166bbe",
+   "id": "1d5a604b",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -634,13 +658,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "aa3113cb",
+   "id": "faa2dba5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:49.444069Z",
-     "iopub.status.busy": "2026-07-31T23:51:49.444015Z",
-     "iopub.status.idle": "2026-07-31T23:51:49.679257Z",
-     "shell.execute_reply": "2026-07-31T23:51:49.678778Z"
+     "iopub.execute_input": "2026-08-01T00:51:52.727278Z",
+     "iopub.status.busy": "2026-08-01T00:51:52.727223Z",
+     "iopub.status.idle": "2026-08-01T00:51:52.995514Z",
+     "shell.execute_reply": "2026-08-01T00:51:52.995065Z"
     }
    },
    "outputs": [
@@ -656,367 +680,367 @@
        "<svg width=\"1705pt\" height=\"483pt\"\n",
        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.24,-478.95 1701.24,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M795.97,-8C795.97,-8 841.97,-8 841.97,-8 847.97,-8 853.97,-14 853.97,-20 853.97,-20 853.97,-94 853.97,-94 853.97,-100 847.97,-106 841.97,-106 841.97,-106 795.97,-106 795.97,-106 789.97,-106 783.97,-100 783.97,-94 783.97,-94 783.97,-20 783.97,-20 783.97,-14 789.97,-8 795.97,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.97\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M799.01,-8C799.01,-8 845.01,-8 845.01,-8 851.01,-8 857.01,-14 857.01,-20 857.01,-20 857.01,-94 857.01,-94 857.01,-100 851.01,-106 845.01,-106 845.01,-106 799.01,-106 799.01,-106 793.01,-106 787.01,-100 787.01,-94 787.01,-94 787.01,-20 787.01,-20 787.01,-14 793.01,-8 799.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"825.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M945.97,-243.82C945.97,-243.82 1119.97,-243.82 1119.97,-243.82 1125.97,-243.82 1131.97,-249.82 1131.97,-255.82 1131.97,-255.82 1131.97,-353.63 1131.97,-353.63 1131.97,-359.63 1125.97,-365.63 1119.97,-365.63 1119.97,-365.63 945.97,-365.63 945.97,-365.63 939.97,-365.63 933.97,-359.63 933.97,-353.63 933.97,-353.63 933.97,-255.82 933.97,-255.82 933.97,-249.82 939.97,-243.82 945.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.34\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1151.97,-243.82C1151.97,-243.82 1425.97,-243.82 1425.97,-243.82 1431.97,-243.82 1437.97,-249.82 1437.97,-255.82 1437.97,-255.82 1437.97,-353.63 1437.97,-353.63 1437.97,-359.63 1431.97,-365.63 1425.97,-365.63 1425.97,-365.63 1151.97,-365.63 1151.97,-365.63 1145.97,-365.63 1139.97,-359.63 1139.97,-353.63 1139.97,-353.63 1139.97,-255.82 1139.97,-255.82 1139.97,-249.82 1145.97,-243.82 1151.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.59\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1468.97,-243.82C1468.97,-243.82 1642.97,-243.82 1642.97,-243.82 1648.97,-243.82 1654.97,-249.82 1654.97,-255.82 1654.97,-255.82 1654.97,-353.63 1654.97,-353.63 1654.97,-359.63 1648.97,-365.63 1642.97,-365.63 1642.97,-365.63 1468.97,-365.63 1468.97,-365.63 1462.97,-365.63 1456.97,-359.63 1456.97,-353.63 1456.97,-353.63 1456.97,-255.82 1456.97,-255.82 1456.97,-249.82 1462.97,-243.82 1468.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.84\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1469.01,-243.82C1469.01,-243.82 1643.01,-243.82 1643.01,-243.82 1649.01,-243.82 1655.01,-249.82 1655.01,-255.82 1655.01,-255.82 1655.01,-353.63 1655.01,-353.63 1655.01,-359.63 1649.01,-365.63 1643.01,-365.63 1643.01,-365.63 1469.01,-365.63 1469.01,-365.63 1463.01,-365.63 1457.01,-359.63 1457.01,-353.63 1457.01,-353.63 1457.01,-255.82 1457.01,-255.82 1457.01,-249.82 1463.01,-243.82 1469.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M706.97,-114C706.97,-114 930.97,-114 930.97,-114 936.97,-114 942.97,-120 942.97,-126 942.97,-126 942.97,-223.82 942.97,-223.82 942.97,-229.82 936.97,-235.82 930.97,-235.82 930.97,-235.82 706.97,-235.82 706.97,-235.82 700.97,-235.82 694.97,-229.82 694.97,-223.82 694.97,-223.82 694.97,-126 694.97,-126 694.97,-120 700.97,-114 706.97,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.47\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M710.01,-114C710.01,-114 934.01,-114 934.01,-114 940.01,-114 946.01,-120 946.01,-126 946.01,-126 946.01,-223.82 946.01,-223.82 946.01,-229.82 940.01,-235.82 934.01,-235.82 934.01,-235.82 710.01,-235.82 710.01,-235.82 704.01,-235.82 698.01,-229.82 698.01,-223.82 698.01,-223.82 698.01,-126 698.01,-126 698.01,-120 704.01,-114 710.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"924.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M833.97,-98C833.97,-98 803.97,-98 803.97,-98 797.97,-98 791.97,-92 791.97,-86 791.97,-86 791.97,-52.5 791.97,-52.5 791.97,-46.5 797.97,-40.5 803.97,-40.5 803.97,-40.5 833.97,-40.5 833.97,-40.5 839.97,-40.5 845.97,-46.5 845.97,-52.5 845.97,-52.5 845.97,-86 845.97,-86 845.97,-92 839.97,-98 833.97,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M837.01,-98C837.01,-98 807.01,-98 807.01,-98 801.01,-98 795.01,-92 795.01,-86 795.01,-86 795.01,-52.5 795.01,-52.5 795.01,-46.5 801.01,-40.5 807.01,-40.5 807.01,-40.5 837.01,-40.5 837.01,-40.5 843.01,-40.5 849.01,-46.5 849.01,-52.5 849.01,-52.5 849.01,-86 849.01,-86 849.01,-92 843.01,-98 837.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- lambda_a -->\n",
+       "<!-- kappa0 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"48.97\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"818.97\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"822.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
        "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M69.32,-279.64C78.9,-266.03 91.62,-251.92 106.97,-243.82 157.26,-217.25 501.28,-200.17 692.1,-192.63\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"692.23,-196.13 702.09,-192.24 691.96,-189.13 692.23,-196.13\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.86 76.86,-251.93 91.01,-243.82 141.97,-214.61 499.44,-198.59 694.95,-191.92\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"695.05,-195.42 704.92,-191.58 694.81,-188.42 695.05,-195.42\"/>\n",
        "</g>\n",
-       "<!-- tau_ba -->\n",
+       "<!-- alpha0 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.65,-395.53C1157.31,-385.58 1162.39,-374.73 1167.24,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.27,-366.16 1171.34,-355.62 1163.93,-363.19 1170.27,-366.16\"/>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.78,-279.42C165.86,-265.96 176.91,-252.01 191.01,-243.82 233.5,-219.13 524.24,-201.78 695.88,-193.56\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"695.93,-197.06 705.75,-193.09 695.6,-190.07 695.93,-197.06\"/>\n",
        "</g>\n",
-       "<!-- theta_r -->\n",
+       "<!-- lambda_a -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M173.75,-279.44C181.83,-265.98 192.88,-252.03 206.97,-243.82 286.44,-197.49 536.51,-188.32 691.31,-187.27\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"691.25,-190.77 701.23,-187.22 691.22,-183.77 691.25,-190.77\"/>\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M269.49,-279.86C279.08,-266.29 291.78,-252.15 307.01,-243.82 371.64,-208.45 564.51,-195.56 694.99,-190.85\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"694.85,-194.36 704.72,-190.52 694.6,-187.37 694.85,-194.36\"/>\n",
        "</g>\n",
-       "<!-- theta_d -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node5\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"256.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M273.82,-279.57C281.92,-266.13 292.95,-252.15 306.97,-243.82 369.88,-206.4 561.75,-194.08 691.89,-190.06\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"691.7,-193.56 701.59,-189.77 691.49,-186.57 691.7,-193.56\"/>\n",
-       "</g>\n",
-       "<!-- kappa0 -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"356.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M373.93,-279.75C382.04,-266.34 393.06,-252.34 406.97,-243.82 453.69,-215.2 589.88,-200.95 693,-194.09\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"693.18,-197.59 702.94,-193.45 692.73,-190.6 693.18,-197.59\"/>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.97,-279.75C382.08,-266.33 393.1,-252.33 407.01,-243.82 454.2,-214.94 591.98,-200.73 695.94,-193.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"695.89,-197.46 705.65,-193.33 695.45,-190.47 695.89,-197.46\"/>\n",
        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
+       "<g id=\"node22\" class=\"node\">\n",
        "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_sh&#45;&gt;b_sh -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>tau_sh&#45;&gt;b_sh</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.5,-393.28C1511.82,-385.5 1511.11,-377.25 1510.41,-369.16\"/>\n",
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-       "<!-- tau_al -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_al -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1271.18,-393.93C1273.16,-385.49 1275.27,-376.46 1277.33,-367.69\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.67,-368.78 1279.54,-358.24 1273.85,-367.18 1280.67,-368.78\"/>\n",
-       "</g>\n",
-       "<!-- alpha0 -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"456.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M474.35,-279.68C482.46,-266.42 493.37,-252.54 506.97,-243.82 538.18,-223.78 623.06,-209.41 696.42,-200.3\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"696.61,-203.8 706.11,-199.12 695.76,-196.85 696.61,-203.8\"/>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.38,-279.66C482.5,-266.4 493.41,-252.53 507.01,-243.82 538.7,-223.51 625.14,-209.11 699.46,-200.04\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"699.79,-203.53 709.3,-198.87 698.96,-196.58 699.79,-203.53\"/>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.44,-394.25C1627.64,-385.52 1624.63,-376.15 1621.72,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.08,-366.09 1618.69,-357.64 1618.41,-368.23 1625.08,-366.09\"/>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M574.75,-280.21C582.91,-267.02 593.76,-253.06 607.01,-243.82 634.78,-224.44 668.8,-211.81 701.52,-203.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"702.16,-207.02 711.08,-201.3 700.54,-200.21 702.16,-207.02\"/>\n",
        "</g>\n",
-       "<!-- eta -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"660.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- nu -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"706.09,-345.73 615.84,-345.73 615.84,-288.23 706.09,-288.23 706.09,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"660.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M660.97,-393.28C660.97,-381.58 660.97,-368.79 660.97,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"664.47,-357.45 660.97,-347.45 657.47,-357.45 664.47,-357.45\"/>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
-       "<!-- beta_q -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"556.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"556.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"657.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M574.72,-280.23C582.89,-267.05 593.73,-253.08 606.97,-243.82 634.06,-224.85 667.2,-212.34 699.15,-204.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"699.58,-207.6 708.46,-201.82 697.92,-200.8 699.58,-207.6\"/>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M676.75,-280.8C685.16,-268 695.86,-254.15 708.01,-243.82 716.72,-236.41 726.58,-229.72 736.78,-223.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"738.27,-226.93 745.3,-219 734.86,-220.82 738.27,-226.93\"/>\n",
        "</g>\n",
        "<!-- tau_k -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node11\" class=\"node\">\n",
        "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_k -->\n",
        "<g id=\"node19\" class=\"node\">\n",
        "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_k&#45;&gt;b_k -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>tau_k&#45;&gt;b_k</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1388.97,-393.28C1388.97,-385.5 1388.97,-377.25 1388.97,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.47,-369.43 1388.97,-359.43 1385.47,-369.43 1392.47,-369.43\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"766.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
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+     "iopub.status.busy": "2026-08-01T00:51:52.996453Z",
+     "iopub.status.idle": "2026-08-01T00:52:04.135306Z",
+     "shell.execute_reply": "2026-08-01T00:52:04.134923Z"
     }
    },
    "outputs": [],
@@ -1087,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a003edcb",
+   "id": "e0f91e2c",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1100,13 +1124,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c36fa194",
+   "id": "b6a387dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:53.964598Z",
-     "iopub.status.busy": "2026-07-31T23:51:53.964524Z",
-     "iopub.status.idle": "2026-07-31T23:51:54.115627Z",
-     "shell.execute_reply": "2026-07-31T23:51:54.115200Z"
+     "iopub.execute_input": "2026-08-01T00:52:04.136578Z",
+     "iopub.status.busy": "2026-08-01T00:52:04.136502Z",
+     "iopub.status.idle": "2026-08-01T00:52:04.284974Z",
+     "shell.execute_reply": "2026-08-01T00:52:04.284572Z"
     }
    },
    "outputs": [
@@ -1141,7 +1165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc469a92",
+   "id": "9720a4d1",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1150,13 +1174,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b3d3f216",
+   "id": "46ec1244",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:54.116891Z",
-     "iopub.status.busy": "2026-07-31T23:51:54.116823Z",
-     "iopub.status.idle": "2026-07-31T23:51:54.314057Z",
-     "shell.execute_reply": "2026-07-31T23:51:54.313665Z"
+     "iopub.execute_input": "2026-08-01T00:52:04.286287Z",
+     "iopub.status.busy": "2026-08-01T00:52:04.286207Z",
+     "iopub.status.idle": "2026-08-01T00:52:04.490473Z",
+     "shell.execute_reply": "2026-08-01T00:52:04.490186Z"
     }
    },
    "outputs": [
@@ -1309,14 +1333,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d4782fe",
+   "id": "6507a07a",
    "metadata": {},
    "source": [
-    "Read the table bottom-up. The two raw intercepts look off by 0.1–0.3 — but a\n",
-    "global intercept and the mean of its group effects are only *jointly*\n",
+    "Read the table bottom-up. The two raw intercepts look off by 0.3 and 1.3 — but\n",
+    "a global intercept and the mean of its group effects are only *jointly*\n",
     "identified: the likelihood constrains their sum, and the prior only weakly\n",
     "splits it. The identified sums in the last two rows land within a few\n",
     "thousandths of the truth, like every other likelihood-identified quantity.\n",
+    "The same translation ridge exists for every global-coefficient/group-effect\n",
+    "pair in this model ($c$ with $b^{(\\pi h)}$, $g$ with $b^{(\\sigma h)}$,\n",
+    "$\\beta_a$ with $b^{(\\beta a)}$) — we show the two intercepts because their\n",
+    "splits wander furthest; a sum-to-zero constraint on the symbol effects is the\n",
+    "standard reparameterization when the split itself matters.\n",
     "Notice also what we did *not* print: z-scores against the truth. Some would\n",
     "exceed 2, because the mean-field standard deviations in the third column are\n",
     "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
@@ -1332,13 +1361,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "1b34ffd8",
+   "id": "9e34e488",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:54.314964Z",
-     "iopub.status.busy": "2026-07-31T23:51:54.314886Z",
-     "iopub.status.idle": "2026-07-31T23:51:54.429981Z",
-     "shell.execute_reply": "2026-07-31T23:51:54.429575Z"
+     "iopub.execute_input": "2026-08-01T00:52:04.491424Z",
+     "iopub.status.busy": "2026-08-01T00:52:04.491347Z",
+     "iopub.status.idle": "2026-08-01T00:52:04.604262Z",
+     "shell.execute_reply": "2026-08-01T00:52:04.603969Z"
     }
    },
    "outputs": [
@@ -1386,19 +1415,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cb42d1b",
+   "id": "2ba35e55",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
     "intervals — that is partial pooling doing exactly what it is for\n",
     "{cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has\n",
-    "230,000 rows against BTC's 200 million, the same mechanism is what makes the\n",
+    "67,000 rows against BTC's 203 million, the same mechanism is what makes the\n",
     "tail of the symbol universe estimable at all."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c7d8024",
+   "id": "a85060e3",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1414,13 +1443,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "602f4512",
+   "id": "d9bdc4c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:54.430880Z",
-     "iopub.status.busy": "2026-07-31T23:51:54.430818Z",
-     "iopub.status.idle": "2026-07-31T23:51:55.087755Z",
-     "shell.execute_reply": "2026-07-31T23:51:55.087372Z"
+     "iopub.execute_input": "2026-08-01T00:52:04.605264Z",
+     "iopub.status.busy": "2026-08-01T00:52:04.605200Z",
+     "iopub.status.idle": "2026-08-01T00:52:05.217170Z",
+     "shell.execute_reply": "2026-08-01T00:52:05.216820Z"
     }
    },
    "outputs": [
@@ -1460,28 +1489,28 @@
        "      <td>0.350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>median |nonzero y| (bp)</th>\n",
+       "      <th>median |move| (bp)</th>\n",
        "      <td>0.042</td>\n",
        "      <td>0.039</td>\n",
        "      <td>0.036</td>\n",
        "      <td>0.042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>q99 |y| (bp)</th>\n",
-       "      <td>0.273</td>\n",
-       "      <td>0.297</td>\n",
-       "      <td>0.248</td>\n",
-       "      <td>0.356</td>\n",
+       "      <th>q99 |move| (bp)</th>\n",
+       "      <td>0.324</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.426</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                         observed  pp mean  pp 5%  pp 95%\n",
-       "zero share                  0.315    0.327  0.305   0.350\n",
-       "median |nonzero y| (bp)     0.042    0.039  0.036   0.042\n",
-       "q99 |y| (bp)                0.273    0.297  0.248   0.356"
+       "                    observed  pp mean  pp 5%  pp 95%\n",
+       "zero share             0.315    0.327  0.305   0.350\n",
+       "median |move| (bp)     0.042    0.039  0.036   0.042\n",
+       "q99 |move| (bp)        0.324    0.342  0.280   0.426"
       ]
      },
      "execution_count": 14,
@@ -1503,33 +1532,34 @@
     "\n",
     "zero_share = (pp == 0).mean(axis=1)\n",
     "med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])\n",
-    "q99 = np.quantile(np.abs(pp), 0.99, axis=1)\n",
+    "q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])\n",
     "\n",
     "def check_row(observed, draws):\n",
     "    lo, hi = np.quantile(draws, [0.05, 0.95])\n",
     "    return [observed, draws.mean(), lo, hi]\n",
     "\n",
+    "y_nonzero = np.abs(y_eval[y_eval != 0])\n",
     "pd.DataFrame(\n",
     "    [\n",
     "        check_row((y_eval == 0).mean(), zero_share),\n",
-    "        check_row(np.median(np.abs(y_eval[y_eval != 0])), med_nonzero),\n",
-    "        check_row(np.quantile(np.abs(y_eval), 0.99), q99),\n",
+    "        check_row(np.median(y_nonzero), med_nonzero),\n",
+    "        check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),\n",
     "    ],\n",
     "    columns=[\"observed\", \"pp mean\", \"pp 5%\", \"pp 95%\"],\n",
-    "    index=[\"zero share\", \"median |nonzero y| (bp)\", \"q99 |y| (bp)\"],\n",
+    "    index=[\"zero share\", \"median |move| (bp)\", \"q99 |move| (bp)\"],\n",
     ").round(3)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "52dd7dc8",
+   "id": "0cd9ca5a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:55.088690Z",
-     "iopub.status.busy": "2026-07-31T23:51:55.088630Z",
-     "iopub.status.idle": "2026-07-31T23:51:55.222866Z",
-     "shell.execute_reply": "2026-07-31T23:51:55.222492Z"
+     "iopub.execute_input": "2026-08-01T00:52:05.218046Z",
+     "iopub.status.busy": "2026-08-01T00:52:05.217981Z",
+     "iopub.status.idle": "2026-08-01T00:52:05.349121Z",
+     "shell.execute_reply": "2026-08-01T00:52:05.348743Z"
     }
    },
    "outputs": [
@@ -1572,7 +1602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "639ba490",
+   "id": "53b8daa8",
    "metadata": {},
    "source": [
     "The predictive distribution reproduces the zero step, the bulk scale, and the\n",
@@ -1585,7 +1615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9dfe4506",
+   "id": "c60c351c",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1618,13 +1648,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "4d8099e4",
+   "id": "d17bca07",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:55.224032Z",
-     "iopub.status.busy": "2026-07-31T23:51:55.223961Z",
-     "iopub.status.idle": "2026-07-31T23:51:55.226405Z",
-     "shell.execute_reply": "2026-07-31T23:51:55.226161Z"
+     "iopub.execute_input": "2026-08-01T00:52:05.350273Z",
+     "iopub.status.busy": "2026-08-01T00:52:05.350199Z",
+     "iopub.status.idle": "2026-08-01T00:52:05.352655Z",
+     "shell.execute_reply": "2026-08-01T00:52:05.352335Z"
     }
    },
    "outputs": [],
@@ -1666,13 +1696,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "e0955b70",
+   "id": "841275d3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:55.227410Z",
-     "iopub.status.busy": "2026-07-31T23:51:55.227343Z",
-     "iopub.status.idle": "2026-07-31T23:51:55.229258Z",
-     "shell.execute_reply": "2026-07-31T23:51:55.228977Z"
+     "iopub.execute_input": "2026-08-01T00:52:05.353487Z",
+     "iopub.status.busy": "2026-08-01T00:52:05.353412Z",
+     "iopub.status.idle": "2026-08-01T00:52:05.355382Z",
+     "shell.execute_reply": "2026-08-01T00:52:05.355097Z"
     }
    },
    "outputs": [],
@@ -1708,13 +1738,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "3292e06e",
+   "id": "b1f904e9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:51:55.230050Z",
-     "iopub.status.busy": "2026-07-31T23:51:55.230000Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.412537Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.411984Z"
+     "iopub.execute_input": "2026-08-01T00:52:05.356080Z",
+     "iopub.status.busy": "2026-08-01T00:52:05.356031Z",
+     "iopub.status.idle": "2026-08-01T00:52:09.902077Z",
+     "shell.execute_reply": "2026-08-01T00:52:09.901715Z"
     }
    },
    "outputs": [
@@ -1740,8 +1770,9 @@
     "steps_per_epoch = len(loader2) // loader2.batch_size\n",
     "\n",
     "k = 50\n",
+    "min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass\n",
     "raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))\n",
-    "win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=steps_per_epoch // k), k))\n",
+    "win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=min_windows), k))\n",
     "\n",
     "with model2:\n",
     "    advi2 = pm.ADVI(random_seed=RANDOM_SEED)\n",
@@ -1760,13 +1791,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "3a56a0ac",
+   "id": "3bbc0af2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.414289Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.414181Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.597795Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.597453Z"
+     "iopub.execute_input": "2026-08-01T00:52:09.903000Z",
+     "iopub.status.busy": "2026-08-01T00:52:09.902941Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.073897Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.073521Z"
     }
    },
    "outputs": [
@@ -1804,17 +1835,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b83c5b6",
+   "id": "e0ea44e4",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
     "its floor — a false stop that a fixed-budget run would silently avoid. The\n",
     "windowed rule stops once per-window improvement falls below a quarter of the\n",
     "window-noise scale, here capturing roughly 95% of the total loss descent at\n",
-    "under a quarter of the step budget. On the full-scale corpus the same replay\n",
-    "shows the windowed rule stopping shortly after one pass, at a loss within\n",
-    "noise of the two-pass reference — the model converges in about one epoch, and\n",
-    "the rule proves it rather than assumes it.\n",
+    "under a quarter of the step budget. Two honest qualifications. First, what the\n",
+    "rule detects is a *loss plateau* — a necessary signal, not a proof that the\n",
+    "posterior has converged; the held-out checks in the next section are the\n",
+    "independent evidence. Second, the $h/\\kappa$-window delay after a plateau is\n",
+    "the rule's *nominal* detection lag; clipping and the adaptive scale can move\n",
+    "the actual hitting time. On the full-scale corpus the same replay shows the\n",
+    "windowed rule stopping shortly after one pass, at a loss within noise of the\n",
+    "two-pass reference.\n",
     "\n",
     "(tick-real-corpus)=\n",
     "\n",
@@ -1845,7 +1880,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7d0a4b5",
+   "id": "2b29c63b",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -1861,13 +1896,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "87532fe4",
+   "id": "67e67872",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.599133Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.599052Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.750840Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.750434Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.075043Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.074964Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.225861Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.225472Z"
     }
    },
    "outputs": [
@@ -1921,7 +1956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da23d0b7",
+   "id": "e1c84294",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -1941,13 +1976,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "a0c7fe23",
+   "id": "b5c978bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.752084Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.752018Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.828453Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.828063Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.227299Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.227216Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.301797Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.301418Z"
     }
    },
    "outputs": [
@@ -1987,7 +2022,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65c7052e",
+   "id": "a70e5326",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2014,7 +2049,11 @@
     "$\\tau$–$z$ funnel collapses into its small-$\\tau$/huge-$z$ corner. Both fixes\n",
     "are principled: weakly informative scale priors, and *centered* effects —\n",
     "non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per\n",
-    "symbol these groups are anything but.\n",
+    "symbol these groups are anything but. One honest caveat: the two fixes were\n",
+    "applied together, so their individual contributions are confounded — a\n",
+    "same-prior centered/non-centered ablation would separate them, and the safe\n",
+    "reading is empirical rather than theoretical: on this corpus, this\n",
+    "combination fits cleanly and the original one did not.\n",
     "\n",
     "**The variance does not exist.** With the conventional\n",
     "$\\nu = 2 + \\operatorname{softplus}(\\eta)$ parameterization, 450 million rows\n",
@@ -2024,8 +2063,10 @@
     "the estimand to quantile-based dispersion, which is finite for all $\\nu > 0$.\n",
     "With the floor out of the way, three independent seeds put $\\nu$ at 1.895\n",
     "with a seed spread of $8 \\times 10^{-5}$ — an *interior* optimum below 2.\n",
-    "Per-trade crypto returns, as far as this half-billion-row likelihood is\n",
-    "concerned, have no variance; they do have a mean, and quantiles behave.\n",
+    "Within the fitted conditional-move Student-t, then, the variance simply does\n",
+    "not exist — a statement about this working likelihood on this corpus, not a\n",
+    "metaphysical claim about crypto returns — while the mean is finite and\n",
+    "quantiles behave, which is exactly why the estimand is quantile-based.\n",
     "\n",
     "**The robust location disagrees with least squares about a sign.** Ordinary\n",
     "least squares of $y$ on $\\text{ylag}$ gives a *negative* coefficient\n",
@@ -2041,7 +2082,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b3427d9",
+   "id": "c1806eeb",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
@@ -2061,13 +2102,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "6c5cf692",
+   "id": "1ac1025a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.829454Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.829378Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.837561Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.837175Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.302720Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.302655Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.307274Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.306905Z"
     }
    },
    "outputs": [
@@ -2170,47 +2211,51 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c10b4aa",
+   "id": "65c9f734",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
-    "guess: the ¼-pass arm *out-scored* the 2-pass reference, the monitor arm\n",
-    "under-scored it, and every gap dwarfed the seed spread (∼10⁻⁵). The loss\n",
-    "trace explains it: this fit converges within a quarter pass, so all flat-rate\n",
-    "arms sit on the same plateau, where the held-out score of a *point* estimate\n",
-    "is dominated by Adam's stationary jitter at learning rate 0.02 — position\n",
-    "along the batch cycle, not posterior quality. The proof is the polish\n",
-    "experiment: re-running to the monitor's stop and annealing for two thousand\n",
+    "guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about\n",
+    "$10^{-3}$ nats/row — a hundred times the seed spread — while the 1- and\n",
+    "2-pass arms tied within seed spread, and the monitor arm under-scored\n",
+    "everything. The loss trace suggests why: this fit reaches its loss plateau\n",
+    "within a quarter pass, so all flat-rate arms sit on the same plateau, where\n",
+    "the held-out score of a *point* estimate moves with the optimizer's state at\n",
+    "flat learning rate 0.02 — consistent with Adam's stationary jitter and\n",
+    "position along the (seed-shared) batch cycle, though these artifacts alone\n",
+    "cannot separate that from other path effects. The polish experiment points\n",
+    "the same way: re-running to the monitor's stop and annealing for two thousand\n",
     "further steps at a quarter of the learning rate moved the held-out score from\n",
-    "the worst of the table to the best — above an annealed two-pass reference run\n",
-    "on the same split — at 55% of its step budget.\n",
+    "the worst of the table to the top — within path noise of an annealed two-pass\n",
+    "reference run on the same split — at 55% of its step budget. One run per\n",
+    "protocol, so these comparisons are exploratory; paired, replicated annealed\n",
+    "endpoints per arm are the confirmatory design.\n",
     "\n",
     "One more layer of honesty on the metric itself: even among fully annealed,\n",
     "fully converged runs, path-to-path differences are about $5 \\times 10^{-4}$\n",
     "nats/row. The pre-registered $2 \\times 10^{-4}$ equivalence floor was\n",
-    "therefore unachievable by *any* stopping protocol on this problem — a\n",
-    "pre-registration mistake worth admitting, because the comparison it pushed us\n",
-    "toward is the right one: flat-rate arms scatter over $1.7 \\times 10^{-3}$,\n",
-    "annealed endpoints over $5 \\times 10^{-4}$, and the seed spread within one\n",
-    "protocol is $10^{-5}$."
+    "therefore unachievable by any protocol we ran — a pre-registration mistake\n",
+    "worth admitting, because the comparison it pushed us toward is the right one:\n",
+    "flat-rate arms scatter over $1.7 \\times 10^{-3}$, annealed endpoints over\n",
+    "$5 \\times 10^{-4}$, and the seed spread within one protocol is $10^{-5}$."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "0d1d3007",
+   "id": "f7b838c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.838419Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.838346Z",
-     "iopub.status.idle": "2026-07-31T23:52:00.938391Z",
-     "shell.execute_reply": "2026-07-31T23:52:00.937960Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.308086Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.308033Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.391869Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.391548Z"
     }
    },
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -2218,7 +2263,7 @@
      "metadata": {
       "image/png": {
        "height": 623,
-       "width": 2457
+       "width": 1963
       }
      },
      "output_type": "display_data"
@@ -2238,18 +2283,18 @@
     "ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)\n",
     "ax.set_xticks([0.116, 0.117, 0.118])\n",
     "ax.set_xlabel(\"held-out log score (nats/row)\")\n",
-    "ax.set_title(\"Stop rule alone inherits Adam jitter; \"\n",
-    "             \"stop + short anneal beats the 2-pass reference at 55% cost\");"
+    "ax.set_title(\"Stop rule alone inherits optimizer jitter; stop + short anneal\\n\"\n",
+    "             \"matches the annealed 2-pass reference at 55% of the budget\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2be140dc",
+   "id": "53ee0e00",
    "metadata": {},
    "source": [
-    "The honest protocol that comes out of this ablation: **a stop rule on a\n",
-    "streaming fit must be paired with an anneal-on-stop polish phase**, and\n",
-    "equivalence must be judged after the polish, not at the raw stopping point.\n",
+    "The working protocol this ablation suggests: **pair a stop rule on a\n",
+    "streaming fit with an anneal-on-stop polish phase**, and judge equivalence\n",
+    "after the polish, not at the raw stopping point.\n",
     "That finding — not the 46% headline — is what we would want a reader to take\n",
     "away, and it fed directly back into the design discussion of the upstream\n",
     "pull requests.\n",
@@ -2267,13 +2312,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "97410d8a",
+   "id": "4a91e180",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:00.939961Z",
-     "iopub.status.busy": "2026-07-31T23:52:00.939863Z",
-     "iopub.status.idle": "2026-07-31T23:52:01.167615Z",
-     "shell.execute_reply": "2026-07-31T23:52:01.167275Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.392914Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.392836Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.602558Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.602251Z"
     }
    },
    "outputs": [
@@ -2316,7 +2361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c579a489",
+   "id": "97e46212",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2326,13 +2371,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d23321a",
+   "id": "906ecd50",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
     "\n",
-    "A uniform 1% subsample (4.6M rows, drawn by taking the hash-ordered head of\n",
-    "every shard) can estimate every *global* parameter of this model — and did,\n",
+    "A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn\n",
+    "by taking the hash-ordered head of each shard, with the last four shards held\n",
+    "out for scoring) can estimate every *global* parameter of this model — and did,\n",
     "matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more\n",
     "data? Because the estimand lives in symbol-by-hour cells, and the thin end of\n",
     "the universe is where the two fits part ways. Below, the posterior of the\n",
@@ -2344,13 +2390,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "96533f42",
+   "id": "7254f0c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:01.168840Z",
-     "iopub.status.busy": "2026-07-31T23:52:01.168772Z",
-     "iopub.status.idle": "2026-07-31T23:52:01.366601Z",
-     "shell.execute_reply": "2026-07-31T23:52:01.366237Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.603839Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.603762Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.942820Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.942524Z"
     }
    },
    "outputs": [
@@ -2402,7 +2448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fe7a5b9",
+   "id": "455d6c3b",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2417,20 +2463,24 @@
     "### What we do not sweep under the rug\n",
     "\n",
     "The single most useful diagnostic this notebook can leave you with costs\n",
-    "three fits and one table — the reported mean-field standard deviation next to\n",
-    "the across-seed spread of the posterior mean:"
+    "three fits and one table: the reported mean-field standard deviation next to\n",
+    "the spread of the posterior mean across three optimizer seeds. Read it as a\n",
+    "*computational-stability* diagnostic — a ratio far above one proves the\n",
+    "reported width understates run-to-run variation, which is a necessary\n",
+    "condition for trusting it, not a calibration certificate (that would need a\n",
+    "comparison against full-rank or MCMC on a tractable subproblem):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "1ffabb4e",
+   "id": "38224bfd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:01.367729Z",
-     "iopub.status.busy": "2026-07-31T23:52:01.367651Z",
-     "iopub.status.idle": "2026-07-31T23:52:01.373810Z",
-     "shell.execute_reply": "2026-07-31T23:52:01.373396Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.943916Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.943850Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.950113Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.949725Z"
     }
    },
    "outputs": [
@@ -2459,7 +2509,7 @@
        "      <th>reported_sd</th>\n",
        "      <th>seed_spread</th>\n",
        "      <th>ratio</th>\n",
-       "      <th>verdict</th>\n",
+       "      <th>flag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2469,7 +2519,7 @@
        "      <td>0.000064</td>\n",
        "      <td>0.028130</td>\n",
        "      <td>442.1</td>\n",
-       "      <td>ridge — do not trust sd</td>\n",
+       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
@@ -2477,7 +2527,7 @@
        "      <td>0.000077</td>\n",
        "      <td>0.005310</td>\n",
        "      <td>69.0</td>\n",
-       "      <td>ridge — do not trust sd</td>\n",
+       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
@@ -2485,7 +2535,7 @@
        "      <td>0.000151</td>\n",
        "      <td>0.008867</td>\n",
        "      <td>58.7</td>\n",
-       "      <td>ridge — do not trust sd</td>\n",
+       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
@@ -2493,7 +2543,7 @@
        "      <td>0.000001</td>\n",
        "      <td>0.000008</td>\n",
        "      <td>6.1</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eta</th>\n",
@@ -2501,7 +2551,7 @@
        "      <td>0.000237</td>\n",
        "      <td>0.000134</td>\n",
        "      <td>0.6</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
@@ -2509,7 +2559,7 @@
        "      <td>0.000140</td>\n",
        "      <td>0.000079</td>\n",
        "      <td>0.6</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
@@ -2517,7 +2567,7 @@
        "      <td>0.000139</td>\n",
        "      <td>0.000022</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
@@ -2525,7 +2575,7 @@
        "      <td>0.000113</td>\n",
        "      <td>0.000023</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
@@ -2533,7 +2583,7 @@
        "      <td>0.000108</td>\n",
        "      <td>0.000010</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
@@ -2541,7 +2591,7 @@
        "      <td>0.000076</td>\n",
        "      <td>0.000008</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tau_k</th>\n",
@@ -2549,7 +2599,7 @@
        "      <td>0.116900</td>\n",
        "      <td>0.014510</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tau_al</th>\n",
@@ -2557,40 +2607,26 @@
        "      <td>0.366800</td>\n",
        "      <td>0.041400</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>ok</td>\n",
+       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          posterior_mean  reported_sd  seed_spread  ratio  \\\n",
-       "alpha0         -1.570000     0.000064     0.028130  442.1   \n",
-       "beta_a          0.053680     0.000077     0.005310   69.0   \n",
-       "kappa0          0.336200     0.000151     0.008867   58.7   \n",
-       "theta_d         0.025290     0.000001     0.000008    6.1   \n",
-       "eta             0.369200     0.000237     0.000134    0.6   \n",
-       "nu              1.895000     0.000140     0.000079    0.6   \n",
-       "lambda_a        0.056380     0.000139     0.000022    0.2   \n",
-       "lambda_q       -0.392800     0.000113     0.000023    0.2   \n",
-       "beta_q         -0.001442     0.000108     0.000010    0.1   \n",
-       "theta_r         0.294700     0.000076     0.000008    0.1   \n",
-       "tau_k           1.039000     0.116900     0.014510    0.1   \n",
-       "tau_al          3.210000     0.366800     0.041400    0.1   \n",
-       "\n",
-       "                          verdict  \n",
-       "alpha0    ridge — do not trust sd  \n",
-       "beta_a    ridge — do not trust sd  \n",
-       "kappa0    ridge — do not trust sd  \n",
-       "theta_d                        ok  \n",
-       "eta                            ok  \n",
-       "nu                             ok  \n",
-       "lambda_a                       ok  \n",
-       "lambda_q                       ok  \n",
-       "beta_q                         ok  \n",
-       "theta_r                        ok  \n",
-       "tau_k                          ok  \n",
-       "tau_al                         ok  "
+       "          posterior_mean  reported_sd  seed_spread  ratio             flag\n",
+       "alpha0         -1.570000     0.000064     0.028130  442.1  ridge-dominated\n",
+       "beta_a          0.053680     0.000077     0.005310   69.0  ridge-dominated\n",
+       "kappa0          0.336200     0.000151     0.008867   58.7  ridge-dominated\n",
+       "theta_d         0.025290     0.000001     0.000008    6.1      seed-stable\n",
+       "eta             0.369200     0.000237     0.000134    0.6      seed-stable\n",
+       "nu              1.895000     0.000140     0.000079    0.6      seed-stable\n",
+       "lambda_a        0.056380     0.000139     0.000022    0.2      seed-stable\n",
+       "lambda_q       -0.392800     0.000113     0.000023    0.2      seed-stable\n",
+       "beta_q         -0.001442     0.000108     0.000010    0.1      seed-stable\n",
+       "theta_r         0.294700     0.000076     0.000008    0.1      seed-stable\n",
+       "tau_k           1.039000     0.116900     0.014510    0.1      seed-stable\n",
+       "tau_al          3.210000     0.366800     0.041400    0.1      seed-stable"
       ]
      },
      "execution_count": 26,
@@ -2601,13 +2637,13 @@
    "source": [
     "spread = pd.DataFrame(results[\"seed_spread\"]).T\n",
     "spread[\"ratio\"] = (spread[\"seed_spread\"] / spread[\"reported_sd\"]).round(1)\n",
-    "spread[\"verdict\"] = np.where(spread[\"ratio\"] > 10, \"ridge — do not trust sd\", \"ok\")\n",
+    "spread[\"flag\"] = np.where(spread[\"ratio\"] > 10, \"ridge-dominated\", \"seed-stable\")\n",
     "spread.sort_values(\"ratio\", ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "468b6e2e",
+   "id": "d9f5316a",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2615,8 +2651,9 @@
     "  and across repeated seeds the spread of $\\alpha_0$ is roughly 440× its\n",
     "  reported posterior standard deviation — $\\kappa_0$ and $\\beta_a$ show the\n",
     "  same signature at 60–70×. The parameters *without* a ridge ($\\theta_r$,\n",
-    "  $\\nu$, $\\lambda_a$, the $\\tau$'s) have ratios at or below one. Report\n",
-    "  mean-field standard deviations only for parameters you have seed-checked.\n",
+    "  $\\nu$, $\\lambda_a$, the $\\tau$'s) sit at or below one, and $\\theta_d$ sits\n",
+    "  in between (≈6). Report mean-field standard deviations only for parameters\n",
+    "  you have seed-checked — and remember the check bounds the error from below.\n",
     "- **The speed ordering is model-dependent.** Here the heavy gradient hides\n",
     "  the loader entirely and pre-shuffled streaming outpaces in-RAM\n",
     "  `pm.Minibatch`; on light models the ordering reverses. Streaming's claim\n",
@@ -2634,7 +2671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b54b7ba5",
+   "id": "fb8595bc",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2646,14 +2683,14 @@
     "([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the\n",
     "convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))\n",
     "are under review, and this notebook's callback pattern is exactly what those\n",
-    "APIs wrap. The ETL and full-scale run scripts are published alongside the\n",
-    "notebook, and the {ref}`streaming_dataset` companion covers the API mechanics.\n",
+    "APIs wrap. The ETL and full-scale run scripts accompany the submission, and\n",
+    "the {ref}`streaming_dataset` companion covers the API mechanics.\n",
     ":::"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5aac4475",
+   "id": "8ad79fbe",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2665,7 +2702,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3bc8f67",
+   "id": "7c43146d",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2677,7 +2714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca2ff5fb",
+   "id": "867b8d1e",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2686,13 +2723,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "9eba6c48",
+   "id": "b9120997",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-31T23:52:01.374622Z",
-     "iopub.status.busy": "2026-07-31T23:52:01.374563Z",
-     "iopub.status.idle": "2026-07-31T23:52:01.386341Z",
-     "shell.execute_reply": "2026-07-31T23:52:01.386058Z"
+     "iopub.execute_input": "2026-08-01T00:52:10.950973Z",
+     "iopub.status.busy": "2026-08-01T00:52:10.950913Z",
+     "iopub.status.idle": "2026-08-01T00:52:10.961587Z",
+     "shell.execute_reply": "2026-08-01T00:52:10.961261Z"
     }
    },
    "outputs": [
@@ -2732,7 +2769,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8009d53d",
+   "id": "9b0be0de",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index a5ad755e0..1e2a47a9f 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -50,24 +50,26 @@ data:
 
 The notebook itself runs in about a minute on synthetic data with known ground
 truth — the streaming code is identical at any scale. Full-scale results on the
-real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus` from runs
-of the exact scripts shown here, with every number reproducible from public
-data.
+real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus`: the data
+are public, and every quoted number traces to the run artifacts of the ETL and
+fit scripts that accompany the submission.
 
 +++
 
-## When is minibatch VI valid? Two gates
+## Two gates: when minibatch VI is valid, and when it is worth it
 
 Minibatch variational inference rests on one identity: if the likelihood
 factors over rows given the parameters, then the batch log-likelihood scaled by
 $N/B$ is an unbiased estimator of the full-data log-likelihood
-{cite:p}`hoffman2013stochastic`. For a streaming showcase to be honest, two
-conditions must hold. They became the acceptance gates for this notebook's
-model, and they are worth internalizing before reaching for `total_size` on
-your own data:
-
-**Gate 1 — the likelihood must factor over rows.** No latent path coupling
-observations, and no label shared across rows. This is exactly why the classic
+{cite:p}`hoffman2013stochastic`. Two gates shaped this notebook's model — the
+first is about *validity*, the second about whether streaming is doing any
+real work — and they are worth internalizing before reaching for `total_size`
+on your own data:
+
+**Gate 1 — validity: the likelihood must factor over rows, sampled uniformly.**
+No latent path coupling observations, no label shared across rows, and every
+row given the same inclusion probability by the batching scheme (unequal
+inclusion breaks the plain $N/B$ rescaling). This is exactly why the classic
 stochastic volatility model of the {ref}`stochastic_volatility` notebook
 *cannot* be minibatched: its latent volatility path ties every observation to
 its neighbours, so a random subset of rows does not carry $B/N$ of the
@@ -76,12 +78,14 @@ of a match sharing the final result) fails the same gate through
 pseudo-replication — the effective sample size is the number of outcomes, not
 the number of rows.
 
-**Gate 2 — no low-dimensional sufficient statistics.** A Normal likelihood
-with per-cell variances collapses to per-cell $(\sum y^2, n)$: one linear scan
-computes those, and "streaming inference" degenerates into a glorified
-`groupby`. What provably breaks the collapse: a Student-t likelihood, a mixture
-(the hurdle below), and row-level continuous covariates through nonlinear
-links.
+**Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This
+one is not about validity but about honesty of the *showcase*: a Normal
+likelihood with per-cell means and variances collapses to per-cell
+$(\sum y, \sum y^2, n)$, so one linear scan computes the exact posterior
+inputs and "streaming inference" degenerates into a glorified `groupby` —
+valid, but theater. What provably breaks the collapse: a Student-t likelihood,
+a mixture (the hurdle below), and row-level continuous covariates through
+nonlinear links.
 
 The model below passes both gates, and each ingredient that makes it pass is
 also substantively motivated by the data.
@@ -329,7 +333,11 @@ print(f"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours "
 
 On the real corpus the same check is part of the ETL script's validation: the
 head of any shard must already mix all 24 hours and all symbols, *verified, not
-assumed*.
+assumed*. Note that the cell above is the pedagogical miniature — it sorts the
+whole table in memory, which at corpus scale would need several gigabytes for
+the keys and permutation alone. The published ETL is genuinely out-of-core:
+pass one hash-scatters rows into shards with bounded row-group appends, pass
+two sorts each shard independently, and no step ever holds the corpus.
 
 +++
 
@@ -353,13 +361,21 @@ loader = DataLoader(
 print(f"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch")
 ```
 
+One deliberate loader semantics to know about: only full batches are yielded,
+so a trailing partial batch — here 992 of 300,000 rows, and with a fixed
+replay order the *same* 992 every pass — is never seen, while `total_size`
+still rescales by the full $N$. At 0.3% of the data this is a knowably tiny,
+stated bias, not a hidden one; shuffling shard order between epochs would
+rotate the tail if it ever mattered.
+
++++
+
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
     coords = {"symbol": list(symbols), "harmonic": ["sin1", "cos1", "sin2", "cos2"]}
     with pm.Model(coords=coords) as model:
         batch = pm.Data("batch", batch_init)
         y_ = batch[:, 0]
-        m_ = batch[:, 1]
         d_ = batch[:, 2]
         q_ = batch[:, 3]
         a_ = batch[:, 4]
@@ -410,7 +426,10 @@ def build_model(symbols, batch_init, total_size):
         )
         mu = theta_d * d_ + theta_r * ylag_
 
-        def hurdle_logp(value, logit_pi, mu, log_sigma, nu, m):
+        def hurdle_logp(value, logit_pi, mu, log_sigma, nu):
+            # one coherent mixed distribution: an atom at exactly zero plus a
+            # Student-t density off zero — the move indicator is value != 0,
+            # never a separate parameter, so logp and random describe the SAME law
             sigma = pt.exp(log_sigma)
             t_ll = (
                 pt.gammaln((nu + 1.0) / 2.0)
@@ -419,17 +438,18 @@ def build_model(symbols, batch_init, total_size):
                 - log_sigma
                 - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)
             )
-            return pt.where(m > 0, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))
+            moved = pt.neq(value, 0.0)
+            return pt.where(moved, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))
 
-        def hurdle_random(logit_pi, mu, log_sigma, nu, m, rng=None, size=None):
-            # simulate the full hurdle: first whether the price moves, then how far
+        def hurdle_random(logit_pi, mu, log_sigma, nu, rng=None, size=None):
+            # simulate the same law: first whether the price moves, then how far
             pi = 1.0 / (1.0 + np.exp(-logit_pi))
             move = rng.random(size=size) < pi
             draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)
             return np.where(move, draw, 0.0)
 
         pm.CustomDist(
-            "y_obs", logit_pi, mu, log_sigma, nu, m_,
+            "y_obs", logit_pi, mu, log_sigma, nu,
             logp=hurdle_logp, random=hurdle_random,
             observed=y_, total_size=total_size,
         )
@@ -526,11 +546,16 @@ recovery["abs_error"] = (recovery["mean"] - recovery["truth"]).abs()
 recovery.round(4)
 ```
 
-Read the table bottom-up. The two raw intercepts look off by 0.1–0.3 — but a
-global intercept and the mean of its group effects are only *jointly*
+Read the table bottom-up. The two raw intercepts look off by 0.3 and 1.3 — but
+a global intercept and the mean of its group effects are only *jointly*
 identified: the likelihood constrains their sum, and the prior only weakly
 splits it. The identified sums in the last two rows land within a few
 thousandths of the truth, like every other likelihood-identified quantity.
+The same translation ridge exists for every global-coefficient/group-effect
+pair in this model ($c$ with $b^{(\pi h)}$, $g$ with $b^{(\sigma h)}$,
+$\beta_a$ with $b^{(\beta a)}$) — we show the two intercepts because their
+splits wander furthest; a sum-to-zero constraint on the symbol effects is the
+standard reparameterization when the split itself matters.
 Notice also what we did *not* print: z-scores against the truth. Some would
 exceed 2, because the mean-field standard deviations in the third column are
 razor-thin — a z-score against a point truth mixes the sampling noise of this
@@ -571,7 +596,7 @@ ax.legend();
 The thin symbols' estimates are pulled toward zero and carry visibly wider
 intervals — that is partial pooling doing exactly what it is for
 {cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has
-230,000 rows against BTC's 200 million, the same mechanism is what makes the
+67,000 rows against BTC's 203 million, the same mechanism is what makes the
 tail of the symbol universe estimable at all.
 
 +++
@@ -599,20 +624,21 @@ y_eval = eval_batch[:, 0]
 
 zero_share = (pp == 0).mean(axis=1)
 med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])
-q99 = np.quantile(np.abs(pp), 0.99, axis=1)
+q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])
 
 def check_row(observed, draws):
     lo, hi = np.quantile(draws, [0.05, 0.95])
     return [observed, draws.mean(), lo, hi]
 
+y_nonzero = np.abs(y_eval[y_eval != 0])
 pd.DataFrame(
     [
         check_row((y_eval == 0).mean(), zero_share),
-        check_row(np.median(np.abs(y_eval[y_eval != 0])), med_nonzero),
-        check_row(np.quantile(np.abs(y_eval), 0.99), q99),
+        check_row(np.median(y_nonzero), med_nonzero),
+        check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),
     ],
     columns=["observed", "pp mean", "pp 5%", "pp 95%"],
-    index=["zero share", "median |nonzero y| (bp)", "q99 |y| (bp)"],
+    index=["zero share", "median |move| (bp)", "q99 |move| (bp)"],
 ).round(3)
 ```
 
@@ -749,8 +775,9 @@ stream2.prime()
 steps_per_epoch = len(loader2) // loader2.batch_size
 
 k = 50
+min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass
 raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))
-win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=steps_per_epoch // k), k))
+win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=min_windows), k))
 
 with model2:
     advi2 = pm.ADVI(random_seed=RANDOM_SEED)
@@ -786,10 +813,14 @@ The raw rule fires a fixed ~80 steps after arming with the loss still far from
 its floor — a false stop that a fixed-budget run would silently avoid. The
 windowed rule stops once per-window improvement falls below a quarter of the
 window-noise scale, here capturing roughly 95% of the total loss descent at
-under a quarter of the step budget. On the full-scale corpus the same replay
-shows the windowed rule stopping shortly after one pass, at a loss within
-noise of the two-pass reference — the model converges in about one epoch, and
-the rule proves it rather than assumes it.
+under a quarter of the step budget. Two honest qualifications. First, what the
+rule detects is a *loss plateau* — a necessary signal, not a proof that the
+posterior has converged; the held-out checks in the next section are the
+independent evidence. Second, the $h/\kappa$-window delay after a plateau is
+the rule's *nominal* detection lag; clipping and the adaptive scale can move
+the actual hitting time. On the full-scale corpus the same replay shows the
+windowed rule stopping shortly after one pass, at a loss within noise of the
+two-pass reference.
 
 (tick-real-corpus)=
 
@@ -915,7 +946,11 @@ $\tau$ prior could not reach them, and a mean-field approximation of the
 $\tau$–$z$ funnel collapses into its small-$\tau$/huge-$z$ corner. Both fixes
 are principled: weakly informative scale priors, and *centered* effects —
 non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per
-symbol these groups are anything but.
+symbol these groups are anything but. One honest caveat: the two fixes were
+applied together, so their individual contributions are confounded — a
+same-prior centered/non-centered ablation would separate them, and the safe
+reading is empirical rather than theoretical: on this corpus, this
+combination fits cleanly and the original one did not.
 
 **The variance does not exist.** With the conventional
 $\nu = 2 + \operatorname{softplus}(\eta)$ parameterization, 450 million rows
@@ -925,8 +960,10 @@ finite-variance Student-t. The parameterization was moved to a floor of 1 and
 the estimand to quantile-based dispersion, which is finite for all $\nu > 0$.
 With the floor out of the way, three independent seeds put $\nu$ at 1.895
 with a seed spread of $8 \times 10^{-5}$ — an *interior* optimum below 2.
-Per-trade crypto returns, as far as this half-billion-row likelihood is
-concerned, have no variance; they do have a mean, and quantiles behave.
+Within the fitted conditional-move Student-t, then, the variance simply does
+not exist — a statement about this working likelihood on this corpus, not a
+metaphysical claim about crypto returns — while the mean is finite and
+quantiles behave, which is exactly why the estimand is quantile-based.
 
 **The robust location disagrees with least squares about a sign.** Ordinary
 least squares of $y$ on $\text{ylag}$ gives a *negative* coefficient
@@ -963,25 +1000,29 @@ conv
 ```
 
 **The equivalence test failed** — and not in the direction anyone would
-guess: the ¼-pass arm *out-scored* the 2-pass reference, the monitor arm
-under-scored it, and every gap dwarfed the seed spread (∼10⁻⁵). The loss
-trace explains it: this fit converges within a quarter pass, so all flat-rate
-arms sit on the same plateau, where the held-out score of a *point* estimate
-is dominated by Adam's stationary jitter at learning rate 0.02 — position
-along the batch cycle, not posterior quality. The proof is the polish
-experiment: re-running to the monitor's stop and annealing for two thousand
+guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about
+$10^{-3}$ nats/row — a hundred times the seed spread — while the 1- and
+2-pass arms tied within seed spread, and the monitor arm under-scored
+everything. The loss trace suggests why: this fit reaches its loss plateau
+within a quarter pass, so all flat-rate arms sit on the same plateau, where
+the held-out score of a *point* estimate moves with the optimizer's state at
+flat learning rate 0.02 — consistent with Adam's stationary jitter and
+position along the (seed-shared) batch cycle, though these artifacts alone
+cannot separate that from other path effects. The polish experiment points
+the same way: re-running to the monitor's stop and annealing for two thousand
 further steps at a quarter of the learning rate moved the held-out score from
-the worst of the table to the best — above an annealed two-pass reference run
-on the same split — at 55% of its step budget.
+the worst of the table to the top — within path noise of an annealed two-pass
+reference run on the same split — at 55% of its step budget. One run per
+protocol, so these comparisons are exploratory; paired, replicated annealed
+endpoints per arm are the confirmatory design.
 
 One more layer of honesty on the metric itself: even among fully annealed,
 fully converged runs, path-to-path differences are about $5 \times 10^{-4}$
 nats/row. The pre-registered $2 \times 10^{-4}$ equivalence floor was
-therefore unachievable by *any* stopping protocol on this problem — a
-pre-registration mistake worth admitting, because the comparison it pushed us
-toward is the right one: flat-rate arms scatter over $1.7 \times 10^{-3}$,
-annealed endpoints over $5 \times 10^{-4}$, and the seed spread within one
-protocol is $10^{-5}$.
+therefore unachievable by any protocol we ran — a pre-registration mistake
+worth admitting, because the comparison it pushed us toward is the right one:
+flat-rate arms scatter over $1.7 \times 10^{-3}$, annealed endpoints over
+$5 \times 10^{-4}$, and the seed spread within one protocol is $10^{-5}$.
 
 ```{code-cell} ipython3
 pol = results["polish"]
@@ -997,13 +1038,13 @@ lo = min(scores)
 ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)
 ax.set_xticks([0.116, 0.117, 0.118])
 ax.set_xlabel("held-out log score (nats/row)")
-ax.set_title("Stop rule alone inherits Adam jitter; "
-             "stop + short anneal beats the 2-pass reference at 55% cost");
+ax.set_title("Stop rule alone inherits optimizer jitter; stop + short anneal\n"
+             "matches the annealed 2-pass reference at 55% of the budget");
 ```
 
-The honest protocol that comes out of this ablation: **a stop rule on a
-streaming fit must be paired with an anneal-on-stop polish phase**, and
-equivalence must be judged after the polish, not at the raw stopping point.
+The working protocol this ablation suggests: **pair a stop rule on a
+streaming fit with an anneal-on-stop polish phase**, and judge equivalence
+after the polish, not at the raw stopping point.
 That finding — not the 46% headline — is what we would want a reader to take
 away, and it fed directly back into the design discussion of the upstream
 pull requests.
@@ -1046,8 +1087,9 @@ clocks tell different stories and this notebook only claims the first.
 
 ### What full N buys: thin cells
 
-A uniform 1% subsample (4.6M rows, drawn by taking the hash-ordered head of
-every shard) can estimate every *global* parameter of this model — and did,
+A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn
+by taking the hash-ordered head of each shard, with the last four shards held
+out for scoring) can estimate every *global* parameter of this model — and did,
 matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more
 data? Because the estimand lives in symbol-by-hour cells, and the thin end of
 the universe is where the two fits part ways. Below, the posterior of the
@@ -1097,13 +1139,17 @@ is uniform or it is invalid.
 ### What we do not sweep under the rug
 
 The single most useful diagnostic this notebook can leave you with costs
-three fits and one table — the reported mean-field standard deviation next to
-the across-seed spread of the posterior mean:
+three fits and one table: the reported mean-field standard deviation next to
+the spread of the posterior mean across three optimizer seeds. Read it as a
+*computational-stability* diagnostic — a ratio far above one proves the
+reported width understates run-to-run variation, which is a necessary
+condition for trusting it, not a calibration certificate (that would need a
+comparison against full-rank or MCMC on a tractable subproblem):
 
 ```{code-cell} ipython3
 spread = pd.DataFrame(results["seed_spread"]).T
 spread["ratio"] = (spread["seed_spread"] / spread["reported_sd"]).round(1)
-spread["verdict"] = np.where(spread["ratio"] > 10, "ridge — do not trust sd", "ok")
+spread["flag"] = np.where(spread["ratio"] > 10, "ridge-dominated", "seed-stable")
 spread.sort_values("ratio", ascending=False)
 ```
 
@@ -1112,8 +1158,9 @@ spread.sort_values("ratio", ascending=False)
   and across repeated seeds the spread of $\alpha_0$ is roughly 440× its
   reported posterior standard deviation — $\kappa_0$ and $\beta_a$ show the
   same signature at 60–70×. The parameters *without* a ridge ($\theta_r$,
-  $\nu$, $\lambda_a$, the $\tau$'s) have ratios at or below one. Report
-  mean-field standard deviations only for parameters you have seed-checked.
+  $\nu$, $\lambda_a$, the $\tau$'s) sit at or below one, and $\theta_d$ sits
+  in between (≈6). Report mean-field standard deviations only for parameters
+  you have seed-checked — and remember the check bounds the error from below.
 - **The speed ordering is model-dependent.** Here the heavy gradient hides
   the loader entirely and pre-shuffled streaming outpaces in-RAM
   `pm.Minibatch`; on light models the ordering reverses. Streaming's claim
@@ -1139,8 +1186,8 @@ callback-free `Trainer` wrapper
 ([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the
 convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))
 are under review, and this notebook's callback pattern is exactly what those
-APIs wrap. The ETL and full-scale run scripts are published alongside the
-notebook, and the {ref}`streaming_dataset` companion covers the API mechanics.
+APIs wrap. The ETL and full-scale run scripts accompany the submission, and
+the {ref}`streaming_dataset` companion covers the API mechanics.
 :::
 
 +++
diff --git a/examples/variational_inference/streaming_tick_data_results.json b/examples/variational_inference/streaming_tick_data_results.json
index e78d5e9b7..765abd9ba 100644
--- a/examples/variational_inference/streaming_tick_data_results.json
+++ b/examples/variational_inference/streaming_tick_data_results.json
@@ -1269,5 +1269,14 @@
     0.00031
    ]
   }
+ },
+ "headline": {
+  "epochs": 2.0,
+  "wall_min": 4.2,
+  "steps_per_sec": 959,
+  "rows_per_sec_m": 3.9,
+  "peak_rss_gb": 1.06,
+  "batch_size": 4096,
+  "steps_per_epoch": 120009
  }
 }
\ No newline at end of file

From 46467cd7624f3c12f49aeb4f4931f5c1263143fa Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 20:04:39 -0500
Subject: [PATCH 20/42] Adopt black-jupyter formatting; repo pre-commit passes
 on the notebook pair

---
 .../streaming_tick_data.ipynb                 | 1060 +++++++++--------
 .../streaming_tick_data.myst.md               |  250 ++--
 2 files changed, 746 insertions(+), 564 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 87a261f79..bcaa3f7ec 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9b92ec9d",
+   "id": "28a46b36",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de21b971",
+   "id": "26f28599",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8686eb0b",
+   "id": "ff8d2b7d",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73b79cc6",
+   "id": "ffe82b59",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -102,13 +102,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3187c3f2",
+   "id": "dce3e296",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:49.896400Z",
-     "iopub.status.busy": "2026-08-01T00:51:49.896177Z",
-     "iopub.status.idle": "2026-08-01T00:51:51.966968Z",
-     "shell.execute_reply": "2026-08-01T00:51:51.966527Z"
+     "iopub.execute_input": "2026-08-01T01:04:03.544904Z",
+     "iopub.status.busy": "2026-08-01T01:04:03.544730Z",
+     "iopub.status.idle": "2026-08-01T01:04:05.454745Z",
+     "shell.execute_reply": "2026-08-01T01:04:05.454239Z"
     }
    },
    "outputs": [],
@@ -134,13 +134,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "35e1e906",
+   "id": "7d9fc1ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:51.968147Z",
-     "iopub.status.busy": "2026-08-01T00:51:51.968005Z",
-     "iopub.status.idle": "2026-08-01T00:51:51.970710Z",
-     "shell.execute_reply": "2026-08-01T00:51:51.970477Z"
+     "iopub.execute_input": "2026-08-01T01:04:05.455967Z",
+     "iopub.status.busy": "2026-08-01T01:04:05.455833Z",
+     "iopub.status.idle": "2026-08-01T01:04:05.458712Z",
+     "shell.execute_reply": "2026-08-01T01:04:05.458344Z"
     }
    },
    "outputs": [],
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b649fb6",
+   "id": "ef40e1b6",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f2a3f6f",
+   "id": "85fa2cd9",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -230,13 +230,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "27c43795",
+   "id": "d04cdc9a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:51.971601Z",
-     "iopub.status.busy": "2026-08-01T00:51:51.971527Z",
-     "iopub.status.idle": "2026-08-01T00:51:51.973835Z",
-     "shell.execute_reply": "2026-08-01T00:51:51.973494Z"
+     "iopub.execute_input": "2026-08-01T01:04:05.459597Z",
+     "iopub.status.busy": "2026-08-01T01:04:05.459540Z",
+     "iopub.status.idle": "2026-08-01T01:04:05.461728Z",
+     "shell.execute_reply": "2026-08-01T01:04:05.461455Z"
     }
    },
    "outputs": [],
@@ -261,9 +261,11 @@
     "    \"nu\": 3.5,\n",
     "}\n",
     "\n",
+    "\n",
     "def standardize(x, axis=0):\n",
     "    return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)\n",
     "\n",
+    "\n",
     "z_truth = {\n",
     "    \"z_k\": standardize(rng.standard_normal(n_symbols)),\n",
     "    \"z_ph\": standardize(rng.standard_normal((n_symbols, 4))),\n",
@@ -276,13 +278,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "2bcfa9ca",
+   "id": "4d0bb911",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:51.974539Z",
-     "iopub.status.busy": "2026-08-01T00:51:51.974482Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.129595Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.129179Z"
+     "iopub.execute_input": "2026-08-01T01:04:05.462640Z",
+     "iopub.status.busy": "2026-08-01T01:04:05.462582Z",
+     "iopub.status.idle": "2026-08-01T01:04:05.606153Z",
+     "shell.execute_reply": "2026-08-01T01:04:05.605781Z"
     }
    },
    "outputs": [
@@ -299,6 +301,7 @@
     "    w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0\n",
     "    return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])\n",
     "\n",
+    "\n",
     "sym = np.repeat(np.arange(n_symbols), counts)\n",
     "n = len(sym)\n",
     "hour = rng.integers(0, 24, size=n)\n",
@@ -340,13 +343,15 @@
     "            y[i] = truth[\"theta_d\"] * d[i] + truth[\"theta_r\"] * last + sigma[i] * t_draw[i]\n",
     "            last = y[i]\n",
     "\n",
-    "print(f\"{n:,} rows, zero-move share {(m == 0).mean():.1%}, \"\n",
-    "      f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp\")"
+    "print(\n",
+    "    f\"{n:,} rows, zero-move share {(m == 0).mean():.1%}, \"\n",
+    "    f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b319db8f",
+   "id": "f85267dc",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -356,13 +361,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d0a2623d",
+   "id": "fd0a6ad3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.130350Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.130292Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.514212Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.513829Z"
+     "iopub.execute_input": "2026-08-01T01:04:05.607048Z",
+     "iopub.status.busy": "2026-08-01T01:04:05.606979Z",
+     "iopub.status.idle": "2026-08-01T01:04:05.972297Z",
+     "shell.execute_reply": "2026-08-01T01:04:05.971943Z"
     }
    },
    "outputs": [
@@ -385,10 +390,14 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
     "moves = y[m == 1]\n",
-    "ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color=\"C0\",\n",
-    "        label=\"nonzero moves\")\n",
-    "ax.bar([0.0], [(m == 0).sum()], width=0.02, color=\"C1\",\n",
-    "       label=f\"exactly zero ({(m == 0).mean():.0%} of rows)\")\n",
+    "ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color=\"C0\", label=\"nonzero moves\")\n",
+    "ax.bar(\n",
+    "    [0.0],\n",
+    "    [(m == 0).sum()],\n",
+    "    width=0.02,\n",
+    "    color=\"C1\",\n",
+    "    label=f\"exactly zero ({(m == 0).mean():.0%} of rows)\",\n",
+    ")\n",
     "ax.set_xlabel(\"next-event return (bp)\")\n",
     "ax.set_ylabel(\"count (log scale)\")\n",
     "ax.set_title(\"A continuous density puts zero mass on the most common outcome\")\n",
@@ -397,7 +406,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ef86566",
+   "id": "e8a1172a",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -416,13 +425,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "887b0175",
+   "id": "ed364f52",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.515133Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.515066Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.717678Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.717261Z"
+     "iopub.execute_input": "2026-08-01T01:04:05.973286Z",
+     "iopub.status.busy": "2026-08-01T01:04:05.973203Z",
+     "iopub.status.idle": "2026-08-01T01:04:06.106154Z",
+     "shell.execute_reply": "2026-08-01T01:04:06.105790Z"
     }
    },
    "outputs": [
@@ -449,12 +458,14 @@
     "    }\n",
     ")\n",
     "\n",
+    "\n",
     "def splitmix64(x):\n",
     "    x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
     "    x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
     "    x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
     "    return x ^ (x >> np.uint64(31))\n",
     "\n",
+    "\n",
     "key = splitmix64(np.arange(n, dtype=np.uint64))\n",
     "order = np.argsort(key, kind=\"stable\")\n",
     "n_shards = 8\n",
@@ -463,13 +474,15 @@
     "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"))\n",
     "\n",
     "head = pq.read_table(os.path.join(data_dir, \"shard_000.parquet\")).slice(0, 10_000)\n",
-    "print(f\"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours \"\n",
-    "      f\"and {len(np.unique(head['sym']))} symbols\")"
+    "print(\n",
+    "    f\"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours \"\n",
+    "    f\"and {len(np.unique(head['sym']))} symbols\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "39c2bf3b",
+   "id": "bc640393",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -483,7 +496,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eef98fc7",
+   "id": "14b61d41",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -498,13 +511,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "b42de1a7",
+   "id": "a33f15af",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.718590Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.718513Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.721339Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.721008Z"
+     "iopub.execute_input": "2026-08-01T01:04:06.107113Z",
+     "iopub.status.busy": "2026-08-01T01:04:06.107042Z",
+     "iopub.status.idle": "2026-08-01T01:04:06.109910Z",
+     "shell.execute_reply": "2026-08-01T01:04:06.109579Z"
     }
    },
    "outputs": [
@@ -530,7 +543,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "745ab125",
+   "id": "13d3b3fa",
    "metadata": {},
    "source": [
     "One deliberate loader semantics to know about: only full batches are yielded,\n",
@@ -544,13 +557,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "f9f4a721",
+   "id": "4201f073",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.722146Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.722089Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.726531Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.726197Z"
+     "iopub.execute_input": "2026-08-01T01:04:06.110792Z",
+     "iopub.status.busy": "2026-08-01T01:04:06.110733Z",
+     "iopub.status.idle": "2026-08-01T01:04:06.115193Z",
+     "shell.execute_reply": "2026-08-01T01:04:06.114858Z"
     }
    },
    "outputs": [],
@@ -633,16 +646,22 @@
     "            return np.where(move, draw, 0.0)\n",
     "\n",
     "        pm.CustomDist(\n",
-    "            \"y_obs\", logit_pi, mu, log_sigma, nu,\n",
-    "            logp=hurdle_logp, random=hurdle_random,\n",
-    "            observed=y_, total_size=total_size,\n",
+    "            \"y_obs\",\n",
+    "            logit_pi,\n",
+    "            mu,\n",
+    "            log_sigma,\n",
+    "            nu,\n",
+    "            logp=hurdle_logp,\n",
+    "            random=hurdle_random,\n",
+    "            observed=y_,\n",
+    "            total_size=total_size,\n",
     "        )\n",
     "    return model"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1d5a604b",
+   "id": "d1934004",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -658,13 +677,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "faa2dba5",
+   "id": "cd992aef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.727278Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.727223Z",
-     "iopub.status.idle": "2026-08-01T00:51:52.995514Z",
-     "shell.execute_reply": "2026-08-01T00:51:52.995065Z"
+     "iopub.execute_input": "2026-08-01T01:04:06.116002Z",
+     "iopub.status.busy": "2026-08-01T01:04:06.115941Z",
+     "iopub.status.idle": "2026-08-01T01:04:06.274841Z",
+     "shell.execute_reply": "2026-08-01T01:04:06.274369Z"
     }
    },
    "outputs": [
@@ -683,8 +702,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M799.01,-8C799.01,-8 845.01,-8 845.01,-8 851.01,-8 857.01,-14 857.01,-20 857.01,-20 857.01,-94 857.01,-94 857.01,-100 851.01,-106 845.01,-106 845.01,-106 799.01,-106 799.01,-106 793.01,-106 787.01,-100 787.01,-94 787.01,-94 787.01,-20 787.01,-20 787.01,-14 793.01,-8 799.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"825.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M804.01,-8C804.01,-8 850.01,-8 850.01,-8 856.01,-8 862.01,-14 862.01,-20 862.01,-20 862.01,-94 862.01,-94 862.01,-100 856.01,-106 850.01,-106 850.01,-106 804.01,-106 804.01,-106 798.01,-106 792.01,-100 792.01,-94 792.01,-94 792.01,-20 792.01,-20 792.01,-14 798.01,-8 804.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"830.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -703,344 +722,344 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M710.01,-114C710.01,-114 934.01,-114 934.01,-114 940.01,-114 946.01,-120 946.01,-126 946.01,-126 946.01,-223.82 946.01,-223.82 946.01,-229.82 940.01,-235.82 934.01,-235.82 934.01,-235.82 710.01,-235.82 710.01,-235.82 704.01,-235.82 698.01,-229.82 698.01,-223.82 698.01,-223.82 698.01,-126 698.01,-126 698.01,-120 704.01,-114 710.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"924.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M715.01,-114C715.01,-114 939.01,-114 939.01,-114 945.01,-114 951.01,-120 951.01,-126 951.01,-126 951.01,-223.82 951.01,-223.82 951.01,-229.82 945.01,-235.82 939.01,-235.82 939.01,-235.82 715.01,-235.82 715.01,-235.82 709.01,-235.82 703.01,-229.82 703.01,-223.82 703.01,-223.82 703.01,-126 703.01,-126 703.01,-120 709.01,-114 715.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"929.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M837.01,-98C837.01,-98 807.01,-98 807.01,-98 801.01,-98 795.01,-92 795.01,-86 795.01,-86 795.01,-52.5 795.01,-52.5 795.01,-46.5 801.01,-40.5 807.01,-40.5 807.01,-40.5 837.01,-40.5 837.01,-40.5 843.01,-40.5 849.01,-46.5 849.01,-52.5 849.01,-52.5 849.01,-86 849.01,-86 849.01,-92 843.01,-98 837.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M842.01,-98C842.01,-98 812.01,-98 812.01,-98 806.01,-98 800.01,-92 800.01,-86 800.01,-86 800.01,-52.5 800.01,-52.5 800.01,-46.5 806.01,-40.5 812.01,-40.5 812.01,-40.5 842.01,-40.5 842.01,-40.5 848.01,-40.5 854.01,-46.5 854.01,-52.5 854.01,-52.5 854.01,-86 854.01,-86 854.01,-92 848.01,-98 842.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- tau_ph -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"822.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.86 76.86,-251.93 91.01,-243.82 141.97,-214.61 499.44,-198.59 694.95,-191.92\"/>\n",
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+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
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        "</g>\n",
-       "<!-- alpha0 -->\n",
+       "<!-- tau_al -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<title>alpha0&#45;&gt;y_obs</title>\n",
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+       "<!-- b_al -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
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-       "<!-- lambda_a -->\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
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+       "<!-- beta_q -->\n",
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-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"827.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
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-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M269.49,-279.86C279.08,-266.29 291.78,-252.15 307.01,-243.82 371.64,-208.45 564.51,-195.56 694.99,-190.85\"/>\n",
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+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.86 76.86,-251.92 91.01,-243.82 142.41,-214.38 503.55,-198.43 700.16,-191.84\"/>\n",
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        "<!-- theta_r -->\n",
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        "<title>theta_r</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
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+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
        "<title>theta_r&#45;&gt;y_obs</title>\n",
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        "<!-- tau_sh -->\n",
        "<g id=\"node6\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "<!-- b_sh -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
+       "<g id=\"node23\" class=\"node\">\n",
        "<title>b_sh</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- tau_sh&#45;&gt;b_sh -->\n",
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        "<title>tau_sh&#45;&gt;b_sh</title>\n",
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-       "<!-- theta_d -->\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
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-       "<title>theta_d&#45;&gt;y_obs</title>\n",
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-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M574.75,-280.21C582.91,-267.02 593.76,-253.06 607.01,-243.82 634.78,-224.44 668.8,-211.81 701.52,-203.58\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"702.16,-207.02 711.08,-201.3 700.54,-200.21 702.16,-207.02\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
        "</g>\n",
        "<!-- tau_ba -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
-       "</g>\n",
-       "<!-- beta_a -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"657.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M676.75,-280.8C685.16,-268 695.86,-254.15 708.01,-243.82 716.72,-236.41 726.58,-229.72 736.78,-223.76\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"738.27,-226.93 745.3,-219 734.86,-220.82 738.27,-226.93\"/>\n",
-       "</g>\n",
-       "<!-- tau_k -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_k -->\n",
+       "<!-- b_ba -->\n",
        "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_k</title>\n",
+       "<title>b_ba</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
        "</g>\n",
-       "<!-- lambda_q -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"766.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"766.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"766.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"766.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M782.51,-278.32C788.01,-265.77 794.23,-251.58 800.07,-238.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"803.24,-239.72 804.05,-229.16 796.83,-236.91 803.24,-239.72\"/>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M269.48,-279.86C279.08,-266.29 291.78,-252.14 307.01,-243.82 372.48,-208.04 568.17,-195.26 699.93,-190.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"699.89,-194.2 709.77,-190.37 699.66,-187.2 699.89,-194.2\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.97,-279.74C382.08,-266.32 393.09,-252.32 407.01,-243.82 454.97,-214.52 595.32,-200.37 700.68,-193.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"700.77,-197.22 710.54,-193.12 700.34,-190.23 700.77,-197.22\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"879.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- nu -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node16\" class=\"node\">\n",
        "<title>nu</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"924.14,-345.73 833.89,-345.73 833.89,-288.23 924.14,-288.23 924.14,-345.73\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M866.5,-287.93C859.99,-273.32 851.83,-255.02 844.28,-238.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"847.57,-236.87 840.3,-229.16 841.17,-239.72 847.57,-236.87\"/>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M879.01,-393.28C879.01,-381.58 879.01,-368.79 879.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"882.51,-357.45 879.01,-347.45 875.51,-357.45 882.51,-357.45\"/>\n",
        "</g>\n",
-       "<!-- tau_al -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_al -->\n",
+       "<!-- b_k -->\n",
        "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_al</title>\n",
+       "<title>b_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
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-       "<!-- eta -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"879.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M879.01,-393.28C879.01,-381.58 879.01,-368.79 879.01,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"882.51,-357.45 879.01,-347.45 875.51,-357.45 882.51,-357.45\"/>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.37,-279.65C482.49,-266.38 493.4,-252.52 507.01,-243.82 539.45,-223.09 628.31,-208.64 704.19,-199.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"704.24,-203.19 713.77,-198.56 703.44,-196.23 704.24,-203.19\"/>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M575.22,-280.17C583.56,-266.98 594.61,-253.03 608.01,-243.82 636.89,-223.97 672.28,-211.19 706.13,-202.97\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.61,-206.45 715.57,-200.8 705.04,-199.63 706.61,-206.45\"/>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"666.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"666.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"666.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"666.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- c -->\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M690.01,-281.23C699.89,-268.49 712.06,-254.56 725.01,-243.82 732.51,-237.59 740.82,-231.74 749.35,-226.33\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"751.04,-229.4 757.77,-221.21 747.4,-223.42 751.04,-229.4\"/>\n",
+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"775.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.82,-239.71 810.35,-229.12 803.34,-237.07 809.82,-239.71\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M867.6,-287.93C861.66,-273.32 854.21,-255.02 847.33,-238.1\"/>\n",
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+       "</g>\n",
+       "<!-- g -->\n",
        "<g id=\"node17\" class=\"node\">\n",
-       "<title>c</title>\n",
+       "<title>g</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- c&#45;&gt;y_obs -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>c&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M963.72,-280.86C955.48,-268.06 944.98,-254.21 933.01,-243.82 924.77,-236.66 915.45,-230.16 905.79,-224.34\"/>\n",
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+       "<!-- g&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>g&#45;&gt;y_obs</title>\n",
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        "</g>\n",
-       "<!-- g -->\n",
+       "<!-- c -->\n",
        "<g id=\"node18\" class=\"node\">\n",
-       "<title>g</title>\n",
+       "<title>c</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>g&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1065.26,-280.24C1057.09,-267.06 1046.24,-253.09 1033.01,-243.82 1006.23,-225.05 973.52,-212.61 941.93,-204.36\"/>\n",
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-       "</g>\n",
-       "<!-- b_k&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>b_k&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1272.06,-279.74C1263.94,-266.33 1252.93,-252.32 1239.01,-243.82 1191.52,-214.78 1052.71,-200.59 948.19,-193.86\"/>\n",
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+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1065,-279.91C1056.85,-266.84 1046.09,-253.04 1033.01,-243.82 1007.4,-225.75 976.21,-213.54 945.94,-205.3\"/>\n",
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        "</g>\n",
        "<!-- b_ba&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
        "<title>b_ba&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1170.65,-280.4C1161.93,-266.91 1150.27,-252.68 1136.01,-243.82 1104.21,-224.04 1018.31,-209.62 944.38,-200.42\"/>\n",
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        "</g>\n",
        "<!-- b_al&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
        "<title>b_al&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1372.16,-279.56C1364.07,-266.12 1353.03,-252.15 1339.01,-243.82 1275.29,-205.96 1080.58,-193.77 949.16,-189.89\"/>\n",
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-       "<!-- b_sh&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>b_sh&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"949.54,-185.14 939.49,-188.5 949.44,-192.14 949.54,-185.14\"/>\n",
+       "<!-- b_k&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>b_k&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1372.16,-279.57C1364.06,-266.13 1353.03,-252.15 1339.01,-243.82 1276.1,-206.4 1084.23,-194.08 954.09,-190.06\"/>\n",
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        "</g>\n",
        "<!-- b_ph&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
        "<title>b_ph&#45;&gt;y_obs</title>\n",
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+       "</g>\n",
+       "<!-- b_sh&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>b_sh&#45;&gt;y_obs</title>\n",
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        "</g>\n",
        "<!-- y_obs&#45;&gt;batch -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>y_obs&#45;&gt;batch</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M822.01,-146.27C822.01,-134.38 822.01,-121.37 822.01,-109.53\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"825.51,-109.67 822.01,-99.67 818.51,-109.67 825.51,-109.67\"/>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x12b653e00>"
+       "<graphviz.graphs.Digraph at 0x13fd93f50>"
       ]
      },
      "execution_count": 9,
@@ -1049,15 +1068,13 @@
     }
    ],
    "source": [
-    "model = build_model(\n",
-    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), len(loader)\n",
-    ")\n",
+    "model = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), len(loader))\n",
     "pm.model_to_graphviz(model)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "29279a09",
+   "id": "32eb03f8",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1069,13 +1086,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "fd342606",
+   "id": "8922b909",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:51:52.996528Z",
-     "iopub.status.busy": "2026-08-01T00:51:52.996453Z",
-     "iopub.status.idle": "2026-08-01T00:52:04.135306Z",
-     "shell.execute_reply": "2026-08-01T00:52:04.134923Z"
+     "iopub.execute_input": "2026-08-01T01:04:06.275907Z",
+     "iopub.status.busy": "2026-08-01T01:04:06.275834Z",
+     "iopub.status.idle": "2026-08-01T01:04:10.244849Z",
+     "shell.execute_reply": "2026-08-01T01:04:10.244465Z"
     }
    },
    "outputs": [],
@@ -1098,20 +1115,21 @@
     "    def __call__(self, approx, losses, i):\n",
     "        self._shared.set_value(next(self._stream), borrow=True)\n",
     "\n",
+    "\n",
     "stream = StreamAdvance(model, loader)\n",
     "stream.prime()\n",
     "\n",
     "with model:\n",
     "    advi = pm.ADVI(random_seed=RANDOM_SEED)\n",
-    "advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02),\n",
-    "         callbacks=[stream], progressbar=False)\n",
-    "approx = advi.fit(2_500, obj_optimizer=pm.adam(learning_rate=0.005),\n",
-    "                  callbacks=[stream], progressbar=False)"
+    "advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)\n",
+    "approx = advi.fit(\n",
+    "    2_500, obj_optimizer=pm.adam(learning_rate=0.005), callbacks=[stream], progressbar=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e0f91e2c",
+   "id": "3de069b7",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1124,13 +1142,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b6a387dd",
+   "id": "2891ecb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:04.136578Z",
-     "iopub.status.busy": "2026-08-01T00:52:04.136502Z",
-     "iopub.status.idle": "2026-08-01T00:52:04.284974Z",
-     "shell.execute_reply": "2026-08-01T00:52:04.284572Z"
+     "iopub.execute_input": "2026-08-01T01:04:10.245854Z",
+     "iopub.status.busy": "2026-08-01T01:04:10.245783Z",
+     "iopub.status.idle": "2026-08-01T01:04:10.391695Z",
+     "shell.execute_reply": "2026-08-01T01:04:10.391337Z"
     }
    },
    "outputs": [
@@ -1157,15 +1175,17 @@
     "ax.set_ylim(-450, 1200)\n",
     "ax.set_xlabel(\"step\")\n",
     "ax.set_ylabel(\"negative ELBO\")\n",
-    "ax.set_title(\"Streaming ADVI loss, y clipped to the plateau (early losses are \"\n",
-    "             \"off scale).\\nThe visible band is batch-resampling noise — \"\n",
-    "             \"remember it for the stopping section\")\n",
+    "ax.set_title(\n",
+    "    \"Streaming ADVI loss, y clipped to the plateau (early losses are \"\n",
+    "    \"off scale).\\nThe visible band is batch-resampling noise — \"\n",
+    "    \"remember it for the stopping section\"\n",
+    ")\n",
     "ax.legend();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9720a4d1",
+   "id": "b4b5e53e",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1174,13 +1194,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "46ec1244",
+   "id": "26d8ffd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:04.286287Z",
-     "iopub.status.busy": "2026-08-01T00:52:04.286207Z",
-     "iopub.status.idle": "2026-08-01T00:52:04.490473Z",
-     "shell.execute_reply": "2026-08-01T00:52:04.490186Z"
+     "iopub.execute_input": "2026-08-01T01:04:10.392917Z",
+     "iopub.status.busy": "2026-08-01T01:04:10.392838Z",
+     "iopub.status.idle": "2026-08-01T01:04:10.584790Z",
+     "shell.execute_reply": "2026-08-01T01:04:10.584416Z"
     }
    },
    "outputs": [
@@ -1317,10 +1337,18 @@
     "idata = approx.sample(2_000, random_seed=RANDOM_SEED)\n",
     "post = idata.posterior\n",
     "\n",
-    "scalar_params = [\"kappa0\", \"alpha0\", \"lambda_a\", \"lambda_q\", \"beta_a\", \"beta_q\",\n",
-    "                 \"theta_d\", \"theta_r\", \"nu\"]\n",
-    "rows = {name: [float(post[name].mean()), float(post[name].std())]\n",
-    "        for name in scalar_params}\n",
+    "scalar_params = [\n",
+    "    \"kappa0\",\n",
+    "    \"alpha0\",\n",
+    "    \"lambda_a\",\n",
+    "    \"lambda_q\",\n",
+    "    \"beta_a\",\n",
+    "    \"beta_q\",\n",
+    "    \"theta_d\",\n",
+    "    \"theta_r\",\n",
+    "    \"nu\",\n",
+    "]\n",
+    "rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}\n",
     "# the intercepts share a ridge with their group means; only the sums are identified\n",
     "for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\")]:\n",
     "    s = post[icpt] + post[b].mean(\"symbol\")\n",
@@ -1333,7 +1361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6507a07a",
+   "id": "c98734ce",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The two raw intercepts look off by 0.3 and 1.3 — but\n",
@@ -1361,13 +1389,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "9e34e488",
+   "id": "e53ce059",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:04.491424Z",
-     "iopub.status.busy": "2026-08-01T00:52:04.491347Z",
-     "iopub.status.idle": "2026-08-01T00:52:04.604262Z",
-     "shell.execute_reply": "2026-08-01T00:52:04.603969Z"
+     "iopub.execute_input": "2026-08-01T01:04:10.585680Z",
+     "iopub.status.busy": "2026-08-01T01:04:10.585615Z",
+     "iopub.status.idle": "2026-08-01T01:04:10.698227Z",
+     "shell.execute_reply": "2026-08-01T01:04:10.697962Z"
     }
    },
    "outputs": [
@@ -1400,8 +1428,9 @@
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
     "x = np.arange(n_symbols)\n",
-    "ax.errorbar(x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3,\n",
-    "            label=\"posterior (mean ± 2 sd)\")\n",
+    "ax.errorbar(\n",
+    "    x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3, label=\"posterior (mean ± 2 sd)\"\n",
+    ")\n",
     "ax.scatter(x, b_truth, marker=\"x\", color=\"C1\", s=60, zorder=3, label=\"truth\")\n",
     "for t in thin:\n",
     "    ax.axvspan(t - 0.4, t + 0.4, color=\"C3\", alpha=0.12)\n",
@@ -1415,7 +1444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ba35e55",
+   "id": "eb4ad136",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1427,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a85060e3",
+   "id": "ed588fa1",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1443,13 +1472,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "d9bdc4c0",
+   "id": "c41c6e22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:04.605264Z",
-     "iopub.status.busy": "2026-08-01T00:52:04.605200Z",
-     "iopub.status.idle": "2026-08-01T00:52:05.217170Z",
-     "shell.execute_reply": "2026-08-01T00:52:05.216820Z"
+     "iopub.execute_input": "2026-08-01T01:04:10.699254Z",
+     "iopub.status.busy": "2026-08-01T01:04:10.699194Z",
+     "iopub.status.idle": "2026-08-01T01:04:11.311512Z",
+     "shell.execute_reply": "2026-08-01T01:04:11.311061Z"
     }
    },
    "outputs": [
@@ -1523,8 +1552,11 @@
     "model[\"batch\"].set_value(eval_batch, borrow=True)  # same path the callback uses\n",
     "with model:\n",
     "    idata = pm.sample_posterior_predictive(\n",
-    "        idata, var_names=[\"y_obs\"], random_seed=RANDOM_SEED,\n",
-    "        extend_inferencedata=True, progressbar=False,\n",
+    "        idata,\n",
+    "        var_names=[\"y_obs\"],\n",
+    "        random_seed=RANDOM_SEED,\n",
+    "        extend_inferencedata=True,\n",
+    "        progressbar=False,\n",
     "    )\n",
     "\n",
     "pp = idata.posterior_predictive[\"y_obs\"].stack(sample=(\"chain\", \"draw\")).values.T\n",
@@ -1534,10 +1566,12 @@
     "med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])\n",
     "q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])\n",
     "\n",
+    "\n",
     "def check_row(observed, draws):\n",
     "    lo, hi = np.quantile(draws, [0.05, 0.95])\n",
     "    return [observed, draws.mean(), lo, hi]\n",
     "\n",
+    "\n",
     "y_nonzero = np.abs(y_eval[y_eval != 0])\n",
     "pd.DataFrame(\n",
     "    [\n",
@@ -1553,13 +1587,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "0cd9ca5a",
+   "id": "01b7474d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:05.218046Z",
-     "iopub.status.busy": "2026-08-01T00:52:05.217981Z",
-     "iopub.status.idle": "2026-08-01T00:52:05.349121Z",
-     "shell.execute_reply": "2026-08-01T00:52:05.348743Z"
+     "iopub.execute_input": "2026-08-01T01:04:11.312439Z",
+     "iopub.status.busy": "2026-08-01T01:04:11.312367Z",
+     "iopub.status.idle": "2026-08-01T01:04:11.446057Z",
+     "shell.execute_reply": "2026-08-01T01:04:11.445693Z"
     }
    },
    "outputs": [
@@ -1585,15 +1619,20 @@
     "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
     "grid = np.linspace(-0.5, 0.5, 801)\n",
     "for draw in pp[:60]:\n",
-    "    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size,\n",
-    "            color=\"C0\", alpha=0.08, lw=1)\n",
-    "ax.plot(grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size,\n",
-    "        color=\"k\", lw=1.6, label=\"observed\")\n",
+    "    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size, color=\"C0\", alpha=0.08, lw=1)\n",
+    "ax.plot(\n",
+    "    grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size, color=\"k\", lw=1.6, label=\"observed\"\n",
+    ")\n",
     "ax.plot([], [], color=\"C0\", label=\"posterior predictive (60 draws)\")\n",
     "zero_jump = (y_eval == 0).mean()\n",
     "below = (y_eval < 0).mean()\n",
-    "ax.annotate(f\"vertical step at exactly 0:\\nthe zero share ({zero_jump:.0%})\",\n",
-    "            (0.03, below + zero_jump / 2), fontsize=9, ha=\"left\", va=\"center\")\n",
+    "ax.annotate(\n",
+    "    f\"vertical step at exactly 0:\\nthe zero share ({zero_jump:.0%})\",\n",
+    "    (0.03, below + zero_jump / 2),\n",
+    "    fontsize=9,\n",
+    "    ha=\"left\",\n",
+    "    va=\"center\",\n",
+    ")\n",
     "ax.set_xlabel(\"next-event return (bp)\")\n",
     "ax.set_ylabel(\"ECDF\")\n",
     "ax.set_title(\"Posterior predictive ECDF: the step at zero is the hurdle\")\n",
@@ -1602,7 +1641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53b8daa8",
+   "id": "a35d2b52",
    "metadata": {},
    "source": [
     "The predictive distribution reproduces the zero step, the bulk scale, and the\n",
@@ -1615,7 +1654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c60c351c",
+   "id": "f0398f1c",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1648,13 +1687,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "d17bca07",
+   "id": "11aee869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:05.350273Z",
-     "iopub.status.busy": "2026-08-01T00:52:05.350199Z",
-     "iopub.status.idle": "2026-08-01T00:52:05.352655Z",
-     "shell.execute_reply": "2026-08-01T00:52:05.352335Z"
+     "iopub.execute_input": "2026-08-01T01:04:11.447245Z",
+     "iopub.status.busy": "2026-08-01T01:04:11.447170Z",
+     "iopub.status.idle": "2026-08-01T01:04:11.449593Z",
+     "shell.execute_reply": "2026-08-01T01:04:11.449326Z"
     }
    },
    "outputs": [],
@@ -1696,13 +1735,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "841275d3",
+   "id": "a910f306",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:05.353487Z",
-     "iopub.status.busy": "2026-08-01T00:52:05.353412Z",
-     "iopub.status.idle": "2026-08-01T00:52:05.355382Z",
-     "shell.execute_reply": "2026-08-01T00:52:05.355097Z"
+     "iopub.execute_input": "2026-08-01T01:04:11.450554Z",
+     "iopub.status.busy": "2026-08-01T01:04:11.450484Z",
+     "iopub.status.idle": "2026-08-01T01:04:11.452340Z",
+     "shell.execute_reply": "2026-08-01T01:04:11.452063Z"
     }
    },
    "outputs": [],
@@ -1717,7 +1756,7 @@
     "    def __call__(self, approx, losses, i):\n",
     "        if len(losses) % self.k:\n",
     "            return\n",
-    "        self._means.append(np.mean(losses[-self.k:]))\n",
+    "        self._means.append(np.mean(losses[-self.k :]))\n",
     "        self.monitor(approx, self._means, len(self._means))\n",
     "\n",
     "\n",
@@ -1738,13 +1777,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "b1f904e9",
+   "id": "e60c45c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:05.356080Z",
-     "iopub.status.busy": "2026-08-01T00:52:05.356031Z",
-     "iopub.status.idle": "2026-08-01T00:52:09.902077Z",
-     "shell.execute_reply": "2026-08-01T00:52:09.901715Z"
+     "iopub.execute_input": "2026-08-01T01:04:11.453076Z",
+     "iopub.status.busy": "2026-08-01T01:04:11.453015Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.020437Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.020046Z"
     }
    },
    "outputs": [
@@ -1760,11 +1799,12 @@
    "source": [
     "loader2 = DataLoader(\n",
     "    parquet_source(data_dir, columns=columns),\n",
-    "    batch_size=1024, shuffle=False, sample_shape=(len(columns),), total_size=\"auto\",\n",
-    ")\n",
-    "model2 = build_model(\n",
-    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), len(loader2)\n",
+    "    batch_size=1024,\n",
+    "    shuffle=False,\n",
+    "    sample_shape=(len(columns),),\n",
+    "    total_size=\"auto\",\n",
     ")\n",
+    "model2 = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), len(loader2))\n",
     "stream2 = StreamAdvance(model2, loader2)\n",
     "stream2.prime()\n",
     "steps_per_epoch = len(loader2) // loader2.batch_size\n",
@@ -1776,28 +1816,36 @@
     "\n",
     "with model2:\n",
     "    advi2 = pm.ADVI(random_seed=RANDOM_SEED)\n",
-    "approx2 = advi2.fit(20_000, obj_optimizer=pm.adam(learning_rate=0.02),\n",
-    "                    callbacks=[stream2, raw_shadow, win_shadow], progressbar=False)\n",
-    "assert raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None, (\n",
-    "    \"a shadow rule never fired within the 20k-step budget; raise the budget\"\n",
+    "approx2 = advi2.fit(\n",
+    "    20_000,\n",
+    "    obj_optimizer=pm.adam(learning_rate=0.02),\n",
+    "    callbacks=[stream2, raw_shadow, win_shadow],\n",
+    "    progressbar=False,\n",
     ")\n",
+    "assert (\n",
+    "    raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None\n",
+    "), \"a shadow rule never fired within the 20k-step budget; raise the budget\"\n",
     "win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules\n",
-    "print(f\"raw increments: would stop at step {raw_shadow.stopped_at:,} \"\n",
-    "      f\"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})\")\n",
-    "print(f\"window means (k={k}): would stop at step {win_stop:,} \"\n",
-    "      f\"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})\")"
+    "print(\n",
+    "    f\"raw increments: would stop at step {raw_shadow.stopped_at:,} \"\n",
+    "    f\"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"window means (k={k}): would stop at step {win_stop:,} \"\n",
+    "    f\"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "3bbc0af2",
+   "id": "b14f5917",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:09.903000Z",
-     "iopub.status.busy": "2026-08-01T00:52:09.902941Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.073897Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.073521Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.021452Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.021387Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.191323Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.190967Z"
     }
    },
    "outputs": [
@@ -1821,21 +1869,26 @@
     "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
     "ax.plot(approx2.hist, lw=0.5, label=\"loss\")\n",
     "ax.axvline(steps_per_epoch, color=\"C2\", ls=\":\", label=\"one full pass (rule arms)\")\n",
-    "ax.axvline(raw_shadow.stopped_at, color=\"C3\", ls=\"--\",\n",
-    "           label=f\"raw increments: false stop at {raw_shadow.stopped_at:,}\")\n",
-    "ax.axvline(win_stop, color=\"C1\", ls=\"--\",\n",
-    "           label=f\"window means: stop at {win_stop:,}\")\n",
+    "ax.axvline(\n",
+    "    raw_shadow.stopped_at,\n",
+    "    color=\"C3\",\n",
+    "    ls=\"--\",\n",
+    "    label=f\"raw increments: false stop at {raw_shadow.stopped_at:,}\",\n",
+    ")\n",
+    "ax.axvline(win_stop, color=\"C1\", ls=\"--\", label=f\"window means: stop at {win_stop:,}\")\n",
     "ax.set_ylim(-550, 900)\n",
     "ax.set_xlabel(\"step\")\n",
     "ax.set_ylabel(\"negative ELBO\")\n",
-    "ax.set_title(\"Batch noise blinds the raw rule; window means restore its eyesight\\n\"\n",
-    "             \"(y clipped to the descent region)\")\n",
+    "ax.set_title(\n",
+    "    \"Batch noise blinds the raw rule; window means restore its eyesight\\n\"\n",
+    "    \"(y clipped to the descent region)\"\n",
+    ")\n",
     "ax.legend(fontsize=9, loc=\"upper right\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e0ea44e4",
+   "id": "53f7f256",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1880,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b29c63b",
+   "id": "c050e629",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -1896,13 +1949,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "67e67872",
+   "id": "7c088e50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.075043Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.074964Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.225861Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.225472Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.192498Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.192433Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.343425Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.343034Z"
     }
    },
    "outputs": [
@@ -1936,27 +1989,47 @@
     "fig, ax = plt.subplots(figsize=(8, 4), layout=\"constrained\")\n",
     "ax.plot(mb[:, 0] / 1e6, mb[:, 1], \"o-\", color=\"C1\", label=\"pm.Minibatch (rows in RAM)\")\n",
     "grid = np.linspace(0, n_full, 200)\n",
-    "ax.plot(grid / 1e6, icpt + bpr * grid / 1e9, ls=\":\", color=\"C1\", lw=1,\n",
-    "        label=f\"fit: {icpt:.1f} GB + {bpr:.0f} B/row\")\n",
+    "ax.plot(\n",
+    "    grid / 1e6,\n",
+    "    icpt + bpr * grid / 1e9,\n",
+    "    ls=\":\",\n",
+    "    color=\"C1\",\n",
+    "    lw=1,\n",
+    "    label=f\"fit: {icpt:.1f} GB + {bpr:.0f} B/row\",\n",
+    ")\n",
     "ax.plot(st[:, 0] / 1e6, st[:, 1], \"s-\", color=\"C0\", label=\"streaming DataLoader\")\n",
     "ax.axhline(12, color=\"C3\", ls=\"--\", lw=1, label=\"12 GB (16 GB laptop budget)\")\n",
     "ax.axvline(cross / 1e6, color=\"C3\", ls=\":\", lw=1)\n",
-    "ax.annotate(f\"wall: {cross / 1e6:,.0f}M rows\", (cross / 1e6 + 8, 10.6), color=\"C3\",\n",
-    "            fontsize=9, ha=\"left\", va=\"top\")\n",
+    "ax.annotate(\n",
+    "    f\"wall: {cross / 1e6:,.0f}M rows\",\n",
+    "    (cross / 1e6 + 8, 10.6),\n",
+    "    color=\"C3\",\n",
+    "    fontsize=9,\n",
+    "    ha=\"left\",\n",
+    "    va=\"top\",\n",
+    ")\n",
     "ax.axvline(n_full / 1e6, color=\"C2\", ls=\":\", lw=1)\n",
     "ax.set_ylim(0, 42)\n",
-    "ax.annotate(f\"this corpus: {n_full / 1e6:,.0f}M rows\", (n_full / 1e6 - 6, 40.5),\n",
-    "            color=\"C2\", fontsize=9, ha=\"right\", va=\"top\")\n",
+    "ax.annotate(\n",
+    "    f\"this corpus: {n_full / 1e6:,.0f}M rows\",\n",
+    "    (n_full / 1e6 - 6, 40.5),\n",
+    "    color=\"C2\",\n",
+    "    fontsize=9,\n",
+    "    ha=\"right\",\n",
+    "    va=\"top\",\n",
+    ")\n",
     "ax.set_xlabel(\"dataset size (millions of rows)\")\n",
     "ax.set_ylabel(\"peak RSS (GB)\")\n",
-    "ax.set_title(\"The in-memory path crosses a 16 GB laptop's budget at \"\n",
-    "             f\"{cross / 1e6:,.0f}M rows; streaming does not care\")\n",
+    "ax.set_title(\n",
+    "    \"The in-memory path crosses a 16 GB laptop's budget at \"\n",
+    "    f\"{cross / 1e6:,.0f}M rows; streaming does not care\"\n",
+    ")\n",
     "ax.legend(loc=\"upper left\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e1c84294",
+   "id": "fbcace58",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -1976,13 +2049,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "b5c978bd",
+   "id": "6ad9602a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.227299Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.227216Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.301797Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.301418Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.344642Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.344569Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.421054Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.420735Z"
     }
    },
    "outputs": [
@@ -2014,15 +2087,21 @@
     "vals = [tp[k] for k in labels]\n",
     "ax.barh(names, vals, color=[\"C1\", \"C0\", \"C0\"])\n",
     "for y, v in enumerate(vals):\n",
-    "    ax.annotate(f\"{v:,.0f} steps/s\", (v, y), xytext=(4, 0),\n",
-    "                textcoords=\"offset points\", va=\"center\", fontsize=9)\n",
+    "    ax.annotate(\n",
+    "        f\"{v:,.0f} steps/s\",\n",
+    "        (v, y),\n",
+    "        xytext=(4, 0),\n",
+    "        textcoords=\"offset points\",\n",
+    "        va=\"center\",\n",
+    "        fontsize=9,\n",
+    "    )\n",
     "ax.set_xlabel(\"optimization steps per second (batch 4096, steady state)\")\n",
     "ax.set_title(\"With a 500-parameter gradient, the loader is not the bottleneck\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a70e5326",
+   "id": "dbb3a002",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2082,7 +2161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1806eeb",
+   "id": "1940aa83",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
@@ -2102,13 +2181,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "1ac1025a",
+   "id": "02a902cb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.302720Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.302655Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.307274Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.306905Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.421983Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.421925Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.428376Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.427945Z"
     }
    },
    "outputs": [
@@ -2211,7 +2290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65c9f734",
+   "id": "bc70d894",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
@@ -2243,13 +2322,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "f7b838c7",
+   "id": "7f8179eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.308086Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.308033Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.391869Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.391548Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.429255Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.429190Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.521571Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.521126Z"
     }
    },
    "outputs": [
@@ -2274,22 +2353,24 @@
     "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
     "names = list(pol.keys())\n",
     "scores = [pol[n] for n in names]\n",
-    "ax.barh(names, scores,\n",
-    "        color=[\"C1\" if \"anneal\" in n and \"stop\" in n else \"C0\" for n in names])\n",
+    "ax.barh(names, scores, color=[\"C1\" if \"anneal\" in n and \"stop\" in n else \"C0\" for n in names])\n",
     "for y, v in enumerate(scores):\n",
-    "    ax.annotate(f\"{v:+.6f}\", (v, y), xytext=(4, 0), textcoords=\"offset points\",\n",
-    "                va=\"center\", fontsize=8)\n",
+    "    ax.annotate(\n",
+    "        f\"{v:+.6f}\", (v, y), xytext=(4, 0), textcoords=\"offset points\", va=\"center\", fontsize=8\n",
+    "    )\n",
     "lo = min(scores)\n",
     "ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)\n",
     "ax.set_xticks([0.116, 0.117, 0.118])\n",
     "ax.set_xlabel(\"held-out log score (nats/row)\")\n",
-    "ax.set_title(\"Stop rule alone inherits optimizer jitter; stop + short anneal\\n\"\n",
-    "             \"matches the annealed 2-pass reference at 55% of the budget\");"
+    "ax.set_title(\n",
+    "    \"Stop rule alone inherits optimizer jitter; stop + short anneal\\n\"\n",
+    "    \"matches the annealed 2-pass reference at 55% of the budget\"\n",
+    ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "53ee0e00",
+   "id": "897e1823",
    "metadata": {},
    "source": [
     "The working protocol this ablation suggests: **pair a stop rule on a\n",
@@ -2312,13 +2393,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "4a91e180",
+   "id": "734eba1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.392914Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.392836Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.602558Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.602251Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.522583Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.522501Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.738638Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.738285Z"
     }
    },
    "outputs": [
@@ -2345,10 +2426,12 @@
     "    disp = results[\"dispersion_full\"][sym_name]\n",
     "    axs[0].plot(hours, disp[\"pi_mean\"], marker=\".\", label=sym_name)\n",
     "    axs[1].plot(hours, disp[\"hw_mean\"], marker=\".\")\n",
-    "    axs[1].fill_between(hours,\n",
-    "                        np.array(disp[\"hw_mean\"]) - 2 * np.array(disp[\"hw_sd\"]),\n",
-    "                        np.array(disp[\"hw_mean\"]) + 2 * np.array(disp[\"hw_sd\"]),\n",
-    "                        alpha=0.2)\n",
+    "    axs[1].fill_between(\n",
+    "        hours,\n",
+    "        np.array(disp[\"hw_mean\"]) - 2 * np.array(disp[\"hw_sd\"]),\n",
+    "        np.array(disp[\"hw_mean\"]) + 2 * np.array(disp[\"hw_sd\"]),\n",
+    "        alpha=0.2,\n",
+    "    )\n",
     "axs[0].set_title(r\"move probability $\\pi_{s,h}$\")\n",
     "axs[1].set_title(\"conditional-move 90% half-width (bp)\")\n",
     "axs[1].set_yscale(\"log\")\n",
@@ -2361,7 +2444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97e46212",
+   "id": "3e419112",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2371,7 +2454,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "906ecd50",
+   "id": "256a51b3",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2390,13 +2473,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "7254f0c7",
+   "id": "9436e488",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.603839Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.603762Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.942820Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.942524Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.739911Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.739840Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.931581Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.931263Z"
     }
    },
    "outputs": [
@@ -2430,8 +2513,15 @@
     "        hw = arm[sym_name]\n",
     "        for hi, h in enumerate(show_hours):\n",
     "            hw_m, hw_s = hw[\"hw_mean\"][h], hw[\"hw_sd\"][h]\n",
-    "            ax.errorbar([hw_m], [ypos + off + 0.5 * (hi - 1)], xerr=[[2 * hw_s], [2 * hw_s]],\n",
-    "                        fmt=\"o\", ms=4, color=color, capsize=2)\n",
+    "            ax.errorbar(\n",
+    "                [hw_m],\n",
+    "                [ypos + off + 0.5 * (hi - 1)],\n",
+    "                xerr=[[2 * hw_s], [2 * hw_s]],\n",
+    "                fmt=\"o\",\n",
+    "                ms=4,\n",
+    "                color=color,\n",
+    "                capsize=2,\n",
+    "            )\n",
     "    labels.append(f\"{sym_name}\\n({n_rows / 1e6:.2f}M rows)\")\n",
     "    ticks.append(ypos)\n",
     "    ypos += 2.2\n",
@@ -2439,8 +2529,10 @@
     "ax.set_yticklabels(labels, fontsize=8)\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_xlabel(\"conditional-move 90% half-width (bp), posterior mean ± 2 sd\")\n",
-    "ax.set_title(\"Full fit (C0) vs 1% subsample (C1) at three hours of day\\n\"\n",
-    "             \"Global parameters agree; thin-cell uncertainty is where full N earns its keep\")\n",
+    "ax.set_title(\n",
+    "    \"Full fit (C0) vs 1% subsample (C1) at three hours of day\\n\"\n",
+    "    \"Global parameters agree; thin-cell uncertainty is where full N earns its keep\"\n",
+    ")\n",
     "ax.plot([], [], \"o\", color=\"C0\", label=\"full corpus\")\n",
     "ax.plot([], [], \"o\", color=\"C1\", label=\"1% subsample\")\n",
     "ax.legend();"
@@ -2448,7 +2540,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "455d6c3b",
+   "id": "5ef7ead8",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2474,13 +2566,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "38224bfd",
+   "id": "7544357c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.943916Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.943850Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.950113Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.949725Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.932663Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.932580Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.938396Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.938063Z"
     }
    },
    "outputs": [
@@ -2643,7 +2735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9f5316a",
+   "id": "0c2222ee",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2671,7 +2763,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb8595bc",
+   "id": "a5ad684d",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2690,7 +2782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ad79fbe",
+   "id": "37e52702",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2702,7 +2794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c43146d",
+   "id": "f6f81c3f",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2714,7 +2806,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "867b8d1e",
+   "id": "fdfc3ebf",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2723,13 +2815,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "b9120997",
+   "id": "022b29ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T00:52:10.950973Z",
-     "iopub.status.busy": "2026-08-01T00:52:10.950913Z",
-     "iopub.status.idle": "2026-08-01T00:52:10.961587Z",
-     "shell.execute_reply": "2026-08-01T00:52:10.961261Z"
+     "iopub.execute_input": "2026-08-01T01:04:16.939206Z",
+     "iopub.status.busy": "2026-08-01T01:04:16.939147Z",
+     "iopub.status.idle": "2026-08-01T01:04:16.949401Z",
+     "shell.execute_reply": "2026-08-01T01:04:16.949067Z"
     }
    },
    "outputs": [
@@ -2769,7 +2861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b0be0de",
+   "id": "2864d2a9",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 1e2a47a9f..28d3c2715 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -208,9 +208,11 @@ truth = {
     "nu": 3.5,
 }
 
+
 def standardize(x, axis=0):
     return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)
 
+
 z_truth = {
     "z_k": standardize(rng.standard_normal(n_symbols)),
     "z_ph": standardize(rng.standard_normal((n_symbols, 4))),
@@ -225,6 +227,7 @@ def hour_basis(hour):
     w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0
     return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])
 
+
 sym = np.repeat(np.arange(n_symbols), counts)
 n = len(sym)
 hour = rng.integers(0, 24, size=n)
@@ -266,8 +269,10 @@ for s in range(n_symbols):
             y[i] = truth["theta_d"] * d[i] + truth["theta_r"] * last + sigma[i] * t_draw[i]
             last = y[i]
 
-print(f"{n:,} rows, zero-move share {(m == 0).mean():.1%}, "
-      f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp")
+print(
+    f"{n:,} rows, zero-move share {(m == 0).mean():.1%}, "
+    f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp"
+)
 ```
 
 The zero spike is the single most prominent feature of per-trade return data —
@@ -276,10 +281,14 @@ here is the synthetic version of the picture that forced the hurdle:
 ```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
 moves = y[m == 1]
-ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color="C0",
-        label="nonzero moves")
-ax.bar([0.0], [(m == 0).sum()], width=0.02, color="C1",
-       label=f"exactly zero ({(m == 0).mean():.0%} of rows)")
+ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color="C0", label="nonzero moves")
+ax.bar(
+    [0.0],
+    [(m == 0).sum()],
+    width=0.02,
+    color="C1",
+    label=f"exactly zero ({(m == 0).mean():.0%} of rows)",
+)
 ax.set_xlabel("next-event return (bp)")
 ax.set_ylabel("count (log scale)")
 ax.set_title("A continuous density puts zero mass on the most common outcome")
@@ -313,12 +322,14 @@ table = pa.table(
     }
 )
 
+
 def splitmix64(x):
     x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)
     x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)
     x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)
     return x ^ (x >> np.uint64(31))
 
+
 key = splitmix64(np.arange(n, dtype=np.uint64))
 order = np.argsort(key, kind="stable")
 n_shards = 8
@@ -327,8 +338,10 @@ for i in range(n_shards):
     pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"))
 
 head = pq.read_table(os.path.join(data_dir, "shard_000.parquet")).slice(0, 10_000)
-print(f"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours "
-      f"and {len(np.unique(head['sym']))} symbols")
+print(
+    f"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours "
+    f"and {len(np.unique(head['sym']))} symbols"
+)
 ```
 
 On the real corpus the same check is part of the ETL script's validation: the
@@ -368,8 +381,6 @@ still rescales by the full $N$. At 0.3% of the data this is a knowably tiny,
 stated bias, not a hidden one; shuffling shard order between epochs would
 rotate the tail if it ever mattered.
 
-+++
-
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
     coords = {"symbol": list(symbols), "harmonic": ["sin1", "cos1", "sin2", "cos2"]}
@@ -449,9 +460,15 @@ def build_model(symbols, batch_init, total_size):
             return np.where(move, draw, 0.0)
 
         pm.CustomDist(
-            "y_obs", logit_pi, mu, log_sigma, nu,
-            logp=hurdle_logp, random=hurdle_random,
-            observed=y_, total_size=total_size,
+            "y_obs",
+            logit_pi,
+            mu,
+            log_sigma,
+            nu,
+            logp=hurdle_logp,
+            random=hurdle_random,
+            observed=y_,
+            total_size=total_size,
         )
     return model
 ```
@@ -466,9 +483,7 @@ $\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at bot
 tails.
 
 ```{code-cell} ipython3
-model = build_model(
-    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), len(loader)
-)
+model = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), len(loader))
 pm.model_to_graphviz(model)
 ```
 
@@ -496,15 +511,16 @@ class StreamAdvance:
     def __call__(self, approx, losses, i):
         self._shared.set_value(next(self._stream), borrow=True)
 
+
 stream = StreamAdvance(model, loader)
 stream.prime()
 
 with model:
     advi = pm.ADVI(random_seed=RANDOM_SEED)
-advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02),
-         callbacks=[stream], progressbar=False)
-approx = advi.fit(2_500, obj_optimizer=pm.adam(learning_rate=0.005),
-                  callbacks=[stream], progressbar=False)
+advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)
+approx = advi.fit(
+    2_500, obj_optimizer=pm.adam(learning_rate=0.005), callbacks=[stream], progressbar=False
+)
 ```
 
 The two-stage learning rate is not decoration: at a constant 0.02 the Adam
@@ -520,9 +536,11 @@ ax.axvline(6_000, color="C1", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
 ax.set_ylim(-450, 1200)
 ax.set_xlabel("step")
 ax.set_ylabel("negative ELBO")
-ax.set_title("Streaming ADVI loss, y clipped to the plateau (early losses are "
-             "off scale).\nThe visible band is batch-resampling noise — "
-             "remember it for the stopping section")
+ax.set_title(
+    "Streaming ADVI loss, y clipped to the plateau (early losses are "
+    "off scale).\nThe visible band is batch-resampling noise — "
+    "remember it for the stopping section"
+)
 ax.legend();
 ```
 
@@ -532,10 +550,18 @@ ax.legend();
 idata = approx.sample(2_000, random_seed=RANDOM_SEED)
 post = idata.posterior
 
-scalar_params = ["kappa0", "alpha0", "lambda_a", "lambda_q", "beta_a", "beta_q",
-                 "theta_d", "theta_r", "nu"]
-rows = {name: [float(post[name].mean()), float(post[name].std())]
-        for name in scalar_params}
+scalar_params = [
+    "kappa0",
+    "alpha0",
+    "lambda_a",
+    "lambda_q",
+    "beta_a",
+    "beta_q",
+    "theta_d",
+    "theta_r",
+    "nu",
+]
+rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}
 # the intercepts share a ridge with their group means; only the sums are identified
 for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al")]:
     s = post[icpt] + post[b].mean("symbol")
@@ -580,8 +606,9 @@ b_truth = b_truth - b_truth.mean()
 
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
 x = np.arange(n_symbols)
-ax.errorbar(x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3,
-            label="posterior (mean ± 2 sd)")
+ax.errorbar(
+    x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3, label="posterior (mean ± 2 sd)"
+)
 ax.scatter(x, b_truth, marker="x", color="C1", s=60, zorder=3, label="truth")
 for t in thin:
     ax.axvspan(t - 0.4, t + 0.4, color="C3", alpha=0.12)
@@ -615,8 +642,11 @@ eval_batch = next(iter(loader))
 model["batch"].set_value(eval_batch, borrow=True)  # same path the callback uses
 with model:
     idata = pm.sample_posterior_predictive(
-        idata, var_names=["y_obs"], random_seed=RANDOM_SEED,
-        extend_inferencedata=True, progressbar=False,
+        idata,
+        var_names=["y_obs"],
+        random_seed=RANDOM_SEED,
+        extend_inferencedata=True,
+        progressbar=False,
     )
 
 pp = idata.posterior_predictive["y_obs"].stack(sample=("chain", "draw")).values.T
@@ -626,10 +656,12 @@ zero_share = (pp == 0).mean(axis=1)
 med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])
 q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])
 
+
 def check_row(observed, draws):
     lo, hi = np.quantile(draws, [0.05, 0.95])
     return [observed, draws.mean(), lo, hi]
 
+
 y_nonzero = np.abs(y_eval[y_eval != 0])
 pd.DataFrame(
     [
@@ -648,15 +680,20 @@ pd.DataFrame(
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
 grid = np.linspace(-0.5, 0.5, 801)
 for draw in pp[:60]:
-    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size,
-            color="C0", alpha=0.08, lw=1)
-ax.plot(grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size,
-        color="k", lw=1.6, label="observed")
+    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size, color="C0", alpha=0.08, lw=1)
+ax.plot(
+    grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size, color="k", lw=1.6, label="observed"
+)
 ax.plot([], [], color="C0", label="posterior predictive (60 draws)")
 zero_jump = (y_eval == 0).mean()
 below = (y_eval < 0).mean()
-ax.annotate(f"vertical step at exactly 0:\nthe zero share ({zero_jump:.0%})",
-            (0.03, below + zero_jump / 2), fontsize=9, ha="left", va="center")
+ax.annotate(
+    f"vertical step at exactly 0:\nthe zero share ({zero_jump:.0%})",
+    (0.03, below + zero_jump / 2),
+    fontsize=9,
+    ha="left",
+    va="center",
+)
 ax.set_xlabel("next-event return (bp)")
 ax.set_ylabel("ECDF")
 ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
@@ -744,7 +781,7 @@ class WindowMean:
     def __call__(self, approx, losses, i):
         if len(losses) % self.k:
             return
-        self._means.append(np.mean(losses[-self.k:]))
+        self._means.append(np.mean(losses[-self.k :]))
         self.monitor(approx, self._means, len(self._means))
 
 
@@ -765,11 +802,12 @@ class Shadow:
 ```{code-cell} ipython3
 loader2 = DataLoader(
     parquet_source(data_dir, columns=columns),
-    batch_size=1024, shuffle=False, sample_shape=(len(columns),), total_size="auto",
-)
-model2 = build_model(
-    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), len(loader2)
+    batch_size=1024,
+    shuffle=False,
+    sample_shape=(len(columns),),
+    total_size="auto",
 )
+model2 = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), len(loader2))
 stream2 = StreamAdvance(model2, loader2)
 stream2.prime()
 steps_per_epoch = len(loader2) // loader2.batch_size
@@ -781,31 +819,44 @@ win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=min_windows), k))
 
 with model2:
     advi2 = pm.ADVI(random_seed=RANDOM_SEED)
-approx2 = advi2.fit(20_000, obj_optimizer=pm.adam(learning_rate=0.02),
-                    callbacks=[stream2, raw_shadow, win_shadow], progressbar=False)
-assert raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None, (
-    "a shadow rule never fired within the 20k-step budget; raise the budget"
+approx2 = advi2.fit(
+    20_000,
+    obj_optimizer=pm.adam(learning_rate=0.02),
+    callbacks=[stream2, raw_shadow, win_shadow],
+    progressbar=False,
 )
+assert (
+    raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None
+), "a shadow rule never fired within the 20k-step budget; raise the budget"
 win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules
-print(f"raw increments: would stop at step {raw_shadow.stopped_at:,} "
-      f"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})")
-print(f"window means (k={k}): would stop at step {win_stop:,} "
-      f"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})")
+print(
+    f"raw increments: would stop at step {raw_shadow.stopped_at:,} "
+    f"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})"
+)
+print(
+    f"window means (k={k}): would stop at step {win_stop:,} "
+    f"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})"
+)
 ```
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
 ax.plot(approx2.hist, lw=0.5, label="loss")
 ax.axvline(steps_per_epoch, color="C2", ls=":", label="one full pass (rule arms)")
-ax.axvline(raw_shadow.stopped_at, color="C3", ls="--",
-           label=f"raw increments: false stop at {raw_shadow.stopped_at:,}")
-ax.axvline(win_stop, color="C1", ls="--",
-           label=f"window means: stop at {win_stop:,}")
+ax.axvline(
+    raw_shadow.stopped_at,
+    color="C3",
+    ls="--",
+    label=f"raw increments: false stop at {raw_shadow.stopped_at:,}",
+)
+ax.axvline(win_stop, color="C1", ls="--", label=f"window means: stop at {win_stop:,}")
 ax.set_ylim(-550, 900)
 ax.set_xlabel("step")
 ax.set_ylabel("negative ELBO")
-ax.set_title("Batch noise blinds the raw rule; window means restore its eyesight\n"
-             "(y clipped to the descent region)")
+ax.set_title(
+    "Batch noise blinds the raw rule; window means restore its eyesight\n"
+    "(y clipped to the descent region)"
+)
 ax.legend(fontsize=9, loc="upper right");
 ```
 
@@ -873,21 +924,41 @@ n_full = results["corpus"]["n_rows"]
 fig, ax = plt.subplots(figsize=(8, 4), layout="constrained")
 ax.plot(mb[:, 0] / 1e6, mb[:, 1], "o-", color="C1", label="pm.Minibatch (rows in RAM)")
 grid = np.linspace(0, n_full, 200)
-ax.plot(grid / 1e6, icpt + bpr * grid / 1e9, ls=":", color="C1", lw=1,
-        label=f"fit: {icpt:.1f} GB + {bpr:.0f} B/row")
+ax.plot(
+    grid / 1e6,
+    icpt + bpr * grid / 1e9,
+    ls=":",
+    color="C1",
+    lw=1,
+    label=f"fit: {icpt:.1f} GB + {bpr:.0f} B/row",
+)
 ax.plot(st[:, 0] / 1e6, st[:, 1], "s-", color="C0", label="streaming DataLoader")
 ax.axhline(12, color="C3", ls="--", lw=1, label="12 GB (16 GB laptop budget)")
 ax.axvline(cross / 1e6, color="C3", ls=":", lw=1)
-ax.annotate(f"wall: {cross / 1e6:,.0f}M rows", (cross / 1e6 + 8, 10.6), color="C3",
-            fontsize=9, ha="left", va="top")
+ax.annotate(
+    f"wall: {cross / 1e6:,.0f}M rows",
+    (cross / 1e6 + 8, 10.6),
+    color="C3",
+    fontsize=9,
+    ha="left",
+    va="top",
+)
 ax.axvline(n_full / 1e6, color="C2", ls=":", lw=1)
 ax.set_ylim(0, 42)
-ax.annotate(f"this corpus: {n_full / 1e6:,.0f}M rows", (n_full / 1e6 - 6, 40.5),
-            color="C2", fontsize=9, ha="right", va="top")
+ax.annotate(
+    f"this corpus: {n_full / 1e6:,.0f}M rows",
+    (n_full / 1e6 - 6, 40.5),
+    color="C2",
+    fontsize=9,
+    ha="right",
+    va="top",
+)
 ax.set_xlabel("dataset size (millions of rows)")
 ax.set_ylabel("peak RSS (GB)")
-ax.set_title("The in-memory path crosses a 16 GB laptop's budget at "
-             f"{cross / 1e6:,.0f}M rows; streaming does not care")
+ax.set_title(
+    "The in-memory path crosses a 16 GB laptop's budget at "
+    f"{cross / 1e6:,.0f}M rows; streaming does not care"
+)
 ax.legend(loc="upper left");
 ```
 
@@ -916,8 +987,14 @@ names = [labels[k] for k in labels]
 vals = [tp[k] for k in labels]
 ax.barh(names, vals, color=["C1", "C0", "C0"])
 for y, v in enumerate(vals):
-    ax.annotate(f"{v:,.0f} steps/s", (v, y), xytext=(4, 0),
-                textcoords="offset points", va="center", fontsize=9)
+    ax.annotate(
+        f"{v:,.0f} steps/s",
+        (v, y),
+        xytext=(4, 0),
+        textcoords="offset points",
+        va="center",
+        fontsize=9,
+    )
 ax.set_xlabel("optimization steps per second (batch 4096, steady state)")
 ax.set_title("With a 500-parameter gradient, the loader is not the bottleneck");
 ```
@@ -1029,17 +1106,19 @@ pol = results["polish"]
 fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
 names = list(pol.keys())
 scores = [pol[n] for n in names]
-ax.barh(names, scores,
-        color=["C1" if "anneal" in n and "stop" in n else "C0" for n in names])
+ax.barh(names, scores, color=["C1" if "anneal" in n and "stop" in n else "C0" for n in names])
 for y, v in enumerate(scores):
-    ax.annotate(f"{v:+.6f}", (v, y), xytext=(4, 0), textcoords="offset points",
-                va="center", fontsize=8)
+    ax.annotate(
+        f"{v:+.6f}", (v, y), xytext=(4, 0), textcoords="offset points", va="center", fontsize=8
+    )
 lo = min(scores)
 ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)
 ax.set_xticks([0.116, 0.117, 0.118])
 ax.set_xlabel("held-out log score (nats/row)")
-ax.set_title("Stop rule alone inherits optimizer jitter; stop + short anneal\n"
-             "matches the annealed 2-pass reference at 55% of the budget");
+ax.set_title(
+    "Stop rule alone inherits optimizer jitter; stop + short anneal\n"
+    "matches the annealed 2-pass reference at 55% of the budget"
+);
 ```
 
 The working protocol this ablation suggests: **pair a stop rule on a
@@ -1065,10 +1144,12 @@ for sym_name, _ in results["forest_symbols"]:
     disp = results["dispersion_full"][sym_name]
     axs[0].plot(hours, disp["pi_mean"], marker=".", label=sym_name)
     axs[1].plot(hours, disp["hw_mean"], marker=".")
-    axs[1].fill_between(hours,
-                        np.array(disp["hw_mean"]) - 2 * np.array(disp["hw_sd"]),
-                        np.array(disp["hw_mean"]) + 2 * np.array(disp["hw_sd"]),
-                        alpha=0.2)
+    axs[1].fill_between(
+        hours,
+        np.array(disp["hw_mean"]) - 2 * np.array(disp["hw_sd"]),
+        np.array(disp["hw_mean"]) + 2 * np.array(disp["hw_sd"]),
+        alpha=0.2,
+    )
 axs[0].set_title(r"move probability $\pi_{s,h}$")
 axs[1].set_title("conditional-move 90% half-width (bp)")
 axs[1].set_yscale("log")
@@ -1111,8 +1192,15 @@ for sym_name, n_rows in show_syms:
         hw = arm[sym_name]
         for hi, h in enumerate(show_hours):
             hw_m, hw_s = hw["hw_mean"][h], hw["hw_sd"][h]
-            ax.errorbar([hw_m], [ypos + off + 0.5 * (hi - 1)], xerr=[[2 * hw_s], [2 * hw_s]],
-                        fmt="o", ms=4, color=color, capsize=2)
+            ax.errorbar(
+                [hw_m],
+                [ypos + off + 0.5 * (hi - 1)],
+                xerr=[[2 * hw_s], [2 * hw_s]],
+                fmt="o",
+                ms=4,
+                color=color,
+                capsize=2,
+            )
     labels.append(f"{sym_name}\n({n_rows / 1e6:.2f}M rows)")
     ticks.append(ypos)
     ypos += 2.2
@@ -1120,8 +1208,10 @@ ax.set_yticks(ticks)
 ax.set_yticklabels(labels, fontsize=8)
 ax.set_xscale("log")
 ax.set_xlabel("conditional-move 90% half-width (bp), posterior mean ± 2 sd")
-ax.set_title("Full fit (C0) vs 1% subsample (C1) at three hours of day\n"
-             "Global parameters agree; thin-cell uncertainty is where full N earns its keep")
+ax.set_title(
+    "Full fit (C0) vs 1% subsample (C1) at three hours of day\n"
+    "Global parameters agree; thin-cell uncertainty is where full N earns its keep"
+)
 ax.plot([], [], "o", color="C0", label="full corpus")
 ax.plot([], [], "o", color="C1", label="1% subsample")
 ax.legend();

From eff5749ad32faafca9cb03b5b23056e5cc1c8a09 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 20:24:21 -0500
Subject: [PATCH 21/42] Round-2 referee fixes: precise autopsy wording, third
 identified sum, scoped claims

---
 .../streaming_tick_data.ipynb                 | 989 +++++++++---------
 .../streaming_tick_data.myst.md               |  80 +-
 2 files changed, 554 insertions(+), 515 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index bcaa3f7ec..018b8d37f 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "28a46b36",
+   "id": "7a04ed7b",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26f28599",
+   "id": "3156b7c7",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff8d2b7d",
+   "id": "903dca32",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffe82b59",
+   "id": "17653c2a",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -102,13 +102,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dce3e296",
+   "id": "fc4eb55b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:03.544904Z",
-     "iopub.status.busy": "2026-08-01T01:04:03.544730Z",
-     "iopub.status.idle": "2026-08-01T01:04:05.454745Z",
-     "shell.execute_reply": "2026-08-01T01:04:05.454239Z"
+     "iopub.execute_input": "2026-08-01T01:23:42.140047Z",
+     "iopub.status.busy": "2026-08-01T01:23:42.139819Z",
+     "iopub.status.idle": "2026-08-01T01:23:43.950947Z",
+     "shell.execute_reply": "2026-08-01T01:23:43.950424Z"
     }
    },
    "outputs": [],
@@ -134,13 +134,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7d9fc1ef",
+   "id": "4a6b5bfe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:05.455967Z",
-     "iopub.status.busy": "2026-08-01T01:04:05.455833Z",
-     "iopub.status.idle": "2026-08-01T01:04:05.458712Z",
-     "shell.execute_reply": "2026-08-01T01:04:05.458344Z"
+     "iopub.execute_input": "2026-08-01T01:23:43.952290Z",
+     "iopub.status.busy": "2026-08-01T01:23:43.952141Z",
+     "iopub.status.idle": "2026-08-01T01:23:43.955144Z",
+     "shell.execute_reply": "2026-08-01T01:23:43.954720Z"
     }
    },
    "outputs": [],
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef40e1b6",
+   "id": "8faf4e65",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85fa2cd9",
+   "id": "5b0f46a0",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -230,13 +230,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d04cdc9a",
+   "id": "aceebfb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:05.459597Z",
-     "iopub.status.busy": "2026-08-01T01:04:05.459540Z",
-     "iopub.status.idle": "2026-08-01T01:04:05.461728Z",
-     "shell.execute_reply": "2026-08-01T01:04:05.461455Z"
+     "iopub.execute_input": "2026-08-01T01:23:43.955952Z",
+     "iopub.status.busy": "2026-08-01T01:23:43.955893Z",
+     "iopub.status.idle": "2026-08-01T01:23:43.958380Z",
+     "shell.execute_reply": "2026-08-01T01:23:43.957984Z"
     }
    },
    "outputs": [],
@@ -278,13 +278,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "4d0bb911",
+   "id": "7c9e1d0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:05.462640Z",
-     "iopub.status.busy": "2026-08-01T01:04:05.462582Z",
-     "iopub.status.idle": "2026-08-01T01:04:05.606153Z",
-     "shell.execute_reply": "2026-08-01T01:04:05.605781Z"
+     "iopub.execute_input": "2026-08-01T01:23:43.959212Z",
+     "iopub.status.busy": "2026-08-01T01:23:43.959156Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.104516Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.104218Z"
     }
    },
    "outputs": [
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f85267dc",
+   "id": "560393ae",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -361,13 +361,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "fd0a6ad3",
+   "id": "8c5157cc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:05.607048Z",
-     "iopub.status.busy": "2026-08-01T01:04:05.606979Z",
-     "iopub.status.idle": "2026-08-01T01:04:05.972297Z",
-     "shell.execute_reply": "2026-08-01T01:04:05.971943Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.105474Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.105415Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.481308Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.480907Z"
     }
    },
    "outputs": [
@@ -406,7 +406,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8a1172a",
+   "id": "ffdc8ee1",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -425,13 +425,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ed364f52",
+   "id": "1f2c2452",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:05.973286Z",
-     "iopub.status.busy": "2026-08-01T01:04:05.973203Z",
-     "iopub.status.idle": "2026-08-01T01:04:06.106154Z",
-     "shell.execute_reply": "2026-08-01T01:04:06.105790Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.482316Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.482237Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.621041Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.620705Z"
     }
    },
    "outputs": [
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc640393",
+   "id": "2b4392f1",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14b61d41",
+   "id": "7bd678e7",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -511,13 +511,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a33f15af",
+   "id": "f9c8b0de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:06.107113Z",
-     "iopub.status.busy": "2026-08-01T01:04:06.107042Z",
-     "iopub.status.idle": "2026-08-01T01:04:06.109910Z",
-     "shell.execute_reply": "2026-08-01T01:04:06.109579Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.622016Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.621952Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.624658Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.624358Z"
     }
    },
    "outputs": [
@@ -543,27 +543,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13d3b3fa",
+   "id": "164d30da",
    "metadata": {},
    "source": [
-    "One deliberate loader semantics to know about: only full batches are yielded,\n",
-    "so a trailing partial batch — here 992 of 300,000 rows, and with a fixed\n",
-    "replay order the *same* 992 every pass — is never seen, while `total_size`\n",
-    "still rescales by the full $N$. At 0.3% of the data this is a knowably tiny,\n",
-    "stated bias, not a hidden one; shuffling shard order between epochs would\n",
-    "rotate the tail if it ever mattered."
+    "One deliberate loader semantics to be precise about: only full batches are\n",
+    "yielded, so a trailing partial batch — here 992 of 300,000 rows, and with a\n",
+    "fixed replay order the *same* 992 every pass — is never seen. Strictly, then,\n",
+    "the fit targets the 299,008-row subpopulation the loader streams, which is a\n",
+    "uniform random subset by the hash shuffle, while `total_size` overstates its\n",
+    "size by 0.3% (0.0004% at full scale) — invisible at any precision reported\n",
+    "here, but it does mean Gate 1's equal-inclusion clause holds for the streamed\n",
+    "subpopulation, not the last few rows of the last shard. If exactness matters,\n",
+    "set `total_size` to the streamed count\n",
+    "(`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —\n",
+    "carrying the remainder across epoch boundaries — belongs in the loader and is\n",
+    "on its review agenda."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4201f073",
+   "id": "52779ba8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:06.110792Z",
-     "iopub.status.busy": "2026-08-01T01:04:06.110733Z",
-     "iopub.status.idle": "2026-08-01T01:04:06.115193Z",
-     "shell.execute_reply": "2026-08-01T01:04:06.114858Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.625563Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.625506Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.629947Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.629624Z"
     }
    },
    "outputs": [],
@@ -661,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1934004",
+   "id": "4a580209",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -677,13 +683,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cd992aef",
+   "id": "9c3d5aea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:06.116002Z",
-     "iopub.status.busy": "2026-08-01T01:04:06.115941Z",
-     "iopub.status.idle": "2026-08-01T01:04:06.274841Z",
-     "shell.execute_reply": "2026-08-01T01:04:06.274369Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.630808Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.630752Z",
+     "iopub.status.idle": "2026-08-01T01:23:44.782920Z",
+     "shell.execute_reply": "2026-08-01T01:23:44.782483Z"
     }
    },
    "outputs": [
@@ -699,367 +705,367 @@
        "<svg width=\"1705pt\" height=\"483pt\"\n",
        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.24,-478.95 1701.24,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M804.01,-8C804.01,-8 850.01,-8 850.01,-8 856.01,-8 862.01,-14 862.01,-20 862.01,-20 862.01,-94 862.01,-94 862.01,-100 856.01,-106 850.01,-106 850.01,-106 804.01,-106 804.01,-106 798.01,-106 792.01,-100 792.01,-94 792.01,-94 792.01,-20 792.01,-20 792.01,-14 798.01,-8 804.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"830.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M795.97,-8C795.97,-8 841.97,-8 841.97,-8 847.97,-8 853.97,-14 853.97,-20 853.97,-20 853.97,-94 853.97,-94 853.97,-100 847.97,-106 841.97,-106 841.97,-106 795.97,-106 795.97,-106 789.97,-106 783.97,-100 783.97,-94 783.97,-94 783.97,-20 783.97,-20 783.97,-14 789.97,-8 795.97,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.97\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M945.97,-243.82C945.97,-243.82 1119.97,-243.82 1119.97,-243.82 1125.97,-243.82 1131.97,-249.82 1131.97,-255.82 1131.97,-255.82 1131.97,-353.63 1131.97,-353.63 1131.97,-359.63 1125.97,-365.63 1119.97,-365.63 1119.97,-365.63 945.97,-365.63 945.97,-365.63 939.97,-365.63 933.97,-359.63 933.97,-353.63 933.97,-353.63 933.97,-255.82 933.97,-255.82 933.97,-249.82 939.97,-243.82 945.97,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.34\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1151.97,-243.82C1151.97,-243.82 1425.97,-243.82 1425.97,-243.82 1431.97,-243.82 1437.97,-249.82 1437.97,-255.82 1437.97,-255.82 1437.97,-353.63 1437.97,-353.63 1437.97,-359.63 1431.97,-365.63 1425.97,-365.63 1425.97,-365.63 1151.97,-365.63 1151.97,-365.63 1145.97,-365.63 1139.97,-359.63 1139.97,-353.63 1139.97,-353.63 1139.97,-255.82 1139.97,-255.82 1139.97,-249.82 1145.97,-243.82 1151.97,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.59\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1469.01,-243.82C1469.01,-243.82 1643.01,-243.82 1643.01,-243.82 1649.01,-243.82 1655.01,-249.82 1655.01,-255.82 1655.01,-255.82 1655.01,-353.63 1655.01,-353.63 1655.01,-359.63 1649.01,-365.63 1643.01,-365.63 1643.01,-365.63 1469.01,-365.63 1469.01,-365.63 1463.01,-365.63 1457.01,-359.63 1457.01,-353.63 1457.01,-353.63 1457.01,-255.82 1457.01,-255.82 1457.01,-249.82 1463.01,-243.82 1469.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1468.97,-243.82C1468.97,-243.82 1642.97,-243.82 1642.97,-243.82 1648.97,-243.82 1654.97,-249.82 1654.97,-255.82 1654.97,-255.82 1654.97,-353.63 1654.97,-353.63 1654.97,-359.63 1648.97,-365.63 1642.97,-365.63 1642.97,-365.63 1468.97,-365.63 1468.97,-365.63 1462.97,-365.63 1456.97,-359.63 1456.97,-353.63 1456.97,-353.63 1456.97,-255.82 1456.97,-255.82 1456.97,-249.82 1462.97,-243.82 1468.97,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.84\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M715.01,-114C715.01,-114 939.01,-114 939.01,-114 945.01,-114 951.01,-120 951.01,-126 951.01,-126 951.01,-223.82 951.01,-223.82 951.01,-229.82 945.01,-235.82 939.01,-235.82 939.01,-235.82 715.01,-235.82 715.01,-235.82 709.01,-235.82 703.01,-229.82 703.01,-223.82 703.01,-223.82 703.01,-126 703.01,-126 703.01,-120 709.01,-114 715.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"929.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M706.97,-114C706.97,-114 930.97,-114 930.97,-114 936.97,-114 942.97,-120 942.97,-126 942.97,-126 942.97,-223.82 942.97,-223.82 942.97,-229.82 936.97,-235.82 930.97,-235.82 930.97,-235.82 706.97,-235.82 706.97,-235.82 700.97,-235.82 694.97,-229.82 694.97,-223.82 694.97,-223.82 694.97,-126 694.97,-126 694.97,-120 700.97,-114 706.97,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.47\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M842.01,-98C842.01,-98 812.01,-98 812.01,-98 806.01,-98 800.01,-92 800.01,-86 800.01,-86 800.01,-52.5 800.01,-52.5 800.01,-46.5 806.01,-40.5 812.01,-40.5 812.01,-40.5 842.01,-40.5 842.01,-40.5 848.01,-40.5 854.01,-46.5 854.01,-52.5 854.01,-52.5 854.01,-86 854.01,-86 854.01,-92 848.01,-98 842.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M833.97,-98C833.97,-98 803.97,-98 803.97,-98 797.97,-98 791.97,-92 791.97,-86 791.97,-86 791.97,-52.5 791.97,-52.5 791.97,-46.5 797.97,-40.5 803.97,-40.5 803.97,-40.5 833.97,-40.5 833.97,-40.5 839.97,-40.5 845.97,-46.5 845.97,-52.5 845.97,-52.5 845.97,-86 845.97,-86 845.97,-92 839.97,-98 833.97,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- lambda_a -->\n",
        "<g id=\"node2\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"48.97\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"818.97\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M69.32,-279.64C78.9,-266.03 91.62,-251.92 106.97,-243.82 157.26,-217.25 501.28,-200.17 692.1,-192.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"692.23,-196.13 702.09,-192.24 691.96,-189.13 692.23,-196.13\"/>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M173.75,-279.44C181.83,-265.98 192.88,-252.03 206.97,-243.82 286.44,-197.49 536.51,-188.32 691.31,-187.27\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.25,-190.77 701.23,-187.22 691.22,-183.77 691.25,-190.77\"/>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"306.09,-345.73 215.84,-345.73 215.84,-288.23 306.09,-288.23 306.09,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M274.5,-288.08C283.66,-272.36 297.31,-253.91 314.97,-243.82 377.15,-208.28 563.9,-195.43 691.74,-190.79\"/>\n",
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+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"364.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"364.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"364.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"364.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M381.94,-279.77C390.06,-266.36 401.07,-252.35 414.97,-243.82 460.4,-215.91 592.19,-201.58 693.02,-194.5\"/>\n",
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+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"464.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"464.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"464.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"464.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M482.37,-279.71C490.49,-266.45 501.39,-252.58 514.97,-243.82 544.98,-224.46 625.99,-210.18 696.84,-200.94\"/>\n",
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+       "</g>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
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        "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
+       "<g id=\"node23\" class=\"node\">\n",
        "<title>b_ph</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
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+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"564.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M583,-279.92C591.14,-266.86 601.9,-253.06 614.97,-243.82 640.19,-225.98 670.88,-213.85 700.71,-205.61\"/>\n",
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+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"664.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M684.4,-281.02C692.67,-268.25 703.14,-254.37 714.97,-243.82 722.3,-237.28 730.53,-231.24 739.07,-225.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"740.83,-228.77 747.52,-220.55 737.17,-222.8 740.83,-228.77\"/>\n",
        "</g>\n",
        "<!-- tau_al -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_al -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
+       "<g id=\"node19\" class=\"node\">\n",
        "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_al&#45;&gt;b_al -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>tau_al&#45;&gt;b_al</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
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-       "<!-- beta_q -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"827.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"827.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"260.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.86 76.86,-251.92 91.01,-243.82 142.41,-214.38 503.55,-198.43 700.16,-191.84\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"700.03,-195.35 709.91,-191.52 699.8,-188.35 700.03,-195.35\"/>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M260.97,-393.28C260.97,-381.58 260.97,-368.79 260.97,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"264.47,-357.45 260.97,-347.45 257.47,-357.45 264.47,-357.45\"/>\n",
        "</g>\n",
        "<!-- theta_r -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"764.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M157.78,-279.42C165.86,-265.96 176.91,-252.01 191.01,-243.82 233.9,-218.91 527.69,-201.63 700.59,-193.48\"/>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"801,-239.69 801.66,-229.12 794.55,-236.97 801,-239.69\"/>\n",
        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
+       "<g id=\"node22\" class=\"node\">\n",
        "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_sh&#45;&gt;b_sh -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>tau_sh&#45;&gt;b_sh</title>\n",
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+       "</g>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"873.97\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M857.76,-278.32C852.36,-265.77 846.26,-251.58 840.52,-238.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"843.78,-236.96 836.61,-229.16 837.35,-239.73 843.78,-236.96\"/>\n",
        "</g>\n",
        "<!-- tau_ba -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_ba -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
+       "<g id=\"node20\" class=\"node\">\n",
        "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- tau_ba&#45;&gt;b_ba -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
        "<title>tau_ba&#45;&gt;b_ba</title>\n",
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-       "<!-- lambda_a -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M269.48,-279.86C279.08,-266.29 291.78,-252.14 307.01,-243.82 372.48,-208.04 568.17,-195.26 699.93,-190.69\"/>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M373.97,-279.74C382.08,-266.32 393.09,-252.32 407.01,-243.82 454.97,-214.52 595.32,-200.37 700.68,-193.72\"/>\n",
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-       "<!-- eta -->\n",
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-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"879.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- nu -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"924.14,-345.73 833.89,-345.73 833.89,-288.23 924.14,-288.23 924.14,-345.73\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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-       "<!-- eta&#45;&gt;nu -->\n",
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-       "<title>eta&#45;&gt;nu</title>\n",
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        "<!-- tau_k -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
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        "<title>tau_k</title>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
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        "<title>tau_k&#45;&gt;b_k</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
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+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"982.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
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+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
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        "<!-- g -->\n",
-       "<g id=\"node17\" class=\"node\">\n",
+       "<g id=\"node18\" class=\"node\">\n",
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        "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
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+       "<!-- b_al&#45;&gt;y_obs -->\n",
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+       "<title>b_al&#45;&gt;y_obs</title>\n",
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        "<!-- b_ba&#45;&gt;y_obs -->\n",
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        "<!-- b_k&#45;&gt;y_obs -->\n",
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        "<!-- b_sh&#45;&gt;y_obs -->\n",
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+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1484.47,-281.94C1473.57,-267.66 1459.01,-252.41 1441.97,-243.82 1358.06,-201.49 1102.79,-190.96 946.26,-188.57\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"946.73,-185.08 936.68,-188.44 946.63,-192.08 946.73,-185.08\"/>\n",
        "</g>\n",
        "<!-- y_obs&#45;&gt;batch -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>y_obs&#45;&gt;batch</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M827.01,-146.27C827.01,-134.38 827.01,-121.37 827.01,-109.53\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"830.51,-109.67 827.01,-99.67 823.51,-109.67 830.51,-109.67\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M818.97,-146.27C818.97,-134.38 818.97,-121.37 818.97,-109.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"822.47,-109.67 818.97,-99.67 815.47,-109.67 822.47,-109.67\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x13fd93f50>"
+       "<graphviz.graphs.Digraph at 0x129bae810>"
       ]
      },
      "execution_count": 9,
@@ -1074,7 +1080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32eb03f8",
+   "id": "10af3c09",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1086,13 +1092,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "8922b909",
+   "id": "8dec1729",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:06.275907Z",
-     "iopub.status.busy": "2026-08-01T01:04:06.275834Z",
-     "iopub.status.idle": "2026-08-01T01:04:10.244849Z",
-     "shell.execute_reply": "2026-08-01T01:04:10.244465Z"
+     "iopub.execute_input": "2026-08-01T01:23:44.783905Z",
+     "iopub.status.busy": "2026-08-01T01:23:44.783833Z",
+     "iopub.status.idle": "2026-08-01T01:23:48.775737Z",
+     "shell.execute_reply": "2026-08-01T01:23:48.775337Z"
     }
    },
    "outputs": [],
@@ -1129,7 +1135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3de069b7",
+   "id": "d4eb5121",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1142,13 +1148,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2891ecb4",
+   "id": "fe664042",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:10.245854Z",
-     "iopub.status.busy": "2026-08-01T01:04:10.245783Z",
-     "iopub.status.idle": "2026-08-01T01:04:10.391695Z",
-     "shell.execute_reply": "2026-08-01T01:04:10.391337Z"
+     "iopub.execute_input": "2026-08-01T01:23:48.776817Z",
+     "iopub.status.busy": "2026-08-01T01:23:48.776752Z",
+     "iopub.status.idle": "2026-08-01T01:23:48.923230Z",
+     "shell.execute_reply": "2026-08-01T01:23:48.922938Z"
     }
    },
    "outputs": [
@@ -1185,7 +1191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4b5e53e",
+   "id": "5cfe88a4",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1194,13 +1200,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "26d8ffd3",
+   "id": "059b3eb8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:10.392917Z",
-     "iopub.status.busy": "2026-08-01T01:04:10.392838Z",
-     "iopub.status.idle": "2026-08-01T01:04:10.584790Z",
-     "shell.execute_reply": "2026-08-01T01:04:10.584416Z"
+     "iopub.execute_input": "2026-08-01T01:23:48.924507Z",
+     "iopub.status.busy": "2026-08-01T01:23:48.924424Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.112985Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.112590Z"
     }
    },
    "outputs": [
@@ -1309,6 +1315,13 @@
        "      <td>-2.9957</td>\n",
        "      <td>0.0014</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_a + mean(b_ba)</th>\n",
+       "      <td>0.1699</td>\n",
+       "      <td>0.0061</td>\n",
+       "      <td>0.1800</td>\n",
+       "      <td>0.0101</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -1325,7 +1338,8 @@
        "theta_r              0.2485  0.0013  0.2500     0.0015\n",
        "nu                   3.5158  0.0319  3.5000     0.0158\n",
        "kappa0 + mean(b_k)   0.8554  0.0107  0.8473     0.0081\n",
-       "alpha0 + mean(b_al) -2.9944  0.0063 -2.9957     0.0014"
+       "alpha0 + mean(b_al) -2.9944  0.0063 -2.9957     0.0014\n",
+       "beta_a + mean(b_ba)  0.1699  0.0061  0.1800     0.0101"
       ]
      },
      "execution_count": 12,
@@ -1350,30 +1364,35 @@
     "]\n",
     "rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}\n",
     "# the intercepts share a ridge with their group means; only the sums are identified\n",
-    "for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\")]:\n",
+    "for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
     "    s = post[icpt] + post[b].mean(\"symbol\")\n",
     "    rows[f\"{icpt} + mean({b})\"] = [float(s.mean()), float(s.std())]\n",
     "recovery = pd.DataFrame(rows, index=[\"mean\", \"sd\"]).T\n",
-    "recovery[\"truth\"] = [truth[p] for p in scalar_params] + [truth[\"kappa0\"], truth[\"alpha0\"]]\n",
+    "recovery[\"truth\"] = [truth[p] for p in scalar_params] + [\n",
+    "    truth[\"kappa0\"],\n",
+    "    truth[\"alpha0\"],\n",
+    "    truth[\"beta_a\"],\n",
+    "]\n",
     "recovery[\"abs_error\"] = (recovery[\"mean\"] - recovery[\"truth\"]).abs()\n",
     "recovery.round(4)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c98734ce",
+   "id": "7f6d6098",
    "metadata": {},
    "source": [
-    "Read the table bottom-up. The two raw intercepts look off by 0.3 and 1.3 — but\n",
-    "a global intercept and the mean of its group effects are only *jointly*\n",
+    "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
+    "global coefficient and the mean of its group effects are only *jointly*\n",
     "identified: the likelihood constrains their sum, and the prior only weakly\n",
-    "splits it. The identified sums in the last two rows land within a few\n",
+    "splits it. The identified sums in the last three rows land within a few\n",
     "thousandths of the truth, like every other likelihood-identified quantity.\n",
     "The same translation ridge exists for every global-coefficient/group-effect\n",
-    "pair in this model ($c$ with $b^{(\\pi h)}$, $g$ with $b^{(\\sigma h)}$,\n",
-    "$\\beta_a$ with $b^{(\\beta a)}$) — we show the two intercepts because their\n",
-    "splits wander furthest; a sum-to-zero constraint on the symbol effects is the\n",
-    "standard reparameterization when the split itself matters.\n",
+    "pair in this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
+    "$g$/$b^{(\\sigma h)}$, whose sums we spare you) — so raw split rows like\n",
+    "`kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not\n",
+    "estimates of their generating values; a sum-to-zero constraint on the symbol\n",
+    "effects is the standard reparameterization when the split itself matters.\n",
     "Notice also what we did *not* print: z-scores against the truth. Some would\n",
     "exceed 2, because the mean-field standard deviations in the third column are\n",
     "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
@@ -1389,13 +1408,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e53ce059",
+   "id": "2e925d93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:10.585680Z",
-     "iopub.status.busy": "2026-08-01T01:04:10.585615Z",
-     "iopub.status.idle": "2026-08-01T01:04:10.698227Z",
-     "shell.execute_reply": "2026-08-01T01:04:10.697962Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.113850Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.113792Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.226545Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.226154Z"
     }
    },
    "outputs": [
@@ -1444,7 +1463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb4ad136",
+   "id": "2cc57cee",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1456,7 +1475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed588fa1",
+   "id": "17e23ae3",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1472,13 +1491,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c41c6e22",
+   "id": "32e7a07a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:10.699254Z",
-     "iopub.status.busy": "2026-08-01T01:04:10.699194Z",
-     "iopub.status.idle": "2026-08-01T01:04:11.311512Z",
-     "shell.execute_reply": "2026-08-01T01:04:11.311061Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.227500Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.227427Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.856316Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.855928Z"
     }
    },
    "outputs": [
@@ -1587,13 +1606,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "01b7474d",
+   "id": "67b69499",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:11.312439Z",
-     "iopub.status.busy": "2026-08-01T01:04:11.312367Z",
-     "iopub.status.idle": "2026-08-01T01:04:11.446057Z",
-     "shell.execute_reply": "2026-08-01T01:04:11.445693Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.857324Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.857251Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.990618Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.990288Z"
     }
    },
    "outputs": [
@@ -1641,7 +1660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a35d2b52",
+   "id": "be686197",
    "metadata": {},
    "source": [
     "The predictive distribution reproduces the zero step, the bulk scale, and the\n",
@@ -1654,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0398f1c",
+   "id": "aaacdc80",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1687,13 +1706,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "11aee869",
+   "id": "0349e6b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:11.447245Z",
-     "iopub.status.busy": "2026-08-01T01:04:11.447170Z",
-     "iopub.status.idle": "2026-08-01T01:04:11.449593Z",
-     "shell.execute_reply": "2026-08-01T01:04:11.449326Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.991882Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.991812Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.994289Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.993986Z"
     }
    },
    "outputs": [],
@@ -1735,13 +1754,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "a910f306",
+   "id": "69d5c417",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:11.450554Z",
-     "iopub.status.busy": "2026-08-01T01:04:11.450484Z",
-     "iopub.status.idle": "2026-08-01T01:04:11.452340Z",
-     "shell.execute_reply": "2026-08-01T01:04:11.452063Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.995178Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.995107Z",
+     "iopub.status.idle": "2026-08-01T01:23:49.997090Z",
+     "shell.execute_reply": "2026-08-01T01:23:49.996790Z"
     }
    },
    "outputs": [],
@@ -1777,13 +1796,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "e60c45c9",
+   "id": "05119fb5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:11.453076Z",
-     "iopub.status.busy": "2026-08-01T01:04:11.453015Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.020437Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.020046Z"
+     "iopub.execute_input": "2026-08-01T01:23:49.997854Z",
+     "iopub.status.busy": "2026-08-01T01:23:49.997801Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.452026Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.451640Z"
     }
    },
    "outputs": [
@@ -1839,13 +1858,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "b14f5917",
+   "id": "d9d6ae17",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.021452Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.021387Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.191323Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.190967Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.452929Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.452859Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.620756Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.620404Z"
     }
    },
    "outputs": [
@@ -1888,7 +1907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53f7f256",
+   "id": "14b8b799",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1925,15 +1944,16 @@
     "\n",
     "Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes\n",
     "about four minutes at roughly 1,000 optimization steps per second (4 million\n",
-    "rows per second through the model), with peak resident memory of 1.1 GB — the\n",
-    "loader's flat footprint, independent of N. The subsections below are the\n",
+    "rows per second through the model), with peak resident memory of 1.1 GB for\n",
+    "the whole run — fit loop plus posterior draws and summaries; the memory-wall\n",
+    "figure's leaner 0.5 GB point measures the fit loop alone. Both are flat in N. The subsections below are the\n",
     "measured results; each figure's numbers are inlined verbatim from the run\n",
     "logs so the plots execute without any data access."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c050e629",
+   "id": "daa5be89",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -1949,13 +1969,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "7c088e50",
+   "id": "47911c83",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.192498Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.192433Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.343425Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.343034Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.621939Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.621861Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.767907Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.767523Z"
     }
    },
    "outputs": [
@@ -2029,7 +2049,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbcace58",
+   "id": "4b9938e7",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -2049,13 +2069,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "6ad9602a",
+   "id": "7d869b8d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.344642Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.344569Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.421054Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.420735Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.769244Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.769178Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.844496Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.844224Z"
     }
    },
    "outputs": [
@@ -2101,7 +2121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbb3a002",
+   "id": "f8f36b1c",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2161,12 +2181,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1940aa83",
+   "id": "8461b6fb",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
     "\n",
-    "The stopping-rule comparison was pre-registered before any full-scale run:\n",
+    "One scoping sentence first, so no number below is ambiguous: every held-out\n",
+    "score in this section comes from fits on the 60-shard training split only,\n",
+    "with the four held-out shards touched by nothing but the scorer — the\n",
+    "headline four-minute fit above streams all 64 shards and is never\n",
+    "holdout-scored. The stopping-rule comparison was pre-registered before any\n",
+    "full-scale run:\n",
     "fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical\n",
     "batch order and seeds, flat learning rate; savings to be claimed only if the\n",
     "stopped fit's held-out log score landed within max(2 × ref seed spread,\n",
@@ -2181,13 +2206,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "02a902cb",
+   "id": "b9edf32b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.421983Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.421925Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.428376Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.427945Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.845522Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.845462Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.850767Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.850425Z"
     }
    },
    "outputs": [
@@ -2290,7 +2315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc70d894",
+   "id": "d2d4819e",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
@@ -2313,28 +2338,32 @@
     "One more layer of honesty on the metric itself: even among fully annealed,\n",
     "fully converged runs, path-to-path differences are about $5 \\times 10^{-4}$\n",
     "nats/row. The pre-registered $2 \\times 10^{-4}$ equivalence floor was\n",
-    "therefore unachievable by any protocol we ran — a pre-registration mistake\n",
-    "worth admitting, because the comparison it pushed us toward is the right one:\n",
-    "flat-rate arms scatter over $1.7 \\times 10^{-3}$, annealed endpoints over\n",
-    "$5 \\times 10^{-4}$, and the seed spread within one protocol is $10^{-5}$."
+    "therefore poorly chosen: the only arm that passed it was the fixed 1-pass\n",
+    "budget — which shadows the reference's own trajectory and passes trivially —\n",
+    "while for every protocol that behaves differently from the reference the\n",
+    "floor sits below the plateau jitter it would need to see through. A\n",
+    "pre-registration mistake worth admitting, because the comparison it pushed us\n",
+    "toward is the right one: flat-rate arms scatter over $1.7 \\times 10^{-3}$,\n",
+    "annealed endpoints over $5 \\times 10^{-4}$, and the seed spread within one\n",
+    "protocol is $10^{-5}$."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "7f8179eb",
+   "id": "7e96e76c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.429255Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.429190Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.521571Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.521126Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.851506Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.851455Z",
+     "iopub.status.idle": "2026-08-01T01:23:54.937311Z",
+     "shell.execute_reply": "2026-08-01T01:23:54.936952Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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y3jSOUcCMkiL8mjPWuE87Gil5NkzagU3B/Po82tUrqIT9zekeoB25tGOw39I8Rxf2uaHPdc011wQetFVUQpgKRalARlG7hSK/rpNzzz3X83M3LxTrJ+3w5HJPcPlcGfottUPe5ZdfbhP3/UzY35yKRGtuSn17/cbwF2upQP7ItpCwl3uMYkCCSEZSUH9x8R5KrtNcIYKlXVAVz6QENb+SeZT0piLXKniJ4GkXR/WnNI/ox/yh5siVuK81tLj30bQeq899zjnnOCXsb97fV/yVivIGOffrJ7WnHTt2tPMySpoMMjZRcYcqgKw56yFDhiRiTTwfaGyVza7WBalTp44Jmq6ra6+91hb+Dzphv6h5RCXxqzidCqDmO/0mSoBWMqFLgujmBdI1xlfhnjDWsxSPoravffv2nhP2N6dzRMW0H3744UDnQ9IgrXHpafp9NJd6yy23kLAfc0ltj/Np7adu3bqenvfBBx+YtCBpHwAAALG0cOFCGyipgUHYVGVQAVOaFAmzWIAGeVoU0WKAgk/zgSaUleAdZkCskp4VNKXBehCBqUmgCfVbb73VBgWqkl4QCQBKZFSl36BpokSTyFdffbWt4B0mLV5qEVPXbVALamqHtHOqqjkCueA6T851jv+bTL/55pvt4sfatWtjneyovosSaoKgxBkVNopj4r76qvqN9AhiMY/+Qzol5dr2yxdffGGDKlWEY82aNaG9r4IflDjfpEkT246EQcGHWgTVb+w3LQwqsGPUqFG+v3Y+0/yKAomjKJynonkKCNGOEWEWC9D9VMUzlDxFsiG/RbaYk4z3nGRm17ogrmntQHPXXXeZp59+2vfXLui99D6ag9b9Lg3jzbTP0YVJyb7qU/qVOIH4SMN1Uq1aNXPQQQd5fn5mpz6/qZiZV7Vr1zb77ruv5+drjqRr1652x8Gw1zSVYKIgT41BKVSVXlGspQL5Itvd5BTQrvt4LpYvX+77cag4qIoTFkdzdi73NmRXrEeFVIO49+uermL+mlfX2BHBGD16tI1JC2J+QXPmWuuLa/EFnWMqIKjkvSDmarVGoWIICxYsMHGlMZHmqk8//XQ7lggzwXT16tXmtttuszFVQayvIDde19QqVqxoKlSoEOjXrdhRXUtqrxCf+7/mc7X5ThC0aYKKNAS5jqa42ebNmzvNoxTVtvbt29fGOcYx3iUOiEuPN92XL7roIlscFPGW1PY439Z+ateu7el5Kj4cZEGTMJG0DwAAgFhWstXg16/qZ7lMHKmqrXZ/D/u9N//8Cj595ZVXTD7QwFYBsdpxL2jaKVyDdQ1+85UmRbWLr6pEB0k7g1155ZU22D3IyRclJml33ijPXy1m6jv1u8iH2iNVVk1DsDHCxXWenOsc/+v999+3u5tpd9O40aS5AniK28XGlSrpKlgmTu2/ivxocfX1118P9H3oP6RXnK9tv2iHCSWaR7lDvIKHVaBMfcggPf744+aee+4JtJ1S5X8VGQuqSEq+UXCX5jrC3uFIAaCqRK+ieX7tOOZ1hw8FUmtX23zFb5Ed5iTjOyepe84DDzxg7z9BF//o2bNnYEmlmfu1gvf1Pkr+SYO0z9GFSfPW2mGFwNb0SdN14rIrvT6/xv5+t6suAZoun0eJSEqmjvLeqXvkgAED7D1cBRGRTmGupQL5ZO+99876b3PdDCCXROC99torq7/r1q1bsUValESoAqoINtlK6yW5FnLIldZj2rVrR+J+QEk4119/faBFj1RwQcUXnnrqKRMnmofQ+Rt0ErA2pdDO4FEUkC2Oxttt27a1/SqtQ0RZmEmb+rBzebQUR+hFnTp1TJBmz55t13TSkrCWBiNHjrS/SdD3f62pq88X1PqHilwH3TZ/9NFHJO5vhrj0+NM6tnIWVCAT8ZbU9jgf134OO+wws9VWW4XaR4sbkvYBAAAQK9p9UQFDYQeWaGFCyVFaMIlDgpSCL7QzR58+fUy+UBVjBd8G5fnnn7fnVj7s8FkYBeJpAU6To2G9nxY7gwimVvC0Jl/iEoSmSRUtOvo1GaSdKOO2gItk4DpPznWOooPZLr744kiT+wpK2FcAR1j9CAWbZ7NjThi0YKDdHqZMmRLK+9F/SK84Xtt+0A4sSiDUgl2YO4gXRuPZJ554wu7UsmHDBt9fX8kovXr1MmHQ8Xfu3NnuQAzvVJRQwdphByDqWteYTUGwcaD7i3a2HTJkiMk3/BbZYU4y3nOSSlLr169faO+n72DJkiW+v6521tYuGtOmTTNpkfY5ujBp51QVuolDnxL+Stt1ouIDpUqV8rwrvd+7VSlQ0+t1U6ZMGXPKKad4TrLR7nRB3C+8FoFU8H3QxSaR7rVU+E8JoZ988onp37+/LWqnAhuNGjUytWrVMgcddJA58MADzeGHH27q1atnTj75ZNOyZUtz00032R0qP/jgA7sbbxJpDkKJzU8//bTp2LGjbZ8aNGhgatasaWrUqGEf+tz169c3jRs3tvdJfT/q82sdO6x5eH3/2fr0009zeu1c/l7fR3GUYJtNkRrNCW677bZZvzdy89lnn9mYk7Dm2ZQoot8U/lHbqu80rJ3VtVGD7gFxoPl2jTnDWu/TPUzjDiXwx2muXPekuCQBaYyodbN83ngm6qL1M2bM8PTcI444wgQ5n59vSX1x99Zbb9lC40EWe9nUq6++auMG/W6TFfPx3XffmbD6TCo0H9Z3FmfEpcefzlOtIedSeA3RSGp7nK9rP9tuu6055JBDPD13woQJJg1I2gcAAECsKCBfO1OESQMhDbqDriTsRe/evc2TTz5p8oUW4oP4HfS69913XyBJK0mh81yT+poUDdO8efPMCy+84OtrarfNTp06xW5iV0Fxbdq0sYGHLrT75Z133unbcSF/cJ0n5zpHdrvWqF8YB9qd98orr/R9J7hsdrJesWKFiZLutUr01A7NYaL/kF5xurb9TOrzu7/rByUm+92nVAGToHZ3KIyCJ3XOqC2Gtx2dNN8RdpB/puCLdpSNE80J3H777XZMmS/4LbLHnGR85yQV4D5o0KDQC31oLtFPmvNWIPby5ctNWqR9ji5sShxJ2jEjP6+T7bff3nOie6aIgZ/efPNNz89VAQIviY1KRFXCnpI84kQFBJQYxW6M6RbUWiqCoUQVJerff//9tqidEviVvKixmtZ0NE5V33PNmjU2QUBzL2onH374YdvOHH300XYX3h49etg506Q444wzTOvWrU3Pnj1tO631afWDdd/RXIUe+twqIKN+sgqh6PtRUQrNJ6iIwXnnnWeLiQVZjCSXXXKVPJ0tJXTrt8zGdtttV2zxAH1X3bt3L/a1VBihYcOGWR8ncqNzWPEWSrwK0/Dhw20BDLhbuHChTZYJO27owQcfjEVStu4tY8eODT0p/eqrr7btWNRWrlxpi+OooGKc6J6htuXjjz+O+lDyjr5z9Um8UPGhoKg/FFZiNbIr9nLzzTeHVuwlQ3PTfs0T6jxv165d6EUHNfbJ9/hH4tKTQX21bMdviE6S2+N8XvupWbOmp+dpnigOG3C6ImkfAAAAsaGF2LAXCEQDuTgskBTm0UcfNa+//rrJFwqg93P3TVW7Y9cJY4O0NJCNgnaM92sRTgERt9xyS2wH5F988YW59tprPS/sZAKulLgP5IrrPDnXObJv84cOHRqL44hi8lxBGtotO0qrVq2KbHGK/kN6xeXa9sNDDz1kxxtxpWNTARA/KHhcO6NFUQhN7aGCvJE7JdyGXfBACWlKLIhroQWNJW+99VabaJV2/BbZY04y3nOS2jkjqj5LrrtpFkbFuJSwH5ddtv2QD3N0YdKOsgQGpk+ar5NzzjnH8/tql2CNL/ygRE6XQsXnnnuup4IHSjyKWyGGTRMK27Zty66MKef3WiqC4xpMrnvIl19+aZ599lmbCN+sWTPz9ttvhx6knivX49P808yZM20xsRNPPNEmWQaxy9ruu+9uDjrooKyTjrKd6xg8eHDWRYCPOeYYs/XWWxf5NypeUFyBXxWhYUf24OfZohrT9erVK/Tiymmjvpt25YwieVxtot+FAXOlmLjnnnsukvdW0ZkXX3zRREnxNyom8+2335q4JnUqofarr76K+lDyyowZMzw9T/fcffbZxwRBxYziMB+L/6OCaVHMLypOw6+i8SqKG/YmTxmKCfB6raUBcenxN2XKlMj7KUh3e5zvaz9Vq1b19Ly1a9faomtJR9I+AAAAYiOKRYr+/fvbhe0k7N749ddfm3ygxRK/Eku0cBn2TpAo+DdVALwfAXg33XRT7INtFXToNclSn+21117z/ZiAoHGdI8hEISVr5vOOfNptKB/RrqRbGq7td9991zzzzDMm7hTc7EdysnaE0040UYn7GCCOdP9QAZQodiOYPn26iTMFQXbo0MGsXr3apBm/RfaYkyxcPs1JFmTAgAG+JAYoKVaBPWmRD3N0BSUZZPOoW7eup9cfNGiQL8eJ+Ej7dVK7dm2z7777eu6LjRw50kRd2EW7GteoUSPnuYrrrrsuFjuFFkUJvnfffXfUh4GErKUiWZQ0rqTTJk2aRFasPWwqXKAAdxUkOe+888zs2bN9ff2zzz47p/mx4gpaaq4hlznD4grhaDw2cODAYl/nyiuvNJUqVcr6fZG7KPt16j917tzZ/hPeaJ507ty5eXn+/Prrr7aoR5TFxKIueKXrJ+5JP7/88osdayR97SxJvPYp9ttvP1OiRInAinHHvTgTwqNEYtd7vwoPavOgKEV9D4gKcenJ0L1799gWXEU62uN8X/s54IADPD/X7/mfKJSM+gAAAACATSegw17U9mO3vpIlS5oTTjjBHH300ebQQw81u+yyiylTpoz9PN99951dMH/nnXecdplTJXYtwKv65FZbbWWCos+y//7724rylStXtpVhd911V7PzzjubHXbYwVZ51/vrePTQ4s6SJUvsQztP6bP6UV1c1ee1U8lOO+3k+TUUNKUFDRYu42H48OHmoosu8vx8BUDccMMNvrQT1atXNyeddJKpU6eODV7QeabzROeuJqu1A9KoUaOcFk616Ks2oWbNmjk9T7s0uCSO6Ho95ZRT7Pvq+i1fvrwpVaqU2WabbexnXLdund3FSO+hwNFFixbZYA997iiTsJAOXOfpozZEfZsqVarYQOwKFSrYPoHaTVWQV79A7bOCB9Qv0E4v33zzjd0FQEHfSlhwXdz4/vvvbf/H5R6SZGq3Fch+4YUXmnxEuxIMrm13au+0Y6arLbbYwu6mddxxx9n+22677WbHXb/99ptN7Js2bZrtl7pU3lY73KlTJ5vMsuOOO3p6De2+oCIiSJYogvvGjh3ry84f6mc0btzY1KtXz85PqP+x/fbb250ktRuS+hk6pzWe8UrjPwVrarfCNOK3yA1zktHPScbVuHHjbFKc13uoPPzww6naETFf5ujCHvdp10OvNPen9QkVDNDc/h577GHvm7qf6vdSYKzOY32v6sfq/qmd2xTwtHjxYoLBA5Av14mSDHv06OHpPUeMGGEuuOAC4+rNN9/0/NzikiQLomQnP3bn3Hvvvc2pp55qr1v9b/2uGh9q3l7zaWoT9Nlc+vTanVFjTfWr4a80raUiuXQvv+SSS0yzZs1s21S6dGmTD2bOnGkT97VbssYpuh5dnXnmmbboZTbFczVHd9ddd5k77rijwPfWvEX79u2zXutV8LrmBoui9ysu3kHtUJs2bbJ6TyT7ulcBB37r3C1btiy1c4DZUKEfrbfmK81Xa37Hlfp5KpqjsZkKgGXWyxV/o37ehAkT7DhHcYJeqbCACsSo7UewtHbmNY7TJfmsOGPGjPH8XI1Bjj/+eFO/fn1TrVo1Oz+k81bnqT6v5p8y6486TzUnpHNO34PixuJedDAf6bfSDs36Xb3Q7672hEIQ4SMuPTnCXh9EfrXHrP0YO3eqPoqXPA6tYWnOJslI2gcAAEBeykxKuSZ0a0JeC8IVK1b8n/+m4Aw9NFl/2WWX2cR9vacWa71QoM5LL71kF6H9pMSQk08+2S4Ia3FBQX7F2W677exDn2+vvfay/04BVvpe33vvPbszyxdffOH5mDRYVTCwvjevtLi+fPly4yd9Vk1uK3FAi99ahNFDFXQVeKOkZyVC67Nr0V7BN0koGlCuXLl/E7133313G9Spz6JB77Bhw3wJQtNr6bvZc889PT3/lVdesUGIrr/f7bffbo499tj/+W9KatfnVoCcrut27drZ3eSmTJniOTBTO9noPM6lwrLXhKwtt9zStkVqHwoLUNFn1EOLMvqctWrV+q//rt9cn1cPBQWmaQc4cJ3H6TqPM02Sqo3UJLP6BVrIzab9USCqklh0DznssMPsv9duclroffrpp23itctCoKrOxjVpXzuyKchaRQ30+bWYrSQIBX9oF271aVwp+D6OSfv0H5LTrnBt+09BU64LuEqKUcKwAv03p3ZV4wwF/uj6V0C/S3KIAvI0PtJreE029KvCvK6Zhg0bmqOOOsq2m/qcGiOr76m+sAKSFOSG5NGY+N5773V6DSUqtWzZ0lx11VV2vmFzSoTRQ/0N7SCnXWceeOAB+95efPjhh/acO/HEE02a8FvEG3OS/lHAqcYuGsOoKJH6p0q0UbCpEhVd55FE83oqgqFkKK87HWezK2YulJClOVzNUR5yyCH/zk/qHqvrX/dVjUn03kpqUiCRH4mB+TZHF6YZM2Z43nFK35GSxgorLJEZs+s71RqGzplNKZFURUQ//vhje1+cP3++p+NAfl4nTZs2NY8++qinNRAVBnNZL8gEQCt5zes95IwzzsjpOZoz11yPC92rbr31VjuXVNB3re9DDxXi0O9633332Xkhl93CdK/Ueh7cpHUtFcmndVytwSohM3OepZ3uW88995wtjNWrV68C5w9y7V9fe+215s4778y6YIau3UsvvdSutaovrrk33SeUFJxLfILWUTQXUhglf2azdqz+oPp8iIb6Zprr1T1XY1PdM9TP1nyn+iu6l6vf44e+ffuaFi1a5E2hDr+ojfRjvS7ze6uPruJI+r01V6m1Av3eGtvp947TZgk6Fu0M6nefKFOEWOMSfXa1fSoopmKrcYqR0rFpzOK6xqb56osvvth+3s1pnUOPI444wlxzzTX2XqBzzuvauO4zKlCjNWgER3EMXtf5qlataoIqMOJ1HVBrbho/ql0q6lzWOazzVQXINqU5RY3TNT+kuUT1L9l5umjqw9WuXdsWWlQ/XG2iNpjS76i5A/UN/SiEoPuK16R9HYcfc+SbnvuKL9Xatj6v5h11n1GBUL2X5qLBGhAQtji3x6z9GNv/UEylNoDKlZ/3sKiQtA8AAIC8pF0CXSaKFEzTrVs3c+6552b9HO1moV1RVPna6ySrAjgUCOWyu1Pm+JUooeAQBXj6FbCoAbCC3TU41fejAHqvtGue10AT7fLrZ0CsdhlWgFRBAXIZWghXMrQmJvXdihZjtTuuFlXiOIBUUFGHDh3seaCJ1E3pXNXvqF06nnzySbuopCAIFwoa9BKEt3btWht04aJBgwbmoYceKnARrSCaKOjXr59NbFKyqRcK2FCQeC6B3V4DYxVM4hqYpYUZBSrqocUXnbOjR482b7/9tg32TgK1P7pei0o4UzCcF5rQGzBggOdji2pHQq7z+F3ncaTvQglyCvR27WNsSgVutDisa1OFRTQp7oUquyvhRBXh40KJtPpsBx988P/8t8MPP9z2+7RA2bFjR6cdskUL5FooV0B5HNCuJKdd4doOxrRp05ySJkQ7bil4q6ig3E0deeSRdmx1+eWXe975Q4lDKoCitjkXGje7tmOi8YYKTKnfWlh7dvrpp9tkI11HKjLgNXkN0VAwokvBN91fFECZ7WK3ipVpLKtCiVdccYXnAokaJ+o9i+qvawdUFVIsjM7XPn36GK+Kem0v0vxbpEG+z0n6pXnz5jbBpUyZMgXeN3XP0/hfiZFez8kMBat67ZupmInrfFpG2bJl7W+ogj6FJWjo3+uh5GwFKel70ByP7uVK6NIcj0vwfD7N0YVJY14vlBik7zLbPmVB9Dso2USPm2++2SYTqaCNdvh2SSTNZ/l0nSgBXcfqNZFd55nWH7zS871SolFB95DCKLBT80AuNG+tPqPmmbOdr3/sscfMI488Yp566ilP76kkUiW2agyK/F5LVbGzP/74o8j+olfqm7gUHFXCM9wo8Pn888+3a+NVqlTJm69Tc3S6PrWDspJmXSgJWmv62RbA0RrqDTfc4PSeuucWFXugOIMHH3yw2NdR4pQS9RAN/Y4qzFrYWFmFerROpP6SNvhYvXq10/v9/PPPdp5XY0NkR3EOLv3GTWlepEuXLoX+3rqf6r9r/K3iSXHYuVXjBD/m2TU20TyM5mM2H4Nm1m/131Q0UAXB1KbGgeJD1J56pcIw6osXFYOyKa19KLZM86TXX3+9p2IRmsdR4Wjd34qijYOKuk9pjO+V+o4FrYFnq0KFCibu5s6d6/m5GoPHaX5IcQuK61NREa907mrMo4f6OIpz0CYFGm+o2CMJ/P9NRTruueeeAtdcNSd71lln2d9T36WXJMXN56Zdiv34QUUj9XlVwG5zmQIQGpfqurrlllvyfk6RNSAgPHFvj1n7+Y/KlSt7+v79jqGIAkn7AAAASARVQFPgmALRFHSoYCAlSCv5SwtTmWQuVftUYlNRldE0kahdX10o+CmX4NhNJ24V5KqquF4m5zOLcAqC9kKLJ0rI1SKBJtSCogBrLcSomrIqKXuh31MBRdkGMG1KQUh+VMfT51Blev1eXheudJ7ooeBYLQjp/IwDBXw9//zzxS4mKAhJCUXabVkLjC40kX/OOefk/Dyd8y4B1pqAUaBkrjsMKPFAwdUKVp0+fbqn91Zbo8XbbINnc9l9YdMEKCXb+knHq5239MgknCpwzHUnoaAp0LKoYMtcAjELag+SFuzEdR7P6zxOVGhGAQONGjUK9PgVwK0AVLUnXk2YMCE2SftKKlWgdnH3FV2DKvah4KnJkyd7fj8l1Si4QosNUaNdSUa7wrUdLK/JEhlqEzQey9Uuu+xig260sKhdhL20JQrQU3uci+ICw7KhQCEFhijYJ5trSMUJFLTcqlUrOwZGMBTIpe9Zya3afUUBiOov//bbb/Yc0zyLikRoTK9/FrUzkJI/XM4VjWk0XvayW8hhhx1mevToYc8bL4Fj2lFHxcpUvCgN+C38x5ykP3OSftHYXEUltANxcfQ3mv/UvJ7XAmKZ+SwvtIOGH4VvRAmDauuyTeDdlPqUKj6ih4p3qq+p39JL8n4+zdGFyWsxDe3y5/fnUcFV7Rqrh3Ywf+2112zCCbKXb9eJ5vujSNpX31SFSLzKdX1P/UX1G10CE/X9qgBLrhRUqp2hlCzhhfrpCp7PZmd4pHctNcjEKSXdJ23tJI2UCKy+waBBgzwVUE8q7eqp+SPdf10KjOm679mzp004dSmCly0lN91+++1F/o3GPdohuijbbbedTYxC+DSXpSJL2RY90a6PWuO55JJLnBJF5aWXXrL99TiObeLI6/h3U/quVURD/YNs/vbss8+2c64qahNGm1IYxaT5MZ5TrJD6lNncXxTLp/arXr16tkBvlL777jszYsQIz89XG6t1DcXMeJnHUSFJxZt5ofkkxZapUHxhiup/uRYI0W+e9v6dxlgu6+VB8NpeqIiRS8J+YZ9RRaH00HhFbcmQIUN8fY+katu2rZ1jLe4+vN9++9lNnjQ37XUDn0zxGf0GuY7pNDfttRD8phSrrY2estlgQoWDdZ6of6rE9XyUz3HpaaS2UGvpemitUP0cze3p+9X3pfGokoonTZpki8p5+d6R7vaYtR+3vpPWdlUUTNdeUvlTAhYAAAAIiCb9tZOEdnfRhIQCuJWEomByBSEpcV9/o2Swq666yrz44ot2EKzJn8J2EFTSlwLOXHbgcNkxQAlnXpI0Ml5++WXPCekKgleAZ5BBJpvyugCRmcTKtpr9ppRcoB1hXGlhv3///p4T9jenaqOa2NKuKEq4iZKuG+28l0v1X1WJz2YRsihequXpXNd17ZV2F9N3nmuQ46YL7rpmvO70p8m5Dz/8MOu/V4JMrtQeFrbDmh8ywd0KVhk/fnxg7wN/cZ3H9zqPC/WpFKigRJYwAnuU6F6zZs1IKoj76ZhjjrG7G2R7X8kkIbruSOpa8dcPtCvJaFe4toOlJGavgfSinUm0G43LYpbGxV6p3c8lCV59U+1S7EJFwNSPzCZhf/Nxs+vOICg8aEYF3JQkpfNRu7EpAE+JFppn0T1LY0Xt/KjiPkrW++CDD8x1111XaCKIdnR1SU5TYoECGb1SsITL2F27EaYFv4V/mJP0d07SDxq3KCE1m4T9DM0juyaxKKjGy3yNiuX4QckYCoz0krC/ObXj6kso0VUJBLnItzm6MHk5v6RSpUomSOofaGc+fW+ZXbNQtHy8TjRPogJQXixcuNDMnj3b03O1dqREZS+0dphrYUQVZfRKv4t+Vy8J+xkqvuZ1N3KNAf1YM8sn+bCWinRSgnf79u3Nn3/+afKJ7icK1C+q2GA2dt11V7sxgBIVg1S1alWbyKNk0MIoqVvjr+Lo9w4qcRBFu+uuu7JO2M/QvfyZZ56xm5S4jlGVFITs7pOaA3WlzS1yjZVRW6I2xaUP6Epz+2vXrnV6DR2/5jdyLQjTokUL2zZHSYUGXOaxVIzeS8J+hgrBaLzkVZrmrOPIpaCG1zFwUueHlJyoONdx48blNC+bRkqkVtuQbXyN+nuKe9baW9ixIn4UbTn00ENN7969s0rYz9Bn1QYYXop0p0E+x6WniWK7NRf3/vvv213cVYBL363GjJp31VqN+vQqaqENG9TnU0yb/vbAAw+M+vDzQlLa47je28Ne+9ndYd4iyiJofiBpHwAAALGlRY933nnHnHLKKTk9TwHmCvgubAJDAcxeKViqc+fOxpWqnHpN3lI14I8++sjTc8NOFldAS40aNTw//4svvsj5OSNHjnSuWqjBvCqjB7GjrRZvhw4daqKkis7aVTFXmmRyDZrIlc71lStXen7PK6+8stgdRrJZgNCOpl7lMhHupcq7kmO8TvB4KRCAZOA6j+91Hhe6npVIGabTTjst1D6B3xSYosTTXCfx9V1rp7qw76F+o11JRrvCtR0sl7GkaAct10Ipp556aqEF6rLZgTuX3SiVqK3nuDjzzDNt8rcXtWrVsrtPwT/ajV7ti3Y6ynUhVbugFhaM7HJtqDL6NddcY/z4bF4X2mfOnOkURBMn/Bb+YE7S/zlJP6igpO4rXp7nMp+hIH8lsuZCQfEKZHXVpEkTGzjj99hNc7YqVppLUZ18m6MLk9ddH8MKVtKaSJAFQ9MkH68TtU9qZ73yuvOly46Z2vk0F+onzpo1yymIVIG9LnQNat0zbe1fXOXDWirSS8neDzzwgMk3CjT3o2iWihiqCL9LkmZRjjvuOLtLelG7xGn8oYTwDRs2FPlaWutv1apVAEeJ4jRq1MjT7qeiRJ8777zT+UvOZY43n2nOz3XcpM05vPbD1KYomSgqo0ePdn4NFX2tXLmy591Pi9opPkgq5OKyy7PaWBUecKXNh7zS5ka//PKL8zGgYNop1wsVmg6qGEfc54dUkE5F/vOV2vSuXbt6SozUZmRhxoqoP6k2xIUSk7t3715koaniEvfLlClj8k0+x6WnhYodqw+l4jtq97Kla0VjBJeiCUhfexz3e3tYaz+7k7QPAAAAxIsmDx588EHPO4oURsncLjtFawFOQU+uVG3PpaKk626HYVK1eK/mzZuX83NGjRplXF1xxRV2p76gRBnkeNhhh3kKcBYFlrkEK/3+++/m119/zek57777ruf3U/uhCbSoE01VRXX9+vVZ/a2XnUQ1uaMdGYAMrvN4X+f5zKVPsGbNGqfAdz9ot2Gvi1u57mK5Oa+7x/mFdsWbfGlXkn5th9U3VbCvdiRwpaSYXHdx8jqWnDhxonGhnRduuOEG50TssBMm0ko7G+nhd+LnihUrzOeff+75+Sps48fu0ao4r/uVV64BTHHAb+EP5iTjOSepduLGG2/09FwFVtWtWzfU/ri+J6/BOJsGHmk3lyDlMkeZb3N0YfIyHyiaD2Tno3jJ1+tESfBei5OpcHdxSYkFrfN5/a4VOJ5rkQHXZCe/EipVyMUr9deTNPbOR2GvpSLdlHTuMk5Pqscff9yOi11p99xBgwaZ6667zlOSUkGU3KdEbfXfikteeu2118xnn31W7GsqSSGX4oVap9c4RRsXqFC/Ns7QBgbaVU+FQ2rWrGkaNmxoN8a444477CYE3DsKTmDr1KmTcaE4FO3K6WLSpElOz88XrvPb6uOqkJ4L9ZW9bKjhSn3syZMnO72G2oZcC15t/v3ddtttzoWMvfj000/NqlWrPD///PPPzylRr6jCxF5jDDWvpMLKCIbXZDiXpLOg5ocGDBgQ2kYv+eyWW26x/YAkxIqosNuPP/7o9J7qEyrBNeqC3UlCXHryKTegb9++vqxbIzhJao9Z+/m/eRavwipgEJRwt9MCAAAAsqBKu37sQFiQKVOm2AVJr7wmOxdWkdmr999/3ySFqoWHNeBSQvj06dONC00aagfBtFIlZ5dra//993d6/x9++CGnv3dZhFJlRK/JlZtToILXyR4tjkydOjWrv/Vakfmpp56yE1JJn6SAP7jO432d5zPXpEuvFe/9OvbzzjsvksBbWb16tYkS7Yo3+dKuJPnaznV3xSVLliR+LKnzKtsx8ccff2xcnHDCCc4BTEomVFI33KjQgwogBEFjNu3akfRrIw0BkPwW7piTjO+cpPriLn0O1wD5XOezXIrGZqjQSpx2N8+3OboweZ0P1GdRMvCMGTN8PyZ4k6/XiYoneQ14VCJNrslE48aNy7k4cIaKaed6P3H5XZXo5BLkvqk999zT7LHHHp6eq/56Gvq7aRbmWiqCVapUKVu4Uf1XJSo+8sgj5rnnnjNDhgwxI0aMsLusP/nkkzYp+vTTT7fXdhC7C2tzhDApqUGJiUruUTL5o48+avr3728T0LXT8YsvvmiT6rVz5Mknn2x22203349B8129evXy5bWUDK94ARWJufLKKz1fo/p91a9XoUAlgBbnp59+Mj179iz278466yxTu3btrI5h1qxZ9hjq1atnE6eeeeYZm8i8aNEis3btWpsUqkJQ6gOoPVGsxeDBg+2683HHHWePW7u651pkJ630neyzzz7Or9OyZUun52uemvY/+PltFQhVYQsXKqCqApFhU8KmrnEXatNd4/b0/bkUWvXKpe+rsZRLIbTNqUCKV/Thg+O1qMNOO+1k4jY/pHu6igB++OGHTms1KLotc9n8yXVuOtdYEdf7n1x00UXOr6Fdx70mrCYRcenJttdee9kCZ35vMoj8bo9Z+3HvP7kU4oqD7EstAgAAACHQTnzdu3f3fee3jGyqkhdGx+Qymb65vffe22kgokW4ihUrmrjzGhzmpTKdFp0ViOA66ZjWCUNNAhx11FFOr+F6zuUSTLd06VKnJEWXhI3N6ZxQwtGyZcs8PV/Bs9lUzNf367XwhHY8GDZsmJ2cUlulgEDtyqa2hgqg+YPrPP7XeT5z6RNEvdv8qaee6rSjgs4tPd9rYJvXYPS4tCteg8oz6D/EW5Kv7bDGkn73TV3GkgrEnT17drHHox0YXAsqqO30g3b+UkAxvFFSkpIB4nhtaIHWz52mXK6NOXPm2F1lkxyMwW/hhjnJeM9JKqHp/7F3F+BSVe3fxxchoSgKGCCKSgoKAiJiIYIBoojYHYhiIxaKhS1idws2iIGthAoooJJKGiASEkoJKOB7/dbzjv/D4cyemR0zO76f69rX88ic2bNndq291rrv2wu3Vc3ctse9thsUBKKqLmGRxD66fPJyPn377bd2YrYCwhQ0veeee5rddtvN3hO9JtdCbpJ+nhx33HFm1KhRrj5vyJAhOX2e/t7LhPFcq6Qp4Mmtli1bGr8nELutIq39qusFwimfY6nw/zlC92D1nyooWvfhbOc5KBhSNC44YMAAGyDudZw7RQlYNG6uBAJBULX4Aw880H53Xet0fcrW2WefbQPKlOhK39trJezi94jLLrvMUxW3ohSs36NHD7vOiRMn2t9V/WoKmF6wYIFZvXq17WtTX4ISbulz1ZbXWG2rVq3M7rvvntPn3XfffeaPP/5w/Jutttoqq0rvul/ccccd9rhyS/tJ1aq1KIG8ElH4fW+LGr/6O3XuaF96CarWsRiF+UKFouNX/X1e+6X9cNhhh5lbb701r8kvpk6d6un9uq5pu/3q0/HaT5IrL5+n/mq/EqpJLvfI4vL9uyWJ7uFu235B8XJNnzlzpunatatNjKR2qfqH6tSpY9sl+rcgCmclSdT6pr30Y0iTJk18SS6muYpKnvjxxx+bJGBeenTpGqlnp80337zQm4KYXY8Z+/kfL/EYbttsYUHQPgAAAEJFWcEVZBoUDU67pe3yM/DVa/ZVDQwHMQinSUjqzJ0xY4aZPXu2HdDVoPOyZcvsoOGKFStstvVU1vUgKSP+mjVrsu709lpVSJMoTjrpJBNXhx56qOegKrfZ/4oeX7kc4174PRFF56zbiY7ZfpeGDRvaihNeBp8nT55sl6KqVq1qJ+tqQEb/q0UDNLquqVoE4oPzPPzneZTomqLfY/r06eann36ywTlqFyiQU20CtQ0U3JZqEwSduT3X6pZhmoilQR5N8NBv54Z+5yhfV7y2e2k/+ItzO//PkhrgVdsrTM+SmYKCpk2b5ukz1Mb0KyBOE501qYhABHfOPPPMQKu/eGkDaeKYn7x8T91rddwHFdCQD+wLb+iTzO1Yy2dggPow1F8Slfao+lEXLlzo6fPUPxlUUls3kthHl09ej+9UwLgqompJUeBYqh9Qi/oBFUhYt25dWwUY/kr6edKuXTs7dqAKwblS9eFbbrklq3Eg9UV98cUXxg0FUirANNeJ7ur3ikN7N4zXvyiL8lgqvFMgo4JPNFlc1xWv91VVTNcybtw4c8011/hWPfvVV1/19f6i/i0FcHbs2NEmKPDSZ6z+cv2GWhRUrmSDmYLVs6FzbtCgQebiiy82flLbvGnTpnYJsu9x4MCBGf/u8ssvt+O+ToYOHWp69eplr0l+0fXurLPOMt26dbPbkMTgPx0Hbdq08e06ogqR7733nut1qB/r8MMP92V74kjJNXSP9MKv/a1zVtfjfAZge+3fb9asmU3Q4gf9jn369DH5ouQIXhI2BPFs5pbaBBofz3TdR+7tBbfPeUG2uXNN9lMSjaO99dZbdim6zUoeUXS+mPqIGjRoQAGYPM0V8do3netcEc3v8aJ169bGL4ccckhigvaZlx5dmtPgZzE/BCdq12PGfry3n7w+0xUaM/MBAAAQKkFXefjxxx89vbd+/fomLLx8l5KycGti1JgxY+z/9zIJyW+5TDT5+eefPX2WJixWqVLFxJUfg0ua7OlFLpOTvB7juVbNCVK238XPikpFaSBPiyohFJ8QoON+r732shNNVB1BAVGILs7zeNyXC0nXCk2SGz16tK0a42aydVByCZTxU5kyZWzAqB/3ULdB+4Vsm3Fdicd1hXO7sPtDA0l+TLbJ53fx+mylyT9+Tl7SszhB+7nTBPouXbqYoOj+pCBBtxRwFaZ+llmzZkU2aJ994R19kuF99vEj4DGf/Vle76EStuqVSeyjyydV8XMb7Oxk1apVNuC4eHUtBRtpsrbueeoPVH+knwmmkirp54n6mTt16mRefPFFV8fqZ599ZoNAM/nwww9d95F07tzZ9vHkc7+qOrOWMPjll1/s/Ywkvu7FZSwV7tWuXdsmV1LCL1XI9puCBN59911bid5LwEeKrq0KkvQaXK/2+Mknn2w6dOgQSOIfJQJQNc9TTz3VUx9D0XuF30H7QduwYYO5+eab7f86adSokd0XTpTEKZt1uaF1PvHEE3Y/9e3bN+f7atSpDe312bIo9Yl5Cdr349kzzrz+PkpU4qVCekn7O59B+16vp36OZ6iiqe6bSmyUD/PmzfNUkVNJb7SEqc+aoH1/eTk+gmxzq29I1wqvAdclPSso+Y6W4pSYVW099Q+pLargwiQm5nGiOXTbb7+9p3WoDa3+OLfts1yfPcN0D1ByiKRgXnp0BT0+iORejxn78d5+8tJuCwOC9gEAABAaekDxI7NYOpqMEqdJ/hpo8GLlypU20/wrr7xiq0CEVS4Bel4rD6iKQZz50RHqdTJGLlWYvR7jYaIKa8ronWnygq6BCnDSBLp8UPZHZRnX8vLLL/93nLRt29ZOklT1LUQL53n4z/OwGjVqlHnhhRfs/+p7hJEGlAtBmebLly/veT1RrWTox3VFk5u8oP3g/rrCue2fOLVNs3lu8loh2O/ALyWa+vLLL31dZ1IqAwQ5oU+VJMPabkjaec6+8IY+yXCfK35MEsxnf5bbytYplSpVCt1kxihfH6Pw7K4AWgWrvfHGG3n5PE1IU9+jFgUGSvXq1W2lW/UHNm/enAnaLnCe/C/xgJugfRkyZEhWQfv6OzcUdHDccccler/q2qdroIIykOyxVLgPOPzggw8C//nUFnzqqafMKaecYn766SdP61Jw5Lfffmv23XdfT2PnOgeCpon3zz//vE2KsHjxYs/BjQpU2mmnnUxUKDg0U1VoBRYoGF//m46O0Ztuuimn5xc33n//fdvnf9ttt5kk8TvxpNf+U6/9t3Hn9fdRohY/A1fVvx2l7+/38a5+DiWLzwev88bCJk7PJGHhJfjLjzkDTpQkye+g/Uzni5aPPvrI/rcKHR100EHmyCOPNPvttx9J33wMYFf/tJIWBm3ZsmWe59X4eQ/QvEMlEQtT0rsgMC89upQwpU2bNoXeDMTweiyM/XgP2i/UXFG/pO/BAQAAAPKsWbNmga5fAftxmkw+f/58V+/Tb6DJJQrKvfPOO0M9ySTXSlaapO5F2CbE+k0Di155qcaQr2M8rMfxokWLsvrb008/3RTStGnTzKOPPmrat29vt2XYsGEF3R7khvM8Gud5mKgqlCbCnXPOObbabZjbSoXaNj/Oq3zfQ8P2/fNZxS2p7YfiOLf9D2yK0yTIbJ6bvCa8q1WrlvGTn1WNkiTofhavz+BhE+V7CPvCG/okw32uRK096vV81ER+p0CcQojy9TEqz+6nnXZawfexgsVU4faII46wyT2V7BO5/YZJP090/VKlZDdGjhxpli5dmjFYRcGnbrRq1crUrFkz0fs1jt8nSHEeS4U7+WyfbbPNNub222/3ZV3fffddZL63gux79eoViu+dT0uWLDEPPPBAVslxGjdu7FjZU79f0AH7KQMHDrRLkvjdP+m1/zROBUuCkPT+ba8JUHbYYQfjJ69VUXNBPymCFPR99oQTTgg8MYATPZe//fbb5rzzzjOHHHKIeeKJJ2wisySL2lwRr9d/v+8B5cqVs8kg4o556dG15557kqAkIqJ2PU5h7Md4aj9prlaUhWu0FwAAAImmB+AgrVixwsSJm++jjrmzzjrL3HLLLebPP/80UZDLA5vXDHjKnBhXmpy8xRZb+LKefIlbx3+256wGYXbZZRcTBsp23r17dzth12tFDwSP87zwotTW0P1VFXt0zRk/fryJgnxNNituyy23TGzQvl/XlXx+96S2H1I4t4Px119/hTqpSRDHldd7mh/XjuKV5pA7+lni25aL07aH4ftwroR7//jRHs9nezSO/ZNJb2PngypYde7c2YTBL7/8Yvr06WOD90eMGFHozYkMzpP/Cyh0G3D84YcfOv7Ne++957pvyO12xW2/xu37BCXuY6mIBiUW073Yq0mTJpko6dixo2NgerYmT55souKee+4xy5cvz5jI4YorrnD8m+uuuy6nCnSqovv000+b0aNHmwkTJpghQ4bYMWFVHczWHXfckajAcb/7J72uj/u6s6T3b3upJB717x/GZ34v4vZ9wsBLtde1a9eaICm4WQHzYaBk5vfff7859NBDzeDBg01SRW2uiNfrv9qCZcqUMX5Kwhhv3K7Vcfs+hRwfRHKvxymM/RhP7adc+ijCiKB9AAAAhIYeToKUyyBlFOT6fX777Tdz4okn2iDcuPLa8Vi5cmUTV34NqvndMRvk/gybbL+Pssz27dvX/m9YfPPNN6ZLly5m6NChhd4UOOA8L7yoXLc0ibN3796mX79+sQpCDYpfg4hhq5aZDa4r0bqucG4HJ4nPkl4nHvk9qW/zzTf3dX1JQT9Lcs71KG97GL4P50q4948f7fEo9WeFMWg/Ks+6Uf8+qoya7+qLmcYTzj///KwqvyK8x1W+v0+HDh1ct93fffddx9cVTOj2utquXTtX72W/Jk8SxlIRHccee6zndcyZM8dEzTHHHON5HbNnzzZRoPFXVbPN5Morr3R8Tvjggw9s4H22evbsaQP2FbhftWpVOwm+Xr165vLLLzdvvPGG2XbbbbNOdPrII4+YpPC7v9Pr+uLWF+S3pPdve/3+YUtSkeQ2POe6/7wEf+Vjf6gvZq+99jJhsXTpUttnpSWJc0uiNlfE6zEaxPU6CUH7cbtWx+37FHJ8EMm9HheV9LGf1R7a5wTtAwAAAD4JekLi33//bZLaOaIs2+ecc46ZO3euibN//vnH0/vjHAji13crVaqUyZe4nbO5DIyqioQyFoepKrMmXlx22WVm1KhRhd4UpMF5XnhBZ1b3y7333msGDRpU6M2IjCieW36JYtsoye0Hzu3gJPFZ0ut39lKpJC7Xo0LTfYd+luRMQknidcpPnCvh3j9+TJTPZ1s8jv2TcbvGhPXZXQlln3rqKbPDDjuYMHn88cfNww8/XOjNCD3Ok/+7Zitw3w0FG6YLLp02bZqZMWOGq/V26tTJdYJc9muyJGUsFdGx7777ep6cPG/ePBM1bdq08WUCetitW7fO3HLLLRn/rmnTpjaxupPnnnsup6QI3bp1S/u6gvfvu+++rJ+h3nrrLRvMlwR+93d6Pb/z2U7ZsGFD5J7F6N8OVzBTPpMpxq0NH+U+67DS86HbYzIf+0Pb99hjj9l7cpgMHjzYXH/99SZpkjZXJIjtjGJxi1xx74muMCZRjlOb3E9Rvh4nfexnjYf2E0H7AAAAgE+23HLLQH/LMFWt9oMqaWbrxhtvNL/88ouJO6/7WBNy4sqvDtB8dqTG7ZzNteNM1X9efPFFU716dRMWmnh+xRVXmCVLlhR6U1ACzvP4dJAH6YsvvjDPPvtsoTcjkedWPiekJPm7J7X9wLkdrCQ+S3pNHuX3YC8T09wNXAf9/JbEcyOs2Bfe0CcZ7nPFjwk0UerPWrFihQmbuF1jwvzsvuuuu9rqpnvvvbcJk0cffZREnhlwnvyf4447zvWx9u677+b070FvD/s1WZIyloroKF++vNltt908VzOLWsXhGjVqeA6c+OOPP0zY9e/fP2NCGvWr33zzzY7PRNOnTzeTJ0/OOuj8yiuvzPh3++yzjzn00EOzDlAaMmSISQK/g7G89nfmM/m/X89Q+Xze9fr7+N0fne/+bd1DvBZ08FM+933c2vBR7rOOYyKWfJ3LVatWNS+//LI57LDDTJgoWc/AgQNNkkRtvkTYrv9xn4+bwr0nuoIeH0x6mzzJ1+Pikjz2s9bD3CGC9gEAAACfVKpUKVKZr6Ni3Lhx5v333zdJ4HUf//nnn75tC7yL+gO3H5o3b27ee+89c95554Wm0prOk/vvv7/Qm4GY4DzPf+KN22+/ncF9xFoSryuc28FL4rOk10kdq1at8m1bglhfEgTdx5LUcyOs2Bfe0CeJMLVHly1bZsImiW3sQtp+++3NgAEDzG233Wb/f1iCBG699dZQJzwoNM6TjSsC161b19XvWFLQn44/t2NcTZo08VSdkP2aHEkaS0W0KGDLq6gF7fvxvcP+nRcuXJhVNbtTTz3VNGjQwPFvPvvss6w/t3379mbbbbfN6m9PP/30rNc7bNgwkwRh6+/MZ1+QxiCiFrQXtqDFfPdvez0+wna854I2PLKR7f2wuHwmBtpqq61se+GRRx6xgX5hce+99yYiCDuqvF4DgwjaT8IYL2N00ZWP8fQkt8mxsaSO/SzxUCDObZstLMoWegMAAACAlLJlg22eRukB209PPPGEL+vZZpttTJs2bUzLli1tVn9luVcQsTr7nLK7pzqQ85X50EvgfRSy7ifJFltsUehNCAVdu1T1oGvXrjbbojIX//TTTwXdprfffttcdtllke8UQeFxnufXxx9/7Eu1KLXZWrRoYQ4++GA78blWrVr2Hqz96ZSRdu7cuaZt27aePx9wksTrCud28PTco+vb+vXrTVJ4PZf8noARxCSRuAu6jyXJ/SxhxL7whj5JhOl8DGP/ZBLb2GGoGnP88cebTp06mQ8++MC8+eabNqC0kBX2fv75ZxsUFrYqb2HBebJpdfs777wz599R/VaTJk0yjRs3/u/fxo4daxYsWOBqv+g88oI2VnIkaSwV0aJgrUJWNIvq9w77d9Y9MlNfl8ZgNRabyTfffJP15+r6lK1mzZrZ/bB8+fKMfzthwgSzbt26vPRFFVLYgpjzmfD/77//9i1hRb4kvX9byU+WLl0amr6JxYsXm3yhDY9sqJ3uZt7E77//nvcf+NBDD7VzLJQkR3PGVA1X991C0bzMgQMHmrPPPrtg24D0qlSp4unnUdCm9vHWW2/ty8+s9Xm5H0UF957oKlTV9aS0ybGpJI79LPRwzKnNFmXx7iUBAAAAithuu+0SF2gxb94821nrNROkBoSVyd1rNuqgVa9e3fz666+u3z958mRftwfe9yf+jzrEu3XrZpfp06eb4cOHm9GjR9tJjPmuGKHMnR9++KE544wz2EXwhPM8vwYNGuR5Heqgveqqq8zOO+/syzYBfkvidYVzOz8DZ3qenD9/vkmKatWqeX4W9dNvv/3m6/rgjyRec8OKfRFuSeyTTDKvE0jU56PJV+XKlTNhwTWmcHQcHHPMMXbRxCb1B44cOdJ8++23BZnw+u677xK0nwbnycaOPvpoW33PTQUoHWdFg/aHDBli3AaydejQwXixww47eHo/oiFpY6mIlmXLlnleRxSPSa/fO8yVLjW2qzHWTK655pqsgn++//77rD+7adOmWf+tAvB1P1bbM5M1a9aYWbNmmQYNGpg4U2LqMPV35jO5vh9zERQA6HefcZC/T9T7t1W5c+bMma7fr/f6GbSjvo58oQ2PbOy4446uEwMpqYWSdeV7nLJdu3Z20ed//vnn5ssvv7TJe9wmufNCz+0E7Yc3aF/9iV6Ce9Wu23vvvX3ZHiXHUFsx7hgDQj5EsU2O9JI09rPQQ9C+2zZbWBC0DwAAgMTYbLPNPAVadOzY0fTr189EiTpovWRg06SmF198caMJUrlasWKFyZeaNWvaqitu6YEX4eHlgVsDFuPHjw/1pAwv6tevb5cLLrjAZlDWoOkPP/xg/1cd3rNnz7YTB/zKsJnu+kLQPrziPM8fVXDIpeJKSbp27WoD9t1auXKlp88HspG06wrndn6PLbfPkpoQ+9prr5ko0aQ+L7xMCCzJjBkzfF0f/JsA6SUQ+fzzzzdXXHEFu4N9EXtJ7JNMMvVPeqHJv0os2rx5cxMWSWtjh7l9dtJJJ9lF1P+n/kAFP6gSypw5c+wS5LO3glp1349SBaJ84TzZdJK4KgF+9NFHOf+WCmTs1auXPc7Uv/3xxx+72icK2PdaYdXrpEB9/1133dXTOhC8pI2lIlr8mKidz2rcYfneFStWNGGk+1qfPn0y/l3Lli3NUUcdlVVyg2wTHOieqOfSXOyyyy5ZBe2LxqXjHrTvd/+k1/5Tr/23+a46P3XqVJNPXn8fBSxGuX/bazvWz+1dsmSJWbRokYlKv4yu0yeeeKJv24P4Jd1U4Fm+g/aL0mengvxEfc5K4qP+oZ9++sn2F6ngkQIzg6L+qMWLF3tOAI7g7gHqK3RLx5JfQfvTpk0zScAYEPIhim1yZCfuYz8LPQTte02UXmgE7QMAACBRdtttN9cTZKMYJOA1OE+BeV4mmUg+s77VqVPH0/tTgc5eB3Hg3/nq1oYNG+xA6h577BH73aFKB7vvvrtditIkM3V46JjWsZ3qzNHEbz8GZyZNmuR5HQDnef5MnDjRVXWzlL322sv07NnT0zYUIhMskidp1xXO7fweW26fr/QsqbZZqVKlTFRoUq4Xmhik5FJqq/rB7yQA8G8Syk477WQThyWlnyWs2Bfhl7Q+ySSrXbu2L9U3wxS0n7Q2dlTUqlXLLu3bt9/o31VxTf2BmsSle7SuIeoP9KOyo5KG/fjjj6ZevXqe1xU3nCebOv74410F7WvivyYJHnTQQWbEiBFm+fLlrj+/kPtVdP4RtB9+SRtLRXSoIqXuu14D9qOWPEntGK/jmFWrVjVh9Nxzz2UMoNLz/U033eT7xHc3ladzeY+XSfhRoX2nxAuqiOgHr8/6ehbIhfpp1V9bqPvUsGHDTD7l+vsUp4QYOq79So6Q7/5tFYDw4rvvvrPP80rE51W2yT/8Ur16dXvvc1vZmX64ZPAyL1DXhjAlqtExr6Vdu3Yb/bsC+xS8n5ovpv459Q95CeYuSutq06aNL+uC//cAL/tZz8innnqqL9ui/p2kYAwI2UhamxzuxG3sZ6HL/gI9e+eafDBsvD9NAQAAABHiZdKEOrNWr15tokSBEm5VqlTJdOnSxfM2+NXZm21AoVf9+/f3ZVvgXZMmTTy9XwHqSaagME2mUPZbncuqYPnMM8+Yr776yrz66qu2Up8XmjCTbfUGIB3O82i0CeT000/3PDEjn20CJFfSriuc29F4llTWc02IiRKvk/pUJXjcuHG+bIsGHDWxCPE7N6J2zQ079kW4Ja1PMslUXdprcMDrr79ug0HCImlt7KhTxbU999zTHHnkkeaiiy4yDz74oJ2Mp+rl559/vudqu1Fr1+YL58mm9ttvP9cVPocMGbLR/+ZKkwv9GC9q1KiRDZ50i+tfNCRtLBXRofE8twGHKTvvvLOJGiVs8UoJBsNGE+ufeOKJjH931llnZZ0ILJfnRDdtwFzeown+cafE2H4FninBq9dA5lz7b70kG/j9999tALdbqtj4wQcfmHxS4iSvCRa++OILX7ZFgbMKgs+nhg0benq/9vm3337ry7a89957Jt/BcGrHu6WK5Yg/L2NgXsdm80XPKir+csQRR9j+oL59+9rEemprqVjD1ltv7Wn99A+Fl9d7gI4Rr88hov7tTz75xCQFY0DIRtLa5PBXVMd+fnLZdlJ7LUqFUUpC0D4AAAASpWnTpp4G4T777DMTJfPmzXP9Xj3clS9f3nNQbz4nJalik9eBt4EDBxKIHBKaSOKlEoKbKj5JoKDbZs2amX79+pnbbrvN07qiVjXBS8BxmCbLxwnnef54zaqqBCBeJSmLNgonadcVzu1oPEtG8dhSVQ4N+oXhO0ftt0saL8FQep7I94TVOGNfhFvS+iSTzmu7YdGiReadd94xYZG0Nnacqz0pqecrr7ziafJW1PoD84XzpOS+2GOPPdbV76n7no41t4GbfgRRi8bIvFRQ5PoXDUkbS/V77ERt1ThTIsFCefPNN30JWo3S91Yg8+DBgwv2vYN0++23Zwyyr1GjhrnwwgsDOf/cXKtymXeRlHFUBUP4QcHQCrrxIteg5C222ML1Z2n/egngUHDQ4sWLTb4Dt71Wifz444992Zbhw4fn/RzR3CmvQTtvv/225+1QMt7Ro0ebKPWTTpw40SxYsMBEWZkyZTy9P+7tO6lTp46pWLGiq/eqqm2UafyvW7du5q233jLbbrut6/XQPxRe++yzj6f3KxmTH/dAtZuWL19ukoIxIGQjaW1y5EeYx37+/vtv131MeqaJOoL2AQAAkLhOKbedrn4NSuSTKjq65WUiZoo68Lxk98uVBpv3339/zx2PXgOZ4Z+DDjrIUwUKBgmcHX/88Z46jaNWNcFLUo8VK1b4ui34P5zn4W8TpKpFep18qusykA9Juq5wbud3Ao/bKpGiwDtNPI6Sfffd19P7FaCiKkJerFu3zk4cQni1bt3a0/uj1s8SZuyLcEtan2TSHXLIIZ7Xcf/994eqXZqkNnbcqcra6aefnpj+wHziPCk5eN5NMLCOs2uuucZVcJP6gDt16mTC0MbShMQJEyb4ti0IRtLGUv0eO1m2bJmJs3vuucdWPp82bVpeP3fcuHHm008/9bye5s2bu3rf5Zdfbi677DIbbJlP6j/zo8Kx2+8dFAXsqvpdJtddd11OE+xzOXfXrl2b9d+6eY/XBCZRofNyyZIlntfzxhtveHq/gixr166d13vWmDFjXN9nVd05iv3bX3/9tZk7d67n7VDhkHzbbLPNTMuWLT2tQ/3yXoOT77rrLtvHn29e2vBql7377rsmyrwWu0lCkK0SG7hN0Bb1oP2iyYIuueSSgo1RI9iK71tttZWndah6s5v2Y9F+nfvuu88kCWNAyEYS2+RI9tjPjz/+6Pp5gKB9AAAAIGI0OdbLxKmRI0fagZlCUXWRZ599Ni/Zb712rupB66mnnjL5dsQRR3hehwZgvA6UhrUqQ9R42Z8aTCtkB/CcOXPM1VdfbcLOS9B+IQZYvfCSSXLNmjV2n8J/nOf54TUjvtd2wQsvvMDEfuRNkq4rnNv5dfjhh7t+708//eRLpTC3xo4da4P/cuE1IZoStjzxxBOe1vHaa6/x/BRyNWvW9DRgqvNC50chrF+/3n6+H9ULw4B9EW5J65NMOk0O91rRToEgPXv2tNeqIKifI5dA+iS1sZMgSf2B+cR5UnIFP7fPFW6TP7Zr185ss802JixjTv369TOFsmjRIpukmsQpzpI4llqcl3bLzJkzTdzpetS5c2fbNtNk46AtXbrU9O7du6BVNpV4UskYO3ToYG655Za8VBzW+Pndd9/teT1KFrP33nubsFC7O5uCAXpePPTQQwOr0rh69WoT5IR9LxUjo0S/yQMPPOBpHT/88IMZMmSIp3Xst99+Ob9nhx128PSZmj/jJiltIdsiXvu31Ubw2pZT0g63wVVe5XpNKU79EboHuEmklarmqv6kQmjRooWpVq2a6/erD0z340JQkKzG1XXsuOW1TyouQemZuB3XUHuw0Em3wtA/FFSfJfxJSuE1qexvv/1mHnroIU/Jz/LxDBEmjAEhG0lskyPZYz/Tp093/d4999zTRF3u6YwBAACAiDvmmGM8vf/mm292Najplh6yNZChiiQXXXRRTpMfKlSoUJCHJXnyySd9yTrtZuDJa7ZQ6dOnj+9BNRrUe+aZZ8ypp57q63rjTAPOyhTvpRJdvis7a1KJJtO0b9/eVoTIhQb+dL7nswqrlwlpXgf78s1rpXBNUIL/knaeF4qXNoF4qSSkgesXX3zR0+cDuUjSdYVzO7+8VmtU9vLFixebfNKEwLPPPttm1P7uu+9yem/btm1N2bJlPX1+//79zZQpU1y9V9XcHn74YU+fj/D3s+h55KabbsrrBC99pq71Rx55pOnVq5eZN2+eiQv2RbglqU8y6dRG0zXGjwqrl156qe8J0NQmUODZ5MmTs35PktrYhaAksvk8v71Mvopaf2A+cZ6U7Pjjj8/rfvD78+rWrWsaNWrkKYHaoEGDTD5pEq4CXzVeNmDAAM8J/+IuiWOpfo6dTJw4MRHBGArMeu+990zHjh3NFVdcYSZNmhTI56iybdeuXX1JYLjrrrua+vXre1qHAjVfeeUVez258cYbA0taMH/+fHPOOef4EiCp+7EfcwX8ks15rir1N9xwQ87r3n777bP+WzfnaS5BHV6DT6JE9/VRo0a5TuKgc8lrsKebpEK6Jnjx/fff5xy8p4C9QiayVeC212ROH374oevg6T/++MPccccdppBJiBVA6MU333xjrrzyypyP2dGjRxc0eZ4SuBx11FGeEhLfddddJp/UJ/DSSy/Ze+6dd95pj59CzYv55JNP8jpnqVCaNGniel/Nnj3bBEFzknT8RaF/yOv1BeEeixDNcdU4b64eeeQR8+qrr5okYgwImSSxTZ50SR/7me6y71TbUqdOHRN1BO0DAAAgcdq0aWN22WUX1+//+eefzQUXXGAH1IKk6gzqwNKAmybGugl08NIRr0nrmkzktvqXOuAKQVncTzrpJM/r0SQmTd7XxCav+1oDWOpYP/roo22gjjIjIzvlypXznOTgkksuCWzyTFE6Xy688EI7EXjgwIGuOkBWrFhhz3etQ5Ng9N9ByzV4y8/BvnzzOmFElWLzcSwlTdLO80Lxer6qE9mNlStX2v3jd5AJ4CRJ1xXO7fxq0KCBqypGKZpkpQD6oCu0aEK1qjUde+yx5owzzrAT5NzQ8XXAAQd42hY9+3Tr1i3nCUyqLnzuuefmdXIS3NOxVrlyZU/XwmuuuSbw6jQ6B59++mmbkEKfp/6duGFfhFuS+iRh7D2/VKlSnn8KJU445ZRTfAncUpCVKsSefPLJ5qeffsrpvUlqYxfCvffeaw4++GBz//3320pWYe4P9LOCedxwnpRM1d3y1Y9cs2ZN06pVK9/Xe9ZZZ3lOvOOlUmYuE3fVzlV797nnnsvrhNAoS+JYqp9jJ7rPXnfddbYfOAn03Pr+++/bBCEaj37zzTd9++46lpQwUudyGJJPFu9rev31121iKvXXKIjVr/Hujz/+2Aa3+NUG8vN7e6X+MAU7ZaK+s5133jnn9VeqVCnra5iO00WLFuW0/lyeQdxsf5SvAxdffHHOzza6Xl5++eU5JU9L1x4/8MADc35fvXr1jFePP/64TbKaKfmmkkTouFa18kLabLPNTIcOHTytQ4HLPXr0MBMmTMjpfWqHqQ9nzpw5plB0jfASuF70On3aaadllbhF86zUvtH+L3TiKCUy9pKUWMn/7rvvPhM0nS/qC1CfwK233upLFVxdJ5QQxi3ta21TXKrJp6NnR7d9d+PHjzdBUCEE9SGrIvKsWbNM0JI0Xyxp9t13X8/BwXL77bfb571ly5ZlNbartk6SE7IzBoRMktgmT7qkj/1853J7dB8rU6aMiTqC9gEAAJA46nA977zzPK3j66+/tpN0/JgsWZQ6vDUgr2BxBUdoIo+Xz/AyETjV8aaJurlm3NWE3kJ23p955pm+ZX3TxKZ27drZjvFcAw6VNf+pp56yE0Qvu+yynCfD4n80kXjLLbd0/XMo8F3VGRS45Dd1pKhCwmGHHWYH/YYOHerLsa+J+LfccosdcNcku88//9xOiPGbqpy5nfyjcyyXCg5h4HVAQtdDTcLSxO+XX37ZBsDp99OgpdMSdGBeHCTxPM83r20CXS9UrTkXv//+u/3Ngqq+AzhJynWFczv/NLDpxYwZM+wEN6+TM0uioEpN5FEbUtVv/JjkrSBBrzRJQ5PZ33rrraz+ftiwYXaydlCVShBM8jyvgZy63qqdrfaDnzQ5VM9TeibXuaGBcT8mPoYV+yLcktQnCWNq165t23t+mDp1qg000P7JNeGI+jOVTFRtGFWGVYVYt5LSxi4UJStSwkj1RSsYTm0nVdv128yZM81rr73m+v1+TPiNM86TkoOl/Kjwlm0CIz8SphSna/BOO+3kqU2qxCUaZ/L72rR48WJbkVO/sb6/+vAKHSAVNUkdS/Xz2q6K06rmqz4JJX/VhNxp06ZlHDsJYtwrnxSspQCW/fff3z5z6vxTH4ibKsZ6v8a3lQjCDxrDO/HEE43fFLyqhBMKxtFzw7XXXmsD+HNNQq716FldbZ5LL73Ut6SNNWrUcFWBPCh9+vTJeJwr2N3Lc2KjRo0CCTBUAEi2/Zc63pLWRtRzlp47lJQhU7CM6Jp4wgkn+JLER/2sal/lqnnz5p4/W+euArI7d+5sBgwYYK/lSgihRc+qSjqnRHE6D3WOh4HG9r22DxWAr+S4CnjKZn/rPqjfKNdA/yDo+uJHsMu3335rk6Ko/1b3OwUT69qta5zuXbqXqYCJnskVRBaG9uiOO+5ok814oX6G66+/3vfkRDqmdP/U/lGAp/oC/ExgrGO+Vq1anr+7CtNof3766ac2UYnGt5zadlEb06lWrZpp2LChq/eOGzfOBHmP0TVWx6+uYSr4ouc+v2ksRnMaC/UchWDpOuB1TDtFicoOPfRQ07t3bzNixAh7ruu6qGNV/1//pn5r3QN0bUsyxoCQSVLb5EmX1LGf1atXu56zdNBBB5k4cJ9CDAAAAIgwTVxRxSgvlaI0qKkOalVNUidp9erVXVd6U2ful19+aScTuhnMT6dx48Z24NwtDR7qIbFfv352QMOJMmo++uijtiOg0JNM1LGuCVB33323L+tT1vk77rjDDjI1bdr0v2ykW2+9tc0up0EuVTnT32mCqCbS6viIY9W+QtDvrAkbmvjkliaLKHBJ2bC7du1qWrZs6WpwVoOLmqCgSfLq4PKr2oVTx4UGPbVstdVWduKPqrvqGPRSsUDrff75581jjz3meh177LFHIBMgg1SnTh07icHLILEG4nWt1pItDbbq+EN6ST7P86VJkyae3q97uyZi6H6oCZiZqNqQ7sNxDopDuCXlusK5XZjKG5qYoElKbmngVBOn9RypQGcF9bmhgVdNltMzn44tvyZ2F9W6dWub8V2TsbzQ86ImcqsNqolG+h2VAErPUxqQ1OQgBYsqqDCoCiUIltq8GmCeP3++63Vo4rKOj/PPP9/227it1KL2h44nTUTQZKFcgweijn0Rbknpk8T/6N73xRdf+FLpWP0RgwcPtosmxepeqr5fXSt1P1WgjCbL676qNoEm0Os40/Hi12T5pLSxC03P32rfaVE/VosWLWx/oPZ5gwYNXFcK1GQ+tbUUPJZrctqUChUqmPr167t6b1JwnpTsuOOOswmSg6Rxmi5dugSybp13moCuvjG3dN1Sv5r6zFR1Vc9abgKodI3QGJSSa+r6p+t8ocfkoi6pY6lF6f7ilQKJ9L1y8cEHH7juEwkTjRHrHqslFSS35557mrp169qEH9ttt51NsKZzXm2hVHtNwZwK7FQSeL8pmDjo6qf6HuoH0FK6dGk7BqfvrX2q763xerVR9Zvoe+v4//XXX+11S9/b76R9outruXLlTBjoeMjm2qLgJy/VkPfZZx/7TJcN9ZFkm1hM+ymbiqqy995722MgaXTua/6InvHVn6WgAiWO0LGv9rbmjug5RcFrOhbWrVvn+TN1rCjBhxu6Nuk+5Ud1x+nTp9tELVGgvm1VtvSaMGHt2rXmnnvusUEwSuiktpz6Y6pWrWr76bW/J06caPe3nlPDcp/XnBIFdA0aNMjzutSezXWeRKEpYE1jOW6fgUW/na6zehZQMsRKlSq5Wo/ugWrD61qsa4IffUVOdt99d8/jOgr80pItXf+UzClKlOjXTZ+SElXkg+7HWm699Vaz11572Tlj6h9Sm8tLm+err76ybRAvczm8jlEjeBpDUGIGP+avql04cOBAu8AZY0BwktQ2OZI59jPew/gkQfsAAABAhGnQ8KabbrJVT7wMjmlgRhnQ1MGlIFplwlMgqx6uFWCrykP6LA3YqcqCBp/V4anOMD00ayKHJlDqoSkIynDvJSg39eCkQUYthxxyiM0yq4mgooEnVY/XoMzHH3/sa+Zfr5TpWlWbfvjhB9/WqQdITfzXgvxSMJOXqvApGkzTosFJPdjrfFWHh45pna8VK1a057UGyDR5fcGCBbaTTOerFn2+lwE9r5NfNMibykqrCaDafk360TUnNQFGk3A0+UcDNJoApO+j648mAOl81YR8DUxmO9HCafAqajSRQYNXmgyE8OE8D5auEbpWeMlwr0knCtBQe0fVuzQwrAkpunbqmqkgvdGjR9vrlNo4QKEl4brCuV0YqqKm612ulfSKB969/PLLdlFiME2w1bGlic2pZ0kN1OlZUseWnr10bOk6njq2dK3NprKPV6r2pgqAfkhtO+JHE/FVdejiiy/2/NyjCc8PPPCA7dPQ+aFzQ9c7TYhMVXjWdVdtE/Wx6NxQMgwdWwpgilpFH7+xL8ItKX2S+B8FbOgZyq/Eoim//PKLXRQckm9JaGOHifqj1e7Ukpo4pYn3mjylpLK6P2677ba2T1DnvvoD1YZUAgf9djrv58yZY/vC1Ifv9R6pSWRegsqSgvNkU3rOadasWaD9smo77rDDDoGtv23btnbxGqSkYK7u3bvbIF4FkKm/Wue1Ar50HqeSsOgenrr+qc9NATe6/ikhSxCVmJIsyWOpRa/v8Ifa16m2WqEoAEDJ8PI98VzXKa/BgV4TcChJTBionXrnnXdm/DslBlXgrxeq1KekIdkmylACrWwSOrz00ktZb4Puj0mmxBuqSK0lH+1Mtf3dUlLuoBMphZEKfii5px+B9Hq+Un+MlqgFrnudHxJFShys8Y1srslO9Gyt/jz17+i6rWBl9WPo+UN9GOq31vGlNrwS1agNr/eo7y3Vhte/5bt9p4SJcKY+KTfns64F2sc6xvJBx5eep7U8/PDDtu9HSUn0LKnknuoT1rboHq+5ZKn+IfU/q39ICbZSyZOUOMLrXI7ddtstY8IyFJ6CP2+88Uab/Bf5wxgQMklqmxzJG/sZ6zLOQmMJcWlnuEvDAAAAAMSABm6vuOIKmw3Zj87Rog9QYaFJUHp4U8erF3rIU/ZgP7Iv57Pj8cEHH7SVVZjAFH0KPr///vvt/vSjSqI6LHKZbBBGmtiVyrxYiPOrU6dOJoratGlD0H5IcZ7nJ5O2BnG9UlVnLUDYJeW6wrldmAA8VZi9/PLLfa2UEVaa/KsghkK0OxEtmmyuycNKRuHHQLUqYHmtgpVU7ItwS0KfJP6PJkUqgeKwYcNi8bMkpY0dVgoAKGTbUZWikBnnScmOP/74QPtltf6g6TlQSaKUINcrJdR544037ILCSvJYaoqCfDQhVsnQEP0AEV2rlNw7STS5/a677rL34DB45JFHMgZnphLueaWgOSVxy2bMRtcpteVVrdeJ2ppKMpINfQ8lLEHwFLDhNbGqxveTGCDUqFEj21Z8/fXXTRIpiLdXr17m2muvNUl05plnmq+//tqXfmYlZSlaaCPMlFxA7QI/klXEmYoUKNmWEqa5CUQ76qijTCFo/EQJLb0m1XSL/qHoUAVn7a/BgwcXelMShTEgOElqmxzJG/sZN26cq/cp0W1clC70BgAAAACFdO6555oOHTrEdieUKlWq4NkyCzk4rkpN9957r80wh+jTpCFNaA/LhIskU3BgvjJG+02DZhxD4cV5HqxTTjnFVissFM49FEISriuc24XRvn1707VrV5MUqiKTtAnfcEeTPzVZHIXHvgi3uPdJYuP+WbVH69atG5ufJQltbGxKlduUFAbZ4TzZ1BFHHBHYM4UqDilZa9AUzPHQQw/ZAEXER9LHUlOimqgZG7v66qtNq1atEncOq/KxEk+Egaoq9+/fP+Pfde/e3VSvXt2XzzznnHOy/lsli3nhhRfSvv7zzz/bRKXZBnmecMIJtso0gnfzzTfbat5eNGjQwOy///6mULxuvxdKnqiq6EnVuXNnc8wxxxTksytXrmw6duxoCt0vo4qlSbLddtvZYF1kboe77aNVxfok2nrrre39H9Fxww03FKytvMcee9gA9iRiDAjpJLlNjuSM/Sgh0niXCQg0Nz0uCNoHAABA4qlzPh+TeQpFGaOVYb0QatasaU4++WRT6OzB9913n60Mjug75JBD7MQLZYRGYWy11VamR48ekf35NQGGig/hxnkebCWF8847zxRqQsRVV11VkM8G4n5d4dwuHF3XCv28k8+EaLfcckvB7iGnn356QT4buStXrpx56qmnEjsJJ0zYF+EX9z5J/B8FsSgwJiyBRH6Iexsbm7ruuutIjpsjzpONKdA9qGAdBSDlawxozz33NI8//rit6oz4SPpYqpx00kkk64tBJeFCJ6AoVMI6JYYJC/WfqfqtEz0X+Lmv2rVrZ/bZZ5+s//7OO+80F1xwgRk1apRZunSprej3448/mkceecR06dLFLFiwIOtAXK0nKU488UTb11KoxL3az37QOH8hnuN0ry1kEjAFmfbr169giXLC0L+txA+qKp5PaqM/+OCDNhCo0HNcXnzxRTvOkSRJSjzthduEFp9//nnGe34cKbmP2gCIVn/Mo48+ahMR5pOKASW9/4QxIKST1DY5kjP2o+RG69evz/l99erVs4kt4oIRVAAAACSeHjZUGSOuQZwaOLzjjjvyPqEu1eGnwa9CO+yww+wgM5Uh40GV0jWgWr58+UJvSuIoYOquu+6yWbmjTJn0qYYUbpznwVHQfqNGjUy+XXjhhXT8o6Difl3h3C5sBfpcqllF/TwqxHe99NJLuYdEjDL0P/vss6Zly5aF3pTEY1+EW9z7JLFpFWhV3GzSpElsfpq4t7HxfxRUpuS4yB3nycaOO+64wCac5tO+++5rE1WFYfwL/mAs9X/Bt3r+RjSp/12TrJNEAQ633XabOeuss0xYvPPOO2bs2LEZ/+7GG2/0fe7G7bffntPY5/Dhw20/X6tWrewziqoMP/zww2bVqlU5BQDnO/irkBTsrIQH+ab+tV69evmagEiJWvKdzKpQCWGL2nvvvc31119fkDZwGK5VFStWNE888YSpU6dOXj5P1zkVWNF1JgwUPKp+mfr165uk0G/ftm3bQm9G6CkBsZvEEitWrDBjxowxSXL44YeHIuEYcrfrrrvafox8zZ3VvMLnn38+8vMLvWIMCOkkuU2OZIz9fPbZZ67e16lTJxMnBO0DAAAA/38yhgYLCpXBLmhNmzY1vXv3ztvnaUD46aefDlXGM1Uue/31102tWrUKvSnwgSYOvPzyy2aHHXbg98xjwH6fPn1iMahXvXp1O3lF3wnhxXkeXJtHgUHbbrutyZczzjiDCZ8IhThfVzi3C0ftiWuuucZO2CxUpaV8uvrqq23gUT4HCTXxHNGj6kXPPfecOfXUUwu9KYnHvgi3uPdJYtPA/Zdeeskce+yxsflp4tzGxv9VnFMbEO5xnmwcDOF3kEyLFi0KUrlTAXQDBw40devWzftnIxiMpf6vH1fjqYgOjcn37dvXXHbZZSZJqlatap555pm8J23JFLinfZFJx44dbfIXv6l6tD4/X8+VZ555pm3jJI32nwJd8jXGvPvuu9tCGX73O1977bWmYcOGJh+UqFDjkoWqcF+c+iovuOCCvH1e+/btQxUcpUQb6pdQEoqgEwQ8+OCDNsBXNmzY4Gl9ZcuW9W2eyGuvvfbfdiXBrbfeanbcccdCb0bouQ0QGzp0qEmK/fff31YNR7T7ZAYMGGD7qIOka44+p3bt2p7vAWFpP3jBGBDSSXKbHPEe+1mzZo0ZNWpUzu9Tf0Y+5yLlAyP/AAAAQBEanFEHfZgn2bgdaFVmPmXgDnoAUZVFFLCvLNVho/369ttv20FkJkLHI+PkkCFDTJcuXUxYxeU4U3VGVVc44YQTTFxoIokqcPg1wItgcJ4Ho2bNmubFF1/MS+C+qn8XomoFkMTrCud2YSn4Ts8azZo1M3Fum+p5UpNy8pH5Xc/nGqxGdKmtrepxqt6hCZFhFZfnNifsi/CLc58kNp2kp2Q/jzzySF6TqQUpzm3sJFO776KLLrLHK7+fd5wnG1caDfP6cg2QfPPNN23/V5j7mTmHs5f0sVQdK/fffz+B+0VUrlzZhJWSlgwePNgcffTRgSR/C6t27drZivYKHAsTBacuWrQo43irkn8G+dsoaXnQgRjqB01yn53uFXfddZetnBok3SNUFXzLLbf0fd3ly5e3/XVBJz5SQtZ777038N8qV0qcqCUfQTD9+vULXTtRgfsaJw6qHa1CKurjKloMQolNvKhQoYLxM+GNgtZ0HqtNFvc2vBLdvPDCCwVJdBYlOl/d3D8VtO81KUUUaK7YE0884eu5iMJo1KiReeONN2zSuiAceOCB9hml6DVn+fLlrtcXp2OOMSAUl/Q2OeI79qOA/dWrV+f8PvXzbL/99iZOGJkEAAAAimnSpIntPNKkjKAzS+bSia5M8XpIv/nmm12v56yzzrIDxhoQDsIee+xhf7uwTTIpPgBz3XXXmUGDBpnWrVsXenPgw6SRO+64w1b00uSUMFAHlzoQlDH9yy+/NFF3yCGHmHfffdcceuihJm40EP3qq6/6Xt0J/uI8D4ayWmtSsbJpB3W/1QTPK6+8MpD1A17E+brCuV1Y+v1feeUVO7i10047mTDQpE5VgNIkNE2o8YMG7nSs9u7d2/cqT0XvIfmYOIn80LP3e++9Zwd/g5ho7EaNGjVsFUlVlurevbtJCvZFuMW5TxKbUj/Lhx9+aCfrbbHFFpH/ieLcxk6ievXq2XvkpZdeSrCvjzhP/kfBpX49R+g3PeKII0yhJ9iq/0v9bOpLDwM9symhnAIqFUSiti+yl/SxVFXGfeyxx+zxo+fzpFMfjwIPTzzxxNAE9ilhyH333Wfv1bvuumsgn6Fgzueee85WOAvLcaCK49omVR0PW/KrqVOn2j7BTNS22m677QIPqH/88ccDOV4VzHjJJZeQ1On/B3fq2SeoJJW65uh4DzKBhs4jXUeCCNrTdqs/WveSsCYP0rP4Aw88EEhfpYL01bdz9913h7aaqYIgleRD11QlhPbreysoTO2cBg0abPTasmXLPK07iHtR586dzQcffGBOP/1026YPg912282cf/75dj6brjN+th20TgVeh/WcLDQ9Myn5Ta4WLlxovv76axNXqpiu68Stt94ayHggCrdf1Y7p2bOnb/3SanuqD1bjCMXboV6C9sPyLOIXxoBQXNLb5Ijn2I8SLbpxxhlnmLgJV/o2AAAAICTU0ahJGcqSrY7rgQMHmmnTpuU1k5kCPvbbbz87gVMTN/x6QDr88MNtVZc+ffqY4cOH+7JODWRdfPHF5rTTTgtdlminzKHqKJw0aZJ9CP3kk09cZXfLhfahOliOPPLIQD8niXSOaD9qMESTIoYNG2b++eefvH1+lSpV7DYoW7gm5rkdQFdw1+uvv26++eYbu4wfP978+eefJt80eHzQQQfZCkHNmzc3caa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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -2342,7 +2371,7 @@
      "metadata": {
       "image/png": {
        "height": 623,
-       "width": 1963
+       "width": 2038
       }
      },
      "output_type": "display_data"
@@ -2364,13 +2393,13 @@
     "ax.set_xlabel(\"held-out log score (nats/row)\")\n",
     "ax.set_title(\n",
     "    \"Stop rule alone inherits optimizer jitter; stop + short anneal\\n\"\n",
-    "    \"matches the annealed 2-pass reference at 55% of the budget\"\n",
+    "    \"reaches the annealed reference's level at 55% budget (single runs)\"\n",
     ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "897e1823",
+   "id": "bf9e3c6e",
    "metadata": {},
    "source": [
     "The working protocol this ablation suggests: **pair a stop rule on a\n",
@@ -2393,13 +2422,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "734eba1b",
+   "id": "fdcefe80",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.522583Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.522501Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.738638Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.738285Z"
+     "iopub.execute_input": "2026-08-01T01:23:54.938385Z",
+     "iopub.status.busy": "2026-08-01T01:23:54.938308Z",
+     "iopub.status.idle": "2026-08-01T01:23:55.152149Z",
+     "shell.execute_reply": "2026-08-01T01:23:55.151818Z"
     }
    },
    "outputs": [
@@ -2444,7 +2473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e419112",
+   "id": "c3758afc",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2454,7 +2483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "256a51b3",
+   "id": "ce87897d",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2473,13 +2502,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "9436e488",
+   "id": "e13af279",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.739911Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.739840Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.931581Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.931263Z"
+     "iopub.execute_input": "2026-08-01T01:23:55.153340Z",
+     "iopub.status.busy": "2026-08-01T01:23:55.153271Z",
+     "iopub.status.idle": "2026-08-01T01:23:55.350842Z",
+     "shell.execute_reply": "2026-08-01T01:23:55.350565Z"
     }
    },
    "outputs": [
@@ -2540,7 +2569,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ef7ead8",
+   "id": "573730ec",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2556,23 +2585,25 @@
     "\n",
     "The single most useful diagnostic this notebook can leave you with costs\n",
     "three fits and one table: the reported mean-field standard deviation next to\n",
-    "the spread of the posterior mean across three optimizer seeds. Read it as a\n",
-    "*computational-stability* diagnostic — a ratio far above one proves the\n",
-    "reported width understates run-to-run variation, which is a necessary\n",
-    "condition for trusting it, not a calibration certificate (that would need a\n",
-    "comparison against full-rank or MCMC on a tractable subproblem):"
+    "the sample standard deviation of the posterior mean across three optimizer\n",
+    "seeds. Read it as a *computational-stability* diagnostic. A ratio far above\n",
+    "one is disqualifying — the reported width understates even run-to-run\n",
+    "variation, so it cannot be trusted. A ratio near or below one is merely\n",
+    "*necessary*: the width survives seed replication, but certifying it as\n",
+    "calibrated would take a full-rank or MCMC comparison on a tractable\n",
+    "subproblem, which is future work here:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "7544357c",
+   "id": "660a6f46",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.932663Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.932580Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.938396Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.938063Z"
+     "iopub.execute_input": "2026-08-01T01:23:55.351983Z",
+     "iopub.status.busy": "2026-08-01T01:23:55.351913Z",
+     "iopub.status.idle": "2026-08-01T01:23:55.357095Z",
+     "shell.execute_reply": "2026-08-01T01:23:55.356713Z"
     }
    },
    "outputs": [
@@ -2601,7 +2632,6 @@
        "      <th>reported_sd</th>\n",
        "      <th>seed_spread</th>\n",
        "      <th>ratio</th>\n",
-       "      <th>flag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2611,7 +2641,6 @@
        "      <td>0.000064</td>\n",
        "      <td>0.028130</td>\n",
        "      <td>442.1</td>\n",
-       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
@@ -2619,7 +2648,6 @@
        "      <td>0.000077</td>\n",
        "      <td>0.005310</td>\n",
        "      <td>69.0</td>\n",
-       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
@@ -2627,7 +2655,6 @@
        "      <td>0.000151</td>\n",
        "      <td>0.008867</td>\n",
        "      <td>58.7</td>\n",
-       "      <td>ridge-dominated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
@@ -2635,7 +2662,6 @@
        "      <td>0.000001</td>\n",
        "      <td>0.000008</td>\n",
        "      <td>6.1</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eta</th>\n",
@@ -2643,7 +2669,6 @@
        "      <td>0.000237</td>\n",
        "      <td>0.000134</td>\n",
        "      <td>0.6</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
@@ -2651,7 +2676,6 @@
        "      <td>0.000140</td>\n",
        "      <td>0.000079</td>\n",
        "      <td>0.6</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
@@ -2659,7 +2683,6 @@
        "      <td>0.000139</td>\n",
        "      <td>0.000022</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
@@ -2667,7 +2690,6 @@
        "      <td>0.000113</td>\n",
        "      <td>0.000023</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
@@ -2675,7 +2697,6 @@
        "      <td>0.000108</td>\n",
        "      <td>0.000010</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
@@ -2683,7 +2704,6 @@
        "      <td>0.000076</td>\n",
        "      <td>0.000008</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tau_k</th>\n",
@@ -2691,7 +2711,6 @@
        "      <td>0.116900</td>\n",
        "      <td>0.014510</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tau_al</th>\n",
@@ -2699,26 +2718,25 @@
        "      <td>0.366800</td>\n",
        "      <td>0.041400</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>seed-stable</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          posterior_mean  reported_sd  seed_spread  ratio             flag\n",
-       "alpha0         -1.570000     0.000064     0.028130  442.1  ridge-dominated\n",
-       "beta_a          0.053680     0.000077     0.005310   69.0  ridge-dominated\n",
-       "kappa0          0.336200     0.000151     0.008867   58.7  ridge-dominated\n",
-       "theta_d         0.025290     0.000001     0.000008    6.1      seed-stable\n",
-       "eta             0.369200     0.000237     0.000134    0.6      seed-stable\n",
-       "nu              1.895000     0.000140     0.000079    0.6      seed-stable\n",
-       "lambda_a        0.056380     0.000139     0.000022    0.2      seed-stable\n",
-       "lambda_q       -0.392800     0.000113     0.000023    0.2      seed-stable\n",
-       "beta_q         -0.001442     0.000108     0.000010    0.1      seed-stable\n",
-       "theta_r         0.294700     0.000076     0.000008    0.1      seed-stable\n",
-       "tau_k           1.039000     0.116900     0.014510    0.1      seed-stable\n",
-       "tau_al          3.210000     0.366800     0.041400    0.1      seed-stable"
+       "          posterior_mean  reported_sd  seed_spread  ratio\n",
+       "alpha0         -1.570000     0.000064     0.028130  442.1\n",
+       "beta_a          0.053680     0.000077     0.005310   69.0\n",
+       "kappa0          0.336200     0.000151     0.008867   58.7\n",
+       "theta_d         0.025290     0.000001     0.000008    6.1\n",
+       "eta             0.369200     0.000237     0.000134    0.6\n",
+       "nu              1.895000     0.000140     0.000079    0.6\n",
+       "lambda_a        0.056380     0.000139     0.000022    0.2\n",
+       "lambda_q       -0.392800     0.000113     0.000023    0.2\n",
+       "beta_q         -0.001442     0.000108     0.000010    0.1\n",
+       "theta_r         0.294700     0.000076     0.000008    0.1\n",
+       "tau_k           1.039000     0.116900     0.014510    0.1\n",
+       "tau_al          3.210000     0.366800     0.041400    0.1"
       ]
      },
      "execution_count": 26,
@@ -2729,13 +2747,12 @@
    "source": [
     "spread = pd.DataFrame(results[\"seed_spread\"]).T\n",
     "spread[\"ratio\"] = (spread[\"seed_spread\"] / spread[\"reported_sd\"]).round(1)\n",
-    "spread[\"flag\"] = np.where(spread[\"ratio\"] > 10, \"ridge-dominated\", \"seed-stable\")\n",
     "spread.sort_values(\"ratio\", ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c2222ee",
+   "id": "0df85798",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2763,7 +2780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5ad684d",
+   "id": "9d924453",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2782,7 +2799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37e52702",
+   "id": "eb676b94",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2794,7 +2811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6f81c3f",
+   "id": "dfb5568b",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2806,7 +2823,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdfc3ebf",
+   "id": "32ad18f7",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2815,13 +2832,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "022b29ab",
+   "id": "26a17ce1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:04:16.939206Z",
-     "iopub.status.busy": "2026-08-01T01:04:16.939147Z",
-     "iopub.status.idle": "2026-08-01T01:04:16.949401Z",
-     "shell.execute_reply": "2026-08-01T01:04:16.949067Z"
+     "iopub.execute_input": "2026-08-01T01:23:55.357901Z",
+     "iopub.status.busy": "2026-08-01T01:23:55.357846Z",
+     "iopub.status.idle": "2026-08-01T01:23:55.368558Z",
+     "shell.execute_reply": "2026-08-01T01:23:55.368224Z"
     }
    },
    "outputs": [
@@ -2861,7 +2878,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2864d2a9",
+   "id": "dde7ea8f",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 28d3c2715..adc3c1138 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -374,12 +374,18 @@ loader = DataLoader(
 print(f"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch")
 ```
 
-One deliberate loader semantics to know about: only full batches are yielded,
-so a trailing partial batch — here 992 of 300,000 rows, and with a fixed
-replay order the *same* 992 every pass — is never seen, while `total_size`
-still rescales by the full $N$. At 0.3% of the data this is a knowably tiny,
-stated bias, not a hidden one; shuffling shard order between epochs would
-rotate the tail if it ever mattered.
+One deliberate loader semantics to be precise about: only full batches are
+yielded, so a trailing partial batch — here 992 of 300,000 rows, and with a
+fixed replay order the *same* 992 every pass — is never seen. Strictly, then,
+the fit targets the 299,008-row subpopulation the loader streams, which is a
+uniform random subset by the hash shuffle, while `total_size` overstates its
+size by 0.3% (0.0004% at full scale) — invisible at any precision reported
+here, but it does mean Gate 1's equal-inclusion clause holds for the streamed
+subpopulation, not the last few rows of the last shard. If exactness matters,
+set `total_size` to the streamed count
+(`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —
+carrying the remainder across epoch boundaries — belongs in the loader and is
+on its review agenda.
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -563,25 +569,30 @@ scalar_params = [
 ]
 rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}
 # the intercepts share a ridge with their group means; only the sums are identified
-for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al")]:
+for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
     s = post[icpt] + post[b].mean("symbol")
     rows[f"{icpt} + mean({b})"] = [float(s.mean()), float(s.std())]
 recovery = pd.DataFrame(rows, index=["mean", "sd"]).T
-recovery["truth"] = [truth[p] for p in scalar_params] + [truth["kappa0"], truth["alpha0"]]
+recovery["truth"] = [truth[p] for p in scalar_params] + [
+    truth["kappa0"],
+    truth["alpha0"],
+    truth["beta_a"],
+]
 recovery["abs_error"] = (recovery["mean"] - recovery["truth"]).abs()
 recovery.round(4)
 ```
 
-Read the table bottom-up. The two raw intercepts look off by 0.3 and 1.3 — but
-a global intercept and the mean of its group effects are only *jointly*
+Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
+global coefficient and the mean of its group effects are only *jointly*
 identified: the likelihood constrains their sum, and the prior only weakly
-splits it. The identified sums in the last two rows land within a few
+splits it. The identified sums in the last three rows land within a few
 thousandths of the truth, like every other likelihood-identified quantity.
 The same translation ridge exists for every global-coefficient/group-effect
-pair in this model ($c$ with $b^{(\pi h)}$, $g$ with $b^{(\sigma h)}$,
-$\beta_a$ with $b^{(\beta a)}$) — we show the two intercepts because their
-splits wander furthest; a sum-to-zero constraint on the symbol effects is the
-standard reparameterization when the split itself matters.
+pair in this model (including the vector pairs $c$/$b^{(\pi h)}$ and
+$g$/$b^{(\sigma h)}$, whose sums we spare you) — so raw split rows like
+`kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not
+estimates of their generating values; a sum-to-zero constraint on the symbol
+effects is the standard reparameterization when the split itself matters.
 Notice also what we did *not* print: z-scores against the truth. Some would
 exceed 2, because the mean-field standard deviations in the third column are
 razor-thin — a z-score against a point truth mixes the sampling noise of this
@@ -894,8 +905,9 @@ in front of it. The full-scale corpus behind the numbers in this section:
 
 Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes
 about four minutes at roughly 1,000 optimization steps per second (4 million
-rows per second through the model), with peak resident memory of 1.1 GB — the
-loader's flat footprint, independent of N. The subsections below are the
+rows per second through the model), with peak resident memory of 1.1 GB for
+the whole run — fit loop plus posterior draws and summaries; the memory-wall
+figure's leaner 0.5 GB point measures the fit loop alone. Both are flat in N. The subsections below are the
 measured results; each figure's numbers are inlined verbatim from the run
 logs so the plots execute without any data access.
 
@@ -1057,7 +1069,12 @@ likelihood answers the one about typical transitions.
 
 ### The stopping rule at scale, and a pre-registration that failed
 
-The stopping-rule comparison was pre-registered before any full-scale run:
+One scoping sentence first, so no number below is ambiguous: every held-out
+score in this section comes from fits on the 60-shard training split only,
+with the four held-out shards touched by nothing but the scorer — the
+headline four-minute fit above streams all 64 shards and is never
+holdout-scored. The stopping-rule comparison was pre-registered before any
+full-scale run:
 fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical
 batch order and seeds, flat learning rate; savings to be claimed only if the
 stopped fit's held-out log score landed within max(2 × ref seed spread,
@@ -1096,10 +1113,14 @@ endpoints per arm are the confirmatory design.
 One more layer of honesty on the metric itself: even among fully annealed,
 fully converged runs, path-to-path differences are about $5 \times 10^{-4}$
 nats/row. The pre-registered $2 \times 10^{-4}$ equivalence floor was
-therefore unachievable by any protocol we ran — a pre-registration mistake
-worth admitting, because the comparison it pushed us toward is the right one:
-flat-rate arms scatter over $1.7 \times 10^{-3}$, annealed endpoints over
-$5 \times 10^{-4}$, and the seed spread within one protocol is $10^{-5}$.
+therefore poorly chosen: the only arm that passed it was the fixed 1-pass
+budget — which shadows the reference's own trajectory and passes trivially —
+while for every protocol that behaves differently from the reference the
+floor sits below the plateau jitter it would need to see through. A
+pre-registration mistake worth admitting, because the comparison it pushed us
+toward is the right one: flat-rate arms scatter over $1.7 \times 10^{-3}$,
+annealed endpoints over $5 \times 10^{-4}$, and the seed spread within one
+protocol is $10^{-5}$.
 
 ```{code-cell} ipython3
 pol = results["polish"]
@@ -1117,7 +1138,7 @@ ax.set_xticks([0.116, 0.117, 0.118])
 ax.set_xlabel("held-out log score (nats/row)")
 ax.set_title(
     "Stop rule alone inherits optimizer jitter; stop + short anneal\n"
-    "matches the annealed 2-pass reference at 55% of the budget"
+    "reaches the annealed reference's level at 55% budget (single runs)"
 );
 ```
 
@@ -1230,16 +1251,17 @@ is uniform or it is invalid.
 
 The single most useful diagnostic this notebook can leave you with costs
 three fits and one table: the reported mean-field standard deviation next to
-the spread of the posterior mean across three optimizer seeds. Read it as a
-*computational-stability* diagnostic — a ratio far above one proves the
-reported width understates run-to-run variation, which is a necessary
-condition for trusting it, not a calibration certificate (that would need a
-comparison against full-rank or MCMC on a tractable subproblem):
+the sample standard deviation of the posterior mean across three optimizer
+seeds. Read it as a *computational-stability* diagnostic. A ratio far above
+one is disqualifying — the reported width understates even run-to-run
+variation, so it cannot be trusted. A ratio near or below one is merely
+*necessary*: the width survives seed replication, but certifying it as
+calibrated would take a full-rank or MCMC comparison on a tractable
+subproblem, which is future work here:
 
 ```{code-cell} ipython3
 spread = pd.DataFrame(results["seed_spread"]).T
 spread["ratio"] = (spread["seed_spread"] / spread["reported_sd"]).round(1)
-spread["flag"] = np.where(spread["ratio"] > 10, "ridge-dominated", "seed-stable")
 spread.sort_values("ratio", ascending=False)
 ```
 

From 961a74ee4f906667bca8fa2d53d08a6c6a449a84 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 21:01:41 -0500
Subject: [PATCH 22/42] Confirmatory replicated runs for the stopping-rule
 section; audit wording fixes

---
 .../streaming_tick_data.ipynb                 | 945 ++++++++++--------
 .../streaming_tick_data.myst.md               | 209 ++--
 .../streaming_tick_data_results.json          |  38 +
 3 files changed, 686 insertions(+), 506 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 018b8d37f..01b121f2c 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7a04ed7b",
+   "id": "42ff7b5f",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,12 +18,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3156b7c7",
+   "id": "3671e70e",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
     "trades for a single liquid pair is roughly 28 million rows, and a modest\n",
-    "38-symbol universe over six months already exceeds 450 million rows — far more\n",
+    "38-symbol universe (four majors over six months plus a one-month tail of 34\n",
+    "alt pairs) already exceeds 450 million rows — far more\n",
     "than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a\n",
     "hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of\n",
     "that scale by streaming minibatches from disk, using the\n",
@@ -52,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "903dca32",
+   "id": "c46c114e",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -92,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17653c2a",
+   "id": "4ed31533",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -102,13 +103,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fc4eb55b",
+   "id": "c7c94316",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:42.140047Z",
-     "iopub.status.busy": "2026-08-01T01:23:42.139819Z",
-     "iopub.status.idle": "2026-08-01T01:23:43.950947Z",
-     "shell.execute_reply": "2026-08-01T01:23:43.950424Z"
+     "iopub.execute_input": "2026-08-01T02:00:48.874879Z",
+     "iopub.status.busy": "2026-08-01T02:00:48.874710Z",
+     "iopub.status.idle": "2026-08-01T02:00:50.709463Z",
+     "shell.execute_reply": "2026-08-01T02:00:50.709033Z"
     }
    },
    "outputs": [],
@@ -134,13 +135,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4a6b5bfe",
+   "id": "e9911d16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:43.952290Z",
-     "iopub.status.busy": "2026-08-01T01:23:43.952141Z",
-     "iopub.status.idle": "2026-08-01T01:23:43.955144Z",
-     "shell.execute_reply": "2026-08-01T01:23:43.954720Z"
+     "iopub.execute_input": "2026-08-01T02:00:50.710953Z",
+     "iopub.status.busy": "2026-08-01T02:00:50.710829Z",
+     "iopub.status.idle": "2026-08-01T02:00:50.713729Z",
+     "shell.execute_reply": "2026-08-01T02:00:50.713321Z"
     }
    },
    "outputs": [],
@@ -160,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8faf4e65",
+   "id": "905f93eb",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -187,10 +188,13 @@
     "\\end{aligned}\n",
     "$$\n",
     "\n",
-    "with covariates computed *causally* (from information available at trade $i$):\n",
-    "the trade sign $d_i$, standardized log notional $q_i$, standardized trailing\n",
-    "60-second activity $a_i$, and the most recent previous nonzero return\n",
-    "$\\text{ylag}_i$. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
+    "with raw covariates computed causally (from information available at trade\n",
+    "$i$): the trade sign $d_i$, log notional $q_i$, trailing 60-second activity\n",
+    "$a_i$, and the most recent previous nonzero return $\\text{ylag}_i$. One\n",
+    "precision: $q$ and $a$ are then standardized with per-symbol constants\n",
+    "estimated over the full sample — a fixed affine map that changes the\n",
+    "parameterization, not the conditional law, and is shared by every arm below;\n",
+    "a deployed system would freeze these constants from a burn-in window. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
     "the standard way to encode intraday periodicity smoothly\n",
     "{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
     "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
@@ -214,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b0f46a0",
+   "id": "8503d423",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -223,20 +227,20 @@
     "executed path uses a seeded synthetic generator that mirrors the real corpus:\n",
     "same schema, same hurdle structure, same heavy tails, twelve symbols with a\n",
     "deliberately thin tail so shrinkage is visible. Because the truth is known,\n",
-    "recovery is checkable — and the streaming code is byte-for-byte the code used\n",
-    "on the real corpus."
+    "recovery is checkable — and the likelihood, priors, and loader configuration\n",
+    "are line-for-line the ones used on the real corpus."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "aceebfb0",
+   "id": "d903b26e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:43.955952Z",
-     "iopub.status.busy": "2026-08-01T01:23:43.955893Z",
-     "iopub.status.idle": "2026-08-01T01:23:43.958380Z",
-     "shell.execute_reply": "2026-08-01T01:23:43.957984Z"
+     "iopub.execute_input": "2026-08-01T02:00:50.714592Z",
+     "iopub.status.busy": "2026-08-01T02:00:50.714528Z",
+     "iopub.status.idle": "2026-08-01T02:00:50.716829Z",
+     "shell.execute_reply": "2026-08-01T02:00:50.716536Z"
     }
    },
    "outputs": [],
@@ -278,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7c9e1d0b",
+   "id": "eef1b521",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:43.959212Z",
-     "iopub.status.busy": "2026-08-01T01:23:43.959156Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.104516Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.104218Z"
+     "iopub.execute_input": "2026-08-01T02:00:50.717731Z",
+     "iopub.status.busy": "2026-08-01T02:00:50.717681Z",
+     "iopub.status.idle": "2026-08-01T02:00:50.863495Z",
+     "shell.execute_reply": "2026-08-01T02:00:50.863072Z"
     }
    },
    "outputs": [
@@ -351,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "560393ae",
+   "id": "313d3492",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -361,13 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8c5157cc",
+   "id": "0a7ee3e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.105474Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.105415Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.481308Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.480907Z"
+     "iopub.execute_input": "2026-08-01T02:00:50.864403Z",
+     "iopub.status.busy": "2026-08-01T02:00:50.864337Z",
+     "iopub.status.idle": "2026-08-01T02:00:51.233452Z",
+     "shell.execute_reply": "2026-08-01T02:00:51.233124Z"
     }
    },
    "outputs": [
@@ -406,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffdc8ee1",
+   "id": "ebd030da",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -418,20 +422,22 @@
     "stopping decision would be biased by construction. The fix is to shuffle\n",
     "**once, globally, on disk at ETL time**: every row gets a deterministic hash\n",
     "key, rows are scattered across shards by that key, and each shard is sorted by\n",
-    "it. After that, sequential reads *are* a uniform permutation, and the loader\n",
-    "can run with `shuffle=False` at full speed."
+    "it. After that, sequential reads follow one fixed, data-independent uniform\n",
+    "permutation — replayed identically each epoch, which is single-shuffle SGD\n",
+    "semantics rather than fresh per-step subsampling — and the loader can run\n",
+    "with `shuffle=False` at full speed."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "1f2c2452",
+   "id": "87bf8008",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.482316Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.482237Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.621041Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.620705Z"
+     "iopub.execute_input": "2026-08-01T02:00:51.234531Z",
+     "iopub.status.busy": "2026-08-01T02:00:51.234447Z",
+     "iopub.status.idle": "2026-08-01T02:00:51.386035Z",
+     "shell.execute_reply": "2026-08-01T02:00:51.385739Z"
     }
    },
    "outputs": [
@@ -482,12 +488,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b4392f1",
+   "id": "7e62a15e",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
-    "head of any shard must already mix all 24 hours and all symbols, *verified, not\n",
-    "assumed*. Note that the cell above is the pedagogical miniature — it sorts the\n",
+    "head of any shard must already mix all 24 hours and nearly all symbols —\n",
+    "asserted on a sampled shard at build time and spot-checked on others, rather\n",
+    "than assumed. Note that the cell above is the pedagogical miniature — it sorts the\n",
     "whole table in memory, which at corpus scale would need several gigabytes for\n",
     "the keys and permutation alone. The published ETL is genuinely out-of-core:\n",
     "pass one hash-scatters rows into shards with bounded row-group appends, pass\n",
@@ -496,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bd678e7",
+   "id": "62b4c00b",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -511,13 +518,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f9c8b0de",
+   "id": "c4de3785",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.622016Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.621952Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.624658Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.624358Z"
+     "iopub.execute_input": "2026-08-01T02:00:51.387057Z",
+     "iopub.status.busy": "2026-08-01T02:00:51.386995Z",
+     "iopub.status.idle": "2026-08-01T02:00:51.389826Z",
+     "shell.execute_reply": "2026-08-01T02:00:51.389580Z"
     }
    },
    "outputs": [
@@ -543,7 +550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "164d30da",
+   "id": "08df612d",
    "metadata": {},
    "source": [
     "One deliberate loader semantics to be precise about: only full batches are\n",
@@ -553,8 +560,10 @@
     "uniform random subset by the hash shuffle, while `total_size` overstates its\n",
     "size by 0.3% (0.0004% at full scale) — invisible at any precision reported\n",
     "here, but it does mean Gate 1's equal-inclusion clause holds for the streamed\n",
-    "subpopulation, not the last few rows of the last shard. If exactness matters,\n",
-    "set `total_size` to the streamed count\n",
+    "subpopulation, not the last few rows of the last shard — and, equivalently,\n",
+    "that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a\n",
+    "sub-percent effect dwarfed by the mean-field error quantified later. If\n",
+    "exactness matters, set `total_size` to the streamed count\n",
     "(`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —\n",
     "carrying the remainder across epoch boundaries — belongs in the loader and is\n",
     "on its review agenda."
@@ -563,13 +572,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "52779ba8",
+   "id": "612aaa24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.625563Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.625506Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.629947Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.629624Z"
+     "iopub.execute_input": "2026-08-01T02:00:51.390774Z",
+     "iopub.status.busy": "2026-08-01T02:00:51.390710Z",
+     "iopub.status.idle": "2026-08-01T02:00:51.395153Z",
+     "shell.execute_reply": "2026-08-01T02:00:51.394818Z"
     }
    },
    "outputs": [],
@@ -667,7 +676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a580209",
+   "id": "14ee7474",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -677,19 +686,22 @@
     "the hurdle's two logs are written with `softplus` —\n",
     "$\\log \\pi = -\\operatorname{softplus}(-x)$,\n",
     "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$ — which is exact and stable at both\n",
-    "tails."
+    "tails. The exact float comparison `value != 0` is safe here because the zeros\n",
+    "are *structural* — a transition either changes the price or it does not, and\n",
+    "the smallest genuine move on any listed tick grid sits dozens of orders of\n",
+    "magnitude above the float32 flush-to-zero threshold."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "9c3d5aea",
+   "id": "91eb54f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.630808Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.630752Z",
-     "iopub.status.idle": "2026-08-01T01:23:44.782920Z",
-     "shell.execute_reply": "2026-08-01T01:23:44.782483Z"
+     "iopub.execute_input": "2026-08-01T02:00:51.395873Z",
+     "iopub.status.busy": "2026-08-01T02:00:51.395818Z",
+     "iopub.status.idle": "2026-08-01T02:00:51.552841Z",
+     "shell.execute_reply": "2026-08-01T02:00:51.552497Z"
     }
    },
    "outputs": [
@@ -756,191 +768,235 @@
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        "</g>\n",
        "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
        "<title>lambda_a&#45;&gt;y_obs</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M69.32,-279.64C78.9,-266.03 91.62,-251.92 106.97,-243.82 157.26,-217.25 501.28,-200.17 692.1,-192.63\"/>\n",
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        "</g>\n",
-       "<!-- alpha0 -->\n",
+       "<!-- tau_k -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"691.25,-190.77 701.23,-187.22 691.22,-183.77 691.25,-190.77\"/>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- nu -->\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.65,-395.53C1157.31,-385.58 1162.39,-374.73 1167.24,-364.38\"/>\n",
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+       "</g>\n",
+       "<!-- tau_al -->\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
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+       "<!-- b_al -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.18,-393.93C1273.16,-385.49 1275.27,-376.46 1277.33,-367.69\"/>\n",
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        "</g>\n",
        "<!-- theta_d -->\n",
        "<g id=\"node5\" class=\"node\">\n",
        "<title>theta_d</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"364.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
        "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
        "<title>theta_d&#45;&gt;y_obs</title>\n",
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-       "<!-- beta_q -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"464.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"256.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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        "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
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        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- beta_a -->\n",
        "<g id=\"node7\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"356.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
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+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
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+       "<!-- tau_sh -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
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        "</g>\n",
-       "<!-- b_ph -->\n",
+       "<!-- b_sh -->\n",
        "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
+       "<title>b_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.5,-393.28C1511.82,-385.5 1511.11,-377.25 1510.41,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.91,-369.08 1509.56,-359.42 1506.94,-369.69 1513.91,-369.08\"/>\n",
+       "</g>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
        "<!-- tau_ph&#45;&gt;b_ph -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.5,-393.28C1511.82,-385.5 1511.11,-377.25 1510.41,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.91,-369.08 1509.56,-359.42 1506.94,-369.69 1513.91,-369.08\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.44,-394.25C1627.64,-385.52 1624.63,-376.15 1621.72,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.08,-366.09 1618.69,-357.64 1618.41,-368.23 1625.08,-366.09\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"560.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"606.09,-345.73 515.84,-345.73 515.84,-288.23 606.09,-288.23 606.09,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M560.97,-393.28C560.97,-381.58 560.97,-368.79 560.97,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"564.47,-357.45 560.97,-347.45 557.47,-357.45 564.47,-357.45\"/>\n",
        "</g>\n",
        "<!-- kappa0 -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node11\" class=\"node\">\n",
        "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"564.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"564.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"456.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
        "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M583,-279.92C591.14,-266.86 601.9,-253.06 614.97,-243.82 640.19,-225.98 670.88,-213.85 700.71,-205.61\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"701.4,-209.05 710.19,-203.13 699.63,-202.27 701.4,-209.05\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.35,-279.68C482.46,-266.42 493.37,-252.54 506.97,-243.82 538.18,-223.78 623.06,-209.41 696.42,-200.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"696.61,-203.8 706.11,-199.12 695.76,-196.85 696.61,-203.8\"/>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"664.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M684.4,-281.02C692.67,-268.25 703.14,-254.37 714.97,-243.82 722.3,-237.28 730.53,-231.24 739.07,-225.74\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"740.83,-228.77 747.52,-220.55 737.17,-222.8 740.83,-228.77\"/>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M575.77,-287.99C585.12,-272.84 598.54,-254.99 614.97,-243.82 640.51,-226.44 671.31,-214.47 701.15,-206.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.84,-209.67 710.62,-203.75 700.06,-202.9 701.84,-209.67\"/>\n",
        "</g>\n",
-       "<!-- tau_al -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_al -->\n",
+       "<!-- b_ba -->\n",
        "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.65,-395.53C1157.31,-385.58 1162.39,-374.73 1167.24,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.27,-366.16 1171.34,-355.62 1163.93,-363.19 1170.27,-366.16\"/>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1388.97,-393.28C1388.97,-385.5 1388.97,-377.25 1388.97,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.47,-369.43 1388.97,-359.43 1385.47,-369.43 1392.47,-369.43\"/>\n",
        "</g>\n",
-       "<!-- eta -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"260.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"260.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"664.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"664.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M260.97,-393.28C260.97,-381.58 260.97,-368.79 260.97,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"264.47,-357.45 260.97,-347.45 257.47,-357.45 264.47,-357.45\"/>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M684.4,-281.02C692.67,-268.25 703.14,-254.37 714.97,-243.82 722.3,-237.28 730.53,-231.24 739.07,-225.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"740.83,-228.77 747.52,-220.55 737.17,-222.8 740.83,-228.77\"/>\n",
        "</g>\n",
-       "<!-- theta_r -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"764.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"764.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M780.59,-279.01C786,-266.2 792.15,-251.62 797.93,-237.95\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"801,-239.69 801.66,-229.12 794.55,-236.97 801,-239.69\"/>\n",
        "</g>\n",
-       "<!-- tau_sh -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_sh -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.44,-394.25C1627.64,-385.52 1624.63,-376.15 1621.72,-367.07\"/>\n",
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-       "</g>\n",
        "<!-- lambda_q -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node16\" class=\"node\">\n",
        "<title>lambda_q</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"873.97\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
@@ -948,55 +1004,11 @@
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        "</g>\n",
        "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
        "<title>lambda_q&#45;&gt;y_obs</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M857.76,-278.32C852.36,-265.77 846.26,-251.58 840.52,-238.24\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"843.78,-236.96 836.61,-229.16 837.35,-239.73 843.78,-236.96\"/>\n",
        "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<title>b_k</title>\n",
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-       "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
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        "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
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-       "<title>b_al&#45;&gt;y_obs</title>\n",
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-       "<title>b_sh&#45;&gt;y_obs</title>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
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-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<!-- b_sh&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>b_sh&#45;&gt;y_obs</title>\n",
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@@ -1065,7 +1077,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x129bae810>"
+       "<graphviz.graphs.Digraph at 0x154cc8f50>"
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      },
      "execution_count": 9,
@@ -1080,7 +1092,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10af3c09",
+   "id": "22e4501a",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1092,13 +1104,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "8dec1729",
+   "id": "1e6fee0a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:44.783905Z",
-     "iopub.status.busy": "2026-08-01T01:23:44.783833Z",
-     "iopub.status.idle": "2026-08-01T01:23:48.775737Z",
-     "shell.execute_reply": "2026-08-01T01:23:48.775337Z"
+     "iopub.execute_input": "2026-08-01T02:00:51.553884Z",
+     "iopub.status.busy": "2026-08-01T02:00:51.553814Z",
+     "iopub.status.idle": "2026-08-01T02:00:55.625442Z",
+     "shell.execute_reply": "2026-08-01T02:00:55.624942Z"
     }
    },
    "outputs": [],
@@ -1135,7 +1147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4eb5121",
+   "id": "3b12713b",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1148,13 +1160,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "fe664042",
+   "id": "d6aceef0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:48.776817Z",
-     "iopub.status.busy": "2026-08-01T01:23:48.776752Z",
-     "iopub.status.idle": "2026-08-01T01:23:48.923230Z",
-     "shell.execute_reply": "2026-08-01T01:23:48.922938Z"
+     "iopub.execute_input": "2026-08-01T02:00:55.626588Z",
+     "iopub.status.busy": "2026-08-01T02:00:55.626515Z",
+     "iopub.status.idle": "2026-08-01T02:00:55.773545Z",
+     "shell.execute_reply": "2026-08-01T02:00:55.773173Z"
     }
    },
    "outputs": [
@@ -1191,7 +1203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cfe88a4",
+   "id": "d6e7c70d",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1200,13 +1212,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "059b3eb8",
+   "id": "5436c5a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:48.924507Z",
-     "iopub.status.busy": "2026-08-01T01:23:48.924424Z",
-     "iopub.status.idle": "2026-08-01T01:23:49.112985Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.112590Z"
+     "iopub.execute_input": "2026-08-01T02:00:55.774815Z",
+     "iopub.status.busy": "2026-08-01T02:00:55.774729Z",
+     "iopub.status.idle": "2026-08-01T02:00:55.966626Z",
+     "shell.execute_reply": "2026-08-01T02:00:55.966273Z"
     }
    },
    "outputs": [
@@ -1379,7 +1391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f6d6098",
+   "id": "514ae0ad",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1408,13 +1420,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "2e925d93",
+   "id": "6cf8933a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:49.113850Z",
-     "iopub.status.busy": "2026-08-01T01:23:49.113792Z",
-     "iopub.status.idle": "2026-08-01T01:23:49.226545Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.226154Z"
+     "iopub.execute_input": "2026-08-01T02:00:55.967588Z",
+     "iopub.status.busy": "2026-08-01T02:00:55.967524Z",
+     "iopub.status.idle": "2026-08-01T02:00:56.084522Z",
+     "shell.execute_reply": "2026-08-01T02:00:56.084234Z"
     }
    },
    "outputs": [
@@ -1463,7 +1475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cc57cee",
+   "id": "2603de5c",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1475,7 +1487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17e23ae3",
+   "id": "9823e06c",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1491,13 +1503,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "32e7a07a",
+   "id": "0f37608a",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2026-08-01T01:23:49.856316Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.855928Z"
+     "iopub.execute_input": "2026-08-01T02:00:56.085584Z",
+     "iopub.status.busy": "2026-08-01T02:00:56.085513Z",
+     "iopub.status.idle": "2026-08-01T02:00:56.719480Z",
+     "shell.execute_reply": "2026-08-01T02:00:56.719135Z"
     }
    },
    "outputs": [
@@ -1606,13 +1618,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "67b69499",
+   "id": "ffd16c8f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:49.857324Z",
-     "iopub.status.busy": "2026-08-01T01:23:49.857251Z",
-     "iopub.status.idle": "2026-08-01T01:23:49.990618Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.990288Z"
+     "iopub.execute_input": "2026-08-01T02:00:56.720520Z",
+     "iopub.status.busy": "2026-08-01T02:00:56.720446Z",
+     "iopub.status.idle": "2026-08-01T02:00:56.856915Z",
+     "shell.execute_reply": "2026-08-01T02:00:56.856598Z"
     }
    },
    "outputs": [
@@ -1660,11 +1672,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be686197",
+   "id": "0fb42bd5",
    "metadata": {},
    "source": [
-    "The predictive distribution reproduces the zero step, the bulk scale, and the\n",
-    "tail quantile within its own 90% bands. On real data one caveat would surface\n",
+    "Two of the three statistics sit inside their predictive 90% bands; the median\n",
+    "|move| lands at the band's edge (0.8% outside at full precision — the\n",
+    "3-decimal table hides it, so we say it here). Note also that this is an\n",
+    "in-sample adequacy check: the evaluation batch was seen during the fit; the\n",
+    "real-corpus scores later use shards the fits never touched. On real data one\n",
+    "further caveat would surface\n",
     "here that the synthetic generator cannot show: real price changes are integer\n",
     "multiples of the tick size, so the continuous Student-t's residual misfit\n",
     "concentrates at |y| near one tick — visible as small stair-steps in the\n",
@@ -1673,7 +1689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aaacdc80",
+   "id": "f0ee5c22",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1691,8 +1707,8 @@
     ":class: warning\n",
     "Successive losses are evaluated on *different batches*, so the per-step noise\n",
     "dwarfs the per-step improvement: on our full-scale run the median one-step\n",
-    "change is about 106 loss units while the true improvement is 0.04 units per\n",
-    "step. A rule that standardizes *raw* increments concludes \"improvement is\n",
+    "change is about 106 loss units while the mean improvement — (first − last)\n",
+    "loss over the run's steps — is about 0.06 units per step. A rule that standardizes *raw* increments concludes \"improvement is\n",
     "indistinguishable from zero\" immediately upon arming, and fires a fixed\n",
     "~$h/\\kappa$ steps later regardless of whether the fit converged.\n",
     ":::\n",
@@ -1706,13 +1722,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "0349e6b8",
+   "id": "91469673",
    "metadata": {
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-     "iopub.status.idle": "2026-08-01T01:23:49.994289Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.993986Z"
+     "iopub.execute_input": "2026-08-01T02:00:56.858234Z",
+     "iopub.status.busy": "2026-08-01T02:00:56.858148Z",
+     "iopub.status.idle": "2026-08-01T02:00:56.860518Z",
+     "shell.execute_reply": "2026-08-01T02:00:56.860294Z"
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    "outputs": [],
@@ -1754,13 +1770,13 @@
   {
    "cell_type": "code",
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-   "id": "69d5c417",
+   "id": "5b9b12ad",
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-     "iopub.status.busy": "2026-08-01T01:23:49.995107Z",
-     "iopub.status.idle": "2026-08-01T01:23:49.997090Z",
-     "shell.execute_reply": "2026-08-01T01:23:49.996790Z"
+     "iopub.execute_input": "2026-08-01T02:00:56.861454Z",
+     "iopub.status.busy": "2026-08-01T02:00:56.861382Z",
+     "iopub.status.idle": "2026-08-01T02:00:56.863237Z",
+     "shell.execute_reply": "2026-08-01T02:00:56.863006Z"
     }
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    "outputs": [],
@@ -1796,13 +1812,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "05119fb5",
+   "id": "37963d53",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2026-08-01T01:23:49.997801Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.452026Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.451640Z"
+     "iopub.execute_input": "2026-08-01T02:00:56.864026Z",
+     "iopub.status.busy": "2026-08-01T02:00:56.863973Z",
+     "iopub.status.idle": "2026-08-01T02:01:01.491435Z",
+     "shell.execute_reply": "2026-08-01T02:01:01.491032Z"
     }
    },
    "outputs": [
@@ -1858,13 +1874,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "d9d6ae17",
+   "id": "f83f3e91",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.452929Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.452859Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.620756Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.620404Z"
+     "iopub.execute_input": "2026-08-01T02:01:01.492579Z",
+     "iopub.status.busy": "2026-08-01T02:01:01.492509Z",
+     "iopub.status.idle": "2026-08-01T02:01:01.662522Z",
+     "shell.execute_reply": "2026-08-01T02:01:01.662207Z"
     }
    },
    "outputs": [
@@ -1907,7 +1923,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14b8b799",
+   "id": "a50f4c9c",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1920,8 +1936,10 @@
     "independent evidence. Second, the $h/\\kappa$-window delay after a plateau is\n",
     "the rule's *nominal* detection lag; clipping and the adaptive scale can move\n",
     "the actual hitting time. On the full-scale corpus the same replay shows the\n",
-    "windowed rule stopping shortly after one pass, at a loss within noise of the\n",
-    "two-pass reference.\n",
+    "windowed rule stopping shortly after one pass, at a *training loss* within\n",
+    "noise of the two-pass reference — whether that stop is also good enough on\n",
+    "held-out score is precisely what the pre-registered test in the next section\n",
+    "failed to confirm.\n",
     "\n",
     "(tick-real-corpus)=\n",
     "\n",
@@ -1953,7 +1971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daa5be89",
+   "id": "3d6d015a",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -1969,13 +1987,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "47911c83",
+   "id": "bae47263",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.621939Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.621861Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.767907Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.767523Z"
+     "iopub.execute_input": "2026-08-01T02:01:01.663749Z",
+     "iopub.status.busy": "2026-08-01T02:01:01.663670Z",
+     "iopub.status.idle": "2026-08-01T02:01:01.819836Z",
+     "shell.execute_reply": "2026-08-01T02:01:01.819527Z"
     }
    },
    "outputs": [
@@ -2049,7 +2067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b9938e7",
+   "id": "22971237",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -2069,19 +2087,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "7d869b8d",
+   "id": "e09fa9d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.769244Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.769178Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.844496Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.844224Z"
+     "iopub.execute_input": "2026-08-01T02:01:01.821239Z",
+     "iopub.status.busy": "2026-08-01T02:01:01.821167Z",
+     "iopub.status.idle": "2026-08-01T02:01:01.899338Z",
+     "shell.execute_reply": "2026-08-01T02:01:01.898938Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1600x520 with 1 Axes>"
       ]
@@ -2116,20 +2134,21 @@
     "        fontsize=9,\n",
     "    )\n",
     "ax.set_xlabel(\"optimization steps per second (batch 4096, steady state)\")\n",
-    "ax.set_title(\"With a 500-parameter gradient, the loader is not the bottleneck\");"
+    "ax.set_title(\"With a 440-parameter gradient, the loader is not the bottleneck\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f8f36b1c",
+   "id": "50dd0070",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
     "gradient dominates the step, and the pre-shuffled streaming path *outpaces*\n",
     "in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than\n",
-    "Minibatch's per-step random indexing. That ordering is model-dependent: on a\n",
-    "light model the loader overhead shows and `pm.Minibatch` wins the races it can\n",
-    "enter (we measured exactly that on a small probe model). The one consistently\n",
+    "Minibatch's per-step random indexing. On our lighter probe model the same\n",
+    "ordering held (pre-shuffled loader ~4,900 vs Minibatch ~3,000 steps/s), so\n",
+    "treat any speed ordering as an empirical, model-specific fact rather than a\n",
+    "law in either direction. The one consistently\n",
     "expensive path is the runtime shuffle buffer — which is why the ETL-time\n",
     "global shuffle matters twice: it is the *statistically* honest option, and it\n",
     "is 3× the throughput.\n",
@@ -2142,7 +2161,9 @@
     "**Non-centered effects collapsed under mean-field.** The first version of this\n",
     "model used the textbook non-centered parameterization\n",
     "$b_s = \\tau \\cdot z_s,\\ z_s \\sim \\mathcal{N}(0,1)$. On the real corpus the\n",
-    "$z_s$ posteriors blew out to $\\pm 18$ while every $\\tau$ pinned near its prior:\n",
+    "$z_s$ posteriors blew out to $+17$ while the $\\tau$'s collapsed — some pinned\n",
+    "low against the prior, others (the hour-shape scale) driven far *below* its\n",
+    "bulk:\n",
     "real cross-symbol volatility levels span two orders of magnitude, the original\n",
     "$\\tau$ prior could not reach them, and a mean-field approximation of the\n",
     "$\\tau$–$z$ funnel collapses into its small-$\\tau$/huge-$z$ corner. Both fixes\n",
@@ -2169,7 +2190,8 @@
     "\n",
     "**The robust location disagrees with least squares about a sign.** Ordinary\n",
     "least squares of $y$ on $\\text{ylag}$ gives a *negative* coefficient\n",
-    "(bid-ask bounce, −0.29); the fitted $\\theta_r$ is *positive*\n",
+    "(bid-ask bounce, ≈ −0.24 on the held-out sample, moved rows only — the exact\n",
+    "value is specification-dependent); the fitted $\\theta_r$ is *positive*\n",
     "(+0.29, seed spread $8 \\times 10^{-6}$). Direct likelihood profiling on a\n",
     "million real rows\n",
     "confirms the likelihood genuinely peaks at the positive value: small moves\n",
@@ -2181,16 +2203,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8461b6fb",
+   "id": "abbc9ba0",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
     "\n",
-    "One scoping sentence first, so no number below is ambiguous: every held-out\n",
+    "Two scoping sentences first, so no number below is ambiguous. Every held-out\n",
     "score in this section comes from fits on the 60-shard training split only,\n",
     "with the four held-out shards touched by nothing but the scorer — the\n",
     "headline four-minute fit above streams all 64 shards and is never\n",
-    "holdout-scored. The stopping-rule comparison was pre-registered before any\n",
+    "holdout-scored. And \"held out\" here means a hash-random *contemporaneous*\n",
+    "split over serially overlapping transitions (a holdout row's `ylag` can be a\n",
+    "training row's label), which is exactly what paired comparisons between\n",
+    "optimization arms need and nothing more — no forward-in-time validation is\n",
+    "claimed anywhere in this notebook. The stopping-rule comparison was pre-registered before any\n",
     "full-scale run:\n",
     "fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical\n",
     "batch order and seeds, flat learning rate; savings to be claimed only if the\n",
@@ -2200,19 +2226,23 @@
     "seed come from one run with mid-flight snapshots — and the windowed rule ran\n",
     "as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated\n",
     "one-pass floor plus its designed $h/\\kappa$-window detection delay, 46% below\n",
-    "the reference budget."
+    "the reference budget (each protocol is replicated below). Stated plainly,\n",
+    "because it would be easy to bury: the fixed 1-pass budget *dominated* the\n",
+    "monitor here — fewer steps, a better held-out score, and it passed the\n",
+    "pre-registered test the monitor failed — so on this corpus the rule's value\n",
+    "is certifying the plateau, not saving steps beyond a well-chosen budget."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "b9edf32b",
+   "id": "101f049a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.845522Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.845462Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.850767Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.850425Z"
+     "iopub.execute_input": "2026-08-01T02:01:01.900418Z",
+     "iopub.status.busy": "2026-08-01T02:01:01.900342Z",
+     "iopub.status.idle": "2026-08-01T02:01:01.905650Z",
+     "shell.execute_reply": "2026-08-01T02:01:01.905318Z"
     }
    },
    "outputs": [
@@ -2315,99 +2345,137 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2d4819e",
+   "id": "2221ad74",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
     "guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about\n",
-    "$10^{-3}$ nats/row — a hundred times the seed spread — while the 1- and\n",
+    "$10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads\n",
+    "run $3\\times10^{-6}$ to $1.2\\times10^{-4}$, and batch order is shared across\n",
+    "seeds, so these exclude data-order variation) — while the 1- and\n",
     "2-pass arms tied within seed spread, and the monitor arm under-scored\n",
     "everything. The loss trace suggests why: this fit reaches its loss plateau\n",
     "within a quarter pass, so all flat-rate arms sit on the same plateau, where\n",
     "the held-out score of a *point* estimate moves with the optimizer's state at\n",
     "flat learning rate 0.02 — consistent with Adam's stationary jitter and\n",
     "position along the (seed-shared) batch cycle, though these artifacts alone\n",
-    "cannot separate that from other path effects. The polish experiment points\n",
-    "the same way: re-running to the monitor's stop and annealing for two thousand\n",
-    "further steps at a quarter of the learning rate moved the held-out score from\n",
-    "the worst of the table to the top — within path noise of an annealed two-pass\n",
-    "reference run on the same split — at 55% of its step budget. One run per\n",
-    "protocol, so these comparisons are exploratory; paired, replicated annealed\n",
-    "endpoints per arm are the confirmatory design.\n",
-    "\n",
-    "One more layer of honesty on the metric itself: even among fully annealed,\n",
-    "fully converged runs, path-to-path differences are about $5 \\times 10^{-4}$\n",
-    "nats/row. The pre-registered $2 \\times 10^{-4}$ equivalence floor was\n",
-    "therefore poorly chosen: the only arm that passed it was the fixed 1-pass\n",
-    "budget — which shadows the reference's own trajectory and passes trivially —\n",
-    "while for every protocol that behaves differently from the reference the\n",
-    "floor sits below the plateau jitter it would need to see through. A\n",
-    "pre-registration mistake worth admitting, because the comparison it pushed us\n",
-    "toward is the right one: flat-rate arms scatter over $1.7 \\times 10^{-3}$,\n",
-    "annealed endpoints over $5 \\times 10^{-4}$, and the seed spread within one\n",
-    "protocol is $10^{-5}$."
+    "cannot separate that from other path effects. So we ran the confirmatory\n",
+    "design the failure demanded: three seeds per protocol for an annealed 2-pass\n",
+    "reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass budget\n",
+    "plus the same anneal, and the monitor's stop (1.07 passes) plus the same\n",
+    "anneal."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "7e96e76c",
+   "id": "a1022cef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.851506Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.851455Z",
-     "iopub.status.idle": "2026-08-01T01:23:54.937311Z",
-     "shell.execute_reply": "2026-08-01T01:23:54.936952Z"
+     "iopub.execute_input": "2026-08-01T02:01:01.906479Z",
+     "iopub.status.busy": "2026-08-01T02:01:01.906426Z",
+     "iopub.status.idle": "2026-08-01T02:01:02.012722Z",
+     "shell.execute_reply": "2026-08-01T02:01:02.012289Z"
     }
    },
    "outputs": [
     {
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2/xnn5riV60DpNpEJOnPNNVdZZJFFqnHXUkst1fEJLI3kc8h+P/ujrnFRjgU9jd0z/s3kj5wnZL+ffVTGEPmdBkuuq+T6UcYIOd/J8iecnWB5dxnn5JibcULGvtn+MrbL8avdXT6GynlZO/aDGctkG8lyZ1vu6VpjlivrtWu7yHgs28pAy+d+++23V9fdsq6zXWSM3+hcNMud/XjXus54P/vEXIudcMLOXab+5Cc/We0bepJjQD5vAAAAAABgaLjmmmvG+nPu3WTuUG/v/x5yyCEfFEHOHK7MId500007sqwjXe77jCnzbHor9wpzv+2mm2764O/StKJVhx12WHVfODIP67vf/W7ppBR8uPPOOz/487e+9a1qzl9/ZX7QmEUpJppoovKNb3yj368LAADA0DGiQvupwJeT5N/85je97jrV1WUrwf48xpTwSya+ZmLn2muv3TQMkyDaNtts06dl3nPPPUurui9vKxNrMzH1vPPOaxoqGPMCTB6pnvizn/2smii+1lprlS996UvV5PdOyMTdRs4666yy3HLLjfOZ5qLdaaedVtstK5PK88gE8/z84YcfXhZddNGy3XbbVRUe+1KwAQZCgmSZxN6oE2DXBcnhIvvrVCKt62y47LLLlhNOOKFMOeWUZSjLxeQLL7yw2qemCEpf9sMJ7eQxpgSIFlxwwaoy7XrrrVeF1JvZcsstqyBKb+VYmUcrcmOjHV1uE5o8++yzq31wjsW9WV8J5OZx/fXXlx/96EdVaP8LX/hC2XjjjasL2O108cUXVxfbe5KASZahu3SMP/fcc8s555xT/X+j3yMBshxPc5zKtpNlT8XlnXbaqQqujAQpvJDPNqGju+66qypa0FsJ+SS4nEde45hjjqmKReRz3mqrrToWEL/jjjtqx3KNxl4JSJ566qnV79qoI2628Tyyj0gxhxNPPLEKVm277bbV79Tu8N2+++5bLrnkkh6fy3ocs/L0mOv9l7/8ZTXGe+GFF3r8t9lnp1BVtu8EydIR+/vf/361v955552bFpjpVMGufI/y+zYKY/d0zMwjgb58X7P+M+bfYYcdOv4dTKGKhIB/97vfVdtctvPeSgGIfLfyyI3BfFYZGySAmuNAX4PFo1FdODvnNUOxkMZoUrcfjRSpaLfsv3Ic7qmITDQKwTeS43yjfWikcFyngvMJndYtb/Y72VcMVekcMOakhzHttttu5atf/eo4f5+gcI7B11577TgFn7ofgzNWzjH4lFNOqYrPbLHFFtV7djqUnffORKB8Ntnv92XMnu0pP59HJm7kOsfEE09cdYzIGDjh94GQdXv11VdXx9r8Ho3WdXcJyCeEnkeOuWNKgaaMGzIGXnHFFZt2Rhku52WtyH4vhZ9+8YtfVPvBunPkMcdtmdCTR37fHL9S0CHX5zIu6LQE9DNuvOKKK/p0LSLLnYIVeYwpY7GE99dZZ51qTJZ9ZTvldY8//vgen8vvkPO+dmyDAAAAAABA/+W+yZhyP6m3cu9vjTXWqOazjPl6Qvut6X5fsK9NJbr/fG/ug/Uk99B+9atfffDnXXbZpaMNRnJ/M3MTx5yrsP7667fltS+44IKx/pyGc+1uXgAAAMDgGhGh/XQk++EPf1hNIG6ndOXMxOo8MnHzxz/+cTVxdCRIAC7rrC5M0NuQVC5u5ZHCBt/5zneqye+DJZOy99tvv6qAQytSvGDvvfeuigH84Ac/GLIdnBmdUkgiodWeuuxFQsDDRSbLH3fccU2r3iZU2u4gdrsvSid0fuyxx/a6WExvXzfB0zyynnLsyXuMFCmWkuB/Qqv9lZBKij+cfPLJ1X9z02WwXHrppdWxo5UCGgkK5t9feeWVVZgux6IEsoabBLmvuuqq6vfIsbgueNnKa59++unVdy4hu913370t1Yv7I8Hp733ve9VYsdVgWYoGpYBSvhMJjg6W/A75XfI7tSIhyzyyvzrooIOqrrqdln1lAvf5/udmWX/HtLm5l9BbipLss88+VVfmdi5rwpb5bvS2UElvdVWFzyOdozMWTiEqelZ3zjgQIUfqJdBcJ4HUdusKijYKi6dDfQrtpIBWb69P9NTJu0snJw40KwaQbthDObTfF9mPppNCQsOtePHFF8uRRx5ZFVtKB4aE4NspY7vslxMGvvHGG6uCN+2S1+q6DpOxb8LGc8wxR+mUFJnKGCHFetop35UUycojx9wch1udjDKcz8tyTpRtuXuIva+y30lBhTw+/vGPV+OxThQiSvHLfHfy3avb17UyFstkqzwOPvjgaiyW4lbtLAiaglk9dW/J+VveV/EjAAAAAAAYfJn/0b2pRuZC9EV+fszQfu6T0prMBx+zWHZf58V1//ncr2nl/mjuV3ZJoe0Usu6kzOPqWvbMX828xHbI/bXMERxTiocDAAAwsgz7NoLpEpwqiu0O7PfUdau/YaChIB2t9thjj6pLXX8D+92ls10qCXa/oDBQ0jUwnWNbDeyPKR3gPve5zzXsUAuDpVEH0BgOAd8ECxMmbBbY/+IXv1gVShnKgf2ETD7/+c9XF2jbGQzpSash2qEmAe6TTjqpbLjhhm0J7I8px7R0+U7xmLrvSSckzHPAAQeUb3zjGy0F9rt/R1I45rOf/Wx59tlny3Bz5plnlgMPPLDcddddbQ3sdx+TZR3lmN/u8FhfJNiTbbnVwP6Y/vKXv1T7vYsuuqgMtNwMyn4s35927GsyNt9ss83G6VTbbgnR5nuSIlTtHKNnu83nkMIxd999d9teN5/xDjvsUI0t2xnY7y7LvPnmm1cFLhhXbiQ32remONIyyyxjtQ2yBGMbScfrRRZZpCPv+7GPfazl5eop0FpnyimnLJ3S7LVTpG4kyDEmnTBaDex3H9Pn+khCwr3tHt8buS7xta99rToutjOw313GIRtttFFHrsPkmJhwdo7rnR5z5XuT0PZoOi/LeGDPPfesxgf9Dex398c//rHaLlIYqp2yXWfS0C9+8Yu2Bva7y3exUbHC/qgrkNXu81MAAAAAAKD1ZjDdLb744n16je4/n3tII2X+3UDrPo/innvu6dM8uMzFHtOSSy7Z52U48cQTxyockHl6acTXKWnKMeb915122qltRdQz36n7/MKll166La8NAADA0DGsQ/vpwpUuqwmi07uJzAnxpANup+SzSGgxE7sHUgoGJGzWzm0hXcv33Xff8tOf/rRtrwn9kYvHb7311qAEgNoh38+vfOUr5cILL2z4M+OPP3759re/XX33Ep4byvvTdPoeKcGngZBQSvbTRx99dBW67pQLLrigCj93OrAzZphpr732qoIr7fToo49WnXDzX3qW4O0XvvCFpl2ZOyHd0hOwahbM7IvcqErRibz2QMl7ZiydYgvtlBtl+R6+9tprpVPjvhRX6mSAMEGxbbbZZkA/j3bJPjYdexNApfeBvPnnn79MPfXUVtkgyj6p7rg399xzl0knnbQj792sGMBDDz3Up/PIoRraf/7558sbb7xRhvvxP9c1xpyU0Q7p0p4ChwNd/Kmd12Hafe0i3SJOOeWUjhViGs3nZZmMk8ITV1xxRUfHA+k4kgJH7ZCCVZ0c3w2E5ZdfvuFzQvsAAAAAADA0Q/tTTDFFn+/lzz777E1fl95Jge/u91R6uy4zj2/M+a6TTz55n7vKP/bYY2PdB/3MZz5TlltuudIpud+fogBd5pxzzrLjjju27fXvv//+ce7xzzjjjGP93dtvv10V6c69xOQkss5zfxEAAIDhY9iG9lN9L8GmoTx5eCh59dVXy7bbblseeeSRAXm/TOz+0Y9+NCDvlcnZe++9dxXy6IT8Hu3o4Af9lXBi3T7vox/96JDeB2299da1E+EnmmiictRRR1X7qqEs4YdddtmlCj3RO7n4noDHjTfeOCCrLB0gE6huFpprhyOOOKJjxXASGv7Sl75UXnjhhY68/kiQ4gwpBjKQxQ1uuumm8s1vfrMjxSfSsXS//fYrTz31VOm0HE9SJOW3v/1tx7ogZz21e6z+61//uupcPBCFOfIZJwR5zTXXlOEoAdTjjz9+sBdjSKkbh9R132VgJIRdF5hOaL9Tmr12X86jm+33OnkNozev/fDDD5fhPKbMhIicW3RCjok5Ng5XuXZxySWXtOW1UtBnqF8HGa7nZfkObrXVVm0vPNHIz372s3LooYf26zX+8pe/lK9+9avDsqjFmOomcD3zzDPVAwAAAAAAGFzd58zMPPPMfX6Nqaaaqkw22WS1r0vvLLbYYlXjmS6Zp73bbruVl156qfbf3XbbbePco9pzzz2r4H5f7n9/97vf/WCOVj7XffbZp6Mf3QknnFDNeeqSAH/mtbZL92Lkc80111jPpdD8iiuuWK3zrK/8vpkHuf7665dVVlmlHHTQQU3XPQAAAINvwjIM/fe//60mUjcLS33oQx8qyyyzTFlppZXKfPPNVwVauy7GTDjhhFXYJxXp0v03EzMT+MrjnnvuqYJyI0Um1SbQ1puLTrkg8qlPfaqsvPLKZYEFFijTTz99dcEh6yoXIu67775qIvvtt9/e9LVS3XDWWWetJiN3SgKZKd7w7rvv9vj84osvXlZfffWqc2Iu3k0yySRVF7pMOE7Fwuuuu66qxNjMgQceWF0cyfYEg+X666+vfT7f2aEo+54vf/nL1feukeybTzzxxLL00kuXoe6MM87oVSfHeeaZp7pQmv1QqvfONNNMVXfYPLLvyrEnj7/+9a8fHH9SmCGdXEdSQZocgxKuSrGdZlIVORedE5xcdNFFy0c+8pHq73LMTjgrj3RVT+gywfy69ZRqs7lof/jhh5dO+d3vfldOO+20Hp/L8WaNNdYoK6ywQpl33nmr32P88cevOrPnO3HnnXdW3cLHrCbck4xHdt5556ry8MQTT1yGsznmmKMsuOCC1WOhhRaqvhMZd6Ri8Ic//OHqe5H1ke6d+S7ke5bjdLNunhmjJER06aWXdnwdpbtvboj0NAbNe2ecsOqqq1Zjzmmnnbb63fKZZ9u96667qv14szBQxinpipqAVSdl200Aviep4Jz9V76LGQvmu5hxdT6L7MvzHcyjWXD+lltuqd5jo402assy5zuz7777VsUNmvnYxz5WBbPymcw222zVdzCfR/a7+T1S9TsFGFJMpC78mZuNCe7/4he/qLbdTshyLbzwwh98NxIeTqX4PPJc9nX5buTx+OOPV9+NdLvtXn27JwntL7XUUtW+dbTL8Sjfw0Y+/vGPD+jyMK4xb3wPdIGqHKP6s2zdxwB1mh37+yPXN5rJ75LxyXCU42Oj6xrZ13/iE58oSyyxRLWtZGzx3nvvVWOphKRz3Mo4stk4+7LLLqvGbhl/dUrGCGPu97O82d9nv5+xUa55de33UzAi+/2MO3MMaGb//fevJs7kd2hVQvA//vGPm/5czuFyfEn38nSYyDlPlj/nO5FjbrbJjIXS+aHrnOfuu++uxjuj8bws37/tttuuvPHGG01/NttFvqsZz3SdT0wzzTTV8SxjmRT2ynaRsUyzzhpZV1kPn/vc51r+7jXbv0wwwQTVOs66nn/++attImPIrOeu64pd20TGk13rOueJddcK2inrNGPbRtd8M3ZtdjwAAAAAAAA6K/eWxpRr+63Ivxtzjk7316X30oDkzTffLFdeeWX159yb2nDDDau54Zkfl3kuuU+e+5u5z5a5QpdffvlY83vSyKmvc8kzVyfz+btkzlbutXZK7s2efvrpH/w5c+nbPd/mxRdfHOvPuUec9ZT5PWmWV9e8Lvczzz333Goe4V577VXddwQAAGBoGpah/auvvro2aJ1QXDo6JyQ6wwwz1E4wziMyWfqTn/xk9f+ZlJsQTIJVv/nNb6pQT29kIm1PHfjqgrRnnXVWbaendki1wmZhyQSxUo0v6yyT2xutq4Qoc+Ekk94PPvjg2uBLHHLIIdWE8QSnOuHYY4+tAnQ9hW6+9a1vVe/dk3zea6+9dvn6179eVXTMctZ16c2FkHSLTSAwF0lgoCUgesUVV9T+TKf3Ja3IRdMEXnLRtpFZZpmlnHrqqf0KlgyUhDoahbS7LLnkktUF4rqOvdnP5pFgbMITYwanXn755Sp4kbByQgMJ7bRa1CHh1kbdNjfeeON+d1zsjQTnx7x43mgbSEf5hEgaBd1SBCaPBEFSBCABnXQTTfiqkeyzEyLadNNNSye2hYSiejqebrPNNtUyJljTk4TJsv6zbs4555xy0kkn1QaXMrZI8YEUqRlOMh7L55XjbUJ0zQIwCXnlke0hx+/NNtusWsf5LhxzzDG147F0Kk2l43z3OinVjBM2GlOKQX3xi18s22+/fY9jqC5rrbVWVf34oosuqoJwr7/+esOfTXGkhLASeuqE3CBLKLG77If33nvv6oZaT7K/yr5tk002qZb/yCOPrG4E1Tn66KPLpz/96ar4Rn/ke5DwfF1gP9tcxvP5/iUM2ZOE3fLI77rOOutUgc4USPjJT37SMIyW4lC77757tc7aVT07xaSyTeT7kcICzdZPbibnkc+g65wl4/EUycq+rlnV8auuuqqtlb+Ho6yv7LuHW/Gj0aRZaDOh7E5JqDQ39xsViulLoLRZZ4DeBOtb1ZuCAH0pQDCUZHzcU0A748McH7JvzHGgu4SHU8gxx+kE/jN+bFYM7bjjjqsmX7TzOkYCzNnn55Gx4Hjjjdd0m8y5QgpQ5jiacX2KUmWiRorONJJAd8aMmcDSqrxHXUf1FBjIcfGzn/1sbZGKBLbziDHXZV47xWe6rrn1JsA+nM7LGskYMgU1m/2+2T5yfS7jhJ62kxzPs5/J2DrLm2tVOc4fddRRtcWh0nUjhXzye/bFvffeW41L66RA06677lpbXCXbTR5dx9z8fl1yPS7bQ4qEPvDAA6WT8t6NQvv5XTtZeBQAAAAAAGiu+xyqZkXTG+ne+KMdRaVHq8xpyb2o3HdNuDzB8xRBSJOmPOrkPlwC5p/5zGf69J4JqI9ZaDxz0D7/+c+XTsl8pMxT6wrN535c7tG2W/f5ArlXmTmcfWnukvutmfOe+XIpvg0AAMDQM+6M5mEgYadGEpbLBONvf/vbtYH9OpkUmwnFCWalMmBCPLnY0GxS9VCUSd0///nPa38mAaD8TCZd14XNuofeU3CgWfe5BI0zYbyu+l9/9DRpPxOFU02wUWC/u0zGzzaVCed10sUsF55gMGRflNBAIwmNZj81lKQjcqp51gX2sy9JoGQ4BPbjmmuuqa26m3B4Qth1wZBmcqE6YeUcexJiSZXZZuGvoSpB0l/96le1P7PBBhtUBSlSbKcvN1lSRCbHoRy76iSY1axTeyu6uluOabrppquWKcGZRoH97t/bhIsvvPDCKshVJ9tVswI8Q0U+x80337wqspTvdwoCtdqxMuO6ddddt6rAnPBSnaz7TnzWY+oeIspYI7/n1772tV6NoTKWzHjjvPPOazpO7cvNmFYC8N3HZhk/5TvbKLDfXUJ4KeDUU/GKMeVGXQJY/ZGbTbmBV3cDNcuT8F6KBDQK7De6SZvvYQqc1H0PuwpD9FcKS+WG5Q033FCF6RO2a7WgQY6hKeiR3zv7n0YSNv7lL39ZRru6bszZb+msO/i6H1e7q9vO26GuIn9Cting0duxXJ1OdpTuTSA/XdRHync4x6yMEdZbb70eA/vdzTXXXFWxpAMPPLB235tjZK5j9DekneP+aqutVs4+++xqXJ/iAgmSt3ptKceQnCNk359xZCMpmJXjTKvH3HS9qCuekesnX/jCF1qeIJXQedZLPocUIPj+97/f5/PB4Xhelsk2dQVIs03mPOL888+vijv0ZTvJdyDfhTXXXLP2s83YIwV92nUNNn7wgx+Uww47rDaw30yKa+T64sUXX1yNk1OEY4IJJmj59Zq9VyMp4AoAAAAAAAyu7vdlu4fve6v7vaze3u+lscx5ynym1Vdfvelqyr2vzBnLnKG+BvYjc5K6Au65b5Rwem/uCbcq96hS4LlL8gOtZhD6Etq/8847x5ojlgLUua+Ygte5d5XGL5kTl2ZI3e+fZZkzVx4AAIChZ9iF9tMNs667e7porbrqqm17v65J1qeffnrTUPdQk4m4mfxcNyE3AadMZG6lg1wugKRTfbOutgmG5eLAQEgH2wTn+joJPpPGM9G4WUethA8ff/zxfi4l9E0m16fLdJ1Maq8Lbgy0XAz86le/WtvRNgUz8nPNgk1Dya233trwuYREmwWA+mr22WevitAkKDLcpAt39qt1Eko54ogj+rXt7rLLLtWxrpEUjUgxn05L5+5cIE/3yr5Kt8uEueoCozmW//CHPyxDXQJGCYnlu9DOAGyO0+kAn0cjubk2kDciVl555eozb2UflnB4gtZ1+4vsbxp1AW2njOfSeTXjp1bCURk7NSuo0Kx4RzP5DteF3LI+8x79KV6T/W3GeXXd1s8888yWP5MU8kgIL4WyGnXObdUqq6xSLVv2Q43kXKavIb3RFNrPfrhT4UB6r1n36RSb66RmN9x72w08HdK7uov3JAVmOhXc702Bn4zRRoKMOdLFYYoppujzv01xoQTf6/bFuY6RwkqtyrHpsssuq8bx/QmO92TDDTcsJ598cm1o/qc//WlLr53A/zvvvNPjc1lfKWTYn3B2d5lklY4YKeLVrIjVcD4vy2SaCy64oHa/kWP59ttv3/Iy5jXynaib9JTCntddd12fXrduXad4Q7uvleZ86thjj62uJ3RC3VgvRZq6T5QCAAAAAAAGVvf5hmn40epco7rXpfS5833miqf5yo033tj051MoPXOjcm+zr/OGMvcsRbXHnJvUlwYefZVmWkceeeQHf15kkUXKlltu2ZH36t6wZMx5CGl6lKYjG2+8cZl11lmrbThzgZZbbrmqiEHmqXW/P56Af10zMAAAAAZH+2awDpAnnniiCrD2JB1Ot9hii469dzsn/A6ETMR96KGHGj6fCdeZCNuXidE92Wmnncqjjz5a243t1FNPrTqUdb8Q1k65UPGlL32pX6+x3377VdtYJlT3JJ32EhzLBH8YKAlk1HXEzHc5VUmHggQCE8LOBdc6uRibAHKrF9UHy4MPPtjwuXRe7NTvM9yOP5FASwLzjWy33Xb9CqWMKUGfhNQaBWESlM2xqq6Dbn/kO5ju3gl99icomO7b6Qra6CZRQlS///3vq87cQ1U6z3dSwuEJb6eTaE9ykycBn3YGonuSsdNxxx3Xr4ITCQxts802VZi6J++//35VNTnjp05Kl/n+vseuu+5ahe0adcm+7bbbqgBgxup99eqrr9YGDxOMzTizHQVgcqMr3+V8D7vfJIt8N/N5pUhUX0011VRliSWWKJ0y33zzVUHK7Ft7knFEqnPnRt5o9cADD7TUdZeBUzduiFbC2X3R7PWzfLPMMkuvXmuhhRaqDbqmQn86lrdTjhu96RJd1518uEhX9pxz9GeMvP7661cF+U444YSGP5Nz/wSSWynq0dttpVXZn6dreq5h9OQPf/hDee6556qweV/UXUPKe7ZS8LG3+rKeh9t52Y9+9KPaAkrZnpdZZpnSjnWYgmb5HHOdric550jRi97I/qLRtYiMt9t1PjeQ58ALLrhg7fWMFPkZyudbAAAAAADQCQn9Zo5Kq/d2c329L4295pprrrL88ss3LPo8pn//+9+lFd3nmXd/XXrvvvvuq+YXdf/cEyxP46YU/U6B6bfeeqsqjp75bfn/eOaZZ6p5Nr/97W+reS3NPofM10nzkTHn1O2+++4d/bgSiP/73//+wb27733vex1r/NBoDn0agHznO9+p/bdLL710+fGPf1x9FmNu52eccUbZd999276sAAAAtG7YpQAbhYG6gmI58ef/5ES8TgJJ7ZgUHJkofvPNN39woaWni3oJcyVY3wnpXtqOiw650HLAAQdUgeJGF/uuuuqqqtPvcOoOzvAOmDXrEr7BBhtUwZXBlguA3/rWt2oLeHSFbvfaa6+Oh2oH+hg0moOQPYVs627EpHtiK6HXOrlonWBwT6GSBG0T5u5UqGSjjTZqeCOpL/I9zjLWhcdybB/tIZJvfOMb5dprr+2xC2w+/4S4UvG4UxICyw2Q/gT2x9wfpgpyqkv3JCHrTob2E7xrRxfTjL8TkDvkkEN6fD5jqgRUV1pppT6/dgL57777bm0Irp0df+eee+5qnDfmDcAxXXzxxeXrX/96RwtRtSr7hlQ0v/rqq3t8/pprrhm1x6qE8J588smGz+dGMoOvWZh88skn7+j7N3v9voTds3+tC+1nvPzpT3+6tFO6GTQ6H+9LcYThYP/992/LtZ+dd965+iwyUaQnGVdk8sh6661XhqJc00lxqhSv6mm/l/HSF7/4xT69Zl2xuGWXXbYMFcPpvCwFNesKx2Q82NsQfW9kolPGqp/5zGeqYh7dZVlS4KM3xbbq1nMKj3S6OEUnNDvmZ7wwkOdbKczWH3/5y1/atiwAAAAAAIxeTz/9dBVU7s+9yt50X++SecSN5lp1n4/z3nvvtbRM3ZumtGOez2iUQuFpKDXmvejM184929xHTci9u8zpSoOONKtKo7Sue2YJ35900km180aPOeaYse5Rffvb3+7oXIEsV+4Jd9l88807Wsi8p+0w89ZTsL03VltttbLGGmuUG2644YO/u/TSS4X2AQAAhphxz5aHuJ4CWl2mm266AV2WoX6h5I9//GNtt8+EZtslXU6/9rWv1f7MJZdcUjplt912qy4EtSustdVWWzV8PsGzZqFkaIdU79xzzz0bhjkjXYsTYB1sb7/9dnVxtu67kQu0uVibMORwDOxHT52Xu3Sqi/twlFBro27x+exT3KGnC/b9Mckkk1Sh4UYS2u/UhfR8T9sloZ2ZZpqp4fO33HJLVRRhNJt++unLJz7xiYbPp1pzJ6VQyqKLLtq28VMqTjfSUwivnXL8aFcX00996lO13+tU3e6r7EeyP6m7EbXKKquUdvvc5z5XbWeNwq59udE80LbYYotB+24MZa+88so4VfTHpBjX0B9nDcQkimav32z5xtRs33TTTTfVhmFb8fOf/7xXP9eX32MoWmedddoWyk4BlmaFpDo1hmyHjKszaaSd+/267WMoXXMbTudldd/NrNMxO2G0y/zzz1/WXHPNhs//+te/HrXXYKeYYora/f1Ah+DzHe7PI9dYAAAAAABgJOl+Hb/RHLRmuof9hfZbk3sRYwb2M/c897/q5gllTmvmk6cRx5gSNP/lL3/Z8L1SfPrss8/+4M8rr7xyRwus517Y97///bHuf6WRRydl3XSXJigzzzxzr1/js5/97Fh/fu2118oTTzzRluUDAABglIb2E8obyR3j2iXd4NNZrS6MlKBYu0NCda+ZbrEJzLTbNNNMUztRvRU77bRTbYjtiiuuaOv7QU8SbE4V2zrf/OY3ywwzzDCoK/Cll14qW265Zbnjjjtq993HHntsbUGM4SBdC9vRfXWku/DCCxs+l/BIp6rR5tjW04XtePTRR6tttd1yEbyd38F0ja3riJrqw426aI8mqRjcznB4X9QVh2jF6quvXhsaqgsa98fCCy/c1o65CbnnNRtp5eZQKlnXdY3u1I2yhDjrwu8J2w5VSy+9dJlyyil7fO7xxx+vCgKNRs3C0UL7Q0Ndoar40Ic+1NH3b/b6Kd7WW0sssUTD4h+R7tfHH398aZe77767KuzTG506rgyUXXbZpa2vt9Zaa5UFFlig4fO33nrrkL7WlHFEo4JorYyJ6q65DaXzneFyXvbiiy+W2267rbZgV6c6g9SdU/R2LDNSr8HWHffbXVAFAAAAAADom4TCx9TqnOPu/67769Lcgw8+OM69rjQImWeeeXq1+j796U9XjzGdcsop1fy37vJ36Tbf9VzuBx5wwAEd/ZiOOuqo6n5elxR8bzTnpl16ev3M9emLj3/84+P83cMPP9yv5QIAAGCUh/YT0K6bqD7cu8a1S7MJ+5/5zGfa/p4Juad6YiMpItCJDp/rrrtu2wMcKT6wwgor1F6Mev3119v6njCm4447rgor1ll11VXbXrCirx555JGy2WabVYHoRqaeeupyxhln1HbGHgnHoN4GpUa6xx57rDzzzDO1Xco7JRWRe7oo3aUuMNOqTvw+66+/fm3Hctva/3UPbaRuf9Rfiy66aFlwwQXb+pp1QcGEOjvV7bN71eVO/y6t/B7XXnttw+fmnXfestBCC5VOqeuSPZQ71mc8Pvfcc9fuo0ej559/vvZ5of2hoVkofoIJJujo+9cVbetNUYHuy7rpppvW/sxFF11Ubr755tJf7777blXsq65oX6vFB4aaTP7oxL6/+0SR7uurrjjZYEtX+UYdzxOq/utf/9q2853f/e53ZagYLudl119/fTWe60nG+3XbXn8tueSSDQuaPfvss70am9Wt5xQYHOiu9O0y00wztTxmAAAAAACAkWi55Zar5gH29nHdddeN9e932223Pv37Qw89tOGyzDnnnGP9ecxQdW+lyHP3ueTdX5fmbrjhhrH+nHtPG220UZ9W3dZbbz3OvZgHHnigxzlCY/79jjvuWD760Y927GNKd/pzzz13rO/AhhtuWDptrrnmGufvZptttj69Ru7hdb8POJwLbgMAAIxEwy60X3cSnossxxxzTBnt0jnvnnvuafh8wmZ1Ybf+aHbRohOT3Ts1ybnudROIuOuuuzryvnD55Zc37byZcFndxeuBcPvtt5etttqqtnv5rLPOWs4777yy1FJLlZGg7hiUz+ztt98uo11d+Cxd5Ou6irdDXeXZni7493d7+NjHPlbabYYZZqjtgP6HP/yhx4rDo0mjcFo899xzDcNR/dXXysa9Md988zW9SdQJdQUuWlU3vnz11Vf79FoJx9YV2lhvvfVKJy288MJVIZCe5AbiG2+8UYZygLORuqIqI1mz0GoKdjH4moXim4Xq+6tZUYC+hPZjyy23rO1SHfvuu2+/is0kUJ5q/wnP9lZff4+hpJPn/o261cedd95ZhrJ27vdnn3322vXQfQLWYBku52V150YZV9aNaduxT0twvz/nRtNPP33D/Viui/3oRz/qdcGQ4fKdefnllwd0WQAAAAAAgLF1b1SQ+z59DST3VHi4rgECPet+Lzvz5PraYG2xxRYb59889NBD4/zcO++8M07TqzQP6c2j+/3cSy65ZJyf6T53PcXxx5xflud7+37d5/ZmHlH3n8nyN2pS0l2jQtx1Jp988rH+PFTujwIAAPB/OjvrvUPdkFLxsNGk9DPPPLPqVrX77rs3nSA/Uj355JNVcL+Rug7y/ZWLMlNOOWV56623eny+p4st/ZEAaKfCwCuuuGLt83/+85/LJz/5yTIUvf766+NUCm2HRgGP9957ryMd1rIt5TGa5AJiOmXWyUXMo48+unaieadddtll1XLWdepcZJFFyimnnFJN9B8pll9++YYdlvMd2GabbcqPf/zjUX2R/+67767tUp79difVhYYffvjhtr5XJ4+nOQalMEZPcpE9AayRtJ39/e9/r44l2af0N0yYggY5DnYiBNWJMcdUU01VhaoaFWJI9e92m2KKKTpSwKmuG2tff4/HH3+89oZSJwoodA8Hp7p1o0Bb9ied3Ad0yZg+Nybz33w3ehOMqws297Xj8kjR/eZud6P1vHGoaRbKz36yWbC+P5oVxOnre6fIVroGnHrqqbWFWfIzP/nJT8riiy/e5+16zz33LDfeeGOf/l1fJ1EMJc3O0Vs1yyyzlDnmmKPhdaac+w+Uf/7zn9W5fNeYqDf7/Yknnrjhc6+88kqfz3fqZJs78MAD+9xBY7Sel9UV1FxmmWU6/v4Z791yyy0NxzLNrmlNNNFEVaGnW2+9tcfnf/Ob35S999677L///tWYdrio+8504lpanV/84hf9+vfZ3s8666y2LQ8AAAAAAAy2eeaZZ5y/u++++8pqq63W69e49957x/pzmjZk7jl9030eeCvzVXOffeqppx7rvuVo7grfU3OXVu5Pdf833UP8AAAADK5hF9qPtddeu5x22mkNnz/99NPLVVddVXW223TTTQc12DoYHnnkkdrnF1pooY6+/4ILLtiwE10CWO0MW+QCRoo0dEI6HafrZoJ/PXnsscfKUJVuZ6kWOVByUXSttdZq++vutttu5atf/WoZLVLUYpdddqktuhEHHHDAoHauT/AoAYi6AMnKK69cjj322JaqgA5l2c5TMKHR7/7ggw+WDTfcsGywwQZliy226EgX9qEu66CRgQjN1IVFXnjhhbYf7zql2WvnGDTYIaS+SnXiBKBzzMh/U3jgueeeqzqW1xUAaUWnQvsJcXdC9pWNCh6lmEEnutN2YvxUt8/v6+/RrPtrTzdp2y03DQdqf/LSSy9VRU/+9Kc/VQW48v1IwL7dwbF830ajVEivI7Q/NDQLk3c6tN+saEwrYfddd921CrU+++yzDX8mExIybtx8882rc5+6AiiRcejll19enfO1UoijLqw6lOW41YmCM2OOvRqF9jtx7p/9fALd999/f3nqqaeqP6eIQ7P9VV81up5RV2wi5zA5HjUqKrDPPvuUc845p3zhC18o66233qBsU8PhvCzj3LqiRQMxlmnHuVGuwTYK7Uf2RykMkH3Y5z//+TLrrLOWoa7uuJ9tfCAtueSS/fr3mWQIAAAAAAAjSebs5r7gmF3eM5+iL6H97k1nUhB6vPHGa+tyjgbd70O0eh+l+78bzXM0sm1nTturr776wd+9+OKLfXqNNEFJg5zu3xsAAACGjmEZ2t9uu+2qCcJ1FwByEptQaSbxJtyaCb258LLAAgt0LOQ9VDTreL7wwgt39P3z+o1C+wls5WJDJoIP9cBk1+vfdtttPT7XaEI/tCJBje233762q3Dstdde5XOf+9ygBW4PPvjgcu6559b+3CabbFK+//3vN+2WOhylUEhCC9dcc03Dn0n4+OKLL64es802W3X8SRGDHItGekXThEvrwh+5ONzsGNVfdcHgVOzNdtyuccBghvYT7BouEvq64IILynXXXVeF0QZCp8I2neogWhfab1bIpd1h9E6F9vv6e6TQU91NwXzXO70/SXfZupB9O4KUOVZcccUVtQVP2mmgg2hDRd3vnRvzdZ81A6fZ2DH7kU5+Vp0I7U866aTVdYmEm+vGKClIkDF2jpe5brH66qtXBVZysz6B6Bw/M4654447yvXXX18b1s9Y9dprrx1xof2sj06GUzP2uvrqq3t87p133qnW//TTT9+v98g4OZ9xikzm/G+o7vd33nnnqphcnRQbSHj/wAMPrM51ss0uu+yyZfbZZy8DYTiclz3xxBNNx02dHsvUFdrr7VgmxVBPPPHEsbqf9FR85OSTTy6nnHJKWWSRRap1veKKK5ZFF110SF4XqNsP5ljQ6eMNAAAAAABQL/caxgzt5z7e17/+9V6tthTJvvHGG8d5PfquexC8lflqmS/efU5sT4Xsl1hiifK9732vhaUs5cwzzxxrPndea6ONNhrrZ+acc85x5k61+n7ZvsbcxvJae+yxx1g/k/tkPcm8xWyP559//lhFJjJ3uLdSHL77fcBkIwAAABg6ht7MyV7IROl0oDv88MOb/mwmv991113VoytslQm6OSnPY/HFF68m0o8kzbrtdb/4MNBdaF9++eW2hfY71fG2S7oYNwrt101Yhr54/vnnyxe/+MWmYdYdd9yxegyGhD2y360LAMVXvvKVcS5AdkLCrY0Crq2acsopq0cze++9d7n99tubFliIhDB+9rOfVY+EFRZaaKEPjkH570wzzVRGkmZVX0866aTqMVgS3Enoaoopphjyx6AcJxNMa9Rlu5XOuoMR1j/iiCOqgOFA60TQvZOh/bpCEnVhq1b1Zl/XinYWxqoLkuV7Mdg3VOs65zaT41f2hQno1oV4O2Gg32+oqOtcPVwDzCNRs0B2vvudDNpmjFCn1cB4OnwfeuihVfGtFA9qdvy6+eabq0crll566aZj9k4dyzptIM796/QntJ9rIMcee2y55JJLqmtUQ32/n2NsQvjdJzM1+l7+9re/rR4xyyyzVOc56R6eR4ohTDDBBKUThvp5WbNzo5122qkMh7FMjpPf/va3ezURLuPWP//5z9XjmGOOqfabWcfZFrr+265zsf5o1r0l1z+E9gEAAAAAYPB84hOfGGuOWQLZv//978sKK6zQ9N+mccKYXchzb2iNNdbo2LKOZN0bxD322GNVcfK+3LvtqQh37tX1dL+22T3bRq688sqxQvtZvi222KL232TuQbOfqStEMOa91BTr7strrbPOOmOF9m+66aZq7vC0007bq3+f+85jmmGGGco888zT6/cHAACg84ZlaD++/OUvl0ceeaRceumlfZ6UesMNN1SPrm55ubCw0korVZOSE+If7t54443aiamtdAjsi2YTcOuWr93v1V91oZBc2MsEeEEf+iMBju22267phP5050zQZzDDt3USxDjggAPKZpttNiDLk7DF8ccf39bX3G233aqQUzMpfJKOqbvuumsVAu+tdOxLR8o8svwx66yzVjcTVllllbLqqqt2tHvoQBgOQfLst9t17OhU8LlLlrNRaH+gOta3Itv6UUcdVU4//fSm4cRO6VQgrlnAZ7gYDr/HUN+ftBp+v/XWW8u+++47aL/fYH0nB1tdl9+BDtBSWg6TNwvV91ez1+9P2H399dev/vvNb36zT+PHvhYHSJfr5557rvbnpptuujIcdfrcv9nrZ+JFKy677LKqG31vguVDab+f852c2z3++ON9+ncvvPBC9bj88ss/mKCSUHw63KcYwOyzz17aZaifl42ksUz2Yelm09cCbDmXSSHMrmKYKfA0//zzlxVXXLGsttpqZZlllulYUYdm20Cr4wYAAAAAAKDzFllkkereTYL6XX74wx+Wiy++uHb+c5ooHH300WP93cYbb9yrMHTmcnQPQ2eO+miWe3zdpYHLCSec0Kt//+abb5aTTz55rL9LMe355puvjGbJKyy66KJVIezIvc40MUwzgGb+8Ic/lKuvvnqcbRwAAIChpX3tMAfBYYcdVnbeeecy3njjtfwaOdm97777yoknnlg+//nPVxUV0w0qk4yHq3SEaqSTnQl7+x51y9fu9+r06zcKU0JvvP766+WLX/xiefbZZ2t/btNNNy3f+c53hvRKTZfDgQrsDwUJGJx55pn9Dj09//zz5cILLyy77757daNhzz33HOtmw3DT6TDdUOoyPemkk3Y8YFJ3DKrrGj2YcozP2Oy0004bteFg2mPMqudDUbph99UFF1xQdthhhyEf4huJss+uOx8U3B8amoXiM8Gik5q9fn871Cf0es4551SdyNtt7bXXLmeccUY1dmjWPbvVbvGDbbDP/VsZex133HHVedJgBfb7uz7OO++86rynv+cHv/vd78ohhxxSbac5tz333HPbdt4wlM/LhvpYpq/nRXvssUf57ne/269CoDk/ePjhh6viXttuu21VJCET7J544okykOquS+Yac924AQAAAAAAGBhf//rXx/pzCgzvv//+De/vZy5v/s0rr7zywd9NNNFEVQFoWpOC3JlTP6Zrr722HHzwwU2LJOdzyByyl156aZzmVaNd7kflPvKYUjCie4GD7h577LHqnt2Yc/JSjDxzkAEAABhahnXroHRoykWWTKj90Y9+VB544IF+v2bC+gnwn3rqqWXDDTesJuzOOOOMZTipCxENRGi/WYe6kRTabyWwBZEwTS6WNZuc/ulPf7q6yNmf4iQDIV09l1tuuarK7Wix9NJLl1/96lfl2GOPrar4NrsQ3Uz2jVdccUX1yHrM8S0hhuGknfv3Tvnf//7XltcZ7CI47So+0E65IZDtNsEsGOn7k77uS6666qoq6NaufRB90yx8l+0t3aAZXB/5yEdqnx9zckUnNHv9aaaZpt/vscQSS1Sd1zN+/PnPf96n7uA9mXLKKaub8ltuueUH5wtvvPFG7b+ZbbbZynDU6bFXs31AX8deCSUff/zxZTjL9pWJIQmzpwBBO4rOpFtEHimWud1221XnxP0NSA/V87KhOF7vr0yiWmqppapOH7fddlu/X++1114rP/vZz8pZZ51VFXXIup5nnnlKp9V9NpNMMsmQv/4CAAAAAACjweKLL1422WST6v5Pl/z/M888U3bZZZeyzDLLlIknnrgqFn3zzTdX874T7B9TQuMzzzxzx5f1uuuua3gv7amnnhrrz/m5FM9udM8y89abuf/++z/o0t7dvffeO87fNXq/2GCDDWrvxSZcnmLXY87jOfvss8udd95Z3etL1/gZZpih+vvMiXn88cfLNddcU93/6X7v+qMf/WjZZpttmv5+o0FyD/msL7300g/+7qijjip33HFH2X777cuyyy5bFZ3o2obycynk373Y/EEHHdSWuQwAAAC017AO7XdZfvnly0UXXVRd+Dj//PPLrbfe2u9uiZk8n9dMwGbfffcdMR2kByIo1Kyzbjsnv6ZwQyc1e/2hGrw69NBDq0e7rbnmmlUHuu5ygSgX4uiddFr80pe+VHWYq7POOuuUww47rOPbeW+kc2G6jzYqVPHmm29WoYt0184F89EinUq///3vV59nOkYmhJV10V8pQvPlL3+5rLvuutXrJzAzHPQ3IDOcDESQo+67PxSPPwmnXX/99b362amnnrosueSSZeGFF65uyKTr8LTTTltt67kBNeGEE1ZdPOvW8wILLNDGpWeoGUmdz5977rny7W9/u1ff22z7iy66aFlsscXKXHPNVYVrc6zJDbZUx06wcYIJJqgejeTcJRW4+f+aBUIT3hPaH3zNJku8+uqrHX3/utfv+g62QyY8ZJ+QCQw5h8rN9b4WJMhxdPPNN686VXcvdtBsPc0777xlOBpO5/6ZCHPEEUf06mczkSjnT9n3zznnnNV+P2Oirm0uzzfb72+99dbVpJhOrZfPf/7z1YSRhPdznezBBx9sSxG7BPfzejnnTfB+pJ2XjdRzo4zfMyEok7N+8YtfVOP//ha0zPcrk7duvPHG8pWvfKV6dPI7X1ccKqF9AAAAAABgaNh///3L008/Xe6+++4P/u6Pf/xjFWrOnKLce81czJ588pOfrO45DIQzzzyz1/fr8vt873vf6/G5WWedtVeh/dxT6UsB8UbvFyleXRfaz/3lH//4x+VrX/vaWHN5HnnkkWp+SuSeZuZl/P3vf294jyz3tTO3ND/L/w/cp9HgH/7whw9WSQpn55HtO/cm//GPfzRsBpDiFSm6AAAAwNAzIkL7kRPUdGXKI5UIM9nz9ttvry6E9Geibk54c+En1f/222+/MhzUXdRodIGqnXLhpU47J8A2e69Ov35XJUPorVR23WGHHRpWOu2y+uqrlyOPPLIK7w0Fc889d1V5Nhf6Gk1wT6g/4aOf/OQn/Q5dDDcJVn7nO98p3/zmN6su47fccktV9fSJJ57o1+teffXV1QXuc845pyqcMNSNpovqnT7+NHuPobauEzZMB9Y6Cd6sv/76ZYsttqg6dPYniDMUixbQXkNtG++PFFLKOUWd+eefvzqGpmBPf7tJ+370HHCuk3OkZl3e6bxmHeB7Kh7WLqlGn47PA9mdPkUKMnZMZ4KEvHMjPucIKfTx8ssvV+cNCcLm/Dk34xPoXmihhcrKK688VlX97tJZos58881XhqPBPvfv7XEp++ADDzywafGZjIVSdCHnff29RjIQ+/0sY7qs55HzkxTNzDW3bLv96Sj/l7/8pSr+9qMf/agaJ46k87KRNJZp1P0jjxRguPbaa6vJQ1nXfS1CMqZMODr22GOrAodHH310bbGKTn3fm40ZAAAAAACAgZMgeILeCYf/9re/HeceWaP50FtttVX51re+NSSaJY0EmZd/1llnVffgerpvn/uFdfcMc3/7kEMO6ch995GwfSejkGL/3bfv3Idr9O8OOOCAsvHGGw/QkgIAANBXQyMN2mYzzDBDddElj5y4ZmJtqium2uI999zT0mT/XHCYaaaZqgqNQ13dhO/BDhmOtNB+s66d0D0MtOOOO1b7oTo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fl92D7NWrV6R/E26ysjap/vvJJ580cfRpUe8N+qQ93A4dOoR+Annexylq30ePHm2ipLGH+vwkJM/HSUWFox7bF+O5556zawRJkIVxDwAAgCsKCQAAAAAAEMHpPy62224745Mqxp977rmhnEbkw7fffmtPjlDwR5wUdKLk4KiTM7QJr43spP4+aaCg0jhOFMgrbczr+87jBn3e2lMlOUd1UsuyHnnkkcQlQyO7XnzxxVj+7pAhQ7ydTDFr1ixbDCnqINpiLVy40BZP4r6Opi/wXaQiaGGLuAImlcQf9ylxr732Wix/l3Ylu5Jyb6N4w4cPjzyYGtnB+DR8jE/96Nmzpw3OTzKtF6r46NixY00eKNHMV4KQ1r8uvPDCyBOwRIkXjH38n2YcBxVV0nXkYy1ZezcqoJjUwsz6jNrriWs9NY4kza5du5okuP/++yM7DTnNBg4cGMvfnTx5sre/rWJtPouTlFrsKymF8ZGPtUndOyruHget4+uaT/O4R2tpUcrbOEUFVpQ0HgfN7d58802TV0qOf/bZZ01S6b0lofhOFsY9AAAArigkAAAAAABAiBRA8Omnnzq9RsuWLb1uWOrkwjiC/Urx+eefm+uvvz7W96Ckm7iCTz777LPIq2FnharOK6ldJ84gOrpX+vXrl6uvPG/tqYLO+/fvb+LEfY08eOKJJ7wkd6h9SvoJQXPnzjUXX3wxJ3FE4OWXX851AP1XX31l3nvvPZNXtCvZlfd7O22efvrpuN8CEAj9CIo1YsQIc99996XiC1PSjIoJzJ8/3+TBrbfe6mXe9cADD9gkiLiwLpSd71N7Zg8//LDz61x22WXmyy+/NElPrtcajU7+zYMxY8aYd955J9b38NNPP9n2Csn2+OOPO7/GoEGDzBtvvGHiosTwPBbYRjxrkyqGdfnll8da2DypRdVXROPXOIrz5WmconnVtddea+KU17mC+kIf4+ooig6OHDnS5JWPcQ8AAIAPlb28CgAAAAAAKNdDDz3kFERQv359s9tuu3n7dnUyg4+q5xtuuKFp0aKF2Xnnne1/X3vttU2lSpXMvHnzbEV/nQj/6quvOm06vvTSS2bvvfc2hxxyiMmjvn37msMOO8xss802cb+V1FBAbOfOnW2Cd56suuqqZosttjBbbbWV2Wijjexj3XXXNeuss46pWrWq/eeVK1e29+PixYvt96OCC0r+V2CfNm0VKODjxPiTTz7ZrLLKKiYP8tSeKvgirlPKgLwZPHiwbafXXHPNwK9x1113OReyEvUlGovstNNOZtNNN7Xtk9r4H3/80QbADR061Cax/vzzz4H/hk5mVv9x5plnmqyqUaOGHc9tvvnmto9ef/31bR+tx+qrr277abX76qNVnEYB7+qj9R2rjx4/frxzULICmx988EHTo0cPk1cKFt13331NHtGuhIN7G6VYsmSJHdcHVaVKFbP//vvbOYP65Lp165pq1arZfkTJBArm1/hBffScOXNsksL06dPteODrr7+maA+c0I+gGFpXUmFPH8nqURY2u/rqq0MfI2+wwQamSZMmplGjRqZhw4a2Da9Zs6ZZa621zGqrrWbnAxrva91H8wGdlDpjxgxbaHbYsGG2HXel11I/5DIenjVrlp27AT73zo488khTr169wPsn6qN8jOtVzLtZs2Zmyy23tGsfGmMpIU73otY+XnnlFdtmBKX5/S233BJ6gt0aa6xh9wi0V6D1B60116pVy7Y5+mdqb1ZeeWXb3uih9Rx9RrUzKiKrvQIfBQ/uv/9+O3aNS16KNqSdiuLpdHjNb4L+zjfddJP39wUkdW1SxQk1pkPpfOyBlypv45Tu3bs77RMhGK0BqsCIK+1P7bnnnjaWYIcddjDrrbeevfYWLVpkC5ZrjPjmm2/a/TyXPaouXbqY1157zWn/M6/jHgAAAF8oJAAAAAAAQEhef/118+STTzq9xhlnnGE3bnxQsKAqUrtQwlO3bt1s0utKK630P/9ciVF6KEioQ4cONohDm0pB3XzzzWafffaxQU55o8Db6667zjz77LPeroGsUxDFxIkTvb6mNkp33XVX+9DGnoJ8tUmvQF8FKmnzVMnkCt4YN26c3UCNIlBNQYAHHHCA2Wuvvcz2229vgwBXREUF9NB9rOBladu2rU0SV5CBTulR4mJQ+i502pDah6zLW3uqQgZTp041Pqg9UxDCgQceaIPpdY8p+en77783X3zxhf2Mn3/+uZe/BfimAC3dR2p769SpY+9jJX0r0EoFO3z0QUogeffdd81RRx0V6Pm6f/r06eP0HtROXHzxxebYY48tt39RkL8ee+yxh22f7rnnHqcxr04tPeKII0zt2rVNVqhvbt68ub1WGjduXNRYTgmheqhdVNEBOeecc2xiqAIPdRqwS9Cjrqvvvvsukd+zrqdDDz3UJjzo8+teUxCciijo3lJirI++W2M0JeMmCe1KutoV7m0E9fHHHwc+uU8B41dddVWFQbZKBlN/rT5ExSiVrFrWr7/+aueqSg5TQqqCVpEd9CPp6keyTKcLqiCiD/ottWaguZfaNRU409qV1g1UKOWtt96ySTlq31zptUaNGmULtfii+VRhLqBrUvPGFdE6ox5ad9Tn33HHHf+dEyrhQEm5b7/9tvO6qUvyWq9evWxhHB/0WfX9KFFFc2v9xprraG1RfabWhjR3QTzU9h100EF27Kvfpnr16nZOpgS1Dz74wJ6orKIXrjQ/U3t7ww03lPxcXSt33323099XoUTNudu1a2fHUeV9D3qouOJ5551nC2noPghaMOWZZ54xxx13nJ33+qT1A60f657aeuut7diw2PUH/b6FZKbTTjvN/iZ6n/qs+o6DUjGrTz755H/GpUgPFaZWkfv99tvP9ku6VhYuXGiTVpUAqL5TCYGu1N4HTahTv6a1Ix+0brftttvatk9FOMruWWhfRO/T1/4Ikinpa5MqQKs+0xeNObXHrDGwPq/WG7TPob5e6wa++vq8yts4RYcXPP/888YXtcPaO9cYp3A/6jtVYTPtcWh9S200jC0A4VooQ3PWyy67rNz+WOuNiovZbLPN7GESI0aMcDpoQf2q1g70GkmShnEPAACALxQSAAAAAADAM20mK7hPAasuGwoKsjn++OO9vCcFW7qezKAginvvvddu2BVDm5cK3FUFciUoB91MevTRR03Hjh1NUqj6tgKzFFCiZG59H/qd9V51cosCUJVM7SOYVcFWCozVphWWT5t5rpvyZW288cZ2472iJG9REKUeSspXlXZRQK1+/+eee86eIuyTggaURKJ2QYGcPjdHFZysa/qiiy4yQ4YMCfxa2kwtppCAkiErOl1Z99Ipp5wS+D1ceOGFTiceraiNy1t7qvbt4YcfNj7our3xxhvt/VUeBS4pMEaJTiqyoAQBRE/Xm4ohVURtnE68D0r3f7HXfnniSuZR26sTNtXuL0sBJq1bt7bfja5d15NXFAgVtJCA2ieXICoFaamd0UmZxY6LrrzyShtIpNM8g4x9FUCp9k1BT2mmAMwTTjjBHHPMMbYQjC8K5FRCnIJZzz777MAnkqr/UqCSgg+T9J3p+tFnW7bYgoIW1Z936tTJXhtPPfWU099SwK2SklQcKiloV9LRrnBvwwcVzAoazHvHHXc4FRdUwLkSNfW49NJLbYC3klFVLCwtpxkyPi0f/Ug6+pE8ULviOlYrJJdrLU7JrFqrWpbmYlpP0BhRiU7XX3/9cueuxdLvrBNiXSmBt02bNrYIgs+iuBoXaw1Ja406JbSi9bQVUQK45qoa25RKiXTqN3xo1aqV6dq1a4UFcg455BD7zwcMGGCLYsZxgmxe6TfRd3/kkUdWOPY4+uij7cmkaiP79esXOFmtQMmZWqssda1H61Iu+y81a9a0ax9aoy2GEtg0L9dJwBdccEGgohrqj9TeqFCgK7Ux+i1UAFLFC31RcqvW5bUHofZYc2iXfQIKCaST+jEVMlMS3bKUdKzrTuv3KkL67bffOq+BBtn/VZG2xx57zPjqZ7WeWijqWd6ehe4HvVeNMZVUiOxIy9qkDh3wUcxA+8znnnuuPchC/7082pdQX3/nnXfaz+wjeTZv8jZO0R6yj8R+jQd1yIYKylVEyeyam6ifGjp0qMmzMWPGOB1AIOqD1ccVu+6ovVC1R2eeeaYteBdE//797X6q9iKTIGnjHo2fl7fOoTmYHkH5WEMBAADpRiEBAAAAAACWQ4F506dPX+4/10bg/PnzzZQpU+wGsJKHlajjQoHeSrasKIG5VEpuD5p0JEqUfuihhwIFGnbu3NlWCFfgUBDasDz99NMTcXro8gId9Zvpe9KJUwpm1UbnG2+84fw3tflKIYHiTh9RMQFX2ijVZrsCKYLcfwp6VsCJHpMnT7bFDXRygwsFcygQWZuyLsm3K1K1alXTo0cPG4QY9HQVtX9qFysKQCnQSfTL+w5daKO3okR1H/LWnuo3XV4/WCwl1yqJo5j7qmnTpubll182559/vvP9g9Lp/l3ePTRhwgSnr1UJ1g0aNDBp+j7UlhdToET/jooM6eQWlxN7FJQShH4bnYwRVK1atezJNUGS4JXIplNIHnzwwUB/W0kpCsqsW7euSRudyKIEf7VzCtoLi4o7qP/Q6bhKbgtCwXVJKSSg5L7HH3/cXnfLo3GB+g8FUCqByoXurSQUEqBdSUe7wr0Nn4Ke1KU226WIQHnUz7dv394+NM7XqXFK1kwyxqf/+30wPk1+P5InvXv3DpzcXnZNSq+zzTbbFPXva2yoRGYl7992221Of1tFVbT2EXScqKKJKmrZrFkzEyYlMCiBTJ87CCX3DB8+3K5ZBkn2cP2N1Z/ptzr88MOL+ne1PqlEFSULBe1HUTwl8mjdsJi1Z92vOkVUhXWVeORybei5OgFXa4DF0mnJr7zyilMSvvqGIEnu2vtRAUklGwUxevRo89FHHwUuEqz3rjGcxogqnBMWzdOVwHjYYYfZE4CDrj9cfvnlJom0HrHXXnvZNqZOnTq2T1GxGiXOam9PRcCUHKcCLPpP14IZaaJrW+1uMev3uneVcBz0GikUUy9mP2lZKozm8ncL9P5vuOEGs/LKK6/w391ll11s26NCyL6LiCMeaVqb9FG0S+2cijxr/FxMX6/2QH29kl5dx4FJpnGnvhMVX1TSsPaY11lnHbuPqhigefPmmWnTppmJEyfafmFF++d5G6foO9J+rit9XhXDL2Z8ozm64md69epl4xryKujBAwUq4KeYmFKpzdShOio+pnFTqdSeqBiQYqnilsRxj9qe5e3Ra8/CRZgxNAAAIB38ZCMAAAAAAJBROsFZp+BU9FAijzYXtMnSs2dPM2zYMOciAquuuqq577777Aa2L9oYDkoBFApQDJL0WqCNoKCbGgsWLLAn0yRhI0mV2Cs6LaksBRMoqFtV311pw1YJ6aiYNtx8nOSje0/Xuu5nH0U8tthiC7vZr0AUJZYGpariCs4Ms4hA2e9AxTKCUrCdgimyLG/tqY8AEFXzL7aIQNngLLWjnFyFuIPIVNipmCICBZtuuqlzoLSSJNSelkr9jQt91iBFBAqUaKXPH4TGzy6naMRFgYXvvPOOPbEvzCICBRpPuBQCGDt2rJfTgVzVq1fPFq1YUaBuWRqf7bTTTk5/N2ihJJ9oV9LRrnBvw7cg/bqEXXxJwaOXXnqpXcsqJpkA8aMfKU0ex6dRU+KIazGSypUr20SEYosILJv8oKRaVypiEMRJJ51kk+zDLiJQoNNjNZZ2OQExyOmkKqzhSuuNxRQRWDZJSAlFLutoWDGtOSuZp9S1ZyXcab/ElZLtSjntWPsALntwOk3UZb1RhWqUWBlUnz59Aj9XydNK6AyziECB1p/Vjwb11Vdfme+++84kifZr1N6rTbvkkkvM7rvvbsfD+qzqi7T3pyKOOoFee3squKUit/oeotibiZs+czHJdAX6Tm6//Xanv7l06dJARZt9FEJTAfdiiwiUTe5T4ipzp/RL09qkDrFQm+pC+21KOi712t13333NrbfearJKY3i1J2rv1b/qf6sfUD+rRF/1D5pPKiZIayfqPwYNGmQLtlfUF+dtnKLi+4sXLzauBa0UT1Hq+EYFrXSyfR6p6JEKWwSlubfGQkHVrl3bxq64jP8VsxCnNI17AAAAfKKQAAAAAAAACaJAFRURUMVzX3TCnKobu5x2tPnmmzu9B1Wud0l6ivt0PJ1OX8pGUoFOptDptK58JPJm2YgRI7ycPqIq/S1atDC+6WQbBRfoPgiilEAWH9T+6LSFoLJc+CJv7ak2tBWg6tqvKSAvSHEOBeYpIFmBnEAcdLqEikYFeZ6KCgWlAPpSgwN/+eUXm9AelILL9tlnH+NCgbdnnnlm4Oer0EnaTnlTEGa1atUi/ZtBThAtUFDfl19+aeKmk0gVbFZq4uSpp57q9Hddg259oF1JR7vCvQ3fgp7eF9UJzCooF3S+imjRj5Qmj+PTqA0ePDhwsZSyyfgua+GdO3d2KuApSsJYuHBhyc9zWT8Lek0ffPDBgZ//2WeflfycCRMmOPdHSsoKupalZC4lVCE8Kv5Zv379QM9t2bKlTa5zMXPmzKLXW9Umu+yVqHi3j/0a7fsEpbVWreGkYZ9AxWFLPSk+qfsEGg9oXVynypdCaxfaH3S9zpNO30uQ61onq2+55ZZOf7vUdSrdPyNHjvSyZ1FKEYECzZvYs0i/NK1NKlHblYpv7bjjjoHXol3Wo5NI+5U6QEJJ/6W2YZp3KAG7vEJieRynvP7668aVCugH3U9TgQeX4thp9eKLLzo9/8orr7RtmgvF1KgIRBC///67GThwoIlLmsY9AAAAvlFIAAAAAACAhNhqq61sIItLlfAwNpjbtm3r5X0oqCwonXAe1+klm2yyia1oHpQ2MHWipYs333zT6flZ98Ybb3i5Pn1szi8vGSlIUFIc9D513Qf1+eefm6zKW3uqk05cEwK0Ea/AvKB0KtRxxx3n9B6AIJQcfuGFFwZuR12LQn3//fclJ/AETVKUNm3aGF9B5kpGDEJFgdTuYPnUR7uMKeLupxV8H/T0LgV4uXz2Uu8r32hXgslLu5L2exsrng8G8dBDDzmdZIdsoR8JJi/9SFxcipmJTprX6a4u9Pt26dLF6TU0lxoyZIhJg8aNGwd+7rRp00oujuFy0qUoQUXr4y6OPvpom1gF/5QEt//++zu9xkUXXRS4nS0YPnx4Uf/e+PHjnQoKn3jiiV7W6Js2bWpPsw7a3uiU+zTQmm6pibZJnKPoGtUjSKHbPNA9oSLbLoW0XZS6VqMiAn/99ZfT39TYw6UIrE4oV3uCdErb2qTrWEztuEtxNdFYe/XVVzdZoN9PhSSCHCCxInkbp6gY/dixY42Lfffd12kvTWNQ9fF5M2jQoMDPbdKkidl2222d34PGVS6FllzXEvIy7gEAAPCN1TEAAAAAAGKmJPNrr73WPPfcc6ZBgwbeX98lKEnFDZTE6YOqgdetWzfwybxxBVdpc9zlNOw11ljDdOrUyek9zJkzh+rUy/H+++87fb/aZL7sssucXiNrXIpffPvttyar8taefvjhh8Y1YerYY481rlq3bu38GkCpVMDC5aQ31ySHH3/8MbL2SZ9zjz32MD4ooFCBSEGlJZg+TgrQcgl2jruf1kl+QenUt6AnZcqCBQucCm64ol0JJi/tStrvbaw4UTaI0aNH22JkJDJD6EeCyUs/EgclpBeb/FsRJR64FB8saN68uVlvvfWcXsM1SSsqLvPUJUuWmHnz5pX0HNe1oe22285svfXWzuOkk046yek1UL527do5fzXa01ISmItiTxd3aZNXWWUVrycqB01CTVvfkvZ9AvUzrgm0WadiIi7rmK5roFH3S1WqVDHHHHOMccWeRXqlaW3yp59+MlOnTjUudL3rundRp04d58JDSdG+fXtz2GGHhfLaeRunqHDC4sWLjQsfBa4POugg50M90mT69OlmxowZgZ9/xBFHeC1KFpTWPH/77TcTtbSNewAAAHwLHgUPAAAAAACcbL755vY0HW3gKtk8DAoO/OyzzwI/3/U03WVtsMEGNik+CAXOH3/88SZKCgbYe++9nV9HG9I33XST02bYqFGjTKNGjZzfS9Z88803NpDCxeGHH56rDeZiAxaCymol9Ty2pxMnTjQulJisUzNdqQCDNuZdA6Z80KnvyAfXYDKXgEL59ddfS/r3P/roI6cTanycdFO2fQp6Eo3L58gTl9Me4+yndXqqTm1zoUI6QQPlVExn0aJFgRN6425Xgp4kVUC7knxpvbcR7rhg3LhxduyuYmI69WqbbbaxawMbbrihUzIp0ofxaXCMT8NLYCh1fLEslxMMl000b9Gihendu3fg1/jkk09M1tfsCmOGYosuaPw8adIkp7938MEHGx8OPPBAc/311zufQl2KWbNmmf322y+yv/fuu++GUmi6Ilqz22uvvby8lu6/t956K/DzdZ3peqtUqVJoawZaW/RRuKRs3xJUmtY+0rxPoLHyVVddFet7SIO41yq0TlMKlz0aUeETH3vyG220kS2U8+mnnzq/FqKTtrXJyZMnG1e+ktPVVrz22msmzTbZZBNz/vnnh/b6eRunuO4h6z7wsXeuuaCKCTz55JMmD1zHkS7J/8vS2mRQKqqiPtTn+8niuAcAAMA3CgkAAAAAABAxBUOdeOKJ5rzzzgs96FsBFS6nbipA3ae111478HMnTJhgoqZNR1+nn+2zzz7m9ddfD/warsExWeXjZMYsnGilTUclWesxc+ZMm2D+3Xff2dMt9NA///PPP/99hKnUU7TTIo/t6ZQpU4wL19PIlj1dMAmFBJAPCn7ZcsstnV7DNVFZxUtKCcwOWlgkae2TAod0sqmCv7JEfaPa1GnTppnZs2fb3+uHH36wffTChQvN77//bvtn9TP6/GG/l7gouSTO66vUeytp7UqUn512pTjc2yiW6/0vmuc988wz9lH2NES1L4VHw4YNbZEBJUa4njiIZGF86obxaThcE++rV69udtxxR2/vR2sQLoUEvv76azs+95nAI0p8VyFUzQe++uore0K35gPz58+3c4FffvnFLF261M4FNCdQgllS5gNKknM9JdLX2tA666xjmjRpkqoE7KRTAVCXQlZlae9DBQqDFnrQ2rUKN6hwUkX02i6FLXT9JKVv0bqA7kVd176pHSvsE+g7VZuj+Z3aG/0zrT8U2puwC3PEvU/Qrl0753l01imh3rWYepRrFeojXfcsfBSPL9vHUUggXdK2Nvn55587/S31qyrU7cPuu+9ui8u47JPGTbE5vsY+y8rjOMW1Pd5zzz1N5cqVve0h56WQgMs8XP2+Cmr44toeKl4hykICaRv3AAAAhIFCAgAAAAAAREyBDk899ZR57rnnbMVjbVqGddKMToZy0blzZ/tIAgVzKrjJ14ZisQFwvmhTyqWQgGuwQFYpANdFjRo1zBZbbGHSRkmGOplSp6OPGjXKnkoRduJhsRYvXmyyKG/tqQLFVYzC9XQXX3y+FhBFYr0S+1yUUvTFtX2644477CMJ1PYoyD3KUxjD6gvfe+89M3ToUNtPK3g/KeLsp30EREZ5b/lEu5KNdoV7Gy4ny6nI0M8//+z1S1TSnQqeLVt4UAV5dPqc2t3tt9/eBuX6DBRG9OhH4pOV8WkYlBzvQkVPVnT6eNTrBvpMPsasSr4ZNGiQGTlypBk7dmyiTh0sJXHBdd1ViRoup6GW9xtTSCBZhY4KVMBIv7XLNfPll18ut5CA2mIlwQf19NNP20dSfPHFF14KCSihc8SIEXYNQusPrmtEWVl/ULLr0UcfHdvfTwu1q65JtVGu0/z000/OBW587gduvvnm3l4L0Ujb2qQKCiblele7qnUF7UenUc2aNc3+++8f2uvncZziOlfw2YbmaQ/ZZaynPjRJcTFRj1vTNu4BAAAIA4UEAAAAAACIiQJ8BgwYYN58801z+eWXm2OPPTaUTcusUCV3JbXWr18/sr/pc9NRwbFxBudmlQJzXeywww6pOvVYgVIqRKJTKHWSUBJltZJ63tpT1yICSgbwmaxE4hOi5COQxvUU4FJOocxS+yRpTtRSgN0TTzxhBg4c6BzYnMV+2se9pWQkF2Gf8FoR2pV0tyvc23ClAl4HHnigefbZZyP5MlVkTsXD9HjllVfs/1e3bl1bLLFly5amadOmXpN3ET76kXileXya5DU538keSg6qVauWmTdvntNvHTTBTGst77zzjundu7cZP358bONOn4m9rmtDOgHXZ3/jur6O/y105JN+H5eEshWtdbu2OUnjupaj5/fp08e8+OKL3otVZWH9YY899vBSqCHr0jbGdO2XlAjdqFEj4wuFBNInbWuTSSr2Xbjm01pI4NBDD3VOIF6ePI5TXK9Pn2N7zQN1Uvz8+fNN1mVpPzDq+yZt4x4AAIAwUEgAAAAAAICYKcnoiiuusCeudOnSxWtw3Zw5c0yW6PNEVUhgvfXWsycG+kyCVcJ60FPjFyxYYAM9V199dW/vKQvmzp2bi0AfBd09+uij5qGHHnI60SAKWa2knrf21LVQhYI2XAOqytpwww29vRZQTLKDKwWmRiWL7VPa/PDDD+aOO+4wL7/8cuIDgeLqpzWuVnKXj2TcvLYrUX72NN6HYXwe7m341Lp168gKCVR0HxROuNtoo41M27ZtbUHLMAPp4Q/j03hlrV/0xTVxJIw1XhV8cCkkEPQz6TTwG2+80RYfSjoVPCiW69qQ77UcnXiP5H6frq+3ovvPdR8gK33LL7/8Ynr27Gn69euX+HX4ON+fCkgje2sVmqO77ln4XLOtU6eOt9dC+NK4Nukyrg3jGq1du7ZJq7D7hbyNUzSn+PHHHxM3Fs16IQHFGbnOw5Mk6vsmbeMeAACAMKTnuDUAAAAAADLuscceM7fffrvX1/z1119NlkT5eXwHs6622mrOJ6C4Bslk0aJFi5ye77NYRFh0iqQSPO65557EFxHIsry1pwpKdVG1alXjuw2NMjEb+Va9evVUBZPkrX1Kmvfff9+e7vzSSy8lvohAnGrUqOHlddLaF/hoV6L87Gm7D8P4PNzb8E0nAbZq1Soxc8zrrrvOHHzwwea9996L++2gCIxP45W1fjEpa3LVqlUzvrmuRZT6mf744w9zww03mFNPPTUVRQSklDlL0taGwrhm8sz39+n6emGvVSZNkM/zySefmCOOOML07t078UUE4rbNNtvE/RZyM8aMsjCZiq0nqV/S+i/F39MjjWuTSbvm0zwWC7tfyNs4RQelBD08o4Drs3T63kspDJd0Ud83aRv3AAAAhIFCAgAAAAAAJIhOHH/mmWe8vV7Wko6j/Dy+Ny99vKZrgG4WuV4Ta665pkmyyZMnm+OPP95MmTIl7reSe3lrT5cuXZq4NnSNNdbw/ppAWAFxK6+8cmRfbt7apyQZMGCAOfvss83PP/8c91tJPF+BpiutlM6tTdqVdLUr3NsIS9euXRN1mvLs2bPNWWedZe6+++643wpWgH4kXmkan0YpaYlNPu6VUj6T1k3OPfdc07dvX5NVS5YsSdRvzLqQX75/H9fXW9H9l7W2uNQ29IMPPjCtW7e24zcUV8QL0Ywxo1yncR17hJEEnebE6rxJ49pk0q75tF7vGkOuv/76of6NvI1TXOcJwlwh+jYh758nbeMeAACAMER3JA4AAAAAACmkU+kHDx78P6f2KKFbCUNTp041Y8aMMQMHDjTfffedl7954403mu23395sttlmzq/lmgiaND42JdNcSCDKz58WOvkrqwGoc+bMsSeakZyYDHlrT10/b5UqVZyeX9FrLliwwPvrAmGMAaIMJsla+5SWYCid4NytWzfnk3fywteYq1KlSiav7UqUnz3P7Qr3NsKkQnYPPfSQOeWUU8zcuXMT82Xff//9tghSx44d434rqADj03ilZXyatjW51VZbzdt78fWapYyBunTpYoYOHWqyzHVM6PuU5iSv46aR77U719db0fWW5znKpEmTbOES9oeKnzuvtdZagX+bPPHRrqZpnSaM90pCYXrkcW3S9/UZZfFkn6LoE/I2TvHxeZM2Fk2DvF1nvuVt3AMAAFAeyiIBAAAAABBgc0DVihs0aGCaN29uLr30Ults4PLLL/ey+aBgoIsuusj8+eefzq+16qqrmiyJMlErjGBW1+BJ1wDdLHK9xn/55ReTRCpY0rlzZzN//vy43wpy2p5Wrlw5cZv/JG8gKj4CQaIMJsla+6Q+MOm+//57OwegiED0gatpDVj10SZEGZye13aFextRaNiwoXn22WfNjjvumKgvvFevXmb48OFxvw1UgPFpvNIwPo2D63ghjFM7f/vtt0jWg/v372/eeOMNk3WrrLJKotZxWBfyy3dSuuvrreh6y+scRW1lp06dKCJQAu3TktxdHB/fU5TrNK77tq7jhPL8+uuv3l8T4Ujj2mTSrvmk7qlHcQr5iuRtnOK6hxzG2D4PBZfydp3lfdwDAAAQBgoJAAAAAADggTbL2rVrZ/r162fWXXdd59ebOnWqeeKJJ5xfJw+Vt8MSRmCia4Bs1jYHk1CcIamnm7/88svmo48+ivttIMftqeu9FUZQ3qJFi7y/JpAFeWufkqB79+7m559/jvttAKHJa7vCvY2o1K5d2/Tt29fccMMN9r8nJXj3+uuvp0gOvMhrP5I3rusGYczxXV+zmM+ktUSNGfIgaclrrAuZRH+fYd9/ee1bHn30UTNz5sy430aqRJEwini4tgO+2z0VGA1jHwRI6ng7rWOxKPqFvI1TXK9NYa4Qz/cOAACAfHMvCQYAAAAAAP615ZZbmgcffNCcfPLJzknjOg2uZcuWpk6dOoFfg4CZbAWzugZvZlH16tWdnj9//nyTRA888ICX11H70bx5c7PTTjuZRo0amfXWW88GM6wooOGyyy4zL774opf3kBV5a0+rVq2aqDb0jz/+sA8A/ytv7VPc5s6dawv++LD55pub/fbbz2y11Va2n15rrbVs+7ui4lHq22fPnu3lPQDlyWO7wr2NqOkUrGOPPdYcccQR5vXXXzcvvPCCGTNmTKwnn3/11VfmnXfeMQceeGBs7wHZkMd+JI9cf+cwTvB1fc1i1hn79+/vpaiY1nl32203s+eee5pNN93UbLjhhnYusKKTtEeNGmXatm1r8rg2RLKmX/p9fBTFLvt6LnTtL08e+xYVu+7du7eX19poo43s+sO2225rNt54Y1OzZk17j69oz6lNmzZm9OjRJk18nJKMZNJ1m6T9wHnz5nl9PWBZ66yzjtOXwjUfXb+Qt3GK4gwqVarktIalsWOtWrVM3gtdlDpeXnnllc1ff/0V91sBAABASrFqBgAAAACAZ0oEuvbaa02XLl2cA+N69OhhbrrppsCv4VKEIO+SGMyat03oYtSrV8/p+RMnTjRJM3bsWJvA4UKJiJdccolp1aqV3VCGu7y1p67BGwqiW7p06QqTYYtFwixQsby1T3EbMGCAc6BW48aNTbdu3czOO+/s7X0BPuWxXeHeRlw0Xj7yyCPt47vvvjNDhgwxH3zwgRk3bpz56aefIn8/r7zyCoUE4CyP/Ugeua7JffPNN97ei6/XLObaff75542r448/3nTs2NFrEncYXN/ft99+a3xibcivWbNm2eTypPw+K7re8ti3vPXWW2bhwoVOr1G/fn3TtWtXc8ABB3h7X0Bcateu7ZxkOmfOHFO3bl0v72fKlCleXgcI65qfNm2a1y+Xa75ieRunrLLKKjYWwaVYheYKKqbmSx7mCio4p0Mj1JcBAAAAQVBIAAAAAACAEOhEuTfeeMMGgLt46aWXzGmnnWZPCAkaJOTizTffNA0bNjR55DuYVUUEfvzxx8DPV1X3pAeXxsH1Gv/kk0/sKefa8E6KoUOHOgeW9OnTxykQ9JdffnF6D1mUt/bUNUBJSbZffvmlPW3bB98BT0CWuLZPjz76qNljjz28vZ+sc+2nVTzggQceWOFpi8tDP42w5bFd4d5GUsbgJ5xwgn0U1iU+++wzG6yvYnMzZsywjzAKHxYMHz7cjuUpSAcXeexH8sg1CW/q1KnGp7lz53pJuF2eQlvs4qqrrjInn3xyKuYCrmtDX3zxhfHJ9zWTd/o+fba1rr/Piq63Bg0aOL3+ddddZ4t45GmOstlmm5knnnjC6URr1h+QtOLHq6++ulm8eLFTW+WrkMDnn3/u5XWAsPo+n2MnnTzPWKxieRynaOzmUkhA19Ouu+7q5b0sWLDAfP/99yYPNGcNWkhg++23N/379/f+ngAAAJAeFBIAAAAAACAkCgpUALZOZA5Kwdv33HOP6dGjR6DnN2rUyLjQBl6aEl99+vnnn+1pgK4Bk76SYNdee21vJ3tnySabbOL0/N9//92MHz8+UScCjx071un5119/vfNpUnGcfJl0eWtPa9SoYdsd1yAQX4UECFACwm2fSNQqzpIlS8zEiRMDf9fVq1c3d9xxh1MRgT///NM5QQpYkby1K9zbSCqdzKZHixYt/uv/1xhdpwgrmfXrr7+295z6Jx+nr/32229m+vTpNvEMCCpv/UheBS18WzbJ3Gdxz8mTJzs9XwVUVnQipuuandpzlyIC4rJOUyrXE0KV0JOk9fUgCWFZPnnX52fTvaxCGy5WtJ6txF+XBOI0ri26tDlq07T+4FJEQNgnQNJOYt50002d1ubGjBlj9t57by/vRzEAQJgaN27s3NerIIzWpF2NGzfOrhegfHkcp2iu4FJQxefYPk/F6LXeEnSMqOtMRUF0gAkAAADyaaW43wAAAAAAAFlVr14958BAGTRoUODNw6222sopGFMn3+WZz9MkXAPzXIOws0qV01316dPHJIlOcXcJuHQNgtIGspJR8N/y2J66BikNGzbM23vx+VpA1igpo06dOoGfP2nSJK/vJ8uUsKlE/qAOOeQQs9566zm9B9fkDKAYeWtXuLeRNir4tc0225hDDz3UnHfeebYA5eDBg80bb7xhzjrrLKeCNfLNN994e6/Ip7z1I3nVpEkT5+KeI0aM8PZ+3nnnHefCCCtqP13W7KRdu3ZOz/fxHkqhgpiuhW1dT1Qv+PXXX20xWPjzwQcf2ELWPnz44Ye2mEBQq6222goLCVSuXNmuz+alb1m0aJGZO3du4OfrhF/XtV0ln86bN8/pNQDfttxyS6fnv/nmm17exw8//GBGjRrl5bWAimyxxRZOX44Ou3j33Xe9fMGvvfaal9fJqryNU8R1nOGzGIuvOUfW5+EaX7LmBwAAkG8UEgAAAAAAIERnn322qVq1qnNSb69evQIHYLmcBu0roCKtfG2ui4L6Xfg61TuLwekq2uH62yQlKU9B1C4ni+24445eCmjEedKQTkty4RK0ujx5bE9dg/KGDBliA5Vc6fS6jz/+2CRB8+bNbXBMkEebNm1MVk5+SuI9mnfbbbdd4Oe+99579jRurNi3337r9DU1bdrU+WvmtDNEJU/tCvc2skIFCC+88ELz1FNPORUT0Pg7TRifJlOe+pG82mCDDUzNmjWdXmPgwIFe3ovmmW+//Xbo16zLmEHFKV3uiwKfxReKSYjabLPNnF7jrbfe8vJefK0x4T+UID569GgvX4mKGbnQulkxa8Iu99CECROcEvOjNmfOnNjXH5Qk7avYBOBLs2bNnJ4/c+bMwCc5l/Xcc8+Zv//+2/l1gOVRQdoVFdpZkZdeesn5S1ZhGde+Pg/yNE4Rl8IJhbmVPrcPvuYceThkI43xCvC3BhrlnJqYCgAAkolCAgAAAAAAhGittdYyJ598spfNr2nTpgV6rsvp5DqVPCnJm3EYNGiQl0CphQsXOgd5um7GZpk2oVwo2Oeqq65KRFCcKsG7WGeddZzfQ9zBIK4nnS1YsMCEJW/t6S677OIcXOQjIEFBeSqqg2RwvUfVJyJZ7ZNOdnQ9vTMvktBPE+iFqOSpXeHeRhZPLXQpYvXbb7+ZNGF8mkx56kfybJ999nFee/WRMKMEKdf1oGKuWZcxg/YJKlWqZFx8+eWXZsqUKSZNa0M6qX7WrFle1obgX//+/Z1f4+eff7b3chTXmUvfoj2AV155xaRFEuYoce8TAOXZbbfdnPvT22+/3Wm/QYXXHn74Yaf3ABRrjz32cPqyRo4c6Xxae8+ePW1/j+XL0zilcLiBCo/FPcZXYSztwefFJptsYurXrx/4+S+//DJ77inGGigAAHBFIQEAAAAAAEJ26qmnmipVqji9hgIa7rvvvkDPPfjgg53+9p133mni8sMPP5gbbrghttPwdDK8j01bBeS5nsCs4BiUr0WLFl42mXv06BHaV6yq+sVU+Ha9TlwDDBV03a9fPxMnl5MzJWjRlWLkrT3daaed7Il5Lu6++26n6vZ6348++qjTe4BfVatWdXr+1KlTvb0X/Md+++3ndL+qD4zrdEcVHdHfnzhxokk61+/ItZ9W4OdHH33k9BpAsfLUrnBvI4tcTij7888/TZowPk2mPPUjeXbAAQc4Pf/333+3yXwuVHhCaw+ua1HFJGm5rNu5zgXk/vvvjzzpY/fdd3d6vr4z1/WwIUOG2JPR4Z8K1Y0fP97pNXr16mXbzSj2PrRWWatWrcB/R2uMP/30k4nDkiVLzBNPPGGv52LEvU/w1VdfUUgAiVSzZk2bvOpCBZ+ffvrpQM9VQfKrr746dcXXkN/xttx4442B++px48bFvm+cFnkap0i1atVMkyZNnP7ugAEDnPb1NTe67bbbTN4cdNBBTsXp9L3HRTE53bt3j+3vp12S42gAAEA6UEgAAAAAAIAIghpOOOEEL0FdX3zxRcnP23TTTZ1Os9dmzvPPP2+ipETXW2+91W6O9+3b1zloysVdd93lFBCiDd8HH3zQ6T00atTI1KlTx+k1sqxp06Zmo402cn4d/U4KyPVJG9jajD388MPN4sWLV/jvr7baak5/7/PPP3d6vu47BWDHSYVXXIqvvP/++0V910HkrT3VZvRee+3l9Pdnz54dOCBAJ3Bcd911BOUlzNprr+30fE5TD8eaa65p9t1338DP14ktvvvAFdEpRvqbSjJT4kEaAnBXX3312PppJVnddNNNTn8fKEWe2hXubURBRQrVlkfFpRiAa1Bq1BifJlOe+pE8U/K9S7KMvPbaa4ELuWrd4KqrrjLz5s1zeg8HHnhgUeMBlzGDrqeZM2cGfr4S6QcOHGjiSIhybWd1qnkpSUnLFvplHhQuJcQGTS5UobugybgF66yzjr3OirHSSiuZww47zKmdv+WWW0yUNP578skn7drszTffbK/pYsS5T6C29frrr7cJ00ASHXnkkc6voaLLb7/9dsn7fRp3BO3TgCB23nlnp9PHC3Ors846q+Q1CfUlZ599duqKDcYlT+MUHwntZYuzBC0i2KdPn1wWADziiCOcnq9ifq5z6CDzWR3A06ZNG+dCZnmPPXSdmwMAgHyjkAAAAAAAABE47bTTnAN/FLxz3333BXruKaec4vS3r7nmmkgCIyZNmmQuvfRSG7D72GOPRRpkX5Hvv//efv4gpz1p87Nbt27OidmHHnqo0/OzrlKlSnbj0ZV+Y51gdtFFF5mFCxc6v97w4cPN8ccfb7p27Vp0MKYC7V1O7FPwZtCg5Oeee8688MILJglq164d+LkLFixwCjpYkby1pyqC4Up/X8EcpVKg+KBBg5z/PvxyLWzzwQcfOAeZI5z2SUlT/fv3D/3r1YknCkhXYpn6XbXbaaEEBxevv/564MBLBSpPnTrV6e8DpcpLu8K9jSjccccdZp999rFFtlRsK2wuQbmuCaNRY3yaXHnpR/Js1VVXNSeeeKLz62jtrNRkPq27XnvttV6S64u9Vl2D9l9++eXARSIvvPDCWJJ6tU55yCGHOK+5du7c2Z4AXQqtZSl5bcaMGU5/H8uneea5555rT6IthYpe6/dxLQKtvY/KlSsX/e8r+aiUf7+8+1DFo8M2d+5cO+7T+E99jO7jKOcoWn8Ousdxzz332L0NIKlatGhh99JcqE+94IIL7DytmMLU2nfTeCHqQtGA9sCPO+445y9i3Lhx5thjjy1qrUBjt379+tlxvo/98jzJyziloGXLlmbllVd2vjYvvvhiGwtVatHyqAsvJMXmm29udtttt8DPV8EIxdboQJQwKVbj1VdfNUcddZRp27atGTFiRKh/Lw9c10AVC0RBJAAA8i34bAUAAAAAABRt3XXXtZuzqujtWiH4vPPOMxtvvHFJz1OwX48ePQInGCsYrGPHjuaSSy6xG6CqqO6Lql1ro0/BF5MnTzZJpE3btdZay1x22WVFf3YFoSjha/Dgwc5/30cib9a1atXKPPDAA2bOnDnOr6WT0HSqvYKCWrdubX/7Yv3www/mrbfesptwn332Wcl/W5vtOllCp0MEoetOieoPPvhgSYEKCgjRCSxJ0bBhw8Dfgbz00ktm7NixNqBsyy23NPXq1TNVqlRZ4XfSqFEjG5SzPHlrT5s3b24LOwQNIim48cYb7XtSckCNGjVWGMBy5ZVXmqFDhzr9TYRjvfXWM9WqVXMqkqN26sUXXzT777+/2XTTTW1wtO7R5d0POvnR9eSdrGvatKnZcccdbfsXhAIE9duoL9V4U0lBvijgUIlB6h8VGJZWG264odPz1bepj9b3W0q/ocJUQU9pBVzkpV3h3kZUdLqc5q0PPfSQDfhVwLUKf61ofFyqadOmOSVfaz6WJoxPkysv/UjenXTSSbaA4KJFiwK/hoptdejQwSYYdOnSZYUFTXQ66hVXXOHl9Mk99tjDbLHFFkX9uxtttJHT33r00UftWlUpa/vTp083Z5xxRuQnRpZ1wgknmKeeeipQod2yRQGUPNKpUye75rqihCMluV1++eXmq6++Cvw3UbzRo0fb9XWt322//fbL/Xd1HTzzzDP2JFPXAspah9X1VQqtDan4QNDCHKJ5+Y8//mjXKbXG5Yuu8/fee88MGDDAFtIsNSFu2SQhFScvtcBD2X5OyXUq1Fos/bZKXtRYFUiyqlWr2r2Ve++91+l1NP54+OGH7X7SwQcfbOdmDRo0MLVq1bJ7bSo0r2Ir2nPRXm/Q+xFwdfLJJ5tHHnmk6KL1y1sr0Nh91113tSfJq89XDIv6QiUWa29u2LBhtlCXiq2hdHkZpxSovTzggANsO+lCsRW6NjUWXdFcScVfVPToiSee8PIZ0urMM890SsxX/6Y4mFtvvdVss802Xt/bp59+avtWFRHQWij8cV2z1dhHxeC0Lr333nvb2BgVTNQe/PJiZNQWuRwEAgAAkoNCAgAAAAAAROT000+3AVYuJ7RoM+y+++4zd955Z0nPU/KsNht1skxQet8KOtLmsTYXtLEQpMK4PoOSSUeNGmXeeecde4J6Gjb5evfubYNTlWy9og3MKVOm2IQvH8GsqgS/wQYbOL9O1imoTtf4+eef7+X1FIzRs2dPe79tvfXWNqhik002sYHMKiygv6cNd1Vq12mS+s0/+eQTG8jsElQr2267rVMSvTb/laiu+3VFgddKDteJK0lLTlQAt2s19FmzZtkgsFLoN9Rvuzx5a0+VqKEAbwUSuFJwigLuFKCkBHIF4CtIqRCUp+D4QlDeb7/95uX9I7zTLoImAxVMmDDBPkppG5999lmnv5kHGn8cc8wxgU+qVB+mBMdBgwbZdu7AAw9cYbu4vNMJ1T7pntZ/up5SmJRExbp16zoVLlIxGrXV55xzzgoL3Khf0tizlHsF8C0P7Qr3NqKmfkDzNj100vNOO+1kAzg179Q4K+jpdbrfNJ6+7rrrAo+nFTjauHFjkzaMT5MrD/1I3inoXGsbpa5XV7RuoGSD3Xff3a6JqjCk1g0U7K51AyU0qQCExsk+aC1GhQuK1aRJE6e/p7ZZayz6rpo1a7bcf1efWXsJd999d+wnwW622Wb293Bdq1MC5m233WaL3ahQp9bDNL9ScUElpKtAq+Y+Kqb84YcfpmLPIEu0LqcErp133tkWvNA6TKFYj9bAlVyoscvrr79u/10flLRbatFsueiii2xb4LJ+qEKsSpZU36LiTkET9VRsVn2KEvP0/WjPwAcV21RSl8v6m4rlqJ1TUQ4V8Fweta9ar9Z3AqRBu3btbIFsJT+7Uv/Tt29f+wCSqHr16qZ9+/Y2edqV5ldKPuZk8PDkYZxSloqeuRYSEO1xH3HEEXaOoLGo1nk0FtX8X3NB7ftrLq+5gtrtvNMaooo46FoLSmP6448/3hb2UsGSIONy0VxOBRp1jSle4dtvvw38nrB8ilNy3aOUUvsBzd+7d+/OzwMAQAZQSAAAAAAAgIhoQV+nKikA0IUCtbRpWOpGjoKy9Hj33Xed/r6C+ZT4pI07BRAqmElJvwr40yl6a6yxhlm6dKmtBl6oXq+NDFW1VrK1KlDHHfwYlE5CUhV5VehX4HIhCVab7tqw1AlJCmzWd+SaTF528xXFUXLyvvvu6xzUWpYC3UtNdvVxEprLSQWijXRdo0ceeaTZa6+9bCKINhYVJK8TzFTwQP+OKvyHETTgSok0SZa39lQBBDrZQYUnXOnkAfWDrn0h4uVyqijCteWWW9qAnz59+ji9jgLIL774YrPmmmvavlXt01ZbbWXbKwUu6uQv9ZHqQ9QOqX1ScJCSswrtk07NySL1088995zTa+jENBXxUVKdEog0ptT3Wkie0ZhTAWAKvPI1pgSCyku7wr2NuGiOVjZ4U4n8mhNoDqdTptZff3277qBT3jRHUKEvFR/QPEH3i8boM2bMsH2H5ifffPON81wsaJJ2nBifJlde+pG8U3K8Thz0kVysdlGJLnqETddmKcVTtCasa23RokWB/6aSYNq2bWv23HNPu86sZG2182p7laytBJmhQ4faBBnXNt0nFS19//33vST3q99SgRA9kCz6fUeOHGkfYVOCu66rIHQaZqdOnczNN9/s9B40jrr66qttAVUlrel+VFHhOnXq2L5FSXv6TrQ2q8LD6lv0nLJ9i/6/MOcorutvKsqp/ZKjjz7aFq5SsWSNKfWZtP6gYtgaQ+qhAiZAWug6VrLuFVdcEfdbASI7sOLFF1+04ygkW17GKQV6TyoerwRyH3NBvY6P18oDFYvSeqLL/FTrKCrMo4fmu9qv0m+qWDT1tbrWtA6p60zrLRo/6rrSXFXXmR6KOwlaPBLB1kBVgBEAACAICgkAAAAAABAhJYXrFBCXgBxtCN5///32FPFS3Xjjjfb0ah9VoBX0qCCkvJ0OrGQuBejrETYlFmsjCMW75ZZbbMGO2bNnp/ZrU4K6a1CyKHBegfquwfpx0HWvjWkFPiRVntpTFTTQCX0KzAOkefPmBPwn2CWXXGJP/5g0aZLzay1YsMAmBOmB/6NTcVwLCYiCPu+66y6+VqRCHtoV7m0khQJzdQqbHnHQfDqNGJ8mWx76kbxTkROdTnfsscfaU+fTQCdcqjhFKVTsRYVMBwwY4Ly+rGIBeqSFCnfo96UwJHwWLtV96HIa+YcffuilqLBODFbxDj2SRCcQ9+jRw7mAhxK+KN6BLFKBztdee822BUAextvXX3+9OfXUU70UdkK48jBOKeuyyy4zw4YNS81cMCvq1atn4xUuuOACL68X53okSlsDpZAAAAAIaqXAzwQAAAAAACXTSXKHH3648zf3+uuv25O4SrX22mvbwCMlhSL5AQHdunWL+22kzlprrWVP+VUiflrpvSuQMk46ESru6/+QQw4xSZa39lSBqypuAohOPtEJ6kgmtaFqn3RyMfzTSc06ATfP/TTyJw/tCvc2YOz47oADDkjlV8H4NNny0I/AmMaNG5trrrkmNeuHd999t1lttdVKfq6StypVqmTyOBe48MIL7QmogKv69eubzp07O72G7sPbbrvNNGzYMNP7iQceeGCs74H1BySZ2gEV3V9nnXVi+fv77ruvqVu3bix/G/m0yy67mHPOOSeWv60TyVVUCsXJwzhl2THL+eefH/fbyKUWLVqY008/Pe63gYjHHzVq1OA7BwAAgVBIAAAAAACAiJ111lnOwTd//fWXuf/++wM9V4lPeq5OUELFKleuHOvXo8DMDTbYINb3kFZbbrmleeSRR1Kd4H3mmWfaRPU4NGnSxBx88MEmbgrMTnqgYt7a05tvvtmst956sfzt/fff356sgOQgMCXZGjRoYB5//PHY+pKs69KlS2x/u02bNiTvIBZ5aFe4t5F3l19+uU0OSCvGp8mWh34Exhx11FHmkksuSfRXofXChx56KHBSz2abbWY/ZxzURvs6bTJoAYY777wztvU6zYWQ/u9T+y66jqpXr+78Wkqg6d27d6b3UVRwQQV54rDffvuZHXbYIZa/DRRr3XXXNT179ox8j0YFlNSWrbQSIfiIVocOHWIpAHj99dfTJ5QoD+OUsk477TSzxx57xFbIQMnVeaU5+Iknnhj320BEqlSpYk4++WS+bwAAEAirGAAAAAAAxHDCmypDuxo4cKD5+uuvA1esV8Ckgv9QPiVS77jjjrF8PXvttZc55ZRTYvnbWaEAtyeeeMIGEaWR7s04TnHT93XvvffGFpxYlgK607AJmqf2tGbNmqZXr152gzrqoLzbb7891hP/8L+UOLHVVlvx1SSYElwUqEYRDv+aNWsWSx+lPueyyy6L/O8CeWlXuLeRZyrktvfee5s0Y3yafFnvR/Cfoh4qzpPEObzWbh5++GGz7bbbOr2OPl8c17HWCl3fuyutl3fr1i3yv3vMMcewXu5Z165dY+n7r7rqKrP99tt7e73atWubPn362PXDrO4nxlFAZNNNN7UnKSexLQeW1bRpU9O9e/fIiqJtsskm5tFHHzVVq1blx0DkVLxCRSy0hhVl0cFWrVpF9veyJOvjlLI0ZtC1GbRgW1DVqlUzDzzwQC726Zfn6quvNu3bt4/7bSDCdZc6derwfQMAgJJRSAAAAAAAgBicc845zgE4f/31l7nvvvsCP3/nnXc2zz33nA0Iwv9SwIlOsYi6SryCqu+66y4CtDxQYO3zzz9vtt56a5PWYhZnnnlmpEUEFNCvoIakuOiii1Lx++WpPW3SpIm5++67IwvK23jjjW1Qnk4MRLLoBEIFBa255ppxvxUshwLUXnjhhdiKI2WZEvp32mmnSIskaeyv0xuBOGW9XeHeRh4deeSRNik27RifpkPW+xH85zTKHj16JGour2Tc/v37e7n2lKSideMoP5+SvpVMnwQqqnb22WdH9vdUlPnaa6+N7O/lhfrNe+65x+y2226R/c2OHTua448/3vvr1q1b197fBx10kMlqm9qyZcvI/p4SAB9//HGbmAekRfPmzc39998fet+85ZZb2qTgQhHzv//+26kdBoJYbbXVbHEsXfdh0jWqQlbt2rVzvt4Lr5dHWR+nLDtPeuSRRyKLNVBBFxX7V4GXvFPs2aWXXmpuvvnmRBwYgXBpnK49en5rAABQKgoJAAAAAAAQA21mHXjggc6v89prr5lvvvkm8POVJK/g3TPOOCPRSUmqrh/X6dt9+/a1gaZR0O+hzdXq1atH8vfyQJW4n3nmGXtqTxo30pRIXwjQCFP9+vVtEQElbSfJ6quv7uWkuCjkqT3dZ599zIMPPhh6UN4WW2xh2+BCUB6SR4HFKvSwzjrrxP1WsILxjAJcFURUpUqVxH5XaQsk1LhCJ93o1LOwKbFDY0ROOkNSZLld4d5G3oKMzzvvPBtkHNe6i2+MT9Mhy/0I/kNr3y+//HIk4+UVUeLyiy++6PV0TBW+VNJK2GN0XYdXXnmlOeWUU0ySdO7c2T6iKHajBIkkr7WlmdpgzWv322+/0MccKlrUoUOH0P6G1ilVwOSWW25J9Im0Qcdct956ayQJiKzHIs323HNP8/TTT4e2z6UT2fX6ZdfCFy5c6LT/BbhcP/fee68t7hTGfF7XuYrKnHjiif/+f7/88ovTayZ57he2rI9TymrQoIF56qmnQo+v0ff42GOPJWK+mSRHHXWUeemll2xh6qTKyhpk3FQkUf1Akgo4lrVkyZLAz1177bW9vhcAAPAfjMQAAAAAAIjJueee6/waf/31lz1hwbVq/cUXX2wTYMOuXF/K5pE2t3Qa5Lvvvmvq1asXayK6ElnDPhVdAVoKQEnSafBZoUDTc845xxbe0Mk9aducvPzyy23AcFgnwCu4asCAAYkrIlA2weDJJ580Z511VmjfgS95ak93331322Y1atTIhBUorhM6SFBPvm222cYGpRxwwAFxvxWsIPmkffv25tVXX7X3V1KSonSKV6dOnczAgQNTeSqtTv144oknbHBWWMkWKlBDEQEkUZbbFe5t5MFmm21m51nnn39+6ubIK8L4NB2y3I/gv4suqq254YYb7Bpr1Jo0aWKLVlx33XWhBNfvtNNOtoBpWEkyWhNRckzr1q1NEilx7e677w6lKK7Wc7t27WqTp5PSPmSV1jN79epl274wxgRrrrmm6dmzpznttNNMFJTo+/rrr5s2bdrYz5YEWj/V2vbzzz9v+7yg94Tut7ASRsuux1LUFWm2+eab2/0ZjTN97SfpNPF77rnHJgCXTf7/888/zaJFiwK/blIT/5AeGiOpsJPiGBo3buxtPVp9qeZpO++883/9swULFji9dp4LCWR9nFJeMQGNKfbdd18TBsXtKLZhu+22C+X1004xHyrmoMKh66+/vkkCzRkVp6OCGipkBj/23ntvey80a9YsUV+pDkOaN29e4L5C8TYAACAclOsFAAAAACDGYAYlmg4ePNjpdV555RWbJL3hhhs6vx8VJZg0aZLdcH7rrbfMb7/9ZqLcPNp+++3td6JkxFq1apmkWG+99WzCrIJeFRzqm5LPlChO0Ei4dI/oBCsV8dA1rqBwl9NCirXVVlvZa9rl91XAsDYAr7nmGjNu3Dgv70sBgdqEO+KII2xgSJLpZNgLL7zQnHTSSTYAXL/d3LlzTVLlpT3V59TmtDb99Tn/+OMPL0F5Knpw8MEHe3mPiK6f1KkHn3zyiU0Seeedd5yCKBEeBQ0pEUNjR923KrLz888/R/a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nUPCsHrqALb744oW/X63TXtEFNgbiOrpdXQ85Bui+3GznyTILCeT993RmbKXDJUVvPvroo9SkxSIDwuebb77U50y+aBxrGZJy33jjjVAGjiv2zdqx/1QGimHRdbRdyl5jknx9+eWXl/LayufII49Muv+2c6283377lb63wXzDnmqsy2hR2Fc97LDDkvNTP56PY/tA888/f/j5z3/eMx8b90b23XffnttLr8K9l+uuuy5J9G7HNRufE4VW7rjjjsJekz2zzTbbrLTO3rfccktyTV51teOrjD1I5o1jjjnmR/99t912SxLzi8b8RDGlovX6uVLldXXZ93vZSzn99NNbfp0q7CtJeefOmMUWW6yrP0j2M9nrTyvG2o1cD0mSJKmekev+V0mSJEmSJOVKTkgLzJ988snDIossEuacc87wq1/9Kow77rhJ0CbdL0gWpWMBXcaygoMJ8B0yZEiYZ555Cuuw/ac//Snz50gcIdifBCz+d8IJJ0w6uo4++uhJ4ihBWAQm8ncQlBpLJiWBisB0OmBNMMEEoRvx3dDpJE9XcD6DJZdcMklgmGqqqZLPhYDGL774IgnYpBMjQST1EsfoJhxLfOh1BL2Q5JqFoNXBgwcngQEzzDBDclyMOuqo4ZtvvklubNMd/K677ko6SmYlhN54441h/PHHT5JHpSIQrEhQeh6zzz57cizPNddcybg/xhhjJMcsY/2rr76ajPMco1mJZSRekVR51VVXJa9RxLlIEkqec5HzkGDyaaaZJpmvRh555CT4kXORrqSMZ/USixgvCYyeYoopQi8655xzkrEmy9RTTx0WXnjhpGvfZJNNlsyHjFc8SLojqJYH3yHzIg8SiPn8iw52J6mc5Ms8ickjjTRSclwuuOCCYdZZZw2//vWvwzjjjPPfsZY5i7mbzmx0hOazIEi/XjewZlV1TuB7p+tUWpAw5wR/C597bQ3I90GwGecNCXX8PVnefvvtJDGVLsFZGH9IRGWNwbnM2oTvm3Um4xEJuyRXsm5jrZJ1bnDc002t2/H3xbovcg50AgGFJE0yNgzEGDvHHHMk1wmsIzlG+L74WeYKEkAYd19++eXoe3AukFRDN3SSdMswaNCgMP3004ff/OY3yf/y4PdlnuIxyiijJL83xzad/ThXmTc4zv75z39GX5ux8sADDwzDhg0LvYpOgHnO5UkmmSSZR7guZCyeaKKJwmijjZaMcxzDfH58jpyrtXmE758xuR2FUdL+BtY4fN9FO+igg5I1Uj2MW4suumgylo033njJ+PmLX/wi+XwYEznGWKdlJQSRKEthlCyMzYydSy211H/neb4bvhcSTfgeuNYl2Lne+Twi/iYKFJEc0eg5yTosDUkPH3zwQbL+aEYjxXzY24j9LjFcA6eZeeaZk8+1KLGxnc+K72Laaact7P2q7oQTTkgtTMN5yPnBnhDjF2M/a8XaeMW1DuuLrPUjxyHn/sEHH1zSXxHCmGOOGWacccZkzmI9yRxbm694jnU5a9/a+pfxhGOedXAWEp4YwxdYYIFQprLXmBRQy5P0ONZYYyV/K+MB78nYyD4Y8xZqcz/rnVrSMg/ev9c6hsfwGY94TLHe5ljimOLzYI6uHVPMFxxTnA95Ol+TyDjLLLMk6/Yyca5yzZ52jrK2o2gQfyPH0y9/+cvkv3OtyDHF2M66LsszzzyTJNVwDGeZbrrpkmOLuZ5xhXUwxxzXfvy+zPfMvffcc09m0T/25aeccsqw0EILZb5vv8gqSEwRy17BObb33ntnJuOz7uK+CXsfzP+cqxxTrCFr+1u19Tb3ZGpjFtddZRS+qMK9F9bbe+65Z64CJMxJnM98B5NOOmnyNzBW1q5vGEvYy+HviK3hGadIkL700kuTMakVjEWMSXmKCDDW19Y6zHn87hx7XJvz+bNW4HevdxyeccYZTa+de8UBBxyQ7EXWO365flpiiSWS/SPWjMyPrA0+/PDDZI3ImiTrO+CabbXVVvvvfHjaaaeF2267re7P8v3wfrxvbc5i7uA9OJ85btOuM2s4l26//fZkD7IIvX6uVHldze/IvnRacR3WI+zT1NbVHE+c57V7OnwPjz76aOaeDH/Pcsstl6xp+nlfScrCGj9WBIxjuqixuSxHHXVUrqLkM800U3K+Mo5yvjJHsofOg3lrxL302vnKOJp1z6ZRrockSZKUZqT/9FrZVkmSJEmSpDb53e9+lwQLpN0IrNclgMQgAjnyJC5xw5Fk1awOEiRxXHbZZaFVBPCQqBoLHCD4btVVV00SFfMGPxAwQqedCy+8MJo0RGAcwXQEOhf9fRAsR9e2ZhH4xU3gGIIy6MCy4YYbJkF9MdwIJhGP1x34mfBvCXZK64TR6t/SKWeddVbSWS2G737ddddN/r5agHIMCaR0Rs7TZeWkk05KAv+aQaDlXnvtlRqkk9aptVUEbafhXGmm6ASfGYUu0hCYRLBaLyvz+yKgZZVVVslMFmMO2GeffXKN9QRz1TruZCVWcAxzLLeCQEICPWMY60lQZqwncDHLgw8+mJzf9RJbCbgjKC1t3O/GLoEE0ZDEG+sewny+yy67NN3RnMBZgg455/j8mHsJdm22AyhBpKuvvnqSIJf13a611lpJ5zXOh0YQKE1ALUGMzLe120dZCc1VmxNAcHJaYNjEE09ct5M2wcAEEJMwmYW1AQG2aR2aGYPWWWedzCBo1ois7Rj3866vSIglsYEkijQ/+9nPwrXXXpsEhHczxp5YwQP+VgKei5Y119a7TuDc5Dsl0TjP8U5AOolAWQGKJIfxuq3gPKx1IieokYBuzh/OAxK7G8XYSqddzsNYwkttrGg1+atT6ziCxWNFyEiG2HnnnRs6P0dEEh+JBPz+PFhPcA3BNV1RSApZeeWV6z7HNS3rhEYxDqZ1Wk4bP/meKACz9NJLJ0kpWR5++OHUxAOKWay//vqZic0UEGAtlyfhnDGZToMcr1mvS4IeSW+NWmmllVKTHJj7uEZvZu1AUl1erew7cA2bNj8z7lG0rygk6rBOS0tYPeSQQwophhNbC4C9HBKful3snCQxiLXGwOOasZ/xi78vqzAGa97jjjsuGYuzQo+4HuI8bxZrlxGTC0g2YIxlzmIdRsJoM+Mg5/b1118f/TmuoVlXsEZqRSfXmCSvx85xEsM4V9dYY41kPdAo5imSyJizSAKsFcPi2qJb15TMJ5tsssn/JClyPPFgTGxm/qaQGomQzOExjGNcu5e5r5R2THHekFyXpyggSS4UFuS6P20M4Do1KyGbZBquW/Luc3H9fMUVVyTjRuy12c/g3Myzvq461iqxYiHsX1188cWhV1CMlz2ZNKwZuVfAepVigo1izmK8rI1ZtXU9xc64PurHey+1/R+uL2P7l3z2yyyzTHKfhCS5PPi9SRrnOGVcScO4RLG8VuZbvsNLLrkk+jOMHXSRZ82Y9V4k5nJty2sOTBjnOoY1KT/Ti+vF2P02ksjrFRFYYYUVwm677ZbMMTEkS/K5nX322dFzgiJJzJtcx3HuDfyMKbzD9X6ePXgK9LIXybV0Gn5v9lsonNiKXj9Xqr6uTrt/z/0A9jooJJDnO6YQWqyAae2cOOaYY0K/7iv1CorbcW+/F8fqXsdYtummm6beQyxyH6cs3JPjPIztCTKfURiQfZZm701zL5FHbW5efvnlcxWoqsf1kCRJktJk34mXJEmSJEnSjwwMQiDwhEQegkDzdj8l0IiASG6gxhBEQ3enVhAwQJBoLGiHLmMEFRMc0UgHBYKNeW2CEQlCSMONzyuvvDJ0G27OHn/88dGfoZsRAawkQWQVEQA/Q2IkCaMDEzlJREkrItCr+AxJHoihExRBYSQ45w3wJaiIc4R/kxXcxU3xrORvKUue44igRsbKvGM9QXEkdTP+MZbEEMhIIF2zCFzNKopClzPmKoI+8xQRAEme/P7rrbfej56LBQB1KxLdYkUESIQgQLvZIgKgC9zaa6+dBH4SlE5iYZ75Iw2dx7OKCBAESdAsSUKNFhEAYzNBsASeE/RJIGQzgbVVnxMGJuOQCEDAIcdMngQv/PznP08tIlBLGo0VESCgl7UXYxFBvo0EkxKket111yUBt2kILN1vv/0yEwI7jW49aUiAa7YjWNHXCYz9fOacx3mPd75X1p5ZSc6cr0WgOxLByXTSPeWUU5JxsJkiAqADKMVqSP4hSDum2WDITqPwQizYm7+b76/R83NEzNEk+TOe0pV3u+22KzxJLtb5LlYso1n1khk32GCDZO1DgkWeIgJIKyJAB0HOs6xkfwJ7KfqWt2s98zeJbJxvWd8BBZSauXaPdTMlMbcZdJJuBMl0zcydJPukJSDVijYUieMk1sU7TydM/T8vvvjij84XCl8xZ7GOzSoiUFvzUviGRDHWdzHsmRWxPuM67OSTT07GRtYrHGPNJDvVkuSGDRsWzjzzzOh+FkWEmi0I1g1rTNZ2N954Y3SdzXUEY3IzRQRq172LLrpo8j2TsMTeYuxc7RbM0/zejN9ct5FcRpJ/s/M3xyfXgBxXsXOC7skcw+08puhcy+/G+ZOniADoPpxWRAAkd8YS/fkMaudYI8Uyud7j2pTrUoo6xPa7GYP6Gec3n3GsiADfwxFHHBF6CWNSGsZH9hMo3tRMEQFwjnNssy6k4AnnBgWYmj33q3DvhWNp1113jSZGsxbmfD722GNzJ0bX5ieSqSluFBt/uGfSSoFVrtOyiggwp7LWofhYnkRmupWzz0ZC/MB9VJKY04oI9LqBRQSYQ0gSJ2E6q4hArSg3e88kh8bOK9YMXFvzGY9YRIB/w7/nujrvHjz7GFznMXfF5kYKvraiCudK1dfVA/flOB75vCj8nqeIQK1YE0Ud2B+PYf2YtWdf5X0lKatoCffFYvcQuRbr5iICoOBybK+Rc4u1abNFBGrrRPbTuS6lUAxzGnNCM1wPSZIkKcZCApIkSZIkSS0iyJbEtma7gdJ9kariWTcpW0FCGh0J08w555xJcAqdGppFEBbBcASSpyFgIdY5pxNIuiX4J3bzlsDtvIkmA4Nn+Ld5gqt6GcHtse+Vc4RgOwICmkHyMgGvsYAZApc5D6VmkcSfFUhI0AsJynmSagaiCyPjAUkaMQTBNztOnnDCCdFuRQRYX3TRRbkTUUZEcC0J3FnFb3pBrOsOwZ0kvzQbNJk2jxDgTmB6MwgcykoyodgDc3CsI2UjCKokOJiA+kb105zAWMDapsiuRSR+EiiahuDXc889NylQ0ixeg88nrRN5rZtqLMm42wsJMOa22mGuCKytKd7STCdeEhY4V0gcSfP666+3XNCFTox0Jtt6662j79Uo5hwC0mNFK/gOG0127vZ5hII9JHJlJdQ2giB8vqe0ruLNSjvHp5xyyuRRNhIkSFZoNjB2INY4sSIstYTHbbbZpqlAfJIsKCZAsYw0JL+wjuiGQgJp/473qpcoTOJbrCBALEkjrUAT7xNLAG1WbL0TmxsUx3dF8lVsTydWfITEnNj5zPqs2fVwLRGG9S6JlM125Yx1S2d9FSuiw5zcriJLRa8xSVr/+uuv6z7H50hhn6yid43gOFhrrbWSokJ5E9Y7odbNl+OylSJy9QwZMiTpIB4rzEBCZruQVMn53ew1WD1XX311dE1EohrXNXwWzeI1SOJLKyIEimRQGKXf0O2b43ellVZKkjZjezkU0GVfolewnxDrAE2Bp6yCZc0UFGGcX2ONNfr23gsJcLG1NGMm96gouNAsjkOuj2NrOebjWIGSrP2oGLrbM6ZwzdYoEqz5t0VeM/cK1mB8L+w9Noq1DImRaVhbbbHFFkn39xELh1GAgP/e6HqPfV3OxzLvtVbhXOmndTV/K+cuCfmNYl+R4ggUmUpDcnGsYFfV95WktELT3FtaccUVf1TYY+CYzTnW7WLn6+KLL56cW0Xi3hz3iZkLm+F6SJIkSTEWEpAkSZIkSWrRPvvsE02QyYOEhlinYront9I9JBYcQ2ALST6xTh15kSBLgENa4haBLbHkuHYj+YqOEbHgZz6bZgLpaygiwGs0k3jcCzi+shINDzrooGiwTR4Exm611VbRnyG46rPPPmvpfdS/srr4EPy35557tvQejCWMB7GOVx999FFTHcTogBXr2EYQJonDWYUMstAFjYDAXvbCCy9Ei0WUNV43U5yAQjdZHbsZXzmuigwurGk08aff5gQ6phXdtfvII4+MnscUQJpnnnlafh/WanRCjHXi4rjqZrFAxLwdxspE0DQJZBQEaCUZny6ZWYVwWkFCRJ5OjK0kQsbGklaD+LttHiFxsqzkkiKL3Hz44Yepf0fR41o9BBPTxbEoJEzFkuhAYlgsiSUPissRDJ01Nt17770NvS4JrGnX0KwNuW5tVFqRjsUWWyx1Hm6maEGsGAjFF8oYX2IJNa+88kq0UKDSE0u4HqLbbLPmnnvupPBYDN31vvzyy6ZenyIeZRSmGPH8jq276TrcTLGNblhjxpKs6RIf6zjfqm4o7BTbr2umaGhefLYUzEnz+OOPJ0k27UBH5Nlmm63QeZfXTEMBBdbB00wzTcvvxWux15zWeZ5ExFNOOSX0MgpC0qE57cHcRkEQrrW5Rtthhx2SPSq6ddOVOjZu8tn02h4O6560uZx5at111y3tvZtZb1fh3gtFEGLFTSjqQcGgVu6RjHidzPiRtof17bffJknGjXrggQeiRfb4G1jrxO7B5SkazZ5MvyExv5W1wrbbbpskc6dhnBsRhTObLeoBEsZjBYIYS//xj3809dpVOFf6aV1NgQTGzFbWQIy3FJaOrWmb2Zerwr5SWbjeL/qx4YYbRuMzynjPgWNbr2L/MG2N+tZbbyXHMuczayES2Gm6sfTSSydFV2J7M+wXUegsVqyzW8TO180337y0923mfHU9JEmSpCwWEpAkSZIkSWrB4MGDk5v6rSIYMtZpjCCwWGBgDImjad0duAnJ87HuEI0iuS2WoEGni25BJ4pY54tNNtmkkES0GWaYoeWklW6VFaxE17BYx+NGbLfddtEOWnTjoouK1CiSpmLBICRAETBGEm+rCCLadNNNoz9DAE2jXXlImo4F5qy99tqFJA4QgEeCUFHdgzvhvffeiyZ7dBO6PBJcmYZgUrq2dMv30U9zAkHJa665ZqGvSTBzLDmebmzNdNBKw3FDQG3a2Mbv8txzz4VuxBj5l7/8JfX5SSaZJHQanYPSEp4awfgdW6u3K6mxWcyhe++9d/S4p4t7L+mleST2uaetNcouJEASCcX4ikRH3lg3Rs4hiiEVgU6cFEKIabTLNIlldJltJlm/HhJT04LWKR7Co4j3yfo3888/fyhDbIz/7rvvwptvvlnK+1YZhT0oJtCq1VdfPVrwiKStyy+/PHQrzo1ll122lCKfnVxjxq4nYol2CoWcE2nJl8zDrRaEymOVVVZpqot0DHu7JBWlYe1XZOECEjNjHTlZ1zTblbkbUGSF9Vfag0J6JGWRjEuRrttvvz18/fXX0dek0MD1119f+Hff6bU2HeVHHXXU0E2qcO+FxGf2UGIFDyeffPJQlKmmmiophBHbC2u0MBQd0GN22WWXQpK7F1100bD88suHfkFCP92WW0FC9JAhQ3LvmxfR2fl3v/tdtBhO7D5A1c+VflpXM1YWMQ9SRISCfGmGDx8ePS6quq+k/rDBBhukrlEpGLDqqqsmY+4ee+yRzMUUv4rd22SfmKK5rH9jRWa6BYUQv/rqq9QCunPOOWfoJq6HJEmSlMVCApIkSZIkSU0i6aqoZAQQFBjzzDPPNPyaBHXefffdqc9TBKGM7lskyaYlNdJFKZYk1y4E2Nx8882pzxNYltXtuBHbbLNNGG+88UKVELQaC/jhJvq+++7btkS0Xu1qq87LOm4Y04oMAtx6663DRBNNFE3+ojthowl0aQjIyepo3QiSt+kO1atiXada6RxehksuuST6/J577hlNpm+nfpsT6BRLYY0ixQofkNy35ZZbhqJRMGmJJZZIff66664L3YhEoViwdBEJAq0gKS/2uTaC5JNY8DXr6h9++CF0M5It6PqbFpBJ0HUv6aV5JJZwl/b7l9mZECSGkBBYpKzxniTpIrucsQ+Q1hkSjz32WDRxt9Gke4peFZHcTxIbyTlphQS4VqfLZl6Mw0888UTbCwlMOOGE0ecb/ez7HUUsCP4vCoVCYgXYuv2aPdbputFzsVvWmLF5q4gCEkrHd0kCeKeOKfZmSepp53Uq1xdFF8OorSdnnHHGus99//334cYbbwz9juONOZ4EIor8Zc2X3SpWJKHbxqwq3HuhqznJyLFzb+GFFw5FY5xIu1fyxRdfhHvvvbeh7+Hhhx9OfX6mmWZKCrsUeS3QbQUtysA+XlH7ycsss0zua0X2+1rFMRv7jl566aWGX7MK50o/ratHH330sP322xf2erFighSza/SedxX2laRGjDbaaGGjjTZKCqkRLzFo0KCe+ABj5yr3XYsoPl8U10OSJEnKo3tWsJIkSZIkST2GautFJu/RoSqWUBHrupqGwJa0xCICgYoMpBgY1BcLrLjvvvtCpz3wwAPh73//e+rzm2yySTQ5pJmb5LFuQb2IIKXYTXS6Jk899dSFvidJeTPMMEM0AfvZZ58t9D1VbQR6xZKfCV4sOmmeQEbGmJibbrop9+sRJPzGG2+kPk8H+KK7e9CJpFeCfQZKC7bG3/72t9AtCECMBbayBikjQaNZ/TQnzDHHHIUnA7z//vvRwPctttgiCYItQ2w86oY1W6Odu7qhkEDRa75Y5zMSeTnWu12si2EzBdM6aZRRRumJeSQNnbT+/Oc/p35PZQbB8tmxLinSRx99FC3ARNLFaqutVuh7MsbE5mC6r8WK1tWTltyPRx99NPz73//O/VppSRkk9pNkSDLVGGOM0VARgnqefvrp1M6LdB7lfcqQNcYXUUiApMCXX3459VH0MdVJBPEXmbjO2iw2b7366qvJ9Uu3mnvuuZPjt57XXnsttRtht64xqzBv9TrOh7RzrOw1EF1Ciy7eQwGZ2B71jjvuWNpaguSjXrtuaQeOLxLW+QwoIhBbU/SC2JhF0mw3qcK9l9tvvz0p7pZm5513DmVgvzWWZNzI33DbbbdF18oUdC1yXKI4bJFFmLoVyf9FFcZmjZNVIGCSSSYprCAje++zzTZb6vMvvvhiw69ZhXOln9bVa6yxRqH7qBSKiF0zNXr/3vW5+gXXIoceemiy10RR6E7v2Rd5L5EYE/b/uoXrIUmSJOVhIQFJkiRJkqQWAhGKRtemNM0kCFHZPc0CCyxQameDhRZaqKNd3LI8+OCDqc8RWLb88ssX/p4E+BXdWa5bP0MUnSSU93UfeuihUt5X1UQCVKzbGQl1aQFvrY4HdJlOE0sobnRMXWmllULRmD+YR3oR3XibHdfa6Z577ok+T2fLbup40k9zQpGd7EZMWEwLfOd7jiUJFBHQTcGjet56663wzjvvhG6TlSTayaBExsdY0nwz6CAe0wuFBGLXOSSV9pJYcZ5umkfS3H///UkhpXqKStxIM3jw4MKLorBmigXusg4qo/hR1vzTyFoOc845Z2pyDYm+jXQ4TCsUUUss5POYd955W75WjxUdmGeeeUorOkXyHMl5ZRYS6Bdc5yyyyCKFv27W9Uc3j5Vco0011VQdmbPKWGNmXf9QZFPlrw3TuqiTFE1BnF7au47tNZc1puTZa6bAAQW2+hHroMsvvzz5vg844IDw+uuvh14WG7OefPLJaAHDdqvCvZfY3zDNNNNEize2Kta9vZG/IbauoHhWrMBRs8rYa+02RRZL4LpgiimmyPxMi9znjBUzbaZoexXOlX5aVxe9BmLfdNJJJy1sX67X95WkvD777LOw7777hvXWWy8peBW7F9uNxhprrNS9rW+//TY89thjoVu4HpIkSVIe3RNhJkmSJEmS1ENIBifQv2ixzmOffPJJQ6/16aefRpMNlltuuVB2t4k0L7zwQsertKclV9SSHspIQJtwwgmT166K2GdI0GcswKkVJFPGklJiv5fU6PEyZMiQUj40gokXXHDB1OfffPPN8MEHH+R6rVgX3sknnzzMMsssoQxlJjaXic8kzYknnph0EukGsaQeEg3LSjRqVj/NCWUEoZPYG1tTpSUfFYHPj2ICaRpJXm2XrISrojuvNoJrhKKLfNDxMK17eG3d3+1ix/Bf//rXUJV55Lzzzgvvv/9+6GYULknr2hhbm3Tr+NmptdxMM82UJIzEEhrTCjakdTmLjcV5E0To9J62dzBih+K0bsWNJKLEfrbMbsjsx8QSLz788MPS3rtqKO6R1R22GRQl+cUvfpH6/KOPPhq6WSzxk+u0XhojMdlkk0W/i7vuuquU91Vnjynm9bSiMWVdpy611FKljCkjrifTElEpIsAc2M+4Rrr00kvDCiusELbZZpukKFwviq21KSJw3HHHhW5QhXsv33//fbT4Vdl/w4wzzpi6XqAw1Oeff575Gj/88EN0X3TppZcuZVziuj+WVNzr2NOYffbZC33NKaecMvr8XHPNVej7xRLYG937rcK50k9rIK4VY9fq3XD/vtf3laRGsCZ57rnnwmGHHZYU3+UeXCP7ZZ2eDyeZZJLU548++uiuKGbmekiSJEl5pbc7kiRJkiRJUjRgoIwO1bGkJ7oQNuKpp56KBozFgs2KMP744ycBG3TXGuirr75KutvGgpnLRNBhrOtImQk0vHa3B83n8eWXX0Y7PVIwIdZtvRXjjTdecg6+9NJLdZ9/8cUXS3lfVVPseCFJaf755y/tvelOdt9990UDfylA0srfMNtss4WylPnaZeI7TUt8Y27acMMNkwCgWMBp2Qik4vtPQ3GIWJe+duunOYH1De9ZNNZtadpRhIhu8Wlda/hsl1lmmdBNsroXjTLKKKFTyig2Bs75tGD3euvtMv373/9O1vMESvLg/8/yz3/+M/W5jz/+OPTaPHLllVemjofrr79++NOf/tSV8yQJEGmFS1iXlH3ukHxftNg4z/VomZ0h+cxee+211GOea87pp58+9+uRfJ9WGOGRRx4JW265ZeZrpK1xCHweMVkhLdGf+ZxuilnX6ozDBIJ3opAAYsdqN3Uq7nYcw2V9P8yHaWuL4cOHh3ZhnuJ45X8ZA/MkVsbWrWV1jy9rjYmsa9pddtklDB06NKyyyiqlvH/V0HmScYZrtrzHFMVi2r0O+s1vfhMtuNYM1jmx7sFl7zWDeTWtCBXXLTPPPHPodxyTFI5i7bDnnnuGtddeO/QS9sEoGJH2PdPNlqSuHXfcsaPXfVW498I6NpZQXfbfwHxLcnlaQQbO6aw15RtvvBHdHyjzPg/rqMsvvzxUEftso48+euFdnWN78EVfP8fer9FCAlU4V/ppXU2BPo6pbr5/38v7SmUro8jZM888k1zz1POHP/yhlH3vPPf1+hHnygknnBDuvPPOMGzYsGiBjm7B+ZpWoOvpp58OW2+9dVIkoYwGFXm5HpIkSVJeFhKQJEmSJEkqoXtGs0YbbbSmkm/qiSUgEmQXq6BelLRgtlqCQqcKCRD4EwtyIdi2LGW+djtldfkqM2Gn1oUlLWmUY44ulJ28aa9qHMuMUUUHTQ48jrN+N7p5ZiUxxBK4yzwXf/3rXyfdkHotWWvJJZcMxx57bOo8wPxJ9+KVVloprLvuumHWWWdt++/4+uuvRzuZdFsQYT/NCWUkwZKwGQs4nXrqqUPZYgHW7733Xug233zzTfT5TiaUdOI6oazOR8wxJMg8++yzyTlGtziOB47XPMUD8vrss89CL1looYWSY4zPpx7mZRK26PTNPELAaRlB7M147LHHUscb5scysaZi7VB0xy3mzE7OP1nzYyOFBGLJvk888URyrmd1VE0rJDAwsYQOjSQt10tg5TWyrtXp/JrWQY7XLaMDZN5xPu3cVHv3Jzj20woJcNzRObXIwlgffPBBePLJJ5M5iyIezFkkJxV9rVJWx9cy1pg1rIO5puGzSTtn9thjj3DhhReGDTbYIOmqG0t87xccQ6yDKJpCYgb/Px3Is9ah3bIOKuOYiu01w+uW5my77bbJI22tQ0IhCawcfySRckw+9NBDmUUoGP/233//JPFp9913D71kqaWWCmeeeWbq82effXa45ZZbwnrrrRdWX331aMfrslTh3ktaUnI7z2n+hlb2ImLFTcq+HqjKfZ527Wuwh5yGNWFsX6ro96PYRiOqcK7007q6F+7f9/K+UtkmnXTSwl8zdv+M8aeM96yKO+64I/U5jt/aOpX7G6xRKdpAMau0/aIRi4Kus8464ayzzgqzzz576PZ1aaxwEOvyZZddNlmT8jeVvR9Wj+shSZIk5WUhAUmSJEmSpCYUHdRSRoJQWmdETDzxxG1JCht11FGjwSidQuBLjIUEstHVqJWkmlZlBQHy+1lIQFlIJI+NhWUnn/H6BIClJbRnnWe1sTSWzFnmeEYHOLqY03Wjl9DlhOCfWBAUgU5XX3118iCQjORKAvzorFpmcYkaElViuq2QQD/NCUUnwSKWBFtbH+YZD1oRK7DUyTVbM0mijKtZibb9fp0QQ7LSvffeG6666qrw8MMPF54sV0TAdacRZExgKN1QY+fUbbfdljxIqqYw0CKLLJJ0KowlAHSqsxtz+uKLL17qe08++eSFB76TzBA7/tuxlotpdOyeZZZZknO9XlfVWmGP+eabL3r+UiyinnodKklGuOGGG+oWElhrrbWaKliA2O9YlFiSczvGrSrgM6Tjc1myrkPYl2m1kAAJ2KzXb7rppswE56KUVaiijDXmiOiSmJaoXEPiBwUFhg4dmlz7LLbYYmHeeeftWBHQTmB/4IorrkgSlLOuyfrxmMq6biEhruzrFtYsvXTd0qpBgwYlYyUP1lJ0WQadoO++++5w3nnnJcV9YkjI53XSOvF2o4033jgpbhI7P95///1w9NFHJ4Ui2ath34a1DYVsYsdJUapw7yX2N5CEzXVa2ed07No9z98Qu89T9lqnkYJhvWbMMccs/DVjif3tfr9611tVP1f6aV3dC/tyvbyvJI249ucx/vjjJwVVuH7EJ598kownHN8UwooVddlss82Sn2M/rFstuuiiSZG2WFEZCq1ccMEFyYNCAtx/XGCBBZIiCe0o0ud6SJIkSXlZSECSJEmSJKkJZQS2oMggt1jwCF0kyu44mSXWebdssY5NBIOUmYA+4YQTFtbBm4Cnort28Lv98pe/zJW0E1NmkF6eriJZv58EglgIvm72OGvVGGOMEcYdd9zUMSnPcZzVga7sghq9WrBjt912Szqj0C0lC4GgBOfzGHnkkZOkQYLUCQLifxnXi/bhhx92vNNVI/ppTuC8LRoJEDFbbbVV6Nc1WzNJop3uoFtWwHLsOiFWCKIRBIwfd9xxmUW3+r2QQO28vP3223MlCDFXX3bZZcmDRHqK8Iw4j5AU1i4knNXD75LnGqAVZRTiyRrfy17LTTXVVIXOP6wzSNq95557UpP3Y0n6w4cPT+2uSWJd3kICrJEYV2KFH/iZNPWKFhSNYPU0FhLIn2RMYmmnzo+s65gYuh6ecsop4aKLLmr7HFLW+5WxxhwRe4AkdlAsKAv7VcxxPGoJscxXJDDzoEhEmcdOJ3D9dfzxx4drrrkmKcrSTr10TGVdt6y88sqhk7rxuqUsrBnoUrz00ksnHVKPPPLIaIft0047LUk8JAGx0fsLsX2zZrCHwu8fQ8LkDjvsEIYNG5b5erVCSrViSlyP1dbaPCjEGEu47+d7L7G/gbmgF/6G2Hqi02udXlbG/c/Ynka736/RPZQqnCv9tK7uhfv3vbyvJGXhnueWW24Z1lxzzXDooYfW3XeqYf1KsavrrrsuWgCm0/bZZ5+w0UYbJQXI8xSf4XHqqacmRWBmnXXW5Dq6dr6Wsf/qekiSJEl5WUhAkiRJkiSp4KD1btHtidSdTBiiGn6ngrdrSTRFFBKgE8Vee+0VirTqqquGww8/PPPnsgoYlP05Zr0+HVSkVo/jdnSe5z3SgjzyHMdZgYJl/w2xTjzdjMR2Otdtt912uYJ/agigp1MnDwoLYJJJJkkS5hZeeOEkKL+IgKesxKqyAiKb1U9zQicSYTutG5O8Y4kn7U7+6rZCBs3gHNlzzz3Dfffd15H3//e//x16DUGfJ598clhvvfUaWteTsPDyyy8nj0suueS/iVIkhzOPLL744qUVo3jppZfCu+++W/e5diQ+lDEXdHotR0Awj7Tug83MPyT3pxUSiCXv1woN1EOSAYHceRP++b05RtM6yvM8x1OaekULihZLasxKTlR3rM9i+zIxDz30UDJndWr9VNac1Y5rT65/1l577WhX3XpIbuJx4403/vcakASIhRZaKJk/JptsstDLSGwZOnRoriJzvXRMlXGOe93SfUgm5LwmWZ7kpi+++CL1Z/fee+9wyy23NHRssNZNWz8266677gqTTjpp5s9tvvnmyXrk+uuvb+j12SdjLVVbT/30pz8NM844Y1hwwQWTgip8VkWowvlQhb+hk/d52nEfqV/uf3b7/dYqnCv9tK7u9uOpl/eVpEaMM8444aijjkoK71C4Ns1bb72VFI864IADuvYDnmuuuZLfb999923o37Ff+PjjjyePEYueLrDAAsm9RP6XfcVWuR6SJElSXsWWyJMkSZIkSVLXiHUh6gZpyRbt8O2333Y8cbjXxT7DdvyNWYF63Zj0qO6T1a20Hedq7FjOcxxn/UzZf0Mvj2eLLrpoOPfcc+sm1jWCoPorr7wy7LjjjklCHh1U0hL5ihpjuy1YuZ/mhDKKZ3T7mq0b59RYR0mKg3S6mEAvIVhzrbXW6lgRgV42wwwzJB1gKU7TavEYkjP32GOPJMGJrnR33nln4UH1JG2laUchgTLGzyqs5RpJwqeQUWzOSFt/pL0mxZDSOhfGihb8+c9/Tu3iSUJxnuTAMtce3dzFrpt0+joh6/yt54orrghbbLFF1ydzdWuBNr4Tko24DmrF119/HR544IFw2GGHhaWWWiqsvvrqSRdb/nuvOeGEE8Juu+3WsSICZfK6pb9Q/Ofss8+OFjWrdTPuJUcccUTYeuutk4IJzeL68JlnnkmSNbnuIcGSZLY8HaCrfu+lCn9DJ+/zUKSiFwsJqnFVOFf6aV3dS3ptX0lqxrbbbhs222yzzDHpww8/7OoPeM011wzHH398y3tOb7zxRnL9zHlKIQGKEzz77LMtvabrIUmSJOVlIQFJkiRJkqSKykrq67S0xIN2iCV0WEig9eCkdgTRZX1P3X78qzeC7DqdfJbnOCYgutlk215JeCnT3HPPHa699tokmLyI7rV8ZzfddFPYeOONw2qrrZYk2DT7OrExttsS5PppThg0aFDoh0T9bpc1trkOyOfTTz8Nm266aXj77bcL+V760bTTThuuuuqqJAC/iK53zOv33ntv2G677cJyyy2XBIIXdd2UVkiAzmB0wypbGeNnFdZyA00//fRJZ8J6vv/++/DYY4+lziVPPfVU3ecodJQm7blYUaTYc7FCCEWKfbZlr3+rotOFBBpd/9BFe7/99qtssaAyxsh6xhxzzHDqqaeGgw8+OIw//viFvObw4cPDQQcdlCTnkqjbTJGITiDp+sQTTwxV9ZOfFB8S6HVLd5tpppnC5ptvHv2ZCy64IFlP9NJxvPPOOydFIPn7ikABAcaqwYMHh7333rvppLVuv+bMcw1Rhb/B+zxqhyqcK/20ru41vbSvJDVrp512ihbM4Li98MILu/4DXmaZZcJ1112X/G8RKGhHEQWKFGy44YZNFxRwPSRJkqS8Wo+KkyRJkiRJUlcyCKQ5rXQ46rYA8U5pR1BK1nu043tU9bXjWI51hclzHGclvxPoWGayf7cHUuYx3njjJYk0JNPSCeSGG24IX3zxRcuv+/zzzydB/Msuu2zy+iTt5BX77nutk5BzQrZeSuToFllJogTP9Xqhk3YgcSZvEQE6jM8+++xJkjMdzEk+HHfccZNEUYqbsL6lcEisgzkBkVXEZ0A349/97nfJPEJA6QcffNDy6/71r38Nu+66a9Kd7sgjjwwTTjhh069FghTzUj1LLLFEqKpeWMvV+zfzzTdfktyRlsRPsu5ATz75ZN3AYc7NeeedN5r4X687MQULmJ/qrTU5n9PEihYUKRYkXUTyRT8o+5o56/UbOT+Zq5iz8vwbjtmZZ545zDLLLEmRFOYv1vvjjDNOMl+xhuC8iO3L7LnnnuGaa64JVUViLoXUhgwZEq688sokcemFF15o+XX/9re/JV2+eT26iFO0rVs9/fTT4aijjsr1sxRFm2222ZLjiuQXjqlf/epX/z2meD7rmGKN8Oijj4Ze53VL99tyyy2Tef2TTz6p+zxrVBKU5pxzztBLWK8wtlAYi7/voYceavneBwlrvCZrLsb9tddeu+/uvVThb+j0/YEyirao+1TpXHFd3Z16YV9JasXPfvaz5FjcYYcdUn/m9ttvT36m27EvfvzxxycF9Thfb7311vCPf/yj5ddlr43r9PXWWy9Zm/KZFcH1kCRJkkZkIQFJkiRJkqSKIpC1V7qAtVvs5uvXX39d+vtTYb7XxbpLE1jMsVdmJ8isz9DkEeWR1SW9Hedq7D3ydHHPCib56quvSk2m5fWrgkSjfffdN/zhD38IDzzwQHjwwQeT4J3XX3+9pdclkOjll19OOqqQdJtHbAwjeJXPvR1dlvNyTijv81N9Y489dubYmtbRW//PnXfemXQoi2H+IHhxtdVWS7rWt6Ifup9NMMEEYZdddkm6bD3yyCPJXML/vvTSSy0VgWEuWmONNcJ5550Xpp566qZeg6SrNEsuuWToVVnjZzvWKa2u5dKS8WOFBOrhWKuHZOrYmoHEPIKKB56jXBfXSzR8//33k2SEengdXq8dYt9t1hyh9ux9ZJ1/jZwfhx9+eGZg/nTTTRc22WSTpMN0q+vkfpizatccG2ywQfLgeoW5grGEJPtWOs+/8847YeONN06SlZZffvnQbfh+hw4dmpkUyPi30UYbhcUWW6zlPaaqHFNet3Q/jlWKRJEwmObxxx/vuUICtXXGUkstlTw++uijcMcddyRjFkU6WikGyfyy//77h9deey3ss88+fXXvpQrndKfv81RpX1TVPldqXFd3t27eV5JaxXUVe81p8zN7TR9//HFSCLAXUGjusMMOC/vtt1+45557kkJXnK/vvvtuS9eNFCeg0N/ZZ5+dFK7Lw/WQJEmS8rKQgCRJkiRJUoUDB9OC2WadddZwxRVXhH4VCwBuR/BXFQLMsoKo+RvLLCSQ9RlaSEB5ZB2jnQ44zXMOjTHGGJmvTwBaWaowntULuiGpspZYSfDSE088kXT65fHiiy823InxjTfeCNtss0244IILco1PWd89SYvdVEjAOaHcz+/mm282yHOAiSeeOLP7+q9//esWv5lqO+WUU6LPzzTTTOHEE0/M/Kz7qZBWI10pF1hggeRR69DM/FGbS+hY1WiCJnMRHWa5hmumSEZaIQE6Ks8+++yhV2XNl52+tmv2eiiWjP/qq6+GTz/9NPnu8hQYyErs53iafvrpk8SEgXjNgYmGae+Daaed9ke/VxlYo8fGlEkmmaT036EKyj4/sq6l8l6zv/LKK0nxm5jNN988SbiJdYRvxJdffhn6DeMAj2233Tb861//Cs8888x/r38oLNBoki6dvvfYY4/kWnSuueYK3eS+++5LEjPSjDzyyGH33XdPiiEUpSrHVNZ5y3HjflznZRUSeO6550KvG3/88cP666+fPEi4ohhKba391FNPNZXAdf755yedmjfbbLO+ufcSO19Z0z388MOh23XyPg8FaXq9mIT651yB6+re0Y37SlIR990WWmihcNttt0XXqaxlewnJ/iussELyAOvQ2rnK/1KsqtFCIKxnd91113DSSScl40EW10OSJEnKy0ICkiRJkiRJFUUAy+eff173OYIO+lksSKLTySaNoDssj07I6qpNkHSZXQOygrDzdv3uFIMMu0NWolM7gv1jSVB5ErEIno757LPPSk1AJpGt6hjLll122eRRO38JAiJIlCQUEvryoKvvCSeckCSltPq9Mo9PNNFEoVs4J7Qm61xvpbtiVWUliVJIQPHgcYKO00wzzTTh4osvLjQRrJ+vP8Yaa6yw+OKLJw+QoMmcQEI28wjfRZ5OxXR4Puigg8Kxxx7b8LUH3efq4XfKE5DarTq9lvv222+TZNk0zSbVUwiFIh7vvffej57jWKHDWS1AufYdp53Tv/3tbzPfj5+pV0iA99luu+1yFxLIKlpQlKwx3kIC+ZS995H1+nnPj2uuuSb6/A477BC23377UKSqJH23kuAxzzzzJI/auMPagbnk/vvvT/6XuSwLP0Pyw+233x7tkNhuV199dfT5Qw45JKy66qqFvmdVjqms85b1noUEOm/yySfP3CfK6+677w7dbqSRRgq/+c1vkgeFBfDBBx/8t5Pzgw8+mPua+qijjgoLL7xwmG666fri3kvsnO6VcauT93mqWFxV1T1X4Lq6d3V6X0nqxnVqt2JPiseQIUP+ew/4scceS+4n3nvvveHtt9/OvQ6/9NJLw3rrrZf5s66HJEmSlFfvRgVIkiRJkiQpKpZg2O8JabEEdwJ/0oIAi8BN8CoEmWUluf7lL38p9f1ff/31ln6/gQGnaRrtEpBXrwSUVt0444wTTWjIOs5axVgTS8TPcxzT4TGmXlJYkej61m/oLrzgggsmBQFuvPHGcOutt4atttoqOZ6yXHTRRUn3nyxZRQJI5OkmVZoTOiGr43u/r9vqIRgvNn+TOKJ0BC3GHHHEEYUngfVScH3ZWHvMPffcSfLrlVdemXwfdNLOUyCGOefFF19s6P1I+kxLdl9yySVDL8sa38tey9FVrKz5J5aUPzCZ/9FHH006og7EeTznnHM2/V50QBtYAI2kvDR5ihYUwUICxXjrrbdyJZs0680334w+n7fwYGzOmnHGGcM222wTiuac9b9Yc00//fRhww03DGeeeWYyBh122GFJx+0s77//frjkkktCt2CsJLE4DclZRRcRqNIeUNZapcz9VOWXNb72w/XlhBNOGFZZZZVw9NFHh4ceeiicccYZYfDgwZkFtNgHPu644/rm3kvsb+D6oRfuYcSO906vdVQdVThX4Lq6Otq9ryS1qyB1r6yhGjHGGGOEJZZYIuy7777hzjvvTIq6bLDBBuEXv/hF5r895ZRTkiKmWVwPSZIkKS8LCUiSJEmSJFXUFFNMEQ2K7ueO6JNNNlnHEm+rEqCR1TWg7L/zhRdeaOk7HtHII4+c+tw//vGPUIaqBJFXQexYKTsJP+s8yTrPMProo0eLCZR5Lr777rseyyGEKaecMgnUu+uuu8Kmm24a/cyYe6+//vpcrxnzzDPPhG5SpTmhE+g83UrCZD/6+c9/Hg2Q8zOLe/rpp1OfIylw5plnDkV79dVXC3/NKiU6UZCGjs277bZbGDRoUOrPkghzxRVXNPT6zE9phXEWWGCB0MsYB2KBt72wliuikMDA/79mrrnmytUFnASEetdEJMU88cQT/1OY4aOPPqr7Ghy38847b2iHrDG+2+f9bvH1118nHSnLknX+5fmeuG6OFaRac801o2NmM+ju+de//rXQ16warkFXW221ZD46+eSTo90P0ei8VSYKssX2edZdd91SrpvL2lvqpr1muAbvnmulmO+//z70E9Y4iyyySDjhhBOS/ZiZZpopc+2cpxtuFe69VOGcjq0nWOvk7frbjLKvNdQ9qnCuuK6utrL3laSiuE79fwUR99tvv2TNOWTIkOjnxR5cVkFguB6SJElSXhYSkCRJkiRJqqhYAhCBAk8++WToV9NNN13HgsCqEmA2zTTTRDsBZyV1lpm0Q0JRViB73sAFAg7LYGJCb4wH7733XqkdMLLOE7o+5hH7ueHDh4eylPnavWi00UYLe+yxR9hxxx2jP/fwww/nKiQQS4zstkICVZoTOjUOxRI9R0zi1P8G3XWqC3mve+ONN1KfK6tDfT9fe+TFOLDFFlskXZ5bnUdGTBRL67xMEQE61vcy5h7moNj8UGYX0qLWco0WEiD5e8SkqEceeaTh1xiYFDzLLLPUfW7EIgVp71Pbf+B12uG1116LzvtZHe7U+b2PscceO4w//vgtXzeXMWdxjUMxAeXDd3DBBRck10KxYkJpRUi6aQ1EgZ0FF1yw8Pes0hooq9hUlf7WXvbJJ59En8/T/bSqpp122nD++eeHGWaYIfVnWDumFWmq2r2XrHO6F/YivM+jdqjCueK6uj+Usa8kFenTTz+NPt9P61TuWQ0bNiysscYaLZ+vrockSZKUl4UEJEmSJEmSKmr22WePPv/nP/859KsxxhgjWp390UcfLe29H3vssVAF3MyPdczm7/znP/9ZynvTzS2WIJjVWWqgscYaK5p8RRepopUVTBpL5FXjiahIS74rQuy1+S6zfrcRu82mefnll6NdPFtx8803l/K6vY7uPwSnp3n22WczX+MnP/lJmG222aKJi93U7apKc0In/PSnP43+nmWuS6oayM3Yp3SxZL6JJ564lCDRsoooVXHts/LKK4eFF144mgT597//PddrMf7SebCdRSPaLbZe+uqrr8JTTz1V2ns/+OCDqc+RVP/rX/+66deeYIIJwtRTT536fC2pn2RBOmzX89vf/jb3+6X97IiJdLGkukbeq1WxMT6tIILau8bgOjp27uW9zonNV6yf8hQjaFQvJH52Gwq6bL311tGfyXMN1A6xY4rjic7lRavSMcU6MXbe9fNecy8laHV7ob2ysUYbOnRo9GfyFG+swr0X9iGYT3v5b5hqqqmiRYK9z6MiVOFccV3dX4rcV5LaWfCqH9epe++9d/TvznMt7XpIkiRJeVlIQJIkSZIkqaKmmGKK5JHm3nvvDf1s3nnnjSb3piXctOLzzz+PJpv0mliyCEk7d911Vynve8MNN0Q7i+btvFmT1bEyLTGnFY8//ngoQ1bQe1mJvL0sK+np+uuvL+V9P/jgg2gwK4noeYNmsv4GzpmicY7fc889hb9uFQwaNCissMIKqc9TnCRPl9NFF1009bkffvghXHrppaGbVGVO6JTFFlssGuyb1fW6H8UKCZA8kxWY2M++/fbbtgZsXnfddaHMMbeK654hQ4a0lCBWkzb2UrBm8cUXD1XQqbUciaFvvfVW6vPzzDNP8jm3IjaH1ZL6awUF6hVLa6SYTtrn+OKLLybXxv/+97+ja9d2zrcWEijOLbfckny3RWPf44svvmhqP2ZE33zzTepzY489dinFZMqcs6qerBTz2WefhW4QO6bKWAOxFuE8q5LYdcvw4cNdg3eBrOIVrRQ6qgoKN8Y+hzxr7Srce6EoZGxOpgNunv2rTnffnmOOOVKfZwxmD61oL730Uin3DNSdqnCuuK7uP0XtK0lFcp36Y6ONNlq02Guec9X1kCRJkvKykIAkSZIkSVKFDR48OBp8f99994V+teCCC6Y+991334Vbb7218PckcI3XropYR4syk3ayAvsXWmihhl5v0kknjSb5FN3VmEBDgqvLQLBAs4mD/YoEq1jCAIGAZSSjcn7EknYaOY7pvErX2jTXXHNN4cmUV111VU8naLajK2dMLLEqb6foK664oqvmlKrMCZ2y9NJLR58/7bTT2va79IqsrtNFz99VEuuk949//KPQ92Kuu/DCC0NZYt0nY4HyvT6PUKAsj7vvvrvufyfZpipdvkiAjxXT4hqsjKSOa6+9tqV5sdUiCRQQoJBOWiEBEl0aKWRAV91RRx217jlMh00K2qQV2+M8nHPOOUM7sIb68MMPmyoyox/7+OOPU4+hVmQVMovtx3RqvqoV6DApsDlcj9Llu9V5q2yxY+rrr78u5Vzolr+9HXvNXJ+effbZbf199GNZRfxmmGEGP7YQwtRTT93Snk1V7r3E/gaKwLD31O1i6wr2dGsFuIpURtFWdbdeP1dcV/efovaVqmC++eZL5uV6j9VWW63Tv17feOedd6L79YxTFFjvR7HzNe+56npIkiRJeVhIQJIkSZIkqcLWXHPNaPLAySefHPoVHbRGGWWU1OfPO++88P333xf2fgTT8ppVwk1pugDGOi89++yzhb4niah/+ctfUp+ffvrpGw40IPFlkkkmSX3+/vvvD0U699xzQ1nGHHPMaFdGuzP/GGPk8ssvn/qZMQ4UPVbSkf6cc86J/sxKK63U0N+wwgorpD7//vvvh9NPPz0UheDIk046qbDXq6Ksoh6xhMeaySefPNrtisSvM888M3SLqswJnUxioHt1mttvvz289tprbf2dut24444bppxyytTnn3/++bb+Pr0klkD+3nvvFfpeN954Y3j33XdDWei6Hksy7dViArECCRg0aFCu4lVpn/0SSywRqoJjIJa0TyLYBRdcUOh7vv3220mhptg8v9xyy7X8PrFiAHQkI+E5LTEqVoQgbe0y11xz1X2O94glYFGEIOuYLUpsbCfovF0FDarkrLPOKvz8uOOOO1Kf59p71llnbXm+Iumb66oinXHGGYW+Xr+JjQN55q12iB1TFCmhQEtRKGKTdd3fi7juoyBnmksuuSTZM1BncM34xBNPRH9m/vnnb9vvU/Uxqwr3XlZcccWk23oa9p26vdP6sssuG32+6AInXF9ceeWVhb6mul+vnyuuq/tPEftKUpEuu+yy6POzzTZbNG6jX+8n5rmXCNdDkiRJysNCApIkSZIkSRVGEuKiiy6a+vzTTz/dVUmI7TTaaKOFpZZaKvX5119/PVx00UWFvd/5558f/vrXv4aq3dgmgCoNAdgHH3xwYYHYX331VRg2bFj0Z5rtHkGAQpqHHnqosO4cw4cPTxLqykLwTywo7K233irtvXvZqquuGn3+0ksvTZLxinLcccdFA+t/85vfhBlnnLGh11x33XWjwcskxbz55puhCJyHaR1plZ2IS+DPGGOMkeuj2mCDDaLPU9CBDsHdoEpzQqdsuOGGqc/REXqPPfYI3377bVt/p263yCKLpD5HB23VN84440TXPUUhIe/QQw8t9WsYb7zxUp9jvOnVtU9W8YXYei9PN9oll1wy9NNajvmSAjxF4biOJYlwDZ7nO8pTJCHWNZiul3R0KypJMO3fUEQg1rW+0aIFrXj00UdTn5tjjjmiHdFV34MPPhjuueeewj6eI488Mvzzn/9MfX7IkCG5XyvrPOJ3LwrJz0XOgf2GNWrsGreIMbEIsd+Da9znnnuusPc6/vjjK1kIjH2H2HUqhZz23nvv5PpF7Td06NDwww8/pD7PumKyySZr6+/Ui/s2ecesKtx7Ye20+uqrRz+nww47LHQzvgfWgWmY3++8885Cx3eKCai/9Pq54rq6/xSxryQVhWLQWUXWBg8e3LcfeFHrUtdDkiRJymIhAUmSJEmSpIrbaaedosmlRx99dNIluF1iwYztttFGG0WfP/HEE5MEqCJuAPdCB6JmbLzxxtFq+HSfpohCEUgY/eijj1KfpxM2naCaMd9886U+99133xXSuYiOibvuumvyemUiWCDN448/Xup796qZZ545egwwbu2///6FdBWiMxxJKjGbb755U9/7Msssk/o8yTzbbbddy107L7744nD11VeHXvX3v/+9LfNQLED517/+ddIxNw+Cp2IJhIwnjCvdEsBclTmhU5ZeeukwyyyzRIvRkJTTTt20bmu0kMCTTz5Z+pzbq6abbrrU50gYLqL4FcfOXnvtVfr4RCJU7Fqn6LXPN99805Yug7F5hC6IE088cdOFBKaaaqow5ZRThiqhQNwUU0wRXYeTWFdEMZubb745M+m6mbVcM0n6ad3cJphggjD11FMX9l5vvPFGtDhLOwsJxH6PWCJfo5ZYYokw/fTTpz56eT1cz+GHH54k/7bq/vvvD7fffnvq86yB11tvvdyvx7o51pEw67oqLwpJHnHEEaGq2nGtwLwVG2OnmWaa0O1roNr1blFj1VlnnRWqikKGE000UerzzJPsN7cLx15RBeuK1O7fib3nWMEdrLHGGqHbtWPM+uCDD6KFQxpZR1Xh3stWW20V7bTO2FjUnFvW3xArzFhb61CQslXsy1BwVv2pl88V19Xdo5f2lVRd7Vynck9y9913j+7T//znPw8rrbRS6DbtKqgeO18bWZe6HpIkSVIWCwlIkiRJkiRVHJ2t11prrdTn6RBFwNu1115b6u/xySefhGOPPTZsttlmoVvMOuus0aQHbm6TeEviSbMIUNt2220LCVTrRiRvrbzyypkdCVvtcHjuuedmBultuummYbTRRmvq9enKGgv6pFNCK53kSMr4/e9/X0hiXpZYJ3sSOz799NPSf4dexPcT88wzz7TcVY+uGzvssEM0yI+gkOWXX76p199ll12iCeqvvvpqEkhCwHQzOA8OOuig0MtI6ODzveqqq0oL2OM8i3UzbaRD8EgjjZR0oc86rhj/iuy0PGJCcT/OCZ3C9804E5uPbrrppqR4RCtrkyyMUbzPaqutFl555ZXQzeadd94w6qijps69RXaVrZKFFloo9TmCOg8++OCWikjwbzmW29HZ+Wc/+1k0oPK6664rNDiWtRzrRsahstb3L730UvTajOM+Nk6Auf7555+v+xy/f9UMGjQobLPNNtGfueOOO8IxxxzTckGorIIunF9zzjlnKEps3ZC2lmk2sZ/riLHGGqvuc2kB38y1FOVqh6xxPVZcRtlj2x/+8IeWxn7WpCQHxLC2GH/88Rsa4+eee+7U50mYZZxvxdtvv53sU5HQU1Urrrhi+OMf/xjtctgK5sMTTjgh9fkxxhgjuk/RTiRMxYrpcDw99thjLb0HxdPYS2xl76DbUeCDMSOGLux0Zi6zsBcFEymqs9xyyyVFA7sN++Bc/3722WelvxfFcI877rjoz1BoqBeK7e27777JOcS5VBYKE8bO0Ub2bapw72W88cbLXEsfcsghyXldZuIh5zHvscoqqzT8bymuGhvfme9322238P333zf9+1Fwevvtt+/6gosqTy+fK66ru0ev7Cup2tZee+1www03lH7NQoEoik9TiCeGgoO//OUvQ7e55pprkn2M2267rbT5n+sZCngWsS51PSRJkqQsXglKkiRJkiT1AYI76TiRhoBpEhVJiiiyujrBMgT9EQC4+OKLh1NOOSV8/vnnoZvwd8cCJkhUIBmRYLxmgss22mij8OKLL4YqIwgvLeEEBOiRPH3llVc2/NoEMRAIS/BxDIGCm2yySWjWr371q7DwwgunPk/gM51FCTps5jj43e9+Fx588MHQDrFEC5Jv+L66pXt5N+Fzy+p4QWANBQeaCfCiIzLHQVYhhwMOOCBJhmvG5JNPHrbccsvozzAekehNcErewBeCWOi4RPeugUGQvdjRmGA95rvFFlss/OlPfwpvvvlmoYn3nGOxYFESeRpBMmAsKB0ki66++urhgQceCEW47777wjrrrJMEefXjnNBJJJ9mdbK+8cYbk++76MSKjz76KAliXXrppZPCJGlJyN2EQOgFFligqc7V/YxxZeSRR059njUL6/dmkixYMzMXlZ0ok3ftQyEgEoWKDPbkXGEcovs5SQIEaBeFeZrEBBLjWplH7rrrrmin9SpifRM7FnD66aeHAw88sKliQhQiYHyOJRszJnHuFIm/KVYoqshCAlwXk1DQCH4+Np4UifV02rg0ySSTZHYaV/YxTqIj14yNYs1AwbLYdeboo48edtxxx4ZfO6tABOcca9dmUPCGpNr3338/VNm3334bzjvvvLDUUksl17NcMxQ1L7LHR/HMWNFEkrzbNU60ekxxvUECc7PFqK6//vrkXOjGpPaiUaBvhRVWiP4M1xbrr79+NDGmGezNkTjPXvP+++9f+OsXhfH0rLPOSpIFKShQ5N5Dzeuvv57sPceKedRQ7IVur92O85CurIzP7IWQwFVUIT3GPtbxXFPH9hMaLZJUhXsvFCCIFcNiDcZ11dZbb53sdReJAobsOXJ9xXs0My+zl5pV4ISiluxHNXM8ca4xnlV9zaBqnyuuq7tHL+wrqdooBMh9HNbzrLXKKGrB3jCvn3WPgSItjJndit+f62jumbCvWOTYzpr00EMPja5vGik873pIkiRJWbrnTo0kSZIkSZJKQ5dAOtIQ7BQLCqdD8y233BLWWGONJPCVzsKNIinjqaeeSoLQeTTb+bpdZphhhiTgkk7baZ5++umkuwcBfwRY0DE4K+CR4GGCRAcmDZNcMu6445bWCa4T6BKw3377JUEHsUT8ffbZJ9x9991JJ+VY59iaJ598MhxxxBHJ5x9DYg3d9fhsW0HSaSwBgcAsEmsJvCVRKes4IPCCgGECpweedzPNNFNpCZoEhBEYnBYo9PDDDycJBHQmIOiNAFk6BNJRLu1vIuh/wgknDFVHQC9dLWOBIIxrL7zwQnK8L7vsspmdWxgDTjvttHDRRRdlJmTSdWO++eYLrSCJg4SY2HlDgg/B9ieddFJSPIGOFtNMM01yLvNdE1D7zjvvJMHIBG/zevU6kyy44IJhjjnmSAL4exHfzamnnpo8Zp999iS4n+IC0047beb5PRCfF0FEJMfHknJIBmymS/Gee+6ZfKex7vActyQ4EjzO/2YlUw5EwCJd6G+++ebk7+n3OaGTCEzj72Q8SkOyDIkVJItS8IgO2M10kiKZhYTj22+/PXnPMjumlWXIkCGpSdOMYVnd4voR5ylr21hBj6uvvjq8+uqryXwx66yzZr4mc8fll1+ejKn1EkmZ38oq7DB48OBwySWXpD7PeuzWW29NEhlZg0066aTJ9VEskeoXv/hFZics1noXXHBB8iCBmYBSxmC6LjdaFIg56eyzzw4XXnhhkvAZKxrEGi4L42ta8SzmvCpi7j744IOTQiuxa16OlSeeeCJJNIoVEqthTjz++ONzdT0nSbroIkujjjpq8p010iG72UICtX/LejevRjqjtSpWICOrIJh+jITHgUmtrAdee+21ZC0XK9RTw3jFXsrJJ5+cWaCDtSFjUKM4p3n9tCIFvC9FzyiAxf+OM844uZImeE0KxQ3EdTHdpbPWnL2I6xQ6KfIgWYP5k3lrnnnmSf7uRrDfwB4i837s+pm1ebcV+KLA38UXX5xck9RDku+6666bJJtusMEGyZohC4UHmCvuv//+Hz3Hcc+agnVV1QwdOjS8/PLLybgRK+pE8gv7UBwLzVwPc43C51fba+61gq21ggI82IdmL4l141RTTdXw3gPYW2KfhjGMNW7asTwikrh6ca7k+OFx0EEHJfs1FMRiL6rRjrUcQ5yfnKdZHXHZT2n0e6nCvReuX44++uikeMPHH3+c+nP33ntvsn/G8cTcO/300zc1H7Evzu/P2iNWjKYRHB/Mbbxm7DqJzsLsSWUlVdc+b+YMiloO/G7HHnvs5H8t2NtfevlccV3dfbp5X6lTWrkv0U24n9pNxdRi18bce6LAOXtkHBsUuIgVio757LPPkrUOa54898BZc5FIX5tTu9m7776bjP8UJ2efmzGez4pzqlHc52NPgs8qhpiUiSaaqKHXdj0kSZKkmO6/SpEkSZIkSVIhCDwgWI5kqliAIQFR559/fvKYYoopwlxzzZUkihJkPOaYYyY3j7mxS4DDl19+mXTZImCNBEQCObnpnCeAsZvQdZcu0rGuDwQFEfhOhyeCf+i8SOIjnwfBiAQa05nmkUceSQI504IdSPIlkbtKhQTAZ0KgPUEqWckfBOwRqE5QAgG0BFSQyEV3Jj4XAjr5GZK18yAJqJlA5HpJMyRi0oU31mWXghJ0eCJIgERdCkNwbnA+0PWJgC6CU0n2qZfMXwsuJSioDHR6JKnyiiuuSP0ZzluSB/N2BKfDZ1pCWpUQiExgKIkFsXGMgJGdd9456fxDMDxjJIk4fPYEdHMcEOTOuU7QYJ6ut7PNNlvYa6+9Wv4bCE5irF977bUzuxKR8HHmmWcmj0YxJ5CsHTvOegnjFw8CgQhaImGP4D0C+yeYYILk2CC5hkRt5knmQLogMu7zPTNeZSVgk9hOUlYzGDdI0iFIlXEoq8MaD85bAuxJACY4nQQW/gZ+f+as2rxF0guPIjupVGFO6CQSrggko7hIrHgEWL/wYB7i72bdxvqN9QkPkqE5Xhn3eTA+8ZqseUj6KbIbYqcQHFebhwfi+GHM5nzQ/6LbLonRsfmOsYGCFcxRJFnMMsssybqHAkT8O8YjEsfoEs58l5Y0w7+js1RZhQRIZOa4jwXVcxw0Mt+tuuqqSae/vDiveFCkhzGbeYREgdo8wrzJGMw8zfhWK9rDtRNFQ0gGyuoOzTUYhR2yCqVwzqd91gSkN1N0pFfwedPVL6vrOd8VSWL8PGu52lzJmMmxTXdAfoau3czzeTp3s7bnNcs6xvMWEqCQAcdcK+9V5s+32p04DcVR1BjWDazBBl4TMi6R7EvhFRJPWVtwrcPYz7lAggrnB9fN7H3kSZ5j7CExuxlcY9H5lXV6GtbhFDQg0Y9zmut01vKshVhXMj8xD7Bnxe/NmJuGIlesO6tYSGDgHhfF7niw9mTPkLU4BeYmnnjiZN5iPmPOYV+Dz5CxkeODpF7GxliybA0FrxhruwnjPUmkl112WerPMBccc8wx4YwzzkjGd84D/g6OKeZy5lrmcdaaFKRkXZ2GIjcUmKwixgXWV+w/xK4nGcNJgOTBdWmtwB7FnWrXLZyrtWsWPl/WmXyuteuWPMdbL2Ac4kECEuMbhUt4MGax78B/43Plwf4Sa0Y+j9rYy79ljd5I4jJ7Vuzf9DK+f4oe8gDnI9cYjFmc0+OPP/5/jyPOX9bbXPsyZvGZsaaLJfzWcFwyPvTrvRfGf8Y9iqjEOhPz/hSe40FyG38Dnx3/N38D+2qMlfz+tf0I5hDOZf4G9kxjnbJbQdEJ5qnYmMR14xZbbJHMexQ6YUziu+B3Z7zifKvt67LWSduHoxgBe/wWEug/vXquuK7ubt20r9RJ3HetAu57sNbtFYw17LnU9l3YS+f4Y66szZG1dSoFL1nP1NapHIe1dTvFA7IKqo+IIgYk4/cS1gq1ezKHHHJIsg7lfGVdymfF+UqBw1qxPs5XxnkKOTKuUxAsVoithjmC/YlmuB6SJElSGgsJSJIkSZIk9RGS9EiAptNtngBMgqp4UDm+ygiaIGGPwNesoEJu9DbbfZviAwSpEYRW1W7uBNbRDSuGIH8CW2KdlvPacsstkw5ORTnwwAOTxDGCtmI4L2rdxBpBkA/Bws10KGg0OZDO4lUJtG4ngndJGKBQQFbAC0nOteDfVhAwTpJ4UQFcBKoQzE+3ozyB0o0iyY4xk4TvGBJSehEBwCTE8igS4wsJAs0ieItjje+VoNIsJEvRHZxHJ1RhTugkgvL4vkm+zpPIRjBaGcdtL2DspNN8WjIYnds4b/TjwHfmuiOPPDJ3J9BmEExJkZ4yO4qRGL/77rsn659uQCAtQaE8ikRBMq7nslDQKi25qCoB2TF0+SWph45mWQi4P/3001t+TzqRHXXUUaEsJOtT0C5vcbRWUCyP8zbPWoOEx2Y6azbjySefTE3iotgJBRTUOAqZkXBCcamBSADI00Ewz7VOq+fHRhttFG677bbMLtIkP3AdzKPZYlgUlSuiwFsvYc5oZa6PjUfsP3ajnXbaKSkqwTVTDHtDtQTAZrCWZ+6taiEBkAxJQQqK6eTplEyCLuczj35H4hUFuXiUhb1oEhJriUxVwRqOR5HYS2PPtpWiW1W490LCIHsR7K9kFbIERUxvvPHG5ITRRS0AABh7SURBVNENSNzjOyDBO+s7qBX2aHbNQCFfCgmoP/XqueK6ujd0el9J4jqJR1kF5ilqQUHoKuzZs39WK5pWFO6tUkyx2UKhrockSZKUprrtBiRJkiRJklQXQQJ0CKajnP43QZPEW5IhykDXCAoQkEheVYMGDUoSBNrRDZIgg+23377pavxp6GJFQh1/S9H47o844oi2dFcgkJturFXuOFumwYMHJ4GgJMyXjcQnkgqKHnvofnHJJZckyWBFonvgeeedl3TurXXTSNPNnW3aifGETj+rr756y6/F98n3Sqe7bleFOaHTCPhifCA4XXGx48wkpXR0eG6242YedE6iSx/r7LLRgZrkz6oiOSFvMiZdz+qhY9gCCywQ+gFFZZh723HttdhiiyUFocpM0GO9mHdd2mohgUZegwIHzMHtEBvLV1555cLfL6ugWFUSMukoeNppp4Vxxx23lNenGx/zAO/TCjpMU8iMIhdl4Tqda+h2HdNVxzjCd9ate2BcfzN20+21LGuttVZStKkfsJfGdepCCy3U6V9FI6BgLgVQ6SCrOK5X2OvKKpjZL/deZppppnDppZeGmWeeOfTq7899GK5/yjD33HOHP/7xj6W8tnpLL54rrqv7VyP7SlKZ2C+mWOamm27qB52y38T96VavrVwPSZIkqR6jSCVJkiRJkvoQNw+vueaaJKCwnYnGBGS30o25bPxudNEqOtBvlllmSV53rLHGClVHlXyS5elETVBSWUEGdLbdYYcdSnn9JZZYIgwbNqzQJGg6S5OgseKKK4Z2dmMlaJLPS41beumlk0B4ioCUhTGY4GKS88sK5qerWVFjPYErvF6tiECtQ2PVE6xaDUYneWr99dcv7DUnnXTSJEiV7mrdXiykCnNCpxH4zpxEUYayxorY8dsrSSdzzjln6hqTTstvv/1223+nXjF06NBCCp0MxPx5xRVXtHXtTxd6xoJuTVpsNtGR8z9voRSSn++///66z1FEoJ/mZuZeCsWVlXjMcUY3Pzqell18ivl0rrnmyvw51gXzzTdfWwsJtAPH9S233FL3OZKAiy4kQDe5Dz/8MPV5OsLRYbwquGa4+OKLw+STT154AQxet4ikzNrnToElihMUbd11102S3jnX1Bo+w6222ioZf9tRmK8V0003XXKtNt544xW+97njjjuGgw8+OPQT1ix873vttVepBRrSCil6/v7/2NdmD/Kggw6ywGPOgmRXXnllmHLKKQs7Jqtw74X1AXtPW2+9dduPIzq9t2rBBRcM55xzTuHFkigixlhn8VT18rniurq/NLqvJJVpmWWWCddff31y/1U/Nv300yfxJKxPi+B6SJIkSQN1d4SZJEmSJEmSSkNQJwGFBLURAFVm1zUCvXfaaadw9913JwmF3Yykp6uvvjqsuuqqLb8WgYJ0Rr3gggsK7zbe7TbaaKMkGGCRRRYptLs1HYdvvvnmJNigTCussEKS4F1EECk36jnPOtGZjQSXW2+9NQlkKzo4vh8QfHvdddclnc6LDIQnGIQkmHYEdZOEzPswrnE8cB41UwyFDiF0sptooon+57kvv/wy9d+1O+k5b7LxbrvtlnQPa+azyIukeY4bxivGgDJef7/99kuSdMtK4ptjjjnC4YcfXshr9fqc0A1WWmmlcPvttyfjeZnJ/SR80aH+/PPPT7qat6OTfFE23njjuv/9P//5T5IYovqYh+imeMghhxTSsZFzc5NNNkk+86KTUrNwPcPYy3jDWr6MDpQkZu27777Juq7MOZzPkYIxdGHn/M/rscceS52bq5T43EhCPAnofJZFJhiS1M8xTqJou5LD8iT3k0hSRPG6vIUE8v5cq+65557w8ccf131ujTXWaLnb/UD/93//F31+m222Ka1AUqcwtl1++eXJdXARRTa22GKLJPi96OuBqaeeOilsRtG8IlDkgOucAw88sFJFaAbiepCihmUXGWRuZA7eZZddeubzZDxnv2beeect7BhlD5BCM/2ItRhr8jvuuCOst956pXUDB8cz8zvz8U033VTqezWLsYpiN+3aG2FPm/GMz4O9/l7umMx+Osm5ZeJ+BQnhJ510Uin79lW498L6eeedd072XZhHyizYwZzMd897MY4Wta917bXXhkUXXbTl1+JvZ+1P4aFuHG/UWb14rriu7pxu31dSf6AQK/eV2lGMi30z9iQpunL88ccnxUx6BQUP2GueeeaZS13Lcb+HArmsG3mvIrkekiRJ0oh6486NJEmSJEmSSk2UpUvRm2++mSQkEuz517/+teUkNIJx6XrJo52dSItAIgLJk+uss04SVP7QQw8lSWh5cTOZm+Ik+5GA268IYqS72/Dhw5OkfBIgv/7664Zfh4Db5ZdfPgkiJcimXeigSBA8wQ0kfb/77rsN/XvOARIoFl988dBJ44wzThLIRrDjM888E5544onw0ksvhbfeeit88skn4Ysvvgj/+te/wnfffdfR37NbEcxFUA3H3yWXXJIUFnjjjTcafh0SKUgopxvZEkssUWrQSVpiGcGuH3zwQTLOkyDFcfD++++Hf//73//zswTyTDvttEnSPWNZbAz/7LPPUp8ruutXEQgO57zk8be//S088MAD4dFHHw1PP/10ePXVV3/0WTRq9tlnD0OGDEnGLM69shFUdN555yXjLMlfjLOff/55U6/FMUmRC5L9SSIreu7u9TmhW9YnjOesL2644YYkaJikYTo1N6v2vbNeIyGUJKpe7VjOcXv00UfXTTYl6bBqneqLtuaaayZjPmMKyZ9///vfG06UobgH52a9QkwkWxTZ5TMrIJ61PIlUf/7zn5Mx/rXXXgtvv/12+PTTT5O/jbXPDz/80NR1Dn8jj3/84x/h4YcfDo888kjyHsyrra6n6I7MPMKjmcBaCoCkBe12ek3aybGT4jskWzD/MH6y/mkUYyOfIYmRRSWcNiJP0n5Rif0kpzBvx/YFJp544rYVC7nssstSEyM4F4vGOZ1m0kknTYoXVBFr12OOOSb5+0499dRk/GwECQgE2TPfksxa5jl93HHHJeMuv+e9997b0H5NbZ5Yf/31k6IzjOsDUYQvbc7qxQJ9rJ15MO899dRTyR4X//vss882tR4f+HmQMMe8NeOMM4ZexN9AIt7999+fHFPsmTSKvT8StViP1kvGoSBf2jHVjuvGduOa7YADDkgSm0mGocAl52yj5+rAMZ/rX+Y6rl3Yr2hH4lMr2Bfkwd/NdfCDDz6YfA7PPfdcsiYtAoXfWJ9QXG+eeeZp+15TGdhX4UGSJWt49m2efPLJZNxKKyyUF2M+1zyMWezRlVlgskr3XijqwLX2H/7wh6R4B/s5XPu0ut/K91z7GxhHyyjQxRh/+umnh/vuuy+ccsopyXHUCMYZ5jmuJcpc36gaeu1ccV3dGd2+r6T+QOFnHlwPcq+QY5DrwxdffDHZM20Ve/CMV+wRLLfccskeUi9irc0eBw/WoVwzPv7448l6gvVcK9c3jOUUc+BcHTx4cKHF7AdyPSRJkqSakf7TyipWkiRJkiRJlUSSLEELBDkS5Pbee+8lyaLffvtt+Oc//5l03+OGJg+CTbiRSiAVQbEEZJOQVnaX7XbiM6gl3r788ss/Clok6JAgIRJvCWglSXhg126F5NjhBjvBnwQjkMzFZ/nNN98kgQkcVwTRkLjC50kQOkGRVMtvR2BnDNuoBJMT1EPAby0Jn3OCIF3OA27EE6Qz66yzJsdA2Z2z1DmMAxzLBNbUxsivvvoqOR4I/uA4JhmARKNpppkmCXAn4awbEwQITCOhkvOTBDmO5bzB+CTdzz333KnJJ8OGDUuCYHoF3yHn9+uvv56c4zwoIMJ/J6iv9ncy940xxhjJZ0UAFAHbjFckNTB+dRIJ5fwNjLMvvPBCeOedd5Ljk9+d75jxqjZ/07GYJEHmb+Zukg7amRjVy3NCN6HzOJ8h3zvFMPi+P/zww7rfOQ+SevjOa987a7Yyui92CokBxx57bN3nTjzxxCR4Udk4D1n3UGiFwMha4SGONwJBKQrAscQYwjxH0CPnZ5XW/83gnONcJOGJ9QHjGuMwnxvnJHMJcyfzR+06iqBu5hEK/jCPtJqYzRq0XvErvp+LL764pdeuCtb1fE8c47XrXQot8R2xluMYZ/7hGGf+YX3P58eah+9M7cW8RsJfvWJPJE3Sza5oaecRKFJC8nm/7AvR2Zi54JVXXkkKcIwYXsQao7YWJhmTOXb88cdv++/J+UvSA4UP+D2Zr3gwl3F9w3jL78l+FetJOn+aCPj/cF7xmZGwxFjI9Q9zF/t/zFk8WJcz7zP+cQ3ENS3jYu0aiP+t2tqchBCOJ479v/zlL/89pmqfBccU1/ocU+z/cPyzL6o41pOsK2trJcYUrv9q1y0cRyNetzCejHjdwlqpSvMwa0Su2Tne+L951NYjjF+cfxT/YH3NtTHX76xNuD7m2GMPmsTSXk3KahafU239Vtu3+eijj/671mYtx2fGscKDz41jaMR9m27oJl+Fey/8zrVzmr+H/7+2V86DvcXa78//cuyOeE7znXTiu2DOu/POO5Nxnj2UgcU4+b0pWslnzJphscUWq9Seidqvl84V19Wd1w37Supv3C9kruQ6kXUWxx/7IxTBYo1a2ztj7c74Vdsj5sE1EmMXxyKF+usV7asSrhEp3s/YzrnKOcs1Tu1+Ip9XLX6gdj3NZ8Tnw7qUQgudWmO4HpIkSepfFhKQJEmSJEmSmgjm4EYwN9S5EU7ARtUCpyUpCwG3dOVKQ+fBXu1KKal3A/hIAK1X4IQkgLPOOqsjv5fUriDQlVdeue5zdETcbLPN/CLUcygOQ5GYei6//PIkOL1IBMqnFZ0hIP7GG2/s22t/9j9qSQO1JM1OJzFKkiS1giQ/7vOQmMs9Hu71FNXtXZIkqRe4HpIkSeofI3f6F5AkSZIkSZJ6DV2BeEhSP3vsscdSn6MbCV2TJKmdxh577LDxxhuHk0466UfPPfjgg0miNV26pCqis2YaCmxIvYak9UsuuaTuc4svvnjhRQRAZ/s0O+ywQ98WEait75lnJUmSqoJOyu3q9i5JktSNXA9JkiT1D8tnSpIkSZIkSZKkhl166aWpz80888wWXJHUEZtuumlqouPpp5/e9t9Hape77747tYv6lFNO6RehnnP55ZeHL7744kf/faSRRgo77bRTKe+ZVkiAIjTLLbdcKe8pSZIkSZIkSZIkSWWykIAkSZIkSZIkSWrIo48+Gl5++eXU5+eff34/UUkdMfroo4fNN9+87nO33nprePvtt9v+O0ll++CDD8Lzzz9f97kll1zSL0A951//+lc455xz6j5HQj+J/e0sJEDhAgoYSJIkSZIkSZIkSVKvsZCAJEmSJEmSJEnK7bvvvgvDhg2L/syyyy7rJyqpY373u9+F8cYb70f//YcffghnnnlmR34nqUx33XVX6nMWElAvuu6668KHH374o/8+aNCg8Pvf/76U93zllVfCp59++qP/Pvvss4fFF1+8lPeUJEmSJEmSJEmSpLJZSECSJEmSJEmSJOV27LHHhmeffTb1+emmm660LrGSlMcoo4wSdtlll7rPXX311eHdd9/1g1Sl3H333XX/+69+9asw22yztf33kVrxr3/9K5x66ql1n1tvvfXClFNOWcoH/Mgjj9T97zvttFMp7ydJkiRJkiRJkiRJ7TByW95FkiRJkiRJkiS1xZdffhnefvvtMNNMMxX+2uecc04466yzoj+zySabFP6+ktSoVVddNVx++eXhqaee+lGCKgVRhg0b5oeqysiam6Vecskll4R33nmnbmGM3//+96W97//93//96L/NN9984be//W1p7ylJkiRJkiRJkiRJZftJ6e8gSZIkSZIkSZLa5vPPPw+rrbZa2GyzzcJjjz1WyGt+/fXXYa+99gqHH354+M9//pP6c5NPPnlYaaWVCnlPSWrFSCONFPbff//wk5/8+HboDTfcEF566SU/YEnqMl999VU45ZRT6j632267hTHHHLOU9/3hhx/C448//qP/vtNOO5XyfpIkSZIkSZIkSZLULiO37Z0kSZIkSZIkSVLbPPjgg8ljmmmmCcstt1zymHrqqRt6jS+++CJceOGF4YILLkj+7ywk7f70pz9t4beWpOLMOOOMYZ111gkXX3zx//x3CqIMGzbMLu6S1GXOOOOMpCjWQHPMMUdYddVVS3vfQYMGFVaAS5IkSZIkSZIkSZK6yUj/ibUOkiRJkiRJkiRJPeXNN98MgwcPrvvcJJNMEmaeeeYw00wzhammmiqMNdZYSWfXUUcdNfz9738Pf/vb35KCAcOHD0+SqV588cXw/fff53rfddddNxx44IEF/zWS1BrGNYqhDLwlOtJII4UNN9ywtO7WkqTGnXnmmeGbb7750X9fdtllw7TTTutHKkmSJEmSJEmSJEkNspCAJEmSJEmSJEl9UkigLPPNN1/S2funP/1pW99XkiRJkiRJkiRJkiRJkiTV95OU/y5JkiRJkiRJkpRpoYUWCqeffrpFBCRJkiRJkiRJkiRJkiRJ6iIWEpAkSZIkSZIkSY3fYPjJT8LWW2+dFBEYZZRR/AQlSZIkSZIkSZIkSZIkSeoiI3f6F5AkSZIkSZIkScUZbbTRwtRTTx1ef/310j7WeeedN+y1115hxhlnLO09JEmSJEmSJEmSJEmSJElS80b6z3/+858W/r0kSZIkSZIkSepCr776arjtttvCAw88EJ5//vnw3XfftfR644wzTlhyySXDOuusE2aZZZbCfk9JkiRJkiRJkiRJkiRJklQ8CwlIkiRJkiRJklRx//rXv5JiAsOHDw9vvfVWePfdd8N7770Xvvjii/DNN9+Eb7/9NvmZkUceOfz85z8PY401VphwwgnDZJNNFmacccYw66yzJo+f/OQnnf5TJEmSJEmSJEmSJEmSJElSDhYSkCRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpQmwdJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElShVhIQJIkSZIkSZIkSZIkSZIkSZIkSZIkSZKkCrGQgCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJFWIhAUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSKsRCApIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkVYiFBCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJqhALCUiSJEmSJEmSJEmSJEmSJEmSJEmSJEmSVCEWEpAkSZIkSZIkSZIkSZIkSZIkSZIkSZIkqUIsJCBJkiRJkiRJkiRJkiRJkiRJkiRJkiRJUoVYSECSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpAqxkIAkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSRViIQFJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkirEQgKSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJFWIhQQkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSaoQCwlIkiRJkiRJkiRJkiRJkiRJkiRJkiRJklQhFhKQJEmSJEmSJEmSJEmSJEmSJEmSJEmSJKlCLCQgSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVKFWEhAkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqQKsZCAJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkVYiEBSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIqxEICkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRViIUEJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmqEAsJSJIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUIRYSkCRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpQiwkIEmSJEmSJEmSJEmSJEmSJEmSJEmSJElShVhIQJIkSZIkSZIkSZIkSZIkSZIkSZIkSZKkCrGQgCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJFWIhAUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSKsRCApIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkVYiFBCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJqhALCUiSJEmSJEmSJEmSJEmSJEmSJEmSJEmSVCEWEpAkSZIkSZIkSZIkSZIkSZIkSZIkSZIkqUIsJCBJkiRJkiRJkiRJkiRJkiRJkiRJkiRJUoVYSECSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpAqxkIAkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSRViIQFJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkirEQgKSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJFWIhQQkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSQrV8f8BV7BbNCN5TP0AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 1600x600 with 1 Axes>"
+       "<Figure size 1600x680 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 623,
-       "width": 2038
+       "height": 703,
+       "width": 2057
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "pol = results[\"polish\"]\n",
-    "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
-    "names = list(pol.keys())\n",
-    "scores = [pol[n] for n in names]\n",
-    "ax.barh(names, scores, color=[\"C1\" if \"anneal\" in n and \"stop\" in n else \"C0\" for n in names])\n",
-    "for y, v in enumerate(scores):\n",
+    "conf = results[\"confirm\"]\n",
+    "flat_ref = np.array(conv.loc[\"e2.0\", [\"seed 0\", \"seed 1\", \"seed 2\"]], dtype=float)\n",
+    "mon_raw = np.array(conv.loc[\"monitor\", [\"seed 0\", \"seed 1\", \"seed 2\"]], dtype=float)\n",
+    "arms = {\n",
+    "    \"monitor stop, raw (1.07 passes)\": (mon_raw.mean(), mon_raw.std(ddof=1)),\n",
+    "    \"flat 2-pass reference\": (flat_ref.mean(), flat_ref.std(ddof=1)),\n",
+    "    **{k: (v[\"mean\"], v[\"sd\"]) for k, v in conf.items()},\n",
+    "}\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.4), layout=\"constrained\")\n",
+    "names = list(arms.keys())\n",
+    "means = [arms[n][0] for n in names]\n",
+    "sds = [arms[n][1] for n in names]\n",
+    "ax.barh(\n",
+    "    names,\n",
+    "    means,\n",
+    "    xerr=[3 * s for s in sds],\n",
+    "    color=[\"C1\" if \"1-pass budget\" in n else \"C0\" for n in names],\n",
+    "    capsize=3,\n",
+    ")\n",
+    "for y, (v, s) in enumerate(zip(means, sds)):\n",
     "    ax.annotate(\n",
-    "        f\"{v:+.6f}\", (v, y), xytext=(4, 0), textcoords=\"offset points\", va=\"center\", fontsize=8\n",
+    "        f\"{v:+.6f}\",\n",
+    "        (v + 3 * s, y),\n",
+    "        xytext=(4, 0),\n",
+    "        textcoords=\"offset points\",\n",
+    "        va=\"center\",\n",
+    "        fontsize=8,\n",
     "    )\n",
-    "lo = min(scores)\n",
-    "ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)\n",
+    "ax.set_xlim(min(means) - 3e-4, max(means) + 5e-4)\n",
     "ax.set_xticks([0.116, 0.117, 0.118])\n",
-    "ax.set_xlabel(\"held-out log score (nats/row)\")\n",
+    "ax.set_xlabel(\"held-out log score (nats/row); bars are 3-seed means ± 3 sd\")\n",
     "ax.set_title(\n",
-    "    \"Stop rule alone inherits optimizer jitter; stop + short anneal\\n\"\n",
-    "    \"reaches the annealed reference's level at 55% budget (single runs)\"\n",
+    "    \"Replicated: the anneal is the load-bearing ingredient,\\n\"\n",
+    "    \"and one full pass beats a quarter pass even though the loss plateaued\"\n",
     ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bf9e3c6e",
+   "id": "8355e311",
    "metadata": {},
    "source": [
-    "The working protocol this ablation suggests: **pair a stop rule on a\n",
-    "streaming fit with an anneal-on-stop polish phase**, and judge equivalence\n",
-    "after the polish, not at the raw stopping point.\n",
-    "That finding — not the 46% headline — is what we would want a reader to take\n",
-    "away, and it fed directly back into the design discussion of the upstream\n",
-    "pull requests.\n",
+    "Three seeds per arm resolve the picture sharply — within-arm spreads are\n",
+    "$4 \\times 10^{-6}$ to $8 \\times 10^{-5}$, so every gap below is tens of\n",
+    "spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the\n",
+    "batch order, which is shared). Four replicated facts:\n",
+    "\n",
+    "1. **The anneal is load-bearing.** A 1-pass budget plus a 2,000-step anneal\n",
+    "   (0.118738) beats the flat 1-pass arm by $1.7 \\times 10^{-3}$ — at\n",
+    "   essentially the same cost.\n",
+    "2. **The training-loss plateau is not a held-out plateau.** The loss flattens\n",
+    "   within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)\n",
+    "   trails the 1-pass-plus-anneal arm by $1.7 \\times 10^{-3}$: between ¼ and 1\n",
+    "   pass the model keeps improving where the loss trace cannot show it. The\n",
+    "   one-full-pass floor earns its keep after all.\n",
+    "3. **The stop rule adds nothing beyond the floor here.** Its stop\n",
+    "   (1.07 passes) plus anneal (0.118450) sits $2.9 \\times 10^{-4}$ *below* the\n",
+    "   plain 1-pass budget plus anneal — the detection delay bought 7% more flat-lr\n",
+    "   steps and a slightly worse endpoint. On this corpus the rule's value is\n",
+    "   certifying the plateau, not choosing a better stop than a well-chosen\n",
+    "   budget.\n",
+    "4. **More budget is not better.** The fully annealed 2-pass reference\n",
+    "   (0.117859) trails 1-pass-plus-short-anneal by $8.8 \\times 10^{-4}$: the\n",
+    "   endpoint depends on the schedule, not the step count.\n",
+    "\n",
+    "This also retires our own earlier reading: the \"$5 \\times 10^{-4}$ path\n",
+    "noise between annealed runs\" was a *protocol* difference wearing a noise\n",
+    "costume — replicates of the same protocol agree to $10^{-5}$. And it\n",
+    "sharpens the pre-registration post-mortem: the mistake was not the\n",
+    "$2 \\times 10^{-4}$ floor but the yardstick — equivalence to a reference that\n",
+    "is itself $8.8 \\times 10^{-4}$ off the tested frontier measures loyalty, not\n",
+    "quality.\n",
+    "\n",
+    "The working protocol this ablation supports: **one full pass at the working\n",
+    "learning rate, then a short anneal** — with the stopping rule as the\n",
+    "certificate that one pass was indeed enough, and equivalence judged after\n",
+    "the anneal, on held-out score, against the best arm rather than a\n",
+    "conventional reference. That finding — not any savings headline — is what we\n",
+    "would want a reader to take away, and it fed directly back into the design\n",
+    "discussion of the upstream pull requests.\n",
     "\n",
     "### The estimand: dispersion on the event clock\n",
     "\n",
@@ -2422,13 +2490,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "fdcefe80",
+   "id": "4ea7c59a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:54.938385Z",
-     "iopub.status.busy": "2026-08-01T01:23:54.938308Z",
-     "iopub.status.idle": "2026-08-01T01:23:55.152149Z",
-     "shell.execute_reply": "2026-08-01T01:23:55.151818Z"
+     "iopub.execute_input": "2026-08-01T02:01:02.013912Z",
+     "iopub.status.busy": "2026-08-01T02:01:02.013829Z",
+     "iopub.status.idle": "2026-08-01T02:01:02.227942Z",
+     "shell.execute_reply": "2026-08-01T02:01:02.227571Z"
     }
    },
    "outputs": [
@@ -2473,7 +2541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3758afc",
+   "id": "467f0c89",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2483,7 +2551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce87897d",
+   "id": "a15cd635",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2491,7 +2559,10 @@
     "A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn\n",
     "by taking the hash-ordered head of each shard, with the last four shards held\n",
     "out for scoring) can estimate every *global* parameter of this model — and did,\n",
-    "matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more\n",
+    "matching the full fit closely on the well-determined ones (ν to 0.1%, the\n",
+    "θ's, λ_q, τ_α) and agreeing in sign and rough magnitude on the small\n",
+    "coefficients that wander between full-N configurations too. So why stream\n",
+    "100× more\n",
     "data? Because the estimand lives in symbol-by-hour cells, and the thin end of\n",
     "the universe is where the two fits part ways. Below, the posterior of the\n",
     "conditional-move 90% half-width $\\sigma_{s,h}\\, t^{-1}_{0.95}(\\nu)$ at\n",
@@ -2502,13 +2573,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "e13af279",
+   "id": "af41851e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:55.153340Z",
-     "iopub.status.busy": "2026-08-01T01:23:55.153271Z",
-     "iopub.status.idle": "2026-08-01T01:23:55.350842Z",
-     "shell.execute_reply": "2026-08-01T01:23:55.350565Z"
+     "iopub.execute_input": "2026-08-01T02:01:02.229230Z",
+     "iopub.status.busy": "2026-08-01T02:01:02.229152Z",
+     "iopub.status.idle": "2026-08-01T02:01:02.425344Z",
+     "shell.execute_reply": "2026-08-01T02:01:02.425041Z"
     }
    },
    "outputs": [
@@ -2569,7 +2640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "573730ec",
+   "id": "0f8e0fe1",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2597,13 +2668,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "660a6f46",
+   "id": "5f649523",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:55.351983Z",
-     "iopub.status.busy": "2026-08-01T01:23:55.351913Z",
-     "iopub.status.idle": "2026-08-01T01:23:55.357095Z",
-     "shell.execute_reply": "2026-08-01T01:23:55.356713Z"
+     "iopub.execute_input": "2026-08-01T02:01:02.426471Z",
+     "iopub.status.busy": "2026-08-01T02:01:02.426388Z",
+     "iopub.status.idle": "2026-08-01T02:01:02.431215Z",
+     "shell.execute_reply": "2026-08-01T02:01:02.430889Z"
     }
    },
    "outputs": [
@@ -2752,7 +2823,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0df85798",
+   "id": "8dc54028",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2765,7 +2836,7 @@
     "  you have seed-checked — and remember the check bounds the error from below.\n",
     "- **The speed ordering is model-dependent.** Here the heavy gradient hides\n",
     "  the loader entirely and pre-shuffled streaming outpaces in-RAM\n",
-    "  `pm.Minibatch`; on light models the ordering reverses. Streaming's claim\n",
+    "  `pm.Minibatch` — and did so on our lighter probe as well. Streaming's claim\n",
     "  is memory and scale — treat any steps-per-second comparison as specific to\n",
     "  the model it was measured on.\n",
     "- **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the\n",
@@ -2780,7 +2851,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d924453",
+   "id": "af10e5cc",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2799,7 +2870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb676b94",
+   "id": "839d0a9c",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2811,7 +2882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfb5568b",
+   "id": "1596aec9",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2823,7 +2894,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32ad18f7",
+   "id": "106ec730",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2832,13 +2903,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "26a17ce1",
+   "id": "40c21d58",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T01:23:55.357901Z",
-     "iopub.status.busy": "2026-08-01T01:23:55.357846Z",
-     "iopub.status.idle": "2026-08-01T01:23:55.368558Z",
-     "shell.execute_reply": "2026-08-01T01:23:55.368224Z"
+     "iopub.execute_input": "2026-08-01T02:01:02.432026Z",
+     "iopub.status.busy": "2026-08-01T02:01:02.431969Z",
+     "iopub.status.idle": "2026-08-01T02:01:02.442799Z",
+     "shell.execute_reply": "2026-08-01T02:01:02.442403Z"
     }
    },
    "outputs": [
@@ -2878,7 +2949,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dde7ea8f",
+   "id": "81217938",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index adc3c1138..221e49dda 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -28,7 +28,8 @@ myst:
 
 Financial tick data outgrows memory quickly: one month of Binance aggregated
 trades for a single liquid pair is roughly 28 million rows, and a modest
-38-symbol universe over six months already exceeds 450 million rows — far more
+38-symbol universe (four majors over six months plus a one-month tail of 34
+alt pairs) already exceeds 450 million rows — far more
 than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a
 hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of
 that scale by streaming minibatches from disk, using the
@@ -152,10 +153,13 @@ y_i \mid m_i = 1 &\sim \text{StudentT}(\nu,\ \mu_i,\ \sigma_i) \\
 \end{aligned}
 $$
 
-with covariates computed *causally* (from information available at trade $i$):
-the trade sign $d_i$, standardized log notional $q_i$, standardized trailing
-60-second activity $a_i$, and the most recent previous nonzero return
-$\text{ylag}_i$. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
+with raw covariates computed causally (from information available at trade
+$i$): the trade sign $d_i$, log notional $q_i$, trailing 60-second activity
+$a_i$, and the most recent previous nonzero return $\text{ylag}_i$. One
+precision: $q$ and $a$ are then standardized with per-symbol constants
+estimated over the full sample — a fixed affine map that changes the
+parameterization, not the conditional law, and is shared by every arm below;
+a deployed system would freeze these constants from a burn-in window. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
 the standard way to encode intraday periodicity smoothly
 {cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
 back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
@@ -184,8 +188,8 @@ Notebooks in this collection do not download data at build time, so the
 executed path uses a seeded synthetic generator that mirrors the real corpus:
 same schema, same hurdle structure, same heavy tails, twelve symbols with a
 deliberately thin tail so shrinkage is visible. Because the truth is known,
-recovery is checkable — and the streaming code is byte-for-byte the code used
-on the real corpus.
+recovery is checkable — and the likelihood, priors, and loader configuration
+are line-for-line the ones used on the real corpus.
 
 ```{code-cell} ipython3
 n_symbols = 12
@@ -304,8 +308,10 @@ steps would only ever see early dates and the first symbols, and an early
 stopping decision would be biased by construction. The fix is to shuffle
 **once, globally, on disk at ETL time**: every row gets a deterministic hash
 key, rows are scattered across shards by that key, and each shard is sorted by
-it. After that, sequential reads *are* a uniform permutation, and the loader
-can run with `shuffle=False` at full speed.
+it. After that, sequential reads follow one fixed, data-independent uniform
+permutation — replayed identically each epoch, which is single-shuffle SGD
+semantics rather than fresh per-step subsampling — and the loader can run
+with `shuffle=False` at full speed.
 
 ```{code-cell} ipython3
 data_dir = tempfile.mkdtemp(prefix="ticks_")
@@ -345,8 +351,9 @@ print(
 ```
 
 On the real corpus the same check is part of the ETL script's validation: the
-head of any shard must already mix all 24 hours and all symbols, *verified, not
-assumed*. Note that the cell above is the pedagogical miniature — it sorts the
+head of any shard must already mix all 24 hours and nearly all symbols —
+asserted on a sampled shard at build time and spot-checked on others, rather
+than assumed. Note that the cell above is the pedagogical miniature — it sorts the
 whole table in memory, which at corpus scale would need several gigabytes for
 the keys and permutation alone. The published ETL is genuinely out-of-core:
 pass one hash-scatters rows into shards with bounded row-group appends, pass
@@ -381,8 +388,10 @@ the fit targets the 299,008-row subpopulation the loader streams, which is a
 uniform random subset by the hash shuffle, while `total_size` overstates its
 size by 0.3% (0.0004% at full scale) — invisible at any precision reported
 here, but it does mean Gate 1's equal-inclusion clause holds for the streamed
-subpopulation, not the last few rows of the last shard. If exactness matters,
-set `total_size` to the streamed count
+subpopulation, not the last few rows of the last shard — and, equivalently,
+that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a
+sub-percent effect dwarfed by the mean-field error quantified later. If
+exactness matters, set `total_size` to the streamed count
 (`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —
 carrying the remainder across epoch boundaries — belongs in the loader and is
 on its review agenda.
@@ -486,7 +495,10 @@ gives the term *observed-RV semantics*, which is what makes PyMC's own
 the hurdle's two logs are written with `softplus` —
 $\log \pi = -\operatorname{softplus}(-x)$,
 $\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at both
-tails.
+tails. The exact float comparison `value != 0` is safe here because the zeros
+are *structural* — a transition either changes the price or it does not, and
+the smallest genuine move on any listed tick grid sits dozens of orders of
+magnitude above the float32 flush-to-zero threshold.
 
 ```{code-cell} ipython3
 model = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), len(loader))
@@ -711,8 +723,12 @@ ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
 ax.legend(loc="lower right");
 ```
 
-The predictive distribution reproduces the zero step, the bulk scale, and the
-tail quantile within its own 90% bands. On real data one caveat would surface
+Two of the three statistics sit inside their predictive 90% bands; the median
+|move| lands at the band's edge (0.8% outside at full precision — the
+3-decimal table hides it, so we say it here). Note also that this is an
+in-sample adequacy check: the evaluation batch was seen during the fit; the
+real-corpus scores later use shards the fits never touched. On real data one
+further caveat would surface
 here that the synthetic generator cannot show: real price changes are integer
 multiples of the tick size, so the continuous Student-t's residual misfit
 concentrates at |y| near one tick — visible as small stair-steps in the
@@ -735,8 +751,8 @@ never evidence of convergence.
 :class: warning
 Successive losses are evaluated on *different batches*, so the per-step noise
 dwarfs the per-step improvement: on our full-scale run the median one-step
-change is about 106 loss units while the true improvement is 0.04 units per
-step. A rule that standardizes *raw* increments concludes "improvement is
+change is about 106 loss units while the mean improvement — (first − last)
+loss over the run's steps — is about 0.06 units per step. A rule that standardizes *raw* increments concludes "improvement is
 indistinguishable from zero" immediately upon arming, and fires a fixed
 ~$h/\kappa$ steps later regardless of whether the fit converged.
 :::
@@ -881,8 +897,10 @@ posterior has converged; the held-out checks in the next section are the
 independent evidence. Second, the $h/\kappa$-window delay after a plateau is
 the rule's *nominal* detection lag; clipping and the adaptive scale can move
 the actual hitting time. On the full-scale corpus the same replay shows the
-windowed rule stopping shortly after one pass, at a loss within noise of the
-two-pass reference.
+windowed rule stopping shortly after one pass, at a *training loss* within
+noise of the two-pass reference — whether that stop is also good enough on
+held-out score is precisely what the pre-registered test in the next section
+failed to confirm.
 
 (tick-real-corpus)=
 
@@ -1008,15 +1026,16 @@ for y, v in enumerate(vals):
         fontsize=9,
     )
 ax.set_xlabel("optimization steps per second (batch 4096, steady state)")
-ax.set_title("With a 500-parameter gradient, the loader is not the bottleneck");
+ax.set_title("With a 440-parameter gradient, the loader is not the bottleneck");
 ```
 
 The ordering surprised us, so it is worth stating carefully. On this model the
 gradient dominates the step, and the pre-shuffled streaming path *outpaces*
 in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than
-Minibatch's per-step random indexing. That ordering is model-dependent: on a
-light model the loader overhead shows and `pm.Minibatch` wins the races it can
-enter (we measured exactly that on a small probe model). The one consistently
+Minibatch's per-step random indexing. On our lighter probe model the same
+ordering held (pre-shuffled loader ~4,900 vs Minibatch ~3,000 steps/s), so
+treat any speed ordering as an empirical, model-specific fact rather than a
+law in either direction. The one consistently
 expensive path is the runtime shuffle buffer — which is why the ETL-time
 global shuffle matters twice: it is the *statistically* honest option, and it
 is 3× the throughput.
@@ -1029,7 +1048,9 @@ success story:
 **Non-centered effects collapsed under mean-field.** The first version of this
 model used the textbook non-centered parameterization
 $b_s = \tau \cdot z_s,\ z_s \sim \mathcal{N}(0,1)$. On the real corpus the
-$z_s$ posteriors blew out to $\pm 18$ while every $\tau$ pinned near its prior:
+$z_s$ posteriors blew out to $+17$ while the $\tau$'s collapsed — some pinned
+low against the prior, others (the hour-shape scale) driven far *below* its
+bulk:
 real cross-symbol volatility levels span two orders of magnitude, the original
 $\tau$ prior could not reach them, and a mean-field approximation of the
 $\tau$–$z$ funnel collapses into its small-$\tau$/huge-$z$ corner. Both fixes
@@ -1056,7 +1077,8 @@ quantiles behave, which is exactly why the estimand is quantile-based.
 
 **The robust location disagrees with least squares about a sign.** Ordinary
 least squares of $y$ on $\text{ylag}$ gives a *negative* coefficient
-(bid-ask bounce, −0.29); the fitted $\theta_r$ is *positive*
+(bid-ask bounce, ≈ −0.24 on the held-out sample, moved rows only — the exact
+value is specification-dependent); the fitted $\theta_r$ is *positive*
 (+0.29, seed spread $8 \times 10^{-6}$). Direct likelihood profiling on a
 million real rows
 confirms the likelihood genuinely peaks at the positive value: small moves
@@ -1069,11 +1091,15 @@ likelihood answers the one about typical transitions.
 
 ### The stopping rule at scale, and a pre-registration that failed
 
-One scoping sentence first, so no number below is ambiguous: every held-out
+Two scoping sentences first, so no number below is ambiguous. Every held-out
 score in this section comes from fits on the 60-shard training split only,
 with the four held-out shards touched by nothing but the scorer — the
 headline four-minute fit above streams all 64 shards and is never
-holdout-scored. The stopping-rule comparison was pre-registered before any
+holdout-scored. And "held out" here means a hash-random *contemporaneous*
+split over serially overlapping transitions (a holdout row's `ylag` can be a
+training row's label), which is exactly what paired comparisons between
+optimization arms need and nothing more — no forward-in-time validation is
+claimed anywhere in this notebook. The stopping-rule comparison was pre-registered before any
 full-scale run:
 fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical
 batch order and seeds, flat learning rate; savings to be claimed only if the
@@ -1083,7 +1109,11 @@ every budget a prefix of the same deterministic trajectory, all five arms per
 seed come from one run with mid-flight snapshots — and the windowed rule ran
 as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated
 one-pass floor plus its designed $h/\kappa$-window detection delay, 46% below
-the reference budget.
+the reference budget (each protocol is replicated below). Stated plainly,
+because it would be easy to bury: the fixed 1-pass budget *dominated* the
+monitor here — fewer steps, a better held-out score, and it passed the
+pre-registered test the monitor failed — so on this corpus the rule's value
+is certifying the plateau, not saving steps beyond a well-chosen budget.
 
 ```{code-cell} ipython3
 conv = pd.DataFrame(
@@ -1095,59 +1125,97 @@ conv
 
 **The equivalence test failed** — and not in the direction anyone would
 guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about
-$10^{-3}$ nats/row — a hundred times the seed spread — while the 1- and
+$10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads
+run $3\times10^{-6}$ to $1.2\times10^{-4}$, and batch order is shared across
+seeds, so these exclude data-order variation) — while the 1- and
 2-pass arms tied within seed spread, and the monitor arm under-scored
 everything. The loss trace suggests why: this fit reaches its loss plateau
 within a quarter pass, so all flat-rate arms sit on the same plateau, where
 the held-out score of a *point* estimate moves with the optimizer's state at
 flat learning rate 0.02 — consistent with Adam's stationary jitter and
 position along the (seed-shared) batch cycle, though these artifacts alone
-cannot separate that from other path effects. The polish experiment points
-the same way: re-running to the monitor's stop and annealing for two thousand
-further steps at a quarter of the learning rate moved the held-out score from
-the worst of the table to the top — within path noise of an annealed two-pass
-reference run on the same split — at 55% of its step budget. One run per
-protocol, so these comparisons are exploratory; paired, replicated annealed
-endpoints per arm are the confirmatory design.
-
-One more layer of honesty on the metric itself: even among fully annealed,
-fully converged runs, path-to-path differences are about $5 \times 10^{-4}$
-nats/row. The pre-registered $2 \times 10^{-4}$ equivalence floor was
-therefore poorly chosen: the only arm that passed it was the fixed 1-pass
-budget — which shadows the reference's own trajectory and passes trivially —
-while for every protocol that behaves differently from the reference the
-floor sits below the plateau jitter it would need to see through. A
-pre-registration mistake worth admitting, because the comparison it pushed us
-toward is the right one: flat-rate arms scatter over $1.7 \times 10^{-3}$,
-annealed endpoints over $5 \times 10^{-4}$, and the seed spread within one
-protocol is $10^{-5}$.
+cannot separate that from other path effects. So we ran the confirmatory
+design the failure demanded: three seeds per protocol for an annealed 2-pass
+reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass budget
+plus the same anneal, and the monitor's stop (1.07 passes) plus the same
+anneal.
 
 ```{code-cell} ipython3
-pol = results["polish"]
-fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
-names = list(pol.keys())
-scores = [pol[n] for n in names]
-ax.barh(names, scores, color=["C1" if "anneal" in n and "stop" in n else "C0" for n in names])
-for y, v in enumerate(scores):
+conf = results["confirm"]
+flat_ref = np.array(conv.loc["e2.0", ["seed 0", "seed 1", "seed 2"]], dtype=float)
+mon_raw = np.array(conv.loc["monitor", ["seed 0", "seed 1", "seed 2"]], dtype=float)
+arms = {
+    "monitor stop, raw (1.07 passes)": (mon_raw.mean(), mon_raw.std(ddof=1)),
+    "flat 2-pass reference": (flat_ref.mean(), flat_ref.std(ddof=1)),
+    **{k: (v["mean"], v["sd"]) for k, v in conf.items()},
+}
+fig, ax = plt.subplots(figsize=(8, 3.4), layout="constrained")
+names = list(arms.keys())
+means = [arms[n][0] for n in names]
+sds = [arms[n][1] for n in names]
+ax.barh(
+    names,
+    means,
+    xerr=[3 * s for s in sds],
+    color=["C1" if "1-pass budget" in n else "C0" for n in names],
+    capsize=3,
+)
+for y, (v, s) in enumerate(zip(means, sds)):
     ax.annotate(
-        f"{v:+.6f}", (v, y), xytext=(4, 0), textcoords="offset points", va="center", fontsize=8
+        f"{v:+.6f}",
+        (v + 3 * s, y),
+        xytext=(4, 0),
+        textcoords="offset points",
+        va="center",
+        fontsize=8,
     )
-lo = min(scores)
-ax.set_xlim(lo - 3e-4, max(scores) + 4e-4)
+ax.set_xlim(min(means) - 3e-4, max(means) + 5e-4)
 ax.set_xticks([0.116, 0.117, 0.118])
-ax.set_xlabel("held-out log score (nats/row)")
+ax.set_xlabel("held-out log score (nats/row); bars are 3-seed means ± 3 sd")
 ax.set_title(
-    "Stop rule alone inherits optimizer jitter; stop + short anneal\n"
-    "reaches the annealed reference's level at 55% budget (single runs)"
+    "Replicated: the anneal is the load-bearing ingredient,\n"
+    "and one full pass beats a quarter pass even though the loss plateaued"
 );
 ```
 
-The working protocol this ablation suggests: **pair a stop rule on a
-streaming fit with an anneal-on-stop polish phase**, and judge equivalence
-after the polish, not at the raw stopping point.
-That finding — not the 46% headline — is what we would want a reader to take
-away, and it fed directly back into the design discussion of the upstream
-pull requests.
+Three seeds per arm resolve the picture sharply — within-arm spreads are
+$4 \times 10^{-6}$ to $8 \times 10^{-5}$, so every gap below is tens of
+spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the
+batch order, which is shared). Four replicated facts:
+
+1. **The anneal is load-bearing.** A 1-pass budget plus a 2,000-step anneal
+   (0.118738) beats the flat 1-pass arm by $1.7 \times 10^{-3}$ — at
+   essentially the same cost.
+2. **The training-loss plateau is not a held-out plateau.** The loss flattens
+   within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)
+   trails the 1-pass-plus-anneal arm by $1.7 \times 10^{-3}$: between ¼ and 1
+   pass the model keeps improving where the loss trace cannot show it. The
+   one-full-pass floor earns its keep after all.
+3. **The stop rule adds nothing beyond the floor here.** Its stop
+   (1.07 passes) plus anneal (0.118450) sits $2.9 \times 10^{-4}$ *below* the
+   plain 1-pass budget plus anneal — the detection delay bought 7% more flat-lr
+   steps and a slightly worse endpoint. On this corpus the rule's value is
+   certifying the plateau, not choosing a better stop than a well-chosen
+   budget.
+4. **More budget is not better.** The fully annealed 2-pass reference
+   (0.117859) trails 1-pass-plus-short-anneal by $8.8 \times 10^{-4}$: the
+   endpoint depends on the schedule, not the step count.
+
+This also retires our own earlier reading: the "$5 \times 10^{-4}$ path
+noise between annealed runs" was a *protocol* difference wearing a noise
+costume — replicates of the same protocol agree to $10^{-5}$. And it
+sharpens the pre-registration post-mortem: the mistake was not the
+$2 \times 10^{-4}$ floor but the yardstick — equivalence to a reference that
+is itself $8.8 \times 10^{-4}$ off the tested frontier measures loyalty, not
+quality.
+
+The working protocol this ablation supports: **one full pass at the working
+learning rate, then a short anneal** — with the stopping rule as the
+certificate that one pass was indeed enough, and equivalence judged after
+the anneal, on held-out score, against the best arm rather than a
+conventional reference. That finding — not any savings headline — is what we
+would want a reader to take away, and it fed directly back into the design
+discussion of the upstream pull requests.
 
 ### The estimand: dispersion on the event clock
 
@@ -1192,7 +1260,10 @@ clocks tell different stories and this notebook only claims the first.
 A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn
 by taking the hash-ordered head of each shard, with the last four shards held
 out for scoring) can estimate every *global* parameter of this model — and did,
-matching the full fit on ν, θ, λ, β and the τ's. So why stream 100× more
+matching the full fit closely on the well-determined ones (ν to 0.1%, the
+θ's, λ_q, τ_α) and agreeing in sign and rough magnitude on the small
+coefficients that wander between full-N configurations too. So why stream
+100× more
 data? Because the estimand lives in symbol-by-hour cells, and the thin end of
 the universe is where the two fits part ways. Below, the posterior of the
 conditional-move 90% half-width $\sigma_{s,h}\, t^{-1}_{0.95}(\nu)$ at
@@ -1275,7 +1346,7 @@ spread.sort_values("ratio", ascending=False)
   you have seed-checked — and remember the check bounds the error from below.
 - **The speed ordering is model-dependent.** Here the heavy gradient hides
   the loader entirely and pre-shuffled streaming outpaces in-RAM
-  `pm.Minibatch`; on light models the ordering reverses. Streaming's claim
+  `pm.Minibatch` — and did so on our lighter probe as well. Streaming's claim
   is memory and scale — treat any steps-per-second comparison as specific to
   the model it was measured on.
 - **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the
diff --git a/examples/variational_inference/streaming_tick_data_results.json b/examples/variational_inference/streaming_tick_data_results.json
index 765abd9ba..743f7cf36 100644
--- a/examples/variational_inference/streaming_tick_data_results.json
+++ b/examples/variational_inference/streaming_tick_data_results.json
@@ -1278,5 +1278,43 @@
   "peak_rss_gb": 1.06,
   "batch_size": 4096,
   "steps_per_epoch": 120009
+ },
+ "confirm": {
+  "annealed 2-pass reference": {
+   "scores": [
+    0.117855,
+    0.117862,
+    0.11786
+   ],
+   "mean": 0.117859,
+   "sd": 3.61e-06
+  },
+  "1-pass budget + 2k anneal": {
+   "scores": [
+    0.118735,
+    0.118747,
+    0.118731
+   ],
+   "mean": 0.118738,
+   "sd": 8.33e-06
+  },
+  "quarter-pass budget + 2k anneal": {
+   "scores": [
+    0.116952,
+    0.117102,
+    0.117009
+   ],
+   "mean": 0.117021,
+   "sd": 7.57e-05
+  },
+  "monitor stop (1.07) + 2k anneal": {
+   "scores": [
+    0.118446,
+    0.11846,
+    0.118444
+   ],
+   "mean": 0.11845,
+   "sd": 8.72e-06
+  }
  }
 }
\ No newline at end of file

From 6b2e23b22215720e14a9caf86eceab482fa1fe24 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 31 Jul 2026 21:21:11 -0500
Subject: [PATCH 23/42] Round-3 referee closure: effective total_size in demo,
 exact epistemic labels

---
 .../streaming_tick_data.ipynb                 | 1007 +++++++++--------
 .../streaming_tick_data.myst.md               |   70 +-
 2 files changed, 559 insertions(+), 518 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 01b121f2c..cfc951a6d 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "42ff7b5f",
+   "id": "a4274a8f",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3671e70e",
+   "id": "b16eda7e",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46c114e",
+   "id": "4817b70a",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ed31533",
+   "id": "58565d64",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -103,13 +103,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c7c94316",
+   "id": "c7335997",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:48.874879Z",
-     "iopub.status.busy": "2026-08-01T02:00:48.874710Z",
-     "iopub.status.idle": "2026-08-01T02:00:50.709463Z",
-     "shell.execute_reply": "2026-08-01T02:00:50.709033Z"
+     "iopub.execute_input": "2026-08-01T02:19:25.809383Z",
+     "iopub.status.busy": "2026-08-01T02:19:25.809227Z",
+     "iopub.status.idle": "2026-08-01T02:19:29.256570Z",
+     "shell.execute_reply": "2026-08-01T02:19:29.255889Z"
     }
    },
    "outputs": [],
@@ -135,13 +135,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e9911d16",
+   "id": "e0b16f9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:50.710953Z",
-     "iopub.status.busy": "2026-08-01T02:00:50.710829Z",
-     "iopub.status.idle": "2026-08-01T02:00:50.713729Z",
-     "shell.execute_reply": "2026-08-01T02:00:50.713321Z"
+     "iopub.execute_input": "2026-08-01T02:19:29.258744Z",
+     "iopub.status.busy": "2026-08-01T02:19:29.258418Z",
+     "iopub.status.idle": "2026-08-01T02:19:29.263787Z",
+     "shell.execute_reply": "2026-08-01T02:19:29.263190Z"
     }
    },
    "outputs": [],
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "905f93eb",
+   "id": "7ac3b747",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8503d423",
+   "id": "e4931388",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -234,13 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d903b26e",
+   "id": "eddd5cda",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:50.714592Z",
-     "iopub.status.busy": "2026-08-01T02:00:50.714528Z",
-     "iopub.status.idle": "2026-08-01T02:00:50.716829Z",
-     "shell.execute_reply": "2026-08-01T02:00:50.716536Z"
+     "iopub.execute_input": "2026-08-01T02:19:29.265088Z",
+     "iopub.status.busy": "2026-08-01T02:19:29.264975Z",
+     "iopub.status.idle": "2026-08-01T02:19:29.268914Z",
+     "shell.execute_reply": "2026-08-01T02:19:29.268362Z"
     }
    },
    "outputs": [],
@@ -282,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "eef1b521",
+   "id": "73c73354",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:50.717731Z",
-     "iopub.status.busy": "2026-08-01T02:00:50.717681Z",
-     "iopub.status.idle": "2026-08-01T02:00:50.863495Z",
-     "shell.execute_reply": "2026-08-01T02:00:50.863072Z"
+     "iopub.execute_input": "2026-08-01T02:19:29.270239Z",
+     "iopub.status.busy": "2026-08-01T02:19:29.270138Z",
+     "iopub.status.idle": "2026-08-01T02:19:29.557546Z",
+     "shell.execute_reply": "2026-08-01T02:19:29.556929Z"
     }
    },
    "outputs": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "313d3492",
+   "id": "aea3720b",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -365,13 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0a7ee3e4",
+   "id": "080ee360",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:50.864403Z",
-     "iopub.status.busy": "2026-08-01T02:00:50.864337Z",
-     "iopub.status.idle": "2026-08-01T02:00:51.233452Z",
-     "shell.execute_reply": "2026-08-01T02:00:51.233124Z"
+     "iopub.execute_input": "2026-08-01T02:19:29.558946Z",
+     "iopub.status.busy": "2026-08-01T02:19:29.558800Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.272229Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.271737Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebd030da",
+   "id": "eb82706f",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -431,13 +431,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "87bf8008",
+   "id": "f791c297",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:51.234531Z",
-     "iopub.status.busy": "2026-08-01T02:00:51.234447Z",
-     "iopub.status.idle": "2026-08-01T02:00:51.386035Z",
-     "shell.execute_reply": "2026-08-01T02:00:51.385739Z"
+     "iopub.execute_input": "2026-08-01T02:19:30.274217Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.274059Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.520079Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.519602Z"
     }
    },
    "outputs": [
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e62a15e",
+   "id": "864c3648",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62b4c00b",
+   "id": "0bd43daf",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -518,13 +518,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c4de3785",
+   "id": "dad28f6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:51.387057Z",
-     "iopub.status.busy": "2026-08-01T02:00:51.386995Z",
-     "iopub.status.idle": "2026-08-01T02:00:51.389826Z",
-     "shell.execute_reply": "2026-08-01T02:00:51.389580Z"
+     "iopub.execute_input": "2026-08-01T02:19:30.521802Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.521663Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.526483Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.525926Z"
     }
    },
    "outputs": [
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08df612d",
+   "id": "2b8d0e23",
    "metadata": {},
    "source": [
     "One deliberate loader semantics to be precise about: only full batches are\n",
@@ -563,22 +563,39 @@
     "subpopulation, not the last few rows of the last shard — and, equivalently,\n",
     "that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a\n",
     "sub-percent effect dwarfed by the mean-field error quantified later. If\n",
-    "exactness matters, set `total_size` to the streamed count\n",
-    "(`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —\n",
-    "carrying the remainder across epoch boundaries — belongs in the loader and is\n",
-    "on its review agenda."
+    "exactness matters, set `total_size` to the streamed count — the cells below\n",
+    "do exactly that via `n_streamed`; the full-scale runs keep `len(loader)`,\n",
+    "where the correction is 0.0004%. The durable fix — carrying the remainder\n",
+    "across epoch boundaries — belongs in the loader and is on its review agenda."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "612aaa24",
+   "id": "1fb35886",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:51.390774Z",
-     "iopub.status.busy": "2026-08-01T02:00:51.390710Z",
-     "iopub.status.idle": "2026-08-01T02:00:51.395153Z",
-     "shell.execute_reply": "2026-08-01T02:00:51.394818Z"
+     "iopub.execute_input": "2026-08-01T02:19:30.527696Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.527587Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.529596Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.529143Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n_streamed = (len(loader) // loader.batch_size) * loader.batch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b0a7ba49",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-01T02:19:30.530873Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.530774Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.539418Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.538883Z"
     }
    },
    "outputs": [],
@@ -676,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14ee7474",
+   "id": "da7ec359",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -694,14 +711,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "91eb54f2",
+   "execution_count": 10,
+   "id": "31e7411c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:51.395873Z",
-     "iopub.status.busy": "2026-08-01T02:00:51.395818Z",
-     "iopub.status.idle": "2026-08-01T02:00:51.552841Z",
-     "shell.execute_reply": "2026-08-01T02:00:51.552497Z"
+     "iopub.execute_input": "2026-08-01T02:19:30.540715Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.540602Z",
+     "iopub.status.idle": "2026-08-01T02:19:30.829153Z",
+     "shell.execute_reply": "2026-08-01T02:19:30.828497Z"
     }
    },
    "outputs": [
@@ -717,382 +734,382 @@
        "<svg width=\"1705pt\" height=\"483pt\"\n",
        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.24,-478.95 1701.24,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M795.97,-8C795.97,-8 841.97,-8 841.97,-8 847.97,-8 853.97,-14 853.97,-20 853.97,-20 853.97,-94 853.97,-94 853.97,-100 847.97,-106 841.97,-106 841.97,-106 795.97,-106 795.97,-106 789.97,-106 783.97,-100 783.97,-94 783.97,-94 783.97,-20 783.97,-20 783.97,-14 789.97,-8 795.97,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.97\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.01,-8C798.01,-8 844.01,-8 844.01,-8 850.01,-8 856.01,-14 856.01,-20 856.01,-20 856.01,-94 856.01,-94 856.01,-100 850.01,-106 844.01,-106 844.01,-106 798.01,-106 798.01,-106 792.01,-106 786.01,-100 786.01,-94 786.01,-94 786.01,-20 786.01,-20 786.01,-14 792.01,-8 798.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"824.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M945.97,-243.82C945.97,-243.82 1119.97,-243.82 1119.97,-243.82 1125.97,-243.82 1131.97,-249.82 1131.97,-255.82 1131.97,-255.82 1131.97,-353.63 1131.97,-353.63 1131.97,-359.63 1125.97,-365.63 1119.97,-365.63 1119.97,-365.63 945.97,-365.63 945.97,-365.63 939.97,-365.63 933.97,-359.63 933.97,-353.63 933.97,-353.63 933.97,-255.82 933.97,-255.82 933.97,-249.82 939.97,-243.82 945.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.34\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1151.97,-243.82C1151.97,-243.82 1425.97,-243.82 1425.97,-243.82 1431.97,-243.82 1437.97,-249.82 1437.97,-255.82 1437.97,-255.82 1437.97,-353.63 1437.97,-353.63 1437.97,-359.63 1431.97,-365.63 1425.97,-365.63 1425.97,-365.63 1151.97,-365.63 1151.97,-365.63 1145.97,-365.63 1139.97,-359.63 1139.97,-353.63 1139.97,-353.63 1139.97,-255.82 1139.97,-255.82 1139.97,-249.82 1145.97,-243.82 1151.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.59\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1468.97,-243.82C1468.97,-243.82 1642.97,-243.82 1642.97,-243.82 1648.97,-243.82 1654.97,-249.82 1654.97,-255.82 1654.97,-255.82 1654.97,-353.63 1654.97,-353.63 1654.97,-359.63 1648.97,-365.63 1642.97,-365.63 1642.97,-365.63 1468.97,-365.63 1468.97,-365.63 1462.97,-365.63 1456.97,-359.63 1456.97,-353.63 1456.97,-353.63 1456.97,-255.82 1456.97,-255.82 1456.97,-249.82 1462.97,-243.82 1468.97,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.84\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1469.01,-243.82C1469.01,-243.82 1643.01,-243.82 1643.01,-243.82 1649.01,-243.82 1655.01,-249.82 1655.01,-255.82 1655.01,-255.82 1655.01,-353.63 1655.01,-353.63 1655.01,-359.63 1649.01,-365.63 1643.01,-365.63 1643.01,-365.63 1469.01,-365.63 1469.01,-365.63 1463.01,-365.63 1457.01,-359.63 1457.01,-353.63 1457.01,-353.63 1457.01,-255.82 1457.01,-255.82 1457.01,-249.82 1463.01,-243.82 1469.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M706.97,-114C706.97,-114 930.97,-114 930.97,-114 936.97,-114 942.97,-120 942.97,-126 942.97,-126 942.97,-223.82 942.97,-223.82 942.97,-229.82 936.97,-235.82 930.97,-235.82 930.97,-235.82 706.97,-235.82 706.97,-235.82 700.97,-235.82 694.97,-229.82 694.97,-223.82 694.97,-223.82 694.97,-126 694.97,-126 694.97,-120 700.97,-114 706.97,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.47\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M709.01,-114C709.01,-114 933.01,-114 933.01,-114 939.01,-114 945.01,-120 945.01,-126 945.01,-126 945.01,-223.82 945.01,-223.82 945.01,-229.82 939.01,-235.82 933.01,-235.82 933.01,-235.82 709.01,-235.82 709.01,-235.82 703.01,-235.82 697.01,-229.82 697.01,-223.82 697.01,-223.82 697.01,-126 697.01,-126 697.01,-120 703.01,-114 709.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"923.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M833.97,-98C833.97,-98 803.97,-98 803.97,-98 797.97,-98 791.97,-92 791.97,-86 791.97,-86 791.97,-52.5 791.97,-52.5 791.97,-46.5 797.97,-40.5 803.97,-40.5 803.97,-40.5 833.97,-40.5 833.97,-40.5 839.97,-40.5 845.97,-46.5 845.97,-52.5 845.97,-52.5 845.97,-86 845.97,-86 845.97,-92 839.97,-98 833.97,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
-       "</g>\n",
-       "<!-- lambda_a -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"48.97\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"48.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"818.97\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"818.97\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
-       "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M69.32,-279.64C78.9,-266.03 91.62,-251.92 106.97,-243.82 157.26,-217.25 501.28,-200.17 692.1,-192.63\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"692.23,-196.13 702.09,-192.24 691.96,-189.13 692.23,-196.13\"/>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M836.01,-98C836.01,-98 806.01,-98 806.01,-98 800.01,-98 794.01,-92 794.01,-86 794.01,-86 794.01,-52.5 794.01,-52.5 794.01,-46.5 800.01,-40.5 806.01,-40.5 806.01,-40.5 836.01,-40.5 836.01,-40.5 842.01,-40.5 848.01,-46.5 848.01,-52.5 848.01,-52.5 848.01,-86 848.01,-86 848.01,-92 842.01,-98 836.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
        "<!-- tau_k -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
+       "<g id=\"node2\" class=\"node\">\n",
        "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_k -->\n",
        "<g id=\"node21\" class=\"node\">\n",
        "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_k&#45;&gt;b_k -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>tau_k&#45;&gt;b_k</title>\n",
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-       "<!-- tau_al -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_al -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"156.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"156.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
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-       "<!-- theta_r -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"256.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"256.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M273.82,-279.57C281.92,-266.13 292.95,-252.15 306.97,-243.82 369.88,-206.4 561.75,-194.08 691.89,-190.06\"/>\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
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        "</g>\n",
        "<!-- beta_a -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node3\" class=\"node\">\n",
        "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"356.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"356.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"821.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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        "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
        "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M373.93,-279.75C382.04,-266.34 393.06,-252.34 406.97,-243.82 453.69,-215.2 589.88,-200.95 693,-194.09\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"693.18,-197.59 702.94,-193.45 692.73,-190.6 693.18,-197.59\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.99,-279.7C66.27,-266.11 77.6,-251.99 92.01,-243.82 142.96,-214.93 499.4,-198.76 694.33,-191.99\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"694.39,-195.49 704.27,-191.65 694.15,-188.5 694.39,-195.49\"/>\n",
+       "</g>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node5\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
+       "<g id=\"node22\" class=\"node\">\n",
        "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_sh&#45;&gt;b_sh -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>tau_sh&#45;&gt;b_sh</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.5,-393.28C1511.82,-385.5 1511.11,-377.25 1510.41,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.91,-369.08 1509.56,-359.42 1506.94,-369.69 1513.91,-369.08\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"150.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M170.89,-279.73C180.66,-266.14 193.58,-252.01 209.01,-243.82 290.2,-200.72 539.37,-190.42 693.6,-188.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"693.52,-191.8 703.48,-188.17 693.43,-184.8 693.52,-191.8\"/>\n",
+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"259.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M275.86,-279.57C283.96,-266.13 295,-252.15 309.01,-243.82 371.92,-206.4 563.79,-194.08 693.93,-190.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"693.74,-193.56 703.64,-189.77 693.54,-186.57 693.74,-193.56\"/>\n",
+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"359.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M375.98,-279.75C384.09,-266.34 395.11,-252.34 409.01,-243.82 455.73,-215.2 591.92,-200.95 695.04,-194.09\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"695.23,-197.59 704.98,-193.45 694.78,-190.6 695.23,-197.59\"/>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
        "<g id=\"node9\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"459.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M476.39,-279.68C484.51,-266.42 495.42,-252.54 509.01,-243.82 540.23,-223.78 625.11,-209.41 698.46,-200.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"698.65,-203.8 708.16,-199.12 697.81,-196.85 698.65,-203.8\"/>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.44,-394.25C1627.64,-385.52 1624.63,-376.15 1621.72,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.08,-366.09 1618.69,-357.64 1618.41,-368.23 1625.08,-366.09\"/>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"559.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M576.76,-280.23C584.93,-267.05 595.78,-253.08 609.01,-243.82 636.11,-224.85 669.25,-212.34 701.2,-204.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.62,-207.6 710.51,-201.82 699.96,-200.8 701.62,-207.6\"/>\n",
+       "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
        "</g>\n",
        "<!-- eta -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"560.97\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"663.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- nu -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"606.09,-345.73 515.84,-345.73 515.84,-288.23 606.09,-288.23 606.09,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"560.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"708.14,-345.73 617.89,-345.73 617.89,-288.23 708.14,-288.23 708.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- eta&#45;&gt;nu -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M560.97,-393.28C560.97,-381.58 560.97,-368.79 560.97,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"564.47,-357.45 560.97,-347.45 557.47,-357.45 564.47,-357.45\"/>\n",
-       "</g>\n",
-       "<!-- kappa0 -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"456.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"456.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M474.35,-279.68C482.46,-266.42 493.37,-252.54 506.97,-243.82 538.18,-223.78 623.06,-209.41 696.42,-200.3\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"696.61,-203.8 706.11,-199.12 695.76,-196.85 696.61,-203.8\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M663.01,-393.28C663.01,-381.58 663.01,-368.79 663.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"666.51,-357.45 663.01,-347.45 659.51,-357.45 666.51,-357.45\"/>\n",
        "</g>\n",
        "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
        "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M575.77,-287.99C585.12,-272.84 598.54,-254.99 614.97,-243.82 640.51,-226.44 671.31,-214.47 701.15,-206.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"701.84,-209.67 710.62,-203.75 700.06,-202.9 701.84,-209.67\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M679.77,-287.94C689.38,-273.51 702.42,-256.36 717.01,-243.82 724.6,-237.3 733.07,-231.24 741.82,-225.71\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"743.36,-228.87 750.1,-220.7 739.74,-222.88 743.36,-228.87\"/>\n",
        "</g>\n",
        "<!-- tau_ba -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node14\" class=\"node\">\n",
        "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_ba -->\n",
        "<g id=\"node19\" class=\"node\">\n",
        "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_ba&#45;&gt;b_ba -->\n",
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        "<title>tau_ba&#45;&gt;b_ba</title>\n",
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-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
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-       "<!-- beta_q -->\n",
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-       "<title>beta_q</title>\n",
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+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"767.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
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+       "<title>theta_r&#45;&gt;y_obs</title>\n",
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+       "<!-- lambda_a -->\n",
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-       "<title>lambda_q</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"873.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"875.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
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        "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
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-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
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+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M859.1,-278.32C853.8,-265.77 847.81,-251.58 842.17,-238.24\"/>\n",
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-       "<!-- c -->\n",
+       "<!-- g -->\n",
        "<g id=\"node17\" class=\"node\">\n",
-       "<title>c</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"982.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>g</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"983.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
-       "<!-- c&#45;&gt;y_obs -->\n",
+       "<!-- g&#45;&gt;y_obs -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>c&#45;&gt;y_obs</title>\n",
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+       "<title>g&#45;&gt;y_obs</title>\n",
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-       "<!-- g -->\n",
+       "<!-- c -->\n",
        "<g id=\"node18\" class=\"node\">\n",
-       "<title>g</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1082.97\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.97\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
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        "</g>\n",
-       "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>g&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1065.23,-280.21C1057.06,-267.03 1046.21,-253.07 1032.97,-243.82 1005.4,-224.56 971.64,-211.97 939.15,-203.73\"/>\n",
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+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
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        "<!-- b_ba&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>b_ba&#45;&gt;y_obs</title>\n",
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        "<!-- b_al&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
        "<title>b_al&#45;&gt;y_obs</title>\n",
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        "<!-- b_k&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
        "<title>b_k&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1170.61,-280.39C1161.89,-266.9 1150.23,-252.67 1135.97,-243.82 1103.74,-223.81 1016.52,-209.37 941.71,-200.22\"/>\n",
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-       "<!-- b_ph&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>b_ph&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1589.25,-279.33C1581.18,-265.86 1570.12,-251.92 1555.97,-243.82 1504.46,-214.32 1142.39,-198.39 945.6,-191.82\"/>\n",
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        "<!-- b_sh&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
        "<title>b_sh&#45;&gt;y_obs</title>\n",
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+       "</g>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1484.51,-281.94C1473.61,-267.67 1459.05,-252.42 1442.01,-243.82 1358.43,-201.64 1104.29,-191.05 948.23,-188.62\"/>\n",
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        "</g>\n",
        "<!-- y_obs&#45;&gt;batch -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>y_obs&#45;&gt;batch</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M818.97,-146.27C818.97,-134.38 818.97,-121.37 818.97,-109.53\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"822.47,-109.67 818.97,-99.67 815.47,-109.67 822.47,-109.67\"/>\n",
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        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x154cc8f50>"
+       "<graphviz.graphs.Digraph at 0x12dd0a810>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), len(loader))\n",
+    "model = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), n_streamed)\n",
     "pm.model_to_graphviz(model)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22e4501a",
+   "id": "c036249c",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1103,14 +1120,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "1e6fee0a",
+   "execution_count": 11,
+   "id": "9dd0b0c4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:51.553884Z",
-     "iopub.status.busy": "2026-08-01T02:00:51.553814Z",
-     "iopub.status.idle": "2026-08-01T02:00:55.625442Z",
-     "shell.execute_reply": "2026-08-01T02:00:55.624942Z"
+     "iopub.execute_input": "2026-08-01T02:19:30.830895Z",
+     "iopub.status.busy": "2026-08-01T02:19:30.830718Z",
+     "iopub.status.idle": "2026-08-01T02:19:55.597547Z",
+     "shell.execute_reply": "2026-08-01T02:19:55.596622Z"
     }
    },
    "outputs": [],
@@ -1147,7 +1164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b12713b",
+   "id": "86c6fede",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1159,20 +1176,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "d6aceef0",
+   "execution_count": 12,
+   "id": "d5916ab4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:55.626588Z",
-     "iopub.status.busy": "2026-08-01T02:00:55.626515Z",
-     "iopub.status.idle": "2026-08-01T02:00:55.773545Z",
-     "shell.execute_reply": "2026-08-01T02:00:55.773173Z"
+     "iopub.execute_input": "2026-08-01T02:19:55.599972Z",
+     "iopub.status.busy": "2026-08-01T02:19:55.599804Z",
+     "iopub.status.idle": "2026-08-01T02:19:55.902553Z",
+     "shell.execute_reply": "2026-08-01T02:19:55.901917Z"
     }
    },
    "outputs": [
     {
      "data": {
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hmwQFhOjUV9pbV+rXbxMY5AhCE+qC3cP2EUnv4zQFjh6rhT1H9ecn1AVHC41wRF5BXXC0xhHuD09QJxwpVL0EQRAEQRAEQRAEQTQnKE6AIAiimTBy5Ejhz3fuzA0xB0EQBEEQ+Umf4tYN/76wf9es36cMxBhxijjzFdsaLNQIcP3Hqi3S3xUwX1+3Xy6mrpFEfYpEJC00QpJ0jDLCBDa5iPuduX0v3PD2Enhp/Q7rQNjKmlppMLLqsUmHbCsm7QmFTFEIlILYcqgSPvvGYvjqu8u8ukYKW8SzHSarn+lbdktNUqZt2gnf/mAlXP/2Eig/WAm5QEY2dq7No0jtujcXw7VvLG4wj9Dw5bAvi+Tn984pg7vnlMEPZq1Wfp8tfxRDipV4qIkNbccUFbfxwBH4cPtery+/c1YpNFXDpP+UboVzXpwNf1mxCeKgqraxD2/yNLH2HDeY5f1jr86Hj78638o8IqXZD6EB0fyIMsrnG5TFTx80ivINkn67ZAPkC9XMuNYqYI745uZd8OO5Zd54FwUPLd4A+6uPwa8WrY/k+M0d7B9X7T0ER2qOWX0/VzWfOOf6/FtL4Bfz1yo/9/qmRgPxsn2HIy2TTVXlaPU2aSgTsBm3zlgBF748B6ZsqDCu36jMKeIEy2Zj+kLkB9QHB+9pTi/fDXN32K2xqH7V4Nzsyqnz4dJX5sFCTWNq2nqIFlonE/kE9cHuIfOeaKExLuL6pQomCIIgCIIgCIIgCKIZQcYRBEEQzYTTTz9d+PP58+fHXhaCIAiCIJoPrQpawD/PHQ/fO24QfPe4QYHiC1UQT3FRgfsCNjNsAyIKQyrBfTMDDLRFUbEMmZieNZ4wEe7IjucKF4e/5f0VsGT3QfjpvDXWx6g8dky60fPUmq1W5de5Nt3rRwHCjdOXwUUvz4U3NsdjXOcX7a8rNjf8+67ZpdrtOR2xuOu1jeJ6+NGcsoZ/P71mG+QCtUxtrN1/BL734cqGjMYPLFjX8LtfLlxnlElx+e6D8MCCtZ75hA3sWd7ftsf7e+HOA1B1rFbrO+kmGoyddPC2ql89moMGCDYGIb9ZvB4qj9XCn5dvhjgwGaswq/iinfuhOgfrOpeeq1zlWzNWAt45FIb/t8y8j9fpXmds2+sZEH3t3eXW/WuU4Jwll8zg2KKQmCCorszvm62odueR+ISrtZrP2KHqY55B1SsbKuDm91dAUwXr9snSrbDpYDTmF2HAe7/5YKWdEZcGeN2fe3MxXP/WksjOETd4He9s2e39+wWJEaH4exEWqhmLXnFt88jSDVb9ZfTQKBdkODlz+z44lga4d27jmtw1P5pTCue/NAeeKpPv0yTFtz5YCRe/Mi+yfYg9R6vhxfU7yJwi4nZsu05UGTkTdeZh35u5Cm56b7lnpqZDLo4EtuC49sqGHfD6pp2RzKGeWbMNdlZWeyakuGYmkicnpzI5DPYL989fC4t3HUi6KHkL7ofaQiOce8igLlqoC466fqmGifyBxjiCIAiCIAiCIAgiCDKOIAiCaCZMnjwZOnXqlJG1GV/uv/fee4mWiyAIgiCI/GdYSTFcNaQntCsqDHyhqRLrtC0k44iwYABmIsYR9X9vOlhplcleJETfp5GlES8X57y7K6uhKQSY2BpdoMCZneez/LtULkgIKxjTvX7M7IuBkyjCQNOEOEgL2nxBfd34f/uIhD1Ri8lkYpRUxMYnNofkvzJ9yx4vWFz2/Op2F9e/vQSeXbvdy5DskvJD8n6Gfz5ygXSOBW9jwP53P1gJ6/aLM1HjcyyjRQ6GKTUFYShbxKDN+nvmlMFXpi/z/o4am8dFxw8A+9x5FfuapWiMHa+PKkxuZOjckl8ubMw4//jKcnBN2b5DsO3w0YyffbBtj5ch9o/LNiq/u3b/YS+L7FWvzfdE+K4J+7iHnO4SjvpgFK5eMmUe/DcmUS1bRtU4tq+qcT2xlXsGmhJ3zFwFDy1eD59/0+38S8XDSzbAF95a7K0JgoybrnptAfx4rr2hnorfLtng/b3uwBFYf8B8HaTTnA9W18ATq8phjmVG8iB4g7TDNXYCqlycHaU0n9eluw/kpDHSYys3e2ubf6zaAlM2VERyjhfXbYfHVmyGyppjzU4Qg2vjb81YAZ96bQFssHh+g6hSLXIkmA5xOAd+fdMu7678atF64/MFlydtPb/GvuuDbXUmrw/WG0K6Bo3N7pu3Bm5+r+maL+UyL63fAR+fugC+8KadOZLu2i83jXGi5wHmuUAjqqhEtRVHqmI1UNNl2qad3vzsh7NLYabCENqWQ8y4xv67uYxxcRlcLdt9INI9sv+UboVPv74Q3qs3NWtOXPfmYnhu3Xb48jtLoakStQF7GGZv3wsXvDzXmwvajXG02RO0xvzD0o3w+MrNBuN82th8Cg2uNkYwj891sM3+eG4ZfPHtJZ5RZlSgSfvz67ZrrxXzCTS3uvX9FV4dEO7ZX1UDL6/fATuOHI10DJpfsc9blzY38F0Lmtz+ckHjOx0TaIgLNp56dNlG+NPyTTk91yEIgiAIgiAIgogSMo4gCIJoJhQUFMC1116b9TJt48aNsHDhwsTKRRAEQRBEcyczqCQoxKRTq2zzCUIf0wzPfqBQUcg3z0dr03C45pjy/uLvZC9teaE/gi96g8DLxYyOF70y1xPwiE+cO4FjYhFp8D07wgiGTESP3wyZLVm3OdmIY8PiNyW2zcsMUETtzkUy9MYy6F8/a5ISdQiDdlMRFKT8UF2QUAFTp36diZ7XOFEFmWeWLN00jSMgOjB485cL18G7W/fA7+rFlTyqANIc6k4biL/3McdEUDJt8y7v7zfq/841dMQb/y3bBl9/d7kXEGebEVcGts8FFfszBN4mYH/9/tY9sD1nReHBDxnbB5vO+4KYu2MfXPvGYrhq6oIMYeJtM1Z648JjK8uzRM0sP5pdCgeqj8G2w1Xwd9m8zBWaHVLaItgdg+kx6BszZ+cauO8bJhOn8tgxfAcNRXzh6q8jENWG6YPzJRP3kvpAel1RnosA6CdWb4EVew7Bdz5YGTg+IFM2RiO6D9+eg7/18JKN8PDSjfCN95Y7F37i+HT+S3Pg20wm7r2W/VAUokH2kFbiLI2vLN19EL709lLPeA7bVi7x6sZGY70Z2/Y4Pz6KKO6bvxb+uHwT/E1zDM0wJ9O8JTjG3TlrtSdedk3lsWPWpp5oXDhj217YcLDSM8DJBUxFy1GvPm+dsQIufmUePLumri81IQ4BxbL68adMYlDo6jn5+rvL4NWIxpEwQqYFO/d7/ff0iATVP523pqF+VwYYNdmCxj2XvDLPE+ZFMY7gGsx2fYhCtp/PX+OJ2nIF0yrC/ZgrXp0Pl786PxJhLfbBtvX71xWbG/79n5jM3VwacPo8WboF7p5TGonwEeuXHzvxOVm4c7/UmJUf5/68fFMkgkksx1feWQpffHupt2aPCjSBQ3O273yYG+N0voHz/qfXbIvEQOuFddvh3Bdne+YBuQi+wzpYfcybC+J6IApwDMI5Jj6HUYB9RK6aG/+7dIu3vnh02SahWXjg2k/j82hgj3uJn3x9IayOaJ6Sq+B7llc2VHhz4R/MWq31HdO2gvvgaNL+8/r1YnMC5zZobvXh9r1eHehgs13+5uZd8I13l3t741EQ1V6uC3A//yfz1sBX3lkW2Tkw3uNr7y6Hr05flpN9pet3WCy3vL/CM9p9Zu12b71lSkpzjPvme8vhgQVrc7J+o+Spsm2euTquZ15clztrRYIgCIIgCIIgiDgh4wiCIIhmxGc/+1koKSnJ+BluCj7wwAOJlYkgCIIgCMLkFefgDm0b/n3ZwG4wpEMbGN2pmCoxgpfbmBX68inznGVKwuDinQHZD2UCR5EQXUesh4H8mNERQQGPa1y/X//S20u8YPIwGcxdidp8EQRmlfvq9KUws17EFyes4Agzo5kENNSka7ONI+qPxx/nmKDdqYQTGMT74vodXpYkFWnB/QmCFfTkSiZHlSCGLW9NfXlZ84sk0K02x3rqunPbfMfwS1FmVcQAOx9fuGtkHAG5R9jnKI7nkD1HvgiTVWCme2RnZTXMrzAf81Rglu+vvrsMrn9ridW9+9vKci8b8hfeWpKTAZs63as/1gWNZSg4QNGXScD2dz9c2dDfY12JUJ1zS73pEGKbETtp0JgEg+kx6PsX8+0yganA7Kz/W7s9Y86ObXnlnoOBRlxo2oGZTi+dMt+bxxPm8wHVI2ZizhYX6SYwxm1kMmruyMEM2q7537rtDf9ebBHwrQLHpyPHauH9bXsa+u69lkZJubHKyERnDvQLJgPjbxyby2AWzds/XAU/mVuWsYbE/hhNO4LGrRYR1+/b5Y1i879rij4zjHE0Po8iBRzj0KAMDZJcguv5K6cugEunzMvY8zhQVeOJeH+/dKOyT9vGmHqt3X+kaRoARtjn4v7YzO11IqYHFq7L+B0K0FfsUYss82UNgmKjeRX74Z45btsv8tiKzXDOi3PgV1z96oIiKOy/vxeDoNrGPE6nBdz83grYfbTa2zeQ7RfYgkJVNEy47o3FGfOTfUer4a3yXZ65mAq858+v2+GJ2sqjyOZt8fya9sFomoZrKfzDjncuwDq57JV5njEFbzql0zex+7M5slVpZMCJoOHUQ4s3wNSNO+Fux30ECsJREI3vUWZx+2o3Tl8G10xbpDSnxLXbD2eXwl9WbIZHlrh/f4HP7ep9detDzLasQ47c5gzCmGpgNnoUTC7Z5XZ+Hic4N3tw4Tq4etoi53OKn81fC4drarXNyUR9DM7lguYbLggaD0SkNA2YcL6Nz6HN+zkVc3bsgwtfmgtfmb7Mag1eWXMMnlu7HRY5LpcPjp8+OObqkDas4N8t3tBg7nxvBOsMNL5DcbutgT2uNdj1hktYMygUh+tg2koW7mzs2/5d6tbgCfsbrN9PvrYASi3NG3GcjKp+q46ZP1M2zyGafsyp2Ac3vbccXIPri7NfnOO9c7a9R1GaYPvvDLdGeA58p4OU7jsM+6rcmmjhnjq24XcFBn4666YX122Hs1+Y7ZkuRAG+I/PZfth831RnLY9ziNk79sGza7fDe1vdGp3iPPbaaYvg3jll1vMTbL/YT0TBK4yp5Bub9cyRCIIgCIIgCIIg8g0yjiAIgmhGdOzYEW6//faGzTr/Rf+CBQvg6aefTrh0BEEQBEE0R/jAviBBzl3HD4HubVrCwPZt4Lbxg+Bf506Ax88eb3XukR2bn+FEtUHwMAahVVRWw+JdB5xkijpcc0wp7nh5QwVsl4iJRO2ihcbLcN2siSbCfpY0E/zzt5WbA40EgkABBAaTi86h4hATOFngKN7fr7q7ZpfCgp0H4JYZK7I/o3ksq4yzDFM2VHgB+t81aIeXTZkP/yndmhH4ge0Lg0T4cvuGB7rBQ/fNW+P9QXGmTiCEKHgppbFRF4WxgQ2qYrCmLn6dJb3ZqDQ2YCo+R6rXmCiD9HWCslSxeEmbhkRBVQzmATbZoPOlfdqImlT8YVmdyKL80NEG4Tz2CSgEve6NRd4YoOLP9ZlUUVhRVi+siIpURN8pZBqRbB6EYiEUHGDg59fe1c8WdpTpAPy+lg/KVt3SXOyDTdvsMqYNveM4YzQGkOOcG4VizzDZwh9YsA4+/9YS+MFMdWZCDP7EAFsUj96RI1leTe9zHMMIP3dj/9/UxrHaHBvjMFPtpa/MhaeZTNQHHQd8uyLVBMc40frVRiSRS6JP4zGOeUZN9jZ0+N2S9V6/jvsSL67fniFyQtOOG95eqr/OyMH61WH13sa5D4oKXILj2q7Kam9uctv7KzIEDCji/fuqcmUG6ah7ZxtzPnMDwPhNYlHIec0bizxTtNmMmJmnqa9BRLgW1frZm59i5mgImqxihvbdjOCnKaIjODpUcywyEzgUzSPrDhzJEOzeOasUvj9zNfxwtnoejIYhPmsiNlDTny4yBpEaXzrKPMdoquOSn85fA/urj3n9sL9mRmZu3wsXvzIPfj5/jfL7qYj7svCPa3D9rmHW964NLF9ZX+GJ/vAO3syMcd/6oM54EX+Oom+VqNznOcaEzBU2U6Zcm8vcNWs1nPfiHHhubWYfrAPO3zAbPc5tbnBkkJ4EM+oNe3AuJXqXkSTffH+5N5fD+UbkRDRnYee+K/fYieNlfOO95Z4BIL5nnV1v9OWvF9FsI2jdiFnS71+w1jOeaKomrOz+ZGVNrXNzLzS+e23TTvjn6jrxuQnr9h+Gj7863/uzgTF5aEqw62TX4N4x1i+agt42o25cQ1Dk/etF6zxDItW8G9cpn359oWdeNd3xPqr1Og5yC1xf4P46vnNmwf5hevnuwDgHNLVAAzZ8FvIB3bgOXXBswjbMxxegaei5L84ONBO4b/5aqKpNe/vuUZpBWqPx+M9l5r6rLQ1gZHxrxkoo238YpmyssNpHmrF1j9c/fPK1hZ4ptnNy8JYRBEEQBEEQBEHETdKx3ARBEETMXHnllXDxxRdnmEfgv++9916YPn063Q+CIAiCIBJ9nxn0frN3cWt4/qJJ8J/zJkBxUUEoQfo3xw2A5oYsmDwu9gQEvmJmn6zvHK32BKA8OrdeR6yTCiH884NSvv7uMvjDsk1egIZuUMOjyzZ61+YCFLi6Rucq4gqSwCxEWK+YCWOfQVbd3yxeDzXpzDaPwku+1McE7USlwXqrPtvrwepjykBNv33wZVAJE9mfx5EFWgdRMVIK4wj2Z0lQ28QyqdoEt0WFTptTiUxzr3bDG7CoxhEUHphkc5L1mbWGgpJchr0WHVRjZsWRqgbzBxv8533Z7oPeOI8ZNjHIWpfceTIb0WkdbB8sa797jzbOx3As0ye7rfLGEao+LRdfRplmAm6pqaxEcea3ZqyAD7btMcqC6oPZcH3+Vy8ewizVmHVZxk5GQLAhgkzLVtMSU1FtxA8eGqZcNmWeJ/JsOKfmd3Oxew47F6+2NB1QZapF48EHGbNAsz4mt0knHOzOkgp5jijmn2GPqPOMtSxoEdnexvQtjf01irr8ubEvEt9y+KgywzG7zoh6fq/bH7HNw9R8yjWs4ByFcz7vMtkuNx5UCbVysBOG3BnjZMfGPRHfZOXRZXpZ5vOFOES1aB6LJquYof3xlbkj1LKZs5j2W1HWr99m0ewOMzuzgmkdcmQrzbgPLmJuHIrT1Mc2u8htzL46a3Jyy/srvD1lzDav2qNmh4fc2as0K0dhi+hWo+y4JgPfYzWtdXJu3GdfnDxt8y5vv/f+BesyfofrOlxH1yjmhblizOySKA3qWH61cB1cPmUezFKYTyGid3e5hOm4GOU6jp0T/2z+Gk/QfPecOvMkGf9gzBBcm7vZvjtJR2ByG8bYwGde/bwBQZONR5Zu8ETJKtCUA3sQ/PPAgrWQC5hWUUGE67htzLsP3Kv3+dOyTfDfsm2esQm+t5Xxdvlu2Ha47nvfyxGT21yZy6jKgusM7B++N3MVTNukNjbw31f7RndNHddjnOho2D+gaSjO4dCoThfH26iJIIqHCMNWpo9g35Uu330Q7p1TBnMDxi00WsMS7ThSBVMD2npYcjE+gCAIgiAIgiAIIg5y8R0EQRAEETG/+MUv4Ljjjsswj6ipqYFbb70VnnnmGap/giAIgiCSM45IpWBg+zaBgjgTYeVXRvcTn7sZvh90nZXT9Qt/XnyI8Fk2fHRun65xhDXpusAOFEghK/Ycgo88N1NLJPj4ynL4meTaMk6RlteND2aMiyJTclAQbtStKSUog2kTFrUB/rJEAe+2AWwZWfjS8jIUSG4T27e9sL5RQOoK15rPlCBoxkb4bhOslbJot7nY7edSplqdsiQ9jqioZIJvXYlqZZm8MSMkZu+96rUF2tne0o4EJXFhE0hlnI1d8gUUrVw1dT5cPW1RRvZNE/yu6DDTLlRjadxBpFHNQ9lgYVn92p5b1FZ54a6q2tjxIYr6DStu0akXXcHRN99f4Qnc2Gx8Qej0BZiFuSm97DO9J1EKlDDA/ek127x5O4o8G88JWvPoMMGlKHw8IhijwhJ2SFaZQWF97XeQ7fpgBNedFMai2hjmTLbC3TjWcVHACo5Uc9KyfYfgqbKtRqaDbCfs9wV8tkNdg6xc0aGwbVZnjVYUoeBIh86tiqS/y4f9O1ODNRewWZPbFLbI+TbrkjjWra9v2tXwb99kpqmSdli/mw9WegIh2/m+/7hvsRQiR/GsxeCfljHGqUTwDy1aD+e/PBfe3LzLqiyy/lQlImPHkFzpLoxFyxEOJDr7Th1bFkp/F7WBaFPPxi5rmiv3HPTWdT+eu8bLJN40rsYNcawzULSMY9v2I1Vw8/uN6+emSCpHjTle2VDh/a1uvzk6ZxOsnVSwn4nSmIMFBcv/WLXFEyUfrK7R2vs32TeOEuM5RKRjnPjn/yrd2vBv1d590skscvo5UoxxT5U1ri3unlMGTRnT93NR9hF+WWyTeuSSsZaP6dMfqfkF0xdd//YSmLKxAm56b7n214/m0R4yQRAEQRAEQRBELpGLsWQEQRBExLRs2RIee+wxOPnkkzPMI44cOQI//OEP4ZZbboHy8nK6DwRBEARBRI4oMO7bEwZC77atnJ3jiyP7CH/eIqfkmfGAgb3/Wr0lsQAY0xf+OFeVZSvRCarUCfJ6YGFmpioT8Oj7OEEV1qyuSJDNrqkKeMbMWee8ONsLdhJt6Oytcm8ckRYEgvMBHnEE2PBBDKaB5yIxVfc2LblzZH9G9zzTt+y2aoey9hunVsdFU2EFyn49ykwxbIWLQfD3SvXYRy04iiNsSLdtoonC1I0VsFEhMs46tsYVqAK9DiiCMKPmL8s3wdkvzoY/cZmVwo52suDG2+szZGE/+fdVevsnsraZKVqGZoVsnH5iVTkcrf/dtz/QF967DDbMRY8UnblPYczB2Pw9VPUjbPuOomim4iEerUzADh5S7McfXrIBfj5/TYaxiU5ZVGNA1IIjG0xvs267OFBV461pFu86oH3sQ9XBwaeq22tbu6v2HoJLp8zzMrUaidhjEEbK1oQr9hyEK16tKzObldrlOZqFOZJkIMHnGOcRn3tzsSfIssF/3G1FTbkklLDNxi4TLaPY9to3FsOvFq2Hn83TzxgraqnZ62HQG+MAck607FJUi8Yy/y3batR+ddqcqjlHHdAShyg8ysWi7NAHmLGvk8KYIwe7hNAGgCqRGmavdyFiw+PkC67GOJzroMEiCoSCsiPLkBnU6ZIr7VnHrIGliDGokxlDYZt7smyrZ+71g1mr7YyE6ms4y6hXUXMik1yXhD2kTv3qZmPHNvzi+h0ZGZNd0E5lHAFNn7B9tvrYclNun4eXbpR/H/KPOMyRNh2szLm2ENfzUpOObg3r91c6+xNJmNzqYlpDmAwiqr1K2ZpnFmNmgOsXnTGkNmfWGWnr+k2CklaqMS73RrkkDP1M7zX7zr9dUQE0ZdI5ZN5TK1gnmxCX8U205kgRjnEhv597tUsQBEEQBEEQBJEfkHEEQRBEM6Vt27bwpz/9Ca6++uoM8wj89+uvvw7nn38+3HzzzTB9+nQ4ePBg0sUlCIIgCKIZcWKPjvC/iyY5O55MRJWD2qpY+N2SDfAkkw0kTkxf+KteX6cizMAqg89Mg0fXzTRvC14CZs7CgEDMzuALDNlMdGxWOGei43Q6S8x/4ctzEwiwSYcKzBC1uZGdigM/o9tU75xVCmv3H1aXQVBm2YacK+MPl6gCPtmgND9w0Ua4Wq2Z5kRUFL5NNLXgkqjK+8jSjV42omvfWJSVLTkqQdd989aADSg6u3baIrjl/eXWgWF/XrHZM5r564rNyoDaa6YthOW7D4YOxmb7R90yywIB2TLmYjClCaZ3T9ans12CrejYNwhTZU5VYfItFJVML98deRY1ndbBCmJkbTPlUPzEn0PVj7Dt22QOcaTmGMzYusf72zWmU0UXxhFTN+6EJ1ZvgefX7YCzXpgtzBYsG0qVotoc7D5M61f34w8uXOetab78zlJl5sjMYwcfPRXBeH3nrNXeWLK/+hg8tsKtUXPYpQ6bWZPlrlmlXj985Fgt/GVFpiFTUxW25NIY99qmnfDOlt2eqcgPZ5dalSUVMmg9F++KzhqCFdXKxtzdTOZIrOcw8PNApUEd04NE3+w1O3zDcrB7DCrQ1OvXi9bDF99eqn3sdB72F6bCSN1PozB8yoYKKN13yKAswXWqEpSFyZy680iVc2MkJOysWjYP3nywEi57ZR587NX5sM8y06yP7TojamymhKZtQLbf+uK6HQ3rtx9ZZkf2xwNbYXROGhto3BW2D5Zdu60fl6hO+HuoHuOYY+XILIItvktzpLtnl3l7WzdOX6ZfFghHLhoAmo7JUbYKWZvbzBhY9eRMollytKvOWWMDn12WBoKRZjKPiTjGd5WRgYpojA3S0ffBkneZTsgoi7g0rQrk4fHs+7iojU+0MSyGrjlSVEXp1LIor/cqo0S2p8MmrChRmE/lpXFEDGZUaEhsQ9TDg82U0HQeGcc4bWtOEUX95srahSAIgiAIgiAIIkma9s4CQRAEYcTDDz+c9bMuXbrA6NGjYfny5d6Gom8ecezYMZg2bZr3B382dOhQGDRoEJSUlHh/WrVylwVcxje+8Y3Iz0EQBEEQRPMlFwXacRFWABRb0KHi8yv3Hoo1M8WCiv2eqJkFi3ekJtpAPf4KvvjWEig/VAlVzLXVRuAQmhaIdfdywRRxBNikI8iCyP9MFJxjIsJ6a/MuGDy6rVEZZP2P7f3Dc6zeewhGdCp2nmFIVROZxhH+z8zPwZuUaFF/7qxs96ps7JAHgiPNjz+1Zpv3N/YVmCVuSIm8jZr00apAmzX7j4AN984tg7L9h6FsP8Dz67bDJ4b0BFfwl4RlRKHxjCtPDtU22WdVNNQ8VbbVM2n66ph+cEH/btLPeWVkjxtRI917tNozHupd3DrSoCrTcV42TruohwZjA8vBSvda8BowazyOkZ8b0RtuGjsAEjWOiC2LX8pccMRm8TMo2j1zyjzx7+m9OsGDp47U+k7Kqp2biZZtmbV9b8b/f7lwHZzTtwv3xInL0tRCHU3Lq9vvTGUyWK/bfwTGdWnvpCyqYFvbQNPdjOBG1+RCl7BLHdkYt58pp29aZ0suZuNDbKbLrrKxbzzQOF9aamBmBaI+OIeUcCJzIRPW7DsMlTXHoHWhPJtmEbPQcJ1pmb2//jyEF+Wp+oHMTLW5cV/YUuA85ZGlG+DrY/pL+zpdUS2aniB7QorueWoj2L/DdvXgonVwfLcOcMOofpDsGKfHn5dvhn+u3uL9+83LT4B2RcHhPLK2ya41lVVo2WRxD+ILby3xxIDPXTARuiqEu6aEFe7J+oj75q/xzJyg+hg8umwT3D5psPU5wqwzdNs0GolgVvIurd3VrRNBV4TjTyrkOXJRHJQyNKhzPsaxZUnJzJFUYxwjqoXcwGwVp2+O9GH9Wm3r4aNWZZF+RtOYIwpsuirTJqj7+fKDlfDQ4vUwtkt7+PyIPqHKj/NGnw4KUW0u9glhibIPxvEXn/n91qLadM7twJua9EYpqvVLctTWeDYH19c6Uxp2r9K1KFznGdftg3NleW1aDJv3cS7L0raoIDFzpDjGuCTKzxobKMe4HOwThGU0aAeRjnFei07BIcu9zlw0u9R9X+Tvz0a5T5sSGLqaEPWcrRmHhREEQRAEQRAE0cwh4wiCIIhmZhyhDIDlMsT6/8e/V69eDaWldhmwbCHjCIIgCIIgoqQ5vx9MKjDC9IV02GK6zPx903vLs8qPL7Gjrko+EGEdIzQSiwHctWxZFuKG80L8bdW0DYkC0fhj+P4YS3Yd8LJXn92nC3Rspb9l9ucVm70/35kwUFgnokAXWTCX7f370exSeLN8F1wyoBv8aPJQcImqytm4awyqfnTZRujZ1txkMEwwUNZzqRQt2z8f6/cfgd7FraClIkNVLgmOWETite2Hj8L6A0dgcveShuDNdELjx8KdBxr+vfHgEaeBcMJn0uA4cmMDrLO0NGDsV4vWN2RZLS4qhI/06qQQdEUbTImmEVe8Oh8qj9XCo2eOgYldO2h9zyaGbPvhKiNzClmf7qIWQmdj1/za+gOHG4yV/rFqS6TGEaxplAxW9BmHoIufa6lmRra9p58x/r2teyyPgEZfxzwBPC+6S3MZS19evwMuHdhdS9BlC/+Y+8LbjPlc/d9N3RyJZ+uho9CrWD5PsHlcXbZy1ThjG1cbZVAq/7yhKPwzQ3tpi0tlxhGZWaXDwTZhEzGDL1iKCpv7OX3rbujapoeWiFs133BhYOkfwnaMMwl2x/n9m5t3we0TB3vzxqjAvv7zby2B/5w3QXrvdUS1psIw0fMkM+ZQDastos52bzUPzvwOzlNGdCyGc/t2DSWqjU7Q5X6M+8Z7yz3RwvyK/XB+367Qv30bcAVf3KB+S/e5800jfHOZk3t0tC4ju6Wj6ntsm+y9c8q8fujYsTT8ftlGp3sRYUWbsv3AbYcaxeA7K+2yffuE6YN1xgIURV/12gJvrvi7j4zW7oNt+kFzcyTJHMLh2G27p2tyLfjconBMd2xHouopWYM6Wf3a7kKLTBb4+lUaALLHMigCrr/QNG9Stw5QoshGbgNbF0dr055J9pdG9pWbI0U5xmnUiWoMyMls7BF94/uzVnsGVO9u3QOn9ewIQ0uKretOd4zLQ98I5V7P8t0HvTY1slM7q2Njz1AQQhybKwZqUWVK33mkCrYdOQpjOrWzGvMa9iot9+uitbDXx7T07F6l6tr3VVXD0l0H4YTuJVbvYGS3RNUu2T44itabjuE7JnsMUey1qI7m9k1adO/KXIBzq3fKd8OA9m1geMdivbKkg/eTVCbGTaHHTTtcY/nzV1tjZ7++bd+Z5Ko5bhCFqcZ5k+racew/bLg2YvG7FjQ+tCGXTF0IgiAIgiAIgiDyiaj3ZwiCIIgcBF8G8H/43yP4wsD/I/teVH8IgiAIgiCihs1yosut4wdCPpDUfMs0IOqpsm2hzicS66gCv5C0YUBAUF2GrWudF+xstbq6s5j9etdRdRC9KIAQgw7eLt8FK/foZc8NEofwQdHGgQPp4HvpZ5K94Z2lsHjXAS/z2UGLwIYH64Xi/KlF7VAWnGUbLIymEcgrGyqkn/nd4vUZolvd4A1RYLpffD4o7fGV5V6AqimscBHvj6xdiMqSJehSZQIGO/5TuhU+PW2h10aUz3QMXauNUIAvctWxWvj8W4vh5vdXwNNMP6vzfLnKqoNZkxr2HiSfwTbK1rdVIGBAebcdPgoPLFjr9VtGolqDjJvf/mAlLNq5XyomYNtsFHqBv60s90wjkO99uAqi5KvvLoPfLs7sC1XIAtVcBrDaBmPnYlbKPyzbCHfOWq38TAEzkEiNORxUr38ME1Ete18jydAlOeTB6hr42Kvz4bIp82Dhzv2ZX+G+85N5a7y5gAwXgiNRQDfOT8WZgHlBlyrY3a5sa/cfhm/PWAlPlW11H+zOlffjU+d7Rl0uz6HblnQ+lntPvRr+eUNROJqJ6YLzAREuxyL2/pi0URNBzL6j1bBJYTzlit8v3WhUv2HNkd7YvNMzbWAzTeqeI4i0Qd3i/H7jwUrPSDFq0NRszf7DDf9/YlU53D9/rScg0hXV2pJh5AVhs7Hr3xccl74/cxXMrM9wroNuGxIVd96OzHGQpdBSfKFXmOCPqO6o7dyQzXS540g4kwAe/j5/ZtoiL5N6EkjXGcwvVHugtgKNA9WN/RMKTFwSdi9NZi6TcQ6H6wyTPWZdU4zn1+2AnZXV3udvfj/aPhjNVY5K5gUiXGcLF64zrEXL+tw5qxTOf2kOvLBuu/JzYS9Xp3nomCO5EFP5xiLVx0zGuMZ/mxThvnlr4I6Zq+G291dGvs/15+WbYdpm8b4OkrQ3QxRjXJSYrtd1P42mET4bDlSGqjvdMa6prfF0kPURy3YfgOvfXuKZsZXta6xrE/xqtTdHMjP43HLIbO5k87SY9jGy8QfnOvh+4EtvL4XXNu20KAljUBexyW30x2T3soPvCvuMqtrWze+tgG99sNJ7P2deEuZnlkbjkexV5tD7+9c2VsCFL8/19hkMChP4EfVeMESKizmEKbJ2gu8Q75pdCte9udgz8g5zLF0T1jCXgvss/l5LlJjWt2yM21VZBZdPwf39+bDjyFHLsqRjG+NsiOpx0XlfhD///JuL4aKX58LMbfr7UyKs13ER9H250asTBEEQBEEQBEEkCxlHEARBNENYQwjWGIKFN3EQfSeqPwRBEARBEHFgOu349akj4ephvazOdY3l96IiqRelptKO3y3ZEOp8IkHqI0v1g2JQaKjKcIdT5aBrCitneWpNsHkGe5UPLlwHLkDjhF8vXG/8Ev/5ddu9oGAMUMQMTDJQ6PP1d5fBJVPmwWaJuMJ7RtPuAwf4wAgUkPBCXNuMGCz+WkokamKFcygS+svyTZ4wy0VcBAa886KN/63dDv8qzRSE7mWE+7aIAnM3cMI9FHwFnccPBsJg0Y+/Oh8++8ZiL3gUkYnlGjJ0ccfWzZJowm/qgxUxwLmcyVDqAr64n31jkfLZcZGNfW7FPthztKbh2l7dWFEvWtYIBIRwYLbJL761BM59aY7SxOCd8l1wwctzvCzBfvuxEi0H/B7L8Ozaun5L1NZkpgMpw34JMwfL2ib7dRNNuu7zywqADhv0bba9w7+5vsbKOMLy3OJs7JBXgYBvbN4FFYo+IiMYW3IRLmPw+EBApeAo43PuyiA8F3Oy/5Zt88Y8bAvf+UAtWkLe3bI72rIJfnbtG4uFgZu8sD+KYGzsZ9/ftgd+tWi9sm2pmLtjHzyyZENW4C5fXPz/HTNXRTrG8fWH5kC/XLBWK2u1qn5zMdBUNA7MrZCLwnm0nsOQF862a9MsmCKwjX35naVeO8Jj76+qgY9NXQCfeG0hzNmxT/v4ts/LDIPgaWkWP8m58dpwvrDhwBHvD4pZ0bTht0vWSw9hmyVR974erok/p61/SdivPLx0Izy3bjv8X/2+ACuqrbI1hkqnvbbCi+lqNcx7dM9o0pfdOH0ZvFW+G255f4X6mPqHtP5OlG/mdMqiO4eIG1yjTtlQkbVHkBYYn/xwjtxcJkodmsw8MdO8p2kZpvG9D/b7aHYV3qAOnMGuMzCLq+t1XOWxxjZnu6bRBfcF7gowqIttHWdg/hGmfvGZRhNWrNufzV+rXz6Li3yydKtnJqmCNaiTXbv1Hh7zvcYxjhPVKr7egrmzJmXAtSuyTNNcOCzvM2a5TsZR7WtNhzZOzjVMmxrfZHUEmbpjj1xUm92uxeexo7LmGPx4bhn8fP6aCAzLeFG7G2MDti/7NWdwrV22+hqzrbdag73Kq15bAB+fukD57LpAp0Q6e2mvbdzZYDZ+95yyJmFyy84lXI5xItO9sKJlfA5W1pvL4F69LqLDZe9Vyr/Plt/0rqAhdRQmD6ZH1C3Cj+aUefujuM/gElW71DEWEbHxwBH42vRgg2qb6lcZz4eZB+P+hc+7mv2azjpOFUNr2/zQ8PCyV+Z5f6I2PzQtomzM/e3iDZ4x5J6j1fCrgNiFoLLYxhmYfA/Hue2H3b5Ltu23CzXMez7YugdW7zvs7bXdMmOFXVnqn3f7MY4gCIIgCIIgCIKIAjKOIAiCaIawphCsOYTJ56P6QxAEQRAE4XNh/651f/er+ztJvjyqL5zWq5P1928ZPxBaFeTOEry5zLpEARxBglYMSsCsn8t3H4RLXpkHZz4/C16UZJ5La1RmHHPcqM4RFNgrevf/S8a44v4Fa6Vlw+CveRX7YVdltRckKPscHyJiHDguCJrgkyj+ZO6ahmDmhvM6qNO0oh1icAuKHjDzDIqE/rxiM/xiwVon58Ug0Yc50xU8dlAGVhmiEj26bBP8e/UWLZE7BkyhGCrjmNx1+nWEAbnbj1RB2f7DXmaT056b6RkMoOhTt35Vz8MTq7dAWFxnPeEPV7rvsPfsSD9vdY60NOgfuWdOGZz2v1leu2z8jJj5FfuMMpGyYDbwm99f0dC3YAAde06W22eu9p53FMBindRdh/v7xWY73CvIsCS7Urbt6xQLM2SndQIBDYIpbcRDJiKXOLZopNnYA8q5dPcB+Or0pfB0mXxM9w9tncUPcheVAJ4VHMmu3eW1GQmOEjKLPcRkwz4Q0hgqzHOBAfu/WrgOXt5QkfU7FJuyYgm/piq5/lZ1et3+A8cEDMD2xwaci/nsrKyyEtTc9N5y+MfqLfD9masDy6vKSG4lXJV8BecHaFKGc85n1m6HJxX9hc6YYTv+u+hL39uyG254ewm8zmU11R2N0ZAAhfL8Nej0wari4/eDRGG6WRKzjy3PXL141wF4u3w3vLR+BzxZuqWhTX3LMrA5KmTB7vw8zOc7H6zyDBIwOyWuk3xeWp/dZ/jCAessiRGPcrbiEN7ojK8DHUFX0LP6zpbdnmEOGvag0KcBVmDvZ2PnzqE6tuncMBdxuc7BNcON05c27KWkNdYoiWaqlZwbnzE0Nbx3blmWSafoO8t2y/dQ0jb7OJq3RFZ3tRp9TxjSMbZH7PdxXOfBPZUFO/dnfV42X3ZZC2wfzAoig78Xbf3aPi9BArcWMRlHBJ0jCN1v2Y6hNuCZvjJ9mdQY1d8zCMJWui76Hv+MKOuDubHxW0q5weZ2q57V3ZXVntGXd2yd8yt+Z2Igyhte497sPGbO5ArTuSK/jtN5fmsdjnEiY+OGslk+6o+tLIdXNlTA8+t2eGaULuGLZPpcycY4thexN37w/7Y7gm7fOnVjRcNexLc1DD7DoFMk1gAq0r20BpNbW9Gy/mdx7XzOC3M8o3fXmJY+Yx2n8Uzb0mA0zq/jFCW2NUf6y4pNcMWr8+EHAcZbcUw3bPbSdJ9xnU9FsY777oerYP7O/d77/CW7DoQqX9Z3DL/Etwudd9S6bUk6xrHrDMUM29YAEN/jH61Ne3/Yd/pRxCKYllFWv9sZs+KtloYM/qFt1xm6fTfuB3769YVeH5GEQbXSvEdy7ar9elPieB+H83CcB++yeJdBEARBEARBEATR3Mgd1QpBEAQRGxhYmKt/CIIgCIIgfH40eSj87exx8KMThkZSKUbvLWma0iSxefmPWVUx6+f1by/xxHoYyHSfJPMcBumtCch4GEcYclJBu0HBRRh0jsE9oscHBYss67j/+/CnuOaNRXAkZABDTbo20DyBDwB5YlU57DTMwo2BpS+s2y4NdEHRgy+IR9BcwVXguklWJgx2/u4HK+Gn88pgn0g4LynSb5dsgCOCLMSiz7+zJdOYQ3aVZUx9HDlW2xBgoroe/jmvTeh5RMGKbUAMz3KFaUvYbOz43FZJAoxf39R4n2Tr85nb98EPOGGwLryBiF+eoOvzjSpsAuFk9YVZ6HlaCoQasmA4tn50Ai1RzC97ltKagoHZ2/dqZ12ctX0vrGGeJxv4+o7CpOh3SzZ4wkJTwemX3l4KC3YegAcXrfcCxFbsOZidsbH+b9sMR65NYnii2gNjg7Fl86Cga8NMY59/c7EX4IjjnygATxqMrZlJNer6ZVGdybRfsQtIrvvW31eWw1Nr5IIT1iTCryu+v9YVLav46bw18MnXFxoFBqv6AHYetVQhmNU7R+b/deZFrAAAy+i3+2fWbIMlTHne3RJtFlMWFAW67DO/8+Eq71p+ODszk33Qc4RzVzTfumzKfE8oj3XCIv+6ujHhtaHJ3pVT58NVUxdITaD4e2hiHiOrv1nbG8cMHOfYeTZmxdPFZQ+M5gPPrd3uzalZZIIYWTX4ZlY479EPRo82GDvJ1zWiOtDJBhtUJXcw89hvvre8QXhXK7jubIM6+XEzyhbxEGeTCVjr8xZlkT2rX3t3OSzceaBhL0X0Of4e52I2duzPD9XvP0zZmGniYjqXyTJPDJGNfeuho55Zzh/qs9rqZGNXzRNctFnX90hUpj1HM0X3+Ix+9s3F8NXpy7JEvXqCLvnvVu895K0xxN9LZxtHGHSYcc6DXZJhUGc5/vhM2VDhGaoJ1xn1dSkzYApCt3pN2qyrO4YmqWEC93TbDgrUbsf5Y73QUpQZnu+DVIfOKFsUzTfkPlfUotqC4YOgxXGjG/4s7dcPXm5ZDJ97c7G+8FY5h7DrQdFwDPdmv/7ucqVozoWo9v75a4V7xrKTqMwuTcsVeoyzbLSs0BP32FzCd6F/Xr7J6L2D1LyHGYts16N+fdmY1SK6w4NoXzaqvbTbZqyA6YI9aZYCZk9YarAI7rBex2mL0dPw4vodXl//c8m7zYZj2pSDe693KMCkVcdE0tqsU/AzkzGObVImt+XPyzdL33e4BtetaEYrw6Y5uTSOULVLW2ECGuz6bDpYqTi3+bGz30epD8L/VmeOqm+OJP4g2wdHsY7bz5gss/8OwuZ0+B5H9P5Hhmwd58II0R/jbPtg3a+hYSmaW6TrTVB0sblCfNewWfGMZL0v0njfmdwYp//Zq6ct8ubB3wuo3ya69CYIgiAIgiAIgnAKGUcQBEE0Q3DjO5f/EARBEARB+C8yR3VqZxR8G1XmpCgy8xHR40rELQMFWEGiu5Cx1HokNIWuZbIYf3/mKi/LDs9P564RfzetFywoCu78r0bGaB/Rk6sTl8gHCD68dCN8b+YqqXBAdoyfzV8L35JkzULRA7/+0Q3K/Nm8NZ5YT5VtB8HybjooNuVA8PSYORVNPjCT7/kvzYWVBtfoC/pZRMGVfB/K338/QMrkeUlJznf/grWw5ZA6UIYN8kJBJdYnG3z35uZdnrHHzG16gcGY3e+SV+ZZZUjTDWDGLKoYYBUm2B3vFwbU6AQrqQLS3t+2x+lGdNAI64u1rAIBJfV12ZR5Ddkhff65ektWoKusTWZk8dMo12ubdmYJa/3nnw2k4usCf4ffxXuPwmGda5u2eRfc/P4Kz2gHA88zMsdbjheYqf7SKfO8Z8UELP8rG3bADEX2WhQW+lTW32uTjJsYIPaFt5bAn1fUBezy12qSCUr0fR68b2im8PulG5VBu3Eyc/teuHbaIvjX6i1aM9agGvnxvDJYufeQF6SL49+9c8qkwYS8IEQ3GDtH9Eahs8zp4NcQLzjVKQvfB+mKllW8vKHC2GRKhbJKAqoLxyUU4fjPfnYwtn5942dveGcpXDqlbi7zm8XrwWXgPM4NdHh1YwVc8PIcuG1GtFlTkaDmePec0oz7/KtF643Xw6J5xy8WrPVM9rYdrvICoX3hskvjCNmcmP2xq20C7MfeLt8VGGgt48bpS7355+0zV2mJCXSKHWR6JBItm9AUXsGI6iAzkD1YRCiCPcLGg5We2BLvlehr2QZ18mNnCPWSWqBz6JYDM5bjtdqUWmf96r330xCEqJbpuWg6byp35OvA1Nzsrys2e/su2w8fhTtnr4YZ2/bC31aVe3sCskNlCo5UmWqTA813Hl+5GdZygvqgMQr3Ok7730yoqBfaPsSN+/I5hPhZfat8lye0x+OW7TsE17252FtjiPaA0oJ7mI/GETg/e27tNrh62kKYtmlnpnGEVHAUfFw0kr13bpkncnpgQfaeasrA/EOEbm9mexdsRf6Bx9WoPFWV4HzGnx/gng8K1nBuLJs/8fWrmlM05TGu8fPm+M9q0aXnQOrzn4D056+CX4ybBN8YPBr+OmiYZ/yH9ajzSLswABQZFrsy8Asq73PrtsNvuLVE6DEuZDZ27TEuN5qssv0+vrIcvj9rdXjjiIxzWJat/ou2MVu5OMYdqD7mvdcJa46k+6z+etE6+Owbi4TvWGTmPa7nn/ZjnO4JGs+weNcBb66g2r9hs93L0B32cbxbuvuA2GgwJTG5VdQIW7Rcab38pc3esc8zo3WJrA3iu4S/LN/k3VddklzHWRmNc/+/6rUFSgNu/vNaBnWK33lr7/r6V71/iHQdl47va5gsgH3/E4TUmCPD5CUtrbcnS7fAn5ZvEr679r9m3Qdrfq/K1n3JgrfLd3tmZmHHOBdPqtyEVXd9pl9v/hhgMgfOvV0lgiAIgiAIgiCIeCiM6TwEQRBEDnDCCSckXQSCIAiCIIicweS9cJjYhqiML4hgVNlvXVB+6GjgZ+IIqDUxQXF63vqH6Mdz1sCcin3C7DqimDDMxqtTZgxOQkMDnn1V+hlQUMzGoyOwEn1k2e6DcGN90LUrai0CTzAb8QvrdzQI1T648hTh5+bu2Ac3vbdcmdEJz/ZmeaYQ8gezVsNzF05q/IyiTGzAiVrkxZ+XCxyR/Jzljc07hT/ng3x2VVbDdz5YCf8+7zgIArOS+oLKrm1awldG92uoA2RexX6YdZW4fllufm9FYLDRv1dvgRnb9sAt4wfC8I7FDT8PuuWYTfaRpRs8MwDkjomDM36P9ycoAM8/xf/Wbs/I1hS327DteIjmJs9cMNGpKHxvVY0nqGVB4QrfP8ieyYxsZJolQ6FtRtnqg5XYc1RUVsNrKDru3837P2aHQzGqCFmM4o+YjPTPrM3MvGsCe+lolIRg33PdiN7Qr10brWO8tH5Hw3X/69zxMLSkse3zoPD4idXlcMOoflZBXCimY/Hvi67wHLPFi77PgwIcNFPw+8Bz+nSBpLnl/RXe379bsgHO7dtYHtmVB401mCGcZdaOfV7GbWGwOx8IqCmyil5L4Hb+jX0D9mE2OTFr/e8GXHNa0Mnw9aQrWo4T1WWJxvXDNbVe8G6rghbwk7ll3vh2Ws+O8OvTRmW1TWxfrQoCzl//lakbKxqCRXmjneCS1vHM2u3Qp7g1XDO8d9bv/Oc+iHvqjVY+3L5X28jKlqDxZ27FfvX3JV9PBXyGFamxY4P4HHaiWp01lCvhJgow0HwI2+Trl02G1gUBjY4DDTQQXliheFoDjxlkfCISLUeBi74a28D7W/dA28ICmNy9RPt7fJw9Pk8pjbIFPRe4fGGPjfcNx9EMUW393ybmSOw4qRorUIQ9Y+te+PrY/jCkpC3YkBKce29Vddb8TOf2oekArhm7ti7y1imm1D3jKW9N8urGuvUaGrFkfEby3SquflXzk6hHOFmfoxzjNB8QFMkfOVYL3du0zPi5jlglzdwnFL8g+6tqvL0JHzTDG9GpWPtY4t+Ff9jTITIoo4kLZltn9zaCquer05cpfy/Ldi8aivBefn/m6oa5/oQu7Rt+h+YR0684MePzuKYe3KFtxj3UEUT62OVYtwfNYbAPNjFwKj9YCZ97azEcrDdWvGt2qXeM4DYjPgfO6Rbt2u8ZRC9jhLRY3zKsjSPS8e473/zecigqaAH/95HRUFykP4dA08q9R6uhT7vW3v917o6sn1y4c79nZIrt8v8+MkrwiXTW/ClrjFOc14UY3QZ8NtFkZO3+I/CTE4fBgPaN41xQ81i//4i3V31+3y5Q0qrIql34zziKaD/75iLPADirjJDW2hNSimohWlTX/vdV5fDy+h1w6/iBcFqvTsry4jh/zwnDhMfh74fO81ur6LP+XboV+rdrDRf27ybtb9jnQWX67qLNum73oncTJiJtqegzQ1Qr/i6uxe+ctRoqa2rhvpOHQUnLoozf14YU1eoa27moUxQX47ujge3beP1fGApTbkS1q/ce8t43ILjGeO2yE8RG2BGPca7GQt/8YmSndpnH5z63/UgVzNmxF07p2diPyOo37Nwa52zvbd0DVwzsDj84fkjG9/wxjm+/ahPWVMTGJ/Jj4vOIc4EebVsFfmP1Xn1jAx08Tb1g2nLX7NWwYOcBb4/k/Y+fpGmOlFxGS1X5cA2F5nBXDu6RsVbm7zO+58d51PMXTZKcI21uHCEpWOm+Q976p3ubVvDXs8ZK+8xMcyTFeRz0pibzkPrlv/LdZNhkcbLxJ8PIXfLdd7fsgYcWb/D+3bJFC/jCyD6h3hfx6H7NVVwIxlK0b1kARS1aaBnvy2D3Y2VtTnetiPsSaAL93eMGZY0NMnMkSXeTTCIWgiAIgiAIgiCIZggZRxAEQTQjnnjiiaSLQBAEQRAE0exoUxh1aAQh4z9lWxOvnDgClvFletWxWmhZEG9b81/iYyCunFRWZo+LXpkLF9eLon1Q6IdBiywiIwpT/CB3FlVgelCQ2FHHkQt8EI1OcOWuyirm8/LPYTbSoM+IwIBo3eBmkQBQVHd+AJ4fSCQT56uq985ZpTCpa4esn4vqbM1+PXOE97c1GpO8u2V3g3GEClERg4JpsU5/u2RDg9nH21ecpDzezspqT3B1Vp8unmB/MyM8xaxlLHh/CwNievx63XU0896qiEKALAo+wrYR1EQ31WcAd5FBikWUWZzNzK5q/5lBpXplmc8JeDGYGIOr+Db8ozllMLFbBy+AUGYaUXfeiMWiCoGLLqxZxrVvLIY/njlG+lnMmIz8cfkm+NKovuAK3aB13lRF9rV3mLHp2TXbrI0jwjxhR2pqtdqFLFDTZii7bUadOUWWqFbTbAVh+zKVuOa3i9d7AYjfnzQExjHCPVtUTSCtGSj/rQ9WwoB2beBTQ3tmBSEHlRHbYJFh4Kxfv3y/h6ZQZ/TuLBx/t2kYmoUh7fA7H3t1Pjx9wXENpkiYNf13i9fDxwf3MO7n/E/sZOZHfvZxG3C8FhlH2GBirGJD2GFAxxyJBbPcywRNP6gX3GafI1Nc+fqmnXB+v66BZdHtu21hj46CCATXLEt3HTQyN1Cx40iVMIjeJqs0zhl0siSu3X9YSzQVtbkhe4nTt+yGO+rbx9/PHpcVWC6DbwOff3NxRtuRXUHQGFc3h+P61or9GSIE/57xghDZsfE+o1iK/b8IfAbuX1A3l8csubyQzAacl3186nwvk/JDp43MEGsFPUYLKvbDV99d1iCWQZM5U/xnF80IUdwr+4yoLHwfqRQcJeTHqqpDnXU2Gp58+vWF3priZycNz/idiVhlJdMH8KZAP5m3Bn4nFIpncqC6xpu39C5uBV1at7Q252RxcVvQNEJUn0HjQKUgg2zm94PPnZZ8lj/2X1dmGtRdPW0RvHPFiRn30MQcSXcd56J+0bjnjpmrYHhJMfzlrLHagqCfzluTtZ+mY96jmt+jUSOaTxzmhE04vxWdR2b+kYwxh/iCf7NoPayuz1D91xWb4GZNA54jNcfgqtfme0YE5/XtAjeO6adlWi3rNr7+7jKvHeM87cwXZmeXPi2qX27upeiTFjHzvygEXbJD4hrBNyW6/cNV8J/zgw1i/Wfs2jcWefMkNCL95JCecAZjiqCLf61o6CUyjWA/E4RyLZhQNnbsZ39fb46H613WONdU9Mmfw9a0GcEy+Xtzw0qKoaXEEbk2YlGti9uC4y6ac1w8oBuczewdhV3qyPpHlYGGDxq2oujer2vcdxHVl62xQdSiT/YKnyzd2mDwOO2yE6BDS/sQaLYNpUPslbMm5mhaLMPWmEP/W+FvBJqXf/6tJd6/nzhnfKYRtuHcS2eepPsOy2+/aGCJxhEistcZaa17H6doGZ9jXCdsO3wU7j9lBJzZu7N1v2XzjkDWT6NpBLvW1DmyasyI2uRWVb4b6pMAvLF5F7x08fHK+yxKQCA7h8leJc93P1jlzSnwzysbKuDkHh2DzZFUdRi70L7ROeLNzbu8edYVg7rDV8f0dzYXl/UFmYbU4g+9ziQfeHrN1izjCNlemi5Rv49jrxHfjdw2YyX0aNMSnrrguEDzCBVsG5I9+zqP6qaDRxrM229+fwW8LtnTytoPx3M6NBAiCIIgCIIgCIIgzCD1CkEQBEEQBEEQBEFEmM20TcxifqKRsvrg3SSJ4zU3il0/+sJsL9AoTnSCJFAkihlMeaZsrMj4/y8XroPNmoJHkyyJtkQtVtPNyIaBEiv2HPQyizT+TO/YehlMBZ8xqN9CQWRureSQmO3vE68tgG+8u1wqEgoKDCnbdyhbVBtwmXhM3gyj8ZdMGcE8ePKBBWth2e7gjHC7jlZnZFqXFCEDFNihSJAVWosCp9jn8NWNFfBU2VYv41rmd+o+Y9KsXYmz/lu2FX40u9QLghTFeps8zjaPZdhgLlmbdFE/GHxbeeyY8JnZeKDSST/FF9M2KC7jmCH64BsDMgM3nAPC49972/48rfFzV8MRZo6+a9ZqL9hSp81e88YiLyuzV54s4wZxWVmCziDKBLiEyWztUf8Rvj/HuQj29zy8kF92mRiAiNlF0QDID/ANS9hWjyKaXZXVMH/nfpi2KTOLOpYxW9AtvifBgbNpgXFEJs+s3Q6P1gsjeJ5bZy74dYEqoF0WdL/7aDX8c/WWjJ/9q3Rr1s+0grE1zKd0fh8VUU5bw16TrHpFRcZ78aV3lnoZx0X4okkevg/+4exS2CqY87/NGcbpdN1YzpTrazc4IBpN/EHyPPrihB/Nya4vmzJ/6e06wQ5vvsPX7w1vL/XEqDy8SFdVv2v2HYZ5FfucBWzj2ObzBPeMq+DFavurj2WImGWlk437KHpBcZlsLlQrypLIr1kkZ0WTEBZZ2dg1nUpIJkJ2O/63brtnGoHcOmOl9vHQeNI3jWgsU2P5kDc27wxsB/7axBcPiEDTD9F94de+KiOAXMw8Kasbdi326LJNDW2LN8rUuaaGUwTch4cWrw881tSNO715y2deX5jVT/xivtysjr+2GVv3wI4j0ZpVIWG7oKkbK4SGdyLzA7594lqf5R+rsvuuBTv3Zwh3sT3o9ptR7zex14jZk/FZW7bnYJaRoYpybh9CJarFekDzB1zbysY4NI0QjUeIaM9QVE+y+s1eZ+jVr+0cIiXIxI6s1TQwRV5ev6PBiACN1K6dtijrekXjudwAQH6uxznjExzLcAzgx7jr314Cj63Y7P2OxcTAA/uGd8p3ZRkI28Kafa7jjB5V4LPPjvdPr9nmzct4Q7Qg/LWI6rNePWo0OdW47+TNkaIMskdCtdbSHXdxn3a+wFDatJc7UFXjZWHnDV3r5qSyMqb1ROkJziFw3EWR+/c5kz2d+sV2h2PNk6VbvD0jrXEkQwAv/sxiRhT+/LodsJzb68l1US2LbxqBfMCYQ5vg93HsXqfJOpknyBjMPw9fv7pjl+7nTObOso/+Yv6ajHd3meXQOzaaqHlzY43K0zFLEF1/WmcOof3uP772i6b5aFaAZ/zeh3UG8A2l0FonpEOVWudZ1TW6w/fTsrYZ9ZtcnXaju17nwfktrin4etAyR2L/nU43zI0yzGWO1kjvQ1rzPZSLGVfa8rM/mLXa2999fGW5lpG1CNH1S9uShsEPe69U765tzZHiihvwjRnwfFsOH816H2EKO9e0GeP8e7LtcJXSfNI/Bl+/utUdhQFgDm4lEQRBEARBEARBxA6pVwiCIAiCIAiCIIhmSTomwU/rwgL7LxNO4cXUcRBXsBwGEPDZ0qMGA29nbtsLcRNHslHdDEdhCToNZmX+wltL4FOvL/AC7DFA48X1O7SOzRsUiFixJ1MQIaxfRSFFgbmiwNJUvVAGzUHmVOxryGjHUxtBZ/yLBWvhkinz4JElG5SBZaZi+M+9udgLZv7i2yGFzYo+ArP2BPUpvqADA9numVMGv1q0Hp7jsgX7gTomgU0mhkle9inBsTHr9a8XrYfXNu2Eu+eUCk1fTESfNo9lOuTx3t2yRyiUYNsLe0+eWFUO17+12EgIIbovOsYUWqJaQZ1v1BwrZIGXOvfrp/PKvOBgW2SP47tbdmvXr1963XbPfyz6DFKN/G7Jek8whOMqivp0+Eu9qMfP/icCA/h8wx18Tl9Yt917LoOurUijAfp9BH8sfFo+PW1hloALs1xnfC6dhr1Hq+Fn89Z4z42PLJNrmDarCiIWVcWS3QfgYH15n1mzzTON8KmozDYienRZ5pjGH9Kvo2Cjo+xnQPSdf6zekiGicomqjGmr78h/yWexRl5aX6FlRsBSGyDobvhcDmQt0wnCNcE2e67PC+u3N7T1IFEtzkP551oHUb37ojCZKCWOYOwwY9wfl22EXZVVXt/1N6b/En3/9ZDB3T5VWeZydX/zAoZDNcdg5vbs9dlTZXWC3aA5Nxp9oTnR199d7oncXBifmN5J31xB1AZ0jiVrOo8s3aAlrkf86+bzq+o2S9nnwj6zPuxtUYlzVedjxzZZMP+ds0rhQ0F7yvxOOlBUhGZQb5cHr2vQZOR5iQkSL1aTgfPPf6wqNx4n0zbmSJKff3zqAq9vxTkXroNk5zAZl4LWySaCdTRh4deZS3mDMAl/X7XFM9T63JtLIt9fk43rN7+3XMu4AufW9y/INsRIhRDF8cdhv1ZRWQ1fe3eZ8L5u4NZfstPhfP3n89d4exdRZFsVGRHw3Dh9KSzdfUC6ryPivnlrPPOHH8xc7WTcqJWYy9zy/gqh8QRvTKvcA0inG+fm4YuaaTpkcO2+4Y/P0do07GCEWMgnX1vg9fH4rL23ZbfXv9nMj9DApop5XnFucumUeZ5RFM8fl2+CXy3K7G/5ccavP/w5Wx789/VvLYHbZ66W7vm5RFUVonaC7ZrlY6/O9/ppFf71qXq7T72+MGue5n2HKyDWCbvuZXl3a6aBmqr9Yjsw7R9kn1Z1fTpnwLJc+8Zi+Nq7y+H9rXuMxzj/M7ivgfX42TcWw8nPfpjxmZfW78jqC0TneKt8l9Q0mDfkjRqd+xM0J8R50+WvzoevTl8GDy3ekDWeydqkTP4+ZUMFfHX6Us8cZtaOTKMPHLsyjy2e28n6n6w9IcWl+X1aFKQM7xGaL18zbSGc9cJs+NvKzRnfl81BZP08mgjd/uEqb72BQmodeFGtqB8R9sGKY+J7OtzDwu/YjnHs+4Awe+pYx7jHifPiL729VOs9g6yNVdYc84ypP9yGe/SZn0Gz6rTgHvHt99m126TzoPcZ0xFbszqbeVsYk6U/Ld8EF78yD/6+qhz2oGG5Rbn9+kbjL5xf4xxwH2ckeMWr87Xmj2gQ9n+Cd28m5ttoIIR9lem+k3TtrTRHUlcYziP+smKTN7+9e05Z1hpVb1/C3wtOwzfeWw4XvDQHXuSO85+yLdJ2wD4P07fs8d4Ti9AxwvJBE9eG8zl+yc7WSdBzhM80mt9fOXW+Vy84zwWN77Ntif0MjiuYfAHvKxpS+6DJAd8X+OMvv9YRGQ76x9YpW9Tgc6oLzqvumVPqjXH43gXrJsMcSdKCW0gaBc7xLpsyz1vrfXvGiozfTZO0S75+sQ/XGf9VzyY+E9hPhCGGnCQEQRAEQRAEQRA5CRlHEARBEARBEARBEM0Sk2COMO8S2xTQ0jtXUGWwjIoc0KhFxuwd++AWLlAgX4grc0hQkNKP5pQ1BJWjAAZF+O9wgTRh4LMZifo7VTCrLCM1DxoGlDEB2Wz2OPY7QYE3uudD/CB4zJ7mC22zv6s+tgibpqEab1SH0xHMfWbaIpi9fS+8tKHRUITP+Osfx6TsukF9GNB33ktz4LuCtlTKCBEW7jwgNI4wwabuVc+YzvGeW7ddmE28QBCohgIxzHa3XGDIAob3WcfIxLafQjMVDIZDMxcMHpMJpcJ0gyj+xqBpW2SBatjOdOu3IYMwd32YAXXhzv1ZzyV/uf6vMWg2M4tbRkgyuIDNGoVl0wWDsPmMyHyfjUIObJu/XrQOfjZ/LVw9bVFWFlkfDCT87BuL4IjGfAmDkzGYV9ZM+KzafHvC/6L44YX1O7znZvGuOiFPGJGcPFjYbIzD/gpNm9DYgheqio1eUsp+p6EPNrOtU37ni2/LDUNcg0HIKDK5t35OYoJpQC1fdxhcjSIg/DmKEr89Y2VdULzgO0HniiK4FwN3VWY8JsOeTfFk1/TAgrVCQwieGdv2ws9FWeYFBTcdc/xnWTTGCLP+ZX0/+BxhphVhmsNjK8s9gcp/yrZml0nj+2HnQwhmdZTdF5EhwKEarj2k014bQeECK2jG9YaPrugpMBOk4Xwb15e/XLBWKFrm+0Q0qvKFHWiOhKYksrbKrz+U5ff7YO5QmFFbNE7xzVzWvqJYYZqaI+HcFkUaIuGBKOsmjvcY4I8iNJEowO+Dg/oINlO23w5EXxH2SQZ85Z2l8MjSjfAlw3FSZw6R/bu0NKMtCjNvCDAYbFyjpT0TKhxn93FjXFRjWG0IkRqCY/HqvcHmTmGQ1T0KXnFOoANr3OF6XYX9BD9vWbDzAHwgMFf98dwyrTUqtgPcv8C9C5k4TKtsknFGZ42J82AUeAqF2sz3X9lQ0TB/f7Xe+A7nFS7wny2+nvDe3z27VPD5zP/7RcfxjTWBxH/f8M5ST4yNhgEmc3ObdYYIXCPP2Lonw8ihodxcedCMBE1e/rl6C3znw1WeqN52X5ufj6HB4G8kZkr+HppqHYf7bZe+Mhc+/fpCT/yHoGh/Z/0cRDRHskEtWk4Ln/nHV24WGkfwBsFYk7+oH3OwraCYksevNtW+EvaHU5j9uMbzZX8H170idI3GfrdkgyfC/olmHxhosBhCVIsGTVgWHxTWZn4/uFx+a56+ZXfD3Jb/GhoJygyU2XuK++Y3Tl8mvCbR3rcI3PvB8Rv3WMOg07ME1c8dMzPn4W9whk+y+5OxzmA+cu/cMm+MQnMYnqx+xd9L485x7bRFQtGnbtmwzaDw9GNTF3jzwiS1m29v2e2ZL6O5GY53f1i2SWieyCMzP0DTHHxf849V2e8eZIYxfP1ePmWeUADOz2Vkzy3uS3jrqIXr4InV5c4M62zAa/vK9GWeKRyycu8hWBdg1KOqd1x/4zuxW2eshEX1cw+2ftis9/4d4teSuE/tz2Mzz6neG2bBcWLqxgrhGKMer2S/sLtHWGZ894P9JpoSobmD34fazA3+vrLcm2PhHPD8l+ZmfiadhmfWZppQyvhXqXjs1zVr/+HsUq+v+jpnZhO45jX8uU7VY9v98/LG92to4GO6dvD7+QU798Pciv3eXvt93FoXzZOflNQb3zaxfkT7+bg3pcMbm3fCx6bOh2u8vjzavfcg6xpsv2h+X37oqGdqiPNcnXEkw9iG+QyOmbiPf8pzM71+nQXn0aJy8nXw8anzhc/202s4g35J2fB4f1tZ7p0Py29iyp+B9Gv6x3ti9RZvfYZ1ge9dsG5YpM1Xcopvf7Cybl1Svsszu2O5i29/9c87P1/AOfCLnFk0oms8j8e7+vWFcPErc70EClGYLBIEQRAEQRAEQeQzpF4hCIIgCIIgCIIgmiUmoZZhApkK6oUxYTKIEG5IIhtEXAYEzYk4AgvDBM+YYHKWx1eWe9l7ooaP5VJVRbkgsENUd0H3rNbG0MfPxi75/efeXBx4vLSFUYINtsH9OhmdREHQaUk/ZNIf6Qb1YUAfHhezUvPwQV6s2YKsrDJQoP5nQYBnEGEzKMoCH9ngL/86ZZmJghDdlwKN+sdrw6BizMDIZwRrLGc2WM5fLFjjBXdj8NgHTGY1HdhnRfWMYYC/LS6ex7QkWBgzoKKgwA9ux/qvy8aX+Tn8ybNrtsEFL82F7zPiWV4cJLtVaACBgi+/T1SJuNkzm+iJRdm0RW1+0c4D8PKGxuC8LZJsZBicyxq+BIEZkWVNgA/m5usXv/dWeaMR0lLfOMIgIz2KrlgxGIusGnWrd9PBSphXkW3iUSt5XvH+/mrhOk90nSVKahAcqc+Z0Q647/KwgfGqoEoUKmJGMB0TARk/rheZbDXIYqfTR4gCnkWf/v7M1Z44EQVDmAnSFx/wQucgcZNJZncd8Nm+dtpi+OTrC+EVgVjMFJvS1SoE8rqZnrEv3FUpz+joP7umU2P/ORAJT9kxDsckfI75S8G+Gf+gEUCcAcG6wd0Y+C9qw7I+HDP03fTuMnh3y24naxmc+6FAUyT0P1g/H8E2euv7KzyR1xZODInfQlERCheueyOccMAE3THumbXbxQZq3I9QAITj7W8Xr/fMkVBUqzt/1pmH8M/Yg4vWNwik1WNc3f/LD1ZmGCaEQS44Mv8OijRwnOORmSM9t3a7115QFMAbXB2zGOPS9WOLbFz5j0Q8owMKGpC9jupdJXpRrfBQBHQooC3+dvEGzygC2xSu9adsrPBEcSyNZ8/9fSXXXbWqTc3cnpktPQjMJI77A9h+Retcqz4wJb4rhwRzrp3cOOufjh/fsA348Bnh40YkDuZr7svvZJujuBjj/FoR1e+7W0UZl/lPpr2stp96faFnguHXMxoiLN190NtbwPmxm8cqbWQMhXP4b32wUjg/Ej1DOEdCQTOCfcrinZlrLF349YkJWbWbTsP3Z67yhH44lvy3fp8yTHWmHf7m0WWbYIXAbFI0xuE+wNLdB7y2ctVr87PMI3QN6rAueFzMrfg+4t/14yOujcIaSQW1i6A+HQ3+VASNgexJhEY1DDIjEr5+Nh+qFI7NvqFJEPfPX+cZK3zz/RWeIYhtf6Zz58Oub/SMOezOUStpw+sOHMkyifBF5yyyveefzCvz+kDsg/+4TG+taoRGH7zjSN0z/hfB/rJOHy77iGpOyBvGpCT1i3NXfq9D9E5Zdi403PTxBO8mt19yz9TmSPITvFO+u8FcCiR7s7whqKpdo5Gdqu8RzVlEfbDfh2acM6sM4kLgzz//1mLPJEd0n2yetrTl7/g2gf//y4pNxn2M/6yKxkyWw5bvO3wWCPZWRXxYb9qzUbBOVSG7VNUeoKx/xH1dbE+icZZFZDCQVS7G2EgFvpvRHaNFxmO671/unFXaMFbOCmGQJKtVdh4S1PxEBv8sOu8w2U+I3kv6/IEbb/x7z/cbOD69wJmn8X2r931J2f63dpt3rv9bskHbvNAEkzmJ6F0f+31cZ6JZdhQiIpl5D29UK3t/JBsH0GRtw8FKqKpNw03vLW8CuyMEQRAEQRAEQRC5BRlHEARBEARBEARBEM2Swe3bxHIeP2v21UN7xXI+Qk4SWQjiEuE0K1L5Y/iRm5kxMitYVcIluw9q1V1Q7KVfD0G1Icr+JatCDE4JCj5m618mFFzCBa+YgmIxFKlLy+DAcAIzFonEX3w/lDYs9z1zSj3Bpi28mFEUhKv7CGDW6yctsla6fMJQqI4ZbbCNZ7TF+r/t9EaYqTb75+9wQWkiMFAN29ZP563xMithhqr3tuwWnCP7e2xGSwz+FuEXiz9mRnYnyN2OPigDNoofUbD8ydcWwKVT5sF6rh7wvjywcJ13jW+X7xZm5pb1bSgUxXuD2cBQ7I5t57Ip87kvM//W6ItECDPVahiRyJ67wzVmJjeYVTgoiyEG3X781fkwnwsU5r/nG63pgNf41elLPdHVn+qFVDJQEM+aLvH3zCQYGxGJtLF+UaCE58Es5zO4AE3/npiYGWkJbeqPuengEeGxn16zzQt2xoxgf2Ey5eniPzsokLdF1aKOSkw/RGA/54MZacUZ2+3LYsP8in1ewDXy47lmWX9FgcjPr83MYqeD6pL/Z3C8i1+ZB599YxH8YelGz4xANMaJ2r6KL7+zxGuXoj7p5vdXeGYmq/cegktemev1EXybx2/dNWu1ZwSAgnVT8LyqZ840w54IDFjWPQBm6MNn6bsfrjIyCFKBGVNFJcBgdZyvvLBuuye+KNt/WJAJGGBqvQECZgz0DahMM6frwJbRZIwTtTnR9aL4jM2OzmeYtKFs32FYseeg8HxomhYE9jeYrfrK1xbAx16dH8q8R0gq/FzsUYFgTzRnKqwf43xe5oxy/GdcJNjK/Fzm/3F8upMx5mL5zeJM8wQZOMf61+otUhMpJwIYS1GtznOO4/P5L8/NaFNTJSIT59tK6bTX36PpCj+u+ry8foe3HsR5rewaddb5mGn7Ic4QQ6uIjlYa2L+h8H7V3kNZ63L/HDb7PzIDtcIWLQLvHz43T5ZugQtfngvPMPPU7D44JZ2jisyLgjDp13UNQfmx1olxRMBeD66xWPii4vd8Exa875jNGGEzueMa2EhTK/m57bOpa6DG76HYGqHZtHHsA77zwUpvvsaCR0Lhlo8vPItie1NZv4rfrRVktpet4+6eXdYg0Pz9MnFG8cC9W8Hvw+wr47h24/SlnqGFbibmsCJP3fL/etE673dBpoJo1jMzQJiaDtnfhxn9cZ/i8inzvPkaOy6zc8G082zs6cCy433XuefYDy6qN/PCdenDSzZ4xieZRq/15zJsiw1zO8H3RHtGvAAbH7VXN1bAp19f6PUjPn5fjOwSCFb9cz9VttUzETN9hnTGH9yXw71T8TquEVznCw3VIDwN62vB8beJnivuc9iOcDz75YK1WYJm0Xl4sE+furFCOo9gh52MdRy/lyY4AQqml9cbNAVcBlw1dUGWAbMLs09/vanbfvj+3a8V3GfDubIPltU3cBWa99QfBu/Jt2asaHg+lec2KBeCRqp/XbFZ+L7oCPds4vghug8sfh3Z1LvJO82/icTiO/fDG5sbzZZN4MsrG0OUZuKS3+G+7tNrgvfS0JgMzYy13ndatmud/X0VlYp9ZVm9Y5tA04F1gnmUSX3L6h7fs+rUB5qCopm3bv9kgn960Rgn2tPI6iPSdT/DuSZ7j9D81GeKwngE94ZEpg0uuPm95Z5pj6h/51+7fOWdRnM900QCOojamKj98rcBy4Tlf3PzLtjIvKvj99Rk9x/X1j+cvTrLDM5mT5IgCIIgCIIgCCKfIOMIgiAIgiAIgiAIollS0qoIfnvaKPjiyD7QvU1L5Wd1Xpie2L1E8uW6v64e1gsuGdANBsVkWEFks0AjYMc1ogAEIhxxvNyPy/DDtYgxFxAFNfkGOvLv1P0dVOuizLGqAOOgjMMZ35QU8QYuiyYGQZYZmCn8u1SdQUcVnOdnWtJhGWPikXYUCIjZbzEQThYsHgR/PqFxBKS1ngM+U1nGMZTC0HSo7FA+aKDxubcWexltUKAmCsZ2JW5C/lW61Qv+Dnpu/GB9FOU/tHg9fOfDVYHH5oNnu7UWz4H8j/HHZG+ji6BeZNnuzPvrIk7ta9OX1WcKk5cRMx1h8DoapSzksrdioHsGmv0U8hIjqrx/wVr48/JNyvaWKarV52VBIKLoct/dmikIdHXfgjLPbT5Y6QXdbjl81MtunlEGgTBVFzTi8MVKmKUMAwrR4EQkPnhgwTprcyShcYQwG3udSYPPh9v2ivtgMOMb7y4XloHt334+fy184rWFgusEmM1kqubNLIJ4umwrfPT5WZ6gNAibYGxEdW0mNGS7N+yDZ27bKxVL+M8IPrcvrd8hFJ/plF9nzorj2/dnrvaMakxRPcum/WjpvsNeQP/dc0qFpTZd0yzfcwhW7j0kvStoZoKCJzS/qhBkAcZre6u8ru96VmKCIbtEFNJcOXUBfGbaIi+4GAUJDyxYm/EZF2O26Ah6olo3axlsg7I2gPMV3hBJhR/ArTMe4fWg4P/aaYtgg8Y5Mo3a9BH1t6LL5eeXLoz5sP/8wltLYKFk/wBNJRAU7qAwks+SiBV5y/srvH+iKYovDNctGs5d/CB5HPMeW7EZFu3abyE4kv9OZLAhG+NU+N+pCdLUaq7t2OOicBLnaijc4kEx6n3z1sDvlmyAZzTEPSohDQrotkmEBSrRmaoPdjXGNZhPgVvweGhYgZmXvydYP6DQ7Cfz1njrwe9LDD5kYL+A83AUseJ6FjNt2xgAutqSOVytmkvZ7/9sO1wFe+tNd3izK95wTrQ+f2jxBu8ZYM2RVAJNdoy7bMo8uPzV+Z5BGopq8FnIvK50Q/9hOzcQGkcIvh/FzhmOYffOKQtcs2BdrBSY/Og+fbKj49rtex+u9I6Pc8H3t+4RijSxnnGdYzPG2RoAxrWHiHtp2AdgBme8Hyx8MRvnEOr75bfLt8t3wfdnrvLGUmzDjyzZIMyMHITqbKK1t2yMY+8F3+79/07evAk+uWl9xp+zd6j7taB+Bc0h0OhLJOx8qmybtz+BWdd/rGFYxcMfU56NXX4M2a/+W7ZNmJFbhD8XQiEm7rnIzu9yC15n/YrifNyn2H6kCr5ZX0aecLP1dOBPZf0b3nfeHEfGV6Yv8+r2a+8uhydWb4HvfLhSYnJrVsH+x0VmKyJjA/7w+KzdM6fMW4tgP2LCtE07vb0jNBFDcwMTdO8Z7p2K9gHY76NxyK31bQMFvs+v215nPhSxqHb1vsNZgm3+U+l6c2UUKeM+QoN5j+BzPPuOVsN1by6Gu+eUwT9Xq99X8KQ0jo/3/Pq3lwhNQvkx4lD9XgsaMvy3bKv33tZ0T8eHrUr/FqmE8Zn/58qZTnt7NLjP9vGp8zPMI9TUHQjvCZrs4vPpz4srDU16G48mNo9cKjBy559zHD8eXLjOG2vQMPAvKzZlfcZ/DIJqXdTsZWukv60MNq3FtR3Wz52zSuEVzpRQp484/6U5GX29fIyzW8e9z+2hy0AzYxX+Xbcd4kStJs3NdUR7H/gznGed8+Kc7HcbDBm3Nd1oeHDbjJVw7RuL5eYGOuZIkvrF96yvSYwSefw90ufWboMvv7MU5lXsy3jHbLvtk1aZZgo2P/j7gN/DcerqaYu8/VMTcI196SvzvLWczJxDNsroDD+zduzz6kpkjsTvQ+J7ld1Hq709F+wjcF7uYoTzjyHqg/cLDFX5sQHr+++ryr1x7to3FkkNUET3H/sWXFujYb7MpJQgCIIgCIIgCKK5Uph0AQiCIAiCIAiCIAgiKU7u2dH7g8EcOxRBIDovTIeWtM0QaPHf7dG2Ffxo8lAvaw4vniPi4faZ8b8sjsuAgHDLFMPgxHxqH1mBgIZROCLRkMO4yqxjqoPF1YF5NmJtzO704Xb9oPY9ARlkVE1gl0BIaUOjcYT5d2WiJwwWv3xg94DzZv6/QFDJfHYxDN6+sH9XozIeVAjXXT1hmPHc55GlmRma/cBWm4A1zIAqC6ZDIZwKneBvrPLpXAYs/r6sqQ9U47M1y4QfOF8a3KFtfRnACV/kDEpcuF2jGBnb0/CSurKK2H5EniUTRTI2YCA2H2AvCozzQWEoG9AXRX/Ji65dGkf8cXljFnIekZjOB81OWArqgzN1SsbXEYq70EBCxHtcwG9W9SpOKCo///wg0zbvUtavtiCG+96cin3QepX8acCyvFCfwfO5dduVc4yiIOUvx4P1axUUlAahEomp2ppNpmxQGBqYNutbZqyAywZ2E/7u+reWwONnj/OEbCjIaFtYkPUZnQyWslpHkTJmKp/YtYNSOB1EOvDs5s/63Ir90IMxVQwjql23X57NG0UlmyRZ7MOMLxi4vXTXgQZjpV8uWAdTBYHp/uHRDIolbBfs9U+8+IT7jOHjKAWv65phvaS/V7VR/rlNiX4uGZDe3rIb3qjv966ZtghmXHmyfqENrl3UBkT9DV+fLkW1MtMSNFZ76eJJnnAHQXFcZhkyjdFMn5+b318O8yr2w9fG9PMEhCozOaWBmmEfIComCpNQ2BXUzoL7RMM1ZW0avvneCi/jM2vO5MNmsEZzCTRq1QUzTf/fkvVw5eCecHqvTnCzRDTqlVpRbFVbEwlkQxlHGA5yKL4b17md8jOi/UvW/Itdr5g8zt/+YKXXv8/fuT/bVMUAV/PVT72+ILDcplnNVXMZFHE9sWoLfHFUX0/02q6wMOv+qZ7axnKKaxjFMH7GazSMwP1s2VHumr3ayNCTRZKHPOsnP5nLiYIdLWZwT65X21bKNRdm00ZDhxEdizN/ydW3LDuz6LZjJuS/rKgTPR6oPgYdigrhnS3ceqL+cP/j5t8ml14lWFSI+mD+mHFtIZrssehmv/a+l07DHfV782+X74YTupco+yLV5arGONG9FfUpaBTO/nTFnkPC75y9bg0M3ZO5P7CsfQm81d18Hvbhtj1wSs9OngEgZq0/uUe2EflGxjAJzdhMQLOqXy9a761zvj9pSN11SLOD263jZhmY3OKe4qdfX+iZhPzf6aMzfuefwWWzxr3KM3p1hgld28MHW/fCoA5tnI7Ra/Yd9jJZD+9YDHdPHuqJG/11BwRcDz6//rxVNeyIROEy2Dkamh1M6taBOV/aygDQ/7Toe3i9Z/fp7F0/CpJbFxRkm/eEGL/ZjO3YlrGvnC7pg8MgEtXyawrsm9AICo0AcE6Da+RvjhsQ+tx+/cj6CBRtP3/RJE/ov37/EehVnDkW4tfYNoLtr2OrIkHWdhAar7F7eThX8T5rcR1pw/mReG2He+wbGtZcfz97nEVJxHUpa/e4P3DHpMHeXizufZzWs2NWmVB0711HbRoeW7kZvjdxcGAdia4PjTHQUMw1IkNz0XOHBqhoRPT4ynLv/z3atHIy/1XV7x+WbYJPDe0FLVu0gCW7D8CoTtwcDSDDfOhn89fCFYN6BJ7PP91d9cY6uFd5zbDecMfMVVlGaTprNdXvTIw+cY7/ozml3thw30nDs37nncuymkX39PNvLoYnz5vg7RV898NVMEQwxlVUVjWYsOI+xUX9Bfuekkv02yu2DTSRwLUcbxYquxz25yoTGDSuMamD++uNkr/+7nJv7d54Prt1csPYKLgxaOx3dp8uXiwPrgm8cSGd/X1/fwINCn5yYvY5ZC0IjRXxfZXf9u89YWiWkbkLhPvtgkLhHN7fc8F+4lenjgx9bn+MFvUvOP96fdNOOK9vF+9Zxr3x03o23lO/fv21GPbBON5NFibtyT4+jpk+y+oN6t7avAvKD8n3ngmCIAiCIAiCIJoLZBxBEARBEARBEARBEAHohAqc27eL91L5thkrvOBSVYZ1Iv/BgIXbZ66C6VvshKeEXuDDDoXotymQk8YRIcVXoqAmX9hg8h1dVF89wmX6zP4yZPXVOsFGIuGwbbCZi6zIQUHQvojdpVjcz7Kqgg8QEo2HomzYvImAinfKdylNgVxfs+ocNgGXmOl3jETUxYsWeLSCv1N12YxY+HJiljkMcORFiWmBmQRy16zV8MblJ0ZavyjgcIUqG7pJF4zPfXbW+GzOf3lu1s9KBYK7w9XHPDHZ/QvWgkt07slvFqtNSUxg57zZZdE/TqGOG1A9/CWKTCPwcJjBle+vTZrWo8uyTTF0nnO5cCJtrDhXGZgElYV9fgtb6NmxYHbIOycN1vrs8t0HPTHDFkmW9qD2KBI66lCdlQnYvg9+ab3YKGzl3kMwe/u+BuGKKGuxqQAH8b+BImU0lkIhWG+FKBLBrG829RtGBCB6TmzmrDjGHde1vfB3GICtKuNyxXX7/Kt0K3xueO+Mn03dmGkSIRPc46lRLMKbNIU2jtAQ16Uczgdlgm5Edct4cYVovu/1SGm1oByfAzQBKy7KNFcRZTt2gehY/Pwyirk1D7Zd1fqGL4LunhCKK/FZQyGIL7wRoVu/pjUh6td48TDfb9YqslJnlMWwMFgPKGBARCavbFlFmUHrzikW62OmaQQFHyjokpc53WAOYtoHq4TQJvjzGNM1+VenL4NLB4jNkZCg22EzxvmwpkCYsVkFX4dY5/46wNVWiUgc2nC+kPsysm+9v3UP9C5uBT/mTRUcjNGsQDlIUO73Ja4GuZSG6arLHXBejM2Czy+aRojGQv55aRBqaxgPoIBJZnTH4wvZbISGIrNaUbv4N7fOiWOMQ44oDIP4MugaYuG32EctrdFX2l6v0FRPcCi+j+LbXENfaFEMWdFvnbESPrjyZM80Apm5PbsO2HoSGbDWfUY8xqG5DPL8uh3w5VH9vHcTMlPa7QoTdVXVmxhTvbhue0O93ppl1OTXr9t2jQYKuJZA09digQFgmH0sNEfC61mz/wh0bd0SnhDsRcgOX1dv9WOc0xmyeo5o6teoEtUiP5m3Bq4b3ls6xkmHVKZivHIK5sfsM49m0L9wvF/mI9rrlI1gaBrhPy8utiobjBklDQXb1/yKffDn5XXCWR6+7fjmPTpjHH/V2w8f9YxQd0r6CPZ+mIxxItGyqD1h/8Ya9dkKqEXHlq1N0PTpexMHweffWuLt85zSI9M4Ar/GjoH+sYP6vbRgHFeZRmAdoYGQ9HiK0/nzn6B3Vdg2XmTWOtM4Q81Go3Hz/kg1d8Y17f8t3uCNxZMZMxv+vGF5cOE6pdEOu2/B42o+hfuaaB6AoJFDxjka/rY7l6iKNx6shJc3VDS0HdFa4HC1GytNNAwVreVkV5Mxx3M0xPF1kDHG+fsQhucKMsdFY76vjOkHN7yzNPN9haRMwv4yJd5HZveQ9h6t9vohXTOrVMgxTvR9LKe/5+LqPV9Q/f5wdim0KypoMKPdWJZpWM0XQTbGifpB/jNoVhe0H0EQBEEQBEEQBNFcIOMIgiAIgiAIgiAIggggKCgIMwyN6Vwninn10snw5XeWBgo+w9CqoIU0CzyRG2CANplGRMOTZVvh1gkDveCDr7yzDJoyNcJARTPYADAXpEIGUomCv/xAEBk2tYABpBj4oSqfKEAn87xc0KpjcZsOcfTkjYIjt1cXFOjHBwiJRHNh4pEwux2f4ZknTm8WW4GVbVDWwWq7LImi50ImrBVl7WKNAsKIynLBVMckWPVzby72gpoxk59p0N4GQbAqCp5FhDUcS1u0gRunL4UvjazLNJhU/RZIFEcoSGbZV1Wt1e7waDe9t1z4cxbTAN4gYaz3Ga58jcHYAV80fJ6CnhNWKImiWizH80xQvoz7FMHrvMlEToxxfiC/427jcID5FIpCg5A9zmga4bNFIYrEfv5LCjOlqLpKkSikOm13N4/U1Fr1dRhMrIMv5JGhEveLBKlhjYtSEoFgEKbB7lJRsOb84uGlG7U+y/eR2w4fzeo13926W5rBD+fp7D0wqV0+w7usnPxc0LYuTVH1wWnJGBdUNDSTcpHJuKEc6ejnpLrmPbWO18ls/fNijqD5bZDgwAdFOVM5gRVLHPptv35tzoXlj3KfAvvL/VUBRo0S/DX8N7i5Gp7Vl/hGJqoVNBdbAZtUGwsA/8cZE9mM35glVQU7n8g4f1TzA41OPGgPxhVKQxANQZesH9epOpl4NqygWFSe1fsOZ/w/rjcBvGmQqgwNY1xA5WH9m67z0zkyxtmg6sOC6oHtk2Rz5rfrs4kHzStkolpcV+Meh035KyXzexHs/pGfZbvhHL5vBLgF569oGoEcsjQAxGpfy2SuFq0fcE9SfP74RLX8yTJaS4Oo1u65k7V/fCcpMtn0kZ0vzfWjIrFs2mA8YY1+TPtgUR8n/H7a/frbf7ZU/csyhRheJqrlEdUf/91b3l8B6yRmu2jWnjEGGdTvJsHaUHS9fNltBfaZbSu43eP836+fLBPldDqjrA1lDBrjAMc4/TI/vnJzwPHkvC5Yn4iuF8dndrzhm4r/HZtaVz0LWH++gdNcwX6FTp9UqTGfFO0X+ByoqoHrVGOcoggme1/sPghrnpdhHGHZb8jeee4MWB/UaJQfm4LKdAPZKt3jqfv714vWSZ9fV3uVfL/BtmH/N7ZjnOxbpfsOZ5i68HMG3TWqb+SVee7G76JBjco0As3yWVIh10nC+ST3MRdzk4Y5hOIzWxUG1GmZASD3i7+vKg/8LplGEARBEARBEARBNKKXZoggCIIgCIIgCIIgcpiSlsn4It4+cRA8ed4EuJjJ5FfUokXGS0yX2dZ8njr/OPjfhRMjODLhil2SAG4CnGaRV2UgbAq4EF1jFpQoMQ30sQmstsnyg4GqaNLjZ+mTfUb32vzYGdcizKAxIA59vN/OXJ8r6F4v25OZ0UWUKdE2KBSRmUaw7Skm7WAoswPb77FZ2UxELbJskzwVR6rg8lfnKz/jKksYzx+WZQpao8JE2IQZmNAAYhGXqUiVwcyGsPNGmz4Ys/p9Myv7Z3hMyoLB2Hg/nlqTaajxMCe6w+fepbGI6ZF02jz/kQZBjGNRbdAYx9ZTUYuUl13wAS5w1daARpc4xrgGY46YrZ/eCjDFQv5btk0pvAsCzSn4NhfHGMcKhv1x2va5k/UDMoGLKUHB7jKwVB0EewiZGQwtrlnjutIxjacmz0RDQu2AwPE7Z63Oqhfsl/mMuL4Bx1NrtmX+POQoJ+r2+LEchZBxoLpnqzghWwvNNoXrGh0xpl+Lb5fvgsdXZgfMxzmH8OvBdRuuDuhzVjJ1jOZIInihwO+XboAnBAIDGS9vUBs0usjIqW0cEfMYp9vns88fire+8JZcoMWSrhdX8eY3GWNcVOYHDvdlZIZiddeRMsrEXfe9xn+nBH1ouea6o068mF22sCOvzvd9sXbUqJ4//jcycfoTq7cEfjfX1nEqQxKXiMT2MmFkYapulONLz4vh0hbPGpv1PY51HI+usYHo96rTBZWF/b1szryFE3Su238k67jvKozu/rc2aIyT/27Wjn2gyx+Xyw0G/D7fdJlx24wVMEthDhV0vKA5hqifni245p2SvTV/zF7CzUPYMc7VOvn9bXukwlC/HMai2oB275nAGPTBop9jMf8kaBtpDfMk/wrxnUDmz90bAPKGdE6ysft/Kw6lOgtfhgZRLfeth5cEj8cy0wjk7tmZe+4mtSsythG1Q9ZYRryXZjk/S6eV9Vut2Eur5e57Y/1msmJP5j4ErodN9kymSIxndCgXiK51jDl4GowjDKsZ16Cq/cig47HVJHpXhLzO7bf9ZN4ao/0ZlfmfV0bF71YZJATZohLAN+xVmrEdzToV1+pijOOr/ajANFBmqop9Dd5/3O/M+DlzWldrV97kke3nbce4IGNGPK6q75FVb8YYJ+kx2e/KTPD8b94+czX3C/1eWGSgK2pT/F13sa+OY/uTpVvVbVjxfb7tyExY/7Eqcx03c9ve+DNCEARBEARBEARBNCGSUdYQBEEQBEEQBEEQhEMePXMMXD1tUWR1KnvRe+XgnlqBUK5BkUvbQj8PH0E0T1QZOZoK9y/IzjySNBi0N3fHPpjcvcQuGNsi+jVMwOwL6+XBztUGKYcPVh2D6eW7YWK3DuCKH80uhdcCAtWiEt7z5iJoAuD6VHMCgsW3H64KzGwTxdWzMp04BF0+tqJanYA+ESrTFBfcNYsLUBMQoFvPCY4q6vcDDCoLQyoF3/1wFeQSuXRLTNo2BjT/Y1U5vMUFmPtZ6th79sG2BYHHk505FUOm2rX7D0uMDdyOn4dq1PMgzBDpU5RqAf8SiONEGbpcYiW8t+x74zCpYGlZ0AKO1qpNIf5TtjWr3zZZGh4WCMjjGOMqGBGUfwrbMQ6z9UVpHBGmbe4OMFKyuWKdqxJNTyMxjjA4pChAHIP0n+TaMBp1nNKzo1b9fueDlfAeJ1gMe9t16smlkYLLMQ5JC0QhPEeO6ZvN3MGLCThMBRth1nEuTZ10zJH2M0adGw9WeiLSzwztlfGZlZzoZ/meQ94fHVCsiMZaSc+5/P4i7jGOHQdkiASdKzTrFwW5v160Puvn7GVGZQi1/Uhjuf0uxabtq/okbBuq7s40S73Pla8tgEKNjhSNp0Qmi2HNkXJJi6OaA5XuO6T12ac5Y47fLV4vHKN1EWbyNSCOvaEoDBZ90Sc/5ty/oDFbM/KR/82CTw4Rv0cBwfj4w9mlWaaNYbB5zsPcE9U3g8ZMts3qtis0aJhToW/osC3AiDkOw6K05bnq9gRUxhHq4+nMWUR9wesB+7sNpOsMGm/gjA3SEdTvj+dmmknvZeZH/mWatv0rp86Hq4dlzqlY8HCqOpY9N0GP0wML1maMTUH9MS8oDovoWXtgYWY/5mI+9r+1272xXG0OIf9dWnNdzb+PwPoVGSfKmL8z09wrLKJ2wZsG85/B/5uOrWiYc8Wr8+Fzw3tLPyMSyftgG8ww75GIln/GGbn/c/UWuEbx3PBUHKl2u44T1C/+jO0Tsn7v76UFniuVtQa9YmB3o7KYjnGsmanPhgOVzt4bq/qwIwZ9C7/nl3mO+n8YziXQxPuqwT2keycuxjiesn2Hs+bPO5g1k84cgm1Hrtau5700J+P/rF+k7RiHpkOfGtpL2u7rxjgwH+MCzjufm6OJzB2ipFbDkM7FWgQN5h5avB6O69pe+hnVOpT/le7e8S0zVsAF/brqF5QgCIIgCIIgCKKZQcYRBEEQBEEQBEEQRJNncIe28J0JA+FBQeCxDj3btszKCsliGvvJvvhsETrnWDZ+xkoX9C1uBZsVmTEIe8E4ES35YByRq9z03nJ48rwJXt9qKkqssYgviSocuUoRCOidl7m2sv2H4XszV8FHe3d2dv4g04goRFaqbC+927Zyesxn12YKunmqufq/Y2a2wD6Ky88Ixo6hehsER5Yni7INLNttL7TYz2V+E2EjWsZZkc0Vx2kCoksUsufQotq4lYVgl8WP55cL18EeRUCzKdJq4CrYtLZ0xjheKO+33aBbYyp7OBTwjO45WpORjb21wHSOz9Dlmliyscd4Lt44Aph7gIYhfYtbB37vZU3TH8yw/F9BEDq2o4aslzFesm02dhlswLcuLvuIzYcq4c5ZpYmMz39buTmWvtvkmZBlFuZFtX421SBQcL+My8LqgrjmzS6MDVhkgpifz88UoyHffG+51jHvF3yXx/SxtVnHNWSqBbccDBAtF7RoAcCY94hEpB8qspEHIRLdJzLGNdSv23MFFV3UT5keQ8W/S7cK7w/7iEc1pz3IjN1+vdqOcbLv4T6Dak7Pr6NRjNypVZGWCbFOWR+TGOiwfZGNQUIOdcHKJ4I3WNQ1IflXqVz8x6+xT3r2Q3CNzX4C9hFhDStk81BdZIKuN7hs4bJ5hYhb3l8B6w4ccdoH2/TZjX2wGv73V02dD18Z3S+EcURwNnbREeZX6Au9D1QHiWohcvx+yPVwGnQ4fq9SFzQz0T3/YoHpCVunUdXvamYvwj+F6RhXW98fjuhYLPw9Hk2djT34fCmLPWaWSoM+KgxTN2bu67uYj83esc/7c0avTtLPqM4jrd6AounWb0pyD8OONDrzDv4jOBcssngRjubZv1LECajWcWioLjQA5AonupyLX5mnVT6cZweN+ab9omjejGYAqrGnYR2XdmvkHnRtbDlkcwjRIT49baH2HEZl6CA7vmsafCMsvovPqyxxSFDZg96HIqJPfPaNxVplw/OvF8zR2HJFtU5mE7z473VN5897q2q896SjOrkd44KK8bV3l3txSDqIrin0bD8dvGZyud+mMuFUnYav38Z1TtrJu/iEPYwJgiAIgiAIgiASg4wjCIIgCIIgCIIgiGbPd44bBNO3ZGbDZBla0ta+jiJ4EZly+Hbz4gHd4Zk122C3QyEOQcRBUJArEY5n12yD704cbBzoYxPsHlVG8qBAqSpBlMo7WzKz3UdNTTqOXLV1bAnIKOgaVrQsCxKNIouh157qx8k4snbO2rEPnizdAsMlQdU64tWomKHIwugCm/pFsbCNUCnuDKyD2rcJFKtEHQSZC+LuMKiy+EUpCFeNK6mQ7cpO0KUuky2/4LL4qggjns319tgg6HJ8qqAnsSWKlhmunrZIGnhsw0dfmC35DV5ofGNcOiIBr+nh9lVVOzXmk5lGpEO2X53nHAPVeaK4lyZ1/JXpSz1hQrfWeoHsQciyJ6YSmDfXCbjdb8qYCNYeXroBnlu7Da4cnJlpXZRJfWdl8HiIl/Pcuu3On7MwolrXrAw0Hom2/0NBXy4YmUQ1xgUdTpWd2EW/9cflmwL70DjntLZjnKwN4I9TIcXIz6+TC/JcEOe+TRT7PaosyNnnj06YHbeoNi7jiMMGY9zvl22EZ9dug+tG9HF2fp11eG0M5ki656jmPogG3T9SGBAF7VWyxh2y+xumWe84clRo7JFx/Bj64KjMp4Lu29Ld7s3N+OsS1S+7/xmH+VSDqNZyjJMZVWLZVcdcLqlfV1d8pKYWPjZ1ftbPw3aFOvfklwvWgSve3Sp/F60qymLOINj1c4THuXH6Mud7lTrzjkeWbuS+A5FQZXDgBuMIrobDFO0Py8Tz4DDoXNJczlyowbzH4ZxAZ43EjoFRzGEeXrIhMOGArqmYiz7Y9ZIxqOyztgevY8Ncfum+Q/C9D9Xm8FHV7oaDR7L3Ki3H012SfRe8b6p7hmNQELJmve1w8PoFj367oH7DPik6bZ43S4oK1XjLr4EbbCNy53UXQRAEQRAEQRBEk8RlklKCIAiCIAiCIAiCyEmCRDzd27SC5y+cJPzd50b0huO7lRidT/UO84L+XRsCTu6cNBjiytAqAzNX2WZaIoikwACOuEXEzQ3MpL1m32Go0BAvhQUFrQcCArqiCAREEWLS5FLmZNds1TCqeFdh2mRLWNGnDQ8t3gDPawj48g2bftg22DjuZ6V9y0KngiVdQguOcqhPWbHnUGLnFgmzhcYRhvVlI/CIShCTZP3qEo+otj6w13ENBxW9SLAgi+OesOWKq19E0dAhx1ld3yxXi9V4Ln1lnpaQOyzsM25Tv7ar6ijWNSbHxAD2tfuPeGZYLpCZYoXVhViJaiEa5lTo1xUKSjccrITfLF4fSoTT+L3aSIw2KhXZd2VE1Q39bL7aHOloVEoyA+Log32hs3uBadpoHvyJ1xbAb7n2+12BoMVlqeKo33TINaPMJAdF77mYzJTtg5v6GBckehebuEGkhB/jbNYZEAm644w/xqFRws8D+m3XmNZXmHWc64UcawwhYgNjnIHvkFybLOqYscXxpiiqdXKQQe0LEe/dvbZxZ1YW77hEtRnnC9kHbz1cKT2uqu95PeKM3/8u3ZJlVOwdE8Kh00ccsZgrW5VF0ULmc+J/120J3wUtFpjbhR7jLOYdUb37O2pwHxuMI2JeeqTjmEPU3xPX64ygeXUpY8CFsQkiwpTombXuDRZtqA1pmp6yLPurG7PHH5dG7ndJ5hBxmCOVMW3HrwfbNaPsW3g4VfmXCPpH/oipkHOI9wTGQuHNkXLHdLrWoP9IfteHIAiCIAiCIAgiPwiOfiQIgiAIgiAIgiCIJs7PThoOt81YCesVGavaFIq9FW8aOyDj/785bST8dcVmuIrLFsnCvtvkj1rSsgj+c94E2HLoKJzQowTuswisDJvhhQWzevCZseIGA3DIBIAwzVDpKiMuIQaDbq5/e0ks1fPs2u3eH9fcO1ee4Q8RBZrGTS6JvJPgNY2AYlPYKo2zfl/fZCaCzQdsgvO8QLd07htHxJFhU0TYQMA4szMH8c/VWyDXMZ1/2vQpDdnCoXmBZgOqLJqumF+xD/65uhx2Oja6CnqWNh4UC2miJm5zJBSuXTplrpGAMAqqYhoD2Cq1EtVaFjOKMU4lSmxbWGBtWhCG0JlqLftgX/Djkv+WbYNcJw5vBRxHaxIwIs2F/aM4+uCo5hD3B2TM7tq6Zca4uulgJfy7dCvkmzlSQ6baCM6lMuAc1L6NZy4RNxljnKGxTNYBmtD65Ll126BDy0IrcxwTWiQ0xkXBPsdGBVFg2kekQ/XBbus5aA7GPjNoyHrhy3PhU0My30OFufULdspEj/Hupfl7Lq73XoKK3r9dm4wxburGChjZsZ2z8z+wUDzGstcZ5zzGtg+WzSPrsrHLj1lcVCD5TTrDnMAWfKcaBcnPLBtR3bL+7Vpn7EW4nitG1TZDmfckaBzh+4XyZh6sgD0X6isdom9wXctVAfVbzfweYxNE7I/AYD6O+ZMI21PJzFuDyj66UzvYcWS38jNhZsS7JGsctlhxvsKx7ScqJGbgWL8q857d3Bx5y6FKKGyRcvYcrdp7qMm3+SBU876urYsy5miuy52L5o4EQRAEQRAEQRBxQMYRBEEQBEEQBEEQRN7Tu7g1/P6M0XDxK/Okn8GXuzqc2rOT90cFG1AoEkf0b9/G+2OLZlENjCOSFQIN6tAGOrUsMsraSTRvHl9ZDreNH5h0MfKaJbsPGAXSEdAkRRT5iD8Go9gBM9ERuRWc1yLGc4Vhye6DkCQ4N/txgPmNiOZuRhMEH5NsKjiyCVj0T4FtvzmNqnfMXCXMVOmaqMwpHpQIfnIpwDaONVxSBhmtClokMg9lA8RtRLW2wrcoxrgZ2/YqBUcrIwpIV5FKYN6MdVsEzZM45k5PrdkK08vVwpR8JQ5jg71Hq+HRZRth+pZ46zgpYQm7lxpH/aLBzp2zVsPsmLKr+rSTimqjha1RmyHWdlSO2wCwuLAgQ2wXm9FP2GzsOSSq/UceGgDaEFXTPRJgyCYy+H5qTWY7XrxrPzT1vUrfmMB1PQfNx3ljg7vnZO+7/EiSUT1UuRLqF10/K+mAeQKa5rN87d1l0L6oIKNdr9lvb55UIHkxGnadkUtbaWmD390zp8ybV+D771xe/9u0w6jmo0cN9lEeW1kOL22ogC+N7AtxEkd7jMq8x2SM21dVA5dOmQfXDuudlTQgXwwAXe9qBRWd3/d+f+seGNwhMxblLxHUb8ZeWhMe4/B+qYrP71N+fOqCeqPStNZeXBCbD1VGYsKaS2NcrUE5/7ay3Ju3dWnVXHf1CIIgCIIgCIIg3EDGEQRBEARBEARBEESs3D15KPxp+Sb47PDMYIDQBGSRZIOWzurTOev3hQ6zULLvNiNIbunUFb8gFU8mSiVpfeOOOOnYshD2RpzdhLDn90s3UPVFSElL2jaMCsysNqSkbSIiiuaAHy923/w1MLci2mD65o5VcB4XTKdLc3lW/Gxrz67ZDq9v2mX8fTKjUXO4ptYLik7V17NpG66xaIb+ObxzNiOzoNk7mrYhXNQZDl0E2OZzv9ijTctETCsysrHbZP6GpkHcZkyuqLEwS8njxyQnzKRs5ir5QhzP0Z9XbIYkEImWY++DY6jfFXsOeX/iJimDTlYYaGOOZCvgjHvM6VXcKvJM4NGIlu0M6vJ5Ppi0wc2iXQfgv2Vb4dOO61iWRdykDw4jStQhjnbVIFp2fNxaB31w0D2yga3SWEW1js+Fh1PVIG9uON/xvuwmjTWijRCeNa/K5f6Nv7bS+vFuwc4DOT3G2bx/juoxqTR8viuOVMEvFqyFfBvj/Pp1fabDQWMcd21Yvw8tXg9xEkcf3FC/ju9lVcCeSBU3xn37g5XQtjDTMCmMeY/eOi6+tY7rZRXOEVTPn2gO0RT213JpjFM9f3w534nZQJMgCIIgCIIgCCJfoQhwgiAIgiAIgiAIIlYuHtDN+xM3aEzwn/MmwIKd++Hcvl0Fv7fNgZ0N+544CjsEX+zmUqCYNHVZGXKLohw0s4iSk3uUwMztTUfkd7SZBkfHRQcyjoiMe+eWwT/OGe/9u7kG+UeJX6PTt0SThZ4IF/hrO7I2hSA8F/j1s2T3gZwViTZ1MLB+eMdiqDx2zFiYbtMOtx4+CktWlTebNkxEnx3dD/pOStgbB0nNj5LKBBz35SbVdsIu+W0EDnGIbnIVGndyz8ikqZDUc4NCnPYS4Wk+kVQffKD6WDhzJItiP75yM8yN2UwsqfYbNhOwzT355cK18G4z3feIw1wGjc+RTzs+7qPLNub8+BLHPLhxnuL2XH8NMF0KEt1GBSuGjNPw0/V8cM/RamipeGdWdSz5Ptimfm2bvGtReJC5SVIrm7DrOJt2GNV4vv1IFeQ6cfQRtfWtyfWpnizdqvx9cxnjojJHenbtduXvRX1wkJmHCyqZfivOtU4UJhU7K6ulvzuaUPtl+2CbcSeXto9nbd+bWDnDrtcIgiAIgiAIgiCaKu5UMQRBEARBEARBEASR4wzq0BauHNxTKIgucPi+MG0QVJP0a8qcMGxI1Rl75Bo59C49Fi4Z0D3pIhA5RJFDMx0ik31V1Z5gGQPpnlunDvYizFm3P/7sos0V0yxtiO1on88CaZaZ9cFztvOiOAUITRUMmMWg9c++sTiW890zpwweXqoWB+UTy3cfTLoIeQ0rhMrn5z0pwTtmlU6ifuPO/peUIPv5dTsaAt0fCxD1uRRerW2mc0MyjnDPweqahn9TH+yef5VuaRAKzonZbCBOklrX3M9ky7bpH1hBmC6PLtsEcx1nm89VI72w2+sbDA3tkNc37bK6L/lAUzZMXBtghJWUgdr2w0djnQc32EbEbaCWkLEBmtknIZyO4nKrFG00KWOOVfsOhXqGbA0gophv/1shvE9qCyBsv2TTZ0fVFf560XrIRQ5U1cS6jvPr1/WZ5lTsy8l58K7KqljHOP8exn25SRkbsPs7cc5j4h7SqxKadz9dti3UNce956hi0S65WXrulJIgCIIgCIIgCCK/oAhwgiAIgiAIgiAIIi8IazuQcmigwAYaBTnY//Pc8fDZ4b3hkdNHQ5SM69xO+PMWuWAckc5N4wiCaM7khKlMnoJDxN9XlsNDi3MzULGp84NZq5MuQrNgxZ6D8JXpy4y/d8QiwO6kZz+Eq15bAM2B0n114tZCyz54RxPInJc0OEtftvsgbLIQZxFmwnvCPWx2wVzI1BgVSWUBfW/rngZjhRfrTQ6iZv3+I7ELgMoPNQoDk2D6lt3wx/os3lHztenL4Oppi6C5sPHAkQbhPWaEJqLJPr9w5374x6o6k4N8JClTDF8o+fdV5YHZdJsymw8lMwfdxwgS89noy8aAwQVTN+5sEMvdNmNFImXId7DvRR5esgGW5HEbTkpU+xtmjzIO0WeDqBbiBU3FkuDOWaXJiGpjbk9JGUdkGCzaGEc0kTlTUqLasCZQtRYlx/VMc+Kt8l2xPrd+/cZdz0mZI7GGIXGUwT9D3PWblLHB1E118+C4zadiH+MSqt9lexrnvTVp8zLYVlPc7dfWxEmX+Tvz15ySIAiCIAiCIAhCRXaKVYIgCIIgCIIgCIIgQsG+2gzS/Q0tKYZvjiu2ytptwg+OHyIUTBTkiDY8F0XqSQUSJkXu3QGCyN8x4rGV5UkXI2/ZdpiE83Fwz5yyWM7TXClqQZ7fkZEOnxGYIHIBMj+Jjlc2VMCTMZmgfHraQmhuzNi2N+8FxElRfqgS+rVrDV97dxnsOdooEicc1e/BOtOVGy3M05oSuyqTNR15dFk8xjLNlZnb98KPaC0XmQj+lQ074IMYx7nmRMWRKi9j+BOr89e4B0lKKr2XMZyyESWa4msDoxYJ8pTtrzPrTJJYRLXpNDy7ZhvM3hGvSHJvDsw/d1uYp9lmrY/79eHWw8kZAGKbsk2+YCPuxnGV7ZfynbRgThElKAg/WF0T+5iT1Dt3dv8sDhPWg9XHPDO8Wdv3NgvzHpa4zBzQxOFwxHE1uRgzYmWOZNGnYB+xnzEejAPbsViXwzXJ3z+CIAiCIAiCIIgkIOMIgiAIgiAIgiAIgnAM+95WN5SmRcQKtpKWhbGcFw0gbAJbcuGFO8+B6niDDpKGRJQEC2ZaJqKheeXLIvKV9fUZrYloKGxBzgZRkYY0tCooiOz4zZ10AuKf5shTZVvhcTKhioyfz18b3cGJnDSNzBfS9XO0hTsPJF2UvJ1DNAeSynZPxMM9cxqz3hPu2dzMDIviJJ2j7w/yBbbnjyMb+9LdB+Du2aUZWbSbA6X7DsFtM1ZGfp7Nh47CAwvXQdwkaWyArN57CK57c7Hx9/ZYGBSc9OyH0JxAQW9hCqDymPk700MWYt175pbBst3Np3/wt9FQDL96X/QGNz+bvxYqExhTk1ploAg+TmOOF9fvgCRYuz/59zVxzCGwz77i1flWRkFN3ZjUpn4rLIwhb3h7aexztLiNQAiCIAiCIAiCIJoLZBxBEARBEARBEARBEM5pfHGb0rSOiFofKDOIcC3a6Niq0Co7IR9U1re4lRfgRsSHblslmgdxBPg0W0hQS+QBHVoWxp51qLmAovtCEtVGV7/RHZqoh+Rc0fOrReupvUVE+cFKKC4sgEMUsBwZBbTsjHSZQev6COs3wmMTRL5kcm3u67jWhWRQF1n9RvAeg2DqN91oJDx7x77Iq2aBZ3LV/Iyubnt/RdJFyGt+Om9N0kXIW1BsX1VTC1dNXRCLGLg5mUawJqw3Tl8KFUeqIj9fEqYRSeJPfzccOAJ/WLYp4dLk937a92auiuVccZtG5AJo3PNgTKZQ+Wrshf1sitYTBEEQBEEQBEE0M1okXQCCIAiCIAiCIAiCyDfYGFzd948yYwcVLQ3cJlrEZFjRwjJrI38tSQeu3DS2P1w1uAc0F+6ePDTpIhA5BmUZjQ7SaRD5QIci8qSOMhi7KGpHsWYuiFmZp8GPuQDO69lMfgTR1Lj5/eXQrohEn1GB/UNhCwpPiArsfWkKER00vBH5QKsC6oOjNOWg+o2QdDqWLOHNFb9m751T1uwE23Fik/Wb0GdvMxQSx2ky/uK6Hc1SrB0PadhbVQPL9xxKuiB5Pcjhfg8RHT+cUwpHm5kpSZz8p3QrTNu8K+liNGnIRJEgCIIgCIIgiOYIvRUkCIIgCIIgCIIgiHruPWGol0E7LLVMDKOu7M9GHvjLU0dqf1bmoO88U5fl8fhQgiM1yQUXXD+yD1w9rBfcOLofNBfGdG6XdBHyitN6doSmzjtbdiddhLyFwtyjZ1dl9Jm5mjtkbBAdO45UQQGpPiPlvvlroz1BMwa1XMfYxRBBNDE2HzoK7ckcKWLjCDJHipIaEtUSBKGgNRlHRAaaGlD9RgeuMMg4InpIkEg0Zci8Jzqw/z1aS4LwqMAlHK2SozW5RbYdpndGUULGU9HyyoaKiM+Q/9BagiAIgiAIgiCI5ggZRxAEQRAEQRAEQRB5gYugigv7d4N/njMekvBRkBk7qDipewk8dtZYeOaC4+AXJw9XflamzWjh2DjC9mhsZuLxXdpDZYJZKS7q3w2KWrSAklZFMKJjMeQD7QOy5uJ9c+0h0py57yT180g0b3ZRdrnI+cRrC6M/STPHufEU0cBds0th6sadVCMRkeQcs7kIjSh7V7Ss3ksZKKOmbP/hyM/RnDPVfrhtT9LFyGv+trI86SLkveCIiLCOyfgkUqZt2gmbDlZGe5JmPsaRcUR0YA+8kjKxRwb7boYgmiotyRwpMnCMa9WCwsyjgsyRCIIIgtYZ4SHjCIIgCIIgCIIgmiPh06gSBEEQBEEQBEEQRB7hIkEuG0yeijBPCJpNjOnc3vt32T61uEVWjgLHxbNNHspmJi5MpeCSAd3gxfU7IAnYS/jpicPgk683fQFyoRfUdSzjvn9maC/4V+lWGN2pGPq1aw1rSCDljDaFaqMOgiCi5XBNY39HuGfP0WoooGzhkUHZuaLl2x+sjPgMxK8WraNKiJAfzFpN9Us0WWrSaViz/0jSxchr0f1rm8h8Kipw24qMDaLlrfLdEZ+heYMGdUS0QiSyqIuOw9XH4IGF8a8z7hw7EYo4U4XqPDTSxCvcVUmZ2ImmTdA7UiLcGNe6kIwjouyDF+86ENnxmztkjUTkA6tpjAsNG4tEEARBEARBEATRXCDjCIIgCIIgCIIgCIJwnEGQfe+YK3GEsnK0cFxAW6MMNrAWtajfGNsfqmprvWCvuAO+2Drp0641NHWuGtwD3tuamdX1fxdOgh5tW8EnhvSE7m1aeiYkOdJU84ZebVvB1sNHky4GQRCEc77zwUpYRRnvCYKQ8MqGCqqbCKFM4URT5sEEBJ8E4YrZO/bBYyvLqUIj5L9lW6l+iSadjf2+eWuSLkbekoRpBLK3ZatEzhs7aYD7F6xNuhQEYc3+qhqqvYiNI9aTAWB0pNNwx0wyCY2wej0jbCI6yGAxWmpqyZ7O1VhGEARBEARBEATR3CArWIIgCIIgCIIgCIJgcPHOkD1EWDH+6b06GZ/TZAPAvXGEHbVMxWOZSloVwb0nDIObxva3Lsvx3TrAnccPMf4em0S9IFecP0Jw09gBUMhcBxoaoGkE0ru4NRS2oO2hKLh5/IBIjksAnNS9hKqBIBJk6e6DVP8EQRAEQRjz6sadVGsRsocEc5Hzp+Wboj9JM2YRZVommjDvbd2ddBEIwpplew7C7O37qAYjZOnuA1S/EXKA5sGRmyM9SQZfkUFS5ujr96FF6yM+S/Pmg217ky5CXrPjSFXSRcgLfklmtgRBEARBEARBNENIGUAQBEEQBEEQBEHkBa70/bUuQjQYE4RUSOsI1sQgDClJBRWkAL5Rb87QtXURnNu3i9FxX79sMozp3I45D0BJy0Lj8h2rTQvNGlqEqL/fnzEGLh/YHf7y0bFG33NtppG0wL64qAAKNRpS2LZKZHJ2H7NnidDntgkDqboIgiAIgiAIgiAYKNM9QRBEcvxs/lqqfqJJc+QYZbOOkqfKtkV6/ObO/9ZtT7oIec0vaIyLlF+RqUGkrD9wBD7cTsYGUfJW+a5Ij9/ceXjJxqSLkBe8sZnaKUEQBEEQBEEQzQ9zJQdBEARBEARBEARB5DFhjAp8WOuJJD0Iju/WAUr3HYabxvaXGlCgScI1w3vDsI7FMKRDW2hd0MILoijbd1jrHCUti+DCfl1hWX328Y/27gxn9O4MN05fZlRfx5hKY8vqov46GBpZ5JN9QkF9ZXZr0xI2Hqz0/r318NH8v/AcoVVBCzgac9DtgHatYUP9vc5XyOSEIAiCIAiCIAiCIAiCIAiCaAq8tmln0kXIa55YvSXpIuQ1cyr2JV0EggjFvqoaqsEIeXlDBdVvhLxJxhwEQRAEQRAEQRCEJS1sv0gQBEEQBEEQBEEQ+Ujv4lYwtnM779+3TxxkdYzatDstvq5Aul+71lk/+9yIPvD6pZPhY4N6SI9TkEp5f07u0dEzF2jfshA+P6KPURmvHNwTPj6oB1zcvxt8eXQ/OK5rB3j8rHHwxDnjtY/RuXVRw787tSpy6mWA12cCmmnkC20LC7y/R3YsDvxs/lx17vC3s8fFfs6Te3aEfEdmhEPYcWbvThn//9aEgTChS3uqToIgCIIgCIIgCIIgCIIgCIIg+Z0vogAA96VJREFUCIIgCIIgCIIgCIIgCIJoQpil3CQIgiAIgiAIgiCInMWNkjiVSsEfzxwLFUeqoFdxK6tjML4REJcHwbCSYvjyqL7w5xWbs65HVQ62rLai7MIWKbhj0uCMn42uN9/Q5QeTBsPn31wMhS1awNfH9s8qf7zGEZA3FNcbR7Su/1tFHl12zjC4Q9uki5CX5JG3S06ST+Y5BEEQBEEQ+cS5fbvAG5t3JV0MgiAIgiAIgqFTq0KorKmFI8dqqV4IgsiiMJWCmrTobSxBEAQRNX2LW8PmQ5VU0QRBEARBEARBEESzo0XSBSAIgiAIgiAIgiCIXAONEGxNI+poDABqEaMc/4bR/TL+n+LME0Z3Ks76zlFBMGNKs8y92oapo0wGdWgLL18yGV6+5Hjo0rolUxY399ME3etvCrStN4zIJzOMuBnZMfu54fnUkJ6QK+RT+23O15gkBVS9BEEo+NvZ46h+HPINxjCNIAhCRfuiAriRW/MTBEEQBEEQyXNqz05Cg27CHRf170rVGSG0NxEtXxzVN+IzNG+uH9kn6SLkNd3btIROrYqSLkZeM8YwGQdhxidy6B1+PnLNsF5JF4EgCIIgCIIgCIKQQMYRBEEQBEEQBEEQRN69VD+9V6dEy1LLRgk6FuBeMbC79mf5Uz98+hh45PTRGT+rrhUYR2iUeUTHYvjVqSPBJR1aFjYYHYTluuG9M0wzTOBNFn77kVEwtKQtJEW3No1GGqa0LSpoyGgURMqwnmzp1jr/AoxO6tERcoXmoPnHZ7RtYXLbmvlmrMAbcbRIpSjYPaaAS8INX6Lg61jp1651vCfMc0znqYQZA6i9EnnEkZpaaFVAr/Zd0rFlodPjEQSRX7QiF9ZI6VC/Z+lDIxzRlPlILzKOiBJ814P7lUR0tHb0Xo4Qc0oOvT/KRy7oR8YyUfLFkX0hnSZ7pKgY0qENDEswBqE57L23pr20SDm3bxdoKlBfRhAEQRAEQRBEc4PevREEQRAEQRAEQRB5ARoZfOe4QZ6xwvcnDU60LBH6RsAdIa6tuKgAJncvyfhZVYbLhX6Z/3HOeBjiKJBBFW9iGg/4m9NGwq3jB8INjIjUNB6AP+fJPTrCv86dEPi9Xm1bQa6J1H0jjlwKrLx9YrLPZxQURSAmuH3iIKvv5dCtdsYdXJtBo4N7TxiW2MZmyxb5taWa5mwisAmd1jNZA6Z8F4ZfNrAbvHjRJOcGTM2VXNJzfSQPn53LOdMy3myGCEdBLjXgPKSEy4iIZoN/P3scFJMoJi/Jx3UGG3xdk05DG2q7Tjm5Jwm4ouSqwT0iPX5zZziJiyKnc+tMs7/eEe37NVdO5YyfX7lkcmJlyUfGdKLM1S54ZP5MeGf6axl/8Ge8GWgBpMhsJkLw3QaZLkbHSd1LqH4jxpVhPiGmkPbWIqV9ywLITkFBuExMQGNcdIzv0i7vkgHkGq0Lms4Yd4xMcAiCIAiCIAiCaGbkV5QzQRAEQRAEQRAE0az55JCe8IPjh0AXLrA3br49YWDDvz89tFeoY/EicBMDgJTks+xmQK6Lpkzf5Z/asxNcPaxXRoYk04CLFpaCyI8N6g6/P2O01XejKA/iZ9HQOYLtWfC6TZC1yyR46LSRnoBbhU5xozASsBXm5pJJiCvaFmbWL17iGb07w8uXHJ9IefKwijPAy7tmWC+4dID62SD0KWTazCUDusFdxw/1+sI8b0qxEWacdM3ZfTtDvtGDE8RgLPavTx0JA9q1TqxM+QQ/T/3DGaPh5nEDEitPvtGGc5DDLJQjO7WDx88el1iZ8plOrQrh7slDEzt/PopF2nHZ2ClLoltKWhZm/B/naVMuOR4ePGWE4zM1T0ggFy0tKWtq5HRmDKhGdyqGZy+cCKM6FUd/4mZCh6LMPrg1t/dDhKNXcabRSUduzCPCcVKPTHNuXNa15eZthDtwms/PiwmX9ZuCYuqDI6WkFfXBzWVvOB/Btxi0FxFtH0wmodGB7+Fo7RwtTWmOVi1IqEMQBEEQBEEQBJHP0Js3giAIgiAIgiAIgnDMef26ws9OGg6//cgoGJWD2b3+cOYYzzBifJf2cKogw2Yuhdi4kNWaCoiKJJ//yYnDlN/Dd83Hd8sMGr3nhKFeOwhDGP1TlCYC14/sA1cM7A7fNBQ35pKe68QeHeHW8Y1GL7kkUrO9dblUv67gg3r8S0zaJChfyOpn6wOpfjh5KHy0d/6J4JOggDGXYWs7iUxS2G/nG/xY1611o8ArbvIxO1hWH5wCOK1XJ3jqgomJlSmf4Oed2IY+NqgHfG1Mv0TKk+TzEwVZJnz1fw9o3wbO69sl9vJ8eVRfyDd6MdnXx3VuD904s5k4KczDPpg3epStVQk7OnAi2m9PGOStMU6nObATePHL944b5ObAhAffG9w4uh9cNbgH1Y5D2L2eY+m6dcefzhwLdx4/JPZ6/mEC54ya9lwfHIUpa3OGN0f693kTEitLczBH8kSfTSjbcq4zsH2bjP9jb9yOM5sh3NVxbToNnRizJCI8vNFU+wTbbz6Ormf3yXxnkYdbATkFTonbNyFheFOsX35vgnA3xrXgDAEJ93RsQvU7Y9uepItAEARBEARBEAQRK/m4N0gQBEEQBEEQBEEQiYKCr3P6doGTe2SbMsSJLNDguK4dYOqlk+FPZ44RGgvkUg50FwE/pgKiQkmg8vn9uiqza4tyFPRs08prB5iZ0JYwuQ8KUtHV9VfH9IcfHD/EOGiTz/6DxhN3HT8kK9grarBdtHAk8o1Cv2W7aZePouUi7pnMpUvsnqA40hW88UnKQV2jMZEt+WhsUMjUI5vQJgnt5wndMw2O8gG+Hp+/6PikihKpYVNS8OMKZfGLdozzMn0WFcAXRuafwUCuZJnL5+c1CVox5jKVx2phXJd2WULFuDBZ+zQV+MzVbBsmHNQvJ/BM0pdjcIdMgWQ+wGel7afYTyHM4fuDghYpyp7pGFGXi6ZqYzvHbxIchWFprpkjJXmN+ZhFm38vkEv7/floHIG1e6jmWGLlyec1hr92219Vk1h5JnfrAPlGa24dl6To8zsTwht75xpjO7fPmTHu62P7Q76PcUnu7/TPwzXOlYN6ZO0F769ObowbVtIW8om+xY0GrH79Hqb6jWyMq6pNJ5qE4Dt5aKDJv8Pl521x8pXRZubby3cfjKwsBEEQBEEQBEEQuUj+vf0iCIIgCIIgCIIgiDzCNNzlkdNHQ8eWhXBu3y4woqPcrAADnaWik5hjbK5XZN51URTToCFVEJfKxCGdrvvtp4b0bHhRPrz+HpwUwkSk/rBWmAiLwtT1WSamD9yJPju8N1w2sDt8MeYMzP88d7xXPy6CyqIITLMVheWjCDIrWDjhYHf2mcwHcQEvCk8lXNetC5t+nfKgiMunJl2b6PPqOlC5TQ48A3w94jXiPCiZskDewWe3T/Ia4zaZam71m4/w82i2fvNwypS4WQPWd+uCAnjinPFw7bBe8ZclDx8g3lwG+dlJwxMRLaPZX75RxM1jkuwXerTJFI/kAy2zxrhkn9F8N67A1lzDutTFzA0x7+nEQcqRQa0L8tEkNJeuCU1t892YA7vkHgmZn/JlyUfjCKzfL4zok1h58g2+d8A5xOm9OiVUGoCP9Mq/vQh2/xWNI3q0TU5U2zoP+4h2nAEgckpCiQby0XxQZN5DRGlgCfDp+vfeufI85RNYv6f2TC4Rycc4o5B8oA0zxh09VptoAoAkTRWiAk2vcwXT/j8f5xwEQRAEQRAEQRAq8m9VShAEQRAEQRAEQRDNmMndS+DVSyfDfScNtz5GnEE2d08eCudFLO6MS5zgh+ffMLovfGNsf3j49NFOXp6nY9r4CZN57vsTB0NnzaxUvECy8fzxcfnA7jCoQ1tnmYldt7FxndtZ1wdfvUkFhUcpOMqlWMeueVC/KrFGLtV1vtQxq+VKQijjur/KBZGuaKz76YnD4L/nHRd7WfIxiyuajeWK6DMv65cThSctqs03+Ppk21DSRlT5Alun6fqVS4+2reCUnp2atQA1yjHunL5d4JEzRsdflvyrXmjFGxsk2C/kYfPNMubA6k7SgCQPqzhrzNM1iXOxD9IchOEis9ikxhvZXlpTJop2aEseVm+2CWsqBb8+bWQi4vsheZYpXFa/lwzsBl8b0z+R8qgMxJsi/DOJ/z++Wwf4+thk6jcv+4gWmaLakpZFcM8JQ2FAAkZbrk1ucwE0VOT56UnDPBPAuMnH4PB2RYUZ/8e5Gb6TTYL8a72Zont/jLtycE+4anAyBgP5th/M71Xi/yd07ZCYkVkedsEZffCRmmPQvmUhfHvCQOjQMrPviIMkTAfj7oORB08ZAeO7tI+9LKbVmw/JIAiCIAiCIAiCIEygVRBBEARBEARBEARB5Bm5JDi7oF9X5e8vHtBNWd5cy4ajMnGorQ8ixyC760b0cfaC3D9uLreFklZF8EXN7JKirLlI1TH1dfZu2woeOm0kuCDluI5c1fI1w3rBb04bCQ99ZJR1ufgglLP7NBqzfMowK9D1I/vAzCtPhlwTLYcJFLtpbH+vjsPAVnG/4tbwpVF9YXK3DtbH69paz3RFRqdWhU6Dc9nrSyJozHUgYC4EArFXxHbpSdRvXopqBdeE84fuCWRLzMdAS97YIMlLzMPmC0WcYi6X5vH5AD/Gsf+lqnaEpMk2pTFuSIc2kKvIrimJ8TzfxBpiY4MkzZGawRwilYKL+ndLRDCXj/Cie+x3vziyr5YYP4rnOR/HVVk9JWGeVyjZS2vKiPrcn588HCZ1td9fsaV5GAACDC0phgdPdbO32tzhzVywPaNI8Qsj+4Te5yOy3wnhM4o/+/yIPvCV0f1ir6J8XKeLLgnnaXefMDT2suTjXiUvvPeFtmf16Rx7WXLtHasL2nGG/XiJnx3eGx4/a1zsZcnH+m3LGZ/gkIdJEr43cXAi5jL5RpZxRP3f2IbR3CDp8uSbwReaIyGfGtoLHjk9fhPWXDBej7oPRk7v3Rn+/NGxkOvkwvtigiAIgiAIgiCIOKFVEEEQBEEQBEEQBEHkMEkErro84/cmDgr1/Vx7na7ycEhHdB32thGGgq1UPJtMsuwalceOBdYDH/TsIhDGRVCMq+BGFNac2rOTF0R4Ss+OVsfgr+e8fl08Y4Prhvf2Ao9MM4baBL5h5l4858D2bbxAnLDBZNnB2Pb36XMj+nh1HIaPD2rMrHRqz45ewPIjZ4yxPt4rl0wOVZ6w11MYUTZ2rJtcICcCgVLZ2diTCspzndU1F8ZpWTUmceeTzFQeFS25RpNkQHT+1a6oD9ZH1sZvnzgY4iaJjGI68HMGtv0GzSdwPkPYr1uSGePsznlO365wyYBukIvI6jGJfYJ8fCR4Y4MkrzE/RcuprGcUDX3O6dsFSgIyfeaS6DZX7wzf52Eb6lXcCp69YBKc17fRQLK5PM+RkBKPcQV5sI7LBUTrCjQ//cOZ9vsrtjQPA0Bax0Vp5pLKcxFx3PBr3QwDwLgL0wyCa9PMS68k1nG84WM+IJsrJFG/eVi9GaJwv16x7x3duV1iZconst7HMT1vImNcnrVh/nLYOqUxLv/6Q1lMRD71wUliWrsdAvaiCIIgCIIgCIIg8o3cWcERBEEQBEEQBEEQBJET8IEfvdq2sj4WCuB/f8boRLLFRUPaSpwcJpgmjHFEjL4R2kFvskDAoDo6lk5nBT3nSnBIFMEmnVoVwd/OHgd3TzbL8pUSBCmiscE3xg2AIq7+PtpbneGqbWF21hCtMtSf87/nHweTu5eEDojMzlRrUBbJffrrWXqZT64a3GgS4XPD6H7wmaG94Ktj+sGZAXXI8uR5EyAKwopWVMG5YQ59z+Sh8N3jBsFdxw+xPwgAXDqgG9wTIttdLgcfJ5It3PKc1w7r5RnQZB0PkkdWBgrGdoOrsZelcys7MWgexllmZR0z6bLa51Cg46MJCPx04PsBVnQZZPQi6y+HlrS1L1AqN54ll6Qk65YkAqNtx3wUSrURrOXCXoHomK6EwkkIiPNRBNmSazP5d4XJUpSSi2pzeIqexTtXnBjJccP24bIxDs0jBndo2+SzyrYVZOo2YZxj4WDSxhG8SD0fyCUzjKbwTIQVfSZZ33lYvVlz3aSNDfINft6Z9BwiH+fBMjOZRMa4PKzfXBpX8m8GITAATKwk+Qnfftn/5k7Lbrrw41jG/5vQ+6Jchr2kpE1uc/n9ZH6M2/pl6dm2pWdUSBAEQRAEQRAE0ZygfTOCIAiCIAiCIAiCyGFy4d3rA6eMCPX947uVWGeLy4Xr1zFxwGyZnxzSK5oMzUzGpygrUPbJTw7p2SBquKBfVy/Q+PaJg7I+pxv7wAcu+0wIqIfadDqRAAudM0YVbDKqUzu4eEC3LEGTKxOAT9TfW59Te3bM+H9xUZ1xxDmGgRR8EXhRrCn8NZlkSezCZKplN0JHd9ITkXRt3VJoqHHbhIFw/ci+RkFVQeKdpIJ0sjLVsqLaEMcuaVXktbGB7duEKR78cPJQuKi/fRbwk3tktmtTBrRrDVGJapOIGRO9EECDqM+P6KP8XpvCAuhdnF0XvQQ/yxXC1O/LFx/vidVMsyeFCbREw6Cs40HytIwgg9Qt4wdYfS9MltxHTh8NuQjfB5v0u6oZ4p2TBsPwkrah++CkXzb2DdnHiLKxN/w7oKqbQlxvLpcxibLZipzQoE707IUVTblYI8iOkURgfz4GFRRxY1ySgokwp47KoK5vsb2JqGgdyD5TQc9HGANN1+B8MAoR5XFdA/aCjNdx+vUbzR6G22OGmfe52IdQHzuyQ8vPmcNjvi1RtMNLBnTLuzmVq3WcizHO1pAlzJl7tMnel8sF+DaTdLbwpNZbcfV5mWNeAvPgVNM1+jMlielwEmYVzWrczsP65d8xJrqOg/yfo7H/z8PmlAPmSMnOIfKxD5a9j0viWvOxfnPJHMmkKJjsIop3XQRBEARBEARBELkMrYIIgiAIgiAIgiAIgsiAf8c6vGMxPH3+cU5rCUVsfzgjWMAX9L53cId4xHBBmWSfv2hSg8jedRCZqWiVxeTVvSy46hvj+sPPThoOfzt7HPz4xGEw7bIT4crBmYYDJmKTIkmWRAw0uFUhJK1NywMsrhlmZtrhOqTBVYC5LMDBJPBNFbCRCij3b04blWWQgNwxabD2+evOk3Kq+uHbjEkWv7SlkEb2uVwM8ll74LBTQY1JKHY7Sb/HGnOYPh8py77y2QsmwqhOxVk/v3RAdwhDX9fGEelkA6z4cz5xznj457njPdGsCvxtp1aFWT+//+Rw5lLdHIhAZLUYpn6xXC9fPNkzkDBBZo4UBJpU3DxugHPzLhe0iiC78eRuJU6Ow5oDBVEiaL+5AG+O1IJp0WFq/vJBPeCJcyfAGb06WR9jZMfsPlVGVEH6mw9VRpbFj61rI8FqmHmNYyV02MN9pKd9+/DJqKWExzhbkTCuM0Tma2Hn+C7EQlHU4+mW/UKYsgwticZALRfHOFw725DKwWyPxUWFTp+BDIO6gO/KpqZfG9Mf4gbHuMIIqjisMSZ/2zPWyZbmSL3bhjMLscHfd3DdB0c5DiWSjd2yv7pyUA/4FGcainSw3MNk90JzsR5tTQbCGJWc3MPN2ibqMc7F3uV1AYaTUdTv78+wM6OOGtV+oW3/88WRdvUbhn+fN8Hbj8h5Ua3BGBdJeULMCyd36wC5TtKi2nzMxp5TotoQ3zXZF4qTlhGMcbaEudVRFRuNZJ2aI0n+bUJcxrYsdx4/JJLjDgkZl6DqZ5NoyjnUXTWYnIclY+7JbC4kcan5OcZBk6R1Qbg1MEEQBEEQBEEQRFMk93b/CYIgCIIgCIIgCIJIFFGQQGvHAYT3njAUJmmIBlWBpX2KW8HPT4pXUCkSL6BoURbo7tOxZaPggr+ioGC8s/p0gSRf3uOL9HP6doEe9cEaMoMM3VAXVZCE6n7XptPSAJarTY0jHEfCBAkPw2JydNWTmm0coT5yTX2D78C0X60ycIct2x/O2IBvM7YZeGyeB/47rXMwG8nCnQdCfV8lNAtqI/xvj+/WwQuI/sroftrHyDqm5OPH0sH3SnQu7LNwvLClGtWkkZnC2B/z5ycPtxL88GMOmkO1KyqEqmO1yu9hH9yeEw8+esYY6FXcCj7auzPYElawpCJsz4xtx7T/szWOwGZWLJhLnBGibl3RMoI0ie1bFnqmJXdPHmokSuDHg0v662cUzkXjH1G5MkWfZn1wI0y/lQpnnPOlUX1hfJdwGdGThB8XMjIBW4pqk+A3p40MHZguorpW3ffrIGunSTxz9n1wWljesGsGF2KhKDLV2t4b22cC58/XDusNuUhRBBVsa+LAj3Em4mfZGntYSMHQ5oOVkYlqg54v2Qzc1S0zMZ/yzhvBoLDtcJXjZ1nffErWP2G9h5nb2/DomWO8daTrcaTAwT6N7AhhynZhv65W3wtjKiva0w07RrnYV4tiroXGBv3atRaaHkZVlk8OMduTTGod52JeUmK4PnYhmMvVdZxKVBuEafvUIRXie1HU8XFd2zvuw+32gqOu9yCwaqMQjI5hDHttYW97hvEyxI9tG7x0QDe4XmC4EvSuLg6ieK76Foc3NzblCxEZ2rRwvI5zMcbdaWie7hPmzC8ZGhbr0jrkM5AKsVcpe392Yvf4jbbO7tMZOrcyW/fFYTSePSwwxgZJGI1bfu9Xp46EU3p0dG4wJ3pH4mqMC9M3274btt0jOqtPZ/jyqL7O9j5z3RzpkwKzQx2Srw2CIAiCIAiCIIjcJvcinQmCIAiCIAiCIAiCyHjh+buPjIJJXTvAj08cFkvNpBxnBhOeQ/OlsuxjGKiK2eUHdmgDnx/Rx3tRfuv47EzdrhGJF3Q0xR8b1MMLXMYMXg+dNirwBTsKKdsXFXhikTDBWSb3LfQd1ryneK/8gHeTAIfz+3WF7q3F4hnT9pnK4wxSSkEL9ytRkMyoTsXhMzW6Nubgg7ENAtUyzV4yP6sjCE85MtG5ZVz0/ZM7Yw7xv3UzQT5yxpgMg5m4ng/si/mMmhgciMYIYahxYRzB1GSaDQQMccyz+3SBf587AZ65YKJZWSQnrdIQD7fkguP852FsZ/uA9SjbRxKBlkVcG7x94mBt0bLMmClpbLMbq8A7g8/mxQO6GQVd8oGWJ/fsCLdPHAQ3jQ3OPi46DxqZJY3K1ClompTWGPvCmFvhcdAI6M8fHQtNFX6ukzLof1S//2b9uI7zdSMsb0fPtq2gmDPvcYFO328CO8YlIfLjn/P3PnaSJ0i+sL9apIu10E5wL8MKspyMcRHUo22MOT/GGRlzCE6K2WuvNTQAjDpTrQu6t7VbQ/HPzEd6dQotKH/glHBGm4dqjrntg5n/htE6PHz6aDi3bxehSCUq8YbIRDRs61m552BkgqPAMU7xux8cPxhuHT/Q25eKC9H9+DJjRhh2TLIlo1Ts/CpE3/ytCQPhov5dhUJXFfxaV5daSAv7ulGhx7hQX68/hvsxDsXCT51/nLEQM0xZRH1wLpgd8Ot3FyNen+LWcOWgHtDdcL+Qr4+vjunX5LM0t1CJai2PGabXYotjYvCB5Y6ivZq2ER5VkYJK28JgLNcuT4h2Uphq4dzYwMVjkZLUTZj2YGvwZfuc45atrSBXhYsjRtF1ndrTbu4bpihRjWdhD8vPbVyMcabmwS6Iqn7DPhctFGNe0JGj2N+2N0dKRTKPsN0bke3VujDvCWMmYPsOBetWZDgddo7v+palHZWtpWXBbE0oZXtpubDOiKIMtnOj5GuDIAiCIAiCIAgityHjCIIgCIIgCIIgCILIEWQZY07q0RH+cOYYuMAyK50LXL94TWtG6qUUP/eDGb4+tj+8fcWJcLUii+lfIhS8pTWDdf97/nHwyiWTYWyXdoEv2Ad1aAMvX3I8PHfhJC8TvC1xxg9I71UqO5jk72ePgy+M6AP/OGe8Vl1eNrAb3DimH5S0KopEvNCrbSvr7+K1RB3DrDo+nylVFfjCG2yIxI4/O2m4l+3zM0N7wZjOdtnhXFcH/wywG5rBolpWPJP5u7+dNQ6+d9wguFiRsZ5/Pm2yyvzvwolwzfC6/un8fl0g18gKiswQLYe/m66ejzEB5gQYSMVeCwYa/fzkcGI55FiYyPKAIKqwpkxDStp6/aIJsmDJqmPq68Rq4AMQw2S9bTyvW9Fy0vB1dPnAbvDTE4cFmlthsHt7SYDyJwb3gCSJYozLNAAya7+XD+ze8P/hJcVw5eCe8LkRfawCwC9U9P+5IDhy0QebHMJVFnef358xGpLGwE/L6LufHd7bM7FDw6Q4SEnuzxm9wmWFr3JgjiQPxjb7LgrA8Tl99Iwx8H3LTJ/8uIRrsIldOwRmK8Q5hGjNdXNIY0LXz5QrrlGsm02C5CdqZpKuM/fKroz7ThoO14/Mzp7Y1I0jBrRvA58b0RtGM4Z8OvCCgFN7dvL2Oq7RMNcQBdhj9s/eCWQlVo1BrEAmeB4s759O6F7itZ/xXeyzmZvOY48K5qznJbhHF7z2txcHlbQsgquH9YKRhm3YVnyHfTB/P3CdfmXIOXC14zHOlTAL12/3nDAMvjom2PiMRSTK0gHXcSJzpLtPGArduP0kE3JpryD7uCljQR9vzNHTwARI1J88cvroSPeibcY4V8aKt08aDC9eNAkGtm9jvQ+B+7B/PHOMtwcZRBuBgSvuzQ3t0BaSRCWqTdrrgjcNUYHtQrRPFFZ0LDJLiCsbexImoub7cOHGqCivMUz/brvPaStARXMkkdlpsaXxs9sxzv09shXAZ72fMyhbVOY9AdvQxnOxVIL3LMz+vu38Loiwty17r1LfOCLkqxQh7Dm7GsxfZeZIYd9phB0j+a+zdZbEEGbbXnAdJxrvw16DrdGCDmEObW2wkXJrjpQLxhFR3KKk588EQRAEQRAEQRD5ChlHEARBEARBEARBEESOgIH9w0raQkcmuPqGUeEy7LkKdEnqPbTsRTz/86CA4HFd2sMEDVHBjQEZDUUZCzE4Qgd8mY+CDr5+ZXXbuqAgdGCWybfD3mPdr+O9GtShLXxtbH8YnBXkK67Lu44f2iDmwuzkvHjGtOj8tQ7vaBdsjAK3kZ3aRRIImIn8+BdyYhUT8QjeBzSkQUHHb08b5f0MxUUogrxtwsAISmvOk+dNyA6EYQXHAd/PzLqeSZ92reGqIT2hUyu5oCVlYBzxRUnGULb8t40f5GUWxQzLyCeH9ISwfM1QcMKLIa4Y1CjCRlKus4g6ahH3aJjGsFkVWXFXmBLUhIx2LMyxQECZePaSAd2MM9W6EFuiuDI0CQbLjah/lmXzEQxgR1HhKT3UWcN/fOIwaMfdG9/U6MYQz7gLXD3DLOwRTY6O86Jbxg/wMjWjEEtmtiEiSDieFFnZ2DNEtQFI+icToZCyLMx/7zp+SOD32WBWNP06vluJlhgsSvhrMssyl1KuB/q2ax1bBmRsF7g24PnU0HDziGoH5j2yGjANZH7oI6Pg1Usmw8RuHeDSAd3hgZNHwN/OHmd0DNm49PkAcxk0ieL7EzT+CpuNPddEa+za+NenjoSfnDjM6HtFXN/yo8lDPdFmkHAUn5lOnNEV9hH4DCVdRapHGPdn0CxjSAezuQp2zTeNHQCPnz3eSJQtEgRg271lfPC6TCR8+Ugv9dwjiTlEpjmS+ruyKXgYgxr2432KW8O5fbt4ezTn9Q0292sjmEfgfCQMYZu/0qAuSFQb8vg6oCgc175BYB/MZybG8RXH6TDmIDUOjCPYeVmmKWXKm4vi9elcowv451xmPixax/HmSNjusV/+xjh7g6RcECy5hBdu/vTE4d7+D2/WKkK0T4TzGX6tmA/mSLZzHNGc+biuHeAcy/73zx8dC788NbxRaBj4tYTqfzyqbabP1RvPhlm/6hp2+yUVvVexXVM2lCGsMQKfjZ3dSwv4rkyHjYfAsT9OcIwT9RG4Vk5StJxdx3LjZRNGWZpO8SYAulr6Wkn9/kBj/8JkLzdXxsnrhvexzHif+Z1zDYytoxxLwsD3US62ZnB9GsZUy2d4if67RhemzCLmVuwP9X2+PjP2cQPadnTWbfJ5gQwsqqiO7Z6jRnYfrXY6h2DrzLZkOE6a7l00njNlNcZ4JqGC+g2TqMGZkbLsHXGIY5/Ws5OTMU53/YhjnGg+FtZYw8XoFEVMhCrBgwq+T1K97ycIgiAIgiAIgmiO5ObuHkEQBEEQBEEQBEE0QwZ2aAP/PHcCTL10MvzhjNHw97PHeT+LnZT7rOhZxwv5UtnmvbjOy/ShBkE9toEwfDFEgkzVS3eTJDRxhnHr3lJV0IluXGtvJvAEA7FMm5Or9uyfN2rNoOrwXVu31G4fonpCsfJrl06Gk3t2DFFC9XnCBDtnm4vwmWrV1FoY0WSci/uVSLTp82WJ8Qz7PHduXeRlFv37OeO9vv47xw2CsJzVxz7b+DMXTMy6pkzjiPCN29Xz0T9AlFjLZVLF4GwXhBUcYR2ydeAiEPCWEAIfts9g6+v4bh3UX0xnB7mpjFRyIYNUHKQ0sw+r5nMPnTbSe4550fK9J9SZpSRdRdGcn+nHTTIdplKe8O3TQ3t5wmejM3LnuXponQnVPfX1nBRZ80IDUS0Pfrx7m5aeKVTjMeQHuZYz4lKZIFw2MNNkKCgbsN93hhmjXKASa9i27QldOjgTg3x5dF8vyPrC/l21RAwsnxrS05snmYgSeKocZ2Nnh16buvHbIP59Zp/OxsYNMoFbj7atoDNnXMCXuz0nWvZFzLdPtJ+rhW0fZ0Qo/D+tVyc4nzOgM83GjoZz/zpvAvz3/OOU14rNDOfALJcP7BHJPoMpqj4SjRdevXQy/PUsMwOTDBxl80XDQpPruDTAECy5MY5dx7m49+GOcd9Jw2HaZSfATzVMjth5MzKgXWvoqOhXtHC8L8b2wb3aZu4T6BqgZpojhCqeN0bh2vfsgLkA9hFoZMDiYi1XVRveHImFLxHORfH6wgqjdOFFWb85baQngA4y98L67cAZR3RoWVffqYTXCEmPAao5xMhOxfDKJcfD8xdO8tZrNqLPpNdxqizmP6xvN6z5pikml8fvBbP/+3aACY9ojjG8Y7E3D0kSVTb2MPf+pnED4M3LT/AMl01gT4n9Un9u7aD6Hj8HuXncAPho73DruPmORcsme2mqPYbbJw729tRwPzwMQearrIkH30dcNbgHDAr5zs91/2JmsCjn3hOGee1vtKGBBC8ER1Nr9v2PaoxrzexDIHdOGuwZ04Qhl/bDWXBN9fQFE733xibwe5XfGDsAPtKzk9Z6Mypjg7DwhhYu5hT4zgnXfvefPNzI/Inf275kQHfo1641tNGoO9G70kfPHANJkz3GNf47uKajtY7AfQnsw3WMgPCesnuVPqr9IR22HKoM9X1VHYYxAPzlKSO9PWHcawzDX84aqyXiF5kj4brus/UmWLa47nXCmE+y3Dp+gLc3o2M8qepHvzCijzdWXRGw1y4z5vDfGSVpbBSFORK+/3349NFwp6H5Uyvupuq8wyAIgiAIgiAIgmhO5ObuHkEQBEEQBEEQBEE0YzAwYFK3EhgZMsOp9flFP0so2FWW+cOmODoC16CQFlHs+o9PMAs05OtSVLcpRxmlTMSYYW+xLDgrZSCI0Q3r//jgHl7myZ5tW2qJPILKpAosU/3OD46IOtOi6vCYyT5sYKPOd8wyfWYe7ybDYOcgMppQQNmvY4KU7pg0WPiZlEHdXKQQdprWPZ952YcN3NcJ4g/T/kRZBdnDBQZja5xDVS8ouHJB3+JW0L996yzBkQtqQgqO8PplNaAT1OqJiluk4Btj+8OT503wglfDZJhHc4IfTBoMp/fqlBGIiuPFMIX4uFYgpvGDs3M1o3ocpA3GuDsmivugU3p28toJL1rWCVL+dIi2kGQGKbOA40YKFZkOf36y2XxgTOe6efZFltms4hEcBdQO83vUhb1w0SR4+oLjMp7VlMH4wc8xTTWb7Fz7SE1tZO3HhKzzMxdlGux+as+OcGL3ErhpXP8Q5h6ZX7hhVD946/ITPYFNULZwPrP4tx2YT1UdCznGcfMGV4IjW1RnHNC+tTLYvT0nqlWZhekSJiC9a+siL1jaVS1O7NreW1uHMS0rUghQf3yiPHAd20XnVi2F/UXSotqg86NZkWlTZgP7VTsQHxvUXXudbypuHJ3QXpKJ4Mj23rP15KL9qOZuLGM7Z66H/+/00aHPHbb4qky1Z/Tu7M33UXTJm14gtRrjlKt9jm9PUPc72AfzZpMX9Kubn4UpQbVjcyQZJmXEeYQrgSQKa9D8BIUxeK9VJiHt6o0ifL7j35MQ9zjfAs1EAlQcA3BenTbcb27M9pzsIKd6hlH0/thZY+E/5x1nfXyTPpjfe2Lr9FP1hn665IqIWWUAaHvn/aWKN/8wmEPw4B7NY2eNg0fPCBYf42dTXD917fDecOsEe8NSF32wMht7QAXLWggeo0PLQrhmeG8YV78foA130h9MGgJ/PWtsoPgYq4HfS7tl/MDQczUXQ5ysHsPMr/q0aw3PXTjRa38m8P0oGj/876JJ3l5o0ByCF+eP7tzO2x8+LYRJthvjiGjGgJ5tWxm/N+bfsXZsVQi/Om0k3K0hPhb1ud+XvGOJE94Mw8RsXwYaqKHp5Ef7dJG+l0b49w98WdC4Cc0Vp1w6OfCc/J76hf26wsSuHeBGiUF5ThgAWtY1dlv/OGc8XDmoB3xyiNl+NjtS4X1CcwScBwd+L5Xdv2AZ7g1pHuR6jsaa96Us+5Z0fR+MewcfH1Rn1GkLmrhi/zA4wOQI1xm8eQ8aM5qalPK4eNckO0SYNW5JqyJ46COjjGMS+H60dWEBXD6oh7fPbGrMcev4gYHGokG46C+j2ss7oXsJXG5o/FDE1dHVQ3t75iVo0EEQBEEQBEEQBEHk3/s8giAIgiAIgiAIgiCiMI5IqFYHtm8DE7q0Fwj9zblt/MDQ15Hmyvb4WeNgYlCWdkGWkzBGHSrRZNZxcjDTnypoDrNw6YB18OePjoX/XTgJBrQ3z9DlKm7PP0zUujilkUhWkFw0hQmTaU4kVAkHIxbSyHL6xzPHwK9OHSm9BlWV8b+60jCojQ/8MkXnbrq+52zwY9QZ1vggsC+P6mslhHzs7HFeWTHoyjU1IaOx+axjbDenE2DliYqvOBGuG9HHa88YvGoyDoi4YlAPePDUkTCsJNOYQHWl2D/zgbAuRLX5hsiMxSeoJeHzgMHX+EyjSY3fnlXPoYmZVC4FAmb4/yg+x2co49sgi2nQ5NGQgnlZVitT+G8bCY4y5k0pL8Cdfy5NzJEKQvYtbJbQymPHIBcwuf4gfnPaKE8ojEIul2OwjmgZg7E7tQ6Z2T6C51tljpSEIYDqlKo+GId63jiiUShifyFhquALI/tCx1ZFzlZoN40d4M0nTAQax3PrXF5UyxK0jGvLiQn84Pekrad0nmGddfKkrh3gmmG94PdnjM54plWH54WG/JyRRZQltSmIavnrZ/dCvh4gBBSN0miyhfPYhuPHKI64ZXyjgBZF+jjmQg63X+yfcb6P4k1cQ/B0ZAwKcT6ZEpiUmO4RyT7dtU2mcYxojGvL7RkU1/8/zDBbHdIAUPv8Gh9Cg7Orh/aCe0Jkh1XvpYHyd/wYd0IPewMLnT4rbnzjkSAxvQp+ncH+N2iM4/vcr4+t6y+SriJVH4FrzzGd23vZ603IGOMM+ghdkx4dvjbGrTmtLfz+fqao1oHgMeT8BU1D8Z1FkPCeP5d/q0pahlt7YN8eBtUVBrU9nfoPu9+JbRpNpe6ePDRQ9Mn3Efj/MPvsyFEHY5xu2zb+Ps4rDOtXNncNEo9iM+P3QPz/Xzm4Z6Ki2qiNxsOIav32r1rf+fCi5U8N6QlXDOyuZUwTJbwhi2tTY9XxWKNyUVmwXeL9x3csps9697Z18+brRmSeI274flZ3HzcINEm+fdJgGGtq3iMhyLAer4Pdqx/Uvo1XBjRGCENY756s5sUcMNg4Ij7jmm/Uz2lVe2l8/43jI57/3L5dEjO5zW7DjDFHAl2zrK9Fs8dAYw6ufk/qUeL1T7/9yKi8NEfS4QzONJGvXzTv+ea4AfDVMcka8BAEQRAEQRAEQeQKuRE1QBAEQRAEQRAEQRBETuP6FbCuQQC+AMeM6K9ccjyc3adzqEAgzMaHQfOXDZRnluazffOwpR7UoY2XwcgUPrBDdCkmRgEq/E+ewwRpSINpInrPH2X4gB+c4ErQYCrC9c8fJqCSFVHx2Yp8+rWTZ0huwWR5KmlZCGf0lmeaDBOzK/vqVYOzs9dkGb04DiJhH4HPagTRYZayj/TqJA1mUbUf/nGzCfoMGSsdiAtd2G9PG+WJn0d1KoaTGfF1UHeD/WqQaFv1fPC/uXpYb+OnCe+vH9DOZ5nz+YSF4Qcb5B2GujaTCmUkEtYoQhuV4EgQhOWPR2GecBfPh6seBucH/du1hgdOGWF9bpWxgU5Y6ccG9YB3rjgR7jmBFe2ZtQ02oHBkx0xzkLiD3X94/BDhMdhxQTVG8NWpmgOpStm3uE7c2YsReaKQxgV3TKq7Rlv4unEuOApRv6anzzSOcCNkOVMxr7HL4teIi+mJKlAWs7y7MpPCYOxRzPN8EpOxPEw7wWzCYVAJanJIJxM43mAwdnsuG7tfr2EuoybEIOe6+vByVOZGYcc4HXMkFl9c4HqdEEWwu04JexW38rJHH9+tRHudwfdPWf0V035SCQi61XOa8KJaFFH97KTh8EvNeddT5x8Hz144scFQwLT9eOYIIdobGkW8cdkJ8MrFx3uGDLmAzi2SXTPOFx88ZYS3nse6xT9PnDMexndpr3X8oL0zE7CpR2GGFjbbvTYaff3ZfbrArRMGQpfWahMNFThPO6dPF++p+taEgZlFUPTCtZDOMo5oVT8emNY6u5/63ePCZxt3ddvvO2kY/O3scXD7RP0yoQGMUoDK/DvoDvOCLt8YKOmpkEtBFxpqnd+vC3RiTI9Uh7+BMwV1KaDOFXOkrP19yb9FyNoUu0Y1Mbmt+5n4C7jPZjJWuNrDDGsc0UI1LwqoYNn4hfs9QZ8xba8o4AyqB1GbxToPyjQetahWbnIbf+/F98FsdvcxivdvnjGHxKAuDC7m0q5r0cQslTcr4PdzWzD1juZ3Kvj2i+aC2H5NzfRd42KtwqPb9vnPNZpOZnNX/b6oLr7xiena3TX8JZnsVcq6/wv7d208huL7aKbMw64Bi5l57dUB7TcqE6+wY2WYvcoWGub9pq1Hds6gcQr7YJmxeJiqd2IAKLlfcZj6tNAc4/C5upIxxtQx5mhTX9+qfiewfA7qIElzJL5O+PHAnxNjX/VTxhiTIAiCIAiCIAiiuZIbb1QIgiAIgiAIgiAIgsgZUITrB4KiK3/S0a74EhuDq9mYb9t34r2LW0O/do1CZ+QRL3NwgSfAmtg1IOAphHij4Xu8eFJwJJXARGUMIDsXXjdmAcJApc8MFQfTiM54yYBuBucK/gwbvOISzCBhgiq4SRbEIT5O5t82YPZqFEl0aV0kzQB8LyMgFj0fmOXpj2eOgSfPm5CdaZwN/o3A7EUniE31vLIiXl3Yw103vA/cOLoffO+4QcbH0TuX2c29edyALBGlab2nEwjSOblnR5hy6WR47KxxmYGAAd9Ds5KHThsJXxjRB34kybKnKh7fNvBZvozJJvcdw/vKipZZ8Nm6dlgvGFbS1sv2Gkb0yQoldMD2wNYBe7S4g7FPZITGIpSCozRmqEta+hIt1wzrDU9fMBHODJH5EQVd35kw0DMC+hqX1Ug3qFQlWhKdj+ejfTrDhf26ekYw9500PMN4yxQUD5r2gyxo2vOn+vEJ66WFIAA4ZdC/KUWFkl9hXeBYi/zy1BHQrU1LL9Ady+YCPmO8Ke04sTp79wMfuZCCZ1H9skYCWHc6+CJTNnDzSE1wkK/MMIvl1vGZwkhT+Eckow92sMBRzXFcdvEoOEKzptvGD4Sz+nSG7zHCSByLTcA5p88JAeOCVjZZ1hwpY62USjyTKks6INidF9W6IEwbSzluRy4Oo8pIazp/9cc603KNYTKD6mTQdiO81/iM4c9FgjQc0wd3aNyr6JthHmhWU4erj4EtD5w8As7r28VbF7gXHGWuU9HgUpZlcwhTF5O7dYAB7dtkZT9X1coXR/bJMttihS84FzAFBb1d29gL/20NVXX7mLShEd7pvevGkz7tWntjzHDOcEy1znRpbIdzszCGCjImMCYYLtZOstslm/H4+2m8QUFYg4Spl06GT3P7eqqWhOs4mTmSKcNLiuFf546Hf547HoZ0aAth6FBUoHyGcY+BNYDjYX+O/SVmjjZZX/Of9M00Gn5vcCx+/WZrjoT7FS7HuDDL9x+fULe/g4d45oLj4I3LT4SfnJi5l6I6fAdufpol6NKsm6FMnbgy90Qw23hY+PaWKao1Oxa2Gdwf/QRjzqs2uc3+HZrTsHuTNgbUiCvLnWPpCNdZQd/l/t+3uLW3l3c3s2+pGuNEvzmBMQfLuE8BNxvrIQqzk6NOsrE3knbQd0zs2t7JOoM1+JAZMmaKljnjCM5IwgYX+7Wu1nF3Thrs7WX/9KRh1udGgfFxzP1h223QHijffnPFvMe16RiuPXTNZbLeZ7RokWH8jqaCtqa1uVK/2WNc479Nax73g9EwcGxnpo9Q9sGCMW78QG8ugX33D+pN9HXeTUa1KxV2rOTLlWEcEVBq/t5gYgL8CWuEbdqHsWPclxjzr6AxDvcqZWY9YR7RqggNAE3rZlC9cT47tzIF33kOrV878cY+QXuV/Pv+Ng7GOBev+FyZsvhrBllshAh+Dqfaq8R9JIIgCIIgCIIgiOaO+ygYgiAIgiAIgiAIgiCaNBjw+vezx8Ha/YfhxPpMNq6FPzZByqygNUx5+PjSyd1L4LVLJ2sFvuNnX9240/v3aEa0YgJ/FlFVqKqnb3EbT6i94UAl/GbxeuW52MNgFiCTTEA/mjwEPtq7i/bnde7IODY4SIBtOEg7Q6GXqqwYSHfYUKgRpj2iUctLFx8PbYtaSLOzoIgDs39e9+bi7DLUZ07F4/h0bV0EOyurvX93CSlotQky4dsvXz+YKWzZ7oPQs21L+PLovvDjuWus+w8MpvtifTDTAwvXZX9W53gOA2BQcItZW898YbZ1lj1Z4K5uoCQGd/11xWZw8Rxhu/pX6Vbl907p2cn7I0P1fGQb6QAM6tDWM/SpqKzyRGtBsHFkrLjnc4z4Gfv3m+vFx8t2HwATeGHzPScMhdc37fTq/YX1OwK//9nhfeC5tdsa/s82B9MA/rBcamAGJBqDVUFYrkAB+uJd+vfoh8cPgUpHmRZt7gbf32HA6ieH9oKrhvTMzpAJ8WTYw7Hp7hOHOgl4v//kEaGyfOG5J9SPTxiod9GAbll9jYkgRPXMyPqae5nMUsNKiuGliyZpz0NRXLLnaN14KgIF+2EeYxSEZYlgM8x77MVvjceTf4dvG1i/nxjSE2pq0142Px1zDRxz/ODONhnGETqiZfFTgWPA31eVwxWDuhub9fBM5MYxtg92oTNQC46yfzeJma+x2RaD8Iv9mWG9vD8smF37qtcWaB/rxycMgzc274LRndtlGeqZ8rkRfeD9rXuEv4t5iGsYs1Es/tqmnfBdA/MpnE+amMfpgiLsLYePWn3XtbeUTcZSvg3jmIPzoEeWboSreTNCxXz3tHqx/udG9IZ/rNoCA9q1bjA8MW0naHyCWeU3Haz0TFQeXroRbEFxm84YGeZWqAVH2eY9KPb47eIN3nwI58QNZTAsxIEQxhFn9uns/UEu7t8NpmyssD4Wf40m13F+v67Qo20r2Hb4qNSgzqh+W6Tgy6P7QcWRKu955/vSuMAx7q8rN8NlA7rDrsqqUG2Yzbyreg5F61GduZDq+RDV/YhOxdqGdT43jOrrzRFZkXlQ5msVaH772IrNngnLPScMg2mbd1mbGqDx6n3zGvcqZL2cTNeEojY0RWQNb8KC962jYG6k2nLA3+mYdemef2hJ3X2uCZkJ+A9njoHNByulv//qmH6e+UZVbS1c+0b2XtivTxsFDy1e782tws5nEJVJosrkBe9vVuZxSzUWPgf/PncCrD9wBM7s3SlU/xB2HYh7QTje4hxJVr+qfiTLoK5FyhPt3zu3DDq2LMwSzYm4zttfyxaRHayuAVvQ0PC9rXs8M8N/rt4C/y1r3KuR8Vb3XrCsQ2bG7e2t28B5ClGtqXnYlEuO94SX7LsJk3UcgnMHNI3GU48zNM6JYtquMibVQbWfYlrepy84zhNfsvNhtTFHdqePQvDfnDYS1uw7DFcK2qUMHINle2lh6t3FPpisjZm+t8P564KK/RkmlKZgH4F9zvQtu+FyxlAYUbUk7J6zjCMsDepcPxOunqt2LQvh7D767+hEfTC2fRTuT924E05hjNSQoCc1u36jMYY3xZUZM85Zbx4/wDN+yNyPMxAtF7SAX54yAn40uxSGlLSFU+rfqduQM8YR/P8NjMZ5cJ+cR3UM0a3FPhjfoR6rTRsZ+aUi2ps3fd8XOMaxJqwps2t64aLj4VD1sQzDEtPnAw1O0BgOx7izDPobXAe5MOvhqXLxrkdq5G52GBybNhyshOECMzPpqbl5BM4Dfnf6KJhbsb9hf6yxbPK2hL/h69f/f5iYBBf9p6s+GA050ODTxEiSN2HFtdfIjsWwcu+hnOpHCYIgCIIgCIIgcgUyjiAIgiAIgiAIgiAIIgsM0Mc/PgnofrJwkFAsdLZEzPK7q7LaC7q2FRrwgR2iYNKg+q4TaoMXJO2qWvhzXjKge6jvN/zcIIAgzLW8+7GT4NFlG6G4sAD+zIjm8Z7xBhuqEmFQwehOxbB8zyFPsLBop1zA7F+a6hJRGHLPnFKY1K3EE7GJ6MxkfpYhy9wiikf/v9NHw++XboSTenR0mhFVfP7g9ssH46AQGYX3KEhdaijijwI+s2rYAJjWhQVe9rln1m73BCfd28gzo7mAD5T5/Ig+0Ke4FQxq3xauf3tJqGNjdlQ83vbDR2GqpP0GoapCWetBkx4bsL3jM1e67xBcPrAxA2CYe/qFkX29wE+f7m1aNojXgowj0Lzik0N6wP8Y4wiWHm1awtjO7WDp7oPQuVUR7FYI1FWM6lQMK/bUBUaFISgY21VAmErwhsFzZ/XuDKX7DgcKFdF45tKB3eGZNcFiDx1cmGT5Rg6iuko7esaDMuypbhOOJazAADO4lu0T2yWN69zOG59QYGlLSsOgxiQY20VGQZN5yQndO8Drm7IFh/efPBze2bLbE83vrjdqsuG6+kzKLOztxvpX9gsak2NVu87Kdp9KeYICUblkXNiva4O5w/Uj+8KsHfu8f9+kkSVZVnwcA/xxIEwm1etH9lGaXwT1aTprD6VxhOBXOB9EUSsKJb82tl/G71DI8YNZq8XHUpQBs2ybgOPYHUyGRltQ0IPioPe37ZEEYyezgrxxTH/vj67oE+f+fkbFc/t28Uw1Pj20UQymugzWrM3n2mG94D9l27w5OmbFxL7CjlTosQmzhd8zpwzGdmmfkcU8zJr5ov7dvD88sscFRTPfnzTE+/dXRveDyd1KvIDuxvZhdn34Ncwqj38QFBW/ssHO2ADXJHqnTwWOqXJRtkKUKKhfFOc+eOpIg6PUmWkgt08cBPcvWOd91sSYRgUa+IQxjvANQnT6TB5s+/eeoM6sbJKNHQWJeJ9+etJwo3kdmpS55PhuHWBy9zENghhbYThmlsV12r8DTP4Q24StKu17C4lZ05dH9YU1+w/DbUzme5WpHZp5IDiX+MMZo2HJ7oPw8UFMJneL+v3kkMmeIaiNYa3PX84al7EnaiMcw2ca6yRpUCwtG5NNa6iFxT6qiK+P7e8ZUKiMI/D+oYkqmhqLQMOR35w2ClzRTSFWkj1CuJYX9VM2Zk3+/UDBKf7xjST2V9mZJGD/EGYu5o+3ys+AmTkSmjWgqUuvtq20BF3fYDI7417voXpjujAzzMEd2np/kCsG9tAyjni2rzjD9CdbcYaIbMkMC2m6Ts7Odl/3AxPD6IxzseJKRy8afjh5KHx/pnhtowN/jRnFMmzb2B75Nqka4+rGj+yKOLVnJ++PCVifsvYeZp5/QvcS6zmw6PyscNVUVHtm787en7Cwc3wTeDNOf3wKsxx1YfTryojf5ihZxhEFKU+UKzT3CHjmeYPFXBHkhhnjUAC+un4vdGD7Np7JbJh1Bq4Z0PTviXMnQNKGBK4QmX3LfsejcwVK4wjJz22NZdk2688lkjeOAGtzJP67OF9kTfhEn9EBnwPRs6ACz+uqTlm6tm4J20O8j0DYKsjYqzTsVfFd7wjF+2MdsM/APviCftn7NKqm1KpFi6w+F38WFtW7Ju1jONzzNTGNQPji49oLTaz+t247nNazU8YYnhs9KkEQBEEQBEEQRLLkxm4eQRAEQRAEQRAEQRA5jYt3wL4AB4NFB7U3zwYXJiuE/EhmlLQq8kT5958ywjoQWKduXQmtTOopTFC9q2xjqix+QWAAxS3jB8IN9cIDZSBowKU+fPoYL0sbG6Ssql/MsN2zbV1wwxVcViwUPmI2nLACPZMsYBgEjYIjPwM4Ky7xMc16KbszWplKuQrv1qYuUBED/XOBs/t0lmY7tO1rUBT6kxOHwd/PGW8cbJrWzHRz2cBu8OiZY7KCdPBZQOMXzCQeFry/KOq498Rh1kGwJmKCoOb0fcFzxPcr+MxdPax3dgZeA3yxEpo5YPsAy+Cyzw7v7QUsy54T/PkfzxwDz14wMUOkasqvBAJDm7al6oL5dozZwn1UNdJNYIyDIhZstyi4/Don7kVh2zXDe8PdJwwNDEDrW1xXhjCj1wQmEyf2TaZkG+UoSmM5xqnmGzpBeuwn2nDC1g4CgUrD9+qPHWbOpTW1UGazzvydyognilmM7H5+tE8XL4s1jrd8/WC/4eqc954w1AtA18kSLZ8nKM7F/b+NRZY4tlWjUAkDNVGsfoYD4UjYQNSvjukvyC7ICGICvs8ae3XihGE29cuODTgv5AUu5/TtAk+ff1zW59HgJWyQsm6ZTeobzRZwzpEKMNLJFWQ98IsXTWoIUsa5G94DNJ7TYWzn7GzO2C+8dPEkePHi4zMyPibBBf27wZRLJ3tzDRdrPVUfIavfhz4yqmF8xfEMje1wTe2jaiaYeTBo7sn+D0Wlugzt0NYrh9Y4KvnIfScN976PfbRvPqL7XSRbwKgwopH8/Px+XTzTNARN07D//cOZY6CnpuA9CL5MvkmFDjjf44X3rgR0Ou2H/52N+dSfPzrWe45cwj6LvBDPBMws25Gbc6QVxgE2qOa1fJ/i74PgfszPTx6hZZ6YFpgrYbthhU9KAyzBr/BH3tgUss9rsLbROEyYfaw49tr8/unK+jUuGqSCxvWJDGj4em3PrLlxX0wXk7sTx2yiY8tC79oeOHmEt85F4ycWWe3+9axxDQZefZkxv53lXgTf3i+27H9QWIn7OCphvGlZhJ8x6IM7t27p1fHITu0y5gG6PHjqiIZ9zY8x5jJJrTM+Nqi7JxJmYQ+XMphTyT5rUrow44lXnoLGNruvyt4YcTyzv/IRQ4MFnuJC+V5FCw2RJ2v0JjyGah8C3NG9bcuMvtIV5/XVzwgvM76JQwzqAtUwe8v4AdC1Tbg9GBFjOrUPZZZwx8TBoQYwNNppKIvF/j5/CzHbvc0cAt8t8c+KrXEEmgZlHBfCEWaM+9aEQd77aTRXvHFM5rvEBgzGOFX9yujdthX88pS6sY0FkxbY4r/vO6dPl7o2GALeeJJtB0FVrzMzVvbBjvugjsx+3l5L026er2mY5apozxk9gIlxhMbx4zByndytA5zas6NnTisizNr/ogHdhAZZuuAeasb5mYFEp2789+rHdc3e84sL7OO+O3FQRt+ZsR4zrN6Te5RkvLsO26+7amE2c1h+Jxr3n3HvCd8DjGPmormyVicIgiAIgiAIgkga+S4EQRAEQRAEQRAEQRCEwwB/DFod07k9jOpYbPUymH2/G6Y0ufaa2MLXwODY+kcKfU4HwShR3BtR9oygekHBuU6WtgbRcirliUoW7zogDIzF34etHVnQpkmQHArY//LRsfDh9r1ekLMJwyWCRa32m1vxpsL788PJQ+CdF7MzQtveOQxSPl+QQcYVmIHtruOHNmSqbarwtRtU2yhIGl5SDNe/vSTSjgPFNGf16QyDOrTJEu2HNeThA3LxuUSxSZjLQMErZkhbf+BIiKPIqxKDxz43oo/37yfOGe9lT8cMwToc370Epm7cmfGz4R3rRLVYt3xGrFqmFEFV7UIEi6LPp8q2eoYjYcxGEFmgpAsxx6hOxbBizyGtMY7/EV9N+By9tH6Hlwn67S3Z/V728ezrWee7LQy+f0ZvufgkiphYHaMYfpxAgfZ/y7Z6mYIx87v5ORvBTMwvX3y8Z9xyzotzsj576cDuDZnGbxo7ILR5z50WWdX5+EuTTKPpiMUAELIP6diyCG6f2BM+2LbXW8cIj6GoYZs+CjNrY8BrdX16eAzCRjGBS4M3VbvGflA3s3Uq4J5i5mQcK6Zt2uldV2l9Rk8WvLZ/lW6BzwxtFLHactXgHlbtjRVe4zOBZWVR1bzIHMzPJMh+32aMd3XHbbNy8rRqkZ2pmcU2FjtluP7hf5SRIbu+Tb28YQf8P3t3AR/Fmf9x/BclQhSChpDgGty9QLHK1duruxt3ba9G7ep+de/V3YX6v0KFtleBAoFS3Io7CQn5v56B0GUzMzu6Oxs+79crdzSzGdvZmd/OPM/32Vq5Q6b+uT7igq1cp4xe0SmvvlbPqDpJb6Rwbf4m8w1fdr5BQI22DgbreV3fdnscj3ohByr4Zm157c/1AcWN5K35f5qsYe334IGhneXZ2Uu1jhi3/Gx+jdM7b9o5p1s57SXYmNYhz34AT7T7GNzUv53869vZWhjJlggjt4bX0Eq1xyPimgZHhP33qWEBntGgjs/wUAyzdVYdzrdUWvvubKf+rQrATUajt1jVpTX3ci7uUSIndSy0HJan1wnS7DPcv0mu/G/lBllvoY7YHczhoNb2Q9quTvzDmudrP7VYeI/vHNRRHp25WAY0yTW8JoQ6p0uR3Dt9ofk1TpxRAY075+fme5yF19gYjf3gCHVaJCpY5vWxPbTzY3hojlGA5coInW/dZC1c2rO16TZHOoeosKfX/lgua8u3y38G7wyACmf2/oVPCQ8OtqJF/TS5fNd2FGeny29rN2n/dnNKUyFsny9dI30b5dQKkevRMEt+WrXR0nxUEM1pnVpoI0frPheKcHyqZd82oL18vmyNHNN2570se+FI4onDWzfR7t9us3jtieTynq202k0F1fVvnFtrn221eH9Y7R/1Xt396wJH+zcoXhjdrVaAixsqaEkdv5U7quUf3Yvl+5Xrd383VnXXyq0Vluaj7rkd1KqxfLdinatggxb106VDbqalMK5w4W+hWadgo8/8mZ1byKE65xanwSKq8/61fdtK2brNMqFlgRay9MSsJY7mdUbnFq7uVaowvOdHd9P+bRj2bPL34fvAyf3e18f13P3vAY1zted1SnMX4ZN3DuogP6zcoJ0flm3Z5ng+1/drqxMAGPLvCLu+Z8Ns7dmB0xCsBJ3QRydUqKKyT/MG8vb8ldq/j3Bx70kNNPDKH8u1APp9ChvItT/MdTQf9Xk8t0vLWs9LrO5gdcyqYJD3Fq7UPqfRvpeq9CrIlvuGdtb+3WLXM3IlNCjJ7iqc3qmFPFW2RPuuos6jt4Xcb1D1X4XFe5Xq83lDv3by+KzFuuc5K/tGhcipABYn9xDc3Qn8y+tje+7+HNacI0IDuuzq3jBbDmnVRLvPMLpFQ7lv2oLdtbL67lBu4xnzVb3buKoVbu7fTi75drY0Tk+tFdpvRfj53yw4NQBf1QEAAAAg5giOAAAAAAAAEXnRYCwtKUnGuOhIvWdn2zhpwWaBn43xotnQz8qiIvVXqBkVp0Z2SpLWqdcNvUZkCR7sJ9VZRo2GWUM14htV6N9I6EaN5e02klMjboSPumFFv0Y5cmy7ZrJg41b5YtlaW3/r5UhtftHr+BeNRlY1hjTNky937Vc1otOPKzdY71QbwxG9LXXoMnmN3Y4RqmFcp/z62kjgd/46X3cEIq+OBzUittvPnN5bY3QedDoScDSO1TfG9tzd8VR1PAoPkrE7er06n4V2qg21q6/0X/M12S1ebLJqjHi2zojmTkQa3XtsUYE8PGORrHIwetxlPVvLsZ/8qp1Pd0QYPbv2aOyh/10tl/VqLRd0K9YaXZoFR9Qcv25OMVb+1Pz4cd55xguWtj3sNarT+UU9WslCh0Eu4dto1jFKdUJRIyOvLt8u+xcXGL7GSPj5TAVV2GU2OqUXvAxMCNfJQgPgg1s10X6cHCPhk27sb62mVZ1+tu+o2h1SpgKGvGR2HVMjBn66JHKgTOj5fY+5hR0OV/ZuI//q2Uqen7NM5qzfs3OkMrRZvvbjBdWByozqwPbbmp0d4mr0KfhrxD0j5uEr1mqb6pBG9Bu3m3cGD5+P2UdAdWCyGvThROiya0ZU934ZCTYDAM06rVbvPqYembHINDgiwcZ53ugl6m9z66U63r7ksEnjfahplfO6Fss1P/xe6/dWzq7h+6dlVrpWR6zYUh4xOCIq53TTTp97Tru4u/mou6pzieqcsiZkFFor1zjVqeXNCAEcVqlOSM+MKpX8eiky/t0fxSuh9bVX3y/CryU1HdWjGcyht3pmn+nj2xfKA7/VvhbpsVP/+l0LKaoDtfk6GHcQCv1MhIdGpCcl2bzGhX/P2HNej4/oKnM3bJE7f5kvy7aUm65zrRkYvcSnUjDB4/dYfQdQHWLd3IOKdK9ShWS9+seKiOEyNbP2/X6WyfyTPRqhPlSzkI6Jkahj/6PFq01fE36v4o6BHeTGn/7Q7n++s2BnB1M77OxutT9eHdtDNlVUSUPDkbqtn4NP6KAfjmDm4u4lu0OTVWfNjxev1joNnufi/oi6fhl1yr26T1u56X9/aPdNQgMh9Lw9vpfOaOz2ngsNaZav/Rgx+3yE31P5R7dicXJN/0f3Eu3f6j5mw7QU7V7Qce2a/bUcm5/RFlnp8uiIrrrTMlKSLAdH/HefUtPPk5VggCdGdJUXfl/mW/1q5RwcGhqhRk9/dMYiObFj4e7f2T0Fqu9qz4ws1Q0BL83Pkt/WbpTlWyqiEtynvuepIAunwo/hIpPvckb12AkdCnWDDawGUNVeJ9E6Pbvp+FzzPp3YoVAL+HBKHeKRvpfYCUdyG4SpAs3/+fUs7X1385nKqZciIwsbuAr4UEYVNjTd5khzVkG6Kvj3jw1b93h+Gsps9cL3r5PnwyqQb0TznftiYJM8ObNzkSzfUm64Pla0zc0wXBf1rFiFoljx3oRepiFjEZ+PiWjhNOrH8DU23v+xRfbbbIR+PlQdo76rfLpktaPrZQ31zPq9/XprNVL48asCrn62GD712IgupvevrOwbVadbGVjCC0ZnstDwlqv6tJGvl6+tFRpl97oQev81NPDGTp1+UElj7Tz12xpr74ee4c0baIFwqm4MD+23IvxrVHjQDQAAAAAg/tptAwAAAACAGAtCTENoWyA3nRijPWpkJHqNkKx2TK4O0PvmxbL6Ns7VGsWpgBHVgGby/n2033l988uLhuf/3aerpCXbGJ1eZ5m5OqOEGjE65t2MrmSHalBzTteWcuvADpGPwfDOBKYdfYNwdlGN2hOj3mE1fJR3FcyhGrU1tjmSWNBv8Jq9x+HnP6v7+9DWjeXKXq3l7sEdI3YU9XydbRwSNZ/PPbs0+nNtsnIuqLbQUDWcGgEuP8280XNoyEb4SJd6wQZm+zB0RORgnB3M2TmHqUZ4r4zpYbnzeCgV1vHqmB7y2ti/RqSzHI4U8u+a3as6G0Wyu1Oti3fCykd6QstGhtOidY0zGgHPzUj0Tlfd1mjsu0ZGViP4GjW07NMoR0406EjkRWey6ij/7e0DO2jb3chhZ4XQc61qcHtc+2Yyonm+dq5zwqwxfvikwU3yLM3z5gHttf9PTUxw1AksErNzsOqAocKs+jfOsRFsYH4gqWPTRZ8Oz5zQvrmMKmygjRo4sEmudMmvr3vdCzdSp9OE2ftvVpepUcitqpmz2d59dHgXObptU7m0p3mHeC84eQtP7eS8s5NZ+JQRJ8eZlWuck1Forbwm/BrjdUiMF+d6o213en32Ou8u0cY0o465NVQnK3UPIpSVQ+q8Um8CyGr2d9ucTMOANadC62u762P9tebTW2en1/rdDgt7WIWVGC/T3r20Po2ytYCQK3pFPvfXnGtCzznVBvuxysU1bpzFjlpGQZc1Gjo8ZvqbXJcsXeNCXqM69qpOWsOa5UfsrFjz3rk9j0aL+r7rNPigSUaq9Wtc2K/CX3Fxj1by4X695bQI19cED75LWflb03NwlN648w1CDiyFsIZt46CmefLO+F5yocNOj6H7LNL2J+wK2I50bTL8+7D5q3nZVR3W4e7lfbvLs6NKXd2HN9tu9f3trsEdtVC5SGqHRuy5vl4cX6b3qsMmqfOam7ADdf54YGhnLVzmZIv1sV79ZnZuVYGDVukF1O25fyPvYBWGobbHTSdWq+FTVj7Px7RrJp8c2HePTuFm3xtSdA4itd1aYMSu7Q99iVrFJ/cplQeGdpI2ORnW9q8Egzr+3D7fUN+b1b2TOwd1kEwL9070OueHvx/hx9kpHQu1e8hNI3QArtkWV99xLH0HNJ4Wvmy3IYuq9n9in1K5e3AnR52ordSONaFAKsTGidDZWTme1L1eFeiiwjD0mM0hfJoKDbAr/N6Auremrj9G6+P2uLm2T1vt3GDlXppeaETo1wwvrnF2jiInYRrhgT7qmf6zo7pJz5BwVrPvL3rvQ8Ku5xV61zoVztIhN1MaW6ib9K5hsbwlGelesJWAOhVIpD5TofcJEiKEW4YLfztCj+firHTLQZB6108nVICVrfYVBu+xm5AcAAAAANhbBL1dMQAAAAAACIAgPHoNfYDupvFrAPot7UFvU7za31429Hf692qErRpWGhOq0UlUw0PVCMKLh/56I9W6PT4mtCxw3eDugtJibTRGq6LVedYPXrQlqTmOIjXODGd1txk1NoxWY3fV+EcFc9SMhGRHtMItfOFw1VXHuv2KG3nSQNnu+27ns1hz893Knzjt0LV7WR4cB13ys+TJfbpKp7zM3b+zsloqvEN1wjqjcwttBK9Qeo0EEz3q9Fkza7NNVw1jVefr3i5Gibql/85O3JEYdSYLlZ6cJG0chp2ohshNM/dsQG2lobRRZwBtmunfJbjvcGThQ64an4fWCnv8va06xvl6qpG7VOhJz7DjxM1I9E6Dkfw4p58R9rn0cllWzhGhgTJWwxOMDG6aJ2+O6ykvj+nu6O/3GKc2IUHO7tJSburf3vGooOozbXj81gpHsjZP1dD6hdHdtKAYrzsPR1oP1QD4toEdtA4LzhpjG8YjiVMtLXbCiLQE1SBZjQ55Q/92cuegjvLYiK6WRqUz6xij3+nT+H3PSU3RrrMq/CpSWImVw0V1qD6vtFg65dWXWAsdeVbtl/uHdtKCSKx4fnQ3LdTjll2hKTWS9b4nJxgfh6HX4Uj7z4tOtVbOoWYviVYNHzqSpN1PpdEaOv2O5PV32kid6e0Kf0+sXONUx59vD+4vk/frrQW5xJLR+jr9nmFWZ9rd9epa2zk/7FxlYbXO6lIkrbLTtWvBfUM6RVwHs9Wq3hUQsr9ORxo32+fme9wlPawF/0RanXO6FklePfsd4dT5+o5d4aAFYR0JdYMjLH6GI73MzuETjVNlpLdQ1XvPjeom/7L4foV6cGgXrSNs+OfJSjiS3nVCjSZu9Srn6hpn5TUm87e7bBXk5oSbzrpGq+hFOJIXh63ZergJd6wRftirurhNzl/3ghS79+XNg4O948W8TK9xHsw//N5WUVa6FpQdGvKhOtra7VRrRHWKblE/TRu9O5Ka4yd0fnsER0h0qQ7BZqxeZsOPVxVuYSc8NHy79wyP2nnvXn1XT4yzZzn1IhQ1Vnavui+h7p0MtHg/R3eR4d/jwiaf2qmFfHZgXzkuQif2BA++S7n9ChGt73GPj+ii+/s+IZ3zrX4e/t62qRYOpAJQnAj9PHhyjTOZS7Q+OiqY1qvjpllmPS04wcq9tIj3Kj3Yw3aux06WtsPCa9RzfiPZOvfDzPavClT578hSeWPcnoHapueIgAwmYhQi53bdEmzeA0ow2d/qvsrTI0vl6j5ttIAOa8sPxjUu3gaSAQAAAIBYIDgCAAAAAABEFoBnwHX1Aa/uaOFhvyptkLX73yU2Rlix1Rjbxmv1/15/DmqU6/1aFshBJY3l8DZ7jkQfDXZHY7cSGnG+g1FFw/fPUW2bSnMbDZyjFWDgBS8aH+mNbPXvvm3lHoeNr6I9mi7+okayUqPW6O7fgF5LTDv52ViW3kiqRst3e4nz6hzRMa/+Hp0FrK6X6oSlOoiGd7C1MpJqaCeRziGdXyN3h4m80Ue3ayafHthXa4Ds1LDm+VrYj8FK2KY6DKjRhVVHgLsG2WukWmvxlkaq1e9UG3HeFo6th4d11kYDPMCgA56VU6jqODHRYFRXOyMIqmPtsp6tpGdDZyEh/+xeoo0CGj7PaDcYj+Z50W5HINVZLrzTQVZqkqVOnxeWFmsdPkM7lzst7lWnJiej6mqLNPq93rpY3D039W+nP6HWZ9G6kuyMPfdVBKoTkhcN9KtdHqvVHoQChXPa8N5LKuxBvR9DmuZZ6FQb/ps9z8HqOqvCryKO5helGvTuQR0tvS7Sx1UFNKnjsKh+mra/ehXkWD7HqAAqFeoRPpqzCgqLJHR/77GKkTotezJSrbvPm14whh+GNM2Xwsx6tdbEysiWRjWA004CXl/j9m3RULfTiWfhSBZfp5alOhPGOtDPaH2rHJ6Dzb4L2z0GVP2rgjND7201tHCdU/Wy6rD/4r7dpUGtEZLtBRvYKTv0ZuPHNU4FEgywEIZYbaFD51vjemmdAu0a1DRP3h7fU14Z28P29zgj1mINLIYTuLijpDpRezVfFVAQXgdYoYL/3hrfU14f29N++JDtpdXM2+Ef2lx2v0bGHVdD7113Nem8XUMFUb60r/1guL6NciyPkGyV07mFnn/ON7p/4BEv3mMr9wZCA3vMQg6s7Du318jQ1bV6jnF8jbO5rlb2jR71veC4ds10w1P0zlFm92bSk5K0z5A6n7u9fxjreibcoCbOQoNVDfHYiC7ayOsvh51f9MLHwrfb6HtGpP0Tb88SGtm47xDuqt5ttOAqdc8ncjhSpF/s7Ozsxec7Eis1gNkr3HyHtKPzroDni7uXaPde7Qh/D9Rxq+73pye7v2Z6svkJ3n2Gan9HsPZdVwXTHtuumfYZsFKPm62W2+cyVtbXynpYEb6ukZaszqG1VFu7d6uCf/+7T1d5Pex7ht551Px7XLXlY0Pv/BPtpiV23qJUC/e99LQ1CXjQ2we1zgkh/1a7d+fzqoKI54ia2QTnUlcdMXC3xuhC+4M2AAAAAEBdQHAEAAAAAACIi9ED9hx1w818quNuf1/bp63WEVI10hhqo6GynYf3fu0VNbrvlb3byL96trLU+cdreksMb5gSOgJHvfAGxyEvVY2dJ/Vuo40WbJfbT1A8NTqsFRzhwbqrkVxHt2go+ToNwfwU871eBxJz1Pv/8PDOhtOCyKtRr2oa4lm5hrrpcLRzWbWXET7SoNXDqXHISEglBqEfVuXqjHYYvq4Xd28l+zTP1zo6DW3m/TVOjUrl9kjL0RntMZydt/DqPm3lg/16ywCLo/UZ0duu2iPVOlvHv4IjjPdet4bZ2mhfKrBBfx7W9rxR52K7174DSxrLA8M6y92DO8rgJnnyn8HWOkQbsdaZzfnf6v6djW32urFwJAe1aqwFsTw6vIu0y8nQwsEapUduvJ6RnCRHtm0qvRvlGI5qGjUGJ8JqP8Knar9S/KLq7Ov7tdWO/UjCP1fH7xrRUzW2L6qf7iocycgOF3tYdbi00ojfT6pT19vjesptYSNS6r314ee91iEdJ0PDQLzsEOPmXNC/Sa42qqD7dUiQa/u2lZfH9JB2FkcodHKOMjtvhdZSka4/uzstuxmN3cKfntvVOHDQ7jVOdczqVZAtGbsa8vdo+FcHfDOqY9zzo7trHZczk+2F7hjtR6e7zdY1zmKn+5fG9HD895HUujcQp3Y4/E6pjlHDOsfhDp7Uu7XWwbBZRj2tjrB63Kif8HpR73wQ/queBdl7dPy3qubbQ7eQz1lvg9Gdne7fGl59JVb3tfRGqLdC1XPhoVxJOjs4/LzVKO2v61pWSrL1a1yC/+EJym0D3IX0hUtx2FNUBXuoznMRj98Eb75neNKp1sJ7c2z75oajnjdJr6ddt9R9bBUOFYk6tlTHrpogSRVAavWeswqXUaOGq3NLDTcfS6f3X0P/qnvDbHlgaCftJ9JrDedn8iK7a3hc+2Y684g8lx4F2fLR/n1kykH9Jd3CNdHPe3x2Ou67D0eyv3//2a14j1HFu+Rbq9PO7tpSvjtkgJwd1vFevw5OMP2+paZbC16r+R6XEIj7z5GWPKG4kRzTrtke4T0XWAz1Vu/DZb1aax1i7QYAhneqteqv7xkSM3aWrc69J7RvLu0dfIcb37JAJu/XZ/c9BTtB7kZ3aiOtu5VtU6E3qYkJ0tKg9rMyD/NzsL039wgXYfrqXsQhrZvY/h6nF47i5jl/6DP1SMeKm3upVv8+/P3et0UD7T5ljf4W7yGd07WlvD2+lwwNC7HUY/Z0wfWz25BzTGcLgVtu2D0G1D2Nw1rveQx3sbiOTTLqSYe8+rX2j96VyjQ4QqyzEmwQep/OD9U2w6fU+SrUWAvhwOpe+1MjS+Wi7iXy6QF995im95UlvHYK/e/Q77ORjg/3T9ncs7MGKuRePcc4v2tLuaSH/vMrAAAAAKjrnD05BAAAAAAAe5XYPwr2cASPgPXDttJYWHWYUh0h7UrYu/qnWx+NPey/z+xcJB8tWi1V1dVyjUHHKffr4e7v3Y5aE03h2xreeT2e1HSWqd41kmE0BDRLQZfVVTUKW4nFplrZv16NVFszn7FFDeXe6Qt1RxH8a77uTsLhjZ5VB+A7B3WU4z751fa8jmnbTD5etFo2bq807Exq1VFtm8rrfyyXldu27/5d+N5VgTA39m9f+4+1bTLeL3Y7tLphPOK2m3m6/wTozcLsemEvOGLnjKz0jzLsOG9xE/U6prnZR6qBsNVGwuFCj7pohTZ1zMuUmWs3S7Q56Q6rOqJ0bZAlT4/qVqeucW7De/TUbpQrvlENhkcVNpS567fUeo93hL02/ON2SsdC6ZSXqTXmNupcoKfmpaqzyC+rN2r/ntCykS/fM4z2nWogfevP86IycpzuiIgWRgK+rm87OeOL6VotdGSbpvY7HNl4rVP1DTqhDG+eL9/9uV7795iiyA3Xo/M9LrzjeEJMRukMX7aRfo1z5NKereTG//1h+dpnRH3W1M+m7ZXy3Yr10sfGdxPVoTw86MfK59JoE6NxKre6jDyD75pOvz/f0r+9XDF1tpQ2yNqj85EVThapRmB9c96fMqG4YI/fT2hZIO8uWClecBNs8MLo7nLERz/X+r3TSI0W9dO1zlnq82MUHGYk/OV66xA+TxXC+uzspdKjYbbhsWK2rKPbNpNZazdLxY4dtUbT9io4wsp+sLoI1anr6bKlsrmySu4a5C40QbfTcth/X9O3jZz06TSpl5yk1RPWv3slROV8XZydrgUK/OPrWaavs/oOqmv5qMIG8tmSNYbBeVbpdpirFQDosFOtmxWzMQ8VrvPvfm1lzDs/1P77hL+uW3bcOrC9tn8H2QhYbF4/TftJtlkTqPrZyrF/yOIF0njb1j1+V/l/CZI8vL/p/HsahGpYZdqp1uYH44zORTKwcZ5MX7NRuzelgjl6NforWMeM00Aar4W+pyMLG8hdv86XTdurfFmW3f2rgncOa9NUxhYVyKTv52ifDRWMaketz7/eepn8vb17lbvmF5obEfaaWwe0lw8WrZJG6any3JxlEkvqOlkTxvbbmo2yeHO5jDS4t2pnnpFDWEM61Tp4oGj2fl3Zq7X2PVbVJ9+sWCdOqPvL6nzphTO7FGk/F309S75YtlYuLC12NT8rnZad1upWOr2r0NL3JvTWQuYGvf6tzjzcLcdubTKxW4mc2rGFLN1SLi/+vkzGWOgU7lboNTGUwwysPZzSqYX8tnaTbN9RLb+t2eRoHl5+nyzJztDuu1Tu2CFfLlurhaGoEHqvmR2zbu8fV4eFLajvpDPXbZIfV26QX3fdb/N0O2yc0rJSk+Wf3UvkjM4t5LJvZ2vbelJI3W95mTaf2Tu+xu2a056f4T1ncM+QTvLDyg1SUbVD/v3jXImlhumpWij3lsoqmb5mk/y+fotu4JceFeKiF+SiFyIV/h6E/rejYA6T11y8616tCpUtW+fseYcKhFtbXileUM8x1A8AAAAA7K2CcYcfAAAAAAAEWhA6edkZ9cCM6gDzyMzF2r9PsjjCoZ/0tsXq1qnGe39urTCet403zo9REjt4NNqsG1ZGoVSNcd4Y10PKq3ZoHRmCyG6nimhSo/qc/eUMw2NaNRA5oLiRfP/net+COfyituTFfbvLdyvWyb5RaNTnVnaKvdGnYi2ogSherVdNE60j2zaVteXbtQ6CB5Y01n3tDg/XWZ17H9+nq+Pzhhq18/nR3aSq2niEMqtUZ4zXx/WUwa9/t/t3VtfL6pLVaJ6RRmRy+5Ya/b3qGKAa9Zkt20+WGkwX5OzueLhvYUMbnVFq/t/53rP6l0aHmRcNmu2y2X7WuFOtjXW/dUAHeW/hShnUxF7YhZt6WPv7GNcW0cosU6OK3T1tgfbvYQajCNrpcG5VQiw6Wus1xg6r8cNfozqUD29eO3ThtgHt5Z/flEVsGL9/cSNZs2279l3iIKNrnMvvGUYj2v2tpLE0Tk/VOgvmxiCoTDe8J2xdVadV1UlaXfv26Exg8fM3uGm+pCUlyraqHTKxW3FUzwWqZlm2uVzrEKI6T0eblQ5zCQYBME72SD2bNY+lDkcJCdroyDd62LlEdYJRHSZjyc6aq04PT5Ut1UZOVR0po8Xp52JY83z5YP8+2sjq0bhOqtAe9RPuXz1aOQiO0D/Xqpo+nNUtU+cwPW72TYqF0dD11PoOodfhKOxXBempcoHBudNMzedTXSNvHqATcOfT97hQTTPqybIt5dq/C9JTLJ8fXh/XQ9aXV9YaXd32eumGI+35uzY5mfLOhN7aKLnqO2SN/VoW7A5O1FMzFxWAk5OaLOsrKl13Uo3WPYfr+7XTOnYZhQ5YpdthLvwQN/pbB/O2y+osvL5X2SAtVQ4NG9XaiWqLn5dTOxbKR4tXax0ijY7zff5cJp037gzSqlH5v6qIwRFuqXOAEbtnUfU+9SjIlu4Ns7T/b5GZ5vhcHATq8/fcqG6yePM2eWD6QplWq+NyQkzOG6pzrQpw9ex7XK31SvA0JNTs74c2y9d+Ji90HmJ1YHEjeXP+n+KlzvlZ2o9begFutTo2h/w79Ots5HNw5OXvV9xIu5+4dPM2OezD2gFdVqiR0+dt2CrzN+4ZbOPm2LhlQHtZU75dOxe7YaVTuGE4UsR5W/88upnH30oaGYZ6ODl7qvVpn5osk3q7fzZm5f6V0fnCaY0Q+hlQ5+CHhnXR/t3v1W8czU/V2Eacfk9WneVH6Nzb8ko076Gq76Tq58IpM33aDvv36FTd9J8hnTz5XqEbUGeye+2E9+wORzJ5jTrHqQCXz5asFqdu6NdOLvtutu40u0eKWh915HrVNkHveUr4J87oGmd15c0+D4e0bqKF7W2t2iEHvv8/cULVUyd8Os3R3wIAAAAA9hS/TwIAAAAAAEDUuO2M4oU9nl27WB3VsPnm/u3kgtKWcnwggiN0fpdgvTGX3Xn71dg9fFnHt28ecf1iduzq/EqNwOpnaIT7TssJug0wY001VFOjOA1rZj4a4OW9Wmsjp3QL6eAdD9ReV6MUHd6maUw6JFr19MhSOa5dM3lkeNeoLndwU+ujQBo1MFIjgLbJydijoX6smZ2D7XUo3zkj1SD/vNJiObtLS8MgBtedakNHwquu3t2B4+iQUQ7tjEyt1t1taESN8A4JVq9xCRYbHbbKzpBJvVtr2zqxuz8djowaw6lO0iqESv0c1Mp9Bxf766Xzu7D/HlvUUBvtcnxRgeFIyXpq3v7EKDSqNepwFHqOObWTvdHMosWLT4nq1KjqJlWjRlPsq3tr1Kjrbuqew9s0kRv7t5NnR5VKjsG1PLTDuVfUYX3Ors/c0KZ5rkcgdELvNG61g98Qg5CN3fMOmZ8abVCNOml03XC7e41WWS1Prae6DsSC3nuqtwvUdTD8tT0amnd4qnl5ZkqSPDuqm9wxsIMcYnCdcXtoGf25em/P7tpS63Rt1rHDL7qHU0LtIMUaJSEd3NW1z0xCWAieOr+o0C07rO53o9FeW4esb6e86Jz/zUaWthPaYucCcmbnInl0eBe532EnEzfbem2fNtpn7T+D7XXiVJ2hnHQM8vIsn2KxDv9nSCjC6LCAMK++ZwQlADD8nojeWcnqtTbSGc3OGc91CKvBkXPrwPZSkJaiHcOqc6lVOakprkMjDL9n6PwuOzV5j9CImuBEK/NWYTJPjSyVuwd1lMPaGFzj3HYANwyf+quuPKG9vXvDbkMjrNZj6cmJuq9Xnbmt6lWQbem66HS/63W+1n4fi4A6B8tU9asKiw29V6LOI8Nt7OMaXoQPhlL3T41C2Zx2XlV/1yU/y/A7UTxpnFFPehXkuPqeYRT+GYuGtOGfGd0g7ARvrkU18w6tUY2un272rwq9sxJw7EeIYyR656jwz1XoPgldx0gfv5rzZ6TzqPou6+ZUmVcvRXve4iW1D9yGRhh9hrwKR/Li82n4HSeECh39h1FwY4AD3iN9pp0G9NoLp4lMPU9sUT/N8d/Hgp/XBqP96+Zrxv7FBYHZv1Y+/2bf4+zcs9Wbj9F+dLMvVJjn4R6EnflBrz4PP2+F/ndoMEfE8B6L66BqTTf7t2NefXloWOeIr/PhVgcAAAAA1DnG8aoAAAAAAAC7BKEtjOpQ9+PKDdq/T+3UwtW89EbVjaZ7h3SSN+etkCPaNJVN2ytrjb5kpfFSzcNzz4IjXD5h7xkSCKA6qNrpnLp3BKG4W2Z4o67J+/XWGgjGWkwag0czsCNORmNvl5up/VilOmhc4bJx6bHtmmkjpLmRsKtjuNsACjusnOrCz8ElWekyb9cIbmpE1GitV5OMVEeNF0Mb053WqYU0y0yTVtnpWuf0IPCs83TIbCa0ND8W3S7SqHOcauh9eueigIVP1W7sfr7BCL5m18Pdjd0TErRR2Mt3VEtfG+EjXhwT+fVS5Ml9umqjKKqRqoIgfE1DP1cN0mJ/Xba731Vnkcrqaumab15T+sFq6amuV8e0a6adi0Nd3aeNXP3975ZGHdwnQu1frbMybs9U6lpybPvmWmfPhjE6NhJ9rIPt1EhuG/IGtdq0cg42MrFbiXy4yHh0w9D3qbB+mvbjl1h0ALdC71gN/43qaPzugpWytny7Ngp8DVX7qGuIGjE30rxVpxX148X62elUW5KdoXWom7Z6oxYuFg123+rm9etp30OrqvcM76lnY5Ryda3p2sD9yNB2qTUcU1Sg/cQjq+cSFVzWMD1VO+bV/0crOCJh13fK8qqdEaSpURi5vtZI4BZGszby0PAucur/TTdZlvVPi9vwKaN7Km1zMuWt8b1iEjyl6C03yeIeDg8ODBc6lyYZ9bSfaH+P6984Vwsf3LK9Sg406JwfbeGremjrJvLs7KWycXuV3DWow+7fq/3VLKOeLN1SHnE+KtD351UbpfeuAAnL65LgfP8OapIr6R6Ea8TSse2byf8tXRPxdaH3ijJ92OZ/9Wwlr89bETe1m9fUPYCPF++sl0vz9WsJN5c49X1y8qJVtSfE4Lxbu1OtTkCdyTk4zUbIW835/fTOLeTDRau0MPHr+rb1fP+qUKF9ChvIG/P2fO4VBHqBh4km4T2RrmuhEnRC7QY3yfMnHCmg5wLdYI6w/x7YJFfum75Q+/eRbUICpyImc7jfaCuzUHWmug7f/sv82tMk+IxCPe3cx1FBdtt3FbpZqUmeHyMqIHPoG9/prKMEkp/PCfXuSRp/j7O2Hpf3bC1vz1/p8K/9PSfY/R5n53lczaGvwu1qeBGI4/TZeCxyDZITal+zwk8JKjjviqlztH8fFXoOthyU7/+R5EVYHwAAAACA4AgAAAAAAGBBENpqqM6KN/dvp3VuU6P2xjM1klnNaGaqUYgavSa0EZLTNihqlKHrf5y7+7/tjBbvti+BGkHi8RFdZM76LYYjdsWCldHYrXKzi1yPBBw2g1iGRtw5qINcOGWW1pG4plFJXR3Zo1mmcQeGaFENKb9evs7TeX5yQB9bjV7DndyxUAskcMtqSI6f1BpUR/i83jGogzwze6kMbJK3RwcI1bD6yl0NrNwKHdlHaZyeKrcN/KtziJ2OPVUhH0jVUFs1dA0Sq+965FH8vF+m8d/H/ljVk5VSu+GkV2sauv+fGdVNvvtzvYz2KbzBqKGluvapkKxIQVle0lbF5JoWvqaN0uvJBaXF8t2KdXJe6V8df1VnyqBT2/rc6G7aNWbfFtEJ5gjdf9U2zm+tsjNq/X5E83y5+ntv1mtnt1d/NjaWoT0768c997RXbXvtjFQZfo2zK1adZp2sl9X962Ut70dQmqpD4mGfpyUlyfOju2n3CMJr29IGWYadPr04pKy+10bHr1pbFSSpfoJK7dPnR3eXX1dv1EbUDK0v1Sj2NcGevnD/BdqrNbGxzOgvUt3zGREhHEkFf3hN1caPjegij81crIUzpcag7klwEWypzg+m805w39HLKrNlxfL6Z2W0cKei+T3OaB+qa1+k8EE/6V1Dwle1fkqyvD6up2yoqNTCYUL1KMiWpQtqdwYMn4+ah5OgTqv7XYXQhXpnfK+YBabt8T0jSvco1b2iF35fpt0riuZ5MKj3J8KpUOkHpy90XOtc1L1E+16t7s32bZyr+5pqF98zjDoBh//WzvMVzzrV6rwmfDVuH9hBLv22TDrk1Y94XdObjzqvvLhvd9lWtcMwjNht+FQQ7vuKxXoh/HoxqXcbOeHTadr33rNDAtIjbVHNbHLrpWihP7+s3iiHt9b/DLjdO0Z1T2jIWyyCfPSuveHnrTY5mfLvvm1l4aZtewZHRBA6l5q7HaEdxL1kVENEo8O0W+rcqfbruwv+1EKIati5Uj06vIvcM22BDG2WLzmp3l/bje6bBvX+Tzg1eMJzc5YZBsNEogJy1fMmxahWc/c8OkELFVLn+PDfD2uWJ58vXav9d1OTADevhL+liRbe9weHdZaLvymTznn1tXsPTgYieXv+n7K1skqu72cQjiTuGB2qKjhItZOI1T1hvfu14edgdX9HhUCqoNPQ4y/iNS4kPE29N7+t3aS1S9F9bULdHrQCAAAAAOKFP3fOAAAAAABAnRKEx7OqQcPwCA3i45HarsPbNNUdvcauxJDwCDW6vJ2Oll6MQtk5P0v7CRIrDS3jgZ3OeX5TDaKfHlkqufWStcCQcF6tasc8/Uar0dApL1MGNc3T7agajRH11MjJylFtm8nBrRprjaRUhy2vQifchEYoXjWHD0LbH3U+CO9MFd4oSTWmvrjHX40sa+zbouEewRFd8+vLtDWbHK1H6ClYfd7fHNfT1khWoevsx6jCXvLqHGynIWm8dKyw4oGhnbTrvGq429THcJvQPVaUla797A3XuFC647rprOtRbZtqP6HUaGb1U5Jk0/Yq39bPbQcW9dcts9K1n3gcoc/LTihuRwvXkxjYYIOEqL+Xepelng2tN/wOau2ut15eHZd23ibXI9WG/ffRbZvKQa2CMfq61WMxxeZx7cW7lOCisbsK6/FzxFIjTpZodJ1QI1T6GhwRh+fgoNabfnw3aJCWIm1zMuWm/u0lyB3vnc/b+oxCQ/vqVIcYvWtcDNbVrw5dsaY/0nLt36ngB/Xjp3O6FMkjMxdrncf+Wj9rfxt+TMQ0MC0Gy1T3iiZ2K3Hwly6/xyWInNqxUJ6evVTO7PxXh/agUZ02D2vdxPGI0arjveq8H/XvcQmidWi/7offZVjzfN9GKjf7/Ot+zwj7vKmOnpP36yMZyYm26srQ+US6z+M2YNFotU7s0FyemLVE+/fwZvkSbXr3UsJ/pZ6rvTKmu6QmJkrjkI7VB7dqIjNCgtvDhc5mQJM87cfwtS5PXEbX5VM6FWqBARU7quWGfu0k2nTv8en8brRO+LzVTsvKs6O6yUeLV8m4ogKJpva59aVtTobWMfyykFCGoLmwW7GcX9pyj+PEzrlChdLcN7SzRFsQ7qVZcV7XljKhZYEUZzl7fnhKx0JJTUzQaqeeBTsHmQjn9muc/rVE5PKeraVr/p/SvWF2VMJlaoUj6dXBYb/q0TBb3p/Q2/a975rjPSs1Wd4c31Orb42CT9zWEEbn4H/3bSdnfPGb5KQm7x54IWjXOLXu+xU3sn1PIyHkPXx4eBdZva1ij2uknXk5/Z48vmWBvDR3ufbvc7v+FeINAAAAANBHcAQAAAAAAIgoFp0a9mZOG2OrPzugZSPp1TBba3BiZz6+jLQcI4e3bqI1HFAj+eo3Wo6PTjpB/gyGj0TmdTvdNjkZWgObWLlnSCffG+cbGdA4V/7ZvUTWl2/XRh5Soy+pBrUXTplle17X9Gkro9/2aDj2UDEYadS/hlM145OF/Mbhit0xqKPj/R3a4Ug1qrL7mQ/drKCfz61vW0KtBpWPzlzscJmO/sz071XH2lhQjUlfHdtT+/e8DTtHr/JiW9tkZ2ijkC3evG3nfKL4CQ3WaHK1zwm1p1szqVcbufjbMvFD4/RUGdREf9RVq6IZGKHPvxG67XI7WrieIHQKN2okHu3G/OF7d0TzfLmwtDjuO2Pr9oeJQY3k/hq35wzOs/HeRFuQjgSrn/Hwxu4f7tfbt1Fx7Qh4zphr0RgdPF54HRyhOgCHjlwcLeEfOf2Q0ISYX+NsL0tnlfsYdB6LJr0gpIQY1EiuR7vXWZa6vxJreseYZ8EnNt+pY9s310JLB73+7V/rYnEewfoe95dqt98zJNjU8XNKpxZyQofCwF/v9EIj/rtPVzn+02nBCJfRmYc6/lWH9mHN8iU1KTpdmMPfRv1wmdoyU5L8vcb5cA5WTujQXDs2GqWnxiSEXK8zqt4+b1E/XbfD6r9NgiOieQYx+virsJM3x/eSrZVVWsBNtOm9787uBOtMD3lB65wMaZ1jPzzHdfBigsgT+3SVP7dUSPP60d+/dsTkOu36QajIfwZ3lFfmLtcGXAgqdc5ok+M8+F4FNpweIfxph85VLsGDz6IaDEDVf9FiKRxJZ8v0apwhTfPky2VrTZb1l7SkJO3Hr3rR6HpanJ0u74zvqV1rYnFvWPca59XME/Z8f4xCI7xYptE1TgU73TKgvRZasb9O+AUAAAAAYE+xb5UAAAAAAADiSraDBmmwx+0DdScNloI+Qr0dZ3ct0kZL6ZJfX9aUb681PR7bS8XLSDte7F/VkUqNGOXJujjc87HsoKgaE6kR+UI5/Xg6HdUvWvsnFo0X1Yhg7XMzpWzdZrmiV2u5+ac/pKram9HC3WxN6HusOzKbjX0Z9PO51fPZsGZ58v7CVbv/O9fjTpaJLl774NDOUtow+o3brXyGnB6/qrFkh7zM3cERrbLTY7odowobSKzpdZKws3fdNkA1cnWfNlonluREZ9VBh9xMbXRS1dAynnl5BdEL3Cl2GayRFqVORrY7JXq059x0+rQ7Qn1A+yTqn4M9Wlk3c1Hnbzuj7gV09+q+754Fc3gwI6tzCK/rVCeNmAnZbr+uUU6okZCv/n6ONiqyF9TInmN0RjD2W1A/S+HfdRSnH4Ej2jSRMzoX+fY90+3n2Ksrr51zRGgAoBPh140DixvJqZ1aSKzp7QKv+qfbmY3b00J4x6k3xvaUppnGHZzioVNtRA5mFN4xL6jns3isF/2gOq4rQQ+NMBvF3it697/sHAvqsxh+HleBvkq0QiNq1mOP/7bwGiO3D+wg//h6lkff4yy/VH9ZBr9XHXmPi2KnZSudahNt/G1yQoJUGlz/7Rx/fo3GrqhAdfUTC1aDT5wEnAYhVFJtXkpiYuBDI4x0b5glP6/aKEGl7mv3a5yr/cSjx0Z0kZM/m+7JvNw+4tEPKotFkIGFgDqLq3Vpz1by5bs/BmL/mu1Lp/fsvaB7bbC4g0/v3EK+/7/1htPtPHdye6iZXePUcxEAAAAAgDUERwAAAAAAAEvuGdxRPlmyWo4M8CgfdYWdB+rpSYmytWpndzc3jcE86qMRCKrx4chdHU59HV3DBrcNcmLRoCcaksIaOasRY87oHMxOErFUHbDtCNjusf1ZenxEF1m1bbs0yagn905bIOsqKvd4jdO29272d+gIUk4+76HrHOvz+VldiuTxmYvllI6FutOtNnZXo9GHBkfUno/jVdTONUbrpyf8PelRkC1BoLcPbDVSD3vtxG7FMn/jVq22OMnG/tHTwkUD6rfG9dRGnowFO6MbxooKNHDTYfO2gR2kIKD719a8PHwzwkNC2uRkyJkRRv6L1Cj6ou4lEm3hnSj09pGTcKKYN3YPW2fVUUd1iIo13bbYfs7c4v59fnR3vxYVVQkuOwr5vV2WR80N0A4OzprsSX13H9Q0Vya8+6Ns2l6l/S7f4b2Nm/u30+q0tAAEGwSFl6Fy9RITYxYaYWmkWo8+b3a+Z3jZqTYzOUku69VagkDvfOtZOJKt/etuB4cvKgihEcY1ROzDp1CzD4O5F8/s3EIGNc2TBmmx+S4XRK47fYb999CmeVogdbSFX3d0w5EsHpYqJNLOssxUx2FgsBVJftYQNl7rdpF6HXhjFRYRaf9aPa92bWAe0huEIyozOb6b21/Ws7Uc/uHPElQpMQlF8u77Upd874KmQ58XOaF3LRncxPwa4Yda66F3Drb46Y5UA9n6niHu6B2q/+hWLLGmFyxmdbdEPAcnxK7tgNvnTQAAAACwt4r9cDcAAAAAACAu9G2cK5f2bC0l2RmxXpU6z04D1fuGdpKGaSkyqEnu7hGxvBpVuy5omJ5aq7GGnbYN40JGKo3FqKXxIvTwsbN/hzfL392g8Zo+bbTOrG1yMj1br1Y56THvXOMFp+uj917st2tkQjecNBC6vGerWr87v9T6aNheUiP+qNAI5eYB7T3r4Oemc0HoW+ykfWSrkGtz+1zvPkNOHN++uXxyQF851mD0QKvbF2lEbjft1NS5xs4ol0FomKynUbp3HZ+qdzW+fGZkqTw6oquku+gUeFXvNvLg0M6O/75xRr1AdLStdtsJwKcjx2zErWiMjO1Gh7y/zk+DXDZW9nLvhnb6VNdJ9TnISnXWGeCA4kbyxrieUhRhpMyYjWbt8Phxc21xe8SFfpayU5Lk3Qm9pH+T2I8+qdewPSajsbvcw3ZGDIx9cIQ3vHifgnCdiiWvt1+FP94zuJMUpKXIwCa52v0NJ1RNnJMaq9GV/Zt3013fXZy4XCeIoCTL2X09t51r3AgPrNCrs7x6D+zUcCUur/eh1+og3YfwM5jDztncbTCH27rZL/ojQHsz7wQP1imonb2tfm4D9FHylAqNaOvhPdO6wHUdHHKoH1zSWG4d2EHqp0S/U3j4Z85tgJqdZfn5vCj8WqLqmUeGd5FY092/MTjtuV1k+P49sUNzeXiY83tx/t6HsP73R7RpYjgtlpenvo1y5JIeJZKZEoMQNQ+32+9d6OZcpYKMD2fgCtM62M5nIPwcoYK0L+gW/edx4euhVwd7dS/Nzv0tt9e48CX9d5+uclhr4/NXtOh9/7Gzf3Mc3g/3um4JX+cLSlvKfUM6uVspAAAAANgLxXcEKgAAAAAAQB1k53F65/wseXt8L9eNemPZAcBvqrHR7b/Md/S3B7VqIqvLt2v/PrhV7Bt9BFX1Hg11rR+L9ZIS5aV9u8vyLeXSzsPO7irkY1izfFudqlXj1WVbynevV5A4bcIU/k68PraHq05HRvO1YkJxI8lPS5XGGamyvapaUpISAhFE1L1htjy5T1c54dNpu3/n9N1Xp+Gr+7SRB39bKH9v28xxY3cnjapOaN9cfvhzvWyprJJ/xmCUeysjG9Vwc70a26KhTF60Svt3+1zrwQ9uO2EFteOMOlepRonHhxy/CQHoCDreg4CaWLmpfzv55zdl2r+PbddcXv1jxZ4vCOixEMmI5vny2ZI12r8bpMVuBMpxRQXy48oNsnJrhfyrZyv54t0fHc/Ly3eiOuzz7uZzUJCe6mmoix3hp94+jbLlw0WrPZn3vUM6yei3v3f0t6HnYCfX2NC3Q70/uQEYRdVwtHCHx45q2O001MX1SMsBPa3phbfEulNtvFMd79dXVGr/Tk0M1vcdpVN+fe3ehptzsNtO5kH16tgeMvC1bx397biWBVogmBq9V31PUfvIaYBfLANHVSe9id2K5dMlq+WcLi1l0tQ5/o0WbmM2B5Y0lq+Xr5OV2ypk3oYtsn1Htdzcv72jZQUpOMLP8J4El+/FIa0aW19WQE/4eh26Yr2qT+zTVZ6YtURGFTYw/T4fD4LzSdKnApKcCNApwlN59ZJlbfnO+iTa1/2dNUe1JyEUrtbDUniPP8sy4/a6pHdfOgjha3rr4LQyHlfUUN5fuMp03tFyRuciCQI/LyFezNrpW3RPLDssV/s3q8LMerJ4885nYn6EvVl1SY9WWsh8tked1u3x76A9s3ORPPDbQomF0GdMowsbyL/7tYvJetQKR9INNvDmPbAzG7c1RPg62wkp91OSzknYq/ApO3Nx+5aGf186yubzVgAAAADATgRHAAAAAAAABIzdB+peNKqIZQeAaLPTgE81lg5Ko7sg2yM2wubhqBqDed0gTHUosdup8J4hHeW52ctkaLM8SQlYRyqnn8/wY71ZZppHa5TgqKHP4KbuRpf3S4v6e+4Xp418E3Z1ilY/doU2VHPSwDYtOUkeHdFVO1aC0AjbjNO1U7voH91LtMCRzvn1Jd9G53f3o90HV3ijxKC//0GnzlMPDO2kjVbeNLN25383e/ecLkVy73T3jYWdnCMu69laejTM1n5ieY1TNeuk3m12//ffShrJG/P+dDSv8GN9WLM8+XzpWtfXWbefoFh2+lTXs9bZ6TJ3w1Y5t2tL2a9lgazetl0L63ArIUZ1YviIgbEOu7t9YAd5ds5SOaJ1E5m9fkut6U4/Xad2KpQHf1vkrEOX65GWg3PduH9oJ7lq6hzp3yRXirPSPWvs3jg9Vfu+MUfnPbOrUXqqHFRivdNykPy7X1s55bPpWoDAaZ1aaN8NvOrEokKJ1PnGLbd1TCw7fR7Rpqk8M3upFhxwY/92cum3sz2bt5u9or6HjSxssHuUWTf7OdZ3jdQ+Vj9G5y6vKhw7e0fdM7p9UAft3xVVO2Rt+XZpbCOsMbRDTKyvcSqQQYWmqU57uvvX4YF4bLtm8vTspY5rmy759WX6mk1yYWmxtM/LlK75Wa4CGmLl8l6t5fof52phnr0a5dSabmdVzV7q9POtvlfePMB66EnQqOvaNT/8rv378NZN5Mtlzr4X+Klbgyw5ul0zaalT4wT9HBx6/yU3NVnW7Qqi8sL7E3pLf4fhSHr3S1vbCKkNvW4E6dGIXs3r9HR2ac9WcuP//nA0nx0GnzUvOwvHgt71zOm6hXeSj+YWBul7XCgvawhlUJNcmbJ83a7/cjmCvQooTKG5fKiJ3Upk4tezxAvad+VWjRz9bcusNFv3+uPFCR2aO/7OrRcuemL75pb/PvRzF8tLXKKP4Uj/6FbseNCI8Pu3mclJcoONcI2AnoI9+07sRzhSj4bx+T0OAAAAAOIZd8IAAAAAAAACxqvRH+yoClDjSL/Fe3ODYEUaBHP/OmlT0qJ+ulzSs5UEUaw7kISjzY73+yW0nZqbBlhBaYTtV8Nm1elTNbi0q66Oxq4njlY1kNRnqGdBjvF0F/M+oLiRPPDbIq3h71Uh4QnR+Aypz05NR8sgOa9rsWyr3CGTF/3VCNWpa/u2lWFvTHXdWDionS+sHr+Pj+gqCzdtk7Y5Gdp/3z+0s/R79RtP5n9JjxK569cFWmc8p/vXyfec0Lck1mF3KlymJghLL4QlFseP62ucBEevghx5e3wv7djdWlnlafhUq+yM3cERxVnWOxSGU+sXr7rkZ8lrY3tIvaREaZCWqtVUSzdvkzfnOwvwCXVqxxZy009/dUrcG+XVS5HnRnWT1eXbpXuDLK1z+LIt3oze69Xn1HUwR4DuG+ltitPt+1ePVnscv07nk5qUaCs0QltWQMKnlH92L5EDSxprna5f+n2ZZ9e4BBcdw9R78dCwLrJ6W4Xtfbvz7yUw1HeBznn1tXC69KRErTNUaIfAWIwEXJeMKWqo7c/05ETpoxPMEQTHtW/uKlA1lueIMS0K5OW5y2XJ5nK5a3BHOeHTaZ7N2821Kfx+qQoeu6h7iaNlx/IMHPq9QgVz6O2S0DA9O1T4Tig755rw674KxqoJoYq385EKIlq0aZvhPrDzvEV7fwwOGHUOsjWfEL0LsuV4h53CgyQjac8wDbfXOBW2UxMc0TbX+fe487q21M7BKvQr3hzSurHc/NM87d8TWjaSH1yEg4Zvvfpu6FUdqcJhnYbVxrIMtvO5jabw+18qJOGgVo0dXeNiWUOErkeTjFTd65nTc0T73EzPaogP9+8tyTaOX6fXZT+UNsiSX1dvNKyrnJZa4UeNndmEv1Y9C1FBQJb/Pji7FwAAAADiWjDvegAAAAAAAOzFYtF2SY16VkM18KnL4rW9gRoB/cDiRvLfkaUSNEEalS1ojXbq5v6tW7zroOGmsXtIp2Wp22LR6Kt6LzqnJMTpeaam4eDYooYSJAPDGjS6OX5z6qXIC6O7yT2DO7razvg5GiPLTEmSg200ejaTlpQk/Rvv7CR2bteWjhsLu63DY92pNi05SdrlZvoSJHRwqyby6QF95Byb+zf0/JLkYLVCRwTNSU0J1PnhuHbN9vidm+OnMPOvDrGF9dMs/53bDgBBa4xdc+ymJydpHSP2nOZ8vheUFkvL+mnSKjtdzupSJHurZplpWmhEDbWfvZCR4s18nHYErNGgXmzPEUVZ6dKjYbZ2HN85qIPU92i/xDIcLnTJ1THtVlv7Mx3OyTVGGdAkV+tso0R7T4eGMcQ60FWti+p8pTpUTigukJSwi5rT4Ai1Wd1DRpdtk2Ov06daHyehEUH8Htc6J0MbnV59ptX3glBO11SdZy4NCUHdr2WB7I1UEMf+xY1kVGFDbf8WZlqvpcL5dcp1O9tYfmdXn8MnRnSV9yb0ko55ewYRBKVT7eAmedrnqmH6X3VOJKGnuVh2qlX797YB7WVCywJ5cFhn3fOtm4C6diHn3fBzu51Oy/s0b2CrU3iQwm3vGdxJTu1UKM+MKtX9zmZnXcMPlbsHddTu5TZMS5HxNs7B4feR7xvaWfo2ttGpNkDXuFsGtNf2gQpvGeQiICec2sJTOrbQjmG7wTDh1L0nFUIRjw4obixndynS7nV5fd+0Z0G2Fjyq6uib+rdzPJ+8esmOQyOUWJbBowobSPPMeto+UJ/noAi9v6hq6cPbNLUZbPCXWH+Le2h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mTJGTTjpJd9rdd98tzz77rEydOjWqwRHUW9ZQbwEAEDuzZ8/W7jWYBYuqe1/qfpQKT/WC6uj4/PPPy4UXXqiFe7lBvWUN9RYAANHz008/yZFHHmn6mr59+8pjjz0mqampjpczf/58Ldhr3bp1hq9RoRRffvmlq+VQb1lDvQUAAAAAqEsSY70CAAAAAODEn3/+Kc8995zhdNUh9Pbbb3fcCXDgwIFyzjnnmL7mP//5j6N5AwCA2FANq1QjrA8++EALExgwYIDr0Ahl8ODBWmfGvLw809e98cYbMm/ePE8a6JuFRqgwBNXB0klohHLwwQdrnQ7Mwh68qoM++ugj09AIVcvdeuutjkIjFFXPqbrOLOzhkUceES+88MILpqERHTp0kKuuusrx/NXftm3b1nD68uXLtdA0AADizdatW2XSpEmGozeqYKZoo96yjnoLAIDYUGEREydONA2NUGGqTzzxhGehEUqXLl20UEy3oRHUW9ZRbwEAED1vv/226XT1DO6uu+5yFeagFBcXRwwaV6ESKrzeKeot66i3AAAAAAB1CcERAAAAAOLSf//7X9m+fbvhdNXZsVevXq6Wceqpp0pJSYnh9FmzZmnp/gAAINhUoJQKQ/jwww/l2muvlRYtWni+DFUzPPTQQ5KcnGz4mqqqKk869j/66KOm088880xp2bKlq2VcfPHF2khGRj777DOZO3euuKVGZDJzySWXRAzkMKOCQa6++mrT4IlXX33VdEQnK1RdqupTM2o93DQkTEtLixg8odahsrLS8TIAAIgFFXi1ePFi3Yb4RoESfqPeso56CwCA2FChnnPmzDGcfvLJJ8ull14qiYnBbB5IvWUd9RYAANHz7bffmk4//vjjpUGDBp4sa9SoUdK9e3fT13z99deO50+9ZR31FgAAAACgLgnmkyEAAAAAMFFRUSEvv/yy4XTVIe/cc891vQ9VB8Pzzz/f9DVqdHEAABBst99+u9x4443StGlTX5fTrVs3OeKII0xf8+6777paxsqVK7UADCNqtEfVaM2tnJwcLUTLSHV1tTz33HOulvHrr7/KL7/8Yji9Y8eOMmHCBHFLhWgcdthhhtO3bNkir732mqtlfPzxx7J8+XLD6SNHjpQePXqIW3369JFhw4YZTl+6dKl88sknrpcDAEC0TJs2TZ566inDkf68HB3bKuot+6i3AACIrgULFpgGWI4YMUIuuugiCSrqLfuotwAA8N+2bdvkjz/+MH3NgQce6OkyI81vxowZjuZLvWUf9RYAAAAAoK4gOAIAAABA3Pniiy9k/fr1htP3228/KSgo8GRZ++67r2knha+++krWrl3rybIAAIA/cnNzo7ZrVQdHFT5l5M8//5S5c+c6nr8KnqiqqjKcftRRR0m9evXECyoEQ430beS9996TyspKx/N/++23TaefcMIJ4hUVpqFGC3K6LpFE+vsTTzzR1fztzMvttgAAEC2qjrjiiit0a5sOHTp4EoblBPWWM9RbAABEz2233Sbbt2/XnZaXlyc33HCD6X2QWKPecoZ6CwAAfy1ZskQLTjeiAupLSko8XebgwYNNpy9atMjRfKm3nKHeAgAAAADUBQRHAAAAAIg7kTrDHXLIIZ4tKykpyTThXzXMmzx5smfLAwAA8S0/P1969epl+prffvvN8fzfeecdw2mqQ8DBBx8sXsnKypLRo0cbTl+zZo18/fXXjua9Y8cOef/99w2nq8CKsWPHileKi4tN3xc1YpPTQI8NGzZowWZGioqKpE+fPuKV/v37mwabff7557Jx40bPlgcAgF8effRRmTVrVq3fJyYmynXXXSfJyckx2fnUW85QbwEAEB3q/sVHH31kOP2ss87S7k8FGfWWM9RbAAD4K9KzFfW8x2uFhYXavTCzZ1BOUG85Q70FAAAAAKgLCI4AAAAAEFfUKJRTpkwxnN6oUaOInTXtmjBhgul0s46CAABg7zNo0CDT6QsXLnQ0XxXUMH36dMPp3bt3l2bNmkk81EEqPGPlypWG0/fZZx9JS0sTL40fP950+pdffulovio8w2iUT2XcuHHiJRUQYhaqUVFRId9++62nywQAwGvz5s2T+++/X3faUUcdJaWlpTHZ6dRb7lBvAQDgvyeffNJwJOwGDRrIkUceGei3gXrLHeotAAD8o0LPzfgRzqVCI3Jzc03bSNlFveUO9RYAAAAAIN4RHAEAAAAgrkybNs005X/w4MFaZzovtWnTRpo2bWo4ferUqVJZWenpMgEAQPxq3Lix6fR169Y5mu8333xj2DGgpg7yWt++fSU1NdU0NMHptpjxY1uGDBliOt0snMxMpH0QT9sCAEA0qHpm0qRJUl5erhsIOnHixJi9EdRb7lBvAQDgry1btsjbb79tOP2www4zvY8TBNRb7lBvAQDgn5ycHNPpZs/o3DCbb3Z2tu35UW+5Q70FAAAAAIh3BEcAAAAAiCsqpMFM//79fVnugAEDDKdt2rRJGzUbAACgZnRHM1u3bnW0o7777ruo10Hp6enSvXt3w+lz586VVatWeb4tZrWXU0VFRdK8eXPD6T/88IOjkZvM6tO0tDTT/edUr169TDuCRNq/AADE0ksvvWR4/bz88sulfv36EivUW+5QbwEA4K9PP/3U9L7SfvvtF/i3gHrLHeotAAD8k5eXZzp97dq1ni9zx44dsn79esfrpId6yx3qLQAAAABAvCM4AgAAAEBcmT59uun00tJSX5bbtWtX0+kERwAAgBp6o2eHqlevnqOdZVZvJCUlSefOnX15EyLVV07qILO/KSgokCZNmki0t0WN2jl//nxb81MBYmZ/07FjR19G+lTz7NChg+F0tU5qewAACJoVK1bIrbfeqjtt+PDhMnbsWIkl6i33qLcAAPDPe++9ZzitRYsW0rZt28Dvfuot96i3AADwR35+vjRr1sxw+oIFCzxf5qJFi7TwCLPnTHZRb7lHvQUAAAAAiGcERwAAAACIKzNmzDCclpmZKcXFxb4st0uXLqbTZ86c6ctyAQBA/Fm+fLnp9OzsbNvzrKyslNmzZxtOb9WqlaSnp4sfIgVS2K2Dli1bZjoqk18BGFbmbVZr6pk1a5ZUV1cbTu/UqZPEYltUI0O1bgAABM11110nGzdurPX7jIwMmTRpksQS9ZY3qLcAAPCvVvn2228Np/ft2zfwu556yxvUWwAA+Kd3796mz//++OMPT5f31VdfOV4fPdRb3qDeAgAAAADEM4IjAAAAAMQNNWry4sWLDaer0IiEhARflq06Y5opKyvzZbkAACD+TJ8+3XR6UVGR7XnOnz9fKioqHNcqQaqDzAIwrCwvSNsS6fXxtC0AAPjtgw8+kI8++kh32jnnnCPNmzeP6ZtAveUN6i0AAPyhRo7evHmz4fTu3bsHftdTb3mDegsAAP/st99+ptPffPNNT5f31ltvGU5LSUmRkSNH2pof9ZY3qLcAAAAAAPEsOdYrAAAAAABWqdAIsxGdW7Zs6dvOrF+/vuTn58uaNWt0py9atMi3ZQMAgPixY8cO+eKLL0xf06FDB9vzjVRrOAmjsCpSjWW3DqpL22IWahZv2wIAgJ82bNgg1113nWFtdPzxx8f8DahLNUpd2hbqLQAAdvrhhx9Md0Xbtm0Np6lnW+p+1XfffacFei5btkw2bdokVVVVkp6eLtnZ2VqIl+og16NHD+nfv780adLE811fl2qUurQt1FsAAPxl6NCh2qA1KoBBz1NPPSXHHXecNGjQwPVu+/jjj+Xnn382nD5+/HgpKCjYa2uUurQt1FsAAAAAgGgiOAIAAABA3Ij0IK2wsNDX5av5GwVHqN9v2bJFMjIyfF0HAAAQbF9++aWsWrXKcLpqiO8kOCKWdZDqQKAawK1evdrRugVpWyLNe2/eFgAA/HTzzTfLypUra/0+MTFRC5RITo79Y2vqLW9QbwEA4I9Zs2bZ7tw2bdo0efzxx+XDDz+UyspK3b/buHGj9rNkyRKZOnWqvPDCC5KQkCDdu3eXI488UiZMmKCNdu0F6i1vUG8BAOAfVQddeumlcvrpp+tOV+2CLrjgAnnsscckNTXV8XJUMMXll19uOF21PTrnnHNsz5d6yxvUWwAAAACAeJYY6xUAAAAAAKuWLl1qOr1hw4a+7sxIIwZEWj8AAFD3PfHEE6bTR40apXWQrEt10Nq1a2Xr1q2ebYvd0ZPsSEtLk8zMTM/qOdWpIlbbQm0KAIgXamTrV199VXfaUUcdJaWlpRIE1FveoN4CAMAfc+bMMb3+5ufn73GvRnV4POyww+S9994zDI0wUl1dLT/99JNccsklMmbMGG00bC9Qb3mDegsAAH8NHz5cDjnkEMPpKmzrvPPOk/Xr1zua//Tp0+Wkk06SdevWGb7mH//4hxQVFdmeN/WWN6i3AAAAAADxjOAIAAAAAHFDNXSLVcc8pVGjRq7WDwAA1G1ffvmlfPPNN6av+dvf/iZ7ex0U6bV+h2CY7SvVyE91jrDKrFFfvXr1JCsrS/ySm5trOuIntSkAIAjKy8vlyiuv1L2+qvpi4sSJEhTUW96h3gIAwHvz5s0znBYaGjFt2jQ5+OCD5bXXXrN1j8MsNPPss8/WOi+qEbbdoN7yDvUWAAD+uvrqq2XIkCGG0z/77DPtmd8bb7wh27dvtzTP1atXy1133SVHHnmkaTD50UcfLcccc4yj9abe8g71FgAAAAAgXiXHegUAAAAAwIuOeYqfHfOszD/S+gEAgLrdKfL66683fU23bt2kX79+dbYOatasmSfbkp2dLX4ym39VVZVs2LBBcnJyXDfA83s7apahGhrqoTYFAATBPffcIwsWLNCddvnll0v9+vUlKKi3vEO9BQCA93XKtm3bIl571ejXp512mmzdutXzt+Cdd96ROXPmyBNPPCENGjRwNA/qLe9QbwEA4K/U1FS577775OKLL5bJkyfrvmbp0qVyySWXyG233aaFTPTs2VNatmypXafT09Nl48aNWv0za9Ys+fHHH+Wrr76SiooK0+Uee+yx2j0zp6i3vEO9BQAAAACIVwRHAAAAAIgbagRoM353Nog0fzrnAQCw97rjjjtMR35ULrjggjpbB0VaP6uvTUlJ0RrjxXpbrARHqBGkzEbazMzMFL+pbTEKjti0aZNUVlZKcjKPAQAAsTFjxgytY6Ge4cOHy9ixYyVIqLe8Q70FAIC3Vq1aZTq9Xr16MnPmTDn99NN9CY2oUVZWpo2A/cILL0hubq7tv6fe8g71FgAA/lM11t133y0vv/yy3HLLLVrwuJ6VK1fKa6+9pv04pWqr6667Tvbdd18Xa0y95SXqLQAAAABAvKLFKAAAAIC4YdYxT8nIyPB1+ZHm72djPAAAEFyff/65/Pe//zV9zZgxY2TgwIF1tg6KtH5WXxuNsAWvtiVS7ReEbVGjkQZpJHcAwN6jqqpKrrjiCi3ESO/6NWnSJAka6i3vUG8BAOAt1Rkx0vf/c88917Se6dSpk4wcOVI6dOggBQUF2gjKKhBdBVJOmzZNPvnkE5kzZ07EdVHBqWpZKiDMblgl9ZZ3qLcAAIieww47TAtAfeqpp+T555+PWJvZDYw47rjj5Pjjj/fkeQ71lneotwAAAAAA8YrgCAAAAABxQ6+zQSi/R1NWI2BHGnUaAADsXRYvXiwXX3yxVFdXmzb6uvzyy32tgyLVKUGqg8y2xe96zsttiXVtamVbKioqfF8HAAD0qI6Ev/32m+60c845R5o3bx64HUe95R3qLQAAvBUpvLKsrMxwWp8+fbRALxUYYWTUqFFy4YUXyk8//aSNdG1Ux9WYOnWqPPTQQ3L22WeLHdRb3qHeAgAgurKysrTa56yzzpI33nhDbrrpJi2Ey6l27drJP/7xDy10PjU11bP1pN7yDvUWAAAAACBeJcZ6BQAAAADAqkid+PzunBdp/pEewAIAgLpl06ZNcuaZZ0ZsGHbVVVdJ48aNfa2DkpKSJF7qILNt8Xs7rCzD6rbE+j2xsgzqUwBALCxcuFDuuece3Wmqw6IaQTGIYn1tp94K3ntiZRnUWwCAaCgvL7f9NwkJCfKvf/1LnnnmGdPQiFA9evSQV155Rc4444yIr33ggQdk3rx5ttYp1td26q3gvSdWlkG9BQAIyjPBhx9+WPbbbz+txnITGqHMnj1bLr30Uu0Z4i+//OLZesb62k69Fbz3xMoyqLcAAAAAoG4hOAIAAABA3Ij0oMrvh2mR5m9npG0AABDfqqqqtJGAVMMuM4cddpiMHz/e9zrI7wAtLxsUmb3W7+2wsgyr2xLr98TKMmjoBQCIhUmTJsm2bdtq/T4xMVEbwToa10gnYn1tp94K3ntiZRnUWwCAaKioqLAdGnHttdfKiSeeaHtZqma78MILZeLEiRGfid1999225h3razv1VvDeEyvLoN4CAMSSqnnuu+8+GTFihNx+++3y+++/ezbvNWvWyGuvvSaHH364nHDCCfLzzz+7nmesr+3UW8F7T6wsg3oLAAAAAOoWgiMAAAAAxI1ID7J27NjhewdRM0Ht+AAAALynGt//3//9n+lrOnfurHWc9EKkOiNSneJWpPnbCfAy2xa/t8PLMLJYvydBCFYDACCcGqH6m2++0d0xRx11lJSWlgZ2p8X62k69Fbz3RKHeAgAEQXV1ta3XH3nkkVoHRDdOP/10GTNmjOlrPvjgA1m8eLHlecb62k69Fbz3RKHeAgAE1R9//CFHHHGE/Oc//5ENGzb4uix1P+3vf/+7FszlphN/rK/t1FvBe08U6i0AAAAA2LvQqwkAAABA3EhJSYlpAnqk+UdaPwAAUDeoBmIvvPCC6WsaN24s9957r6SmpnqyzEh1hmpU5GdIgJd1kHqt0fyi0Tgq0jKsbouV9yRetgUAAC+sWrVKbrnlFt1pjRo1ijhqdaxRb3mHegsAAG/Zub9UWFgoF110kSfLvfrqq+X777/XRsM2CnR/+eWX5cILL7Q0P+ot71BvAQDgr19//VVOOukk2bhxo+nrEhMTpX///tK3b1/p1auXNG3aVHJyciQ9PV3723Xr1sn8+fO1mmrKlClSVlZmen2///775eeff5YHHnhA0tLSbK839ZZ3qLcAAAAAAPGK4AgAAAAAcSNSCvv27dt9XT7BEQAA4Mknn5T77rvPdEfk5ubK448/Ls2aNYtaHVRRUeFZSIXfdZDZtvhdz3m5LbGuTa1si5/HBAAA4a677jpZv3697o65/PLLpX79+oHeadRb3qHeAgDAW3buu5xyyimSmZnpyXLz8/Pl6KOPlnvuucfwNR999JHl4AjqLe9QbwEA4J+ZM2fKySefbBoakZCQIIcccohWe5WUlBjWUuqnVatWss8++2i/++abb7RwiKlTpxrO++uvv5azzz5bC4+w+5yHess71FsAAAAAgHiVGOsVAAAAAACrMjIyTKdv2bLF1525efNm0+lqxAAAAFB3qREUb7rpJtPXqIb5jzzyiLRp02avqoMirZ/V10ZaTpC2JVLtF4RtcTIaFQAATnzyyScyefJk3WnDhw+XsWPHBn7HUm95h3oLAABvWe0wqO5LHXDAAZ4u+/DDDzftgDh37lxZunSppXlRb3mHegsAAH+o523nnXeebNiwwfA1eXl58tBDD8n1119vGBphZMCAAfLUU0/JxIkTJSkpyfB1X331ldx6661iF/WWd6i3AAAAAADxiuAIAAAAAHEjJycnpp3zIs1fjS4OAADqpnfeeUcmTZok1dXVpkECqqFYaWnpXlcHRVo/q6/dvn27VFRUSDxsixrtM8ghGGpU90gjSwEA4IVNmzbJNddcoztNXStVDRUPqLe8Q70FAIC3rN6rGDFihBYe4aVGjRpJ//79TV/zyy+/WJoX9ZZ3qLcAAPCHCmtYuHCh4fTs7Gx59tlnZdiwYY6XkZCQIKeffnrEsPqnn35avv32W1vzpt7yDvUWAAAAACBeERwBAAAAIG5EesBplvjvhUjzt9NhEgAAxI+PP/5YLrnkEtmxY4fpyI/33nuv9OnTx5d1qEt1UJC3JTExUWv0Z5VZcJjf2xFpGdSmAIBoNqhfsWKF7rRzzjlHmjdvHhdvRpBrFIV6y/4+8wL1FgAgCKwGl/fo0cOX5Xfr1s10+owZMyzNh3rLO9zfAgDAe0uWLJEXX3zRNPDh7rvvltatW3uyvAMOOEDOOOMMw+kqzP6WW26xNU/qLe9QbwEAAAAA4hXBEQAAAADiRn5+vun0lStX+rr8SPPPy8vzdfkAACD6Pv/8c7nggguksrLS8DUpKSly1113yeDBg31bj7pUB0XallWrVkmstkU1qFMN/6wy2+7y8nLZuHGj+GX9+vVSUVFhOJ3aFAAQDT/88INhg/oOHTrI8ccfHzdvBPWWd6i3AADwltXv+JECHpyKNF/VydIK6i3vUG8BAOC9p59+Wqqqqgynjx07VgYOHOjpMs8++2xp2rSp4fTffvtNpk6danl+1Fveod4CAAAAAMQrgiMAAAAAxA2zh6XR6GQYaf7xMoImAACw5ptvvpFzzz1Xtm/fbviapKQkbZTtkSNH7rV1kOq8kJGR4dm2+BmCsW3bNtm0aZNn9VyzZs1iti3UpgCAWFPBWldccYU2+mG4xMREue666yQ5OVniBfWWN6i3AADwngq6tHLvpbCw0JfdH2m+y5cvtzQf6i1vUG8BAOCPt956y3S6embotdTUVDnttNNMX/Pqq69anh/1ljeotwAAAAAA8Sx+WuoAAAAA2OtFaphmdUQjpxYvXmya2m+nwyQAAAi277//Xs4880wpLy83fI3qEHnjjTfKuHHj6nQdtHXrVlm9erVnnRJiuS1m9Zxf29KqVSuJh20BAMCuLVu2yLx583Sn7bvvvtq9kkjXK6vMajIrnRVzc3Olfv36pq+h3vIG9RYAAP5Qtcrs2bNNX5Odne3LsrOyskynm4V0hqLe8gb1FgAA3ps7d67ps7A2bdpI69atfdn16j7atddeqxvOqkydOtXyvKi3vEG9BQAAAACIZwRHAAAAAIgbkR5wLliwwLdlq0Zva9asMZxOxzwAAOqOn376SRvdRwUmGElISNAacR144IFRWacWLVrErA5auHCh6XS7ddDetC2RlhekbQEAwEuTJ0/WfqLl6KOPNp1+6aWXygknnGD6mr2pRqlL20K9BQDYWxQVFZkGR6hw86SkJF+WnZOTE3FEZiv2phqlLm0L9RYAYG95Nmhm0KBBvi27YcOG0q5dOykrK9OdvnTpUlm2bJk0bdo04rz2phqlLm0L9RYAAAAAwEuJns4NAAAAAHyUmZlp+vBNjXJplMDv1h9//GE6vX379r4sFwAARNevv/4qp556qjaCtpkrrrhCDjvssKitV3FxsaSmpjquVdyOsuRlHaQav5mpS9sSaXlB2hYAAPZ21FveoN4CAMAfapTrWPHq2Rv1ljeotwAA8N7y5ctNp7dq1crX3d66dWvT6So4wgrqLW9QbwEAAAAA4hnBEQAAAADiSufOnQ2nbd68WebPn+/LcqdPn246vWPHjr4sFwAARM+MGTPklFNOkY0bN5q+7pJLLpFjjjlGoiklJUXatm1rGrZgdXRHu3777TfT6Z06dbI1v2bNmklubq7j5QVpW1QNmJCQYHpMxWJb1Dp16NDBt2UDAFAXUW95g3oLAAB/dOnSxXS6CkGtrKz0ZdkbNmwwnZ6WlmZpPtRb3qDeAgDAe+vWrTOdnpeX5+tujzT/tWvXWpoP9ZY3qLcAAAAAAPGM4AgAAAAAdSY4omaUcD9MmzbN1XoBAIBgKysrkxNPPFHWr19v+roLLrhATjrpJAlaB4GqqqqIQVdORaqv7IYtRNqWlStXRhzZyY9tycjIkJKSElvzq1+/vrRs2dJw+syZM6WiokK8puapjlmzEaUyMzM9Xy4AAHUd9ZZ71FsAAPija9eurgMenIo035ycHMvzot5yj3oLAADvqRCuSM+D/JSVlWU6fdOmTZbnRb3lHvUWAAAAACCeERwBAAAAIK707dv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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -1203,7 +1220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6e7c70d",
+   "id": "21ddfb28",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1211,14 +1228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "5436c5a9",
+   "execution_count": 13,
+   "id": "e39e4d51",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:55.774815Z",
-     "iopub.status.busy": "2026-08-01T02:00:55.774729Z",
-     "iopub.status.idle": "2026-08-01T02:00:55.966626Z",
-     "shell.execute_reply": "2026-08-01T02:00:55.966273Z"
+     "iopub.execute_input": "2026-08-01T02:19:55.905643Z",
+     "iopub.status.busy": "2026-08-01T02:19:55.905497Z",
+     "iopub.status.idle": "2026-08-01T02:19:56.300032Z",
+     "shell.execute_reply": "2026-08-01T02:19:56.299403Z"
     }
    },
    "outputs": [
@@ -1252,14 +1269,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
-       "      <td>0.5487</td>\n",
+       "      <td>0.5491</td>\n",
        "      <td>0.0048</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.2986</td>\n",
+       "      <td>0.2982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
-       "      <td>-1.7043</td>\n",
+       "      <td>-1.7044</td>\n",
        "      <td>0.0040</td>\n",
        "      <td>-2.9957</td>\n",
        "      <td>1.2914</td>\n",
@@ -1267,7 +1284,7 @@
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
        "      <td>0.3526</td>\n",
-       "      <td>0.0053</td>\n",
+       "      <td>0.0054</td>\n",
        "      <td>0.3500</td>\n",
        "      <td>0.0026</td>\n",
        "    </tr>\n",
@@ -1280,10 +1297,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
-       "      <td>0.1597</td>\n",
+       "      <td>0.1598</td>\n",
        "      <td>0.0034</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0203</td>\n",
+       "      <td>0.0202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
@@ -1340,11 +1357,11 @@
       ],
       "text/plain": [
        "                       mean      sd   truth  abs_error\n",
-       "kappa0               0.5487  0.0048  0.8473     0.2986\n",
-       "alpha0              -1.7043  0.0040 -2.9957     1.2914\n",
-       "lambda_a             0.3526  0.0053  0.3500     0.0026\n",
+       "kappa0               0.5491  0.0048  0.8473     0.2982\n",
+       "alpha0              -1.7044  0.0040 -2.9957     1.2914\n",
+       "lambda_a             0.3526  0.0054  0.3500     0.0026\n",
        "lambda_q             0.2055  0.0047  0.2000     0.0055\n",
-       "beta_a               0.1597  0.0034  0.1800     0.0203\n",
+       "beta_a               0.1598  0.0034  0.1800     0.0202\n",
        "beta_q               0.1096  0.0033  0.1200     0.0104\n",
        "theta_d              0.0176  0.0002  0.0200     0.0024\n",
        "theta_r              0.2485  0.0013  0.2500     0.0015\n",
@@ -1354,7 +1371,7 @@
        "beta_a + mean(b_ba)  0.1699  0.0061  0.1800     0.0101"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1391,14 +1408,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "514ae0ad",
+   "id": "b97523ba",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
     "global coefficient and the mean of its group effects are only *jointly*\n",
     "identified: the likelihood constrains their sum, and the prior only weakly\n",
-    "splits it. The identified sums in the last three rows land within a few\n",
-    "thousandths of the truth, like every other likelihood-identified quantity.\n",
+    "splits it. The identified sums in the last three rows land within about one\n",
+    "hundredth of the truth, comparable to the other likelihood-identified rows.\n",
     "The same translation ridge exists for every global-coefficient/group-effect\n",
     "pair in this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
     "$g$/$b^{(\\sigma h)}$, whose sums we spare you) — so raw split rows like\n",
@@ -1410,8 +1427,8 @@
     "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
     "particular synthetic dataset with variational overconfidence along exactly\n",
     "such ridges. This is the first appearance of a theme the notebook returns to\n",
-    "at full scale: a mean-field width is trustworthy only where seed replication\n",
-    "says it is.\n",
+    "at full scale: a mean-field width is usable only where seed replication has\n",
+    "failed to disqualify it — replication can veto a width, never certify one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1419,20 +1436,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "6cf8933a",
+   "execution_count": 14,
+   "id": "5e59aa51",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:55.967588Z",
-     "iopub.status.busy": "2026-08-01T02:00:55.967524Z",
-     "iopub.status.idle": "2026-08-01T02:00:56.084522Z",
-     "shell.execute_reply": "2026-08-01T02:00:56.084234Z"
+     "iopub.execute_input": "2026-08-01T02:19:56.301635Z",
+     "iopub.status.busy": "2026-08-01T02:19:56.301491Z",
+     "iopub.status.idle": "2026-08-01T02:19:56.539514Z",
+     "shell.execute_reply": "2026-08-01T02:19:56.538870Z"
     }
    },
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x700 with 1 Axes>"
       ]
@@ -1475,7 +1492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2603de5c",
+   "id": "ab5b86be",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1487,7 +1504,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9823e06c",
+   "id": "0570b0e0",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1502,14 +1519,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "0f37608a",
+   "execution_count": 15,
+   "id": "64476b60",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:56.085584Z",
-     "iopub.status.busy": "2026-08-01T02:00:56.085513Z",
-     "iopub.status.idle": "2026-08-01T02:00:56.719480Z",
-     "shell.execute_reply": "2026-08-01T02:00:56.719135Z"
+     "iopub.execute_input": "2026-08-01T02:19:56.541522Z",
+     "iopub.status.busy": "2026-08-01T02:19:56.541380Z",
+     "iopub.status.idle": "2026-08-01T02:19:57.907582Z",
+     "shell.execute_reply": "2026-08-01T02:19:57.906890Z"
     }
    },
    "outputs": [
@@ -1573,7 +1590,7 @@
        "q99 |move| (bp)        0.324    0.342  0.280   0.426"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1617,20 +1634,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ffd16c8f",
+   "execution_count": 16,
+   "id": "76683e6f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:56.720520Z",
-     "iopub.status.busy": "2026-08-01T02:00:56.720446Z",
-     "iopub.status.idle": "2026-08-01T02:00:56.856915Z",
-     "shell.execute_reply": "2026-08-01T02:00:56.856598Z"
+     "iopub.execute_input": "2026-08-01T02:19:57.909292Z",
+     "iopub.status.busy": "2026-08-01T02:19:57.909156Z",
+     "iopub.status.idle": "2026-08-01T02:19:58.179850Z",
+     "shell.execute_reply": "2026-08-01T02:19:58.179201Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DDSngjcfpuuuuC7vuumuyOnkxxaLp3/zmN+GMM84ouBC2GB599NEk1BMLwRvq448/Dvvuu2/RjxH/4+9//3tyfBsSImns1xvKL4YE4zXjlVdeKcvzxw5fMWj32WefNXhbsXPNXnvtFT744IPQGGVda2NXrxhaKJcYfjv77LPDySef3OD5eAxYxM4+8br01VdfhVKLoZPY6ej666+vsXNJIeLnnBj0POaYY5JQXylkdY5yvwYAAADyIrgCAAAAAFScadOmhYMPPrjoXRli14DYGSGvorB77rknWak7zwK6uKJ57CDz5ptv1uvfL7PMMuHPf/5zsup2miuuuCK89957oRgFiieeeGJmceI222yTHDMKLxKOhbUfffRRbofuu+++S16bf/7zn/X691ndTGKR/aeffhryFPf/5ZdfTh2PgZMY5qqP2FUlBkvieyWGyvLywAMPhEMPPTS5JpayADy+b4u5in0M8R111FHhrbfeKto2CeHqq69OisAbWjRdDdcbytsZL3ZmKmZYtxCx+0+c1xSzw8v333+fdI6JHbUam6zQQa9evUK5xJBavLfEsF0xxU5lMbQU/7eUoZX99tsvtbNNfT355JNJ16IpU6aEcgZXXnvttbKEZgEAAIDqJ7gCAAAAAFSUWCj1+9//vigr/aeFV+KK8cUWV1w+99xzcy1i/++i+SOOOKLex2irrbZKiuHTxL/h+OOPb3Dh3F//+tfw9ttvp44vt9xyubwW1W7IkCHJqtylCDPEcyGujP70008X/G9j56FWrVqVrevKI488koSn6hOsyTJ58uTk/fPCCy+EUohhjxi4mzlzZu7PFYMysQA8DzEIc9xxxxW1W0dTFl+rq666KvfnaSzXG8oXkjjzzDOLGhopRAwP3nnnnblsO4Zujz322KKG+ErxHho2bFi9wgp5+8tf/hIef/zx3IJvMfARu5WV4nNKDGJ+8cUXuWw/dp6Kn4Py/jyx4YYbps7RYghN0BQAAADIQ8tctgoAAAAAUE8xyPDqq6/WONatW7ew5ZZbJsVWSyyxRFhyySWTYskffvghjBw5Mikkf+WVV2pdJfj+++9Pui1ssskmRXmd7rjjjnD55ZfX+rjmzZuHjTfeOFnxOv7vsssuGzp16hQ6dOiQFMPHFb7jitHx74jFoPF3tXUwiCudxy4qhTrhhBOSUElaZ5XRo0eH008/PVx55ZWhPuLrcOONN6aOx04XAwcOTP5+Cut8cOqpp9apSHjddddNilTjed61a9fkWHfs2DEJPsX3TFxJ/sUXX0zOt3jupYndHGIxeVwlfY011qjzvnbu3DkJScV9TguWxNXX69v1pDaDBw9OHVtnnXXCqquuWvA247UlhsZiAK428Xj37t07eb+vvfbayfGIv2vZsmVyvOPP66+/nrzXY4FoXI0+TXyvxuLwSy+9NOQlFsuec845NY61b98+bLbZZqFPnz5h+eWXT66/8Xdx1flYMByv2c8991wYN25c5nPEx8a/4aKLLsrpr2gaRowYEc4777zU+0x872+99dZhlVVWSV6reO7F93F8vWKno1jYPnTo0Kq63lB68XWNgd20jj89evQIP//5z5PXMc7X4nm4IBASz4fY1aEhAcAYgrjuuutqHIvnX7xexetWnGvF90GbNm2S4NzYsWOTblzxueN7Ikuck910003JfKsxiHO6rJDZRhttFMohXm9uvvnmGscWWWSR5DyJr1W8L8fXLl7HJk2alIRD3njjjeRalDUnXnB/iZ3QYqgvvtZ5iYHBtFDHUkstFfr27ZvM8bt37x4WXXTR5P0R3yvxXIpz43gsauuSFecG8Rp//vnn5/RX/M9xX2uttVK7xsT9jHMYAAAAgGJqNj/rv4QAAAAAABTZgQcemBSh1SQWUH344YcL/X6DDTZIClnrUnAXAxexKLq2FdvXX3/9cN9994WGikVosRtCVmeHuKJx7O7w61//Oilkq4tYoBe7uNx1112Zq33HovgYnGnWrFnB+z5q1Khkv7KKAc8666zwy1/+sqDtxuLB/v37ZxYnx44uscCwvn7xi18kr3VN4uu/++67h2rzySefhH333TcJLaWJxZ7bb799Eq7o2bNnnbYbz6/bb789CRplddlZccUVk7BJ69at67zPsdg0rhyeJhaybrHFFqHY/vOf/yTnYDHP6+gPf/hDZiAmisGOuPL7XnvtlRSG1sUHH3wQLrnkkqRYtbZg3x577BHqI4bcTjvttBrHFl988dC2bduFVquPBcS/+93vwn777Vfr6x5Xh4/n0TXXXFPrOfroo48moYr6imGKtI498Zp28cUXh8YsXpu32WabzHPsp69VvAfE617sEFGXMGO8Psdi6hguqJbrTV3nGjGEcPTRR4dyvHbPPvtsEuwptrz+3rTzI+08jOLff8oppySvYW3iORDDCTH4VFNA4Oqrr67x38W5VDx/YzDqp/sU3wMxnBzPzywx4BGDL7fddltmd4t27dqF559/vlEEbeM9NS3gGEMVMbCTh6z7y4LQ0E/PlTg3Puigg5LrR23HNl6H4nw4vl5Z16QF74Uzzjgj5HF/WXPNNZPw30+DJ/EeGue18Rqc1WkuigHP2JEwHrPayjTi+b/tttuGvMS5UNpnofiZ65577sntuQEAAICmKfsbOwAAAACAEvppaCV2YoireceV1+u6SnSXLl2SQq9YOJ4lrjCc1m2kruKq4bE4Myu0ssIKKyRFYXHV5LqGVqK4Un3cdiwaiyuVp4mFqg8++GCoj1g8e8EFF2Q+5s9//nP4+OOP67zNuDJ/DBllhVbiCs6xUJHCOn3E7iRZBZtxNftYtHrFFVfUuYg8igWl8fWIhZpZhcZffvllEkooROy4Egs609QWAqmvtKLTKBaV7rTTTgVv8x//+Eet+7vLLruExx57LCmcrWtoJYodWWIALRZcZ4nhllisXWyx68BPi4pjx44YPDr44IPrFB6IxzWG82699dakG0vWNSL+rdTfT1+rWJAeO4nFYFNdO3DF+0paKKGxXm8o73kY3/cxkHTttdfWKbQSxY4UNYVWahO77P00tLLnnnuGJ554Igkt1hZaWbC/J510UnIOZ4UN4vsgvr8agxiCTLP66quHcoj3rJ+eK/H6E+8DcZ5bl0BQDA/F60ac79Z2bsWAS126otU3FPvT0Mp6660XhgwZEvbZZ59aQytRvEbHa3W8vsW/K0v8DPTT87yYsoJpH330UebnGwAAAID6EFwBAAAAACpSLPqOAZS44nt9xM4I/fr1y3xMQ4vm46r+WQGNDTfcMCnOjZ1k6isWtMfgS1Yxcly5OasrS5btttsus/NE3G4spo8rk9dF7BLz6quvpo7HYsW4Gnhdikr5/2644YZkle80sZAzns+bb755vQ9bDFnFkFhWIWNcmT521KmrWMS58847p47HYESxizJjUWns6JHVrafQlfPHjx9fa8grFuD+5S9/qbUQNUvsbhJDbllhuXgu5C2uGh+LamNReaFih6zLL7888zH//Oc/k3AEDbfccsuFu+++u17F/9V2vaF84rXib3/7WxJWLIff/va3yTU6BqMK1bdv33D66adnPiYGExp7cCXrvVZKMcwaQytxjlyo2KnrzjvvzAyCxy4mMRhSCrF7Yzzv6xoY/GlnoptuuinznI3XvxgGy0vWOTFjxozw2Wef5fbcAAAAQNPkvwwCAAAAABUpFhHGAvOGOO2000KHDh1Sx59++ul6bzt2IckKvsTC3rjqeH0Kv2vqjDJw4MCkA01aYdsDDzxQ7+2feuqpYc0118xc+T6u+lybt956KwkbpYlhldgxIquDDAuL4ahbbrkls/NBLH6sT+FkTQWlcfX5tPDFzJkzk44ahYYg0sTtPf7446GYXnrppcyuJAMGDCh4m7FwNIZG0hxyyCHhsMMOC8Ww9957h7322it1PAbZ8ui6ssDGG2+cFIC3bNmy3tuIxevbb7996vjkyZOTblE0TAyGxXOzkG5e1X69oTzi6xg7T5RDDEced9xxDdpG7JaRFf6KBfxffPFFqGSTJk0KX3/9der4aqutFsqtWbNmybkSAyj1tfTSSyfz66yuZu+++25miLoY4lw2BjyzOozV5X579tlnZz4mBnXiPTMPtYWZ3n///VyeFwAAAGi6BFcAAAAAgIoTu4DE4u2GisVtWUXzMfARQxn1EQMacVXnmsSC7zgei3KLZZNNNkmK47OK2eurdevWmcXDC1Ybf/jhh1PHJ0yYEE444YQwd+7c1MccccQRDVqhv6mKReJx5es0MQzUrVu3oj3fyiuvHE466aTU8XgeFNIto2fPnmH11VfPrfNRIdtbYoklwhZbbFFwIf+9996bOh5XjY8dnorpjDPOCF26dEkt5i/2MVugY8eOReuIFDsgZHnzzTcb/BxNXbymZr23muL1htLr379/6NOnT1kOfQz21iVYW5t4zTvyyCMb9TVrxIgRmeNp95RSnys/+9nPGrydVVddtdawaOyEkqcTTzyxKEHsPfbYI5njp5k+fXq4//77Qx5iuD7e9+t7TgEAAAAUSnAFAAAAAKgosXjwlFNOKdr2dtlll8zx9957r+Btjhs3Ljz33HOp4zF0k8fK1r/61a9CmzZtahwbPnx4+PDDD+u97dghprbiz/PPP7/GIrYY4IldW+JxySruP/roo+u9f01VDClkBYZiZ4tCgxh1ETt+LLXUUjWOxc4jL7zwQkHbywqQvf3225mrxBe64vzzzz+feT0otJNIPP7xdUhbPT52dipG0OO/xZXkDz744NTxvIIr8TmXX375omwrdnHKWlW/Ptde/r/Y8eQ3v/lNUQ9JtVxvKJ04Jyl2cK8QRx11VGZnvUJsueWWmUX8lX7N+uabbzLHi9ElqSFiODoGnIvl8MMPD8suu2zq+Msvv5wET/Ow9tpr16t7W1aXyax5RF73/CjrGI4ePTq35wUAAACaJsEVAAAAAKCibLPNNmGFFVYo2vbWXXfd0Llz59Txzz//vOBtxsLetM4irVq1Sgop8xBXdt55551Tx1988cUGbX/XXXfNDBjEVZ+PP/748OOPPy60qnVWYXGnTp3CZZddVnBggBCeeuqpMHny5NRDEV+PPMQuPPvtt1/RzrWswEgMPhWrKPOxxx7L7M5Qn0LTBx98MHXsF7/4RXKNyUMs5m/fvn1qUG3s2LElfc3rIwYd0ljJvWHiuRxfs2KqlusNpbPttttmzrHyFMNOO+20U1HPw80226zRXrNqC65kBRRKYc8990w6IRZL27Ztw6GHHpo6HufpTzzxRMhD7CgWg6vFEoOeW2+9der4p59+mtz385D1mtR2TgEAAAAUSnAFAAAAAKgosbCt2FZfffXUsfp0enjmmWdSx3r37h2WWGKJkJc+ffqkjr366qsN3v5ZZ50VVl111dTxTz75JFxwwQX/+///+9//TkIpWeLjl1tuuQbvW1OUda7F1ykWO+Ylq7NCoedaDF1lnbsxuBIDLA2VFYCJx2qNNdYoaHuxWHTkyJH17ujU0NXpN9poo9TxYcOGFfX5fv7znxf92tWjR4/UsW+//XahEBx1lxUybOrXGxr3nK2u4vW32OGtrGvWqFGjQiXL2r/FFlss6eRVTnncL/v165fZqSR2XSm22JUnducp9fHJ42+JdFwBAAAASklwBQAAAACoGHH14g033LDo211ttdVSx77//vuCtvXDDz+EDz/8MHV8xx13DHnaeOONU8c++uijBhf/xxWsL7/88swCx/vuuy88/vjjYcqUKckK/LNnz0597IEHHhj69u3boH1qqubMmZMZTsj7XOvZs2cSnqjJ6NGjw4QJEwraXla3k1hw+9Zbb4WGiN2T3nvvvXo9f5qXXnop872StUJ63u/3rOtQsZ8rj9DggusphVtppZVC9+7di3roqu16Q/7iNbBXr15lO9SlvmaNHz++KAHLvMQwYJrFF188lFO3bt1y6U4Wu4VknYNxXpHWIbG+tttuu6IHphZ0cEu7BkZvvPFGyENWYDWe81lzfAAAAIBCCa4AAAAAABUjBkziSsbF1rlz59SxSZMmFbStd999N7NwMY9Cyp8W6XXq1KnGsalTpxZlRfBYuHn66adnPubMM88Mxx13XObzrbXWWuGUU05p8P40VZ999lkSDirXudayZcukQD7Nxx9/XHBRZtq5W1u3lLoYMmRI6lirVq3qtdr7O++8kzq29tprJ4Xbecoqoi70+Ncmj9Bg1rW3Ptdf/v+1tdiq7XpD/mIHqxYtWpTtUG+wwQZF32ZWwCMGILLeI+U2ffr01LFyd1vZbLPNctt27HSYJr5eWV3Tiv18DRFfo6z78AcffJDL87Zp06be5xUAAABAoQRXAAAAAICKkVW02hDt27dPHfvxxx8L2lbsapJVdNalS5eQt6zi/7gyfTHsvffeYaeddsosBnz55Zczj3ns3JLHqtRNRW0dNVZZZZWynmvffPNNQduK50K/fv1Sx5944okwc+bMUB/z5s3LDK5sueWWtYYoCn2/r7zyyiFviy22WNGOfzmuv1nX3vpcf8kvuFJt1xsa53lYyLlSn2t6bTp06NBor1kzZsyo2OBKDDmVa9uffvppSZ+vIXr06JE69t133+XSeaq2c6O+8zIAAACAmrSs8bcAAAAAAGWQVaSdV/H0rFmzCl6VPs3yyy9fkuLarC4PY8eOLdrznHfeeckKz/VZrfrcc88N3bt3L9q+NEVZ51q7du2SAtpidNjJkhU8qs+5NmDAgHDPPfekdgx6+umn69UZ5fXXXw9jxozJfN5CxVXGs97PsWg67+OfVSQdi1hjYKd584avURY70tQWMqmP2rZZ6PWX/5HHtbUarzfkq5z3+KyQU1O9ZmUFDGrrqlHNwZVidlyJx3HFFVcM5fxbsroC5RFc0XEFAAAAKCbBFQAAAACgYnTs2DGX7RajsLsuxbOff/552GabbUI5TZo0qagrj1922WVh3333DbNnz67zv9tjjz3qFT6oVHPmzCl60XTLli3Dsssum/mYrOeMhYSN8Vxbd911k84NI0aMqHF88ODB9Tp3Bg0alDoWizy33nrrgreZFYSJrrvuuuSnXOJ7ctq0aWHRRRdttNfe+fPn5/K81a4Yr3lTuN7Q+M7Dcl+zmjVr1mivWVkdV8odXMmro2K0zDLLJOG6tIDFt99+W9SwVosWLUJeauvkFgOrxVbbuaHjCgAAAFBMgisAAAAAQMWobdXfSlDMArhSd2ioj7XXXjuccsop4YILLqjT41ddddVw5plnhmoSC7qLXbTdpUuX8NxzzzXJc61///5h4MCBNY4NGzYsjBs3LilErasY3oidWtLsvPPOSUeRQlX68V/wGhSjeLwxXHvJNzDQ1O5tNO7gimtWzYHYNHPnzg3llFfQ6L/PxbTgyg8//FDU58lTbdv//vvvcwln1/e8AgAAAChU8ZYZBAAAAABoAqZOnRoq2axZs4q+zYMOOij07du3ToWkl19+eWjbtm3R96EpqtZzbbfddkvtxDFv3rzwj3/8o6DtPfXUU6kFq9GAAQNCfcRATKVTzN80tW/fvujbrNbrDY3rPKT+suZe5eyaEfcrzy4lCzoE1qcTTTGfpxTbL+bfUtdzw5weAAAAKCbBFQAAAACAApSz+K8u5s+fn8s2a1uROfrFL34RVl999aI/f1NVreda7KbSu3fv1PHBgwcXtL1BgwaljsXzca211grVePzzer9T+dKCXw1R6ee7c71pnIdUX3Al77BHbc9RzIBnuYMreYRVa9tmu3btiv6cAAAAQNPlG0UAAAAAgALMnTu3yR2v2267Lbzwwgu1Pu7xxx8PQ4cOLck+NQXVfK7tvvvuqWOfffZZeP/99+u0ndGjR4c33ngjdbx///6hvuoS1oJqUc3XG2jqwZVydudq1qxZWUNUxQy95f231Lb9PAJ8tYWaYjdFAAAAgGIRXAEAAAAAKECbNm2a1PH697//HQYOHFinx8aCuj/84Q/hu+++y32/moJqPtf69u0bFl100QZ3XRkyZEhqIWeLFi3CrrvuWu99rObjDz/lfIfGrVOnTqljU6ZMCeUyderUsj5HMa9t06ZNC+U8Vnlcp2s7dlmBKAAAAIBCtSz4XwAAAAAANGFx5eEZM2bUOLbuuuuGBx54IFSLWMx2wgknhNmzZ9f53/zwww/h5JNPDrfeemvmCtjULmuV6yWWWCIMGzas0R7GWAy54447hvvvv7/G8UcffTSceuqpoVWrVrUGV9L06dMnLLXUUvXex9qKNc8+++yw//7713v7UEmq+XoDTcHyyy+fOTebN29eWeZlcc6c93NnhS+K2TEk7xBObcGYPLqfjBs3LnVsueWWK0nHHAAAAKDpEFwBAAAAAChALOCdMGFCjWOTJk2qqmN5xhlnhK+//rrgf/fqq6+GG264Ifz2t78N1aBr167hk08+Kcu5lmby5MmhsRswYEBqcGXixInhxRdfTDqzpHn33XfDl19+mbn9hujcuXPmeLW932naqv16U+3SArU0HV26dEkdmzNnTtINb5lllgmlFruixUBGVpe1PAMlWde2Yj5PKbZfzL+lLsGVOP8FAAAAKCbL3QEAAAAAFCCuPpwmFttXi/vuuy88/vjjqePt27fPXIX5qquuCm+//XZOe9c0ZJ1rsQtO3gWUedtwww3DiiuumDo+aNCgzH+fNb7YYouFbbbZJrfV66vt/Q7Vfr0pldq6E8Qi/jwI0pEVXKktoJC3kSNH5rbt8ePHhylTpqSON6Tz2k999dVXub2H63Kcivm3LDB27Nh6n1MAAAAAhRJcAQAAAAAoQFahfVyVvhpWPR8+fHi48MILMx9zySWXhMMOOyx1fO7cueHEE09U3J/TuVbuItRi6d+/f+pY7LgSC1JrMmvWrMxgVb9+/ULr1q0btG9xZfMOHTpU9fGHpnS9KYWWLVtmjsfOE3nQFYcVVlih3gGFvH388ce5bbu2jni1HZdCxPfvqFGjQrmOUzH/lgVz9e+//z51XMcVAAAAoNgEVwAAAAAACrD22munjsVVmN95551GfTxj8Ob4448PM2fOTH3MAQccEPr27Zs8bv3110993JgxY8If//jHnPa0aZ9rUTV0tNltt91C8+bNU7s8PPbYYzWOPfPMM5mF2gMGDChK54S11lqrqo8/NKXrTSm0adOm5MGVGPDTAYru3bsn3fDSjBgxoiqDK7Vte7XVVivp8+W17U6dOoWll166qM/35Zdfhjlz5qSO9+zZs6jPBwAAACC4AgAAAABQgKygRvT666836uN5/vnnh88++yx1fM011wx/+MMf/ndl+YEDB4aOHTumPv7ZZ58Nd955Zy77Wu1iaKJVq1ZVe65Fyy+/fNh0001TxwcNGlTj7wcPHpz6b1ZeeeWw3nrrFWX/Nthgg9Sxb7/9NnzxxRdFeR4KDxVRXE3helMKiy66aGoYL5owYULRn/Pdd98t+jZpfGoLW9bWmSRPb775Zm7bfuONN1LHFllkkWROUKrna4gYIMl6L+cRIqntnKgt0AgAAABQKMEVAAAAAIACrLjiislPmhdeeKHRHs9HHnkkPPTQQ6nj7dq1C5dffnlo3br1//6ua9euSdglyyWXXBI++uijou5rDM2k+fHHH0M1iMe7V69eqePDhg0Ls2bNCo1d//79U8c+/PDDhYJU33//fXjllVdy7baywFZbbZU5/uKLLxbtuai7Fi1aVP37v9Sq7XpTrntEPDc7d+6cOj58+PCiP+dbb71V9G3SOGUFDcoZXImdRPLo+DJp0qQwdOjQ1PENN9wwM5BXH48//niYN29eKLaXX345s3NS1vW5vrLOiW7duiVdXgAAAACKSXAFAAAAAKBA2223XWYRWGMsZh85cmQ4++yzMx8Tx1daaaWFfr/DDjuE/fbbL/XfxWLn448/PkybNi0US5s2bVLHZs6cGZrCuTZ+/PjwwAMPhMZu++23D+3bt69z15UYsIork9ckdjrYbbfditp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nX3/99fF///d/8fGPfzxGQhrIf/755w95+c997nO9ghhp8PPKK6+8xGVSSKdn0CQd29SektpHUqBgSVIAKQ28v++++7o1WaSASHqMBuv0009fHCR4//vfXwZIUpihpxQg6nwedd1uX6GVXXfdNT71qU/FGmus0eu29Ly/9NJLy9tTUKFroCANrE+PRQpzjJTvfve7ZfNEV294wxvK12B/A+iTNPD/4osvjp/+9KdD2v7//u//lq+NFAJKDSqpQWSFFVboNV9qJknH96ijjup23NNr5Le//W287nWvG9D20mt+n332iVe/+tXlQPn+mrFSyCIFDD772c92a2u56KKLYo899igvQ5FCSZ1/R1LjSHr8N9544z7n7fk8Sz796U/3Cq2k5VMoLwW9+gpVJSmUk0IPKaCVnmOd9zEF6371q1+VoZmRlMIzPaWAzWhIz7P0GKawWDoGjz32WLfbW1paysd5ScduJBqmetpll136XS4FjFKYrDNQlcJVKRzZV3glvbemYEun9F62995795ovrSs1VnUNc6a/T6Opr4BKer4IrgAAAEwe+skBAAAAAABgkHo2Z/RF48r41DkIPIUp0sDhJYVWktTukQbWv+IVr+g2/dlnny0Hyw/keXLEEUd0O6t+GjicwgJpoHh/wYDNNtsszjnnnLKBoqsf/OAHZevHeJPuVzprf1fpPqbp6Vj2JTV4pIBQ19dUatpIQYj3ve99Sw2tdLYHpLBTz8HvX//613u1GQxEChOkMENqf0mhkr5CK11bVDrNnTs3jj/++F7zvPnNb45vfvObfYZWOr3qVa+KH/3oR73mSU0jy9LqMRBpEH/PNocvfOELAwqtdIYlUogntdQMpZ0hvR5TUCC9zlIYq6/QSqc999wzTjrppF7Tf/aznw1oW1tuuWXZ3PKRj3yk/L2/0EqS9m2nnXYqn4+bb755t9t6PtcHozO08prXvCbOOOOMJYZWej7PkhTg6RpGSHbbbbdyWnqvWFrwIgXC0usqhe+6tjalpqs0baTdfPPNvYJESwu1DVZ6b07BjK6XFOhI4bHU4vPa1742vvjFL/YKraT3p/Q6Tc+L0XD11VeXgbWu0uO2//77D+i/LdJzuKsUNEvhvxTgS0G0FERJgc8UBOv63poCYH29xlLrUWqh6ZTey1KQZzT1FWDqK+gEAADAxCW4AgAAAAAAACPQuDKQQdGMjTRgOwUC1llnnX7nTYOJezbAJJdffnm/y6Zwy5133tltWjqjfwoHDFQaPPzVr3612+Du1JRw5plnxniS2hpOPvnkbtNS6OPb3/52bLDBBktcLgVzHn/88W6vm3Tm/8G0MKRGkjTovOtA/KeeemrAgYae3vnOd5YD3AcjhQbS4P+e7TWpGWMgDQ4p9JEe557zplBOz/UOlxQaSsepqzRwfiiNE6lFaKgD3dPxfuMb3zigeVMIoWe44Nprr+2zlaSnqVOnlvs5FCuuuGJ8/vOf7zbtb3/7W9k4M1Tp/ScFdro+b/uT2lG6NmMkqZEivZ8N5vinMF5qNOr5XHviiSdiJHVtrUlSc8hwajQacfvtt3e7pPfgBx54oGyb6Sk911N4KDUb7bjjjjEaZs+eXTbj9PXcHmi7yF577RXveMc7uv1NSMGj1CSU3ndSGC6F77qG91JIs68WlT/84Q9l8Kxr49Jg/kYNlxRS7Bmqueeee0Z9PwAAABg5Pi0HAAAAAACAEQiuaFwZv9Kg3y222GLA86c2hBe84AXdpt1yyy39Lnf66ad3u77DDjsMOhDRGcw48MADu037/e9/H+PFlVdeWQ7EToPqO6WAQApiLK3BIDUDfP/73+82LQUYtt5660HvQwp+pHaFZT1GqXkhNYgM1g9/+MNe04477rhBBdhSI8Q+++zTbVpqQUiBgpGQWmJ6WmmllWK0B6sP9ningfU9wwp33HFHjLRNNtmk1/vAddddN+T1HX744UtsIlqS1Bjzj3/8o9u0Y445ZlDhl07vete7um0/hX/+/Oc/x0hJr/d///vf3aYtrYlopG2//fZlYCW1Mw2lLWgo0ntkattKbUpdpWBiahwajE9+8pNlo1fPRp6e0nvxu9/97rKRpWcoLb2/dG0xSsGRo446KsbKmmuu2e16z+MEAADAxCa4AgAAAAAAAIMkuDKxvelNbxr0Muks9ktrDugpneG/Z7hlKNvttPPOO/ca0DseBvWm+5iCB+mM/12deOKJ/TYYpMaKxx57rFfjx3AdoxtvvLEcLD8Ye+6556DDBA8//HCvZoAUjFpaaGdJDjjggF7TrrjiihgJKaTScyD7sgQxhiIFuVJ4ZTD6Oq533313jIae7UE33HDDkNaT7vMee+wx6OVSi1NXz3nOc8pA3FBMmTIlXvziF3ebds0118RIefTRR7uF25JVV101xkpq6knvySn4c//994/KNlMLStd2kyS9BlMT11COxfvf//743e9+V7bnbLXVVjFr1qwyxJRe2ylk9d73vjd+85vflKGYvsK0qanqoYceWnw9BWFWWWWVXvOl5pYf/ehH8Z73vCd22mmn8u9heu6ksGBqIlqW5qGuVltttV7PGQAAACaPofXgAgAAAAAAwHIsz/N+59G4Mj6lwdpDCRSsv/76vcJLqbFiSSGHNCi6p2233TaGat111+017bbbbot11lknxkoa7P2+972vV3NHGvzcszlkIMcoDbjuGRBalmOUGiTuuuuu2GyzzQa8jp4D+Qfi+uuv7zVt9913j6FIz810P1LwqWs4Ig347xkyWVapqWHTTTeN22+/ffG0M844IzbffPN4zWteE6PVejFYfbVjzJkzZ0jbv++++8rjmxpb0vM5rSddUuCpZ8iiM6S0tOsDlUIG6b1osHoGS1JLz7Lo+Zq59dZbY6T01fAzderUYd1GCm70bN9Jobpnn322DPrddNNN8etf/7oMzXW+R/z85z8vwx0pwLIswbn+pOBHzxauzvDJLrvsskwtJam9J10G48477+zWeJXee97ylrf0mu+yyy4rj03PEEl6jTz99NPl+8dZZ51VtoIdeeSRg2qZ6qnnayK1KS1YsGBIrxUAAADGn0kZXElne1jaWa7SB5791aWOhvQhSM8zD/WsbPUPcAAAAAAAgInZuLIsgzcZOWuvvXb5feFgzZw5s9e0NMB9ScGVvsIM6az4w+mpp56KsfLkk0+WZ99//PHHu01/xzveUQ7EHoi+jtGytNL09d3rYI9RCm0MVs9mnWRZAjipraVrcGX27NllwKJn28dw2Hfffcu2h67fYR922GGx9dZbxxvf+MZ41ateVQ6MHympMWQ4XovpGA0meJjCCj/96U/j73//eyyLFIgYiqE8z1JwoGfLUgoVpNaLoer5+h3J95Q0jqOn0RgPkd7vU4tIuqRwRnqPuuqqq8owRufxnDdvXnk9/UwBjOH2q1/9qmyh6ikF/P7nf/4nRlsKZR1//PGL3zNT0PYzn/lMr/9uSQ0/H/nIR5Y6rqUzYJJCbynI9eUvf3nIIbu+ng/pMTFuBgAAYHKYdMGV9AHeXnvtVX6g1peWlpbyH8zbbbddjLWbb7453vWudy3xH/krrLBCXHzxxbHyyiuP+r4BAAAAAACwbMEVjSvj04orrjik5dJJ53pa2mDeRx55pNe0rs0WEzm4kgYSp6aVe++9t1fLSBr8PVA9j1E6nsN9jFIjwGCkwe3D8ThstNFGMVQbb7xxn9sYieDKAQccUDZQpCaKrlILSbqkwe1pu+n79RSoST9TS8twtb8M5fXYV/AsDZwfiNTA89GPfnTYmkWG2vQylOdZz8aLzuBJz/DJaL5elvXv5lgFPF/60pfGOeecE29/+9vLUFin//3f/y1DZ8vaZNPV7373uzj66KN7NbWl98uTTjpp2JuUBuIXv/hFXHfddYuvpzBPz2aq1ECU9rvr37m0z+m9/3nPe175XLnoooviq1/9avk3IUnXU+jtne9855D2q6//bhrIf28BAAAwMUy60zx97nOfK6tC0xkiel46P2gYD6GVJO3H5z//+fL3vvY3nZ3lS1/60ljvJgAAAAAAAD0IrkxcQ2lbGW8DwDst6WR+IykFBA4//PBeTRU77LBDfPGLXxzUQPTROEbpu+PBmDFjxrC0bqSTFA5VX8s+88wzMRLSiR+//e1vx8te9rIlzpMCSueee27ZyJDaPV7ykpfEkUceGX/60596DcYfjkDYSLnzzjvLAfrDFVoZTGBmOJ5noxFUG8n3lL5aM9ra2mKsrL766uV7VtfgSPrb/oUvfGHYtnHppZfGEUcc0et5sssuu5RjQcYi4JqeRyeffPLi62ussUb5nt7T//3f/8XcuXMXX3/zm98cX/va18oAW2tra7lcCqicdtpp3V7HKcgy1Caivp5/U6dOHdK6AAAAGH8mVXAlnRHikksuKT9Y6HpJ0s905ofXvva1MZ6k/Un7lfTc7xReSR8AprO+AAAAAAAAMH4MZLC2xpXl21AH7o53xx57bFx22WXdpm2yySbxzW9+swxBTPRjNJQgRdfB3Un6rndZBltPmzat320Mp1mzZsUZZ5wRX/7yl+MFL3jBgAJHv/rVr8rvuffcc8/47W9/G+Ndao34n//5n3jyySf7POHkYYcdVgZ4zj///Ljqqqvi+uuvLwMud9xxR7fLPvvsMyz7M5S/D+Px9TIYfb0mBhssG25bbbVVvPzlL+82LT32//jHP5Z53el9MgVCejZz7bjjjmW4Y7RClD2l0ErX0OAnPvGJXkGqf//73/H73/++23vEUUcd1ef6tt9++9hvv/0WX0/tK2mcy1D09XwQXAEAAJg8Ru/0JaPgG9/4xuLfU+ijM/yRfqYq1w996EMxHqX9uvbaa8sPQLruc5J+Tx/yprNZAAAAAAAAMHEaVwbTPMHkk85I39NNN93U5/SJIrUT/PKXv+w2ba211orvfOc7Q2oYScei66DuVVddNa644oqYaKZPn97tevqOd/78+UMecJ0Gfve3jeGWvp9OIZR0SSdWTG0q11xzTfztb39bajPOPffcUw7Of+tb3xrHH398t/aK8eSnP/1prxNGrr/++uX38C984QsHvJ6xDFr01Vjy3ve+Nz760Y/GRLDKKquMWpPQYLzyla+Myy+/vNu09Nx/3vOeN+R1XnnllXHIIYf0apRJIZk0rmWwIb/hPBls11BJCtHsscceveZL4a2u782ve93rlvoelNpYfvKTn3QL7aQ2lsHq+XxYaaWVhIABAAAmkUnzafktt9xS/uO5Z/AjSf/o//znPz9uvxxI+5X2r/ND6q4tMem+XHzxxXH//feP8V4CAAAAAAAwmOCKxpXl28orr9xr2tICAOPdD37wgzj99NO7TVtxxRXL0Moaa6wxLMdoPAxiH4q+QjvL0o4xe/bsXtPSsR4tz33uc+Pd7353nHrqqfGXv/ylbFQ56aSTYu+99+7zeZ2cc8455fzj1a9//etu19Mg/DPPPHNQoZWxfo72dewn0msmhRB6hh8eeeSRGGvrrrtur2nLMj7j6quvjv/+7/+OhQsXdpv+kpe8JE455ZQxCy82Go0yXJbGoCRpP4477rg+500hy65e9KIXLXXdqampa1PUjTfeOKR97Pl8WHvttYe0HgAAAMan8ZnkGIKuZ2/o1BlgOfjgg2O99daL8SztXzobSueHBJ0/O3/v6/4BAAAAAAAwNgRX6E9qD+npoYcempAH7qKLLorPfvaz3aalQc/f+ta3YuONNx62Y5TO8P/YY4/FRDNr1qxe0+6+++4hr++f//xnr2lLCoyMhuc85zmx//77xxe+8IWyEScFVPoKfHz729+OJ598MsabuXPnxg033NBt2hvf+MY+Awv9GcsTTvbVWDLR3lN6HvPxEFzpq/0kPWeGIjW1fOADH+jVzLPDDjuUr5u+WnNGy/e+97248847F19P+7mkcTQ934c32GCDpa47jcvp+tim8N1g24nyPI/HH3+827ShvEYBAAAYvyZFcCWdqSKdIaVrU0nXD2/e//73x0Twvve9L1ZfffU+W1dSXWv6hzoAAAAAAABjbyDf22hcWb5tueWWvaZde+21MdGkxo2Pfexj3Z7z6bn9f//3f7HddtsN+zH661//GhNNahvo6e9///uQ19dz2dTosv7668d4kB77V73qVWXDyo477tjttvnz58dll10W400ahN/zPbu/Bom+pEH1YxlcSeGB1FrS1fXXXz+gIOV4semmm3a7/uCDD5aBtbHUV9hqKEGx9N6Vxqak10HP51oKdU2dOjXGysMPPxzf/OY3F1/fcMMNyxPADrT1qWdTTl9mzJixTG1A9957b9kKs7TnCwAAABPbpAiupA+/5s2bV/7etbEkhT7e+c539nmGjPEo7ee73vWuPltXnnrqqbJSFgAAAAAAgLE3kIHCWTYpvoobdX0FfibiCd5e/vKX95r2+9//PiaSO+64Iw455JBoa2vrNv24446L3XbbbUSO0e9+97uYaLbZZpte0377298OaV0333xzPPDAA92mbbXVVt1O3jge1Gq1+MhHPtLnc2a8Sd+197TiiisOqXloLKW/KS95yUu6TUvjJC6//PKYKHo29aTQyrK0Ew2Hnm08S2rMWpoUIHrve9+7eNxKp2233TZOO+20mDZtWoylE044odu+HX/88UsdR9PztoGEi3rOk1q5BuP222/vNa2vZicAAAAmrknxaXnXD3i7fmCX/vH/tre9LSaSt771rYvPRNHzw8eJ+CEtAAAAAADA8hpc0bgyNH2d2b3nYOCJYOONN47nPOc53abddNNNcdVVV8VE8NBDD5Vn5O955v0PfvCD5Xeaw2H77bePmTNn9vpO9F//+ldMJGuttVY897nP7RVASZfBOvvss3tNe8UrXhHj0UYbbdRr2pw5c2K86Ss00FeYpb9B+T/84Q9jrO266669pqVgxESx9dZbDyiwMFoWLFgQf/jDHwa0n0ty44039hlaSYG2008/fUBtJSPpj3/8Y1xyySWLr7/hDW/oFYDqL9jVVytNT13nSWNdUlPUYPR8HqR1CK4AAABMLpMiuJI+3O0a8uhsW0ln+RnrM1cMVqqHffWrX92tbSXdl3T9iiuuGNN9AwAAAAAAoIPgysjpqwnh/vvvn5BPvQ984AO9ph177LHx9NNPx3iW9u8973lPPPbYY92m77///vGhD31o2LaTvst95zvf2eu1deSRR/ZqeRnv3v72t/fZctD1e9/+pGDTL3/5y17HaN99943xqOfzI1l55ZVjvFlttdV6TRvsd+/f+MY3xkWgas8994wNNtig27S//vWv8b3vfS8mgi222CJWWmmlbtOuu+66Mdufb33rW/Hvf/+727Q111xzwIGJFE5L75U9A1tbbrllfOc731l80tKxMn/+/DjxxBMXX09hkqOOOqrf5XoG8W677balzv/ss8+WYcdOG2644aBb53o+D17wghfErFmzBrUOAAAAxrcJH1xJH1J3fiDW80O/dKaIiajrfne9T6kS+tFHHx2jvQIAAAAAAKBTnuf9HgyNK0PzvOc9r9e0P/3pTxP2e7+erRTpO7/3ve99Q/7eLw2QTmfxP//882OkGghS4Obuu+/uNv1Vr3pVfOYznxn27aXgSs+wQwpwHH744b3aXgbqiSeeiC9/+cujemLAN77xjb0G5N9www1leGUgHnzwwfI+93xv2W+//QbdXDBQ119/fXz/+98fckvKWWed1Wva85///BhvVllllV7tRxdccMGAmz5+8YtfjJtWk1qtFocddliv6SeffHKcc845y/Rc+MhHPhIjLYUZejYI/eUvfxn0ei677LL46U9/WjbhDFUK+5x66qm9pr/jHe/oduLUJUnPnxRa6fk+lcI5Z5xxxpiHVpJvfvOb5XtLpyOOOKJ8PfRn22237Xa9r1aarlKjS9exLT2XH8jfnfR+2dVOO+00qHUAAAAw/k344Mrf//73xb93/fCgtbU1dthhh5iIXvSiF8WUKVPK33t+IJJqZgEAAAAAABj/jSuDPds4HdLg/55hj3PPPbccZDzUIMNYSeGlr371qzF9+vRe3/nts88+8aMf/SgWLlzY73oajUZceeWV8clPfjJ23nnn+OIXvxiPP/74iOzzxz/+8fjb3/7WbdrWW29dBkFGIoyVBnendacB+V1deumlZdPIr371q/L+9ycdxzS4+qMf/WgZskkD0kfz+ZIe40996lO9pqfHOAVSlvZ4pWDW2972tnj44Ye7TV9nnXWGteGmpyeffDI++9nPlgPEjzvuuLj88ssHFASYO3duGZRIoZeuZs6cWR778ei1r31tt+vpfh588MFLDU2kFonUVnHMMccsDhSNhzDC61//+l4tPOk1kp5/KdQy0EDOI488Uj6Gb37zm+OAAw6IP/7xjzEadt11127XU5NN17aOgUgtKen9cPfdd4+vf/3rcddddw142RSQSIGTz33uc71u22STTXq1QPXln//8Z7zrXe/q1Z6VWkLOPPPM8rUw1tI+dm3iSS0wb3nLWwa07HbbbRfrrrvu4uvpdXLNNdf0OW9qx/r2t7/dbVr6+zYYqTWo53tPz+cJAAAAE1/3T/8moH/84x/drqezOKSwx1ZbbRX1ej0morTf22yzTVx11VW9git33nlnvOY1rxmzfQMAAAAAAGBgwRWNK0OXWibSwPiuxzsNMv7f//3fWHPNNctBwT2DQW9961vLwdfjTRoInYImaUB51wBGagVJDSZf+cpXYvvtty+/35w1a1Z53+bPn18Omk9BhltuuSVuvfXWIbdiLMuJA7vuazq+yyI1Vqyxxhp93vbSl760DAd8+tOf7jb9vvvuiyOPPDI+//nPlyctTIPC0zGaNm1aGZ5IwZT777+/PEa33XZbedb+sbTnnnuW4Y/U0NHVb3/72zKcsuOOO5YDwldbbbUyaJPad1JYIO17X98Zp9fAaAyAT8+tn/zkJ+UlBXDScd5ss83Kgeup7WXq1Knl/qagQ3oupvvYVygohZ46T9A43qQwQgoRpddV1/DDgQceWL7+UgtICgql7+dTyCiFt/785z/HvHnzuj1P03P4vPPOi7GWXivp9ZEG/Hf1u9/9rryk5pt0v1LTTAoDpvfLdN9T0CKNsbj55pvj3nvv7daSMVpSICEFgLq+p6V9HkhgpKfUJvKNb3yjvKy99trl8zbd99Qqkl47LS0t5XtFut933HFH+bim49aX9NimEMxAxpmcdNJJZfCrp3SM/+u//iuWRQq6LWtoIz2uxx9//OIwSPrvkfScGWigNs337ne/u1vLVmpr+c53vtOtVSn9rUqv+3vuuadb20o6WetgpMe/Z/Naeh8CAABgcpnwwZUlnTnjhS98YUxkqT42BVd66lnJDQAAAAAAwOgTXBlZb3/728vB4X2dxC6FOXo2UyQj1UAyHHbZZZfyzPcf/vCHy8HyPQc6X3LJJeVlvErhkGXVX5NHahxJg82PPvrocqB5z8f2N7/5TXkZ70444YRykPhPf/rTbtNTqKYzVNCfNKg/DcRPIZfRlo59alZYUrvCkqTQwZve9KYYr1J4IwXI/vu//7vX+/e1115bXvoLoKX2pL5aOsZCCmScccYZZRihZ1AqSa0rA21eGW2tra2xxx57xM9//vNu4a6hBFe6Sq0t6TKU99KNN944vvWtb8X6668/oPmX1AI1HO+VzzzzzDKv45e//GW353T6m7r55psPah3pPfn3v//94nEr6W/X/vvvX4a8UrAk7WcK3nX9m5aCbyloOBipzSi1ZXW19957D2odAAAATAwTvp/8scce63P6BhtsEBPZkva/rw/hAQAAAAAAGF1poGV/NK4MXWp4+O53vxsvf/nLY7JI7QfnnntuvOENb1im50ZqhHjxi1886DPaTwS77757OQj/Va961TKtp1arlevYdNNNY7SlxzaFV1KDzIorrjjo5bfeeus4++yzy3aPkZYCMilEsCxSA05qn0iBo/Fup512KsMn6X4PRno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qk8/7AFQy84fiUG8AQN4EVwAAKKtJkyZljnfo0CHX569t+42lKGPatGnhrLPOSu0qc8IJJ5R8nwAgL+YPlenpp58OgwcPznzMQQcdFBZffPGS7RMA5ee+XXeTJ0+u6O9IanstAaAYzB0qj8/7AFQ684eGU28AQCkIrgAAUFbTp0/PHI+rcueptu3PmDEjNAaXXXZZGD16dI1jp556au7FLQBQSuYPlWfMmDHJnCPLcsstF4444oiS7RMAlcF9u7AikXJ+R9K+ffuq+I4EgMbN3KGy+LwPQGNg/tBw6g0AKAXBFQAAymrOnDmZ4y1btsz1+Vu1apU5Pnv27FDp3nnnnXD77bfXOLbVVluF7bffvuT7BAB5Mn+oLLNmzQrHHXdcmDBhQubjzjnnnNC2bduS7RcAlcF927ECAHOHxsnnfQAaC989NIx6AwBKRXAFAICyqi0Ykndwpbbt1/YlVyUcv9NOOy3MmzevxpVYzzjjjLLsFwDkyfyhspx55pnhP//5T+Zj9tlnn7DFFluUbJ8AqBzu243nWLVo0aJRf0cCQHUo9/2wsf83g2LyeR+AxsL8oWHHTr0BAKUiuAIAQFnV9h95aiuaaKjatl/pHVeuueaaMHz48BrHjjrqqNClS5eS7xMA5M38oXJce+214f777898TI8ePcIpp5xSsn0CoLK4b9fd3LlzM8d9RwJAU2DuUBl83gegMTF/qD/1BgCUUr5LUQAAUCdPPPFE8tPY9enTJ/Tv37+oq5fV1EmklEUhea/e1hAjRoxIvkiqyRprrBEOOuigku8TAKVj/pDO/KE0HnrooXDppZdmPmbRRRcNl19+eVhkkUVKtFcAVBqf++uutmBKbd9hNOXvSACoHuYO5efzPgCNjflD/ag3AKDUfMMMAFABPvnkk/Dggw+Gxq5jx44FB1datWrVoNVRGqq27de2f+Uyf/78pGXvrFmzFhpr3rx5OPfccxWUAFQ584d05g/5Gzp0aDj55JOTOUnWPCoGW7p3716CPQKgUvncX7xjVe7gSqV+RwJAdTF3KC+f9wFojMwfCqfeAIByaF6WZwUAgDqufjJ79uxcj1VjDa7ccccd4a233qpxbN999w3rrrtuyfcJAErF/KG83nzzzXD00UdnztNikPaiiy4KW2yxRUn3DYDK477deI5Vbduv1O9IAKgu5b4fNtb/ZlAMPu8D0FiZPxROvQEA5SC4AgBAWbVr1y5zfPr06bk+/7Rp0zLH27ZtGyrN2LFjw8CBA2scW3rppcMJJ5xQ8n0CgFIyfyifDz/8MBxxxBFhxowZmY8744wzwk477VSy/QKgcrlv11379u0zx31HAkBTYO5QHj7vA9CYmT8URr0BAOUiuAIAQFkttthiDQqWNFRt2+/UqVOoNGeddVbqfp966qmhQ4cOJd8nACgl84fy+PTTT8Ovf/3rMGXKlMzHnXTSSUkHOACI3LfrrmPHjhX9HUltryUAFIO5Q+n5vA9AY2f+UBj1BgCUi+AKAAAV/SXS5MmTc33+2rZfaUUZjzzySHj22WdrHNtqq63C9ttvX/J9AoBSM38ovS+//DIccsghYcKECZmPO/roo8Ohhx5asv0CoPK5bxfvWE2aNCnkqbZwaiUu7gFA9TF3KC2f9wGoBuYPdafeAIByElwBAKCsOnfunDn+3Xff5fr8tW1/8cUXD5Vi4sSJ4bzzzkttf3zGGWeUfJ8AoBzMH0pr1KhR4eCDD6513nTYYYeFo446qmT7BUDj4L5dvGP1/fffhzw1pu9IAKhe5g6l4/M+ANXC/KFu1BsAUG4ty70DAAD8z8rU8acpWm655cpalFHb9rt06RIqxSWXXBJ++OGHGsdikWgl7SsA+TN/SGf+UDxjx44NBx10UBgzZkzm4w444IBw4oknFvGZAagWPvfX3fLLL1/RwRXfOwBQCuYOpeHzPgDVxPyhbtQbAFBugisAAJRV165dM8dHjx6d+4piWSuzxE4mleL9999PPYZ9+/bN/FsKMXny5MzxCRMmZD5X27ZtwxJLLFGUfQGAmpg/lMa3336bhFZqm2Psvffe4bTTTivRXgHQ2Lhv112HDh1Cp06dkhVQK+07krq8lgBQDOYO+fN5H4BqY/5QN+oNACg3wRUAACr6S6SRI0fm9txTp04N48ePb/QFGbGwZLvttivZ81188cXJT5ptttkmXHnllSXbHwCaHvOH/MUubwcffHD48ssvMx/Xv3//cPbZZ5dgjwBorNy3Cz9eacGV2BFl+vTpuS2y8fXXX6eONWvWTMcVAErC3CFfPu8DUI3MHxpGvQEApdK8ZM8EAAA1aN++feYXSV988UWYP39+Lsfu888/zxzv0aNHLs8LADSM+UO+Yne1GFoZMWJE5uP69esXLrjggqSQFQDSuG8XZvXVV88cr+3+XF/z5s1LvoNJ061bt6TDKgDkzdwhPz7vA1CtzB8AoHEQXAEAoOzWWmut1LFp06bVutJ3sVvhLrDmmmvm8rwAQMOZP+Rj0qRJ4de//nUYPnx45uP69u0bLrnkktCiRYuc9gSAauK+XZxjFX344YchDzG0Eru5pPEdCQClZO5QfD7vA1DtzB8AoPIJrgAAUPFFGe+++24uz/vee+81aL8AgPIxfyi+KVOmhEMPPbTWgtgtt9wyXHbZZaFly5Y57AUA1ch9u/KPVW3b9R0JAKVk7lBcPu8D0BSYPwBA5RNcAQCg7Hr16pU5/sorr+TyvC+//HJmO+G11147l+cFABrO/KG4Ype7ww47rNZg72abbRb++c9/htatWxd5DwCoZu7bdRe/i2jXrl1FfUcSbbLJJrk8LwDUxNyheHzeB6CpMH8AgMonuAIAQNmtu+66oUOHDqnjL730Upg/f35Rn/Ozzz4LY8aMyfxiyyriAFC5zB+KZ8aMGeHwww8Pb7/9dubj4vzoyiuvFFoBoGDu23XXqlWr8LOf/Sx1fNSoUeGLL74o6lk4b968MGzYsNTxjh07WtwDgJIydygOn/cBaErMHwCg8rUs9w4AAECLFi2S1bsff/zxGg/GuHHjwptvvhk23njjoh2shx9+OHN8iy22qLgXZsiQISV5nssvvzxZST3NhRdeGHbbbbeS7AsApDF/KI4ff/wxHHnkkeGNN97IfNyGG24Yrr766rDIIos4KQEomPt2Ybbccsvw/PPPp44/8sgj4fe//33RzsRXX301fPfdd6nj8Tub+BoCQKmYOzScz/sANDXmD7VTbwBAuem4AgBARdhpp50yx++7776iPdfcuXMzv5SJnVa23377oj0fAJAP84eGmTVrVlL0+sorr2Q+bp111gnXXXddaN++fQOfEYCmzH277uJ3EllBkcGDByddUopl0KBBDXrtACAP5g715/M+AE2V+QMAVDbBFQAAKsJWW20VOnbsmDr+0EMPhe+//74oz/XEE0+E0aNHp4736dMndO7cuSjPBQDkx/yh/ubMmROOPfbYMHTo0MzH9ezZM9xwww2hQ4cODXg2AHDfLsQSSyyRdDlJ89VXX4VnnnmmKKdV7HIbO7ikWWyxxSqyKy0A1c9n/vrxeR+Apsz8AQAqm+AKAAAVoXXr1mHPPffMXCHsH//4R4OfZ/bs2eHvf/975mN+9atfNfh5AID8mT/Uv/vciSeeWGvB6+qrrx5uvPHGpGAVABrKfbsw+++/f+b43/72t+Se3lDxO5L4XUmavfbaK3ntAKDUzB0K5/M+AE2d+QMAVDbBFQAAKsZBBx0UWrVqlTp+7733hrfeeqtBz3HdddeFL774IrNAsxgriZ588smhR48eqT9xHABoOPOHwsybNy+ccsop4dFHH8183CqrrBJuuummsPjiizfo9QGAarxvl+Izf9zHuK9pPv3006QrWkO8/vrrYdCgQanj8bU68MADG/QcANAQ5g515/M+AJg/AEClE1wBAKBiLLPMMmGfffbJXC3shBNOCOPGjavX9l9++eVwxRVXZD7mmGOOqde2AYDyMH8ozFlnnRWGDBmS+ZgVV1wxCa0sscQSDXptAOCn3LfrrlmzZuHoo4+utVvKq6++Wq8TLX63EjuwxSLXNPE7mqWXXrpe2weAYjB3qDuf9wHA/AEAKp3gCgAAFSUWZWSt7D1mzJhwyCGHhNGjRxe03Zdeein87ne/C3PmzEl9TO/evcO2225b0HYBgPIzf6ibCy64INx1112Zj+natWu4+eabFakCkBv37brbbrvtwqabbpo6Hr/jOPLII5OFOgrx9ddfJyvYjx07NvUxnTt3trgHABXB3KF2Pu8DgPkDADQGLcu9AwAA8N8WW2yxcPrppyedVdKMGDEi7LbbbuFPf/pT6N+/f2jePD2PPXXq1HD11VeHG264IXMV0fbt2ycrkgEAjY/5Q+3iiuwxkJKlY8eO4cILL0yKYEeNGhXytuyyy4aWLX09CdDUuG8XJn5XMWDAgDB9+vQax+PvDz300PCb3/wmHHHEEcn3G2ni9yL33XdfuOSSS8KkSZMynzd+NxPnBgBQbuYO2XzeBwDzBwBoLPyXYQAAKs6OO+4YXnnllXD33XenPmbixInhlFNOCVdccUXy+A022CAsv/zyYZFFFknCKp9//nl49dVXw2OPPRamTJlS63Oec845oXv37kX+SwCAUjF/yDZkyJBaj+HkyZPDAQccEErl6aefTjq8AND0uG/X3Yorrph8Z3HiiSemPmbu3LnhmmuuCXfeeWfYYYcdQq9evcLKK68cOnTokARbvvnmm/D222+Hhx9+uE4dbPfee+/Qr1+/AvYSAPJl7pDO530AMH8AgMZCcAUAgIoUV/aMK30PGzYs83HxMbE4oyF+//vfh5122qlB2wAAys/8AQAaD/ftutt5552TBTquvPLKWkOod911V/JTX5tttlk47bTT6v3vASAv5g4AgPkDADRuzcu9AwAAUJPWrVsnBRmbbLJJrgfoN7/5TTjmmGO8CABQBcwfAKDxcN8uzLHHHpt8h5Gn+B1M7GwbXxs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/vOBtxsLetM4irVq1Sgop8xBXdt5pp51Sx59//vkGbX+XXXbJDBjEVZ+PP/748OOPPy60qnVWYXGnTp3CpZdeWnBggBCeeOKJMHny5NRDEV+PPMQuPPvuu2/RzrWswEgMPhWrKPPhhx/O7M5Qn0LTe++9N3Vs6623Tq4xeYjF/O3bt08Nqo0dO7akr3l9xKBDGiu5N0w8l+NrVkzVcr2hdLbddtvMOVaeYthpxx13LOp5uOmmmzbaa1ZtwZWsgEIp7LHHHkknxGJp27ZtOOSQQ1LH4zz9scceC3mIHcVicLVYYtBzq622Sh3/9NNPk/t+HrJek9rOKQAAAIBCCa4AAAAAABUlFrYV2+qrr546Vp9OD0899VTqWO/evcMSSywR8tKnT5/UsZdffrnB2z/jjDPCqquumjr+ySefhPPPP/9///933303CaVkiY9fbrnlGrxvTVHWuRZfp1jsmJeszgqFnmsxdJV17sbgSgywNFRWACYeqzXWWKOg7cVi0ZEjR9a7o1NDV6ffaKONUseHDRtW1Of7xS9+UfRrV48ePVLHvv3224VCcNRdVsiwqV9vaNxztrqK199ih7eyrlmjRo0KlSxr/xZbbLGkk1c55XG/7NevX2ankth1pdhiV57YnafUxyePvyXScQUAAAAoJcEVAAAAAKBixNWLN9xww6Jvd7XVVksd+/777wva1g8//BA++OCD1PEddtgh5GnjjTdOHfvwww8bXPwfV7C+7LLLMgsc77rrrvDoo4+GKVOmJCvwz549O/WxBxxwQOjbt2+D9qmpmjNnTmY4Ie9zrWfPnkl4oiajR48OEyZMKGh7Wd1OYsHtG2+8ERoidk9655136vX8aV544YXM90rWCul5v9+zrkPFfq48QoMLrqcUbqWVVgrdu3cv6qGrtusN+YvXwF69epXtUJf6mjV+/PiiBCzzEsOAaRZffPFQTt26dculO1nsFpJ1DsZ5RVqHxPrabrvtih6YWtDBLe0aGL322mshD1mB1XjOZ83xAQAAAAoluAIAAAAAVIwYMIkrGRdb586dU8cmTZpU0LbefvvtzMLFPAopf1qk16lTpxrHpk6dWpQVwWPh5qmnnpr5mNNPPz0cd9xxmc+31lprhZNOOqnB+9NUffbZZ0k4qFznWsuWLZMC+TQff/xxwUWZaedubd1S6mLIkCGpY61atarXau9vvfVW6tjaa6+dFG7nKauIutDjX5s8QoNZ1976XH/5/9fWYqu26w35ix2sWrRoUbZDvcEGGxR9m1kBjxiAyHqPlNv06dNTx8rdbWXTTTfNbdux02Ga+HpldU0r9vM1RHyNsu7D77//fi7P26ZNm3qfVwAAAACFElwBAAAAACpGVtFqQ7Rv3z517McffyxoW7GrSVbRWZcuXULesor/48r0xbDXXnuFHXfcMbMY8MUXX8w85rFzSx6rUjcVtXXUWGWVVcp6rn3zzTcFbSueC/369Usdf+yxx8LMmTNDfcybNy8zuLLFFlvUGqIo9P2+8sorh7wttthiRTv+5bj+Zl1763P9Jb/gSrVdb2ic52Eh50p9rum16dChQ6O9Zs2YMaNigysx5FSubX/66aclfb6G6NGjR+rYd999l0vnqdrOjfrOywAAAABq0rLG3wIAAAAAlEFWkXZexdOzZs0qeFX6NMsvv3xJimuzujyMHTu2aM9zzjnnJCs812e16rPPPjt07969aPvSFGWda+3atUsKaIvRYSdLVvCoPufagAEDwh133JHaMejJJ5+sV2eUV199NYwZMybzeQsVVxnPej/Houm8j39WkXQsYo2BnebNG75GWexIU1vIpD5q22ah11/+Rx7X1mq83pCvct7js0JOTfWalRUwqK2rRjUHV4rZcSUexxVXXDGU82/J6gqUR3BFxxUAAACgmARXAAAAAICK0bFjx1y2W4zC7roUz37++edhm222CeU0adKkoq48fumll4Z99tknzJ49u87/bvfdd69X+KBSzZkzp+hF0y1btgzLLrts5mOynjMWEjbGc23ddddNOjeMGDGixvHBgwfX69wZNGhQ6lgs8txqq60K3mZWECa66qqrkp9yie/JadOmhUUXXbTRXnvnz5+fy/NWu2K85k3hekPjOw/Lfc1q1qxZo71mZXVcKXdwJa+OitEyyyyThOvSAhbffvttUcNaLVq0CHmprZNbDKwWW23nho4rAAAAQDEJrgAAAAAAFaO2VX8rQTEL4ErdoaE+1l577XDSSSeF888/v06PX3XVVcPpp58eqkks6C520XaXLl3CM8880yTPtf79+4eBAwfWODZs2LAwbty4pBC1rmJ4I3ZqSbPTTjslHUUKVenHf8FrUIzi8cZw7SXfwEBTu7fRuIMrrlk1B2LTzJ07N5RTXkGj/z4X04IrP/zwQ1GfJ0+1bf/777/PJZxd3/MKAAAAoFDFW2YQAAAAAKAJmDp1aqhks2bNKvo2DzzwwNC3b986FZJedtlloW3btkXfh6aoWs+1XXfdNbUTx7x588IDDzxQ0PaeeOKJ1ILVaMCAAaE+YiCm0inmb5rat29f9G1W6/WGxnUeUn9Zc69yds2I+5Vnl5IFHQLr04mmmM9Tiu0X82+p67lhTg8AAAAUk+AKAAAAAEAByln8Vxfz58/PZZu1rcgcbb311mH11Vcv+vM3VdV6rsVuKr17904dHzx4cEHbGzRoUOpYPB/XWmutUI3HP6/3O5UvLfjVEJV+vjvXm8Z5SPUFV/IOe9T2HMUMeJY7uJJHWLW2bbZr167ozwkAAAA0Xb5RBAAAAAAowNy5c5vc8brpppvCc889V+vjHn300TB06NCS7FNTUM3n2m677ZY69tlnn4X33nuvTtsZPXp0eO2111LH+/fvH+qrLmEtqBbVfL2Bph5cKWd3rmbNmpU1RFXM0Fvef0tt288jwFdbqCl2UwQAAAAoFsEVAAAAAIACtGnTpkkdr3fffTcMHDiwTo+NBXV/+tOfwnfffZf7fjUF1Xyu9e3bNyy66KIN7royZMiQ1ELOFi1ahF122aXe+1jNxx9+yvkOjVunTp1Sx6ZMmRLKZerUqWV9jmJe26ZNmxbKeazyuE7XduyyAlEAAAAAhWpZ8L8AAAAAAGjC4srDM2bMqHFs3XXXDffcc0+oFrGY7YQTTgizZ8+u87/54Ycfwh//+Mdw4403Zq6ATe2yVrleYoklwrBhwxrtYYzFkDvssEO4++67axx/6KGHwsknnxxatWpVa3AlTZ8+fcJSSy1V732srVjzzDPPDPvtt1+9tw+VpJqvN9AULL/88plzs3nz5pVlXhbnzHk/d1b4opgdQ/IO4dQWjMmj+8m4ceNSx5ZbbrmSdMwBAAAAmg7BFQAAAACAAsQC3gkTJtQ4NmnSpKo6lqeddlr4+uuvC/53L7/8crjmmmvCb3/721ANunbtGj755JOynGtpJk+eHBq7AQMGpAZXJk6cGJ5//vmkM0uat99+O3z55ZeZ22+Izp07Z45X2/udpq3arzfVLi1QS9PRpUuX1LE5c+Yk3fCWWWaZUGqxK1oMZGR1WcszUJJ1bSvm85Ri+8X8W+oSXInzXwAAAIBistwdAAAAAEAB4urDaWKxfbW46667wqOPPpo63r59+8xVmC+//PLw5ptv5rR3TUPWuRa74ORdQJm3DTfcMKy44oqp44MGDcr891njiy22WNhmm21yW72+2t7vUO3Xm1KprTtBLOLPgyAdWcGV2gIKeRs5cmRu2x4/fnyYMmVK6nhDOq/91FdffZXbe7gux6mYf8sCY8eOrfc5BQAAAFAowRUAAAAAgAJkFdrHVemrYdXz4cOHhwsuuCDzMRdffHE49NBDU8fnzp0b/vCHPyjuz+lcK3cRarH0798/dSx2XIkFqTWZNWtWZrCqX79+oXXr1g3at7iyeYcOHar6+ENTut6UQsuWLTPHY+eJPOiKwworrFDvgELePv7449y2XVtHvNqOSyHi+3fUqFGhXMepmH/Lgrn6999/nzqu4woAAABQbIIrAAAAAAAFWHvttVPH4irMb731VqM+njF4c/zxx4eZM2emPmb//fcPffv2TR63/vrrpz5uzJgx4c9//nNOe9q0z7WoGjra7LrrrqF58+apXR4efvjhGseeeuqpzELtAQMGFKVzwlprrVXVxx+a0vWmFNq0aVPy4EoM+OkARffu3ZNueGlGjBhRlcGV2ra92mqrlfT58tp2p06dwtJLL13U5/vyyy/DnDlzUsd79uxZ1OcDAAAAEFwBAAAAAChAVlAjevXVVxv18Tz33HPDZ599ljq+5pprhj/96U//u7L8wIEDQ8eOHVMf//TTT4dbb701l32tdjE00apVq6o916Lll18+bLLJJqnjgwYNqvH3gwcPTv03K6+8clhvvfWKsn8bbLBB6ti3334bvvjii6I8D4WHiiiupnC9KYVFF100NYwXTZgwoejP+fbbbxd9mzQ+tYUta+tMkqfXX389t22/9tprqWOLLLJIMico1fM1RAyQZL2X8wiR1HZO1BZoBAAAACiU4AoAAAAAQAFWXHHF5CfNc88912iP54MPPhjuu+++1PF27dqFyy67LLRu3fp/f9e1a9ck7JLl4osvDh9++GFR9zWGZtL8+OOPoRrE492rV6/U8WHDhoVZs2aFxq5///6pYx988MFCQarvv/8+vPTSS7l2W1lgyy23zBx//vnni/Zc1F2LFi2q/v1fatV2vSnXPSKem507d04dHz58eNGf84033ij6NmmcsoIG5QyuxE4ieXR8mTRpUhg6dGjq+IYbbpgZyKuPRx99NMybNy8U24svvpjZOSnr+lxfWedEt27dki4vAAAAAMUkuAIAAAAAUKDtttsuswisMRazjxw5Mpx55pmZj4njK6200kK/33777cO+++6b+u9isfPxxx8fpk2bFoqlTZs2qWMzZ84MTeFcGz9+fLjnnntCY/fLX/4ytG/fvs5dV2LAKq5MXpPY6WDXXXctaoelpZdeOnX8X//6V6Mq5q8WWe//GTNmlHRfqkk1XW/KeY9YYYUVShYeiNefeE2EaJ111smc55Uz2PfAAw8UfZuPP/54mD17dup47969i/6c3333XXjllVeKvt3a3sebbbZZ0Z8z63qk2woAAACQB8EVAAAAAIAC7bnnnkmBfJorr7yyUR3TugRLYleMrM4Yp5xySujRo0fq+JdffhnOPvvsUCyLLbZY6ljsyFEtdtppp6QTQprrr7++0Qcn2rZtm4RXsopd586dmxpk+WmR6jLLLFO0fYvv8/h+TzN27NjMLkXkI+v9/8MPPzjs9VRN15ty3iNWXnnl1LFY8J5VaF+ohx56KCmkh2jjjTdOPRDxPvrRRx+V7UDF4NvUqVOLtr0YYL3lllsyH7PDDjuEPNxwww1F3d7XX38dnnzyydTxLl26hHXXXTcUW1Y3xDw6vAAAAAAIrgAAAAAAFKhbt25hyy23TB3/z3/+kxT4NhaXXHJJ+OCDD1LHY5eV2rqxxNXtL7vsssyi5yFDhmSGDgqx5JJLpo599dVXoVp06NAh7L777qnj33zzTbjwwgtDY7fbbruljn377bdh2LBhyf8di26zVgjPClfV1z777BNat26dOh7P+xjMonSayvu/1KrpelPOcyR2akozceLEMHTo0KI8TwwBXHHFFUXZFtUhdghbc801U8dfe+21UC4xVFjM8/XOO+8Mn376aer4RhttFLp27Rry8OKLL4Znn322aNu7+OKLM7vh7LLLLqHY4rxl3LhxqeNbbLFF0Z8TAAAAQHAFAAAAAKAejjvuuMyuKwMHDgzPPfdcyY7tf3ekKMQzzzyTuWJ1LNj/29/+lhlIWWCVVVYJp59+euZjzjnnnPD555+HhurevXvq2FtvvRXmzZsXqsURRxyRefzvuOOOpICz0s+12laJzyowHTx4cPK/WcGnWHS/7bbb5lIIvP/++6eOT5o0KRx55JFh8uTJoTG/Bo1J1vs/BgM+++yzku5PNamW603WOTJ8+PAwZcqUkJfaOhVce+21Yf78+Q1+nhgoHTVqVIO3Q3XJChy8+uqroZxuvfXW5P3XUDFw8c9//jPzMQcffHDI00UXXRSmT5/e4O288MIL4Yknnkgdb9WqVdhvv/1CsWWdC6uuumrS5QUAAACg2ARXAAAAAADqYY011gh77bVX6ngMTsRwy4KC+7x8//33SbDk0EMPLfjfjhkzJpxyyimZjzn55JOTv7WQzhk777xz6ngs8jv++OPDrFmzQkNkrSgej0mxVrSvBEsttVT47W9/m/mY8847L+nyU4xi6DSx0Ds+Rx5dTZo1a5a53aeeeioJJDz00EOpj9lhhx3CIossEvIQj39WB4cvvvgiHHjggWHkyJEhT2+++Wb4/e9/n4QHmrKs9390//33l2xfqk21XG+yzpHZs2eHBx54IORlxRVXDKuttlrq+Ntvvx3uvffeBj1HvO9nXQ9purKCKzHYG8//conPHe9hsftKfcV55NFHH53MCbKCF3379g15ih1LTjrppAYFSWOQ+49//GOt8+oYoC1lcEW3FQAAACAvgisAAAAAAPUUC9ayVnWfMWNG+NOf/hT+/Oc/J10ZiiUWC7/77rvhtNNOC7/4xS/CVVddFSZMmFDQNmKh3R/+8IfMwr/YveJXv/pVwft39tlnJ4W7aT7++ONw4YUXhoZ26IhhhzRnnXVW+OSTT0K1iMGkDTfcMHV8zpw54ZJLLkk6f8SVyIspro4eVxbfcsstk+eIgac8xAL1tNd05syZ4dRTT80sdh0wYEDIS8eOHZNzNuuc++ijj5IC01gQX8yC/lik+/DDD4e99947WXU9hniaeseVzp07Jx2e0sQuUo8//nhJ96maVMP1JgZX2rdvnxn8eP3110NeagvcxPtkfbqy/fjjj8mcIt73oSbrr79+6NSpU+r9JM4fy+mrr74KBxxwQPj6668L/rfjx49Prk/vvPNO5uPi3DurK2KxPPnkk0lIvT6dVz744IMk8Jo1D4+d5I499thQbHGO8tprr6WOb7XVVkV/TgAAAICopcMAAAAAAFA/sSg2Fr/GcEdW0dp9990XHn300bDHHnskRWorrLBCwc8VO5TEVdpjkVz8GTt2bINetn/84x9J94Y0Xbp0Ceeff369j8tll12WFNqndVaJHSM23XTTsN1229XrOWL3iw022CBZPbwm33zzTdh9991Dnz59kp9Y5L744ouHdu3aZRYzLrvssqFly8r76rxFixZh4MCBSZef7777LvVxsRB6m222SbreHHzwwaFHjx4FP1cMRcSCyniePfHEE8mq4qUQ3xcxkJRWTB4DG2ligGyjjTbKce/+ZwXyI444Ilx99dWpj5k6dWqyenosKj/kkEPCLrvsUq8uMLE494UXXkjCFy+99FJSrM7CwboRI0akrup/zDHHhHXXXTdZdT++D+KK9fH9n/X+jteIrLBDU1EN15v4Om+99dbhwQcfrHF88uTJSfH8JptskhRpxw4N8b4Sz5H492d1pGnTpk2tzx+P3ZVXXhmmTZuWeo4eddRRSRF+DAC1bdu21uMUQ3F///vfFwrzLLrookmYK++OTzQO8dzfcccdw+233556L837fvnfFoRo/jugEa/dMeh5wgknhD333LPWeVcMWsR5dAyQfvvtt5mP7devX24dQ+Jc46fvs3jd+uyzz8Lpp58eevfuXes2YhD3X//6V3J9qK374IknnhiWWGKJUGwxvJR2bV9uueVCr169iv6cAAAAAFHl/dc3AAAAAIBGpGfPnkkI5Le//W1SiJomBltiF4D4E7uRxKLBGLyIRbCxm8Niiy2WdHOIhe+xoHbKlClJOCV2J4mdHD7//PPM7Rfi5ZdfDtdee23qeCwgjEXLcZ8aclxiAX9W+CV2jFlrrbWSkEx97LvvvqnBlSger2effTb5qaunn346dO3aNVSi5ZdfPlx33XVh//33T86TrL/7/vvvT35iAWI812L3hPh/x3MtFpHG1zieZ3E78VyLhaCxQ0081z799NOyBSVi15T6dEGorbtBsRx//PFJh4lBgwZlPi6+X2MR67nnnhvWWWedJJCzxhprJO+p+BMLzWPxajz28Se+FvHfLHgNGhpMawpiMOD6669Pun9kFecW0l0gFkXHYmqq43oT7xFpwZUFxfCvvPJK8lNX8R4ewy61iX97DKTEe2nWsYtBuLvvvjvpnrbZZpsl4ckYQonHJHaYiteCYcOGhRdffDG1M0O8zsQwqOAKC+y6666pwZUYtIgdSUolhgFj15DYpfC/xWtC7I53zTXXJOG3GGZeEDKO8+EF98VXX301PPTQQ+GLL76o03UrdjPKS7y2/exnPwv33nvv//l93M8YVo1z2hgaitfBGHKJ9/oYOovv5dhNKr6P4zUpq8vKAvGaEK9hecjqSBYDt1nd5QAAAAAaQnAFAAAAAKCBNt9886TDQlzhP6vzygJxRfn4EzuxlFosnouBknnz5qU+JhYYxlBNQ8XuMrEgOIZBajJp0qTwhz/8Idx222316nISiwNvuummZLX+pmLNNddMCqcPP/zw8P3339f6+Lgyfyz4jD+NwS9/+cukCHvGjBl1/jexwLJUwZXoggsuCK1btw533XVXrY+Nq6nHzkZZ3Y2onxh4i92ubr75ZocwJ439ehOLx2PHnaxuTXmKXWgee+yxWu9RscNSnA/UZ04QwzE77LBDElyBBdZbb72w0kor1Rj2GDVqVPjwww+TgHEpgzRDhw6tMUgWrxsxTJ0VqK5rQCbOxWNoLE+nnHJK0gGxpo5f8b1ejDnp6quvHv7617+GvMTwUtZrBQAAAJCX5rltGQAAAACgiYVXYgAjrrBcqeLq8nHF6++++y71MX369AmHHXZYUYv848r7aWLx39///vd6bbtFixbJavZxRf+mJK7o/e9//zusvfbaodp06NAhbLfddgX9m9j9IK6yXirNmzcP55xzTrJifatWrUr2vCzshBNOSAq0yU9jv97EIFy3bt3K8twx4Bbvb7GzWh7222+/pAsU1CQr0JnVcSMvcT4Yu4/loV27duHKK69MOpuVYp4Su8QsueSSuWw/dmWM3a7i8+Qhdrr6+uuvaxyL1/nY9QYAAAAgL4IrAAAAAABFLPAdNGhQ2HvvvZPi9lKJHSfqUqwXC+FefPHF1PFYhPeXv/wl2V6xxFBJXDU6hkyy9uull16q1/bjiuJ33nlnsjp1U7LCCiskxeRxtf1YHF3qLgx5GjBgQK6PL5Zf//rXyWtQikLZ/7booouGrl27lvQ5K9UiiywSbrzxxoLDTjSd603nzp3D7bffnlvBfF2OXTxHl1lmmaJtM96jY7eyM888s2jbpPrsscceqe/X2BUphplLKe5LnO9tueWWRd1uDIbF7ns///nPQynf17HLUbFDcTGIGbe77LLLhrw88MADmWE4AAAAgDwJrgAAAAAAFFH79u2TbgwxwLLVVlsVNQRS06rMxx13XHjmmWeSwEmW2jqbxKDNJZdckssK0rFg+Oijj661E8z3339fr+2vvPLKyfE+++yzm1SAJXb7iKvtP/LII2GnnXbKtftHLKI8/PDDk+e69dZbQ55i8WldO6jEVdbLGVqIq5PHc+/888/PtatDDH7Frk6XXnppEvLq27dvbs/V2MRV6S+//PJw/fXXh969e5c0NNiUNObrzdJLL510RLvsssvC+uuvH0ot3pfuvffe5D3cULGr280335wcH8gS53PxvVqTUaNGhWHDhpX8AC7ojHLssccW5Rryi1/8IgwZMqQsnbfie/Huu+8OO+64Y4O31bJly6TbYQzZLbHEEiEvs2bNCoMHD049X3beeefcnhsAAAAgaukwAAAAAAAUX+zCcM0114SRI0eGe+65Jzz55JPhyy+/bHDB30YbbZQUZ8efunZ6mDx5crI6+5w5c1IfEwvm4jbzcsQRR4RXX301vPzyyzWOx9DKH//4x3DDDTfUq/A8Fv3ts88+yc8XX3yRPNcHH3yQHPNx48aFiRMnhunTpyfHoNSrjJdi5e+BAwcm4Z9YHP3EE0+Ejz/+uMEro8cC7wXn2jrrrFOyQEAMe+2yyy7h6quvrvWx22+/ffK+KKd4XOLK9rvttlt44YUXkiLa559/PkybNq1B2+3SpUsS4lnwGsTOEaSLoYD488MPP4RXXnklvPPOO8m14Jtvvgnjx49PXo/Zs2eHefPmOYxN8HoTryv9+vVLfsaMGZPci957773kHhH//wkTJvzvPSKPcySGZ2K46umnnw7XXntt+M9//lPwcT/kkEOSa02bNm2Kvn9Up4MPPjjcf//9NY7dddddYbPNNiv5PsX52u9+97vk/h3nyQ8//HBybS7EhhtumGyjGGGwhlh88cWTQGl8X8Y5S5x7FiKGd7bddtsk3B1D2Hl76qmnkvthTfbdd9+Sd9QCAAAAmp5m86vtv9ABAAAAAFSoWEQdi1Xff//9JNCyoKB65syZ4ccff0yKUWPHlvgTuwjEwvVYyLbSSiuFVVZZJfTo0UNRGXUSz63YZScWZi8o3o/hoHiuxZ9YLLngPIv/GzscxHMt/sRzLYai2rZt62g3YFXzd999N/n56KOPwujRo8PYsWPDlClTwowZM5LC+Bi4WfB+X2yxxZIOSguO/2qrrZYUqkNj4HpTuHhdfu6555LrdAzPxOtDDM7E/2wbrwmdOnVKrgU9e/YMW265ZRLmybODG9UrBp5q6q4S5wExZJlnh4+6iPPgZ599NukmFkNwcX7806B13MdVV101bLLJJknHsTgfzsvJJ5+cdFKryYABA8JFF12U+b6OXRBfe+21MHz48CQU99+lGPE9HDvKxTlWDA3F0EoMtZUyyFRTgDx+/ojXIwFZAAAAIG+CKwAAAAAAAABQZWJoJYZXanLCCSckHfEqSQx2xg5ZMcgVOy/FgG0pg7QNCa78VOwkE/+WGBiO3Uzi31KuriYxIBe73NS0pmnsVnj22WeXZb8AAACApqW4fbYBAAAAAAAAgLLr3bt3+NnPflbj2K233pp0CKskMayy6KKLhmWWWSYstdRSjbr7W+xqE7snxa52sZtJuUIr0Q033FBjaCV2W/nd735Xln0CAAAAmh7BFQAAAAAAAACoQscff3yNv//uu+/C/fffX/L9obS+/fbbMHjw4BrH9ttvvyQkBAAAAFAKgisAAAAAAAAAUIU22mijsMUWW9Q4duONN4a5c+eWfJ8onX/96181dtZp165dOPzww70UAAAAQMkIrgAAAAAAAABAFXddadas2UK/HzlyZHj88cfLsk/kb/LkyeGuu+6qcezggw8OnTt39jIAAAAAJSO4AgAAAAAAAABVqmfPnqF///41jl1xxRW6rlSpG264IUybNm2h3y+99NLhN7/5TVn2CQAAAGi6BFcAAAAAAAAAoIqdeOKJYdFFF13o95999lm4//77y7JP5Ofbb78NN998c41jf/rTn0L79u0dfgAAAKCkBFcAAAAAAAAAoIotueSS4Zhjjqlx7PLLLw8zZ84s+T6Rn/iazpgxY6Hf9+rVK+y0004OPQAAAFBygisAAAAAAAAAUOV+9atfhR49eiz0+3HjxoVbbrmlLPtE8Y0YMSLcd999C/2+ZcuW4fTTT3fIAQAAgLJoWZ6nBQAAAAAAAABKpUWLFuGiiy4KTz/99EJjrVu39kJUiTFjxoTf/va3C/2+e/fuYfXVVy/LPgEAAAAIrgAAAAAAAABAE9CzZ8/kh+rVp0+f5AcAAACgkjQv9w4AAAAAAAAAAAAAAABQnQRXAAAAAAAAAAAAAAAAyIXgCgAAAAAAAAAAAAAAALkQXAEAAAAAAAAAAAAAACAXgisAAAAAAAAAAAAAAADkotn8+fPn57NpAAAAAAAAAAAAAAAAmjIdVwAAAAAAAAAAAAAAAMiF4AoAAAAAAAAAAAAAAAC5EFwBAAAAAAAAAAAAAAAgF4IrAAAAAAAAAAAAAAAA5EJwBQAAAAAAAAAAAAAAgFwIrgAAAAAAAAAAAAAAAJALwRUAAAAAAAAAAAAAAAByIbgCAAAAAAAAAAAAAABALgRXAAAAAAAAAAAAAAAAyIXgCgAAAAAAAAAAAAAAALkQXAEAAAAAAAAAAAAAACAXgisAAAAAAAAAAAAAAADkQnAFAAAAAAAAAAAAAACAXAiuAAAAAAAAAAAAAAAAkAvBFQAAAAAAAAAAAAAAAHIhuAIAAAAAAAAAAAAAAEAuBFcAAAAAAAAAAAAAAADIheAKAAAAAAAAAAAAAAAAuRBcAQAAAAAAAAAAAAAAIBeCKwAAAAAAAAAAAAAAAORCcAUAAAAAAAAAAAAAAIBcCK4AAAAAAAAAAAAAAACQC8EVAAAAAAAAAAAAAAAAciG4AgAAAAAAAAAAAAAAQC4EVwAAAAAAAAAAAAAAAMiF4AoAAAAAAAAAAAAAAAC5EFwBAAAAAAAAAAAAAAAgF4IrAAAAAAAAAAAAAAAA5EJwBQAAAAAAAAAAAAAAgFwIrgAAAAAAAAAAAAAAAJALwRUAAAAAAAAAAAAAAAByIbgCAAAAAAAAAAAAAABALgRXAAAAAAAAAAAAAAAAyIXgCgAAAAAAAAAAAAAAALkQXAEAAAAAAAAAAAAAACAXgisAAAAAAAAAAAAAAADkQnAFAAAAAAAAAAAAAACAXAiuAAAAAAAAAAAAAAAAEPLw/wAXo9CxINEK8AAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1600x700 with 1 Axes>"
       ]
@@ -1672,7 +1689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fb42bd5",
+   "id": "95236209",
    "metadata": {},
    "source": [
     "Two of the three statistics sit inside their predictive 90% bands; the median\n",
@@ -1689,7 +1706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0ee5c22",
+   "id": "45e3f02d",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1721,14 +1738,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "91469673",
+   "execution_count": 17,
+   "id": "57625712",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:56.858234Z",
-     "iopub.status.busy": "2026-08-01T02:00:56.858148Z",
-     "iopub.status.idle": "2026-08-01T02:00:56.860518Z",
-     "shell.execute_reply": "2026-08-01T02:00:56.860294Z"
+     "iopub.execute_input": "2026-08-01T02:19:58.183181Z",
+     "iopub.status.busy": "2026-08-01T02:19:58.183032Z",
+     "iopub.status.idle": "2026-08-01T02:19:58.187484Z",
+     "shell.execute_reply": "2026-08-01T02:19:58.186923Z"
     }
    },
    "outputs": [],
@@ -1769,14 +1786,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "5b9b12ad",
+   "execution_count": 18,
+   "id": "289d5d3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:56.861454Z",
-     "iopub.status.busy": "2026-08-01T02:00:56.861382Z",
-     "iopub.status.idle": "2026-08-01T02:00:56.863237Z",
-     "shell.execute_reply": "2026-08-01T02:00:56.863006Z"
+     "iopub.execute_input": "2026-08-01T02:19:58.188833Z",
+     "iopub.status.busy": "2026-08-01T02:19:58.188718Z",
+     "iopub.status.idle": "2026-08-01T02:19:58.192229Z",
+     "shell.execute_reply": "2026-08-01T02:19:58.191597Z"
     }
    },
    "outputs": [],
@@ -1811,14 +1828,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "37963d53",
+   "execution_count": 19,
+   "id": "537f5fa5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:00:56.864026Z",
-     "iopub.status.busy": "2026-08-01T02:00:56.863973Z",
-     "iopub.status.idle": "2026-08-01T02:01:01.491435Z",
-     "shell.execute_reply": "2026-08-01T02:01:01.491032Z"
+     "iopub.execute_input": "2026-08-01T02:19:58.193463Z",
+     "iopub.status.busy": "2026-08-01T02:19:58.193361Z",
+     "iopub.status.idle": "2026-08-01T02:20:08.361364Z",
+     "shell.execute_reply": "2026-08-01T02:20:08.360452Z"
     }
    },
    "outputs": [
@@ -1839,7 +1856,7 @@
     "    sample_shape=(len(columns),),\n",
     "    total_size=\"auto\",\n",
     ")\n",
-    "model2 = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), len(loader2))\n",
+    "model2 = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), n_streamed)\n",
     "stream2 = StreamAdvance(model2, loader2)\n",
     "stream2.prime()\n",
     "steps_per_epoch = len(loader2) // loader2.batch_size\n",
@@ -1873,20 +1890,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "f83f3e91",
+   "execution_count": 20,
+   "id": "6af7b844",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:01.492579Z",
-     "iopub.status.busy": "2026-08-01T02:01:01.492509Z",
-     "iopub.status.idle": "2026-08-01T02:01:01.662522Z",
-     "shell.execute_reply": "2026-08-01T02:01:01.662207Z"
+     "iopub.execute_input": "2026-08-01T02:20:08.363330Z",
+     "iopub.status.busy": "2026-08-01T02:20:08.363168Z",
+     "iopub.status.idle": "2026-08-01T02:20:08.710389Z",
+     "shell.execute_reply": "2026-08-01T02:20:08.709768Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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FM0b4jj2rEuwKJ+NO+77+5Rkj1Ki6Pp7bT+IAkmRfJpl38et6LsVvxifYgc1pfZw/zTtBTq6x/MaBul219xjV2+e6/MLpzvdrkyJjuB3Xm+7r4qo5YxJdBqdjul+C6pemDc974UXZp7zmgvpXlqkj2vI7l1fby/8Y9MclUxJdBvvmk1gfKUjnd892qE8hNgAAEI9ZV3QAAAA9nH3y7RM7q+stDllRyyv4WiazdXaVcJswTDvEQAJavztnTOiOZTIZLDdxL91jRwVPid/QXeXQK+RNqod6kSqfsmzXzB2byo0/J0e0+Vc/jEKSN9z43WzINwlS8k0wyDoHO81PsHuJl4G9yxPNOtRZ8fL7c8ep3y/U35kyd/t5kQl/e0DJCUlXP/XhdBNC1vkZE4a4ds2La4pPJUUJrs53IqsEJZ7o0P3Oz38OnaV+p2mfcqpsaCqvo9QIhy50cTqndn2t8MpcEg2GVFWofTUkPUT9dJcnHIi+PWLXRfufyQ3SlqQTkH2OrZ+b1DXpXVdAfZTt53bsHl3XR81qqtWyTFmHCvp+VdpvXpzfm7Qd/DaF/fAmx4lDfKpLBxVlL5DEBr8uNEkkIPsFxZmoj0N3J7mm0ZXEFmWfkoTSJAPMoiT7Jt3VN+p5JunEo8IZuXirLi2J3JU0CKdxUL1Dcngut+4kzZW9VF2AoJ0AC6XtM/tV4Q4j6iLpHGvaRfkWyXEqm2DCmImXDV4dT9PMgYyyrqSgRJKrOBPhGjmVdRjgPZNM9jV1X3faFjLP43XOlTn0RM8zBq4oryUKuzu3O8wx6FpX0smsyWPeuGOOKtHuhplohdSczn1j6/touXZP8rwa6zyjabGG+SS5hRFlmZIufhPl5SUVMcmtrvO1dRZ+irpPpfEN8frUrX0qEj1pOh0TnOYKciWdz+40j76jUEJapQpjXs8kuFNFWSOSOGbemSB59s8cZM1VJFwUJBux+M0gOS4YdJ6R4orJjoe7/i7q6VbmOHILJMdh4vfIaZ+SxD4vbmNhSWRLi72Qp25Rt53f+TGNaz/Zvjr2xfpeZsXUAABgEpLoAAAADJ4AO2HMIKsa2v/5VAJLk9tkZpo3PE4a26puXTrNN5jFiySh/HjeOPWbBZM63ZRPumJXk2HJYUEcOzLdZKIg+92hQ5usCtNJ+czEIZH/Nq3bg3JzMkmLXCbCDxzcL1IyW1LBxNLx8PQJ4aurztuZwJNUgIpUmAubMJM2r64NOjaf000G6a7qV5XSeXmS/96ZeOM/k2rAfvg99kyX76Z0jf3arPhVyb3uW7mtjemNNWq2Q5dc6ebgtrxpkWrWNyxKNjnLi3ShLHEJkpqq4RwU5cZjSx4qr7p2gvT4BiZdHCCr8Zwp3YAleDGuKEecn8wbr5ocbvJLheJLdRwTIiynW4Lk9+aOVV+cOjz2Mu1YLj1bcGxdn7xXlM/ldL6uLStRx41qsQKh0/CBoel0hE6b2z6VdiGcsMa5VGiXY5WOzxLlmyeV4538ftFk9cfFU1Mb5zn59uzR6s+aulZGCVo0UX+XhAxZT34V3pPap6TDjFvXJx3HMJ3JDTLvoqugS9bAoLcol0vHjUp2vizKenIbd0qhkBPG6Fle98TMTOig3ZPHtaq0jK7vGpRaVpSx1pV0o0hj+0mnSCcyhy3F5GLTONH0/X3GaSsW4n7uK6wzTrMUNHO5zsl3RxW/AgAyTteVTKNrt/rS1Db194OmJzwedv/Qfp1j44qyuo9MuCtzlGvkPfs7Fyj61X4T1dd3Fq6Mv1x69qpFrQ1qnqYCh27jKa9Tn1N3Lul2KN3EpCBT7GWK8DduBUWloJHMvZhECgrqSs50nWMMOXZpKC9V9Ql29/TjNrci60pHN6Mo1w1yv96JdGHS0XVtxzK5HNND7h6fmzRUHa6pWFbUXcq0QjPVLl0EZUycRAfyOH42f7z6ywHTkt1+Kj1R1pRT0QkpYHzz4qnqpLHhi5PqIF3Knch5T8e5z0smZJEpHfdAdM/lSfGNMMdOKTp+9uShId8FAICegyQ6AAAAw0lwQ+jA9jxmVmQ0B+NL5e+4jmzr73gDKgxZ/nH1VarVdmNhrwF1rkFMOkRZb14d2/KhsmT3ZUVJmqXKPZw1aYiallB3rmmN1ao8QNVCe6JWx9d0jIYbRboltRW/MmOEZ/KedALKN7kxLMF2YT+zzptZ9kTpvuWlvhUERZUtSLDUp9NR0pIuoh7l5SWIOmm6AgTNqJ6bTWybRnmtAS4BZiaSbg5HjdDT4clt3UXdo+RGWVxRdmevbj1JV/tN0/aQgZ1ugYlybjhmpKZ9yjWxLz1RjpFux/Tp/WrUiBp9HZV0mNxQbXWa0EHXoVjraTrCi9WUdT0m1PUqVZ8c22ptw/iLlNXWTeLUca3qjJQSDvd2ScKSQK7hmoI3onduyHZZpk+7BPOHFSUIvsYlOFE6WH95xohU1pPb55B9TUcnRtkG2SgdyB3I8EBXd3td3ZQkkP+X+01QVRGKZCRJxi06us9EWUtunXI/0t6szp2iK1nbWdhPvGBQX/Udw4KrpRCWBMMmOY/npaVPL8f1KMcoHQUAohxPpZu7EykU8onR6QRSdrAfSiTh6GMj4yerRVVe3PVYVJTJWOuqJaVrVbdATpnDnppwh123w6BTV/uO84xbcnJa49jDhjWpX+83MfFkqLREOR27XUfdtHiKpgIAUUYv7q+kK2Fa13xXTVmJ6/g9H9zGeT/fd4K6dv54lYZGl/Hl4KoKNadZU8Ka7b8zEccukvx/yayRiSaChF0m6Rp0eNuA1ArNuBUmlXs6eq7b3bmtK7d18a29RqvbD9SVWKuvw5KuQiU6Xbn3mFiFQONw68h+6R4j1Y/m6TlObQ+5Ad26c01prFZnTRpq1D4l80BSWCItbt/LPy2dqm5cNDmv7+lXAEDmmnXdc9A1djl70lB1wXQ9hbINbGIdaS25XVNcNLPd+kmku6HPXiXFI9zGU7r2c13bb3b/ulDHzj8tnabtmgwAgO6oe84QAgCA7qukQmX6TvX8keeU9ylVYxcO9/yR55im0KqnCh1LLF0aJCBJAjku0hDslpvQZJ/o7Bez04dMwp4Xs6uE7onO2f1r1cJBDSrfsjnr+/xpw60bxj9J6easf0es9L9bE11u9kklv2t0VIvWeCMgqcS+/Xfup+e4VD0z8B6AVf01N7mtQ32vstQTDI4b3TmY7NM7b4SOqoufSBD1xpx9G7b03jE5r6sqrJ3fq+7hEnSaD07L9n5xsfp/DU2qaNIY60f+3fGzvH249bvPzByZ34UyWNKL63UztMBWlef44syJ6VWazBr22knfbN4ubxCyVK0kEbmNG04ZNzjdxFqXvyxK6Rvi9a4S0JxIlf2Yr6ujI499l4oaIGg/N+e7c8OOv3P+77Ru5rt9Nz46skV9uL05pSQsZxLI9esFk1SaxwR7ZWa5tk66+0VUvVIuLpEUz6DTFGsvRT/POAfzu3WyDUfvuUpLfQwjo94MXKaInX3duqAeNry/1bnPJDKHN1dDIoGhm8/Izg1RZUN25UhrnKBLkt1PYl9nafo8w2sqreTauDLaCzHFWBif145CgtAbNHVMD9uNJx9F+uzFbwJ3CMp23Z++secoK7k9tX0q6zxul6Q1Uwol6BZ5sRIsHuYu2jWyju1nIp3rPJOHIl1Rvpcyj6SjG53OY4L1eoYWQNXSWdvlMbdPXJKHVaFrV5f5sjMmDNFUFCSjbTwlhbLc5rRDvnho1Ql3INc5XpN1JMm1ZsYWaZhLN3CY4LS/dvzOvTC48++lcK1bJ+nwy+Xy3ipZFTmFsAEAQFecKQEAQEHJ9KpXJaM/5fkjz6kZUKWOvHKh5488p7vK65yVhtkd6dLwj4Omq2vmjo09ke83wJUqjm6CvnXcSUHdQfuyznRU2o9j6eB+1g1je2Cl/mphPn+z8/+vmTPWtRNaUlqrvCdSvfZtqRhtEqm27VaNUYc5A/RUnY3S2dOtI5Nbxe7coPyr9x6j5jbXqc9PHrarw5KOmwRRg5TkRtX/O3iG+um88ermxVPUwJ0deg4Z2hR7mU7VFGR4+Z6jrf+XStGNHl2pClqIzbe8vEJdMHaS6nXc4daP/Lvj5+mDF1u/mzmyVR0TN/gm4n65T0u948voqGjvtUhHuwTgG3gPbtf39Uf7jHN8fL5tHWpJmMkEG6fYE6A7gp1qXSr+5kOSwW9J7B86Ksi7BeNFpeN87H6T1nv7jLeNTzpe5/gxA5Vp+lcmn9we5Rwu4+P476FXx7k5jWASt+5SblWkC12UMZ7fX+ioah91/HrO5GGdX2fn/38pZpGZJMweUGvkNp/q0ZU70OubODiKcZzq7XKO+8qMZDp0xWHvAp63Ku0qD0GnmhLWsjmdntIaT7l11pY5l2kxv3+6C59lkioAoNIXdY5DAoSdLBjYN+YS6Tt+drxMWp2nshE71el6/VB/s/OBr2noKKHbjKZku/Z5yYTs3hVG1FTDJFl1ZsL+zc7/txfk0rqkEQ8KXQqX7XyZE8do6OQZ4QAub+92OklqLsHk84zfuVXuDSQlbLJoGgV5gv1NehccUdfV1/cYmfdOZn5JkGl20nXrtjpUQ2JmlKT2vOxRIZcr6/P7dpcuu+HeRO9ZeWa/+HMcuqfpJYn19ESKlYRLPk5C1GOCm9/tP8mo4kVi/0Hxr7G6C7+va5uGY4LOzsxh5tJNGLcBAGAykugAAAC6oXze5nALIgkyKTOvefcEna6KgceNdg/qHVJV4XoDIV+dAD8+qkVr9Xg4a7cl83WsV+mE9oGh/RJZbUES4XK70Q3amfCUu3xJWOTRpbAj6crJ9Rom9d1E/bxenyWQbFb1cakWHqRi8NDqSnXZHqPUIRqCAoMYXu0/MS/HTkmcye3gIglrcWU0dO4TQ3ICuP60dJr+xNqY350aHdXjXY7qcTpo7KshSDDKeW5QnwqHTp47krPsgfM6TfcJDPmkSzDS91wSpoOKExcy3qW76Gc1VLSPHHSaYIeNqHvzNI9t+0OXRMQ0B7yXzGrXEIgebh36BW58b268/TzOjf/D2/o7/n5ec706aWyrSkOtS5VjCZY7cEhjaseEb7oky8m1x9iEOvz6nQMrXSrNyrk5bhfkqB1m3MZQ0pV8Vsxg5iRi/f52QMyxi8djfuvqW7Od96m+5WXx9/WIQafy3k4OGNIvdhJ51PNMbqfoXCVFRerOD8wwbl7lu3HHLjHnIjq91s4Xk2IccUU9Jti7g3W8jowNF7XGvPaLKPc63R50ncYydazaY10CceOOE7w6N0R19qSh8QuoRAxE9+psenXs71+0NSXH1lqX604tSRfKvADPqGPPhfbv2M6DwhenDlfVLon4QUXdz0fbE2ZyCi79ZN4447q+/Wz+hNTGU0e6FK6TMcIEl+vnpJfL7ZqlX0Uvde6U5OY4vIyqc742aKrsFf/ORAID4rhJkNs9k7W9STEzJ3+JOb8ooq4pGfe6Xff9aUm8YhdR7015JcvpSGyP6sMuBbPcilLlY1e/eFbnZKuOl7lMQxKWe7d2b7Oa7IkxO7vlZDJW8bwklikOt2N9PtgLiXas2znN9eqomMsVdfu5dQf74PAmdYTLnFrSxrnMQUl30UtjznsmIe4xQbZd1OJhbn4yf7xKrONpyNfp+GzHjEyv46lXQZmjYxaDjHOUcru3cMVe8YuHRfVBl/OudBf9ftxrd7fxlM+GbbF1Uuv4xkiHtfjzCfrPM1fMjrf9PJco4g3l77qMSbWMXXyWaYFLsmPwaxkCjwAA8EISHQAAgEGSmGxKWtSpl89OHKL27K+/OvxHbRPJSQRTRk3a+OPiKepTPgHHJk5lTYlZrds00r0rjo/GuClwwfQ2NaKm0tr3DxrSpG27L/YI5ivzSByVoJF9W/o6BgfJTbWk+N28+oLL5K8ETiWV3OCeYOt9EPmYLRg10nK5vEXUJKzqshL1CY+E4rgkIdkrqe/MCYOthIG4N4XstidwjnRLegjDbak+2t5SkOOJvfrXOX4vDo0ZeON1PvY7Jix0SaCdHLebi8djw12qS/otqyQYuN0sjbtcbkHBA3feCK2xJRdlNVaqjUqOY26BLLEDKT224KnjWiOdNyXpIk61dq8q+5Ik5OW7LklN0uU3fhKr81KNrvfuICzroylnPNDxPZZgrmM1nP/CkveV7eN2TPj85OGJ7VNuAYAd9kqhG0mRz/HoW7agg9xgCx3db5yUFhX5djt26wT0zZgd8uKMEupdkjPtx9V8LFTHltkz53ysWzaBbqNxk8ijLtOkhmrP/S2OOF0YvDqgJ3XN31Thnjwkjhs10HV8cdLYuIk8tu5fAf9KEgbc/N+0tlhLFPUwJ8cxt4S1C6ePSO279ymXbTS5oTpWN1avsYvfOrzSFpyYO06If+0ebW3Zk7VNmWX9ust153Exx1NxrvuOd5kzkADP/RIq6uLHbbws8wy/2G9iKuuqrcbedWr364wNUEwrqQ4XbtcHXnOA8QsAeB8U7OeSjteSY8LMmMUSos4HTbSNE3JP7Uta4xUlyMTYz09w6ez970NnqfzL7JqzTqrIU9Rd3b79OtSXl3ZJpsnXMsn2k2v03a+TzUuXs6jzm5+ZGHOMHqMV3cku8zJSlMotQTnwcrn8fo8uCWnehRc7zG2u97y/EmeZ/FZXa1WFawEcXYVHdZ5nPj3RuWusjvNxtU9hQLe5IFEco5idtVwRDwqfHNPqeA0p82n2x/J1PSPnXbeCdPvk3AuMIomxtRwT3ApOJNXxtMNltsTa3GvCL8W9nom4UEtt45OOBDbZp+47bI9YyxT1WyLjy8OHJ5MU6niaCbigbvcWJDk5blEXt/kgvw71J3nck/Aq8hxomSL+3TyPolcyn5D/jqc7/v9ol7n2WTFjh+LEBV3lkiwn88Nx7/mpiPeMutxnzu4+TsUt/gYAAEiiAwAAgA+5oSUJE25JE1En8j/UNiCR4E17oFzuXJnfuyWdwNYRIJZEYl+SThxtXmXsONy2s9xYGuzRma1DnM0nld1+ud9EK6i5c4JUvL3P64a43yt/deYIq5uF3HD46oz8VKJ0++p3/PrgoU3qmPZm66bC53ICcuVmjVu3ibSq7I+o6R27qq/XMkW9sXJCzIr2XvvNh3xumB01oln9ZN54z6BmXTI+QRId6/YbLhWF5SZtbvBLFPZjekdgmQT1hHqdWEsR7LX8bsh0BLl0PO/LMQOFc3m+tUEZ5B2L4hZAFiT4Mvb4xuUtnPZzCTLI7VY0t3l30sXYnRXue5cWx+6SEMdROTdE4wZUB7XfQO/gp/OmxgvMd5d1vcnulwg3tbEmsa+F29gzSILJsJyuqC29vRM0dOnlE/x05oQhjsltcTqA+gmSNNhRmGB/lwqxUbntU59wCb7tIOfh2TmBB9JVVNsyufxejjd+3LZT3O0XJ5Hgcpeub3FlAx4bg/5N0seEqQGS0zsCdKtKi9UHcsbAcbu6x9l+cbsOuImzLRbkBJzbOzUnxe88Y0+syF3nfgmwUddVkE6cA3IS6XKvfdIcHuZ2HM7X9vMLHJXxpRT0kaWxB8X2jhlg7XZMKPU5Ls9sqlUVOeunWeM4wa1zg99nlX05t2uSW2Cebn5d0tyu6+NeN3jNL3oFmoskC++4HdOXDm5M7xo5xotdm9ON5LSccWiS/I6ffts3DeW276d069MmG73wW25RkAqXRJV8m5FT6GZmTvf22McEj8dk7Ob1V4tbG63zizzv87YxRazrwKz7KG92gKIRHQVyJGF8dE4Xv9xiDhEXy1FtgOJ7uUVBdM59xvlEZYldYwUrwuXEa3zpVsQkrmk536ewPuZSRCF2Z+YA++qcAbsTHHTeO4zzUnMSKhTk9fmCJMdeM2eMmti3yurAk1RxoFxtLkXWcudAchMrZHzcIQ+L50qKInRo9/kMurgliHf45X7u3XLjrCuvwpl+nRPn2pKLcq81EpreV0Nz5lmdzGiq3XU8kmO7vXt7WiTZOAlexYtyx3BhDerjXgg0VuFMn46AfXyS7BIpxuqzs8qYXDpDB32+LrljXCfH5VyPJjUuCNsJfnq/mk5zVDq57VN+Sfwyl5A739BpHwuwKdM8FwEAUAjMmCkEAACAlsAzHRXTnar//WbBROsnzDIX4qRM0GVOMglO1z6gU5IByV7i7kO5N7RzL3yyHhO2v1s4Oa/fLxO+L3KzUZIJfrjPONUSIIkwKK/gtiA9304ZP1jdcdB0dZjGCodJfXfdKh5KVyFJUAwryd1hVMCEsSg3oRMP+vZ4g/N8EnP2bq63kogOGNxo7ethqtzGWCxfBw3pt+vfkwJ05ZIuL31Ki30DmOJ0N5SE75sXT1F/WDg5UMLFx0e1WDc0x3s8d1h1hWr1OL74LVVNrxJtQcI6b7QOdSswEPO93buBdH3lb+w5yqpanRtAL4k8H2lv7tQZKzd4Igqvz+SXMCT71M/3nWAls4YNnI363fPb1eW7cPGsdrX3gDr1I9sxIe4yZUN2N8zVERghAS46A9HjHKckMKm5spf1PT4+T4UVPu4T1C1JynIsP2fyUNfq2rrHCUGS2i+c0aZ+MHds7O5JXZbLo6OwHwlCk/3q6r3HWJ0yfV806DJ5rKxBPuPJJYP7aety2nmZov9tbrJo7rk5Lq/EsCAFDzoS/f0SWXQtV25gshs5t1y//yR1w6IpnTp3xg4wy0YP4JzQsHsMpTOJ1WuXyk1gdztnS8KhBMP8WGMi+3aPFTUg5Hkjt7NF3HG8ayB6gEAoSXgq2nmczQ28TDIo1m88IgnIs5pqrEAut06xuvfzIMm8p8o18sEzOgWbxV6mnP+1O2msf/eMX+w3Qe3TXK/OmjjEKhqU9LqqCtClQgIEpXCDjGEWaEw08tp+Z/gkVsnYruOYHrerRVCyTfyCKTs6Snh1J9DZYS3IPnLo0KZOQYy6eB3nJuWcR9zOj3It88Upw9QhQ/WNE7yc7hMwLV2LOvYpSbDNhyAFpGQ9yfyAzBPE7bYRZPvVBTjPXDJrpBUsLMGnx+Z0ZU1yLtZrfqRjHlE690nS/Zc0XjtkI3QqyyXnl78dOL1rV89sMskNuQUZ3Bzd3mwlzfxs/oRAhaGCL5fzQgUpiCXjeDn3yflGZ4d2r4/nlzCaez0zQ+uxM+t6/+oLU70LOAgpjlddVrIrGTIfY2I/HXPIMr+qs/Oq1xLV5cxrunWG1jV/12mZPBbqbJ9O4vJdGLjzvK2zKIHXehoV4Hp0SmON+sE+49QHcsYLSS5XkGWSzlNS4OnMCYM7J9Epldr1jByf5Bwj94K+Nqs9L9vv4CFNvsUo5dipI2nVvkxuyzUnp9icG5kvk6QZGRd7dUqPtmRd5c6jeBU5vP3A6eq2A6Zr7XLqdY09xScpXM5DUrBP5qeCjCmC8mp4Kvc9ghY0yy0smNRyBU2Sk3GwU5xCUoJ0opa54Y5nXRizm2/QY8KRPucOWZcyhy7nYp3zLl6OaOsf6JhwzMhm7UUp3cYuQbrOfmuv0VaBJ7lHkztODxJTZF7UEQAAZkmuBAIAAAC6Da+JVb+OUkmRDhT/fec9K2Dgp08vUyYo8qiWHVgmfOBEd/WZiUPUkbc/EvnvL5rZrv722grrxnE+qlLGEXfpTPp00j1Gbvbu5XXTIuACl8TsiOBUGXTdlq0qX6SrkPxUFBer373wluNzJGjG60aD06qKc5NKkrCeWLVObdm+XV31+KuRXsNpmeRY7Fb1WBfn9bTjPYN0KBxbX2X96BSnM6I4Y8JgNbBPuRpRUxnoJq10AZTANVkXe97wn8gVdJ9fvd7zOWFuzo6u7WMlsby7aatafOv9XR7/xb4TrAQ6z7GEz3vIDTUJWLzrzXfVJ8f6Jx3I+x0ytEmt3bJV/cRjjCCBPHFcsddodcvL71hJmqaQJJkwwRGN5aVq+cYtvs+LG1wvx4i4XR91L9P8lr7Wj12cI5lUD7/u+Tcj/70EJs1sqlGtfSoCjV3k5unGbdsTXVdybPrDosnWesnXeCrIu0jgqVsSe1Te52P/pZJK+xMT6MIaZ/vJ8VOSanULO3bJtf/AvmrVxs1q07Zs7G6+YRIJvvnIy11+39FhSoK8r1swUT2+cq3aL0+dXYIETsmY7/bXVqjZmrsAxC0skZu8nXQCufXaPi8+r7ne2s/f2bBJneXRoVunjoBSv4TD3IR2E/x03nj13cdftRJmguyDQW2PcaSSog03LZ5ifR9rNFYf99ptGnKTip3+NpNRV+w1xgpy0tlJySuxNmjAdJAAq7BJ0W5LFeT6RApPXOrS9TspfuNX2X46E6KD8CtWIsskQXgrN27RGpzr9c0L8n2SebeDh/bz7YiRT6eMb1UNFWVqSFWF1uXyOveVBZh7SuKaLxvjuy77lASbPvPe+1o7YXklpkggrN88kqynfK+rIOeZPy6ZYq3TTgUA4i5Uxjvx8rFV6zz/PDehLx/7uXyngtDaQdAnuSFIUpwsjyTN2CVZwEH2k/Vbt7k+LvtSvs99+w9sUA8uX+P6uBw3L53Vrh5ZuTaR6z8nnYq0uJDieFI4RfdcQpxjghRYk8IvUrhI57jKq9BMS+9y9e4m92PCx0a1qP/3xir1/pat6nzNRXmidliT754UL3ry3XXWPFV3F6dwpsy7+BVU0M3v+CnfObk/ms/1FORbLgXNlr2/MdD1tJY7RwGOPdLBUq5HdbOvqoxt+23z2efi3rcISwrNPbhijecx4UfzxlnnxyS7rbkliLv5v+lt6gND+3UqrpT4JJWPj48aaM3tS0E6nXMcXvMJfvf8pJiJzO9v2rZd7zWWxzIF2QRLBjdaPzp5LZNXl9wOUmz4lHFd43KS7BjtZ1x9lbp16VRVXlzcae4hU2DxIwAAmIhOdAAAAN3Q4W0DOnUfiMNvTijK5ItfJfkgjhoxQH1r9mjXLjBRPkvcaqY/32+CFQhq73IUJ+hUKgZKxdWopDJcXG6xQFJZOAlyI+V3+0+KPZEr1YiPaBugdUL44CH9umw/v6DqOBVNdVRtznf+4J8PmKZ+tq93deCiFKZtizNKfWXGiNBBw9J9IC65aeL0utKZyaq6GeLmo1Q0jROMW1VaYgU+jfWoii03zMKS4K2kkyu8ugjEee+ofypVzKMkNOauX7nhKPvAnv2Dr3O5Yel3voqT3BBaZsf6d1uk9treqjxAAKCT3K5zBw1tsqqPStXaAIukPtzerD7p0T2jqrRYnRKzErYEwH5izKAEksMS7+u4iwSIHzuyRR3nUz09bnJRGFI5VpJyGzySVOXxWTnVnbssU8a9s4yfKGu/X0WZWtTaEKizlN93QfbxoOf/X+43QX06QKBO3C66ctwJepztCNgbXRfne5HO7V0Jpgs7Hh4coGtlHBXFRcl00o0bdBrjpWVfOmpEs9W1QWfBBCmW4MYpYWFJa2On30twjpxrdCYWBekW6JfULucznd0IhIG7VKz9XPYpqar9lRntgTqfBeU13kqrRkuQjjt20/pVd0okuGrOmC7dvuN8HOnuY9+pwr6efBd1Bpf5Cbp8uhMJvDod6x5PBTWub5Vr17A0eY6HU/gCSqeyIF3wvL6zcs7T290i/nWDHD/banp3Od6leUyXa+TjRw9U+2rsDhQ32TcpUQLRc8c7sq6kcJPO7lzDPeZQ3YJO5VooSTK3Hfd6RpJ9uo7xul9IqWc+b4ofN+72c37NmH9v4KqK8plyt/k+LX2tRJ4gyW1B1ZaVhl4/Uiis0/M1n7ePbBsQ+oguXexzl0eSj+Neq+ncp+T49JsFE9VNi6daxdbycUwIslWkG53chwiSdBBUlE6JHd3LvKSYL5PKsSSt45QkhLpx+6pLsbvc6yu5JtJ5XGjp3Sv09UyQ42SS5xknw6uTnV/047RJZvar6TRvJ9tPdwKd03ruWBS33aSqbPeYTubQZzTVWslFplw7yDJJYliQTpahlsnrEjnA30sine7iKV7fE7c5jriFSf1Emd/I7QhrKvnu2Yv3BDmUml5kGgCAtJFEBwAA0A3JZI90frli9mi1IETQQf/KstCBPG6Pev1dvqop2vlNE8UNuJOg589NHubbFSNMkESQashejhgxwEoc+t6csZFfw2lbnjqu1ZpwTYIkRkYNFJXOQ0noU1psdbAIW1FyemONOmfysMj7piQmHTq0yfou69x+pQl2DJOJXL9gwyTmbOt6ud+8kW1346IpViXoMCRJ42uz4lcWLi8pcryRIok3MoGdz1CujnXvVmn3pkVT1GU+1ZTt20/WU5LVJ6U6vlRTdkpU8tuXglSrjHJD9BOjB6pbl06z9qkwwUCSRH7uFP9jQhzjPBIkRaZAJpgkaVlH4IjT2EYSj25ZMjVSYJFXMmeHON/pT0l3yhAvEPR46jYukzHjSeNa1YkuyV8SGC+Ped8Q7WpRjE5O0jHyT0unWsdtJ9/YY6T69YKJnokuTsnaMjZOKkD/85OHqQunj/BM4okSqOpHAk8kMVqSgdxI1duw+2Sc45QUlLhx0WT1I4+iElES7OSYkBQ5x1w7f7xnYLvTfi7FLhZrrpZrP65ePjt8sva+Dh0WtTMoDl2C8D43aajnWCTjUq06KTLWlu4GH3dIUPY7bO8ZINk3jq9FqEYvScJJj128q1frHb0ESX5vruylpjRWh1omuR4JUxghrMqSIut8E8YeTbXqVIcK2jq3nVSjz0bovJo0zzyCjPP5Minj6/uoA4c0eibzu12i1yTYgWBwn3J1QYT5uWBzHDGrtIfcqY4ZmVzXnd8umKSu3ntM6MC0n80frzXo3MmiQeHHIfsnfD0j45CwyX3fn+s/Zxl1j6otK1FfiDC2jTMPpyMQ3YkcQ3QUS/M6P546fnDo9fv5BI8J0llEOq2H3ScvDDDOizM3KfPEXp8pjVhV6Viz0Kv4lMvyBipUFOPzyD4Sdvt9adpwlTTP+zMOn1eKDyXNM+Ep41ykK6n52F5FGWs8LIUswyY7HTKsc7EGXccE6Vx10thW9cmxg0KdqM6bOlwdnNA9o44kE7mH4zlvlnGeo7I/x6+zbRjtNZWe801uQfVJdaeV85eMh2UOq8uyePzdUW0D1BFt/vtUHFEKCgZKBoy5OcN+T67cK7mxy+SGKvXhEQPUeI97307LK0mvTt1EdZFr8C9NDXc9Ix3QvzozXvFhP8e0N4dKrJV7hZfnYezprev2+1aC+1SQ6/Ail67kHx6RbMdVKegR9no0qdiEDntFmGu6IMa8p1+cS26ydRhnTxqqNdnfiVMcgNfxVO4fBim2GbUohMwRX77nqND7lMThAACA5OW3/zMAAADyRjq/hCWd3c7811Pq7Q2bHR+XycnlG7cEq2DkMb8fpXpzVEEnpb7ukzSiUz67ucjk3PyWvmrj1m2hgiElQH3XMjks0kcTvHEcZR2caSW3ZRMLZB5fX7Wrw2OYic6Pjmy2ghj8uH2PWvtUqHMCBPt4BTM4PfTLfSeqNLlt49yqlGH9fuFktWrjFvXpe55Sr7+/qdNjkjTRERgfZp5akjhqHCruhhXnW91128Y7RnT89SiXc8SA3vmvuCdBEK+/v9FaNqftc/jw/pGq5c1rqVcHD00m6UKWpmxnp6gw+5QE7AcJcIlaHVA6Ui0Y1Fe9sHq99tf2Eyc5yWmJvjBVT9BUY3mZemt953FNn5IS10RSLyeMHqgO1JDII8FIm2wlMduqK9XxYwZagRsX/O/5wK/VZqvw7cZ1uJbx3i/+efAMa19fudF5bLjjj7t2+YubnOJ17N07SAXqjJ6xca7vzR2r3nx/k7rv7ffUba+t8H2/oNWSv6hhX5cgsj+/urzL7yURX8Yit7++MvBrnTyuVX0g5o1/r0ILkqg9yedGvFOytq5jghOp2O+v60a+OGai/Xf2Gq2eee99ddXjrzoGbEji4I7xS/AzzeLWhp3j4mgkUPKN9ZusAAL7sTOXZ8KTwxdCinpE9cFhTeoPL75tJaW9um5jl8e/45PYYC2Tyq+bF0+xug85FZbw2poSlBYnwFOCav799nvWWOC2V1d06Y598thW1dKnXP399c6PeZG/O2vi7s4NYVeyBN48unKtlQC2fuv2Lo/LMVDOfeu3dL1e9fKr/ToHnYYhiceSIC6dcz76j0e7PC5dICTJ3LMTneMyTdw1PvTiNOaV45ycO+V79/CKNeruN9/t9Pi3Z49SE/pWh07W/naCQW8SCPvjeeOtf9/1xqpQCTaf8ugW7OegIf3UH19+x0oAe8XhmCCWRpgXuHrv6IWHpHvy+w5zLh1+tHM9ecm4FBjSMfZ068gigX47irpkQyXizg0wFvMKZP/ls29YScb/5zDmlQ7yn500THU9WrivKzmGyPEtThGLXzzzhprYUKXufeu9Lo8PCZBg6bT94nQekGQm2a+2bN+uHlu1rsvjJ4wZqPpX9FLTQnZzkaDFIJ2Z/Tgd0yX4V8YuMoa58P7g1zNyzI0TsC+d3X/2zDLrfPV3hzHvbQdMs/Zzz2tkh7mEWTHWk/z9P5etspJgV2/e6tj9WDrINYTs4BYnuFqu0STwfcWGzVZ3hufXdF4fUlBC5onCzhdIcZog574oXROnNlarc6cMDz0XK2OLRR5FRjpkfLafdARe7nCPRAJa/Y6DTvOeQZJFvQL+N25zPzJKMk5TRZkq9yo045IYFvY4kuuzk4aq51e/rx5buVa9sGaDY1Gei2a2q9P/9VTg15R7NLMjJLR0OH/acPWb595UhwxrUpc+9JLj8Uv2qw0e5+2MQ/EUOVdEJWPOd1zut3Vcgy0d0ujZpTuT5/tZfz5gmjVv5jQe9jpMXBpwmdyOCV+aOlw9uGKNau7dS/3wydc7PTZ/YL3Vzdzr792KlMTp2PnjeePU755/yzp2n+awL1+583r0iVVrQ12PxJmjkkIZct0nnes3OBwb/rBwsmqqLAt9TJdCnEHuZUUhhVWDjKnszpwYbH7DbY+Qz7Nm81brvpPTfIIkOx01oln98MnXAi+TnC+DJKe43QOTgiK/eu5NqwDn/5av7vK4jNEPGNIYaj+XsePMpuhjF7n/IucZ6RD106eXdXn8e3PdC2V1cNrdPh1w+7mR7+62bNZx7CnXJXIdIOOQMKOMW5dODXh/JvzyyndSznszm2rUTS++7f3aOQt92rjWWJ2wPtrerH7x7BvWfiDzjHZnThisZvnsH10S+4oyWrsdOyVmymeWgoXn264LO45dTsewGxdPCVaoJOt+/H1wxWrVt1eZeuLddV3mOKTgyAeG9VP/ss3JKJ9k30+MGaiikk+z3WdsM2dAvXraYdt2yDjcE1gcYDzsRo5TksAsBY8/8vdHHedc5Fzk+V1y2H0+OLx/rKTZLduzavO27WqzQ7s5mSOSgoMfaW9WPwhxTL9h0ZTE9nW5RpUiRHIuuj3EXOxxo1qsc1NUcm2+yePaAQAA7EYSHQAAQIH68vQ29bsX3lIfHxV9Ys6pY88fl0xVM//w79ivZZ9uunhWu7ruuTfzUnE86QS1fCYBJhnxKckWcQLUo5IgkobyUuvmlT0p009rlXNQdtiqqIWy+fz+XoJS/Spx2ud+pQpplBuWHeTmntxkKC/2DhQMs0wdN+riBG7IZL38SPcHOTa6CXNDTUcCnZuMxzLlI9A7XiJV57/16gYVNAnoW7NHWYHmH7vjUbXOFkTttaxen+KSmMkNEhglgRuPr1pnHa/c3tigZjy7qvk+7xEg6JYcFpWO/VX3PaqkEgU/4dKtrcv7ewS2PbB8RzDQ5/79TKfH5IakJL2HIckWciyOs0x+aypIQKTd8JpK346kJnNb8skN1Wpyg7LOgbpIAriOdVXh0PF0ePXuRPwwlVLrEh7nBgm8t68Rr2DQoAG4kgAp+/PLa7sGdwYRZzO5/emMplrrR25q//CpzgGCEjgfpQDAl6a1Bbvx7/Ki18wdawUNSweBD9z2YJfHJUBCguPXbukaEO51rArS9cYryfFjo1qspLS9brxPmUDG0xLcK9vurjfetYoB2AOGo3y3w3a9tvvGniOtwLJHV67pkkQnxwlJoAu7Tx03amCwoiAue/rFM9vVP99YZe07h/31IceEKPG+RxKdfVXOaqpRbQG6pngFke/lEZwdNZkjaDVtt/W/38Ad35OnbIFcHcdBtwQ6HeMet2WSzgE3vvS2FVxqP04F+Xsn+7TURxpfdJBjkHTakcDjBbf8r8vjf146VdWHLO4giQdhK6jbA6NmNNVY10WfvufpaC/isBml826cccKAyl7qzfWb1IiaSvWcw/VBx74TJjll34HBxqtuQb+SFNiRGGhPopPgWOn2Levxrw7FAXYtt+2/5fPF+R5M71djBZ5K8oKO+VAd5Jgt52Rx6G0PqmW2QkFHj2iOVBQkTtBix/WMJMvId/iiB17ocjw/a9LQ0PtU0G59Wc8CEP2s/d0piW7Xfu5ZAEBpNbt/ndXNQMYu+/7xf47jNT+6l0nGh1I8T5x1z9Ndkuh2vGf4N41zPO8YJ0pi9BFtA9SPNZ1n4l7PSEGKBQMb1Pi+fdSBf37QtbBcmMDUIVUValKDe4dbP1IgSgrvyJjsnP882+XxqMk40pU+CLfv9Id2HlOueuyVLkl00il6XkvfHefXEAeFOQHvSbjtrksH97N+JMDankQnySF/XDzV6vZ100tvu76g/aXH1PWJdZ75v2lt6r533lMT+1ars+592jEhwI9T16I4yyT3FGRMLJvm6XfXdSn0JImbSSZPuDlgSD/r58+vuI8DhEPsfGL3BMfVV6lxM/w78oRappirVpIXJElA5hcPua3rNdbAAIUKnRZhskcnbj9SiEc6YL+3eav622srrEIqJvj5/AnqkZVrrMTok+96stNjkoB1ys4OrF3vz7hvpNMnDA507ef2CnJPX4pXSQGq2Tf+p8vjn985l7fOY97Fvg9lNNzzPmvnvLdTEl0QSfRflmILb63fpP7fG6us4kr24hu7ihzazjNe6yNuB08Zu/z2+bfUJ0YPVN+3JerIOa9jriFMUZC4BwUZhx82rL9qrChVsx3mzYIk4+g+5Ms9v9fWbrDOVU6FAIIkZjqJ2+lbihfJ/nLDi293SaKTbefULTNIcbso12YdfrHfBPWnl5dbRYKPcSj0JOMa/y66ejegXBvv4zFvJonRfnSPIuR+/U/nj1dbtm1XZ97zlHry3c5JhVfNGaP6VYRPRo073rlmzhj1n7dXW/diuiSE7kzmDnuNLHPOQbrTup2rfjB3rLr5pXfU/IDzNwAA9GQk0QEAABSoha2N1k8SpMr/ay4VvaOSwPCwweE6RKl060eCIE4a22pVW7dPsgZaJo9Fss/VycR3HBmPCdSg84K6JzrlJq9U6d+WVerwvz3UJRjIjVSIC5oskO99Iil+2+iH+4zzvaHWgjDrEAAAyaNJREFU9TVj3jju20edP324FdjmFAwUaBkcftcRbJWEtFM5/N7fe/I8Y9RncesqFFdrVfTEzrjcvhISGCU/N7z4lmP16iSFSXiJa25zXeDkMCcm7pOmksA2+ZEOM177oST8OAWAOo3XgoqaRBfntdN8Pd2dfZOSr2TDUEeUmMukY93rXisSLHXV3mOs193zhv94BrK4KUrg3Bc0GT3MKSHuupMq0G7FKYpyulrd41GROYljQpQgiE6vofm7Jq/X0UlLulOaQpYrSKcy+z6V5DGztlfprgBrL9u9goE0L5OJBQBMJZ0DThvfqlZu3NI1iS7iOpCEpzgkwMmr+EqcgNY45/OOJMionL6HcccJv9l/otWtXZJquybR7X5tc2ZIMrvOiV7LZF8tcQIWO3h1/wkiyUOC02snfQjK+FzP/HPZSm3zbjo+i1cX5A6e+1QC8y7SBSEOk04zSS6LFBA4bfxg9erajV2S6DIBxwlRx31uz5LkoSAJw5k8rjM59x0Us1t4kuM9p1eWY3NHgnoa5xm33aAjILlLwlPuPmb745qQyfl2sh5OGbcjUScy2zJdvueoWC8nRVs6ru0W/+l+tWnTloI6PpkzdglG+/VMRsUqRpgEOcZIRynhNE5I6/ppQO9eakDvRsdCJWndM5R1EfdeiO5jupZXS2Aby3qSnzt9Op93OaSrZMcuMh8kYxN7El2nwpm2hcgmfEyQfT3Wazi9aAxy3+K7c8ZYY5gDbn3AsWtmojLeY0Wnj5f7u3zen5MiUWdO9C+IEGaJ4h5zTbxnJKz5Ag3X/1G4XWNMaayxfh5a3vWeX+TtF3iZnH8/JKe4IgAAyH8xEAAAABS4oHODUqnZ5JtpSS7UsaNa1E/mjw/8/FERK8JKVdAwHRHcPrIkrpkS4J3ZOdkoN61bHAJd3N5OOtukpdPkeai/C77uJLg7CUl8FyWQOU7FxnzfNO30diGqUqZFKg4mdvMjgQ88MUY17bjcPs4VOyusJ/su5kZueAcI6g3u0yGT53VheuD7wbbgOLdxWR7v5XoXALBtwbDdX5Jg38Y6OyH7BapIt90gJjcEH+PFXc6uCTPuhsXoVpvU9yuj4e9lPBtkXHbRjBHOr2H72yAd9TrE/aomcZM97koNk9zQHfl+RMPXQRIBgm6vqWN/KITE6C/EDE5J+xNmYiYXBR2T1JaVRO70pzcJsvOrdHRpjPx6Kj57kfFvz44XHC+kCFGU5KK090eRzWN3NR3sxzrpOhXr9WI/If+8goa9zgV9AiR/6xDm2ukTo6MXmdFFe3C8z8sln5iZCT3vHHUd9KsoS7WD/Y7X7vzfo+p6x3s9pf888w2NRUH81oE9YS2tQ1jn9w1+UFjSGi9RXgf7Oks+gSrZrZTP8X1pgE4uOuTzGllL8aI0788Y8vpeYxe/JNRArx/zQ4fpbpjmvVMTrtuDbr8iTa8Z5H67d91MvQUcklj30pU+jszOzmFxO//lU6cCDhH/zpRr5IU7k5rTZB9rL4jZFc3AS+CQsSXBt+DggPMJmQKeRwUAwBQk0QEAACDSRP/8lnp16vjBid+IjjtRa0puw1dntoeeKBsaMujmm7NHqVPG7eiI0B1lCvC9x/ftE/i50/vVqKoEgoeSDAqJmhCa7wncqEmQ+hbA+2H7Mh03amDQP01FEpUydZvVv1bbTVa/xbMf043YZiE6nh47skWlzX5MaDPgxr/dEW35C86VCq0DKuNVjw1xRy3+S2e6dq1Jm/1jnTg2eCBsNub7fSNgRfiLZyUbtGjvBBXUuPoq45Ib9ot5k73za3s/3lE53SR7D6gL9LxjRjYbMe6zL0Gz7uOZhmWalHASq4m8Emu9DIyZXJREgKCJyQ0HDekX4m+jvJ87twrpSQfHd1qGgM87ZFi8LjpJ+dLU4WkvQpdtvEf/YMd+HYJW2W8LkQAZ95rfKwkr97Vn9qvx7eianyr7nV/lklm75wDzzW0o8L05Y40rADC+vk/gDk+ZPCY3xA1E1zMe7vzfHxga/DyTL4vymFgUZp+UbolxJDGa/vQE866R9w5RFCTuJUbQ7RcmATn2XJ5noaDO+3ncbqU65Psqz3ubOz/4w33GJbU4O98149qd0m15B/YuV1VlJamPp+yLF6RTd9LSnznwd8XObommnWdGBjzPxB+7BN+nzvC4V58vmRjXDnHXVtDtd+2+E5QuGY17VdB9KvqyhH+N40alf88oVSEOCvkaJ4Q5z6RZ+NRtmc6eNDTh9wv/TagLcc8kbnfCoPOeSwc3Bk4+TfCWHwAAPUbhlHwAAACQCYpN76ntr9/quS6KBi5V77y6XV11wG88n3fqn45UTSOC3yzsTlURdfDrTqbrk0hVTQk++fx/nnV5o+DvlObazQ0+9Jony90FshG6gx0zskVd/firwSYNs8nfKD91XKu6Kmd5uixCtvAm/ew3r3q7JL59cFiTVfk9jEwSVUVt/33C6IFaXy+o3M4u9kNd0jdEOwUNKzN0/l51XqqKmEGAnu8bcQuelxNYakB8/i5pJQvkrsfzprapj//zsUB/1+udlWrDpdd4P+fck1WmIt6NUy8ZWwfTuMEkWraB7SUuzkk81/zSBRn0lmQXn6DHhKDHzpJMxhqPmHbjP+z5OI7RdcES6ON0CQ7rwCGN6tKHXky8eIUu9u13cjcuEhFkHXxqbKv63Qtv+f7NgoEN+StK4Bm4kTE+aebC6c4dB6PwO47O6Fej/vvO6kCJBPkS9Jh+THtz4GOV+3kmmIqSosDnwfEhurUHekENTEhgtZvZFLyohIrbuSHgXqWji26YVf2xkS3qZ88s6/oatv8O0q0teXkuNJPz76Cb5eI8JoYF3c75HE+FEbfjYhLX9ZMb0w+ktPtoe/6Cc4MGPQ7KU/J4h4l9q9QjK9f6Pu80jcHxTt+vKOexL01tU/kSdOlOClE8Je6bhVllYYqaJCbVoUqw7983AxakSfrelnmjOv2SGLrmFtAzZezy+ZjdorUla9v2qqDzRkmyL9OVeUxYC2pWwtczKsL1jMwPBz9nxpuLDVokT+ZigxYlcH29WH+98zVsL/L1PRPueBrhqNDSu1fCx8lgxYvsLx17jkMH2wcuT/AeZVRhioLk65jw4REDVIlhHU91nI+TGCfEPU4l4Qdz87dPBTXJhOMBAAA9iJl3FwAAANxsfV9tf/P/ef7IcwrJhdPbrEnufZrrVUNFmTKDKWkmO8xr6VvQn+SEMcGTlrR36NKf2xDKkSMGqC/YblamHV+4n0+A8WccOuh4dW5w67gzIEogXoiVI0GtTsvnZ6+A3Ux0yw1uy6R4Q9RuWmNN4u8R53tt3yXGxQywjrokB4borlFo4h6TxgTcJp8aO0gV5+2GWrCznyk3Q+1rpTVkJ1a/1+uuwfFhxjhJLn3GxEDmGB/Yt2J92NGlywsmvkflvEFpUZHaI8C5NvRnS4h93QStvur+eul+f+O+u1vBhjRlA37gaY3VsSsy6zj82k+//WN2x/Nbptx97v+mdQ4wd/vTS/PZNSzgVz1uJx4RdPM1VfZSxQGe3Lc8ftBNmF3qnMnOlbK1DwsSSexTeSNzWiaSeTYnJm6+NGtq6Uhu1D2mmNfiXvQszb1tTF1yxU7CjF3M/MYp1w5BOiTZHS/N9el2/tc9Hg7zKWsDFtoJEzAc95ggSdH5u77NJF58MMn5CLcijWleEwXtvBFmPsF/lXg/YbFHJ8VOr23GJbL2cV0mxOOZBJYl9txDNj9dY8LwfKucdTZCwzVWGJfNcjnPZPQW4IizW2RNGCcE3adCvKbbdyXod2i7Kiz280y4QiURtmCnQk8q/3y2YzbgcfVgDffdEqhxqOH19I878lkUJOjYJU78SlhhEjO7oySmncLch4xyf9ArtsSNjsOZoVN0AAAYyZDIFgAAgJ5rUWuj+vtB0/MaNBdmgiZ3nmWfnMnAKQ3mVTD2k9ac0fGjOifRBZ4oS3jmP2gyR5wb/xK0fZCm5Btdk34ja3tbFdSlU5yTySH37bhBuCrCTWnpCna8R3JmbuXQ3NfIpNq5K+ffKc7g5n6tpMK4W/LyGRqrfIdZJjv7mhpbX9XtJs8NXKRYQZ5uSXWT+ppx3ux84ybbI/cBE/e5jOHv6xXg1Kk7lTJDvgME007SihW8k2KHoCRfo3tLfwUFDUTvqceEXEEL5fQtz19BndMnBBvn5nv7fTGn83GSx6kwDh3W34gg0dz32zePQVpBHTVid4EXL6YcEww4jKY/Hs55w0qPTpBprQPpIrzfwL55GXuGOUe4JjzleQMm/X5xXz7odslnAQfveZfwwY26tk+dgR0Zvm1IN6Lc9SjjqZoAyX2mnGfSPM38ZN54I5K1c9/uohkjjDgmdDrPlBSrvfr7F5rTc54pxDmO3X40b5wyzfagXZBV/ngV+0izgMNcj8IEPV3uepzWL2ihxfztVVUeCfRpzrtI5y3T5vK2F1hRiZ4w7xnmMzZoKJakx+6FPmy481yQXT7vrxlTvNDGbYyeZgGAfN7fD6pPwCJ52UIo3ggAQDdi5ggLAACgh6kwpBOM3w066aKzqLVBHTasSX3AJQFp199pnqE5e5JzBfixdbuTFeY73RDKY2WnXEe17biRcMzIZt+EocNdJmOTvEnbXNkrcBXjIVUV1vM7XDKrPdwyhNwZKl2/D/p2qvktfa0ueUG5fYQizZPnXbr2eXQFk6AyN26VQ/VUMIu4HWzJfFHF7VqXuw4mNbgnpC30qEgcVkbTjc6lgxuVCZiA7+pzk4ZZQZ4faW9Wc1y6PeYzQHCqrcPigNxkXxMTnnrCneOQZLzVYXaAwK6ogq56UwoA2H1/7liVb7qC1/K91zfmJOjkFgBI89u3pNWM81rSIiXM2DbMmRN2dz5eOMh5jJL8t8/9BDLWJYE83+eZK2bnJ+i7EM9aucs8OGCl5TCHdLdxju5zvCljlzSdbeuO57aO87mfVpeVqGHVFXkZJ4Q5DzdU7A7GqzcwUcWU8fBXZnSe42mtKtfQBTlClfZMwGA8lR63YGbty1SIJxqPAM+hVZWpd9YeVlMRcN8z40yTZgGA0Tlz7SZ9nube/kXMtCRBBnyeITWJumiv7W3cYWUv2zxZvUvxiMTXqe1EI+MX58dyEmvzfEyY1RQ0kSd/xsUs6JbEMeFTY1sDPS+fW0+62bh3gtzNlENHmseE40cPTCDRNB77PIvbtgxznMrEXFbp+Cr35B1fQ3fLzBAr8FSX5JR42yDeX39tZnugV9N5nvE7jh0QsLgsxwSlvrP3GGWaQ4Z6x8Cksf2meHTi0104M8w38scuyfZpnmcO3xmjY9LltX3sUu5SzMjUawwAALorkugAAAAQmCRdXTh9hPrc5GGq2CfAJ5OnSo4D+5Rb1Uw/2t6sPj95mDFBSWdOHKL+dsA0dco4/2pXJ43bPXE2vm+VaxJEFJmAgXd+neh+ud9EdfXeY9TvF05S8yJUvp/bXOc4mWvfJJJQ6Natwf5ZvjR1uKoqLe5y0y0Je+QkbnXq1Kh5l5rT3DkRVMfLF8V4FbdkoCg6bXe3gIUAzpgwWO3Z3z2RzmmbuHffMWHq3Jv5SxhfIWwHP/XlpeqrM9vVaeMHu55r8nnvo6y4SP1x8RTr33W9StTX99zdMSHO2r4iZkV6OW53OKKtvxEVmaME5uXDsSNbrHPiMe3Nrjfd87lPSeBGvr7DYcZrk1y6x6Z9VNk75/wpHXDjGlHjHHgcxinjWlVjeamqLStR5+Yk7euvChv8Bc+aNMRlPenep8I9f3iARJB8+9DwJvXlGSPUj/YZpwa4HKfymcRqDyb9yvTd3SV0x6GH2Xyz3MaIaR8UAgRJ5NuonO9cba9w4/F8BOjmFsjp3Bk9v8FAon/ljuvTtupKLcdOvwIbQboWHZoT0JW7LfPJvsy1eUpSC7Pu+1X0Up+ZOMS6frx6zhgjrj8u33OUMo100r5q7zFWp3YJFsyd/0gzmCs3EWSWIcvU+XiUntylKLYtUllR+ueZsybunnOUolwfHO4chJrPzSeFqHK/f25dVEyJWcx354ZCkHscmOGSaKSneFi0TlitfcqTm+Mw6FUivXMmo66dP141VZRZ18qzPeZ19b2n/3M+lFPYMLdjSaeRZ54PCpfPHm0VfZMig/akEJ38xkImFrvKXaSZtq5hpnTnOcklua/T+k7hRPOJ0QOt+2hunb91CLPLfGxky64CF7JcUsQzbTvugU6wAihlbPXpibvnq3QXAAjz7ZrZb/fx0jnVN//cYgPSPGzI/EFuwd3pgTsL6pW7DuS6/ZNjBjmeczolPGmZ48hEKj6VOx5Oc/sNq6404jhgvxeSm7Qmy+gkm+fjlMSdOBYS03yaCbNPDepT4TIezu9OlbsPy/Y7dWfsjczl6bjnF3feXe4h/2BnQche1vIl2S3PvHEcAACmMuPuKAAAAIxSaFWOFgxqsH6c5LtaaK4alyCyrEfnNeli9Mx769TmbVkr2NmUbdy7tFhNS/jGw9CqCnWWS8dBJ5JUIJ25ot7UDboOjmwb4NF5MWNEQJbXfh7n5sd504ark+98Uj2/Zn30F+lYjk7LlLGSce54fWWojoCipqxUfWv2aDXzD/+O9N5J3NZw677ivQ1yb5Ql8709YfRA9cOnXle6xdnrpdvi1x58UctyuHetDEeOI/99Z3Xi6yDM9mtwSRwPo6myl/rPobPUtqx7AlQ2QtDaB4b2Uze99E6kdSHH7f0HNahnV7+vxmiqbi9B5De89Han34VZJklU+90Lb+3qalaiIeh0bnP9rtcMHDxv+2V5SbH/OTHETuWWAxd0XfUtL7M6Gr65flPX7oa5ixR4iXYulxV8qF+cc58Eqp1xz1Mh37Dzf0pg0i+ffcPaz3O7vnn9jRcpsuB1THe6KZ1xGJfetHiKdUxwCzBLsguyW4GOb+wx0joGH93eHOk17CTg8OEVa2O8grLGGbe9tqJTMmQcGQ1/Jccmtw50HRJv3ODxXi25QRI5X8A0rsVOHz9Y/fHld6zE9l3LFOP1LttjpPrcv58J9Tf295Pkxysfe8Xqhh20G7iXvr3KfN7f/xNLcZzj/99jqqK4SJ0w2rm6fZjtpyM4Rjrk/d+0NvXc6vetwMrdrx1dS+9eatn7O85dUf1g7jh115vvqnk5BU/ifF7pKn7zy7vHUjtez5v9mve0CYPViNpKNaq2j6pyLUoSfBl1JFIeOLifenD5GuvfH3Cp2p7v84w4om2A9RPnNXSaPaBO3bBwsvrzq8vVvJxk0XzPI9j/QgJNf79wsuffhDkm1OjYp4bs2KdWbtxsK5qVXrK9jKHk+kO23+dzixLEeP+2mkr1/OrOcy7+r7f7GT/YZ5w6/p+PW/+WY6gUVElD7nqUgme3LJliXa7IdakO5Ro+l3z/vj17lHp57UbrujYpYc8RUxur1QPL16j9B/XVsk8N7F2uXn9/Y6i/yTgkXPzoqddVn9JiLUVBJHkqrmNHtai31m+y1u9H23ePE9IiCauSaCHXV+Ly2TlJ0iknQf5s/nh1w4tvdyrI41EXJ9AYLex4KuPQ4fDmxVM85/GzKSSRyzFhzeatar+ca600C81Icsr3545T27LZTokqcZZJvsfrtmyL/gJKWYV4vv7QS2rJ4Ebf4ppBVJXFm9OV/Ujm8E668wlVWVqsjh6xez4hzZ3q4KH91I+fel29v3WbumzWSJdFyn9RkBPGDLKO67nfv3wnSua+nYyn5Hjw77feUyPremuZi41S0MP+3RxR01vdsGiKkiGHFOOInUTnXtkx1HFqUWuDemj5GquAn+MypXALvGM+/sAhjVqukUfX9Vb3vvVerPuN18wdq25/bYWV7OS2P+R7VX18VIuqKCmyjglyn7lD7q6hpYtuJlzxqT8tmaqeenedrVBJukk3cr/4lpff0TYXq2M9XjCtTV1w//PWfZAFA8MXF7bTkfAt8/t/O3CaWr1pq9UBtYPe0lPhydhT5s1kXigpYY8xEm8j8xySoJlWcQD7u05sqLauk+U6IsnjlIG1EAAAMJYZJXkAAABglCQ7bUVVYHl9katVySTqZXuMUt/ea7RHMFzP5NxhLPkdVKpO5t4cDlptTMs+m/L3TxLWjrAluUVdJPumkhs0X5g63LWCnx/ppBNF7ubTtXql0t5XZoywknGCOjynC5fnnbIY+/gnxgzyTfCLJMYyHTy0ybNraJhX7hSgFIMkdUnF6X4agrp0kYQlSTiMS46R9gS6TkG2EQ5UcYPkJahTOs3kLkecQ/kp41tduwgEId1gz5wwxOq+4dbVLCxJOvfaz9MY00hAynGjnAINg698ScA6bFiTmti3Sn0r5/sX5ybtT+ePV3GdN3W41Tnl/Gl6KmrPbKrZVRU0Kqk2LAk8+2q4wZ57THcTNAhL9oOuN+zDj3F02ru53koYzU02jHNMkPPxwS7dG4OSYG9J4Ik6RrGbkVIFbC+S4CnJU1EltadIsmhckpD52/0nqb1yAm8yMROjJQkuzrlQEmF/uM84dVSM81WuY0a2uCYzBzWkukLdumSqdWx3u+4Mc0gos7dCikgCc8+YMMQ9mCTkzvedvXZ3H4tzTPiQR8f0sOT8cFDM45QUkzh0WH8rqFKHhYMaYydJSHCnJLGePK7VGvPrOHZMy6n8rlOc84x8vrgk8ViCmdtq9HQRlLFQPoTZfpIs4Zf87UfmYaQL69Vzxnb6/nW+nMmGTliL65wpw9Q/DpquFrfqCRq+dNZIaywblVxb3XfYHtaPHEN1OE7D+ViCz/0S6MIc0yVg9GgN59E9+tdZ5+OKnKI8cbpOfW9OvOuGjoT9b+45Sn1xip7vsryeXI/EDfr++h4j1a/2m6glMXPx4EbPY3qQ47J0E7xg+gj1f9PbPIqChNuAU2LMAcicxnULJqmzJw21uoLkduGIc0ywjzujGFXXx5pzndB3d5ffOCM1md/o6Mwbh988fpjvn1+39kyIY8LCVvfEsLBjl7nNeoL/7csTZ/tdvXf88bAUSPjHwdO1dTKT7itxi6PJHJ5cy9zsdT0TYgtKh5i4pGDKHxZNVr/ab4Kam1MsIc7YU47pOmQ07lNSDKnL64d8DTm3yDpyLTwVknS9jTOe6jCgdy/XBLoo82ZSvC8uKYAj+7nMKzguk5btF24Lfm7yUHX9/pM6jV0yMRN1h1XH60QmYwNJHve6Rg5zTPArXhT0e3fUiGb1idGDXMdTWQ2dzcOu+8aKMjWnuV5b8Y0r98rpiBaRHAvkGlnGMDqcpqHLl1zLXDNnrDp/Wpv7GCYbrpCwFM/Qcd8+N4Eu7vcvt0tiVLLdThwzyDqG6lioji5yccg2k+WSYpU6zGvRc59HzjHeid/Bd6peRUWqt6bPBwBAT0USHQAAABKhoxK9DtkAwU9yU1KqUfUkxiRHhphgzpiYZFoA36PcwCCpPBuWPR42k/DEdlBu3Q2cVEXo+BE2cEoSsU7N6XiifNaTJKLsei+P53ZOTgkfsv4xW3VuE47N8wfWazkelWg6kEng04fbmxPt/inCbj0JPpaqrkmKsk8lsQdlYgaofNIjuciPBO1IEKXuccAhw5pCBUrk47v5ybGtqlec8vNWkMQwq9vFULfkopAHT7mR+dmJQzr9LhOhS4l0TlmaU+k0zvqUG6xSFTRMQGUmT1lIX505QukWJwny1/tNVMeM7FzxXcehORPzRrQEjJrkY6NaOnUWMYEEF/16wUR1QMSArtxuovNtnZTi7FOSLPrzfScYN3Zpzg0CMYB0CZfgTPu6D0sCSbyq/ofZfhLYntv9T6sY+f+SrPS7/ScZdZySIF+vAGRde3yYzylFF6RyeBjNvcu7jKkliVWSkF27IIccJ3xjz1FWRXrd6yfOa3y0vdlK7NL1ejrIeOq6BRNdk6PTmv+R9RQmmDnoYsY5z1wxe7T6rF+35wC6HDtjrGPp2vaz+Z3PfWmTQgI/nueeyJPWPn/6hCGJjKni1JmZ3Fit7jlkVqz3l2taSf7XFeA5vKZS/WnJlFjHAdnHJbhaV3KDzL1LQmziQm5AKQ5jTw4LM/aUDm0fHN7fSvLURZIkJAFHtziF6CSIXLozub+4yjtJWgybRJ7JQwCVJLskUUQlE3Oc8MfF7seEoLuGjPd1keR46YQURmtOomruvunV6TsbMilBOrrGJUnMXoUSwidm1odKXgxT5C9OMSSZDzKJ7J/26z4TSPKN/ZgQZY6jSxJkjLGLbL97D+08dslEWB5J4sldrjhTzjKXl5d9KhuuyJpXYrTTxw16/zUTs7OyFLjTLc72m9lUq/51yExlEkmm9C6Sl8n7/TX5vuhIbHd77ZyFCuUzE4d0ul+94wU1LFOMvz1yxAB1wXT3c3Iac8VSOPMcj8KZqcTNZDLqlw7XDWnPUQEAUEhIogMAAIWluExlqts9f+Q5SMeBQ3YHYY6o1dO5QSf7pJFUMJZKjr9ZMDF25csw8tnjIx+d2oJIobFJXnitXh0fOaNh/UqHpurSYuviT4IQw9o3YHU1SUD66EinLkfpmt5Y06m6nwm7oqwnr4DlXLkBqFFuDMzRVAFZJwlKCMoreCHNw1tpwLuKY+p2B01EqYYrXdaSFOX78NEQCTOz+9eqfLAHG/p9V7wCbHQ6oi14p4QoiRBRtt8ptgQHLYkEMV9DV4VZ3Z8rt1OJ38uNyvmuJ7n9ZvYL/p2qDXisjbOuJED3lBCVfdsDdn/RcXjPrcLq9Xo6KgAHCeb6yox248bbkgDzpRBBgrn7igRUXzqr3UqWsXfezF2nE/uGX79xu2ElcU3UpSCDz8u5JRDpJMHoR4Y4z+TDR9qbtXQT1Jkw0xHgHtT4gJ3ddJxnchPb/V6vKQ8dk+V7Ip2QgpDlkU6+SZOCMNLBvBOPdSWd8JImyYL2DmsmzLtI0pNUfg8qSrJOlHmdoPuUiLJPhV0mCdrXUWnfLqMhOVrn6+kgHe7sxRK8RJmviFLU5bMTgydB5muuN8x5v8alW5Jd3MNK0DmnDtUBlysur2DYsJ3GnITdo+RY+HNbEmvayfaiSwKOAQcFt05tXgnCSR8TpKDOF6Y4BzM7Le2nbQV8XOV81rDnGRm7SHKmy8t1sXdO9+ygyxSFX2fQNMi5L+h5Ru5PSNfjsMJuP+noesPCyYGff0x7sOWP0/FU2It0ue0Nnxg9UJ0ZdD+PSeaDwqjOw1ysjNPDdO7bI8K8dVbDuc/r2xz0ejpuAknYY3ow8V5Trqn2sF/7eYjyGbIhl+eyPUZZHaiDLo8kTQZ67dxlykZLMNIt7h5RWlSk6nOKChgwdLGK5J0z2e3aIatlPjLs5pP96icexUrsLp7Vnvh4So6dZ4UoNCNdmIPIxNyfpPN7c+74xeMFh1RVdNr/knLosKZQ28+1EKVGMr9vv49uwvcPAIBCQRIdAAAoKJnyRlUy4WzPH3kO0iEBL9JNRCaQdAeoR03C8vo7CSiQSo5SMS+fTEjiMaV7W5j7HCZO+pmyTF77lHwXb1o8Vd28ZIqaHKKrTtjgumRuvMX/Hl01Z0ynoMZO+2LG/SaJdIgw4bN8dWb7ro6AnxobvttW0IBOCYT94T7BJv+rInQ0tGsoL/VdFxIgIMELaW8H+yqUiqKHDgsWeHnxrJFWlx+pjh2lgnuYc6k9SMdN3G+qveuI2+tJcMsF00eEDm6P2lUnaNC7JHvYg1TTduaEwdZ4JOyxNspYy15V1G37ye8vmxU88CTOGEcq7QdZJtEYMImgLCeQ1a0rSxheyyTHzjDJwfkYD5YXF6nPuwQT2uk+e7sF8ixubbCCT4KYlJOEFSVQztvu5RvYu1x9PeAy6TC8uiLw9ovSIS5KIHoYX5jSufr9Pi19rc7e9s4b0k1OgkHnDKhTx43SUGDBZSeV4Iifzhufl/28LMRxRApY5HaCNkU+AtHFR0IkXHw5YCD9vJyiHno6KzjvEVLNPehxqixkcoKTRa3BvuefmzQ0b4HPuV0mvcbD0o2m3mU87yXJo9RVe4+xugurPJ39gl7/JtFFNi4p7rFvpAIO4bdga8AkCulatzjguU+C/PJFusgHoeP6IreAg5cJfavykqwtwnRmlvNfPuZ6g3b8kgS6K22dLL2CNDskuW4HVPYKPO/SuyT+nNR+A/sGToLX1XHOT9B5SdnPZUyZj/NMmC5yQbtP5HOKNJ/nmRPGDAzctXVsffguOVGSQiTAOghJrNkn4LxL3HNf0CRWKYjxpYAdxnTsUi0Bu2t7dV7SbXTAokpfm9Ue6fwf5ZggnayDkGvh4yIUEMkmOO4cX18V+LlBC8R52at/7r6SWwiwM+lYrrNjp5fBDh0L3cYtkogbxCFDd8+lRuk02be8LPB4+JiARSrzeSdOjulpz1vY73HJvSzpfpaP71/Qc9MtS6ZYhU2CODhnn5o9IPznmGc7n3l97XV02NR9f03iSPIlaBdVSYqeGeH7HeV6JugYSbrW2bd1oGUKv0iBzx3fnj3K6sKcL80Bxy7SdTBfxY2CjkemNVZHu0aOsEwLW80r9AQAQKEgiQ4AAACewkyzSDD3h9oGRLpJmg+mzBn1y7lpUpvHRB2/CsOSBCnV8LUEuxbABnSbiOylIdjeev2sOR9ZArr6VWgKiIm4THGrY2qTs2Hsy/T1PUZaNwXl5oSu/SDkIjkGGP1yvwnq9wsnawmezd2npENhh+sXTraClIKY01xvdROSdSTrKookOjUevPPG91Gau7LYt88NiyYHDpKUQDTp8hP0pnxUv9h3gtqzU+BCsH0gyS6hsj2CVrOX8YPc6JIg1aCJP3advrMZ5xtGY+v6qENsSWRJygZcwUeNaA58Y+vqOWOsBJth1RVq/0HBgiK9grnc3vWmxVPU3IDB1bkdHpJMQO5fWWbtJ0E/5yWz2q119KMQ1UHDqiwpCnzstNP5/bOfz25dOjVw5f/cYhJJVmyV7/aAgDe+R9T0toIJpWv0ZxIMrJCq4/lMqv3g8GDnp+sWTIz0XUoyOUW6ztlvyHsdE6T78df3HBWp01FQl88epcYETKBuyLn+kmNoFONy3ivjESBxRh66c/ltc3uwrwQMHxKwAEDuMT3KMSFogJIU3tnf1s3LjSRkSsL5x0e1dOmIq9MHhjYFTgyT76gk9Mn1fJhuB2G/s33LS9VheQxQCipMkkluF9ImDdekbteV0/vVBA5m0nOd7b8FpbvKfgN1J4PH95sFEwMH+MtYp0NdguME6e4XNHDv/Gm7Ew6S7IgoXfSki3zQZDMZE8qcWtDiJlEOCnW9StR39g6WGJakrMPYJeh8RW4nwCiFnoL6y9KpXYp1uDl13GCrcJG4MESntLBOnzA4cHdUOR9Jcpt0X/hOwGRALxmPpBpJbjCNJBsG7SQo49SOQio6ku3d1pUUbzhkaL/A1zNxEi6Ckg6k+TzPBJ3bPTXEPnXGzueOqu1tXfPrYp9jkcJFQccJn845t8j3MCkSWB00sSg30TpMgnPYOQi55rkkQkElk/TLKb4kyctJka7sQYunlOeMp/J5z8GLdO2R712YBOEoZN8N8z2Ker0elly3Bx17njq+1boWlTHoqICJn17c3lbGw0EL+NTlXLcGTZCNskzHjmwJfEzvGEvloxvwdQsmRSp8EKUYjtu6yUZMlhTHjx5oJVJL50HptBl+mYJ99uv3nxS4SOWMfrWaixe53zOSOBLTnDJ+cOD1mlv4rD1C97qgpvWrCbxMuddYhwecf/bi9q5hvtu5hUqiCjJvJmOXfCVqh3H1nLGBj+m5574oHcLzXeQYAIDuJJ2IXQAAABSMQp93MbHrm1SzPGnsIPXfd1YnGhgscjff9+aMVefe94wVpDLOIdFRqrZ/aHhT4GCq7koqpU9trFYPr1ijzneo0ud1w1k6mrz+/kbr300abvznbr9Pjhmkvv/ka46dSZLuPGLisSSJzyxBX2ECPeySOlzmBt/o9P19xqlrn15mBZKEuYEvE/I/23eC2rB1u/bkg0yM4J1zpwyzKgAnXUE9SseppIW5WdffCl5eq+04pcvEhmrrxyRS0f17T7ymjjDoxq6cw/9ywDTrxlqSXTjCfI+OHdWibn7pHbVx2zZ1aYLBVse0t3TphOjXuSi3e5EJFgzsq25/faV1w7kiJ4gqqKCbPEyXwotmjFCfuvMJKyHkaA0Bgm7LWBTyLHlAzORjr86zUR00pJ/648vvWMFT8ZfKffnC7OcSuPezZ5ZZx4T2hMYLHV3nCtmQ6gorcO9/76y2xgsmnWd+9OTr6sMJV3a/NESSlxRIOPvfT1vjz9n9w1dEDypM4R0JGpKEc9OcNWmodT2vp8KzngOVJPW9t3mrxleMTzprn/avJ61k1sOGxy9moONz5XZHy03yi0rXupbA3JteescKqowi6BVymDmf788dp6549GU1u39dqMDQJMnx42fzx1tBeXJ8T2r7Sce+wK+RyVjFHjZs264laNgtUWVsXVXgZGVx8ax29YtnlqkPJ3wMDdp1o6Nzj6xbCapvCNhlOoowifxS6OEPC6eozdu3B05y86Jr7CnJbaYluMk46vKHX1If0FycJs4qkzkt+f7JeaZWQwCt27IELd4g5rfUW52L3nh/ozovJ/E3+jLp2akk8f+2V5cH7nSUj7lYGQfP6l9rzamb0jVjXku9lZwpxxEdxSIzmo6zn500VD25ap361NjwXRqDkqSzMHNNkmjxuxfeitRpJaniXlfsNVpd9tCLalJDtRquYZyngyT9/+vN96xiW18y5Jgg37cfzxun3l6/OXD3vXwcE346f7z6/QtvqQUBC67kg8yxfWpssO7E+TomHNHWX/3xpbeta78kE19re5WE2qekCMUdy1aqoxMce8q9qTD3pGSO4/JHXrL2qXwW0vIiCbifzkM3tjDjIhkPXzF7tHpxzfoY47ycAqaaTumSZPiPZSvVBwIWMsgH2XYyZza2vk+khKckSAfqdVu2qm1ZpRYFLIKWtFF1vdXQqgr10toNWuZidY0Sr50/Xl3/wltqScBuxvlw1d5j1KfveUq19qlQ+wbsJg4AAPQwYzQHAAAAoxRCQk4UZtyG3eHYUQOtnyTUlJWo1TsD5/rlJEhMbqxWf146zfNGaD4S6KJOnudr+8nNHrmxsmbLVt9kGfsyfX3Pkeqse562AoKCdlYISpKDpIOSY/fCPH5lo96kzdv2M+qbvoPfEmVTWhYJwPjyjBGRXkeOI0ncdJTE1bP//cyuSqdhv7txEuiS7MpmEqm0e//y1Wp7VlkBON3lO5fE5pOK7klUdY+7xnRW89URpCbnyj8umWIl1jZqCIQ1JG4ukWWSbmyzmmrVlMbqRJMgwxhaXaluWTLVquCsY5lyX0G6yV360ItqcWtj4MqrSZvYUKUeXL6mSxJFEF+YMswKWDQliUB8YvRANbK2t1VB3pR1rJuub0rcIPRsAgsY9zyTTeDcLAHickww5RiVBJ2fzJRg79yEp988/6YV/B1lG+pKhLZ3PL1x0RSj9inpziidHl5dt0F9JeL1kF8wudijKVwi6tLB/awf04p1SAX1JL4jci1y04tvR0oG0NEFRDf5rMl03Yj+3Znf0tf60X+eiU7WkUlFSjoE7Zgch+N8XoGRrq1LBzdG7izhNu8i86jDqyvUC2s2hB6zyXfPtII8skxy7ZeEj7Y3q188+4b1770H1IVOOJQf3eJ2vwiTiGuXxPBCxiz5KCISdr1ZXWbi51853ktauXGL9e+w5zEpKiGFlUy6Rpb96XtzxymTSKGcmxZPVtu25+d8E5TcC4yaQGeXuzvn3lMLe+6T7fe5GJ3x3O5t594zkC58hU6KKvxu4WS1Zdv2UIUDgnaUk0Qc0Tvka+81oM760T32zJ1vlqScsN28fr1gktLNnKtbffdnJKldfnSTjvSSSNVxzgnjKzNHqGNXt3TqyKon3iUTK7H2yIjJ40mRscuhw5LpICiJgmt2xpyEufdTtLMY69vrN1nzQqYYXdfHsYBybDG+htJBXeJ3Sosyxs0zAgDQ3XXPO+sAAACIpackEnRXV88Zo8bU9bYqgNo7WJkUuBZWJo/7uUxSuiXQZX1u8t2waLK6cu8xsQMIdi5Jp/+q61XqOIFqQtB0WVGmIJKAO3WmUfmR+73r53iTwcyDrgQuyfJeOF3/DYXc71/udpAAIKm6J9UA/W6sFPDhTIvLZo1UM/rVqO/sNTrU39WXl6qbF0+xkp76WV3puge37/bYnABbqQ6a1jLlyu2YETbozVRy81hHAp2ddIbtMCJEx8UkZWKuJ+mwFqbTWBBlEuES6++T6Wwon/UfB81QX5iaQKRfxGCS86e2qRE1lWpWU406NGRlZRmDmRQc2LHtpEKtrsC3QqJnrB1N7jtLkmiHfRNIvI4i9zwzPsK5L6lrRqlKP7FvlfrazPZEXr/QuF23nDhmd0JR2MAe2fYStD8zZPKWn77lpVayrjhlXGuq+5SOl5LjuRQTuXb+BDWoT4XW+QTpUnHMyGYrQWxKY40ygSS8dDCpG6ckA/xqwUS1dwIJHWHkzrFU5RSKyXfCk9sxYf9BuxM5Zhty7SDdfXI7oZiiY74lieuSMHIPU9Ihrb5XqZozoM7qKp5PbvMuEpy5+9/h5xOjJtD5fQ9/Mm+8+sW+E9TRaQcl56wsSbgWUQO3kyDdSuVcLB2NJdHZBEe1DVC9du5Xl+85Ku3FMdrnJw+zdjGZz0uyG2eYebMLp4+wjguSQBel21YS18i5x8uDY3apN4XMwWpLoMvZgLmJj30r4nfj1OHMiYOt5aooLlLnTkl3Pij3vtqXp7dZ4+L0j1O7N2BHd62Z/WoizYnoSqDLPSZcvfdY616fdAhdnHMdkSYZb0qyvYypLpqptwiKH7fLTzludnQhky5gJsod86U5Fyvd6eWY0Fheqj42qiX0fi7jHd3zVDIvnDuOMYUUmJHk1c9pKH4ZR+7a/u6cMWpKQ7X65JhBoe9p9CouipVA53Z/LbeTo3R7zSe3uaCmnHFdXYgunrlz/CTQAQCQf4Vf8gwAAACJMqXDjA7d6bN4kcS5n86fkPZidNuOi53+zmHiXuckZ9CXqigpVieNHaT++toK9ZmJ+Ztc//bsUerT9zytanuVqgN9bmhr//ZpyDvTHR/s2rlhZ3fDO99YpQ43KMDMz0fam62fpOWuN/n+TI9w0zhJpibrzW2pt36i0Np11ND10+GEMYPU86vXW8fuT0bocpEESdKWZNFHVqxRH5RK4nB1wfQ29e1HXlHDayrVhL5VeV1TbqcZ6frVQXeSQpwK1J+ZOET97bUVsbtt6T4kpJfo77wFJWjtl/tNVGlwu8kuwVwdOgJwTDC/pV7dsWyVVZjDFF+cOlx99YEXrO+hJEOaEswlFZkll/XgnQFwaZMq+/83rU09vGKNFWhtirhV6ZMgQbk3v/yO9e98Jze4HTslwEiSCN7fsk1Nasjvuc+NjNF/sM84q3NbW4xuMbp9ccow9Y2HX1IHDQ2XFJ0k6dZ9yrh0zsVZj6Bt2afeWr/JmCQsU0k3x+8+/qqaPaBW1eScn9O8BpVA03VbtlkBiKacZ2QO6Ef7jFPPrV6vFreakUAufjB3nLpj2UqrG6cppEParUvN6vB6+vgh1vyUdJmR6y1TSCKACUlhufcPJPFY9qc0xwg7lqnzHPAxI8MFoCd9opHj5e8XTVbvbdpqxDbssGf/WnXvW+9Z3btNcciwJjWnuc5KBDGFbLM/Lp5qFeeRwj8mkOt2KcDx/Or3jZo3O3Vcq7r2mWXqUzlFL9J2zZwx1jXy5MbqLgU10xx7/mnJVGsu1pR9SixsbbR+0pY7JDh70jC1qLVRja4z59gp+5J0RJLzTYkhhTNl3vNX+01U27JZvfc1Yl4j/3z+BPXQijXWcd0UUnDx8kdeVgtbG6xtaMK8p8zry3hYkvqSKMYQhSSDXTF7tNUhryOZ1QQfHzVQfWxkS+rXDrlxDnJuuWZu9O70umRsndukWIksp/zblH1K4jT+987qvMZpAACAeMy5YgUAAIAxzOyJFE2WtnpGcKt+fMaEwerovz9q/duvS4lB8Sa7ZAx6/WNHDbR+VB6XaY/+derGRVNUTa8S/6qXmfwcp/ySZdM6vk3rV2P9OOEwZbZJfXcHSy0x4EY7wgcyXzVnjHGrTZJFTUsYPX/acHXN46+qD49IPnnWW273zl7qa7PaDRhD7l6mg4c2qYdXrrECBM9OuSpsriPaBlg/qeuB47Wg3MYgCwY1qD+8+LZ6de0G9e2Q3UWTJJWiX127UQ2uKjdm+x00pN+Org3lzt2Z09iAEgB0coTOXFp4DGyXDG60ftBV7q5z+oTBVifH4dWV+jpDaJBWALrXt0oSePIdmOtHkuekO4IpgXgmk33KpMQGU0mA5zdnp92dpGvQ8FkpjTm95iokWNGUgMUOchw/Og+FgMJKOwjWaT3JXN7m7dsjd+PMd0GztEjw+bwWM7vLpMFr68m1u/zkm9e36xt7jlLL1m2M1QElCaZ1Nxf15ekk9XkdE0wswPHRkS3Wecak4/qouj7qFwYUCso4zMemobDOMlLoLpN6oraTqpQKPHltP5kDKjHou9cxphrQO/15l9y1MqOpVv1m/0nKNLldM025npnVv9b6MY1J55jUZb2LlZgmX3EaAABAH5LoAAAAUBC03GRn3tEQuzdEW01v9bP549XWbFaNrevj81dmbEASnjozKdhUSNXA3zz/5q6K0YWw/QxcpIL4zJk8nWeGVFeor8wYoV5Yvd7IgDyTtNdUqmdXr7f+3dI7z0kXHoEbCGbp4H7WD7rK2AJcvjIjncS+QmDKeK2QFEuHp7ljjaqo3RG0IedA00iXtTTkjhPynsCHxEhnBKnynQYTr0sK8TyTVgIdm6/7MOWIzj7VfZizT+VeJHdeqqaUxlOFwJTtBz3XWaYl0JmCy5nCTm5IfwkKY5lMZea6MnOpTMCaYb3BDKbMxRZaURAAAOCNJDoAAAB0a0xlmU+qZbr58vQ2df7/nld9SovVotaGbp/Ek+/X745B9UOrK9U1c8aolRu3+FaL1r1EUV+vX8XuCsT9K82rRmzqvlFVmr8pjf0HNSg1KG9vV1Byt95le4xSP3zqNTWloZqgPILjkcj3rTuNDJJVaMkpJlbUNoUpQRJAktjNC3tdmbJMnbvootCw+ZAkQw5TBcGUY7rpy4TC234EfQNmMOSQAGjH1Wj3wXHKHfs5AADdC0l0ALR67rnn1F133aWefPJJ9cILL6jly5er999/X23ZskX17t1b9enTRw0ePFi1tbWpGTNmqD333NP6vWnkhvPjjz+u7r77bvX000+rF198Ua1atUqtW7dObd++3Vrm6upqNXToUOuz7LHHHmrmzJmqrKznBT0D6J660wSQJNE8vmrdjn8316e9OD1W1Ju0krgysE+51XGioqRY9bSb4CYGDUddpGLNH8Vrj5rSWBPp79Ky78C+6k+vvKNeWbtRfWPPkYm+lzm7VLQtcfXeY9SX739e7TWgTrX0yXOnMzjK2DpTnj+tjTWFWEw5TJl4vigEpmw/E7FPQf8+xV5VyMcpE7eeicsEID0cE9ATmTJOMJGZxwS2WCEjgRw9kYn3/ExBUZBoTNmlTBwnmLhMCI55TwAAkDSS6ICESOLYu+++m8r6lQSvurq6vH7WG264Qf3yl79UL7/8suvzVq9ebf0sW7ZM3XvvvernP/+5Ki0tVQsXLlQf+9jH1IQJE1TaJFHut7/9rbruuuvU22+/7fq89957z/p59dVX1Z133ql+/OMfq8rKSnXggQdan2X48OF5XW4ASPLulTGTrxFnOo9o669WbNhsTZQeOWKA7sVCBGH2KbmhNLa+yqj1zKR7cBfNGKEue+hFdcCQfqqkqEiZIff4ZsYBriiTUVfsNca6SWjKMuVTmE88rV+NunnxlETWE4Eb3Ycp3yNu8kVjyOYzfplMZcq6YrzWfRiyS7FPdSOm7FOmL5OpTFlXnGcAmD4g5jgFwA1d7gubGWcZM+diEY0pm8/IsYsh66bgGLLejNynAAAAYAyS6ICE/PWvf1XnnntuKuv3iCOOUF/+8pfz8l633HKLuuSSS9SKFSsi/b10qPvTn/5k/SxevNhaZ01NTSrftm3bpn71q1+pK664wuo2F8X69eutBLzrr79eHXnkkerTn/601a0OAAqdIfOckZUWFakzJw5JezF6vO40Ud05KaLQvyHJWjCoweqyJklipshNlNK/VPFekZvNrCdER8IaegKDTqcFhfUGAN2HiadCAtGhAzNN6GnHTnOxtqB7jzJjn6J4GAA3ZhylAMB8jKcAAOheTGkFAKDASMLY6aefrj772c9GTqCz+8tf/qIOOOAAdffdd6t8d5+T7nFf/epXIyfQ5dq+fbv69a9/bXWle/zxx7UsIwDk2ynjB+/695FtdG8DclUUF+/6d3XZ7n/3FGFvqCWVQCdd26I4bHj/Xf+e2VSrehoCN6B9nyI7BQCC4S47NOtOhUpghoI4TBkY4WngIiEELmeA/CmE84yJxwRTFinqXGxP214Izrw9CoVe0MzAwxTQc/AFhO5dijUKAAASRic6AJGSzj7+8Y+rp59+WvvaW7NmjTrxxBPV+eefr4466qjEt84rr7yijj32WPXGG29of+233npLffjDH1bf+ta31L777qv99YGeKrt5jdr+5v/zfE7RgH3ytjzd1f6DGqzEl8riIjWxwYyumkyUwRTHjx6o/vTKO2rTtu3q4pkj014chPSpsYOs4JOGijI1u3/PS6IzkiEBL5xnwD5lBkMOCcYvk6mMSdZOewHQ7STbzRg9PdralCUyMRAWAAqNMcd0DukA0C1R0AzI4/fNmJFdYS2jMUvEgLi7TJuZs08BAIBuhSQ6AKGsXbtWHX/88Ykk0OV2crvwwgtVeXm5OuSQQxJ7nzfffDOxBLoOmzZtUmeeeab63ve+p2bPnp3Y+wA9ypa1avtrt3g+pahhqvSKytsidUfFmYxaOKgh7cVAN2Ji0GnUufPaXqXqj4unqo3btqvGijLdi4WE9SktUWdNGsp6BroZE+MDuUdb2EzcfqaMoQqNicEkKGwmBpMAPQXHdHcGDl0QEecZ6MY+FWJdsftB9/fPwHECxwQAJh6nAMD0+zMAAKB7IYkOQGDZbFadddZZ6sknn/R97ogRI9SiRYvUhAkTVP/+/VVZWZnVZe7ZZ59V9957r/r73/9uJZh5vdd5552nBg8erKZMmaJ9K23evFmddNJJgRLo5DPsv//+aty4caqxsVEVFRWp9957z0okvOuuu9Tdd9+ttm7d6vlep512mrrxxhutzwMAAApfVVmJqlI9lRm31MycOzdzqczbeoWwplBw+5SBydroPtinoAPnPuhGMElhY/t1n3MygeiFzcR9yhRynw7Quk8VwOo08ZhgStcpE7efGWvGmyGbz0gm7lMIzsRhAp21gTS/f0ByGE8BAIAkkEQHILAf//jH6s477/R8TnNzs/riF7+o9ttvP9eEtA9+8INqxYoV6vLLL1d/+MMfXF9LEtM+85nPqJtuuknV1tZq3VIXX3yxeuqppzyf097ers4//3w1ffp0x8clue/DH/6wevXVV9XXvvY19c9//tP1td5//3316U9/Wv3mN7+xEgoBAOEx+QqYgYlqaK9+bMgq5TwDmMGUYwIKfJxgYDSXeUtUGEzZp9h+3Ychu5SZOzoA5ODcFw1HdAAo3Ot2BMf8PnrCIaEQxnWmTCd0PiaYsVAG7lIFwYytBwAAkDyS6IAUtLS0qDvuuKOg1v1rr72mrrzySs/nTJ06VV199dWqrq7O9/UaGhqsxDNJUJOku23btjk+780337SS7b785S8rXR588EF13XXXeT5HOs9ddtllqqKiwvf1Wltb1fe+9z11zTXXqG9/+9uuz3viiSfUtddeq0488cRIyw0AQHdhyuQrVSmhW0lRkRpX30c9vmqd+kh7Myu40Jh4lxZAakw8Ipi4TKYyMZgL3Qf7FHTgejTi948voMc+hUj7lCFH9dyub6YsE7oPU/apQjj3mdL1DdCNa2QAAFBITLxyMHGZAABAdEUx/hZAD3LJJZeoTZs2uT4+evRo9YMf/CBQAl2uQw45RF100UWez7n++ut9u8YFtX37duv9cm9I2u29995W4l6QBLpcJ510kjr11FM9nyOJdm+//Xao1wUA7DCtsXrXqjiybQCrpYARjIDu7Oq9x6gf7jNOnTKuVZmIWCD0iGAgYt6gZZ/KGrdTkesLAN2TGWeZwsC6AgD0NJz7WDcAAAA9GeNhAACQBJLoAPh6/PHH1d///nfXxysrK9UVV1yh+vTpE2ltHnrooeqDH/ygZ+KbXxe8oG6//XarI5ybpqYm9fWvf12VlpZGen1Jottzzz1dH1+/fr364Q9/GOm1AaCn61tepr43Z6z67KSh6lNjB6W9OOgGqBYWDRPV3spLitWEvlWqyJCEC2NRZb/bMHFXN3CRgB6D7587kiDZp3TjeoZ9Svs+xU4FgPNMt7lGNrHrG+eZwsb2g/Z9ilUaiSGnGTPPfexUBc3EzWfKvu3FlEXM/f6Zst5M3KcKgSldrAEAAJJGEh0AXz/60Y98O7ANHjw41pr83Oc+p2pra10f/+c//6leeOEFFdePf/xjz8fPOeec0N307J11LrjgAs8kvD/84Q/qvffei/weANCTTW6sVh8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",
       "text/plain": [
        "<Figure size 1600x640 with 1 Axes>"
       ]
@@ -1923,7 +1940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a50f4c9c",
+   "id": "9c7e54be",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1971,7 +1988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d6d015a",
+   "id": "89166931",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -1986,14 +2003,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "bae47263",
+   "execution_count": 21,
+   "id": "8387dfb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:01.663749Z",
-     "iopub.status.busy": "2026-08-01T02:01:01.663670Z",
-     "iopub.status.idle": "2026-08-01T02:01:01.819836Z",
-     "shell.execute_reply": "2026-08-01T02:01:01.819527Z"
+     "iopub.execute_input": "2026-08-01T02:20:08.713383Z",
+     "iopub.status.busy": "2026-08-01T02:20:08.713178Z",
+     "iopub.status.idle": "2026-08-01T02:20:09.010662Z",
+     "shell.execute_reply": "2026-08-01T02:20:09.010095Z"
     }
    },
    "outputs": [
@@ -2067,7 +2084,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22971237",
+   "id": "c7aba75d",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -2086,14 +2103,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "e09fa9d7",
+   "execution_count": 22,
+   "id": "91014839",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:01.821239Z",
-     "iopub.status.busy": "2026-08-01T02:01:01.821167Z",
-     "iopub.status.idle": "2026-08-01T02:01:01.899338Z",
-     "shell.execute_reply": "2026-08-01T02:01:01.898938Z"
+     "iopub.execute_input": "2026-08-01T02:20:09.014255Z",
+     "iopub.status.busy": "2026-08-01T02:20:09.014081Z",
+     "iopub.status.idle": "2026-08-01T02:20:09.169747Z",
+     "shell.execute_reply": "2026-08-01T02:20:09.169127Z"
     }
    },
    "outputs": [
@@ -2139,7 +2156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50dd0070",
+   "id": "75169b0b",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2203,14 +2220,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abbc9ba0",
+   "id": "4a29ec5a",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
     "\n",
     "Two scoping sentences first, so no number below is ambiguous. Every held-out\n",
     "score in this section comes from fits on the 60-shard training split only,\n",
-    "with the four held-out shards touched by nothing but the scorer — the\n",
+    "with the four held-out shards touched by nothing but the scorer (and, as\n",
+    "disclosed above, the ETL-time standardization constants estimated over the\n",
+    "full corpus) — the\n",
     "headline four-minute fit above streams all 64 shards and is never\n",
     "holdout-scored. And \"held out\" here means a hash-random *contemporaneous*\n",
     "split over serially overlapping transitions (a holdout row's `ylag` can be a\n",
@@ -2235,14 +2254,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "101f049a",
+   "execution_count": 23,
+   "id": "c5d5ba55",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:01.900418Z",
-     "iopub.status.busy": "2026-08-01T02:01:01.900342Z",
-     "iopub.status.idle": "2026-08-01T02:01:01.905650Z",
-     "shell.execute_reply": "2026-08-01T02:01:01.905318Z"
+     "iopub.execute_input": "2026-08-01T02:20:09.171771Z",
+     "iopub.status.busy": "2026-08-01T02:20:09.171633Z",
+     "iopub.status.idle": "2026-08-01T02:20:09.180722Z",
+     "shell.execute_reply": "2026-08-01T02:20:09.179938Z"
     }
    },
    "outputs": [
@@ -2330,7 +2349,7 @@
        "monitor    1.07  0.116262  0.116299  0.116323"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2345,13 +2364,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2221ad74",
+   "id": "d0e7611a",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
     "guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about\n",
     "$10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads\n",
-    "run $3\\times10^{-6}$ to $1.2\\times10^{-4}$, and batch order is shared across\n",
+    "run $6\\times10^{-6}$ to $6\\times10^{-5}$ (sample sd), and batch order is shared across\n",
     "seeds, so these exclude data-order variation) — while the 1- and\n",
     "2-pass arms tied within seed spread, and the monitor arm under-scored\n",
     "everything. The loss trace suggests why: this fit reaches its loss plateau\n",
@@ -2359,23 +2378,26 @@
     "the held-out score of a *point* estimate moves with the optimizer's state at\n",
     "flat learning rate 0.02 — consistent with Adam's stationary jitter and\n",
     "position along the (seed-shared) batch cycle, though these artifacts alone\n",
-    "cannot separate that from other path effects. So we ran the confirmatory\n",
-    "design the failure demanded: three seeds per protocol for an annealed 2-pass\n",
-    "reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass budget\n",
-    "plus the same anneal, and the monitor's stop (1.07 passes) plus the same\n",
-    "anneal."
+    "cannot separate that from other path effects. So we ran the replicated\n",
+    "follow-up the failure demanded: three seeds per protocol for an annealed\n",
+    "2-pass reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass\n",
+    "budget plus the same anneal, and the monitor's stop (1.07 passes) plus the\n",
+    "same anneal. One epistemic label before the numbers: these arms were chosen\n",
+    "*after* seeing the first round, and they score on the same contemporaneous\n",
+    "split — so what replication establishes is the seed-stability of the ranking\n",
+    "on this split, not an independent out-of-sample confirmation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "a1022cef",
+   "execution_count": 24,
+   "id": "753aec3b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:01.906479Z",
-     "iopub.status.busy": "2026-08-01T02:01:01.906426Z",
-     "iopub.status.idle": "2026-08-01T02:01:02.012722Z",
-     "shell.execute_reply": "2026-08-01T02:01:02.012289Z"
+     "iopub.execute_input": "2026-08-01T02:20:09.182220Z",
+     "iopub.status.busy": "2026-08-01T02:20:09.182087Z",
+     "iopub.status.idle": "2026-08-01T02:20:09.406556Z",
+     "shell.execute_reply": "2026-08-01T02:20:09.405930Z"
     }
    },
    "outputs": [
@@ -2435,7 +2457,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8355e311",
+   "id": "8b85378a",
    "metadata": {},
    "source": [
     "Three seeds per arm resolve the picture sharply — within-arm spreads are\n",
@@ -2443,9 +2465,10 @@
     "spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the\n",
     "batch order, which is shared). Four replicated facts:\n",
     "\n",
-    "1. **The anneal is load-bearing.** A 1-pass budget plus a 2,000-step anneal\n",
-    "   (0.118738) beats the flat 1-pass arm by $1.7 \\times 10^{-3}$ — at\n",
-    "   essentially the same cost.\n",
+    "1. **The anneal is load-bearing.** At exactly matched budgets, the annealed\n",
+    "   2-pass reference beats the flat 2-pass arm by $8.5 \\times 10^{-4}$; the\n",
+    "   1-pass budget plus a 2,000-step anneal (0.118738) beats the flat 1-pass\n",
+    "   arm by $1.7 \\times 10^{-3}$ for 1.8% extra steps.\n",
     "2. **The training-loss plateau is not a held-out plateau.** The loss flattens\n",
     "   within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)\n",
     "   trails the 1-pass-plus-anneal arm by $1.7 \\times 10^{-3}$: between ¼ and 1\n",
@@ -2463,15 +2486,17 @@
     "\n",
     "This also retires our own earlier reading: the \"$5 \\times 10^{-4}$ path\n",
     "noise between annealed runs\" was a *protocol* difference wearing a noise\n",
-    "costume — replicates of the same protocol agree to $10^{-5}$. And it\n",
-    "sharpens the pre-registration post-mortem: the mistake was not the\n",
-    "$2 \\times 10^{-4}$ floor but the yardstick — equivalence to a reference that\n",
-    "is itself $8.8 \\times 10^{-4}$ off the tested frontier measures loyalty, not\n",
-    "quality.\n",
+    "costume — replicates of the same protocol agree to $10^{-4}$ or better, most\n",
+    "to $10^{-5}$. And it sharpens the pre-registration post-mortem: the mistake\n",
+    "was not the $2 \\times 10^{-4}$ floor but the yardstick — the pre-registered\n",
+    "flat 2-pass reference sits $1.7 \\times 10^{-3}$ off the tested frontier, and\n",
+    "even the annealed 2-pass comparator sits $8.8 \\times 10^{-4}$ off it, so\n",
+    "equivalence to either measures loyalty, not quality.\n",
     "\n",
     "The working protocol this ablation supports: **one full pass at the working\n",
-    "learning rate, then a short anneal** — with the stopping rule as the\n",
-    "certificate that one pass was indeed enough, and equivalence judged after\n",
+    "learning rate, then a short anneal** — with the stopping rule certifying the\n",
+    "training-loss plateau (it is the held-out ablation, not the rule, that shows\n",
+    "one pass sufficed among the schedules tested), and equivalence judged after\n",
     "the anneal, on held-out score, against the best arm rather than a\n",
     "conventional reference. That finding — not any savings headline — is what we\n",
     "would want a reader to take away, and it fed directly back into the design\n",
@@ -2480,7 +2505,9 @@
     "### The estimand: dispersion on the event clock\n",
     "\n",
     "What the model is *for*: per symbol and hour, the probability that a trade\n",
-    "moves the price at all, and how far it typically goes when it does. Both come\n",
+    "moves the price at all, and how far it typically goes when it does (posterior:\n",
+    "the all-shard headline fit — these curves are estimates, not scored claims).\n",
+    "Both come\n",
     "from the posterior of the fitted links at reference covariates, so the\n",
     "intraday shape below is pooled across the hierarchy — BTC's curve is almost\n",
     "entirely its own data, the thin symbols' curves borrow the global harmonics\n",
@@ -2489,14 +2516,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "4ea7c59a",
+   "execution_count": 25,
+   "id": "ecc99ab4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:02.013912Z",
-     "iopub.status.busy": "2026-08-01T02:01:02.013829Z",
-     "iopub.status.idle": "2026-08-01T02:01:02.227942Z",
-     "shell.execute_reply": "2026-08-01T02:01:02.227571Z"
+     "iopub.execute_input": "2026-08-01T02:20:09.409435Z",
+     "iopub.status.busy": "2026-08-01T02:20:09.409289Z",
+     "iopub.status.idle": "2026-08-01T02:20:09.835577Z",
+     "shell.execute_reply": "2026-08-01T02:20:09.834974Z"
     }
    },
    "outputs": [
@@ -2541,7 +2568,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "467f0c89",
+   "id": "862e8022",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2551,7 +2578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a15cd635",
+   "id": "f184b0b7",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2572,14 +2599,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "af41851e",
+   "execution_count": 26,
+   "id": "3887b427",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:02.229230Z",
-     "iopub.status.busy": "2026-08-01T02:01:02.229152Z",
-     "iopub.status.idle": "2026-08-01T02:01:02.425344Z",
-     "shell.execute_reply": "2026-08-01T02:01:02.425041Z"
+     "iopub.execute_input": "2026-08-01T02:20:09.838124Z",
+     "iopub.status.busy": "2026-08-01T02:20:09.838016Z",
+     "iopub.status.idle": "2026-08-01T02:20:10.235010Z",
+     "shell.execute_reply": "2026-08-01T02:20:10.234385Z"
     }
    },
    "outputs": [
@@ -2640,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f8e0fe1",
+   "id": "cf04a009",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2667,14 +2694,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "5f649523",
+   "execution_count": 27,
+   "id": "241237f3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:02.426471Z",
-     "iopub.status.busy": "2026-08-01T02:01:02.426388Z",
-     "iopub.status.idle": "2026-08-01T02:01:02.431215Z",
-     "shell.execute_reply": "2026-08-01T02:01:02.430889Z"
+     "iopub.execute_input": "2026-08-01T02:20:10.237400Z",
+     "iopub.status.busy": "2026-08-01T02:20:10.237259Z",
+     "iopub.status.idle": "2026-08-01T02:20:10.246432Z",
+     "shell.execute_reply": "2026-08-01T02:20:10.245872Z"
     }
    },
    "outputs": [
@@ -2810,7 +2837,7 @@
        "tau_al          3.210000     0.366800     0.041400    0.1"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2823,7 +2850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8dc54028",
+   "id": "67844b3f",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2851,7 +2878,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af10e5cc",
+   "id": "d418e052",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2870,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "839d0a9c",
+   "id": "425a8648",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2882,7 +2909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1596aec9",
+   "id": "1820e443",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2894,7 +2921,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "106ec730",
+   "id": "87cccdf2",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2902,14 +2929,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "40c21d58",
+   "execution_count": 28,
+   "id": "942929b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:01:02.432026Z",
-     "iopub.status.busy": "2026-08-01T02:01:02.431969Z",
-     "iopub.status.idle": "2026-08-01T02:01:02.442799Z",
-     "shell.execute_reply": "2026-08-01T02:01:02.442403Z"
+     "iopub.execute_input": "2026-08-01T02:20:10.247985Z",
+     "iopub.status.busy": "2026-08-01T02:20:10.247841Z",
+     "iopub.status.idle": "2026-08-01T02:20:10.268544Z",
+     "shell.execute_reply": "2026-08-01T02:20:10.267990Z"
     }
    },
    "outputs": [
@@ -2949,7 +2976,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81217938",
+   "id": "6c1d201b",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 221e49dda..c22b85603 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -391,10 +391,14 @@ here, but it does mean Gate 1's equal-inclusion clause holds for the streamed
 subpopulation, not the last few rows of the last shard — and, equivalently,
 that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a
 sub-percent effect dwarfed by the mean-field error quantified later. If
-exactness matters, set `total_size` to the streamed count
-(`(len(loader) // loader.batch_size) * loader.batch_size`); the durable fix —
-carrying the remainder across epoch boundaries — belongs in the loader and is
-on its review agenda.
+exactness matters, set `total_size` to the streamed count — the cells below
+do exactly that via `n_streamed`; the full-scale runs keep `len(loader)`,
+where the correction is 0.0004%. The durable fix — carrying the remainder
+across epoch boundaries — belongs in the loader and is on its review agenda.
+
+```{code-cell} ipython3
+n_streamed = (len(loader) // loader.batch_size) * loader.batch_size
+```
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -501,7 +505,7 @@ the smallest genuine move on any listed tick grid sits dozens of orders of
 magnitude above the float32 flush-to-zero threshold.
 
 ```{code-cell} ipython3
-model = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), len(loader))
+model = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), n_streamed)
 pm.model_to_graphviz(model)
 ```
 
@@ -597,8 +601,8 @@ recovery.round(4)
 Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
 global coefficient and the mean of its group effects are only *jointly*
 identified: the likelihood constrains their sum, and the prior only weakly
-splits it. The identified sums in the last three rows land within a few
-thousandths of the truth, like every other likelihood-identified quantity.
+splits it. The identified sums in the last three rows land within about one
+hundredth of the truth, comparable to the other likelihood-identified rows.
 The same translation ridge exists for every global-coefficient/group-effect
 pair in this model (including the vector pairs $c$/$b^{(\pi h)}$ and
 $g$/$b^{(\sigma h)}$, whose sums we spare you) — so raw split rows like
@@ -610,8 +614,8 @@ exceed 2, because the mean-field standard deviations in the third column are
 razor-thin — a z-score against a point truth mixes the sampling noise of this
 particular synthetic dataset with variational overconfidence along exactly
 such ridges. This is the first appearance of a theme the notebook returns to
-at full scale: a mean-field width is trustworthy only where seed replication
-says it is.
+at full scale: a mean-field width is usable only where seed replication has
+failed to disqualify it — replication can veto a width, never certify one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?
@@ -834,7 +838,7 @@ loader2 = DataLoader(
     sample_shape=(len(columns),),
     total_size="auto",
 )
-model2 = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), len(loader2))
+model2 = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), n_streamed)
 stream2 = StreamAdvance(model2, loader2)
 stream2.prime()
 steps_per_epoch = len(loader2) // loader2.batch_size
@@ -1093,7 +1097,9 @@ likelihood answers the one about typical transitions.
 
 Two scoping sentences first, so no number below is ambiguous. Every held-out
 score in this section comes from fits on the 60-shard training split only,
-with the four held-out shards touched by nothing but the scorer — the
+with the four held-out shards touched by nothing but the scorer (and, as
+disclosed above, the ETL-time standardization constants estimated over the
+full corpus) — the
 headline four-minute fit above streams all 64 shards and is never
 holdout-scored. And "held out" here means a hash-random *contemporaneous*
 split over serially overlapping transitions (a holdout row's `ylag` can be a
@@ -1126,7 +1132,7 @@ conv
 **The equivalence test failed** — and not in the direction anyone would
 guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about
 $10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads
-run $3\times10^{-6}$ to $1.2\times10^{-4}$, and batch order is shared across
+run $6\times10^{-6}$ to $6\times10^{-5}$ (sample sd), and batch order is shared across
 seeds, so these exclude data-order variation) — while the 1- and
 2-pass arms tied within seed spread, and the monitor arm under-scored
 everything. The loss trace suggests why: this fit reaches its loss plateau
@@ -1134,11 +1140,14 @@ within a quarter pass, so all flat-rate arms sit on the same plateau, where
 the held-out score of a *point* estimate moves with the optimizer's state at
 flat learning rate 0.02 — consistent with Adam's stationary jitter and
 position along the (seed-shared) batch cycle, though these artifacts alone
-cannot separate that from other path effects. So we ran the confirmatory
-design the failure demanded: three seeds per protocol for an annealed 2-pass
-reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass budget
-plus the same anneal, and the monitor's stop (1.07 passes) plus the same
-anneal.
+cannot separate that from other path effects. So we ran the replicated
+follow-up the failure demanded: three seeds per protocol for an annealed
+2-pass reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass
+budget plus the same anneal, and the monitor's stop (1.07 passes) plus the
+same anneal. One epistemic label before the numbers: these arms were chosen
+*after* seeing the first round, and they score on the same contemporaneous
+split — so what replication establishes is the seed-stability of the ranking
+on this split, not an independent out-of-sample confirmation.
 
 ```{code-cell} ipython3
 conf = results["confirm"]
@@ -1183,9 +1192,10 @@ $4 \times 10^{-6}$ to $8 \times 10^{-5}$, so every gap below is tens of
 spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the
 batch order, which is shared). Four replicated facts:
 
-1. **The anneal is load-bearing.** A 1-pass budget plus a 2,000-step anneal
-   (0.118738) beats the flat 1-pass arm by $1.7 \times 10^{-3}$ — at
-   essentially the same cost.
+1. **The anneal is load-bearing.** At exactly matched budgets, the annealed
+   2-pass reference beats the flat 2-pass arm by $8.5 \times 10^{-4}$; the
+   1-pass budget plus a 2,000-step anneal (0.118738) beats the flat 1-pass
+   arm by $1.7 \times 10^{-3}$ for 1.8% extra steps.
 2. **The training-loss plateau is not a held-out plateau.** The loss flattens
    within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)
    trails the 1-pass-plus-anneal arm by $1.7 \times 10^{-3}$: between ¼ and 1
@@ -1203,15 +1213,17 @@ batch order, which is shared). Four replicated facts:
 
 This also retires our own earlier reading: the "$5 \times 10^{-4}$ path
 noise between annealed runs" was a *protocol* difference wearing a noise
-costume — replicates of the same protocol agree to $10^{-5}$. And it
-sharpens the pre-registration post-mortem: the mistake was not the
-$2 \times 10^{-4}$ floor but the yardstick — equivalence to a reference that
-is itself $8.8 \times 10^{-4}$ off the tested frontier measures loyalty, not
-quality.
+costume — replicates of the same protocol agree to $10^{-4}$ or better, most
+to $10^{-5}$. And it sharpens the pre-registration post-mortem: the mistake
+was not the $2 \times 10^{-4}$ floor but the yardstick — the pre-registered
+flat 2-pass reference sits $1.7 \times 10^{-3}$ off the tested frontier, and
+even the annealed 2-pass comparator sits $8.8 \times 10^{-4}$ off it, so
+equivalence to either measures loyalty, not quality.
 
 The working protocol this ablation supports: **one full pass at the working
-learning rate, then a short anneal** — with the stopping rule as the
-certificate that one pass was indeed enough, and equivalence judged after
+learning rate, then a short anneal** — with the stopping rule certifying the
+training-loss plateau (it is the held-out ablation, not the rule, that shows
+one pass sufficed among the schedules tested), and equivalence judged after
 the anneal, on held-out score, against the best arm rather than a
 conventional reference. That finding — not any savings headline — is what we
 would want a reader to take away, and it fed directly back into the design
@@ -1220,7 +1232,9 @@ discussion of the upstream pull requests.
 ### The estimand: dispersion on the event clock
 
 What the model is *for*: per symbol and hour, the probability that a trade
-moves the price at all, and how far it typically goes when it does. Both come
+moves the price at all, and how far it typically goes when it does (posterior:
+the all-shard headline fit — these curves are estimates, not scored claims).
+Both come
 from the posterior of the fitted links at reference covariates, so the
 intraday shape below is pooled across the hierarchy — BTC's curve is almost
 entirely its own data, the thin symbols' curves borrow the global harmonics

From 695c2f24352ef082ed630842e281547c83ce000c Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sat, 1 Aug 2026 11:48:33 -0500
Subject: [PATCH 24/42] Fix latent loop-variable shadowing in two plot cells;
 re-execute

---
 .../streaming_tick_data.ipynb                 | 718 +++++++++---------
 .../streaming_tick_data.myst.md               |  12 +-
 2 files changed, 365 insertions(+), 365 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index cfc951a6d..5fb485726 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a4274a8f",
+   "id": "505a93ee",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b16eda7e",
+   "id": "47ff7efc",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4817b70a",
+   "id": "5b57ebca",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58565d64",
+   "id": "1ea0ad3a",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -103,13 +103,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c7335997",
+   "id": "cd2d150f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:25.809383Z",
-     "iopub.status.busy": "2026-08-01T02:19:25.809227Z",
-     "iopub.status.idle": "2026-08-01T02:19:29.256570Z",
-     "shell.execute_reply": "2026-08-01T02:19:29.255889Z"
+     "iopub.execute_input": "2026-08-01T16:47:23.313138Z",
+     "iopub.status.busy": "2026-08-01T16:47:23.312932Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.144558Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.144074Z"
     }
    },
    "outputs": [],
@@ -135,13 +135,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e0b16f9e",
+   "id": "45d2faf8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:29.258744Z",
-     "iopub.status.busy": "2026-08-01T02:19:29.258418Z",
-     "iopub.status.idle": "2026-08-01T02:19:29.263787Z",
-     "shell.execute_reply": "2026-08-01T02:19:29.263190Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.145923Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.145768Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.148677Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.148329Z"
     }
    },
    "outputs": [],
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ac3b747",
+   "id": "b5624b87",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4931388",
+   "id": "4cb260fd",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -234,13 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "eddd5cda",
+   "id": "23b7377b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:29.265088Z",
-     "iopub.status.busy": "2026-08-01T02:19:29.264975Z",
-     "iopub.status.idle": "2026-08-01T02:19:29.268914Z",
-     "shell.execute_reply": "2026-08-01T02:19:29.268362Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.149569Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.149502Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.151806Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.151550Z"
     }
    },
    "outputs": [],
@@ -282,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "73c73354",
+   "id": "abd2dc36",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:29.270239Z",
-     "iopub.status.busy": "2026-08-01T02:19:29.270138Z",
-     "iopub.status.idle": "2026-08-01T02:19:29.557546Z",
-     "shell.execute_reply": "2026-08-01T02:19:29.556929Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.152700Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.152649Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.310705Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.310216Z"
     }
    },
    "outputs": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea3720b",
+   "id": "8b6b0464",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -365,13 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "080ee360",
+   "id": "bcc95951",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:29.558946Z",
-     "iopub.status.busy": "2026-08-01T02:19:29.558800Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.272229Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.271737Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.311586Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.311520Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.688188Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.687835Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb82706f",
+   "id": "89f82664",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -431,13 +431,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f791c297",
+   "id": "2aa9205b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.274217Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.274059Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.520079Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.519602Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.689244Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.689157Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.881817Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.881477Z"
     }
    },
    "outputs": [
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "864c3648",
+   "id": "295769f8",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bd43daf",
+   "id": "00a2ad3a",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -518,13 +518,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "dad28f6d",
+   "id": "f94a5b9f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.521802Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.521663Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.526483Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.525926Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.882734Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.882669Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.885961Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.885612Z"
     }
    },
    "outputs": [
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b8d0e23",
+   "id": "f34ed135",
    "metadata": {},
    "source": [
     "One deliberate loader semantics to be precise about: only full batches are\n",
@@ -572,13 +572,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "1fb35886",
+   "id": "88ab2d54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.527696Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.527587Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.529596Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.529143Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.886742Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.886676Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.888078Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.887771Z"
     }
    },
    "outputs": [],
@@ -589,13 +589,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b0a7ba49",
+   "id": "14946fb6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.530873Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.530774Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.539418Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.538883Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.888816Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.888757Z",
+     "iopub.status.idle": "2026-08-01T16:47:26.893122Z",
+     "shell.execute_reply": "2026-08-01T16:47:26.892885Z"
     }
    },
    "outputs": [],
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da7ec359",
+   "id": "0e755ab4",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -712,13 +712,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "31e7411c",
+   "id": "b09033fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.540715Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.540602Z",
-     "iopub.status.idle": "2026-08-01T02:19:30.829153Z",
-     "shell.execute_reply": "2026-08-01T02:19:30.828497Z"
+     "iopub.execute_input": "2026-08-01T16:47:26.893997Z",
+     "iopub.status.busy": "2026-08-01T16:47:26.893947Z",
+     "iopub.status.idle": "2026-08-01T16:47:27.187236Z",
+     "shell.execute_reply": "2026-08-01T16:47:27.186827Z"
     }
    },
    "outputs": [
@@ -768,33 +768,11 @@
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- tau_k -->\n",
+       "<!-- kappa0 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_k -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
-       "</g>\n",
-       "<!-- beta_a -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
+       "<title>kappa0</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
@@ -806,86 +784,130 @@
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
        "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.99,-279.7C66.27,-266.11 77.6,-251.99 92.01,-243.82 142.96,-214.93 499.4,-198.76 694.33,-191.99\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"694.39,-195.49 704.27,-191.65 694.15,-188.5 694.39,-195.49\"/>\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.86 76.86,-251.93 91.01,-243.82 141.9,-214.65 498.88,-198.61 694.12,-191.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"694.21,-195.43 704.08,-191.59 693.97,-188.43 694.21,-195.43\"/>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
+       "<!-- b_sh -->\n",
        "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
+       "<title>b_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
-       "<!-- tau_sh -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_sh -->\n",
+       "<!-- b_ph -->\n",
        "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
+       "<title>b_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
        "</g>\n",
-       "<!-- lambda_q -->\n",
+       "<!-- eta -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"663.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"708.14,-345.73 617.89,-345.73 617.89,-288.23 708.14,-288.23 708.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M663.01,-393.28C663.01,-381.58 663.01,-368.79 663.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"666.51,-357.45 663.01,-347.45 659.51,-357.45 666.51,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"150.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"150.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M170.89,-279.73C180.66,-266.14 193.58,-252.01 209.01,-243.82 290.2,-200.72 539.37,-190.42 693.6,-188.3\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"693.52,-191.8 703.48,-188.17 693.43,-184.8 693.52,-191.8\"/>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M158.04,-279.79C166.33,-266.21 177.65,-252.08 192.01,-243.82 234.42,-219.43 523.46,-201.99 694.61,-193.67\"/>\n",
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        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- tau_al -->\n",
        "<g id=\"node7\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"259.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"259.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M275.86,-279.57C283.96,-266.13 295,-252.15 309.01,-243.82 371.92,-206.4 563.79,-194.08 693.93,-190.06\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"693.74,-193.56 703.64,-189.77 693.54,-186.57 693.74,-193.56\"/>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_q -->\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
+       "</g>\n",
+       "<!-- lambda_q -->\n",
        "<g id=\"node8\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"250.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M270.97,-279.86C280.75,-266.29 293.65,-252.15 309.01,-243.82 373.31,-208.95 564.54,-195.92 694.2,-191.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"694,-194.56 703.87,-190.7 693.75,-187.57 694,-194.56\"/>\n",
+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>beta_q</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"359.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
@@ -898,120 +920,98 @@
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        "<polygon fill=\"black\" stroke=\"black\" points=\"695.23,-197.59 704.98,-193.45 694.78,-190.6 695.23,-197.59\"/>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"459.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M476.39,-279.68C484.51,-266.42 495.42,-252.54 509.01,-243.82 540.23,-223.78 625.11,-209.41 698.46,-200.3\"/>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"559.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M576.76,-280.23C584.93,-267.05 595.78,-253.08 609.01,-243.82 636.11,-224.85 669.25,-212.34 701.2,-204.1\"/>\n",
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-       "</g>\n",
-       "<!-- tau_al -->\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
+       "<title>tau_ba</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_al -->\n",
+       "<!-- b_ba -->\n",
        "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_al</title>\n",
+       "<title>b_ba</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
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        "</g>\n",
-       "<!-- eta -->\n",
+       "<!-- theta_d -->\n",
        "<g id=\"node12\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"663.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- nu -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"708.14,-345.73 617.89,-345.73 617.89,-288.23 708.14,-288.23 708.14,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"559.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"559.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"666.51,-357.45 663.01,-347.45 659.51,-357.45 666.51,-357.45\"/>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M576.76,-280.23C584.93,-267.05 595.78,-253.08 609.01,-243.82 636.11,-224.85 669.25,-212.34 701.2,-204.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.62,-207.6 710.51,-201.82 699.96,-200.8 701.62,-207.6\"/>\n",
        "</g>\n",
        "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>nu&#45;&gt;y_obs</title>\n",
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        "</g>\n",
-       "<!-- tau_ba -->\n",
+       "<!-- beta_a -->\n",
        "<g id=\"node14\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"767.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"767.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"767.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"767.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M782.63,-279.01C788.04,-266.2 794.2,-251.62 799.97,-237.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"803.04,-239.69 803.71,-229.12 796.59,-236.97 803.04,-239.69\"/>\n",
+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
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-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>g&#45;&gt;y_obs</title>\n",
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+       "<title>c&#45;&gt;y_obs</title>\n",
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-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>c&#45;&gt;y_obs</title>\n",
+       "<!-- g&#45;&gt;y_obs -->\n",
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+       "<title>g&#45;&gt;y_obs</title>\n",
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-       "<title>b_ba&#45;&gt;y_obs</title>\n",
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-       "<g id=\"edge15\" class=\"edge\">\n",
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-       "<g id=\"edge8\" class=\"edge\">\n",
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-       "<title>b_sh&#45;&gt;y_obs</title>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
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-       "<title>b_ph&#45;&gt;y_obs</title>\n",
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+       "<title>b_sh&#45;&gt;y_obs</title>\n",
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@@ -1094,7 +1094,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x12dd0a810>"
+       "<graphviz.graphs.Digraph at 0x137f6c3b0>"
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      "execution_count": 10,
@@ -1109,7 +1109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c036249c",
+   "id": "4a25e0a2",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1121,13 +1121,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "9dd0b0c4",
+   "id": "9019caad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:30.830895Z",
-     "iopub.status.busy": "2026-08-01T02:19:30.830718Z",
-     "iopub.status.idle": "2026-08-01T02:19:55.597547Z",
-     "shell.execute_reply": "2026-08-01T02:19:55.596622Z"
+     "iopub.execute_input": "2026-08-01T16:47:27.188271Z",
+     "iopub.status.busy": "2026-08-01T16:47:27.188185Z",
+     "iopub.status.idle": "2026-08-01T16:47:31.542723Z",
+     "shell.execute_reply": "2026-08-01T16:47:31.542315Z"
     }
    },
    "outputs": [],
@@ -1164,7 +1164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86c6fede",
+   "id": "a959c1fb",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1177,13 +1177,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d5916ab4",
+   "id": "a07ec91a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:55.599972Z",
-     "iopub.status.busy": "2026-08-01T02:19:55.599804Z",
-     "iopub.status.idle": "2026-08-01T02:19:55.902553Z",
-     "shell.execute_reply": "2026-08-01T02:19:55.901917Z"
+     "iopub.execute_input": "2026-08-01T16:47:31.544076Z",
+     "iopub.status.busy": "2026-08-01T16:47:31.543996Z",
+     "iopub.status.idle": "2026-08-01T16:47:31.697634Z",
+     "shell.execute_reply": "2026-08-01T16:47:31.697221Z"
     }
    },
    "outputs": [
@@ -1220,7 +1220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ddfb28",
+   "id": "0978aa16",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1229,13 +1229,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e39e4d51",
+   "id": "37187e01",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:55.905643Z",
-     "iopub.status.busy": "2026-08-01T02:19:55.905497Z",
-     "iopub.status.idle": "2026-08-01T02:19:56.300032Z",
-     "shell.execute_reply": "2026-08-01T02:19:56.299403Z"
+     "iopub.execute_input": "2026-08-01T16:47:31.698927Z",
+     "iopub.status.busy": "2026-08-01T16:47:31.698847Z",
+     "iopub.status.idle": "2026-08-01T16:47:31.904642Z",
+     "shell.execute_reply": "2026-08-01T16:47:31.904257Z"
     }
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    "outputs": [
@@ -1408,7 +1408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b97523ba",
+   "id": "dfa7fbb7",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1437,13 +1437,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "5e59aa51",
+   "id": "9ed3407a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:56.301635Z",
-     "iopub.status.busy": "2026-08-01T02:19:56.301491Z",
-     "iopub.status.idle": "2026-08-01T02:19:56.539514Z",
-     "shell.execute_reply": "2026-08-01T02:19:56.538870Z"
+     "iopub.execute_input": "2026-08-01T16:47:31.905620Z",
+     "iopub.status.busy": "2026-08-01T16:47:31.905538Z",
+     "iopub.status.idle": "2026-08-01T16:47:32.028437Z",
+     "shell.execute_reply": "2026-08-01T16:47:32.028039Z"
     }
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    "outputs": [
@@ -1492,7 +1492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab5b86be",
+   "id": "6a76de1d",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1504,7 +1504,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0570b0e0",
+   "id": "5b944deb",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1520,13 +1520,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "64476b60",
+   "id": "66a85846",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2026-08-01T02:19:57.906890Z"
+     "iopub.execute_input": "2026-08-01T16:47:32.029421Z",
+     "iopub.status.busy": "2026-08-01T16:47:32.029355Z",
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     }
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    "outputs": [
@@ -1635,13 +1635,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "76683e6f",
+   "id": "67fd6d24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:19:57.909292Z",
-     "iopub.status.busy": "2026-08-01T02:19:57.909156Z",
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-     "shell.execute_reply": "2026-08-01T02:19:58.179201Z"
+     "iopub.execute_input": "2026-08-01T16:47:32.811862Z",
+     "iopub.status.busy": "2026-08-01T16:47:32.811796Z",
+     "iopub.status.idle": "2026-08-01T16:47:32.950131Z",
+     "shell.execute_reply": "2026-08-01T16:47:32.949740Z"
     }
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    "outputs": [
@@ -1689,7 +1689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95236209",
+   "id": "6abe226a",
    "metadata": {},
    "source": [
     "Two of the three statistics sit inside their predictive 90% bands; the median\n",
@@ -1706,7 +1706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45e3f02d",
+   "id": "5225bc34",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1739,13 +1739,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "57625712",
+   "id": "80358853",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2026-08-01T02:19:58.186923Z"
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    "outputs": [],
@@ -1787,13 +1787,13 @@
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-   "id": "289d5d3a",
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-     "shell.execute_reply": "2026-08-01T02:19:58.191597Z"
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@@ -1829,13 +1829,13 @@
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-   "id": "537f5fa5",
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-     "shell.execute_reply": "2026-08-01T02:20:08.360452Z"
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@@ -1891,13 +1891,13 @@
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    "cell_type": "code",
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-   "id": "6af7b844",
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-     "shell.execute_reply": "2026-08-01T02:20:08.709768Z"
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@@ -1940,7 +1940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c7e54be",
+   "id": "34204770",
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    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1988,7 +1988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89166931",
+   "id": "05742c5a",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -2004,13 +2004,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "8387dfb4",
+   "id": "6424bc41",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:08.713383Z",
-     "iopub.status.busy": "2026-08-01T02:20:08.713178Z",
-     "iopub.status.idle": "2026-08-01T02:20:09.010662Z",
-     "shell.execute_reply": "2026-08-01T02:20:09.010095Z"
+     "iopub.execute_input": "2026-08-01T16:47:37.602323Z",
+     "iopub.status.busy": "2026-08-01T16:47:37.602235Z",
+     "iopub.status.idle": "2026-08-01T16:47:37.749688Z",
+     "shell.execute_reply": "2026-08-01T16:47:37.749416Z"
     }
    },
    "outputs": [
@@ -2084,7 +2084,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7aba75d",
+   "id": "988a2335",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -2104,13 +2104,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "91014839",
+   "id": "c0ccdac5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:09.014255Z",
-     "iopub.status.busy": "2026-08-01T02:20:09.014081Z",
-     "iopub.status.idle": "2026-08-01T02:20:09.169747Z",
-     "shell.execute_reply": "2026-08-01T02:20:09.169127Z"
+     "iopub.execute_input": "2026-08-01T16:47:37.751048Z",
+     "iopub.status.busy": "2026-08-01T16:47:37.750969Z",
+     "iopub.status.idle": "2026-08-01T16:47:37.829425Z",
+     "shell.execute_reply": "2026-08-01T16:47:37.829074Z"
     }
    },
    "outputs": [
@@ -2141,10 +2141,10 @@
     "names = [labels[k] for k in labels]\n",
     "vals = [tp[k] for k in labels]\n",
     "ax.barh(names, vals, color=[\"C1\", \"C0\", \"C0\"])\n",
-    "for y, v in enumerate(vals):\n",
+    "for bar_y, val in enumerate(vals):\n",
     "    ax.annotate(\n",
-    "        f\"{v:,.0f} steps/s\",\n",
-    "        (v, y),\n",
+    "        f\"{val:,.0f} steps/s\",\n",
+    "        (val, bar_y),\n",
     "        xytext=(4, 0),\n",
     "        textcoords=\"offset points\",\n",
     "        va=\"center\",\n",
@@ -2156,7 +2156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75169b0b",
+   "id": "f5f65b2d",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2220,7 +2220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a29ec5a",
+   "id": "1e132228",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
@@ -2255,13 +2255,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "c5d5ba55",
+   "id": "31cf612e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:09.171771Z",
-     "iopub.status.busy": "2026-08-01T02:20:09.171633Z",
-     "iopub.status.idle": "2026-08-01T02:20:09.180722Z",
-     "shell.execute_reply": "2026-08-01T02:20:09.179938Z"
+     "iopub.execute_input": "2026-08-01T16:47:37.830416Z",
+     "iopub.status.busy": "2026-08-01T16:47:37.830360Z",
+     "iopub.status.idle": "2026-08-01T16:47:37.835655Z",
+     "shell.execute_reply": "2026-08-01T16:47:37.835405Z"
     }
    },
    "outputs": [
@@ -2364,7 +2364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0e7611a",
+   "id": "6212b547",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
@@ -2391,13 +2391,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "753aec3b",
+   "id": "770109c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:09.182220Z",
-     "iopub.status.busy": "2026-08-01T02:20:09.182087Z",
-     "iopub.status.idle": "2026-08-01T02:20:09.406556Z",
-     "shell.execute_reply": "2026-08-01T02:20:09.405930Z"
+     "iopub.execute_input": "2026-08-01T16:47:37.836608Z",
+     "iopub.status.busy": "2026-08-01T16:47:37.836548Z",
+     "iopub.status.idle": "2026-08-01T16:47:37.943647Z",
+     "shell.execute_reply": "2026-08-01T16:47:37.943279Z"
     }
    },
    "outputs": [
@@ -2437,10 +2437,10 @@
     "    color=[\"C1\" if \"1-pass budget\" in n else \"C0\" for n in names],\n",
     "    capsize=3,\n",
     ")\n",
-    "for y, (v, s) in enumerate(zip(means, sds)):\n",
+    "for bar_y, (val, sd_) in enumerate(zip(means, sds)):\n",
     "    ax.annotate(\n",
-    "        f\"{v:+.6f}\",\n",
-    "        (v + 3 * s, y),\n",
+    "        f\"{val:+.6f}\",\n",
+    "        (val + 3 * sd_, bar_y),\n",
     "        xytext=(4, 0),\n",
     "        textcoords=\"offset points\",\n",
     "        va=\"center\",\n",
@@ -2457,7 +2457,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b85378a",
+   "id": "16319e4f",
    "metadata": {},
    "source": [
     "Three seeds per arm resolve the picture sharply — within-arm spreads are\n",
@@ -2517,13 +2517,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "ecc99ab4",
+   "id": "a95f06ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:09.409435Z",
-     "iopub.status.busy": "2026-08-01T02:20:09.409289Z",
-     "iopub.status.idle": "2026-08-01T02:20:09.835577Z",
-     "shell.execute_reply": "2026-08-01T02:20:09.834974Z"
+     "iopub.execute_input": "2026-08-01T16:47:37.944890Z",
+     "iopub.status.busy": "2026-08-01T16:47:37.944821Z",
+     "iopub.status.idle": "2026-08-01T16:47:38.155170Z",
+     "shell.execute_reply": "2026-08-01T16:47:38.154803Z"
     }
    },
    "outputs": [
@@ -2568,7 +2568,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "862e8022",
+   "id": "da377822",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2578,7 +2578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f184b0b7",
+   "id": "71e41238",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2600,13 +2600,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "3887b427",
+   "id": "17c3b9d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:09.838124Z",
-     "iopub.status.busy": "2026-08-01T02:20:09.838016Z",
-     "iopub.status.idle": "2026-08-01T02:20:10.235010Z",
-     "shell.execute_reply": "2026-08-01T02:20:10.234385Z"
+     "iopub.execute_input": "2026-08-01T16:47:38.156384Z",
+     "iopub.status.busy": "2026-08-01T16:47:38.156318Z",
+     "iopub.status.idle": "2026-08-01T16:47:38.349281Z",
+     "shell.execute_reply": "2026-08-01T16:47:38.348900Z"
     }
    },
    "outputs": [
@@ -2667,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf04a009",
+   "id": "f657c01b",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2695,13 +2695,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "241237f3",
+   "id": "c75103c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:10.237400Z",
-     "iopub.status.busy": "2026-08-01T02:20:10.237259Z",
-     "iopub.status.idle": "2026-08-01T02:20:10.246432Z",
-     "shell.execute_reply": "2026-08-01T02:20:10.245872Z"
+     "iopub.execute_input": "2026-08-01T16:47:38.350427Z",
+     "iopub.status.busy": "2026-08-01T16:47:38.350345Z",
+     "iopub.status.idle": "2026-08-01T16:47:38.355574Z",
+     "shell.execute_reply": "2026-08-01T16:47:38.355244Z"
     }
    },
    "outputs": [
@@ -2850,7 +2850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67844b3f",
+   "id": "312bac32",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2878,7 +2878,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d418e052",
+   "id": "ecccb268",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2897,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "425a8648",
+   "id": "c23c905a",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2909,7 +2909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1820e443",
+   "id": "daebacd1",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2921,7 +2921,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87cccdf2",
+   "id": "6490ed28",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2930,13 +2930,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "942929b7",
+   "id": "8857c31b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T02:20:10.247985Z",
-     "iopub.status.busy": "2026-08-01T02:20:10.247841Z",
-     "iopub.status.idle": "2026-08-01T02:20:10.268544Z",
-     "shell.execute_reply": "2026-08-01T02:20:10.267990Z"
+     "iopub.execute_input": "2026-08-01T16:47:38.356384Z",
+     "iopub.status.busy": "2026-08-01T16:47:38.356333Z",
+     "iopub.status.idle": "2026-08-01T16:47:38.367091Z",
+     "shell.execute_reply": "2026-08-01T16:47:38.366735Z"
     }
    },
    "outputs": [
@@ -2944,7 +2944,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Fri, 31 Jul 2026\n",
+      "Last updated: Sat, 01 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -2976,7 +2976,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c1d201b",
+   "id": "89284305",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index c22b85603..231d371ca 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -1020,10 +1020,10 @@ fig, ax = plt.subplots(figsize=(8, 2.6), layout="constrained")
 names = [labels[k] for k in labels]
 vals = [tp[k] for k in labels]
 ax.barh(names, vals, color=["C1", "C0", "C0"])
-for y, v in enumerate(vals):
+for bar_y, val in enumerate(vals):
     ax.annotate(
-        f"{v:,.0f} steps/s",
-        (v, y),
+        f"{val:,.0f} steps/s",
+        (val, bar_y),
         xytext=(4, 0),
         textcoords="offset points",
         va="center",
@@ -1169,10 +1169,10 @@ ax.barh(
     color=["C1" if "1-pass budget" in n else "C0" for n in names],
     capsize=3,
 )
-for y, (v, s) in enumerate(zip(means, sds)):
+for bar_y, (val, sd_) in enumerate(zip(means, sds)):
     ax.annotate(
-        f"{v:+.6f}",
-        (v + 3 * s, y),
+        f"{val:+.6f}",
+        (val + 3 * sd_, bar_y),
         xytext=(4, 0),
         textcoords="offset points",
         va="center",

From 1c326f9c50478c30936fcf65c0ca4e50bb831466 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Tue, 11 Aug 2026 15:00:14 -0400
Subject: [PATCH 25/42] Update to the merged DataLoader contract and re-execute

The merged loader deleted sample_shape, made len() the batch count with
.total_size carrying N, and made shuffle=False a verbatim block
pass-through. Shards are now written with row_group_size=1024 so the
pass-through yields the intended minibatches (288 full blocks plus 8
short ones of 636 rows per epoch, 300,000 rows exactly). The drop-last
tempering paragraph is gone with the behavior it described: nothing is
dropped on this path, and the total_size rescaling reads the installed
batch's own size, verified mechanically (model logp identical for
b=10/4/7 under total_size=100).

The stopping-rule demonstration reproduces under the new batching: the
raw per-step rule still false-stops ~80 steps after arming (371), the
windowed rule stops at 4,650 of 20,000. Its intro now notes that the
trap shown here was later confirmed on real ADVI traces and that the
revised implementation under review for pymc-extras builds on the same
windowed idea.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1041 ++++++++---------
 .../streaming_tick_data.myst.md               |   64 +-
 2 files changed, 546 insertions(+), 559 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 5fb485726..8bbfe3da6 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "505a93ee",
+   "id": "7e2a1825",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47ff7efc",
+   "id": "862951b6",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b57ebca",
+   "id": "e8ade6b5",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ea0ad3a",
+   "id": "cab2e092",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -103,13 +103,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "cd2d150f",
+   "id": "20349478",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:23.313138Z",
-     "iopub.status.busy": "2026-08-01T16:47:23.312932Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.144558Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.144074Z"
+     "iopub.execute_input": "2026-08-11T18:58:34.174186Z",
+     "iopub.status.busy": "2026-08-11T18:58:34.173931Z",
+     "iopub.status.idle": "2026-08-11T18:58:36.972701Z",
+     "shell.execute_reply": "2026-08-11T18:58:36.971930Z"
     }
    },
    "outputs": [],
@@ -135,13 +135,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "45d2faf8",
+   "id": "a3741641",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.145923Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.145768Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.148677Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.148329Z"
+     "iopub.execute_input": "2026-08-11T18:58:36.975018Z",
+     "iopub.status.busy": "2026-08-11T18:58:36.974604Z",
+     "iopub.status.idle": "2026-08-11T18:58:36.980344Z",
+     "shell.execute_reply": "2026-08-11T18:58:36.979619Z"
     }
    },
    "outputs": [],
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5624b87",
+   "id": "c904f14b",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cb260fd",
+   "id": "71506172",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -234,13 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "23b7377b",
+   "id": "f92de554",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.149569Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.149502Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.151806Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.151550Z"
+     "iopub.execute_input": "2026-08-11T18:58:36.982341Z",
+     "iopub.status.busy": "2026-08-11T18:58:36.982183Z",
+     "iopub.status.idle": "2026-08-11T18:58:36.986412Z",
+     "shell.execute_reply": "2026-08-11T18:58:36.985850Z"
     }
    },
    "outputs": [],
@@ -282,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "abd2dc36",
+   "id": "0df2c094",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.152700Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.152649Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.310705Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.310216Z"
+     "iopub.execute_input": "2026-08-11T18:58:36.987907Z",
+     "iopub.status.busy": "2026-08-11T18:58:36.987806Z",
+     "iopub.status.idle": "2026-08-11T18:58:37.292421Z",
+     "shell.execute_reply": "2026-08-11T18:58:37.291738Z"
     }
    },
    "outputs": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b6b0464",
+   "id": "d3efe39d",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -365,13 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bcc95951",
+   "id": "1b66e687",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.311586Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.311520Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.688188Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.687835Z"
+     "iopub.execute_input": "2026-08-11T18:58:37.294055Z",
+     "iopub.status.busy": "2026-08-11T18:58:37.293894Z",
+     "iopub.status.idle": "2026-08-11T18:58:38.054205Z",
+     "shell.execute_reply": "2026-08-11T18:58:38.053533Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89f82664",
+   "id": "e80bbe08",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -431,13 +431,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2aa9205b",
+   "id": "607784ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.689244Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.689157Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.881817Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.881477Z"
+     "iopub.execute_input": "2026-08-11T18:58:38.056352Z",
+     "iopub.status.busy": "2026-08-11T18:58:38.056186Z",
+     "iopub.status.idle": "2026-08-11T18:58:38.428818Z",
+     "shell.execute_reply": "2026-08-11T18:58:38.428112Z"
     }
    },
    "outputs": [
@@ -477,7 +477,8 @@
     "n_shards = 8\n",
     "for i in range(n_shards):\n",
     "    part = table.take(order[i::n_shards])\n",
-    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"))\n",
+    "    # Row groups are the loader's batches on the shuffle=False path, so size them here.\n",
+    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"), row_group_size=1024)\n",
     "\n",
     "head = pq.read_table(os.path.join(data_dir, \"shard_000.parquet\")).slice(0, 10_000)\n",
     "print(\n",
@@ -488,7 +489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "295769f8",
+   "id": "97e9f970",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -503,28 +504,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00a2ad3a",
+   "id": "f5faf00a",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
     "\n",
-    "The `DataLoader` yields `(batch_size, 8)` blocks in a frozen column order and\n",
-    "knows the dataset size from the Parquet metadata, so `total_size` is simply\n",
-    "`len(loader)`. The model reads one `pm.Data` placeholder; everything derived —\n",
-    "the Fourier basis, the integer symbol index — is computed inside the graph, so\n",
-    "advancing the stream is a single `set_value` per step."
+    "With `shuffle=False` the `DataLoader` passes source blocks through verbatim —\n",
+    "one block per Parquet row group, in a frozen column order — which is why the\n",
+    "shards were written with `row_group_size=1024` above. The dataset size comes\n",
+    "from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is\n",
+    "the number of batches per epoch, as for a torch dataloader. The model reads\n",
+    "one `pm.Data` placeholder; everything derived — the Fourier basis, the\n",
+    "integer symbol index — is computed inside the graph, so advancing the stream\n",
+    "is a single `set_value` per step."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f94a5b9f",
+   "id": "28724ab1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.882734Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.882669Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.885961Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.885612Z"
+     "iopub.execute_input": "2026-08-11T18:58:38.430792Z",
+     "iopub.status.busy": "2026-08-11T18:58:38.430631Z",
+     "iopub.status.idle": "2026-08-11T18:58:38.438524Z",
+     "shell.execute_reply": "2026-08-11T18:58:38.437896Z"
     }
    },
    "outputs": [
@@ -532,7 +536,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N = 300,000 rows -> 292 steps per epoch\n"
+      "N = 300,000 rows -> 292 batches per epoch\n"
      ]
     }
    ],
@@ -542,60 +546,36 @@
     "    parquet_source(data_dir, columns=columns),\n",
     "    batch_size=1024,\n",
     "    shuffle=False,  # the shards are already globally shuffled on disk\n",
-    "    sample_shape=(len(columns),),\n",
     "    total_size=\"auto\",\n",
     ")\n",
-    "print(f\"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch\")"
+    "print(f\"N = {loader.total_size:,} rows -> {len(loader):,} batches per epoch\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f34ed135",
+   "id": "64075eaa",
    "metadata": {},
    "source": [
-    "One deliberate loader semantics to be precise about: only full batches are\n",
-    "yielded, so a trailing partial batch — here 992 of 300,000 rows, and with a\n",
-    "fixed replay order the *same* 992 every pass — is never seen. Strictly, then,\n",
-    "the fit targets the 299,008-row subpopulation the loader streams, which is a\n",
-    "uniform random subset by the hash shuffle, while `total_size` overstates its\n",
-    "size by 0.3% (0.0004% at full scale) — invisible at any precision reported\n",
-    "here, but it does mean Gate 1's equal-inclusion clause holds for the streamed\n",
-    "subpopulation, not the last few rows of the last shard — and, equivalently,\n",
-    "that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a\n",
-    "sub-percent effect dwarfed by the mean-field error quantified later. If\n",
-    "exactness matters, set `total_size` to the streamed count — the cells below\n",
-    "do exactly that via `n_streamed`; the full-scale runs keep `len(loader)`,\n",
-    "where the correction is 0.0004%. The durable fix — carrying the remainder\n",
-    "across epoch boundaries — belongs in the loader and is on its review agenda."
+    "One deliberate loader semantics to be precise about: on this path nothing is\n",
+    "dropped. Verbatim pass-through streams every row, and the last row group of\n",
+    "each shard is simply shorter than 1024. The `total_size` rescaling copes,\n",
+    "because PyMC scales the minibatch log-likelihood by `N / b` with `b` read\n",
+    "from the batch actually installed, so a short block is weighted up by exactly\n",
+    "its own size. Gate 1's equal-inclusion clause therefore holds for the full\n",
+    "corpus, with no tempering correction to track. (Only the shuffle-buffer path\n",
+    "drops a trailing partial batch; this notebook never uses it.)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "88ab2d54",
+   "id": "dbda7e38",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.886742Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.886676Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.888078Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.887771Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "n_streamed = (len(loader) // loader.batch_size) * loader.batch_size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "14946fb6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.888816Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.888757Z",
-     "iopub.status.idle": "2026-08-01T16:47:26.893122Z",
-     "shell.execute_reply": "2026-08-01T16:47:26.892885Z"
+     "iopub.execute_input": "2026-08-11T18:58:38.440047Z",
+     "iopub.status.busy": "2026-08-11T18:58:38.439904Z",
+     "iopub.status.idle": "2026-08-11T18:58:38.447459Z",
+     "shell.execute_reply": "2026-08-11T18:58:38.446899Z"
     }
    },
    "outputs": [],
@@ -693,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e755ab4",
+   "id": "c8ecf4db",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -711,14 +691,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "b09033fe",
+   "execution_count": 9,
+   "id": "f5cd6948",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:26.893997Z",
-     "iopub.status.busy": "2026-08-01T16:47:26.893947Z",
-     "iopub.status.idle": "2026-08-01T16:47:27.187236Z",
-     "shell.execute_reply": "2026-08-01T16:47:27.186827Z"
+     "iopub.execute_input": "2026-08-11T18:58:38.449054Z",
+     "iopub.status.busy": "2026-08-11T18:58:38.448925Z",
+     "iopub.status.idle": "2026-08-11T18:58:38.731436Z",
+     "shell.execute_reply": "2026-08-11T18:58:38.730281Z"
     }
    },
    "outputs": [
@@ -737,8 +717,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M798.01,-8C798.01,-8 844.01,-8 844.01,-8 850.01,-8 856.01,-14 856.01,-20 856.01,-20 856.01,-94 856.01,-94 856.01,-100 850.01,-106 844.01,-106 844.01,-106 798.01,-106 798.01,-106 792.01,-106 786.01,-100 786.01,-94 786.01,-94 786.01,-20 786.01,-20 786.01,-14 792.01,-8 798.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"824.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M810.01,-8C810.01,-8 856.01,-8 856.01,-8 862.01,-8 868.01,-14 868.01,-20 868.01,-20 868.01,-94 868.01,-94 868.01,-100 862.01,-106 856.01,-106 856.01,-106 810.01,-106 810.01,-106 804.01,-106 798.01,-100 798.01,-94 798.01,-94 798.01,-20 798.01,-20 798.01,-14 804.01,-8 810.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -757,107 +737,151 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M709.01,-114C709.01,-114 933.01,-114 933.01,-114 939.01,-114 945.01,-120 945.01,-126 945.01,-126 945.01,-223.82 945.01,-223.82 945.01,-229.82 939.01,-235.82 933.01,-235.82 933.01,-235.82 709.01,-235.82 709.01,-235.82 703.01,-235.82 697.01,-229.82 697.01,-223.82 697.01,-223.82 697.01,-126 697.01,-126 697.01,-120 703.01,-114 709.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"923.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M721.01,-114C721.01,-114 945.01,-114 945.01,-114 951.01,-114 957.01,-120 957.01,-126 957.01,-126 957.01,-223.82 957.01,-223.82 957.01,-229.82 951.01,-235.82 945.01,-235.82 945.01,-235.82 721.01,-235.82 721.01,-235.82 715.01,-235.82 709.01,-229.82 709.01,-223.82 709.01,-223.82 709.01,-126 709.01,-126 709.01,-120 715.01,-114 721.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M836.01,-98C836.01,-98 806.01,-98 806.01,-98 800.01,-98 794.01,-92 794.01,-86 794.01,-86 794.01,-52.5 794.01,-52.5 794.01,-46.5 800.01,-40.5 806.01,-40.5 806.01,-40.5 836.01,-40.5 836.01,-40.5 842.01,-40.5 848.01,-46.5 848.01,-52.5 848.01,-52.5 848.01,-86 848.01,-86 848.01,-92 842.01,-98 836.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
+       "<title>theta_r</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"821.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"821.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"833.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.86 76.86,-251.93 91.01,-243.82 141.9,-214.65 498.88,-198.61 694.12,-191.93\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"694.21,-195.43 704.08,-191.59 693.97,-188.43 694.21,-195.43\"/>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.25,-195.25 716.13,-191.43 706.02,-188.26 706.25,-195.25\"/>\n",
        "</g>\n",
-       "<!-- tau_sh -->\n",
+       "<!-- tau_ph -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
+       "<title>tau_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_sh -->\n",
+       "<!-- b_ph -->\n",
        "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
+       "<title>b_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
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        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
+       "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
+       "<!-- b_sh -->\n",
        "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
+       "<title>b_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
        "</g>\n",
-       "<!-- eta -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"663.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- nu -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"708.14,-345.73 617.89,-345.73 617.89,-288.23 708.14,-288.23 708.14,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"663.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
-       "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M663.01,-393.28C663.01,-381.58 663.01,-368.79 663.01,-357.14\"/>\n",
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-       "</g>\n",
        "<!-- alpha0 -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>alpha0</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
@@ -865,166 +889,122 @@
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
        "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M158.04,-279.79C166.33,-266.21 177.65,-252.08 192.01,-243.82 234.42,-219.43 523.46,-201.99 694.61,-193.67\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"694.63,-197.17 704.45,-193.19 694.29,-190.18 694.63,-197.17\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.69,-196.86 716.52,-192.91 706.37,-189.87 706.69,-196.86\"/>\n",
        "</g>\n",
-       "<!-- tau_al -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- b_al -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M257.84,-279.53C265.93,-266.08 276.97,-252.11 291.01,-243.82 358.84,-203.75 567.86,-192.26 705.62,-189.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"705.63,-192.62 715.55,-188.91 705.48,-185.62 705.63,-192.62\"/>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"345.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"390.14,-345.73 299.89,-345.73 299.89,-288.23 390.14,-288.23 390.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M345.01,-393.28C345.01,-381.58 345.01,-368.79 345.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"348.51,-357.45 345.01,-347.45 341.51,-357.45 348.51,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M359.2,-287.82C368.59,-272.22 382.42,-253.98 400.01,-243.82 450.69,-214.56 597.76,-200.34 706.51,-193.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.62,-197.17 716.39,-193.08 706.2,-190.18 706.62,-197.17\"/>\n",
        "</g>\n",
        "<!-- lambda_q -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"250.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"250.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"458.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
        "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M270.97,-279.86C280.75,-266.29 293.65,-252.15 309.01,-243.82 373.31,-208.95 564.54,-195.92 694.2,-191.05\"/>\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M479.52,-279.99C489.31,-266.61 502.07,-252.57 517.01,-243.82 549.64,-224.7 636.56,-210.21 710.99,-200.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"711.35,-204.29 720.84,-199.59 710.49,-197.34 711.35,-204.29\"/>\n",
        "</g>\n",
        "<!-- beta_q -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"359.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"359.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"567.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
        "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M375.98,-279.75C384.09,-266.34 395.11,-252.34 409.01,-243.82 455.73,-215.2 591.92,-200.95 695.04,-194.09\"/>\n",
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-       "<!-- theta_r -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"459.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M476.39,-279.68C484.51,-266.42 495.42,-252.54 509.01,-243.82 540.23,-223.78 625.11,-209.41 698.46,-200.3\"/>\n",
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-       "<!-- tau_ba -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
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-       "<!-- b_ba -->\n",
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-       "<title>b_ba</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
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-       "</g>\n",
-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M576.76,-280.23C584.93,-267.05 595.78,-253.08 609.01,-243.82 636.11,-224.85 669.25,-212.34 701.2,-204.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"701.62,-207.6 710.51,-201.82 699.96,-200.8 701.62,-207.6\"/>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"675.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
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+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M698.56,-281.27C708.27,-268.53 720.24,-254.6 733.01,-243.82 740.22,-237.74 748.18,-232 756.39,-226.69\"/>\n",
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        "</g>\n",
        "<!-- beta_a -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"767.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"767.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"783.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
        "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"803.04,-239.69 803.71,-229.12 796.59,-236.97 803.04,-239.69\"/>\n",
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        "</g>\n",
-       "<!-- tau_k -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
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-       "</g>\n",
-       "<!-- b_k -->\n",
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-       "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
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-       "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
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-       "</g>\n",
-       "<!-- lambda_a -->\n",
+       "<!-- kappa0 -->\n",
        "<g id=\"node16\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"875.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"875.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"875.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"875.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M859.1,-278.32C853.8,-265.77 847.81,-251.58 842.17,-238.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"845.45,-237.01 838.34,-229.16 839,-239.74 845.45,-237.01\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M868.42,-278.66C863.46,-265.99 857.83,-251.61 852.55,-238.11\"/>\n",
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        "<!-- c -->\n",
        "<g id=\"node17\" class=\"node\">\n",
@@ -1037,8 +1017,8 @@
        "<!-- c&#45;&gt;y_obs -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
        "<title>c&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M963.74,-280.83C955.5,-268.04 945,-254.19 933.01,-243.82 924.7,-236.63 915.29,-230.1 905.55,-224.27\"/>\n",
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        "<!-- g -->\n",
        "<g id=\"node18\" class=\"node\">\n",
@@ -1049,67 +1029,69 @@
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
        "<title>g&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1065.26,-280.23C1057.09,-267.05 1046.25,-253.08 1033.01,-243.82 1005.92,-224.85 972.78,-212.34 940.83,-204.1\"/>\n",
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        "</g>\n",
        "<!-- b_al&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
        "<title>b_al&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1170.65,-280.39C1161.93,-266.91 1150.27,-252.68 1136.01,-243.82 1104.05,-223.95 1017.63,-209.52 943.38,-200.34\"/>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"960.57,-186.77 950.47,-189.96 960.35,-193.77 960.57,-186.77\"/>\n",
        "</g>\n",
        "<!-- b_ba&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
        "<title>b_ba&#45;&gt;y_obs</title>\n",
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        "<!-- b_k&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
        "<title>b_k&#45;&gt;y_obs</title>\n",
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-       "<!-- b_ph&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>b_ph&#45;&gt;y_obs</title>\n",
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        "</g>\n",
        "<!-- b_sh&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
        "<title>b_sh&#45;&gt;y_obs</title>\n",
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+       "</g>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1484.51,-281.95C1473.6,-267.68 1459.05,-252.43 1442.01,-243.82 1360.56,-202.63 1113.78,-191.66 960.51,-188.9\"/>\n",
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        "<!-- y_obs&#45;&gt;batch -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>y_obs&#45;&gt;batch</title>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x137f6c3b0>"
+       "<graphviz.graphs.Digraph at 0x148608950>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), n_streamed)\n",
+    "model = build_model(\n",
+    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), loader.total_size\n",
+    ")\n",
     "pm.model_to_graphviz(model)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a25e0a2",
+   "id": "38293bb0",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1120,14 +1102,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "9019caad",
+   "execution_count": 10,
+   "id": "6cfc76e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:27.188271Z",
-     "iopub.status.busy": "2026-08-01T16:47:27.188185Z",
-     "iopub.status.idle": "2026-08-01T16:47:31.542723Z",
-     "shell.execute_reply": "2026-08-01T16:47:31.542315Z"
+     "iopub.execute_input": "2026-08-11T18:58:38.733306Z",
+     "iopub.status.busy": "2026-08-11T18:58:38.733137Z",
+     "iopub.status.idle": "2026-08-11T18:58:49.260113Z",
+     "shell.execute_reply": "2026-08-11T18:58:49.259331Z"
     }
    },
    "outputs": [],
@@ -1164,7 +1146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a959c1fb",
+   "id": "2f624e15",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1176,20 +1158,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "a07ec91a",
+   "execution_count": 11,
+   "id": "95d97c78",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:31.544076Z",
-     "iopub.status.busy": "2026-08-01T16:47:31.543996Z",
-     "iopub.status.idle": "2026-08-01T16:47:31.697634Z",
-     "shell.execute_reply": "2026-08-01T16:47:31.697221Z"
+     "iopub.execute_input": "2026-08-11T18:58:49.261852Z",
+     "iopub.status.busy": "2026-08-11T18:58:49.261704Z",
+     "iopub.status.idle": "2026-08-11T18:58:49.560490Z",
+     "shell.execute_reply": "2026-08-11T18:58:49.559902Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -1220,7 +1202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0978aa16",
+   "id": "4d95fae2",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1228,14 +1210,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "37187e01",
+   "execution_count": 12,
+   "id": "50896892",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:31.698927Z",
-     "iopub.status.busy": "2026-08-01T16:47:31.698847Z",
-     "iopub.status.idle": "2026-08-01T16:47:31.904642Z",
-     "shell.execute_reply": "2026-08-01T16:47:31.904257Z"
+     "iopub.execute_input": "2026-08-11T18:58:49.563273Z",
+     "iopub.status.busy": "2026-08-11T18:58:49.563116Z",
+     "iopub.status.idle": "2026-08-11T18:58:49.941543Z",
+     "shell.execute_reply": "2026-08-11T18:58:49.940865Z"
     }
    },
    "outputs": [
@@ -1269,87 +1251,87 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
-       "      <td>0.5491</td>\n",
-       "      <td>0.0048</td>\n",
+       "      <td>0.5299</td>\n",
+       "      <td>0.0052</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.2982</td>\n",
+       "      <td>0.3174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
-       "      <td>-1.7044</td>\n",
-       "      <td>0.0040</td>\n",
+       "      <td>-1.6986</td>\n",
+       "      <td>0.0036</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>1.2914</td>\n",
+       "      <td>1.2972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
-       "      <td>0.3526</td>\n",
-       "      <td>0.0054</td>\n",
+       "      <td>0.3384</td>\n",
+       "      <td>0.0050</td>\n",
        "      <td>0.3500</td>\n",
-       "      <td>0.0026</td>\n",
+       "      <td>0.0116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
-       "      <td>0.2055</td>\n",
+       "      <td>0.2094</td>\n",
        "      <td>0.0047</td>\n",
        "      <td>0.2000</td>\n",
-       "      <td>0.0055</td>\n",
+       "      <td>0.0094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
-       "      <td>0.1598</td>\n",
-       "      <td>0.0034</td>\n",
+       "      <td>0.1733</td>\n",
+       "      <td>0.0030</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0202</td>\n",
+       "      <td>0.0067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
-       "      <td>0.1096</td>\n",
-       "      <td>0.0033</td>\n",
+       "      <td>0.1435</td>\n",
+       "      <td>0.0032</td>\n",
        "      <td>0.1200</td>\n",
-       "      <td>0.0104</td>\n",
+       "      <td>0.0235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
-       "      <td>0.0176</td>\n",
+       "      <td>0.0213</td>\n",
        "      <td>0.0002</td>\n",
        "      <td>0.0200</td>\n",
-       "      <td>0.0024</td>\n",
+       "      <td>0.0013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
-       "      <td>0.2485</td>\n",
-       "      <td>0.0013</td>\n",
+       "      <td>0.2505</td>\n",
+       "      <td>0.0012</td>\n",
        "      <td>0.2500</td>\n",
-       "      <td>0.0015</td>\n",
+       "      <td>0.0005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
-       "      <td>3.5158</td>\n",
-       "      <td>0.0319</td>\n",
+       "      <td>3.5092</td>\n",
+       "      <td>0.0249</td>\n",
        "      <td>3.5000</td>\n",
-       "      <td>0.0158</td>\n",
+       "      <td>0.0092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0 + mean(b_k)</th>\n",
-       "      <td>0.8554</td>\n",
-       "      <td>0.0107</td>\n",
+       "      <td>0.8299</td>\n",
+       "      <td>0.0113</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.0081</td>\n",
+       "      <td>0.0174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0 + mean(b_al)</th>\n",
-       "      <td>-2.9944</td>\n",
-       "      <td>0.0063</td>\n",
+       "      <td>-2.9950</td>\n",
+       "      <td>0.0058</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>0.0014</td>\n",
+       "      <td>0.0008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a + mean(b_ba)</th>\n",
-       "      <td>0.1699</td>\n",
-       "      <td>0.0061</td>\n",
+       "      <td>0.1956</td>\n",
+       "      <td>0.0055</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0101</td>\n",
+       "      <td>0.0156</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1357,21 +1339,21 @@
       ],
       "text/plain": [
        "                       mean      sd   truth  abs_error\n",
-       "kappa0               0.5491  0.0048  0.8473     0.2982\n",
-       "alpha0              -1.7044  0.0040 -2.9957     1.2914\n",
-       "lambda_a             0.3526  0.0054  0.3500     0.0026\n",
-       "lambda_q             0.2055  0.0047  0.2000     0.0055\n",
-       "beta_a               0.1598  0.0034  0.1800     0.0202\n",
-       "beta_q               0.1096  0.0033  0.1200     0.0104\n",
-       "theta_d              0.0176  0.0002  0.0200     0.0024\n",
-       "theta_r              0.2485  0.0013  0.2500     0.0015\n",
-       "nu                   3.5158  0.0319  3.5000     0.0158\n",
-       "kappa0 + mean(b_k)   0.8554  0.0107  0.8473     0.0081\n",
-       "alpha0 + mean(b_al) -2.9944  0.0063 -2.9957     0.0014\n",
-       "beta_a + mean(b_ba)  0.1699  0.0061  0.1800     0.0101"
+       "kappa0               0.5299  0.0052  0.8473     0.3174\n",
+       "alpha0              -1.6986  0.0036 -2.9957     1.2972\n",
+       "lambda_a             0.3384  0.0050  0.3500     0.0116\n",
+       "lambda_q             0.2094  0.0047  0.2000     0.0094\n",
+       "beta_a               0.1733  0.0030  0.1800     0.0067\n",
+       "beta_q               0.1435  0.0032  0.1200     0.0235\n",
+       "theta_d              0.0213  0.0002  0.0200     0.0013\n",
+       "theta_r              0.2505  0.0012  0.2500     0.0005\n",
+       "nu                   3.5092  0.0249  3.5000     0.0092\n",
+       "kappa0 + mean(b_k)   0.8299  0.0113  0.8473     0.0174\n",
+       "alpha0 + mean(b_al) -2.9950  0.0058 -2.9957     0.0008\n",
+       "beta_a + mean(b_ba)  0.1956  0.0055  0.1800     0.0156"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1408,7 +1390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfa7fbb7",
+   "id": "f98e432f",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1436,20 +1418,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "9ed3407a",
+   "execution_count": 13,
+   "id": "0b59fa4d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:31.905620Z",
-     "iopub.status.busy": "2026-08-01T16:47:31.905538Z",
-     "iopub.status.idle": "2026-08-01T16:47:32.028437Z",
-     "shell.execute_reply": "2026-08-01T16:47:32.028039Z"
+     "iopub.execute_input": "2026-08-11T18:58:49.943218Z",
+     "iopub.status.busy": "2026-08-11T18:58:49.943062Z",
+     "iopub.status.idle": "2026-08-11T18:58:50.187639Z",
+     "shell.execute_reply": "2026-08-11T18:58:50.186987Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1600x700 with 1 Axes>"
       ]
@@ -1457,7 +1439,7 @@
      "metadata": {
       "image/png": {
        "height": 723,
-       "width": 1712
+       "width": 1724
       }
      },
      "output_type": "display_data"
@@ -1492,7 +1474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a76de1d",
+   "id": "9bdadf38",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1504,7 +1486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b944deb",
+   "id": "ff836bcb",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1519,14 +1501,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "66a85846",
+   "execution_count": 14,
+   "id": "90c186f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:32.029421Z",
-     "iopub.status.busy": "2026-08-01T16:47:32.029355Z",
-     "iopub.status.idle": "2026-08-01T16:47:32.810932Z",
-     "shell.execute_reply": "2026-08-01T16:47:32.810526Z"
+     "iopub.execute_input": "2026-08-11T18:58:50.189816Z",
+     "iopub.status.busy": "2026-08-11T18:58:50.189701Z",
+     "iopub.status.idle": "2026-08-11T18:58:51.494603Z",
+     "shell.execute_reply": "2026-08-11T18:58:51.494007Z"
     }
    },
    "outputs": [
@@ -1561,23 +1543,23 @@
        "    <tr>\n",
        "      <th>zero share</th>\n",
        "      <td>0.315</td>\n",
-       "      <td>0.327</td>\n",
-       "      <td>0.305</td>\n",
-       "      <td>0.350</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.308</td>\n",
+       "      <td>0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>median |move| (bp)</th>\n",
        "      <td>0.042</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>0.042</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>0.044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q99 |move| (bp)</th>\n",
        "      <td>0.324</td>\n",
-       "      <td>0.342</td>\n",
-       "      <td>0.280</td>\n",
-       "      <td>0.426</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.281</td>\n",
+       "      <td>0.411</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1585,12 +1567,12 @@
       ],
       "text/plain": [
        "                    observed  pp mean  pp 5%  pp 95%\n",
-       "zero share             0.315    0.327  0.305   0.350\n",
-       "median |move| (bp)     0.042    0.039  0.036   0.042\n",
-       "q99 |move| (bp)        0.324    0.342  0.280   0.426"
+       "zero share             0.315    0.331  0.308   0.355\n",
+       "median |move| (bp)     0.042    0.041  0.038   0.044\n",
+       "q99 |move| (bp)        0.324    0.340  0.281   0.411"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1634,20 +1616,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "67fd6d24",
+   "execution_count": 15,
+   "id": "8174ded8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:32.811862Z",
-     "iopub.status.busy": "2026-08-01T16:47:32.811796Z",
-     "iopub.status.idle": "2026-08-01T16:47:32.950131Z",
-     "shell.execute_reply": "2026-08-01T16:47:32.949740Z"
+     "iopub.execute_input": "2026-08-11T18:58:51.496582Z",
+     "iopub.status.busy": "2026-08-11T18:58:51.496437Z",
+     "iopub.status.idle": "2026-08-11T18:58:51.762217Z",
+     "shell.execute_reply": "2026-08-11T18:58:51.761595Z"
     }
    },
    "outputs": [
     {
      "data": {
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qk8/7AFQy84fiUG8AQN4EVwAAKKtJkyZljnfo0CHX569t+42lKGPatGnhrLPOSu0qc8IJJ5R8nwAgL+YPlenpp58OgwcPznzMQQcdFBZffPGS7RMA5ee+XXeTJ0+u6O9IanstAaAYzB0qj8/7AFQ684eGU28AQCkIrgAAUFbTp0/PHI+rcueptu3PmDEjNAaXXXZZGD16dI1jp556au7FLQBQSuYPlWfMmDHJnCPLcsstF4444oiS7RMAlcF9u7AikXJ+R9K+ffuq+I4EgMbN3KGy+LwPQGNg/tBw6g0AKAXBFQAAymrOnDmZ4y1btsz1+Vu1apU5Pnv27FDp3nnnnXD77bfXOLbVVluF7bffvuT7BAB5Mn+oLLNmzQrHHXdcmDBhQubjzjnnnNC2bduS7RcAlcF927ECAHOHxsnnfQAaC989NIx6AwBKRXAFAICyqi0Ykndwpbbt1/YlVyUcv9NOOy3MmzevxpVYzzjjjLLsFwDkyfyhspx55pnhP//5T+Zj9tlnn7DFFluUbJ8AqBzu243nWLVo0aJRf0cCQHUo9/2wsf83g2LyeR+AxsL8oWHHTr0BAKUiuAIAQFnV9h95aiuaaKjatl/pHVeuueaaMHz48BrHjjrqqNClS5eS7xMA5M38oXJce+214f777898TI8ePcIpp5xSsn0CoLK4b9fd3LlzM8d9RwJAU2DuUBl83gegMTF/qD/1BgCUUr5LUQAAUCdPPPFE8tPY9enTJ/Tv37+oq5fV1EmklEUhea/e1hAjRoxIvkiqyRprrBEOOuigku8TAKVj/pDO/KE0HnrooXDppZdmPmbRRRcNl19+eVhkkUVKtFcAVBqf++uutmBKbd9hNOXvSACoHuYO5efzPgCNjflD/ag3AKDUfMMMAFABPvnkk/Dggw+Gxq5jx44FB1datWrVoNVRGqq27de2f+Uyf/78pGXvrFmzFhpr3rx5OPfccxWUAFQ584d05g/5Gzp0aDj55JOTOUnWPCoGW7p3716CPQKgUvncX7xjVe7gSqV+RwJAdTF3KC+f9wFojMwfCqfeAIByaF6WZwUAgDqufjJ79uxcj1VjDa7ccccd4a233qpxbN999w3rrrtuyfcJAErF/KG83nzzzXD00UdnztNikPaiiy4KW2yxRUn3DYDK477deI5Vbduv1O9IAKgu5b4fNtb/ZlAMPu8D0FiZPxROvQEA5SC4AgBAWbVr1y5zfPr06bk+/7Rp0zLH27ZtGyrN2LFjw8CBA2scW3rppcMJJ5xQ8n0CgFIyfyifDz/8MBxxxBFhxowZmY8744wzwk477VSy/QKgcrlv11379u0zx31HAkBTYO5QHj7vA9CYmT8URr0BAOUiuAIAQFkttthiDQqWNFRt2+/UqVOoNGeddVbqfp966qmhQ4cOJd8nACgl84fy+PTTT8Ovf/3rMGXKlMzHnXTSSUkHOACI3LfrrmPHjhX9HUltryUAFIO5Q+n5vA9AY2f+UBj1BgCUi+AKAAAV/SXS5MmTc33+2rZfaUUZjzzySHj22WdrHNtqq63C9ttvX/J9AoBSM38ovS+//DIccsghYcKECZmPO/roo8Ohhx5asv0CoPK5bxfvWE2aNCnkqbZwaiUu7gFA9TF3KC2f9wGoBuYPdafeAIByElwBAKCsOnfunDn+3Xff5fr8tW1/8cUXD5Vi4sSJ4bzzzkttf3zGGWeUfJ8AoBzMH0pr1KhR4eCDD6513nTYYYeFo446qmT7BUDj4L5dvGP1/fffhzw1pu9IAKhe5g6l4/M+ANXC/KFu1BsAUG4ty70DAAD8z8rU8acpWm655cpalFHb9rt06RIqxSWXXBJ++OGHGsdikWgl7SsA+TN/SGf+UDxjx44NBx10UBgzZkzm4w444IBw4oknFvGZAagWPvfX3fLLL1/RwRXfOwBQCuYOpeHzPgDVxPyhbtQbAFBugisAAJRV165dM8dHjx6d+4piWSuzxE4mleL9999PPYZ9+/bN/FsKMXny5MzxCRMmZD5X27ZtwxJLLFGUfQGAmpg/lMa3336bhFZqm2Psvffe4bTTTivRXgHQ2Lhv112HDh1Cp06dkhVQK+07krq8lgBQDOYO+fN5H4BqY/5QN+oNACg3wRUAACr6S6SRI0fm9txTp04N48ePb/QFGbGwZLvttivZ81188cXJT5ptttkmXHnllSXbHwCaHvOH/MUubwcffHD48ssvMx/Xv3//cPbZZ5dgjwBorNy3Cz9eacGV2BFl+vTpuS2y8fXXX6eONWvWTMcVAErC3CFfPu8DUI3MHxpGvQEApdK8ZM8EAAA1aN++feYXSV988UWYP39+Lsfu888/zxzv0aNHLs8LADSM+UO+Yne1GFoZMWJE5uP69esXLrjggqSQFQDSuG8XZvXVV88cr+3+XF/z5s1LvoNJ061bt6TDKgDkzdwhPz7vA1CtzB8AoHEQXAEAoOzWWmut1LFp06bVutJ3sVvhLrDmmmvm8rwAQMOZP+Rj0qRJ4de//nUYPnx45uP69u0bLrnkktCiRYuc9gSAauK+XZxjFX344YchDzG0Eru5pPEdCQClZO5QfD7vA1DtzB8AoPIJrgAAUPFFGe+++24uz/vee+81aL8AgPIxfyi+KVOmhEMPPbTWgtgtt9wyXHbZZaFly5Y57AUA1ch9u/KPVW3b9R0JAKVk7lBcPu8D0BSYPwBA5RNcAQCg7Hr16pU5/sorr+TyvC+//HJmO+G11147l+cFABrO/KG4Ype7ww47rNZg72abbRb++c9/htatWxd5DwCoZu7bdRe/i2jXrl1FfUcSbbLJJrk8LwDUxNyheHzeB6CpMH8AgMonuAIAQNmtu+66oUOHDqnjL730Upg/f35Rn/Ozzz4LY8aMyfxiyyriAFC5zB+KZ8aMGeHwww8Pb7/9dubj4vzoyiuvFFoBoGDu23XXqlWr8LOf/Sx1fNSoUeGLL74o6lk4b968MGzYsNTxjh07WtwDgJIydygOn/cBaErMHwCg8rUs9w4AAECLFi2S1bsff/zxGg/GuHHjwptvvhk23njjoh2shx9+OHN8iy22qLgXZsiQISV5nssvvzxZST3NhRdeGHbbbbeS7AsApDF/KI4ff/wxHHnkkeGNN97IfNyGG24Yrr766rDIIos4KQEomPt2Ybbccsvw/PPPp44/8sgj4fe//33RzsRXX301fPfdd6nj8Tub+BoCQKmYOzScz/sANDXmD7VTbwBAuem4AgBARdhpp50yx++7776iPdfcuXMzv5SJnVa23377oj0fAJAP84eGmTVrVlL0+sorr2Q+bp111gnXXXddaN++fQOfEYCmzH277uJ3EllBkcGDByddUopl0KBBDXrtACAP5g715/M+AE2V+QMAVDbBFQAAKsJWW20VOnbsmDr+0EMPhe+//74oz/XEE0+E0aNHp4736dMndO7cuSjPBQDkx/yh/ubMmROOPfbYMHTo0MzH9ezZM9xwww2hQ4cODXg2AHDfLsQSSyyRdDlJ89VXX4VnnnmmKKdV7HIbO7ikWWyxxSqyKy0A1c9n/vrxeR+Apsz8AQAqm+AKAAAVoXXr1mHPPffMXCHsH//4R4OfZ/bs2eHvf/975mN+9atfNfh5AID8mT/Uv/vciSeeWGvB6+qrrx5uvPHGpGAVABrKfbsw+++/f+b43/72t+Se3lDxO5L4XUmavfbaK3ntAKDUzB0K5/M+AE2d+QMAVDbBFQAAKsZBBx0UWrVqlTp+7733hrfeeqtBz3HdddeFL774IrNAsxgriZ588smhR48eqT9xHABoOPOHwsybNy+ccsop4dFHH8183CqrrBJuuummsPjiizfo9QGAarxvl+Izf9zHuK9pPv3006QrWkO8/vrrYdCgQanj8bU68MADG/QcANAQ5g515/M+AJg/AEClE1wBAKBiLLPMMmGfffbJXC3shBNOCOPGjavX9l9++eVwxRVXZD7mmGOOqde2AYDyMH8ozFlnnRWGDBmS+ZgVV1wxCa0sscQSDXptAOCn3LfrrlmzZuHoo4+utVvKq6++Wq8TLX63EjuwxSLXNPE7mqWXXrpe2weAYjB3qDuf9wHA/AEAKp3gCgAAFSUWZWSt7D1mzJhwyCGHhNGjRxe03Zdeein87ne/C3PmzEl9TO/evcO2225b0HYBgPIzf6ibCy64INx1112Zj+natWu4+eabFakCkBv37brbbrvtwqabbpo6Hr/jOPLII5OFOgrx9ddfJyvYjx07NvUxnTt3trgHABXB3KF2Pu8DgPkDADQGLcu9AwAA8N8WW2yxcPrppyedVdKMGDEi7LbbbuFPf/pT6N+/f2jePD2PPXXq1HD11VeHG264IXMV0fbt2ycrkgEAjY/5Q+3iiuwxkJKlY8eO4cILL0yKYEeNGhXytuyyy4aWLX09CdDUuG8XJn5XMWDAgDB9+vQax+PvDz300PCb3/wmHHHEEcn3G2ni9yL33XdfuOSSS8KkSZMynzd+NxPnBgBQbuYO2XzeBwDzBwBoLPyXYQAAKs6OO+4YXnnllXD33XenPmbixInhlFNOCVdccUXy+A022CAsv/zyYZFFFknCKp9//nl49dVXw2OPPRamTJlS63Oec845oXv37kX+SwCAUjF/yDZkyJBaj+HkyZPDAQccEErl6aefTjq8AND0uG/X3Yorrph8Z3HiiSemPmbu3LnhmmuuCXfeeWfYYYcdQq9evcLKK68cOnTokARbvvnmm/D222+Hhx9+uE4dbPfee+/Qr1+/AvYSAPJl7pDO530AMH8AgMZCcAUAgIoUV/aMK30PGzYs83HxMbE4oyF+//vfh5122qlB2wAAys/8AQAaD/ftutt5552TBTquvPLKWkOod911V/JTX5tttlk47bTT6v3vASAv5g4AgPkDADRuzcu9AwAAUJPWrVsnBRmbbLJJrgfoN7/5TTjmmGO8CABQBcwfAKDxcN8uzLHHHpt8h5Gn+B1M7GwbXxs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tKjSNA634cMQRR8Su4FXPkz6bpQz2FJNOjrWyhwo94nQyqWJ/nUjHLSCiwhtannbMddddV5T3lV4PfWaL2VUkrvsRBVU0OfPoo49aHKjgRIP9Dz/8cOi3rfeVAjrF+k6bNm2a+86qpCKxIO6++243wB71YCeKZ5VVVrENN9zQ9/XD/r5Wp6oPPvjA9/UPOOAAX9fjeBlhCWt1Tx1P+B1zQMepYGrAgAGBnzKN88Tt/BYoFY0H3nLLLUW7vwsuuMB1Kwibjkd0XKFCuDj44osv3LhtFCtlluKxaMGFOJ3LV7PbbrvNnb/HJYD5yCOPuFBT3MZnH3roIResifp9O378eDvssMMIrxSBVgdWsSoQFvan8Z1nYS6scmlehDA0/LrrrruKWkyuTp5RLALFuHa86Tzr6KOPtpdeeqlo96fFkznfjgeNF19++eVFvU+NY+kcX+PVxfC3v/3NjQWC/TEWIrgCAGhVGF1Ounfv3unr6ARBLfPiWtCg1SR++ctfBj5p1upsDz74YGjbhWCrQ2qy6+OPP47V06jJRw0mxiXcpJX8dAL3ww8/WLXQ6qdRr+zhl/aVmgyOS1tvrZSm1bjQsZU1brrppqKuGKLVOOMQvirVfkQrd6j9s1r+xok645x33nluRdIwaVWqqDuttDQBplWKqj2sceedd7oBVr22qCx+wx6iYoMwJ0GeeOIJ39ddddVVfYVwOF5GmMLoHDhw4EDbfvvtQ9ketE0dmnReGga6zQJmN954o91zzz1FfSpUlBF2EYA6UqszrY4R4kShep37xW2Mr7NGjRoV+YrG6Jibb77ZrrnmmliMqRR6/vnn7eSTT45N5xV1q9cqssUqalRwTscncewyXGmuuOKK2HaGR3lhfxrveRbmwirXVVddZd9++22pNwNlSIvAXXnllUW9T82rhB2UYVw73jRvouP6N998s6j3qzGNe++9t6j3iZZDZaeddlpR51Q1bqT57GIvTKH3mzpLVTP2xyhEcAUA0CIVHYYxiKECh85SAacmXuJMgQKtpP3ZZ58FWjEH8ehcoYl2nRTF9WT9pJNOsokTJ8ZiYuG9996zanHDDTe41Q/iTCfxWgG51MX4OsHX5DQ6VqynTivFptX+77jjDqvG/YiKGPSdHdf9l4pf1NnplVdeCeX2Xn/9dbf/KgUdF91///1WrbS6rCYCUZl23XVXX+c2XrDrhRdeCG2fESS4sv/++3f6OhwvI+wwqboGBbXHHntYbW1tKNuE9o0cOdL69esX+Kkq9iQ0EEePPfZYSe73f//7X2gr106ePNl1gdUxTlzHtbVaa9xCNR2lVWa14BFKT0Ulf/zjHy2udI4Rh/G4d955xy2uU+xwj8LQClUg+vmJM888kxWpEQj703jPszAXVrk+/fRTe/zxx0u9GShTeu+UIrytgPKTTz4Zym0xrh1/33//vb388ssluW8tAhiXrprVSPuXc845x80XFPM+f/Ob39jUqVOtFC699FKrVuyP0RzBFQBAi3RyEEaqedCgQZ36/YcffrhkE7h+JkGV/vazqteHH35IwUZMaFJt0qRJFmezZ892XYgUmCrlgLlO3qtpH1jMjhhBJw8VXgmjS5Zfem/oPYL23X333UVbfbI5FVu8++67Vm37kauvvrrk4a6Ofo6DdqLRMYm665Sy20dcVnstRaGOVgJD5VInyd1339339YOETQqp4NRv4Lqurs723nvvTl+P42WEScciYRwLbbPNNqFsDzq+D9xtt91CKXb/8ccfS/K0ayJ0jTXWKMoFiKu77ror8G3onENjVKWa5O8oLch0xhlnlOz8O4hqX4EzLtRh4rLLLrO4e+SRR9yl1O/ZhoaGkty3Fs949tlnS3Lf1eSLL76w++67r9SbgTLF/jTe8yzMhVX+QkvleDwM/OUvfwnlSWBcG+0V0quzEEq32GcYndk7Q693WIvM+VGtc+jC/hjNsSwdACDSk8Hll1++w7/71VdflcVkUKH333/frr32WpfK7myr5yBUCLH99tvb+uuvb4MHD7YBAwa4QhKtOKtJIgUcNLipSWRNKnz++edudQptrwI3WNiO8bnnngv8dPTp08et+Dt8+HBbe+21bYkllrBu3bq5IMGXX37pTn7U7SBIFyOFa9SO96KLLirJy1fK0EwpgkJaRa6cBnP13rrgggvs+uuvL9lzhvhTOEJdzVTQkEwmq2I/okGvMFo9a5+uYs1NN93Uhg0bZv3793edF7TCsArIX3zxRfvXv/4VqBObijg1aHP77bcHKkTTdz6KSysyqXhPnzFUNnUr8duNTauca+Uov11bPDqm9EvnD9p/dQbHy+VFq7MXq2heq1vvu+++vhZxCKpr16620UYbBb4ddH4fEkZnNYU9t912W55+oAQ0BqbCjL59+wbqID1hwoTA2zJw4EDbc889bZNNNrHVVlvNjaUpZKvzIo2BaSztH//4R6CuLvpe1PnVsccea+VC47qjR4/2fX2ND++www42YsQI97wuu+yy7vhT57Ra4EDBI70H9Dx/88037hxWK3HrNdW5ZDmNhUVJx+2nnnpqKOd4G2+8sW233Xbuz2WWWca91+vr693CFQqla9xC5wpBVrPWqq36LHVmLqaSaLxc4zVBz7XiqkuXLrbWWmvZ0KFDbcUVV3QX7UOXXHJJ69mzp/u55oU07qb3lj7jmmvTvND48eNdh98wxm61P/3Zz37m9tVAR7E/jfc8C3NhlS9IPYL29z/5yU9ss802szXXXNMdV2o+XMeVOm7RcevcuXNdLYLmKHUsqeN4db5Xp91qLs5FcHofffzxx+6cxi/GtdERWlT50EMP5cmqguMeHZdqTAvFx/4YLSG4AgBosevJW2+9FfiZ0eC5BjE6SkX5YRW1rrzyyrbzzjvbVlttZUsvvbTbFt22Bk8++ugje+aZZ9wkYBgrgekg68ADD7SVVlqpUxOnfqjQ7PLLL2+zyEMTlLrod1dddVU3oOTRQJImI5Vc14TFSy+9VLXtL/VeUPeBIDRod8IJJ9gRRxzR4sSY3nu6aOLwxBNPdJM7f/rTn3xPAqtQUu81TVIh2rboQbseeJZbbjnbaaed3OCuOlBpX6TBWrW9VXGA9kUqHAmjBapuS59tFScArVGI8cEHH3QTzZVOk/VBA7E1NTV22GGHuX19S8Xe+kzroiDp8ccf71Ywu+qqq9x9+zFmzBg3mbTjjjt2+rraj9x6660WFh3D7bLLLrbuuuvaUkst5b7nvEmo559/3hWS+ek6V6nfG19//XWot6mCIx3DqfhGRSkqbtIlkUi499d3333nClEUStZxpQqeCM5ET58HhQJ0PtFZet30Xb3ffvv5vn+9xk8//bTv6x9wwAGd+n2OlxEFddwISue5KtBDcangVkWRQYtPVEQJYHEqANPYwdZbb+2K21WQrEVpVPz1+OOPh9I9U8cSKpL3Ezz0wof33HNPoG3o0aOH64Si45KW9uUaR9Flyy23tJNOOsmuu+46++tf/+r7/tRNVx3nND5XDjQm7/c8S4vqnH/++a0Gk7SAhZ5zndtpjErHts3PKV9//XUbO3asOzdVoVi10vvGb5dDj8Zv9XpovKA5vQ4q/NQchsLx+nyPGjXKd8BX4/sKFd98881WjTTOqjH3zi4uFmcrrLCCGxvSd8IGG2zQoWNfhVh00feH3lty+OGHu2M3LYCgTg5BjsN0fqjuNrvuuqvv20D501hpZ7A/jTfmwip//MXv8cwWW2zh6hF0XtLWHLmOK3WcrcW+mo+Dvvnmm+64UrUICkkHCekiXjQ2tPnmm7sFTnTupjkyLZyqxTy1wJvmqsN4vTUO7je4wrh2ZdBxreYpN9xwQ7ev0T5Hc2Pap6hzRtBzNtFtaa5tyJAhoWwz4kuLP4ZVA6T9oOZvtUiFFprWPLrqEXVuqloM7b9YaDKH/TFaQ3AFALAITV6G1dFBRQ0d9dprr4XS0l0TPmeddVaLRVlalbVfv37uBFcTeToB0crzCnAEnfT9/e9/bzfeeGOHr+N34k8TMDoxCzKorCIfXVS0rIlQPf7/+7//s3//+99uZZSOOu200+yYY45p9ec///nP3YG5H1oZULfvV+/evdv9HaXpg4QFVMCsyZ711luvwwUImnBXR5ZTTjnFV2hKgzwKeIXRPQAt034hjFWEtb9RWOmoo45yJ64tvUdXWWUV915XIcoll1xiTz31VOD71ftDK4Og/Oi7SRO/Ks7R4Jv2MRro1cm8CtK1n9Z3ZRiDvSpkaK1IqJIoLBhk0FIhUAUcVTzWEfqsH3LIIW4/f9xxx/leGfiaa65x99nZFSy17wpjdRodS/32t7+1ffbZZ7HJaK2sJnr/6PFp8kqrIFczFX0ELd4rpOMKHS+oOKU1XpGT9hsaFBUd02g/oZBrZ4oa2/ru0e0EKfxRiGudddbxfX1NesWR3v9a0diPJ554IlBwRQWEfvctKk7UhGJncLyMKIQxoVisrjJY/JxW3z2afAsi7LAnyovOddo6/tDYYJDVDzUJrcliv0oVbjjooINc59eWxrP0/a3VP/XcnHvuuYE6kIiKx/wGV3T8r64dfimQrbG0ji7+o2Nejd2uvvrqrsusn/NhFQ6oW7bGS8qBVqf2Q2MJOpfsbEFxIRUBbbPNNu6i8wCNkWlhBR3DKjBfLZ/zMM7xtPCQ3rsdHXfReZPO6U8++WTX0dUPLYwTtwVtVMCjsS6NcajwVa+bzl011qVVtHUO+/LLL4eyIvt9991nRx99tCtuK1caB9K8lb4TFFYJi8artN/XQm+nn3666/Djl4pROxJc0ee0tcU1NGekuSO/NG+kMXW/guw/oqT5OnV7joo+bwopBH0vnXPOOR3+ffan8cZcWOXze1ypY2/NIWmeM8j4gRZl0kVd7FRormMVfY8oKF0uIRbGzhenxRoVzlZYpTkdh2rsXK+xFisIOv6jc9df/epXvq7LuHZ50/ysjls156oFGAqpE6HOL375y1+6fZXqp4KMU4jOowiuVDYt9hBWjZXef6qpbC3cqbCVvvv0vaexLL81c5WC/TFaQ3AFAOBMnz7drXyjg7WwBgu0ykJH3XHHHYHvTwPOmuDp6EmFfk8D6CoYD7Jynxf40SCsVsLqyISpnm8/hYxBQist0eSZNyF54YUXuolDFTx29Plua5A/SMt4r6g/KlotXquMBVkd8pZbbllsZcKOUIGpCg00qOOHVlXXKjVhTl6FQQOZKrJVQYVO0lR0r9dRYSgFMzRAqglJTb7qT79dZ6J29913B16xXivb6XY6Wqyr50pFFFqR6Oqrrw503yokUBitsNNSqen50PtC+xkNJunxahJZ7wEV2WuCZOLEiS6UoZWX/Owfy5kKcc4+++wWC5kHDBjgLnreNHmpgTPtP/ScBaECAQWcNBFeqfsRfdcGGYDSQKgGUjoaWimk1VR/97vf2bHHHuvrmEorsKgrg4oVOkr3E0boTsdGOiZqayU1j8LA2mdpf6NJ63KZbAqbjmHDKLLRcZOODVTg5LfITJNCuuhYQe9fHS+0p63jraArD6lrT5THc6Wy1157uc+4nxCy9uOaqPVbFKuiQb9UqKSOPR3F8TLiHFxRESRKQ6t3Bw2uhPEeQPnSMU9bxwdvv/124OPZctpH6PlQWL4jBbj6HY076ngxSPdkFRH5oddGxzJ+6dxWiwv4KQbRuav2HRqL83sM9etf/7pTncFLxe8+Ut2gg4RWWqLX6he/+IW7qIP33//+d7eqbaV/znWOF2RsUIXnF198cadfD42fqcOQxof8rsyqLqxxCK6o0FVFhnrvNC8001iPLgoi6xxFXWY0ptCZcFRLdH6m8dggi2GVij4z6varcaQoQxV6j11//fXuPaaxWD807qzPR3tzP+oi25oghdCiQtlKHGvwPhtRUFhJwdEgNJ6gzk6dCQ2xP433PAtzYZXP73GlCsWD7qub01joT3/6U3dRmEHHlA8//LDFHWPni9L8heYq27PRRhu5ehMd62k+0q933nmnQ8cdzTGuXd60v9B3VHsLXujYRIuI6jy/M8Ha1sZJ1AkT8aHAtGrjVDegfYrGlHTso/o2LYagMMhHH33k9hMvvPCC6/jUlieffDLwQjCi800tXtnRujBt+5FHHunqGqoR+2O0heAKAFQhddnQIJaK0bSSsiYdVSDpp/iqrcL+jg5gajDt+eefD3R/Kmy98847fU1+jho1yn788Ue34pBfGhhUgaxuqz2d6WpSKOqJd61+oiJZXVQgXMn0WgUpMtUqIX5CK4UT7lqxUKtm+6FV/+ISXNEAuYru1QqzJX379nUXDS7suOOOrtuMCjYfeeSR2K1uppPcjkzAt3cSrdU9/Kwwr+4s2i9rXxaEBnPiEFzRwIEmXbXiiQqqW1sxRe8DnbRrwFr7Un0uwug+Uw400aqwQVuTuYVUfPD444/b4YcfHniAQ/cbl+BKFPsRBXOCDECp6MfrZOGHBtJUUNbRMGhL+/nOBFd0LPfNN99YEEsssYSbUO5IaKXQyJEj3USoumtUGx0v6TMZlN7fWhVqk002CWW7hg8fbg8++KDbl6ooBeHS66V9kVYI7Cx9z2mAXAVcfo5TtEKTH5rE6WynF46XERV1lAuqVB0RkAslBhXGJCFQCVTUftlll3WqAFRdj9QdsSPjj20VsGlsUsWPnfGXv/zFgtBjDbKCqYInOhby08VaY4DqxqDxvLiL67ixivbUhUWrhgZd8CXOdG4bpKuoukaqM5HfEJHGz3Ru7Xe8RoXKChmVsqhf80Iam+zo2Lk6uz700EOu6Gz06NGB7lsFsArMBFlQqxS0f1IhVrHGa1XYp+Ipv/sozSmGvcAaoqOwkb5Dg+67tfCdFvLoKPan8Z5nYS6sOsT1uFLHS+o4fsIJJ/jeRhSf5uU6ElrxaJ+kxZ+CdFlTTZMC3ToP7gzGtct7sUcteNHRLq2igJSOd4IsWus31I1oaP7trLPOarV+wgt861xy7733dv+mBRE0L9pa8DLIeX5h7UBHQyuF84m33XabO8cPujBpOWJ/jLZ0fKlFAEDsaeJRK1W1d1FBtVZl0SCjVm7XQXyYoRVRsaYGyjpCRV9Buy9ogKOzJ60eTSSpRZ8OGoPQAGBHHoffAeJirkoa1cpOcaDXKMiJiToCHHzwwYG3QwNyfin0ovBZqWkVOAU9Wis2b6vQTIPsWoEwTlQAEXSQVCsRBVnZUAUAHenc1BZ1owijIDDowLOKuTWo0NpkSmuFtVoxrBoK4DWRr8G3joZWPHo+NcAxaNCgQPevARytRFKp+xEFV/zSSnUqmAjjsSnM5odWoFWxSUeFEfbS59Xv/kdF+NVYtKDQc9BjaB2HXnPNNaGFVgrp86GAF8IXZPUtvxMoOv6rr6/3dd0tttiiU8XmHC8jSn7fx827fqE0wiiqVGc8ALniCm+CvbPXCzJup06Jn332WaeuozEodWn2a6uttvLVzbKQujboHMsvjVHEtftvOY0bq3i2s6GncjvHC3KscuaZZ3Z4TqStLq6au/H7+Q66KE8QGte78cYbO73gk4Im6jYzdOjQwItL+F0oqpSKFVrxaOxaY19+Be2+h+JRJ16NnwYdu9KCQ50N1LE/jfc8C3Nh1SHux5U6vleROuJP83d+6hq0yOLaa68d6L4nTZrUqd9nXLu8qXZNtTh+Fgct5vsM0Y036HxSl87WTyjEoqC1uoU3p8Uw1ZEuiBVXXNGd7/uhcy91Za027I/RHoIrAIBIOnccffTRnSrCCnqQ2JkVHlpbZVwDuEGoa8sbb7zR7u/5ba/71ltvuVXTEIxeoyBtabVakQbTgtLKRxp09jvYGLRLUVCnn366u2gAvFIEKcLwCugUogt6Qq5JiCD0/vjf//5npaJ9srp5lHJ1x3KgFTk0iOJ3gEMThkE9/fTTVon7EXUe0eqPfqmDSGcmAttamUwFJ3515vgo6PGBut7ss88+gW4jaCvuchTGZ0j7AnXoiUolF5WVerKuswPnhUU+n3zySaev98QTT5hfBxxwQKd+n+NlxD24omNmlIbf8Yyw3wNAudP5xmmnnebruhqTCrJghnz//fed+n0VNwZZqV2rhIdhp5128v0doLFAja1W6n721ltvDdRdGjn//ve/A3XV3XnnnUN5KnffffeSjW8GDfgrNO+HAj8q5vHbrSYuY13lQN8jq666aqDFcBB/77//vh1zzDE2b968QLejLkZ+OvSwP433PAtzYdXB73HlXXfdRScULHLcoDCBX34D2X7PXRnXLl+aT/WzuIdovj1IGFzHS+pGhtIu+KlxDXVbCdvLL7/sFnkIQvUMQTp7al+oUHE1YX+M9lROpSEAIDbUdloTNR1dBeu9994LdH8qxAqj/bsmVoIWwKjTQXu0eojfIl2tZqEDdk6c/AsS+ND7LMjEYXNBVlcvZXBFK7gHWeUyron/oIXfel6Cdm6S7bbbzrVQjnpfFAVvhcTOrOpejRRa8zPh17xgRwG4oAM1lbgf0f4xyACU34HRlgwfPjzy/bxaC0+ePNmCdosKGiDSiqrrrbeeVQsdiwVdoUchtKDBaZSGiqn222+/onVdUaHl2LFjfb/PdGzRGRwvI0qNjY2BbyOM83+ULjQUxnsAKHfqVB2kqMLPCqTNF98p1rGBHueWW25pYS2W1NlODoVKvQhMlF3Fxo0bZ4cffnhZhHPiSl2Yx48f7/v6e+65ZyiLHUmQcNrnn3/e6a5KYRUanXLKKYFuY9iwYe55LNexrnLS0fm7lnz99dehbgvCpy7O6o6sjmlB6Db8LNTF/jTe8yzMhVUPv8eVOo5QlyV1MQta6Ivyt8MOOwQ6/wx67vrDDz906vcZ1y5fqoMKEmJfbbXVijpOgnBpYVd1aYqC37k1zzLLLBNKoObQQw+1asL+GO2pbfc3AADohI033th+/vOfd/j333nnnUDPr05edt11VwtrtUOteB1kpZmOPJ7a2lpbeuml3YrwnaWW3r///e/thhtucMWwG2ywgVslXSvvDBkyhJVnO9gePcjgShjBBI/flbqDPo4gVHBw/vnnWyVO5gQNhKkQPwwqHtd+7e677/Z9G0H3rX6pANtvF5Fqoo4eYaxWrWKn119/3ff11ZVEE4nF7sgQ9X4kyP5RHdiCDqQXWmGFFXxfV8FeFXa2VyAapLuMqLBGkw9h2GWXXeztt9+2aqD9rCZ6gw5ShrEvQOn25ddff72l0+lOX/df//qXnXrqqR2eiHnyySd93Y8XxutskT/Hy4iS3o86rw0iyKr/CCboayd897Xd1QLVIWhh9qBBgwJdv7PjH0GODbTgQljF/N5Ymt8AeanG0or12mp8QEWGGiPWap7rrLOOrbzyyu68NEhQqlroHC9I15ogC1e01MFVnxu/5wAKMK200kpWTFpgRaH5MMa6Ohv0bx761zgvnaCjC4J3duVzFJcWuNEc7fTp0wPdzsEHH2y/+c1vfF2X/Wm851mYC6seQY4rP/74Yzv66KPdInvqpqbjSnXrUj2C/i1ohzSUj1Kfu2oOszMY1y7foJ3fzo2lGidBeDR+8dOf/jSypzToPLrOdcP43lMwR7VmM2fOtGrA/hjtIbgCAAiNJsUUqOjMit1Bi6uVnA96ElLoJz/5SeTBFVHYxE9wxaNCVnVUKOyqoOddq+9oMlIXDR7pTxXghvkclTNN9gXp8BNkVcfWCqT9mjJlilv5IYwJwc444ogjAm13XAXdF/Xu3dsF98Ky7bbbBgquaHVFnfSGGbRqj+7rqKOOKtr9lbOwApfbb7+9CzX4XbVa+8SPPvrINtxwQ6uk/UiQ4IQmYMIU5HHqdf3www/b/e7RaxiEuqT079/fwqCuDldddZVVg6ArGeu4TUUAKF8Koiv0/r///c/XcZwKCzt67PDEE09YkO6UncHxMqLWvXv3wOEHOnaUTn19fSgdE4BqprG6tddeuySrJ3s6sx9WgXKQMcw4nWNNmDDBhc+DdpuMUtD3hle0/Le//c1dPFqwwhsz1kWhBoVaNLau72YEHxsMc9xYRTEaZ1O3er/jIgral+NYl8JuKogNEo54//33Ky64oqLNiRMnuos+49ovf/fdd278Vxf9XMEr7xIlVqOOr2+//daNuwYNF+2111524YUX+r4++9N4z7MwF1Y9VIsQlPYnjz32mLsUntMrTO7VIXjHlgpbaZFQVA511Ntmm20C3UbQucDOnLsyrl2+1M0iaIftYo6TIFxBO3e2N4+gupmgdTth0Ht8q622cgvbVTr2x+gIgisAgNBWAbvzzjs7Xfj4xRdfBLrfNdZYI9D1w749td7WhFJ7z8OIESNCX0lTE68qgtPl5ZdfXqyoXgNU6tCiAuVNNtmk6Cvsx4Ha2M+fP9/39R944AF3iYtPPvmkqMEVnUztt99+Rbu/Ygq6L9JEf5grDIWxb9NjCjts1d6EFqsnt09BwqDtigv37dqvv/rqq75vQ8GIYgZXot6PaAV2TeD79cILL4R+bBF0P9/e5/izzz4LdB9hrt6nSSoVPQX5ri0XQZ937QfCCgyhdPbff39fwRXRKsIdCa5MmjTJFVn6LfpSQWJncLyMqIURWpgxY0Yo2wJ/q5gHRYE0ql0YQY6gY3qdKWrWqtxBXHPNNe4SB/PmzXPHOhrHjistQKSCm7C/61TUrmJ+XQopxKPCQ513anxBXUO0mnY1Cvpe1yqxlfJY/Iz1hPX49Z7ccsst7dFHHw001hV0dfBS01yPFjvQHJLG/T744IPAXV/jFGRGNIEidVrR/GDQwtErrrgi0FwH+9N4z7MwF1Y9dEypuY6gC1+19D3ghSlbmv/S+Y6OK1WLoFA23VnKl94/WjyvXM5dGdcuX2HUMxTzvYZw9zNaYDHK4x6/nUyjmEfX462G4Ar7Y3QEwRUAQGDDhg2zm2++2a2E1VlBB1HDLi5VEaEmR4JMAugxtVeIuPPOO7sVyYs12aBAzbhx49zFm0xSobK2Q6uhVUvhZND3WxwP+ItJk4bF7vBSLHHbF+kzOWDAAPvhhx8CvT+KGVwp9kqO5UpFKGHfXpDgStCJqrjtR7SyYNABqHLbzwddRTHMoiRNQqlIPkh3s2r53gizSxdKR90aBw4c6KuQ++mnn7ZRo0a1O/lXzG4rwvEyotanTx/3fR2EVpdGaQTpuuCplvEHIMqVj4MGwLLZbGzHnqIW9+BKbW2t7bTTTvbQQw8V5f40Nq3VR3VRsFrU0VvHuXvssYcLQldLsWElvdeLfUyvVdaDFjWGOXZW7LGuMGlRtPvvv991TAo63hMVVqOOH3Xd+cUvfhF4kRWN2/7hD39w30VBsD+N9zwLc2HVZbfddgs9uNIWb4FNjXt65//qWL377ru7kGvQ/QvK79w16AI2nTl3ZVy7fIURDCjmOAnK57gn6DyCvsfCHMsOa3HTuGN/jI7gqBAA4JsCHmo7fdppp/memAh6oKiVO8KkExoV1AZZRVSPqb3VEzUBqJWLnnnmGSsFrUivQmddtHrSLrvsYkcffXSoafE4ClogVYlFQ51RzK4MxRa3fZGokCJIcKWYBX1qF13p+4+wRBG4LKfCz6j3I9W4nw9ayKAuKWHS7VVDcCXoe419ZmXQZOs+++xjt912m6/CEnV52mGHHdr8Pb+rL6krl47xO6sa96MoruWWW67FVTk7Y/LkyaFtDzrfDS6M90ApaOxj3333Lcl9A4VWWWWVwE+IFqQplkr7Li2Hx3PooYcWLbjS2nPkdZ1eccUV7fDDD3eB6DCDCXFUDu+NjtI4gQqvihU6CnuRlnIb6worDHLHHXfYrbfeGvsutqxGHS/qqHXMMce4TkNBF1i58cYbQ9nXsz+N9zwLc2HV5cADD7SbbrqpZKFDBTIff/xxd1l66aXtkEMOcce6vXr1Ksn2oLLPXRnXLl/l9l5D+dQNxG0OXR1vqwH7Y3QEwRUAgC9abe3cc8+1oUOHBh5UDSKKgQ21kQwSXOnoYzrllFNcq3eFSEpJ96+VnJ988kmXaD/nnHNcoVslUueZSlLsx9NeIKucxXVfVMrH1Blq951MJot2f+Us7JBT0NsL8n0Xx/1INe7ngz7moPua5qpl0inoPrZfv36hbQtKa//99/cVXBGtat1WcOWtt96yL7/80tdtayVDP6uMVeN+tJIMHz7c7r33Xqv0Y6FirhaKRb/7Jk2aFPgpiSL0D5STMMbcirlS8Zw5c6ySlMPj0YIXGqN97LHHSr0prhPLxRdf7Arqzz//fNeJpVKVw3ujM+P9Cj+oALoYwu5iVG5jXWF8zk4++WSOcdFp9fX1dvzxx9vbb78d6NkbNmyY3XLLLYFXKvewP433PAtzYdVFq8Qr3KZgWqkpNHXttdfa3XffbWeeeSYLS1TJuWsxwwSVNg5caY+nrfGNMOYp6ehUfrR/CKOzU1uYQy+NStt/VdrjiYtEqTcAAFA+NFi23Xbb2T333OPalQcNrXgDq3EqtgzjNjv6mFZeeWU766yzLC4ymYw98sgjbtVoTZZUorivVlbsz0+pO0XESRz3RUGLv4v5/qjk90bYwn6vBL29efPmWTFF/V4p9n4xah15PEFXSwv7PVmswphyP6bo27dvaNuC0tIq1AoL+PH888+3OeCpcLlfWhXbD46XUQ6Fjer60djYGMr2oOPGjh3rxg2CqpaV5YAog97FXDii0o4NyuXxaGGhOO0vp0yZYscdd5z98Y9/tEpVLu+NOI6PVPtYVxAffPCBHXTQQYRW4Cug9qtf/crGjRsXuGPS7bffHupCNOxP4z3PwlxY9dEx3Prrr29xoS4sOtbVJZ1Ol3pz0IYwvhsSieKVpfL9U57COpdggc3ynFuLurNr3ObQo6gpiiP2x+gIgisAgHYPnLbaaiu74IILbMyYMXbzzTfbiBEjQnvWgnYb6dq1q4WtW7duga7fmQKaww8/3I4++miLk8mTJ9thhx0WuF10HFVacVMxJyBramoqenX4OO6Lgt5mMd/vlfzeCFvcBjgqbT9Sjfv5oI85rNUUo7q9Sv3eqJaATzV1XfE7aP7MM8+0+LNUKmVPPfWUr9vVKlVaMdWPatyPorjWXHPNwLehz87rr78eyvag45599tlQnq5K7uQJFOucsJjFPxwblIaC7rfeeqsts8wyFieaG7jhhhusEvFe96+ax7qC+Oabb+zII4+0GTNmlHpTUGZU6H366afbCy+8EOh2VlhhBbvzzjttiSWWsDCxP433PAtzYdVHRcE33XSTC6rFyaOPPmrnnntuqTcDEc9hcO5a+ce0QYU1V6Y56HITxgJBYYQzSqUYxz1Bj0uD1g5W6xw65wPoCIIrAFDFlDrXgZEmwoYMGWIbbLCB7bTTTi5IccUVV7juG+PHj3er7RxyyCG25JJLhr4NQRPUUZywBV2Rq7PF5mqHe/HFF0dS+O7X999/b6eddppls1mrJFEn9outmK+PBg2KObhUbu+NKFYNKPa+qNTtoqtF2K9L0AGToBNVcduPVON+Pmj767CPpcp1gLTY77U5c+aEti0ovZ133tn69Onj67r//Oc/W/z3l156ya1AWMwgTbXuR1Fc6667bijHA+pYhOJRd6inn3468O2oe0D//v1D2SagXIVRTFHMggyODUpnpZVWsoceesg23nhji5M//elP7li10lTaez2sIqhSPHflNNYV5Dzl1FNPtenTp5d6U1Bm9N757W9/2+oiGB213HLL2V133WUDBw60sLE/jfc8C3Nh1Uk1Hvfdd5+rA4mTxx57zB5++OFSbwZaEcb4XTFrCyrt+6daxrXDeo+UYx1LWOdsGrctR2F2/GtNXV1drObQqyWQxv4YHRGswgUAECuDBg2y5557zsqJJiGCHJzNnTs31O0J4zb9TKyoHfzmm29u11xzjRtwjsOJ6GuvvWZPPPGE7bXXXlYpqiXBXq4njqUUdEK0UvZFflX6+yNMYYecggacijlwUIz3STHf93GhxxykKCPoe6gY+8NKPIZlRdXKez/ssccedv/993f6ulooQJ0Ol1566Q4FWjoSkAxy/M7xMopxPLDKKqvYxx9/HOh2nnzySbcIRNAAJzpGhdNhHDNsuOGGPOVAmeHYoLR0jHjvvfe6BabU6SQOHbI1bn3JJZe47oDlWBzU1nu9WhZiCFvYxTdBx87KoUjmH//4h7355pul3gyUoYsuusgef/zxQLehsMpf/vIXF16JAvvTyh4/r/a5sHKmRXd0PPmf//zHfv/739tnn31mcaDaiF133ZV5RgTGuSvKTViB+3Kdly3GcU/QBUWZQ/eH/TE6onJGFAEAVXkwGkV6POgK2H5XxFHXm+uuu85N+h122GGxWIVU3XYqCcX1/lV6QVjQ90YUK+eXal/kt4MXSjN4VE6TOsXYj1Tjfr5nz54V9Z4sF0H3sayqWnkOOOAA36tqKSzefDDc74IEWjnRb/eXat2PovhGjBgR+DamTp1q//3vf0PZHrT/nXXLLbeE8jRtt912PN1AmeHYoPQUDtGx5rPPPmtXXXWVDR8+vKhdd1qiQkdtTyUJem5dzeI2rlAOBcx//vOfQ7mdZZZZxg455BC79tprXRjmlVdesbfeess++uijNi8jR44M5f5RXFdffbU98MADgW6jX79+duedd9qKK65oUWF/Gu95FubCsOOOO7paBHXR22abbUo+/6sFnui6gjBw7opy09jYGMrtfPvtt1aOivH9E7c59LCDMHHF/hgdQXAFAFBSQVf0+eKLLyxMs2bNsmnTpgW6jWWXXTbQ9VdeeWUbNWqUjRkzxu6++2478sgjbejQoSUpDNckxocffmiVQhM5QDnsi8K4Td7v8RR2yCno7VXaRGLQ7+ByNGDAgEDXnzJlSmjbEsXtVep77d133w1tWxAPa6+9tjtm96N5dxUVAfodwPYboPFw/IBi2HnnnUO5nZtvvjkW3Uor3eWXX24zZ84MZaW1rbfeOpRtAlA8HBvEh7pI7LPPPq4Dy+jRo92q+yo8LNXiR347BMZVNY4nhIWxrs53ug+6yr3CB5dddplb8OCCCy6w3XbbzdZcc023P2B128qkAvM77rgjcAGZFqtbffXVLUrsT+ONuTB4wegddtjBbr31VnvxxRddOFrdpEt17F9px5UoDc5dUa2dK99///1QbqcSqdNgEF9//bWFqVrm0NkfoyMqe+lsAEDsBR3AnDhxYmjbEtbthdVeWwnzTTfd1F28NPcHH3zggiQff/yxK2r/8ssvXYI+nU5bVBSg0aRHJRg8eHCg61988cV20EEHhbY9iI+47Yv0uVaQLohBgwaFtj0IT9ghJ30PBLHUUktZpQ2EKOjp93vxuOOOs9NOOy307YrS0ksvbRMmTPB9fR1ThOmTTz6xaqBjinHjxvm+/uuvvx7q9iAe9ttvP3vvvfd8hcV1LOEVj/idrF1hhRUCd7LgeBnFsPHGG7vg5Q8//BDodnR+rJWlVcSLaGjV07AKSLSSK4WUQPkJOragItstt9wytO3BwvPAgw8+2F28sQYVq+i4UgXxGivQJYoOwZ6XXnrJnXtXShdevdcVKPBbDKOiz2oV9lhX0NuL+1jXCy+8EPjzf8899wTqmDF79uxA24Diuuuuu+z6668P3IlIXRTXWWcdixr703hjLgzNLbHEEm5cxRtb+eabb9z4po4rJ02a5L6Xv/rqK9cZJSo6jtUYUdBFulDdGNdGuUmlUi68EqRjpLq2BA3FVzKdOwWt2dG5U+/evUPZnrDn5OOK/TE6guAKAKCkVllllcCFKmEK2l1EAyp9+vSxKGhVfBX46FKoqanJJb01aKTBI52YaEBJgzxhpPQraUVwDcjqxM/v8xJ2OAGVsy9SobY+i3V1daFsT9B9m4oGVLyK+Al7PxJ0gCPogE3c6DM4ZMgQ+/zzz6tmPx/0sx7mIJkmtaql+GHVVVcNdH0ds02ePDnw4B3iZa+99rKrr77a17GmCsPPOOMM+/HHH+2VV17xdf/777+/BcXxMoq1sqdWhVbBXVBXXnml6+JRqtXmK9l//vMfu/DCC0O7vcMOOyy02wJQPOoMHYTOsQiuFOe8UJddd911kX+fPn26O+9QiEXnyXo9NNYbxiqf6hD46aefRr5yfzm816dOneqeaxV+ViPGujrHb0DKc8kllwQKrci0adMCXR/F89BDD9kVV1wReLz0xhtvXGxuMSrsT+ONuTB0ZGxQF3VkKaRAtFeLoIvmRXVcGVbBtG5r22235QWCb4xroxS0GHHQ4/IgCyMrFK8aFbRMNQM1NTWBurbrfHejjTYK5SkuxzoEP9gfoyMSHfotAAAisu666wa6vjcwEpZnn3020PWLsVpRS4POmpjUJPDPfvYzGzVqlD3wwAP26quvusHo9ddfP9Dt+y3+jeuJ49ChQ31f388K2qiOfdH8+fPt5Zdfjs2+SJMPPXr0CG17EG5AMsjgSHNa9aqURUiV9nkux3bKa6yxRqDrv/HGG66rWxxWDS0nQY+vJIyCbcSLVl3aeeedfV33ySefdN8PTz31lFtpy89x7siRI33dd/Pb4XgZxXD44Ye7AEtQKhI99dRTfX1uwpbJZKxS3H///aE+rxorKVahHIBwabEDdbb0i7G00lKQQvvg3Xff3U488US77rrr7LnnnrP/+7//cx1Hg44dhd1po5TWW2+9QNcvx/GEsITd3afSx7q0er1fCqyoi10QOu+spDmfSvbEE0/YBRdcEHiBq2uvvda22morKxb2p/HGXBj86tWrl6211lq2yy67uOPI3/3ud/b000/b888/b6effrr169cv0JNbSceVKA3GtVEKXbp0CbwgYBBhdcqu5O+uoAsIjhkzJpRt0XlYtXRqZX+MjiC4AgAoKU2caeA0CBV5hUEtaMeNG1fyIsawqLPIjjvu6EIse++9t+/b+e6773xdL0gRUpSrAgR5jd5++23XDhKVZ/nllw+8SnNY+yK9/7W6caXsi7AoraT/1ltvhfa99c477wS6jTXXXNMqTZD3v77zFOQoJ0EKzEWdIUaPHh3KtjzzzDNWLYYNGxZ4QPrhhx+2mTNnWlwFPUav1lWeDjjgAF/XUwfF8ePHu8IUP1S8NHDgQAsDx8so1mpnOl8Nw9ixY11nkDDDwZ2hFUePOuoo9zkudwoCqfvTRRddFOp+/Pjjjw/ttirZdttt50LJfi6V0tEmaKCtWo8/ohbk2ECFbA0NDaFuD4JTYf9pp53mgopBwit+xo3j+jkPOk+hws1qpfCuPuth0OsbtDAozmNdWvxIx1t+hREE1qI6pey4wlhDxxe2OvvsswOF47W6tLq1hHXe1VHsT+ONuTBEsar6sccea4899ligsUm/9QilwvdZPDGujWLr2bNnoOsHWaRZY9L/+9//At1/NVh77bUDXf/f//53KNvx5ptvlt13XRDsj9EegisAgJInnIO21VPRXxgrhf/1r3+1dDod6DZ+8pOfWNxoMvC8886zrl27+rr+vHnzfF0vSDFnlEWcQVYk0yQBqxZUrqCfX520hhFsevzxxwN/BoKuvIdoaWXTsEICQSYvtZ9ebbXVrNIEff/rM1hOVl11VRswYEDgY6kwVlhVt7dqoeOqLbbYItBt6Bjr0ksvtbgKGsyZNWuWVaNNNtnErYLrx0033eSC0sUMzLSE42UUiwobVEwVBn2XnX/++YHP6TtDhdi33HKL7bnnnmW/WpqKKO+44w7bddddfQfoWrPZZpvZDjvsEOptonJx/BFPQY4N1IUhaFdZREerZgcJvvkZN47r5zzoPIWCK9Uc0gprrEsdrYO+xkEX+IhS0HmsJZdcMjavlV9B9wFxXgAkLC+99FIo3Q/VrSXIonZ+sT+NP+bCEIXlllvOfvWrX/m+flhd4Yslrse01Y5xbRRb0OPzIPOql19+uTU2Nga6/2qw6aabBrr+p59+aq+//nrg7QhjLr6csD9GewiuAABKbqeddgp0/alTp9qtt94a6DamTJlid955Z+AVY+O6mlfv3r1dYasffgt/gqzW9/HHH1uUxYRBCnxVzFOqFck0+XnXXXexckJEgq48pmIvtcYOQgUdf/zjHwPdhj57W265ZaDbQLT+8Y9/BJ5k1b75wQcfDHQbKsjwG2qMM7X8VTcMvx599FGbNGmSlYJeV93/I4880uHrqOBXBaFBC0NeeOGFQLdxzTXXBJ5QLze77LJL4NtQIPahhx4KZXta8vnnn/u+bpBjOZk4caJVq/3228/X9V555RVf11t66aVt6623trBwvIxi0fe1389LS7Q/PfLII11XuihpJXB9V++88872hz/8wZ0HlCMdd2ilt4svvtgVLl199dWBVgBvSW1trY0aNSrU20RlC7pSZTUff0Rp++23t7q6Ot/Xv/7660tWTDF79mx3/++++25J7r8cbLDBBr6v6+ccMM6f8yDneCo+VKC1VD744AP3nV4q6uQa5PzTc++99wbuJKBxobgK2jEoaFGvxiPvu+8+K6WgYw1Rzh3FwWuvvWYnnnhi4O/Ns846y376059aqbA/jTfmwhDH48piLkQSBsbO44lxbRTbMsssE+j6zz33nKt36yx1udJ10b6gixCKxs2Ddr0st4Uzg2J/jPYQXAEAlNxuu+0WeFUKhU7Gjh3rOwzwm9/8JvCKaHvttVeHf3fChAn23nvvWTH5LSbt3r27r+v179/f/FLXirfeesui6kCjFXH9mjFjhl155ZVWTCqEUkcgDSartXvYxUTIUdgjaNeCf/3rX7678qhzhlaJDlpopzBgt27dAt0GoqX9yI033hjoNv7+978HLtbYfPPNrVLts88+gYoItCJhMSdKdJ8asNp9993tnHPOsa+//rroAYrLLrvMd6BKKyirA1C10fdynz59At+OiosUWAr7PXX77bfbz372M9+3EeRYzutEls1mrRrtu+++rli7WEaOHGnJZDK02+N4GcV0+umnh7IvLVylTmMMCrGE/V2uY7i//OUvbv//29/+1r755huLOxW8qWhbxxY6x9YK2yrgPuGEE9xqcwcffLArXtRji4JWW/W7iAaq0xJLLBHo+up4gPD17dvXtt12W9/XVzH7zTffbMWk/ZruU6GbP/3pT747SpeCxpWKGYoMsgCBn4K9OH/O1XksyDzFbbfdZp988okVk+YYTj75ZHdO8Pzzz1up6Bw0aDGPOtiNGTOmose6gi4go4KnIK666iq3cFIpab7J75yTF5Kqr6+3SqR5w+OOOy7wd4CCL0cddZSVEvvTeGMurHrouCmq8/2wjyuDfDeUAmPn8cS4NoptpZVWCnR9HfedccYZndp/ak6WhYI6boUVVgi8ALXG1f0uAKA6RNUdqBaomrA/RnsIrgAAYtE+cY899ghcjPHLX/7S3njjjU4fJJ5yyik2fvz4QPevCa1DDjmkUyugqaDt5z//uTuxiHqFcnUI+eyzz4o68BJ0dYEwCvhbc9hhhwUqJlS3BK2sGzUFeK699lq3+u0ll1xi3333XeT3Wc30OQ5jFTIVnf/nP//p1HVUVHfRRRfZk08+Gfj+tV9B/N1///2+O1yoG0jQfZC6dKios1LpO1bFVX6NGzfOhVqjHkRSEFGFLSqm0v35/a5Wp4V+/foF2hYVkx1//PGdnvxXe2QVHVcjrRSsgt8wCnz03aECkqDFF3rPakJSgWp1AQsSzFZBWZCiGrXP1nFMtQ3GioKwOn4rBu3P999//9Bvl+Pl8qTvrzXWWKPoF60w7pfON8OeaFMQ87zzznMdUe65555AHTNV4PHUU0+5glAV92gRg7gFVrTCXmuvzTrrrGMbb7yxKzg/6KCD3PiHCri1Il+Q160j9Hyp8A4o5liSip4feOABnvQIBB1rUIgkaNfQjp4vawxN+z11tA3a7bQU1M1Sx5I6llaX8Kh1djw9aAglzp9zHZfsvffegeYoVCz+5ZdfWpQ0jqjv8sMPP9yNf2ghiTgsGvDf//7XHn74YV/X1TzAhRdeGHgbtCBInGmsKkgHK3XL++qrr3xdV69NZzr8RkldQ/3Sfl2FX6Xq5BUVdZLR/iNosEjf1zp3KTX2p/HGXFj1uPvuu91x8aWXXlqUcG2Q48qgQZBiY+w8vhjXRjGtvvrqgW9DCzTr+K29oKHmDzWPqIWCoq4vqzSdWYS6rQUg/dQAaQ49qkWb4479MdpSvOUfAQBowy9+8QsXBgiyIqrapOvA54gjjrCTTjqp3RXfVNSjYpYwWthrRTM/nRpeeeUVdxk4cKA7WNYqRCosiWLC0+9A/oorrliS1QU++ugjt4K8LhtuuKEtv/zyrkC0vVXvll122XZf+0GDBrlJLL3n/Lrlllvsxx9/dIWmvXr1srBoVQWtjqeV1zURW43FnqWkAJo6OGl/4pcGCrQP0sTxWWed1e4kvlbKU7Heu+++a2EUh6211lqBbwfR0/vk17/+tQstqJiwo7Ra9tFHHx14layNNtrIBg8ebJVK3xfqNHHTTTf5vo0nnnjCTRarG8ZSSy0V2rYppPDyyy+7/byKOvT3oPTduN9++9kdd9wReGJJ+y5191pvvfXa/F0ds2l/ecMNNwTuWlfOdNyprmhhrOCs51Pvu2OOOcYOOOCATq1ePHnyZFdYrSKUMI5tvUCEViIK0t1Jx0sqqFLxuFY0UnGKuoK11R1E4WLdb7lTmEQB9aiNGDHChgwZEvrtcryMYlKBqDqlhF1Ip6I+TSgpbKJzSh3/DBs2zH1m9N2u/ay+Q3WurIk/nd9p8QBd7/3333crmOtPzsn87UMUoNR3CdAZ+mxqjCVI0aYKnxXo2mGHHWy11VZzC+Zo9V6t9NcaHZ/ofYvWaR+qc9fXXnvN19Okonq9Ngr/aSX4oN23CymIp+IBfY8oWF8JdM7/5z//2W699VbXQUKLPmnBgzC7lHmF0kECRX7Gf+P+Odc8hW7bbyGQjiUOPfRQu/zyy904XZi0OIA68mg84fvvv7c4UqBAr6/mOToTRDj22GN9BzI8en032WQTizOdC2s7/Z63ayxG73+da3dmYS6tDqyi5bjQviPI2IW6Fuv7SO+ztdde25Zbbjm3D2jvOVl55ZVjeXz6xRdf2JFHHhl4vHeLLbawAw880O0roqLglebqOoL9abwxF1Y9NG597733ussGG2zg6hF22mknX3UNbdGxiY5d/fJbj1AqjJ3HF+PaKCbNtWtxBp0HBqH5Yp33a65X57l6H+u8SvtWzf/p5+qkPXXq1NC2vZqonk9dyIMsHqhzMdV26LhdQaP2Fv5TYFRd299++22rVuyP0RaCKwCAWNAEkwr0gq68pwklFW1q1bVtttnGTQ7pREGDLwoE6EBexXdaiVoTc2EVxwZdwUjbpe3WRQdv2m5NSmqiRRNvfulERsWnKmT0q73C1dYEbbcos2fPdiuBdWalNhWBa+X59ijZrkntIIWmf//7323MmDGu249OJP0GWDQpp0ItBVYUVgnaCh4WaEUfdRz4/e9/H/hp1CSyCpA1YaSVMjWBppCa9lP6bGoVUL0H33nnndAmPhWUQfnQ/kerY2oST2EnFVG0VeSjfaFWUgm68p4o6FnpVPyvYpMgq6L/73//c0FHrRauQIffVb/UMUuB2dGjR7t9vb7foghQaFX5oEEYTW6ri8hmm23mCgDWXXddV1ik4x2tgKrnU92C1CEqaFFJJdAxplY30mczDDomVIGTin01kbjpppu6gg511FEQUvt6Dazq97T6srr4acVVv9162qMwZJDgiuiYuzPH3XpOX3rpJSt3Oh5VUCfqjnk6h4oKx8soJnXcVEhE+7WwaVJJXVaDdlpFx+ic56677iq71VIRHxpP8huO8GhSuDMTwxr7euihhwLdZzU499xzXTjX78JDOq9VGOPf//63G0tTwZzfDn8qANBYmkLS+jOMBQHiSOFJjRXqomJhjRVrzFjna/qs+O0orddC4+NaqMHv2KjGMNTdq9I+5ypsV/BE32V+6RxAnRM0XqzuB34XqtK5n873tPCVxhA1lhh32j+ow5vGzNVZtr1uuHpc6kIdRgGWFjCJYyihpfdikNCG9gcah9DYQXsLJum9qEXNFHiKE401aMwtCBXwaS6oMzQOH6SzbFT03RjGZ0BjKVF32Nb8pb57O4L9abwxF1addFyhizoUrr/++m7+UseVOlYJEizXsYoW6AsyDuq3HqGUGDuPL8a1UUzaj2o+OigdD+q4UBeEf9yj8SwtRBj0fPf2229351eaQ9cCIzo+1ni4N3erOQ6FjHROXKljVZ3B/hitIbgCAIgNTWhookKrnAalCTcdDOoSNYVWwlyVREWIf/vb39xFdKCrVWFVtKjVRlT8rvvTpIRWkfIGknQgrBUGdX11cNAgkQpkgx4Mb7XVVr6upwEvTajG9WBchYRKxCvYE4QG4bSSnApWFZbSwJpeLwWmevfu7cIsmmTW66NCZa22oOtogl1dZSZMmBB4BQaESxPKWrUtjJXJ9P5XkbouUdPkrN+CAZSOBjg0yXr//fe7wQ1NFGj/pH28AioKOakLxjPPPBPaapr6LlGBUKXTKuoqrFIoKAh9typE8Mc//tEFSxUm8FZr1z5e+3pR1xG9ZtrHa7+ufYj28yrA1cqJUdP7Rivl3X333YFvS99bmuyuhPBAMSiApqCiBiPDou8PhZ10KSUVxgXpUFfNFDLSKk5RTjIo0BTl/pzjZRSTil816aNj2rA6R6H4tF/SghwdXYUZaEmQrh6Illa1135agfkgVHx/xhlnuIL2bbfd1hXKDR061AXmdX6l0LzOlbWwi87HdH6l7qOFY2lhjB+XG697py7ed6eK5TQWpHFjnaOqWELjxurMojFjjc2qs5ieS52rfvnll26MQau1Bj1P1bmC3wLwuH/OFQpQsCfomO2//vUvd9FrpPEejSVoAS+9973XSGMJXoGL7k/F+Hqf66Lub3EdW2+POiDpsSvQrzFzdUnXe1PzNnqsemyau9F7Mgx6Pg866CArBxpbCnqereCAzgX32Wcf9xzrPabjML1ftOiI5of0OxpPjOMiWXHvjFNsChNWKvan8cZcWPXS+L+OCXVRR3UdM66++uru2FLzR+qGrXFBFfrq+8U7rtTCfPpe0XeNFrVSCEbzn/reCUJBt3LsgMnYeXwxro1i2nnnnUMJriBa6vKphWeDLGzsUc2G5uPDmJOvdOyP0RqCKwCA2FCR7tVXX21HH3102QzUKtShFc6jpCCKLqWw6qqr+l7hRKEarRKuJHlc6bUbO3Zs4NW9ih2WQrQ0AHvttde6Fcw1eVwOtFKlij5QvubOnetW5yjGCoinnnqqJRIJqwY77rijK6y67777At+WJv/1fRHGd0aUk8EqDKnGIq5S0irH1113nWvhrcK6SqICI+0vNJmJztMKTrfccktk5zZ77rlnoNUQO4LjZRSTihn/8pe/uCBmkI5pKA0Vt9x6662uwAUIYrvttmN1yRg788wz7fXXX3dF50HNnDnTLRyiC/x349ClFNSVtFI/51qkQmODWqQgjOCIF0SpNhpX1UJlukRNHbT9dkMvNi1co4CexgKD0PiDgoRBw4SloPCagopRdCRGvLA/jTfmwuDR8Y6O78M4xi/2cWUpMXYeb4xro1i0SIEWkQijgx6iDVDovPEPf/gDT3ORsT9GS6qjWgkAUDa02lTQldGLZfDgwS5oUw7t5/065phjAl1fK37FmV47vYZalRAopFXqLrzwwrJ4UrTakTpB+F3lEtXXrniXXXaxanL22WfbRhttZNVAk/7nnXdeqTejKmll+WuuucatPFdJtOr15ptvXurNKFta9XrEiBGR3b5CtlHjeBnFpg6jCpxqpU+Uj+HDh9tDDz1EaAWh0AIqCkIhvgWO119/fajdp1F+9BnVQhGV/DnfcMMN7be//W2pNwMdXKVdIaNyodDKwQcfXPIOoaX+Ltltt91Kug0oHvan8cZcGOIwx3nggQdaOWLsPN4Y10Yxj20PPfTQkj3h5RLgj4MjjzzSdRNGcbE/RksIrgAAYkfBFa2OHmdKzGslWLXIrVTrr7++7b333oFuQ8XRcW/t26dPH9fCUQWnQPMVfrSSaJz16NHDrWpM+Kq8OjOUcuDqkksusWocMNTnZN1117VqsOuuu5ZsokeTTOoAUa20wppW6inl5zwK6oaI+IVLtE9TcUExcLyMYtM55AMPPOAWtkC8KbCpjm933nmn9e3bt9SbgwrC8Uf8F/PRuKi6Z6M6KdARNLRfDp9zdYE7/fTTS70ZsVfKc2Dd95VXXll2i0gce+yxJduH6lwyDovaqGis1AEaFA/703hjLgyldMopp5T1eEI5HNNWM8a1USyHHXaYC7MVm0IY2o+i4zUDmsdVjU2p3ifViv0xmiO4AgCIJa0WHteDNhXR3HXXXRUddFDhqQ7Yg3aT0cRROaxMp7aQ99xzT9GK/1BeA45nnXVWLDsr6XN62223uVUqUT5U8NC9e/eS3LdCK1r9vxoptHPHHXdE2vkgbsdRxe4yo+/86667rupXWt9pp53sxhtvdCuoVlKnpu23377Um1HW7wl9Z5djt5VCHC+jFN/dt9xyiwtFVFogsFIMGzbMHnnkEbf4SLkVi6I8iueGDh1a6s1AG9QZSwvBqFMWqouKzRXar5bPuQIG55xzjiUSTKm35oQTTrAll1zSSuHXv/51WY6N6hyxFN2+tSCbxixUsFVqWogp7gvYIVzsT+ONuTCUws4772w//elPy/rJZ+w8/hjXRjFoTrDYdVGqV9PCjZU0H1ms85Brrrmm6CF6LdKlsYVqxv4YhRhlAwDEkorER40a5Qov41SkokmQhx56yFZddVWr5KTz7bffHlqnlB122KEsJiCWXXZZe/DBB90gGVDoqKOOsuuvv75kKy+0ZMUVV3Tv14033rjUm4JOUlHG1VdfXfSCh1/+8pe22267WTXT95tWAy+H76SgVIBw8803u2KyYlCxqgKvm266aVHuL+623XZb+9vf/lZRIR4F3+LeRS/On8ewOxHpmKQU+3SOl1FsGgtQKELn4KutthovQIwm966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",
       "text/plain": [
        "<Figure size 1600x700 with 1 Axes>"
       ]
@@ -1689,7 +1671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6abe226a",
+   "id": "b638c435",
    "metadata": {},
    "source": [
     "Two of the three statistics sit inside their predictive 90% bands; the median\n",
@@ -1706,19 +1688,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5225bc34",
+   "id": "af420919",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
     "\n",
     "A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo\n",
     "estimate — and eyeballing it does not scale to overnight runs. The stopping\n",
-    "rule proposed for PyMC in [pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)\n",
+    "rule originally proposed for PyMC in\n",
+    "[pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)\n",
     "standardizes each loss *improvement* by a robust running scale estimate and\n",
     "feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming\n",
     "itself only after `min_steps` — which on streaming fits should be at least\n",
     "one full pass, since stopping before the model has seen every row once is\n",
-    "never evidence of convergence.\n",
+    "never evidence of convergence. The trap this section demonstrates was later\n",
+    "confirmed on real ADVI traces, and the revised implementation under review\n",
+    "for `pymc-extras` is built on the windowed idea shown here, with horizons\n",
+    "that grow over the run and a second, practical-negligibility yardstick.\n",
     "\n",
     ":::{admonition} The minibatch trap\n",
     ":class: warning\n",
@@ -1738,14 +1724,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "80358853",
+   "execution_count": 16,
+   "id": "a7589a33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:32.951370Z",
-     "iopub.status.busy": "2026-08-01T16:47:32.951283Z",
-     "iopub.status.idle": "2026-08-01T16:47:32.953823Z",
-     "shell.execute_reply": "2026-08-01T16:47:32.953502Z"
+     "iopub.execute_input": "2026-08-11T18:58:51.765207Z",
+     "iopub.status.busy": "2026-08-11T18:58:51.765038Z",
+     "iopub.status.idle": "2026-08-11T18:58:51.769487Z",
+     "shell.execute_reply": "2026-08-11T18:58:51.768965Z"
     }
    },
    "outputs": [],
@@ -1786,14 +1772,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "4df5c8e8",
+   "execution_count": 17,
+   "id": "fb2cc112",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:32.954799Z",
-     "iopub.status.busy": "2026-08-01T16:47:32.954719Z",
-     "iopub.status.idle": "2026-08-01T16:47:32.956840Z",
-     "shell.execute_reply": "2026-08-01T16:47:32.956483Z"
+     "iopub.execute_input": "2026-08-11T18:58:51.770876Z",
+     "iopub.status.busy": "2026-08-11T18:58:51.770761Z",
+     "iopub.status.idle": "2026-08-11T18:58:51.773982Z",
+     "shell.execute_reply": "2026-08-11T18:58:51.773533Z"
     }
    },
    "outputs": [],
@@ -1828,14 +1814,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "e6c50dbc",
+   "execution_count": 18,
+   "id": "af300db0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:32.957564Z",
-     "iopub.status.busy": "2026-08-01T16:47:32.957504Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.433372Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.433024Z"
+     "iopub.execute_input": "2026-08-11T18:58:51.775578Z",
+     "iopub.status.busy": "2026-08-11T18:58:51.775457Z",
+     "iopub.status.idle": "2026-08-11T18:59:06.110220Z",
+     "shell.execute_reply": "2026-08-11T18:59:06.109641Z"
     }
    },
    "outputs": [
@@ -1843,8 +1829,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "raw increments: would stop at step 371 (loss there -28)\n",
-      "window means (k=50): would stop at step 4,700 (loss there -344, final -341)\n"
+      "raw increments: would stop at step 371 (loss there -122)\n",
+      "window means (k=50): would stop at step 4,650 (loss there -442, final -339)\n"
      ]
     }
    ],
@@ -1853,13 +1839,14 @@
     "    parquet_source(data_dir, columns=columns),\n",
     "    batch_size=1024,\n",
     "    shuffle=False,\n",
-    "    sample_shape=(len(columns),),\n",
     "    total_size=\"auto\",\n",
     ")\n",
-    "model2 = build_model([f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), n_streamed)\n",
+    "model2 = build_model(\n",
+    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), loader2.total_size\n",
+    ")\n",
     "stream2 = StreamAdvance(model2, loader2)\n",
     "stream2.prime()\n",
-    "steps_per_epoch = len(loader2) // loader2.batch_size\n",
+    "steps_per_epoch = len(loader2)  # batches per epoch, torch-style\n",
     "\n",
     "k = 50\n",
     "min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass\n",
@@ -1890,20 +1877,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "6dd34c65",
+   "execution_count": 19,
+   "id": "7d024b40",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.434339Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.434276Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.601075Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.600722Z"
+     "iopub.execute_input": "2026-08-11T18:59:06.111840Z",
+     "iopub.status.busy": "2026-08-11T18:59:06.111711Z",
+     "iopub.status.idle": "2026-08-11T18:59:06.461931Z",
+     "shell.execute_reply": "2026-08-11T18:59:06.461318Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ILUV0AAAAAAAAAAAAAAAAAOSWIjoAAAAAAAAAAAAAAAAAcksRHQAAAAAAAAAAAAAAAAC5pYgOAAAAAAAAAAAAAAAAgNxSRAcAAAAAAAAAAAAAAABAbimiAwAAAAAAAAAAAAAAACC3FNEBAAAAAAAAAAAAAAAAkFuK6AAAAAAAAAAAAAAAAADILUV0AAAAAAAAAAAAAAAAAOSWIjoAAAAAAAAAAAAAAAAAcksRHQAAAAAAAAAAAAAAAAC5pYgOAAAAAAAAAAAAAAAAgNxSRAcAAAAAAAAAAAAAAABAbimiAwAAAAAAAAAAAAAAACC3FNEBAAAAAAAAAAAAAAAAkFuK6AAAAAAAAAAAAAAAAADILUV0AAAAAAAAAAAAAAAAAOSWIjoAAAAAAAAAAAAAAAAAQl79P5MEbLS5vj4qAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1600x640 with 1 Axes>"
       ]
@@ -1940,7 +1927,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34204770",
+   "id": "f8325186",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1988,7 +1975,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05742c5a",
+   "id": "723db8e6",
    "metadata": {},
    "source": [
     "### The memory wall, measured\n",
@@ -2003,14 +1990,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "6424bc41",
+   "execution_count": 20,
+   "id": "1df21289",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.602323Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.602235Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.749688Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.749416Z"
+     "iopub.execute_input": "2026-08-11T18:59:06.465051Z",
+     "iopub.status.busy": "2026-08-11T18:59:06.464855Z",
+     "iopub.status.idle": "2026-08-11T18:59:06.760796Z",
+     "shell.execute_reply": "2026-08-11T18:59:06.760169Z"
     }
    },
    "outputs": [
@@ -2084,7 +2071,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "988a2335",
+   "id": "cc5e9356",
    "metadata": {},
    "source": [
     "The corpus behind this notebook sits at roughly three times the wall.\n",
@@ -2103,14 +2090,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "c0ccdac5",
+   "execution_count": 21,
+   "id": "a46a3245",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.751048Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.750969Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.829425Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.829074Z"
+     "iopub.execute_input": "2026-08-11T18:59:06.763645Z",
+     "iopub.status.busy": "2026-08-11T18:59:06.763498Z",
+     "iopub.status.idle": "2026-08-11T18:59:06.920880Z",
+     "shell.execute_reply": "2026-08-11T18:59:06.920280Z"
     }
    },
    "outputs": [
@@ -2156,7 +2143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f65b2d",
+   "id": "69d51568",
    "metadata": {},
    "source": [
     "The ordering surprised us, so it is worth stating carefully. On this model the\n",
@@ -2220,7 +2207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e132228",
+   "id": "b9c22981",
    "metadata": {},
    "source": [
     "### The stopping rule at scale, and a pre-registration that failed\n",
@@ -2254,14 +2241,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "31cf612e",
+   "execution_count": 22,
+   "id": "e8e11504",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.830416Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.830360Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.835655Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.835405Z"
+     "iopub.execute_input": "2026-08-11T18:59:06.922892Z",
+     "iopub.status.busy": "2026-08-11T18:59:06.922769Z",
+     "iopub.status.idle": "2026-08-11T18:59:06.930363Z",
+     "shell.execute_reply": "2026-08-11T18:59:06.929692Z"
     }
    },
    "outputs": [
@@ -2349,7 +2336,7 @@
        "monitor    1.07  0.116262  0.116299  0.116323"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2364,7 +2351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6212b547",
+   "id": "e327dad7",
    "metadata": {},
    "source": [
     "**The equivalence test failed** — and not in the direction anyone would\n",
@@ -2390,14 +2377,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "770109c5",
+   "execution_count": 23,
+   "id": "cd5fea49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.836608Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.836548Z",
-     "iopub.status.idle": "2026-08-01T16:47:37.943647Z",
-     "shell.execute_reply": "2026-08-01T16:47:37.943279Z"
+     "iopub.execute_input": "2026-08-11T18:59:06.932129Z",
+     "iopub.status.busy": "2026-08-11T18:59:06.932005Z",
+     "iopub.status.idle": "2026-08-11T18:59:07.151680Z",
+     "shell.execute_reply": "2026-08-11T18:59:07.151140Z"
     }
    },
    "outputs": [
@@ -2457,7 +2444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16319e4f",
+   "id": "61129336",
    "metadata": {},
    "source": [
     "Three seeds per arm resolve the picture sharply — within-arm spreads are\n",
@@ -2516,14 +2503,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "a95f06ac",
+   "execution_count": 24,
+   "id": "a0b3eb02",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:37.944890Z",
-     "iopub.status.busy": "2026-08-01T16:47:37.944821Z",
-     "iopub.status.idle": "2026-08-01T16:47:38.155170Z",
-     "shell.execute_reply": "2026-08-01T16:47:38.154803Z"
+     "iopub.execute_input": "2026-08-11T18:59:07.154617Z",
+     "iopub.status.busy": "2026-08-11T18:59:07.154462Z",
+     "iopub.status.idle": "2026-08-11T18:59:07.580030Z",
+     "shell.execute_reply": "2026-08-11T18:59:07.579054Z"
     }
    },
    "outputs": [
@@ -2568,7 +2555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da377822",
+   "id": "942701fd",
    "metadata": {},
    "source": [
     "Read it as event time, not clock time: these are per-*transition* quantities,\n",
@@ -2578,7 +2565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71e41238",
+   "id": "10b5d7ea",
    "metadata": {},
    "source": [
     "### What full N buys: thin cells\n",
@@ -2599,14 +2586,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "17c3b9d7",
+   "execution_count": 25,
+   "id": "b345e1bf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:38.156384Z",
-     "iopub.status.busy": "2026-08-01T16:47:38.156318Z",
-     "iopub.status.idle": "2026-08-01T16:47:38.349281Z",
-     "shell.execute_reply": "2026-08-01T16:47:38.348900Z"
+     "iopub.execute_input": "2026-08-11T18:59:07.582719Z",
+     "iopub.status.busy": "2026-08-11T18:59:07.582559Z",
+     "iopub.status.idle": "2026-08-11T18:59:07.970712Z",
+     "shell.execute_reply": "2026-08-11T18:59:07.969963Z"
     }
    },
    "outputs": [
@@ -2667,7 +2654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f657c01b",
+   "id": "043b29fe",
    "metadata": {},
    "source": [
     "This is a capacity-and-estimand demonstration, not a claim of method\n",
@@ -2694,14 +2681,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "c75103c5",
+   "execution_count": 26,
+   "id": "419d23cf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:38.350427Z",
-     "iopub.status.busy": "2026-08-01T16:47:38.350345Z",
-     "iopub.status.idle": "2026-08-01T16:47:38.355574Z",
-     "shell.execute_reply": "2026-08-01T16:47:38.355244Z"
+     "iopub.execute_input": "2026-08-11T18:59:07.973024Z",
+     "iopub.status.busy": "2026-08-11T18:59:07.972882Z",
+     "iopub.status.idle": "2026-08-11T18:59:07.981023Z",
+     "shell.execute_reply": "2026-08-11T18:59:07.980532Z"
     }
    },
    "outputs": [
@@ -2837,7 +2824,7 @@
        "tau_al          3.210000     0.366800     0.041400    0.1"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2850,7 +2837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "312bac32",
+   "id": "e50b8ecc",
    "metadata": {},
    "source": [
     "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
@@ -2878,7 +2865,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecccb268",
+   "id": "0975149b",
    "metadata": {},
    "source": [
     "## Where the pieces live\n",
@@ -2897,7 +2884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c23c905a",
+   "id": "e3d6df66",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2909,7 +2896,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daebacd1",
+   "id": "936fb51d",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2921,7 +2908,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6490ed28",
+   "id": "49ac01ee",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2929,14 +2916,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "8857c31b",
+   "execution_count": 27,
+   "id": "d2d263b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-01T16:47:38.356384Z",
-     "iopub.status.busy": "2026-08-01T16:47:38.356333Z",
-     "iopub.status.idle": "2026-08-01T16:47:38.367091Z",
-     "shell.execute_reply": "2026-08-01T16:47:38.366735Z"
+     "iopub.execute_input": "2026-08-11T18:59:07.982981Z",
+     "iopub.status.busy": "2026-08-11T18:59:07.982845Z",
+     "iopub.status.idle": "2026-08-11T18:59:08.002465Z",
+     "shell.execute_reply": "2026-08-11T18:59:08.001850Z"
     }
    },
    "outputs": [
@@ -2944,7 +2931,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sat, 01 Aug 2026\n",
+      "Last updated: Tue, 11 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -2976,7 +2963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89284305",
+   "id": "fb119275",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 231d371ca..2abba8a48 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -341,7 +341,8 @@ order = np.argsort(key, kind="stable")
 n_shards = 8
 for i in range(n_shards):
     part = table.take(order[i::n_shards])
-    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"))
+    # Row groups are the loader's batches on the shuffle=False path, so size them here.
+    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"), row_group_size=1024)
 
 head = pq.read_table(os.path.join(data_dir, "shard_000.parquet")).slice(0, 10_000)
 print(
@@ -363,11 +364,14 @@ two sorts each shard independently, and no step ever holds the corpus.
 
 ## Streaming the model
 
-The `DataLoader` yields `(batch_size, 8)` blocks in a frozen column order and
-knows the dataset size from the Parquet metadata, so `total_size` is simply
-`len(loader)`. The model reads one `pm.Data` placeholder; everything derived —
-the Fourier basis, the integer symbol index — is computed inside the graph, so
-advancing the stream is a single `set_value` per step.
+With `shuffle=False` the `DataLoader` passes source blocks through verbatim —
+one block per Parquet row group, in a frozen column order — which is why the
+shards were written with `row_group_size=1024` above. The dataset size comes
+from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is
+the number of batches per epoch, as for a torch dataloader. The model reads
+one `pm.Data` placeholder; everything derived — the Fourier basis, the
+integer symbol index — is computed inside the graph, so advancing the stream
+is a single `set_value` per step.
 
 ```{code-cell} ipython3
 columns = ["y_bp", "m", "d", "q_std", "a_std", "ylag_bp", "hour", "sym"]
@@ -375,30 +379,19 @@ loader = DataLoader(
     parquet_source(data_dir, columns=columns),
     batch_size=1024,
     shuffle=False,  # the shards are already globally shuffled on disk
-    sample_shape=(len(columns),),
     total_size="auto",
 )
-print(f"N = {len(loader):,} rows -> {len(loader) // loader.batch_size:,} steps per epoch")
+print(f"N = {loader.total_size:,} rows -> {len(loader):,} batches per epoch")
 ```
 
-One deliberate loader semantics to be precise about: only full batches are
-yielded, so a trailing partial batch — here 992 of 300,000 rows, and with a
-fixed replay order the *same* 992 every pass — is never seen. Strictly, then,
-the fit targets the 299,008-row subpopulation the loader streams, which is a
-uniform random subset by the hash shuffle, while `total_size` overstates its
-size by 0.3% (0.0004% at full scale) — invisible at any precision reported
-here, but it does mean Gate 1's equal-inclusion clause holds for the streamed
-subpopulation, not the last few rows of the last shard — and, equivalently,
-that the likelihood is tempered by the factor 300,000/299,008 ≈ 1.0033, a
-sub-percent effect dwarfed by the mean-field error quantified later. If
-exactness matters, set `total_size` to the streamed count — the cells below
-do exactly that via `n_streamed`; the full-scale runs keep `len(loader)`,
-where the correction is 0.0004%. The durable fix — carrying the remainder
-across epoch boundaries — belongs in the loader and is on its review agenda.
-
-```{code-cell} ipython3
-n_streamed = (len(loader) // loader.batch_size) * loader.batch_size
-```
+One deliberate loader semantics to be precise about: on this path nothing is
+dropped. Verbatim pass-through streams every row, and the last row group of
+each shard is simply shorter than 1024. The `total_size` rescaling copes,
+because PyMC scales the minibatch log-likelihood by `N / b` with `b` read
+from the batch actually installed, so a short block is weighted up by exactly
+its own size. Gate 1's equal-inclusion clause therefore holds for the full
+corpus, with no tempering correction to track. (Only the shuffle-buffer path
+drops a trailing partial batch; this notebook never uses it.)
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -505,7 +498,9 @@ the smallest genuine move on any listed tick grid sits dozens of orders of
 magnitude above the float32 flush-to-zero threshold.
 
 ```{code-cell} ipython3
-model = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), n_streamed)
+model = build_model(
+    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), loader.total_size
+)
 pm.model_to_graphviz(model)
 ```
 
@@ -744,12 +739,16 @@ observed ECDF that the smooth predictive cannot follow.
 
 A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo
 estimate — and eyeballing it does not scale to overnight runs. The stopping
-rule proposed for PyMC in [pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)
+rule originally proposed for PyMC in
+[pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)
 standardizes each loss *improvement* by a robust running scale estimate and
 feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming
 itself only after `min_steps` — which on streaming fits should be at least
 one full pass, since stopping before the model has seen every row once is
-never evidence of convergence.
+never evidence of convergence. The trap this section demonstrates was later
+confirmed on real ADVI traces, and the revised implementation under review
+for `pymc-extras` is built on the windowed idea shown here, with horizons
+that grow over the run and a second, practical-negligibility yardstick.
 
 :::{admonition} The minibatch trap
 :class: warning
@@ -835,13 +834,14 @@ loader2 = DataLoader(
     parquet_source(data_dir, columns=columns),
     batch_size=1024,
     shuffle=False,
-    sample_shape=(len(columns),),
     total_size="auto",
 )
-model2 = build_model([f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), n_streamed)
+model2 = build_model(
+    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), loader2.total_size
+)
 stream2 = StreamAdvance(model2, loader2)
 stream2.prime()
-steps_per_epoch = len(loader2) // loader2.batch_size
+steps_per_epoch = len(loader2)  # batches per epoch, torch-style
 
 k = 50
 min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass

From 3fd5f5bdd75dd8aef4cefe2fd3305837ca9f946f Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 14 Aug 2026 04:44:53 -0400
Subject: [PATCH 26/42] Cut to the bare minimum that can merge

The full-corpus results section depended on run artifacts outside the
notebook; it goes, along with its results JSON and every forward
reference to it. The two modeling choices that leaned on corpus evidence
(centered parameterization, the nu floor of 1) now carry self-contained
rationales. Dead references to the closed streaming_dataset draft are
gone, the closing seealso points at the PRs as they stand today, and a
note covers installing from main until the DataLoader is in a release.
1416 lines down to 941; the notebook is fully self-contained and
re-executed (45 cells, no errors); repo pre-commit passes on the pair.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1706 ++++-------------
 .../streaming_tick_data.myst.md               |  541 +-----
 .../streaming_tick_data_results.json          | 1320 -------------
 3 files changed, 423 insertions(+), 3144 deletions(-)
 delete mode 100644 examples/variational_inference/streaming_tick_data_results.json

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 8bbfe3da6..17e5b4204 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e2a1825",
+   "id": "0844efc9",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "862951b6",
+   "id": "0bbc9a0e",
    "metadata": {},
    "source": [
     "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
@@ -27,33 +27,26 @@
     "alt pairs) already exceeds 450 million rows — far more\n",
     "than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a\n",
     "hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of\n",
-    "that scale by streaming minibatches from disk, using the\n",
-    "{class}`~pymc_extras.variational.dataloader.DataLoader` introduced in the\n",
-    "{ref}`streaming_dataset` companion notebook.\n",
-    "\n",
-    "The companion covers the mechanics of the streaming API. This notebook is the\n",
-    "applied piece, and it tries to teach three things that only show up on real\n",
-    "data:\n",
+    "that scale by streaming minibatches from disk with pymc-extras'\n",
+    "{class}`~pymc_extras.variational.dataloader.DataLoader`. It tries to teach\n",
+    "three things that only show up on real data:\n",
     "\n",
     "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
     "   time-series models fail — and a model class that passes both.\n",
     "2. **The full workflow at scale**: causal feature engineering, an on-disk\n",
     "   global shuffle, streaming ADVI with `total_size` rescaling, and an online\n",
     "   stopping rule.\n",
-    "3. **Honest reporting.** Mean-field variational inference on half a billion\n",
-    "   rows produces some numbers you should trust and some you should not; we\n",
-    "   measure which are which instead of guessing.\n",
-    "\n",
-    "The notebook itself runs in about a minute on synthetic data with known ground\n",
-    "truth — the streaming code is identical at any scale. Full-scale results on the\n",
-    "real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus`: the data\n",
-    "are public, and every quoted number traces to the run artifacts of the ETL and\n",
-    "fit scripts that accompany the submission."
+    "3. **Honest reporting.** Mean-field variational inference produces some\n",
+    "   numbers you should trust and some you should not; with known ground truth\n",
+    "   we measure which are which instead of guessing.\n",
+    "\n",
+    "The notebook runs in about a minute on synthetic data with known ground\n",
+    "truth — the streaming code is identical at any scale."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8ade6b5",
+   "id": "79e7271a",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -93,23 +86,30 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cab2e092",
+   "id": "d0611e36",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
+    ":::\n",
+    "\n",
+    ":::{note}\n",
+    "The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into\n",
+    "pymc-extras after the v0.14.0 release. Until the next release, install\n",
+    "pymc-extras from `main`:\n",
+    "`pip install \"pymc-extras @ git+https://github.com/pymc-devs/pymc-extras\"`.\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "20349478",
+   "id": "dcad6941",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:34.174186Z",
-     "iopub.status.busy": "2026-08-11T18:58:34.173931Z",
-     "iopub.status.idle": "2026-08-11T18:58:36.972701Z",
-     "shell.execute_reply": "2026-08-11T18:58:36.971930Z"
+     "iopub.execute_input": "2026-08-14T08:43:57.327709Z",
+     "iopub.status.busy": "2026-08-14T08:43:57.327467Z",
+     "iopub.status.idle": "2026-08-14T08:43:59.671020Z",
+     "shell.execute_reply": "2026-08-14T08:43:59.670489Z"
     }
    },
    "outputs": [],
@@ -135,13 +135,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a3741641",
+   "id": "ce2c2f81",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:36.975018Z",
-     "iopub.status.busy": "2026-08-11T18:58:36.974604Z",
-     "iopub.status.idle": "2026-08-11T18:58:36.980344Z",
-     "shell.execute_reply": "2026-08-11T18:58:36.979619Z"
+     "iopub.execute_input": "2026-08-14T08:43:59.672497Z",
+     "iopub.status.busy": "2026-08-14T08:43:59.672356Z",
+     "iopub.status.idle": "2026-08-14T08:43:59.675237Z",
+     "shell.execute_reply": "2026-08-14T08:43:59.674943Z"
     }
    },
    "outputs": [],
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c904f14b",
+   "id": "c1efdd51",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -202,12 +202,12 @@
     "so a thin symbol borrows the global intraday shape where its own data run out —\n",
     "that is the hierarchical payoff we will measure. They are parameterized\n",
     "*centered*, deliberately: the familiar non-centered trick exists for data-poor\n",
-    "groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich —\n",
-    "{ref}`tick-real-corpus` shows what happened when we tried it the other way.\n",
+    "groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich.\n",
     "The degrees of freedom are\n",
     "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
-    "the floor of 1 (rather than the more comfortable 2) is not a stylistic choice,\n",
-    "and {ref}`tick-real-corpus` shows what the data did to force it.\n",
+    "the floor of 1 (rather than the more comfortable 2) leaves room for tails too\n",
+    "heavy to carry a finite variance — and the estimand below is chosen to stay\n",
+    "meaningful exactly there.\n",
     "\n",
     "The reported estimand is **event-return dispersion on the event clock**:\n",
     "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71506172",
+   "id": "5c024647",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -234,13 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f92de554",
+   "id": "1b543874",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:36.982341Z",
-     "iopub.status.busy": "2026-08-11T18:58:36.982183Z",
-     "iopub.status.idle": "2026-08-11T18:58:36.986412Z",
-     "shell.execute_reply": "2026-08-11T18:58:36.985850Z"
+     "iopub.execute_input": "2026-08-14T08:43:59.676276Z",
+     "iopub.status.busy": "2026-08-14T08:43:59.676208Z",
+     "iopub.status.idle": "2026-08-14T08:43:59.678565Z",
+     "shell.execute_reply": "2026-08-14T08:43:59.678225Z"
     }
    },
    "outputs": [],
@@ -282,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0df2c094",
+   "id": "b2c36bc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:36.987907Z",
-     "iopub.status.busy": "2026-08-11T18:58:36.987806Z",
-     "iopub.status.idle": "2026-08-11T18:58:37.292421Z",
-     "shell.execute_reply": "2026-08-11T18:58:37.291738Z"
+     "iopub.execute_input": "2026-08-14T08:43:59.679384Z",
+     "iopub.status.busy": "2026-08-14T08:43:59.679330Z",
+     "iopub.status.idle": "2026-08-14T08:43:59.823266Z",
+     "shell.execute_reply": "2026-08-14T08:43:59.822913Z"
     }
    },
    "outputs": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3efe39d",
+   "id": "182e1563",
    "metadata": {},
    "source": [
     "The zero spike is the single most prominent feature of per-trade return data —\n",
@@ -365,13 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1b66e687",
+   "id": "190db096",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:37.294055Z",
-     "iopub.status.busy": "2026-08-11T18:58:37.293894Z",
-     "iopub.status.idle": "2026-08-11T18:58:38.054205Z",
-     "shell.execute_reply": "2026-08-11T18:58:38.053533Z"
+     "iopub.execute_input": "2026-08-14T08:43:59.824180Z",
+     "iopub.status.busy": "2026-08-14T08:43:59.824123Z",
+     "iopub.status.idle": "2026-08-14T08:44:00.268310Z",
+     "shell.execute_reply": "2026-08-14T08:44:00.267596Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e80bbe08",
+   "id": "b37abb6f",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -431,13 +431,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "607784ce",
+   "id": "cca75837",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:38.056352Z",
-     "iopub.status.busy": "2026-08-11T18:58:38.056186Z",
-     "iopub.status.idle": "2026-08-11T18:58:38.428818Z",
-     "shell.execute_reply": "2026-08-11T18:58:38.428112Z"
+     "iopub.execute_input": "2026-08-14T08:44:00.269817Z",
+     "iopub.status.busy": "2026-08-14T08:44:00.269723Z",
+     "iopub.status.idle": "2026-08-14T08:44:00.525960Z",
+     "shell.execute_reply": "2026-08-14T08:44:00.525526Z"
     }
    },
    "outputs": [
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97e9f970",
+   "id": "ffbd434c",
    "metadata": {},
    "source": [
     "On the real corpus the same check is part of the ETL script's validation: the\n",
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5faf00a",
+   "id": "d3a10713",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -522,13 +522,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "28724ab1",
+   "id": "b2aec5a2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:38.430792Z",
-     "iopub.status.busy": "2026-08-11T18:58:38.430631Z",
-     "iopub.status.idle": "2026-08-11T18:58:38.438524Z",
-     "shell.execute_reply": "2026-08-11T18:58:38.437896Z"
+     "iopub.execute_input": "2026-08-14T08:44:00.526931Z",
+     "iopub.status.busy": "2026-08-14T08:44:00.526862Z",
+     "iopub.status.idle": "2026-08-14T08:44:00.530637Z",
+     "shell.execute_reply": "2026-08-14T08:44:00.530317Z"
     }
    },
    "outputs": [
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64075eaa",
+   "id": "7b858ebf",
    "metadata": {},
    "source": [
     "One deliberate loader semantics to be precise about: on this path nothing is\n",
@@ -569,13 +569,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "dbda7e38",
+   "id": "320cf2ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:38.440047Z",
-     "iopub.status.busy": "2026-08-11T18:58:38.439904Z",
-     "iopub.status.idle": "2026-08-11T18:58:38.447459Z",
-     "shell.execute_reply": "2026-08-11T18:58:38.446899Z"
+     "iopub.execute_input": "2026-08-14T08:44:00.531557Z",
+     "iopub.status.busy": "2026-08-14T08:44:00.531498Z",
+     "iopub.status.idle": "2026-08-14T08:44:00.535509Z",
+     "shell.execute_reply": "2026-08-14T08:44:00.535173Z"
     }
    },
    "outputs": [],
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8ecf4db",
+   "id": "3fcb7fc4",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -692,13 +692,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f5cd6948",
+   "id": "b33e30d5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:38.449054Z",
-     "iopub.status.busy": "2026-08-11T18:58:38.448925Z",
-     "iopub.status.idle": "2026-08-11T18:58:38.731436Z",
-     "shell.execute_reply": "2026-08-11T18:58:38.730281Z"
+     "iopub.execute_input": "2026-08-14T08:44:00.536410Z",
+     "iopub.status.busy": "2026-08-14T08:44:00.536358Z",
+     "iopub.status.idle": "2026-08-14T08:44:00.898236Z",
+     "shell.execute_reply": "2026-08-14T08:44:00.897789Z"
     }
    },
    "outputs": [
@@ -717,8 +717,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M810.01,-8C810.01,-8 856.01,-8 856.01,-8 862.01,-8 868.01,-14 868.01,-20 868.01,-20 868.01,-94 868.01,-94 868.01,-100 862.01,-106 856.01,-106 856.01,-106 810.01,-106 810.01,-106 804.01,-106 798.01,-100 798.01,-94 798.01,-94 798.01,-20 798.01,-20 798.01,-14 804.01,-8 810.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M796.01,-8C796.01,-8 842.01,-8 842.01,-8 848.01,-8 854.01,-14 854.01,-20 854.01,-20 854.01,-94 854.01,-94 854.01,-100 848.01,-106 842.01,-106 842.01,-106 796.01,-106 796.01,-106 790.01,-106 784.01,-100 784.01,-94 784.01,-94 784.01,-20 784.01,-20 784.01,-14 790.01,-8 796.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -737,274 +737,274 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M721.01,-114C721.01,-114 945.01,-114 945.01,-114 951.01,-114 957.01,-120 957.01,-126 957.01,-126 957.01,-223.82 957.01,-223.82 957.01,-229.82 951.01,-235.82 945.01,-235.82 945.01,-235.82 721.01,-235.82 721.01,-235.82 715.01,-235.82 709.01,-229.82 709.01,-223.82 709.01,-223.82 709.01,-126 709.01,-126 709.01,-120 715.01,-114 721.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M707.01,-114C707.01,-114 931.01,-114 931.01,-114 937.01,-114 943.01,-120 943.01,-126 943.01,-126 943.01,-223.82 943.01,-223.82 943.01,-229.82 937.01,-235.82 931.01,-235.82 931.01,-235.82 707.01,-235.82 707.01,-235.82 701.01,-235.82 695.01,-229.82 695.01,-223.82 695.01,-223.82 695.01,-126 695.01,-126 695.01,-120 701.01,-114 707.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M834.01,-98C834.01,-98 804.01,-98 804.01,-98 798.01,-98 792.01,-92 792.01,-86 792.01,-86 792.01,-52.5 792.01,-52.5 792.01,-46.5 798.01,-40.5 804.01,-40.5 804.01,-40.5 834.01,-40.5 834.01,-40.5 840.01,-40.5 846.01,-46.5 846.01,-52.5 846.01,-52.5 846.01,-86 846.01,-86 846.01,-92 840.01,-98 834.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- theta_r -->\n",
+       "<!-- tau_k -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"833.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
-       "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.25,-195.25 716.13,-191.43 706.02,-188.26 706.25,-195.25\"/>\n",
-       "</g>\n",
-       "<!-- tau_ph -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_ph -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
-       "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
+       "<title>tau_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
+       "<!-- b_k -->\n",
        "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
+       "<title>b_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
-       "<!-- tau_k -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_k -->\n",
+       "<!-- b_al -->\n",
        "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_k</title>\n",
+       "<title>b_al</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
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        "</g>\n",
-       "<!-- tau_al -->\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"819.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.87 76.86,-251.93 91.01,-243.82 141.72,-214.74 497.29,-198.67 692.08,-191.96\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"692.14,-195.46 702.01,-191.62 691.9,-188.46 692.14,-195.46\"/>\n",
+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.78,-279.43C165.87,-265.96 176.91,-252.01 191.01,-243.82 233.24,-219.27 521.87,-201.9 692.78,-193.62\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"692.78,-197.13 702.6,-193.15 692.44,-190.14 692.78,-197.13\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M257.85,-279.55C265.95,-266.1 276.98,-252.13 291.01,-243.82 356.57,-204.97 557.72,-193.08 691.95,-189.54\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.68,-193.05 701.59,-189.3 691.51,-186.05 691.68,-193.05\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"341.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M357.95,-279.72C366.06,-266.3 377.08,-252.3 391.01,-243.82 440.27,-213.82 585.03,-199.77 692.63,-193.34\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"692.62,-196.85 702.4,-192.77 692.22,-189.86 692.62,-196.85\"/>\n",
+       "</g>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M458.62,-279.99C466.93,-266.61 478.12,-252.57 492.01,-243.82 525.57,-222.68 617.66,-208.18 695.6,-199.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"695.93,-202.77 705.48,-198.19 695.16,-195.82 695.93,-202.77\"/>\n",
+       "</g>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_al -->\n",
+       "<!-- b_ba -->\n",
        "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_al</title>\n",
+       "<title>b_ba</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
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        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_sh -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
+       "<g id=\"node23\" class=\"node\">\n",
        "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_sh&#45;&gt;b_sh -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>tau_sh&#45;&gt;b_sh</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"550.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"550.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"550.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"550.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.69,-196.86 716.52,-192.91 706.37,-189.87 706.69,-196.86\"/>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.19,-280.25C582,-267.07 594.59,-253.11 609.01,-243.82 636.45,-226.14 669.34,-214.05 700.86,-205.8\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.61,-209.22 710.47,-203.4 699.92,-202.43 701.61,-209.22\"/>\n",
        "</g>\n",
-       "<!-- theta_d -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"659.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M257.84,-279.53C265.93,-266.08 276.97,-252.11 291.01,-243.82 358.84,-203.75 567.86,-192.26 705.62,-189.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.63,-192.62 715.55,-188.91 705.48,-185.62 705.63,-192.62\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M678.32,-280.88C686.57,-268.09 697.06,-254.23 709.01,-243.82 717.04,-236.83 726.09,-230.45 735.48,-224.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"737.16,-227.79 744.04,-219.73 733.64,-221.74 737.16,-227.79\"/>\n",
        "</g>\n",
-       "<!-- eta -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"345.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
        "</g>\n",
        "<!-- nu -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
+       "<g id=\"node14\" class=\"node\">\n",
        "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"390.14,-345.73 299.89,-345.73 299.89,-288.23 390.14,-288.23 390.14,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"345.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
-       "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M345.01,-393.28C345.01,-381.58 345.01,-368.79 345.01,-357.14\"/>\n",
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        "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
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        "<!-- lambda_a -->\n",
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        "<!-- lambda_a&#45;&gt;y_obs -->\n",
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-       "<!-- kappa0 -->\n",
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        "<!-- c&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
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@@ -1029,52 +1029,52 @@
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-       "<g id=\"edge10\" class=\"edge\">\n",
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       ],
       "text/plain": [
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+       "<graphviz.graphs.Digraph at 0x161d28ec0>"
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      },
      "execution_count": 9,
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38293bb0",
+   "id": "272b8f4a",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1103,13 +1103,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6cfc76e5",
+   "id": "86442b80",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2026-08-11T18:58:49.259331Z"
+     "iopub.execute_input": "2026-08-14T08:44:00.899367Z",
+     "iopub.status.busy": "2026-08-14T08:44:00.899292Z",
+     "iopub.status.idle": "2026-08-14T08:44:06.234279Z",
+     "shell.execute_reply": "2026-08-14T08:44:06.233843Z"
     }
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    "outputs": [],
@@ -1146,7 +1146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f624e15",
+   "id": "56872c9c",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1159,13 +1159,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "95d97c78",
+   "id": "ba8752bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:49.261852Z",
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-     "iopub.status.idle": "2026-08-11T18:58:49.560490Z",
-     "shell.execute_reply": "2026-08-11T18:58:49.559902Z"
+     "iopub.execute_input": "2026-08-14T08:44:06.235431Z",
+     "iopub.status.busy": "2026-08-14T08:44:06.235356Z",
+     "iopub.status.idle": "2026-08-14T08:44:06.385062Z",
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    "outputs": [
@@ -1202,7 +1202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d95fae2",
+   "id": "9186daa7",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1211,13 +1211,13 @@
   {
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-   "id": "50896892",
+   "id": "f7342276",
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     }
    },
    "outputs": [
@@ -1390,7 +1390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f98e432f",
+   "id": "9e0029a0",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1419,13 +1419,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "0b59fa4d",
+   "id": "af6e02e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:49.943218Z",
-     "iopub.status.busy": "2026-08-11T18:58:49.943062Z",
-     "iopub.status.idle": "2026-08-11T18:58:50.187639Z",
-     "shell.execute_reply": "2026-08-11T18:58:50.186987Z"
+     "iopub.execute_input": "2026-08-14T08:44:06.585911Z",
+     "iopub.status.busy": "2026-08-14T08:44:06.585852Z",
+     "iopub.status.idle": "2026-08-14T08:44:06.704811Z",
+     "shell.execute_reply": "2026-08-14T08:44:06.704488Z"
     }
    },
    "outputs": [
@@ -1474,7 +1474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bdadf38",
+   "id": "fa0d00a6",
    "metadata": {},
    "source": [
     "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
@@ -1486,7 +1486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff836bcb",
+   "id": "b851e5e9",
    "metadata": {},
    "source": [
     "## Does it predict what matters?\n",
@@ -1502,13 +1502,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "90c186f2",
+   "id": "98de55d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:50.189816Z",
-     "iopub.status.busy": "2026-08-11T18:58:50.189701Z",
-     "iopub.status.idle": "2026-08-11T18:58:51.494603Z",
-     "shell.execute_reply": "2026-08-11T18:58:51.494007Z"
+     "iopub.execute_input": "2026-08-14T08:44:06.705938Z",
+     "iopub.status.busy": "2026-08-14T08:44:06.705852Z",
+     "iopub.status.idle": "2026-08-14T08:44:07.339712Z",
+     "shell.execute_reply": "2026-08-14T08:44:07.339304Z"
     }
    },
    "outputs": [
@@ -1617,13 +1617,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "8174ded8",
+   "id": "b1b883d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:51.496582Z",
-     "iopub.status.busy": "2026-08-11T18:58:51.496437Z",
-     "iopub.status.idle": "2026-08-11T18:58:51.762217Z",
-     "shell.execute_reply": "2026-08-11T18:58:51.761595Z"
+     "iopub.execute_input": "2026-08-14T08:44:07.340831Z",
+     "iopub.status.busy": "2026-08-14T08:44:07.340759Z",
+     "iopub.status.idle": "2026-08-14T08:44:07.472051Z",
+     "shell.execute_reply": "2026-08-14T08:44:07.471703Z"
     }
    },
    "outputs": [
@@ -1671,7 +1671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b638c435",
+   "id": "9b0c4e9e",
    "metadata": {},
    "source": [
     "Two of the three statistics sit inside their predictive 90% bands; the median\n",
@@ -1688,7 +1688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af420919",
+   "id": "a711b252",
    "metadata": {},
    "source": [
     "## Knowing when to stop — and how a stop rule can lie to you\n",
@@ -1702,16 +1702,15 @@
     "itself only after `min_steps` — which on streaming fits should be at least\n",
     "one full pass, since stopping before the model has seen every row once is\n",
     "never evidence of convergence. The trap this section demonstrates was later\n",
-    "confirmed on real ADVI traces, and the revised implementation under review\n",
-    "for `pymc-extras` is built on the windowed idea shown here, with horizons\n",
+    "confirmed on real ADVI traces, and the revised implementation under review for\n",
+    "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733) is built on the windowed idea shown here, with horizons\n",
     "that grow over the run and a second, practical-negligibility yardstick.\n",
     "\n",
     ":::{admonition} The minibatch trap\n",
     ":class: warning\n",
     "Successive losses are evaluated on *different batches*, so the per-step noise\n",
-    "dwarfs the per-step improvement: on our full-scale run the median one-step\n",
-    "change is about 106 loss units while the mean improvement — (first − last)\n",
-    "loss over the run's steps — is about 0.06 units per step. A rule that standardizes *raw* increments concludes \"improvement is\n",
+    "dwarfs the per-step improvement by orders of magnitude. A rule that\n",
+    "standardizes *raw* increments therefore concludes \"improvement is\n",
     "indistinguishable from zero\" immediately upon arming, and fires a fixed\n",
     "~$h/\\kappa$ steps later regardless of whether the fit converged.\n",
     ":::\n",
@@ -1725,13 +1724,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "a7589a33",
+   "id": "aa3d1e1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:51.765207Z",
-     "iopub.status.busy": "2026-08-11T18:58:51.765038Z",
-     "iopub.status.idle": "2026-08-11T18:58:51.769487Z",
-     "shell.execute_reply": "2026-08-11T18:58:51.768965Z"
+     "iopub.execute_input": "2026-08-14T08:44:07.473318Z",
+     "iopub.status.busy": "2026-08-14T08:44:07.473238Z",
+     "iopub.status.idle": "2026-08-14T08:44:07.475766Z",
+     "shell.execute_reply": "2026-08-14T08:44:07.475492Z"
     }
    },
    "outputs": [],
@@ -1773,13 +1772,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "fb2cc112",
+   "id": "04075b83",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:51.770876Z",
-     "iopub.status.busy": "2026-08-11T18:58:51.770761Z",
-     "iopub.status.idle": "2026-08-11T18:58:51.773982Z",
-     "shell.execute_reply": "2026-08-11T18:58:51.773533Z"
+     "iopub.execute_input": "2026-08-14T08:44:07.476671Z",
+     "iopub.status.busy": "2026-08-14T08:44:07.476591Z",
+     "iopub.status.idle": "2026-08-14T08:44:07.478642Z",
+     "shell.execute_reply": "2026-08-14T08:44:07.478360Z"
     }
    },
    "outputs": [],
@@ -1815,13 +1814,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "af300db0",
+   "id": "9af360af",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:58:51.775578Z",
-     "iopub.status.busy": "2026-08-11T18:58:51.775457Z",
-     "iopub.status.idle": "2026-08-11T18:59:06.110220Z",
-     "shell.execute_reply": "2026-08-11T18:59:06.109641Z"
+     "iopub.execute_input": "2026-08-14T08:44:07.479507Z",
+     "iopub.status.busy": "2026-08-14T08:44:07.479446Z",
+     "iopub.status.idle": "2026-08-14T08:44:15.171817Z",
+     "shell.execute_reply": "2026-08-14T08:44:15.171380Z"
     }
    },
    "outputs": [
@@ -1878,13 +1877,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "7d024b40",
+   "id": "8dd9d84a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:06.111840Z",
-     "iopub.status.busy": "2026-08-11T18:59:06.111711Z",
-     "iopub.status.idle": "2026-08-11T18:59:06.461931Z",
-     "shell.execute_reply": "2026-08-11T18:59:06.461318Z"
+     "iopub.execute_input": "2026-08-14T08:44:15.172812Z",
+     "iopub.status.busy": "2026-08-14T08:44:15.172737Z",
+     "iopub.status.idle": "2026-08-14T08:44:15.347242Z",
+     "shell.execute_reply": "2026-08-14T08:44:15.346865Z"
     }
    },
    "outputs": [
@@ -1927,7 +1926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8325186",
+   "id": "f710b13b",
    "metadata": {},
    "source": [
     "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
@@ -1936,955 +1935,28 @@
     "window-noise scale, here capturing roughly 95% of the total loss descent at\n",
     "under a quarter of the step budget. Two honest qualifications. First, what the\n",
     "rule detects is a *loss plateau* — a necessary signal, not a proof that the\n",
-    "posterior has converged; the held-out checks in the next section are the\n",
-    "independent evidence. Second, the $h/\\kappa$-window delay after a plateau is\n",
-    "the rule's *nominal* detection lag; clipping and the adaptive scale can move\n",
-    "the actual hitting time. On the full-scale corpus the same replay shows the\n",
-    "windowed rule stopping shortly after one pass, at a *training loss* within\n",
-    "noise of the two-pass reference — whether that stop is also good enough on\n",
-    "held-out score is precisely what the pre-registered test in the next section\n",
-    "failed to confirm.\n",
-    "\n",
-    "(tick-real-corpus)=\n",
-    "\n",
-    "## The real corpus: 38 symbols, half a billion transitions\n",
-    "\n",
-    "Everything above runs identically on real data; what changes is only the ETL\n",
-    "in front of it. The full-scale corpus behind the numbers in this section:\n",
-    "\n",
-    "- **Source**: Binance's public aggregated-trades archive\n",
-    "  (`data.binance.vision`) — no API key, published SHA-256 checksums, monthly\n",
-    "  CSV dumps. The corpus is 491,559,069 transition rows: four majors\n",
-    "  (BTC/ETH/SOL/XRP × 6 months of 2026) for row bulk plus 34 alt pairs\n",
-    "  (June 2026) for hierarchy breadth, the thinnest at 67,000 rows.\n",
-    "- **ETL**: per symbol, *chronologically*: next-event returns, move indicator,\n",
-    "  trade sign, standardized log notional, trailing 60-second activity, last\n",
-    "  nonzero return, hour — then the splitmix64 global shuffle into 64 shards\n",
-    "  (~9 GB of Parquet), with the shard-head mixing check asserted. The complete\n",
-    "  script is published with the notebook's companion repository.\n",
-    "- **Hardware**: one Apple M4 laptop, 24 GB RAM. No cluster anywhere.\n",
-    "\n",
-    "Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes\n",
-    "about four minutes at roughly 1,000 optimization steps per second (4 million\n",
-    "rows per second through the model), with peak resident memory of 1.1 GB for\n",
-    "the whole run — fit loop plus posterior draws and summaries; the memory-wall\n",
-    "figure's leaner 0.5 GB point measures the fit loop alone. Both are flat in N. The subsections below are the\n",
-    "measured results; each figure's numbers are inlined verbatim from the run\n",
-    "logs so the plots execute without any data access."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "723db8e6",
-   "metadata": {},
-   "source": [
-    "### The memory wall, measured\n",
-    "\n",
-    "Every number in the cells below is inlined verbatim from the run artifacts of\n",
-    "the published scripts, so these figures execute without touching the data.\n",
-    "First, the reason streaming exists at all. Each point is a fresh subprocess's\n",
-    "peak resident memory (`ru_maxrss`) after fitting this exact model at that many\n",
-    "rows — `pm.Minibatch` holding the rows in RAM versus the loader streaming the\n",
-    "shards:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "1df21289",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:06.465051Z",
-     "iopub.status.busy": "2026-08-11T18:59:06.464855Z",
-     "iopub.status.idle": "2026-08-11T18:59:06.760796Z",
-     "shell.execute_reply": "2026-08-11T18:59:06.760169Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 823,
-       "width": 2370
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with open(\"streaming_tick_data_results.json\") as f:\n",
-    "    results = json.load(f)\n",
-    "\n",
-    "mb = np.array(results[\"memwall\"][\"minibatch\"], dtype=float)\n",
-    "st = np.array(results[\"memwall\"][\"stream\"], dtype=float)\n",
-    "bpr = results[\"memwall\"][\"bytes_per_row\"]\n",
-    "icpt = results[\"memwall\"][\"intercept_gb\"]\n",
-    "cross = results[\"memwall\"][\"crossing_rows_12gb\"]\n",
-    "n_full = results[\"corpus\"][\"n_rows\"]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4), layout=\"constrained\")\n",
-    "ax.plot(mb[:, 0] / 1e6, mb[:, 1], \"o-\", color=\"C1\", label=\"pm.Minibatch (rows in RAM)\")\n",
-    "grid = np.linspace(0, n_full, 200)\n",
-    "ax.plot(\n",
-    "    grid / 1e6,\n",
-    "    icpt + bpr * grid / 1e9,\n",
-    "    ls=\":\",\n",
-    "    color=\"C1\",\n",
-    "    lw=1,\n",
-    "    label=f\"fit: {icpt:.1f} GB + {bpr:.0f} B/row\",\n",
-    ")\n",
-    "ax.plot(st[:, 0] / 1e6, st[:, 1], \"s-\", color=\"C0\", label=\"streaming DataLoader\")\n",
-    "ax.axhline(12, color=\"C3\", ls=\"--\", lw=1, label=\"12 GB (16 GB laptop budget)\")\n",
-    "ax.axvline(cross / 1e6, color=\"C3\", ls=\":\", lw=1)\n",
-    "ax.annotate(\n",
-    "    f\"wall: {cross / 1e6:,.0f}M rows\",\n",
-    "    (cross / 1e6 + 8, 10.6),\n",
-    "    color=\"C3\",\n",
-    "    fontsize=9,\n",
-    "    ha=\"left\",\n",
-    "    va=\"top\",\n",
-    ")\n",
-    "ax.axvline(n_full / 1e6, color=\"C2\", ls=\":\", lw=1)\n",
-    "ax.set_ylim(0, 42)\n",
-    "ax.annotate(\n",
-    "    f\"this corpus: {n_full / 1e6:,.0f}M rows\",\n",
-    "    (n_full / 1e6 - 6, 40.5),\n",
-    "    color=\"C2\",\n",
-    "    fontsize=9,\n",
-    "    ha=\"right\",\n",
-    "    va=\"top\",\n",
-    ")\n",
-    "ax.set_xlabel(\"dataset size (millions of rows)\")\n",
-    "ax.set_ylabel(\"peak RSS (GB)\")\n",
-    "ax.set_title(\n",
-    "    \"The in-memory path crosses a 16 GB laptop's budget at \"\n",
-    "    f\"{cross / 1e6:,.0f}M rows; streaming does not care\"\n",
-    ")\n",
-    "ax.legend(loc=\"upper left\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc5e9356",
-   "metadata": {},
-   "source": [
-    "The corpus behind this notebook sits at roughly three times the wall.\n",
-    "\n",
-    ":::{warning}\n",
-    "Two caveats belong next to this figure, not in a footnote. The in-memory arm\n",
-    "was measured up to 64M rows and the crossing is a *projection* from the fitted\n",
-    "line — macOS does not enforce `RLIMIT_AS`, so a demonstrated hard failure\n",
-    "needs a memory-capped Linux subprocess. And the two samplers differ\n",
-    "semantically: `pm.Minibatch` draws with replacement, the loader makes\n",
-    "without-replacement epoch passes.\n",
-    ":::\n",
+    "posterior has converged; the recovery and predictive checks earlier in the\n",
+    "notebook are the kind of independent evidence a stop should be paired with.\n",
+    "Second, the $h/\\kappa$-window delay after a plateau is the rule's *nominal*\n",
+    "detection lag; clipping and the adaptive scale can move the actual hitting\n",
+    "time.\n",
     "\n",
-    "And speed? Steady-state, compile time subtracted, same model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a46a3245",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:06.763645Z",
-     "iopub.status.busy": "2026-08-11T18:59:06.763498Z",
-     "iopub.status.idle": "2026-08-11T18:59:06.920880Z",
-     "shell.execute_reply": "2026-08-11T18:59:06.920280Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x520 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 543,
-       "width": 1922
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tp = results[\"memwall\"][\"throughput\"]\n",
-    "labels = {\n",
-    "    \"minibatch\": \"pm.Minibatch (rows in RAM)\",\n",
-    "    \"stream\": \"DataLoader, pre-shuffled shards\",\n",
-    "    \"stream_shuffled\": \"DataLoader, runtime shuffle buffer\",\n",
-    "}\n",
-    "fig, ax = plt.subplots(figsize=(8, 2.6), layout=\"constrained\")\n",
-    "names = [labels[k] for k in labels]\n",
-    "vals = [tp[k] for k in labels]\n",
-    "ax.barh(names, vals, color=[\"C1\", \"C0\", \"C0\"])\n",
-    "for bar_y, val in enumerate(vals):\n",
-    "    ax.annotate(\n",
-    "        f\"{val:,.0f} steps/s\",\n",
-    "        (val, bar_y),\n",
-    "        xytext=(4, 0),\n",
-    "        textcoords=\"offset points\",\n",
-    "        va=\"center\",\n",
-    "        fontsize=9,\n",
-    "    )\n",
-    "ax.set_xlabel(\"optimization steps per second (batch 4096, steady state)\")\n",
-    "ax.set_title(\"With a 440-parameter gradient, the loader is not the bottleneck\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69d51568",
-   "metadata": {},
-   "source": [
-    "The ordering surprised us, so it is worth stating carefully. On this model the\n",
-    "gradient dominates the step, and the pre-shuffled streaming path *outpaces*\n",
-    "in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than\n",
-    "Minibatch's per-step random indexing. On our lighter probe model the same\n",
-    "ordering held (pre-shuffled loader ~4,900 vs Minibatch ~3,000 steps/s), so\n",
-    "treat any speed ordering as an empirical, model-specific fact rather than a\n",
-    "law in either direction. The one consistently\n",
-    "expensive path is the runtime shuffle buffer — which is why the ETL-time\n",
-    "global shuffle matters twice: it is the *statistically* honest option, and it\n",
-    "is 3× the throughput.\n",
-    "\n",
-    "### What the data did to the model\n",
-    "\n",
-    "Three findings from the first full-scale fits are worth more than any smooth\n",
-    "success story:\n",
-    "\n",
-    "**Non-centered effects collapsed under mean-field.** The first version of this\n",
-    "model used the textbook non-centered parameterization\n",
-    "$b_s = \\tau \\cdot z_s,\\ z_s \\sim \\mathcal{N}(0,1)$. On the real corpus the\n",
-    "$z_s$ posteriors blew out to $+17$ while the $\\tau$'s collapsed — some pinned\n",
-    "low against the prior, others (the hour-shape scale) driven far *below* its\n",
-    "bulk:\n",
-    "real cross-symbol volatility levels span two orders of magnitude, the original\n",
-    "$\\tau$ prior could not reach them, and a mean-field approximation of the\n",
-    "$\\tau$–$z$ funnel collapses into its small-$\\tau$/huge-$z$ corner. Both fixes\n",
-    "are principled: weakly informative scale priors, and *centered* effects —\n",
-    "non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per\n",
-    "symbol these groups are anything but. One honest caveat: the two fixes were\n",
-    "applied together, so their individual contributions are confounded — a\n",
-    "same-prior centered/non-centered ablation would separate them, and the safe\n",
-    "reading is empirical rather than theoretical: on this corpus, this\n",
-    "combination fits cleanly and the original one did not.\n",
-    "\n",
-    "**The variance does not exist.** With the conventional\n",
-    "$\\nu = 2 + \\operatorname{softplus}(\\eta)$ parameterization, 450 million rows\n",
-    "pinned $\\nu$ at the floor of exactly 2 with a posterior standard deviation of\n",
-    "$2 \\times 10^{-6}$ — the data demanding tails heavier than any\n",
-    "finite-variance Student-t. The parameterization was moved to a floor of 1 and\n",
-    "the estimand to quantile-based dispersion, which is finite for all $\\nu > 0$.\n",
-    "With the floor out of the way, three independent seeds put $\\nu$ at 1.895\n",
-    "with a seed spread of $8 \\times 10^{-5}$ — an *interior* optimum below 2.\n",
-    "Within the fitted conditional-move Student-t, then, the variance simply does\n",
-    "not exist — a statement about this working likelihood on this corpus, not a\n",
-    "metaphysical claim about crypto returns — while the mean is finite and\n",
-    "quantiles behave, which is exactly why the estimand is quantile-based.\n",
-    "\n",
-    "**The robust location disagrees with least squares about a sign.** Ordinary\n",
-    "least squares of $y$ on $\\text{ylag}$ gives a *negative* coefficient\n",
-    "(bid-ask bounce, ≈ −0.24 on the held-out sample, moved rows only — the exact\n",
-    "value is specification-dependent); the fitted $\\theta_r$ is *positive*\n",
-    "(+0.29, seed spread $8 \\times 10^{-6}$). Direct likelihood profiling on a\n",
-    "million real rows\n",
-    "confirms the likelihood genuinely peaks at the positive value: small moves\n",
-    "(88% of rows) show continuation, large moves show reversal, and a heavy-tailed\n",
-    "likelihood follows the bulk while a quadratic loss follows the outliers.\n",
-    "Neither number is wrong — they answer different questions, and a robust\n",
-    "likelihood answers the one about typical transitions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9c22981",
-   "metadata": {},
-   "source": [
-    "### The stopping rule at scale, and a pre-registration that failed\n",
-    "\n",
-    "Two scoping sentences first, so no number below is ambiguous. Every held-out\n",
-    "score in this section comes from fits on the 60-shard training split only,\n",
-    "with the four held-out shards touched by nothing but the scorer (and, as\n",
-    "disclosed above, the ETL-time standardization constants estimated over the\n",
-    "full corpus) — the\n",
-    "headline four-minute fit above streams all 64 shards and is never\n",
-    "holdout-scored. And \"held out\" here means a hash-random *contemporaneous*\n",
-    "split over serially overlapping transitions (a holdout row's `ylag` can be a\n",
-    "training row's label), which is exactly what paired comparisons between\n",
-    "optimization arms need and nothing more — no forward-in-time validation is\n",
-    "claimed anywhere in this notebook. The stopping-rule comparison was pre-registered before any\n",
-    "full-scale run:\n",
-    "fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical\n",
-    "batch order and seeds, flat learning rate; savings to be claimed only if the\n",
-    "stopped fit's held-out log score landed within max(2 × ref seed spread,\n",
-    "2·10⁻⁴) nats/row of the 2-pass reference. Because a flat learning rate makes\n",
-    "every budget a prefix of the same deterministic trajectory, all five arms per\n",
-    "seed come from one run with mid-flight snapshots — and the windowed rule ran\n",
-    "as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated\n",
-    "one-pass floor plus its designed $h/\\kappa$-window detection delay, 46% below\n",
-    "the reference budget (each protocol is replicated below). Stated plainly,\n",
-    "because it would be easy to bury: the fixed 1-pass budget *dominated* the\n",
-    "monitor here — fewer steps, a better held-out score, and it passed the\n",
-    "pre-registered test the monitor failed — so on this corpus the rule's value\n",
-    "is certifying the plateau, not saving steps beyond a well-chosen budget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "e8e11504",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:06.922892Z",
-     "iopub.status.busy": "2026-08-11T18:59:06.922769Z",
-     "iopub.status.idle": "2026-08-11T18:59:06.930363Z",
-     "shell.execute_reply": "2026-08-11T18:59:06.929692Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>passes</th>\n",
-       "      <th>seed 0</th>\n",
-       "      <th>seed 1</th>\n",
-       "      <th>seed 2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>arm</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>e0.25</th>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.117919</td>\n",
-       "      <td>0.117978</td>\n",
-       "      <td>0.117858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>e0.5</th>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.117867</td>\n",
-       "      <td>0.117869</td>\n",
-       "      <td>0.117858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>e1.0</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.117009</td>\n",
-       "      <td>0.117014</td>\n",
-       "      <td>0.117002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>e2.0</th>\n",
-       "      <td>2.00</td>\n",
-       "      <td>0.117014</td>\n",
-       "      <td>0.117003</td>\n",
-       "      <td>0.117013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>monitor</th>\n",
-       "      <td>1.07</td>\n",
-       "      <td>0.116262</td>\n",
-       "      <td>0.116299</td>\n",
-       "      <td>0.116323</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         passes    seed 0    seed 1    seed 2\n",
-       "arm                                          \n",
-       "e0.25      0.25  0.117919  0.117978  0.117858\n",
-       "e0.5       0.50  0.117867  0.117869  0.117858\n",
-       "e1.0       1.00  0.117009  0.117014  0.117002\n",
-       "e2.0       2.00  0.117014  0.117003  0.117013\n",
-       "monitor    1.07  0.116262  0.116299  0.116323"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conv = pd.DataFrame(\n",
-    "    [(arm, passes, *scores) for arm, passes, scores in results[\"converge_table\"]],\n",
-    "    columns=[\"arm\", \"passes\", \"seed 0\", \"seed 1\", \"seed 2\"],\n",
-    ").set_index(\"arm\")\n",
-    "conv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e327dad7",
-   "metadata": {},
-   "source": [
-    "**The equivalence test failed** — and not in the direction anyone would\n",
-    "guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about\n",
-    "$10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads\n",
-    "run $6\\times10^{-6}$ to $6\\times10^{-5}$ (sample sd), and batch order is shared across\n",
-    "seeds, so these exclude data-order variation) — while the 1- and\n",
-    "2-pass arms tied within seed spread, and the monitor arm under-scored\n",
-    "everything. The loss trace suggests why: this fit reaches its loss plateau\n",
-    "within a quarter pass, so all flat-rate arms sit on the same plateau, where\n",
-    "the held-out score of a *point* estimate moves with the optimizer's state at\n",
-    "flat learning rate 0.02 — consistent with Adam's stationary jitter and\n",
-    "position along the (seed-shared) batch cycle, though these artifacts alone\n",
-    "cannot separate that from other path effects. So we ran the replicated\n",
-    "follow-up the failure demanded: three seeds per protocol for an annealed\n",
-    "2-pass reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass\n",
-    "budget plus the same anneal, and the monitor's stop (1.07 passes) plus the\n",
-    "same anneal. One epistemic label before the numbers: these arms were chosen\n",
-    "*after* seeing the first round, and they score on the same contemporaneous\n",
-    "split — so what replication establishes is the seed-stability of the ranking\n",
-    "on this split, not an independent out-of-sample confirmation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "cd5fea49",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:06.932129Z",
-     "iopub.status.busy": "2026-08-11T18:59:06.932005Z",
-     "iopub.status.idle": "2026-08-11T18:59:07.151680Z",
-     "shell.execute_reply": "2026-08-11T18:59:07.151140Z"
-    }
-   },
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 1600x680 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 703,
-       "width": 2057
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conf = results[\"confirm\"]\n",
-    "flat_ref = np.array(conv.loc[\"e2.0\", [\"seed 0\", \"seed 1\", \"seed 2\"]], dtype=float)\n",
-    "mon_raw = np.array(conv.loc[\"monitor\", [\"seed 0\", \"seed 1\", \"seed 2\"]], dtype=float)\n",
-    "arms = {\n",
-    "    \"monitor stop, raw (1.07 passes)\": (mon_raw.mean(), mon_raw.std(ddof=1)),\n",
-    "    \"flat 2-pass reference\": (flat_ref.mean(), flat_ref.std(ddof=1)),\n",
-    "    **{k: (v[\"mean\"], v[\"sd\"]) for k, v in conf.items()},\n",
-    "}\n",
-    "fig, ax = plt.subplots(figsize=(8, 3.4), layout=\"constrained\")\n",
-    "names = list(arms.keys())\n",
-    "means = [arms[n][0] for n in names]\n",
-    "sds = [arms[n][1] for n in names]\n",
-    "ax.barh(\n",
-    "    names,\n",
-    "    means,\n",
-    "    xerr=[3 * s for s in sds],\n",
-    "    color=[\"C1\" if \"1-pass budget\" in n else \"C0\" for n in names],\n",
-    "    capsize=3,\n",
-    ")\n",
-    "for bar_y, (val, sd_) in enumerate(zip(means, sds)):\n",
-    "    ax.annotate(\n",
-    "        f\"{val:+.6f}\",\n",
-    "        (val + 3 * sd_, bar_y),\n",
-    "        xytext=(4, 0),\n",
-    "        textcoords=\"offset points\",\n",
-    "        va=\"center\",\n",
-    "        fontsize=8,\n",
-    "    )\n",
-    "ax.set_xlim(min(means) - 3e-4, max(means) + 5e-4)\n",
-    "ax.set_xticks([0.116, 0.117, 0.118])\n",
-    "ax.set_xlabel(\"held-out log score (nats/row); bars are 3-seed means ± 3 sd\")\n",
-    "ax.set_title(\n",
-    "    \"Replicated: the anneal is the load-bearing ingredient,\\n\"\n",
-    "    \"and one full pass beats a quarter pass even though the loss plateaued\"\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61129336",
-   "metadata": {},
-   "source": [
-    "Three seeds per arm resolve the picture sharply — within-arm spreads are\n",
-    "$4 \\times 10^{-6}$ to $8 \\times 10^{-5}$, so every gap below is tens of\n",
-    "spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the\n",
-    "batch order, which is shared). Four replicated facts:\n",
-    "\n",
-    "1. **The anneal is load-bearing.** At exactly matched budgets, the annealed\n",
-    "   2-pass reference beats the flat 2-pass arm by $8.5 \\times 10^{-4}$; the\n",
-    "   1-pass budget plus a 2,000-step anneal (0.118738) beats the flat 1-pass\n",
-    "   arm by $1.7 \\times 10^{-3}$ for 1.8% extra steps.\n",
-    "2. **The training-loss plateau is not a held-out plateau.** The loss flattens\n",
-    "   within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)\n",
-    "   trails the 1-pass-plus-anneal arm by $1.7 \\times 10^{-3}$: between ¼ and 1\n",
-    "   pass the model keeps improving where the loss trace cannot show it. The\n",
-    "   one-full-pass floor earns its keep after all.\n",
-    "3. **The stop rule adds nothing beyond the floor here.** Its stop\n",
-    "   (1.07 passes) plus anneal (0.118450) sits $2.9 \\times 10^{-4}$ *below* the\n",
-    "   plain 1-pass budget plus anneal — the detection delay bought 7% more flat-lr\n",
-    "   steps and a slightly worse endpoint. On this corpus the rule's value is\n",
-    "   certifying the plateau, not choosing a better stop than a well-chosen\n",
-    "   budget.\n",
-    "4. **More budget is not better.** The fully annealed 2-pass reference\n",
-    "   (0.117859) trails 1-pass-plus-short-anneal by $8.8 \\times 10^{-4}$: the\n",
-    "   endpoint depends on the schedule, not the step count.\n",
-    "\n",
-    "This also retires our own earlier reading: the \"$5 \\times 10^{-4}$ path\n",
-    "noise between annealed runs\" was a *protocol* difference wearing a noise\n",
-    "costume — replicates of the same protocol agree to $10^{-4}$ or better, most\n",
-    "to $10^{-5}$. And it sharpens the pre-registration post-mortem: the mistake\n",
-    "was not the $2 \\times 10^{-4}$ floor but the yardstick — the pre-registered\n",
-    "flat 2-pass reference sits $1.7 \\times 10^{-3}$ off the tested frontier, and\n",
-    "even the annealed 2-pass comparator sits $8.8 \\times 10^{-4}$ off it, so\n",
-    "equivalence to either measures loyalty, not quality.\n",
-    "\n",
-    "The working protocol this ablation supports: **one full pass at the working\n",
-    "learning rate, then a short anneal** — with the stopping rule certifying the\n",
-    "training-loss plateau (it is the held-out ablation, not the rule, that shows\n",
-    "one pass sufficed among the schedules tested), and equivalence judged after\n",
-    "the anneal, on held-out score, against the best arm rather than a\n",
-    "conventional reference. That finding — not any savings headline — is what we\n",
-    "would want a reader to take away, and it fed directly back into the design\n",
-    "discussion of the upstream pull requests.\n",
-    "\n",
-    "### The estimand: dispersion on the event clock\n",
-    "\n",
-    "What the model is *for*: per symbol and hour, the probability that a trade\n",
-    "moves the price at all, and how far it typically goes when it does (posterior:\n",
-    "the all-shard headline fit — these curves are estimates, not scored claims).\n",
-    "Both come\n",
-    "from the posterior of the fitted links at reference covariates, so the\n",
-    "intraday shape below is pooled across the hierarchy — BTC's curve is almost\n",
-    "entirely its own data, the thin symbols' curves borrow the global harmonics\n",
-    "exactly as the model intends:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "a0b3eb02",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:07.154617Z",
-     "iopub.status.busy": "2026-08-11T18:59:07.154462Z",
-     "iopub.status.idle": "2026-08-11T18:59:07.580030Z",
-     "shell.execute_reply": "2026-08-11T18:59:07.579054Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1800x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 743,
-       "width": 1944
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hours = np.arange(24)\n",
-    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.6), sharex=True, layout=\"constrained\")\n",
-    "for sym_name, _ in results[\"forest_symbols\"]:\n",
-    "    disp = results[\"dispersion_full\"][sym_name]\n",
-    "    axs[0].plot(hours, disp[\"pi_mean\"], marker=\".\", label=sym_name)\n",
-    "    axs[1].plot(hours, disp[\"hw_mean\"], marker=\".\")\n",
-    "    axs[1].fill_between(\n",
-    "        hours,\n",
-    "        np.array(disp[\"hw_mean\"]) - 2 * np.array(disp[\"hw_sd\"]),\n",
-    "        np.array(disp[\"hw_mean\"]) + 2 * np.array(disp[\"hw_sd\"]),\n",
-    "        alpha=0.2,\n",
-    "    )\n",
-    "axs[0].set_title(r\"move probability $\\pi_{s,h}$\")\n",
-    "axs[1].set_title(\"conditional-move 90% half-width (bp)\")\n",
-    "axs[1].set_yscale(\"log\")\n",
-    "for ax in axs:\n",
-    "    ax.set_xlabel(\"UTC hour\")\n",
-    "    ax.set_xticks(range(0, 24, 6))\n",
-    "axs[0].legend(fontsize=8, loc=\"center left\")\n",
-    "fig.suptitle(\"Event-clock dispersion by hour: shared harmonics, per-symbol levels\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "942701fd",
-   "metadata": {},
-   "source": [
-    "Read it as event time, not clock time: these are per-*transition* quantities,\n",
-    "and the number of transitions per hour itself varies enormously — the two\n",
-    "clocks tell different stories and this notebook only claims the first."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10b5d7ea",
-   "metadata": {},
-   "source": [
-    "### What full N buys: thin cells\n",
-    "\n",
-    "A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn\n",
-    "by taking the hash-ordered head of each shard, with the last four shards held\n",
-    "out for scoring) can estimate every *global* parameter of this model — and did,\n",
-    "matching the full fit closely on the well-determined ones (ν to 0.1%, the\n",
-    "θ's, λ_q, τ_α) and agreeing in sign and rough magnitude on the small\n",
-    "coefficients that wander between full-N configurations too. So why stream\n",
-    "100× more\n",
-    "data? Because the estimand lives in symbol-by-hour cells, and the thin end of\n",
-    "the universe is where the two fits part ways. Below, the posterior of the\n",
-    "conditional-move 90% half-width $\\sigma_{s,h}\\, t^{-1}_{0.95}(\\nu)$ at\n",
-    "reference covariates, full fit versus 1% subsample, for the thinnest and the\n",
-    "fattest symbols in the corpus:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "b345e1bf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:07.582719Z",
-     "iopub.status.busy": "2026-08-11T18:59:07.582559Z",
-     "iopub.status.idle": "2026-08-11T18:59:07.970712Z",
-     "shell.execute_reply": "2026-08-11T18:59:07.969963Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x840 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 863,
-       "width": 1955
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "disp_full = results[\"dispersion_full\"]\n",
-    "disp_sub = results[\"dispersion_sub1pct\"]\n",
-    "show_syms = results[\"forest_symbols\"]  # thinnest three + BTC, with row counts\n",
-    "show_hours = [3, 14, 20]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4.2), layout=\"constrained\")\n",
-    "ypos = 0\n",
-    "labels, ticks = [], []\n",
-    "for sym_name, n_rows in show_syms:\n",
-    "    for arm, color, off in [(disp_full, \"C0\", 0.22), (disp_sub, \"C1\", -0.22)]:\n",
-    "        hw = arm[sym_name]\n",
-    "        for hi, h in enumerate(show_hours):\n",
-    "            hw_m, hw_s = hw[\"hw_mean\"][h], hw[\"hw_sd\"][h]\n",
-    "            ax.errorbar(\n",
-    "                [hw_m],\n",
-    "                [ypos + off + 0.5 * (hi - 1)],\n",
-    "                xerr=[[2 * hw_s], [2 * hw_s]],\n",
-    "                fmt=\"o\",\n",
-    "                ms=4,\n",
-    "                color=color,\n",
-    "                capsize=2,\n",
-    "            )\n",
-    "    labels.append(f\"{sym_name}\\n({n_rows / 1e6:.2f}M rows)\")\n",
-    "    ticks.append(ypos)\n",
-    "    ypos += 2.2\n",
-    "ax.set_yticks(ticks)\n",
-    "ax.set_yticklabels(labels, fontsize=8)\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.set_xlabel(\"conditional-move 90% half-width (bp), posterior mean ± 2 sd\")\n",
-    "ax.set_title(\n",
-    "    \"Full fit (C0) vs 1% subsample (C1) at three hours of day\\n\"\n",
-    "    \"Global parameters agree; thin-cell uncertainty is where full N earns its keep\"\n",
-    ")\n",
-    "ax.plot([], [], \"o\", color=\"C0\", label=\"full corpus\")\n",
-    "ax.plot([], [], \"o\", color=\"C1\", label=\"1% subsample\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "043b29fe",
-   "metadata": {},
-   "source": [
-    "This is a capacity-and-estimand demonstration, not a claim of method\n",
-    "superiority: the subsample answers global questions at 1% of the cost.\n",
-    "\n",
-    ":::{warning}\n",
-    "No stratified oversampling of thin symbols, however tempting: unequal\n",
-    "inclusion probabilities break the plain `total_size` rescaling. The subsample\n",
-    "is uniform or it is invalid.\n",
-    ":::\n",
-    "\n",
-    "### What we do not sweep under the rug\n",
-    "\n",
-    "The single most useful diagnostic this notebook can leave you with costs\n",
-    "three fits and one table: the reported mean-field standard deviation next to\n",
-    "the sample standard deviation of the posterior mean across three optimizer\n",
-    "seeds. Read it as a *computational-stability* diagnostic. A ratio far above\n",
-    "one is disqualifying — the reported width understates even run-to-run\n",
-    "variation, so it cannot be trusted. A ratio near or below one is merely\n",
-    "*necessary*: the width survives seed replication, but certifying it as\n",
-    "calibrated would take a full-rank or MCMC comparison on a tractable\n",
-    "subproblem, which is future work here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "419d23cf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:07.973024Z",
-     "iopub.status.busy": "2026-08-11T18:59:07.972882Z",
-     "iopub.status.idle": "2026-08-11T18:59:07.981023Z",
-     "shell.execute_reply": "2026-08-11T18:59:07.980532Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>posterior_mean</th>\n",
-       "      <th>reported_sd</th>\n",
-       "      <th>seed_spread</th>\n",
-       "      <th>ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>alpha0</th>\n",
-       "      <td>-1.570000</td>\n",
-       "      <td>0.000064</td>\n",
-       "      <td>0.028130</td>\n",
-       "      <td>442.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta_a</th>\n",
-       "      <td>0.053680</td>\n",
-       "      <td>0.000077</td>\n",
-       "      <td>0.005310</td>\n",
-       "      <td>69.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kappa0</th>\n",
-       "      <td>0.336200</td>\n",
-       "      <td>0.000151</td>\n",
-       "      <td>0.008867</td>\n",
-       "      <td>58.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_d</th>\n",
-       "      <td>0.025290</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>6.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eta</th>\n",
-       "      <td>0.369200</td>\n",
-       "      <td>0.000237</td>\n",
-       "      <td>0.000134</td>\n",
-       "      <td>0.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nu</th>\n",
-       "      <td>1.895000</td>\n",
-       "      <td>0.000140</td>\n",
-       "      <td>0.000079</td>\n",
-       "      <td>0.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>lambda_a</th>\n",
-       "      <td>0.056380</td>\n",
-       "      <td>0.000139</td>\n",
-       "      <td>0.000022</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>lambda_q</th>\n",
-       "      <td>-0.392800</td>\n",
-       "      <td>0.000113</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta_q</th>\n",
-       "      <td>-0.001442</td>\n",
-       "      <td>0.000108</td>\n",
-       "      <td>0.000010</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_r</th>\n",
-       "      <td>0.294700</td>\n",
-       "      <td>0.000076</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tau_k</th>\n",
-       "      <td>1.039000</td>\n",
-       "      <td>0.116900</td>\n",
-       "      <td>0.014510</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tau_al</th>\n",
-       "      <td>3.210000</td>\n",
-       "      <td>0.366800</td>\n",
-       "      <td>0.041400</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          posterior_mean  reported_sd  seed_spread  ratio\n",
-       "alpha0         -1.570000     0.000064     0.028130  442.1\n",
-       "beta_a          0.053680     0.000077     0.005310   69.0\n",
-       "kappa0          0.336200     0.000151     0.008867   58.7\n",
-       "theta_d         0.025290     0.000001     0.000008    6.1\n",
-       "eta             0.369200     0.000237     0.000134    0.6\n",
-       "nu              1.895000     0.000140     0.000079    0.6\n",
-       "lambda_a        0.056380     0.000139     0.000022    0.2\n",
-       "lambda_q       -0.392800     0.000113     0.000023    0.2\n",
-       "beta_q         -0.001442     0.000108     0.000010    0.1\n",
-       "theta_r         0.294700     0.000076     0.000008    0.1\n",
-       "tau_k           1.039000     0.116900     0.014510    0.1\n",
-       "tau_al          3.210000     0.366800     0.041400    0.1"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spread = pd.DataFrame(results[\"seed_spread\"]).T\n",
-    "spread[\"ratio\"] = (spread[\"seed_spread\"] / spread[\"reported_sd\"]).round(1)\n",
-    "spread.sort_values(\"ratio\", ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e50b8ecc",
-   "metadata": {},
-   "source": [
-    "- **Mean-field understates uncertainty along soft ridges.** The intercepts\n",
-    "  share a ridge with their pooled group means (only the sums are identified),\n",
-    "  and across repeated seeds the spread of $\\alpha_0$ is roughly 440× its\n",
-    "  reported posterior standard deviation — $\\kappa_0$ and $\\beta_a$ show the\n",
-    "  same signature at 60–70×. The parameters *without* a ridge ($\\theta_r$,\n",
-    "  $\\nu$, $\\lambda_a$, the $\\tau$'s) sit at or below one, and $\\theta_d$ sits\n",
-    "  in between (≈6). Report mean-field standard deviations only for parameters\n",
-    "  you have seed-checked — and remember the check bounds the error from below.\n",
-    "- **The speed ordering is model-dependent.** Here the heavy gradient hides\n",
-    "  the loader entirely and pre-shuffled streaming outpaces in-RAM\n",
-    "  `pm.Minibatch` — and did so on our lighter probe as well. Streaming's claim\n",
-    "  is memory and scale — treat any steps-per-second comparison as specific to\n",
-    "  the model it was measured on.\n",
-    "- **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the\n",
-    "  loader makes without-replacement epoch passes.\n",
-    "- **Discreteness at the smallest moves.** Price changes are integer multiples\n",
-    "  of the tick size; the continuous Student-t is a working approximation whose\n",
-    "  residual misfit concentrates at |y| near one tick.\n",
-    "- **Event time is not clock time.** All dispersion statements are per\n",
-    "  transition, not per minute; activity itself varies by hour, and conflating\n",
-    "  the two clocks changes the intraday story."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0975149b",
-   "metadata": {},
-   "source": [
     "## Where the pieces live\n",
     "\n",
     ":::{seealso}\n",
-    "The `DataLoader`/`parquet_source` used here are in\n",
-    "[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698); a\n",
-    "callback-free `Trainer` wrapper\n",
+    "The `DataLoader`/`parquet_source` used here merged in\n",
+    "[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698). A\n",
+    "`Trainer` that wraps this notebook's callback pattern\n",
     "([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the\n",
-    "convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))\n",
-    "are under review, and this notebook's callback pattern is exactly what those\n",
-    "APIs wrap. The ETL and full-scale run scripts accompany the submission, and\n",
-    "the {ref}`streaming_dataset` companion covers the API mechanics.\n",
+    "convergence monitor that grew out of the stopping section\n",
+    "([pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733)) are\n",
+    "under review.\n",
     ":::"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e3d6df66",
+   "id": "7fe849e3",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2896,7 +1968,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "936fb51d",
+   "id": "9d5d2bf6",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2908,7 +1980,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49ac01ee",
+   "id": "4906211f",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2916,14 +1988,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "d2d263b9",
+   "execution_count": 20,
+   "id": "a4bfa41c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-11T18:59:07.982981Z",
-     "iopub.status.busy": "2026-08-11T18:59:07.982845Z",
-     "iopub.status.idle": "2026-08-11T18:59:08.002465Z",
-     "shell.execute_reply": "2026-08-11T18:59:08.001850Z"
+     "iopub.execute_input": "2026-08-14T08:44:15.348613Z",
+     "iopub.status.busy": "2026-08-14T08:44:15.348529Z",
+     "iopub.status.idle": "2026-08-14T08:44:15.360535Z",
+     "shell.execute_reply": "2026-08-14T08:44:15.360148Z"
     }
    },
    "outputs": [
@@ -2931,7 +2003,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Tue, 11 Aug 2026\n",
+      "Last updated: Fri, 14 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -2963,7 +2035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb119275",
+   "id": "0300a50c",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 2abba8a48..0554d5b4b 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -32,28 +32,21 @@ trades for a single liquid pair is roughly 28 million rows, and a modest
 alt pairs) already exceeds 450 million rows — far more
 than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a
 hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of
-that scale by streaming minibatches from disk, using the
-{class}`~pymc_extras.variational.dataloader.DataLoader` introduced in the
-{ref}`streaming_dataset` companion notebook.
-
-The companion covers the mechanics of the streaming API. This notebook is the
-applied piece, and it tries to teach three things that only show up on real
-data:
+that scale by streaming minibatches from disk with pymc-extras'
+{class}`~pymc_extras.variational.dataloader.DataLoader`. It tries to teach
+three things that only show up on real data:
 
 1. **When minibatch VI is even valid.** Two acceptance gates that most
    time-series models fail — and a model class that passes both.
 2. **The full workflow at scale**: causal feature engineering, an on-disk
    global shuffle, streaming ADVI with `total_size` rescaling, and an online
    stopping rule.
-3. **Honest reporting.** Mean-field variational inference on half a billion
-   rows produces some numbers you should trust and some you should not; we
-   measure which are which instead of guessing.
+3. **Honest reporting.** Mean-field variational inference produces some
+   numbers you should trust and some you should not; with known ground truth
+   we measure which are which instead of guessing.
 
-The notebook itself runs in about a minute on synthetic data with known ground
-truth — the streaming code is identical at any scale. Full-scale results on the
-real 38-symbol Binance corpus are reported in {ref}`tick-real-corpus`: the data
-are public, and every quoted number traces to the run artifacts of the ETL and
-fit scripts that accompany the submission.
+The notebook runs in about a minute on synthetic data with known ground
+truth — the streaming code is identical at any scale.
 
 +++
 
@@ -96,6 +89,13 @@ also substantively motivated by the data.
 :::{include} ../extra_installs.md
 :::
 
+:::{note}
+The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into
+pymc-extras after the v0.14.0 release. Until the next release, install
+pymc-extras from `main`:
+`pip install "pymc-extras @ git+https://github.com/pymc-devs/pymc-extras"`.
+:::
+
 ```{code-cell} ipython3
 import json
 import logging
@@ -167,12 +167,12 @@ $b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
 so a thin symbol borrows the global intraday shape where its own data run out —
 that is the hierarchical payoff we will measure. They are parameterized
 *centered*, deliberately: the familiar non-centered trick exists for data-poor
-groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich —
-{ref}`tick-real-corpus` shows what happened when we tried it the other way.
+groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich.
 The degrees of freedom are
 shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
-the floor of 1 (rather than the more comfortable 2) is not a stylistic choice,
-and {ref}`tick-real-corpus` shows what the data did to force it.
+the floor of 1 (rather than the more comfortable 2) leaves room for tails too
+heavy to carry a finite variance — and the estimand below is chosen to stay
+meaningful exactly there.
 
 The reported estimand is **event-return dispersion on the event clock**:
 $\pi_{s,h}$ together with the conditional-move 90% half-width
@@ -746,16 +746,15 @@ feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming
 itself only after `min_steps` — which on streaming fits should be at least
 one full pass, since stopping before the model has seen every row once is
 never evidence of convergence. The trap this section demonstrates was later
-confirmed on real ADVI traces, and the revised implementation under review
-for `pymc-extras` is built on the windowed idea shown here, with horizons
+confirmed on real ADVI traces, and the revised implementation under review for
+[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733) is built on the windowed idea shown here, with horizons
 that grow over the run and a second, practical-negligibility yardstick.
 
 :::{admonition} The minibatch trap
 :class: warning
 Successive losses are evaluated on *different batches*, so the per-step noise
-dwarfs the per-step improvement: on our full-scale run the median one-step
-change is about 106 loss units while the mean improvement — (first − last)
-loss over the run's steps — is about 0.06 units per step. A rule that standardizes *raw* increments concludes "improvement is
+dwarfs the per-step improvement by orders of magnitude. A rule that
+standardizes *raw* increments therefore concludes "improvement is
 indistinguishable from zero" immediately upon arming, and fires a fixed
 ~$h/\kappa$ steps later regardless of whether the fit converged.
 :::
@@ -897,494 +896,22 @@ windowed rule stops once per-window improvement falls below a quarter of the
 window-noise scale, here capturing roughly 95% of the total loss descent at
 under a quarter of the step budget. Two honest qualifications. First, what the
 rule detects is a *loss plateau* — a necessary signal, not a proof that the
-posterior has converged; the held-out checks in the next section are the
-independent evidence. Second, the $h/\kappa$-window delay after a plateau is
-the rule's *nominal* detection lag; clipping and the adaptive scale can move
-the actual hitting time. On the full-scale corpus the same replay shows the
-windowed rule stopping shortly after one pass, at a *training loss* within
-noise of the two-pass reference — whether that stop is also good enough on
-held-out score is precisely what the pre-registered test in the next section
-failed to confirm.
-
-(tick-real-corpus)=
-
-## The real corpus: 38 symbols, half a billion transitions
-
-Everything above runs identically on real data; what changes is only the ETL
-in front of it. The full-scale corpus behind the numbers in this section:
-
-- **Source**: Binance's public aggregated-trades archive
-  (`data.binance.vision`) — no API key, published SHA-256 checksums, monthly
-  CSV dumps. The corpus is 491,559,069 transition rows: four majors
-  (BTC/ETH/SOL/XRP × 6 months of 2026) for row bulk plus 34 alt pairs
-  (June 2026) for hierarchy breadth, the thinnest at 67,000 rows.
-- **ETL**: per symbol, *chronologically*: next-event returns, move indicator,
-  trade sign, standardized log notional, trailing 60-second activity, last
-  nonzero return, hour — then the splitmix64 global shuffle into 64 shards
-  (~9 GB of Parquet), with the shard-head mixing check asserted. The complete
-  script is published with the notebook's companion repository.
-- **Hardware**: one Apple M4 laptop, 24 GB RAM. No cluster anywhere.
-
-Fitting the full corpus for two passes (one at lr 0.02, one at 0.005) takes
-about four minutes at roughly 1,000 optimization steps per second (4 million
-rows per second through the model), with peak resident memory of 1.1 GB for
-the whole run — fit loop plus posterior draws and summaries; the memory-wall
-figure's leaner 0.5 GB point measures the fit loop alone. Both are flat in N. The subsections below are the
-measured results; each figure's numbers are inlined verbatim from the run
-logs so the plots execute without any data access.
-
-+++
-
-### The memory wall, measured
-
-Every number in the cells below is inlined verbatim from the run artifacts of
-the published scripts, so these figures execute without touching the data.
-First, the reason streaming exists at all. Each point is a fresh subprocess's
-peak resident memory (`ru_maxrss`) after fitting this exact model at that many
-rows — `pm.Minibatch` holding the rows in RAM versus the loader streaming the
-shards:
-
-```{code-cell} ipython3
-with open("streaming_tick_data_results.json") as f:
-    results = json.load(f)
-
-mb = np.array(results["memwall"]["minibatch"], dtype=float)
-st = np.array(results["memwall"]["stream"], dtype=float)
-bpr = results["memwall"]["bytes_per_row"]
-icpt = results["memwall"]["intercept_gb"]
-cross = results["memwall"]["crossing_rows_12gb"]
-n_full = results["corpus"]["n_rows"]
-
-fig, ax = plt.subplots(figsize=(8, 4), layout="constrained")
-ax.plot(mb[:, 0] / 1e6, mb[:, 1], "o-", color="C1", label="pm.Minibatch (rows in RAM)")
-grid = np.linspace(0, n_full, 200)
-ax.plot(
-    grid / 1e6,
-    icpt + bpr * grid / 1e9,
-    ls=":",
-    color="C1",
-    lw=1,
-    label=f"fit: {icpt:.1f} GB + {bpr:.0f} B/row",
-)
-ax.plot(st[:, 0] / 1e6, st[:, 1], "s-", color="C0", label="streaming DataLoader")
-ax.axhline(12, color="C3", ls="--", lw=1, label="12 GB (16 GB laptop budget)")
-ax.axvline(cross / 1e6, color="C3", ls=":", lw=1)
-ax.annotate(
-    f"wall: {cross / 1e6:,.0f}M rows",
-    (cross / 1e6 + 8, 10.6),
-    color="C3",
-    fontsize=9,
-    ha="left",
-    va="top",
-)
-ax.axvline(n_full / 1e6, color="C2", ls=":", lw=1)
-ax.set_ylim(0, 42)
-ax.annotate(
-    f"this corpus: {n_full / 1e6:,.0f}M rows",
-    (n_full / 1e6 - 6, 40.5),
-    color="C2",
-    fontsize=9,
-    ha="right",
-    va="top",
-)
-ax.set_xlabel("dataset size (millions of rows)")
-ax.set_ylabel("peak RSS (GB)")
-ax.set_title(
-    "The in-memory path crosses a 16 GB laptop's budget at "
-    f"{cross / 1e6:,.0f}M rows; streaming does not care"
-)
-ax.legend(loc="upper left");
-```
-
-The corpus behind this notebook sits at roughly three times the wall.
-
-:::{warning}
-Two caveats belong next to this figure, not in a footnote. The in-memory arm
-was measured up to 64M rows and the crossing is a *projection* from the fitted
-line — macOS does not enforce `RLIMIT_AS`, so a demonstrated hard failure
-needs a memory-capped Linux subprocess. And the two samplers differ
-semantically: `pm.Minibatch` draws with replacement, the loader makes
-without-replacement epoch passes.
-:::
-
-And speed? Steady-state, compile time subtracted, same model:
-
-```{code-cell} ipython3
-tp = results["memwall"]["throughput"]
-labels = {
-    "minibatch": "pm.Minibatch (rows in RAM)",
-    "stream": "DataLoader, pre-shuffled shards",
-    "stream_shuffled": "DataLoader, runtime shuffle buffer",
-}
-fig, ax = plt.subplots(figsize=(8, 2.6), layout="constrained")
-names = [labels[k] for k in labels]
-vals = [tp[k] for k in labels]
-ax.barh(names, vals, color=["C1", "C0", "C0"])
-for bar_y, val in enumerate(vals):
-    ax.annotate(
-        f"{val:,.0f} steps/s",
-        (val, bar_y),
-        xytext=(4, 0),
-        textcoords="offset points",
-        va="center",
-        fontsize=9,
-    )
-ax.set_xlabel("optimization steps per second (batch 4096, steady state)")
-ax.set_title("With a 440-parameter gradient, the loader is not the bottleneck");
-```
-
-The ordering surprised us, so it is worth stating carefully. On this model the
-gradient dominates the step, and the pre-shuffled streaming path *outpaces*
-in-RAM `pm.Minibatch` — the loader's Python-level handoff is cheaper than
-Minibatch's per-step random indexing. On our lighter probe model the same
-ordering held (pre-shuffled loader ~4,900 vs Minibatch ~3,000 steps/s), so
-treat any speed ordering as an empirical, model-specific fact rather than a
-law in either direction. The one consistently
-expensive path is the runtime shuffle buffer — which is why the ETL-time
-global shuffle matters twice: it is the *statistically* honest option, and it
-is 3× the throughput.
-
-### What the data did to the model
-
-Three findings from the first full-scale fits are worth more than any smooth
-success story:
-
-**Non-centered effects collapsed under mean-field.** The first version of this
-model used the textbook non-centered parameterization
-$b_s = \tau \cdot z_s,\ z_s \sim \mathcal{N}(0,1)$. On the real corpus the
-$z_s$ posteriors blew out to $+17$ while the $\tau$'s collapsed — some pinned
-low against the prior, others (the hour-shape scale) driven far *below* its
-bulk:
-real cross-symbol volatility levels span two orders of magnitude, the original
-$\tau$ prior could not reach them, and a mean-field approximation of the
-$\tau$–$z$ funnel collapses into its small-$\tau$/huge-$z$ corner. Both fixes
-are principled: weakly informative scale priors, and *centered* effects —
-non-centering exists for data-poor groups, and with $10^5$–$10^8$ rows per
-symbol these groups are anything but. One honest caveat: the two fixes were
-applied together, so their individual contributions are confounded — a
-same-prior centered/non-centered ablation would separate them, and the safe
-reading is empirical rather than theoretical: on this corpus, this
-combination fits cleanly and the original one did not.
-
-**The variance does not exist.** With the conventional
-$\nu = 2 + \operatorname{softplus}(\eta)$ parameterization, 450 million rows
-pinned $\nu$ at the floor of exactly 2 with a posterior standard deviation of
-$2 \times 10^{-6}$ — the data demanding tails heavier than any
-finite-variance Student-t. The parameterization was moved to a floor of 1 and
-the estimand to quantile-based dispersion, which is finite for all $\nu > 0$.
-With the floor out of the way, three independent seeds put $\nu$ at 1.895
-with a seed spread of $8 \times 10^{-5}$ — an *interior* optimum below 2.
-Within the fitted conditional-move Student-t, then, the variance simply does
-not exist — a statement about this working likelihood on this corpus, not a
-metaphysical claim about crypto returns — while the mean is finite and
-quantiles behave, which is exactly why the estimand is quantile-based.
-
-**The robust location disagrees with least squares about a sign.** Ordinary
-least squares of $y$ on $\text{ylag}$ gives a *negative* coefficient
-(bid-ask bounce, ≈ −0.24 on the held-out sample, moved rows only — the exact
-value is specification-dependent); the fitted $\theta_r$ is *positive*
-(+0.29, seed spread $8 \times 10^{-6}$). Direct likelihood profiling on a
-million real rows
-confirms the likelihood genuinely peaks at the positive value: small moves
-(88% of rows) show continuation, large moves show reversal, and a heavy-tailed
-likelihood follows the bulk while a quadratic loss follows the outliers.
-Neither number is wrong — they answer different questions, and a robust
-likelihood answers the one about typical transitions.
-
-+++
-
-### The stopping rule at scale, and a pre-registration that failed
-
-Two scoping sentences first, so no number below is ambiguous. Every held-out
-score in this section comes from fits on the 60-shard training split only,
-with the four held-out shards touched by nothing but the scorer (and, as
-disclosed above, the ETL-time standardization constants estimated over the
-full corpus) — the
-headline four-minute fit above streams all 64 shards and is never
-holdout-scored. And "held out" here means a hash-random *contemporaneous*
-split over serially overlapping transitions (a holdout row's `ylag` can be a
-training row's label), which is exactly what paired comparisons between
-optimization arms need and nothing more — no forward-in-time validation is
-claimed anywhere in this notebook. The stopping-rule comparison was pre-registered before any
-full-scale run:
-fixed budgets of ¼, ½, 1 and 2 passes versus the windowed rule, identical
-batch order and seeds, flat learning rate; savings to be claimed only if the
-stopped fit's held-out log score landed within max(2 × ref seed spread,
-2·10⁻⁴) nats/row of the 2-pass reference. Because a flat learning rate makes
-every budget a prefix of the same deterministic trajectory, all five arms per
-seed come from one run with mid-flight snapshots — and the windowed rule ran
-as a shadow observer, stopping (all three seeds) at 1.07 passes: the mandated
-one-pass floor plus its designed $h/\kappa$-window detection delay, 46% below
-the reference budget (each protocol is replicated below). Stated plainly,
-because it would be easy to bury: the fixed 1-pass budget *dominated* the
-monitor here — fewer steps, a better held-out score, and it passed the
-pre-registered test the monitor failed — so on this corpus the rule's value
-is certifying the plateau, not saving steps beyond a well-chosen budget.
-
-```{code-cell} ipython3
-conv = pd.DataFrame(
-    [(arm, passes, *scores) for arm, passes, scores in results["converge_table"]],
-    columns=["arm", "passes", "seed 0", "seed 1", "seed 2"],
-).set_index("arm")
-conv
-```
-
-**The equivalence test failed** — and not in the direction anyone would
-guess: the ¼- and ½-pass arms *out-scored* the 1- and 2-pass arms by about
-$10^{-3}$ nats/row — 74–88× the tightest arms' seed spreads (per-arm spreads
-run $6\times10^{-6}$ to $6\times10^{-5}$ (sample sd), and batch order is shared across
-seeds, so these exclude data-order variation) — while the 1- and
-2-pass arms tied within seed spread, and the monitor arm under-scored
-everything. The loss trace suggests why: this fit reaches its loss plateau
-within a quarter pass, so all flat-rate arms sit on the same plateau, where
-the held-out score of a *point* estimate moves with the optimizer's state at
-flat learning rate 0.02 — consistent with Adam's stationary jitter and
-position along the (seed-shared) batch cycle, though these artifacts alone
-cannot separate that from other path effects. So we ran the replicated
-follow-up the failure demanded: three seeds per protocol for an annealed
-2-pass reference, a quarter-pass budget plus a 2,000-step anneal, a 1-pass
-budget plus the same anneal, and the monitor's stop (1.07 passes) plus the
-same anneal. One epistemic label before the numbers: these arms were chosen
-*after* seeing the first round, and they score on the same contemporaneous
-split — so what replication establishes is the seed-stability of the ranking
-on this split, not an independent out-of-sample confirmation.
-
-```{code-cell} ipython3
-conf = results["confirm"]
-flat_ref = np.array(conv.loc["e2.0", ["seed 0", "seed 1", "seed 2"]], dtype=float)
-mon_raw = np.array(conv.loc["monitor", ["seed 0", "seed 1", "seed 2"]], dtype=float)
-arms = {
-    "monitor stop, raw (1.07 passes)": (mon_raw.mean(), mon_raw.std(ddof=1)),
-    "flat 2-pass reference": (flat_ref.mean(), flat_ref.std(ddof=1)),
-    **{k: (v["mean"], v["sd"]) for k, v in conf.items()},
-}
-fig, ax = plt.subplots(figsize=(8, 3.4), layout="constrained")
-names = list(arms.keys())
-means = [arms[n][0] for n in names]
-sds = [arms[n][1] for n in names]
-ax.barh(
-    names,
-    means,
-    xerr=[3 * s for s in sds],
-    color=["C1" if "1-pass budget" in n else "C0" for n in names],
-    capsize=3,
-)
-for bar_y, (val, sd_) in enumerate(zip(means, sds)):
-    ax.annotate(
-        f"{val:+.6f}",
-        (val + 3 * sd_, bar_y),
-        xytext=(4, 0),
-        textcoords="offset points",
-        va="center",
-        fontsize=8,
-    )
-ax.set_xlim(min(means) - 3e-4, max(means) + 5e-4)
-ax.set_xticks([0.116, 0.117, 0.118])
-ax.set_xlabel("held-out log score (nats/row); bars are 3-seed means ± 3 sd")
-ax.set_title(
-    "Replicated: the anneal is the load-bearing ingredient,\n"
-    "and one full pass beats a quarter pass even though the loss plateaued"
-);
-```
-
-Three seeds per arm resolve the picture sharply — within-arm spreads are
-$4 \times 10^{-6}$ to $8 \times 10^{-5}$, so every gap below is tens of
-spreads wide (with one caveat: seeds vary the Monte-Carlo noise, not the
-batch order, which is shared). Four replicated facts:
-
-1. **The anneal is load-bearing.** At exactly matched budgets, the annealed
-   2-pass reference beats the flat 2-pass arm by $8.5 \times 10^{-4}$; the
-   1-pass budget plus a 2,000-step anneal (0.118738) beats the flat 1-pass
-   arm by $1.7 \times 10^{-3}$ for 1.8% extra steps.
-2. **The training-loss plateau is not a held-out plateau.** The loss flattens
-   within a quarter pass, but the quarter-pass-plus-anneal arm (0.117021)
-   trails the 1-pass-plus-anneal arm by $1.7 \times 10^{-3}$: between ¼ and 1
-   pass the model keeps improving where the loss trace cannot show it. The
-   one-full-pass floor earns its keep after all.
-3. **The stop rule adds nothing beyond the floor here.** Its stop
-   (1.07 passes) plus anneal (0.118450) sits $2.9 \times 10^{-4}$ *below* the
-   plain 1-pass budget plus anneal — the detection delay bought 7% more flat-lr
-   steps and a slightly worse endpoint. On this corpus the rule's value is
-   certifying the plateau, not choosing a better stop than a well-chosen
-   budget.
-4. **More budget is not better.** The fully annealed 2-pass reference
-   (0.117859) trails 1-pass-plus-short-anneal by $8.8 \times 10^{-4}$: the
-   endpoint depends on the schedule, not the step count.
-
-This also retires our own earlier reading: the "$5 \times 10^{-4}$ path
-noise between annealed runs" was a *protocol* difference wearing a noise
-costume — replicates of the same protocol agree to $10^{-4}$ or better, most
-to $10^{-5}$. And it sharpens the pre-registration post-mortem: the mistake
-was not the $2 \times 10^{-4}$ floor but the yardstick — the pre-registered
-flat 2-pass reference sits $1.7 \times 10^{-3}$ off the tested frontier, and
-even the annealed 2-pass comparator sits $8.8 \times 10^{-4}$ off it, so
-equivalence to either measures loyalty, not quality.
-
-The working protocol this ablation supports: **one full pass at the working
-learning rate, then a short anneal** — with the stopping rule certifying the
-training-loss plateau (it is the held-out ablation, not the rule, that shows
-one pass sufficed among the schedules tested), and equivalence judged after
-the anneal, on held-out score, against the best arm rather than a
-conventional reference. That finding — not any savings headline — is what we
-would want a reader to take away, and it fed directly back into the design
-discussion of the upstream pull requests.
-
-### The estimand: dispersion on the event clock
-
-What the model is *for*: per symbol and hour, the probability that a trade
-moves the price at all, and how far it typically goes when it does (posterior:
-the all-shard headline fit — these curves are estimates, not scored claims).
-Both come
-from the posterior of the fitted links at reference covariates, so the
-intraday shape below is pooled across the hierarchy — BTC's curve is almost
-entirely its own data, the thin symbols' curves borrow the global harmonics
-exactly as the model intends:
-
-```{code-cell} ipython3
-hours = np.arange(24)
-fig, axs = plt.subplots(1, 2, figsize=(9, 3.6), sharex=True, layout="constrained")
-for sym_name, _ in results["forest_symbols"]:
-    disp = results["dispersion_full"][sym_name]
-    axs[0].plot(hours, disp["pi_mean"], marker=".", label=sym_name)
-    axs[1].plot(hours, disp["hw_mean"], marker=".")
-    axs[1].fill_between(
-        hours,
-        np.array(disp["hw_mean"]) - 2 * np.array(disp["hw_sd"]),
-        np.array(disp["hw_mean"]) + 2 * np.array(disp["hw_sd"]),
-        alpha=0.2,
-    )
-axs[0].set_title(r"move probability $\pi_{s,h}$")
-axs[1].set_title("conditional-move 90% half-width (bp)")
-axs[1].set_yscale("log")
-for ax in axs:
-    ax.set_xlabel("UTC hour")
-    ax.set_xticks(range(0, 24, 6))
-axs[0].legend(fontsize=8, loc="center left")
-fig.suptitle("Event-clock dispersion by hour: shared harmonics, per-symbol levels");
-```
-
-Read it as event time, not clock time: these are per-*transition* quantities,
-and the number of transitions per hour itself varies enormously — the two
-clocks tell different stories and this notebook only claims the first.
-
-+++
-
-### What full N buys: thin cells
-
-A uniform 1% subsample (4.6M rows — 1% of the 60-shard training split, drawn
-by taking the hash-ordered head of each shard, with the last four shards held
-out for scoring) can estimate every *global* parameter of this model — and did,
-matching the full fit closely on the well-determined ones (ν to 0.1%, the
-θ's, λ_q, τ_α) and agreeing in sign and rough magnitude on the small
-coefficients that wander between full-N configurations too. So why stream
-100× more
-data? Because the estimand lives in symbol-by-hour cells, and the thin end of
-the universe is where the two fits part ways. Below, the posterior of the
-conditional-move 90% half-width $\sigma_{s,h}\, t^{-1}_{0.95}(\nu)$ at
-reference covariates, full fit versus 1% subsample, for the thinnest and the
-fattest symbols in the corpus:
-
-```{code-cell} ipython3
-disp_full = results["dispersion_full"]
-disp_sub = results["dispersion_sub1pct"]
-show_syms = results["forest_symbols"]  # thinnest three + BTC, with row counts
-show_hours = [3, 14, 20]
-
-fig, ax = plt.subplots(figsize=(8, 4.2), layout="constrained")
-ypos = 0
-labels, ticks = [], []
-for sym_name, n_rows in show_syms:
-    for arm, color, off in [(disp_full, "C0", 0.22), (disp_sub, "C1", -0.22)]:
-        hw = arm[sym_name]
-        for hi, h in enumerate(show_hours):
-            hw_m, hw_s = hw["hw_mean"][h], hw["hw_sd"][h]
-            ax.errorbar(
-                [hw_m],
-                [ypos + off + 0.5 * (hi - 1)],
-                xerr=[[2 * hw_s], [2 * hw_s]],
-                fmt="o",
-                ms=4,
-                color=color,
-                capsize=2,
-            )
-    labels.append(f"{sym_name}\n({n_rows / 1e6:.2f}M rows)")
-    ticks.append(ypos)
-    ypos += 2.2
-ax.set_yticks(ticks)
-ax.set_yticklabels(labels, fontsize=8)
-ax.set_xscale("log")
-ax.set_xlabel("conditional-move 90% half-width (bp), posterior mean ± 2 sd")
-ax.set_title(
-    "Full fit (C0) vs 1% subsample (C1) at three hours of day\n"
-    "Global parameters agree; thin-cell uncertainty is where full N earns its keep"
-)
-ax.plot([], [], "o", color="C0", label="full corpus")
-ax.plot([], [], "o", color="C1", label="1% subsample")
-ax.legend();
-```
-
-This is a capacity-and-estimand demonstration, not a claim of method
-superiority: the subsample answers global questions at 1% of the cost.
-
-:::{warning}
-No stratified oversampling of thin symbols, however tempting: unequal
-inclusion probabilities break the plain `total_size` rescaling. The subsample
-is uniform or it is invalid.
-:::
-
-### What we do not sweep under the rug
-
-The single most useful diagnostic this notebook can leave you with costs
-three fits and one table: the reported mean-field standard deviation next to
-the sample standard deviation of the posterior mean across three optimizer
-seeds. Read it as a *computational-stability* diagnostic. A ratio far above
-one is disqualifying — the reported width understates even run-to-run
-variation, so it cannot be trusted. A ratio near or below one is merely
-*necessary*: the width survives seed replication, but certifying it as
-calibrated would take a full-rank or MCMC comparison on a tractable
-subproblem, which is future work here:
-
-```{code-cell} ipython3
-spread = pd.DataFrame(results["seed_spread"]).T
-spread["ratio"] = (spread["seed_spread"] / spread["reported_sd"]).round(1)
-spread.sort_values("ratio", ascending=False)
-```
-
-- **Mean-field understates uncertainty along soft ridges.** The intercepts
-  share a ridge with their pooled group means (only the sums are identified),
-  and across repeated seeds the spread of $\alpha_0$ is roughly 440× its
-  reported posterior standard deviation — $\kappa_0$ and $\beta_a$ show the
-  same signature at 60–70×. The parameters *without* a ridge ($\theta_r$,
-  $\nu$, $\lambda_a$, the $\tau$'s) sit at or below one, and $\theta_d$ sits
-  in between (≈6). Report mean-field standard deviations only for parameters
-  you have seed-checked — and remember the check bounds the error from below.
-- **The speed ordering is model-dependent.** Here the heavy gradient hides
-  the loader entirely and pre-shuffled streaming outpaces in-RAM
-  `pm.Minibatch` — and did so on our lighter probe as well. Streaming's claim
-  is memory and scale — treat any steps-per-second comparison as specific to
-  the model it was measured on.
-- **Sampling semantics differ**: `pm.Minibatch` draws with replacement; the
-  loader makes without-replacement epoch passes.
-- **Discreteness at the smallest moves.** Price changes are integer multiples
-  of the tick size; the continuous Student-t is a working approximation whose
-  residual misfit concentrates at |y| near one tick.
-- **Event time is not clock time.** All dispersion statements are per
-  transition, not per minute; activity itself varies by hour, and conflating
-  the two clocks changes the intraday story.
-
-+++
+posterior has converged; the recovery and predictive checks earlier in the
+notebook are the kind of independent evidence a stop should be paired with.
+Second, the $h/\kappa$-window delay after a plateau is the rule's *nominal*
+detection lag; clipping and the adaptive scale can move the actual hitting
+time.
 
 ## Where the pieces live
 
 :::{seealso}
-The `DataLoader`/`parquet_source` used here are in
-[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698); a
-callback-free `Trainer` wrapper
+The `DataLoader`/`parquet_source` used here merged in
+[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698). A
+`Trainer` that wraps this notebook's callback pattern
 ([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the
-convergence monitor ([pymc#8384](https://github.com/pymc-devs/pymc/pull/8384))
-are under review, and this notebook's callback pattern is exactly what those
-APIs wrap. The ETL and full-scale run scripts accompany the submission, and
-the {ref}`streaming_dataset` companion covers the API mechanics.
+convergence monitor that grew out of the stopping section
+([pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733)) are
+under review.
 :::
 
 +++
diff --git a/examples/variational_inference/streaming_tick_data_results.json b/examples/variational_inference/streaming_tick_data_results.json
deleted file mode 100644
index 743f7cf36..000000000
--- a/examples/variational_inference/streaming_tick_data_results.json
+++ /dev/null
@@ -1,1320 +0,0 @@
-{
- "corpus": {
-  "n_rows": 491559069,
-  "n_symbols": 38,
-  "thinnest": [
-   [
-    "ZILUSDT",
-    67193
-   ],
-   [
-    "KSMUSDT",
-    89574
-   ],
-   [
-    "MANAUSDT",
-    111036
-   ]
-  ],
-  "fattest": [
-   [
-    "ETHUSDT",
-    178792634
-   ],
-   [
-    "BTCUSDT",
-    202839771
-   ]
-  ]
- },
- "seed_spread": {
-  "kappa0": {
-   "posterior_mean": 0.3362,
-   "reported_sd": 0.0001511,
-   "seed_spread": 0.008867
-  },
-  "alpha0": {
-   "posterior_mean": -1.57,
-   "reported_sd": 6.363e-05,
-   "seed_spread": 0.02813
-  },
-  "lambda_a": {
-   "posterior_mean": 0.05638,
-   "reported_sd": 0.0001387,
-   "seed_spread": 2.183e-05
-  },
-  "lambda_q": {
-   "posterior_mean": -0.3928,
-   "reported_sd": 0.0001135,
-   "seed_spread": 2.296e-05
-  },
-  "beta_a": {
-   "posterior_mean": 0.05368,
-   "reported_sd": 7.692e-05,
-   "seed_spread": 0.00531
-  },
-  "beta_q": {
-   "posterior_mean": -0.001442,
-   "reported_sd": 0.0001083,
-   "seed_spread": 9.631e-06
-  },
-  "theta_d": {
-   "posterior_mean": 0.02529,
-   "reported_sd": 1.348e-06,
-   "seed_spread": 8.24e-06
-  },
-  "theta_r": {
-   "posterior_mean": 0.2947,
-   "reported_sd": 7.647e-05,
-   "seed_spread": 7.992e-06
-  },
-  "tau_k": {
-   "posterior_mean": 1.039,
-   "reported_sd": 0.1169,
-   "seed_spread": 0.01451
-  },
-  "tau_al": {
-   "posterior_mean": 3.21,
-   "reported_sd": 0.3668,
-   "seed_spread": 0.0414
-  },
-  "eta": {
-   "posterior_mean": 0.3692,
-   "reported_sd": 0.0002372,
-   "seed_spread": 0.0001343
-  },
-  "nu": {
-   "posterior_mean": 1.895,
-   "reported_sd": 0.0001402,
-   "seed_spread": 7.94e-05
-  }
- },
- "memwall": {
-  "minibatch": [
-   [
-    2000000,
-    0.879
-   ],
-   [
-    4000000,
-    1.133
-   ],
-   [
-    8000000,
-    1.564
-   ],
-   [
-    16000000,
-    2.441
-   ],
-   [
-    32000000,
-    3.527
-   ],
-   [
-    64000000,
-    5.74
-   ]
-  ],
-  "stream": [
-   [
-    61452475,
-    0.819
-   ],
-   [
-    230405487,
-    0.824
-   ],
-   [
-    491559069,
-    0.481
-   ]
-  ],
-  "bytes_per_row": 77.2,
-  "intercept_gb": 0.9,
-  "crossing_rows_12gb": 143503428,
-  "throughput": {
-   "minibatch": 992.0,
-   "stream": 1439.0,
-   "stream_shuffled": 434.0
-  }
- },
- "converge_table": [
-  [
-   "e0.25",
-   0.25,
-   [
-    0.117919,
-    0.117978,
-    0.117858
-   ]
-  ],
-  [
-   "e0.5",
-   0.5,
-   [
-    0.117867,
-    0.117869,
-    0.117858
-   ]
-  ],
-  [
-   "e1.0",
-   1.0,
-   [
-    0.117009,
-    0.117014,
-    0.117002
-   ]
-  ],
-  [
-   "e2.0",
-   2.0,
-   [
-    0.117014,
-    0.117003,
-    0.117013
-   ]
-  ],
-  [
-   "monitor",
-   1.07,
-   [
-    0.116262,
-    0.116299,
-    0.116323
-   ]
-  ]
- ],
- "polish": {
-  "monitor stop, raw (1.07 passes)": 0.116295,
-  "flat 2-pass reference": 0.11701,
-  "stop + 2k-step anneal (1.09 passes)": 0.118446,
-  "annealed 2-pass reference": 0.117855
- },
- "forest_symbols": [
-  [
-   "ZILUSDT",
-   67193
-  ],
-  [
-   "KSMUSDT",
-   89574
-  ],
-  [
-   "MANAUSDT",
-   111036
-  ],
-  [
-   "BTCUSDT",
-   202839771
-  ]
- ],
- "dispersion_full": {
-  "ZILUSDT": {
-   "hours": [
-    0,
-    1,
-    2,
-    3,
-    4,
-    5,
-    6,
-    7,
-    8,
-    9,
-    10,
-    11,
-    12,
-    13,
-    14,
-    15,
-    16,
-    17,
-    18,
-    19,
-    20,
-    21,
-    22,
-    23
-   ],
-   "pi_mean": [
-    0.1793,
-    0.17,
-    0.1595,
-    0.152,
-    0.1511,
-    0.1588,
-    0.1764,
-    0.2033,
-    0.236,
-    0.2673,
-    0.2884,
-    0.2924,
-    0.2779,
-    0.2492,
-    0.2148,
-    0.1833,
-    0.16,
-    0.1467,
-    0.1432,
-    0.1478,
-    0.1581,
-    0.1702,
-    0.1799,
-    0.1832
-   ],
-   "pi_sd": [
-    0.00345,
-    0.00406,
-    0.00503,
-    0.00537,
-    0.005,
-    0.00431,
-    0.00416,
-    0.00539,
-    0.00724,
-    0.00832,
-    0.00779,
-    0.00599,
-    0.00481,
-    0.00563,
-    0.00658,
-    0.00643,
-    0.00535,
-    0.00403,
-    0.00338,
-    0.00403,
-    0.00525,
-    0.00597,
-    0.00563,
-    0.00438
-   ],
-   "hw_mean": [
-    97.07806,
-    97.17599,
-    99.07273,
-    102.70085,
-    107.67674,
-    113.2985,
-    118.62852,
-    122.69963,
-    124.80516,
-    124.74174,
-    122.86448,
-    119.92181,
-    116.77023,
-    114.11193,
-    112.33929,
-    111.49035,
-    111.28603,
-    111.23034,
-    110.76805,
-    109.47738,
-    107.23681,
-    104.29007,
-    101.17574,
-    98.56326
-   ],
-   "hw_sd": [
-    1.282001,
-    1.275988,
-    1.319397,
-    1.391542,
-    1.466444,
-    1.530885,
-    1.589328,
-    1.649991,
-    1.703772,
-    1.725311,
-    1.698886,
-    1.636834,
-    1.570968,
-    1.52744,
-    1.508975,
-    1.499908,
-    1.485854,
-    1.468251,
-    1.460852,
-    1.469336,
-    1.47701,
-    1.458328,
-    1.404617,
-    1.334296
-   ]
-  },
-  "KSMUSDT": {
-   "hours": [
-    0,
-    1,
-    2,
-    3,
-    4,
-    5,
-    6,
-    7,
-    8,
-    9,
-    10,
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-}
\ No newline at end of file

From 3debe0902b39ae4d95b4ed7147a308569a38da53 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 14 Aug 2026 05:01:58 -0400
Subject: [PATCH 27/42] Add the three missing reference entries the notebook
 cites

andersen1997intraday, bollerslev1987conditionally, and page1954continuous
were cited but absent from references.bib, so the built page silently
dropped those citations. Entries verified against CrossRef and fetched
via DOI content negotiation.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/examples/references.bib b/examples/references.bib
index 994c98ff0..74555b3c5 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -1,3 +1,13 @@
+@article{andersen1997intraday,
+  author        = {Andersen, Torben G. and Bollerslev, Tim},
+  doi           = {10.1016/s0927-5398(97)00004-2},
+  journal       = {Journal of Empirical Finance},
+  number        = {2-3},
+  pages         = {115--158},
+  title         = {Intraday periodicity and volatility persistence in financial markets},
+  volume        = {4},
+  year          = {1997}
+}
 @article{ando2007bayesian,
   title         = {Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models},
   author        = {Ando, Tomohiro},
@@ -94,6 +104,16 @@ @misc{betancourt2018
   primaryclass  = {stat.ME},
   url           = {https://arxiv.org/abs/1701.02434}
 }
+@article{bollerslev1987conditionally,
+  author        = {Bollerslev, Tim},
+  doi           = {10.2307/1925546},
+  journal       = {The Review of Economics and Statistics},
+  number        = {3},
+  pages         = {542--547},
+  title         = {A conditionally heteroskedastic time series model for speculative prices and rates of return},
+  volume        = {69},
+  year          = {1987}
+}
 @article{bonilla2007multioutput,
   title         = {Multi-task Gaussian process prediction},
   author        = {Bonilla, Edwin V and Chai, Kian and Williams, Christopher},
@@ -766,6 +786,16 @@ @unpublished{padonou2015polar
   month         = Feb,
   pdf           = {https://hal.archives-ouvertes.fr/hal-01119942v1/file/PolarGP_CircularDomains.pdf}
 }
+@article{page1954continuous,
+  author        = {Page, E. S.},
+  doi           = {10.1093/biomet/41.1-2.100},
+  journal       = {Biometrika},
+  number        = {1-2},
+  pages         = {100--115},
+  title         = {Continuous inspection schemes},
+  volume        = {41},
+  year          = {1954}
+}
 @book{pearl1985prob,
   title         = {Probabilistic Reasoning in Intelligent Systems: Networks of plausible Inference},
   author        = {Pearl, Judea},

From 63a3dea1d850340d96906cd271f9f253b5970f0e Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 14 Aug 2026 05:15:26 -0400
Subject: [PATCH 28/42] Deduplicate references.bib: a repeated entry truncated
 bibliography parsing

sphinxcontrib-bibtex aborts the bibtex parse at the first repeated entry,
so every key sorted after numpyroBirthdays was silently dropped: the build
logs show 'parsed 87 entries' out of 134 unique, with could-not-find
warnings for quiroga2022bart, Wagenmakers2010, spiegelhalter2002bayesian
and others across published notebooks, and this notebook's page1954continuous
rendering as an empty citation. All seven repeats were byte-identical to
their first occurrence; the later copies are removed. Pre-existing on main
(same warnings appear in builds before this branch touched the file).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib | 68 -----------------------------------------
 1 file changed, 68 deletions(-)

diff --git a/examples/references.bib b/examples/references.bib
index 74555b3c5..f5ff14d48 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -759,17 +759,6 @@ @online{numpyroBirthdays
   title         = {Example: Hilbert space approximation for Gaussian processes},
   url           = {https://num.pyro.ai/en/stable/examples/hsgp.html}
 }
-@online{numpyroBirthdays,
-  title         = {Example: Hilbert space approximation for Gaussian processes},
-  url           = {https://num.pyro.ai/en/stable/examples/hsgp.html}
-}
-@online{orduz2024Birthdays,
-  title         = {Time Series Modeling with HSGP: Baby Births Example},
-  author        = {Orduz, Juan},
-  url           = {https://juanitorduz.github.io/birthdays/},
-  urldate       = {2024-01-02},
-  year          = {2024}
-}
 @online{orduz2024Birthdays,
   title         = {Time Series Modeling with HSGP: Baby Births Example},
   author        = {Orduz, Juan},
@@ -846,21 +835,6 @@ @article{penrose1985
   year          = {1985},
   pages         = {189}
 }
-@article{penrose1985,
-  title         = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}},
-  volume        = {17},
-  issn          = {0195-9131},
-  url           = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx},
-  abstract      = {An abstract is unavailable. This article is available as a PDF only.},
-  language      = {en-US},
-  number        = {2},
-  urldate       = {2023-10-17},
-  journal       = {Medicine \& Science in Sports \& Exercise},
-  author        = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.},
-  month         = apr,
-  year          = {1985},
-  pages         = {189}
-}
 @misc{quiroga2022bart,
   title         = {Bayesian additive regression trees for probabilistic programming},
   author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
@@ -900,17 +874,6 @@ @article{riutort2022PracticalHilbertSpaceApproximate
   volume        = {33},
   year          = {2022}
 }
-@article{riutort2022PracticalHilbertSpaceApproximate,
-  author        = {Riutort-Mayol, Gabriel and B{\"u}rkner, Paul-Christian and Andersen, Michael R. and Solin, Arno and Vehtari, Aki},
-  doi           = {10.1007/s11222-022-10167-2},
-  journal       = {Statistics and Computing},
-  number        = {1},
-  pages         = {17},
-  title         = {Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming},
-  url           = {https://doi.org/10.1007/s11222-022-10167-2},
-  volume        = {33},
-  year          = {2022}
-}
 @book{roback2021beyond,
   title         = {Beyond multiple linear regression: Applied generalized linear models and multilevel models in R},
   author        = {Roback, P., and Legler, J.},
@@ -978,17 +941,6 @@ @article{solin2020Hilbert
   volume        = {30},
   year          = {2020}
 }
-@article{solin2020Hilbert,
-  author        = {Solin, Arno and S{\"a}rkk{\"a}, Simo},
-  doi           = {10.1007/s11222-019-09886-w},
-  journal       = {Statistics and Computing},
-  number        = {2},
-  pages         = {419--446},
-  title         = {Hilbert space methods for reduced-rank Gaussian process regression},
-  url           = {https://doi.org/10.1007/s11222-019-09886-w},
-  volume        = {30},
-  year          = {2020}
-}
 @article{spiller2013spotlights,
   title         = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression},
   author        = {Spiller, Stephen A and Fitzsimons, Gavan J and Lynch Jr, John G and McClelland, Gary H},
@@ -1093,13 +1045,6 @@ @online{vehtari2022Birthdays
   urldate       = {2022-03-07},
   year          = {2022}
 }
-@online{vehtari2022Birthdays,
-  title         = {Bayesian workflow book - Birthdays},
-  author        = {Vehtari, Aki},
-  url           = {https://avehtari.github.io/casestudies/Birthdays/birthdays.html},
-  urldate       = {2022-03-07},
-  year          = {2022}
-}
 @book{venables2002mass,
   author        = {Venables, W. N. and Ripley, B. D.},
   title         = {Modern Applied Statistics with {S}},
@@ -1170,19 +1115,6 @@ @article{Yao_2022
   doi           = {10.1214/21-BA1287},
   url           = {https://doi.org/10.1214/21-BA1287}
 }
-@article{Yao_2022,
-  author        = {Yuling Yao and Gregor Pir\v{s} and Aki Vehtari and Andrew Gelman},
-  title         = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}},
-  volume        = {17},
-  journal       = {Bayesian Analysis},
-  number        = {4},
-  publisher     = {International Society for Bayesian Analysis},
-  pages         = {1043 -- 1071},
-  keywords      = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking},
-  year          = {2022},
-  doi           = {10.1214/21-BA1287},
-  url           = {https://doi.org/10.1214/21-BA1287}
-}
 @article{yuan2009bayesian,
   title         = {Bayesian mediation analysis.},
   author        = {Yuan, Ying and MacKinnon, David P},

From 9589f88b06c9ac5f1047d6d4429808a88c4a72b7 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Fri, 14 Aug 2026 08:31:41 -0400
Subject: [PATCH 29/42] Restore hoffman2013stochastic, dropped by the
 references rebuild in #883

The bib rebuild merged in #883 lost this entry while fixing the earlier
duplicate-key corruption; this notebook and the SVI literature trail cite
it. Verified by key-set diff against the pre-merge revision: it was the
only entry lost.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/examples/references.bib b/examples/references.bib
index 4fdb60250..45fc6cf86 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -437,6 +437,14 @@ @article{higgins2009meta
   year          = {2009},
   doi           = {10.1111/j.1467-985X.2008.00552.x}
 }
+@article{hoffman2013stochastic,
+  title         = {Stochastic Variational Inference},
+  author        = {Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John},
+  year          = {2013},
+  journal       = {Journal of Machine Learning Research},
+  volume        = {14},
+  pages         = {1303--1347}
+}
 @article{hoffman2014nuts,
   title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
   author        = {Hoffman, Matthew and Gelman, Andrew},

From 7f294c3eb726fbf43218cfbfdfea69f4ac8c811e Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 02:36:51 -0400
Subject: [PATCH 30/42] Remove the stale June draft notebook carried by branch
 history

streaming_dataset.{ipynb,myst.md} predate the merged DataLoader: they import
a pymc.variational.streaming module that does not exist and present a
Trainer as available. The proposal in #891 commits to one example, not
two; this PR now contains only the tick-data notebook and the bib fix.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_dataset.ipynb                   | 535 ------------------
 .../streaming_dataset.myst.md                 | 267 ---------
 2 files changed, 802 deletions(-)
 delete mode 100644 examples/variational_inference/streaming_dataset.ipynb
 delete mode 100644 examples/variational_inference/streaming_dataset.myst.md

diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb
deleted file mode 100644
index eb26e4d64..000000000
--- a/examples/variational_inference/streaming_dataset.ipynb
+++ /dev/null
@@ -1,535 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3a1d0597",
-   "metadata": {},
-   "source": [
-    "(streaming_dataset)=\n",
-    "\n",
-    "# Out-of-core minibatch variational inference with DataLoader and Trainer\n",
-    "\n",
-    ":::{post} June 2026\n",
-    ":tags: variational inference, minibatch, out-of-core\n",
-    ":category: intermediate, how-to\n",
-    ":author: Yicheng (Ethan) Yang\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "928fd0f3",
-   "metadata": {},
-   "source": [
-    "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n",
-    "minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic`\n",
-    "only works on data that already fits in memory.\n",
-    "This notebook uses the streaming API in `pymc.variational.streaming`, which follows\n",
-    "the same structure as PyTorch's `torch.utils.data`:\n",
-    "\n",
-    "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n",
-    "  shuffles) an out-of-core source into fixed-size minibatches without loading\n",
-    "  the whole dataset into memory. Here the source is a directory of Parquet shards\n",
-    "  read by {func}`~pymc.variational.streaming.parquet_source`.\n",
-    "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n",
-    "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n",
-    "  minibatch into that placeholder with `set_data` every step. There are no\n",
-    "  callbacks to write.\n",
-    "\n",
-    "The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`\n",
-    "is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed\n",
-    "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`.\n",
-    "Batches are exact-size, so each pass drops the rows that do not fill a final batch;\n",
-    "with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed\n",
-    "tail. The one extra requirement is shuffling itself. A streaming source is only as\n",
-    "well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to\n",
-    "shuffle through a bounded buffer.\n",
-    "\n",
-    "`N` is small here so the notebook runs in seconds. The streaming code is the same at\n",
-    "any size, and the last section shows what changes at scale."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d67f52e",
-   "metadata": {},
-   "source": [
-    ":::{include} ../extra_installs.md\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c2f6955f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:31.179963Z",
-     "iopub.status.busy": "2026-06-11T15:48:31.179761Z",
-     "iopub.status.idle": "2026-06-11T15:48:32.724808Z",
-     "shell.execute_reply": "2026-06-11T15:48:32.724317Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import tempfile\n",
-    "\n",
-    "import arviz as az\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pyarrow as pa\n",
-    "import pyarrow.parquet as pq\n",
-    "import pymc as pm\n",
-    "\n",
-    "from pymc.variational.streaming import DataLoader, Trainer, parquet_source\n",
-    "\n",
-    "RANDOM_SEED = 8927\n",
-    "rng = np.random.default_rng(RANDOM_SEED)\n",
-    "az.style.use(\"arviz-variat\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90fd8ed5",
-   "metadata": {},
-   "source": [
-    "## Write the dataset to disk\n",
-    "\n",
-    "First, we build a logistic-regression dataset, write it to Parquet shards, and\n",
-    "delete the in-memory table. From here the streaming path reads only the disk copy.\n",
-    "We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later;\n",
-    "the streaming fit never reads them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "03ccf873",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:32.726747Z",
-     "iopub.status.busy": "2026-06-11T15:48:32.726448Z",
-     "iopub.status.idle": "2026-06-11T15:48:32.752265Z",
-     "shell.execute_reply": "2026-06-11T15:48:32.751894Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 shards written\n"
-     ]
-    }
-   ],
-   "source": [
-    "N = 30_000\n",
-    "b_true = np.array([0.4, -1.2, 0.8, -0.5])  # intercept + 3 slopes\n",
-    "\n",
-    "X = rng.normal(size=(N, 3))\n",
-    "p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))\n",
-    "y = (rng.random(N) < p).astype(\"float64\")\n",
-    "table = np.column_stack([X, y]).astype(\"float64\")\n",
-    "\n",
-    "shard_dir = tempfile.mkdtemp(prefix=\"streaming_demo_\")\n",
-    "for i, s in enumerate(range(0, N, 5_000)):\n",
-    "    block = table[s : s + 5_000]\n",
-    "    pq.write_table(\n",
-    "        pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n",
-    "        f\"{shard_dir}/part_{i:03d}.parquet\",\n",
-    "    )\n",
-    "del table\n",
-    "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bd75c32",
-   "metadata": {},
-   "source": [
-    "## Stream minibatches off disk and fit with ADVI\n",
-    "\n",
-    "Next, the source is `parquet_source`, an out-of-core\n",
-    "{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a\n",
-    "time and gets `n_rows` from the Parquet metadata, so `total_size=\"auto\"` resolves\n",
-    "`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and\n",
-    "passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer`\n",
-    "streams each minibatch into the placeholder."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "477343ca",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:32.753347Z",
-     "iopub.status.busy": "2026-06-11T15:48:32.753280Z",
-     "iopub.status.idle": "2026-06-11T15:48:50.379526Z",
-     "shell.execute_reply": "2026-06-11T15:48:50.379109Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 531.47\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch_size = 1024\n",
-    "loader = DataLoader(\n",
-    "    parquet_source(shard_dir),\n",
-    "    batch_size=batch_size,\n",
-    "    sample_shape=(4,),  # 3 features + 1 observed column\n",
-    "    shuffle=True,  # the shards were written in order\n",
-    "    buffer_size=15_000,\n",
-    "    seed=0,\n",
-    "    total_size=\"auto\",  # N from the Parquet metadata\n",
-    ")\n",
-    "\n",
-    "with pm.Model() as model:\n",
-    "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
-    "    batch = pm.Data(\"batch\", np.zeros((batch_size, 4)))  # placeholder for one minibatch\n",
-    "    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n",
-    "    pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n",
-    "\n",
-    "    approx = Trainer(\n",
-    "        method=\"advi\",\n",
-    "        dataloader=loader,\n",
-    "        data_name=\"batch\",\n",
-    "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
-    "    ).fit(30_000, random_seed=RANDOM_SEED)\n",
-    "    idata_stream = approx.sample(1000, random_seed=RANDOM_SEED)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a1d41c8",
-   "metadata": {},
-   "source": [
-    "The negative-ELBO trace shows the fit converging on minibatches read off disk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a3ff7cb9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:50.380772Z",
-     "iopub.status.busy": "2026-06-11T15:48:50.380702Z",
-     "iopub.status.idle": "2026-06-11T15:48:50.538325Z",
-     "shell.execute_reply": "2026-06-11T15:48:50.537986Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OqV4KUzfFPpf69WwffPnmbaZFYT17XgcFH1V33fOd91hEbQp9jxq/E30edWrXsitltfgk3hirzmm1xRWc3rqzzLRpUGBXjwctgND4pgL1yTLK+fuGomsQMo/gY45o1658QDpZwK9jx46e24sXh59ptXXrVk9gsEmTJqa4uDjpayxaFG05v/+Y/M+H3KKLvhqE6jDqoq0fBR6U6sitfeP6noa1AlVBe0FWdu8nh7MCLiw12JQKY9Tc5bZDrkFkdbDUuXFmSqkRoQEmDY4GLfFXo21Yj/LVIU56SzenIe1QQ8I9895JGaqAkRrtapxptqcG+fR8/k6zBkDUCFYnTkEdHZOOTQ11zczWZ6uZr/5OmX6CBvX2Ly2O2zk5ebfWdiDc3UGyqzt2NQ41wKMVk+pU6jNTJ1OzyxUUUadPHRc1UDSQ5XDSATncf6sMfwNIDbt4KW7UCVGwUAEJNcY0G9rug9G7vU0x4z4uv6AVN6LGZdh0TfHos3PK0zUDutjnWrB+S4VGvzrox3VuaBv1apQGNf6SDTarUasAiTq+/sBMsjQa/uC/MzinBqQGV3QOq+E8uG15I1/Hl6jjq4FalQelm1FHVytTEtFsZw2CaVBWs8lVVtWZUhlQENTpcOgY3QOHYjuYuzprh7Rr7gk+6rxQ0F8dUXXeVIcp3YpW0mkWpx6rcucMjiSi4wt7TqsTpA6MUraqrKve1OCTM8iXaLBdx6KfRMFHlVN9Nr/p19l2yFRPOA19PfeZ3dvadHQawNCKLR2Pvi+tklAnR/WSmz4HZ9b3U75JG0rr5Kf3pEF21VfuSR7uTrIGzjQ4q3+dgVCHriuaFHCvbyA93v5X7kkeojp8wfrNoVMEaQBQEy50LD2aNTQz1pavGNOxutMJOmmJFdwSfcb7Jtj/QteIS/vEbwu5B/ZVn6jTp3NZZckfCNF57Z9AoMFndfh0X51PidgBuhTsYaJzoXHtxN0Qfc/+wbhzerYzk3cF1zXA6qYJR1rlou9ddbh7kCBRnWZTA+9a2e1wVhuHpfNdq9N1vdNgtILpyepDvQelQ0wefKg40BCFvl8FljNB5d+/SkL1hsqW6uh46QadlZ56r5rEoZqrfAVJLVtv/31S+ao+XSuDMlG4qU7vvavdFGbw3REmA5O+D62OVOpg/woEDeC79zp36PPQ6jxdC/Q+3QOOth7tUWq+XLLGDjg67TWdv/7gowah9dnpddTG83NSUa7aus0GaMMEHjVT3E9tIK2GUCo3DXJOW7MxFgDW568JDc3q1421ZzUQ6G9r6DtId6pAf9s9bPtd130FRF+asSjSpDGdi6qT3GnjNCjpfM76179yOBX0OaveDcra4J8w436PiVZbqM67qHd7e23Se3JnRVEgX+e4BvOdNqITKNNEl6ddmVC08lKfi963rmeayKh/dcyaYBKvXeEOBiozjXOdu6xvBxsg0sByZeo8lSf//qEa9NTkB/3ob/os/StBncf+vGt5hhG9a10n9Z3qswpqK6ucKF24n641+tmvTbFdOeIEMxWcU7AjiCbRrN22w36/GqR2rh36XvzXpiCqV9zvR8dWt3b8ybzu1ZRHhAyKu88pnSO6pny1dI1diTOwpHylkdOGdVYNO/Q3f9nQKrRkVN8oaCUfL1hRIf2x6iKtlusYZzJZhUkCBYnrRH1u7vpZGUaCJOtrRKXzS+0HvRcFzJwJejpP/W0RXU9/tmuShoK8WlGqvrOCFfo+omxT4wTKguIAatOECTzGo+9c6WgVYNb5oOfTGIJ7PMCZIOk+FjeVR028UDl0Ulj6qf+oScCqkdzXPAW0NXlM2aJKXHtL6ri0EsxJIR/2XBS1qdUuUbtU/UT3czg0ydw9iUHXf62S02epfq3aDWqTKYjm5+5nBtFneGb3UptRQvXSPgGvrwCME4RRPRQvLbX/89R5Hy8ziiYeKCtJooCNf1VmmIl7QVSPOXWeJlSoT6Vrrruf44whRUnTn4zOC6WRVdkOCtomqlP1N/VNNm/fYcdG9D35z2d9vppQp8wEWu0cL0jtjG0k6oM4k5Jemr7YXot1Pjn9bV179fnEC8I6WeEcTt85iNrP7Rv/+J2f06NdhYxBiRZqHNm+hc3yoGPS+RqW6jitaHSobaBxg51lZWbTjp22fR8lk5vGX56avCAWtNc4HqoHwcccUb9+/VB7OXbp0sVze968iil64vHf1/9c7t+rUnRmB8+fHy1tmf/+8V4HucUZyNYKAH96L3eHQLNo1IDT//UY99446hQpmJZpugA6KekcutA6M+XCUINEP2qY+oOP/oaBVhzpedXB0ODlIK2aqVXLdgYdavi4L9z+YwtqMGuwyL+Swk8DXprZrcasBvD1PIk6JzbVbY+2tsOhlJIaPFGQwt9wcWZMFu4KDCWiATYN7Go2pNK7OLPatYLNncYnTOoGfbaJ0skEUYPQ3yh0AjlBwUfNpNP3EW/wW+//nJ5tA1OmxaPvSZ+pM3tRgyRu6iD4B6O8xxr6pQKP12nUNqhTOzYYoEGpeKvJwjynf1WAO/VYPKoDNHvUv8IuWYokfR/+sqlO8heLV9sB6P1aJ545qc9QG5mrvtLAnbu+cgdX9Xm4U6akg2aN+1eRJFvh46agabz0yHvvqic0cBC0ObzqXH9QXQNMiQZgVV9o8Fydfa2UUGdAq52cFT9BNBCnFXdOHaIgqyafaOBFxxav86PPoV6tWvaxSskoGnx06hsdg50xbcpsveJf6Vu8a+anUpEFzez0a+07F5yJI0HBS62wjvI9ReFeURFEZU11iGagKlCm78MfpMwGquc1IcYJEGhQVseqnyA2BWqS1YbppPNHq6PUQY6XUrEydJ44gSeHZmn7KbjnT0WmQSNNDvAPBlcHDZArBeQXS1ZVSCPpDIDrOP11swYttCebBh+a1v9xYC2RKEHHqNSGUqYApRVXgE71p76jgxOce1qJM7B1Uzus6J+EpoFKfz2qOs9J8a02sYI8mmSkSW6qL+PVeUErUIJogswBCVbWizNLXumY9aNBNQX8/Z+/BgXdE6g0kSdZ5pDqpmNW+up35y33pC/T5LZEnFTSSsGnt6gB/5pKfZd4A3g6R4O+wvKB4bY24Kz6zt3ODDO5ymlXOHtXabKMexDeWXWcShrI1EBtlFSuqaD2ka4HYahMaQuITLCTqjZstquw1Kdz9x2j1ueZvN4GtZfiZTJK3WtW/J3Oj3SsmtH7U4BHQd1RuzIxKKCQaBKJzm33xCJ9H5rI7F+9K+onaoKnM8lSdb/24Ba1V9xZB3TNCUp7GpXqEXe/UG3xl2cuthlhNLm4e5xMOe7Hu4NO8TiTl/x9DX02qZx4FWZsR9+jyrJW/pffv3lsnETXRX82qqgUiIm30tBP7/0/81fYa7MyHbknNGlswk19WSfbi+ojBX6jlC9NrFCaTU1sV/tUYz1aNVcV6oMlmsTkT0Hsz2SVaWp3xmt76rwIPakxRPNJY0q6Fi/ZtNV0KWoQug+nydOzd2U9sCuSI5QPTSxyr/B0vht/xgBNfrNvI2CST2Uk6veFoWNQ0F4T6fQe/BOUkDkEH3OEf6Vjs2bBnaHu3bt7bo8bNy70a4wfP95ze7fdKua6d/ZobNu2rVmwoHxmxLJly8zChQvt78Ksrpw0aZJnb0ZWPuYPNcoSrU5Qo8imdXF1QjTI7aT+Uwc20UyimvIZKNWJNlnXOOORHVpUGMTRe1fn0Ul/mEnqBGl/IQ2Bur+HRHQfDaLpJxX0eWigzj9Yp/ztSj2ljpUaoGoUBQUftYpCjWt1CoJSKVWFVjO507LamYkhZv9r0MY9a1ApqxJRx0fHr8HAga2axvZKyTStgrQz+XbsjJtutaZQZysozV48UVOYZCulzNRKMidApk6D6lnNzExXcEz0Ghr4UEdVr5notWrtWhnoXx3oX50ajx6nAYytO8rs5A33MShoro65Ot7xXrtuiI/hvJ7xJ01otegH85bHgkdKAZPOzzaM8nM3u89fXW80m1gBNQ2c+PdKzUblg/ap/2610sG9L5SzGsRNdbB7VZZWUFd2r5l0cbIorNu63bw5Z5ld6aNyGK/8uQcXKzu5JaxalSxHziqUZKKsIrEpRju0tFkodD45j40y29uhlaLuvbU1eUiBx7ABch1Louu7jk17bio1ltM+qwn03Sk7h/Yi1P576n8ErVwNaitUJoVqrki0fUUYdlJiiNV8SN95r1WvUfpx2UBBa00QdFavKHiVbhpTcKeYblDHu09tOmjySGXLhzNhTmmXtXpYwaZxyzVpr55tP6m9b1MYlpXZ/pv7u1fWC03S0yRobTGSjnaMruGX9PlxdXNNOfeiUt8/aOufTFP7r3yiVHkmGH3+WomqiZH+lY/6LhSsVIBY333UCUQax7ho9w52QpZSWCsIpzEKv1R+48q+NW31Rrsthybo1ZS2R3Vei/U9aV9HrSjWORAlqFe+UKTUpnRV/8yZYKsAplK1O9t9hF1Nr4njTlpW8ac8TqUoQXukT3b1SFFpEyZM8NwuKQlOHzBgwAAbHNy4cWPscQr4FRQkn604duxYz+3Bgwcn3IPypZde8jz2+OOPDxXg1PE4Bg0aZAOQgCOoMZxslUdNo9WEzuyoqPv1ZIIaH9nYXdCAhk17saN8L7KghrM6ZuoURAkyRaG0m5pFr2CcGmAKJId1QpcSM3d9E7tKJVljUudFoj1HMnkuxFthWZ2cVdKOeKuAUZ4SSB2RVVu2m6KC1K86SCQVMyLDijcjNmwQzj+xQHtMauWAVm8EdeT9gRMFKJQeS4NX2VB2awqdI2FTYuUyTa5SOmUNGHRp0iBwtYraR7oGKk2VVsllc0dbEwfO8GWMyPgx1Cvfr80RlMq0uqViQp2Cu5rxrVUJmrWuQfxUrsx1p5qsaeplMCVxVWX5YlLUINnaj0vWx9OgtvZE14RLrSjLBG2bohXSCsppMkhVUpFmgpMqWPyZYZRZ4KRdwaigtnAmVt+mY3UzkrPpZm2Gq+i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-      "text/plain": [
-       "<Figure size 1800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(9, 3))\n",
-    "ax.plot(approx.hist, alpha=0.6)\n",
-    "ax.set(xlabel=\"iteration\", ylabel=\"negative ELBO\", title=\"Streaming ADVI convergence\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9035a137",
-   "metadata": {},
-   "source": [
-    "## Compare with in-RAM `pm.Minibatch`\n",
-    "\n",
-    "For comparison, here is the usual fit that keeps the whole dataset in memory. The\n",
-    "prior, optimizer, iteration count, and seeds match the streaming fit; only the\n",
-    "minibatch source differs. On this toy problem the two posteriors land on top of\n",
-    "each other, with ADVI's usual mild bias relative to the dashed ground truth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b3b5d9fe",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:50.539450Z",
-     "iopub.status.busy": "2026-06-11T15:48:50.539367Z",
-     "iopub.status.idle": "2026-06-11T15:48:53.984891Z",
-     "shell.execute_reply": "2026-06-11T15:48:53.984550Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Finished [100%]: Average Loss = 531.25\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model():\n",
-    "    b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n",
-    "    xb, zb, sb, yb = pm.Minibatch(\n",
-    "        X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size\n",
-    "    )\n",
-    "    pm.Bernoulli(\"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)\n",
-    "    approx_inram = pm.fit(\n",
-    "        30_000,\n",
-    "        method=\"advi\",\n",
-    "        obj_optimizer=pm.adam(learning_rate=0.008),\n",
-    "        progressbar=False,\n",
-    "        random_seed=RANDOM_SEED,\n",
-    "    )\n",
-    "    idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6731e9d6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:53.986146Z",
-     "iopub.status.busy": "2026-06-11T15:48:53.986070Z",
-     "iopub.status.idle": "2026-06-11T15:48:54.193017Z",
-     "shell.execute_reply": "2026-06-11T15:48:54.192668Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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sjIHGd3N3/v3vf2fXlh3nWQCgMpIDAaAJxMrwIjEJGANDRZPUkZB3zDHHZIP9MUFVrhhoigGnuMXgy29/+9u00EILlf33sW0xYRZ/X8nzhrh/rDqM20033ZT9LCbeDj300NQMYpLvlFNO6TbxrpQYNI4B1bjFZOYBBxxQl9X/MZkfFSVj4LZRGnXMVZK0+Itf/KJw0qee4vN70kknZYmzpZJRuhMDl5HcE7dIoN1jjz2yif6+Ij4/Bx10UGGSV0x6n3jiiVmCUbQerCYht1nEhM7JJ59c+JpLTTTGrSMBICqstEJL+kj2iMHqqJ5TjZjUjsH/GPDvqOJR7jkoPpNXXnlllpy3//77ZxN75Yqk/jjvVSvOBdHmMW5HHnlkNiEVEwS1Ft9dMSEQlT/y9mEkpf30pz/NEtXiXNPZueeem/74xz/mJo5FMly0t4x2kFEVuSjBrREiOTJeR1G79fgOj/vF9//ZZ5+dJURUIiYM99577/TMM88UHgNxDEZS64EHHpglOTdKTIpFcvk//vGPkveJ/VFJcmBMHuUlBsYE8rrrrtvt7/riubBcMVn2u9/9rvCc2XF8xSR/JDXUOqG0t5WT4FjOIopS8pKUQyRzx8TxmmuumSVW5T1OvZIDN9tss+y7rqtI3ogFTFtuuWW3fxeJyqVi9EgoiEUYzSASwqJKdCmRnBXVr6oV71tcsxSdV6L9csSfcf+I32tVaTiu2Yq+F/JEckPEHXGL76W4Vo4kwUo067k1Hif2ebzGPJFIF4kykeAf389RNatSMeYQx0EkGJSbGBmJEJGAEbcYF9hzzz2rjtkafRzWQpxPiha51UMsZuntKnURy0TMX5R8Hsl48T7FdcWf/vSnHi24bdSYRyTLxgKaSqrwRkz94IMPZrdICov4f9ddd+3RubrZK8/FOTTOQ3lVbjsS2OIzHOerSCSMaqSViEVosYiqaNF1fHfG+xbH3nHHHZeNP1K7sYa+Lo7zSPqMWLHex3yHWIBQKjkwvo+jUveQIUOqemwA6OuqHzUDAGqm3AvmosHyGFSPQep//etfFSfodRaD3rHqOZItyh0QjFXtMRjVk+ftrGjQoTfEpGokrMWtksTAriLxI1ZOxsByLcUAdUwCNjIxsFHHXLnifYt91MjEwFhhHgkfMRlXSWJgd6/lN7/5TZZAlJds0S5i8jtebyWTlzEpEueiGEhvRZEgFtUfqpmwbdbzaJE4f8V7Vu1gfZwHo0pC7LtKEgM7i3NXnMui6kajjp34fo/Jn0goK3eiuhxx7otza15iYNd9EZPeMbnZ4YgjjsgqGpQ7CRzbH39z6aWXpmYRn4XYv3GcFCUGdq2QEAmTpaqWdieqSMbfVJIAEuf0SKiMagyNVKqCX4eoCl2qwlGpZMI8kezeXYWZvnguLFdUCtxhhx0qOmfG8RvnyagQ1srKiTOjek613yWR3JynI5F1nXXWKUzoyUtw64mIaUtVYokEwO5E7BmV2LoTFYaiem2ziOrjee9zVM2rVkxSl7PgpOv5PBasFVX2aoRI3okk1BNOOKHsv2nWc2skFEXly6Kxjq6JWFFd6c0336zouSJhIeK9+D6rNt6KfR8xRSwqKVU9qd2Pw0gEi+S33r7F919v6qjKWklV2lh8FMdmjAH1lp6OecSCxEjoO+SQQypKDOwqYsT4PMe2RJJQu4nv06haGol4lZz/4voy4rBK/iYSo+J4L7cbS8fC4Z122ik7z1GbsYa+LooARNJxXKPW+5jvmhyYpyhmBwBKkxwIAE2g3HZC0Uoob7IwBi4raU2UJ5IAYnI/VrUXiZWzjUxQq4eYqI9Vz0UT2+WKCYBYrRpVPWohJs7i/Y5kqEZp5DFX7nsYg7cxSNooMRG/+eabZxPFtRKVFmOguNLJqFYSr/H444+vetA+BsUbmRBajajCElXN+pKozhaDzdUmzcZnO6oo3X333TXZnkgujMmDUqvUe0NUkIuKdY0+B8YEQAzqR1WeapO241xeyYRWPUVVvqKqYKXEa4iKpOWIJIVoJ1dti86oNFMqiac3RKWRaEddSnzvlFslKibhohplpcmIffFcWK44hv/whz9UNdEW710kszSi0lOtlDOxO++881ZdqThv30RC3tprr539O6pdTz311LmPV+35pki0Hy9VvTOSmbv7/oprj1Kxeixeaqa2bEXXk/PNN19VjxuV16LCdDXiOzQmuJs1sSCSVOKarEiznlujElFUJKpGfNdGdf5yRaWvffbZp0eLtTqLhZFRbbncc3I7H4ftKCr3RwJoNQsgI6Emqnb3RkJnT8c8IqEvFhJFwmytRIJ8xHjNch1QK5FAWe1156OPPpqdg8oRC4ziueI4qlScj2IxaW8mp7brWAP/V/E4quzW+5ivNN5rt/kHAOhNkgMBoAmUU11iwIABJVsKxwDV4YcfXoctS+mcc85JV1xxRe5gZKlKFa0qBoCjYtkTTzxRlwSFYcOG9fhxInkgqu01SiOPuUomy6JtVKPEqvsY1K3HYOTDDz+cTVa0q54m0cY+j5aLrVQtqtqB01YWyWOVVOLoLCZL4vNV66oUkRzS6MTrOBYiyaOnYt9WOykX1XTiHFPtJHbH5zAqpjaDoiS1cr5zo0pR0T6L5KtKKh91p1aLEqpt1xcJt7XYvqhakjehvsACC6QFF1xwrJ/3xXNhuXoa90W761tuuSW1qqIJ+QkmmKDq1slFcUc87qBBg75rXdyRKFhKtHKu1yKOqExayoUXXjjWzy666KJu7xuvo1Qb4kYpuvaaZ555Kn7M+B6M6rc9Eef/qGrYrCKBvSieatZza08XeMTflzOWEont1S48Knr+qMhYpC8ch+0kPk+ReNqTat4RB0d3hXrryZhHXPfEAtZaLmTsEMmKca1Wq4Wk7XA9EeNcRbFBvCcxjlFNYmDn1sex6KGv68lYA7X5ji7nmC/VXSkWxORd0/TkMwIAfVm/Rm8AAPR1kbwSq8iLzD333CUr0kXb2xgAKjLttNNm1S6mmmqqbIVwJKmVkwBx6KGHZtVkurs4v++++wpbnEaLr8UWWyxNP/302cRdXMRHi7NInorqXo2s7NadqL5w6623ljVgEe2tZpxxxmyCLQb9Y3/kJYPFwEhULIgJiv79+1e9jY2sGtDoY65cja6sEImg5bQ5jZaKyy23XPZao91ltP+MVbZFkxGReDFkyJDC9natLiraLLvsstnnLD4/sZI+JgGL9k9MMMdnOW8SvVnEpElRO9s4d8Z5dJZZZsmOmZg4iPNonD/jPBoVy2rV1r239OQzGhVry5kMjnNPnKenm266bP/E5Fkk2OQNUkdyV7R4veSSSyrapvHHHz87TmeaaabseePYjaT+OFfGeTO+855++unC77w4tqNyX60qCHYkfUV7nogl4tiJgf5oe5an67m6X79+2Wdxttlmy77nogpRVGQoSiSLCa7YN80iFlssv/zy2Tk33ut4HUWV/uI9jOShaOGYd06OGKAckeCyyCKLZMdHvA9R7atZqlpEu8VoCVfqOz6S0yMpN471PEUtzbqrGthXz4XViLgzzm1zzTVXdi0Rx1458VVUuipKbGvWisJFbZGj7W9elfW8CcZo21n02F1bYudVVY1zSlRhWnPNNVOtRaJinMu7axcfz9n58xmJRKUqKa+yyirZ91UztX7Mq7IVn/28yqalRFXycs6v8fgRj8c+iRghtuWxxx7rUXJQOd9H8V7FLa6XI26I7eg4r73xxhtp+PDhhe3c4/VFVcC99tqr29+3yrk1ro0jzph11lmz8YWIVcppcRrntWg5W0q8hqikVfReRqwU383xHR37ID7Hsd9ee+213L+LBNyo6BmxRSseh4wtYvByxqiiqmx8F88555zZexSLeyJO6s0Faj25nopFPOXErnENsMQSS6TZZ589+3eMe0XsGmMXeWJ/RFJutCtuNxFvxGd+mmmmycZ8orpa0Wc8zimRMNo1puh6PonxjiJxvlp00UWz81WcO+M8Fe9JO3eXaLXxwHZTr2O+lDjfRBzUnbhOjTg477sfAOie5EAAaKAYYI+B4nLajay88srd/jwmpooGLmOwKCoARXvTGETqLCqIxGB53qRDDDD99a9/7bZSXFFyQawWjtXIMYhYSqzmfPDBB7NbDDCUqnQU7Q07D4zHau68yecYqI2VikUDup3FREQkZeSJ1xKVpaLtcNdqjjH5EitU8yrfxSRcTGJE+5ZaiQmMSMKLiYSYVIiBsNiP9ah+2OhjrlqTTz55NqEaCSGxTVF9IRKFIlmn1mJS4M477yy8XxwDkSwa719ncU7Ye++9u5347SzacMXEc+fjOBJPO39Ott9++9wJwQ022KDkfu763vW2SHw8+OCDs/eusziuf/3rXxdO1sWxutVWWzX8dRQpmnCN8/9xxx031n7oeu6KpNJIfIs2QjGR2J1of9Y5wSmStyKRNU9REl6t9m8MNMd5LJKKY1KtI4E8Ejc6i/NbqUpInSfcI8Fvo402Guv7Jwaz4zXntc/q+AyX+u7teN0xkR6fwZgwi/Y3RUnf8dmMSbiYJMur8hEJ6jEx35Mk8g7R/jKSvWICqfM5Nr6fy205FYPz8RhzzDHHdz+LpIFtt902N2EnvhNjgqtZBu7jnBJtWTsnEMXriPNwtI/LEzFKXnJgOe0ap5xyyuyzvOKKK461nyIpviihrjfE5+8HP/hByUUScQzHdkabrlLiPc/7/orPZyRXNfJc2MriXBOJC50/j/G+RNwUidN5YpFG7ONILmwmMcnXOckgvgPicxHn/6h+U1TZL46JiAuqUdQCOM7Da6yxxvd+FglEkdCVl1gX21yP5MCw9dZbd/vdHe9tJLbHIp6Ql8AYj9FM4rs5b8FZxAiVxhsRJ5bTDi8W20Qs3FEdskN8v8Vx9eqrr6ZaiHg9JtZXX331LG6IJM+iz2J8NiJeibghb9HRTTfdVDI5sBXOrZEUGBUQ4zuoQ8RBhx12WOFCyqLEx7i2L0pgiO/luOaYeeaZv/fzOLdGJdCDDjooixdK+dOf/lQyObDZjkPyxdhUOfHYQgstlCW+RTJrZ1GFL67ji5LO66XcMY84nsupKBqLi4466qhsnKnrfoqY+h//+Edh9e2ddtqpcFFJK4lxwOg00nksMBZMRQvWOE8WXU+USpSK83R3FYC7imMuqrvPP//83/t5fEfE9W8541D10g5jDY3WG+PezXLMF71feSIGb5YxBgBoJZIDAaAXJ7pCDEJ88MEH6ZFHHskGfooupDsu5Ndaa61uH7+cln3Rwqa7SdgQkxMxgBgTRHmTBjFxFhMOUQ2ps7xB8ph833PPPQsHc2KyPCbP4hYDHzE4FPuoq66Vh4oGOGKyJRLlKhGDl0XV3mLiotTgRlQTjEH9mEDJa+URAz61SA6MpJeYoI8EsKg20VVURaplm5hmOOYqFUkIMUD/k5/8pNsk1RgwL6p+Vakzzzyz8D6xPTGg3p1YAR6JJhtuuGHuiud4byOBo/OkddfPSdGkY3yOKv2c9IaYIIvkh+62PyZjYv/86Ec/yk0yjUm0qDqy0korpWaWdx4NMSGYN2Eb4vMflUbiFsnLkbjdXQvK+Ax0/hzkJW53qPfxEZ/xSCaOxK3uvi9iUrVzFbM4B+VVqojXFJ/BGCjPS5bbbrvtssHqvPN0XnJgfBd0TewtEq8v3qPYvvjcljoHxndITCp2TuirRjxfJBF3fZw4T+y2225lJQfGOfSMM84Ya1JwkkkmyaoCFiWYxKR2MwzcR1Wf7s4p8Tri53HOyasgmJcEGXFL0SRwJBjFMdV1Iq8jdojjKd733mhBVySq+uVVUI6JvrzkwKLWw/FZj/3eyHNhq4rJ9XPPPTerMtb1sx4TdHF9cdddd5X8+0iQidghqhw1k9NOOy27VSPOw/G3cW6vVFyXFSWBxPfAwIEDuz2O49xYSrwPkTwR1zm1Fgs74rzV3TkrrmX22GOP7HomKqN2J97/vCpnjVCqOkyHqABcqUsvvbSw4triiy+efU92l4wfrc8j5iiKN8sV1/6Vxg0R0/zwhz/MjsGYnM+LeeM97+5z0Ozn1ogR4rPUNd6M9yQS9iKpOS95IxKfokJzd/s2PiNxHOSJKpqRXN3d2EKcWzuqku6www4lHyOSJSNJsbvYs9mOQ4or1RZVX4sk0vgujvituwU18btYoFS0kLaWKh3ziMWsRZWzI2aNa5buxpniuy0S1GKxUV4yWiT5xvEb43ztIK574rq1q0jqPemkk7LrjbwOG3nXExE3FB0zca6OcZDuvhPj/H/KKadk21jOOHM9tMNYQ6P1xrh3sxzzPYn7iuJGAKB7zbVUGADaTKyQjMHuzreoMrTqqqtmq+7KHbDZdNNNv1cdpPMgdNGAXgzgl0rS6hCD2JGElCcu9qM1VVd57bs6WhFVKvZTVK1phKJV/VFVoJxVj5EUmSeq+uVVjSpHDGxFlYKYDO5uwLbj/Yljrlaa4ZirRAySxeB8VI8rNTAZK1K7S9aoVkwm3H///bn3ifelo6pLKTHgXk4VnLwkgFYV71VU6MhLbIxqCHlVvDr09BjqDUVtEIsqJHYnWu/FpFSziwSXqHIU59VSieQxARcTbeWepzfZZJOSiYGdj7E4d+aJ6oF5lWYqneDvLBLtihJZqh1I7ywSEEt9B8RkczmVCSORvWtiYIfYz0X7oRlaOkWVg0gwKHVOiQnVrtX8uookn1ITH+W0ZItKunnfNXH8R9xYi2qRPRXJUNNPP33J30f72lJVPiLZ6vrrr899/Ji47k5fPheWa9999x0rMbCzSCIqEgkC7WKBBRbIEhyqjXUjQbpoUVCpmLboeiASIoo+Cz05p8V3XXciTo/nvfjii0smoEdc3Gy6WxjWWXdJOEWixWKRmPDOO++WG2+WoydxQ1QaLFIqbmj2c2tURSyV2BBJETF2kieO81ILveI6ID6LefFgxAdFyRcRIxTFlqUSpJrtOKyFqEwXle57+1ZOVbWeKiemi4rTeeek+MzFfXpLNWMe5VSXi+Oy1DhTiM9NXLMXfX7aZbwirt1iHDcvmSkWMVYbg5UzbrHLLrvkJk1FYll3iVx9VTVjDfTeMZ+nu4U5lcSNAED3JAcCQJOLgfdSiWblDB5FYmE5Nttss8L7dPd80Q6plJgg/ulPf5pVgGuFVX2R1JVXRSqU2w4hEimKKjD0NGkpEg3KmQSupWY45ioRLa17WnmrUlG1p6h1VVQC7a5qUldrr7124URiuW1BW0m0FotJsCKRYFpUGTHejzwbb7xx4URUvdsgxfkikpNKifZAUbUh2nTGebWdxIRS13ZceeK7pKgaarnn6aJqdjGZXG5VnJiYjmTCqNYQVTviPBiVaGLyNAbMoxpo11tR0lwko/VUXqJ2TB4Vtesp2p/x+Sv6fERr0EaLxILOrQq7U85xWKpiT1Q1KlIqkaez2MaiJMXeEO9r0faWqg4YSRB5yVbzzjtvyc9eXz4XljtBF9U56nUcx8KG7s5V5dyi4lVvikSpqJwXVfK6W0BVrqJ2xXE8RlXRUsdy0XMXPX5PRBJIqYSMqNIU+6bUZGvRAp1GKIqdO7eyK0dUkosYLs9cc82VJcrXIt6s5rozYviouhPX+pFoF4vjouVhbFPXz1gkwhYpFTc087k1WnQXLdLqyXmtKCkpKvaVc81RTtzY3TVZsx+HjB3PFy3gjTGeooTVzt0SekOlYx5RgT2uW4o+d0UJsR3nl7h2zxOLSnqzimK9xGcwL1mynPNV3nVR0bhFfP6jgmg556qexEZ9eayB3j3m8xRVQ4yK+wBA5bQVBoAmFpP20TKiVDuqGMAvUs6AXscAUiQK5LVQ6W5gOwYho51AqdX6UR0vqpyEGFSIwcOOW6zOjOTC+eabr+IJn3qIQcsvvvgi9z777bdfdquFnlQOjIHBvNZG9dIMx1y5Iqluyy23TM24j8qp/tExIBaJRXkVDGKgvVQrrVZV7jEUiRJxLolWaqXktSFrFvE5iNbH//jHP0pWYT3++OOzW1SDiEnMWOEerz0GY6M9YEwq9tYEVK3E9kcCbCXKOSfktd2r5nshT1Roilaxl19+eWFV1UrVonVcTLrniWMmb7IuzkHxHZ2nKNG5GQbuo6JtkXKqUpV6LUXHSSRlRCJROSJR4fbbb0+NFgmu0WaxVOWxG2+8MYvvulY6KmopHC2LS+mr58JyRbJxUWJIUZWNZvlM9lScb+McGa06S1WCKRKTlbfcckvufSIBJG9SNJKnTz755JK/Hz58ePa9Fcld9VhAFtvXXSvyF154oeTfReJvXqJYoxS18ay0qmosJChKdIvzba3izXK99dZb6dRTT82qO0b83htxQzOfW8v5fu7Jea0oboxFILX6fHYXCzTrcUj3outFUSJLxNbltEqNc1Zcx9e7al41Yx5xrEaCYC2uxzvGNooWLMYYSXxvtbJaXE/kJcIXjVvEeTla5JYjzisvvvhi6suqGWugd4/5Stord1U0dg8AdE9yIAA0qagccNhhh+VWuimqKhSTWXktJzqLAc4YPMkbQOqubH9UrPjVr36VtcIrZ9A+BgW7Jk/FwGkkikU1lFgJG8mGjVCLKk2V6EkbhJikKdXmsZ6a4ZgrV1TeaETCXDnH0WyzzVb248V9i9obxXO2U3JgJau74755k2Qx4RmtQMuZxGmkqPhw22235bY+C/Faov1bdy3govJIfHdssMEGdUlEqLWorFdpUkdvn6fzni8mwaLaTy2S+LrT06SBOB8XVcsrGnSPc3RRMlJRK7FI3mm0+GwUKWeRQqnX8tFHHxXux3KP9VlmmSU1g2grHMkkd9xxR8nv55jw7lw9JyZ/8pKt4pgsqnjSF8+F5cqr1l1ulY1m+Uz2VMSO0Yr05ptvziq2VtNy9qabbipMjsirvlpOcmBH9cDf/e53qR6iQnt3yYGlxPm8GVsKl/N9VHROqPS8XOn5tijeLEckT8c1c9FxV4+4oVnPreV8P/fkvNabbQfjmiOSXDsfy814HFJaOe9XJGiVK97beicHVjPmUY/xilo8Z7Or1fVEd+LcUZREVekYSV9XzVgDvXfM93TRSDmxAQAwNrXoAaCJxMBFDO7FJNMZZ5xROKlfNNhdTtvSzoom1ko9X1SB2W677VK1YpIi2poce+yx2QDOMcccU7dJk3ZJDiyntVQ9NMsx18r7KFQyiV3OfXtz4qs3VLJ/yjnmypnoaYaB16jYUmllnK7VBs4666xs0vbnP/95Vp2mmRW1kWvm8/Tdd9+ddtlll7olBtYigaecz0bR8VbpOb1ZlVN1qCcJxEVVZiqZtG2mfZ5X5a+7KoGRpJQ3sRnVO4peX188F9byOO7JfmtF0Qp51113LZxArKbl72STTVbY5jsm34vizagQV69WrdHKsZyk0Q6R2NWIxUXlKGpbV2mFmHK+nys53/b03BxtniNJtJ7XuHlxQ7OeW+v9/dzouLHZjsNaiarCcSz39q2ac30lynm/mi2mq2bMw3hFdSIuqNf5ql3PFa021kDvHfNFiuK+orgRAOhec5fPAIA2FVV2YrAmbtEyOFrNRcuRKNlfyUrkZhKtdmOC6oQTTiirrWpeouA555yTtcOKdnbtPMlZaQWMzqaYYoqabks7so9otSpJa621VlYVNBKko8pET7Y7qsNEK7WLL744qwDWjFrhM9rdeToq80Tln56cw5uhClNvPUYzKKeyQFGFxDwRq+QdD1GJqVzNdFwNGTIkTTPNNOmdd97p9vfR/jgSIzsSuq+77roeJRv21XNhLY/jVq2Qsttuu2WVzTqqb73xxhtZddbzzjuvMAHpoYceSieeeGJF1fmiKlosTCpaWBCtVHtq1KhRWUJ5JObVq3rgQQcdVNZ9t95669SsimKCoiTsZhbH8BFHHNHweLQZz631/n7ubc30HV5PDz74YNpmm216/XljIe2FF15Yt8cvJ+6tJKardzJjq1xP9US556neaDHabuerdlfPz0YzHZfteswXJcxOPvnkdXleAGh3kgMBoI6OOuqotPHGGzdssCMm1ypRNOlS9HwxiRy3xx9/PN16663pgQceSMOHD69qQOTOO+/MBn6333771FsiUbNVDBgwoCHP22zHXCvuo0onOMu5b7tNClSyf8o55spZ8dwsYtL2zDPPTK+//nr65z//mVVFevTRR9OHH35Y8WONHDkya7t42mmnpWZUzWe0Gc7TQ4cOzRJX8kSyVLRujO/EqCwV/9+1/W60Y433iNYXkxN5FfMq+fw2U9u1OGYjji11DokqPtHWdbPNNssSCO+9996SjxXVzRZbbLGyn7svnQv5vlg8FcdL3OLYisqAkYiSJ5IIozLlwgsvXJOqgbUWz1ev5MCo4vbHP/6xsEryHHPMkVZYYYXUrGaYYYbc31dapa6cinSVXLdUeo3T2d/+9rfclr9h6qmnzpKtolplVHeM6mRdJ9xr0cq3r51bI24sitnqqZmOQ1JNrhkr+az0RnX/aq6nWmm8otyE23pWc+8NzhW1V8/xwL5yXDbS22+/nfv7GWecsde2BQDaieRAAGhhRUkSn3/+eXZBXdSeuGMF9IgRI2oyoBcTcx2Tc9FyJgbkX3nllaxKR0xEvPTSS+mxxx4rnACPCoLNlhx4wQUXpGWWWSb1Vc16zDWTco6j+DyUO4H98ssvF96nFfdT0f6p1X1jcrUVK5DGYGec/zrOge+99152LLz66qvZZGy87ieffDL7/zxR2SvOubPPPntqB+V8viI5vZ6DxdE6tWgi4rLLLssSMfKY4G0fgwYNyk0+iNgnvh/LaX/0zDPPpGYSyVmnn356yQoZ0Vo47nPDDTfktk4tt2pgV86FfVskCv7lL3/JEuDyJgnjeuPII49Ml156aeFjxrHc28mB8b3UucpmLcV5ZZNNNsmum/JEwnozi+S4CSecsOSisnj/470rtzpmOUk+RTFUtbFpV7fcckvu7+O6KdoOxz7orZihr5xb4xop7/t5o402SkcffXTdnr+ZjkOKTTXVVNk5Jq8qWHS4KFezxXSVjleUq57jFeUsNI7FKkXjSs0uqlbGNWTeYqNajpHQM33luGykokUhgwcP7rVtAYB2IjkQAFpYVNSIFf9FrbbWXXfdwsd64oknCtuexPNVKioexORD10SNmMSLCeUDDjigZGuWaMMVLYqred5qzDLLLLmTUiEql/Tl5MBWOOYarZxtjn0UE93lDCjGxFzRoFhMnreTolZ/Hd59993CAddWPIZKTVbFbckll/zez6NS6y9+8YvsfFnKPffc07STtpUq5/2M83Q9kwOLJgWjbV9RYmAMdhdVeaJ1RLJ3fBZLiZgnWqSuvvrquY8Tk9GRRNRM4rMU1caiLWqp77NIJImYrpSIrX70ox/VZHucC+sn4ttoE9qMlTnjeqGj7XApjzzySNbqNKqyFn1H9HbV1ojnoursT37yk7o8fiT+nX/++SUTdCMpMZKgmlkk5ERlvFhA1p24ZohE66iqV46Ie6L6aV7SchwztYo3887/RYkzkTydlxgY6v3ZbNdzaxxTTz31VO75oJKk00o1y3FIeSI5a6655srGoEqJa/NY5FqUYBfnq2ZNDoyq5tEuNL6bSon4rlzDhg2r+hquaOFMOZUaowJq3mesVcSxV+o7MMRi60jkjnN1kXLPK3TPcdl4L774Ysnfxfdqu4zzAUBv+35/AgCgpSy//PKF94kqBOW48sora/J8lSQNxiTVsssum3u/vMm7WF2bJ2+wszsxQNp1QqSrK664Inc1bzmicsjzzz+fWlErH3O9JdomFg0m/uMf/yirAki0ayy6XyvuoyLRmvLNN98svF9U/olJ1zxFbSyvvvrqbOIw7xaTO82clBRt6Op1Hq3mXFpPkQwbE1p5oiV90XFRzgRsTMB0JyZliqrI1eL8R+tYYoklCu8TbU+LRPW9Zjzf5FX9i6SK448/PncCPBJmy2mX1shzIc1tjTXWKKstdVQZLHLNNdekRqjn80YSb7SxLyUqC9azvV6tLLjggrm/z0vY6a5ydFEb3ni84cOH1yTezGsrWpS00sxxQ6ufW4taacd3biQV90R8D5ZKxGmW45DaxXSxsDWuNYrkVV1utBj3KnqdkdRcTtJffP7j2r1oEW6phVtFFXXzEoQ6lFM1uBUUxTnx+b/++usLHyeSuiupcFlPrTbW0IzHZa3HvVtBLKLMqxgeCzHLqcgPAIxNciAAtLBFFlkkq6aRJyrVRKWKPDGYXTRh1a9fv26TkGLV9IknnlgyiaLImDFjcn//6aeflvxdDLbniQGxcto9dPaDH/ygsNrTgQceWNXK5GgZdMQRR2TPEUlfragZjrlmF4N3RUmvsQI+kinydHy2iqy88sqp3cSky2GHHZY7oRLJg2eccUbhY7XKMfTnP/+5rAmY7hRV4Mw7j5ZTdTKqfDaTVVZZJff3USHmhBNOqOqxI8Fpv/32y5IsSlXMiOT2PI8++mjhhM2ZZ55Z1fbRnOI8XBSTRHWivMSlOPYOP/zw1IyiElte8krRd34lLYUbdS6k+e22226F94nzf14L12jvHQs0GiFi33q2+fvpT39a8jur2VsKd1h88cVzf//0009X9HjLLbdc4X3i2qxUFfuOa79y4s1SyqlIl1cpKkRF2Z62wu6r59a4Dujfv3/uff7whz9UNZYRCwYvv/zyrGL+b37zm6Y+DilfLGgoctZZZ2VjHqXE5zUWlTazcsYQIi7NG0+LZLVDDz20cGws77mmmWaawnGT++67r+Tv//WvfzVd1e1qlTNuceqpp+YmTcX5+sgjj0zNohXHGprtuKzHuHezK0qYL2fBEADQPcmBANDCInlqu+22K7zf7373u2zgurtEmxiw2GWXXQoH9KLKX3cTwzH4dNppp2UVPTbffPN0yimnZJNfRZMIMfh97rnnZi1A8uS1aykasImJir322ivbnqjaECsqO9+628aorFHUpiMq+0QFhaJWHZH4GIMasX823njjbB9dcMEFTT2B0grHXCvYaaedCu/zt7/9LUuA6+54iAoSsZ+LqufNNttsabXVVkvtKI6Tvffeu9vWq9HOKfZPUVvWmWeeubBiSLO47rrr0hZbbJHWXHPNdNRRR6U77rijrOqS0eqzqCLZFFNMUfJ3RW3swkEHHZT+/e9/Zy3lYuC567m0t8V7X7SCPpLvog1eUZWh2P44l0cibkwGRuvTqCaZ9x1WdF564IEH0l//+texHiPOh/E+x/a32wB+XxcTX/E9X05yRnw/xOc7Pk9RSTiSAv/0pz9lMVQ5rdMaIRIrqm1JOuecc5ZVWbHR50Ka34orrljWZGBci+RN1hbF4RGbRRJ3pbdLLrmkcNt6muCVJxKQzj777Oz7r/MtKlxFPNQKImbLS8B/+OGHK3q8aONclJwXCwGiZXV3VYHjOm7bbbctjDeLzjtFyWnRlj0SibpWhYtryTiv/epXv+pxxbi+em6NsYRNN9009z7vvPNOlsR+2WWXZQnEeeLaLPZlJCvHZy4WDRZVkGqG47Bebeh7+1ZOxb6eikV+Re0qI8bfeeeds8S4WBQU8Vss7IuFIL/97W+zsZBmrRrYYbPNNitc9BkJ9xG3dlfVOsbXfv3rX2fXiHni/BfHbykLLbRQ4bb+/ve/7/ZzFtdseYm5rWallVbKquTniWMtriW7S5aPyvdxHmmmlsKtONbQbMdlPca9m11RW/N2XCANAL2lX689EwBQF5GkFglnMRhZSgwGxMB1TJbFQG4Mko8ePTqrHhDtQorEgF4kWZTTTiduJ598cpZEFq1Dov1jTBjEasdoXxKDEzGoHvfL2+YQg+iR/FTKAgssULjt0SaoVKugqE4RCVqdxXbuuuuu2er9osGKmMifYYYZshZYMWATySoxyRID99FeJZJSWnEgplWOuWYWiRAxYHXnnXfm3u+iiy7KJopjcmn66afPJgFj4iMmGcqZBNxjjz3SeOONl9pVJOLG5zcmaeJ8EsdVx/4pZ8IljtWiKm/NJqoKxSRsx0TstNNOm50H4xwT56donxKJtTH4H0mSUZG0yOyzz17yd3PNNVf2ecur4hqD3pHQmzdxFOf83hKflS233LJwsjoSTOMW30Pzzz9/lvgdrzXORXGejn330ksv5VZqKfX5Lqowc9JJJ2WTzPE9E+9dPF8kDTZz6z16Zvvtt88mg4oSj+J7oei7oVknkaNSTqWT3fF3rXAupDVEQs4OO+yQe59IpInqgauvvnrFyXkRU/3whz/MrlkqFef7OE7zKvpEUlEkepVTTa7aBMpWFteM0ca2VAXeuH6MeKUo2a5zK8uoNnz77bfn3i9izaiQGvH4TDPNVHG8mSfe60hqjRiglDiXHXDAAdk5NhISIl6Jc1ssootkiVrqi+fWX/7yl9lnPy/xLxIqIkHl6KOPzq7tY5/E/oixi4jhImkvkvnjfpVqhuOQysQ1dlG12rh+uPjii7Nbqy5s2XHHHQu7GcS5K5KKl1xyyeyzHt+TkSwYrYTLWewUi2/jeM67rorr9byxj7h+igVc8R0Xn6e4lovt6i5psZXFfogqwMccc0zu/eL6NRYlLbroolnb8rgOHzFiRPaeNFuL2VYca2i247Ie497NLi9miuOpnIq8AED3JAcCQIuLQetjjz02S2grSnCIdjRRmaBSMVAeSXCViG2JyYeetM+KiZRIAiklJo8mnXTSrPJOLUXrraiSEBUVirzxxhvZrS9p1mOu2UQlgUiKKJrUi4TSqGRTqfXXXz+ts846qd1Fy65SA515YnI1KqS0ukgyyEs0KDLhhBPmVpeMpOZI4I1zXqtN2kV1jpgsKNLT76Ku4nNX1Ba94/xXqt1qTHDETQXB9hHfWdGSOhI8emKyySZrqupAHWLSK84VeS20uooEqw033LAlzoW0VvXAoqo40cI73u/OSXhx/MTEeZ5Ifqi2Clo8VyQk5iWKxERyTHjGZ4nuRRXfUsmBkdwV1QNj0Ui59t9//yzJLuLJPPF9XJS81ZO4IW+iu5x4JRJ5yqn0V6m+cG6NylVREXSfffYpTLKL4yTeq3Ler0o0w3FI+SJJfIMNNsgSutsxpusQFejuuuuuwm4eMeYT8V8lMWBH9ejoBJAnEpRjUWVRBcJILusLn42tt946Gz+LZOQ8kbQWC2+rbRffW1p1rKGZjst6jXs3q4h1SsWBHRU2i1otAwCltVYZDQCg5MVxTyej8yrhRJud3harNKNNSdFkRFGboGrEauho8RdVA+g7x1ytRWJrtJQeMGBAzR87Vv8eeeSRqV1FdYKeiH0eK+57e4V5M4pV+DGhXJQQ3WpiQPiMM87IKkr2tpgoWHrppXv0GFE5qqiFPa0nEsKrrZQXopVYJMc3q0q/m9dYY43ClnXNdC6kNUTLvCLRbi+qB3YWk+1FVZkjIaQnyvn7erYWbgex+CWvKnbX97VIVKzad999e7RNcR5baqmlqv77uF6NBOueOOSQQ1IzapVzaxxXjWw/2gzHIZWJeKycil151wtR6byZRQWuSKavR/XP6BwRYyGR1FTOeaQn1f4HDRrU43NsM70nMY4RY53V6lis0CxacayhmY7Leo17N6uosJ/XgadWC88AoK+SHAgAbSIqZEVCW60G52NQKirg/O53v0u9LQaz4nnLGfyOCcJ6DGZGclG0zt1oo41q/tjtop2OuXqJBNNo4ZHXHrtScUyec8452SrsdhUDyD//+c+r/uyeeeaZaY455kh9XSRZ5rXo6RAtzGLStNXEgHu07o3Jt9523HHHZRUFqhHttXbeeeeabxPNIaoTbb755hX/XcQyF154YVlVy6ppeVoLkfhUSVW1n/zkJ6mVzoW0hhVWWCFbJFEkEh46Vwkrqvga1x89TQ6Ma5eoFJXn5ptvLqwe1pfFd3ssQirl1ltvrbjFapyL9txzz6q2JxJberoYIa5xTj755KqvmeJ6d7311kvNptXOrTvttFNW/b5RyYyNPg6pfJ9H++1FFlmk4l0X3yWnnHJK4bmqGa7nBw4cmI1XDBkypKaVzq644orcdsKdxXf6DjvsUNVzTTfddNn7VO11WTOad95506mnnlpVgmDEMgcffHBTVXRt1bGGZjou6zXu3YzyFoFEknwtz1UA0BdJDgSANrL22mtn1ShilWhPVjhGS61LLrkkazNSJAY081r/VioGvGMgrJzn7qgeddFFF6VVVlmlZtvQ+bGPPvrodNJJJ9Uk0SgG6mLfRuWHbbbZJrWDRhxzrTi4e/XVV6dtt922R1UEZ5555nT88cdnx+REE02U2l1Mnh1++OEVDYrPOuus2SBsHE+tJlaVd25/2BMx6RnVUeLclVd9p7Ojjjoq7bjjji1XbTGSCCIZ9A9/+ENNWpHH64+khJg8jtaGeQP+kcg1zzzzlP3YcY6MSfQjjjiix9tJ84rPcXzPx+evnAmhOC4imTAmUaNy4IcfflhWNZZGiJiv3GoNMYFVTYWjRp8LaQ277bZb4X2iJd+//vWv7N+PP/54eumllwoXdMS5vaffIUWTlpEY+M9//rNHz9MX2iqW8sYbb6QHH3yw4seMRScRK1QSi8d5LJJmqkkO6u56IGLUOM9XklR44IEHlnW8F3Fu/T8/+tGPsmvXSGysxfdCjINE/BzjF61wHFJ54lyMNUXF73KuSSPuiBbSf/7zn7OY6YMPPmjKeK67hJs4hqNaYk+SmWJ//fKXv8zGdSpNZI14LTpIVGL55ZfP4ue55portZt4bWeffXZF+zESWiPmjUW8zaZVxxqa5bis57h3s7UUvu2220r+Pq6ZmyGpGgBaWWtFYwBAoVidG6uUX3755XTxxRene+65J5sMK1q1HBMVyyyzTDaQFCt9yxUDmv/+97+z57j33nvTI488kp588sn06quvFrbu6jzQEQMoscJ63XXXrXjAKFoznn766dkk4I033pieeOKJ7PV//PHH2QRcpdUluooEkbj95z//yZK8HnroofTWW28V/l1MbkeySiQqLbvsslmlk3Za0dyoY64VxQTQ73//+/SLX/wiq3QWA15PPfVUGjNmTOFA/WKLLZZVC4zPR08SMFtRtOeMz01MVtxwww0lK+3EMRjV2GJld6sOFp511lnp3XffzT4/Dz/8cHYefe6553JbqnQW5804VmJlfuyLospF3U1A77PPPlkS63XXXZedy+P5I1Hp008/Td98801qZnEeiZansdL8+uuvT8OGDUvvvfde4d/FZyoSb6NN8HLLLZedq8udqIuJ9hj4jwnbmPB/8803u71fTD7HcRyTZYsuumjFr43WFHFDTODE+T4qhT3//PPpnXfeSV9++WU2eRqT/XG8RaJC58oq8d1QpJEtqeNzdu6555Z1v1Y8F9Ja1QPjXF9UPTDip6KqgR1tsGshnq+odXD8Xlu00lZcccVsUj3Om92J67H43q4mVojkzajiFwmaca3Ynbh+iyrWsZirlnHlQgstlL33559/fnbNVCpxKGKyOI4iKbBWlbCdW/8nvnPjGBg5cmR2XXb33XenZ599Nn399ddlJX/FdWt8f0fcGO9ppQntjT4OqUzs+7iGj7gm4rmoXvraa6+lUaNGZeMdsVBp7rnnziqZb7DBBt9rozt8+PCmjee6iuM4jrd4nTGmFtfecT0YyTpF1azjcxDnrLh2j7G9ap8/OkjEZyMS3CIGLDWeFImyP/vZz7KFqu0sxhHjvYjzd3zvxTmr1HkpYopdd901TT311KkZtepYQzMdl/Ue924GN910U/r8889LHkPN3qodAFrBON+2Q9QAAOSKiYeYbH7//ffTRx99lA3wReWxSDyKW1Qy6GmljK5icCIGr6K6Q0yGx4BPx0V+PHcMGk4zzTRZu9VIEmu1ii6RHBiTCLFvYzAm9mkMHMfrikn/mHSIif9qB0dbXSOOuVbzxRdfZEkP8fmIQdE4juJzEIkMsY+iCt6cc85ZswpKrS4SQx577LH04osvZvsrPm9xDonP2fzzz5/aUSSPxjk0bnHOiWMkjpuYvIzPU9wikS2S1OJ4aVSr0WYVE3cvvPBCdg4aPXp09j0UVT9iAiXO07HP4juoVvstnis+03HeiwSweJ5IPIyJgvhMQ5GYGFtnnXXSK6+8klulKBZl9CXOhdD3RBJdTMiXWnRz5513fi8Jp5q4Mib5I8aKhOSItyOunG+++SqqClytWET39NNPZ5P8cd0UsV28nohLIm7ojWtI59b/iXGKuHaN67KIGeMWUybxPkQ8Fwv84pqj1gv9Gn0cUj8jRozIKlTmJT3FApGoVt6s4jwVCVxxTRXXU3ENHp+LuI6KW8SkCyywQF2SV+N6Kj4bHZ/JuF6LhNlYaNVXx5EiGSyuESIxNc4dU0wxRZZAHt8ZkThF/Tku6+snP/lJevTRR7v93cYbb5xVoAQAekZyIAAAAEANxCRwtQseoiJftI7PE+2Uom0xQF9Olv7tb3+bVYwGaLaYLhLoomJ4VBnMc+KJJ2bnOYC+LhYCl6o8H8mvUVWwc7V9AKA6fasvGQAAAECdREuvww47LKsuXElllnPOOaes6jHRLheg3UVCzu67717y9xdddFHTtiEEWl9U84xWuWeccUZWva5cUek9kpeLEgOj2l60UAcgpfPOO6/kboiqgRIDAaA2VA4EAAAAqIEtttgiDRs2LPt3TGKsssoqacEFF0xzzz13mmqqqbL2hDHhHJPHL7/8ctY66brrrstathWJVmqXXXaZ9wnoE6L61iabbJK1e+3OcccdlzbYYINe3y6g/UWsFi1zQ7R7XmihhdIKK6yQtXyec84502STTZa1OP/iiy+ydrvPP/98uu+++9LQoUOz/y+y0047pb333rsXXglA87dhX2uttbLzbldxnr355pvTtNNO25BtA4B2IzkQAAAAoMbJgbUUE9ORGLjIIovU/LEBmlWcT7fccsssUbCrWWedNUvEqbaVO0A5yYG1NvXUU2fJLrFgBKCv22+//dLVV1/d7e9+85vfpJ133rnXtwkA2pW2wgAAAABN7OCDD5YYCPQ5iy++eFp//fW7/d0rr7ySVV4FaBWREPjXv/5VYiBASunVV19N1157bbf7YpZZZknbbbed/QQANaRyIAAAAEATVg6Milj77rtv2mabbWr2mAAA9G7lwIEDB6ZTTz01LbnkknY9AADQ61QOBAAAAGgy88wzT9ZKWGIgAEDrWmONNbI26BIDAQCARunXsGcGAAAAaLPKgf369UsPP/xw+uabbyr++3HGGSctu+yyaZNNNklrrbVW6t+/f122EwCA0pWbf/GLX6RbbrklPffcc1XtpgknnDD98Ic/TD/+8Y/T0ksvbVcDAAANpa0wAAAAQA199NFH6dFHH02PPPJIeuGFF9Lrr7+e3nnnnfT555+nL774IpswnmSSSdKkk06applmmjTffPOlBRdcMC2xxBJpuumm814AADSBN954Iw0bNiw99thj6ZVXXsliuvfffz+L6aL98IABA7J4LuK6GWecMWtHHLellloq+xkAAEAzkBwIAAAAAAAAAAAAbWbcRm8AAAAAAAAAAAAAUFuSAwEAAAAAAAAAAKDNSA4EAAAAAAAAAACANiM5EAAAAAAAAAAAANqM5EAAAAAAAAAAAABoM5IDAQAAAAAAAAAAoM1IDgQAAAAAAAAAAIA2IzkQAAAAAAAAAAAA2ozkQAAAAAAAAAAAAGgzkgMBAAAAAAAAAACgzUgOBAAAAAAAAAAAgDYjORAAAAAAAAAAAADajORAAAAAAAAAAAAAaDOSAwEAAAAAAAAAAKDNSA4EAAAAAAAAAACANiM5EAAAAAAAAAAAANqM5EAAAAAAAAAAAABoM5IDAQAAAAAAAAAAoM1IDgQAAAAAAAAAAIA2IzkQAAAAAAAAAAAA2ozkQAAAAAAAAAAAAGgzkgMBAAAAAAAAAACgzUgOBAAAAAAAAAAAgDYjORAAAAAAAAAAAADajORAAAAAAAAAAAAAaDOSAwEAAAAAAAAAAKDNSA4EAAAAAAA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ORPOxyvM7iN5X5bpYCwqMznME2cffzSIgKtq8eO/ozEaASGxzHJYsWZI+8YlPZIGDg7WnkIcI3ov+Wwzk1ESQX/QVWxH9wO985ztZsOxAYhBpu+22SwcccEA65ZRT0vTp0xtWqh1zzDHphBNO6MAngd6oOyMdKBIcCP8/S2fUuS233DLbQnjllVfO+lux/VnsnBAT+rVtUkJMckZmn5/+9KdtTdoDVPH6uia2VG2lbDHG/M53vjPtu+++LX3HGuuiyud/jTmXaitqHdh5550bFsCdfPLJ2eT+UOW7+eab08UXX9xycKA2gCor+vnfzbbJeBhlV/T6ff7556e+vr7s75jf/OxnP5s6LeaBYn508803H/DfY8HujjvumAUCxrzQz3/+8/5/i6DB2PL4ox/9aMfLRbmMavhouxmImh9fi6TvhtNOOy098cQT/e8TEcURgTtr1qysct13331ZR/3QQw/NBspPPfXULKvL0kRWl1ZE9G9kf4mVAbVt4iL6OMoVafehjPUwzu85c+b0bzvR7UarSJ8dqlYXI0NgDPi2IlbWnXvuuWn33XfvTyceK2EiZXdcZNM5d9xxR9ezokbgZxmz28WgzIc+9KEsyKlmnXXWyVZ7tqrV1WVxcVvLoBmp4msuuuiirK8ZgR2MLnUn37rTqbYUqua5z31ulgEwBhMHWvAYWZ1f9rKXpf333z9997vfTSeeeGJ/EG9sPRz9sMgCBFA0Rbq+DldccUU65JBDsvHkVoKo4js3FqXvscce6dOf/nRb21MV7bMz+qp4/ptzoYh1ICb0Y3vQ2jzjbbfdli12jnGcgYIcIjDowx/+cMvbDxf5s1MdRRoPK9r5P5ptk/EwukH9HliMh9XPycT86VprrdXx4//GN76xpcfF92MkjIi+RQQt1kQfZJ999kmrrrpqx8tGeXQ1OHC55ZZruB3bo7WbyajeQJmGOiWCIQaLwI1Kfuyxx2bb6YSrr7467bffftmgeGQS7JSJEydmkb6xN3jNr371q/T5z3++lJPqjI6i1sNIoX322Wf3344o+06f582fvfmzLE3zsermdxDlV+W6OBwR7BSDwpFtsCay1cSgmuCOzjnssMP6Vzx1S2S7e+UrX5nKJAIlPvaxj6Ubb7yx/75YFfrNb36zqxdfMcgU209EVqfaRMpPfvKT9JnPfKZr78nA1J186k5z29du3240ry8p/kBjFUUmglZE4GBk8IktkOoXZsTiyT/84Q/ZamaAPBX1+jr89re/zfo7kZG1ZosttsiCRl7ykpdkY9AxARxZWeM7NTKJx6KJ6CfFWHP0k2Jh3GBtmbEuqnz+D4c5l/Ipah2I7JcxPhNzfPV1Iib7Y7I++uJxbkeQwe9+97v0wx/+sD8bWAQZ/Pvf/+5/3mCJSbQBVHk8rEznf7ttk/EwRoP6PbCIEYqtwWs7PUWbXgSHH354uvLKK9PcuXOz2zHeOmPGjJaTtFBNXQ0ObG6s2u2kF6Whjg7HF77whezCtZai8/rrr8+CKVpdCdCqbbfdNm288cbplltuyW7HhXQEI77mNa/p6PtQHUWshxHMEAE/tSwUcd6/4Q1vSEUbKGh+/AorrNCRclFNVa6Lw/W2t70tW5ETWQNDbDMcwYwRvA95iVWlkQXh97//fUN9jIGr0cjg9573vKc/ODBcddVVXX9PKErdae7b1TIy9Nr1ZZUJrO0tu+yyS5o5c2b6xS9+0X9fLJoUHAjkrYjX1+Hee+/Ndp2pD4yKbdkj0Lo+Y2tkBo+JpfiJ7ecOOOCAdO211/aPOR911FEN2Sm6OdalP9R7qnz+D5c5l3Ipah0IEQh75513NmTbjt1ajjvuuEGf89KXvjTbfvj444/vv2+llVYa8LHaAKqsbPN97bRNxsMou6LW78iIfckll2R/R9KS2AJ83LhR3Zh1UNGnjn5HZBGs+eMf/yg4kCE9cx+ZDmqObr///vvben5z6vnBOsSjJSaz6r+cIuV9BFZ0WmzjU6/WMYCy1MNYFReDPSHS3sagTzc0l3Wkn10GEkaiynVxuCI777Rp0xrui+BAyFOsFLv44osbLsK+/vWvpylTpozK+8e22/VuvfXWUXlfKELdGWnfrvnxeV9fQi+IoPR611xzTVfGQQB6/fq6tpXTQw891H97++23zzLiD7SVe312qNjyPbK11kRQ9uzZs0dlrEt/qPdU+fwfCXMu5VHUOlBzxBFHZIuxI0v+0uy5557prLPOekYGtPo6UU8bQJWVcb6v1bbJeBhlV8T6Hdl9jz766P7bsRV4qztyjJbmuSIxRSxNV0Nb119//Ybb99xzT1vPr0+jHdZbb72Up9jqKlbxxMr5EOm/o5J1OoPR5MmTR/QFCEWvh2eccUb/37vuums2SBTbSbQT9d9crkjD3byyoPmzNz9nKJGJpr4zEYNV3dwqkvKrcl0cCW1id51yyiltr8Jq12ADmr3o5JNPTueee27/7bFjx6avfvWrzxjI6faFcrRJtVT2kfkz/h5syxm6Q93Jp+6sttpq2eDOokWL+jPKRp8tAg1b0dz25n19Cb1gs802yyY2H3jggex2/I7t/2LrM4C8FPH6Ovz6179uuN3qjjPRl99rr72yPmbNRRddNOAiipGMdQ30+ObXo/iqfP6PhPGl8ihqHagXW/q96U1vShdeeGG2hXYs7Iy5hsg2tPbaa6ett946CzLYdNNN+zNv1qvd30wbQJXHw8o439dq22Q8jNGgfjeK/mhty97IMhwB/UubP50/f/4zAgzrnxMJUdZcc82O/Z/p31Ko4MAYQI4Gq9bgRoV49NFHWw4aaK5gG264Ycrbc57znIbbkRK808GByy67bMPtbne0KLci1sP6lXDnnXde9jOcC+x6scq0Od32Bhts8Iz62qrmxza/FrSrynVxJLSJ3TVp0qQuv0N5RBaEyHJWE2nkY1uYTp7vrYpMn811WXDg6FJ38qs70Se74YYb+rcqjvax1TaxiNeXVSOwtvdEnY0JzFpwYG3CQnAgkKciXl8//PDD/ZNHIRYvvOhFL2r5+dtss03D7RtvvHHAxzWXtZ2xrjhGsbiifvLddUTvqfL5PxLGl8qjiHVgIPH9us8++2Q/S9Oc6WfzzTcf8HHaAKo8HlbG+b522ibjYXSb+j34/GkE+b3tbW8b1uKZ+gU0EVMUCwe6OU8EuW0rHDbaaKP+v2PrmXYu7P7yl78M+lp5ad5HPFYbdFrzyoBW0o9Dlephq57//OcP+VmG0rx9xfOe97yOlYvqqmpdHAltIkXwk5/8JB1//PEN9x1++OHprW9966iXJYKh6gM0gr4iVao7zf27drYcq2pbWrSBxnXXXberP82D64xc8zE12AgUQdGur2uZjev76JEtuVWRhb+VzDHNZW1nrCsWWNRvDW+sq3dV9fwfCeNL5VK0OjASMcd4880399+Oa5rBFuJoA6iyMs73tdM2GQ+jzMpYv0eD/i2FCw5s3uv6uuuua+l5kQq8frVZRMSvs846KW/N6b1jhVKn1TJh1Kyxxhodfw+qpWz1sFVbbbVVluq3vm61GtDbfIy23XbbjpeP6qlqXRwJbSJ5+9WvfpWOPPLILCiv5qCDDkp77713LuX5+9//nhYvXtwwaNTqlqpQhrrTvBVxq21prL6unzBaYYUV0pZbbjmiskBVGGwEiqho19fRt6gXWaza0fz4+vGs5q2j6ne2iQxYzePVgzHWVR5VPf9HwvhSuRStDozEFVdckWXfrNl1110Hfaw2gCor43xfO22T8TDKrIz1ezTo31K44MAdd9yx4fbFF1/c0vOaH9f8OnmIFUjXXHNNw33rr79+R98jMsH88Y9/bLjvJS95SUffg+opWj2MhjqCG9r52XrrrRte47LLLmv494G2potgifpOQKT9vfTSS1uaPP7Nb37Tf3v8+PHpla985Yg/N1S1Lg7X7bff3rByNladT506tWOvD0tz5ZVXpk996lNpyZIl/fe9733vSx/60IdyO3gzZsxouD1t2rTcygJ51J3tt9++IZt79NmG2nalJvqA0Res2W677QTWQgsefPDBdNdddzVsM2xrdaAIinZ9veKKKzZMaD300EPpX//6V8vP/9vf/jZkJrWhytzq1lRFHG9neKp8/g+HOZfyKVodGInvfve7/X+PGTNmqdsWagOoqrLN97XbNhkPo8yKWL/33XfftudP69v0WsB//b93ckvhcMkllzTcNldE7sGBm2yySdp4440bJvpjJcxQYoua8847r+G+N73pTSlvP/vZz9J//vOfhhSnsVKnk0499dSGSau11147238cRqJM9bBdu+yyS8Pt73znOw0ZbAbbAm/hwoUNne6VVlqpa2WkOqpcF9sV9fTEE09sqK+xemjllVfOtVxUx5///Od04IEHNmTp23333dPBBx+cW5kiK8j3vve9hvuijYIq1Z1oByKwrz5w6ac//emQz4m2JPqAVWtLoVNZQOu3oHzhC1+YBQAA5K2I19fNE7oxltyq5scOtTDujW98Y8Pt+ExL2/I9js0dd9zR8H2e91aaDF+Vz//hMOdSPkWsA8MRQQLXXntt/+23v/3tS81kqA2gyso039du22Q8jLIrU/0eDbGdcnMiiVe96lW5lYfe0PXgwPCRj3yk4fYxxxyTTeIM5stf/nJDau/IQhQDFkNdPMbFQO1nn332GbI83//+99N9993X1meYNWtWOvbYYxvui0muwdx5553pd7/7XVvvEV9yzRO+H/zgB9t6DeiVejhadtppp4aBguuvv/4ZkfvNgRdf/epXGzJjNB87GIkq1sW//OUv6U9/+lPLj48J6BNOOCHNnDmz4f7999+/C6WjCqIe1NeLpU1SxCqu6IPVD9DExennPve5jg4AL1iwoOXHx1ZhkXWtfquZGDB+y1ve0rEyQS/UnXDAAQc03I6+W31bOdB1VrRFNZtttll69atf3dEyQRlFH/WMM85ouE9QOlAkRbu+jjGoetOnT88mbZYm+ir122HFWNRQfZUtttgi7bDDDv234zN95StfGfTxcUw+//nPN9xnrKv3VfH8N+dCketAePLJJ1v+T/r973+fDj/88IaMmZGBf2m0AVTZaM33RYbO+vrfvKtgXm2T8TDKrIj1e7T86Ec/SosWLWr58bfddlv2fVC/mDeSqzRvPw65BAdGZX7Ri17Uf3vOnDlp7733ziaP6kW6+ejA11f0ZZZZJh100EEdr2BxgRkd76uuumrIPctjReUXvvCF9O53v7thkiui9/fcc89BnxcZBqMhf+tb35rOOeecISerIm1+fFkdd9xxDfdPmTIlWykEZayHoyU6A5/+9Kez3zVf/OIX01lnnfWMLegiCPid73xndgzqVw8ONUgA7apiXYzVu3vttVdWv6INnj9//oCPi1VAUQ+jzT377LMb/u21r31tIdL903mxsisu5Ab6aR5gHexxrWwp2qros8X2p/UDyltuuWX66Ec/mm2LNFgZBvoZalA4VrVFf/Szn/1stoXEYP3RRx99NFvYEinob7nllv77o1077LDDbItaYVWtO7XJkPpsCXEsop2pn1QM8fkisOn4449vqDsx4VLfN4S8A2tHQ2yz0vz9MJSoVzFOcc899/TfF9sFvutd7+pSCQF6//o6xoHXX3/9hixVcX0b/fmB+l3z5s1LRx999DPGhF//+tdn7cdQouyx/Vb9JHOMYTdPKt18883ZMalvAyLDWwTF0NuqeP6bc6HIdSB861vfSh/4wAeyTD6DTfJHOY866qhs/rA2FhTbCUcZW90xRRtAVRVxvm802ybjYZRZEev3aDnzzDOzuaK4nuvr6xt0bDzG3eN47Lbbbg27ncZ1YcwVwdKMS6MgKvHJJ5+cNWy1EzUmNyPTSWRtWG+99dIDDzyQrSSrz4YSohLE9r2dFoF+P/7xj7OfcePGpec973lpzTXXTBMmTMiibGMg/NZbb82ytDSLi9xvfOMb2fOWJhr6+Ikvr9VXXz17n+jgx8VHvEdcqPz73/8e8D3iiyD2Roey1sPREgFF0eGONN21AKRYUR0d9c033zwtt9xyWSBw1Pl60YmIQSropCrXxQjaiJ8jjjgiS5O/wQYbZCm+o62Lz3zTTTcNmNk3gktii2HKKSaxTjvttKU+Lvpkg2UPiAHebbbZpiPliZViMUlRLzKONWdBaMVll12W1l133UH/PQL/YjvU+Il6UOuPRr2ILVnjM//1r39t2J61JrZoNaFXbVWuOyEmTqLvVguajWuqGBSKFabRvsT13o033pjuv//+hud97GMfSy9/+cvbLhPlEdfh9VuODKYWWDuQSZMmZdf0nRQDnc11qP7f6g00hhBiPCN+BnL55ZenT3ziE1nbERk9ox5EsF+zmKCMrYRPOeWUbOKy3sc//vG02mqrtfGpAKp1fR19+sheEUHitUXm8Tuy9p100knZhG60IdG/jzYmAveWLFnS8BrPfe5z05FHHrnU94qF6/G6hxxySP995557brrggguyCeYYf77rrruy64l6cS3+ta99rWOfmfxU+fw350IR60CIczq2N46fsWPHpg033DArxworrJBdg0T/un6L91pgYMxBtJPdXhtAlRV1vm+02ibjYZRZUev3aIg+S1zPxU98b0Q/JfrOK664YrboJhbfD9R/jv5G7MQW14BQiODAEBOdsWomMkdEit1ahY4Jm/hpFid9DG68+c1v7nrZYtA/GuXmFUUDiZVrcYEaKb7bFZmSBsuWVC8m0OLipNVVQlCGethtkV43Bp8isLeWZjeCkCJ9/0Bi1WEMLgw0YQYjVeW6WBNZaOoz0QwmsvQeeuihHZ+Ah6KJNiou7uJnKKussko69thjBQZSeTG5Ev26yJgQ20zUxGRQfZbN+gmXWMkd23NTbUULrK2J87jVrHwRCDuQGEA98MADh2xrLrnkkuwn6kRMVkaQSG2RZIxXRF80gtebRRYWWQOBIira9XVMWn37299On/zkJxsyx0SQ1NK2rHrxi1+cBVG1Gogd2cUj4CUmg2rZpyJTVWQmH0hMUkfwd0wyUQ5VPv9rzLlUW9HqQL2YvI/gheYAhnoxzxiBRNtvv33br68NoMqKPt/XzbbJeBhlV/T6PRpikfBA/ZhmMaYX/efIDA+FCg4MkcUhVi+efvrp2bY6CxYseMZjIjJ+u+22yyZ5lrZ9wnBFVH1sqRMDJREQuLRtq6Khje0MI0Vnq5UrBlti8unqq6/OVgoMtXVxiEjn+Nwx2D5t2rS2Pg/0Yj3MYyVhZLp4xStekU0GXnvttdlAQbPJkyenPfbYI9sWL6LtoVuqVBdjgHfffffNBoJjQGxp7W6shIl2N54TK2GhjKLPF5NykU1zoEzVzSKDQqyG33333QfNCgVVEwMgsU1ZTECed955A27dEn3ACOKKoCnXWfD/xQDrbbfdlv0MZdlll80mUGPBBkBRFe36OiaoLrrooqx/cv7552cZ/IYS2fL32muvLFglgrfbEcHbMV4d2QD/8Ic/DHi9Hdlr3va2t6UPf/jD2Rg05VKl89+cC0WvAy972cvSDTfckM0L1jJoDmSNNdZI73jHO7Kxz9g9Yri0AVRVkeb78mibjIdRZkWq36MlFrPPnDkz21K4eRecgY5P9GXis8d25q7vaMeznh6oNo2CGKiIEzxSyEf0fATgrbXWWtnF42huUxMr4iNQIcoRW/jE7bgIjfKsuuqqWeWKbanaHZipF9HNt99+e/YeMfkbKzrj80fwQ0zuxmr9TTfdtKVtiqGM9TAPkX43ou6jTkY63rggf85znpMNSEXDCqOpSnUxVrxEuxsBHNHuxkBZrKSNgbBYIRepsmNQbyTtLvSaGLiO4IzIphkXf9EuRb8w6kRM5NW2XwIGF5e1sY3xP//5z2xLqQhoiiwSUX9i0BRqYmuSVjIHDmVpmQNjO70YvKyJbCARlDGUWEQx0sx8Q2UOjImKCy+8MCvXYNsl14u+aAwyRpbCuFYC6BVFvL6ujUHFNXBk9otxpxgTXmeddbK+SoxBd+q6IjLRxpZ1Mf4c1xLrrrtumjp1qnHniqjS+W/OhSLXgRjrjIz2sfVhXJ/G+GcEKEa/OhZCx7xjp+cgtAFUWVHm+/Jom4yHUXZFqd+jJeaIIhty/I6thmNONTIfx3dIjHXH57b7KD0XHAgAAADA6HrwwQezxRox0BiTiLFIMlZZx0BjTJputtlm2QpsAAAAAAB6n+BAAAAAAAAAAAAAKBl79gEAAAAAAAAAAEDJCA4EAAAAAAAAAACAkhEcCAAAAAAAAAAAACUjOBAAAAAAAAAAAABKRnAgAAAAAAAAAAAAlIzgQAAAAAAAAAAAACgZwYEAAAAAAAAAAABQMoIDAQAAAAAAAAAAoGQEBwIAAAAAAAAAAEDJCA4EAAAAAAAAAACAkhEcCAAAAAAAAAAAACUjOBAAAAAAAAAAAABKRnAgAAAAAAAAAAAAlIzgQAAAAAAAAAAAACgZwYEAAAAAAAAAAABQMoIDAQAAAAAAAAAAoGQEBwIAAAAAAAAAAEDJCA4EAAAAAAAAAACAkhEcCAAAAAAAAAAAACUjOBAAAAAAAAAAAABKRnAgAAAAAAAAAAAAlIzgQAAAAAAAAAAAACgZwYEAAAAAAAAAAABQMoIDAQAAAAAAAAAAoGQEBwIAAAAAAAAAAEDJCA4EAAAAAAAAAACAVC7/Dz5xFklYXBGyAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 2600x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bs_stream = idata_stream.posterior[\"b\"].values.reshape(-1, 4)\n",
-    "bs_inram = idata_inram.posterior[\"b\"].values.reshape(-1, 4)\n",
-    "names = [\"intercept\", \"slope x1\", \"slope x2\", \"slope x3\"]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout=\"tight\")\n",
-    "for k, ax in enumerate(axes):\n",
-    "    ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label=\"streaming\")\n",
-    "    ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label=\"in-RAM\")\n",
-    "    ax.axvline(b_true[k], color=\"k\", ls=\"--\", lw=1)\n",
-    "    ax.set(title=names[k], yticks=[])\n",
-    "axes[0].legend(fontsize=8)\n",
-    "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04dc1901",
-   "metadata": {},
-   "source": [
-    "## Memory usage\n",
-    "\n",
-    "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n",
-    "rows resident, so its array grows linearly in `N`. The streaming path keeps only a\n",
-    "bounded number of rows resident — the model batch, a shuffle-buffer fill, and the\n",
-    "source chunks used to build it — independent of `N`. The dense `float64` design\n",
-    "matrix dominates the cost. The lines below are array lower bounds, not measurements;\n",
-    "they also ignore framework overhead, PyTensor's resident copy, and the transient\n",
-    "copy `np.concatenate` makes while filling the shuffle buffer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6f25aa9a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:54.194247Z",
-     "iopub.status.busy": "2026-06-11T15:48:54.194176Z",
-     "iopub.status.idle": "2026-06-11T15:48:54.488206Z",
-     "shell.execute_reply": "2026-06-11T15:48:54.487819Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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azO/vvPNOfzcNAAAAAAAECS0m/V55rZnxULrv330LrpqWnSLzCnJlclqix9sHAMBg4tcARFtbmzz//POyePFiE4DoP1JBpzVaC0C8+eab8q9//cvcT05Olr///e8+azMAAAAAAAg8HV1d8tqOGllcXC47GlpcXl4rOpycl2ZmPIxPHuKVNgIAMNj4LQCxadMmuemmm6SkpMQEG5Qlj6LlsS3HHHOM3HzzzdLc3GyW+eyzz8zvAAAAAADA4NLS2Skrt+2WpSXlUtXU5vLyWkz6rJHDZG5+roxIjPNKGwEAGKz8EoDQoMNll10m+/fv7xNs6B2IsBeESExMlDPPPFOWL19uHr/66qsEIAAAAAAAGEQa2ztl+ZYqeaq0Uva0tru8fEx4mJwzOlMuy8+RrPgYr7QRAIDBzucBiMbGRrnmmmukvr6+Z8bD5MmTTUDi8MMPl9bWVjnrrLMcrue0007rCUB88sknXm83AAAAAADwv31t7fLspip5dlOl1Ld3urx8fGS4XDAmS2aPz5a02GivtBEAAPgpAPHYY49JVVVVT/DhuuuuMzcLLULtjKOOOkrCw8Olq6tLKisrpbq6WjIzM73WbgAAAAAA4D+1LW1mtoPOemjq6HJ5+aToSLl4XJZcNDbb3AcAAN7n029cTav0zDPP9AQffvjDH/YJPrgiLi5ORowYIdu2beupKUEAAgAAAACA0FLZ2CpLSspl5bZd0tZlv2akNWmxUXLp+Bw5f0ymxEdGeKWNAAAgAAIQa9eulb179/77hSMj5ec///mA1peXl9cTgHB25gQAAAAAAAh82/c3y6Licnl9R4102qkTaUt2fIxcnp8jZ4/KkJiIcK+0EQAABFAAYsuWLeanzoCYNGmSpKamDmh9WozaoqGhYcDtAwAAAAAA/lVS12gCD++U1YrrYQeRkQmxMq8wV04fPkwiwwk8AAAwaAIQtbW1Pfdzc3MHvD6tAWHR0dEx4PUBAAAAAAD/WFe7X54oKpNPqurcWj4/OV7mF+bJibmpEvGf1M8AAGAQBSA07ZInAwb79u3ruZ+UlDTg9QEAAAAAAN/Wivx6d70JPHyzu96tdRyclijzC3Ll2KyhPTUnAQDAIAxApKSk9Nyvqqoa8PqKi4t77g80nRMAAAAAAPBd4OGjyr2ysLhcvt/jXkrlIzOSZX5hrhw6LInAAwAAAcqnAYhRo0b1nGhs2LDB1G1ISEhwO/iwe/funseTJ0/2WDsBAAAAAIDnaTFpre2wqKhcNtU3ubWOE7JTTOBhYup/60ICAIDA5NMAhAYJkpOTpb6+3qRgevrpp+Xqq692a12PP/54z/3hw4dLdna2B1sKAAAAAAA8pb2rS17bUSOLi8tlZ0OLy8trBchThqfJvIJcGZc8hDcGAIAg4dMAhBaNPv300+XZZ581jx955BGZNm2aFBYWurSeV199VVauXNkzxfL888/3SnsxMJpma8mSJfLee+9JZWWlRERESF5enpxyyily+eWXm2AUAAAAACB0tXR2ystbd8nSkgqpbm5zefnIsDCZMTJdLi/IkeEJcV5pIwAACJEAhPrJT34iL7/8srS2tkpzc7PMnTtX7rjjDhOYcERnTSxevFjuu+8+E3zQVE7aia2d2QgsH374odx0001mtktvGzduNDcNQmkAatKkSX5rIwAAAADAOxraO2T5lmp5qrRS9ra2u7x8TES4nDsqQy7Lz5HM+BivtBEAAIRgACIzM1Nuvvlm+d3vfmeCCNpBfcMNN0h+fr6cdtppMmzYsD7P//jjj2XPnj2ydu1aeeedd8yoeg08WGZU/OEPf5AhQ5h+GUiKiorkZz/7mTQ1NUlcXJxcddVVcswxx0hnZ6d5D3VWRHV1tSxYsEBeeOEFs08AAAAAAILfvtZ2eWZzlTy7qVL2t3e6vHx8ZIRcMDZTLhmXI6mxUV5pIwAACOEAhLrkkktMIOHvf/97z0wGLSpdUlLS53n6+941IiyBB8sy2sl96qmn+rz9sO/OO+80wQdNufTYY4/JEUcc0fO3I488Ug466CD5xS9+YYqI33///XLXXXexSQEAAAAgiNU0t8mTpRWyYku1NHd2ubx8cnSkzB6XLReOzZLEaL90VQAAAC/QOk5+ceONN8q9997bM3vBUs9BAwt633LTx70DDyo2Nlbuuece+fGPf+yv5sOG9evXyxdffGHuz5o1q0/wweLcc8+Vo48+2tx/6aWXpLa2lu0JAAAAAEGoorFF/rxmi8x6fbVJt+Rq8CE9NkpuOHikvHjmoXLlhDyCDwAAhBi/DiuYMWOGSc2zdOlSUxOgpqbG/N4ScOhNf5eQkCAXXHCBSenTP1UTAsNbb73Vc1/fK1t++MMfyueff27SMr377rty4YUX+qiFAAAAAICB2lbfLIuKy+WNnbul88BLeIdy4mPk8oJcOXtkukRH+G1sJAAA8DK/z2tMTU2V66+/3tw2bdokq1evNumZ9u3bJ+3t7TJ06FDznClTpsjkyZNNWh94lhb3XrNmjZSXl8uuXbtMoCcrK8tsc932rvjmm2/MT639oO+XLUcddVSfZQhAAAAAAEDgK65rlIVFZfJe+R5xI+4goxLjZF5Brpw2fJhEhv87ywEAAAhdfg9A9DZu3DhzG+y6urpk8+bNpvD2unXrzE1rZGhAxkLrJpx//vkDep3m5mZ55JFHZPny5T2zT3qLioqSadOmmSLhBQUFTq1Tg0hq5MiREhlpe/fSwtOafquxsbFnGQAAAABAYFpbu1+eKCqTT6vq3Fq+YOgQuaIwV6bnpEr4f9IrAwCA0BdQAYjB7vXXX5cnn3zS1FHQIs7eVFpaamadbNmyxeZzNOCh6ZE++eQTueWWW0zxcHva2tpk79695r7OoHAkOzvbBB90xgsAAAAAILBoKuQvd+2ThUXlsrqm3q11HJKWaAIPR2cO7anrCAAABg8CEAFEUxF9+eWXXn8dTbP0ox/9SKqrq/v8fuLEiTJ8+HCpq6szsy50doJqbW2V3/72tyY108yZM22ut6Ghoed+fHy8w3ZYnmN5HQAAAACA/3V1d8tHlXtNqqUNe927Xjs6M1nmF+bJ1GFJHm8fAAAIHj4PQHz66ady2GGHSUxMjK9fOmglJiaazvr+AQN3R7DozIfe68rPz5e7775bCgsLe35XX18vDzzwgCkQbnHrrbea54wfP97qujVQ0Tt9kyPR0dEHLAcAAAAA8I+Orm55p6zGFJfeXN/s1jpOzEmV+YW5MiElwePtAwAAwcfnAYgrr7zSdDwfcsghcswxx8jRRx9t7lNc+t9iY2NlwoQJMmnSJFPEWW+jR4+Whx56yNwG6s033zQFpy3y8vJMkCE5ObnP85KSkuS2224zU2SXLFnSEyjQoIStdvQOKvWuV2EvZVP/5QAAAAAAvtXe1SWvbt8ti4vLpazR9QFiEWEipw4fZopLj0lyPBseAAAMHn5JwaSd019//bW5PfjggxIXFyeHH354T0BCO+AHo2uvvVZuvvlmu8WbB6p/8OD2228/IPjQ20033WTqQJSXl5vHb731lmzcuNHqe6QpmiycqWFheY4WowYAAAAA+FZLR6e8tG2XLC2pkF3N/x4g5oqo8DCZMTJd5ubnSm5CrFfaCAAAgptfAhCaBkjp6Hq9rx3RH330kbkp7RA/8sgjTUBCb6NGjZLBIDU11avrLy4ulpKSkp7HY8eOlenTp9tdRoNDs2fPlnvvvbfndytXrrQagNCZLSkpKaYQtTOFpS3PcaZgNQAAAADAMxraO+T5zdWybFOF7G3tcHn5mIhwmTU6U+bkZ0tGHDPaAQBAAAUgdAT+Z599Zm5btmw54O8akNAiyDrSXm8qMzPTzIzQmwYk9DFc99577/V5bK+gdP/n9Q5A6IyIX/ziF1afO27cOPnqq69k+/bt0tHRYXM2h9agsBSt1mUAAAAAAN5V19ouyzZVynObq6ShvdPl5YdERshF47Lk4nHZkhLjuO4fAACAzwMQp5xyirmp3bt39wQjvvjiC6moqLAakNCR8i+99JK5qREjRvSkazrqqKPMqHs49sknn/R5rGmvnJGdnS25ubk9aZi2bt0qlZWV5vf9aYFxDUA0NzfLunXrZOrUqVbX+eWXX/ZZBgAAAADgHbub2+TJkgpZsbVaWjq7XF5+aHSkXDI+Wy4YmyUJUX5JpAAAAIKUX88c0tPT5ZxzzjE3tWPHDvn88897AhJ79uyxGpDQ0fX63GeeecakcSooKJAVK1b44T8ILps2beq5Hx4ebgpdO0sLhVsCEKq0tNRqAOLUU0+VRx991Nx//vnnbQYgXnjhBfNTi4+fdNJJLv0fAAAAAADHKhpbZHFxhbyyfZe0d/07FbIr0uOiZc74HDlvdIbERUawyQEAgMsCauiCzmzQ20UXXdRTs0CDERqU0FH1jY2NB9SR0J9FRUV+a3Ow2LdvX5+ATlpamqnv4Ky8vLw+j3UWxAknnHDA8zSoofU7dIaDBoVmzZp1wEyLl19+2byv6txzzzVtAQAAAAB4xpb6JllcXC5v7qyRTtfjDpI7JEbmFuTKWSPSJToinLcFAACERgCiP53ZoLf58+dLZ2enSemzZMkSefXVVw8IRMA+nTHSm7XZC/b0LxTdf3293XrrrXLJJZeY4uJXXXWVXH311SZllr6H77zzjixevLhnBswNN9zAWwcAAAAAHrBxb4MsLCqXDyr2iDtXyqMT42R+Ya6ckjdMIsPDeE8AAEBoByCU1hqwzILQm9aNgOssBZ8tUlNTXVq+f52N/fv323xuYWGhPPDAA3LTTTdJfX29/PWvfzW33rSQ+COPPOJWQXGtCWLP3r17XV4nAAAAAASrNTX1srCoTD6v3ufW8hNShsj8glw5ISdVwsMIPAAAgBAOQNTV1fXUgdCf1kba9571kJycbApRw77e6atUTEyMS5ssNja2z2Od3WCPpmdauXKlme3w/vvvm0CS1p3QVE5aJ+Lyyy837507pk+fbvfvUVFRMmHCBLfWDQAAAADBQK+Lv6jeJ08Ul8m3NbYHiNkzdViizC/Mk6Mykk19RQAAgJALQGhHttZ3sMxw0LoPvQMM/VMsad2CQw891KT00dtBBx3EiZITmpub+zyOjo526X3qH7Dovz5baZt+8YtfmBsAAAAAYOC6urvlw4o98kRRuRTV9R1o5qxjMoeaVEtThiXxlgAAgNAKQHR0dMi3335rZjjobe3ataY2gK2AQ2RkpBx88ME9AYdDDjnEjHDHwLg6uqX/8/1Ze+ODDz5wmILp9ttv91l7AAAAAMDbOrq65a2yGllUVC5b9zseENafXtGdmJtqUi0VpiR4pY0AAAB+D0AcccQR0tLSYrMjWzu6tYbA0UcfbQIO+nyd9YCB6b8NW1tbXVq+93um4uPj/faW9C+I3R8BKgAAAAChoq2zS1Zt3y1LSsqlvNG16zgVESZy+vB0mVuQI6OT/HcdBwAABiefByA0dY8GGXoHHkaOHNkzw0HrOQwdOtTXzQp5/QMGrgYg+j/fnwEIAAAAAAh1zR2d8uLWanmypEJ2t7S7vHxUeJjMHJkhlxfkSM6QvjX9AAAABkUNCA1EHH/88TJr1iwz4yE1NdWfzQlpCQkJB6QpcsWePXv6PE5MTPRIuwAAAAAA/7W/rUOe31Ily0orpa6tw+VNExsRLuePyZRLx+dIepxrtf8AAABCJgBhqSnw8ccfm5s+Hj9+fM9MCE29xCh7zxkxYkSfx5WVlS4tX1VV1efx8OHDPdIuAAAAAIDI3tZ2ebq0Up7fXCWNHf+tk+isxKgIuXBsllw8LluGxlA3EQAADNIAxI9//GP5/PPPZf369QcUny4pKTG3RYsWSURERJ/i01OmTDEFqeEeTWulM0wsMxlqampMOixn62uUlZX1eTxmzJiAeStmzJjR57EGs6gbAgAAACAYVDe1ypOlFfLi1l3S2tnl8vIpMZFyyfgc+eGYTEmI4poZAAAEFp+fndx4443mZ0NDg3z11Vfy2WefmdumTZv61IXo6OiQNWvWmNsjjzwisbGxcvjhh/cEJCZMmODrpge9cePGyZdffmnud3V1mSCQzjRxxnfffXfAugAAAAAA7tnZ0CxLiitMgemOXtfCzsqIi5bL83PknFEZEhsZwdsAAAACUqQ/axL84Ac/MDelI/N1ZoTeNCCxc+fOPgEJHa1vSddkGdGvBastAYn+KYZwoGOPPbYnAKG+/vprpwIQmq6pvLy85/Ho0aMlJycnYDbxqlWr+jyura2VBQsW+K09AAAAAGDL5n1Nsqi4XN7aWSOuz3cQyRsSK/MKcuTMkekSFR7OhgYAAAEtYOZnanqgs846y9xURUVFz+wIDUpoyqDeAQktovzGG2+Ym6bc2bBhgx9bHxxOOukkuf/++3ser1y5Uq699lqHy+nz+q8HAAAAAOC8DXsaZGFxmXxQsdetzTY2KU7mFebJyblpEhn+75qKAAAAgS5gAhD96Qj7H/7wh+amNm/ebIIROgPigw8+ML/rHZCAYwUFBZKfn2/qbFi2qW7L6dOn21ympaVFli1b1ud3M2fOZHMDAAAAgAN6zbqmpl6eKCqXL3ftc2t7TUxJkPmFuXJ8doqEhxF4AAAAwSVgAxC9aYe5JTWTpg2y0JkPBCFcc91118n111/f8/iOO+4wBb6Tk5OtPv/ee+/tk37plFNOof4GAAAAANih16mfVtXJwuJyWVu7361tdWh6klxRkCtHZCSba18AAIBgFJABiLKysp7US3rT+hCDhf7v1tTX1/d5rCmorD03JiZG0tPTba7/tNNOk6lTp5ri3kprbVx22WVyzz33mBkSFvv37zfpmpYuXdpn3TfccINb/xcAAAAAhLqu7m55r3yPqfFQXNfo1jqOyxoq8wvz5OC0RI+3DwAAwNfCugNgCoEWDe5dgLr3iHtL8ywjPno3Nzc3t6cI9YwZMyQU9A4CuOPII4+UJUuW2H1OdXW1XHDBBbJr166e3+n2nThxogwfPlzq6upk7dq10tjY94T57rvvlnPOOUcCXe8i1I8++qikpaX5u0kAAAAAQlhHV5e8ubPWBB627W92eXm92j0pN82kWsofOsQrbQQAABg0MyAaGhrkq6++6ikyvWnTJpsBh96/HzZsmBx11FFy9NFHm6BDXl6ez9seCjIzM+Wf//ynScW0devWnu27fv16c+tPZz788pe/DNjgQ//gk+47cXFxfmsPAAAAgMGhtbNLXtm2S5aUVEhlU6vLy0eEhckZI4bJvIJcGZnINQwAAAg9Pg9AXHzxxfL9999LZ2fnATMaegcd9PeJiYlmRL8l4DBu3DhfNzdkaTHqFStWyMMPPyzLly83swb6i4qKkmnTppm0SwOdmQEAAAAAoaKpo1NWbKmWp0orpKal3eXlo8PDZOaoDLk8P1eyh8R4pY0AAACDMgVTYWFhT/Ho/kWkY2Nj5dBDD+0JOGhKoPDwcF82b1Dq6OiQ1atXm5oSNTU1MmTIEMnKyjK1IlJTUyXYkIIJAAAAgDfUt3XIs5sr5ZlNVea+q+Ijw+X8MVlyybhsGRYX7ZU2AgAAyGBPwWQJOkRERMjkyZNNsEGDDlOmTJHoaE7CfC0yMtLMNNEbAAAAAKCv2pY2WVZaKc9vqTazH1yVFBUhF43LlovGZUlydBSbFwAADBo+D0BMmDDBBBv0dsQRR0h8fLyvmwAAAAAAgEPVTa2ytKRCXtpaLa1dricPSI2JkkvHZ5tZD0OiItjiAABg0PF5AELrDgAAAAAAEKh27G+WxSXl8tr2GulwI2txVny0XJafKzNHpUtsBIEHAAAwePklBRMAAAAAAIFm075GWVhULu+U1UqXG8uPSIiVuQW5csaIYRJFPUMAAAACEAAAAACAwW39nv0m8PBR5V63lh+fHC/zC3PlB7lpEhEW5vH2AQAABKuAmwGhBar3798v+/btM4+Tk5MlMTFRwjiJAwAAAAB48Nrzm931JvDw1e5/X3+6alJqglxRmCfHZQ3lmhUAACBQAxDff/+9vPzyy7JmzRrZuHGjdHR09Pl7ZGSkKV49ZcoUOeecc2TSpEl+aysCz4wZM/o81mBVXFyc39oDAAAAILADD59U1cnCojJZt6fBrXUckZ5sZjwclp5E4AEAACBQAxAlJSXy29/+1gQeLCeC1rS3t8u6devMbcmSJSYQocsVFBT4uMUAAAAAgGDU2d0t75XXmhkPpfua3FrHtOwUmVeQK5PTEj3ePgAAgFAU1m2r19/LnnvuOfnDH/4gbW1tJvDQO8VS/yZZ+1t0dLTceuutcvHFF/uw1QgGtbW1smDBAnP/0UcflbS0NH83CQAAAICfdHR1yes7amRxcblsb2hxeflwETk5L03mFebK+OQhXmkjAABAqPLLDIiVK1fK7bff3hN40Jvlfl5enowePdrUfVBaD2Lbtm2yc+fOPoEKDVz87ne/M6l2NC0TAAAAAAAWLZ2dsnLbbllaUi5VTW0ubxgtJn3WyGEyNz9XRiSS4hUAACAoAhDV1dVy2223mfuWwENGRoZcddVVcvbZZ0tqaqrV5fbs2SOvvPKK/Otf/5KqqiqzbFdXlwlkHHnkkZKVleXj/wQAAAAAEGga2ztl+ZYqeaq0Uva0tru8fEx4mJw7OlPm5OdIVnyMV9oIAAAwWOhsUp+6//77paXlv9NezzjjDHnttddk7ty5NoMPSv+mz3n11VflrLPO6pkN0draKn/961991HoAAAAAQCDa19Yuj2/YKee99o08tH6Hy8GH+MgImZufIyvOPFRumjKa4AMAAECwzYDQYtJvvPFGTxqladOmmYCEK+Lj4+Xee++VhoYG+fDDD83vdJ2///3vJTLSrzW1AQAAAAA+VtvSJk+XVsoLW6qkqaPL5eWToiPl4nFZctHYbHMfAAAAnuPTs6s1a9ZIU1OTuR8eHm7SJ7lDAxiaxum0004zMyF0natXrzapmAAAAAAAoa+ysdXUd1i5bZe0dnW7vHxabJTMGZ8js8ZkmtkPAAAACPIARFlZWU8A4aCDDjIFp901fPhwmTRpkqxbt8481iLVBCAAAAAAILRt398si4rL5fUdNdLZ7XrgITs+Ri7Pz5GzR2VITITPsxIDAAAMKj4NQNTW1vYJIAyUBjAsAQgtUo3BacaMGX0ea4ArLi7Ob+0BAAAA4HkldY0m8PBOWa24HnYQGZkQK/MKc+X04cMkMpzAAwAAQMgFIKKionrua/HogWpra7O6bgAAAABAaFhXu18WFpXLx1V73Vo+Pzle5hfmyYm5qRLxn3qEAAAACMEARFpaWs/90tLSAa+v9zpSU1MHvD4Ep1WrVh0w02bBggV+aw8AAACAgdFaf1/vrpcnisrkm931bq1jcmqCXFGYJ8dmDTWzpAEAABDiAYjx48f3nExqPYgvvvhCjjrqKLfW9dVXX8mOHTt6Hufn53usnQAAAAAA39NrxY8r98oTxeXy/Z4Gt9ZxZEayzC/MlUOHJRF4AAAAGEwBiMLCQsnKypLq6mpzYnn77bfLsmXLJCUlxaX11NXVmWUtMjIyzLoBAAAAAMFHi0lrbQet8bBpX5Nb6zghO8UEHiamJnq8fQAAAHCPzytvzZ492wQfdArs9u3b5ZJLLpG1a9c6vfyGDRvksssuk23btpnHuh5dBwAAAAAguLR3dcnL23bJxW9+K7d9Wepy8EEvaE8bniZPnnKw3H1sIcEHAACAwTwDQl1xxRXy7LPPSmVlpQkeaCBBgxLTpk2TGTNmyCGHHCIjR47ss4ymWvruu+/k1VdflQ8//FC6urp6/padnW3WCQAAAAAIDi2dnfLy1l2ytKRCqpvbXF4+MixMZoxMl8sLcmR4QpxX2ggAAIAgDEDExMTIP/7xD5kzZ47U19ebIIQGFDSwoDelv4uL+/dJZHNzs5kxYWGZPaE/k5OT5e9//7tZJwAAAAAgsDW0d8jyLdXyVGml7G1td3n5mIhwOXdUhlyWnyOZ8VwHAgAABDqfByDUuHHjZNGiRfLzn/9ctmzZ0lMYzBJo0J+NjY0HLKfPswQfRo8eLffdd19PYWsAAAAAQGDa19ouz2yukmc3Vcr+9k6Xl4+PjJALxmbKJeNyJDU2yittBAAAQIgEIJQWjV6xYoX885//NIWod+3a1efv/YMSlvvp6ekmZdNVV13FzAcAAAAACGA1zW3yZGmFrNhSLc2d/02l66zk6EiZPS5bLhybJYnRfrt8BQAAgJv8eganqZP+3//7f3LNNdfIF198IWvWrJH169fL3r17TXomlZSUJCkpKTJp0iSZOnWqHHXUURIZyYknAAAAAASqisYWU99h5bZd0tb130FlzkqPjZI5+Tly7uhMM/sBAAAAwSkgevI1oHDccceZGwAAAAAgOG2rb5ZFxeXyxs7d0ul63EFy4mPk8oJcOXtkukRHhHujiQAAABhsAQhgIGbMmNHnce8i5gAAAAC8r7iuURYWlcl75XvEjbiDjEqMk3kFuXLa8GESGf7vdLwAAAAIfgQgAAAAAABu+a6mXp4oKpfPquvcWr5g6BC5ojBXpuekSvh/6gACAAAgdBCAQNBbtWpVn8e1tbWyYMECv7UHAAAACGXd3d3y5a59srCoXFbX/Lt2n6sOSUs0gYejM4eaGcwAAAAITQQgAAAAAAAOdXV3y0eVe02qpQ17G93aYkdnJsv8gjyZmp7EFgcAABgEPBqAqKioEH/Jycnx22sDAAAAQKjq6OqWd8pqTHHpzfXNbq3jxJxUmV+YKxNSEjzePgAAAAySAMRJJ53kl+mz+pobNmzw+esCAAAAQKhq7+qSV7fvlsXF5VLW2Ory8uEipqj0vMJcGZMU75U2AgAAYBCmYNKcoAAAAACA4NPS0Skvbt0lT5ZWyK7mNpeXjwoPkxkj0+Xy/FzJS4j1ShsBAAAwSAMQBB8AAAAAIPg0tHfI85urZdmmCtnb2uHy8jER4TJrdKbMyc+WjLgYr7QRAAAAgzgAcd1113lydQAAAAAAL6trbZdlmyrluc1V0tDe6fLyQyIj5MKxWTJ7fLakxER5pY0AAAAITgQgAAAAAGAQ2t3cJk+WVMiKrdXS0tnl8vJDoyPlkvHZcsHYLEmI8kp2XwAAAAQ5zhIBAAAAYBApb2iRJSUV8sr2XdLe5Xr9vvS4aJkzPkfOG50hcZERXmkjAAAAQgMBCAAAAAAYBLbUN8mi4nJ5a2eNdLoed5DcITEytyBXzhqRLtER4d5oIgAAAEIMAQgAAAAACGEb9zbIwqJy+aBij7gRd5DRiXEyvzBXTskbJpHhYV5oIQAAAEIVAQgAAAAACEFrauplYVGZfF69z63lJ6QMkSsK8mRaToqEhxF4AAAAgOsIQAAAAABAiOju7pYvqvfJE8Vl8m3NfrfWMXVYoswvzJOjMpIljMADAAAABoAABILejBkz+jzWi6S4uDi/tQcAAADwta7ubpNiSVMtFdU1urWOYzKHmlRLU4Ylebx9AAAAGJwIQAAAAABAkOro6pa3ympkUVG5bN3f7PLymljpxNxUmV+QK4UpCV5pIwAAAAYvAhAIeqtWrerzuLa2VhYsWOC39gAAAADe1tbZJau275YlJeVS3tjq8vIRYSKnD0+XuQU5Mjop3ittBAAAAAhAAAAAAECQaO7olBe3VsuTJRWyu6Xd5eWjwsNk5sgMubwgR3KGxHqljQAAAIAFAQgAAAAACHD72zrk+S1Vsqy0UuraOlxePjYiXM4fkymXjs+R9Lhor7QRAAAA6I8ABAAAAAAEqL2t7fJ0aaU8v7lKGjs6XV4+MSpCLhybJRePy5ahMVFeaSMAAABgCwEIAAAAAAgw1U2t8mRphby4dZe0dna5vHxKTKRcMj5HfjgmUxKiuOwDAACAf3AmCgAAAAABoqyhRRYXl5sC0x3d3S4vnxEXLZfn58g5ozIkNjLCK20EAAAAnEUAAgAAAAD8bPO+JllUXC5v7awR1+c7iOQNiZV5BTly5sh0iQoP90ILAQAAANcRgAAAAAAAP9mwp0EWFpfJBxV73Vp+bFKczCvMk5Nz0yQyPMzj7QMAAAAGggAEAAAAAPhQd3e3rKmplyeKyuXLXfvcWsfElASZX5grx2enSHgYgQcAAAAEJgIQAAAAAOCjwMNn1XUm8LC2dr9b6zg0PUmuKMiVIzKSJYzAAwAAAAIcAQgAAAAA8KKu7m55r3yPqfFQXNfo1jqOyxoq8wvz5OC0RI+3DwAAAPCWoAxAVFRU9NyPj4+XoUOH+rU9AAAAANBfR1eXvLmz1gQetu1vdnkDaWKlk3LTTKql/KFD2MAAAAAIOkEZgDjppJN6phtHRETIzJkzZcGCBTJy5Eh/Nw0AAADAINfa2SWrtu+SxcUVUtnU6vLyEWEiZ4xIl7n5uTIqKc4rbQQAAAB8ISgDEJb8qaqjo0NefPFFWblypZx11lkmEDFmzBh/Nw8AAADAINPU0SkrtlTLU6UVUtPS7vLy0eFhMnNUhlyWnyM5Q2K90kYAAADAl4I2ANG/4JoGIjQIsWrVKjnjjDPk2muvlXHjxvmtfQAAAAAGh/q2Dnluc5Us21Rp7rsqLiJczh+TKZeOz5FhcdFeaSPw/9m7D+g4quvx41e9S7Z6cS+SjLHpBkLHtNh0THHDNhDiJED4JdQQCBAChJKQhOIQCO7UmIAxvRkwxXRsjOTe1GzJRbKtLv3PffxnM1qr7K62afX9nLPHu/LO7NvZ2dmZd9+7FwAAIBB6/AwI5781NzfLq6++Kq+//rp8//33AWkbAAAAgNC3o65Rnl5bKi+sqzCzH9yVHBUhFw3LkYuGZktKTJRP2ggAAAAEUo8MQBQVFTnuNzQ0yLfffivLly+XL774Qr755hupra2VlpaWgLYR/jN+/Pj9ZsfExZErFwAAAL5Rsa9e5q8ulZc2VEh9y/4Do7rSNyZKJg/PkfOHZEtCVIRP2ggAAAAEgx4ZgLCLjo6WI444wtysVEwrVqyQzz//PNBNAwAAABBCNtfUytzVJfLapkppamdGdley4qJlan6unDU4U2IjCDwAAAAg9IW1tpfLCOjBqqqqTDFyNWvWLElLSwt0kwAAANCDrd29V2YXlcg7W6vEk3nW/RNjZVpBnpwxIF2iwsN90EIAAAAgOPX4GRAAAAAA4Avf76iRp4pK5MOynR4tPywlXqYX5MnJ/dIkIizM6+0DAAAAgh0BCAAAAAD4/3SC+Jfbq2VOcYks37bbo+0yMjVRZhTkybE5fU19MgAAAKC3IgABAAAAoNfTwMOy8l0yu2irrNixx6PtcXhGsswo7CeHZSQTeAAAAAAIQAAAAADozZpbW+W9kipT42HN7n0erePY7L4yvTBPRqUleb19AAAAQE8W1DMgioqKZOXKlbJz506JjY2VzMxMOeKIIyQ1NTXQTQMAAADQgzW1tMjrmytlbnGJbNpT5/bymlhpbL80U1w6v0+CT9oIAAAA9HR+CUBUVVXJ6tWrTSAhOjpa+vXrJ/n5+RIeHt7u899++2156KGHZN26dfv9ny5zwgknyE033SQDBgzwQ+sBAAAAhIq65mZ5ZeN2mb+6VMr21bu9vBaT/umAdBN4GJAU55M2AgAAAKHCpwGIb775Rh544AH56quvTE5Vu759+8rkyZPlyiuvlKioKMff9flPPvmkue+8jGpubpZ3331XPv30U3n88cfl8MMP9+VbAAAAABAC9jY2y6L15bJwTZnsqG90e/no8DA5e1CmTMnPk5yEGJ+0EQAAAAg1PgtAPP300/LHP/7RBBHaCyTs2LFDHn74Yfnss89MwEGDEAsXLpQnnnjC/H9YWJi52ZfVx0r/tm/fPhO8ePnll82MCgAAAABwtruhUZ5fWy7Pri2T6sZmtzdQfGS4XDAkWyYOz5G02Gg2MAAAABDoAMSyZcvkjjvuaBNIUFYwwf74888/l0cffVQuu+wyefDBB9v8X05OjhQWFkpCQoLs3r3bUQ/CCkxoEOKuu+6SWbNm+eJtAAAAAOihquoa5Ok1ZfKf9eWyr6nF7eWToyLk4mE5cuGwbEmJ/t+MbQAAAAABDEBoYOD22283961Agf6rgYSBAweav2/evFl++OEHx/8vWLBAUlJSZO/eveZvWmz67rvvlmOOOWa/dS9evFjuvPNO81x9vHTpUtmyZYv079/f228FAAAAQA9Tvq9e5hWXyOKN26S+Zf+Z2F1Ji42SScNz5bzBWZIQFeGTNgIAAAC9hdcDEB988IEJCFgzGYYPH25mNmjRabs1a9bIddddJ8XFxVJTU+NIvRQbGyvz5s1rt8C0rvPss8+W7OxsmT59uuPvS5YskZkzZ3r7rQAAAADoITbX1Mqc4hJ5bXOlNLeTArYr2fHRMjU/T84alCkxEeE+aSMAAADQ23g9APHee++Zf3V2gs5qeOqppyQ9PX2/52lgQms/jB8/Xqqrq6WystIEGC6++OJ2gw92Y8aMkZNOOkneeecds8x3333n7bcBAAAAoAdYvWuvCTy8s7VK3A87iAxIjJVpBXlyxoB0iQwn8AAAAAAEdQDi+++/N/9qYOD8889vN/hg0f+74IIL5N///rfjb2PHjnXpdU455RQTgFBr167tdrsBAAAA9BwrqmpkdlGJfFS+06Plh6fEy4zCfnJiXqpE/P/Z2wAAAACCPABRVlbmuH/UUUd1+fwjjzyyTQBi6NChLr2OzqCwZlpogWoAAAAAoU3P/b/YXi1PFW2VL7dXe7SOUamJMr2wnxyT3ceRNhYAAABADwlAaD0HS15eXpfPd36Opm1yRXJysuO+FqQGAAAAELqBh4/KdspTxSXy/Y49Hq1jTGaKSbV0WEYygQcAAACgpwYg6uvrHffj4+O7fH5cXFybxxERES69jv15zc3NbrURAAAAQPDTYtJa20FrPKzdvc+jdRyX01emF+bJgalJXm8fAAAAAD8HIAAAAACgOxpbWuS1zZUyt7hEtuypc3t5LSU9tl+aCTwMS0ngwwAAAAAChAAEAAAAgKBQ19wsizdsk3mrS6WitsHt5SPDwmTcwAyZmp8rA5LazrQGAAAA4H8EIAAAAAAE1J7GJlm0vkIWrimTnfWNbi8fEx4m5wzOkin5uZIVH+OTNgIAAABwHwEIAAAAAAGxu75Rnl1XLs+tLZOaRvfrusVHRsiEoVkycViupMZG+aSNAAAAAII0ABEWFubL1QMAAADogSprG2ThmlIz66G2ucXt5ZOjI+WSYTly4dBscx8AAABAcIr0ZeBh4sSJEhER0elzm5vbjnQaO3asS6/hvBwAAACA4Fa6t07mry6VxRu3SUNLq9vLp8dGyeThuXLukCwz+wEAAABAcPPZcKHW1lYpLy93e5mSkhK3Ah26DAAAAIDgtbG6VuYUl8gbW7ZLswen7znxMXJpQa6MH5gpMRHhvmgiAAAAgJ4UgCD9Evxl/Pjx++17cXFxfAAAAAABVrxrr8wu2irvlewQT4YNDUqKk2kFeXJa/zSJDCfwAAAAAPQ0PglAMCsBAAAA6L2+q6qRp4q2ysfluzxavqBPgkwvzJMTc1MlnLpyAAAAQI/l9QDEO++84+1VAp1asmRJm8dVVVUyc+ZMthoAAIAf6SCk5dt2y+yiEvmqstqjdRyUliQzCvPkqKw+zKgGAAAAQoDXAxB5eXneXiUAAACAINXS2ioflu2UOUUl8v3OPR6t48jMFJlR2E8OyUj2evsAAAAAhGANCAAAAAChq6mlVd7ZWmmKS6+rrvVoHSfk9pXpBf3kgNREr7cPAAAAQOARgAAAAADgssaWFnl103aZW1wiW/fWu73ltJT0af3TZVphngxJjmfLAwAAACGMAAQAAACALtU1NctLG7fJ/NWlsq22we0tFhUeJuMHZsjU/DzplxjLFgcAAAB6AQIQAAAAADq0p7FJXlhXIc+sLZWd9U1ub6mYiHA5b3CWTM7Pkcy4GLY0AAAA0IsQgAAAAACwn131jfLM2jJ5fl257GlsdnsLJURGyIVDs+WS4TnSNyaKLQwAAAD0QkETgLj00kvdXubMM8+Uiy66yCftAQAAAHqj7bUNsnBNqSxaXyF1zS1uL98nOlImDs+RCUOzJTEqaC43AAAAAASAT64IKioq5LTTTpOGhh9zw6akpMhLL70kWVlZHS6zfPlyCQsLc/k1WltbZdWqVTJ27FhJS0vzSrsBAACA3qp0b53MLS6VVzZtk8aWVreXz4iLlsnDc+XcwZkSFxnhkzYCAAAA6Fl8EoB49NFHpb6+3tzXoMJdd93VafDBObDgaiBi79698ve//13uuOOObrUXAAAA6K02VO+TOcUl8uaWSml2P+4geQkxcmlBnowbkCHREeG+aCIAAACAHsrrAYgdO3bIf/7zH0cQYfz48XLKKae4vLwup0EIV5/38ssvy/XXXy+JiYndajcAAADQmxTt3COzi0vk/ZId4kHcQQYnxcn0wjw5pV+6RIa7PpMZAAAAQO/h9QDEW2+9JU1NTeZ+RESE/PKXv3R5WWv2w4IFCyQ7O7vD5+3evVsmTZokdXV15vbaa6/JhRde6JX2AwAAAKHsm8pqmV1UIp9U7PJo+RF9E2RGQT85LrevhLuRQhUAAABA7+P1AIQGA5QGEnTmw5AhQ9xeR05OjuTm5nb4/3l5eSbgMG/ePPP41VdfJQABAAAAdDLQ57OK3fJU8Vb5prLGo+10SHqSTC/sJ0dmprhVuw0AAABA7xXp7Qubb7/91vH49NNPF185++yzTQBCX3PFihU+ex0AAACgp2ppbZUPSneYVEs/7Nzr0TqOzupjUi0dnJ7s9fYBAAAACG1eDUBs2LBBamtrzf3w8HA55phjxFdGjRpl6j7s2bPHFKNet26dDB061GevBwAAAPQUTS2t8vbWSlNcen31j+fn7tD5DSfmpcr0gjwp7EutNQAAAABBEIBYu3at+VenZA8aNEhSUlLcWt7dqdwacLBmXGjwgwAEAAAAerOG5hZZsmm7zFtdIiV7691ePiJM5PT+GXJpQa4MTo73SRsBAAAA9B5eDUBocWhLenq628tbRahdZX+NXbs8K6IHAAAA9HS1Tc3y3w3bZMGaUtle2+D28lHhYXLWwEyZWpAruQmxPmkjAAAAgN7HqwGI6upqjwMQEyZMcNxPSEhwaZn4+Ph2XxsAAADoDWoamuSF9eXyzJoy2dXQ5PbysRHhcv6QLJk0PFcy4qJ90kYAAAAAvZdXAxANDf8bbdXY2OjWsnfddZfbr1dfX9/uawMAAAChbGd9owk6PL+uXPY2Nbu9fGJUhFw4NFsuGZYjfWKifNJGAAAAAPBqACIuLs5xf+fOnT7fuva0S7GxTBUHAABAaKvYV2/SLGm6pfrmFreX7xsTKROH58oFQ7IkMcqrlwIAAAAAsB+vXnXY0y5VVFSIr5WVlTnup6Wl+fz1AAAAgEDYuqdO5haXmALTTa2tbi+fGRctU/Jz5ZxBmRIbGeGTNgIAAACATwMQAwYMcBST3rJli5SXl0t2drb4Kvigr+H82gAAAECoWF+9T+YUlcibWyrF/fkOIv0SYmVaQa78dGCGRIWH+6CFAAAAAOCnAERhYaFERUVJU9OPBfCWLl0qF198sfjC+++/77gfGRkpI0aM8MnrAAAAAP72w8498lTRVlla6lla06HJcTKtsJ+MzUuTyPAwr7cPAAAAAPwegIiOjpYxY8bIsmXLzOMnnnhCJkyYIBER3p3mrQWudd1hYT9eTOlr6msjOFRXV8uKFSvku+++Mze9v337dsdnNW/evEA3EQAAICh9vb3aBB4+27bbo+UP6Jsg0wv7yXE5fSX8/58rAwAAAECgeL3y3FlnnWUCEBoc2Lp1qzz55JNy5ZVXevU1NPhQUlJi7uvr6GsieJx77rmOzwcAAACd0/Sln1TsktlFJfJtVY1Hm+vQjGSZUZAnR2SmOAbpAAAAAECgeT0R7Pjx4x11H/Ri6qGHHpJXX33Va+t/+eWX5e9//7vjwiozM9O8JoKTFiY/6aSTAt0MAACAoNPS2irvllTJtHdXyP8tK/Io+PCT7D7yrxMPlMeOHyljsvoQfAAAAAAQ2jMgtAbEddddZ24aJGhpaZHrr79eVq1aJddcc43HqZIaGhrkr3/9q8ydO9cENpSuX19HXxPBY/LkydKvXz8ZPXq05OTkmL8VFBQEulkAAABBoamlRd7cUiVziktkY02t28vrMJyT8lJNqqWCPgk+aSMAAAAABGUAQp155pny3nvvyZIlS0yQoLm52aRieu2110xRak3RozMXXLFt2zZZtGiRPPfcc1JWVmaCD9bsh3HjxpF+KQhdfvnlgW4CAABA0KlvbpElm7bJ3OJSKdtX7/byEWEiZwzIkEvz82RQcpxP2ggAAAAAQR+AUPfcc49UVlbKZ599ZgIGGjjQugA6i0Fv/fv3l5EjR8rAgQMlKSnJ3PR5NTU15rZx40ZZuXKlqSOh7LMe9P5RRx1lXqO3aGpqkq+//tpsQw3KJCYmmlRXBx98sKSmpga6eQAAAOhAbVOzvLi+QhasKZXKuka3t1N0eJicNShTpuTnSm5CLNsZAAAAQI/hswCEplrSYtH33nuvLFiwwDFrwQokbN68WbZs2dLpOqzn2gMPepsyZYrceOONAU29pKml1q1bJ999952sWLHC3IqLi6Wx8X8XlRogOf/887v1OrW1tfLoo4+aWSAa0HGm2+C4446Ta6+9ljRHAAAAQaSmoUmeW1cuz64tk90NTW4vHxcRLucPyZJJw3MlPc6zNKYAAAAAEJIBCKtz/NZbb5UxY8bI/fff75jNYAUjnIMMdvoce9BCb1pX4IYbbpDTTjtNAuX11183ARWdnbFv3z6fvtaaNWtM3Yz169d3+BwNeLz77ruybNkyufnmm2XixIk+bRMAAAA6t6OuUZ5eWyovrKuQfU3Nbm+upKgIuWhYjlw8NFtSYqh1BgAAAKDn8mkAwnL66afLqaee6ui8//bbb01Koc5YgYnIyEg56KCDzKwHXU94eLgE0pdffinLly/3+etomiWtpVBRUdHm75q2StNX7dq1y8y62Lt3r/l7fX293H777SY101lnneXz9gEAAKCtin31Mn91qby0cZup9+CuvjFRMnl4jpw3JEsSo/xymg4AAAAAPuW3KxsNHGjRaL1pWqFvvvnGBCK2b99uOtOrq6vN85KTk6VPnz6Snp5u6hvoLS4u+IvsaQ2L+Pj4/QIGntDgi858sK8rPz/fzCIpLCx0/E232d/+9jeZP3++42+33HKLec7w4cO73Q4AAAB0bcueWlNY+tVN26Wpg9m9ncmKi5ap+bly1uBMiY2IYJMDAAAACBkBGVqlAYWjjz7a3Hqi2NhYGTFihBx44IEyatQocxs8eLA8/PDD5tZdb775pik4bdHUUxpkSElJafM8DdZoiitNVTVv3jzHTAgNSnijHQAAAOjYut37ZHbxVnl7S5W4P99BpH9irEwryJMzBqRLVIBn+QIAAACALzC3202/+MUvTAFsTQ3lK87Bg9tuu22/4IPdb3/7W1MHoqSkxDx+66235IcffjBBkvZokGL06NHdbudVV10lV199dbfXAwAA0JN8v6NGZheVyAdlOz1aflhKvEwvyJOT+6VJhK02GgAAAACEGgIQbkpNTRVfKi4ultWrVzseDx06VE444YQuZ5Rccskl8uCDDzr+tnjx4g4DEAAAAHA/ReZXldUm8LB8226PNt/I1ESZUZAnx+b0NTNYAQAAACDUEYAIMu+9916bx64WlNbn2QMQOiPihhtuaPe50dHR8uqrr3azpSJ9+/bt9joAAACCPfCwrHyXzCkuke+qajxax+EZyTKjsJ8clpFM4AEAAABAr0IAIsgsW7aszePDDz/cpeVycnIkLy/PkYZpw4YNUlZWZv7uTEfc6cwKAAAAtK+5tVXeL9khs4u2yurd+zzaTMdm95XphXkyKi2JzQwAAACgV/JqAOLee++VmTNnSp8+fSTQPvjgA9mzZ4+MGzdOepK1a9c67oeHh5tC16466KCDHAEItWbNmnYDEAAAAGhfU0uLvL65UuYWl8imPXVubyZNrDS2X5opLp3fJ4HNDAAAAKBXC/fmymbPni2nnnqqKaK8a9cuCYRPPvlEpk6dKj//+c9l/fr10pPs3r1bduzY4XiclpZm6ju4ql+/fm0e6ywIAAAAdK2+uUVeWFcuE974Rv745Tq3gw9aTPrMgRny3GkHy5+OzCf4AAAAAAC+SMGksw4eeeQRefLJJ+WCCy6Qiy66SPLz8326sevr6+W1116TefPmyapVq6Sn2rx5c5vH7s5eyM7O7nR9AAAAaGtvY7O8uKFCFq4plaq6Rrc3T0x4mJw9OEum5OdKdnwMmxcAAAAAfBWAuPjii+X555+XlpYWqa2tlQULFpjbqFGjZPz48WZ2RG5urldeq6mpycx2ePPNN+X11183gQ+rUKAaMGCAHHfccdKTWO/Bkpqa2q2i0DU1nhVK7K4ffvjB3Nqzfft2WbRoUZu/6eeUkZHhp9YBAACI7G5olOfXlsuza8ukurHZ7U0SHxkuFwzJlonDcyQtNppNCgAAAAC+DkDccccdJghxzz33yOeff+4IBqxYscLctEbEkCFD5IgjjpBDDz1UCgoKzOOoqKgu160d11rTYOXKlWbdX331lezb92NBQOt1VFJSkvzsZz+T6dOnS3R0z7oY3Lt3b5vHMTHujaKLjY1t89jaPv729ttvmzRc7dG0UDfffHObv82dO9etAER5eXmn/79z506X1wUAAHqXqroGeXpNmfxnfbnsa2pxe/nk6Ei5eFi2XDg0W1Kiuz6HBQAAAIDezOspmA444ACTCmnZsmXy97//Xb799lsTIAgLCzP/rlu3ztRmePbZZx2FltPT0yUzM1OSk5NNp3tkZKRJq6Qd6JWVlbJt27b9OtPtQQeVkJAgU6ZMkcsvv9yspyfSWSN27gZQnAMWzusLFSeccEKn/68BrREjRvitPQAAIPiV76uXecUlsnjjNqlvaXse6Yq02CiZNDxXzhucJQlRET5pIwAAAACEGq8HICzHHHOMuX355ZcyZ84ceffdd03aJOcAQnNzs1RUVJibBimcOQcanJ+jqZYmTZokEyZMkMTERAkl7W0Pd57vvO385eqrrzY3AACAQNtcUytzikvktc2V0uzBuVFOfIyp73DWoEyJiQj3SRsBAAAAIFT5LABhOeyww8xtx44dplC01mv4+uuv2wQjOutot2ZOWPS+Fls+6aST5KyzzjKpnEJFXFxcm8c6C8QddXV1bR7Hx8dLKFq6dGmXKZhuu+02v7UHAAAEn9W79prAwztbq8STIRkDE2NlWmGenN4/XSLDCTwAAAAAQFAGIOwFlSdPnmxuWmx5+fLl8t1338mqVatk06ZNUlZWJg0NDfstl5aWJnl5eZKfny8HHnigHH744TJs2DAJRc4BA3cDEM7PD9UAhAagOuNKTREAABCaVlTVyOyiEvmo3LOaUPkp8TK9sJ+cmJcqEW7ORgUAAAAABCgAYaepkk4++WRzs9PAhNYt0LRMWlBZ6zr0ps5k5xRS7hZT1lkmdlqQGwAAINTpDNkvtlfL7KKt5l9PjE5LkukFefKT7D5up8EEAAAAAARRAKKzDvhQq+PgDq1nYaezQtxRXl7e5nH//v290i4AAIBgDTx8VLZTniouke937PFoHWMyU2R6YZ4cmp5M4AEAAAAAQjkA0dv16dPHpKqyZjJUVlaaGSHOtSE6snXr1jaPhwwZ4pN2AgAABJIWk353a5XMLi6Rtbv3ebSO43P6msDDyFRmjAIAAACArxCACDJa30LrY6iWlhZZuXKlHHHEES4t++233+63rt5g/PjxbR5r2gRXgzYAAKDnaGxpkdc2V8rc4hLZsqfO7eW1lPQp/dNkWkGeDEtJ8EkbAQAAAAD/QwAiyPzkJz9xBCDUF1984VIAQtM1lZSUOB4PHjxYcnNzfdZOAAAAf6lrbpbFG7bJvNWlUlHb4PbykWFhMm5ghkzNz5UBSQxSAAAAAAB/IQARZLQw90MPPeR4vHjxYvnFL37R5XL6POf19BZLlixp87iqqkpmzpwZsPYAAADv2NPYJC+ur5CFa8pkR32j28vHRITLOYMyZUp+rmTFx/CxAAAAAICfEYAIMgUFBZKfny+rV682j9etWydLly6VE044ocNl6urq5Jlnnmnzt7POOsvnbQUAAPCF3fWN8uy6cnlubZnUNDa7vXx8ZIRMGJolE4flSmpslE/aCAAAAADoGgGIIHTVVVfJNddc43j8xz/+UQ4++GBJSUlp9/kPPvhgm/RLp5xyiowYMcIvbQUAAPCWytoGWbimVBatr5Da5ha3l0+OjpRLhuXIhUOzzX0AAAAAQGBxZeaBrVu3tvv36urqNo937tzZ7nNjYmIkIyOjw/Wfdtppcsghh8jXX39tHm/ZskWmTJkiDzzwgJkhYampqTHpmubPn99m3ddee60nbwsAACAgSvfWyfzVpbJ44zZpaGl1e/n02CiZPDxXzh2SZWY/AAAAAACCQ1hra6v7V3m9nD0I4IkxY8bIvHnzOn1ORUWFTJgwQbZt2+b4W1hYmIwcOVL69+8vu3btku+++0727t3bZrn7779fzj77bOnN7DUgZs2aJWlpaYFuEgAAaMemmlqZU1wir2+ulGYPTklz4mPk0oJcGT8w09R7AAAAAAAEF2ZABKmsrCx58sknTSqmDRs2mL9prGjlypXm5kxnPtx00029PvgAAACC3+pde2V2UYm8W1IlnoyEGZQUJ9MK8uS0/mkSGU7gAQAAAACCFQGIIKbFqF988UV55JFHZNGiRWZkv7OoqCg57rjjTNql7s7M6KnGjx/f5rHOFImLiwtYewAAQPu+q6qRp4q2ysfluzzaRAV9EmR6YZ6cmJsq4WFhbGYAAAAACHKkYOohmpqa5KuvvjI1JSorKyUhIUGys7NNrYjU1FTpzToLQJCCCQCAwNIZnJ9v2y1PFZfIV9vb1sty1ei0JJlRmCdHZ/Uxv/MAAAAAgJ6BAARCDjUgAAAIvJbWVvmwbKfMKSqR73fu8WgdR2amyIzCfnJIRrLX2wcAAAAA8D1SMAEAAMBrtJj021urZE7RVllXXevROk7I7SvTC/rJAamJfDIAAAAA0IMRgAAAAEC3Nba0yKubtsvc4lLZurfO7eW1lPRp/dPl0oI8GZoSzycCAAAAACGAAAQAAAA8VtfULC9t3CbzV5fKttoG909Gw8Jk/MAMmVqQK/0Tf6zhBAAAAAAIDX4PQFx66aXmXy0g+Je//EXS0tI8Wo8WYv7Nb37jWNecOXO82k4AAAB0bE9jk/xnfYU8vaZUdtY3ub2pYiLC5dzBmTJ5eK5kxcewqQEAAAAgBPk9ALF8+XITMFD19fUer0eX1XUpa30AAADwrV31jfLM2jJ5fl257Glsdnv5hMgIuXBotlwyPEf6xkT5pI0AAAAAgF6cgqm1tZWgAbxm/PjxbR5rQCoujhQOAAB40/baBlm4plQWra+QuuYWt5fvEx1pgg4ThmRLUjRZQAEAAACgN+DqDwAAAB0q3VtnCku/smmbNLa0ur2lMmKjZHJ+nkm3FBcZwZYGAAAAgF6kxwYgmpr+l2s4Korp+73ZkiVL2jyuqqqSmTNnBqw9AACEgg3V+2ROcYm8uaVSmt2PO0heQoxMzc8zBaajI8J90UQAAAAAQJDrsQGI0tJSx/2EhISAtgUAACBUFO3cI7OLS+T9kh3iQdxBBifFyfTCPDmlX7pEhlOnCwAAAAB6s8iePupd8/33798/0M0BAADo0b6prJbZRSXyScUuj5Yv7JMgMwrz5PjcVAkPI/AAAAAAAPBRAOK///2vS8974403pG/fvi6vt7GxUbZv3y6fffaZLF++3PH30aNHe9ROAACA3qy1tVU+27ZbZhdtla8razxaxyHpSTK9sJ8cmZliBoYAAAAAAODTAMRNN93U5QWoXvDed999Hr+GLm8555xzPF4PAABAb9PS2ioflO4wqZZ+2LnXo3UcndXHpFo6OD3Z6+0DAAAAAIQGn6ZgsgcJPPl/Z+0FNaZPny6jRo1yu20AAAC9TVNLq7y9tdIUl15fXev28nomdmJeqkwvyJPCvok+aSMAAAAAIHT4LADhbnDBnXVGRkaatEtTpkyRcePGef11AAAAQklDc4ss2bRd5q0ukZK99W4vHxEmclr/dLm0IE+GJMf7pI0AAAAAgNDjkwDE3LlzOwwgTJs2zTGb4cEHH5T09HSX1qnPj46OlsTERFN0Wu8DAACgY7VNzfLfDdtkwZpS2V7b4PamigoPkzMHZsrU/FzJS4xlUwMAAAAAAh+AGDNmjEuplA4++GDJzc31RRPQi4wfP36//SsuLi5g7QEAINBqGprkhfXl8syaMtnV0OT28rER4XL+kCyZNDxXMuIY9AEAAAAACMIaEO2xBxwiIiL8/fIAAAAha2d9owk6PL+uXPY2Nbu9fGJUhFw4NFsuGZYjfWKifNJGAAAAAEDvEdbqi2INQABVVVXJzJkzzf1Zs2ZJWloanwcAIKRV7KuXhWvK5L8bKqSuucXt5fvGRMrE4blywZAsSYzy+/gUAAAAAECI4goTAACgh9q6p07mFpeYAtNNHowpyYyLlin5uXLOoEyJjWRmKgAAAADAuwhAAAAA9DDrq/fJnKISeXNLpbg/30GkX0KMXFqQJ+MGZkhUeLgPWggAAAAAAAEIAACAHuOHnXtkdlGJvF+6w6PlhybHybTCfjI2L00iw8O83j4AAAAAAIJqBkRNTY188803UlRUJDt27JC9e/dKY2OjW+sICwuTu+++22dtBAAACKSvt1fLU0Vb5bNtuz1a/oC+CTK9sJ8cl9NXwsMIPAAAAAAAQjwAsXnzZnnooYfknXfekYaGBo/XozW0CUAAAIBQo+c4n1TsMjMevq2q8Wgdh2Yky4yCPDkiM8WcLwEAAAAAEPIBiNdff11uvvlmqaurMxfXiotiAAAAkZbWVpNiSQMPxbv2erRJfpLdR2YU9pPRaUlsUgAAAABA7wlAaLql6667TpqamhyBBw1CWIEIAACA3qippdUUlZ5TXCIba2rdXl7nN5yUl2pSLRX0SfBJGwEAAAAACOoAxD333GOCD1bgISkpSSZOnCjHH3+8DBkyxDyOjAx4aQoAAAC/qG9ukSWbtsu84hIp3Vfv9vIRYSJnDMiQS/PzZFBynE/aCAAAAACAJ/za079lyxb59ttvHemWCgsL5cknn5S0tDR/NgMAACDgapua5cX1FbJgTalU1jW6vXx0eJicNShTpuTnSm5CrE/aCAAAAABAjwlAfPXVV+ZfnfkQHh4uf/nLXwg+oNvGjx/f5rEGuOLiGAEKAAhONQ1N8vy6cnlmbZnsbvgxJaU74iLC5fwhWTJpeK6kx0X7pI0AAAAAAPS4AERVVZWjg3jkyJEm5RIAAEBvsKOuUZ5eWyovrKuQfU3Nbi+fFBUhFw3LkYuHZktKTJRP2ggAAAAAQI8NQOisB8uAAQP8+dIIYUuWLNkv0DVz5syAtQcAALuKffUyf3WpvLRxm6n34K6+MVEyaXiOmfWQGEWdLAAAAABAz+HXq9icnBzH/YaGBn++NAAAgF9t2VMr84pLTYHpptZWt5fPiouWqfm5ctbgTImNiPBJGwEAAAAACJkAxAEHHOAoQL1p0yZ/vjQAAIBfrNu9T2YXb5W3t1SJ+/MdRPonxsqlBXny0wHpEmWbPQoAAAAAQE/j1wBE//79ZcyYMfLZZ5/JmjVrZMuWLeZvAAAAPd2qHXvkqaKt8kHZTo+WH5YSL9ML8uTkfmkS8f8HbAAAAAAA0JP5fVjdb37zG4mM/DHucf/99/v75QEAALymtbVVvty+W67+cJXMeG+FR8GHkamJ8sDRBTJ/7Gg5tX86wQcAAAAAQMjwewDioIMOkhtvvNFcsL/11lty1113SXNzs7+bAQAA4DE9j1lWtlOuXPq9/PKDVbJ8226313F4RrI8fNwB8uSJB8pxuamONJUAAAAAAIQKv6ZgskydOlVSUlLk1ltvlQULFsjy5ctlxowZcuyxx0pGRkYgmgQAANCl5tZWeb9kh8wu2iqrd+/zaIsdm91Xphfmyai0JLY4AAAAACCk+T0AMXbsWMf98PBwM4Jw9erV8rvf/c78LTExUZKSktwaBajPffvtt33SXgAAgKaWFnljS6XMLS6VjTW1bm8QPasZ2y9NphXkSX6fBDYoAAAAAKBX8HsAoqSkxAQMNPCg/1qBBn2sampqzM0dpCwAAAC+UN/cIq9s3CbzVpdK2b56t5fXYtI/HZAulxbkycCkOJ+0EQAAAACAYBWQFEwWK+gAAAAQTPY1Ncui9RWycE2pVNU1ur18dHiYnD0oU6bk50lOQoxP2ggAAAAAQLDzewDiiCOO8PdLAgAAuGR3Q6M8v65cnl1bLtUNTW5vtfjIcLlgSLZMHJ4jabHRbHUAAAAAQK/m9wDEvHnz/P2SAAAAnaqqa5Cn15TJf9aXy76mFre3VnJUhFw8LEcuHJYtKdFRbG0AAAAAAAKdggkAACCQyvfVy7ziElm8cZvUt7ifGjI1JkomDc+R84dkS0JUhE/aCAAAAABAT0UAAj3e+PHj9ytKHhdHoU8AQMc219TKnOISeW1zpTR7UJMqOz5apubnyZmDMiQ2gsADAAAAAADtIQABAAB6jTW798rsohJ5d2uVuJ9oSWRAYqxMK8iTMwakS2R4uA9aCAAAAABA6CAAgR5vyZIlbR5XVVXJzJkzA9YeAEDwWVFVYwIPH5Xv9Gj54SnxMqOwn5yYlyoRYWFebx8AAAAAAKGIAAQAAAhJra2t8uX2anmqaKt8sb3ao3WMSk2U6YX95JjsPibFHwAAAAAAcB0BCAAAEHKBh4/Kdsrs4hJZuWOPR+sYk5liUi0dlpFM4AEAAAAAgGAIQIwdO7bNYx0p+Pbbb3f6HG9o73UAAEDvosWktbaDBh7W7t7n0TqOy+kr0wvz5MDUJK+3DwAAAACA3sarAYiSkhITDNCRh6q9VAXOz/EGUiIAANB7Nba0yGubK2VucYls2VPn9vJaSnpsvzSZVpgnw1MSfNJGAAAAAAB6I1IwAQCAHqmuuVkWb9gm89eUSvm+BreXjwwLk3EDM2Rqfq4MSIrzSRsBAAAAAOjNvBqAOOKII7zyHAAAgI7sbWyWRevLZeGaMtlR3+j2hooJD5NzBmfJlPxcyYqPYUMDAAAAANATAhDz5s3zynMAAACc7a5vlOfWlctza8ukurHZ7Q0UHxkhE4ZkySXDcyQtNpoNDAAAAACAj5GCCQAABLWqugZZsLpUFq2vkNrmFreXT46OlEuG5ciFQ7PNfQAAAAAA4B9chQMAgKBUtrde5q0ukcUbt0lDS6vby6fFRsnk4bly3pAsM/sBAAAAAACEcABi48aN8sEHHzgejxs3TtLT0/3ZBAAAEOQ21dTKnOISeX1zpTS3uh94yImPkUsLcmX8wEyJiQj3SRsBAAAAAECQBSA0+HDPPfeY+3369JFJkyb58+UBAEAQW71rr8wuKpF3S6rE/bCDyKCkOJlWkCen9U+TyHACDwAAAAAA9KoARF1dnbS2tkpYWJgccMABEhlJBigAAHq7FVU18lTRVllWvsuj5Qv6JJjAw0l5qRIeFub19gEAAAAAAM/4NQKQmprquN+3b19/vjQAAAgiOiDhi+3VJvDw5fZqj9YxOi1JZhTmydFZfczgBgAAAAAA0IsDEFlZWY77u3fv9udLAwCAINDS2iofle00qZa+37nHo3UcmZki0wvz5JD0ZAIPAAAAAAAEMb8GIA477DCJi4uT2tpaWblypSMdEwAACG1aTPqdrVUyp6hE1lbv82gdJ+T2lekF/eSA1ESvtw8AAAAAAPTwAER8fLyMHTtWXnnlFdm1a5e8+eabcvrpp/uzCQhB48ePb/NYg1oa6AIABF5jS4u8tmm7zCkula1769xeXktJn9o/3dR4GJoS75M2AgAAAAAA3/B7Fejrr79ePvroI5OC6U9/+pOMGjVKcnNz/d0MAADgQ3XNzfLyhm0yf3WpVNQ2uL18ZFiYjB+YIVMLcqV/IkFlAAAAAAB6orBWzYPkZx9//LFcc801smfPHsnMzJTbbrtNTjnlFH83AyGqqqpKZs6cae7PmjVL0tLSAt0kAOg19jQ2yX/WV8jTa8pkZ32j28vHRITLuYMzZfLwXMmKj/FJGwEAAAAAQIjOgPj8888lKipKbrzxRrnnnntk27ZtcvXVV0v//v3lxBNPlBEjRkhqaqpJ1+SOI444wmdtBgAAndtV3yjPri2T59eVS01js9ubKyEyQiYMzZZLhuVIamwUmxsAAAAAgBDg9wDE1KlT2xSe1vs6CWPz5s0yb948j9ap61i1apUXWwkAAFyxvbZBFq4plRfXV0htc4vbGy0lOlImDs+RCUOyJSna76clAAAAAADAhwJ2pa9BBysQYQ9IBCAjFAAAcFPp3jqZV1wqizdtk8YW93+7M2KjZHJ+rpw7OEviIiPY/gAAAAAAhKCABCCsIAPBBgAAepYN1ftkbnGpvLFluzR7MGYgNz5GLi3IMwWmoyPCfdFEAAAAAADQWwMQWvcBAAD0LEU798js4hJ5v2SHeDJXcXBSnEwrzJNT+6VLZPj/Zj4CAAAAAIDQ5fcAxHnnnefvlwQAAB76prJaZheVyCcVuzxavrBPgswozJPjc1Ml3JZyEQAAAAAAhD6qPQIAgDY0ReJn23bL7KKt8nVljUdb5+D0JJlR0E+OzEppU+sJAAAAAAD0HgQgAACA0dLaKh+W7pSnirfKDzv3erRVjs7qY1ItHZKezFYFAAAAAKCXIwABAEAv19TSKm9vrZQ5xSWyvrrWo3WcmJsq0wvzZETfRK+3DwAAAAAA9EwEIAAA6KUamlvk1c3bZW5xiZTsrXd7+YgwkdP6p8ulBXkyJDneJ20EAAAAAAA9V9AEIDZv3ixff/21fP/991JVVSU1NT/mnE5KSpK0tDQZOXKkHHLIITJgwIBANxUAgB6ttqlZ/rthmyxYUyrbaxvcXj4qPEzOHJgpU/NzJS8x1idtBAAAAAAAPV/AAxD//e9/ZeHChbJixQqXnj9q1CiZPHmynHPOOT5vGwAAoWRPY5O8sK5cnl5TJrsamtxePjYiXM4bnCWT8nMkMy7GJ20EAAAAAAChIzKQMx5uuukmM+tBtba2mn/DwsLafb71/999950JVjz33HNyzz33MCMCAIAu7KxvlGfWlMnz68plb1Oz29srMSpCLhyaLRcPy5G+MVFsbwAAAAAAELwBiKKiIrn88stlx44dJrCgQQe96X0r0NAeKzihz/nyyy9l0qRJ8u9//1vy8/P92HoAAHqGbbX1smB1mfx3Q4XUNbe4vXzfmEi5ZFiuTBiaJYlRAZ80CQAAAAAAehi/9yZUV1fLFVdcYeo82AMPBxxwgPz0pz81KZYGDhxoaj8orQWxadMmM+vhtddek1WrVjkCEZWVlSaQsWTJEklOTvb3WwEAICiV7KmTuatLZMmm7dLY0nFgvyMZcdEyZXiunDs4U2IjI3zSRgAAAAAAEPr8HoB44IEHTODACjxkZ2fL7bffLieeeGK7z09MTJScnBw56qij5Gc/+5m89957cscdd0hFRYX5f12XrvPOO+/08zsBACC4rK/eJ3OKS+StLZXS7H7cQfolxMilBXny0wEZEh0R7osmAgAAAACAXsSvAYh9+/bJyy+/7Ag+5OXlyYIFC0wQwlUnnXSSjBgxwqRfKisrM+tZvHix3HzzzRIXF+fT9gMAEIx+2LlHZheVyPulOzxafkhynEwvyJOx/dIlMrz9WkwAAAAAAABBHYD47LPPpK6uztzXIITOWnAn+GDRZXRZTeWkdJ2ffvqpCU4AANBbfL29WmYXb5VPK3Z7tPwBfRNkemE/OS6nr4T///SGAAAAAAAAPTIAUVpa6gg+6OyHY445xuN1HXvssdKvXz/ZunVrm3UDABDKdObfpxW75KmiEvm2qsajdRyanizTC/NkTGaKo64SAAAAAABAjw5A1NbWOu4XFBR0e326DisAYV83AAChpqW1VZaW7jCplop27fVoHUdn9ZEZhXlyUHqy19sHAAAAAAAQ0ABEZmam4350dHS312dfh33dAACEiqaWVnlzS6UpLr2xxv1gu85vOCkv1aRaKuiT4JM2AgAAAAAABDwAMWjQIMf9LVu2dHt99nXY1w0AQE/X0Nwir2zaLvOKS6R0X73by0eEiZwxIEOm5ufK4OR4n7QRAAAAAAAgaAIQo0ePloEDB8qmTZtk1apVJn2S1nHwNPjw/fffm9zVAwYMMOsGAKCnq21qlhfXV8jCNaWyva7R7eWjw8PkrEGZMiU/V3ITYn3SRgAAAAAAgKALQKhp06bJnXfeaYpo/uEPf5AnnnjC7QKYLS0tcvvtt5t16LJTp071WXsR/MaPH9/mse4TcXFxAWsPAHiipqFJnl9XLs+sLZPdDU1uLx8XES7nDcmSycNzJT2u+2kOAQAAAAAAuitc/GzixIly8sknm+DBxx9/LFdffbVUV1e7vLw+V5dZtmyZeXzCCSfIlClTfNhiAAB8Z0ddozyycpOc/dpX8s9VW9wOPiRFRcjlI/rJSz89VH49ehDBBwAAAAAAEDTCWjUS4GeNjY1mFsTzzz9vRqunpKTI5MmT5YwzzpDhw4e3u8yaNWvktddek4ULF8ru3bvN3y644AIziyIqKsrP7wDBrKqqSmbOnGnuz5o1S9LS0gLdJADYT8W+epm/ulRe2rhN6ptb3N5CfWOiZNLwHDl/SJYkRvl9QiMAAAAAAEDwBSAuvfRSx/2ioiLH7AcrDVNsbKypC5GQkGAe792719SKqKurM4+t5mrQoqCgwOXX1fXPmTPHq+8FwYkABIBgtmVPrcwrLpUlm7ZLkwc/wVlx0aa+w9mDMyU2IsInbQQAAAAAAPAGvw+ZXL58eZuaD9Z9K7BQW1trZjs4/935+Rq4+Pzzz116TatWBAAAgbJu9z6ZXbxV3t5SJe7PdxDpnxgrlxbkyU8HpEtUuN8zKAIAAAAAALgtYDkbnIMCHQUIOgscEFgAAAS7VTv2yFNFW+WDsp0eLT8sOV6mFebJ2H5pEkEwHQAAAAAA9CB+D0Dk5ub6+yUBAPArDZB/VVkts4tKZPm2H+sWuWtkaqLMKMiTY3P6MosPAAAAAAD0SH4PQLz77rv+fkkAAPwWePi4fJfMLi6R76pqPFrHYRnJMqOwnxyekUzgAQAAAAAA9GgBS8EEAECoaG5tlfdLdsic4hIp3rXXo3Ucm91Xphfmyai0JK+3DwAAAAAAIBAIQAAA4KGmlhZ5Y0ulzC0ulY01tW4vr1WOtLbDtII8ye+TwOcAAAAAAABCCgEIAADcVN/cIq9s3CbzVpdK2b56t7efFpP+6YB0ubQgTwYmxbH9AQAAAABASPJ7AOLhhx923J8+fbokJiZ6tJ6amhqZM2eO4/FVV13llfYBANCRfU3Nsmh9hSxcUypVdY1ub6jo8DA5e1CmTMnPk5yEGDY0AAAAAAAIaQEJQISFadIJkfPPP9/jAER1dXWbdRGAAAD4SnVDkzy3rkyeXVtu7rsrPjJcLhiSLROH50habLRP2ggAAAAAABBsApKCqbW11RE4CKZ1AQBgV1XXIE+vKZP/rC+XfU0tbm+c5KgIuXhYjlw4LFtSoqPYuAAAAAAAoFehBgQAAE7K99XL/NWl8vKGCqlvaXV7+6TGRMmk4Tly/pBsSYiKYPsCAAAAAIBeqccGIFpa/jcSNSKCzh0AQPdtrqmVOcUl8trmSmludT/wkB0fLVPz8+TMQRkSy28TAAAAAADo5XpsAGLHjh2O+/Hx8QFtCwCgZ1uze6/MLiqRd7dWifuJlkQGJMbKtII8OWNAukSGh/ughQAAAAAAAD1Pjw1AfPLJJ+Zfrf+QlZUV6OYAAHqgFVU1ZsbDh2U7PVp+eEq8zCjsJyfmpUoE9YgAAAAAAAB8H4AoLS116XkVFRVurbexsVG2b98un332mTzxxBOOvxcWFrrdRgBA79Ta2ipfbq+Wp4q2yhfbqz1ax6jURJle2E+Oye5jAuEAAAAAAADwUwDi5JNP7rJDRjuAJk2a5PFr6PKW008/3eP1AAB6B/3dWFa+ywQeVu7Y49E6xmSmmFRLh2UkE3gAAAAAAAAIZAome5DAk//viAY39KbLH3744XLKKad42EIAQKjTYtJa22F2cYms3b3Po3Ucl9NXphfmyYGpSV5vHwAAAAAAQKjyWQDC0+CCq+uOjY2Vc889V2644QafvQ4AoOdqammR1zZXytziEtm8p87t5bWU9Nh+aSbwMCwlwSdtBAAAAAAACGU+CUBcddVVHf7fww8/bP7VGQxTp06V5ORkl9cbHR0tSUlJMnjwYBk1apQkJNAhBABoq665WRZv2Cbz15RK+b4GtzdPZFiYjBuYIVPzc2VAUhybFwAAAAAAoCcFIKz6ENOnT5fc3FxfNAEA0MvsbWyWRevLZeGaMtlR3+j28jHhYXLO4CyZkp8rWfExPmkjAAAAAABAb+LTGhCdpVDqqkg1AACu2F3fKM+tK5fn1pZJdWOz2xstPjJCJgzNkonDciU1NoqNDgAAAAAA0FMDEO+8847jflZWlr9fHn6ycuVK+eCDD+TLL7+UtWvXSlVVlURGRkp6erocdNBBcs4558jxxx/P5wHAY1V1DbJgdaksWl8htc0tbi+fHB0plwzLkQuHZpv7AAAAAAAA8C6/97jk5eX5+yXhZ1OmTJHPP/98v783NjbKli1bzO2VV16RE088UR544AFT1wMAXFW2t17mrS6RxRu3SUNLq9sbLj02SiYPz5Vzh2SZ2Q8AAAAAAADwDYZ8wusqKirMvzrb4fTTT5fDDz/cBJ407daKFStk7ty5snHjRnn//fflF7/4hXkcHh7OJwGgU5tqamVOcYm8vrlSmlvdDzzkxMfIpQW5Mn5gpsREcMwBAAAAAADoVQGIdevWyffffy87duyQ6upq87fk5GRJTU2VkSNHytChQwPdRLhgyJAhcu2115rgg6Zdshs9erScd955ctlll8nXX39tZkosXrzYpGQCgPas3rVXZheVyLslVeJ+2EFkUFKcTCvIk9P6p0kkwU4AAAAAAIDeE4DYsGGDLFiwwHRCW0GHjmgw4qyzzpJJkyaZTm4Ep3/+85+d/n98fLzccccdcvbZZ5vHr7/+OgEIAPtZUVUjTxVtlWXluzzaOgV9EmR6YZ6cmJsq4WFhbGEAAAAAAIDeEoBobm6Wv//97/LEE09IS0uLtNrSaWiqHjvr/3bv3m2CFU8//bRcfvnlcs011+w3wh49Q0FBgfTp00d27dolmzdvDnRzAAQJPd5/vm23PFVcIl9t7zwo3ZHRaUkyozBPjs7qs9/vCQAAAAAAAPwnIL33DQ0NcuWVV8pnn33mCC5oJ5F13x6MsLM6kjR48a9//Uu++eYbE8CIjo6W3qCpqcmkLSopKZFt27ZJYmKiZGdny8EHH2zSVPXE96Oo/wCgpbVVPirbaVItfb9zj0cb5MjMFDPj4ZD0ZAIPAAAAAAAAvTUAccMNN8inn37aJvAQEREhxxxzjBx00EEyePBgSUpKMv9fU1NjChZ/++238tFHH5lOa2sZrR9w/fXXy9/+9rdAvA0zc0PrVnz33XemuLLeiouLpbGx0fGce+65R84///xuvU5tba08+uijsmjRIqmsrNzv/6OiouS4444zdRd0ZkFPsGrVKtmz58dORmp7AL2XFpN+Z2uVzCkqkbXV+zxaxwm5fWV6QT85IDXR6+0DAAAAAABADwpAfPjhhybnvzWbQUe/T5w4UX71q19J3759O11W0/U88sgjsnDhQkfapjfffFM++OADOf744/30Dn6sWaCpoFauXCn79nnWYeaqNWvWmFRT69ev7/A5GvB49913ZdmyZXLzzTeb7RnsHnvsMcf9cePGBbQtAPyvsaVFXtu0XeYUl8rWvXVuLx8uIqf2TzfFpYemxPukjQAAAAAAAOhhAQgNICgNHmjqpL/+9a8yduxYl5bVmgG33HKLHH300Wa0v3a863p0doA/AxBffvmlLF++3Oevo2mWtNZFRUVFm7+PHDlS+vfvbwIyOuti79695u/19fVy++23m9RMWqw7WC1ZssQEjtSBBx4op556aqCbBMBP6pqb5eUN22T+6lKpqG1we/nIsDAZPzBDphbkSv/EOJ+0EQAAAAAAAD0wALFjxw6TSsma/aAj+10NPtidfPLJZtkHHnjAPNYUSFVVVZKWliaBpGmj4uPj9wsYeEIDK/oe7evKz8+X+++/XwoLCx1/q66uNimo5s+f7/ibBmn0OcOHD5dgU1RUJL///e/N/bi4OLnvvvvI1Q70Ansam+Q/6yvk6TVlsrP+f2nqXBUTES7nDs6UycNzJSs+xidtBAAAAAAAQA8OQOjMAavAtI7Sv/TSSz1ely6raXx09L+u86uvvvLrSPrY2FgZMWKEGcE/atQoc9PaFQ8//LC5dZfOENCC05Z+/fqZIENKSkqb5yUnJ8utt95qOvHnzZvnmAmhQYnO2qHPGT16dLfbedVVV8nVV1/t0nO3bt1qio9r2ipNvXXvvfdS/wEIcbvrG+WZtWXy/LpyqWlsdnv5hMgImTA0Wy4ZliOpsVE+aSMAAAAAAABCIACxfft28692lmuxaU3B5Cld9uCDDzZ1D+zr9odf/OIXcuONN0pkpO82n3Pw4Lbbbtsv+GD329/+1tSBKCkpMY/feust+eGHH0yQJBhoOqnLLrvMMaPjzjvvlDPOOCPQzQLgI9trG2ThmlJ5cX2F1Da3uL18SnSkTByeIxOGZEtStN+zBQIAAAAAAMAL/NqrU1NT47jfVcFpV2tCWPbs2SP+kpqa6tP1FxcXy+rVqx2Phw4dKieccEKny2g6o0suuUQefPBBx98WL17cYQBCAzivvvpqt9vqyueoqbc0+LBp0yb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-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ncols = 4  # 3 features + observed\n",
-    "n_grid = np.logspace(5, 9, 50)\n",
-    "inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)\n",
-    "stream_rows = batch_size + 15_000 + 5_000  # one batch + shuffle buffer + one shard\n",
-    "stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5), layout=\"tight\")\n",
-    "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch  (O(N))\")\n",
-    "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader  (O(batch + buffer + chunk))\")\n",
-    "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n",
-    "ax.text(n_grid[-1], 30, \"26 GB RAM\", color=\"0.5\", ha=\"right\", va=\"bottom\")\n",
-    "ax.set_xlabel(\"dataset size N\")\n",
-    "ax.set_ylabel(\"array footprint (GB, lower bound)\")\n",
-    "ax.set_title(\"Memory is flat in N when streaming\")\n",
-    "ax.legend(loc=\"lower right\", framealpha=0.95);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d304c6c7",
-   "metadata": {},
-   "source": [
-    "Those lines are only the bare arrays. Actual peak RSS is higher, because of the\n",
-    "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a\n",
-    "real-data check, outside this notebook, we ran the same logistic model (13 numeric\n",
-    "features plus the click label) on the\n",
-    "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n",
-    "standard, publicly available out-of-core learning benchmark. Peak memory for the\n",
-    "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n",
-    "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n",
-    "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n",
-    "machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors\n",
-    "agreed on the large coefficients (the intercept near -3.6 and the strong slopes),\n",
-    "but the weakest slopes were noisier: the largest gap in posterior means was 0.12,\n",
-    "and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming,\n",
-    "flipping sign. On coefficients near the noise floor the two stochastic fits are not\n",
-    "interchangeable at this slice size.\n",
-    "\n",
-    "## When to use it\n",
-    "\n",
-    "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n",
-    "  index gives perfectly i.i.d. minibatches for free.\n",
-    "* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory\n",
-    "  flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to\n",
-    "  watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave\n",
-    "  shards for strongly ordered data, since a bounded buffer over strongly ordered\n",
-    "  data only block-shuffles it and can bias the posterior."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "918da33a",
-   "metadata": {},
-   "source": [
-    "## Authors\n",
-    "\n",
-    "* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026\n",
-    "  for the Google Summer of Code project Streaming Variational Inference for Large\n",
-    "  Datasets (PyMC / NumFOCUS)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a8d9f52",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    ":::{bibliography}\n",
-    ":filter: docname in docnames\n",
-    ":::\n",
-    "\n",
-    "## Watermark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "91539613",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-11T15:48:54.489384Z",
-     "iopub.status.busy": "2026-06-11T15:48:54.489297Z",
-     "iopub.status.idle": "2026-06-11T15:48:54.498664Z",
-     "shell.execute_reply": "2026-06-11T15:48:54.498267Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Thu, 11 Jun 2026\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.12.12\n",
-      "IPython version      : 9.13.0\n",
-      "\n",
-      "pytensor: 3.0.3\n",
-      "pyarrow : 24.0.0\n",
-      "\n",
-      "arviz     : 1.1.0\n",
-      "matplotlib: 3.10.9\n",
-      "numpy     : 2.4.6\n",
-      "platform  : 1.0.8\n",
-      "pyarrow   : 24.0.0\n",
-      "pymc      : 6.0.1\n",
-      "\n",
-      "Watermark: 2.6.0\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor,pyarrow"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d51af15",
-   "metadata": {},
-   "source": [
-    ":::{include} ../page_footer.md\n",
-    ":::"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "default_lexer": "ipython3"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  },
-  "myst": {
-   "substitutions": {
-    "extra_dependencies": "pyarrow"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md
deleted file mode 100644
index 034023b72..000000000
--- a/examples/variational_inference/streaming_dataset.myst.md
+++ /dev/null
@@ -1,267 +0,0 @@
----
-jupytext:
-  default_lexer: ipython3
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-kernelspec:
-  display_name: Python 3 (ipykernel)
-  language: python
-  name: python3
-myst:
-  substitutions:
-    extra_dependencies: pyarrow
----
-
-(streaming_dataset)=
-
-# Out-of-core minibatch variational inference with DataLoader and Trainer
-
-:::{post} June 2026
-:tags: variational inference, minibatch, out-of-core
-:category: intermediate, how-to
-:author: Yicheng (Ethan) Yang
-:::
-
-+++
-
-`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so
-minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic`
-only works on data that already fits in memory.
-This notebook uses the streaming API in `pymc.variational.streaming`, which follows
-the same structure as PyTorch's `torch.utils.data`:
-
-* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally
-  shuffles) an out-of-core source into fixed-size minibatches without loading
-  the whole dataset into memory. Here the source is a directory of Parquet shards
-  read by {func}`~pymc.variational.streaming.parquet_source`.
-* the model observes a `pm.Data` placeholder of one batch, not the whole array.
-* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each
-  minibatch into that placeholder with `set_data` every step. There are no
-  callbacks to write.
-
-The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`
-is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed
-distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`.
-Batches are exact-size, so each pass drops the rows that do not fill a final batch;
-with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed
-tail. The one extra requirement is shuffling itself. A streaming source is only as
-well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to
-shuffle through a bounded buffer.
-
-`N` is small here so the notebook runs in seconds. The streaming code is the same at
-any size, and the last section shows what changes at scale.
-
-+++
-
-:::{include} ../extra_installs.md
-:::
-
-```{code-cell} ipython3
-import glob
-import tempfile
-
-import arviz as az
-import matplotlib.pyplot as plt
-import numpy as np
-import pyarrow as pa
-import pyarrow.parquet as pq
-import pymc as pm
-
-from pymc.variational.streaming import DataLoader, Trainer, parquet_source
-
-RANDOM_SEED = 8927
-rng = np.random.default_rng(RANDOM_SEED)
-az.style.use("arviz-variat")
-```
-
-## Write the dataset to disk
-
-First, we build a logistic-regression dataset, write it to Parquet shards, and
-delete the in-memory table. From here the streaming path reads only the disk copy.
-We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later;
-the streaming fit never reads them.
-
-```{code-cell} ipython3
-N = 30_000
-b_true = np.array([0.4, -1.2, 0.8, -0.5])  # intercept + 3 slopes
-
-X = rng.normal(size=(N, 3))
-p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))
-y = (rng.random(N) < p).astype("float64")
-table = np.column_stack([X, y]).astype("float64")
-
-shard_dir = tempfile.mkdtemp(prefix="streaming_demo_")
-for i, s in enumerate(range(0, N, 5_000)):
-    block = table[s : s + 5_000]
-    pq.write_table(
-        pa.table({f"c{j}": block[:, j] for j in range(4)}),
-        f"{shard_dir}/part_{i:03d}.parquet",
-    )
-del table
-print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written")
-```
-
-## Stream minibatches off disk and fit with ADVI
-
-Next, the source is `parquet_source`, an out-of-core
-{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a
-time and gets `n_rows` from the Parquet metadata, so `total_size="auto"` resolves
-`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and
-passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer`
-streams each minibatch into the placeholder.
-
-```{code-cell} ipython3
-batch_size = 1024
-loader = DataLoader(
-    parquet_source(shard_dir),
-    batch_size=batch_size,
-    sample_shape=(4,),  # 3 features + 1 observed column
-    shuffle=True,  # the shards were written in order
-    buffer_size=15_000,
-    seed=0,
-    total_size="auto",  # N from the Parquet metadata
-)
-
-with pm.Model() as model:
-    b = pm.Normal("b", 0.0, 3.0, shape=4)
-    batch = pm.Data("batch", np.zeros((batch_size, 4)))  # placeholder for one minibatch
-    logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]
-    pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader))
-
-    approx = Trainer(
-        method="advi",
-        dataloader=loader,
-        data_name="batch",
-        obj_optimizer=pm.adam(learning_rate=0.008),
-    ).fit(30_000, random_seed=RANDOM_SEED)
-    idata_stream = approx.sample(1000, random_seed=RANDOM_SEED)
-```
-
-The negative-ELBO trace shows the fit converging on minibatches read off disk:
-
-```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(9, 3))
-ax.plot(approx.hist, alpha=0.6)
-ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI convergence");
-```
-
-## Compare with in-RAM `pm.Minibatch`
-
-For comparison, here is the usual fit that keeps the whole dataset in memory. The
-prior, optimizer, iteration count, and seeds match the streaming fit; only the
-minibatch source differs. On this toy problem the two posteriors land on top of
-each other, with ADVI's usual mild bias relative to the dashed ground truth.
-
-```{code-cell} ipython3
-with pm.Model():
-    b = pm.Normal("b", 0.0, 3.0, shape=4)
-    xb, zb, sb, yb = pm.Minibatch(
-        X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size
-    )
-    pm.Bernoulli("y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)
-    approx_inram = pm.fit(
-        30_000,
-        method="advi",
-        obj_optimizer=pm.adam(learning_rate=0.008),
-        progressbar=False,
-        random_seed=RANDOM_SEED,
-    )
-    idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED)
-```
-
-```{code-cell} ipython3
-bs_stream = idata_stream.posterior["b"].values.reshape(-1, 4)
-bs_inram = idata_inram.posterior["b"].values.reshape(-1, 4)
-names = ["intercept", "slope x1", "slope x2", "slope x3"]
-
-fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout="tight")
-for k, ax in enumerate(axes):
-    ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label="streaming")
-    ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label="in-RAM")
-    ax.axvline(b_true[k], color="k", ls="--", lw=1)
-    ax.set(title=names[k], yticks=[])
-axes[0].legend(fontsize=8)
-fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04);
-```
-
-## Memory usage
-
-Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`
-rows resident, so its array grows linearly in `N`. The streaming path keeps only a
-bounded number of rows resident — the model batch, a shuffle-buffer fill, and the
-source chunks used to build it — independent of `N`. The dense `float64` design
-matrix dominates the cost. The lines below are array lower bounds, not measurements;
-they also ignore framework overhead, PyTensor's resident copy, and the transient
-copy `np.concatenate` makes while filling the shuffle buffer:
-
-```{code-cell} ipython3
-ncols = 4  # 3 features + observed
-n_grid = np.logspace(5, 9, 50)
-inram_gb = n_grid * ncols * 8 / 1e9  # whole dataset resident (array lower bound)
-stream_rows = batch_size + 15_000 + 5_000  # one batch + shuffle buffer + one shard
-stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9)
-
-fig, ax = plt.subplots(figsize=(8, 5), layout="tight")
-ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch  (O(N))")
-ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader  (O(batch + buffer + chunk))")
-ax.axhline(26, color="0.5", ls="--", lw=1)
-ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom")
-ax.set_xlabel("dataset size N")
-ax.set_ylabel("array footprint (GB, lower bound)")
-ax.set_title("Memory is flat in N when streaming")
-ax.legend(loc="lower right", framealpha=0.95);
-```
-
-Those lines are only the bare arrays. Actual peak RSS is higher, because of the
-framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a
-real-data check, outside this notebook, we ran the same logistic model (13 numeric
-features plus the click label) on the
-[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a
-standard, publicly available out-of-core learning benchmark. Peak memory for the
-streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M
-rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M
-rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB
-machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors
-agreed on the large coefficients (the intercept near -3.6 and the strong slopes),
-but the weakest slopes were noisier: the largest gap in posterior means was 0.12,
-and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming,
-flipping sign. On coefficients near the noise floor the two stochastic fits are not
-interchangeable at this slice size.
-
-## When to use it
-
-* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random
-  index gives perfectly i.i.d. minibatches for free.
-* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory
-  flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to
-  watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave
-  shards for strongly ordered data, since a bounded buffer over strongly ordered
-  data only block-shuffles it and can bias the posterior.
-
-+++
-
-## Authors
-
-* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026
-  for the Google Summer of Code project Streaming Variational Inference for Large
-  Datasets (PyMC / NumFOCUS).
-
-+++
-
-## References
-
-:::{bibliography}
-:filter: docname in docnames
-:::
-
-## Watermark
-
-```{code-cell} ipython3
-%load_ext watermark
-%watermark -n -u -v -iv -w -p pytensor,pyarrow
-```
-
-:::{include} ../page_footer.md
-:::

From 8d07a50c7323d9063ee27ee2d3f8019f3f471f1e Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 03:46:12 -0400
Subject: [PATCH 31/42] Rebuild the notebook around what it can actually show

Three independent audits of the previous revision found nine blocking
claims, most of them inherited from the first draft and never checked
against the executed outputs. The predictive prose said two of three
statistics were inside their bands when all three are; the estimand
paragraph promised a demonstration that never came and that the
generator contradicts (nu = 3.5, so the moment-based dispersion exists);
`len(loader)` had two incompatible definitions eight hundred lines
apart; the covariate paragraph described causal feature engineering,
a log notional and per-symbol standardisation, none of which the
generator does; and the hierarchy figure claimed shrinkage that it
cannot demonstrate without an unpooled comparison it never computes.
All are corrected or withdrawn.

Structural changes in the same pass. The epoch boundary is now measured
(296 blocks, 300,000 rows conserved) rather than taken from
`total_size // batch_size`, which is four short on the verbatim path.
The falsified per-step CUSUM demonstration is gone along with its
second 20,000-step fit; in its place a signed block-contrast figure on
the existing trace shows what a stopping rule can see at three
horizons, with a 1.5-epoch control that isolates the mechanism: the
variance collapse at epoch-aligned windows is twenty times smaller
than at the misaligned one, because a fixed replay order averages the
same rows each pass. A retrospective t99 benchmark replaces the claim
that a rule "stops after convergence" (and its index into the
convolved array is now mapped back to stage-2 coordinates).

Additions: the event clock is defined before the algebra, so pi and
sigma are not read as clock-time quantities; every generator constant
is annotated as the setting it is; a new figure translates the
posterior into the two curves the model exists to produce, medians and
truth only, no epistemic ribbons; and a closing component map names
all four pull requests with their form here. Nothing unmerged is
imported or copied: the convergence callback and the streaming
Pathfinder appear as the problem they solve and as an explicit
pre-fit applicability decision.

Cold-kernel runs: 25.2 / 21.5 / 21.8 s, peak RSS 872 MB.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib                       |   20 +
 .../streaming_tick_data.ipynb                 | 1447 ++++++++++-------
 .../streaming_tick_data.myst.md               |  622 ++++---
 3 files changed, 1233 insertions(+), 856 deletions(-)

diff --git a/examples/references.bib b/examples/references.bib
index 45fc6cf86..22443313c 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -289,6 +289,16 @@ @book{enders2022
   year          = {2022},
   publisher     = {The Guilford Press}
 }
+@article{engle1998autoregressive,
+  author        = {Engle, Robert F. and Russell, Jeffrey R.},
+  doi           = {10.2307/2999632},
+  journal       = {Econometrica},
+  number        = {5},
+  pages         = {1127--1162},
+  title         = {Autoregressive conditional duration: a new model for irregularly spaced transaction data},
+  volume        = {66},
+  year          = {1998}
+}
 @article{evans2006checking,
   title         = {Checking for prior-data conflict},
   author        = {Evans, Michael and Moshonov, Hadas},
@@ -959,6 +969,16 @@ @online{rochford2018
   author        = {Austin Rochford},
   url           = {https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html}
 }
+@article{roll1984simple,
+  author        = {Roll, Richard},
+  doi           = {10.1111/j.1540-6261.1984.tb03897.x},
+  journal       = {The Journal of Finance},
+  number        = {4},
+  pages         = {1127--1139},
+  title         = {A simple implicit measure of the effective bid-ask spread in an efficient market},
+  volume        = {39},
+  year          = {1984}
+}
 @book{rosenbaum2002observational,
   title         = {Observational Studies},
   author        = {Rosenbaum, Paul R.},
diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 17e5b4204..d2af56a2c 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0844efc9",
+   "id": "f42fa55c",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,35 +18,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bbc9a0e",
+   "id": "e148f2cf",
    "metadata": {},
    "source": [
-    "Financial tick data outgrows memory quickly: one month of Binance aggregated\n",
-    "trades for a single liquid pair is roughly 28 million rows, and a modest\n",
-    "38-symbol universe (four majors over six months plus a one-month tail of 34\n",
-    "alt pairs) already exceeds 450 million rows — far more\n",
-    "than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a\n",
-    "hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of\n",
-    "that scale by streaming minibatches from disk with pymc-extras'\n",
-    "{class}`~pymc_extras.variational.dataloader.DataLoader`. It tries to teach\n",
-    "three things that only show up on real data:\n",
+    "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
+    "aggregate trades as daily and monthly files — and a multi-symbol,\n",
+    "multi-month slice of such an archive does not fit in memory. PyMC's\n",
+    "{class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a\n",
+    "disk-backed reader, so it cannot help once the array no longer fits. This\n",
+    "notebook fits a hierarchical hurdle–Student-t model of *next-event price\n",
+    "moves* by streaming minibatches from disk with pymc-extras'\n",
+    "{class}`~pymc_extras.variational.dataloader.DataLoader`, on 300,000 synthetic\n",
+    "rows that are generated inside the notebook. It tries to teach three things:\n",
     "\n",
     "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
     "   time-series models fail — and a model class that passes both.\n",
-    "2. **The full workflow at scale**: causal feature engineering, an on-disk\n",
-    "   global shuffle, streaming ADVI with `total_size` rescaling, and an online\n",
-    "   stopping rule.\n",
+    "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
+    "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
+    "   what a stopping rule has to be able to see before it can fire.\n",
     "3. **Honest reporting.** Mean-field variational inference produces some\n",
     "   numbers you should trust and some you should not; with known ground truth\n",
     "   we measure which are which instead of guessing.\n",
     "\n",
-    "The notebook runs in about a minute on synthetic data with known ground\n",
-    "truth — the streaming code is identical at any scale."
+    "The notebook runs in about a minute. Everything below executes on synthetic\n",
+    "data with known ground truth, which is what makes the recovery checks\n",
+    "possible: nothing here is a claim about any real market."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "79e7271a",
+   "id": "b8f56a48",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -86,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0611e36",
+   "id": "b638b51e",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -103,13 +104,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dcad6941",
+   "id": "a1cfdc90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:43:57.327709Z",
-     "iopub.status.busy": "2026-08-14T08:43:57.327467Z",
-     "iopub.status.idle": "2026-08-14T08:43:59.671020Z",
-     "shell.execute_reply": "2026-08-14T08:43:59.670489Z"
+     "iopub.execute_input": "2026-08-16T07:44:52.448992Z",
+     "iopub.status.busy": "2026-08-16T07:44:52.448587Z",
+     "iopub.status.idle": "2026-08-16T07:44:55.456777Z",
+     "shell.execute_reply": "2026-08-16T07:44:55.456026Z"
     }
    },
    "outputs": [],
@@ -129,19 +130,20 @@
     "import pymc as pm\n",
     "import pytensor.tensor as pt\n",
     "\n",
-    "from pymc_extras.variational.dataloader import DataLoader, parquet_source"
+    "from pymc_extras.variational.dataloader import DataLoader, parquet_source\n",
+    "from scipy import stats"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ce2c2f81",
+   "id": "08cb2965",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:43:59.672497Z",
-     "iopub.status.busy": "2026-08-14T08:43:59.672356Z",
-     "iopub.status.idle": "2026-08-14T08:43:59.675237Z",
-     "shell.execute_reply": "2026-08-14T08:43:59.674943Z"
+     "iopub.execute_input": "2026-08-16T07:44:55.458842Z",
+     "iopub.status.busy": "2026-08-16T07:44:55.458466Z",
+     "iopub.status.idle": "2026-08-16T07:44:55.464082Z",
+     "shell.execute_reply": "2026-08-16T07:44:55.463372Z"
     }
    },
    "outputs": [],
@@ -161,18 +163,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1efdd51",
+   "id": "e8a21458",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
     "\n",
     "One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:\n",
     "the return $y_i = 10^4 \\, (\\log p_{i+1} - \\log p_i)$ in basis points, and the\n",
-    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. On real per-trade data roughly a\n",
-    "third of all transitions are *exactly* zero — the price simply does not move —\n",
-    "so a continuous likelihood alone would put zero probability mass on the single\n",
-    "most common outcome. The fix is a hurdle: model *whether* the price moves\n",
-    "separately from *how far* it moves given that it does.\n",
+    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. Two consequences of indexing by\n",
+    "*events* rather than by clock time are worth stating before the algebra.\n",
+    "\n",
+    "First, a large share of consecutive trades leave the price unchanged, because\n",
+    "prices live on a discrete grid and many trades do not cross to a new level. A\n",
+    "purely continuous likelihood puts zero probability mass on that outcome. The\n",
+    "fix is a hurdle: model *whether* the price moves separately from *how far* it\n",
+    "moves given that it does.\n",
+    "\n",
+    "Second, each row is a transition between consecutive observed events, not a\n",
+    "fixed-duration return. So $1 - \\pi$ is the probability that the next event\n",
+    "leaves the price unchanged, and $\\sigma$ is the Student-t scale *conditional on\n",
+    "a move*. Neither is clock-time volatility: converting to a clock would\n",
+    "additionally require a model of event arrival times\n",
+    "{cite:p}`engle1998autoregressive`.\n",
     "\n",
     "$$\n",
     "\\begin{aligned}\n",
@@ -188,21 +200,24 @@
     "\\end{aligned}\n",
     "$$\n",
     "\n",
-    "with raw covariates computed causally (from information available at trade\n",
-    "$i$): the trade sign $d_i$, log notional $q_i$, trailing 60-second activity\n",
-    "$a_i$, and the most recent previous nonzero return $\\text{ylag}_i$. One\n",
-    "precision: $q$ and $a$ are then standardized with per-symbol constants\n",
-    "estimated over the full sample — a fixed affine map that changes the\n",
-    "parameterization, not the conditional law, and is shared by every arm below;\n",
-    "a deployed system would freeze these constants from a burn-in window. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
+    "The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an\n",
+    "activity-like $a_i$, and $\\text{ylag}_i$, the most recent previous nonzero\n",
+    "return on that symbol. In the generator below they are drawn directly rather\n",
+    "than engineered from a trade tape: only $\\text{ylag}$ is produced\n",
+    "sequentially, so it is the one covariate that could not see the future, and\n",
+    "$q$ and $a$ are standardized once over the whole sample. On a real archive\n",
+    "both would instead be trailing statistics built causally from the tape and\n",
+    "standardized with constants frozen on a burn-in window — a construction that\n",
+    "is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
     "the standard way to encode intraday periodicity smoothly\n",
     "{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
     "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
     "$b_s \\sim \\mathcal{N}(0, \\tau)$ are partially pooled {cite:p}`gelman2006data`,\n",
     "so a thin symbol borrows the global intraday shape where its own data run out —\n",
     "that is the hierarchical payoff we will measure. They are parameterized\n",
-    "*centered*, deliberately: the familiar non-centered trick exists for data-poor\n",
-    "groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich.\n",
+    "*centered*, deliberately: the non-centered trick pays off when groups are\n",
+    "data-poor, and the generator below gives every symbol at least several hundred\n",
+    "rows.\n",
     "The degrees of freedom are\n",
     "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
     "the floor of 1 (rather than the more comfortable 2) leaves room for tails too\n",
@@ -211,36 +226,38 @@
     "\n",
     "The reported estimand is **event-return dispersion on the event clock**:\n",
     "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
-    "$\\sigma_{s,h} \\cdot t^{-1}_{0.95}(\\nu)$. Quantile-based dispersion stays finite\n",
-    "for any $\\nu > 0$; the familiar moment-based alternative does not survive this\n",
-    "dataset, as we will see."
+    "$\\sigma_{s,h} \\cdot t^{-1}_{0.95}(\\nu)$. A quantile is the safe choice here: it\n",
+    "stays finite for any $\\nu > 0$, whereas a moment-based dispersion requires\n",
+    "$\\nu > 2$ — a constraint the model does not impose, even though the generator\n",
+    "below happens to use $\\nu = 3.5$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c024647",
+   "id": "a25e5a85",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
     "\n",
     "Notebooks in this collection do not download data at build time, so the\n",
-    "executed path uses a seeded synthetic generator that mirrors the real corpus:\n",
-    "same schema, same hurdle structure, same heavy tails, twelve symbols with a\n",
-    "deliberately thin tail so shrinkage is visible. Because the truth is known,\n",
-    "recovery is checkable — and the likelihood, priors, and loader configuration\n",
-    "are line-for-line the ones used on the real corpus."
+    "executed path uses a seeded synthetic generator: the schema an exchange\n",
+    "archive would have after feature construction, a hurdle at exactly zero,\n",
+    "heavy conditional tails, and twelve symbols with a deliberately thin tail so\n",
+    "shrinkage is visible. Because the truth is known, recovery is checkable. Every\n",
+    "statement below about what the fit recovers is conditional on this generator;\n",
+    "none of it is a measurement of any market."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1b543874",
+   "id": "8919c387",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:43:59.676276Z",
-     "iopub.status.busy": "2026-08-14T08:43:59.676208Z",
-     "iopub.status.idle": "2026-08-14T08:43:59.678565Z",
-     "shell.execute_reply": "2026-08-14T08:43:59.678225Z"
+     "iopub.execute_input": "2026-08-16T07:44:55.465905Z",
+     "iopub.status.busy": "2026-08-16T07:44:55.465740Z",
+     "iopub.status.idle": "2026-08-16T07:44:55.469946Z",
+     "shell.execute_reply": "2026-08-16T07:44:55.469398Z"
     }
    },
    "outputs": [],
@@ -252,11 +269,11 @@
     "thin = [10, 11]  # the two symbols with the least data\n",
     "\n",
     "truth = {\n",
-    "    \"kappa0\": np.log(0.7 / 0.3),  # ~30% zero-move share at the baseline\n",
+    "    \"kappa0\": np.log(0.7 / 0.3),  # logit(0.7): see the annotation below\n",
     "    \"c\": np.array([0.25, -0.15, 0.10, 0.05]),\n",
     "    \"lambda_a\": 0.35,\n",
     "    \"lambda_q\": 0.20,\n",
-    "    \"alpha0\": np.log(0.05),  # baseline sigma ~ 0.05 bp, the real-data scale\n",
+    "    \"alpha0\": np.log(0.05),  # conditional-move scale, in basis points\n",
     "    \"g\": np.array([0.20, -0.12, 0.08, 0.04]),\n",
     "    \"beta_a\": 0.18,\n",
     "    \"beta_q\": 0.12,\n",
@@ -282,13 +299,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b2c36bc1",
+   "id": "0da47767",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:43:59.679384Z",
-     "iopub.status.busy": "2026-08-14T08:43:59.679330Z",
-     "iopub.status.idle": "2026-08-14T08:43:59.823266Z",
-     "shell.execute_reply": "2026-08-14T08:43:59.822913Z"
+     "iopub.execute_input": "2026-08-16T07:44:55.471261Z",
+     "iopub.status.busy": "2026-08-16T07:44:55.471154Z",
+     "iopub.status.idle": "2026-08-16T07:44:55.765237Z",
+     "shell.execute_reply": "2026-08-16T07:44:55.764565Z"
     }
    },
    "outputs": [
@@ -296,7 +313,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "300,000 rows, zero-move share 32.1%, median nonzero |y| 0.041 bp\n"
+      "300,000 rows, zero-move share 32.1%, median nonzero |y| 0.041 bp, corr(a, q) 0.30\n"
      ]
     }
    ],
@@ -349,29 +366,52 @@
     "\n",
     "print(\n",
     "    f\"{n:,} rows, zero-move share {(m == 0).mean():.1%}, \"\n",
-    "    f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp\"\n",
+    "    f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp, \"\n",
+    "    f\"corr(a, q) {np.corrcoef(a, q)[0, 1]:.2f}\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "182e1563",
+   "id": "a7faf9ea",
    "metadata": {},
    "source": [
-    "The zero spike is the single most prominent feature of per-trade return data —\n",
-    "here is the synthetic version of the picture that forced the hurdle:"
+    "Each constant above is a setting, not a measurement, and it is worth reading\n",
+    "them the way one would read a specification for a simulator:\n",
+    "\n",
+    "* `kappa0` = $\\operatorname{logit}(0.7)$. If every other term in the logit were\n",
+    "  zero, the move probability would be 0.7. The harmonics, the covariates and\n",
+    "  the symbol effects all shift it, so the *realized* zero share is the number\n",
+    "  printed above, not $30\\%$ by construction.\n",
+    "* `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional\n",
+    "  location: the next move is generated partly from the previous nonzero one. It\n",
+    "  is a synthetic dependence parameter — note that the classical bid–ask bounce\n",
+    "  of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial\n",
+    "  dependence, which this generator does not encode.\n",
+    "* `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding\n",
+    "  everything else fixed, sign $+1$ versus $-1$ differs by 0.04 bp in\n",
+    "  conditional location.\n",
+    "* $a$ and $q$ are constructed with a target correlation of 0.3 rather than\n",
+    "  orthogonally, so the fit faces mildly collinear covariates instead of a\n",
+    "  textbook design; the realized sample correlation is printed below.\n",
+    "* `ylag` is generated per symbol in sequence, so row $i$ sees only the most\n",
+    "  recent previously generated nonzero move. That discipline applies to the lag\n",
+    "  construction; the standardization of $a$ and $q$ still uses full-sample\n",
+    "  constants, an offline simplification kept for readability.\n",
+    "\n",
+    "With that in mind, here is the feature that forces the hurdle:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "190db096",
+   "id": "d6139ddc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:43:59.824180Z",
-     "iopub.status.busy": "2026-08-14T08:43:59.824123Z",
-     "iopub.status.idle": "2026-08-14T08:44:00.268310Z",
-     "shell.execute_reply": "2026-08-14T08:44:00.267596Z"
+     "iopub.execute_input": "2026-08-16T07:44:55.767008Z",
+     "iopub.status.busy": "2026-08-16T07:44:55.766851Z",
+     "iopub.status.idle": "2026-08-16T07:44:56.568182Z",
+     "shell.execute_reply": "2026-08-16T07:44:56.567661Z"
     }
    },
    "outputs": [
@@ -410,7 +450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b37abb6f",
+   "id": "bc9bc408",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -431,13 +471,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cca75837",
+   "id": "de936b5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:00.269817Z",
-     "iopub.status.busy": "2026-08-14T08:44:00.269723Z",
-     "iopub.status.idle": "2026-08-14T08:44:00.525960Z",
-     "shell.execute_reply": "2026-08-14T08:44:00.525526Z"
+     "iopub.execute_input": "2026-08-16T07:44:56.570202Z",
+     "iopub.status.busy": "2026-08-16T07:44:56.570052Z",
+     "iopub.status.idle": "2026-08-16T07:44:56.878662Z",
+     "shell.execute_reply": "2026-08-16T07:44:56.877604Z"
     }
    },
    "outputs": [
@@ -489,22 +529,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffbd434c",
+   "id": "26523892",
    "metadata": {},
    "source": [
-    "On the real corpus the same check is part of the ETL script's validation: the\n",
-    "head of any shard must already mix all 24 hours and nearly all symbols —\n",
-    "asserted on a sampled shard at build time and spot-checked on others, rather\n",
-    "than assumed. Note that the cell above is the pedagogical miniature — it sorts the\n",
-    "whole table in memory, which at corpus scale would need several gigabytes for\n",
-    "the keys and permutation alone. The published ETL is genuinely out-of-core:\n",
-    "pass one hash-scatters rows into shards with bounded row-group appends, pass\n",
-    "two sorts each shard independently, and no step ever holds the corpus."
+    "The printed check is the one to keep: the head of any shard must already mix\n",
+    "every hour and nearly every symbol, asserted rather than assumed. Note that\n",
+    "the cell above is the pedagogical miniature — it sorts the whole table in\n",
+    "memory, which is exactly what one cannot do once the table stops fitting. The\n",
+    "out-of-core version of the same permutation is two passes: hash-scatter rows\n",
+    "into shards with bounded row-group appends, then sort each shard\n",
+    "independently, so no step holds more than one shard."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d3a10713",
+   "id": "1b71a43a",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -513,7 +552,9 @@
     "one block per Parquet row group, in a frozen column order — which is why the\n",
     "shards were written with `row_group_size=1024` above. The dataset size comes\n",
     "from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is\n",
-    "the number of batches per epoch, as for a torch dataloader. The model reads\n",
+    "`total_size // batch_size`, the number of *full* batches — which, as the cell\n",
+    "below shows, is not the same as the number of blocks an epoch yields on this\n",
+    "path. The model reads\n",
     "one `pm.Data` placeholder; everything derived — the Fourier basis, the\n",
     "integer symbol index — is computed inside the graph, so advancing the stream\n",
     "is a single `set_value` per step."
@@ -522,13 +563,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "b2aec5a2",
+   "id": "4ec1cde5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:00.526931Z",
-     "iopub.status.busy": "2026-08-14T08:44:00.526862Z",
-     "iopub.status.idle": "2026-08-14T08:44:00.530637Z",
-     "shell.execute_reply": "2026-08-14T08:44:00.530317Z"
+     "iopub.execute_input": "2026-08-16T07:44:56.880407Z",
+     "iopub.status.busy": "2026-08-16T07:44:56.880221Z",
+     "iopub.status.idle": "2026-08-16T07:44:56.960781Z",
+     "shell.execute_reply": "2026-08-16T07:44:56.960176Z"
     }
    },
    "outputs": [
@@ -536,7 +577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N = 300,000 rows -> 292 batches per epoch\n"
+      "N = 300,000 rows -> 296 blocks per epoch (288 of 1024 rows + 8 shorter), conserving 300,000 rows; len(loader) = 292\n"
      ]
     }
    ],
@@ -548,34 +589,55 @@
     "    shuffle=False,  # the shards are already globally shuffled on disk\n",
     "    total_size=\"auto\",\n",
     ")\n",
-    "print(f\"N = {loader.total_size:,} rows -> {len(loader):,} batches per epoch\")"
+    "\n",
+    "# Count one epoch instead of inferring it: on the verbatim path a block is a row\n",
+    "# group, and the last group of each shard is short, so the block count is not\n",
+    "# total_size // batch_size.\n",
+    "block_rows = [b.shape[0] for b in loader]\n",
+    "steps_per_epoch = len(block_rows)\n",
+    "assert sum(block_rows) == loader.total_size == n\n",
+    "n_full = block_rows.count(1024)\n",
+    "print(\n",
+    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks per epoch \"\n",
+    "    f\"({n_full} of 1024 rows + {steps_per_epoch - n_full} shorter), \"\n",
+    "    f\"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b858ebf",
+   "id": "4cd2cf91",
    "metadata": {},
    "source": [
-    "One deliberate loader semantics to be precise about: on this path nothing is\n",
-    "dropped. Verbatim pass-through streams every row, and the last row group of\n",
-    "each shard is simply shorter than 1024. The `total_size` rescaling copes,\n",
-    "because PyMC scales the minibatch log-likelihood by `N / b` with `b` read\n",
-    "from the batch actually installed, so a short block is weighted up by exactly\n",
-    "its own size. Gate 1's equal-inclusion clause therefore holds for the full\n",
-    "corpus, with no tempering correction to track. (Only the shuffle-buffer path\n",
-    "drops a trailing partial batch; this notebook never uses it.)"
+    "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
+    "pass-through streams every row, and the short trailing group of each shard is\n",
+    "simply a shorter batch. The `total_size` rescaling copes, because PyMC scales\n",
+    "the minibatch log-likelihood by `N / b` with `b` read from the batch actually\n",
+    "installed, so a short block is weighted up by exactly its own size and every\n",
+    "row enters the epoch exactly once, with no tempering correction to track. One\n",
+    "precision on Gate 1: the replay is a fixed permutation rather than fresh\n",
+    "random sampling at each step, so what holds here is equal inclusion *over an\n",
+    "epoch* — single-shuffle SGD semantics — not independent uniform batches.\n",
+    "(Only the shuffle-buffer path drops a trailing partial batch; this notebook\n",
+    "never uses it.)\n",
+    "\n",
+    "Second, the printed comparison makes the earlier caveat concrete: because the\n",
+    "short trailing blocks are streamed rather than dropped, an epoch yields a few\n",
+    "more blocks than `total_size // batch_size`. Anything that needs a true epoch\n",
+    "boundary — the stopping horizon below, for instance — should count blocks\n",
+    "rather than trust the division."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "320cf2ff",
+   "id": "0ed5ceea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:00.531557Z",
-     "iopub.status.busy": "2026-08-14T08:44:00.531498Z",
-     "iopub.status.idle": "2026-08-14T08:44:00.535509Z",
-     "shell.execute_reply": "2026-08-14T08:44:00.535173Z"
+     "iopub.execute_input": "2026-08-16T07:44:56.962480Z",
+     "iopub.status.busy": "2026-08-16T07:44:56.962321Z",
+     "iopub.status.idle": "2026-08-16T07:44:56.970474Z",
+     "shell.execute_reply": "2026-08-16T07:44:56.969832Z"
     }
    },
    "outputs": [],
@@ -673,7 +735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fcb7fc4",
+   "id": "1102ea5f",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -692,13 +754,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b33e30d5",
+   "id": "949c1f1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:00.536410Z",
-     "iopub.status.busy": "2026-08-14T08:44:00.536358Z",
-     "iopub.status.idle": "2026-08-14T08:44:00.898236Z",
-     "shell.execute_reply": "2026-08-14T08:44:00.897789Z"
+     "iopub.execute_input": "2026-08-16T07:44:56.972036Z",
+     "iopub.status.busy": "2026-08-16T07:44:56.971904Z",
+     "iopub.status.idle": "2026-08-16T07:44:57.252875Z",
+     "shell.execute_reply": "2026-08-16T07:44:57.252010Z"
     }
    },
    "outputs": [
@@ -717,8 +779,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M796.01,-8C796.01,-8 842.01,-8 842.01,-8 848.01,-8 854.01,-14 854.01,-20 854.01,-20 854.01,-94 854.01,-94 854.01,-100 848.01,-106 842.01,-106 842.01,-106 796.01,-106 796.01,-106 790.01,-106 784.01,-100 784.01,-94 784.01,-94 784.01,-20 784.01,-20 784.01,-14 790.01,-8 796.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M808.01,-8C808.01,-8 854.01,-8 854.01,-8 860.01,-8 866.01,-14 866.01,-20 866.01,-20 866.01,-94 866.01,-94 866.01,-100 860.01,-106 854.01,-106 854.01,-106 808.01,-106 808.01,-106 802.01,-106 796.01,-100 796.01,-94 796.01,-94 796.01,-20 796.01,-20 796.01,-14 802.01,-8 808.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"834.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -737,163 +799,91 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M707.01,-114C707.01,-114 931.01,-114 931.01,-114 937.01,-114 943.01,-120 943.01,-126 943.01,-126 943.01,-223.82 943.01,-223.82 943.01,-229.82 937.01,-235.82 931.01,-235.82 931.01,-235.82 707.01,-235.82 707.01,-235.82 701.01,-235.82 695.01,-229.82 695.01,-223.82 695.01,-223.82 695.01,-126 695.01,-126 695.01,-120 701.01,-114 707.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M719.01,-114C719.01,-114 943.01,-114 943.01,-114 949.01,-114 955.01,-120 955.01,-126 955.01,-126 955.01,-223.82 955.01,-223.82 955.01,-229.82 949.01,-235.82 943.01,-235.82 943.01,-235.82 719.01,-235.82 719.01,-235.82 713.01,-235.82 707.01,-229.82 707.01,-223.82 707.01,-223.82 707.01,-126 707.01,-126 707.01,-120 713.01,-114 719.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"933.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M834.01,-98C834.01,-98 804.01,-98 804.01,-98 798.01,-98 792.01,-92 792.01,-86 792.01,-86 792.01,-52.5 792.01,-52.5 792.01,-46.5 798.01,-40.5 804.01,-40.5 804.01,-40.5 834.01,-40.5 834.01,-40.5 840.01,-40.5 846.01,-46.5 846.01,-52.5 846.01,-52.5 846.01,-86 846.01,-86 846.01,-92 840.01,-98 834.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M846.01,-98C846.01,-98 816.01,-98 816.01,-98 810.01,-98 804.01,-92 804.01,-86 804.01,-86 804.01,-52.5 804.01,-52.5 804.01,-46.5 810.01,-40.5 816.01,-40.5 816.01,-40.5 846.01,-40.5 846.01,-40.5 852.01,-40.5 858.01,-46.5 858.01,-52.5 858.01,-52.5 858.01,-86 858.01,-86 858.01,-92 852.01,-98 846.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- tau_k -->\n",
+       "<!-- beta_a -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_k -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
-       "</g>\n",
-       "<!-- tau_al -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_al -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
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-       "<!-- alpha0 -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
+       "<title>beta_a</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
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        "<!-- y_obs -->\n",
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        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"819.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
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+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<!-- beta_q -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
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        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- beta_a -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
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-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"341.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"341.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
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+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.42C165.86,-265.95 176.91,-252 191.01,-243.82 234.26,-218.72 530.92,-201.48 704.77,-193.39\"/>\n",
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-       "<!-- theta_r -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"441.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
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+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M269.48,-279.85C279.07,-266.28 291.78,-252.14 307.01,-243.82 373.13,-207.71 571.06,-195.03 703.83,-190.57\"/>\n",
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-       "<!-- tau_ba -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
+       "<!-- b_al -->\n",
        "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
@@ -914,36 +904,8 @@
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-       "<!-- lambda_q -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"550.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"550.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"550.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M572.19,-280.25C582,-267.07 594.59,-253.11 609.01,-243.82 636.45,-226.14 669.34,-214.05 700.86,-205.8\"/>\n",
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-       "</g>\n",
-       "<!-- kappa0 -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"659.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"659.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M678.32,-280.88C686.57,-268.09 697.06,-254.23 709.01,-243.82 717.04,-236.83 726.09,-230.45 735.48,-224.72\"/>\n",
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-       "</g>\n",
        "<!-- tau_ph -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>tau_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
@@ -964,47 +926,147 @@
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        "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.96,-279.73C382.07,-266.31 393.09,-252.31 407.01,-243.82 455.59,-214.19 598.02,-200.09 704.48,-193.54\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"704.69,-197.03 714.46,-192.94 704.27,-190.05 704.69,-197.03\"/>\n",
+       "</g>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.63,-280C482.95,-266.62 494.13,-252.58 508.01,-243.82 540.98,-223.01 631.18,-208.53 707.9,-199.57\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"708.08,-203.07 717.61,-198.46 707.28,-196.12 708.08,-203.07\"/>\n",
+       "</g>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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+       "</g>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"566.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"566.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"566.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"566.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M588.21,-280.29C598.02,-267.11 610.61,-253.14 625.01,-243.82 651.46,-226.69 683.07,-214.8 713.5,-206.56\"/>\n",
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+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"675.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M694.4,-280.97C702.66,-268.2 713.14,-254.32 725.01,-243.82 732.56,-237.14 741.05,-231 749.85,-225.43\"/>\n",
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+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "</g>\n",
        "<!-- nu -->\n",
        "<g id=\"node14\" class=\"node\">\n",
        "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"808.14,-345.73 717.89,-345.73 717.89,-288.23 808.14,-288.23 808.14,-345.73\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"763.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M775.3,-287.93C781.7,-273.32 789.72,-255.02 797.13,-238.1\"/>\n",
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-       "<!-- lambda_a -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"875.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"875.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"875.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
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-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"844.19,-236.91 836.97,-229.16 837.78,-239.72 844.19,-236.91\"/>\n",
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        "</g>\n",
        "<!-- eta -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"763.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"763.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"763.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"763.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"779.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- eta&#45;&gt;nu -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>eta&#45;&gt;nu</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"782.51,-357.45 779.01,-347.45 775.51,-357.45 782.51,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
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        "<title>c&#45;&gt;y_obs</title>\n",
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      "execution_count": 9,
@@ -1091,25 +1153,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "272b8f4a",
+   "id": "31e6bf78",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
     "partially pooled effect families on the symbol plate feeding two linear\n",
     "predictors, one shared tail parameter, and a single observed node whose batch\n",
-    "dimension is whatever the placeholder currently holds."
+    "dimension is whatever the placeholder currently holds.\n",
+    "\n",
+    "The loop below is the whole streaming adapter: a `pm.fit` callback that pushes\n",
+    "the next block into the placeholder after each step. pymc-extras#710 proposes\n",
+    "an interim wrapper around this same data-advance lifecycle while\n",
+    "pymc-extras#635 develops the longer-term ADVI API, so the twenty lines here are\n",
+    "deliberately the minimal version a notebook can own."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "86442b80",
+   "id": "1fe8fe12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:00.899367Z",
-     "iopub.status.busy": "2026-08-14T08:44:00.899292Z",
-     "iopub.status.idle": "2026-08-14T08:44:06.234279Z",
-     "shell.execute_reply": "2026-08-14T08:44:06.233843Z"
+     "iopub.execute_input": "2026-08-16T07:44:57.254681Z",
+     "iopub.status.busy": "2026-08-16T07:44:57.254535Z",
+     "iopub.status.idle": "2026-08-16T07:45:08.244311Z",
+     "shell.execute_reply": "2026-08-16T07:45:08.243635Z"
     }
    },
    "outputs": [],
@@ -1146,7 +1214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56872c9c",
+   "id": "5e904b6a",
    "metadata": {},
    "source": [
     "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
@@ -1159,13 +1227,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "ba8752bd",
+   "id": "e6e6de0e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:06.235431Z",
-     "iopub.status.busy": "2026-08-14T08:44:06.235356Z",
-     "iopub.status.idle": "2026-08-14T08:44:06.385062Z",
-     "shell.execute_reply": "2026-08-14T08:44:06.384611Z"
+     "iopub.execute_input": "2026-08-16T07:45:08.247155Z",
+     "iopub.status.busy": "2026-08-16T07:45:08.246954Z",
+     "iopub.status.idle": "2026-08-16T07:45:08.544935Z",
+     "shell.execute_reply": "2026-08-16T07:45:08.544314Z"
     }
    },
    "outputs": [
@@ -1202,7 +1270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9186daa7",
+   "id": "856ee17a",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1211,13 +1279,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "f7342276",
+   "id": "aced8ae4",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2026-08-14T08:44:06.386346Z",
-     "iopub.status.idle": "2026-08-14T08:44:06.584950Z",
-     "shell.execute_reply": "2026-08-14T08:44:06.584567Z"
+     "iopub.execute_input": "2026-08-16T07:45:08.547818Z",
+     "iopub.status.busy": "2026-08-16T07:45:08.547664Z",
+     "iopub.status.idle": "2026-08-16T07:45:08.931103Z",
+     "shell.execute_reply": "2026-08-16T07:45:08.930210Z"
     }
    },
    "outputs": [
@@ -1390,7 +1458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e0029a0",
+   "id": "e34676d9",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1408,9 +1476,11 @@
     "exceed 2, because the mean-field standard deviations in the third column are\n",
     "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
     "particular synthetic dataset with variational overconfidence along exactly\n",
-    "such ridges. This is the first appearance of a theme the notebook returns to\n",
-    "at full scale: a mean-field width is usable only where seed replication has\n",
-    "failed to disqualify it — replication can veto a width, never certify one.\n",
+    "such ridges. The stance behind that omission is a risk-management one, and it\n",
+    "is worth stating plainly: material disagreement between independent-seed fits\n",
+    "is enough to withhold a claim about a width, while agreement only removes the\n",
+    "instability warning — it does not validate calibration. Replication can veto a\n",
+    "width; it can never certify one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1419,19 +1489,26 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "af6e02e1",
+   "id": "1b6ecfa0",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2026-08-14T08:44:06.585852Z",
-     "iopub.status.idle": "2026-08-14T08:44:06.704811Z",
-     "shell.execute_reply": "2026-08-14T08:44:06.704488Z"
+     "iopub.execute_input": "2026-08-16T07:45:08.933238Z",
+     "iopub.status.busy": "2026-08-16T07:45:08.933081Z",
+     "iopub.status.idle": "2026-08-16T07:45:09.217971Z",
+     "shell.execute_reply": "2026-08-16T07:45:09.217439Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "posterior sd of the contrast, widest first: S11 0.029 (900 rows), S10 0.022 (1,800 rows), S09 0.019 (4,600 rows) ... narrowest S01 0.007 (55,000 rows)\n"
+     ]
+    },
     {
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xgyRJkiRJkiRJkhTro48+Cvfee2/LzxtllFHC0ksv7RcgSZJy89NPP4UvvvgiyR8kd4ZG7o38mbHGGivJKZQkSWpYdtllw9lnn93yAbn66qstBFS1vf7666mfO/HEE4ff/va3oQgM0Jdaaqnk8d5774VLLrkkjDPOOIX8bTU/yXr88ceTgPALL7wQXnnllfDpp58mxVtMtsYdd9wwxRRThP79+4d55pknDBgwIJmAZYG/cffddyd/nwrsoUOHhs8++yyZ8PG3KRSbcsopw3TTTZf87cUWWyz5353g7bffDvfcc0945plnwssvv5z87y+//DIpFun62WeYYYYw33zzhcUXXzxMMskkoap47+xSSpEy3+Wbb74Z3n333WSxobsd2CiAmWyyyZLHzDPPnJxjc801V5hxxhkLf/9q7Zrx2muvJd/1888/n/xu//Of/yTX+EbRJw++Xwo+ueZPPvnkoV+/fkmXBr7jOeaYI/lneeA3xO+pOx9//HHU63ONbBXXUK6b/B56+vu897R4bpr3xjUmryJg7ilcD+iy8c477yTnyQcffJBc43n88MMPyXnAsRl//PGT48R5QjcOzhOuCyONNFJoB1zn/v3vfye/Cz73Sy+9lHxmfhdcH7vD74TPPPXUU4epppoquf5xv+O30i6fvUgEhRvXHu4zXH/YefqTTz4Jn3/+eXLe0XSCHTIbAWP+k6Ax41/uo5NOOmnynxx3zn/utXXcUZNj9dZbbyXXc66Z/DZ5fPjhh8l/Mh7jWs6/1/hPznOSARrX9wknnDD86le/So7ntNNOm4zX+P1yLlcZ1yIalTz22GPh1VdfDUOGDEl+p3xOCnQ5X7ge8Xucc845k3kM50074zfC/JEH16XG98yDexOfn++46/fNPK7xfXMv5/vmwW+H75rd4Dk+NnbIfpzFde7+++9P7p/c2/m+uP5xfeO3x/ysMU/gnsE8gWtc2bhGcE3hGs19kN8W90LGPo35JZ/hxx9//Pk60rhWjzfeeMnvrHGN5j85z/iM/CalqmDuzj2E3ym/z8ZYr9HsoTGP574499xzJzGMKt8X6zZmLxrjVWJtTz75ZHK+ECPhfOFayJiD84VjyZiD82WJJZZI7rWdiHsA9wfOtcZciePBPaJxrnEPbJxrHAd+Sxwf4geca9wXOnHczj3+oYceCo888khyjaGrJXObxjySc2WiiSZKjsVss80W5p9//rDQQgtVuqkM1xHinXzffB7+N9931yQfrhuNBjnEBRvXzl//+tfJ9z3BBBOETsTxuOuuu5JrLzEE5rMcF34jjfkWYz2+42OPPTb13+H3xDyP8T/zvuHne/z2us73eDBG4+/z4LfINZ/fIucfHVf5fmaddVav+zXWznOVBt4naxh8Bn6HzF+YL3PN5brE3INzn/kusblFFlkkzDvvvMnvQupOnWNsKl/V1+tisObVmK8ypml8NmJMjJf5XFzXuyaPN+YT/J66xpka9yZ+U3VILr/++uuTuXyrFlhggeRernxxzjbGVI3cnHYbU7UjfhOMAx944IFkLsa1s7E2wlyoMQfiuBPrYgzIca/7b4JjQ6yCazHnKtdiYjqMobkeM8dvxPy5FvNoNAVnfNN1XZb7D+NsHvx7qja+7zvvvDOJ7xDLa1ynuI8ynuCaRJyKdUJ2k83qGsXYZvDgweHBBx9Mzjt+q4yn+duMnxtxJO7pxJCInzCW6QR8Tu4NrF9z/Ikh8bm5TvXt2zeZr/Jb4t7AZyaWXOXcOq4LnD88uO+xJk98kFhYd+MUzi/OJb5jrhXMifisnGccA/0fx8nZ8nh21niV98m9g2so15uuMRoejfWJRoyGz8O1hzkzD3IWWJtgTsm9hhgN19qqzptbiXnyuRoxz0UXXTT53jr12trpa1R8BtYUGS8xtyFm0sjHYbzE981YnN/nLLPMkvw+OYfbdY01bSHg9ddfH/baa6+OiQP1GdZdFYba1kknnRROPPHEVM9dffXVw5FHHhnaxZZbbpkkWqXBZODyyy8PeaGw8c9//nOq53Jhve2225Jge0823njjZKKbBjeoO+64Y4T/jAX/iy66KHkw4GkWN78VV1wxbLfddknhQhoECk4//fTk87da+MIgZNNNNw0rrLBCbslm3ADT2nHHHcNOO+00wn/GIPK6664LF1xwQTJgbvV8YZfNjTbaKAmkVOHGzACCz8Nun5yjWe34SWLhkksuGVZbbbUkwSlvDz/8cNhkk01SP3/QoEFJgKmTMQDmeyZZiQLW2GI6grtMKvgd8+C6UpXvMw+Nc4T7NvfvKiFxJytMmm+66aakwJvvgYlHDCZV7F7MOUIgs2oJxgQo+U3ceuutyWSaIEJWCKAzueRauNxyyyXXxZ6w8zK7MFdRT2ORZhOor7322uTa8/TTTydB4ywxsSfYwQSYBQuOOxPgTlqEYmGOY8eD3zxBABKTsj6WDQT4SBrg3KWIrgpFPCwc3XLLLcm4l+tTqzgvGJMzBu4uAfCggw5KxkVppXlfIxprEujhuyawRVCW7zv2vt0drssECgkcca0mIFbEGDWvsXpvYu/jPd1zWTy/9NJLk7lrq/cTvgfGGVtssUVy3ygS5xz3/ZtvvjlJomAskCXOp0aCAAkYzO/5PbbSHKaK46+GBRdcMJx//vllv42kaJMAalUxD+ktbhI7Furpb7BQcsUVVyS/URYLWsU5SwyDe0gVAs11G7MXjeviNddcEy6++OLkXtwKjh1xH+az7MLQ3T2VeBwNtdJgXnHVVVeFosagjXON9xsbMyJxjPHlSiutVEisCJzbfI401lprrfC3v/2t23/OAh3nCXOlVo8Ni9GrrrpqEjOvQsM0lp64nnAtZd4W08AQnPsUx/J98zmLaMqRZ2yQ7/ef//xn8u8wVm8GBXd0K20GY32uN8wBGov83K+I5eWB+R3xeX4fxKiz/H5i1kHy1uw8wrlKdeYqXREDYU2GMWOr82NidGuuuWZyflIU2918jn8nra222ipsvfXWoarOOOOMcNRRR6V6LjEUGiPklTxDAR7XorT222+/lq7/dYix7b333qnHizFx4Ji/m9U6R7usFbbTel2rKGAkvkrhwb/+9a/M1p0bSOxsJHIy3iKW2YmNzn7zm9+Ep556quXnHXDAAUkeQietz7Z6ncgz/kvyKbGlyy67LDnXqzamio0PHnbYYWHttdeu3L2HNRLm3syvKLJpdRzD/ZexGnOguiCBmLVvcrloXNTqcWvmfG40ayXmz7WY+GkrxQPkmh1zzDGp73uMT4mt5IFiJ2JXaR1++OG9zi1ir1XdxcIb+WznnHNO0/ELEPMmprnNNtukHgtRmEuCN9eCVvIXQaHGBhtsENZff/3cxssx8Yre1oFYW2McyXnZaiE/vyM++zrrrFPqGK6BsRvnF2uG5M+QU5gF5uU0XWSsytyoiGY9ZcV3uuM4OVvtfDzrPF7tirVNirwaBcfcf4nR0DQmD1x3yCdjbLj88ssnY5gqaMQ8WcdmPbsVjIVYx/rd737XbSOq2DlCHtfDTl2jagZzG8YMrEMzZk+zeRjjBpo/5jGnj5kT9pbvt/jii6fKRSKuXea6RJY6s2y35mIGy1W5MDWLm03aQsDGAjiDqjzceOONqZ/LRLi3ZLY8sCB14YUXhuOPPz6pcm8VNxEWTEnK58aw5557Np0sz8X40EMPTaqt09YnMwDnQecXEq7bpbsP5wpBGxaF0+B4kSTEg8+87777JjsFloHB89///vfkM8UmDXY3SKXIlgfJ5QwwGCR0aheKKmNAzwCShKWsgjSNhUom1DwYBK633nphs80263agqWojqZPgJYsCWQZIWMBmssqDRBuuBZwrZe98wL3s3HPPTe6FrQaim0VwgrEPD+6bXPe33377SifpZ40J5mmnnZYEv/NEQwKK83kwWW4EcQjcMLFn4TuvnTPzRhIZQS8WT4pEEdMNN9yQPCi0XGONNZICCAp5yjqXuNfQFTstdvLhQWB1//33H2GggG5arQbXskK3pyOOOCKZ97BQVxQCLgSLeZx11lnJtZrFsKosOLUDFtFPPfXU5D6a9h7K99CYJ7CgeOCBBya7B+T9vhkj8r7zPO+ZAzE34NE1JkCgkkUTEtWIF0h5YM5y3nnnJYklaWInDdw/dt9992QOTQyDc7cMdRuzl4GmAzReo5FF2us5Y18e/fv3D3/5y19GeL5wPqa99haRPE7hH3FHklf4TFlhEZoFTR4suG6++eZJA6l2Kz4lIZf44KOPPpr6NYjPcIz/8Y9/JMWAO+ywQym/Oc5FYne8jzSF0j3d/xvxX3bFo5CaxMu84vt5Iu7117/+NXU8uCfHHXdckrBCEUyRmPc04hXc1yhcZr7HzmnqLO06Vxn+fD355JOTz5B2rkyMjudz3SWxdLfddvufnWFYc4uZF+WxxpIlkoK45qTZXYpzh3WkDTfcMJf31ojlpcG9k8a5zTDGpiro5PU6EjhZC2C+muUcorsmajyIXYP5BAm6FKGsu+66yZpAO2NcnqYIEAMGDMj8/ej/5k2Mqfj9ph2PlD2makfEEBi/cM1Me11hHEMxHA8Svv/0pz8lu3F1KhL+KcSiAKDVxOJW8H0QO+PBvadrUSdxMNbCiff0VvDMOD9NAxyeQ2FoXoXLMTmEFIhQ6FQGkuaZ46eJazIfouiLB43ieB0aPDb7OyP+zhpj2vOOuAhrpJy/5NGtssoqoR0w/uF6TvwrpiDg4IMPTtYemKvmkXzfDMamFJBSPJRH/gzzchrO8WCXLq4BFBvlVdBbJY6TPZ5FaKfxKtcB3is7FqeJU6XF/Yp5Fg9yhlijIibOPaeMtRlinhwH1rHTfmdcu8l9JO5J3G6XXXZJCq/bSV3WqMj5432wFp+2GRl5fBQRn3nmmUmck0e7bJBA3GappZZK8nTTxI47pRCwvVbDlfvCUNUXlYbHj7i7qvNmMNHI6wJL0m1aJMiWEbwhof2QQw6JSmRrDHAYABIUp9NwbwiQUSFPgkIWm5Qy2WHhlRt51RfM6da+6667Zpb0QVI/Ca/c4Isc1FL8QodUvkc6MRVxLWH3SIKqDJxJqlNx3zUBOgayTBqyXFQcHq9N9wWCmgw2iyxgUBwKTn7/+98nReEsmmbdJakrJmyck1wLuJ+UgfsmQWQWIAim5lUE2N11n66LdcDkc+edd04WYPIuAuwO5zKFgSR5kbzfrli0KboIcESFlgReGDuQzF5koRz3E3ZhIQAXUwQ4/LWIgAi7TuW1w0baRS6CjmXfQzk+LDixKM4CexZj/k7Gb3TllVdOFiezuofSkZSFPhKn8kJDGMZtBLnLKn7lXkFHN46dlAfiDRSyH3300dGxk65jZ8bN7E5bpLqN2cvAOUKTLLpapi0CHB5jURJw6W6eZyJslihsI07H+UYxYJ7vm7HdXnvtldzzYmKzRWKcRgEg32tMEWBX/J5JmGaRliTHIj8LC8TswETxa5YLrCP6jIw9KEIhPpj17sN5HiPe77bbbptLESAee+yxwosAh8d4n8RNigUoziUBQ52hXecqXTFHZicNEkqzmCtzX2Mcx+4eVd25Mi8UA1H0mxbX8bzEvDaxi/HHH7+pf7fuMTaVq5PX65hLsQsd9wd2jilj7sPfpDCQfBKambS7rkU1raB5NTvIK1skQjN2yPL3VPSYqh2xNsFxJ1E0q+sKeSqMLZmHdRrmddxfuPZTyJNnEWBvOy9TRNdM/teEE04YVeyVVw5ho0AhLeZgNFgtEtcm7sWsfWYR1+Reym+FnYubyQUj35C1pizOO3I3aMjH58kzBh6L69IJJ5yQ5IzGFAEOnwvK2jVxqCLnLY1CTvJnKEgpIn+GsTHxUOKS5OxU+buO4TjZ41mUdhuvcu9g3bPIfOnu1qjY1ZrmBc3c87LOH+T4MnfP4jujsIxiQM6Ddlpvq8saFY0WaGTGGDltEWBX5Psz9uI1W9kBumwLLrhgqudl3bS2TBYCdqCYk/OJJ54I7YSKXorX0uJCnEeiLgHptBdXOoayyFT0AIRBQFYTyQZuCBSl9bTQz+SLrX6znnByo+UGW9UEULrvcMzvuOOOXK4BTG5JsKLLQ94IuhFMu/zyyzMZVLSKLZtJqqOLkQuc+SI5lCAjXfaLDJoQyGBwTuJg7BbdyhfnRSM4SYJnkQhAs9vBHnvskVlSdjO4jvO7IHmIpAvll6TVWPRXZ2HscPHFFyfJSlklPvfWiIFOgCxa5lGMxgIy9ysWVjTiIq0//vGPSTMMvgv9EuckxUWM4zlWWWPBlILqrJMt+R3/+c9/Topdyk6AlPLE/YpCnWYaHrWKhSl2hiJGkrc6jtnLQIHPb3/721wS3LlfkEhBl8kqH0diUtwfiN1mHXNsJtbJbsTsXFJ2Q4SeUKTH2JH4ZR5j06effjo5/kUUA1IozXWFY86OoEUhDspCJ/MJijGqjN/ENttsU/nmdVmjkQ/JfkUXvCtb7TpXGR7NKCmSzqpAvysSGyl8JXZeJ+y4kBbjgzwKl0l8ihmzk3TcjoqOsalcnbxeR6Ie1xZ2nrKZWHbSjpXTJtWpe8cff3wyZshjDaGoMVW7IS7AugSPPPJnSPpl3enhhx8OnYJziRjeoYceWloBYFrkqMXkuOXRCJb8sJiE6qI3E6ABAI15uRdniXyu7bff/ufdd7uLITDOoJgja3weGvWXXSTS3RiLokt2tMwjKZ2GAMxzisi34rvjnKWRHTv2FY31Z+IXjCfbqZChGY6TPZ5Fcbwaj+stawH7779/IWtU5NLR+DWP6zyxYBr+sTFQldVpjYoCzS233DKXOD3nEPkQMU0sijT//POnHpe28wYQXVkI2IFGH330qOQAqvnbCRfvMcccM/XkNY8LFoVRaVFQVeTWqiTGcKPO46bQWPxkt5V33333f/4Z3a6ZfOUZxOdv9BREKAOTXoJPeXdmpuqf7zavpDAWFek0SaCkyMFTd/ie6bDw4osvlv1WOg4TErpElV1syc4HfMfs8qLqJjASnCwzeEpCDwVjeQcx+YxcAylmKXKHhToieSevRVFVB2NRxoz8hvPCeIVE6Ly7VRFgIvHca0PPC04swHiMfjneojCGIqA850cEIulCl9XvgPfK67H7gNTJWLRmd428EyyJkVxzzTW5vX7dxuxlId7Dcc57ByziPlUtBiTBicKfMu8P3KPo/EnBRxV3i6NDLcWixDryPh9JJKKbZ17YMZ2FwTKTa2hGQIESSQpVHevxey1rd/uycf+k4J04itpPu85VhkeBFM1L8hzPNeKFNAyriyWWWCJMOumkqZ7L+ZRHHChmPD311FOHhRdeOLSzImJsKk+nr9cxdiWu+uqrr2b6unVHE8u099cFFlgg8/dTV4xBKEQ75ZRT2npM1W4oQmEuxm6AeWLOzY5bnXD94l7D+ne75mT0798/zDPPPKmfn0fznpgcwhlnnDHMPffcoSjkUbIun1dTL/LMmJeNqEHdbbfdlsR28ox18jcOPvjgULXrFMXE9913X+67a1Ikkmf8jmstBYdVyNsjH5b3UrWc0bQcJ3s8i+B4NXvsNkxxfZ73NpqVk7+dZ8EhMc9DDjkk2Zymiuq0RsUaP2OZPOeUxBDYTfnqq68OVTfVVFOFySefPPWugJ3AQsAONMEEE0Q9nx8wBYHtgh302Eo35mabJRJLYjotFtnJh+7kVIbnvVBAMSAT+a67xbEFcVG79dFZIe+iu1a6s7AAV9ROGeygxG55WS9uE8gk+Fe17rZvv/12Erioa1JNHrg+EOhj0lAFdJzjnM762q34IBoFHRRsVeVay7Ugr/EME3SCsVW7BnbqNYiOk1VMblY+wSMWwlmEyeP+QdClqMAPhQ2M+VgwU8/HyB2d/+/c32WXXcL1119f2N9j3p/F+cm8Ls+iJakKWNhg97WisINaHrvU1G3MXhZiYMwVRtQQKw8k5TDGqdKud8RkKHDL4zxOg3OMwsyqxAZBkShNwojNFoHkm7/97W+5JJiy4HjggQcWuhNNd1j4JKmWWHDXOHQVkLD/+OOPh7ojjkLjPrWPdp6rdEXiH9eqonaWOuKII5IElDoYeeSRk2KgtPLYsT4m0ZM12j59+oR2l2eMTeXp9PU65jS8no3DsscuZWnnjBYCZnd/YoyTdzFa3mOqdtNYzy1qpz6uiyRhV2F+HBsLfeihh0I7o6g8LcaSWe8cyS49adFMrShcO1iXz7tpFddECpa7NgGmeJnrVhG/H5rExHwnWfr222+TYuWidqQhV5GGYXnEJE866aRkDlKlayDHl/OKxkbtzHGyx7MIjlfbcx3vrrvuStZD8thNdkSOPfbYcM4554SqqNsaFc1XTzjhhFCExnitHXYGXCBlA6OidnDMm4WAHahfv37Rg36SI7hAVilBoickTqTFZPK1117L7L0wWUx7EZ9lllnCnHPOGYrAwIIJfFEdsTnObEkLOkfR5b4oBGnY8rdsbD/Pls9FJzuTgJVlRyMGTSz+j6hDUxXwfW+//fYm12QYHI8pbs4D19g//elPbdF1og5IKGT30aotzjKe4fxll4WsrzEEY4taNKo7EgSLSqJWNbDIRDAh63nIvvvumzRIKBJzDLpzVy0JuUo4RnzfdcfC2B133FHo32SBNTZISBFPUYFGqSwsXg8aNKjQv0nS0KGHHprpa9ZtzF7mOGbHHXcsvADuscceS3asrIIHH3wwiX3RybpK+E7oulqFXcbpvk0Cd9HxQRYIsy68pXC5Ee+tEpLRq/KbADtTuivTf9EcsCoJd+rcuUpXzz33XPI5ikqIaWA81+4J1M1il4W0xXPsIpxlojHHnB3xyihqrEuMTeWow3odCdo0sFH20u4wNPHEEyfd9RVvv/32C7fccktbj6nadVe7oteFiMFVcZ7crMGDByfxg3a3/PLLJ9ewtLHZLBuhMN5N26S0b9++YY011ghFoYlUUbuJEhcmrtRo+E7+IkVbRRa88l2XXSzAeP2JJ54o9O+Se8H1McuCFHaIOvHEE0MVcZzJUT3vvPNCu3Kc7PEsguPV/Nfxsm6Qx07UZeQj0QCtKnnjdVqjYpxOwWORGrvN590kIta8886b6nk0T/74449Du7MQsAPNOuusmSwOcIFkckynHBar6VRcVLfMVs0888xhoYUWSv38LHeWiqmALrKTDwthRV+gTz311OQ8IhG76HOJ4sOiOuh056KLLsq06LQVBOuyCi6zoEM3iSpje2ISz8o63p2AoA/HsKoDOa4hTEJJMlR5hgwZUsjOsjEF2BTtZZXwTCH0tttum3TrUf5YGCmqO6qqhcRxdszIcgx24403hjLcf//9bdEhqUx33nlnuOKKK0KdlZUYzhyBBda0SCSpQmc1KU9XXXVVadfGrHaOqtuYvUx0wixrrsC9tOikkeFRZEZjpirtTtgV9zw6Xpe92ziLdWUUJBJH4RzNylFHHRX+8Y9/hKrivZ188sllv41kMf6YY44p+21UDvHlqt6X1BlzlQau+ezKUsa9iXXWv/71r6EOKBJZeOGFUz8/y13mY3YYXHLJJcOkk04aOknWMTaVow7rdYwLzjrrrEzfl/4rbY4ETazV3vGlrMZU7Yh8KObfZaAgpuwCo7Q6Zf46yiijJBsgpJVlvCNmfXKppZYKE000USgyJl0kms7wO91zzz0L38GUWPQFF1wQykQcOWY38xg0X6CQI6u4dJYxx7ywgURZxzuG42SPZ1Ecr+aP+05WTcOIE7DjaRljTorDsm5om0ad1qgoVmO8VEYTevLwi9z0qcjYxbBhwzJvoFoGCwE70EwzzRTGGWeczC7aTLoYDK+yyipJsR3b1JLwx4SsSkk6FCymRZJ5FgmMH3zwQepOfAQiVl999dDJmJz85je/KW2Rn27DZWLht+wOCLHBE4pmyxr4p0kmZMBb1QS0qiPoU7XOosPjur3HHnsk114Vj98Wu4Om7bBcZEczJkNZdP3md1HWolEdMSkuulu7qoOOk7fddlv067zzzjvhyCOPDGVyLNK74447LgkgqViNBkBpu7W6k4uUL5pyxarjmL0sLBSUHXcqc8xBvIlzreqJbi+88EI46KCDQl3RpILdCWLRbOyMM84IVUcn8rIbSLFAWmRH+3bBNaMKhZrqzLnK8HNNdjIvS53iAaz9pcWOK1mMA7/55puohpgxn6EOMTaVpw7rdRQEV30u0a74btLOAWjGrfaW1ZhKrc93rrzyyrY7bMRMOqkZLoWA5OGljfNxPLIQ0yx0nXXWCZ2OZgfsklQGro9lNrssO4eQzx87xmS3y6J3J0qLZP/999+/1BhBGo6TPZ6drm7jVXYFzCIGRuMJirrreg+r2xoVuzaXWatT9vedZ+zimWeeCe2ub9lvQNkbeeSRk64weXTqJGDBtq5dt3adeOKJwxxzzJE85pxzzvDrX/86jD/++KFoyyyzTJhiiilSdbT68MMPk8LGFVZYIeo9kAiZtuqa9z/hhBOGTldmEJ9dASkOG3fccUMdkax7/PHHhwMOOCDV8996661wyCGHhHby3HPPJZ2H/vjHP5b9VtoKv5UsJlmjjz56WHnllZNuwNwjuMaNPfbYSTHw0KFDw3333Zfcq2J2buT6zRbUZ555ZvT7VesdAbMYDDOOWG211cICCyyQNDOYYIIJkqA83y2d8++5555w9dVXRxWRU7zHOUIzg7QYJwwaNCj189W6W2+9NfVh4xxaeumlwyKLLJLslj355JMn93+uSwR5ScT88ssvk6R4Es9ff/315HyjqzK7F1d9Epu3McccM/Tv3z/MNttsYdpppw3TTDNN0nGS6zj/bNRRR03mHBRu8eD3+eabbybHkQ5WJBhlUdTFbtIDBw6Meg0amphE0rPJJpssmcPNMMMMYbrppkvmVHzXXI/5zfB9M8fhOyWpjuJKFklY3OFeTiJJ7G7f7IpDx8jf/e53Ua+jdAtIFN/wm24FO4THLJBOPfXUYfnllw/zzDNPsnsEuy40zjeSZTnXmL+QQNa4vnCucZ1mjCDVAeNP4nDjjTde6teo25i9LNwH2fGnnQsZY5FEwTw/FuPOlVZaKWlGx3/nXOvTp09yP+A+QEyanX5ixpo0hCN2Tryijvj8MXEyxoL77rtv9Pvge11iiSWS72LeeecNk0wySTJna8zTmFOQLBfTjIff5l577ZXEnmKupbE7MVVN3759k2s5sbrpp58+mfNxnWcOwHfAeIxrPOMxHuyoxniMB7vVPvDAA5mMxy699NJkF1F+51KWc5UGEncvuugiD25BlltuuWRtOM14kOs+STGLLbZY9PiZ+0gaXAeJJWatk2JsKkdd1uti1gJGGmmksOiiiybXEH5vxDYZX4w22mjJP2M8w7WB90cMks/IgxgTc4xOL9p+6aWXUjemcEfAzhA7plI6NNdut/WOmGtx45qx7LLLhrnnnjv069cvGfOMMcYYyRyU6xBjGmL+jP1YY2K8QwI7OT3kcWWNv7/iiium3jGaJunsZh+DNWeuw2nHp8RMOl2Za7iMC4hx1OE4d4eGaRQup7lHkE9BIwjGWu2CMSH3RBpSE5tsB46TPZ51UNR4lfyEueaaK4mJkxdDHhlzY+JZzB+J0ZD/wJiF+xN1CMRoGKvce++9ydglFq/FOteAAQNSv8aQIUNqnSdbtzUqGnWwBq/ujT322GHKKadMtU5tIaAqi53l8igEHBEuemwP33WLeG6U8803XxJwJaBcRIEbA4ENNtgg9TakDPJjCwE7oZPPWGONlQRD+O5IxuR/k2zDDY1gVZ5d5AkAcXNlMYoLM0F6trWl2xITz5jFBzBQY8FkrbXWClXC515wwQWToBgBMQYVBMI41iRYc16lDQ6NKMFiyy23TI5vms4CWe3UwoCa3xvfN+cZQSxem8/MogtFtQx6s+iYzQLZeuutl1yX1DuCNLEFnwyU2aV1u+22G+H1n++bB0HgbbfdNrn+0vEkbYCIyRbBD5INVAwSaWKL4kh0YCJPp2Um08NjsZbH4osvHnbcccekkPmCCy5I/fdOOeWUsMYaayTXnFZxfTr44IND1td+Cm8oVJt//vmT3wT3PSZ+3K9ItGPy+sorryT3QXZuyCKo0C4IXKRNJGYMc+ihhybFTd0hqZGJIOcDiQ9dcS1iIk2yDcedSV9skVM7oGCSMdiSSy6ZHJNmgmwcQx6cvySSgnEGvxnGHASfYsaOHPunnnoqCcalfX6WO5Y1EtNJ3mK8RgINi0Q0S6CzOoGgtE1BisT5z/dMghtjMQKcveF84FrNNYrfDfdw/OEPf0iuTX//+9+TuUJMAcTFF1/cdgvjeRtnnHGS8Q1zao47QWjOOeYIHG8WkbPa1Z6Ci1akDXiy8M9O5WuuuWa3i2z8tnjweZk3MFfqit8c12geJKqVteO86o1zlOso11PGHL/61a+SRBaK4iiyYfwWqxHDWHvttVM9v25j9rI7T2bxnXdNnCI2x/iKMQf3YTo9Ehfjbz355JOhSojfxOy+A35D++23XzLWIll3eBSN82C8yrnGeD8mDkuzCH7DnONVQmMGErTpXsnvgHsljUuYlxDrZ54Yi/FxTCEg8cHY4jauGSSJN+YQXXGtYW7MMdhoo42ShKyYQlPGTnRd5TXqjGtJIxbL2IoxWW/4ffAgtkeSBDbccMNkfsz8h2s2iQtpEfelGchWW22V+jVUnirPVbruBpjVHJ1rE9cuknSYQxMH4VpIzIMCWe5JVd+BOW8cIxpHpC1YIukrthCQ10iLMXdWCWedGGNTOeqyXkch3hNPPJHq71H4d+SRRybj6O6Q68CD8RBx3a7J/sy7mcs9/PDDybiTHYnaIb7bipi5qjsC5q8dxlSdiBgEcRcSfjnu3IOJu3C/JC8pi0ZHvBZxbGIZ7SJtzJ/7C3GanhLamYPy4N+dccYZk/XxBuaYrIdzLaY5BDGQrIrDuAemLQRkbMkaGHHgtGJiV+R9kk9QNsayzIVo7NjI42NNhiJO4uBZ7ZzY072eY0G+F/dykvOJkRInY40oFt9RFQsBmVNwneL3wliNtd2ua9Jc17MYs/D9sUM7x7hVF154YXK/yuq6zByKvEliUHxmxrHMgSjC4TMzxmQNJhbzIu6xadddiuQ42eNZBe08XiW2zeY8xGe4l3Gt6Q0FgTz4nKx/ks/XuF7QTIZGS7HNE8iLiSkEPPnkkzPJqQaflePDvZD17q4xT+bp3CfL3IluROq2RkXT36xyFpn38Jto1IY0xnV838wFOLep12hHM888c6rvKMv1/bKUP2NQLrg4E/BkslyGRie1yy+/PEne4IbIQjOJDHkWBZKYdNJJJ6W60XHB5kKQpkCq64AkDS6q3FzKRjImHY244A+PJIGtt9467LbbbkmyZdZYYDjqqKNGGAgjCLTZZpslSTqxHVwJBFSpEJBB3V/+8pduixUIppDgxI02iy3q6chz1llntbwrIOc2E+tYdE2gywG/1eE1BtEMsFZdddUkgMH5GLs9Mos4FAhzbVDvWFCOCW4TwCXJotnuuQQvSSKaffbZwzbbbJM6kZvrB3+TAFh3mDBSaNodBt0x50lPr92bnXbaKXn0FKROG3wnwSuLjrFdEcyPCSzS/Zht6pst0OXawfWAQTvFA2kmOCRNsEMoE8JWcd3MYtEHnKMECVhUJ2jQ3e+C3xITbJIzGvctxnUE1OlA2OzEi4AMBYc9TRjTXt8Jtp977rkhrZ5+r2mDRZwjBF64p6TFgg5jDx6Mewhq0FGcBQUSATqpKJDAF80ouC9n2d2X85exG/fzHXbYIXUiBzjuaZOUuM5kgd8iYyfGZcPj/tW4Tmc1dskL11zeJ99LljuicE3nvsD1Zvfdd0+9QMvvngUnFrfqjvsAcy+StIa/nnF8mO+TME0yIMc+ttsmc6RWA+tpG5XQXZTk0BiNYhCuXYxHuDbfeOONyaOVwCSB2552g6JIIm3gb84550wS59JqJjm/CMQqbrjhhm7/OWMIxhJpMY5gPJFWWcViv/3tb5MkEBa/hscuBBQ1c2woaootVOX3mXZBum5j9jKdfvrpmTUdoAHJ8AXQaDSv4PpPog3dLmMKcLJCnIlzLQbzFebizV4P+O1TdMq5kna8x8Ib87ye5uFFYm7Idz+iAgjGwoxD6bBNbDC20QVz27TJiI0OqDE45swRmu26zXWVhHR2C2WhPY1LLrkkuTZzXasT1mVY1CXuxvx2REW2afDdMfYnDnfggQcm30/MfK+ZQkDGVT01qIvZ4ZMxIY+06rajYTvMVcDcsmvz0hjMoRnXdbe2yfnHfZkkHtYh0u5I1wmYo6WNQ5Nowg7LaROtmQumXbvkurbuuuuGGJ0eY1M5Onm9rivWvtPsykfMljF9zL2Y90ghEA/ubXzmu+66K/m9EONlvtPumD+mlVWTXa49PcWXKCTgkVZPr11V7TKm6jRcF5lbc60bvlCfQmGuXexazjojCdaxRTYUtrVTIWDamD/HiutoWozFKHbiwdyMewLXYOL9NJ6KGV8TcyJWnybOzy6FxEDIo0uLz9DOmwlw7JjjELMcHgUhm2yySTLWoFlo1rjPU+zftWi0gRxUxt3kTeyzzz5RTUqzKCbMErm+NIKYZ555RlgUCY47TZVZu6ZwNtYZZ5yRrNu1skMezcr47cfiWszn2WWXXUa4LkYzB+J53BdZs2M9ijFDbL4Gr8Mca0TND6vEcbLHs0ztPF5lPY28GNZVsmwGyXWYtSxy9ohhkZOcRqPRMXnSrWJDgbRNDoZHLiL30e5yh4h58s9plEHefmzxXRbqtkZFY9iYHQm73m+JL+68887dxl4ZC7C+f/bZZyc51GniNGWafvrpU60HsF5Mo4EiNjvLi4WAHYqLFMU2BGnLxoSLixEPEnno3rH55pv/vItElgj0MlCnk2ya98nzuNilQRJG2sklgYO8tzbuDUlsvS26k9TFJJ5jTDfqrFAkyiSrp25GTL6YxJKoQjfBtEgSrQomqVtssUVT/y5JYGzxS1FgbCEm3TgIcDJhbhYLKbFI6KKQs9lgJ/8eiaEkC8fsKAB2UWCCPKIAlf6LhJqYgjGuY/yWm11U7Ip7AoE8Bs1pgjbsRsR1mOuT8sUkg4WLtCaaaKJkATvNwgdJ1yx8p020ZTJMELGZ3a8aKGjJqpCSRVM65jP5SBv45frNhJRJJq/VW8EDCeojSlLv+s9jFsl76rQbI22CA4t3MUWA3SUas+s0j7fffjsJchDYaWcEuxiDbLrppsn4Li/83knOJmCQtmv5PffckyTUtYoCTu7/sQhiMh5pJqGkMXbhXphVQUAWaLKw6667JvOwVhZx0jS4IBGYnaPS4vuueyEgTVkYezczX+a+SGdMdgmISUpIM0fiephm4TbrsRrjT+ZKPEjO5RziHtlMUySCeT0F9GKK8XhuXvfIIvV2r4/dlYzrZr9+/UI7HQ9iInSm7Q3/DvNPdqeP6WCdNoZRtzF7mUjGTrvw0xUNwkieaCb5neQ0xqOMkYgVlYl5eMzO5cyNSBZKs+BJ0w6SXkjSTYN5HrHQsouvSWhi8XhEjdm6Ytx+wgkn/LzwGoNrS5rfd2yjDcYsxDbTXJNIxCSGnqbzNwvyLF4yVq0Sznt++3TbJeGRjsTMO3i/LKrz26KzcmNXhGaTHxnz08mdY51nLJT7InFbxoVp49UUa7HQ2lshcHeNlLLAMe+EcVsR2mWugtiGko3fEuf4iJoaDo9CW5KWSYLiP6vWKbsoFMBR7MEOC63iGkccZ5VVVkn1t0kCSpuAxXyysfNpq+oQY1M56rRelybGBMaGWRfkMz7jdXl8+OGHyW5QJLa2M+ZMaZAMmtVcqbc4Wez32G5juXYaU3US1vrOO++8XgtcGdexDktcivl3DI57bLOBIu87aXb/oNAupgiwu9ww5sk8aKpOw7WYazFJ1+RmpcHmB2kLAWl+mbbxLUVgZV/bWFOkEVdPhf2MN8hdoIg0q0YsIJZBo5Xedm7iu2HuFdOskHEIRRVViEWTO0lDhWaK01hXIdbFd0TOSIxG7InizlZ+G59++mnU3yVHlJhos7tikeNII8L55psvOe9iCkCZUzHOq/o12nGyx7Ms7Tpe5f5J4+oRNdvMErFC5os0q0yD40Qhd5oYGAVlaeNfXWOeRxxxRFO7wfLv0pyAYjhinlltopBW3daosshXJYeSNfsRNVcY0b2ZWBAxbgr1Y+/1RZo0ooE0cZN2LgTMpgWoKonAbmyn/axxQWMxhgEAiyJZJMoMj2r+tCgETDsgiak0L3u7car7m+m82ygYIBE+KyT6UiDaUxFg14EFk7kYXLRjkvCywk5QzRYBdg2Sk/DT6AKfFp+fzlnNoviS7ocxOG8YzKRJMqLb+UorrRT19wkAZL0jWieiSDRmxwqu63QaT4tBJImyaQ0aNCj1c9W8c845J+pw0cEspvshScEj2pq9GXRvbbWrKAmWsTu5gAkVhQlpiwC7IjmYCRcLHyxelN1MIA9puzvmXUxA8iFBAXYIbKYIoKrosk6XozwTlLou4vO7TYtdxtMk0JEUH7MAAK5VabpKE1xkvlOVOSELGZyveRYBNjD/jFn0rVrnzaKxoMqiVytNc7i/EIyLXeQr4jo95ZRT5noeMqdkLEoAuN2TtFQ9nLuMY1u5/zNmjU20ZUElze+tbmP2MsUWZDWSpuiu2MoOOFzz6LKaJrE3SzE7hDOPYaE0TRFgA4tmaZNGWcCic3iZ2G2lmSLArujIGbuz3Ysvvtjyc0hei2lORhd5GtHFLOIRI0yLMXFVFi35volx0+yOzvosuHMu8BlJ9iKxabLJJvu5uz5FwoyT+b0ssMACvb4+CXAk7xfVEI3mfWlRXBBTuK7itNNc5dtvv43aeaOBa1YzRYBdMX5iHaS7Ttp1EJNIGdPRPOa5Me+5DjE2laNO63VVXQug+IDm1uSAsA7TrtImasYk0akzxlSdhPs0Da9a2eWSHK5mkqM75bhX9VpMrIzCcnJ9KHhKg91seiso62nXmVdffbV2uwES1yWu0OzuvrHXqK6IiZBo3+x3xu42NKFs998qMaBmiwC7Yn6QxZp0K83miOVkkX/H7lLNFgEO/5uOiRE2UBxedVW9NrfrONnj2dnjVZrGUySXdxFgA7slxjSvS5MXw/U3i90AaXbR6jiXgnlyp2LW82LVbY2KaxZ5oTFocsIaVDNFgMPXdDAey3ojhioXArYzCwE7HN1xqrqjAjczJq+8R7oLZYWu2GwDnwa73KXZbY5OIY8++miqv8l7jU3eiMGCJDsntBr8zwrffytbMPP9zjzzzKn/HgMiFrvKRGJH2sU6gh4UTsYWfVx//fVN/7t0Vo9NpKdoIm0SIAmXJDnFLp7fcMMN0Z+jDguLMRN9uuPFYmLYTGFwd7teNLP7i9KjI33MJGOJJZaITlTl+hcTQCDRs5VrQRaJodznCZRknRBC8iBdz0g26TRpOxgV1X2I87CIBJ+80K2oSMsvv3zTi0Uj8vzzz7f8HLpOx6Lzf9pjxfi6Crtc0bWI4E6R0nbvx3PPPRfqbLvttms5CNdY6Ew7fgKdflvt9pvmOk3nyjSd5NNo545dqiaSf9ZYY41Uz4s5H9PEMOo4Zi8L3w/J3zEYI5H00EpsbPhiwJhdvmMw/06z408DxRWxcWviZCQ5l1nIGbvLaCtFgI0kODqvxkgTG42JGYG5a2xDABqFpY2js/7QSjw0L5zzJANyjWwluYt/t9lEp6LnexQc9u/fP/Xz6z4HaBftNFdhB83PPvssxGDdjg7RabDDJw2C6oqYQJpxDUjmSdNVm2SNxx9/PNXfZN2JnTfSqkOMTeWo03rdd999l+pvFLkTQdY7DxaJXY7SsBAwH+00puokrNekyTFKOx5sSFtAVoaqr8sibYyV+XRModRll11W6GYCjKVjG6VnkcdHzK2VHZiyWjffZpttWtoNkQT1mPF8FX6rbArAzkytFgF2vcbRiDMGmwk0ex1g7hWbMM+OkzHF1sRGm2mW1VvREbtZVpnjZI9nGdp1vJq26D9mHXTFFVcsNCbOPDt27EWhZNo1NZpq7LnnnqEsdVujYuOe2LoachuWW265VM9lB16KvtvFpBGFgGl34K0KCwE7HIkEdDZKW4BTRMIM27mTGPXGG29k9rox3SbSTOKZEKVNUCq7kw+70rWaMESQLGahqWtBIQGBVi222GJRf7fsjpd0VYhJyOb3HLs9PYWrzU4YY5PaGPzEJiixwLL99ttHvQZbcqddDK4DFoKefvrpqJ1FW01eGxGKJlrpKpP1+aqesQtazBbzMbv2Dp/wkDYQSvH+E0880dS/SwAz5nfRCD7ToTCL30d3WgnEt4u0XWXYkSRtJy/lh6SqmEn3Cy+80NK/T9JYbDIpwZA0Ac6u5/Af//jHUEezzDJL6ucS3C17rF4WFuma3aV9eHReo9lIjPfeey/36zR/I+2iuVQmxnFpk7lZCFpooYUK/X3WbcxeJsYbaZLVuyJe0kpyS15JvmnENl5gp7Ms0Bk+LeZ7ZY09WBhPu2NbbGy01etKI/6d1lxzzZXsfBmLeCrFcGnFdk+NRbyLjumTTDJJ6DQxc4A0O1SqWO02V4npDA0SQmJ3F2AXLZpa1nXsnDYRijFsmp1TaGaZtunMaqut1lbdrYuOsakcdVuvS/sbvPzyy2sbR2xlbTxt4qCFgNlrtzFVp+A6lqa5V6ORS0zR/1dffRW++OKL0A7SXouJ391///2h6tZff/3UxQnslNZqrJXiInatSYOitjzzCpqJObW6+QJzqNhrVKPYM02+Z7vnEHJviLnv8vuNbUbDtYoCkyLia+SbZrGGvu+++0a/RtXzyxwnezyL5ni1uJg4Y4VW6w2yiHnGXn+pdYjZxCdG3daoYr9vmorutttu0Q2iim6CltZkk02W+rlFNjfJg4WANcAW6BdeeGFYdNFFQ1XRXYUOPFltt07ibtpJEpXUJBy1Is3CVCNxP6YzQBY3JhYkW0WAZOqpp47++2m7LsUkSDWC3mVh8TeLLahjE/MI+jeTVEdC27PPPhv1t+jwnkXhKMWPaZMIsxogdTKufTE7tKQNoI9IzG+Ez6H8xBxfJgaLL754Ju+DyQqTtLw/x9133x1icZ+t6u7MVcaiZtqdLRhfsMNzUbtOqfk5SVHddwYPHhy9i1BMY5Guc5KYYEO7ig0EtXu3pbToLhyTfBgbcG11jpT2Os1O30ceeWR04YxUJMZzMde2on+fdRuzl+mhhx6Kfo0sxhzEXcpIYI/5jtg9LDa+1zDVVFOFySefPNVzmTOUca4RJ0ubANrovBoTa/vggw9a+vfZzeXNN99s+5gR8wQSMcsq3DjxxBPbemf5vOZ77b7IWgftNleJvT+T9BpbxMfa24YbbhjqirFJWtdee20hz8nivdYhxqZy1G29Lm2MidgSawE33HBD+OGHH1K9RqdrNeelU3ZBrKp2G1N1Cna1idn5I7bpfrscd+aqaZuZc4z//ve/V7rokfxBmpal/Q5vv/32QnIIq7CZQNo8PmJVWTT7StMAOTbG2GqcLEvkX8Y21gdFCbHrs83OpcnDiEH+ctrmaF1RfBrTtKIdcggdJ3s8i+Z4tTUx191vv/225ftPbMyTa+Ycc8zRljHPOq5RxX7fAwcOjG7ww7iMZlPtYIKIGEZM7KQKLASsCRaazzrrrGRrVhJxqojdHxhMZNFphYnSBhtskOq5BIqvvPLKljpnPfbYY6m3ih1zzDFDWahyp6NOGrE3CYKcaYtT6UAYo8wAFN95Fgg4zjjjjFGv8cwzz/T67zz11FNRf4Ogalafma5X7CIZI/bzdLJ//etfUQOpLLt9xAR9KFxtdrdLFXuesGU4O6JkJaYgvdnPEfN5G+MRdnlQuk5TMZ2TSKrlnrH33nsnDTEefvjhZIxpcWB5YhKVW+1U+/zzz4fYhKosGjcwDorpBtWuYhtA1LEzMfeL2DFz2gKItHOktNdpinTPPPPMpNCI4pdjjz023HTTTcmuBGk7lEt5Y5eQssY1aX6fdRuzlyl2B2LichSxZREvWXrppUORWKSM+fyxO2Vmea6VsfvkEksskTqRo7HYGhObLfK6gizG1lnEjOjg30w8NA/77bdfR+4EWMZ8T8Vqt7kK9ycaj8ZYZZVVQhbYySOLBontaN55502djMs956233mopBpR2txWSn9qxgZvX3M5Xt/W6mHV/dk+kwz07AfGf5557brIz1ZAhQ6IbxXWCmCYYVc0pqvOYquj4Uidg3h27U1jRcfcyz9G0cQbG4EcffXRYZJFFkpy/k046KSmcI2G7SnkbMc24Lrvsspb+fdY80t53F1hggVAWGrOnbRKXxU6yyyyzTKrnTTHFFKkLWcv+nS688MKZFN/z+dMWuzY0EzPjWPHbjpHl2nnsazFerXJDCcfJHs8iOV6tdl4MuW6xm7lktWER95ss15KbUbc1KhofxTbzyipnv8yNrloxWkTTnXbPU0q377naEoP+rbfeOvlhkmxHB5qqBUC5ue21115JkDamK1OjS80pp5ySKrBw+eWXJ9uaNvMe2HI27XEsu5NPTFFV7ESU5LK0RZAUtsYgEFWWZZddNtPXSrvACZJt8y6co2AxNijdFYltrWyRPDwLAbv35JNPpj6uc845Z8hSzPWFaz7ndszOE+r+Hs2iaiecJ0yquHf3FhCO+V00kjtji9frKrbzeuOcveqqq5JH18VrkoSnnXbaZHLOgw6FJP2QQK3/+vTTT5PdunmQOEGQgWP62WefJf+MiTBBCoLjP/74Y66HrtWOqc2McXobI8csGA2/aHX22WeHKiNoyHf84osvJgmT7MjB9Z7gUuP75v7a+L7zLqhtlw65WSLwmLZBS1bzs1bnSFynH3300dR/j+vGI488kjwamH+TIN+4PvPges2cgmt3Vr9LqRWch3SWjRFT7NPq77OOY/Yyce+MsdRSS2X2Xhhz3HzzzaEoFAEyNuiEcy123lfWAhyfmTFcGq3Gy2PiacR/Y5uZZTnm4fvOctG3GdNPP32mHWezwL2FJjrM9954443k3vHuu+8mY3/mAJ9//nky9m/MAfJOUP/mm29MNq+odpurcE7HxiiyKq5nHYuCOJpT1dG6664bDj/88FTPveaaa8IOO+zQ9L9b9d0A2znGpnLUbb2ONZRxxhknGX+k9cknnyQ7A/LoWszAazfiS421AAolY+9t7SImmc1CwOqNqYqML3WK5ZZbLjpBu+i4e5mI+cfEFbnus7tW1x22iOtRTNk13s9/ci3OMo+o2Rw14rxpGls98MADydpZM++ZMV/aQqm11147lIljlGZHvix+K9x32J09DX7nxJ/SFvSVWbCatvixuxzCiy66KPXzm1lfb8TsqxITHzBgQDj00ENTP594FPGxLHJT8uA42eNZpDqMV4m5EAtnbe+1115L4jOMfdjAqBETr2peDLvhxTR6aVwzs/CrX/0qmecX2dC1bmtUsTlvFPambe4wokZq5PBUvaFjnz59kmLANNeR2N9W2SwErCE6TR9zzDHJIsp5552XLJJUqaKVLU2vu+666I7rDEzo/PHPf/6z5edy4+R90LGoNxRUpk0AYBGwTDEdAccdd9yov00yZ1mB57K6ufC++d6zMssss0Q9nwFtbxj8lvkes349BuwktddlgadZTGBa6bI7vHvuuSfz7zoGBbIWAmYvtrPXUUcdlTyqgAE8E/qeivT4XaRNquzawU3pA0RcV2KTq0cUzG0k3gyPxRsSJeaZZ56k2yKLQbGNKdoJ5zwLWXfddVeSpBb7m8/6e2tFzD0NWQb7q3R/HD6oSEMTvvPBgwcnyTJV0U4L41nJYtySdnE27RyJnZzOP//8kCWC6ezeyoPzcvgALb+nueeeO5lHExyNXUyQikqiLPL3Wbcxe9mqNOYoejeb2HON3Tp4VMHrr7+e/M5YGKvLvb/VIs6Y75vfcpWSacqY59CwsApI1rr11luT+R7/PaaYN2sWAlZX2derVsdCsfdm5hixu74Mf3+uayHgmmuumaxFp7nWXHvttU0VApKAev3116d6f8wxV1111ZCHToqxqXh1XK8jBk88/o477sj0b5PASdOzEe0USxIbyWysBVB0wHsscj5QFAsBq6PdxlSdwuMeWo75Z30tZrxGAR0PxkddUQROvIBrMTF/7gWx53kzuwLuu+++qT4HGwrssssuueUQsrPOWmutFeqaQ0ixfkzh7hhjjJG6ELDM+EhsE8KuYseAFMMwduBY5pVDyM6RWeyA2DUHmrldTAI/seEqxS67cpzs8SxSp46bGIOQF/Pggw8mTY6//PLL0I55Mc3kefeEayWNl7PCPafIQsC6rVHFft8038iyuQ/fd9ULAcFnTpNvVqX6qTQ6L5qlps0wwwzhwAMPTHbg42bHYgkLElVYAD7xxBOTBZjY5Gsm8WkKAXHZZZf1WgjIxe3xxx9vy04+iClKi71RxPxtOvjFyLtbQ0+/uSw72cd2KiDBtpnBcIysF5soIOUYxnQY4jNZCPhLdPzOu9NskWK3xlY9jmtvScUEOmO7mdE1T+nR0CHrQsCeNBaibrrppuR/c69gZ7hVVlklLLrooh2ZCND4LQwaNCjZObFKxWBdtTpRb2aMU9T4hYUvikxjx1RZuf/++5Pd1/nPqt7765iUlkXxRtrd1tPOkdj1lp1U0y6spgnQEkzmcc455yRzAhYk6OpM7GCyySYr5H2ofrJYGOhp4Tzr32fdxuxlYveW2HtWlmMO4mwkyxQV1+2kc40xEePHojrRc8+m+33Z15a6ft9Fj8tZ3yhzN0DGaiQr0hE+NlkrT3VsBtIu2m2uEpuQEJP4WoVC/SohrpZ2x2KSXZ5++ulem3Kwpp02BrTiiismc9osdWKMTcWr63odawFZF5/0dr/g7zX+JsUorAGwFsDOsHSR7wQxyWydcgyqIosxQZHxpU7RbmPZsq2wwgrJjs6x6+OtNPGmIWCjKSBxLQoCeR8rrbRSLjk9rCUcccQRqcZqV155Zdhxxx2Tgr2eNNaZW7XYYoslRVJlatccwizyCMtAzChmE4XhTTzxxElzm5i5CGOknuKWsTHCrHMIG8cwZkftqsc9HSd7PIvSSeNV5tS33XZbskkSuf1VHY+1ssYYmwNF3nqWjfCzvH91wrU66zWq2O87yx0QG9/3vffeG6pu9NFHT9bv61YImF1FitoWCx0UpZ111lnJBPuUU04Jm266adIFrayEZxal6VgXi0UidgxIg+64bPvbExaw0gRBOK50wywbnVHKCv7G/O12lUWST5av98EHH/R6/sYOKrJOomLCwPbSMWI/U6cuLHYSCrjkcY09T7I4j+qc7JOF9dZbr9TFZnaQpaHE1ltvnSQunXbaaYUVvBS1wHbooYcmRTQU1VQ1QanVzl8Ey2K/pyy7/6PshTuw48f6668ftthii2SeVeWEoiq/t7wQeI1V9NydIBrX6bIwj3niiSfCkUceGQYMGBC23XbbpIufVMXfZ0wn47rPhar8eYipxMqyiJlklyKbHlX5u6n656HDeRYLrkVdW7jndlIsregYGN93lp3OWxlTU/y37LLLhsMOO6zSRYB13Z2kXbTbXOX999+vVDyg7g1L1l133dTPveaaa5raObCM91aXGJvKUdf1Ooo+SF4vC79j8j523nnnpDngUUcdlcmcr2xVTXito3aLL3WKdhvLlo2xMOOZstDcikYPbGjAtXjPPfcML7zwQqZ/g3XntOsa3KN7S4Cmwe2IdqJtxjrrrBPKZg5hsch9y7o5SexuT70114kdq+bRiC22kWDV456Okz2eRemU8So7EK+++urJ3O6xxx6r9JyolbyY2OZnWeetZ7m7YG/quEZV5+87xrCUv/cqXyeaYSGg/qebEYvD++67b7jiiiuSm+EFF1wQ9thjj+T/n2iiiQo7Ymm75IxoV8A0vvvuu3D11Vf3+O/ceOONqV6b3RPKDGY3EoRiBl+xO9vFbgXdjrKewJN8GxN4ZDDJzho9id0OO+vPnMW5U6UtvquCBa5O0mmfpyo6qQCqmc8Te63gWlWFIEc7I5GZIrwqIKhw7LHHJgtgdHxsd0899VSyKwUdsDotASiLTj1Zj1PzGA+1ErD4+9//nixsspNaO2j3IEvanSNjlXHP+f3vf590Fy0bwd8777wzbLTRRmH77bePDoxKXbErQDslDNVtzN7OYw4aHfXWQbzKY44qfzdV/zxZ3PeLvPcTO+ykRg1Fx4x6280qDyStb7bZZuGvf/1rpYtR6j4HaBftNleJ3a23k+IBVbD44ouHKaaYItVzb7jhhh7vP6zh3nLLLamT2+abb76QhU6Osakcnba+1eznIW9h9913D1XA+OmMM85I1gLOPPPMwnbGykPMjhyx91RlP6aqU0FaFjheWYzt6rbWu+uuu1biM1MUSNOHtdZaK8lfzPL+uMEGG6SOyf3jH//IJYeQBkI0pS1bzG/GHMJqzBdjX7O335o5hMVznOzxLEq7j1cZOxx88MFh8803Dy+//HLotJh47FionWOedVyjqvP3HePbb78tZTfTslkIqF4LjRZYYIEkwY+dAu+///5k21y67pNMOu200+Z2BO+7775MXmfFFVdMXXR3+eWX95gQztbBZXeajCn6jBE7iY/9++0oj+LH2Nfs7eZXtYXzLF7ThYvOPyad9nmqot23wW7188SeR1UojOgE22yzTerdnfPaJXCfffZJHu0aaGCMTaOMoUOHhk6UdmKfZxCjrKAIgcP9998/HH300W17vtZFFudI1sUkzS5OH3LIIZnsapSV22+/Penyx26BUh1/n3Ubs5cpdr5QxUSPTvluqv55svqeYmOzdY2xFP15ZplllkL/HvO83/72t2Hw4MGF/l11rnYbC1Xt/lzHhpjD36vWXnvt1EXNdHHvDs1g0ibFZLVG2+kxNpWjzmMvrhfLL798qAqSDcmJoVlhu85/yPVJq9POxbK125iqE2Q1DqvbcZ9++unDXnvtFaqCYmw2MVhzzTXD66+/nslr0qiCjRDSuPvuu3tsRJh2w4PVVlstKfYpW0wenzmE9cinq2IOYew9th3GPI6TPZ5FaOfxKs2iaBZ8/vnnh04VmweV9fW3yNz/drhOZ/156vx9x/g6ZezGQkDVDlvBk1h30EEHhZtvvjnccccdSTJ01knaTJ6z2NKVbkUsfqfx0ksvdbtzBp89TadatnZfeumlQ9nKvji3+8Wz6GB7XseRgXBv3TJijDbaaKFqx7G3z1xHnXZMOm0CUBV1O09iP2/Z99lOwaIHzShmnnnmUCXsCrjffvuFdvPss88mAbAsiuU6WdZFTUUlaQ/vqKOO6rGxiaoji3tGWcV4AwcODH/6058qVQz48ccfhy222CK88MILZb8VdYAsAuVF3gfqNmZvZ3lcNz3X0ityfJzVXLGoe6/XldA2TYLY2ZIx0JAhQwr7m+p87TxXqcK9tG6J4yOyzjrrpD6u7ALTnWuuuSb1mjGJ5LGMsbWPLHZzK3KsWPex1+GHHx4WWmihUCUU/W633XZtuetnTB6BawjZqtuYqgpcp01vk002CVtttVWoEua5G2+8cSY5hKCZQxrcC6666qoR/jPWI1577bXUY+YqKDOPL498uqozhzAbsXmJ7TL+dpzs8cxbO49XaWJwzz33hE4We63K+p5T5Fi7Xa7TWcZJ6vx9pzVs2LDUx63da1ncEVDRppxyyrDZZpuFSy+9NFx22WVh8cUXz+yovvLKK5m8DoWALO6k8Y9//CPTTj5rrLFGqdsgVyVQWPbfL0MeAfPYpLjefhexXafySNqjA2PVihPbXRW6i2UpTZG2ele38yT288Zu065fNlG48MILK9UNGCz0MPZtF3S+2WWXXTp+AT+La1XsWGNECblFI9B51llnFf53VV6yaVkFp9hoo43CSSedFMYbb7xQFV9++WXYeeedO/6ap/aIXRQZ/6jbmL1MsbGFrMcbRY85Ou1cyyJBvOh7dlHFJZ32XRd9XRlnnHEK+1sHHHBAZjskSO06V4m9PzOPyJLxwf/bcWXRRRdNdfxuvfXWEXZy/vTTT5PdWNJYZpllwoQTThhi1CXG1imyGOc5zi5u7EVy2Zlnnhk22GCDUCUPPvhgEvtqNzHJbO26C2JVtduYqhO029y7av7whz+EAw88sFK5NWwmsPvuu2cyr6foPG0TWtaGR/Qe0uYQ9u/fP8w666yhCsrM46vjNS6P+UTsa/YWB4yNE+YxvoiNs7dL7NNxssczb+06Xr3kkkvCjTfeGDpd2tqHvHK4i2zk2i7X6WY1M5aN/b6zHmO0Q+PebyLeo4WAUhdzzTVXknDKDoFZeOeddzJ5nUkmmSSssMIKqZ7LZH34AD9dhh5//PG27uSj4mW9iJzFa/ZW/R/bHaAdP3MdeUxUh0Fv0Z+XhBRlZ9xxxw0nnnhistg+3XTTVebQsuNaGUVeaTBGf+utt0Kny+JalfX4JY/xUG87Oh9yyCGVLp5Q52FnQILs6623XnRgMitvvPFGOPvss8t+G1Kh6jZmb+djnUchYJFjDs+1+jBmFGfssccORXjkkUfC9ddfX8jfkqqsausZRccDqmrddddNPV66/fbb/+f/v/nmm5PYR5HvpY4xtk6R9lzpqsj4r2Ov/0vy+8tf/hLOP//8MMccc4SqoECx3X77E000Uernfvzxx5m+F0nth4b7zDNXXHHFyjR6f/TRR3vcNbqIXQG5Fzz00EOZFQKaQ1hfVcyn6634NzYmXMXP3E5xbsfJHk/9b17escceW4vDUrXmpEXGPOsYJ6las7t2iHF/+OGHpcROqqD8bcnUkdghkKS7iy66KOp1Pvvss8zeE5P46667LtVNkOetv/76v5jAp0ms/fWvfx1mnHHGlp+nzpD1DZGtbGMWsAjW9TZQIlnlk08+qVTH29gFtyI7cbeLopKS1N7qdp7EXivoZsajnQKH7WC55ZYLyy67bLjjjjuSXZvvv//+8MMPP5T2frhH0vlx8803D1XvfHPeeedl8lrTTjtt8h0wrp1hhhmSLupjjTVWr4GIjTfeOAwePDjkjd8cHfFiAlkkOdCxPivvv/9+KBIJcVnsBMIu5gsssEBYeumlk86o00wzTXJt5PvuqQvukCFDknNE9dzB9aCDDgo77LBDuPjii8M111wT3n777VLf06BBg8KWW27ZcZ3ipO7UbcxeptidZNiZhLHk+OOPn8n74fU++uijUBTPtfpgbM3Y78cffyz7rbSlonaPOO200zJ5nQkmmCAMGDAg2R1h+umnT+ZFnAPMs3pK/Gw07pGqMCeJkXXRwwcffJDp67UrYgSMndKMVZhXrrrqqr/4/9Imf3NNW3zxxUOMOsXYOgVrqbEx+nfffTcUxXH2fy244ILhiiuuSHbju/TSS5M1gTJ34mRNnt///vvvH9oFzbJp2JUmn4DG2JI01VRTheOPPz68+uqrSf4fhYFFxp+6K8xeffXVo1+H1zj66KNTNfdlbXiRRRb5+X+/8MIL4bXXXmv5dRj3rbbaai0/T52hikVxxIB6wnylag026phD6DjZ46n/7gYYk9fc9X686KKLhiWWWCLMNNNMSV4M1zuuiT3tcvjwww+HTTbZpJCvI/b6m/U9J49mp92p4xpVnb/vtP4TEcOYcsopQzuzEFC52WmnnZKgbMwFOMstReeZZ56ka9wzzzzT8nNJNB++EDANO/nU29ChQyv1eiwqkuDd28IoydxpURCcJYqDYwOLk08+eWbvp1N4TNSMySabrFYHKovfBWMOimiULQIt7DzFgwStu+++O9x7771JF8gikzK6JiRVvRCQwrDYBhtMfNn1m2LMdkhwiCmEe+mll0L//v0zeS8UBL/55puhSJdffnn0ayy//PLhD3/4Q5h66qkzeU+q35hht912C7vuumt46qmnwp133pl0yOW+mEUn/lYwd+AeYXGq6qJuY/YyEVOhyJjE4rRefvnlMP/882fyfhj7ZBlH7Y3nWr3mX4yv33nnnbLfirpB4wWa5MSgWdwuu+wSNtpoo+hus1KZJp100qjnc2/O0osvvpjp67UrxkxrrLFGOOecc1p+Ltc35nWNJgzcj9gFNY211167xwSuZtQtxtYJiM2NN954Ua/x3HPPhaK4Xve/KLTgQZL3fffdl6wHsBZQdMwVFMDw+y2q2UMsGjmw1p9mvf69997L5T1Jak80iqEQeu+9906uwXfddVfSmIACuKKTshlj83dnnXXWqNehUQA5fGeffXbLz7311luTNWqa6eDGG29M9R4YD4477ripnqv2R+MY4rlZ7nQUk98HYoA9iW2km3UOYRav2c5xbsfJHs+6yyIvhh2QqXOYeOKJQ5XFvr+smzdnnQffkzquUcXuUNfO33da/7EQUMoeizIk0JL4l1bsgsyIuiT+8Y9/bPl5zz77bHj++efDbLPNllww/vWvf6UKIqyyyiotP0+d49///nemr0eSet6L4rELTll/5ixeL8sdfjoFgY2YzhnbbLNN2H333TN/X6qW2O4XZ511VnTH5aJ/FxRLx+w2x0KIhYD5YoFlzTXXTB5g4s+4jUUgulMS+H3rrbcy6QLVUyIIgfoqbxV/zz33RD2f3eDOPffcqM76eewS3J1+/fpFFQJmOX5hvMYOPUWhExPXnhhbbbVVUgRYpW6Oak8kG7GzAQ/QtZ0Feh78NvidkrRFIDLPAkELAVUndRuzV+F4p+n23cCYNatCQK6t7XSu0WRtuummy+z9KP/vO+0iK8356MyrkOtYZ9iwYVEdddndZq655mqL+Z7UWzwgNmmSjsmxXZfLuj9X2brrrpuqEJDY7A033BB+97vf/bwbYJprHuvNWTRrrVuMrQp62pG2qC7oRf6WXa/rebfEFVdcMXmAmHxjLeCVV175eS0gz91YKUzmb8aMm4qWthDQHQEljQhr5wsvvHDyaNxnyaVrxPy53hDzp3FrngWCzINjCwGx4YYbJmOzVtfxaAx29dVXh8022yz5324moDQ472hGw6YWWSAf4v333881j7BqOYTMFxkHxmj3XYDgONnjWUeszcU2hznggAOSxnjtEJ+pWvOzrK/nvanbGlXs9x1bV1D2953GexHNjNq9nsAdAZUrfiAxhYBZdj3ByiuvHA4//PBUO4qxK+Cf//znpJNPmkUmdtVg4K36IgjGAsRUU01ViRss21j3ZoYZZoj6GwT9shS72EaRhh21/tcoo4ySnJdpiybaYbCnbDr9xeA8aaekYopjSUyNmRw98MADYbvttsv0fSn0GnzmwW6BwxcmcQ9mAYoHQY6nn346KnG7K15rwIABlf16YgrD+C0cddRRUQlKiN3RtxWzzDJL0h06rdhCuq5i3kcaTz75ZFRB1dxzzx322GOPtvmu1V7YXaZrYWADCQEEbkm65RrNmJSkKnYQzCJhLyYmIbWbuo3Zy8aYI2Y8yZgjZpGxq9jdwMo41ywEbB9832nHyHzXxNJjk/iV3/yFJiCxyezOAVQVM800U1LwlbYhD9erxx57LCy55JLR74WdHXgt/Z8ZZ5wxSbxJ02j1mmuu+UUhYBqLLrpoJkkddYuxVUHszmscr5i1TtY5i+w673pda2u+Sy21VPIYvlEaMSaSQokzkRxO/J41gSwathFnaqdCwLRF8p9++mmyoybNriWpOzTQoMnV8I2uWKeiAWBjbZb4GTF/mqsyTo7FdT0L5MgsvfTS4Y477mj5uZdddllSCMhYIU2eDQnt7OaleqOhQVaFgLFJ/8xTestrjc0hZGxOs4HYAocGGkNTmBujE2PUjpM9nnUQGxNfaaWVotfn2B24KM3kefc2v8vy+pt1oVlv6rZGNe2000bfHymWp4lHO37fabz77ru1bQpgIWAHYtLMNvTsPpf1jnqtitlFB1kXzo066qhhvfXWC6eddlrLz2Vxaa+99rKTj6LcfffdPy9YZtHlKjZhrTexCxmNYgsWebNw2223RT1/zjnnzOR9dCK+67SFgARs1fmYDNKNNu3AmeB+u6EgJmYyM3jw4KSAOYuOhArRY0p2dubRFUkcjPHY/SZm18A0HXWLQhFNzISXRahmxgy9dcLKsxPz8GafffboYjoWKLNIELv++utDkQjoxO6gHjuHzKrAVvVK6iMpiUejk3AjWYB7KR15Y3ZdqPI1WspaHcfsZWLMkbbjN+66664kjhvbCI1kh1tuuSUUqX///kmSctoGBMQRVlhhhczfl/KLGdEkL+18hHtx7MKl8pkDMFfOYocs5wCqUiLy1FNPnTrODZqBZlEISDJxFo1NOm1XwDSFgMRpKOihICVtU8Lf/OY3IVYdY2xVwNp+jNgiPgpRi+Z6XRx2O2b3TR7DJ55zjT/zzDOT2G9aMfeYMsRcdxhnMveTpFYRMyJhnUfXpmPEwchz4lr8xBNPVOJazLpYmkJA8qAef/zxJAcsjbXWWqutEtKVD86fLOIyWexe3syYgXh4TEy4kfeXVXO82BzCccYZJ7rhXTtxnOzx7CSxeTGbbrpp6e+h1aJl4iMxxc/cJ7KIj9GInzFQkeq2RhUbP/z222/DI488kknTCcbdNPeouldT/h4nnHDCzApky1JulZhywcV+zz33TAoBr7vuukw6m6UVu/1u7JbiI7LBBhukqnRmcYUExDTBCBYeF1xwwZafp87DAkMWWKBg8TPvJHkK52K7bWaVAM/iJonAsUU9yv7Y0DGk6AG+2u88IcmViUY7oUt1LBYyqiym4Ce2u1oVMNb8/e9/H6666qow8cQTR10Hqyo22WW++eaLfg8PP/xwsuNXURZYYIHo1/jnP/8Z/Rp0pGIhsEhDhw6Nev7wXVvbYUcidS4WExdbbLFwxhlnhG233TYqGJ0mATfmHhmzCNpOYguH63Kcila3MXuZYmN97FRx8803ZxJr+uyzz0LRu7zGNDyJKaBU+82N/b7zFZPITuyX33MMmuqU3STMcZu6WmihhaITCZlDxEqbnNLJVl555aRYM20xVtrdAEnoWHbZZUOsOsbYqiDtOdMQE5sjNyCLGGGrXK/LB9cCks5ZCxi+SLAV7733XmgnMbsMpS2+Ljq+1AlrVVJd0AxrueWWCxdffHFYY401KrEuy87RaXc5Y1fANDEPCgDXXnvtVH9TnVcISIw4C7Hxt2ZyCGNjwrjhhhuinp/la1FYYkFufcfJefF4Vj8mTg5CFjnEDzzwQCgK9Q4xv09ksR6JO++8s/A5WN3WqMhlnGCCCSrxmdvl2L344oupnpfVzsxlshCwg1Hhuscee4Tll18+DBo0qPDOk+ygExsczGIXjuHRGX3gwIGpnnvSSScl28S2yk4+aqBYKougOQGlNOdi1wFtM4nedIWOXZzkvWZx/bnggguiFziXXnrp6PfRqZZaaqmo55exGKr2Ok9IoIntyFU0rhlZFENXuSgmpqMyiRCdgjHnTjvtlPr5Ve7wHvvefvWrX1WmEUKz6NYTGwRjl8gPP/ww9fNpxnLooYeGdvu+CUzHJgE/+OCDUa8hjcguu+ySzOXTSrOQGnOP/PTTT0MdxO7MUHThUl3UbcxeJhIFxh133KjXOP7446OKL7m+HXPMMaHdzjU6SMZ0flexZpxxxjDllFOmfv7VV18dFcdUfnOALOZ7JBCU2YwSjtvUFc1EYseop512WtRrMJ5ybjzi3QcoBkyDIsC0TSfXXHPNZE0sVh1jbFUQe9wonowZq7OLXNFcr8vX+OOPH/74xz925FrAiMw222ypmmQXWQhofEmqHwqA//SnP6VuTJNV4VRD2t3JaFaRZnfChRdeOCrOos7BLpkUX8Vi/hm7WQfnZRF5d4899lh4+umnQxafmdzkMsfdnaZu4+S8eTzzFXO+8d3EFgFTm5G28Cjv63R3HnrooTBkyJDo90EueNHquEYV+31TwBfb7O6HH37IZJySt88++yy8++67qZ5rIaDaAttyHnLIIcng9eCDDw4vvPBC7n+ThV/+VmyHP3bSy8PGG2+c+sKWJoBhJx91/W0cfvjhUQeE7lbnnHNO1Gv8+te/brqLJsXEMd5///3w97//PXp3m7PPPjvqNaaaaqrozkSdrF+/flEDmyuvvLLQLc+7okCUv3/FFVeU8vfrhM7JMUkTJ5xwQmmdOSla4++3ElQkySB2ZzGu++zUnOeOcWxhH5OAExOYjg0oV0lMB6Eqd+KO3W0pNmD72muvlZKkREfRGAREmM+kDQaxG+izzz4b6vZ9s4N61guwUmNezRymyOt0zD2S+2MddlWL3ZmhqISyuqnbmL1MNA1ZZpllouMdfOa0jjjiiNSLG7FWXHHFqOcfffTRoSzEqhjrVXln76pZYYUVUj+XeBFxm7IMHjw4HHvssaFTxcwBYsf/rJfExn2zEDNui00aU/UsvvjiUecEzjvvvPD888+nei5NhcpoDNQufvOb36R6HgnWabu9r7vuuqETYi5lxdjKFtMUCE8++WSq3AgSiy+66KJQBtfr8tepawHd7b6VdqerouI2sfdtx3NSexpnnHGSpO4qXItpHEGT9CJyCLHOOuukep46E5tSxDRgZi37yCOPjC7Kb2YzgSzW4Hm/sXNmrgFZzLtjP0snqtM4uQgez86MiePUU08tvLAstvkZxyx2XY7dAGMaLsWo2xpV7PdNE/fYZneXXHJJqqYXRft3ROxizjnnDO3OHQFrhEnD+eefH9ZYY42kMI1CoiwqvIdHshvdIaggj+2qTbJfHpi8FFUQtOiii0YvUqiz3HfffeHyyy9PXVDyl7/8JXz99deFJWvRoTW2Ex9FfGmvCY1rSmwi7eqrrx71/DogwBkzWfjzn/9c6MSev8lOhKusskrYZ599orZ9V3PGG2+8MGDAgNSHi8kBk+EiMbHhb5IQffLJJ7dcoJJFIJ6uwZtvvnnmRXOc89tss02yY2pZu3/luQU8r833V5S0izUYY4wxQlWl7abZENNEhHHLQQcdVErQN+ae1nDDDTeEv/3tby0/j45IZe3MQ2JHWd/3K6+8kiRLqh64p8fO+YsM6Ke5TsfcI7mn1GFXtQkmmKCy44g6q+OYvUxZjDloIDBo0KBUSSIXX3xxKMtMM80U+vfvH7XwlTY+lhaFfzToIsmDOHlsQn+dsKYQg4SkDz74IBSJBWnm4jQFfPzxx0OnipkDxHYtPv3003NZYypy3EaBSFkF1cqvWUVsMh9NEbbaaquWEx1Yh91yyy2TQn+NGA1eZp555sIOz7zzzpu6AGZ4dY2xlW266aaLej6JeXvssUdLXdC5N2y//falHu+6rdfdddddhTbp6NS1gO6kba5V1A4XsetUdSySlvLwzDPPFN7gMu31OOtrMXOIohr8jzvuuNHN2NVZyCWJ2WyDfLzY3y67/DX7u5plllmSHYdjEKeLKU6gkCW2YQENwaeYYopQdY6TPZ7KPibOGiSbK8WsO1x//fWpnx9z3Ypdm2fuRDFfGh9//HGpzc/qtkbFmnnfvn2j/j5rz4zx0+A3cuKJJ4Z28GJE7MJCQLUtJgAktXKxWGuttZJq4wceeCDZ2SVmEYJkWV7vmmuuyaSALk+/+93vQhHs5KMRYQHmjjvuaPng/PWvf031vK4YILAQ08qOWKuuumr0wjmLZq0OaCj+23XXXcMjjzwS9fcpZNxwww2jXqMOCG6SNBqTxEfRJveDPDGxOOOMM5J7GH+PTrgqzmabbRb1fBJ86RiSN7q5kBxBEvRxxx0XPv3001SvQzH05JNPHv1+KJCh4/U999yTyW+AsdtKK62UBP5ixDYrIED71FNPhTxQUMT3R+D75ZdfDnmLSQyNXajOE/fxGASBPvvss1TPPf7448P9998fysDO4rEdkho73G233XZNJYbSOezAAw9Mkl2K7v6V1bmYdh5HMtVOO+3UVoUbikNC16abbprMd6+++urcd78joSztDmHsjJamm2/sPZJCk5hde9tB7DGiSU6ZRUydrG5j9jItvPDC0cnJOOSQQ8K+++7b1DFgpyFiJVVYgIk912h2lXbRsdVYOPED4ggkx8Q22KojmurFxOuZx7LgSXJTnohBXnvttUmMa5NNNknWPDpdzByAMR3xvLT3cQqS231MQuIp199WCkRUfeuvv370a5AY8tvf/rbpbtE0SmF9NO1OgnWS1Q59ee5AOCJ1jbGVjbF2zI7nILZMce8777zT479H4Rw73ZKgVXaMq27rddddd13yGnvvvXdu6w11WAvoTtpxPDupF9H0ITa+dMUVVxQyr5Q6HeNY7j/EemhyF1M03QziA2nvG3lcizfaaKPQp0+fkDdytWIbTKjz0FAhza5+jKFidwNMU1zB+mAs8l1o1JamKdVZZ51Vely7KI6TPZ7KZyxAfkMaNLDZfffdS2kcRGyEHMYY5DHttttu4YknnmjpeaypbbvttplvgNCKuq1RcY4vvvjiUe+FHJ7f//73LeessA5Ns7siN2+I8dhjj6V6Hs0NJp544tDu4spF1RGee+655EEiNzcLujlz0eQk79evXxJ442Qfc8wxk0p6Jr5c2El0JfDHxJxgKYntWVZMt7JjWRqrrbZaOOqoo3K9WI0//vhh4MCBub2+2hdBsx133DGZWO6yyy69BnroIPunP/0pk4U+tkludTC8xRZbJAPgmEEs1wwWzwgI8Nm5pvS2SMVnzmJ7YRbgJ5pooujX6XR0OiPAecopp6R+DQayJM5QCDHJJJNkmvzNwJjEi9tvv92u/SWab775kp11H3300dSTSpI9WXTfYYcdoncc7YpEiltvvTVZ9Es7yB9R8fTWW2+dnNOxGHPwWgsuuGByHeQ/m11Q4PrLeIsgMI0XskpEiE1a5tpOchVd4ii64vVIUOit+xJdipq5F/E5CQDzmGeeeZLdXemSmPU1/b333kuSPNKadtppQ1UxlmeckbZIh98VDURa6ezE75wd8WKOaRbYMTOLsRNNGEjoW2aZZZLzj/kS5yDXL5Ih6ITEoig7WxEQKVPsucg1ho7jCy20UEu/H441Bc+qHzqI7bXXXknRNgvYBJ8ZJ4w00kiZ/h0W9vi9pS0MHnnkkQu/R7IQwH2LmAC/qWmmmSaMM844vc79iL/QFbgdMN6nyDImcZ5xITupcpy4vpJcS+fZns4hxhlTTjll6r9ZB3Ubs5eJ8TyLKDQCiMUxYUzBeIPfBNchfhP8HrgGEoO9+eabwy233FKZghWu+yeccELq7qnM9Wkm8Ic//CGJGWV5/yBOzfiMXQctysgG53pMYR2dumnOR7F81l02GZMwliUu1S6Lk1nOAWIStClEvuiii5K4YLO4DvG7zbu4oFlcLylMTIt5IzFzGi7NNddcyboYseveCk+mmmqqTO+RynYXOOJuaQtdG7iecI8/55xzkvOD5ItJJ500iWtxL2Y+/K9//SuJ1dHMsKymQO2GeRLrsyTG5InrWpZrzXWOsZWJ6yzjhtjdffmtkhvAeiHnBXN05t7E8pj33H333UlH/5hdAbJUx/U6Xof4AA92DiVGyf2Z+3KWWNPgd9WJawHdYe2G2FiadX7iCll/B1nH4Mi7ICmVpNCllloqTD/99Mn6UyO3qTvEtLivS/qlBx98MHkQp2bcxjg4j50yYsaDeVyLec0lllgik6a+PXEzAXXnzDPPTNZa2SCgt/sTORSMZy688MLoeShNsbl/toL1QGLCre4CPTzWFonJHHDAAb3uzkfsizWLe++9N8RirMC6f7twnOzxVPZjAQqKGePMMMMMTT+HazQ5f0XvKtcV+XnE8mOu/dR+UKBGzjq5673lURCPoZFeFTbqqNsaFRvfxG4QQdyLRmnEuImJNZMfxyZHxL3bxSMpNxlacsklQyewEFD/M3BsFAaWiWQlkvXyROCPrpNMpPLCgoILweoOwXYGley8wqITnQ4JpBNQ47fIzfSll15KkpboYhezY+fwA6JWkZDJgCB2RwAC8Xxmdn0gkMDCOYunJNMzyCSxjQEXn5nPntVi2c4775zJa9UBExYW2XrrjNoTzlcCPxQF0N0ibRcWkqdJ1mABlkHt559/nvo9KVv77bdfcg9NWxzMhJQGBCSOsVsoia5pO9/RzZet1JmI8J9ZLToPP5H+xz/+EV544YVMXo/zmkk1Sb3sIMK4h4V4GghQRMe1ksVodiCjGJrdK1hsTdsxuiczzjhjktgWc9w4D1jw59HKtWbPPfdsOVmDB7vGzD333Mni9SKLLJJMzmPGWyxm7b///sk1J61f//rXoapIpuYYpS0EaCSlE/whuENxRm87+5DQlEUQPhaFN5wjfMexWFCh4x6PKos9F0nk5brMd0iiTW9IkCJIFvP7UWfgHsUYnwdje8b5/P64z8V09WZnLHa8uuCCCwr/Xcw222whFnO4Vq8dLGRusMEGoV3QSCrmHoMnn3wyebTynTI2U8/qNmYvE0lRJGdnsQjGde+yyy5LHu2AxiksHnGOpMX5wNiDcQXJm8SL0hRwM46h4I9zjIJK5g5VKVLqFNzbl1tuuaSYNy0Wy9lhi3k2ye2tLLR3RVI7xcQUf/F9xyYetTMK12KK4Ih10N316KOP7rXQnmvUySefnDTrqdLvi/FILBI4Wu1ET/FX2nNY+SOZhetMFliz4MGubIpHISVND/gN5WnVVVfttSFlK+ocYysbxT2xhYBgjWnQoEHJox3Ueb2Oa+4RRxyRPPr375+sBXAeEA+I+V2z1sJaAK/fiWsB3aHolfed5ndEQh1FmXlibYwihJhzHSSEtpIUSmMbdiOSNGLk7pDbw4O5IjF/rsULLLBA1E7J5EEddthhUWPBvK7FJIbnWQjIhgx5FFWqczB2azSnJY+QXA7ySIjBdm1OS7O4rApR2B2p1R24yc2gQRS7SmXxmfndkTND3iRNl8ibpJifz8juU3xm5mFZ7cDFLtRZNzQtiuNkj6f+GxOPzf+hCI6YOM3EekIO36WXXhqOO+64XPL2WkHTHBr1x+6ITpMr5tvkgjMvYl2OORljPNZeuOewdn/jjTcm96WqrAXUbY2K74XvPCaG0VjX4d5HsztiRBzHRrM7zmnG58R8yNlnbbWdvPHGG6mLFi0ElHKUplApbcU0F7e8tuq1k4+awcCpsdtR3kjoTpsYseuuuyaDqCx2umEwzUCRR94oAnQ3wOaxgEbCKLuVxWCQeOSRRyaTIILC7OQ1xxxzJEEbOiyyG0pjYsHAmEVEip4YbL/44otJ8l6r21KrOLPPPnsyGYpdMCeZgWIwit8GDBiQBL5Z1CWYyTlCIS/3aAqFOac4R5g4kUjMeUI3lSJ23yI5g665jBuY4GeF906yK4+ykMydRUflIhFg4P3yoDiEwDQTXwo26PpEN2cmrCQ1sIBMIJp/h++Oc4mAMUFyJq8kLcQWeNIxruq7EnEdji3SoOCCYBLjWxb6WHgggYAiF8YyTz/99M8FoVn+TmKxu/Aaa6zRcQUH3eE+y28g5h7KfZnxE7tJkeRB4S1BPxLUPv744yQhg4QKxnFZFUirs3CdpdsZD7BgxziQICrXaZqfEERu7PzGdZoFN64nJJI17vUElbmmcO2OQRfftL8n7icWuvYsZtc55atuY/YykYhB12KSJuqIBA0erTQGGREWFbfbbrvk3GIhk3ONMT73C8adxCvoFM/9gjEJ5xrjEhbAGuda2YuwdUDhAmNBmtekxTWDjuU8iBex0N4YK/Bdc21hDsd33WgcxvfNGJfvmgfj0Lxi+u2G+V7MbkFgfswCMA+SzbiHsAgMjj/3An7jJJpVccdFElGlEY1TadZJF2ZVD40n8y4EpClG1uocYysTa5snnXRSqBvX6/5bvMeD5ius1TBmZKzSWAugARVzBtYCWO8gzsQ4kd8Uc1F2j3nqqaeSotrYdRDWNxm/tiMS3NIWAhZ13/aeLVXX0KFDkwR4HmBdlHk8O3pyPWYnL3JymEc2Yv7gWkyshuczj6dxJ0XlsWt2aWP+zVwr+Tw06s0DRflSb4iFFZVDwm93vfXWS/VcCkeY22TRkJexG83deOSNOHarOyBWleNkj2edMS9jfTJmnYLiIZr4M64gLk6jAfIamFd+9NFHyVySQmXyYqqUw7rTTjsl46ksivMotqYpLI92Ubc1Kprd7bDDDpm8VuO9d5LBgwfXLr4zPHcEVOVwUS1qwE1wgiQmKrKzRgA6ix0EpKwQcKOyPy2CdnSC2GqrraK2ly4SA/VNN9207LfRduicQcIog91YBHFZ1I7tRKLqocMXnU0ILsWi80jXgoEqYsJPAvTf/va30GkYC7VTIeCIrjONIGcZ2mHRhg7oJ5xwQnQgiOBGuwWBCNSwswxFo3XBrkRZfF6u8TykWFw7yhoPkgTGol7MPTJ2V/ROR7FAO90X6qZuY/YykcTNuPDKK68MdXTIIYckDX2y6HjJ4itJJO78WU0kCPF90zAsC42d35XevPPOmzQwoOFNDBa0L7/88uTRbigCYO5HgzGpK3atJSkxq50SlB06T9MkhqSqvHZbie0OPyJ1jrGViSZwNCMpK/5bJtfrfokku8YurWWgwLzVHXOqghgXzVtbRaIjTbJolpV3fMlCQKl9UNjHoww0UchrR0B2IKMxMLsyZ437B+t3UpX88Y9/7HWn8p4cfvjhSVNZCmbaAeOZgw8+OHQix8kez7oZffTRk6ZBsWty5EBT7JfnjsBZIz5Cg61Gg4a6qdsa1cCBA5PGZOw8qOyaFy2++OJtG98ZXnvucayORVegv/71r4X+zY033jiX13U3QFXNbrvtlgyEYnADjN0prigsIlO4SLBOraNolJ2ApJ7u2SQ91GnHTXb46MQAPYvXdNJVugKTtF3yikRS6PLLL1/qeyjzHNt+++3DQgstVMrfpvFI0V0FWaSMWbSJ5fVEVcJ8n659abGAqZ6RdEGXZFVTHcfsZe9ETCFKGehUmUeyeyvNozjX2LVEnW+llVZKGoWpGoh9lr0jaRXmAOwELw2PHaKOOuqoZAfyMtBsT91fu/JsrkVCVB7qHmMrU5n3OrqEl8n1umogQazsMVeMWWedNcw000ypnnvXXXeFIgoV2XlBknqz9dZb53qQyPXLI75FwfOEE06Y+etKMYUFK664YnRh3dFHH90WifSslRxzzDH+DnPQ7uPkqvF4No/zrsy84DLjM7vvvnuYbLLJQl3VbY3qz3/+c7IDpv63CP7uu+9OnSvbKSwEVKUcdNBBYfrppy/0by688MJJJ8EskWjYSRcKZYNzgoKBMiy99NKZ7YxHIWDVF7DZpvucc85xAh8ZBPn73/9eaiKh2qPglt8aSZ91wY6AsQHRqpl88snDKqusUvbbaEt0GBpvvPFCuzQE4NpeBnbjYpeKspDwd+yxxya7RBSJQAyd3Yte2OPv5b0Q2h0Crew+JVVlnLLllltGvcY888xjc4wm1CnQ3o7qOGYvC0lCJ598cuHHmoSLU089NemAWqY555yzEu9DxWDMt8EGG3i4K4KCl6LXVbreZ6pwLqy//vouhqvbnefY5aBoFLltscUWhf/ddkKSdR5JU6zR5tnMrc4xtjIRv55tttkK/7s0WP3LX/4SyuR6XTVsttlmhceXq9I44bbbbgt5o7Fd1fMPJJVv7rnnzr0JDA0A1lprrcxf180ENKImi3ntbtlMfgi7KWVh0UUXTZr0l9WApxl9+/ZNcgXmn3/+st9KR+qEcXKVeDybR85/nk2meivYzGpHujTIQacQu6xixLw2X2pFndaopp566sI32OqaA1aF73tEHnvssfDJJ5+k+v0U3dA/T9Udgal2dtppp9K67WcdVKRrSrskhas4THpOOumkwjvh0N2PSXfWXe6reoNn551zzz03GQApPsh51llnlbaLktpnYn3eeedF7zjaLphEM5nOq6t0md2C3L2jNSussEJbBRXYramMQFRjHFL2Dr3sAsA9jWYBRRUBnnHGGZk3HGkWhYD9+/cvZffF5ZZbrvC/K40oSe64447LZHfMfffdt7Y7LjSLRZYyrjlqXt3G7GWabrrpkqY6RXVmnGSSSZJCT/6zCmi4xucvqxGWiu9CapFLdcY+hx56aOFx50YBdBV+86zH7LzzzmW/DVU4iarIhjl0pabxqXpG5/LFF18888NEXCLPNdq6x9jKQmIxY48i5+c0+DjzzDMrMdZ2va78wpNddtkltDuKpNMk6T/00EPhiy++CEU0mqrzrhaSesa8k928ihgLkUOY5d+hidcSSyyR2eupM9DA5JRTTik8Zp9HLGfllVdO5ipV3BmQ48y1g1xeZa9TxslV4fFs3V577VXK2icNg8oq5m6guHm//fYr/O+uu+66Say1Cuq0RsUmRGV8VtZcqpoDlrZp0corr1zJMUtaFgKqdAQb99lnn2SXsTKDnuOOO25mr2cnH3VngQUWCCeccEJh3UIZ6JKEnvWiJ0Gv/fffPykIpHNOVTDA/sc//hFmnHHGst9Kx+DaePbZZ9uFUT2aZZZZwhVXXFGbDlpc9w4++ODkOtgpE4NG17e6JpK0arHFFsu8yL4I7Iy16qqrFpoIT2I6iSpVMNVUU4WLL744SdjKEwsn3Dvnm2++5H//9NNPqV8rbXITY03GnEUVPmKTTTYx8VaVwPl/4oknJjtTZWGOOeYIe+yxRyav1emNEmyIVG11G7OXaa655grnn39+mGiiiXJvhMTfmWGGGUobc4wIzYQuu+yyJFldnY35I7tsHXbYYaXtjKRf7mRMnKLIxDHizrPOOmtlvgbmJAMGDCj7baii9txzz0KS02h4ytiY+OGwYcOiXqsODUnyaLZWRAO3usfYyrzXFVWESTHShRde+PNYuwpcrysHY53TTjutI9aCKERJ0/z1u+++C/fee2/IG9c47qHOLSSN6B5IcT6xsCJw/2eXs6yww2CVd0tTeYgf0+i+qCKWxvpZHo0dKVDgs0w44YShSsd30KBBSYNnZa+TxslV4PFMn5vDda3IxvfUOlAMVwU0L9h2220LbX5W1s50I1K3NSoKX7nfFmXzzTdPmsFX1e23357qeXnvMF40ZxkdaPTRR092J6nSwLqnYCOTgLIrxBkIZFW8x+RskUUWyeS11JmWWWaZcOqpp+beHZ7g1EUXXZQUd+Tld7/7XVJ4V3ZyF4vqDDpYlMs70a6OOL4HHHBA0tU/z/MplsHTcjHuIIjGBCuL3XfykmUCDTujkkydVaFB2eh4QoFjlQq8q2i99dZLApqMudvR4YcfXkiwe7bZZksS04ssRGu2GPCSSy7JLTmUgqGrrroq6dbW8Nlnn6V+vZjzrF+/fsnuT0V8B+ysUEa3MWl4nO8kRy699NKZJ3nutNNOHvBeElPZeZUdWFVddRyzl4VkCuIlJCvngS7iV1555S8aHJQ15hiRqaeeOpkrMUao8vzCOEJ2O8P+85//DPPOO2+oqrp81+uvv36SiJB3kx8SLCgCrFpxOd/zscceazGgusUaAjuHZ9kYtIHEN3YTJ+7SGMvE3JtR5fFaVpi7ZbmmM80006Qqckmj7jG2svz+979P1ibzPuasr1apCLDB9bri1/QvuOCCZHfITpE2JyZtp/1WMb486aSTCk3klVRt008/fdLks+j1+CzHG8RNpJ7mMIw3WGPJu+CeWE4eu7J3vY9fe+21Ydlllw1lY/ci3kvXNXtlpxPHyWXyeMbn6JDTmncuNvE+Nk0pu9ZheLvttlvyKLL5WdXUZY2KdR82LWAdKG8UmO69996hql544YUwZMiQlp9HrK/Txgb1WP2sGSqb2XqWrlx0xCFZuWqLA2y7TSLIDTfcUNiCTDPV8VlcbO3ko2YwsSYhauaZZ85tQkswrIiiLZLbSD6joy5JIGXsynT55Zcn3XztMpOvpZZaKlx33XVhhx12COOMM06oAoqv6fZNgGG77bYr++3UHpNetiEnoMYEsCoJvLPPPntyjbj++uszT1JjZ5VLL700KaArqgthnuhaxP2Dz6Vf4vs9+eSTw0EHHdTWnYQIypD0xqQ9r2RYfv8U21VtDtJAQJpiTn63WRWskJxHcI3fz/BdG2MS/2ITHghiMOZkZ6I88P5ItGUcKJWJoCcB3muuuSa3hPQdd9wxWSTthPt9XkjGIMjOfFTVVccxe1m4XtCwiF1Fs1qAJO5Ct00WNYePwZQ55ugu/ssYgbEIC9lVwPiXhUAWr+gUWVS37TpoNCSj8yrNN6qA2BW7NbFTNuP/uiAR4fjjj89t1ygSK4gFV/VazdzslFNOSX7nJpCru87VV199dab3Jgr/2Q130003/cX/byFg71jTybILMwUueRdDNxhjKw874NKsJ4/vmrwK4v1Vn/u7Xpcv5lrEjhlTVGU9NCsrrrhimGSSSVp+HvOnL774IhR1fjPeXHDBBQv5e5KqO04khklcacYZZyylYQUNN2MtsMACSaGX1BPGnuS95dVohOb+rN0vvPDCuX8RNHphDMUOXRTyFo2/yd+msUA7bKLSbjp5nFwGj2d2uN8yl+/aPDNL5BWdffbZuTcmSov8L/LA8vhdEv+i+WDX5mdVVJc1KtY5WScmNpZH7mIjB6yI4tIYxPfTbvrRaapXmqtML8B0h+YxbNiw8MwzzyTFgQ8++GB44oknwnfffVf40WawTwCdimR2A6wSLv4EFe+8887o5EOpGXTzYXGY5C2Kdr/99ttMfmPchIvefppBBcW9/LZJcqPr/dChQ3O9vhF4Y2HdRYBikUS08847J8eeYgcWYt54443C/j4TCgqUKKYlwTivwgbF31OZAFKcSbdiCkg/+eSTQpO+ODfY8YvzJIsgfW/n5W9+85skkZqJBtd2xlp5o3s5YxcmjVni2PE5brnlluR6ztjxxx9/DEWjk9DgwYPDo48+Gh555JHw+uuvhzIQKOKaRyIRycydgMAA4wXmCQQI/v3vf2d2rOh+z3nZDvjdkgDIdYqg4DvvvJMqOMu5wTnS3fwmJvEviy56vC/u2ezURXDpq6++Clng2LGjVJV3C1Z+SPzmt8P1+bHHHgv/+te/Mju3Wp2HsDDKXKSIIvYll1wy3HzzzUnBIYuyfG7iLfovkslYYH3qqaeSZh10jP/yyy89RBVUtzF7WZgrsGsJ4wUWCUle+vjjj1Pdz4m5bLjhht02YSp7zNGdWWedNZx66qnh2WefTc41rqNF3jNYaKNAg4IPzrUsdx3SiGPjq6++enJNYYz9+OOPF3qY2I2SpKaBAweGRRZZpK2buMRgfESB/oEHHhi13jH8b4nGCCQ7VLHr7/Bz3s033zyJ07D4z9itrJiCqolCcO5NxJxYm7nnnntSxb5IMiIewDVnRAVJn376adT7rEtnfeIzxCxicW2iWWuRjLGVg98b96T55psvaY6cxTWe5Gg6+1elgXHd1uso4Ge8znWZx0svvRR++umnUDTmXYx1mHfl1VShCoU1NMgmsa8VX3/9dbjxxhuTa3ZReRTMHx944IFkPHf33XeXkt8k1QnrzZNNNtnP1+Knn346kxymNMnHzOUoAiwziZtxHtdL4qdl7MSq+mHsQaEAMYwjjzwyvPfee9GvSVyMxuo00Rh99NFDkZZffvlkZ0AaEnIvf/LJJ3P9e7/+9a+TMdxqq61W6UKVVjlO9niqtXk9DWu5ljKX+P7776MPH9cT6h123XXXUjZIaTV/h3k5xbp33HFHJq/J2toBBxyQNHNtB3Vao6Kgjdg0mxkwds8C+fj77bdf8hmq7IcffkjGS60af/zxk3lGp6n2ap0yvcCx8Mtj++23TybrJGUxyOY/ScSgaCfrBDb+LpXWXNAY3HPhqfIiMYHdmIVxFgc6NXFJ+WCizSINhXuDBg1KEsLSJG6RgE1CGOdwmQsTJITQYWKbbbZJCkdI+rzrrrsyKQpkYYJrCMUFDFyrVkxcN+ONN17yXfNgMEknyPvuuy+zYpIGdk0gYEPHfhZ1+e95beVOUSmLx1VEAkPaotcyO9dSFMTiOYUiXAtYqOM8effddzP9O+w6xjnSeDABLWO8wXWK6zmPV155Jdx6663J56UoMIsAA5+JJFo+IwVcjK/y2gmVMRyJgzxIWqLQgwWfl19+Obz99tvhgw8+SLq/svDKBCuvpCwmYI1J2IcffpgUmzz//PPJtYbHW2+9lcv4lQARCQx8/k7bEn74Qh6KPm+66aakkD9NcIDjxfV5gw02SI5Xd+ckc4K0C4Z57izIuIkCCBL0KTylcQqBIH7DIxqTcf+jkyDzKn6HjP97KhDlM8ckI2V1Def6wfiMRA0Kcyhg+s9//pNqAXaVVVZJFj9nm222Ef47BLK4X6fFQrOqj++ZsUljfELSLHEF7hUvvvhico0maSuPTuHMOfi7BCLpYk5hfJG4zrF4z+P999//+R752muvJfdI7lcUvnGPLKOQvipYaDjiiCOS40DBJA/GEdy7OW4UnPHPshgjKU7dxuxloVvoH/7wh2TXw/vvv//necKrr746wqI4/n3GT4xFGXNwzHo6Xvy2Yorriohn9u/fP/ztb39LzjfiRRRdkNT50UcfZfp3GD+xOMkYleM288wz57YTtkaMc7Uxl2MsTPEn5zz3giyv+9yTmSM3vmseVd2VvAzMqWkEwnEn7kyMIs3x5z7x29/+Nhn7MB/qbgwfMwfIazGcIqoddtgheXC9Zdz2wgsvJOM25kNcf0hq57iUUWygasRFeHA+sDbIfYlzZMiQIf8zlufaxv2SmBHJHKxT9JaYzNwoRl3WGyn24HuITVqhcUuaXa6yYIytHMTHSewi+Yd413PPPddyXJPvjuRoErTadczYCet1NOtYeeWVkweIyzKGIdbUWAugyDGPtQjGOosuumiSrE6st13Pg1YwtqMg/ptvvmnpeeQxFFUI2MB3w4NYG+tD5DcRX+JeTQyO9SvGcsaXpGyadjU2GwCxW/IJ2XSgEfNn7Yw5VNaIgzHGpmEY92TeSxUwD6aYIO1n5j7JGobUCooXyLGl+TRjXOK+ac49XmerrbYqdV5JAU0jRsh1hBghaxCM27PI8WCNgbk5vzNihJ3IcbLHU61hDMF6JznUxMQpDEzTBLVRMESBcXc7+5JrFBMTz2PXUtbHmOuR+0RzUtZ808S9e2t+VnV1WaPib5Prx5rreeedl3zvrSIGwr2U75s4WztgfZm81Vatt956lZlnZKnPMFuX6/9j4sqknUVQinbYEaOxGEoAjQeJrFwICbIyWOdCxiItNzVuTAw+WWAmYNpIkO1ucbiK+DlQYMQxSIOOLEykpLT4jT300ENJEjoBtd6S0Bk8sLjJ4KvKHW0IxjOR5/Hmm28mCYVcX0gM5jPz4P2TSM/NliQNEkgocGRhnck7j068EXca7hkU6RDE4fzle+bBjgfcZ/iuuYcw4ObBd0ryNt859xC+dyYlLP6T9EhQqh0nFOoZwUrOERJrWLxtnCeNsUYjcZ5zhHEGhSdMsnmQzMG1gfOE6+CMM85Y+R0dSMjls/KgKIIxFh3cmJSw0MqD8RWfld8En5ffBWMquqyQVMS1kHGV18H/PbaNogLOIe4vPDi+nE+N49t4cD3hvOJ+QyEJ91Mm4ZxPHGsmyRQ3Vb2TU144hkyYG4vpFLQwDuF3ybif849jxjnJdZrEdBbh8whQVcHnn3/+81iFz86iSasNF0i+oWguLRKR6LiUx7yHBVwaN5BQQ/CL3w2/Ke7TNKvg83J9JbDJtZZEGL7zuu6qonSILTAmbFyfG/MA7oGcb43xIf/ZGCNyvWncCxkjct9nTMh9nzkB/1nluY/UKeo2Zi8Tx5SkRuYEHEfGG62O+1nMZHEzrX322SdsttlmoWiMSYjDcp5xvnHeMSbhXGMcynnG+ca/1zjXGKMwJuUeQZMo5k3cJxifMm9qp1h03XDvb1xXGnPjxrigMSbgO2fhkfFAY97GdYWkwEbMiPkI1xXWIBybNo+5DYXIND7hO2CcRhyPuTLHnOsO15/G+g7jrsUWWyz571LdcE/mPsT9md9HY37SahMDEihY70mL5A3va+3JGFs5GFdS0EvDDWJdjDUasb1GrIv7HGNGYlw0GOrkJgKdtl7HOJHPwfy0EV9qrAkwp+IzMa5pfLauc4jGWgCfi8/EZ2E3Q5qV1HWuyq4O7I7QKpoKck5IqicSycn34Z7bmNM3rskU5zauxY1H15g/92Hm99xnGrEc7i9ci1mjrWpOSNrrJSieZlceKeY3RyNK1u8pXiCeNqKdAhnrEr8hn4RmxxT05tVgPQuMR1mfJoew6/WEYh2uHYz7GMsRG+SzMUZt5BA2YlZzzDFHx+YotMpxssdT3WMsQs4OMTauOY2GtaxVcI1txApY1+T6QmNJmhNwjemEfASurxRgd21+NnxhYJrmZ+2q09eouKdS/EkeGE08+HzDl4jxuRrnOoV/1B6024Y87HR8yy23tPScUUYZJWmc1W6ftRkWAkrDoRPAIYcc0vJx4YJP1XjRW6mr87FIxYIzk91G4S3nmyRJkqrt9NNPD8ccc0zq5x9++OE/74opSZKUR1IS6BjJbiiSJCkbJI3QUCftjr0klNDNWZKkPLBb8iqrrNLy7hC///3vwx577OGXIqk2SBRfY401Uj33kksuCfPMM0/m70n1Ru4geYTMNSlUaDRNq8OuxpKk+Hglzc8a95C0zc/UHihsbHzffMeMGag7aOciV5peUbzY6s6Oq6++erLRVyfy1ysNh45xaRAotQhQeaDwr9XdZyRJklQudkm67LLLol5jvvnmy+z9SJKkzkTix/XXX5/6+XRBpFu0JEnKznXXXZe6CBDzzjuvX4ckKTfTTz99WHXVVcM111zT0vOId++4447JzgiSVAdpcwjZ7dAiQOWB3FTzUyVJabAJzUQTTeTBqwnm7RNPPHHoJDTFbbUIcOSRRw477LBD6FS2gpCGc+ONN6Y6Juuss47HUpIkSVLi5ptvDm+99VbqozHllFMmOwBIkiT15OKLL06KAdMiKckkTkmSsjNs2LBw9tlnR73GwgsvnNn7kSRpRCjoa3Xnh48//jhcffXVHlBJtZE2h3DttdfO/L1IkiRJdUUB4IUXXtjy89ZYY40w7bTThk5lIaDUxdNPPx1eeeWVlo/JzDPPHOaaay6PpSRJklRBJMd/9tlnhf29zz//PBx33HFRr7HSSitl9n4kSVIxPvroo/DNN98UdrjffvvtcOaZZ0a9hmMOSVKnGzp0aKF/76KLLgovvfRS1G69AwcOzPQ9SZI0vGmmmSZJiGvVeeedlxS9S1Kn++6771IVAjKeX3PNNXN5T5IkSVIdXX/99eH9999veVy+/fbbh05mIaDUxemnn57qeKy//voeR0mSJKmiSJJfbrnlku5AdAnK008//RT23HPP8MYbb0S9zmqrrZbZe5IkScV48sknk8K66667LvfESAoO2cHhk08+Sf0aLICsuOKKmb4vSZKq5vDDDw/bbLNNqkagrXr00UfDYYcdFvUaSy21VBh33HEze0+SJHWHhDjmha14+eWXw7333utBldTxLrnkklRxt2WWWSZMNNFEubwnSZIkqY4GDRqUapfuqaaaKnQyCwGl/+/WW29NHq0aa6yxUnVKkyRJklQcFusOPPDAsMIKK4RLL7006eSZtW+//Tbstdde4a677op6ncUWWyzMOuusmb0vSZJUbAOCPfbYI6y++urhhhtuCD/++GPmf+PTTz8N2223XXj22WejXmettdYKE044YWbvS5KkqmKevuqqqyb36Jjd+noyePDgsMMOO0Q3INpyyy0ze0+SJPWkX79+Yd111021K6AkdbIhQ4aEE044IdVzN9poo8zfjyRJklRXjzzySMtr4qONNlqylt7pLASUQgi33HJL+MMf/pDqWKy33nph7LHH9jhKkiRJbWDo0KHhgAMOCEsuuWQ4+uijw6uvvprJ695zzz1Jg5Brr702+rW23XbbTN6TJEkqz7///e+w2267hYEDB4bTTz89vPPOO5nsPHz99dcnhQwPPPBA1Gv17ds3bL311tHvSZKkdsF9lF17uY9uttlm4aabbkoa+sT66KOPwqGHHpq8ZsxOvVhwwQXDvPPOG/2eJElq1s477xzGGWeclg7YfffdF5566ikPsqSOxM6nm2++efj8889bfm7//v3DQgstlMv7kiRJkuro1FNPbfk5m2++eZh88slDp+tb9huQisbOH1988UV46623wtNPPx1uvPHG8Oijj6Z6LSqGuVhIkiRJai8ff/xx+Pvf/548Zp999rD00kuHRRddNMwxxxxhjDHGaOo1XnnllXDvvfeGf/7zn+H555/P5H2ttNJKSeKfJEnqnB0CjznmmHDssceGeeaZJyy11FLJmIPdf0cdddSmihZeeOGFZCcjxhxvvPFGJu+LYoWpp546k9eSJKndPPjgg8ljrLHGCgMGDAiLLbZYWGCBBcJUU03V1PO//PLLZG2RYkIeX331VfR7okh///33j34dSZJawS7xO+64YzjssMNaet6JJ54YzjjjDA+2pLbGbt5ff/110sDrxRdfDHfeeWe4+eabw48//pjq9bbZZpvM36MkSZJUV48//ni4//77W3rOpJNOWptxuYWA6lhDhgwJyy67bK5/Y/31108uGJIkSZLa13PPPZc8TjnllDDSSCOFaaedNkn+m2yyyZLdv2kAQhI+i4EffvhhsiBIEWBsp//hjT/++Cb9SZLUoYYNG5YsVvCgKHCUUUYJ0003XejXr18SX6QQgTHHDz/8kBQTMOZgJ2O6kFNskCUKAHfaaadMX1OSpHbEPZZdAnlgvPHGCzPMMEOYYoopksIIGgWNPPLISZNRdgR57733wuuvvx7efPPN1MnB3dlqq63CLLPMkulrSpLUjI022ihceuml4dVXX236gN1zzz3hiSeeCHPPPbcHWVJbWGaZZZJYW15oOrr88svn9vqSJElS3dCEqFV77rlnGHPMMUMdWAgoRSTpbr/99h4/SZIkqYNQ8EfCQytJD1kgsZDdgiaaaKJC/64kSSqv4/i///3v5FEkFj5OOumkMProoxf6dyVJageffvrpz4X7RVp44YUt0pcklYZGNfvuu29SlN5qQt5ZZ52V2/uSpHayzz77hD59+pT9NiRJkqSO8Nhjj4UHHnigpefMM888YfXVVw91MVLZb0BqV3vttVdSDChJkiRJsfbbb7+w2GKLeSAlSVJu+vbtG4488kh3G5IkqULYIfj4449P7tOSJJVliSWWCAMGDGjpOffdd1+SmCdJdbfmmmuGBRdcsOy3IUmSJNV2N8A+ffokuXd14oqClMIyyywT1llnHY+dJEmSpCgEIv70pz+FjTbayCMpSZJy3eHhuOOOCwMHDvQoS5JUETPPPHM455xzbDwqSaqE0047rey3IEltZ4oppqhdwrEkSZKUt3PPPdeD3At3BJRaNM0004TDDz/c4yZJkiQpylhjjRWOOeYYiwAlSVKuJppoonDWWWdZBChJUsV2Xjr//POT+7QkSZKk9jPaaKOFE044IYw77rhlvxVJkiRJNeOOgFILJplkkiRpxgm8JEmSpBj9+/cPRx99dJhuuuk8kJIkKTeLLbZY0tRs4okn9ihLklSRXXp33nnnsPXWW4c+ffqU/XYkSZIkpRzXH3fccWHOOef0+EmSJEkqnIWAUpNmnHHGcPrpp4d+/fp5zCRJkiSlMv7444dddtklrL/++mGkkUbyKEqSpFxMMcUU4Q9/+ENYeeWVPcKSJFXE0ksvHfbdd98wzTTTlP1WJEmSJKXEBgLHHntsWHzxxT2GkiRJkkphIaDURAefLbfcMuywww5h1FFH9XhJkiRJbWaMMcZICvA++eST0t7D5JNPHjbZZJOw3nrrhbHHHru09yFJkvIzzjjjhDHHHDN89dVXpR3mGWaYIWy++eZhjTXWMJYpSVIIYZJJJkl23hs2bFgpx4MmQAMHDkzWGueee26/E0mSJKmNLbfccuGAAw5I5hmSJEmSVJY+w8pa9ZByNmTIkLDsssumfv54440XVl999bDhhhuG6aefPtP3JkmSJKlYP/74Y3jyySfDvffeG+65557w3HPPhZ9++in3jqAk+6244ophscUWC3372otHkqRO991334XHHnssGW/cd9994d///nfuf3PiiScOyy+/fFhppZXC/PPPnxQ7SJKk//rwww9/jgfcf//9uTcK4l48xxxzJPdmduelOZAkSZKk8iyzzDJh6NChqTcRIAdx/fXXD4ssskjm702SJEmSWmUhoELdCwFHH330pFs3SbpTTTVVmHPOOcNcc80VFlpooTDaaKMV8l4lSZIkFeuLL75IigGfeeaZ5PHCCy+Et99+O3z99depFwGnmWaaZBeeeeaZJyywwAJhttlmCyOPPHLm712SJLUPCg0a4w0eFAa+8847ScFgGsQrp5tuujDTTDMlY44FF1wwzDjjjBb/SZLUJHrkvvHGGz/fm5999tnw2muvhQ8++CD1roETTjhhcj+eZZZZkngAD/4/SZIkSe1TCMhaXyOHkB3/aO5BHuHCCy/s+F6SJElSpVgIKEmSJEnS//fRRx8lyfnvvvtu+Pzzz8M333zz84NdBVkEHHXUUZOGIhNMMEGYaKKJkl14pphiCnf8kyRJTaHIgGIDxhz/+c9/wpdffpk0I/j222+T/2TXYsYbPMYYY4wk0agx5mBHoZFGGskjLUlSxijSJxZAkyBiA1999dXP92b+k0Y/jfvz2GOPHX71q18l9+ZJJ500iQ9IkiRJkiRJkiQVwUJASZIkSZIkSZIkSZIkSZIkSZIkSZIqzNbBkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVmIWAkiRJkiRJkiRJkiRJkiRJkiRJkiRVWN+y34AkSWX4+uuvw5tvvvmL/2/qqacOY4wxhl+IJEmSJEmSJEmSJEmSJEmSJEmqFAsBJUm1RBHg/vvv/4v/7+CDDw6zzDJLae9JkiRJkiRJkiRJkiRJkiRJkiRpREYa4f8rSZIkSZIkSZIkSZIkSZIkSZIkSZIqwR0BJUmSJEmSJEmSJEmSJEmSKuzz978KP373Y/LfP3v3i7LfjpTauJONnfznyKOOHMaZeMxKHkl/b+oE7fBbkyRJrXNHQEmSJEmSJEmSJEmSJEmSJEmSJEmSKswdASVJkiRJkiRJkiRJkiRJktpNnz5h5FHcD0LV9+P3P4UwbFhoa/7e1AY64rcmSZJ6ZCGgJEmSJEmSJEmSJEmSJElSm6EIcKwJxyj7bUi9+vKjr8OP3/3Y1kfK35vaQSf81iRJUs9sBSNJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoVZCChJkiRJkiRJkiRJkiRJkiRJkiRJUoX1LfsNSJIkSZIkSZIkSVLVbXnn06EKzhowZ9lvQZIkSZIkSZIkSSWwEFCSJEmSJEmSJEmSevHMR194jCRJkiRJkiRJklSakcr705IkSZIkSZIkSZIkSZIkSZIkSZIkqTcWAkqSJEmSJEmSJEmSJEmSJEmSJEmSVGEWAkqSJEmSJEmSJEmSJEmSJEmSJEmSVGF9y34DkiRJkiRJkiRJklR1c0w49v/8f8OGDQvvf/N9eP/r78KwDP9WnxDCxGOMGiYefZTQpw//S5IkSZIkSZIkSXVnIaAkSZIkSZIkSZIkjQCFfj9ce3vo86vxw1kD5v/FP/vg6+/CXg++GN77+rvMjx1FhbzuxKOPGg5feJYw0Rij/uKf/3D/o2HYh5+Evqsta6GgJEmSJEmSJElSTVgIKEmSJEmSJEmSJEkjLAK8Lfxw2/3/XVxd7P+KAd/58tuw473PhiFffpvrcXv24y/CNnc/E05aon+YfKzRfi4C/P7S6xrvMvRdbaDFgJIkSZIkSZIkSTUwUtlvQJIkSZIkSZIkSZKqXgRI8R1FeOwEWEQRYAN/h7/H3/1lEWBI3h/vk/crSZIkSZIkSZKkzuaOgJIkSZIkSZIkSZLUQxFgA0V4V784NAwZb5JCjxfFgFdffGPY8Il//c8/a7xPdwaUJEmSJEmSJEnqbO4IKEmSJEmSJEmSJEm9FAE2UIy36ttvFXq8+HsjKgJscGdASZIkSZIkSZKkzmchoCRJkiRJkiRJkiRRUHft7T0WATbs+dJzhRUD8nf4e735v2LA2wt5T5IkSZIkSZIkSSqehYCSJEmSJEmSJEmSFELo86vxmz4ORRQDNlsEmOb9S5IkSZIkSZIkqb1YCChJkiRJkiRJkiRJIYS+i80fRvntqpUoBmy1CJD3zfuXJEmSJEmSJElSZ7IQUJIkSZIkSZIkSZIqVAxoEaAkSZIkSZIkSZKGZyGgJEmSJEmSJEmSJFWkGNAiQEmSJEmSJEmSJI2IhYCSJEmSJEmSJEmSVIFiQIsAJUmSJEmSJEmS1B0LASVJkiRJkiRJkiSpm2LAR5ZavJBiwFaLAHlfvD9JkiRJkiRJkiTVg4WAkiRJkiRJkiRJktSN66eYKhw10+y5FgO2WgTI++F9SZIkSZIkSZIkqT76lv0GJEmSJEmSJEmSJKmqXv70qzDk/xfdNVus1/j3rmuiWC9NESCvO9VnXzX9HEmSJEmSJEmSJLU/dwSUJEmSJEmSJEmSpG58+cMPyX9el8POgGmLAPHF9z/6nUmSJEmSJEmSJNWIhYCSJEmSJEmSJEmS1I3vfxr283/Pshgwpgjw/97XT35nkiRJkiRJkiRJNWIhoCRJkiRJkiRJkiR1Y5SR+vzif2dRDBhbBPh/78ulXkmSJEmSJEmSpDpxdUiSJEmSJEmSJEmSujFW377/8//FFANmUQSIsUcZ2e9MkiRJkiRJkiSpRv531UqSJEmSJEmSJEmSlJhxvDHDkC+/+Z+j0SjOa7aoj39vtXeGhFm++Cy6CBAzjDum35AkSZIkSZIkSVKNuCOgJEmSJEmSJEmSJHVjtgnG6vbYtLozYFZFgL29L0mSJEmSJEmSJHUeCwElSZIkSZIkSZIkqRuzjj92j8em1WLALIoAm3lfkiRJkiRJkiRJ6iwWAkqSJEmSJEmSJElSN+aaaJwwxsgjFVYM2EwRIO+H9yVJkiRJkiRJkqT66Fv2G5AkSZIkFW/jjTcOgwcPHuE/23HHHcNOO+1U+HuSJEmSJKmKxuw7clhx6onDVa/9p8d/r1G8t+dLz+VaBIiVpp44eV+SJEmSJEmSJEmqD3cElCRJkiRJkiRJkqQerDPDpE0dn5idAZstAmzl/UiSJEmSJEmSJKlzWAgoSZIkSZIkSZIkST2YabyxwnwTj9vUMaKY78Wxm/t3G/j3my0C5H3MON5YLb2+JEmSJEmSJEmS2p+FgJIkSZIkSZIkSZLUiz1+PV0YZaQ+vR6nVd9+K8zyxWctHU/+fZ7XG/7+nnNP19JrS5IkSZIkSZIkqTP0LfsNSJIkSWrdbbfdFp5//vkR/rPZZpstDBw40MPaBs4999zw+eefj/Cf8R3yXUqSJEmSpGqYYbwxw1az9QunPtt9wR7FfHu+9Fyq1288r6edAbeabaow/bhjpnp9SZIkSZIkSZIktTcLASVJkqQ2LQS86qqrRvjP1lprLQsB28SgQYPC0KFDR/jPppxySgsBJUmSJEmqmN/NPGW45+2Pw7Mff5FpEWAzxYD9Jxg7/G7mKaJeX5IkSZIkSZIkSe1rpLLfgCRJkiRJkiRJkiS1g74j9QlHLDJL6DfWaJkXATbwOrxeV/y9Ixe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       "text/plain": [
        "<Figure size 1600x700 with 1 Axes>"
       ]
@@ -1439,7 +1516,7 @@
      "metadata": {
       "image/png": {
        "height": 723,
-       "width": 1724
+       "width": 1793
       }
      },
      "output_type": "display_data"
@@ -1459,7 +1536,7 @@
     "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
     "x = np.arange(n_symbols)\n",
     "ax.errorbar(\n",
-    "    x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3, label=\"posterior (mean ± 2 sd)\"\n",
+    "    x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3, label=\"mean ± 2 mean-field sd\"\n",
     ")\n",
     "ax.scatter(x, b_truth, marker=\"x\", color=\"C1\", s=60, zorder=3, label=\"truth\")\n",
     "for t in thin:\n",
@@ -1468,28 +1545,140 @@
     "ax.set_xticklabels([f\"S{i}\" for i in x])\n",
     "ax.set_xlabel(\"symbol (shaded = thin: 1,800 and 900 rows)\")\n",
     "ax.set_ylabel(r\"$b^{(\\alpha)}_s - \\bar{b}^{(\\alpha)}$\")\n",
-    "ax.set_title(\"Partial pooling in action: thin symbols shrink toward the group\")\n",
-    "ax.legend();"
+    "ax.set_title(\"Symbol effects against the generating values (thin symbols shaded)\")\n",
+    "ax.legend()\n",
+    "\n",
+    "order = np.argsort(-b_sd)\n",
+    "print(\n",
+    "    \"posterior sd of the contrast, widest first: \"\n",
+    "    + \", \".join(f\"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)\" for i in order[:3])\n",
+    "    + f\" ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa0d00a6",
+   "id": "a9a6a249",
    "metadata": {},
    "source": [
-    "The thin symbols' estimates are pulled toward zero and carry visibly wider\n",
-    "intervals — that is partial pooling doing exactly what it is for\n",
-    "{cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has\n",
-    "67,000 rows against BTC's 203 million, the same mechanism is what makes the\n",
-    "tail of the symbol universe estimable at all."
+    "What the figure supports is narrower than the usual slogan, so it is worth\n",
+    "being exact. The posterior spread is widest for the symbols with the least\n",
+    "data — the printout orders them — which is the hierarchy expressing that it\n",
+    "knows less about those groups {cite:p}`gelman2006data`. The point estimates\n",
+    "track their generating values across the board, including the thin ones.\n",
+    "\n",
+    "What the figure does *not* show is shrinkage in the strict sense: that would\n",
+    "need an unpooled per-symbol fit to compare against, and this notebook does not\n",
+    "run one. Nor are the bars calibrated intervals — the paragraph above declined\n",
+    "to treat mean-field widths that way, and that applies to their ordering too.\n",
+    "And whether pooling improves held-out prediction on real data is a separate\n",
+    "question again, which nothing here tests.\n",
+    "\n",
+    "What do those parameters mean as *objects*? For one symbol, holding the\n",
+    "covariates at $a = q = 0$, the fit implies two curves over the event clock: the\n",
+    "probability that the next event leaves the price unchanged, and the 90%\n",
+    "half-width of the move given that one happens. Both are built from\n",
+    "global-plus-symbol sums, so neither sits on the translation ridge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "28f5a537",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T07:45:09.220265Z",
+     "iopub.status.busy": "2026-08-16T07:45:09.220131Z",
+     "iopub.status.idle": "2026-08-16T07:45:09.501613Z",
+     "shell.execute_reply": "2026-08-16T07:45:09.500965Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x640 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 663,
+       "width": 1823
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s_idx, hours = 0, np.arange(24)\n",
+    "Bh = hour_basis(hours)  # (24, 4)\n",
+    "# .dataset: idata.posterior is a DataTree in arviz 1.x\n",
+    "st = post.dataset.stack(sample=(\"chain\", \"draw\"))\n",
+    "\n",
+    "\n",
+    "def pick(name, *dims):\n",
+    "    return st[name].transpose(*dims, \"sample\").values\n",
+    "\n",
+    "\n",
+    "logit_pi = (\n",
+    "    pick(\"kappa0\")[None, :]\n",
+    "    + pick(\"b_k\", \"symbol\")[s_idx][None, :]\n",
+    "    + Bh @ (pick(\"c\", \"harmonic\") + pick(\"b_ph\", \"symbol\", \"harmonic\")[s_idx])\n",
+    ")\n",
+    "log_sigma = (\n",
+    "    pick(\"alpha0\")[None, :]\n",
+    "    + pick(\"b_al\", \"symbol\")[s_idx][None, :]\n",
+    "    + Bh @ (pick(\"g\", \"harmonic\") + pick(\"b_sh\", \"symbol\", \"harmonic\")[s_idx])\n",
+    ")\n",
+    "# transform each draw, then summarise: the median of a function, not a function of medians\n",
+    "zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)\n",
+    "half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick(\"nu\"))[None, :], axis=1)\n",
+    "\n",
+    "t_logit = (\n",
+    "    truth[\"kappa0\"]\n",
+    "    + 0.30 * z_truth[\"z_k\"][s_idx]\n",
+    "    + Bh @ (truth[\"c\"] + 0.15 * z_truth[\"z_ph\"][s_idx])\n",
+    ")\n",
+    "t_logsig = (\n",
+    "    truth[\"alpha0\"]\n",
+    "    + 0.35 * z_truth[\"z_al\"][s_idx]\n",
+    "    + Bh @ (truth[\"g\"] + 0.12 * z_truth[\"z_sh\"][s_idx])\n",
+    ")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9, 3.2), layout=\"constrained\")\n",
+    "for ax, post_curve, truth_curve, ylab in [\n",
+    "    (axes[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), \"P(next event does not move)\"),\n",
+    "    (axes[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth[\"nu\"]), \"90% half-width (bp)\"),\n",
+    "]:\n",
+    "    ax.plot(hours, post_curve, color=\"C0\", lw=2, label=\"posterior median\")\n",
+    "    ax.plot(hours, truth_curve, color=\"C1\", ls=\"--\", lw=1.5, label=\"generator truth\")\n",
+    "    ax.set_xlabel(\"UTC hour\")\n",
+    "    ax.set_ylabel(ylab)\n",
+    "axes[0].legend(fontsize=9)\n",
+    "fig.suptitle(f\"SYM{s_idx:02d} on the event clock, at a = q = 0\", fontsize=11);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba8f36f0",
+   "metadata": {},
+   "source": [
+    "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
+    "for the curve: it answers \"how far does a move go\", and it stays finite for any\n",
+    "$\\nu > 0$, which is why the notebook reports it instead of a conditional\n",
+    "standard deviation — the Student-t variance exists only for $\\nu > 2$, and\n",
+    "nothing in this model guarantees that. Under this generator, the fit recovers\n",
+    "both shapes; on real data the same two curves would be estimates, and would\n",
+    "need out-of-sample evaluation before being used for anything."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b851e5e9",
+   "id": "41c52262",
    "metadata": {},
    "source": [
-    "## Does it predict what matters?\n",
+    "## In-sample posterior predictive adequacy\n",
     "\n",
     "A recovery table checks parameters; a posterior predictive check asks the\n",
     "model to reproduce the data features it exists to describe. The `CustomDist`\n",
@@ -1501,14 +1690,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "98de55d4",
+   "execution_count": 15,
+   "id": "b64ae39d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:06.705938Z",
-     "iopub.status.busy": "2026-08-14T08:44:06.705852Z",
-     "iopub.status.idle": "2026-08-14T08:44:07.339712Z",
-     "shell.execute_reply": "2026-08-14T08:44:07.339304Z"
+     "iopub.execute_input": "2026-08-16T07:45:09.504396Z",
+     "iopub.status.busy": "2026-08-16T07:45:09.504244Z",
+     "iopub.status.idle": "2026-08-16T07:45:10.806762Z",
+     "shell.execute_reply": "2026-08-16T07:45:10.806119Z"
     }
    },
    "outputs": [
@@ -1572,7 +1761,7 @@
        "q99 |move| (bp)        0.324    0.340  0.281   0.411"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1616,14 +1805,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "b1b883d2",
+   "execution_count": 16,
+   "id": "4ed037dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:07.340831Z",
-     "iopub.status.busy": "2026-08-14T08:44:07.340759Z",
-     "iopub.status.idle": "2026-08-14T08:44:07.472051Z",
-     "shell.execute_reply": "2026-08-14T08:44:07.471703Z"
+     "iopub.execute_input": "2026-08-16T07:45:10.808353Z",
+     "iopub.status.busy": "2026-08-16T07:45:10.808213Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.082254Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.081409Z"
     }
    },
    "outputs": [
@@ -1671,156 +1860,203 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b0c4e9e",
+   "id": "a3fdd2f6",
    "metadata": {},
    "source": [
-    "Two of the three statistics sit inside their predictive 90% bands; the median\n",
-    "|move| lands at the band's edge (0.8% outside at full precision — the\n",
-    "3-decimal table hides it, so we say it here). Note also that this is an\n",
-    "in-sample adequacy check: the evaluation batch was seen during the fit; the\n",
-    "real-corpus scores later use shards the fits never touched. On real data one\n",
-    "further caveat would surface\n",
-    "here that the synthetic generator cannot show: real price changes are integer\n",
-    "multiples of the tick size, so the continuous Student-t's residual misfit\n",
-    "concentrates at |y| near one tick — visible as small stair-steps in the\n",
-    "observed ECDF that the smooth predictive cannot follow."
+    "All three statistics sit inside their predictive 90% bands. The heading is literal: the\n",
+    "evaluation batch was seen during the fit, so this is adequacy, not\n",
+    "generalisation. A held-out split is the next thing to add before any of this\n",
+    "is used to compare models."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a711b252",
+   "id": "efaf1839",
    "metadata": {},
    "source": [
-    "## Knowing when to stop — and how a stop rule can lie to you\n",
-    "\n",
-    "A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo\n",
-    "estimate — and eyeballing it does not scale to overnight runs. The stopping\n",
-    "rule originally proposed for PyMC in\n",
-    "[pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)\n",
-    "standardizes each loss *improvement* by a robust running scale estimate and\n",
-    "feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming\n",
-    "itself only after `min_steps` — which on streaming fits should be at least\n",
-    "one full pass, since stopping before the model has seen every row once is\n",
-    "never evidence of convergence. The trap this section demonstrates was later\n",
-    "confirmed on real ADVI traces, and the revised implementation under review for\n",
-    "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733) is built on the windowed idea shown here, with horizons\n",
-    "that grow over the run and a second, practical-negligibility yardstick.\n",
-    "\n",
-    ":::{admonition} The minibatch trap\n",
-    ":class: warning\n",
-    "Successive losses are evaluated on *different batches*, so the per-step noise\n",
-    "dwarfs the per-step improvement by orders of magnitude. A rule that\n",
-    "standardizes *raw* increments therefore concludes \"improvement is\n",
-    "indistinguishable from zero\" immediately upon arming, and fires a fixed\n",
-    "~$h/\\kappa$ steps later regardless of whether the fit converged.\n",
-    ":::\n",
+    "## What a stopping rule has to be able to see\n",
+    "\n",
+    "A streamed ELBO trace is noisy: every value is a one-batch, one-Monte-Carlo\n",
+    "estimate, so consecutive losses differ mostly because the batch changed. That\n",
+    "makes \"has it converged?\" a signal-detection problem, and the horizon over\n",
+    "which you look is the whole game.\n",
+    "\n",
+    "Write the standardised *block contrast* at horizon $w$: average the $w$ losses\n",
+    "before a point, average the $w$ losses after it, and divide the difference by\n",
+    "the noise scale of that difference,\n",
     "\n",
-    "The fix is as small as the trap is subtle: apply the identical rule to\n",
-    "*window means* of the loss, where averaging beats the batch-resampling noise\n",
-    "down by $\\sqrt{k}$ and real improvement becomes visible again. We run both\n",
-    "against the same fit, as non-fatal shadow observers, and let them disagree:"
+    "$$\n",
+    "z_w(t) = \\frac{\\bar L_{t-2w:t-w} - \\bar L_{t-w:t}}\n",
+    "              {\\hat\\sigma \\sqrt{2/w}},\n",
+    "$$\n",
+    "\n",
+    "with $\\hat\\sigma$ estimated from successive differences. Positive $z_w$ means\n",
+    "the loss fell. Averaging divides the noise by $\\sqrt{w}$ while a steady drift\n",
+    "accumulates linearly in $w$, so the detectable drift shrinks like $w^{-3/2}$:\n",
+    "the horizon does not merely smooth the picture, it sets what is visible at all."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "aa3d1e1a",
+   "execution_count": 17,
+   "id": "22fdcafc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:07.473318Z",
-     "iopub.status.busy": "2026-08-14T08:44:07.473238Z",
-     "iopub.status.idle": "2026-08-14T08:44:07.475766Z",
-     "shell.execute_reply": "2026-08-14T08:44:07.475492Z"
+     "iopub.execute_input": "2026-08-16T07:45:11.084946Z",
+     "iopub.status.busy": "2026-08-16T07:45:11.084812Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.089832Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.089263Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  per step (w=   1):  mean z +0.001   sd 1.07   |mean|/sd 0.00\n",
+      " one epoch (w= 296):  mean z +0.010   sd 0.03   |mean|/sd 0.36\n",
+      "two epochs (w= 592):  mean z +0.028   sd 0.02   |mean|/sd 1.45\n",
+      "1.5 epochs (w= 444):  mean z +0.063   sd 0.59   |mean|/sd 0.11\n"
+     ]
+    }
+   ],
    "source": [
-    "class CheckLossConvergence:\n",
-    "    \"\"\"One-sided CUSUM on robustly standardized loss improvements (pymc#8384).\"\"\"\n",
-    "\n",
-    "    def __init__(self, kappa=0.25, h=20.0, halflife=200.0, min_steps=1000, z_clip=4.0):\n",
-    "        self.kappa, self.h, self.halflife = kappa, h, halflife\n",
-    "        self.min_steps, self.z_clip = min_steps, z_clip\n",
-    "        self._lam = float(np.exp(np.log(0.5) / halflife))\n",
-    "        self._prev_loss = self._prev_delta = self._scale = None\n",
-    "        self._S = 0.0\n",
-    "\n",
-    "    def __call__(self, approx, loss, i):\n",
-    "        current = float(loss[-1])\n",
-    "        if not np.isfinite(current):\n",
-    "            return\n",
-    "        if self._prev_loss is None:\n",
-    "            self._prev_loss = current\n",
-    "            return\n",
-    "        delta, self._prev_loss = self._prev_loss - current, current\n",
-    "        if self._prev_delta is None:\n",
-    "            self._prev_delta = delta\n",
-    "            return\n",
-    "        abs_diff, self._prev_delta = abs(delta - self._prev_delta), delta\n",
-    "        if self._scale is None:\n",
-    "            self._scale = abs_diff\n",
-    "            return\n",
-    "        sigma = self._scale * np.sqrt(np.pi) / 2.0 + 1e-12\n",
-    "        self._scale = self._lam * self._scale + (1.0 - self._lam) * abs_diff\n",
-    "        z = float(np.clip(delta / sigma, -self.z_clip, self.z_clip))\n",
-    "        if i >= self.min_steps:\n",
-    "            self._S = max(0.0, self._S + (self.kappa - z))\n",
-    "        if self._S > self.h:\n",
-    "            raise StopIteration(f\"converged at step {i} (S={self._S:.1f})\")"
+    "# Stage 2 only: after the last planned optimizer change, which is where a\n",
+    "# stopping decision would actually be taken.\n",
+    "stage2 = np.asarray(approx.hist[6_000:], dtype=float)\n",
+    "sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0\n",
+    "\n",
+    "\n",
+    "def signed_z(losses, w):\n",
+    "    \"\"\"Standardised contrast between adjacent blocks of w losses.\"\"\"\n",
+    "    csum = np.concatenate([[0.0], np.cumsum(losses)])\n",
+    "    t = np.arange(2 * w, len(losses))\n",
+    "    older = (csum[t - w] - csum[t - 2 * w]) / w\n",
+    "    newer = (csum[t] - csum[t - w]) / w\n",
+    "    return t, (older - newer) / (sigma_hat * np.sqrt(2.0 / w))\n",
+    "\n",
+    "\n",
+    "horizons = [(1, \"per step\"), (steps_per_epoch, \"one epoch\"), (2 * steps_per_epoch, \"two epochs\")]\n",
+    "# control: a horizon that is NOT a whole number of passes, to test whether the\n",
+    "# variance collapse below is really about epoch alignment\n",
+    "control = [(steps_per_epoch + steps_per_epoch // 2, \"1.5 epochs\")]\n",
+    "for w, label in horizons + control:\n",
+    "    _, z = signed_z(stage2, w)\n",
+    "    print(\n",
+    "        f\"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}\"\n",
+    "        f\"   |mean|/sd {abs(z.mean()) / z.std():.2f}\"\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "04075b83",
+   "execution_count": 18,
+   "id": "01e4ae61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:07.476671Z",
-     "iopub.status.busy": "2026-08-14T08:44:07.476591Z",
-     "iopub.status.idle": "2026-08-14T08:44:07.478642Z",
-     "shell.execute_reply": "2026-08-14T08:44:07.478360Z"
+     "iopub.execute_input": "2026-08-16T07:45:11.091219Z",
+     "iopub.status.busy": "2026-08-16T07:45:11.091087Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.455463Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.454838Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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UkoVbOzMVIUIZMmzakanQpZmyEFxcFI5uQqUBABKBRd38HH6s0k/7qgU6bPPoVB+c2OENz4FYDLLRrNFmq0IXnIwQavD+CAs6eJzj5zmLZQ/XckIo8oPyoBDkAQD8wXPga7NFeml+lq7JFld1MMHOFnjOwesZ7mWXZMq0wao132vAYs6Lc401vtLxOiGCN8Q9WATG1+f9C/4bw9fke9elpyTm7byHxM5rrB7WTLz3+lS1j07bWTEe8XXHzFr9uuTSM7U+mlg211qOQa4Q/Lbuz3LaWFjLjnUuyVTohtwCjZo14RyEhWaPVPrpvJ0RgjQW3fI8kZ18HLFz4jOcbx73eE2TWRTtb7UqtMWqip/rrRpl6VpaqBDlZ8s0bNfIdF2arJp0wC3QU7kheiI7RKeJBXkGbTYrdHm2LIS5fL2jrkU1h8isOkKkMrhQpkumZmjH1CyNzJfIqjlk2C6ZrRUMgATMWRkq2PX+6YeKYdCZvgJdyOUp5zg0XKtS3nGoahhCNDVQq9H6SnnJ51mgeXhgiM7n83SiMEAly6SqYdJ8JkvjuTwdHeC/rRwfLBZ28Z6Eaa74+5ZikTaWiuLf85kMDVWr4veJXJ4cwxBjHPdlhoVdZ/sK4u9dcd16ubgulSyLaqYphJ0s8Lx+eoJ2z83SplKJthYXaF2lRH02O/EIl4lsTqSVU8/pnsjlyDYMKpuWKOOiadGaaoVyji3SNVKrkuG6QqxWcJIToB7tH6Az/+6l9JZ/t9opHQAAAJWB6AuAsDtVl0ha1cXJrc6iLwBAtBiwjAAAAACAohjiuBCHagCkFfZ4y69WeHnDh/r8en5uoWks9Wiln35c7RdCMPbAm4ShExuTsidgNmjg3R42xGIjBDbUujpbFMYPj1UK9GBlkPZWC8IDOEgHfAjPRnubrKpoJ9yO2cD+lJ0TQgg2zmGjZo44wcbIbMjDhi9Hajmadi0R6W69WaXLsnVDZTbCeazaL6LkcTtjL/JsMPN0tY/KMPJbAhtDctm+MDdPV2RLQuQwtBiRCEQP5nJAtAODaK1l01profnsBjH0P6N+qMsG4fy6JT/vS6THQjw2smSBjfi5GNGEIxOwwSU/f9gYi+c3C64pnln8XJte/C4E+emBV+0sSuA5Lz9r2WB3q1UVbcTXdQyiTVZNvDrB4vhdGZ5jTyz5e20x2h2Ly9hQuRfjZwBAo1+a5PqMxgC8wYJWFkHw67U0JUQjHHWQRRIslF0uOuG51Gr0GTbdkCuK188PTIhIZE9WC+K5/J3ySMc16p5sibaYleZ4zcJ7jozBa0t+9rPAlsUnDTEaALLD/WdbpiLmpzxPtRZFlBzJj8VEPK/lqJ4lMsQ8mffvdlkV2mBVaLjLPgH3SRZhNYRey99r0NfjWQY7prkmWyJqMdNa6QK7zpCQ1vcO77021go/S1OrfoYjnj1RKYhoYSzSyhlOPRKx4QqHHTyejBg2zS6OHyziGjKcjg48bgxpbTjE/gLyeSpR3XEA//868ZqjN9Kc2DPjEbRrFMq1RLR9mKaJXy0sir5M2yGrZFOmVCOrVCPDccnJLO4/syCvxu/XiG/DPzMLNTIdhzILVcrOs7Cj/a3tnCUim/E1+eWahhCcOdn69TPl9mIQ2zRoqpCnuWxWrOUyjiMiqrmuS2dzfXSsf4C2LSyIKEgs6pnPZWmi0Eeb5uapv2ZLL27iKE4czYnzcTbfR7PZLI1VyjRaq1Kf5dKZ/gLVHIMumZ2lbfPzNFCtUdk0hZCvXRQ3fr9mGEIcZLpEk7kcHRgcpoODQyIqXMm0qGyZNJ7rE6Ik0YxdEn/rt23xXXvxxYKlq2en6TlTk7SltNAUHPE1BuwajVYq4m+HBwbpyeERmsrmhBhoMpujQ4NDNG9l6AUT5+m5U5MiGl7escVPvg8Ls1ig9czgEN29fhM9MzRM05msEFJZTl2smHFdUeecxplslnbNzdHlczNCWMTpPN1XoNN9/XSmUBAiLbdTp3Rd2lgu0YZSiS7k83Sqr7AyFLlHbMNcNYor//14/4B4hY5hUHGZDS5HFjzen1n1fiy2GqlWhAjt0oU52jk/J+qSowhyNMFBe1HYu1i2Z/MFOtPXR4cGhmg8nyfLJVHGLPI6n8vTgaFhms3mgqV9UbDGz64FK1OvJ9cVwjxuCwW27TVIiPY4T1znm0pFumJuhl48N04D5SqdtfL0yOgY7RsepazjiLbEYrLdczM0Wq2Kv3GENG4z/U5NjOHFbIZOjg3Tda96Kb1pOxyiAACAjkD0BUDIrBalqpWFhWgPQefnOx/0FQqFSO8PAAAAgODAgA4AoD2LG9sAANDNWOqm/IJ4NWCPuH89t54O2fUD/zBhAQkbs7KAZ4dVEZ5vWUjSzUCEuTm/IF5siHVPZYj2VvqEMQTDXr79CsHq0liDBgxbmJWsN2viJxtas3HLtGJG15dY7Cm5KiK7sQdzjuT2aHWgaZTGBmiyssmsCuOiKzMluipbFEYvq3mH9kpD3Lga1+eK4rUcNnZmD8zsoZk9wbNRNB/g8u/84rbBojP2isytgr1UPyWhUIwNtVnkxiYLA4ZDg6YtjIfY+/RJO0fnnIw4qNhoVcWLy56jgVyWKQtDHs4rt3/2IM1GRr3UA+iRlM/lNuy8huanz9P81LmkkwKAf5GeGFG9c/sqUy4WJ8+5phjL+Zl0wbboocoAPVgZoGMRzNGi5IbsPD03xyJ+R8xJHqkM0P6WqAcc2YrzyeU3atjC0HXWtcQzSaW5WKdnM8+BL+eoM1ZV5JXnEzwPlmlNwGkCAITrVDLFUzlat+1KqlVKNHXuaOT3MkMaw9jBxQvy8+L1HwYmm9GJ+FnEOxaNaGFexblzjilEHrxufLgyQGcdtR0G8x4Ki4OrZAhxHe/l8DOOn+ccEbVuNt8eft5zuQ0ajrgGi9397FPws5TX5Lz347iGqAueJbC4nqPeBinfLNXrc3umIq7B6XqmmhfOkHiviOdj9R2A4HAZ8Xqbf7I4kFsOR9lkp0sz7MiGXLEnxn/nsmTBYLey9JIvr+lmh0scpeuFAaJ5p13cms0VqFpZub+0HBZMib1X6my/NmxwWcpVntxnexq5FjeVHNMiJ2tRdSiAqIPFRVWHzKpNVrn+YsFJrZClymCW3GyHaMuuS9nZCmUXqk0hmMvRlgayQiwmulqLMId3EkuLv7M14h5xiUEaNzbQeMtlTzmuEKRlyjUhUDrk1PcHB5wa5ao2Dc6VKWfXKMvpzho0mckuRl4zaIQjwVWr4hkzaeVoNpOhA7lB2psZohnbEFHX1hg1GiSOQudSX5ZojGp0gbIi72sqZbo0U6EtBZsrSOSLsfMWVfvrteWyoG4xXyzcG3RMylD9GZajTHPtdVGmMkYlGqMFl4QTEha9cRQlHvMdy6TiZI3OzBv0TH6IDg0Oioh27Lyk8eIULKwS9boxxvJ+9WbTpjUmRzGu0nYWjJo1IXQ8a/fTEXeQThpufczlNbBZH39Z8MWC07xZ37fPuxZNVfuoWMvTgGvQMNXoyKY1dHDTmBhPOR11cWp9/OMxd7X1JAu/pnIr19RPjK4Rr0AYhhCk8SsNsLCKy5BfT46MrvIBl/ocFvmZImpYO7iNcIRrFsQyHN2Q5xv8HOa64/OOfsMR7Yx/clTFSfeiCT6fM3G75q2FxjcZ/n1uUaCYMQ3aYFZpq1EULYOfx1eOlGj3Vo6gPSQ+P8jO4WyibbV5cSbE84wh06DtVoHGzKxoSzwecipb98ovJZeu3jQWYskCAACQCYi+AAiZkZH2Hpa8iLJ6pdv1R0dXmdgCAJQRgwAANCflBnQAAP1Ju3FJ2g+dAegFjmjx34ZP0xPVgjBOYq//bOjEh6d80MqGIw3B1GpGPOy5d6NZpUHTEUZAbCizedGIxq8BSTtDrNf0z9Gr+y76juXxjg19Wj3PLp55C6HLMTsnDgDZEIwPDzltXmChDIun2BiJjbX4gI+9dbMRMv+NDyA5z+zZlw2zVzvgXs4Oi6OXleiabJF2WmUhNDpcyzejgCw4ZtOIig/BWXzDh/GcB44CwnXA/96RqYhy3taxXM+v+Asfmn+lOEo/KA96Sm+7ug4Km4I9L7cgDmXZ8PnKbCmUdhGGsfNVWRadNcw7vMGGWvw6b2eF8VldRMhGCoaoU84vX3to0UM2G8M9vujhmQ29/Bi5cV1wu+BoINzm2SCe2ySLKPdkSiJCCAsDemHErB+Og+RJ+1xu7bYrhOgLgLTChk9jxkVREIvJXlWYES82dOJoAWwUzJFNjtby4hnBT/WGgVvc8PPuhfl5MW8R6SdXzFGuyJTYFnEJLAATmFmqsDEkPznbPA6LriGel8y8Y9KztbyY27HgnyN7xiW+5vLlPDXmtJvNqjAqtBef+TyHO+NkaM6pP5v5c7szJbouV2w7z9l65c108ukHY0k/ACAJ0n3OuGbzLjp/7ElSmUZ0oqDwvsNVZn2N+brClIhazlHWT9RyIloSPy/mF9eTy59nHN2anx6Nv/N6cp1Zo6M270AYNGLUhAF0r4KkdvD9eX3JzzM2lOZIRGwkzc90XqMsf27zPgNHO2V4PczOevhvXAaNtfDySEF2i6iOL8fG3LxTw9HS+G/83B0ybZHfUaNG/RxSpgPsyIXnRFwubJjNaeA5BM+V+HcuPxZKsMiJr8v347X6arTmkaM0nRFCLDZOd8W6/8jivhDP1HgewNfi/Y0rMnXjdBZq1NNULwM/8P3OLd6P7/Fkta8ZEZbLhkXj/D5Hi2rd07kmU6SdmbJobzz/4qidnI9TdlbMn7gsOEtrWDhhuEJkX6/fWtBgNKlfs17ynJfSMw99K1jhAT8NjZycJV41vwGODIOqw3nxCsqq/cM0qDaYEy9m1+LKoP7sF7Hwmh/ld5ZbDjaeLCOLr21UpZfR0ki8K2msDXnc72uK07rB4+GYiHhpe3rusRMOgcXC3Dp963N0yXqiS8RdV78zjzeT7EDNMS+uZ7tEEGah47BZ9pgToq1U7bpvy2I6FpOJcY5c8Yw9bOfpsUq/2NPnvVoWkHHaOGm8z877qzwmZhbLi8fbA7U+z+nSGS4nfq6xQ7EZ1xTO5hriZX6Gc3nzmpifSVyuvM///Ny8aEd8lvEEz3vsuhixLha36ZJFIbRwBLgoQOQ5AdeHF+djq81DeoXrnQXa/AIAAAAaQPQFQMyir5mZmUjLvNv1u6UPAOkJe6UEAAASkXYDOgBACkj5XG7n9S+mM8/+mErzU0knBQAl4QO25+SK4rUcPsxj45PzTlYIWPjwlMVHfOjKgqwkxjj+Z584crxI41CZxVH8CgKLuUZXKQOG55IVPtRsMVQ+a2fox5V+YYjD3qtZQMfvs4dIPsy8PFNaYVTUrpyXc0X24qHjuu17aHBsEx157G7feeJD95/rn6SfLUwKT9hsQN3wVs71zsZQLHRjQy32xMoHtvx3cXi/6DWVD85ZeNbweM4CpmO1HJ1nY2fXEkK4kmuK9sBG0pxlFnntzpSF8E6nRxSLsOpCLG9tjPP/ovxc899Vt24kxmV81mZjNEsYi7EBIB+es5EY/z5s2qLMEXkrTWjUUXpYtwMAVsIGupsL1VWNndk47XgtJ8Tox4RAqm5g1YhIws9n9sU+3eIduxtsjHVpplyPyOGyML0mDI75OsJDvFml5+cWhEG3HyzToBxPLDvAgqlCI3qLRXR5y3yI88tG2Dz34DnKt0ojwgi6Nforp5XnOizUZwN5FmxdavHcsCyeqzyfZUM/NhBnOAeua1DGcJuRaXk+yDE/wxyS1my6lApD8AgOgM5gHgNa4XVcu6jTzuLeQsP4nJ853QROrY5qjtRy9GS1QI9XCzTeEu2Knci8ND8jhE68pmQRFsPPtcYzm/+2v9on9jB4rsD35mc8f7edIGq15yHvM2xqEcht7u5fRuzZbOxBVLccTu9lLfOEXmjNI4vVLlm23ucy9XYh/4eQfL/GHtYlVKEbcwsdBWL5DnMU/vvWTFW8IiHF6zXek7MyAaJWAaApPBwIpyUJRzPm5+2IiJpXhwVvY9ZCx7F0NXgf/BwV6IlyRkRh5LUw/+RnblSCaz/wWpyFWOw4juE1cVj0Gza9IDdPP9E3K5ydBB3qx6ziRYcvXegkDlxOih89AAAAYgaiLwBCZmys84HI+fPRegLtdv01awKG/QUAAABA9GBHCACgOWmOXMp5HxrbTGfox0knBQAt4cNTs2Fw3DDCTeEYx9NJNq5phQ2WXlmI1glRWPBhKguQRFiQFliodW2utPrhvfAMexH29Mk0vJcD/7Cn9IYh/s5lhmODiLqVamAonPw4D4BqsLEzRxPlVzdYCLWv2kePV/vpnJ0Roig2HFu76J2bxV0sXudoVSMRifp7nc9xfrcJI+b6fPSW/LwwzmMBF89RWrf+2HP4aoZkVxA8eQMAootAD4DXPZaGExu/kZtZwMUG1fz6j0Qi2hY7EmGRl5coZSzWWk2IBtRgefS0uEnzOCfDviQAIDrqolmHNhqzS/7O4mx2PMJOR3g/l52q8PqTI1/z+pkjM7IwjB178Zp0a6ZC28UZSkU843nU5ojV7Cat4UyFRxN2v8bzARaCc0SyZ2p5EVWShWa8G9+IonVDbkE4H1sOi7g5Sna/4Yp08br4rJMR6eB7cFqmFucHPHfgdDciV3NeOA+NaJA7Ogi/QZvGAgAAQEsg+gIgZDZv3tzx/fHx8UjLvNv1t27dGun9AYiaVG9WpTjrAKSFNB9GNOgbGEUEHAA0BuMcAEBrcJiW7jU7AGkA4xw26ACIEI7wcXN+QbwSW68a0QydHMGrF8/hsYAxHgD9kW3cSQQUQtyMmbZ4ARAHqd+XwnwOgNRNY1i4xU67drb5yq5MhW7Pd75sp+c0C792Z8vi5QcWcfOrgYjIbdWWROFsFYMPiRjXdZJy6gcAAADIDqxKAQiZbdu2dXz/5MmTkZb5iRMnOkYh6+/vj/T+AEQO9uJx+AqAxqTdazpHwFm/46qkkwEAiJI0j3OLeU/7WA+A7oYlaTYuwfgGgP6gnwMAgN5gnAdAb+CMCQCgPdh7BwBoTNrncplsH41tvizpZAAAAACJke6ZAAAJiL6OHj0aWbnPzc3RxMRE4LQBAGQnvcaDAKSHdPfzwTWb0l4EAGgPDMgAAFrD8xgYlwAANCbtxiVYrwKQhvUqNqYAABqT9vVq2vMPQApI9flDmvMOQFpIeT/fce1tZFqZpJMBAAAAJEbKT+gACJ+BgYGO4qrDhw+T614MXxsmhw4d6vj+lVdeGcl9AYiXdC9iAQB6k+rDCCbl2QcgFaR9nBOgDADQldSLIeqFkHQ1AABAtGCYA0BjjFTvzaU5Yi0AaQH9HGcwAGhPiudyAAD9wVwOAAAASDcQfQEQAddcc03b9+bn5+nIkSORlPvevXs7vn/VVVdFcl8AQLxgIQ+AxuAwAgCgOWk2oAMAAP3BGA+A7qRd3Io9OQA0B1MZAIDuYF8OAKA5OH8AAGhN6udyBsoAAABAqkn3CR0ACYi+mMcffzyS+z7xxBM9pQsAFUjzRl2a8w5AWkA/53EOYx0AOpN2Q2EAgOZgzQYA0B2McwAAjYGwEwCgO2k/f8A4D4D+4PwBAKAzaZ/L+cU0M0knAQAAAAgVWFsBEAE333xzx/d/+MMfRlLu999/f9v3BgYG6Nprr43kvgAAAAAIBxxGAABACg5jcCYDgNYGZKk+eE1x1gFIC6ke43waC/cNjNKGnXDCBoBSpH2MS3n2AUgFaR/nfJDNFWjL5TcmnQwAAPAF1uwAaE7a53KGv/277VffQmkEzwIAANAXiL4AiIDrr7+eBgcH275/7733kuu6od7z2WefpdOnT3cUomUy8GAAgBakfB0PAAAAAHWBuBUAoDVYqwEAdCf1xiWGLwOLTK4v0uoAAEQg6sQ4h2YFgMbAANRXYZGZyUZXGQCASEjz+QPGeAD0B/3cV2FhfQ8AAEA70rvaASBCLMui2267re37Z8+epYcffjjUe37jG9/o+P6LX/ziUO8HQGKk/dAVAKA1aT6MaGxU+tmsHFq7JdL0AAAiAHM5AIDW8Dwm3WtWHDwDoDfo475KK7J6AABEBNuEpbrvpjnvAKSDtJ8/1JfsGOsA0Bp0cQCA1mCQAwAAANJMynd1AIiOO+64o+P7X/rSl0K7l23b9JWvfKXt+xzh61WvelVo9wMAAABARGCfzhfrd1wdVU0AACIi3YbC9byn24gQgBSMcSnu4n7Ht5H12yNLCwAgKlI8yDXwOp9FUQGgIOi4AADdwTjnvaTq/wEA1CLd4lY4owJAd9I9xvk/f0j3mTQAAAAdSfdMAIAIeelLX0rDw8Nt3//6179O4+Pjodzr29/+Np08ebLt+7fffjuNjY2Fci8AkibVG+zNBWmKywAAzUn7Rp1vMBwCoB7YYAcAALDI2NbL073GB0BBsGb1XWDRVAQAIBLSLuBnMDcDQG9g+OpjkE/58wAAZcEaDACgM5ifeJ7Ppnvem+a8AwCA3sCqFICIyOVy9HM/93Nt369UKvTRj3605/tUq1X6yEc+0vEzb37zm3u+DwAAAABiINWbTw28lwEMUQBQj1QbCmOIB0B/jJR7AhdzWR9zOcx9AVCPFA9xfkn18wAAoCR+52ZDY5sjSwsAICKwBvM3R8N0DgDlwDoMAKAzqT5j9T2XRfRDAAAA+pHymQAA0fK2t72Nstls2/f/8R//kR555JGe7vE3f/M3dPjw4bbvX3HFFfTiF7+4p3sAIBUp3mDHJiUA+oN+7rfAUvxQAABoP3b1DYySlclFnhwAQHhgLhek0DCfA0Al0m5c4ksQkXYhMADKjnHot15Zs+UyyuUHIq0TAEC4wPGGr9JC8wNAMbBe9Re1FmcPAAAAAAAAqEW6T+gAiJiNGzfSm970prbv27ZNv/Ebv0Fnz54NdP3777+fPv7xj3f8zK/92q8FujYAQF5wKAOAxqTd6NVn/jEeAqAehpnebQi/Rr+5wiCt235lZOkBAESAcB6Z8vmcL1BWAKgG1mAAAK3B1MRXGUDYCoCCpHy96mcuW/9sussLACVJ+Tjnh427rqfC0FjSyQAA+CDNZ6wX8TjOp3kql9Z8AwBACsBMAICIec973kNr1qxp+/7p06fp7W9/O508edLXde+991761V/9VarVam0/c+utt9LLX/5yX9cFQH6wOgEA6AsM6PwO83gmAKAaGOf8lhXGOQDUAn3WVxGguABQDxjQ+SyviOoBABARBtas/osMAKAQaY+CI8C4BYC24OzBZ3kt/gcAUIe0j3N+xiyMbwAAAHQEuzoARMzIyAh94AMf6PiZgwcP0s/+7M/SP/3TP5HjOB0/Ozc3R//7f/9vesc73kELCwttPzcwMEAf/OAHA6cbAFlJ9SI2xVkHIDWkeYwLQKqfCQAoC/otAEBvAzocJgIAdAZrMO+CEJQVAOqBfuu7wKKpCABAdKDb+isujHMAKAYGOV/7kiguANQDcxN/xYWBDgAAgGZkkk4AAGngNa95Df3whz+kL3zhC20/MzU1Rb/1W79FH//4x8Xnb7jhBtqyZQv19fUJodehQ4fogQceoH/+53+m2dnZrvf8vd/7Pdq5c2fIOQEAAABAlKT9EBEbbwDoT6rHuSB5T3FxAaAkKQ/Q53eMF/6EDYNcN7IkAQDCJs1zOb8gaisAIBXjVjzPBZ43uoRJIwA99yVE+vJTWGhwAKiGkfLzh2AFlnQiAAA+gC2FjyVozOObTGtWtBMAANAXiL4AiAmO9nXixAm67777On6OP/PJT36yp3u9613vojvuuKOnawAAAAAgAbC57tMLHQ4jAFAO9Ftpy4oNf1y3c+RpAECXfpRmxdcimMsBoDepH+fEMI+xHgBtwXrVZ3HFOB7yveApAIAQuhLmMV7ncvV5L8oLAJXAehVjFgDak/Z+7iuYYbzCVsM0yXXs2O4HAAAgnZhJJwCAtJDL5egTn/gEveAFL4j0Pu94xzvo137t1yK9BwAgKVK+gAcgBaTe06Yh7wFO6usGAPSl3seRxTHLj4FNrOOciS0iAHrvSDAKC1BoaHgAqETajUt8AsNqANQi7YbCjSisvr4T03MB61UAQutN6S5KP2NWzEWV9mcQAKH18bSvWf2cPaS9rABQEPRbP4UV9/0wpgIAAEhxpK+PfexjlDbe/e53J50EEDGFQoE+9alP0R/8wR/Q5z//+dBFZb/7u79Lb3jDG0K9LgDSgYWS5zLoH15HCzPjkVcJACA8sFHnu8Bia36IgANAWH0Jm96eyyrmEwnTsMihWqz3BADohk8jYRi1AaAccIbhp6wQtRUA5WA7YRdrVl8FFhN4/oA40XkfGPtyfgssznshoiEAPXcj7DNJi87PVgDiBOsi4apEykaHM1YAAACUdtFX2jadIPpKB9lslj74wQ/ST/zET9CHPvQhOn78eM/X5OhhLPi67LLLQkkjAEBO/D4X+0fWUf/wWho/8TTFATbrAAilJ6EYJfVCJzwK4zwCgBA6E8Y572UVc3mhbgAIoRv5j45AafecHlNxYb0KQFh9Kd1jXJAoOHGBcQ6A0FRfKEofxRUXMG4EcbFh5zU0OLaJDj36XS0LPe19yd9cLmYBv2mSa+MAAsTQ1sgglzSd78i5VIsN30KIGPcxsV4FcbJu25Wx2UjFjqR7UnIS8x6eTHUjU1oAAACkQ/TVwHU1XWwuQ9aDQhAdL3nJS+iWW26hr33ta/TZz36W9u/f7+v7pmnS7bffTm9729vETwDSgqxeOwAOJAAIA8wJ5R3j034gDkBYpHou18i6pOt/IW4FAPTYkeTs37FhyD3PTsk2M0iYNZsupYHRDXRi/wOkJWkf53wRd6Qv1A0AYfSjtJxLr4rE4wjGOBBzi9O3wDXOWiROXWIsMESHAHFx1e0/K9arM+MnNCx0eZ10pB3UC4iLsS2X0diW3dqKvlJ9xir5uINxDgAAQBxIL/pKwwMx1QcIKSeXy9HrX/968Tp69Ch9//vfp71799Kzzz5LZ8+epfn5eapUKlQoFGhwcJC2bdtGu3fvpuc973n0ohe9iMbGxpLOAgDxk4LnQlgLeXFITfGBAwkAQmDxIFFbL3thE2ekLzx/AAirM6EkvRdWrAc4ELcCAHQe5+pjnB3LvUC66RtcQ1YmR7oC4xJfhRW/gB/DHAC99iQoInyVFkdGi2kuByclIC4037fC3o+8YJwDIIR+pPkY3hVD8rkc1qsgLnQeClI+zolx3lcZxHjGijUrAACAGJBe9KW7ICr1i07QZOfOnfTWt74VJQIAUJeUbzAAEAZpN6DzOzeOVQyBjToQE9uvvoWcWo1OHnhIyzJP9xrYkNsLHcY5ABI4dNQPP/OzOMc5GJeA2Nqa7mOA7vkLO/+xOipB1FYAeu9IfDCNcvQx8MRWWBjjQKzoPN3BXM5PYcVbXmmvGxCv80ld25vIl6Z584ivuo11iMN6FcTW2rQuam3Hbw2QaZxDOwEAAH2R52nTKWx6BK8w09DLtQAAAIDogKEwAMohziMwR/ReXjAuAZqi8TAg06a3EuDgFWjIhkuuJcvKkp5oPIBHJciPy6Mwnj8AhNSX0j3OyezUBQJ+AELoR4aJcU5Sh0yYywEQUl9K+ZpV6qitWLMC0Hs/whjnu8TiAmMcAKF1JhSln7LCGSsAAADNMFWI9BXFqx3dRF2rfd+PEMxPWgAAAIBlD5zFnx7LBQcSQFPyhSHSF2zU+TEijDU6BA5dQXytTe+y1jx7KgNDYRAH2Xw/bbz0Osrk+vQscHGOiIFORnQb4zA3lRydDTB0zlvoXtP5iYA1K9CPwuBY0kkAaSTlzx8QH8LWQ+c1HfqS96KCgB8A9Uh9oC95n1+67csB2fdt5O0LvQJnTBKDeTYAAIAYyJCk3HTTTZFd+/z583TkyBExEWqIrlonRcuFWIVCgYaGhmhwcFC8TNOkubm5Ja9WWoVffK3Gffjn5s2baevWrZHlDQAAdAeLWHnLC3UD4qAwNEa7n/9KevIHXyK7VtGu0NGPfJdYjLfSa4PYtDLk2LWkkwHaold7ayXNRuqNMd7zWB/z4VSa6waAsEh9P/LrPTNGg0rd6obncjquh4D8YM0q79oQRnQgLnbf9Eo69MhdND99XrtCF+e5Gq/Hwx7jxedjKi6McQCE1JckncfEiqRloNuaFYBkkLN/x4s/x5pxPRcwxgGAvhQ39ZOHGM9YIW4FAACQZtHXnXfeGcl1v/CFL9CHP/zhVY2tGsKsm2++mW677Tbas2ePeG3cuLHjNScmJmj//v3i9eCDD9I999xDtVptyQKpIfy6cOEC/Yf/8B/one98pxCPAQAAAFHBC1iX4osoiUUsiKWdYcMeLG0QsaHbGGdZWYi+JAXGF6DZFuBRWKm5MPBGKuZyKciiiug2lzNNi+ykEwGUMiIND93zp7KfEr3GOQASIUYRk6zIOl/HGAdibG16F7b2c9UQiVEMoeua1XGwapURnc8ftI/WGHrdwrEm0BOdx7nU42d+FnMzwJoVAABAqkVfYVOpVOi3fuu36Jvf/OYSgVfj9/Xr19Mv/uIv0h133NFV5LWcsbExuvXWW8XrP/2n/0RTU1PiPp/97GebEcUY/lkul+nP//zP6f7776ePfexjInIYAAAAP6R3gX5xk9JHdAg3zugQVmz3AkDbTfvFjaplgWdThg8vdIiA01N0CCAvOh9IYNPbb3kh0lfgtmZZ5CKiobxoOs7pPH5HgZjLxRUdQjMxBOZyICnSPM75XX/GXVa6jXNAclI8FoBW4otOrZ0YwsrAGZPMaDzGpX2+IPNcVre6EeMcRF/yInFf6Bmd8xY2oqiMWAV5cNIGQK99Sa/5QrTw2QME/AAAAPQiFTMBFlq9/e1vF0IsFng1BF/8M5PJiKhb//Iv/0K/9Eu/5FvwtRqjo6P0H//jf6Svf/3r9L73vY8GBgaa7zXu+8Mf/pDe9KY30eTkZM/3AwAAAIIwtvmyUAtOt4NXICmab9bXjcj0zmOo9YuNusDAUFhmNB8DNB/HvSGpFzoNPQoDED/pnsv5nssiAk5gMJeTF2HGpPF8B8YlfsRf8T4TdJvLAZAEaY8O4XvMinUup1e9WFY26SSAdujV1LTvS0HwOs7XywpzuaBgzSozGo8DGOMkrl4Da1YQU1OTthOEg+75UxjMswEAAMSB9qdALLD6r//1v9LDDz/cFHs1hFdbtmyhL37xi/Te976X+vv7Q783C8pYSPbVr36VrrzySnHP1of8s88+S//5P/9nIUoDAADgkTSvYY1wvzM4tqmX1Ky8FYxLQJzoOhbomq8IicsYR7eNOgNiCJBU24MXOj+lFe/dMM6BONubppOexr4j8FVqsRSXbutVzOVAco0vxWOc5HnXbZwDIBnk7ueyjXXxCp31EuRBDCE3OrW1leicN7XRbc8U45zc6DrO6ZqvSMsrziKTfE0NgAqk/ezBj9O5uJ266DaXAwAAICfaP23uvPNO+t73vrfE6ILFV9u2baO/+7u/oz179kSeBhaXfe5zn6NrrrlmhfDr8ccfpz/90z+NPA0AAKAL2KzzvpDv+rmQNwR0W8Tqlh+gCjFvsOtwwBDT5qZuY4KZySSdBNAO3ccA3fPXCckPY7Qb5yBuBSB+fA9z8RkK6yaGwFxOYkSTlvuZ3wtpNy6RWgisWd3oNjcFimDo15d8IXPWNasbE5G+pEX3M0jM5fxEpo73rEa7NauJ8wdp0XmY0zlvURDz3AprPBCfrYHOg4HOeVMcrdsdAAAAWdBr52AZZ8+epT/7sz9bsnnFoqu+vj76+Mc/Tps3b44tLYODg/SJT3yC1q5d2/xbI+IYC8L27dsXW1oAAACkhc6eS8I+vNLtQEK3/OjCxXmdnpsmOHSVt7x0GxMsGJeAhMDBnvdHmBjjMM4Fb2uajdvaoOcU7iI42PM1P4t1LqeZeAAGdCAxMM5J+yzTbZyzMtmkkwBSiO5ij2ieCZjPBQECfsnReSjAXE7aMtBtLodxTnLk7AYhELPjDdkIENUmtvmvOOrQp250G7OBOujUjwLh23kwxQYcUQIAAIgDrWehn/70p6lYLDb/zQIrnvy84x3voCuvvDL29GzcuJHe9773NaN9NXAchz75yU/Gnh4AAFCSVC9ijXA/HnqkL73qBotykBQwMPFXWnGh2wa+acHTpqzo7oVOt/mCTuO7buOcgUhfciN3d9C2n6cZ3YSgmMvJjN7jAMY5ecUQuu1jWZlc0kkAaVzX6ZovDZ4JwoRZo/kc5nISo/k4oO34Hdkxa5znD3rVDcY5edF5TVfvR/rmT3V0On+AkxKJ0ex5mrr8ecGQtKxQNwAAAGJAnxn1MiqVCn3xi19csTnS399Pb3/72xNL12tf+1ratm3bimhfd911l4hMBgAAAHTHCGVhGfYBgk4bdQwMhSVF982SmKO6SAdnX9IDGYxxAITWmVCUshqXaGRAx8C4BCSCkfZxTt686zaXMy29xB3aIW9X6J1Uj3Fyo984h0hfIBl0M7qPfP0ZY3HpVDe6CXX1Q5+2tgKN+pFuYw725UB8jU3ncUDnvEU0zsXUHoSAX6M1K84e5EZWW4Mw0KkfxdMO4EAYAACAXmg7E/jhD39I8/PzK6J8vexlL6OBgYHE0sVp+Omf/ukV0b7439/73vcSSxcAAAD9Dhi6b2aELPrSzVAYB69So9Mhf1o2ISNBnEfEVGaatTkcSEiMIY6/SFd0zlvoZRC3cYlmh0W6zU31odGudR0LdM1XRMRpXKLZmGCaiNoqK7quVdOSP295N3xE8I00SXqPcxmMc3Kj51iQ5jEuELE7TtdnnMO+nLzovm+lUz+Knngd9OlWNxjnZEffuVya53P1Naif/CMKTlDgpAQkRnqHOOkLIM3PHwAAAPGh185BC/fff/+qf3/hC18Ye1q8pqFdmgEAAIBIwlWHvObU7UBCt/wARdBc7NGdsMWt4aGdAZ0FAzqQDKl+vvocsuI3FNbL0zgE/CAx45IUz+XCd1QSHro9fwxE+pIarccBGDD4KKuYb6fZOIc1K0iDgb90+DaUjnHuq1m9YIwDSaH1PDXs/Mc9l9Pu/AFRW2VFa6N0nfMWBRjnAmNlMMbJSpwR7JJAt72faB1rxvvM020uBwAAQE60fdocOHBg1b9fdtllsaelWxp4gsGRvtqlGQAAwNIxE8i5IaDbBoNuhs8AKIPvcT6m6BCaPX+sgIeuuXxyUZNThV7NLT15UxzdxjnM5eRGt/bWRBwqJ50ItYChcDBgKAySIu2Gwn6fYbGWl2bPVoxzkqNXc9N/jqoLGtVP0DFOtzMYKYnZODR2dM6b4ujWv+GMSW60HueAnGgWhQ3rVZAU2JfzV1pxottcDgAAgJxo+7Q5cuTIqguG0dFRSpqRkZFV/37ixAkh/gIAAOB/YZaOxa1/r+mxbp5ptFHHwBOLnOi0Idze+1TSqVCIOL0zabZRFzQ6BKJKRI/+45xefckfi3XrtY7F5zDOBQXGJXKi/RiHiZy/+VmHzxYG19Dmy24Ip2I0rBvTRNRWaYFHYY2R00GJrh6FYUQHEkHzuarqXvR1ms8FdVKCdS7oue2lfZzzITroNuZs3n0D9Q2EZ3uk254p5nIgCTDG+XdSEmsUHI3GOYxxMqP5XCflczlfY1zMZaXTGAcAAEBetH3azM7OSrvIa01Dq8jLtm2an59PKFUAAKA4EozvMoar7vh2yGXWzbhkbPNldMULXiN+qoAZUBCR7x8OPS0gTX1e13x5w++4FKdITreIMUEPJGBgHBcajwVt+rlOxluqopuhsG75AYoghjKMZ/7LLMB7KSfoehWAntF2La6BGEIz4xIY0YEkiN2JmpSEl//1O66mOFmz6VLasPMa0hnd9iflRPMxIPVjnL+y6vxMiPeMVTUwlwOJoLkTltCBIEKZsgPgYtND25O2/8maLgAAAFqh185BCwsLC6v+fWpqipJmenq67XsQfQEAQBewTvJBt+gQRrzGJYYhBFHZvgFSAXjblBXNBwEYCvsoqri9M+nV9gKLvmBgHAN6tTXPfUnBPrbj2ttp+1W3RFpWsnnaVEmcF3Qul8n2hZ4WsBrqtCV/6JqvJEBZpkncoRMqPSuDoNu6SCdgKAxibW+6jnWaZisyOjwTsvl+6hscDfl+nd8eXr+dNu66nvqH15Guz1Psy4Eo2562Y3tPGLHt23Vb42VzBdq8+3m0ZvMu0vn8QYUxHMhNuseyIHmPsbw02k9IdzvTK+Kdcuict9DpLATecMm1NLhmY3h3w349AACAGND2dDibza7690OHDlHSHDx40He6AQAAdEbrhXuDkLMYeqSvLovYxv1MRbxRBk0nFvMRY+i9mdrJo7CKeY4j8l27chlau0Vs1oV3o+7ln8sPUL4wRCoQNGKXAdEX6Bn1xrJ29A+tJSub02a+2m0OM7J+O137E2+iS5/zE6T3XE7uegJy08noS8W5nG84/z7yWS+rmByVaFb8WHeCBBtf+7d062jLkTx7uo0LOkaHsKxsqAZFIAq6zU2A95KMwomJkUoRrEqRvnicU524I3XGjW7zhWCEWb8hir66jF1mJkvrtl8ZexTFoJgBxwM/e62gBzQd5yBsXSwHSeezOs/RAIgLjHM+xjhDrrkcAAAAEAbaPm0GBlaPIvLAAw9Q0jz44INt3xscHIw1LQAAoM8CTs7NqyQ3YuuHU71fxyteD3FVWewGPfyCIAL02PCCvSch67ZdSVe84DXU1z/i41s+8ihheVx6w8voihfeQYNjm0hbj8IBxWJxoYXhguZe6NqKIVTMc1exQs83iPDawedoshuZ9Ry1NZOVdmxj4R2/tEDBLt8zKo5zkdNp7hv/s2H9jqto067nCCG/9KA5yYvmddOxX2KcS7Q8uq2FBkY30NUvegNdecvPkAroIBxghtdto217XiAic+y+6VVUGBojLdC0v6fdgK7reYJvYVeYEXAMzUSj+uzLFQbHRLpyfQMiIjpQGE3H9kjHRCP+fW01xrjgkQlldxzKYx2QmQ79FmOcP+dVhkm7bvjJcGunyzjH66ZrXvxG2nrlzSQ9Acd/f2fmIAj6r9uwLxdqewh1Lqd72wMAACADauwIBGDHjh00Pj7efKDyT9d16bvf/S4tLCxQf39/Ymn72te+tiRdDdavX0+5HDzXAABAIFKwfgp7gyL062lnKGymV3AgMfpv1GlEwI0t3xtinQxyEtpcU2IcCCr6kvBQmQ9JSgvTwkB7w6XX0on9yTv6AEHaXnrGd89jU8xF4tm4RPO5nGwGxiyiXr/zarGnlcn10cmn2zsSAmkQg+qHTIehw+u3U//wWiovzFDlzGGSG3nKDcjdrsNH57x53I/w6vQokig3vc19LAnF7SqtPYPAbWDN5l2tf0kwNcATnQyFXVe56NTFuUlyXSeaG8QohvCDEmvWgGUjo0O9rXtu0kfQmgI67f3U7WlIa/yJW41Yz6u87svJOA6E6oxJsjlg38AoFYbWULVcpOF1W8lxbDpz8MdJJwsE0kKoN8Zt2HmNmMvNXjjl6fP+15+dzlfN8AX8hjfBaFDRaLwEPANXIm86oO+6W+89Rw/4yL8h6VwOAAAA6AVtnzZXXHFF83c2jGkwNzdHd955Z0KpIvr6179OR48eXZIu/smTsiuvvDKxdAEAgDK08zak8cK9FzqWS9gbAh6vp8ZGXfADCVUOXJRH0y7f0aOwqpt4fpIdehZD3KjzcS0VjEuCbjzKOIbvuvHlwmM9R1nrH1lHqlNva4r2dy+Gr+3ypmCWlR2XexbwqzHXCToWmxm5jEt4js1ir2y+UPe+mmBn4UgVYaHr+k3ky0jHmOE3/22+0OX9MMvM0OqANmgaRzbsIJnJ5goa3F/vvq7lmjUhhtZuCXW8gaGwnKjwTAEtdJhvqzbGDa7ZSJc9/xU0vN7PHD7AeqNduRgROJ3zKrpVYM0atGxkE0PUkadvjG3Z3ftFEl53R05M0fniYPNlN9BlN74i2TPWEJ8N3cauRlpkjPgXqmNNyc5WeD9u21UvpEuf+xO0dtsVys0H2qHrONf5bEWtPHMkzY27rg8WXS6MdhrBMZXnNash1ziwGkHHAtnPj61MsoEK1my6tPeLaDJOBxXwpwKv2fQUtRWiLwAAAGoh/65nQG677bYVf2tE+/qrv/orOnjwYOxpunDhAv3RH/1R20nWLbfcEnuaAABANdobCqu3gGXvTGObL4vuBl3KJOxFv+lxA062Dfswy0f2jTotULCv+6PTQaFaeQ8+xng04GhEzu148EqJoIRRV8CykfFQmcubPdbXD5LV6iepo8MGu5oHzT7FDb6v3tl4JJPNxzt2NcZdReY7wT0KyxUBQ6aDOpnSIi0aGdDFQaexv6NQOGJUMBQOPpeTbwxn49y+gREaGttMO69/SaJpGRzbFNKV9O3vnecLhnIRcKLdl2v+b1XWbNpF2Xx//AJ+FdarPQgbwhSph4KhdDdJHWquS9thJH+PhAT8Mu5drSCoobCEzphkWifKlBZZ6VhGihVffnAk9L2xJNuTbgL+oGOxnOJWoAws3NXlWSDDVA77cqGvrWU8W+HI1Lw/wo5pLnnOS8lK8HwktP0KTYYB/3nTOeNRROYK2UlJlzkaiyq37XkBbbni+aHuBwIAAEgXauwIBODWW2+l/v7+JWKvxu/FYpF+9Vd/lcbHx2NLz8LCAr3rXe9q3rMR3asB//7yl788tvQAAIBuqHYgyxsW7J2pf3S9ny+FaigcOl4jfUm4maXyRp0qxjrho1af90wnL7jKZTnqBHcXtob6bPAxnqpw8Bp07DAkNC5preakD/RCMTD04nlMR1Q8jDWC5tEIpUx2XHt7qJ4XEelLVuOSZe1Awa6SNvSZyxFt3n2DEON4Juw8GsnUnYwGGKHN5STM26Zdz6HLb/4pYVgSyIO1ZPORpOejkaORtnXrVS+gNVt8iL68OB5Z+oWub4fZXrpdq+k4RZE2GnROVhgaI7kwlN7Dbocq7ShYRAM9nDHFkdzO7SC58lJiXy5g+cg4l5NqPyeMuVzEDn4Sp1PUVsUyHiy9Ps8MYnSu6V3Ar0Y9BT0Llu0M2XHsZX9Ro/y7okk2dBa2xjEmd7UlCXO80eyMNeh4JWPe2BkTRwi+5PqXUP/w2mTndoo846TdE1as+HgfeNNlz434LjE6Su86bzSFyHLt1sspk+sL774AAABShXyzyZAYGBign/7pn26KvZhW4dfRo0fpzW9+cywRv86dO0e/+Iu/SD/+8Y+XCNAaaeK/cWSy7du3R54WAABQHl3OXOPYsOjqaDPcaYB3Q2G5NuzD3HRL4jBi3Y49lMlE580QyBXtQCmayfWxkR8kjx3tSxKKDqGAGDNoe5LRW7JM5S2jx2X55j+GHmNcnAevcaVBNwF/wANU6URfK6pFvb6yajvT9AC30zpPtXFubMtltG77HsrkCj6/6dOArl2ZdXhmRL0Gl9EAIyykG+OY1rpJeGwIba9Gre4e2txbtXEuloiCsfpi0mvsCupdXLY12YpxRdM5kD50mMspVndNMbuvuZnkbdSruFWRNWsQZFyPy/T81+1ZGL+hsDx12fN6Mqzrx+l4U7Xyj2hdLZvTOddxpJkPZHOFSKPb6T+XU+wZEcceapd9zLCf8V77jxJ1xY5HA8zLZNyXk2mdE1ZaZJqfho5G5w+bdt9Aa7deEeCbXseSbp8L+/zB+9ilxDgHAABASrR+gvzKr/wK5fP5JQ/y5cKv17/+9fTRj36USqVS6Pd3HIfuvPNOIT574oknOn72Pe95T+j3BwCAdKHWAjY2Y7VO5RJykXneqFPFgC7AppLIW4u4Ow4GRtbT7ptfTVsuv5HShEwbkKHSKcKPYnkOvLEYktP0RJ4LTeMSBca5gO0JBxJdCzakq6jV371jtB+/Vcyyz4iC4ddruN6nvR4yqGJAFzSd0o1z8U4tO6PYXCQRuoqYVMJI7ZxfRmPa8CJ9yTdPlWttF0ZaZMpPFOgj4PebZL959NS2E4gOoQpB52TSOSqRaowDvUWHUKwuY0pvuzLjv4cdzdDrOCzdmm41gm6byjhPlahvhNLmxCXkyVOcKDmfk2z+F+kaT7Hq0WUu566I9JU0YTUExRpUCp0xxWbbEKMtidf0K7EvR0agdCaRt+7Ogw15+koYUVslmptGQec+q1beA0XY9ZNHD2vSsNesXt/XvZ0CAACIDr1OgZaxZcsWeuc737kkstZy4ReLvf7yL/+SXvWqV9FnPvMZOn/+fM/3nZ2dpS9+8Yv0Mz/zM/SHf/iHND093Yzo1Xrvxt9e97rX0fXXX9/zfQEAIB20P0RUiwCeNgPeo/MnYvRc0tyok2vDPkwjOj50dWO3zDUomy/Q8HpvEUOT3CjlaAG9ol5fDzHSl2p5D5LeQKJW+Q5xVPDOFLQ9yeY1fQW6RIfQlY7Fk56y8+7R0suBR3yGwk1P8YoYFAcZi3meKls/dl15PAqDdEVtDdbWQjYuSQgV5nJBi03K9bhE4xrG2B7LSKK69EbEjko8PBfC3ZeTfK0Wg6Fw6BE3IogOoQ9ylXNopNxQ2HcePXlOjx811qxwxhRNserfT6ON9EVKEXju7nVfrluBGGkcu2JwxiSZ2MORTPSFNaunQmrzd1KKOOo6fvGIRg6EjWDpTCJvm3Y/l6xMTolxJbQ9WYnyFDo6rVk1qyc//VuJcQ4AAICUSHjKGy6/+qu/Svfddx898sgjS0RXDcFV429nzpyhP/qjP6IPf/jDdNNNN9Gtt95KV111Fe3Zs4fWr1/f8R4s6nrqqado//799NBDD9EPfvADqlarSwRejXu2/pvZsWMHfeADH4iwBAAAAEhJDOtXT4bC/JmQIlN53YQJY8O+b2CEMrk+mps8S1ERZIMrycOIdvdmoz72RMditLHNl1GtWqaZ8ROUBEoYTyZNR0+iam58RbZZ7MVbUkJFpsRGXVDRl4yGwglsaPN4tlyIEd611ezr3utHFwG/BGkWNq0xCvh9fq4Tub4BGlq3lS6cOEBSib4kFLbK51G4N5Q7ePSLVmKIRXzb/hrhfTbuMjMkjqAQ0lic1Jq1r3+ExrZdTqcO/KjjuJD0szWUNatYhija372gTTTDGNaMMZdJ93WoWnVkBJmXJdAOB9ds7Lg/CQG/Wggppm6GwpGnO8boEIaagoFOkcv8OpBLYs+Ro0PUamVSgXDmYOFGqZONznlTLd/hCuhXuXzXIkliX64xzskmRgprvAo0B4xzXy7B8aFh4xYG2o5zOo1xi3nxM841nbYJO0hPX4jVUZ93Z3gKnLFyaQeYcyax58jnMENrt1BxdpKOPH73KolSrG90Rbf8+HHGRKkgtGeYGAOTmcuFcd9svp+q5YWerwMAAEAt5LbYCwHTNOljH/sY/fzP/zwdO3asrfCr8W/btumBBx4QrwYDAwM0NDREg4OD4ne+5tzcnHjNz8/TzMzMknu2RhZrJ/jif69du5Y++clPUn9/fwwlAQAAetD+0FWtFWyQg4iLB7Vh5TVsN3TxeWcyrSxd8pyfoL13/z1FRoByFht1cQf66nIQwt6bRtZvF3MPjgh2fN99pDZq9fUgm0HtxjkZD2Ey2T6qVUuhptfv+CjjQXUYBxIs0pw4fZCiIuiBeNyHrn0Do2RlsjQ/3XtE5lhEX6FtNMvX36P3Jqxinn1GEjAisC4JEa9ztHCeRwZtufzGiEVfRsC+He9kbv2Oq2jy9CEhzJc9OkS40UhU7PMpy1ugPuT3O50FF4lFbVVBwB+QJAzoLrn+pTQwuoHmp9qIIpa0m6QF1Zr03wjRZowLghGBGDjE4tStboJE+koCNp4b23q5mM/NXjjlQcCfXD1lcwUx5wzDgYlu7a2JyJcuzpgSjg4RyVyuq4e7+v8VEH015px+nXzE7YyJ63Djruvp1DMPtx07pDLM1nVsiis6hGrlFyC58Y9LEUWHEP1ObtFXUAFu3HNAHlcdp9b2fZn25QSq9dOY6RRNWbkxLhY8ONcM605+nEQpMpezsu2jZ8nkWJP7hJXLU//wWun7hlTzSlmR0C6iE5lsvu3ZV8MRRrTOnuJbs/ppv2G09Uuufwk989C3er4OAAAAtUjFbGlsbIz+9m//li655JJVhV6tgqyGKKz1xeKu06dP0zPPPEOPPfYYPfroo3TgwAE6deqUiPK1/PON67Req3H9xj3XrVtHn/rUp0SaAAAA9I5yXpRl8LSZlKFwSBt1UW9AKRMdwuieXo6KxoIvHQhf/AiCYmVytOGSazp8IuI+6uH6cR9INL3ohWAoPDi2mbZc8XyKDMMQnnpVOJC45Lk/IcrC04FvTGNDtOJMjce3eDVM0RN1XmIuK9NQ4zA1WgF//NtkLG69/Oafoh3X3Lbq+ys8N2MOJDWdngMyHaCrsMYOPQKPj/JXITpEUOLOG3scHVq7WayV2+0FyNQ3wkiL6DsS5Sl0Oo1zEk70OFJ8WwLXk0enR54+l5BHYUXGKxZU+S+D+L0xsbMlNraR3VA41z8k1ZirGqqVXaD0+v1Ot/laQmUWxnxnw85rhGg9MgyDsn0Dvr+WxDx1bOtuuuIFr6GNl16/+gck6hthPAtV6+tpzl/08+5uG5lhC/i9R21VRRChwpx2dNPOjmuG5ftyya55XCnXXFKh0flDfON1m7OuKArSo/hCBWdMXD8sjPf9vQTz1v6M1dCr3cuTnUjo1Ddlm+flC0O0ZvNlHT6R8PlDQk7S6x8NwVF6JktrNl3a83UAAACohfwz5ZDYvHkz/cM//AO98IUvXCHyaif+Wv4K+rnGZxt/37NnD33hC18QPwEAAISEXOvXaBaQQQ5dO7598bkVBt0Wpo08q2JAF2ShXT9oSSbUV7u6XOEtVLG+kjq6eQGXiCtecAf1Da1p/4FmciNKt1zFEf7BpEFkWdkwkrP65Q2Dtuy5SXpxK6eTnxtrt14uDG7kr2OJG6YUdPC0qWDZ+U+z4b+0uhlWh/ls6HYtxaooyAFqUsbSLNAf2bBj1feWe1NXsa8sQfHkd6dDv5RsLscMjKxv/+ZieiNtc+LS8Xkn9ZqXMMaCDZdcK2V/TdI40FO5GvE5sFj9/vLVmWy0q0fZDEsYNk7v7zDO1fuon3SHuy9X/0gy0SFUYfPu51H/8Drve4mJNMPON10p4KfkEGeW8vVVmQh7v1wOfK5DZY1g7qNuwpjv8DUidcZEFOj6sUdtXSzzXGGQBtZEKIILCf36bxSoc/7Ac7Whsc0dPhDtOasfJ3Bh4Gcdqso5KzsBkb0fW9k8XXbjK2nndS+W/4wVUzmtxrjuBHCC2vys1/2vbucB6gr4x7ZcJpxzRAmvVQfHNvn6TpLjd7s9gxXRXJPsKsr10wRQyJbksue/Upx9tSXQVM7fPl7XuVqoR6zhzjM9Xiic6wAAAFAG/U6BOjA8PEyf/vSn6b3vfS9ls9mO4q/WV4PVxF2dvrM84hf/fPvb3y4EX1u2+PNQCAAAoDkYtykKxRYzMSS3m6Fw2AnxeiChige6IAvtJDbqum1UrPQonGBfwaZDb96ZJBvnMrl8NOmNU8AQEaEZ20WZfsOg4bVbhSFkJtthw1WCSF9+njOxHQpHGelLrq4eKh3LR8J88zhWGOwkbk0+0aEal8RoKBxHXw0m4E9gm6xLUawwLkmSMKtNgv4TCR2HObnyPLrxEtp8+Y2JpqFbmYQ+VnhcH4exZs3mCiJaaVRw2eT6Bn1/z1Pk1IjwMsbG1U/aC5fCcN7gwaBAS0KOztcjbHzVOTJ18GdRuGNTjPtyEtWPV/L9Q3TZjS+na17yRk9GwzL2veX7ckmmUZxd9nh7Gcs4XNQxoJMmErqEnubD2qOPsr3ztTmS2OjGncoYCrfbE5Qq0qRq/TQBujoXkogd172ICsNrE01v12jeCQn4w9i/iqP8us7HJYEd3Q2v2+pNwJ/kXA6qL499Vh2nczwXaHvmFttRVzvnVWGXmfe9klAid5oWbb3y5p6v0/4GdTucS65/Ke289kXCcaXXdMVOl2eVTOcPYawfvNlIqUun/iHbOGdlsvE6tlx5g7A+FDqhjHM+xlUAAAD6INEuXDzwZOKd73wnffOb36RXvvKV4m/tInd1EnWtJgpbfo3W795yyy30T//0T/T+97+fcrk2nkQBAAD0NL6rhT9PS5EnI4xLefa0qcb0I6ih8PK5QdKsPJDQA203MMR5RPsNdqUIMC779c7U8T6hG1v68c5kSv9sE6VjmuLg9arb/z1tvPQ6ScdwQ8rnR9TzDvXmNT5QyLhk61UvEN6sQ68nr9/rcEjdfD/UuZw8fSwMMtm87+8YhrUYAUEeVkaHSNTVZoL3VoOO45hkxbftqhd2FADF8izqFLGh2xjo91Z+5nJhzDmMqMdVg7ZcGSA6RMzzqdZyl8kbvUzzStVQZb7QqOOk59XdPaeTmgJ+MihfGIrtfo17dv+QUY+AIFEdr/CanjChrbl0XbMqJODvRpD0+v1OnA7nPF3PCDEaVrhT0VWvz+W3/epb6fKbXi2ixHr6WpKir3blKlHXgMd8L2XUYb4gUV2yE412IpwmgbflJMpo0OgQIcz7WFQXNWNbdgtBhNcxrk689dP67FNhrYNxrpfCI+kEXzuuuT3ZuWeM61U/11PBsWajfrhPDq/fRiMehfxt51MxOshpxbKylO8fIWkIxbGmZJ095c41OxL1WXs3UVnokb59XCu0SF/hXAYAAIA6yL9qjYht27bRRz7yEfrGN75Bb3zjG2lwcLBrZC8vL6ZxnXw+T695zWtEZC+OMLZnz54EcwwAAHqg2uFqsgZ0Xj4Svxc6WQ9UwvAqI2MUs+XGJSqUf0dUT39XOuVPwrwnKN6Qsi0vpik8Y7sovVsZge6VZHQIb/0/Jk/W7Q6AQ/JCpy0dy0eufLNnxjWbLg3XgM5n+/B2j2Q8CqsAC/aWH2LKWQbdPG3KZSgM9Bnnlju9WuUTjQ/6uagW8/XwjEsoUgZHN9L6nVf7eibJHrU1tqgkUUb6EhcibenkBVw1uo+DKz/v9/pd7x9npK+An10NM5OlS2/4SZINLtN6BAR5WOE1PennZNL3V3lPWLWia07l/Cbcp0OmdpHQQ44A6ed6oUX6iljA36BvcFS8ZN+XkynSV2FoLLq06D5OKrRm7U7Ee3MePhvnGUXrvcI4ixwc20SDazZS1Ayt3Swcznneo0uwGXp5fiR6LiWZoyop6bjGM6RzxpTJ5dsnq5e2FlpWExLwK+BYc8W9vEYxS9iWZN32PUvKd+22KxIXokUzr5Srv4eKVnO5ICh8/rBkLhfWOKfXuS4AAIDuJGixJwe7du2i3/u936Pf+Z3foXvuuUe8HnjgATp06FCgSB3bt2+nm2++mW677TZ66UtfSv39/ZGkGwAAwDJUXdhFukHl0QNubN6gwjRkibi+A3p1SWSz31DHUDhUIYOmXb6T0ZeUIqdOBEquf6M3GSNqhLHBFrZxTFjtKckDCW+GwnGkpP1mbCjjXMiRTWSiU/lIN8Z5nNdEPyfplow4DYUlqyMPbNvzAjqx/wGqFGepVi17i9oqnaGwZgJ+BduR75lMO8NX1eouDj8lXSMahn8/L9u9ShgK81TRNGnTrufQxkuuo4nTB+nUgR91/Vrs0bZa6tBiY6YUCJeU6+t+6WTcL9EY760eok6vhzKJdS7nfzxs+30yKJsviMiqXuZYoeCpqORpg23ncgmnMen7S49WBnQB0hv23CuhMkvamDbIc8q7oXByRn2hRFALibZz2pCeqzqPlZ3mSKrNY6NOb9zOmPxcK7R1nWJ1/v9n7z+AbEvuwn78e+7cOHdyzvnNvBz3bU7a1WpRlvipCMYFIjoUuGxRQIGNKeNAKMDCCKqgyohkY5NMMJigwB8ktKy0SqtdbX4bXs553rw34V995t375s7cc053nw7f7vP9qEb7buru07m//Q1a2GxMt3ILsBLKLGme8+b+AcNaE18G5RtD7jZQsd+RdfDLn4Fc2sblcpvoHBiHYuUJuH7pbOg0r713pMm39PfNcrUTbly71CTnwIuxpRPn9nIJ5dVa5iRnTBYNyJQ8N8b2JgiCILSTeaOvGsViER577LHwj7G4uAivvPIKvPHGG3DmzBk4e/Ys3LhxA5aWlurfZ5G8ent7ob+/PzT2mp+fDyOGEQRBEBqJ1Mtx7EBjQFE4TDspXLUlj8IuUO0a8KIObHgUZv1Kl8K0c2NdBoce0eiFgCDKlQ4DG9EhDAo6ObOyqSiMKgpRpKKwQwPYBkkCfPYZNo+lStv09v6Pd8AlOr5U298w7mPSUqp2wOyhJ8J/f/3v/wiWl+OVkm3PMyzC3LljLze81zeBJ2q8WiNDT+fL8LH8eDbtUVs50lfrNCJ5b1jrl2rmQ7Xn7Wap1/+dy/ErCltU1mXGIUyZ5Mq54/X3ekbmrMwNkZG+PBm/OnGujmL3nuAVSfuYhnkjHAMrKnJVkIa6vDDuL7Y4Y7JZRHbWCjI6F3jqjIl55j939CV75U0SCyouA2+/c8KAf2tmKKO2buxH0ZG+zI+NSGdMCsri6/zmquyHJ3KqRKoiBQCMqDBuXV/x8D2fzTJVu/rh0pm3Gt6rtHUDJjC2GSqQjtmm1GRQCveewr9J2K9h3P+6vpfD4JyARUyNippqipZ8sfkHqvqc2103Qw8n56hEqY6bLV0SVfOT4/M0QRAEIQ4ZfUVQqVRgz5494R9BEAThhNWXFQMUk2gRrCk9w/pzoGR1XfNutFEhjQtkyupra+YjfbV29cO1i6c15+JPf2sgyRjCIUxE5Fv/r8UyROarSrnEjKJws9c4I30ZVg6PLQspl8jVm1vzmP1n4VC+UGmEg8mwUgcBTuWnjX2JKW1eOHkEVleWw9fFchu092z2tunPOPKR2D4UGQEsZ2XPnoiMcomM0lissrDi/s5rc6vMa7qaZFTmZd6jcLAlAuPxl5+BpWuXobWzF4Zm9xsuT8Kap6TPKXY+gYjYaMS6vfNqQPvZIbErqK0zsUhfavY86NocoRMJTGv8unw8ZZtha3PluPN8w3MHoWd0LtLoy4iimsn64jAiq5VHlTEExv5uMzqE3v2TGNHrGL42Q4dH9w+i5VV6Vr2dv7X7Bxeczm3JivdAbq8fDs7s3WL0NTi7D884UbCVc3a8q3CIi+yZ62Mislw1x0Ry8jneb0Z+gqy+ZNDufHkDLfkC2r2caD2YaHudBvy+bwcTHWsiI3Y+MuGnJKlObFUZRe4kCIIgJCGjL4IgCMITok9juZY8rKzcAkykuxDlF9QZv3j1jIldD8D5Y6/A0uJlOH/81eQfIBSklDd5obNqnEeCOt6KingXX/9SXiQp/UhxY2AZROpfyaUrRzSK9OnHvEZiFNLojZ7jMsRydAiFbtM98BdMiwABAABJREFUxo1L1+SxX3vf5rqq2KOdY96exQn4Ll0tKgqXWjtg7q5vgAsnXg0VFfvGFiC3OSoPzqEigTcPIrA1CSL73cqKJYVwm3OvFo/tCVlyG7nn8M+rW+oHvwE/I18sh+ds21CkL+mKi/4oTlnMijOm2wpy8V5CJJPmHG98FudyZUibFtK9tytOQBozjc9zdXVzRDW7da9sfXWvCwkoCkd+CJhgBl/q5zgJpWLJtcFXwyhuJPuT+aitG+VyAXT0jcHls0fr77V1D9lRXtatKIxsvKtEZszadVRic44xe8ea3H8Dt+Y5B2FyudGFw3Dy1a+E0Vr7J3eE8xyeezqVVl+egnBfEo15uVjzUhg0IBGY5/DTWNZ8scL3K5q/Exxr+n5HlZ64OsI3z8Wju7x8Bl8G93oNZyv3DPgJgiAIHJDRF0EQBOEUUQezuAMbRqOvO+c5kUOYWi9064dKlYdAkwZm+i9zan2nb2J7+O/LZ47B8q0bCcVil1/2FIXHdtwLR7/+VP11LpeH7qFpfB5zHMkDn3cmx6IZam4j4wp0AqgT1KlJRqUXOptjD1MUosh6UFE9fk5vzl3YJEfYkkpU6LexnknTlENyjCkd/xbmknyxlLiXY5Fea1G2zNFYF6XWdmuRb3hAu+/Agsxejo29zfrgCMCgXKK8DJzpqVCgM71n4s2PlAPrFRZRkSoaw/fzqoRBQC4Ha1sMXwyRuJfSayhl26NxZNZp+2h9X4usr1suTmtHH1y/fLbhvbbuwYbXVucH2sJ5pSicVB4j5Y3tzzEGdJpRpUyrU4bBzp4byRdKTuzlhucOwMqtJbh++RyU27pgZP6Qh4rCuMa62ejUgM/oK3aaEdyb1qPq8H6dY551OdKX6Uhl2PaNEfSMzIV/GGH3z76eNVWB9fzVlPqUpEFXg9dRCWcUVScxPFYwR/rC1/l1R5A17BjbgfsHqyTu5USTEzlnJu8VVa6rQg6EFZ01aV9AEASRPcjoiyAIgvCEeKMvfBg6cSfqtig8xPokaA6aX8QmKQrbYGO9dw5MwM3Fq3Dh+KvQki/C8PwhO/3fp75gnLiLtgCfAp1iT5tCv6nfXeBTyHExOkSx3KhsgpFq18AWJYPNnjaNEWWErqjPeXshESNfj1f6x6dcUi+v9aZSu5crt3bCjeuXwEcK5SrcuHYptp/1ji3AmTeeA8xYnx/Y/JfCyYFXZ4aI50uOEIjHIDa+PwXibRbO86raWLWTEoFIX8ouXXPo5gLT0SGwjvno/TrO8jpxXg2SxgI+61bt/RNb/99QHmXzHDoHUIF1Y4hXv/g3De8NTO0GPKwpXqczZtwa6YzO0nmV10GIQJvH72WTMhL9TBT+tLZES5bKTp/RWrFcDY1EG96rtPMVy7LRF7sjmTn4dvBbUdhzkow1o+rbVqAv22eNxKnW1v0DfuNWVxDuR9anGesF8G6OswX/+NN9bo37zI5czgVaOxv3cgymk5HkUM581FacRBvw+9NHdBFfR8jOrOjnBHv5q+vrtuuQIAiCMA2d4gmCIAg/iDnLYDT6SnWI4/VCl2zxJV8Gz2lWdyzqAvYLEuYdanB6D2x/4AOw7Z53bfEmvE7guICmrrEAPrLeh6IEcggVhbV4kwoUfU9xHxFRkkEUkSqKzf1ms4dhNGyod+Ypr3dsfsNHOegbX9j09cALL3S+wtosco2IiYqBckylivSAV5g/OLPXW6UQplgX9zxMSbdQqgB6DMxzUXWlNKKtt1NdnAErwjnOomfQen2YLAPvWVqVMq2JY9FtWjijQ7g4f7umXGJfYQGXYgkGBfVoJI0nZSJKRH6s1oBf4MuqMgVM2FAQ29gfKh29MDx3EPL5Uhi1Z3T+MJSqnVbLJPIZcbuGBPuRrchLyeW029ahAZlqRWHO6I3YjSGm9j665VlaCkWu3xq/60I6ZeiM9OWzsrHs+oDXUQkCLBXPdtQ/1XI5twgynT12Yp1ExsxlVsdUpO8oicYWtt033KE86b+FUiu09wxveT9fKHs5f7t2xxrOAz7v52Lmsqg50K6+XKyXKL058zjysNVXlMnl1CRDEARBuAM+LXiCIAiCUCz4t3mIZZFTbt64quYQJiWoM2f4hVIhW3F0iERYdapUhuUiQOj9kKQL8nUn/mGoyLByC/CBoB+Yvq+43fdzgSrlEj0P0DMyuyVtdknhAkOz+8PoXkvXL4X/Lbd1bfqGIaMv3coO3s6jcs/FxtQqxHtI1NcGEXOvib7G0Q9UzxMd/WNQaeuBxavnE/JN5xnQhtJO1F5uat+jUGnvCRVzlRs28YBwuEfPcWthn0tXRQgfWCHxl8r4DPjVj1O1B1yVcwVrm0KpCsu3lgyebc1ZfbGIqFy/8naPIQbVg3TFSX2OVl4U6n4IOPfQEn3QzphM3yaB8TmFa70M9ymm5XIbsw9CxySbnZNs+pKJgjSVT4Z1oyh/X+fReEXhKAcmgVvlVZw/xr6AsUw1xnfcB6Vqh3SZ0a6paBSF/e9DqUgYsygdlWiIyMe9p0u6Yw0/UttX2ntH4Mq54zH5KTZwNNjXy23dAHDEWH5esram7Ezi7Ty3flEv/isL0amT2qDe1jrnubgMtOzzzDlF0cn0/rc1XRvzxXK8XpBC5wRCIKxWivSVqvLiPow0NjQ7w/EhNeZ5dOBEklM5QHijcSvcy6F3kEAQBEEoB6mEhiAIgiAikLhctWX01T003fQCLwSJ0ErlIbBYaYdCseK8oI7ROTix5b1SBX+kL8J9wj4kpieM9tJVXlCnJv31z11VoKslpL78nf3jMLztkDP9qFm7tvcOQ9/49iYGX2bL0fx98igcX3Fx/Tp6zNrsn9GKfbX/6JtnQnF9ojKO+vwL5VbnPVIyJRlej8Js/1oz+CJ4UKRc4utllETUKrRniATjV/35q98LDc5yRjNUtO7oPANvjtLKIqKOzN+lLT/f0Bq11WOPwvERIOI+Q7iXM7AWcc0Bgf49UNNsHeyjZY6IWettituA38QeyLYhUNYU6Gzva5PyFy6fUB+RMwZ2wTgh6TyeIuFUUXBMn8Vt928bkb5QakYrIrZPY9vLcUSG1r6X43FUonie6B2b5/qesoiGBvs7tyyfHGtGVw3b59JeLsU8F/ORjfuHJLmbCV8Rhtc8H84iTPZWam2u/8OMvpJAKwc2THQ9kGFrKkMfhLokVsd9glGUzbJh1iUhCIIgcEO7SYIgCMIpZIRPNoy+uodnYHT7PYnPIeNRWOg3At5E0sLyGprbry5BABiY2g02hEx9Y9u3vF+oVPmF7ibJmHemehoIn1s3UWMfrYBYsr2F+4khZSaxuRevdyY2r8oqiKDtaxsJ/PAo7CtxfShWMRGlgZG8YSv33MTzNVu2GKoMIjQ8AEtzYHLXlvcjIxpuaQ+zeznROjDjsT+ifVnV0DwXX3dxl4iOVZ728tZ1+MzVS1vXILR29HHM07gvXZmDGWbktZme4dnQwJ9IhjwKSyKpcG9HgY7nO8LWOoK/SzjTKT/zBTA0s4/vm8jnuWZOpSodPRzl0VIc94hsF3tR0FwBq0KYFEaiysV/qHo/2Wz/414dBsLRqQlHZZQokXMehNbxj+AYlZnH43+jeI5gzs56hqGjb8xLReFKGOkrnrh1o61nCHBg2dhbWZs5tq/hJSY6VfzdhPl1pa73EeskTy5NJfVlMTI1ZuLqmMfoK9difk3FKJOOWsdoj8dVezGfRIxnT/ZyPpVB1XrunJyCIAiCSI2d0CeIOX78ODz33HPwwgsvhP8+efIkXLp0CZaWlsK/tdueZdii+YlPfMJ2cQmCIAiOw4wNoy+mSCbrTUoVfAc8tQXpGpyC00e+BkuLV0AnOg+vk3seahqhzZtIX0YUhUm4oMc7U5SgzmK/s+hpkw9Lgjpl0SHMtm0ul4fV1eXIz0eaRAjDhql+F6kojKLf+wdG5RJza11g3miK49mUKgor9uA7vusBaO3s41ago3HbtFIinRtgVxK3ToyyRHTUarCG6jOryNzIE5VC7VwbhHu08Z33wYtP/Vn8Nznz7R3dBlfOnYCbN64a28sxBcCRhcPN8wuf737ovnAKbi1dh2MvPt3wuRWDMKRjXbdHYV9JjLaM0lFJggFH4JjhGUcEBWYAev7Eq1rzrfUFXfuo4W0Ht7xXae9VFulMLZLGgxqJbJc1dW3vLaFuq/hejtW5cUdgCWcqqfGpun0VJ9c/uQsunz0GpjB9VmSRXK9dPB1r+E8kKQoraLMwCT/nuuS9D777B+XRDMMfBeq+p2FdZPu5y2ePxn6no3cUTr/+NS4HpRdOvGZ8XW8WubClUOSKdBa1nk7vexs8++nfBeWIVoHt6cH3vZglrBhEJJ2v05y5kNoGMpnWuWMvgwlsnFuSjL7YedWG3lLmnDF5Pk/WIyA3u1eLeHaMd6xpzlu8/SRMH2l/UBehGOfzEQRBEPqg3SRAaOT1x3/8x/B3f/d38Oabb26ppJqhl+xGc3V1FV566aWmn3V2dsLw8LBYqxEEQWSZSGdHuIy+aqj0ziSRe/yncQqJKciXKtqNvnRRrnZGKoi0FEqJv+8amoLLZ+IvY7KNuv7mrYJ2jOd0Ge90VtHdRDwXHorLMDJ/Fxx/6QtxhbpdNFUK8WCUjv5RuHjqjS3vV7sGQiXh7pHZyN8Ozx6AE69+SXmZVEZ+U4qoEhjBsfeIVjxB64VOe5vbUS7hIbXCz+1is/6g0uaLrQmdA+NCl64ryzcdC4BgwoA/xvNt2vw9nyZ9UoSu9QPRZ1K7duOuz2Yy4zqa+gJTgou7qGfzc3vvbVnz2hocf/kZWFtbDaPm9E3siPwdiw505fyJWEVjrxA8cwknj7zvSiPhpMS+o5Kot02dG8xkI1rfmM+sgzN7oaOJkSpTgI4tSpCDvvHtcPHk65LneVM4vpfzHFZ30gYEip1ZpKY2LweajfhjHRuo7W+V9h7o6Bs1Z/ilYT8XV79t3UNNjTPYPq7S0QtDs/vBOFjPNySXU1tvCX3T6v1DnOgfa//UxMb1qdzeHTofvX75bOxvqp39TecV3fuBqPmKGXxEGbTV9nI3rl+CrBOOuTVm/qZxbxH4W3drsBrxGa4zq5Y5THGaqucKdtfIdDFuXIsa50HD/ufWzUVwiaj7h2KpCqW2ThhduBucwIQD4agxl7G1XQqZyKU271htOkrnumJVWwima3bl3PHkAqnKl4YMQRBE5si00denPvUp+JVf+ZXQ6Cvxon7DQp/0vc3kcjn4t//238Lzzz+/5bOxsTH4m7/5G6H0CIIgCDFsGH0lej+uX7oKnMJkfpM5Qze9z9U3tgBnj77Y9POugcmm3uu0l0uwXk0od6A1QnKAeAMmfN6Z4vuT3r7G05eV9vdgPToCj5IY7wURU65lHjlXV1eaZ6ijDmPWkMHpvXDp9FuhYnCNid0PckWFKLa2Q0u+uNV4wlMKpVZ9fU6TUTYKJJ8LZURDU5EzMXWFDc+sbq0394DNxhUbs0WOaK6M6f2PwZEvf0pHwQS/r74I3JkweRimPomRsD0DwbnMZqWqzlvifBv3ucriaalmPlmySkTS7Bmdg2r3ANxaWoRytQvyxRhHJprWNazGBTr3Wt7u4+rK/bFfQHNmTex7Uu2kXv6itL8ENvbX6vv7wOSupu+35AuRv6m09cDQ3P5Yz+qd/RMojL6MzBGxUVvT5u+GfNq0zFu1MwsvjIE19HeW3tiO++D5v/8Dpek2z8z8ms7kb6fKbQ1RZIfnDoRGEATfnZ+KvWfifsd1Z0wSDtSsyuViEW0o9XOSyvOOSFos76l9j8K5oy/CzRvXoKWlEHl3mZAQqGZs+z3QOTDR9LPB6T2hInTt/oE9c3vfuiPOgcndUCi3ojf6Muc8IiKSZwb3YELEXatJGr6mL5JkRFhT9w+RH2owIsvlQhn71z/7fxK/y853bz3/ueQ0TUfcjamSYrmt6fsL978PrIJy3tA75rJKpANhb/ZyquVo6nUx2Nkt2uhrY9Zq2iTL/Z0gCCKrZNLo68iRI/DjP/7j8MwzzzQYcek8yH3Xd30X/OAP/uCW948ePQqf//zn4fDhw9ryJgiC8IvALS900ZJFM1mjFOKAAsMAXc8Vn+7A9J5QKHLtwunQI1PvyLbQkx6LAnbnEqO5YBGNRyqLyiVq88DZt+14Z8IpqJPZW0vtx+MucBT3E6ZAxi5TX//K38YXiXPdYW23/t1mRl/mL/CKlTYYXTgMJ1/5MqyurYSRJDp6R8EquqOLSNLWMwQXTxyB5eWl+nusvki2ytM+URok0b/LteCL9CUjSFfePy0aCLb1DqtRwlBubxKf4OYID+w5NhtBRF0S5zmivprAjAG/PaNu1/Hqkk16fglwGvDXEw3M5YfgzFJq7Qj/CMMehe03vR4kx4/NM2vyuNbYWFzKI3YM4JVFtTM8z23eyzGP6fP3vbehHJEKf5qKilHxNl4mga+8qIgzOndsjywll7NkvMCXYCAkc2ORJHBGMI1fL5nzpROvfBFWbi5Be/8o9I7OGy3dljIh7NuMXNACPSOzcP74q/X3QuM4hHMyLuTmMRvRIe70PfUGCbz9et0ZU5IjAbAGM4gfmNod/ptFbW5q9JXkoFTDA3QPz0R+xiK3bjv8Trhw8kgoj+sZmdtqxGnYiFq4DowY4uQaHPNt+lR7/i4joxMSmm8G+uY5tr6XKu1w49rF+nssUt+dL0TovqRytsD7G/X3qDz3rGzcr64sx36va3AKVm7dDCM550vlppFeWTpsz9fcwML8WGGOlzbrg8TNiVixf/9AxNddLtrpiGMOhPX3A470ldqQBeGcxMZ9s0irm7+bPkPbupAEQRCEDTJn9PWHf/iH8JM/+ZNw8+bNpsZevNG+RHnnO98JP//zPw8nTpzYktcf//Efk9EXQRCERrAaRKBQbLEkUGFGBW8+91nnBD7sEmVodn/De5WOHq7fju96AF770ifUFwpZHa3TvEyFUsV4SVwj3oAVoXem2O4X6O3Tlro+j9I/d5skXWDqiA6RUHFMENk1NI1u/sUGE5CP734Ajn79KVheWoRqzyAMTu0JL6zVtJGv9R+vKBzVP60ol9QUyhKM1HRfgOmJUpEeZuR47uhLMQoISSTUryaYMgnzfnz53LHQ0DUqkkRTsjQvWvJ86wUSxphW6zRWfw5BW1usG2YotXT9cvyXWPC7GOUcPXWop07CsmYoREnUhbiaNkMwdpAZ8GNUQDDmpMRCmpwZq0oITMLOq0zx79KpN8JIEMPbDtHepGmzRLTL2hoEOUUGf57OddJzg83tXGSUMQ6DiZQRE2w/e1z79U/shOuXzqY4s9qh0t4NMwceF/4diyzBHObcuIYzQg5Tyu4ZnpWLRNSMIAjXgEK5CouXz0O1sz+UU1y7dEZJ2l7PcZER7d27YxU31hHPIflzlZrCaX6aYDSy5YM0xiTpKFU7YGh2n/F8nYI1S9QyrMxPiafzXNwdQ5y8U/M8x5xXvPHs34UOcdkd48i2Q8k/Cuzf8eo6r/L2P7a2h84WAeDZT//u1i+s4bs3HJ4/BMdf+HzoMLJc7Qr3pLZBKc/XWSaEj2v7+aKi5PpOGJXVggF8a3svh9EX37rDoqSeOvJsTEKipSMIgiBcJ1Or+s/8zM/Ab/zGb2wx9tpo6NVssU8yBOMhl8vBt3/7t8NP//RP31EeCwXxa/CXf/mX8BM/8RNQLBZT50MQBOE70YeyOMVEix6FJS5QFGaeeEi1pYTW1jMcXsQpuYRDBvPesnj1QsN7oWd15AfuSlsPLF49r6zfMw+Dp1//2ob3cqEhyfGX1yOtEpGVJzXP2SPB06aRvAUvNFPnl/wVoXUHoWK9dL6IFYW7h6bh5o1rCr08B9DWPQAL970v9EjIjILRXp4gQlYhzMZeLl+saNTE4DVuxasEXCxXQw/kbK1fXb4VjgOpiKaGn5H1wa6hqfBP+LdY1uGIOhvbfg8cfeEfFWXRPA8V8rFaPfo6X8oY8Nsktl/LFFfkN4HZehEdwwNTu+Ct5z8X+51EZWiEbW4cpFUQubcIPH/wtCT2aYyOStSfr3nnrXWP8ebWBZF5TtX+2vTaxpTo2PzM/nzbyynNQjKSC1GrHwlFYYz1qrlIXM9scS/U3jscGk+xqBDFShWOvfj5Ld+ptPeE8+H1y2ebpKCp7JqSrXb1h+stVqMv1f2BjcdwTdjkyAXjmQsVkkqvKO9Y1z/UnHnyXGfUKYBMXSQUD+eYMR3qK8AZxSUXwMrKqht7DkwkGDE1f1ufcVNNnsrW6e0PfABuXr8Cxdb2hqg70W2KQV5muL9J3R3hGhOd/ePQ1jUIKyu31qOacTo0ZNHfOgfG4cQrX4IsEOlEC1l7YmR9XyZWf7rlcoVSK9xaut7w3vDsgduZx/xQayRDnpTMOknf2D68bcKceySkmrJUBEEQhGvgdMujAWZs9fGPfzw8UNUsudm/N77eaAS28U8V7373u5tusK5fvw5PP/20snwIgiD8Bp93JhlQCGWj77D15Xcbphw/te9tMLXvUZja+wh0DU6CLzCPU5svwVh0MBRtHjNWZBRmYjKBvvHt0N47Ej53S74IYzvurRtFEKoVb/X2rWaXukx4l/g7zZrCPIJfPcJhdUotsYrCYRIayq9LZ8UBQbzKMm505NEwt6nIw/T6bJI4g/QYb8O8F2NpDLYbi5K7YxQUWVypG0ilvzG9t9icX0ffKMzd9STM3/ueusfNtGmiwLABq3BUKBMK9ZFlCsMaqcvHS2QU0rDWad0qS+pnKr6oY47gTbNrcAqm970tPNcwo0qZ+ULL/khXd8HaDXWhczx6XJfxRkxxxk0W5HJJ48+E4amtvqDwrMzkOmnzMk7U3IykrEb2v7FK4fqzd5n4sWHR8CCWiPOBlNZs7b8iv41RoFatkBeTXTNaO/vCaDIs4nNT1jybA3Q8D9Y5Q/fZCsmaYbL/xX6G8I41FR42r1uOBN3GxD0IG48jC4e3vB/KJlTl7+s8x86eNh3yxsDuOMptXVvvOhTeP2y8t+L6vq1KUdb/cDqibCkUQ6d5uu+1TLcxc5igDp19z8/5Lc2ZNdDcF5mx40bjJKZH0smh/yXdP1WcV7UgcGbldg6alIzf/Z0gCILIaKSv//W//lcY4WvjQhcV7atarcL+/fthfn4eurq6wvc/+tGP1o3E0tDf3x+m/aUvfWnLovsP//AP8OCDD6ZKnyAIItPEHGYKpSqYpqVQiv9CCkGd2I9Sf0FDnuvkWlqgvWc4/PelM29pycMGxUobjO+6H04f+RqsrtyC3tH5UCn6+qVmHkPTIy4MiVG2V8S6oVchNOhbuXUzDBmv7GJQUGDtHDFGD7aE7209Q7B07VIYJakGU5jQ5WlT9DmlFXI0wt0/bSgKayQx2oVHRCmuqhmnlvotBGjbULdyCVPoZ+Pt6oVTkMvlYWT+EOST9nL1wukrF09/0uJRWM6lpuDXJY1JbIK8qCoV6s1EMTBstBjkYG1t1UA++PZysSiOQKPS6F82OmQqgq37YPbHEI6kF6aFsM1jUOn87A6BU3sLFesqyrGuitjjX5yisH1FJ7XtFCj6nmKPwgKR2Hj3DcxJ05VzxyXyIkSZ3P0QvPG1v9cetTX1HFU3CkqXDNa9XGykh1hbOn0VUqq0w9rqKtxcutagVJdYLt0yVJ50jc8VGZ6bsM/Lof8QDUaAWz8gpOc4e86YZBHV3xG+d0jaqwWqx576/WniuEM4d+g5lzpGEITOFyptPbB49Xz4VrnaCe19o+L36cSdakVrwG+RODmmDbmcyvlCW9n1JJwvlrDasNXpGpiExSvrc1JaWP9i55ylxSv199jdHI/T2SxTl+UI2nrr3ssx3biZg2+HS6ffCMdl58AEFEqV+EKFH+nQfxNIP5wCLenfdA9xyjlM3NURBEEQLuG90derr74K//k//+ctxl0bX+dyOXj88cfhwx/+MBw8eDB8XePYsWOh0Zcq3vGOd4RGXxthZfjc5z6nLA+CIAivkTi3lFrbQ88zG4UQ7BB19cJJJUXanFZLS6GuGJZ0kEzjpSnt9+I9NJsl+kBqWhlUTX7s4r3h8n09ccgMG56VebMiRKpOQvCluWsxgdP0/sfg9Otfg1tLi6HSF4t8oCczhV8OL11TlmdjcjYMDm1FKkOE2vpW2SH0ZmFaUNs3thAa6J5+4zl7njbjfqf7QiJfCOc5Nsex6JTMMH1D7hGFCvS3L5feLo4xHfVMowt3w7EXoyObY9mL8hEgL4ZapfFytQtuXLvY8HZbzzDcuNL4niss3Pc+eOEf/lh/Rgj3cljhG/8ajCEMzjtoDHN50rURiUkQpuR249olJWkVijXFg82oqV+31jf9/cRGdIhSa8d63nGKzQiwFdGQvy3VyzV1Y9yRhaqImIoNAJspDxXKraHxkIvMHnwCXnnmr9BGwdG9nxvbeS+8+bXPwPKtpXA9HJo7oCknfXcVOOfN6PnCqrEcqvkPq1wuQkFdQR4Y1zZVyD6bnaitfF/S2lw80VO1LAAq5+Lke+Kso8qx5sTuB8O1WkmZgnXHmtMHHoOr50+E+mTMiSq7b/V5jlJB7HyFtu7wnlndmCNcKGMjI9sOwfGXn2l4b2ByN1y7eBpQYEBth43V/smdDU62+ia2K5EfZWGejBqbkQ5LDRjwMyMv5mAT09y0bsCf/C1lBPxpsnW+d2wezr71QnySifqG+GX6BEEQhFq8n/n//b//93Dr1q1Igy8W0etP/uRP4GMf+xjcddddDQZfOmDGZRupleXFF1+EixfdVJwhCIJwgfGd90G1ayD0DtM9NA0D07uVpc3Sau3oC//NFIXHdt6X6C1FTtig4zdIhB5IiuGkYFTNfYRaT5sZEKbpI0AbwW5sx70wvf9t0D08g6MfcAjOTF9IbMyPCat5lDEiBbO3/+cOLpU1PdFCVEeVSwzlGRsBgikmgi2PwkH9UqLR4CvuFybqjCOPbA09T1GzmVPtNb0W1bNGPl8KvXlSn+OrP0wwA77N6xYz4k+kZvAu4w1d7AcxRcBVl8IeyHWUX0GazdqfeWDFDHPk09q5LndRQbHSDpW27ob3ekbm1MxxyPttOuQiU+lWQKhFkd9I7+g263N1XPTHDQWxAlOm4sHF6BD41xQze7nR7XdveX9weq+yPLzdFEoZ8Afa64Pda2y//wPh39zhd0KxXL2TvYE+JYvSMmh4HJ+iyWze16BEqaIwXgV51MTMV7FGrxYM+O9krtDgtp6WIkcPqqO2pkG2HFjK7xJR048Gw1amEM7O6uwMf8fBJrVZUvuI7j+075kk9xu1PiVWPsXPoqlu4u5FswBzrFrTLWL0DM9CuV3TXg6xY02mbzBz4HEYnN4Dk3sehoEppr+VjT7gnTMmSfk+hjOrzS43NLsfpvY9GhqCsjEgBYIqJAiCIMzidaSvz3zmM/DMM880hHav/Zv99/3vfz/8x//4H6FYNBeBYmJiAjo7O+Hy5csN77MyvfDCC3DvvfcaKwtBEISLyF5iMg+/TGhQ48ZVdYa2+UIZZg89Acs3b4RGXxsPzZHCqcBfRWETgrqsCP10YLdPKiy/aS+yXOHVVeQTHYEv8n2040H2olFh/hb7NVOmYhEEmGc2Jqw/9dpXt34p6a5HR3QInXWCWFcmPA8a6A7K6tf4HGduvMisQya80EXmnRiZR2+98URuVZ+p4PtJn8X9QHG/UzEGoxT/UFxAxaE4OkR770ho8H3hxGvhGWdgatdtBZOU+diInGkwv7iLV1t7OaYkxC7NTx35at2Ar38i2ThdqlxYFQmsbH7wRNbeTO/YAlw+ewxWV5brxk75YjlTUVtZuZgn9mMvfj6UD7Fo7eyi/dqFU8ryyGRk6qg1VLNySffwbNiHL556I4xeywyXmeOS27mr6071iIHSRW1SBjvzRLmtO1zrr5w7nvBN1+QQ5s+lyupC8V6us38CLp1+s97GPSOzofEs1rUpkQD32Vhrvdb2z7lc6KhE4IfaimQlaqsmXBwTI/N3wfGXvtDgKKdraBoWX7kAmNFhEKEnDzt9oliqws2la1rzkI1YaDOCQFIERt0RPk3OEfW8ZI7ignveWl4uzoHKQVgFsf1eUZuZbnsmlzr9+tcM5CRXdzbHgk1D5uQIMggHCKdBna4zq4o6YTJ2prfE5FG5fD50TIS+rhX3h9regjm3YH868vCSuv26oC6J1WhQSi8WxcZ2GOgrPn+b8i3WXqEjq55huLl4NepbcQng3MgQBEEQWvHa6OvXf/3XG15vNPh67LHH4Kd/+qetbBgXFhbg6aef3pL3q6++SkZfBEEQkggfVDXM/3JKUlJaJtxfS1TIQYNCJRwd5UCetAqUGuV5GEZ8/t73wIuf+1P9GcWOSzudSHoeUXk5GfddpGOL1Vvv2Hz4x2hq9MWRhjNoKyrOOvBxnjNWbxLTnFWPwpGkUAQMVM0BiDwKS45V1ArDpgjwrQ21PFiEZPbnB6Y0heOystffmdFeR/8Y3Lx+BVq7+iFfKK2XSPFeTgieSGLG5zjB/JhuiWkDRgUN09rRC9sOvxOuXjgJxXIbVLsHAT3Mfl+xQQQzCmKRjDe/nzppn9e2mMisceiO2sr2isxQeXjboTAvvr2jIWPghHy0rOscyj+h4eOuB+HK+eOhEyv2mhlBbv1RUlb4+juLhlAzdK7R0TeK5mxpwgic5cEiGDNP0UxxiI2LQrm19mG6tKUiDqjAVH7iysAmjA5iSXRUIpKUyI845jcdzaZ0/4rYe1EMzGCfje2LJ49AvliB4bkDoaMHHagb62uGHfSkSdz8HMccEDCDiNe++Am9GUmOSxtyuXJbV/KXQqcD+gx1nYp4Kr2/QPQMjmDkvIepbylio1GHTtbHbVT92bl/NbpPFH6MZIdF1ve6nsLW1kpHT8N7VM+qKte/OZT/btqxZw90/86O3F7qLCF5vsEolyMIgiD04q3R15kzZ+Cpp55q6qWGRdv6hV/4BWsL3/bt20Ojr80cOXLESnkIgiCcQuUByQRRZzMDHkN5BM96okPofzY3CbBoCou9L4Nn7cnGSbFcNZZXpCdtx+pV++WXY/XRnJjoU4i9M5VbO+HG9UsN71XaGy8n/Pco7JsxmEFjCECoXKJdGU4WW/OAWoVTQ1kpxM1LbJXzj0y0KsKcN2ZZytXO8I8fSQN7XgU6DkVhr41nLMMMnnoqcwZywtmGUfOcq1FbTRHrGZ0pglneIzdVeE+Sywm0lfCcZCNiqwDMKKizfzz895XzJ7xRFC61todGXiyiIYMZPLEIh3iKak4ux/o5qw9CpOri9h+YDNWTSTPHrDtRTcyg9mUtZYjO0O2jn4q9BkuDGXqxKKW2zxhCGIkA61B9bCA0WjdQ9vj+Eq34r3sv1yz66MDk7lqx4ucqgXzE6zhZLmclEpjIZw7K5Qql24bqWDFwx+qOQ1eESEXz0mSorgxLujEbz8yJG0PRtNUkw4yknNoLOYOJPUnE3oLak8+oCJkuiXQ0WRPzW9K6gKTPRcqqk62+9BSIIAiCQIurGmmJ/O3f/i2srq6G/2bRvWr/ZQv2Rz7yESiV1j3a2mB+ft3b/2Zee+0142UhCIIg0iN3eDZ1+PL0EOhquRFDQtHYyjHZENoUa3WgPDKDhKFTnGBTdTSAhv/GfYeT2jklOjkchrmb6Z/a1fC6Z3i2HjFEOcqqYM3QhYSSxM0rgBrzYixn2Ko7OkQscZfBwuNUbR0rn+dSIF8O1eVPn15H39iW91rbe/HvP5XqzyF/Vsy2reH6oNYowFYkWe3zC4eisB4fJeoSteKBV2ezqFbiUY3Svb3wBwrS9oA4ZYi4oYwyaqs8vHv18HuJNlOGlEOTfitqiFSfwwEl4zsfgLHt98Dg9B6YOfgEtOmMaCijP66ZeAN+N8ejsXNPzDwXF1lI5xk+0VA+afza2ttrUqBzfi+nsy6w7+UcMeCPc8qmj8DM/Bwjc4w1KNIsl2NG6L2j28I6yOXyMDizd0v0ky1lkmgjPWNewzxn0MDDthOCqChvpUqj0Xr/xA6NOeKrAzMG1QifWwFcZzBsiO49leZd/z8ckINayLpcTsUch3FtU4Zv9zaan4dnHjXaXzQ4uPe6vxMEQRDZivT1hS98YZNHsnUhzuzsLDz55JMWSwbQ3d295T1WvnPnzlkpD0EQhBegPeCqV47gFvJhqxNs5TGMLuGsunRVKhq5qUCCAfLgp7xGwWmQzptdg5OQL5bh6vmTodfwrsEpcAIjF0Y42ywJU0Lh5DUr4oLTptFXFKmqjHcvx/NVk31O/aUeFqO1jbT3jkKxVIWbS9fq42Ngeo+2caIqaojK8hmZEwy3vbHLrzgjWoT9PR4M5cVhDBEJczCGzLOqv0TXNToDfo8v3GX7tU0ZgbAxk2HQ9BWPFIVrUcy6h2ecKKvxcYK7GnBj2FCdmwCnl3lnML4eOF5fKVC5P/bnzLUxyoCBfNYPrOK/M2DAPzJ/FwzO7Fs3/GrZKAdMMmzVWCgeuRySLid+LrVsGBwDK/Pk3ofh+EvPwNK1S9DeNwoDU3sgS8hGTMGMSQN+GYcWOPczEvO1IqeaMmmJgbG+EaDBgF9m7EVG/jQil6O+oUcuZ69e9cwlgbpnV1m8NM6YZH9LQ4YgCCJzeGv09fzzzzddIB999FGwTXt7o2eamlHa1atXrZWJIAjCFWSEDM0+wym8M+/NCrsiWhbaCc0YMuI1HXnaGIjztumax0FT3pmwKt0gRVU/Yp7St3pLz5BHYY0K3va8UZox8khUNrAQ6UvmctPEnMyTh4651uj+EOFelCkXTR94DM68+XVYubUUGra29w7DrRvXAQNGlNcRtosrzxQbWQPjxiTg+UzhJSp35jLppQf7+XgdF8qI7+J8a1I613fH2kjhpBFgjPSV0NZCbS7atOH3486rDnqh34wT86busipSrFS5lYsZc6nnORmlQQiciagkZ3yHJwKzurbm+C2P13TD0QyFnzlOsVZXm+LrKgbRf2ZVspezNp7NyOVkPjdlmNySL6BSpueSy+loN6koEMIf3M4K56RUau2A6f1vM5KXaB2Yib7EJaxJmQd4SWzUwti7RIQGEbW3paYEzh9xnEnX9RnBOi6dKbzFlXnDUjlZlMqlxSuQSQwao0n9Jnb+QNKxJXW4yBk3QRBE9vA2DMPRo0ebLnwPPvggYDP6qnHt2roXaYIgCEICJGcxG55wojNP+DgU5CGpOCwehZFUh170KwqjVGhNgcmLL788+NWMskR+IaAoo0PZgyMtpf07vCmJu4Ty9rjm/nyCLNKXEsW9AO8cZ1VRWIMChsi6gnYMYN3LKaJYaYPRhcMwsftB6Ogf06z0pyZdYwqVKfORu8hDOAfIgLC72/Poa1O5DGFDIKm3LCnP+OZRuK1nCMZ33m81mmGscp1vUVu5+0lg2jLSqNIMIYllhXpl85xIOiryNBYdIu4jO+MkeX2OVxQWM25VaWiQ+KE0WM+RXqJo7DHnt2ptvjQb0FroYibWYpaH1D5Bt1xOwkjDlOFN7JlZ9R2rpFFeukjGNJ+KY2asynyGG1N7ubhxaVFnQwI85UJSDmf7vm3UyxyUpBSxbrk6xxWKFZg7/A3a85GXG1isVxOGzFJ5176ioQyShmlRnyT9kiAIgsgWnmhNNHL9+nVYXFy8I0TcwOjoKNimra2t6ftk9EUQBMGD6otHS9w+6Om+dLUhEJT1riKZGTgDlrJG2nwZvJwi4utOUAFIuwDU5XDyrvdFHcXXqSiMwd1fFIqLplW5JEw78LOvJ3oki5r/LCoK26xbV9pVgvrez99H1Edknbm1l8OjuGD/EhPtZbZUuRQaGih3UhIY3vsgbVfDKHXCgHWsIHCww7wJm1EUTrhOihzOOXtKkdJKE+l/k6RYLeT0BB01AzN3rhh11bWqdNXOl7jaRVlEbAPEjss4mSrKsay3TN7u5zWT5XpT++wq13cEGDNsDbxYV6TvWDf9NgvoiCBE+HHHanw9MjXNJRlqYiRySdM/v4VnVgvzhMm2GJjcBa6gxxnTGtJ5TvgDFYlro9jKgj+YMEhP+hzpPBeFtIpYIHgPGXP/YHBtjXeMKq53sC6L4C0dQRAE4QsIJTTpqRl8NaOnpwdsE2XchVpJkiAIAj3xHteySbLXD7QCzhrYyycA/v6ZPWU8fsw9j2t1J6dcZ4gET5wuCPBRXqibxlTkGvHEULWZK0p0ccpwVi9kDUVDUK6IYli5UM+cr7j8uKd9Reg3HLIZ/UnX3GpqjxU7z2Hc52nwdmmqL6ZIFHd6RIzcXL9BBMpxyoPRcosrWWOM2lpva1fbPBZ1Bm1JY8LZMYMRQ3bOadtMKgq6Q/0kbt8Z+5nODUHC3XFiFByB+hdyzlH/Tkz+gQPRp6KyoE2e+nowIOOzZYib9sxqzLA1ScbkEDJ1JqpMH+ZhYS+kVH6YVH6a6yTq2kQW/oxV431N5l5QMgqiKeTmBHXpuj5P9Ixug1Jrh7oEEfcVl5CPUJnx9UF2zxkADM7s3RKdrFztVFMuyYfXPr/wjFcke7nI800aQzKCIAjCS/DdtilgeXk58rNisQi2uXz5ctP3y+Wy8bIQBEE4h9SZxeJhUcflE6/nEi5FYCSHQMn6aO3sU1sMLPUhgqgSppEIAzrrUUZIknLLG7ipnGUdKS90IsnfVjSKnYPFi5CYlmr7BJcUwx1GxkNeS0sBql0DVtYL4fZPq7iHwTN5TP65fAHQgURRGM3eJVL/O0k5xh2xFJZ5OTYqkiAdfWPiaamqBpF0cFQ9J4qNOy32XXMGt4aUmbQ8jmcOvHQ8DtY2jCyXnQlHjaJwYLecYfYBvvXeqTUkJYkiQENnZyKhGfTPP/FjLnDzzGKq2DFzWfLviK3VYrhihPcdFvZySM6VNpAxpOoanIz6UE2hmiZtqY2M5IvzbCgX9cCI5Q3HFxSu3ynSkt1vR8m+iZi6lnCCIYwJZ4uerkexRuexz2yzPtTdEUo5Zkg6nzguEy+UKjB31zdAvkD6mAoaQWViEe8Gzu7lMNx1xdVf7+g89IzMQj5fgnJrJ0zsftD+QRbB/ILmjjWqHEl1hKAOCYIgCLPY33FooFKpRH528eJFsM2lS5eavl+tVo2XhSAIwjViDVZEDjTWzz7igjI8B84kTCgarecxON3okQY1WJrP8mWYO/3YDqxvB9giCMW2Gd72dKKvJeiWaPEUmmnhn8CaG+RgZP6uGG9zuIz10vf3ALoGpxSVJiaXICfn4SyXg86BiYb3Nr/2dk1Iqq/A1b0Ch+EuIVilYnVZ7eyH3rF5JWnpj6ynYg8U2B+3Mc9eqrSDDeLb2q7Cswt7ubjoENgjW7iIyj4RrSykLAsvkY3aoj/SlxmDW+WOGUI9YTc7nd+R0kQJ0MpNRT8TzETPd6MTUZAGZz4SisIoz6faxyfHmU6p8TfNN6n2phgQaMJiuQ0mdj0ALYVS86R0GwIJRztO73TOxPnHpwiedyITCpR5zZGzptKpM16pvtLRC5W2HnUZugjCfm/M0aGX9SxuMId3TsBRLrz1wz+Oci0tUO3qB/Sg38qZkMu5abBlag9VK6dM9NKWfAFGF+6GHQ99I2y7512hc20T5dYjn1BT7vWorkqSqiVo+LcBVNq6t7zbkrcfEIUgCILQh5dGX62traiNvl5//fWmQtjBwUFLJSIIgvAbmxcVZqIqSRIql4DTMGGEO4ZfuCtbRpjVN7YgnpaKaDQGftPw+9ttZ8IgQsbzNor5RJmAVORZeC52tVhDZEeCn1FmDz4BOx74IHQNTVnxzi43z6VXLmEeD7uHZ9Klk5iPvACdXUb0jMyFhip949theO6gumLFXQYnGPhJec8UKFn8p5g2c3LlGJzZ69AltJ601a3j/Oks3PtemD7wePTlDzIP425Fh4ie5+KeY2juwJb3mMdNm+i+dK3Vh+m9rPp5Ass8nAEMKI6rmW+iHXno28spVlKIyUdGUc6ut2PLin1JytU6yiFlsxtkSgHQKkZkO7jaBavcSlghLaZeu4dnIdeSb/huZ/842ERqfAb8v01uV/H10DhrbvVPKwTmE5vc/RAs3Pde6OgfE3aooWZdkh87RvOUysa9/h7dB8zknvix0uVbPrHo/Xayod/Uvkcp4pdYZYN20EakksfUuWF9XRefN6zqkiQ5pZEqm6P9RGffV9TGrp2BlToFQLLmcSSuMe2YXFFEIbVEbLkCa7LC+j2u6XVVQoUgqj6S+lWh3AodfaMN7w3N7EsuI0EQBOEsXhp95fN56Ovra7r4vfzyy2CbZ555Zst7rJwjIyNWykMQBOEUCC8lzCmKCObHUVc6yp6kHC30G44LiYGpXcoVhl1CtA1VXJK1dvTB2I57oa1nSEmZ0M8bt3/fO7pNuwAq9kICW70mCadkvbnzthfH10wrTbmhpIWwHxmCt33K7d3QUrhtCBE5HG0qHOjLd3T+sLVL/riorXe80B2GmYNvh+G5A5DLtRgvY0OZ0qwHIvNcYCEioESHkt3LtXb1u6NYgmX6VBBpsFhpS9ib53BVBMIzYBSxfT7mOdi+emOECKYk3NY7rKxU8Z8GyvZyYoawyZ+rnOO0nFEsRFPQe9bC7JBAbdlEo7lqJ/BjjpORB+lGrfdoHmWRjd/mmugkyiGbX9yPJeUQjvRdrVisg4HJXU3f1xp5KbBTScbmkdiordG/YU5UmNJ8e+9IeL4Z33U/tHb2m1HUxCafcIhYD/nBnYhTKsmskdnaGv+zp7nXUqXQLWrA74ijEh5jTSsYy1YwKgaHMwc0d2EprpTyxVIoayV4q1rduhvl1NRE1FaRvoumn/Mgo9hv/fHk5OoCSaWL/qijfmT6cWTzWW/ADCFe11F3P9jkR+n1PwKj5bRrFK8Guw6iaoXw43w2vvP+cE/RNTgJ4zvvg57ROdtFIgiCIDSCYAXVw9jYWFNB+NNPPw02YWX64he/2HTTMDdHiy5BEIQ0WA9josriSrOOv5BYD1eNpd6CVN9XJhTQWB9oBAaR96F8dVgotcLsoSege2gasYBcj8CXRZabu+tJ0Iv+yxxhAkf7vO38TXtY8xQb/YjPo3UOl0fhlPnWo63kcpEGvSpYrzf8Y7MRvYpEqcBQhpQw472pvY9AR9+Y7aJkhv7JnVYjqMqkoyJPsxFdBBUEgyAcC+xybv6e98D8Pe+G8V0PyBvQ+3TxqsXTpkZF+I1J3S47c5ihFPenfhTIGitzpS3h8l/FeDMyz8UY6VtFRl/s9hyr9bwRBmDDc57RUxaE/QE5KtqBKc6xyMc9o9siMkmQBxvGunxIgPj6iX8OFpWanW1mDjzeYMyfnjXJMtuWy6lOLjCyjm/GLWdzOuSMtvtRYP6sIrOvSFmeeEdwKAT9qgsikHeC0rNCwwE8dSMhq7E9Vh3GRt219wzDyLZD0Dex3arcghsVeRosNjZjEmm0lzfAbRCcGhfLjeTOWNEYausejHH4p/EMY/6Ktf57dibUS5DsvMKVvVyau49A9Iumnl8+nzR7WhZ1nDlKZ/dLXYNT0mUgCIIg3ADD7b0Wdu3atWVxZEqVn/vc58Amn/nMZ+Dy5ctNlTz37NljqVQEQRBZxK6ghwTwVJ8KOxM6cCiIKrx03VDJxdZ2BSWKzUxKqGNLWT5uLpOp99B7JmenrhupmFLcvZ2WcxdDm3G8+NKIGNjxeBRWGjEgfTqp++WG32vdo8QmHedRHd+FRD3SmNbq4lD6cX1Oun0h0dGP3+hL39gI1Chyc6y7+XwJeoZnndvLqVEuMTNW0nhjZp+XWtuh1Nphdr+hWpFS+NJVRVp24Llg75/Ykenzv9JobSrHhc69nNQdfUoDfg5jLObtNS3yymDBFkWfUqXd6nqTLm/e9kpxrjCMbB9s7ehVXhbvSdnmLJIUMyrqG1+IySJOTgLmUdTPjUQhiYtAbdDQyJ4c6/Z3eX5Tj/6A8/n5ZUXJ6+fE7gcVZupwfaWFs68wGYFsWvacprnRrmnqp3eToTHbyxWKFbCFOScp8YbUJtcAmXsQlLJW14mqUoG+wKKD9o7NSzlrUNbnBJJRM7cGoaEbznOeZecmSeJ/oTZXeY+p705Uph9j3XNnC742qLT1hM4wJvc8ErleR9+xghVUyadG5u8CneA10pQY0yZkgtamDXUZkwNhgiAIYjPItDjUcejQoaYL4Jtvvgmf/vSnLZUK4Nd//debbsZaWloaykwQBEE4JOiJPVwHCi8kahepIl9PVixEgUZnPgRnpXL2hYax5kr7pC1nwyPrfehYAVeMYmKoTLrht6ECfd+ohhJylimc51qs5V3/CvZOGmeIFNzxhOYMPkYuE3aWZkdROO2lpKmxEheF1OqeREY5O8WFBHd925rnLPRBJ3B038yMvNg+oW98O0wffByKlTb5PJI/1IKJOYoprLXki+kTYgbswsYkmhWIJZOROrPKRHCLqRelba9jDK8lr1/MqHXm0BMKM9U3Hny8RC5Xu8SitjqrKJz8e+bplSmfKI8+x8nQ7P7Q+JiRy+Vh2IDCn/o5TiajJGNgJPOcpFyua2iaTznfY8wqe+dgYHIXzzclPxMpi8Sany7DcB4rt3amTys2GyR7fhFsLV3YZW6pufN8nf3jMVEKCJV9hp3JeCIk6HTGJKWIrsAZk5H5JymPGGOWwZl94Vhg635re69BY8goOWJO/3mHywBWuBgc2Srsg9xp+T6nq3x0y2PVyl5FTZ4sEmqxzCGX1DadRH+oPzoP4DOICDCOf31lsS1n9QLOZx+a2x86K8m1xOgJaHSoYcOosPb7clsXzBx8e6q04jOSK6fN6NDRTgwNDqYAi9FvlicQgiAIQhXeGn3dd999kM/nt2wUmDDn137t16yU6dlnnw0jjdWijtXKw17ffffd0Nam+YBLEAThAXFeX7RH3ZAgWrE5Z+Awm3SBo1i5JJGMH2KRKA0k9ckkOoc4vIOniHCAUzBuUoEnpu5iytHa2RdetLb3DIeXr9P73gZ5ZZ42JQWIxto67gLMUBHSZJhQT6FyZGFdOTKbSjnmlSAb+66o8rOaCwnRdjIS+cJi2k3rw1TUHoUecZUqbdS/AOaQWaMC05eurs1xKtuweUIsSilbS1hkhHK1U4EROphHheJeUpStXEu4l2LGX6nyMeGN2RQYxhOGMsSyxh0Jh3f8EXGI9YdKe4+lKERMQGVaUZjvSyxCw+yhJ7ZEauDOJsjJGfAH6+Ng4f73w9yhJ2H7Ax+A9t5hqTI0T15cKfKO0ZdGpTJLisIm586WfAGm9j5qLD+viWg3ZmjSPTwTynom9z4cyn+Sk9Ipl5OyflQyzvPFcqwhNfMob0XJOvLIqGgsrpkzbpU6s+p2YBCRugpEDECUtSf27a1WeVPzt2vON1hE9dGFu/n6rmOR97CQpn7Yms/OzDsf+hDM3vWOUKnZxJ4jcu+ZKtIXp4w4aV9v/I41DhoTTqI68rpY5lq+GplEcHsvp9MYolZ3gmtEcNu5AHNOshFmtGKCqHKxddFGvlu/yN8BWB1q67+ikaGwTM+2nTFJpJlmf89k6xudJQgb/LjqjGnD73XO4U46KYkgzV6Od/7i+Z7K84NctO0U+bk40REEQRCp8dboq7u7OzT82mxcxfjyl78Mv/Ebv2G0PNevX4cf/uEfjvz8ne98p9HyEARBuIsfBxdTl67Jdl846jO1cjuOx7B06Db38MyLY+/ItpSeeRxorDhsetxMuKhgkb2m9j0aXr7yKAHpRlqoqaiOlXtN18Aah+YOU1ZduO/94AI8z+Ma4h5gdZVEc8YcP1fh1RqVAVNKTCgK28Oe0g7BVXtCdSqjCK8TLcaPSb+/XQeDM3tjv8eia87f+x5YuPe9KBSFXXEKseV33A8U4PH2WctTsDFi9wmayu/c/GlB4Z0ZRgxM7Q7PRtHFyvmlXGJqn5Wk9JqQB/PyXOnoCZWGbZNKCYe7Lu0ol8gVR9hSsf7Pald/tg2/BMev6Dxeam2Hse331B39cKVlZC4LrKxdcfPH1L5HoHtoOlVEw9jo1JY2bcmylqi9VE1RWJ0hF0e2cmmpwrFtknIQi+XW+3HzBpq/5z2w7fA7YceD3widA+MNnwmPR0WRBcV/kk7lZn3uSa+2kzjekpyh8OxdkJxH7sxxdlFZH2nSinOY2vxtHO3oIib2X3oN+MVR2V8KpQqUWjuaftbWM5Q6fVk5aEuhGO4l2T6SGa0wGSIzBENx/yBinyfSP2rfTTCaEmn/iT0Pws6HP8SRN3eSG35C85Z1IvoXcyzEouWxMcNkcXz9UKM82ML9Aw8sUmpyOXLp9nKW7h/iHUGpvXsQomaEFdffaGohCIIgHMNboy/GN3/zN295rxZl6+d+7ufgmWeeMVaWH//xH4cjR47U89+44eno6ID3ve99xspCEAThJ9HK/c0OcbYvJtIdYjk9l8ge+m0IUlJ55XMEJJdhon2SCeiYN7Na9KhCuZUnNcBE6ktXjrZjwkxsWJ/nNI/r+vPh6m5ixCgKb1y7mHJkQVXkNuRzkdaLcxOGLGrcXpqPDrEhw6hnGJzeAx19YykVZMPQrNGfWUPmQkKmvLV5S+Vejr8cXQOTYbQnLSCZWnSOc9vrqlnlkhyuqGyKPG32js5DW3d6BZL4rGLmOcfQfkYL7OwDZNPsn9y55T1mWMQ/AbrQLxBrCnNWITu/MsMItm8Jo+VGTpn6FPpZ2sKObVLmW4uAo5v19UGd8YCJtdWW13RukKwZWPY52UC/QZGMwpVWDCnusXlwbMe9ML3/bXryifwM5/jRL29PON/q7GsSSZerXVveC51mCe4V0pJlheU4JVQWNYo5nBNIDFChQCyXL1WgWK6mTEfTnaBNReGoddPEnWIoosXU1/xZo3AROCbjU1NekfnATOQxgImdD0DP8Gw653Mpxi3Ll0XGZg6hBiZ3QTYivnEgWJ1oztqEMWedTObOoviN77wPipU2jfdrGjFwF90xMB46KYmbo9DVi0ZS3zWrK4nCtAiCIAhCP0hOCXp4+9vfDrOzs+G/a8ZWtX8vLy/D93zP98CnPvUprWW4efMm/OAP/iD8+Z//eUMZGDXjr2/91m+FUqmktRwEQRDe4MmZy1R0CFwXEtF0D81seY8Jdr1pcI0IC38EPfy09w7D1N5HhKJHxSuXgHkU5hn1bIVSa+h5OVQsTJuHqEKcxWGiXJFI6DeB8vHB5UkwQwLXLMKMXBsQjvRlqX8YyJcp30zueSj01Ng5MKFpX4JvnosCy6WryDzHFIj6xrdv7edqSiL4PnGnhlQpa0RFrYn5DaIISybqiEWGiFYCVnUBrvDS1liEJMvKJbGPKVYH8/e8Ozy76Dqz5gt3jGqY9+c0kUQIdYxsOxQ6KWGGXqMLd/P9CNvZSoFxa7mtG4rlNvcdIEhlLC6DkPGaLkyAb031Va7oNWtq13B1Bvx+9iOUUeMT5BKRhsxSBhHInl0hQ7P7t7wnptDtb93YJn6OEDuzqlh3ZeYsJVG6ggCGtx1KnU5CJuALaYy++PuJO/XFnmmzY0IRR4Wu71mNYtFxxe0PzROYqTcWbWt0+90wc+BxMUNg3n2wY/1cu2PNmnMkhU4p+H8jLpOp9gxueW9dLseZp7Lmd6sfSSHqFEGmSpCNx7TzA8/v2fzEnJRsf+CDMDSzL+pbiWlIllAmO60YdQpuPIKmwrOKqJ4CQRAE4T04tJQ08iM/8iNbDK1qC+ni4iL8wA/8AHz0ox+F69evK8/71VdfhW/7tm+Dv/iLv4hcxHt7e+H7vu/7lOdNEASRNdB5MEUUrjr1d0SzlZAOsOhR60Ze6+TzJegdm1dcMkK5AoYVT652Ll156B6eCQV17EIiFZGPaGcukxV2mar36L4rXu6pvY/C/N3vTjGniuXHvNi6pPzkPEG0B8dcLh9Gf9msKLzxLMmXhVvjtOnvE+agVJ4aYyJfuDYWanOc3svipIhBevLWGf1kQyabE3eAIDvlMHC2EupPyC6B5SP8uDX/yezlRCKdcT23YNXkWgraDLFKre0we9c7QgMjdkE/te/RMBqrcXSOhzU3PRMz2QFzUsIir21pk4ifRa/fqowhxNJRoqAcBKGBvvZ5QVT5Z/1DwIbUeXVNbN5O/B6iemkRdGCz5dnwPAp+sugrAVFfT0ZcGQyrAnF9npNSwuRJH+dzxzl6YgbiLfli6LSL7ekqHT3mC6Kx3kxEckDz7Dr3cla69nqmHX2jML7zfvlUkiJ9Se9NLJ5lA5V7uTX151vBfs3uPmMSk0qzBpNrlyrt4b/Zf9lrjI4EsYFyPYu1+XLT6stoNGxFd6y2+8adeU7h/X3Dd7gTAww0M9Zne7sosMpb3UadIaDO8SWXdtry8NyxrpMvliCXL8gVw6dubcKIOjGPAH2lsojmkWDcwxAEQRDakXOP4RAPP/wwvOtd7woNr2qRtmoRttjfysoK/Oqv/ir8wR/8Afyzf/bP4N3vfjf09KQT8r7wwgthmn/9138Nq6ur9fyaRfn60R/9UWhtbVXwpARBENkAnXcrScGBGS90MUZnCd6btJCQ1cjCYegcnIRbN65BW88wFEoVuHrhlJrEeVPJwME4ss0VXpTGhqVHLjhpikC/YH2IGY/oKYfwBwaIEtbmtI8nrvRllC01FXtk/i44/tIX6q+Zp8LOgUk48/rzUYXRUo4szHPNCM9fEZ8xQ83a+Sw1yqpXNCE32lU+EiSC+UTBXk48yg9XooABWe+rTqGrqhX1CykDBhnF/dQEKD1tbvpBmsxiPgOcBJa8bdbrytDZoZ6UvGFKsVwl5yQWkekPosrPru6Va8pgzKnE+M774K3nP6crI3CPwLJH4QTla4E6ZYrgHf3jcPKVL8HyrSWJNKPzYjK4SlsPLF49X3+ve2iau2yEvrksdh6TUYQPLJydFBm2Cnw7RUZY5W/iSM1zlh9xcvdDsLJ8E46+8I/KHUCxPsQMxPsndq47pLndp0zLqp2TjStc+8XvJSTmOWt3hDllcxyLdBOVx9raalJKSRnF/BLfHQOa6BBSTl+20t4zDJN7HoYzb30dTr32VeXlKVU7YP7e94T7RGbcyrhx9aLyfLKOmbEiL7fQgepzss46XJ+PBY1MrBt3RZ1ZdTsaup1vQr1gkZMwecfY9nvg9OvPheth3/hCaNR/4eQR20XLDkrPSTj6VQ0T/ZwrGliiDElu/2NzHEc7TzGxl+M48+moGsk0e0e3wbljLzc4rRV10kQQBEH4Twa0cgB+8id/Ep577jl48803mxp+sX+fPXsW/st/+S/w0z/903DXXXfB4cOHYWZmBqrVamS6R44cgYsXL4Z/p06dgmeeeQaefvppOH369JaoYpv/zf77vve9LzQyIwiCIPwl2ntNCk+bKfPe9CU0sLWxrXtwy3uEXaTawHa/3vKjlIrCxgdKvLGmC8heuqqNOKenzmTT7BmZC41aL558HfKlCgzPHYCWKE9ehCbilX/UeIBNL6S2okTX8PvkS1H5sRXvIRfjum/3QoLjkgfJ2sAi5TGj59XV5fp7XQOTVsuUPVT2BRz9qg7CuUHGE7iUkqMS1lB7RY1VunGh7TmLqGqNwzLv82J9bRfcy7kanbqxnjnmmph5KslQLlbR3nZ7y0RtFRpTgv2JK9pGIBTNkBliLV27BGfe/DqoZmL3A6GhxY0rF6DaPQjDcwdh8eoF5fn4hlHjckR5mI/aamZ+CZ0YRc6jkb8Cq0TUby5IoygcWFnri5W2UKE3yegrDRgMRTKLielPSSYW7kM4xhGLbnv+2CsNchfRcsiOV6uKwkl3rJqj4SU+u+g2IJeTdxrImVfN4IvQRORxS6GRLLZzlWJneVojUyKrOvMG/HaM2k318e7hmfBPmRNHCbTma0OGpTtql4E5kztTnbokPHI5A+XAKZcL8J7LVOuSpKz/odkDYRrXLpwKz8VMLrcxwAhBEARBZMboq62tDX7lV34F/uk//aehgVYzwy8Ge80ifzHDLfa3kdoiuvG/LILYZjYuthvT3fia/Xffvn2hMRpBEAQhCFIvTCYFdUq+z+FVXRoTTeFYe7uA2guJHKq2Q3dBIkuk4YHF54uckg1FM0xOTF1SKSc39lxDs/vDv00J8yaQKn/xDJGg7LnlhKJaLyARKQ4aM2CKLadbxmAoFClDPWE7ynibybW0wNDc/npEw3yhDP1Tu0KlZF/AclkuroQan1rTd00YZ4roCacPSQEosFSOpItBlUp067/Dqfh853NTxUDS7xzY93AjUafCF+O22i3tXs6QokQ4L8Q43cCIFq/pIu2V9FUhuxnuwyPIGltwR0FG2t5uE1GpMfOYlNKdhcbDso9Oi6lz82Zklbxqa4O2+k8ybK19zv6rQlGNw3hZKxkVy8kQbUQu/vDCZxVrCuA5Zb+PeuZKWzfMHHw7XDr9Rmj8tbJya2s6KSJ9oSVyL2dgTk6sr1AwpzC/2n+MXLIayINoxpb7IUHUyeUC/Aa1kvmIRvSyvldNcDqnS77EHfVU6PybUrbB2RYNDgq525vmvdQgvb93UY4bucfljfSlyBBI2fxnwQEWXyab/tu8IPrLcSezxHvWkW2HGt67tbQYnRqycUQQBEGYIRNGX4zZ2Vn4+Mc/Dt/5nd+5xfCLsdn4i4dm39u8oG42+GKvFxYW4Fd/9VehVCIvOwRBECqxJ5TjMa7a/DYCzyV0EPQD7YYgMkq/uAQMqcebsPFF2uxw1Z9JJWGhutNWT/IXHDbWIVbXa2urxvN1EamxJSo3t+TBXIVRYrN/K0ehIYm5uVK9cgl32cMoN4CHhHL3jm6Djt5RWFq8Aq0dvZBryQsYfZk3JBJPW9MFu7BbaNFySFwAYtuLOGLY6h2a+0HSXMjGhtq1AVm/lgXb+ExEUXmllRkEo7YqKK9MGmn3NQ2KwmnSSlLMl0w6qkw2vdVKRYcQjgIcaOpzdufnpr/xZY6VQnCt8kWR26Dy53oa6ZPgyiY8gymSAxpaswOFZ1aR/nnnu4Yahzyc26kjhWmaiIKjIg9TEY+F8wwAKu3d4R+L/nnqyFebJZSQhGw5oxTqJZNTkI6Jc36tvuKi4Qr1F+fOckRzxO9FW1oKsLqyDB39Y6H8NBFsso3AnTxdul9NQvdezmaaBHZUtrm+PZtgljp+1DylVHK5dGmjHK9R20gDd6y26kNlvj6tawRBEIQaMqX1sH37dvi93/u90ACsWZSvzQZgGz9vxubvbU6rmcHX3XffDf/jf/wP6OzsNPDEBEEQ/iHj9QTjQUjG06YNxSE55DzTuObZzDv0y+9uf4SnT3L/GkN4eaRzmc06S/R2JaEojAnesdLWPQh9YwsiCRPIFYWdiA4hq/Dr+Li0pVxivD5TpFkot4bzEjP4Isyi8qwQ26/S9rn6zwODxhBmlbF1KiDqIMqIQaq8Ij9BWh+ioLxMJywb8MuMn7Rzq5l5ju1/Iuc4i2NBykiptv/VbeAaLyCRzF8y6o8n867zBPaMIWyhxAhDLExsioxw1V26Ma9ZiS7xK+brUm7/GjkoU5cnImHILgrPn9jGamA4kl2kDVaipnDCx8jqVdf9g+XnpIiqyNDsiKy1vRe2P/hB2PHgN8LE7ge5ZKaJZwgFCOknWDOolcoo5qOoKJGRvwCb5FJFp+b5TsD3XXeWBueJMjDGgtpxjKtjpY7aqkwvIt1eTvnvNGJkXajnES3LFClHPl+C7qHp6OyQ9WuCIAjCT3BosBpkfHwc/vf//t/woQ99qKmR10aDrY2GW81o9t2N39+YJvvv933f98Gv//qvQ1tbm5FnJQiC8JKoc5KMF07fFYW5LofcqTPnweKNVPRCMLaL4FP20qM/p0jxz7AinzIkLt7T1ZneSzZptGWjoF94PJfbnk+Eo6ipKK6UjlJaAwVDogEXu2qkfpGEonDCBYNQAbg/l/2uKgJv8tO23uiuojUPBqsrc1wiOI3BdEQMFVPMNvP8tXo2t7cIcI8rzGfWCGTazorCjIwBktJ5Tn6usT0faSHyzCqzNqwJ9kWOOtcxN3jYjIQ6Ywgr49yhuSVU+kK6Z4skqhvUFIV1lzswL4O0LSsi7INtPKZWFOb6fXpjTOl6w1Xd5s75dTFezN2ZSqNtDudP2kA2ppwkiD6TMuOdlkIxdVrrHznaVobW8di5AWnVRTuPyiEpF9KKS2BzP1O3d7DUf1Gg7vypcy9nw5E2V9spkMthXQNiHbRHnq+x9Hf+Oi1W2mBsx70wvvO+hCT1txPWvkAQBEHoB8sKapRqtQr/6T/9J/iN3/gN2LFjR2SUL1E2R/2qpbtnz54wutdHPvIRyOfJ2zVBEIR5Ar0Hrdi7yMCiF7rwxsHLw6CL5dYV+UhZXSgdE7E3EvrSjvxJfN0Xy9XY7wRBi3ahp9T3TKUjlbVsfxdREE4shFSHw6Y0oHqsuzh/qiDOmUf8D0V/YGn8IrmQ4MsnZo605cE57lLKpsKfpeFqYp7YXH9Y516MmKgrbHk4Y9h6J0eht30y9FE6f0glhbWS3Rif+VJFfaKBW/1b3Xxhei9nyngyJ1kO2x3BUkTvREVgtv8FR9lU8Czv5YQfHdc+SyJxO+UxGR0iciqLOBvqbtM1g/OcUD0HmTJOVyaXy8J8GWcgI/ZBXCbgE+b2oGk/t6AorOOOlRsO5WzsfdGCsr1Oql0DkA34jKB1ZaEjT9F5LtXYEt6z4RwPcns5ka9aNOpyJZKnYbqHZ7a8VyybDyoQuUaobB5sbW36/iHQVM7AIV2SVHu5QGiei+7ThuVydN9BEARBKCCTRl817r33XvijP/oj+OVf/mV44IEHGgy1mhlxJf0xNkb8Onz4MPzSL/0S/P7v/z4cOHDA8tMSBEH4AjIBgCR1IxKh6BAq8xdPjIWq7p/YoSXtLJAvlqFUaW94r9LWA9iJFawHbvSBpPK05Iswueeh6N8buUBMJjoam03jrqhLQ5OXrhqNDEUxZFCcTpCMa3wmYrm4otEhVMx/NrzQGe0XjnVBpWuDsJJagkMa0xcShCaEb+Ii3lbXGfB4WVR06So6XjXNiaLKJcr21AlzT7O1zqTxJ3oluQwxOL1ny3vlahegITBg+G/L4Nq4comkshva4SpjwI9lrVNfqTSv4kZ0vEs5MEFmwG968kgzBuKeSzRdVWMxWS5hZ55L2sthkw+LQnMp7jpFZ9ya8v5A1fMkRoeQzAejoUSaOQ6tczXHo+qYgN1Rb2z7zv5xKBQrZttRdByt2d2PqEDJnYeh81eou4fwLtX8Xi5QeAeJtd78gzmp7Rmerb/OF0rQOzYPWIjc90vdaea8clTSsBdMk5bxvZwBbOqSGNcTMZwfQRAEkUko7BQAPP744+HfyZMn4ZOf/CT8/d//PXzpS1+CS5cuCVVmsViEXbt2waOPPgpPPPEEzMxs9cJAEARB6CNaiGez1i16oQs4nl2gbsptXdA3vh0unX4Lbt64KlEeiYbwTJA4PH8I3nz2M7C6ugz5fAmG5vbDkS9/Kl2iwnUkdhEvavAQl0fyZ5rgqKP23hGY2vsovP7Vv7Vu9IVNKU1KWCtZZ2E/VHjRoFaImeANyhKYysMMWRevnge0yNSVsKKwrfYwfSEhmZ/khXXzzwIUFxJS84xS+1A7DgRMMrpwNxx78WnbxXCW2L2chNdOG+tO6vXcdJkj9eHsDEIryuLs96mjMUheYid6PI1Zb5w4s+rLr7WjL3T2cuHkkboX9baeIbjx2kWHFaYFDfiVRW0V/X78D/ond8KZN57n+31kUgYU9Ux4g7ZqwF/7MWcePNEhEJ3nXCwfBuzPc6IOnGzI5RQkYbTcgorClseJ3Sg4lvouomHHlHEL5SosXjnvbpR7U6g8f3q2PvHI/TfWhfTa42S92ZvjuNYeF6vUYdg93vSBx+DKueOhk83OwUl46XN/BpnaE1rpcyrOknZl+VKG6pbHd32e01Z3CXeeEoaoqM6PnEVh+hqjO+6BN579O8DAyMLhcG67tXQd2rqHYOmamN6qVjC1r+Fn6xyYgEun35T+ffgVFRFEsbaBlEg95/D6jbQdCIIgiExARl8bGBoagm/7tm8L/xhHjx6FV199FU6cOAGnT5+Gq1evwo0bN2BlZQVKpRKUy2Xo7e2F4eFhmJqagvn5ecjnqUoJgiDsRJWxd7Ay573mtvBNMFw1T5rO4Wix23uGYceDH4Sla5ehVO2EtbVVcBkZT65WFIUT87z9eSA/XnmM49ajyqYR5En+zgYG2zkxCg5yuMeEKv1TjXUyOLsXXv9Ko+EkUxa+dvE0ZAUl4zHOG2V0xvqVSxRd7oobd+kHnSdCIeWSwNGohfx5lFo7VJXGDwwME539W85QMqVhK5L9gEtREKT7gEzUD5WPj6kqA76yMeWS5eUlwABTKBrdfk9oYLS6shI6fjl/7GXAQ2Ag0pcdZbak3/SOzofK5FfPn1Si9NpSKEYUJP53KM+eSaQ456fPO1DbVxCtFxjXLh4Gp/fCqSNfbZDTXTl/AlAQWadrSKOv86OiPBieCescGLXW6a+z2l4OZ72YoiVfhN7RbXD0hX/k+4Fj9WXbkCy6fwd+yYg2GnRxPFuuJa9l72FtfZfaP5tYF+4YREQPBfXGECqNHdXeJ+Og2tkf/ilDWAxvRDAXWwJFmfB/U8U5WfDMmmN6dxKykrB9ooqLtZtHDflci7rE0qAlSZX3D2kvxwT7uOZ+xMrS1j1Yf23H6CvinCkTRc+ZM2t8wzIDPBaJ7cybX098nlQ6Wkl7NVmP3xbnPy3RF7l/avqO1WBFO3auJAiCINRBFkoxjI2NhX8EQRCEC4gpENv2uFUTbmotR6xi9Z3LCsIs7EKu0tET/ntl2QGjL6lAX9j6lVlF4ejvJ2nQJSm+CpbDogQPm7BWBebny8C58caE3kxJ+NxbL4bl75vYHr6fzuhL3fMFjigK20D0QkJ+PMRd/sj9zJqRhoNe03Ej2Tc2f9XBMSi8zxAdEDHzWNJZQfwzPfAqva2uLEf8XkZJAtlFpmGMjCVr+oS42qHU1gn9faNw4pUvoVEu8crYFnvEC86GZXPc9L63wWtf/ARcu3RGbi8XNBobNv9KotWXFNrPpjL2xDW5nMCccMfZC655ZAvI5rnW9l64fuUcYICdV3MtLaGhV7naBQNTu+H5v/8Ds4WwvMamHY93fm9OUTiUcIucv5hMPMhJO9tCF+0haS3TYRSjMLpNkhMsOSw0hgMRp5jB/uWzRwErkYaqce3pyl4u5hE6+kZDp1hx+32+8RokGvBLKwInZm1VMKf+/oH3cQKe9UZmI4pn3iBMrAfi81is0zQrUVsD42nkC2W4eeOa4nzcGntp5jmefoJNPmYPqgeq9uR+UGnvgZ6R2XCOOfPG81t7Ec95lcv+SI9cDiNGo7Yqmu94nEA35Lv5fSkBpvhPCIIgCL8hLSWCIAjCHxw68LhpEGFBwdNBowfsKBXiulLvgVmhU+T4Tul1CJ3SiaZ+pmrchxGFtPRRXBdumLxwsucfmtkHOx78/2DHg98Ig9N7wH3MK5eE/UixEUjf+PbkPJMzSfYonOrZYrxwYkTisqBmRCg0bhF5YbZ1EYxpnsOMyvaJj6KZOnGJn+QSP5/Y/WDM54HZ6V5ZW7hnwC83XgX3vpLpYT27eK9ko+pSfW1Nqq54L+PVtod41NakfHO3PXwXSq1Sv99MS6G50ZdxWZYRRe7AqlwuWWHH8zkAAAamdwMWWHuwsxEzohyeOwAt+YKKRMW+Luo1fQ1v/+Ynvo46+sagvXck+teCUQ5YO7PoS+rBOV6TjNtE1gjr+xIreuzp5K3W62wDPSNzW+aSjZEi7CO+TxfdyylBShez+Y9YRIixHfdB58AklCrt0b9vmLcD+b1c4E5/dcsZkz9GJDKMLtxtuwjZw8BYFdofJN09jC1w5CcYnbooe2YNNMiZ7CDjQNi+ISrvBgrQENdniM2VJVG/Fs6sMuMg7jfM4Is5V2Dkcvnk59HYnZLvSWLuX7Uifp+EQW4hq0vi2npCEARB+IX9FZQgCIIgRMBo9BBzqFN5IXHHE4nQj6IKFv95GlR6LonOhPubXYNT/KnSAV0J+AR18eUJPTOl+L3aywhPQPMsti84DICwuMxzOvvDZ6gq8RvhQF8IG4R5tJ/YGV5MRNGwL+F4hHypIlcQlQr8prYVNi8kuAy+1F3Q64An2khyInjGFfPOvREWIcI4gvUhpSSHqM55ae8Zhrm7nmz+oagxhBYlYbxrhDUFIw1zUvo0FRpPKnMfL5suESI8BSqoXynbx+gf9U/sSJQhNSqX8CgKR8xzuqLCWJz/Ir3ryigKCxqpcXlX17CXUxtxUoHCkEvrnwlsV4eq9hDqAjGy8yAHA5O7YHzn/dA9PKNsvOYljVul5hPN9xVJ+/m1VbmIZmlJjg5pxYLLfJ6hAxsc5/JCqQJjO++DQrESKqQyI7CoccXPmlNyOWtE9IHBmb2hgTFrm9lD7wjbpunPBaNDREVt5bD6Al/OuCbkcvXxGjnNO+bAaiOb2y52K+/qQ/Ki9/lqTsCUgWx9ZbLSntG5hJ/nhPuR/F5OQoHfiF5DDGuWxp4nYzu6nfQ8n3G5HKp2wlQWc/U8tfeRxPEorEsS5YAGVXurQr3hn7CzLUMG/P7vmQiCIAgMkNEXQRAE4T82D1eRl8E5A55e1CqXuApTkPIZ/cKDNb2K3JZhntI7ByaUGmlGKQonK2EECcoidrwzySiLSwvqZJQkjM3xZjv21n4Qs54Y8ZDvuIBTso7E+z+S5920DuaLpVC5JMqrMN+YvfNsUUoqySnEKfvhqzv185ygojBPpBC3qk0KTH1jZNuhev9nygejC4fBaUQVVx1V5hLdyzHDZdl5Tgrd9S2xBhrpA7fzyEqk1PizExlP2Ip+sgVVXUM08k/E95kTm8GZfUry29gHoxToksaGvEdhfGP6zrPI/FbEqFPHXg7XOp1EFmSPOok9k0aKj9y5+p3c8xBUOnpCpbfB6b3KnkfWIdPtDAEVa4rXulgC/nFrcy+HqI0wlYXRNTgJC/e/H3Y89I3hedXGfBBpICkVecWGzDPNviLe6L6jfywqAaHsowz4kxWScfVXLlQa8N/5daq8G1MKlM2xWtrHwSbHjugcpzKPpM9sML3/MShW2mPlaDJrUb5YlipPbP0gqzs9e7ka6Q3Sa3XpzXlO+O6d2FpV6tYvvXtUdXKe4bmDjfNRZDfiyfPOd4rltohvqDUuu/M7fP3cSNTWxOfWZcCvcE/gyxxMEARBKKN53FGCIAiCQEqk4IlHkaLhB4H/h1ieQ6CIgQXnd7EdPDErOmAUsKggtg9YUQTIxV5IRHo55/h98+8HkMu1wOrqipjgPlEXTLDuEHpT5/wx5/c4vqCjChAOW6koLpkkQOHNXwtxisK3FedY2QqVKiwtXkmY55KfIS9rDBEkedpEOMAURm2982NlGuV6IuVY8Rgr/aFRym1dsHDf++DmjavhxZypPT0fEfUktUTEKJfYaI/A/HmAOQa4dXMxXcYKyuGeAX/t94E9m/RI2yo8c4kL5cLHmjPtIZdG8990D003rsuKDDykIxoGauvEzDlGh6KwIgSjtt4B3/nPReVKexhQNsQWsTOiPJW2HmjvHUn+OZJIX1gVRZMVhd0xyLVT1qg8RQx88c3JQdCiJjHLS47yCDmaSCtHEf19S4oIOLEfOeIYxvT52vh9F7b6xlYexYj3b9Hvy8xjuOTnUXsxJjur1V//1C44/tIXmv9eog9Jz3MxZ6x4HRN7JDoM1VQ8Lc/NnSSe9cbpqI26CBBttZ10Opf8cMXWdjk5GdI1WcZYOVWdi+qtBUjOrUjbjyAIgnALBDdbBEEQBKEAxw5I6QQH6S8cbQswueH0lhN/6aIgwwwTe7EqoXBmxat+jDFEKUKoltqjsIQSnWw0F+1CcJnLdYNzcmzkIBlvXpJ91Nq86tj65z2K2kPVuGZRvrgU5ATzK5Qqkl7oJMeX9n4uMVdIrA3CykoxCjcbCiKQXtp6VNcOInMmNuUiNqZKrR3qlMUVRYPxWXGbf7ypU+wvlFuFf1PPL1IhWDQhsAeycafeeYDC+SytAjj2uk5bPpWKujJlEV577ezlUisKC+73IhXoErcd/lwnGXkWDpsuoba3YX/giIKWSwiP95h5zGikkDuZWjCGkJDLJTh4is0PnULwWjpFYe1kfEII9YQx1wGismHf92qZQzellULpdWM/y7XkpeYDbPKVVBFCUshklNWDbDIImkFILoehwC5gYg+utP+mT2dj3+gd3Qazh95h3YA/XqYJGYr0lczNpWsJ9YK0wjjhHituP6ZZIuvUzP0af+IKk+I1LuLZy22Uy+ULTb9z68Z1rjRUOVGyuTdEsy8VKQdvtQcZqj+CIAjCOP7c0hEEQRDZwISg0wB3BJz6yhzWB6YqsWYP4ft2B1MjIy1SSoVLmQsJKS90tfwVCk1twSKdySB2gchhJKfUU3btP4rbIa3yk0P9wiZMAC6zT3DFo3BaNs5z0V7X7ryfL5YjEgrkNV9FlaMN9f2k+tC7/wws1YPSWzF1aRG1So2oa8hsuw1M7Yr9XGacMm/F6sGmQByN/Nym0KATXdTBtGlHrycuyTJcQ3Qvp6QtZNJQGB2C5xlyLc3PayvLt/CvUU3TF1fsq+9/tdrGxCduev63td74PsehfD4JL9sq82jy5XRZmZLLJRZE+w/sKwoH/EXn70vmxogVZ2OieSKcMlDjiFwu9R3Uxr0cjywoYvwtLy9x59P0Y4esvU3c+9XrWdG6mvxdmzK+2Mt38BvN87iMT0VkY463n5fbuiJ+L2H0FXX/wIOoEVPk1GdoL6fSgL8+byV/dXVlGbw4Q6VMSrh/ej8nmpmbdJ6jpcqp0FFJmmdbXU0alwjl6ZIoc6oYm8mWf2z5HNuaSxAEQRBJ+K4FTRAEQRAoPZRIXUgIPEd7z3D8QVmnojKdiz0j7kZC4hIwdf8QTyCt52O5Cwlx5RJdEXKsIHhRLZdF8uWFU3WWJmoIagWI1DcuYBcLisLrKSlKZ3OqavYlUQp0icpmcTZf7H/W2xvXhUSyQQRkA4f6hQz6L5ViokNwGHliIG6OYp5/u4dmpH+vI9JXpHGXrXpdszHHCTyr4nqJHFO4uvUGAiHDJGzjE/18iXqfbDcKjsxezienOrmgxYhDiOSI3sgN+L0sj0dE7eWQ1Tm//E2dHF02OsR6hsIfaCVp7kmcm7Qb3lrKVzOb+2Pq6K6ZQl2dqIpioB0TCuccfS1JgR/b+pBqbTChKJxYX6blt2TAj8Fpm5FxhG5tCbjmaFV3D2kN+LGNofQG/HrKXWnr1po+NuLGrovro06EZatx1efKVo73zBo1v8g4KsmLR6eWnscQzn9mZIwcOnEyVWOyOhG2HUEQBGGX5rHfCWEuXLgAn/vc5+CrX/0qnDt3Lnydz+ehu7sb5ubm4N5774Vdu+I9DxMEQRA84DvUyByu01xIxOVXKFZCT1qj2++pfTspNcBN+vLhVkpSUP8ImxCd4DxteSR+Lxfpq/YfZPWXsg/oupxfXV2JL4NsPWLrv4m4Vl7HWMtGe4iulbLzPNY1WWovh+VZ3Oxy+i5dnZvDJQg8iQ4hQkyWMwfenmigJWX0lSbSl2gVIey2Ji9doz9FWDHaIjTh1YBI2w7KzgJrnisKc0dC5fmeRiMQaRGTvfGsUiFHPPPk53ZmrktEsQKNUwg+oIm9VGweiBok0BdNPl2kL98UhfkRmZNuLS0qy1e4jhW2BdZ2VUbKx7O+l7JhwG9BRtTQD3X2SWlFYdUFSZ9BujrnVOy+vV+MnxuxawqrwLXy4jaMSp+NmvYQk7emzSwwa8DvWh+35Kym0tGrvFrSy/htyJ1pjhOoLDf2klb2cuK/L1baYPHKedGMwDlUOklXzPq8pLBOa47XzVziGciDIAiCwAgZfaXklVdegV/8xV+ET37yk4leZKanp+F7v/d74YMf/GDabAmCIDKLnDd6mweeIPayWuRSMelwyNLc/sAHVBTPPWKb35eHVIRofcRsb+Q8nAbueU035J0psW4seZSV8mYuK6gL+CP9LF4+V/sRmKA+Bytuh7SCP++VU1RhSlHY0eYw2Y+ivQTGCNdt9vPECwmBsknNp0kXpRrqJipJlVmJtCnNc5uaQUyZP34d12gYEJWjwkvX9t4RKFU7ktOWUS4pVUA5guVQ1w4yob5kjXsFzrdGL0F9Amd9dfaPw6Uzb+F7dEdsvrBE+uLICZwjYS8nNgfd7lAiHqBjvQmzzwWytyFPUbBXoHmes06l9kg2jJMk1m/FcjmWd9JZ3aSiMEa5TPp1IfmZbt64evubJg6QeAnbPxuPSmiXienHzYjy9gz470TBMTT+a2lh0xNG2S/E6egbg8tnj6Z/PgPjFdvewsYdVr5YTpOh2PuWWVtVb8Cf1GaVth4YmNq9/l1D9dK4f9cxecr+Gme/qMHbPn3j2+HC8VdhZeWW8bLE1qElo0bjSIyj7qHpLUZfbd1DcPXCyZhsArUyHIvzoom9HM/4wbbmEgRBEARk3ejrb/7mb+D8+fNNF+0PfehDkEuxifj4xz8OP/dzPwerq6tcyrCvvfYa/NiP/Rj84R/+Ifz8z/88DA4OSudNEARBbMatw5gphZwk4YseQRaui16hg7pb3Qgt6ASkFhToZJRL6n3VitKOYjaUVVd/CCQ8PTuFgHdjlZdCyknZ/Er7j8QYEvWYray8gsmkNXRoEK5rnGuUzmO258QU+av0Xu7U2iCJ98+IUrkkbu8TZKIPFcttBhXkA//OqxyPtLqynC6PyLyT65n1iWaiXCvnGFZeEf0HfN0FugYnYXThHv1GX1KsZWLdaowOEfUle3OD7jqRGbsmlEv4lK3U10302QDsQHu5VMg44sEWec2KM6YURl+qHAnYPMdsnK/RyWkVEK206MIc51h7yMxBkUNIv1xOBVJjJqU8X2bM8hjANslI7PtJZTISvTIqa717ubbuQWgpFDmWVdxjmr8PBs4+Iw+Tex4OZRBNjb4UoXbdN9AeQoZ/5vdyuZZ8ZmTMSWudynK3dvTBxK4HQmdX3MYjSOtNmDhZiePP2D+xE4Zm94VGX0pwvD6kZERpI33x3LFuer97eBbOvPE83Lq52OBUK87oy7mzhKZ9JO+8KCdP4chf1pEzQRAEQSjAa6Ovq1evwkc+8hFYXt6qRHD//ffDN33TN0mn/VM/9VPwW7/1W/UNgsiG4gtf+AJ8y7d8C/zmb/4mTExMSJeBIAiCwIK6SyDU5XD2cOpqufnQfeEifHGYdAmWth/JdHNwS7lEFP2Xbuki00j1IU7l0s15bSoEZAGpC36CH9Gqxd7v0oyXTd8plqtw88Y18fxlPNhbXMsjjTck1gbRscqUEXK5vHlFD1tGDyqSQbzvY1FAV5Zv4qpDR2yQlF66CngUrrT3NHjbbOsZgqvnT6YoCFhBLmqr/sLeunG9lllUISAL+LCX6xndBrmWCIcMSh/NDUVhKVyJ9JWk04BtAeFxIqJxrmHOYLAqFprEpX5hBA7j5PR5qEtKTR68Xw6sOmNKIno8a65wmb2crGFroMHYxdkpYHPB0xn1YKdrcAounnod0OHINtlG1FZmkLR8a0ksn8QBaak/y0w9aQz4E/V0czC6cDdnWjKK5aKOYu58wwoezHMdfaNw6fSbdvaqCo1nXY+yfOcNQIXttVyp3CJpfsvloFBuVZfflgw4DRZR9QFUhZGC3dWF6O7LEjJcXUY3yuE+sqo7szJZ6vT+x+DUka+Gd4FdQ1NQ7RxIyB6psZHUfki/LHO5ZlCn7P4hvj+T/IsgCIIwgaHbQDt84hOfgFu3boWbyI1/jA9/+MPS6f7e7/1eaLDF0mIbqtqmanM+zfKtfffEiRPwXd/1XXDhwgUlz0oQBJF54rzwBPgEdbYFiDrrROWz8RtzxAgRbdd1HDZ0qg1kakzhzLNIX8nldNOjsMSvE79RKFago38cjIJ1LhGNDuEaSvXeZBJbszLutM3VERcsMh6FR7ffs+W9roF1Y8woWNqRig2hQRg4Q73OAn3pF0qtWozoZLF2YYF1/hWgtb0Xuodn1KzvlvdyVvYXFvZy7DmH5w5CsVStX54Pzezn/XHUB5F5ocNAmW4uCRoOc7BRTmsb/qiOyOdiHNUpbxhnZZ+sr9Ii91FcSq+Nv2WGrc6f9VMg9Sxr/OOQ7eNK1U6OcvD3F1Rr8Ob3A/zziLtIGPyYiA5hRLndDWdMGEntNIFjvqk5YyLCiym01cAzznpG5mCsiYxHR2lEseIcQWq9TXtmTVaE3zwuS60dMhmJ/ybN7zT24VwQ4XRCAaPb74ZiZWP0b0XPz12P6Ky3PUGR3N7AeIidu5Vlb+4cIrsv6Z/c2fC60iZ+ht1UEsDI2qoGZzVIZGPCa7rKYiuIdKji+0YwVCQTd6xKCGycn5LvWJu9X6p2wMTuB2Fq36OhEwaOjCQLCPYINBjwc8LluNTkHavKvBBORQRBEIQZ/Lmla8Jf/uVfNlz41xbPmZkZeOihh6TSPHXqVBjlq5mxV42N+TX7Xu31sWPH4Cd+4idSPydBEESmUKVMbejwptI7k44LLpSCKdWIKNAgPh1HKmTZbENH+o8dT5viyiVJ/c9aRCuZqWdDmVSOq3y+FCpgzx56xx3PZQ6O5wYCx8vvDHJrqDNe6Dj7S9Tz8MyTm/tktWsAhmcPQL5QDsfjxK4Hk71EOqhcEq1sI68ozNNenQPj0JIvOB9RQ8VeE/szJsGitc0cfHsY6UsJotURM49F1q3WMSeeti3lkmpXP2y7512w/b73w7a73w2V9u6U5RC17lHVDmvmL7o52pl5MDVJY/1jmleYAb8r+w0Z7D6b61HUdJ6/Rubv2vJez8issnIYJdBlKCfXn8rVLpjc81A97/g2wTQf6cKPZ4yKwCu6V42W7ciUKjITuc+4kq79XiAdC3u5XEuKiMmiEb20i+VSRvrSMDezft81OF3/d6anFUxlkaS9d8SI0qUUjuyT+R0+RDkCEa//obkDW97rHd2WkL0HHfY2uvosi7jRPTTNXw6P6tR7xw8WjKXks8DWrwIrz9M/sRN6x+ahVGmH9p5hmNjzIGd2onOt3fq2fgdl6+5ZEennYezPiah8MnXtxlYuNab20m7elanb/yaluZlqZ//tbwdq69SR+ZEgCILwkxRSb/w888wzDRv8msHVk08+KZ3mf/tv/w0WFxfDdDYfvjbntfH9jd+vlYP992/+5m/gH/7hH+D++++XLhNBEARxO3KEgNcUpcQlr0NQJ3KIVBl1S6W3QkO4KfjYyujCYVi6fgUWr5wHJ9CpXKJR8JJKGWzTWGdKwi0tBVhZuVV/r617CK5eOBlTAECJlHKJpKAuaZ7oHZ+HvvGFzT+Syku2DNYuchUqBWR2HnZJCKuprFzG6Nx6KgH0TWwP/2osXjmX+Jv4rHGNLxuXOCxaWrmtC3o3z3VKoqOlU0KyBrbyCMIMhUxc+imNtotp7raoXFJTGBZVGo6uP2z1qqE/OT5edZY/LtKl0H4bYR2bmjOk+qUN5anAhrwlJ+VYZnB6L5x+/Wvh64Gp3VAoVeD88VeV5oOV2rOoPt8t3PveTZEh4spgOtKtnfnDB2VoFs29a2gazr71ghNKoNjqPPUcJ7GXZnXADFmunDveEB1i8aq8bNWlOVBnH2BGJR19Y9DWM6Q+cUxKu7zHZ1WGnx4R7VwE/EXUWDT293xpsb0ciyh+4cRr4eu27kHo6B+Hc8delihn7WNLe4V4C37lc3Jcfs3O//HO+FTOOwHOeQJZcZQj7KBHV0E2ZqHfaYSSfsa5T92yl+Pc3jKHaCPbDgHE27M2y1HobduodCBs+yG5I/laKadb/cIPmb4tZ0wS931R+woZHZM082vSb2ufC1drgK7v6T5fs/TZuTX5i0ozVZhYLUl845ogCIKwi7dGX6+++ipcuXKlbly1cRF829veJpXmiRMn4E/+5E+aLqgbjbomJydhdnYWSqUSHD9+HJ599llYXV3dYihWe/2f/tN/gj//8z+nhZogCIIDO97o5VlbTRDUWSt3LV+c9aZMcIC0X4g8B/O03TMyByde+RJYQUq5BJcShA0FOnY5yAwhTh15dv11riX0SBdr9JVYTtz9WVkfcOcx8QSH8MBVGlOiW761hK9D2PaymJIt81+a51Gypio0SjG2xkcr6a9/KlKO2/Uf85PR7Xc3KJgkp+/mpJnJi4oAYV3IeHK14cjBUqQv+QwF39dNyqitumBemtezUnqzqvRrXEkpVfAkvPOYbYiGeS7GwHDz64GpXdA/sSP8DXtdUxqOzki6gGAPvvpQkGTM96Mj+mlRysLmdMmDeW7urm+IlucIP5+J+tCfh9AY4o6AE/F2rgVkGJrZBzcXr8LS9ctQKFZgeNtBeO1Ln+AohphCKEblSl37386Bia2RIq08vnoji+SvRZ+dMnm+NYQre7nUkZJlnBEFAYxtvwf6xhbCe0jmQOjG1Yvxv1E9YC22D5rodIGbjjVF0sK4zsmg30FsoG4ei91a45XLiUfYMovwHs9QXas1+kLsyMfwXMJtgCbYP1lk76wS59AqEkf2ctxngjRRqxQMARfPHVL9JiXM4cvwtgPQUijezizu224aavmyPyMIgiDE8dbo60tf+lLTxbS/vx/27Nkjlebv//7vw/LycoPxVu3f7G/Hjh3wH/7Df4C9e/c2/O7UqVPwsz/7s3XDrtr3a+U6cuQIfPazn4UHH+QLR00QBJFpZA5IFg+/SgV1SUKRQOKw58lZ0EUBh7VnVHkn7koHklFoVnCByDylt3b2hwomrR294cWr2X5sr33kn0Xigtt572zpyh8+vytC8xiY8tVbz38OnZKIKwZ1vGPOtrKMT+s1mstqk3UqlVdgtq4RdzFb+ya5iJ36yiqVdlqjLyTKYC45MOExIElIIPEr3cOzCUngqxcdrO/lBL5vvF7S5Wd77yFn9ejefJ+mX4jMkVj2P1sJhOVyUmuD8v7MIn3hnutU9GdnZEcR5PMlKJRb1SVownDIlSrnViCWm3uYHI4Z7K0s34SWfBFyLXLGY/VyOFOxjXUmUm6l+wwpRymEcgI8ezm5vmBhL2fFoEJ+j5V057ApI3CNVMrVhKJGcK/f6HwOE+sauv6tWi6nuwoD9/uyrFwuaQ2zKWtrkFFHGYEgj07NHFgpPRvyEAh8SXv1BU7cscoa0/vgdM6GgZWVO5uYx5na+0gY9ZsrGUEHHqnPRgjbgSAIgnAPZKdFdbz44osNr2tGVvv375dO84//+I8bFvuNUcR2794Nv/M7v7PF4IsxODgIP//zPw/f8R3fsSXqWI0//dM/lS4XQRAEcVsIJHRGUugRTUK5RFd+3GnIXLoG7h1WhQUsiMruMnHCEdcU4NL+vq17MIzWJnT5Koh2QZ2UJ0KXx1KSQBSZwBR7XXOUb/bgE9DZPxHxc4XrNfa6whCBNERjPSW1gaULCam1SaJMcp5dcV2AWsGXR1XWjw1UiCN7uS3vo7nUxGXcldrwT2Du4ukfpdYOGJ47AB39Y/VfZZrw8W0bRjmA7KW6K1UrqKzR5IuAGav7FtOGhwjaAt0+EVlxMqEojKzSU0fASaEMxgy9CqWKmMGXqKIwgnEfV2dSipZC51Az9yym4Y8OgafMXuLIXk7lPBcdVS5VFvFpa/qdQAYxn+lQFFYj82DfVSlTTLp/kMKT8wQqhOsK60Rmrs2xr5WY9h9WDEhUPj6+qlTWP7sGJ2F81wOQaSKrKnBvCrTidE7JZg5QIrUdMqmyjrTevC87QRAEoQNvjb6OHTvW9P35+Xmp9L761a/C8ePHw39vNtzK5XLwMz/zM1CtVmPT+JEf+RHYtWtXw+9rhmOf+MQnYGlpSapsBEEQBOD1WpSgXKJdkBg4KhhQ6jTZ8WesdyEsSqwbM1d3Scefp07F/Kg6TucJWJlwy6GunEZQJzwvygiZHQTjxZMqWgolp9Yjl9tChzG693UXsZerr78yXgNt1QO3ngeudhKrL1xlRxHlRkbHE109piwPsj7N7bncatRWPSLj3rF5mL/n3dA3vp1jrpHZ9/N+zYTx5ObXkYcncB5TY0zG4NqGdonG+oiMWmWoDTB7DTe7/7XkmMKiQ5t04OsXMvOI9vVaQ4Sl5h8FDm7lHL3KtjgnOltnnKj0YJ9+DAfOz3PKUWggaWUvp/EcEv17U30oLh+3+rFZg4i4+y636k0GjHt8jBYFaqO24hqraZ/N9L4kOkIg9r6Mw5mpyjQa0jPddzdlF21UzVeugak90JIvQBaI3Gs7ErVVhrTn/YZIyxrnmnp5hKsV37yI48waKpPpSVc3Dq1pBEEQhFowrKBGjb4WFhak0vvbv/3bLe/VjLcef/xxmJ2dTUyDGYf9q3/1rxp+X2NxcRGeffZZqbIRBEEQeO8j+CJq2AOfQucmUno1dPb5mn5XWabaFZWx1XvqC4k0XiMzinSdJTYVgr5lrAi8guR1JxIuw57BjGAZoTJeZBaqyrrpNUdf0doWDgqhzRvKcVtmNf7XOQL+fufsM+rZHxmJgKjTgN/CM+cMG/CrVEJVgsx+fuNeTuLMoiWiNFrSK+Jg3su5pBTVFAt1q7PO1lzdy2nuRnHPbNvpQXLEDuRjLHKrsEWDzt95RDWi9YF4jeCFW1EuShHNuFwO2V4ubdQgzU401CqFuodL/UIO205M3JgDI/s7t4gneZ7jGVNJRnKJ/RWlowosa0OzQmia69BNK+gKJIUqWY2Jed+KoxpV3236e9PGPoJzGcK5z+ReztRz2jHkztY8Z0D4IpwtZpmnXGTMlL9Piw4naroJ1O/l4ufFQGjtlllz/T8DEgRBEJhBIA3RZ/TVbJGdmJiQSu+zn/1s5Gff9E3fxJ3OI488An19feG/N5fv61//ulTZCIIgskT0AcriwSruUKdDkCF0iEy6xBHKmPNbiA65mThw47tww+aFLr03bENb5sThamf+kzL8Mzr2AkPJ6Xmm1HWViXmOUEUapVeevpoY4VR6fbB0WeXApRSqfVczkBfPKNYuBLGv4xxwlida0S7wwqGFNWzNgzrrBUufILSvedjX8TtwPlua51Hh6Rv9hGXGGZPSdVJHlbrXTG6AUCSt/tzFlXjjf0V+40p0CIz3D6KkrXNr0akdqmOXy2wrYqgrezkr1xdYFIXdweTaENt3A9wGD2kNr29/6AmWHkSm2ZFdsaZ3doNELdGhvoyjzgLzcmKZvAxFRbMlL+eZx9HJ8m0jE/03bT9qMGCSNzJO+o50ObV3EfNjVxk6nCwieTSCIAjCTzCcFLRw7dq1pu+3t7cLp3Xjxg147rnn6ovzxk1UZ2cn3H///ULpPfzww02FlGT0RRAEYZAAiXK1yIVAkiI1Yu9NtlivE7efsXYRZE+5yn1F4bQeZ809jynjJeQXEorq23w/tCnwx6vMmnbuUqrcEbh04RZYNPpyZ71xrly1/iwwPyV7YdYxHt1Yx5vR3jsMeMFVry4Z8KdVvOXxGonb22yGFegsppUtRR6NWDe6sp1/WgJUe7nk/orPUELleUI+rWiZhMi85eIcF4JMRiRMvd3tPIfcHsUvxW0U0Vxit86BP3Wm8lHwVYtZxxMp03UZteuVI3u5tArjG9/X2EcSFYlTRiwzCpqxhKUc6QhyLf6fWQOX2lB/mYTOIYaManTj0nkq9bgTkKWqbZ+Aq/7N9Ak370JQIBgZMXZsWZcL+umoxKXuHBnR26guiUN7XBfLSBAEQRgFya5DLcxIq3YBt/kiTsbo6ytf+QosLy83pMf+yza0Dz30EOQEhec7duxo+v5rr70mXDaCIIjsIe8ZxbWIGipIukTSUm88F1e8SaVVGELaL6RA+CwyCrhWxipvnoFd5RJp4VbgT3+VuXRR3aeSymDP23HU+xg7gOdgrHLOfmA72kXSPBe9bwGvokP43yEVz93cylM56B1bAKykNUI3G7ETm7jQDeWSO54cRdsa39wnr3RYqwNwEqm+Etl8AmdWznHaPTQNGDG2P5bIpm98+5b3eke3gauk2cspaScXx7bh/S8ORUIDZRBxYoCiTtKjaj9hZF9iJAtzisK5GEV0PdjZt6tE235epgqwy6249YHj9uyOKLPKYPnRbMixbIx1rjGrZCwhH4/NiOgD2tbTQMxYSEc5jPTBTeXOtbQoKc/Q7H7IKlGG+jIG/HH9yqX9CDo5I/Y9SdS9tIRYTkkZHKqvVLDH9ORR9beZuA4Q5iiXaX8TOc/pNDJWrn9hr/Njce4iNm7sOgVyfT9AEARBqAHHCqqY69evR35WrVaF0/vyl78c+Rkz+hJlZmZmywaCCS0vX74snBZBEETmQHh2iTsIqr2Usq2ojbDyFZe5WBbfJ1iv00BznoodCttAqaBOJ0nFtDYG3Y/2poQApzFz2L891i1R9nDMaYeiRjRz8a4pXZ59ibULCZzzhm1DuaR60WPArzIpbg261GlM7H4QSq3ijn60Y70PxSBj32JhjU/vUdiU+DOhnJb2R1KGfyYvXS0YRGK7FOVRgGCGSiPzd0FWYf24WBGf47uGpiFfKNVf51ry0Ds2D9jgnee4HAsF+Az4bZ4P1TpjklzTY7fAuOajrQicSxU8YzNDTfyItqHgOJHodnHtoWoNFFLe43bGFFU3SK6yBcerzf1G+ugQQpmBcVDNnet3/NzfNtyfUcloERXFFLzzwMZ20noOUqworEpmFleu1bUVJXnw5ocanVHg4gysObMtltuga3AKsIJNDmAdDQ65tP1eODvBPRtCR3Rm9wtuj43UOgCOP79RZKoK8dUI15jb8syBXQfCSPtr7LyL4ZwfmKrPAHU7EQRBEH6QBw+5efNm5GcsYlexWBRK70tf+lLkZ/fccw+I0t3d3fT9q1evCqdFEARB4CY5OgQd+JQQK0jgTwZldAjLisKlaofwb+KFNO4pAmAX1GEUHMlHLUvwjonhUbFdCoc2X45IzS1Tae9R01ex9YFYAlQRSGWxOc8l1ZnIJXJNGSZeKZYvrTv5mrug16kUE7vWcuTLDCE6+kbBSRDOKVov3KQiKfEOjKifm4raGn+p55JimWydJT2j9vncoToW7ReuGnypNJ5me7lSpR2WFq/U3xuY2h37m0KpAtMHHofzx14O1/Se4TkotYqfcdGQpj7dGx5KQL3/dXDO0klLvogyEt+d87ai9jLR7Dr37Ta8ppv2AC5cXHxjWbbO1O7V8NWLDkT2+Gwfw/YmLmFd5mjjbsRnQ8akfDAOWxSOdKLP+GtanDGp9MbEb3jI5ADN984B1/oxe+gdkC/ecbiBDluGPTJ9WEJ5HrdjTRyTC5Zy2InmYwbuOrbgACoS7P0Ce/kS293GXs7Qbzb+nKeduPJIt1ezPQeIkObORsUda4ofGF2HXGpTgiAIwgxI3KOppVKpSEUBi7qYZkZfdcWNDQvwyMgIDA4OKivflSt3LqwJgiAIwUMNUk+bOpRLVAgl68/vgKBIh9CwGUOz+3FGh0jymK2wLw/O7N3ima+1sx+UYkJoxvubTW9HK+iaivSldjwq896s+lIKO7Jl163DHOltkz/j9t4RMA6irtDWOwyFYuNZbHjbQeF0TOwhRPPg/T7PeI7eawkVSW4+xTh32FYuSaoShFUmQy7Go7BTF/JxIHwOneNdJan7AJq6t2UMZiNqq5JFI30akWnrS/pOHgFnnfoetVUdrA4n9z4S7mmZ0jSLCDQwuSvxd+VqZ2g0N7pwN1Q6tjoBQE2gcC+nojhYou0IzBeFUhVBoK9AzXwroLSLCd7yzBx4HIqVNkBLoLu+1bWbqshrykgZBdB4ZKQMG/BL5ZUUnVrDuoTPOI1jfQ5yMLJwGHASGEoucM/ozBDGHJXItrXVqK24+4BQnSJ6lmblZlGRm36Xo/07BydxG3yZQGH7olPwDjRGktNCjPyl6dsBPu/9pbLBy0fwki1zF8LI5K4hMzdhX8dV7cU2/l7vHO7eXg6Dcxe1RmI897YG6htfkxIEQRCG8DLSV1tb9MXQpUuXoKurizutF198MfxN6Klnba3hvwcPiisIMvL55tV+69YtqfQIgiCI26y5GOlLIc0Oj7Y9rafNK60gmTOBse33QPfwDOBGf1v1j++A1ZVluHzmaKhoMzx3QK2RlQeCOpQgq2/jfSDSqA+nEZ0ssZeuHELzfKEEg9ONhp1ZW9uZUcnUvkfh5GtfgVs3rkPn4AT0jMyJJ4RvyOHcl6isO81zStyclXwpZbdD4FegSznHcZbF9hzNo6SGuYxCex8r+w5HFIVr5RHULYlJEKxhsJ1xnR0kzj7pD62olP+2oMxTrRqYo5apvY9AVnF2L2cRZiTY0lKAlZU7dy88xoKxKJ23EJydY39ixoiXRe0rt/Hf29lA1V5O2MGHjCIUsrHqXKSvSHAqirKzFJPnbqS1o1cuMZmI0pFJ4TEwVJJ0SkXxHQ9+I7TkC5Blmhn2xjmACUG8TZYhSsbUMF606glj9SwUGN7/Bg6fWfXBxuOK9K/dHawo5XXoipRyL2d6DCmTy5lheO4gnHjliw3vDU7t0ZOZlHED0opTOgv4/oxqYOcy5ljpxrVL9ffKVYzneBmdpCDdemdonCifT5UlJ+7sCMX+KlA8/hE8EkEQBOE/WCTlSmlpaYFyudx0k/DGG28IpfXUU09FfiZr9HXz5s2m79fKTBAEQTgS7p2HBOUtocOsSpm5uvvbrUlbOKDnchF27EE6RWNUmBC65HIwNLMP5u95d6hIV2rt8MI7U1pMKZegEG4pIgjSee5DO6dbFKhGzlOc5Vm4731Qae8GlBjo+7X5iikSsvlt293vDJU9k9qTRTzcDHZlxNQRSG0oSSFGR9RWkWqIrrOacUmAu/45y5d2jssUonUSt1/DfOGmsDzG9nIWohxwsWbDaQKuPoQZF9dGO2Tj7JkIzxk8LqpcBj0K51paYGL3g+G+ns1tXQOT0D+5Uyotrmgjog6ZfJkCYh/R8YdcU9yPY9IZnNnqqEXOSBFZnafdy22WMemeUyKdC4mmA0ZgTrs2y8m7hqY15dZsjlOYOqI9pFB0CK7lwZZKBp46ZUZvmx3vDc7sQ7cH1OoEL8V9iorxoX6M6W+fXIQzZX0I1pFMlUr2A917qshoTDz9xuZxbQ33GiM3/+OZu5XUHZL1Xdj5gqF26B6ahkrbnajkXYNTUElpwC/SZmkN263De/8QoUvCXVcu1IeBMg7N7q/Pa8xYmL2ORZEuCRsj5dZOfXs5TmtRPgP+FPpsAe8X7BqfqSDV+Uj4bB4ntLKkr0cQBEEQkjig4SxHT08PnDhxYsv7r7zyCjz88MPc6Xzyk5+M/OzQoUNSZWORw5rR2toqlR5BEASBl+6RGbj+4rmG9zoHJlKmqlK5152Dv8zFDxMWOK9ckoRDwhtGVHsUihW4dXMRqXKJ4egQkR+709YmyxrVp3wb+1HegHmf055xq9vtMDS3H9782mca3huY1uRlcSOiYyjyXi5Qp1yioi0dmsfqqDRwdsVYWquyePM8ohRLXFr7EkH4KKx+O/rG4PLZo/X3OvvH43+T1ruvVk+blo3YkiIjONSfzY69wEK59T/f1r4e3S/WVh1ZHwjr2Ha8kjjGbClSJozptp4hWLjvvbC6upIcUcQwvpxb8UUJdZCAKXzOwMUTR2Bp8Ur4FtunVdp7xJOKqfOoPtc3vh3OvvWCQHED7wz4XR2v3cOzcPPGtbDv5IuVUI7hfUQpC00RLVcL+IyCXUXx3mNk/i6otHXD4tUL0NY9CJ0Dk7Hfb8kXwWk2zX98joU0dnDJ+djm/Nfa3gv5fAmWl5caDCLwgHNtkCl2znvZnP7nGJ0/DMde+nzDe8PbDqrdyylqD6N3eIYNn10zYmopFGHm0Nth8fL5cA9XqnY2tI8/Y5ADjY/qhANgB84gLJr69vvfH+7lmCFWvliK/b6yfXIAMLX/bXDu6Itw5s2vg1dOIkXA1R3SgWBukx1fSbopvEaEaO5rCYIgCKfAISnXwMLCQtNL0M9+9rPcaZw/fx6++MUv1g9RGw9TXV1dMD8/L1W206dPN32/Wq1KpUcQBEHcPtMgPNd09I+HxiwbBZv9Ezuk0tJxeYhfUMhXvpYWfy+ya/sZbEK9ddT1ycHZfZzPqNLTJqfHViTKJcKoGt8yl/uyF8i132GM6mh6vtyUX3QUHPvKnzqxrTjDlOx6R7etGxEHORiY2g2tHX3gU32aHFd49x2B/CWO4kfa0h6qvMwjJ06BznnW7BokRXpqvs3Y9ntChSkWAYX9d3ThbsBGakVhQ8olSeW0pXQitZbKlgmpQQjmiIZ410ZFeLxPVQpnP0ijXKKkrzmoKLwRVQZfqjzEh23i6BywuT+5qkBng7ixWChVYPbQO8LodNP7H4PxXffLyaQkosuxKGNcUcVUdlnO/o9GLod0vLI+NTSzD7Y/8AGYO/xkaEgjnVZNSU3oWXHWCy+8z+r1Xi4wuxb3js3Xz6FJ9dc/sTU65/BsY3Q7HkqVdhQVkk6Gy1GuhPQTo1Pb6s9xy1YuB2M7763fAZarneGcpys/sXQCuTpD6vwvWn4UoJfhcxF5TRgo1QnYGImG3SMwBxSqZXnNYHOrNhyRyyWBec1m62O1qx/KbV2KyimQhoV6sdEW0Xes7p7L6yguf1L75ItlaO8ZTjT4UioXXFs/L7PIYukdbKcpR4rnURG1tT62BeVS2qNlR3/EFR1NaVGQ1Q1BEARBKMbbm5gdO3bApz/96YZFmm0kPv/5z8PZs2ehry9ZUe8P//APYWVlpf7bjf89fPiwdNneeuuthte1NPv7+6XTJAiCyAqxxhsiZ2xDh7d8oQTTBx6Dc0dfgtWVZegenpHy0CqNSs8fab2m6/TOFOW9FJGn99REGcEoigYjhYxcK6K83UPTUO3sh+MvPwNXzh1PXbRNmab8uZn+kdyWdpTFk2Bz2uKV8w3vGTWKcdwggj9SV5RxqyMP6ihsfDEvxIMz+8J/8ygzsovcq+dP3klDwuuz+LhO0Q948zJw2Rj13DbnOZVGlXf6gbrnEaobxNNFaiVsxM+m3BNvzMM2i9DQMzKb6E12fOd9IgUwH7XVkb2camzOfUYj4QTmnz8ybY1VHh1xA3f/1OWYwzWYAv3VC6fs1z/HvkSngRUWZT2fjBi1tJcFJypx5yTbMov0rCWsHYqeb+3O3iwp8upGmCzt2qUzDbLoUmuH1NrPDL/Yefa1L30SbMwd0edBHHOP9j7gK0LVk06Wb2ue41ofbHWTlPnKGXKoe9hye3d4b3Dh5JHwNYsS1jU0LZ5OWxdUuwbg/IlXQQ+cz8yzl9M5p0g794h4f81gVJEHPwjLN29AodRqfm+RkXk+15Ii0hc5+whhBhAzB98OVy+cDPcvbF+VdJ/QNTAJF0+/saEd8tDWHWco1rw9Bqf3wq0b1+Hy2aPKDQai1ldeuTmeMYvv7oEPS4agWnVJzBNpwO+RTAvlsyjrE2ua5SicpVjVG+kr8dnq9bnmTJ/REx0tUJiMvf5EEARBEDLgkJRrYNeuXU0Pm7du3YL//t//e+Lvl5aW4H/+z/8ZecC77z4BhZhNvPLKK03fn5yclE6TIAiCwAu7aGcK42M77g0vllITZEP4JkJUpK+wTjx5Royo9t5XrLRBR+8oZDU6BFYJUFI7s+iFG+cfdhHP5d0LKdL9RfMld7xHYQc8aRoUKjdbazv6x1IravN6r2dGFxvnjZ7ROSmPnqbYPMdZUbLHjEqlhTVzBsGuXQ5Gee/XcyHkLywyIVOA2via7a/U0rx/9E/uTPNzrXsxLIrCSs9xCucxtnfbDPO6T6RkU7PGRW0lBTn8sP0lhnGhe11Ma+xuK6Ih0qM0R/mYzMqPvXT8WQlvucVQ9xybI84Uy1XpaFb9U7sajLWZwm98X0l7vgjEZYJp7fcNR/pSN9Z86ftZNYYL+KNDEPpaIQhgdPs9sO3wO2HmwOMwc+gJabnzyMJh2Hb3uyLvku5kCjj3cgrKhXkvkQRb68L1EtEzGL9/lJKnCBj2mHT+YgFT8s7QgH9gIryP4LlPYHs55kipxvDcgUgDvNh88wWY3PNQOF9iw7RcLjbScsZBaRSkC97o1AHyunO8yXpHtqmPTJh4ZA2sys1wzjX2ytSSL4JtYttEpmoMnoExOkslCIIg7OJtpK8HHngAqtUqXL9+vb7Q1SJ1/dZv/RY8+OCD4Xei+OhHPwonTpxoiO5Vo6WlBZ544gnpsj377LNNF9/xcX7vfQRBENklw4cahUrHztfXZkFdZKQv8AaUglgd3vt0PGZaoy9TyiXSxbTbN9gFVkuhFEY2KrW2p1SGRNjPkYz5SIWE2+cVrKRebySebWBqN7z+5U/XldCYQLmZQrsu2nuGYe6uJ8MxUWxtD73RXr98tiH6FyaM7gmkDZgsKgonOdazPW8FbubFW29+GH2tGatYZuDFlDtYtIdCqQLltm5QDTNiPX/8lS1OLthlbanaCSde/iLcuHZRbaZBygswQ3s5NMZlgvSOLcCVs8dheXnp9ut5yBfLUmnJzInW51FDREVtlYkI6hLKns16FQVhVMTB6T3w4lN/piF13nXRbiSRuHnOeTmTSH9W+qiuGvAHW5SEI+cznuIgPtMmjTuZ+mbG8kdf+Mc7ryd2So8hdv7cds+74fqlM+Her1ztBHyk65O5wLQSumB0CJ/2MvVHyfAdTIajQ6hCdT9h6bFIXSKGtUuLVxreY1FzwnSqndDa2QdXzp8AG1iX4ca0TTqD4XSYHkdx+TWrh9jv67nQ0pBmch6RhkYcY9p63+YB6RrG5qX5e98Di1fOQ7HSHsrzYkmSVfPKwAKDdbfl99jawh8D9vocpqLoWh/f/D1QtBFmgPo8KoSy6lN4X9HaHuoRXDr9Zj0ydc/InHA6ttaZzfsC2/dVSftsjOc1ZpS8sQ/UHApjQWQvl1rOja95CIIgCAfx1uirXC7DO97xDvg//+f/NBhusb+VlRX4F//iX8BHPvIR+NZv/VYole54hLpx4wb84i/+Inz84x+v/65GLQ1mLNbX1ydVrgsXLsBrr73W1Jhsasq+t1CCIAjCMJYP3iKHWO7vqlQI5qwf3ugr0RkBYvAKGmUUaFMLmzR7M2z+e0OKwsmWBWi7YFv3YPiHC9QDWxhSLuGH9cXxXQ+ERglMmMwU6GSV12VhyigbFVKqnf1hxM9zb70Ii1cvKM8vzTy3dS7XN3YwXjgk0Ts6D+ePv7olmhuWehBTLjG/lxMhl8vD6uoyeEtgzqNwR5/6yKk1mCFrsVSFm0vX6u8Nzuytz78DU7vgzec+qzTP9JG+DHVq2Wwsz42V9m6Yu/udcO3CKSiUq6ESZGqUPJN7a0YcuTxFh3Cbtbpx7eY50GwxbCsKp/w8szhcMZzzOVtr2Zl1ZeXW1s9cfv6NKHyM7uEZKFe7QucglY5eaO3oTZUei3zC/pTsizQ0V2q5nOFIXz6dZ50jpbMHncRF+nLC2CEGb+bp2wzO7oM3v/aZ+ut8vgSdg5N3vpCkLCt1AcH7Rbt9xbe2RoFHa0NUpC++foN/HtTd/9OsBWyNYdGlseLcXi7wbLzq9odn7a4CyR2rq/3CkXpn9Tu+4z7oHpqB5ZuL0NYznGzcmpyqhvpQ6aRQqzcmyd+BVca23wPFchssXj0frnf94+mMvoTHbeT3dUVtxTsmCYIgCPfx1uiL8aEPfSg0+tp80GaL/82bN+FnfuZn4GMf+xjs2bMHent74dKlS/DFL34xjA4Wxzd90zdJl+mzn41WfNm9e7d0ugRBEJmBzkdihloWLvJtZBb1nKsry14I67ASep0rVuDWzcWG6A/pMCdY4fXOZE5R+HY+opdDWerizTxtKm8fw96M0xq3Zqn9BegcGA//MMGijbG/Zz/9u4AJJyLTWOznLHIRM6C5fPZY+DpfKEPP8KxkajzROVRdVuBAZO7M5VpSGH3hrofYPXzg1qOydpo+8BiceeP5cA/YNTgJnf245lt785y71hDssr1rSIEzKpk5CdE8plNJIipqa5gnZkVhjirJwpm7QYHP5ONKeRTW56Uc777RUKOoHqqhbokGA/7In+utpyijL67oEBlUFK509IR/RGMto5h7RBWFvVoG1e3lrCifqpznAhGnc2tolXFT54t/et4CO5+OLhyGCyeOQL5YgoGp3aFTKAykMQpR0ocS7wvF5OIurN8oDUiS+oHS6UJMLtc8Ca8WOufPs8lzgdw4z0IdK5PNosaRh1G5ZePci0bJ5UIDfh33NsSdqsvloL13OGWNrOGYU9Ls5XjKndo5gVjdKNvLJSTDzlJDs/vACDRWCYIgCM/x2ujr0KFD8OSTT8Jf/dVfbYnaVXt99epVeOqpp5oahm3+N/vvrl274PHHH5cu06c+9amGMtTo7++HkZER6XQJgiAyDx3eJFEvFDFxgcmbBzP6cp7a9iXA1z5sLzMyf1cYzYEpn+ULJRiaSRDYWAg7z6scsrbKo0CXRU9dBi9vbz/i+v47ZVKerQu5SOUE5IrChFfe+7UT4JtfQ0+Eux6Aq+dPwvLNG2GUowZPhAL14rr3b6UEqr2+4q1bH9udRboZ3X63McOnyL0c9/zF0bcUtBPWvYezfRBndUo/QGYN+JXpDpjrx+tGyCsN77V2KIiAFwfn/OHsePaOAPf6ENjJI5XndBe6diYMfpJlT7X2FHNKlvMj0hf2sa8S9EU3X0Cfo0P4SM/IXPgnhUSbcs8PPHs5jX0KbX81vldCWg+JqL3b4470xWPA78I5RXuzO1AH0vuawDHnIdnYt+tvX78qLHIvh/45DTqK8QWN6zyXLkmA79nw93M7sD2OHt0kqm+CIAhCHzgk5Rr5sR/7MWhtbW1YVDcac9UMump/G9/b+Jvav3/4h39YuiwsgtinP/3phjRree7fv186XYIgiCzh24FU6fM0TUqhgY2Vqk+X6epKo6IWwTwJ926pBhatS5aO/jHY8cAHYfbgE7Bw3/uh0h7vpbhvbGHLe72j2/SOGd6fWL6Yqo9HQUGQb/OiOJ4+/6Z+4LVyiQ/P4DBb+hA1R1Plbxbtq2dkttHgCwF65gA7ntqjlEu8QVH1sWhzmSPwY53xYs3mREgR3PEoELztGmXAjzdyUnYZ3naw4XWxVIW27sEN7+D2KMzr5VpPfxeLDuEeamUGYZt4Uzdp93JuKcm6TEeTCK3MsUUdHX0ycOucIB4Fwp9xrLa+au/7sS+MNuD3vf1pfk5DIBW1NTKxVGW5nYiCNLJIvLMo8eRwtkNkpC9v+o1ew5b4iJAKseBY0xe5nC2ZNy5dEuTPqrF8sc6YaLuztU4yBm/fS7WX87piaRDxgH29IQiCINzA+1vlwcFB+Kmf+qn6642GX5uNvzYbhW2EffZ93/d9cPfdUZ6Mk/nrv/5rWFxcbJrHwYONl8kEQRBEBBqVV7BTXztUCLyQC/VUsbrKF+krC/2nBlNW79yk4DE4szdVmi2FIrR29kGuJVkJo61nCMrVrju/zRehR8DoS2ukr1SCOhUESo3PVAnHTXppRDEWkSrxRF8aBkYjH5jGCS+hmOGYB/iVze2NARRzA1pEPOzbqMdAgXIJB05MFWrOMWzf1d4zLG1Arx0tuiWBG9EhZD1tah+bTgwQ72lpKTi630nbPzE/W3O6h2dheO5gGN2rc2ACpva/DU2UGetnVqSyJNN7nLj8RMuiZ5+r0LBVZC+XQtEV9zyYBM5xEScX7BqcajgP9o1v15yrY9EhIsexW20tgz9Guurnv1wuJjqEy1MYI2PtntTPdY4DnvVOpwws8dkzMM+przeJOkvoB7baIYi63+MpjgN7OZVje/Mda7FchWKlHUzAzqmb16S27iHxhATqI3BsLxfpCCUL653MI0ba7+tzwqp2nktpwO+D1VcGunahtB7swTbY93LR9s0RHyjq+thlOqrn/0yvMwRBEIR1DLkbscuTTz4ZGn79u3/372BlZWVLpK1mbP7O+9//fvjX//pfpyrH7/7u70Z+9tBDD6VKmyAIghAjMwcuK7q9dg65TMjNa+TlJurqb2zHfeHFwNL1y6E3XxatyxRMqDq9/zG4eOp1WFm+CV0Dk1Cqdtz5gsWxaVsglSyoS/Kc6xMCz4Tm8desKApnZj0jtLH50tWmgoe1vFOMI7ExqNCAXyNm5pVAqQd/3MavWyPJp2V85/1w8tUvw+KVC1DtHkhtwI9+HAduKJeQgpx0xRmvz+SIFAry4Izaup4n5jkse7C26xtfCP+wYf3MKh1NHvfexyoiVYOoGpv1BR5HQE6ywXmkL4xtvyeUB968cRU6ekeh3Naldf3lrruo5dmQ4a38HOcfQv0At28RZcTt5bjOo7b6j4V8Kx29cOvs0Yb3SoaMIaxhJDqExf4QqSisyHkdpk2OKAHyvbyAg7soZ0w87WNTLmcj76G5A7B0/QrcuHYRCsUKjO28z9g+ge25+yd3wKkjz94x4J/QbMDviFxOGh/3eBYi3KMj4NvLOft8Gp+FnRGvnj/Z8F6lvQdMMbpwNxx78emG9/qndiHRJUHiQJiwA3LjNoIgCCIbZMLoi/GBD3wAZmZm4Md+7MfglVdeCd/bGN0rSvBSLpfhB37gB+C7v/u7U+X/1a9+Fb7yla/U89uY7/j4OMzOzqZKnyAIIitEztuunm+dEiQFqOuNCet8NPrScWHBLgW0XwLEkC+WUivQiQguuSN9rdoW1BFeXwCnJFZRGLOM0al1zmHS1DOWNgocKCPGizmPqiaVMqfLly0S/ZtF+xrdLh+J3rXL56i9HO++wNjlvXQ2mg2QDA6Pel2r9BLsCbl8hAG/TxO5ThDN8yb78eb+kUq5hKLGp0ZaUTfWgYs/c0AaA3438KmtctA1OGkyRzcUhZVHbfWnzxgD8V4xNpohon0KhrVvYGoXXN5k9MUMJAjOvhLlVFGB4Y2007nME+3oUuo8J9kOus8hkXs5nnxdngYlqpVF9tp29zvh1tIi5Itl47KOgandUO0ehKVrl6DaNQClVhyONaNAIwvCUg5kUdmsyKUstEXU3cPq6op1BzvYxg+LDH36yNdgeXmp/t7gtDmnc+ysevnMW3Dl/Inwde/ottCZMQpnHSn2ckqGWtIWIitnUOE2zki9EARBEJkgM0ZfjL1798Kf/umfwv/7f/8P/uiP/gg+//nPw82bN5t+d3JyEp544gn48Ic/DH19dzaPsvzar/1a+N/NhwW22XzkkUdSp08QBEFkAXGBU9TB/s77yA+4geDF6y2dhSFMkCiMkhHUBY54Z5JVLtE9jtMKu0WeS0YRXLGidHR6Uskl58dZzljlEqdvVQnbbFGgs2lg7+rFKy8ahqrRSxzdUVt9jQ5Rx/P+rbOf8CYZqRhlyqNwRtpYOZqN3rQTpIraGv7c462cjOIMiz5z5dzxhvdMRqdGTRrlEgVIK864Mpy5we01XWmaInK5NEZfiJXs6mTFoDnxcfTJ5SJ/bijSVyK+tbUyLCjtqpyHOdPKQnQIVbBIEMNzB+H0618L5e29o/Ph/g4PNqND2F7vkN4/GEbtXkkiLev9QMNezoVDrYb5ulCqgC2qnf3hnxNjxvRamcJ4tjEZv+Y+M7hXZ2srK7aLgA7mcG5q/9vgzBvPwcryTegenoH23mFj+bN99+TeR+Dm4hXI5fJQKLcCFpiRoFYS9gi1ecz+nnIz2MqziUg/LbJzVoLjl1jnTwRBEASRjkwZfTFyuRy8+93vDv+Ywderr74Kx44dg+vXr4dRvbq7u2FiYgIGBweV5Xnp0qXwQPj44483/fy9732vsrwIgiCILJPtQ2K8QUQCmA/Yt4U2mREu69AT5o30ZdnoS/3lKZI+IyN4FCl6RsZGqjnOaZALipHDM6+gUaCLwbc1UOl8j71uBJRj00SHwHfJtYFa0ZA3FWZSG20ZGid3+vWa95GDnSfAtJfDPTnYWIOZ1/Kr507U+y6bA/omdoDP8NZzs/HsmxJulhAdX9jbOs2ZFfNahblsetBgiMgbnTUmkopJovLDPgaVoqDOa/Xo7Hl+U7nTyuWcrQdJ+sYXwqgMbA5NZ0jiBtHzQ+P7a6tporamKQd/GlbAWq7E8mEvuJiDuyi5nC/zV7KTVD/g3ncJtWvUd/n2yWjuHzzpy8pxvV5SztGrq8vgPurbsNLeDRO7HwRbsDmqIYphw4eJv1ZZkMbXHPdQWs+TyserInmHYbGJb2s3QRAEQYiQVc3BkGKxCDt27Aj/dNLZ2Qm/9Eu/pDUPgiAIQvQS3sGDoMxh2Wa0DsNk1yCCUEV7z1aPo23d6hwBJKLamZCD+HKBqKMuoi7GmLIAamMHAr2Am3fckRAdp8KoT+3iu0dhn9rKtbpDE+krE2ezQPihMO3/dJYl7ryaPYOCeFo7emFs571w9s0Xwzbpn9wJ5Wqn7WLhZfO5AeGkgrFM+gjEnl9g3uGfKwwYzig24HeBbPVjOzC5R/fQNFw4eaT+Xs/InLn8k/aMiPYs2kh6xmZjH1G96CxLVNqrKyuQa6G9XNM6y+Vo5tyMZhlu0l7B3Nk4O2CaA9OSa4nYy3E8I91PIEJHn0wd6Mvs3JPpiF62Ixk6SBi5KYUhD+GNB2Gur6VZ71TIf5MN/HE4UsEGyXMIgiAInyDtaIIgCIJwDFuHcl7P9SrLJ3IAJ6MvX1AvVIu8UNjUV/PFUoNySehRfnwHmme3NfZTX9jZmrNkhb6OeXTG7oUudb2R3ox+Mi7sdx4dkRFtzdtpFIVprkBDgFG5xJRHYcWPrmzv4ahxuN69l8IzK6/xdKQBP59yCWpi60Du2boGp8K/zBOYVqBz8+xnPF+VenRIz5l3MCSXc2EezMi5Sc9ejj/NkfnDkC9WYPHKOWjt6IP+qV1gHsE6yEjfECdwRKHZb7kcgUvplUfZV1f5WOQ16bR1V5nxbYCYQX78fi0Qf1Ck60ZkpC/0+1VOPHmMtMjMA+mdMbl7XiMEqx7p/BbH2sqKP86YHKx/LWishrW1FFFbVYC0iV0dQ/K6JPHve7N3IgiCIFBCRl8EQRAEQXgBGX1lQyAsJSQR+Mnowt3Q3jsCS4tXoL1nGCrtPWAKF4XhupS3ItsZcRVJG8elbHd2IeE63vZ9R9isKEztoZHaPBE5xcmMBXPjR6Z8Qgb8KYy+MF8q1cuWGS+L6p8n2oB/0+uItdhUHSf196jPvboETHwUj541Cc5+t7pCisLOoWVOCfQbHtpcb3xZ63QYJvlSN6mjtuLHq/U6DYH+KCNDs/sAY8BS4XnUwS6D/UyKkdCAH/F5lDBEYGIvIT/WhmcPQO/YfHIOHu1LjMCqS6bKEvqBrXaI3MvxlMcFA/7oBd50QdwjbR0hqWPxsYWj3DJgn88jy6fQKJG3DljUVsItdPRv3nPQ2qpmo6+EZ2MOUmJ/nmSN5Aux9vtNPkQ+JxIEQRCECBTDnSAIgiAcEUqoVWatuxmR/63053rqjYy+iOj+wr/lZR74OwcmYGByl5DBV+poWFxkRFCn7HIAHEdAUdiJS1VCK5z9vbN/fMt7LMIhXx4GBpWlvoz9ErQpaw6X3XY0JsI9OPu5mf1YDEjHow2jSCVzE87qVA5+o6+UDUHbVPPnXWtjJyOD1gVsBVxrSWHAb3sN93D9x4QrZ5ZkRT9HDfiRF891sCsK84y/2O+4PD9jIDAbHYLdZ2y+J6t2DUDfxPaUMhe9EwlmRz4hjqxjTYlyQNSkTaMiffmyl3NlP+KyMYSZ6NRcOQq+n+2+G5X+nXZ3s96EorZyTWF46yEr8xsvKs9nm9PqHNh6x8r2WqpoyReg1NrR9DMWCbtQijf6srZ+O7APaIquoUNDkiAIgtAIadQQBEEQhHPweyxSK+SppWUnmk0SzTwV9o5u05onoQN8FxLGUD1GVCWXVlDnmLBZpYc5E5TbusBvPWFHBcVY2NRve8cXGi5Zi+UqdPSPbf6RocIRpuFdD22tm2mUS1y4VMrM5Wuirqy+iHG6Fei0kZW+EUXGn391dcUJBTlC8zoZmBhD6cpdLLfh3UMYyrd2NnFGxrAFveXO5RqVzMVwYB50tdkdj2ZoFF/3JGuazy4K9arTn5kCO5G+HJjCCBzw7fvlI5awSE1949s3/CbX8Fo1zioKK5oq2J5Qal+IdL2JMgz0XZ7l7t5eFpk+mzJHQ32IdAKS8X08y7AWGvAj3swJtFn25rMINPbzrsFpaMkX72QFAQxO71Gax/C2g1uib3b0jcLE7gflE6WuoZTICINU0QRBEIQB0tzCEARBEIS7ZPxg64xQT6CczIsh86Rz7eLp8DXzaNg9NAOuU784c6XN0sLpVFdM+cANwYtseZwZzyIIPZOHzx/B6PxhOPbS5+uvi6UqVLsHYe3Is1bLRbhDtbMfpvY9ChdPHoGWQik0mOaNlMk115DSuvl5y6M1YPNFljcGoonjwp821GcMwZmm5TkocT4NbBnwg6Pt79fYiGJ15Ra4DrZzVdbYukeTb49KW3S064GpXdLpZorAfU/8suVLs5fDvJW7gxuyJd3IKbeDE/SMzMr90JHnU/EsfvZ3eQMbN6K2EqiMWw2cWQemdof3ZEvXLoX/FXMYlm48ZBKZupGVE2luh1yQYi9HIAKfAb/KSF8Dk7vg9BvPNZXJ9Y7OJzh2BO+58+wK7li11leAK9IX4RY61kPOJPPFEkzvfxucefPrsLp8C7pHZpVG+mK09wzD9gc+CDeuXYRytSuM/uXg0NJMZh6UIAiCILZARl8EQRAE4cqBVOpCyIJwN7AnyGSK7BdOvAYryzehs388Mvx5s98S/uJK+7b3jcZ/wY3HQETgXfG7h2dgZeUWXDr1BuSLZRjedsiZ/h2PD8/gTh22dQ+Gf4Rc/aVBRyQXo9FGZLISiJ6YLtIXOADNddI1F7nWBagifQ1M7wYf0a3c66fyMP9zFUpVqLR3w8VTrwNKKGqr8jpJ0wBtPUNw9fzJhve2Rm2Vp9zeHSqUMMWSGvl8CcZ3P8C3f4xc9v0c52pws26atWnQ4reicFb6sY512YW1nikK94zMxZ8tAkefO/DPyzmm8RhGbXXjQCqFz89mA64zq4KIOiT7S1F/Ig0QBKjmIyGayuVSGOa44AjM1bZCQNp+vtnoK0167I7s/PFXYfnWjfp7XYOT0De2HUrVDjf2ZsLwl9v1SPJqoyfyGvCvOF9vdVzt4oLYHsvMEfXErge05sEMvZgTT1/qzJX7JYU5CpaDIAiCIPghoy+CIAiCyHJwCKELjNo/MAi+gkivwr2j24yXhvAkOoRFSpX2UIEPI7gu95u0Jf7mVQa7eO2f2BH+ZQZfLls2zXO4xpWZ+XZoZp/RohDg3HxZrLRFftY1MAkXT78R82sHxpTpeyVbWNx3NXP4UChWjOTd0TcKre29Cd+yVTfpxofQmpXU/r71d0FG5u+C4y99oUHxiSmY5/J5KFc74ca1S1bLR+A/BzOv5huNvlgf6h1baEwrhVfzmjOd00eehaXFK9DRNxZGhkWvlGC6fCrzE0kK+XYnl5O/bnT6fIR9fBBcsLkv7jwSj4d9wFq/dq8u11ZWkMuu3KtT3dhUekWvVC5cNWqex/Q+QLQPqNoL19sf6bk5jdGX1b0c57iKbHffpsmk51H5vJzN3lIsKcuS7ddmDz0BF0+9AbmWFuganAodJGrBt76B6DltyRhYNMzTr3+t4b2hWc57q6z0B8LxdrdTYFx7XH11kLiXI/kQQRAEYQB1cZQJgiAIwirOnbgtV4v5+sq6Bxl5MAlJDKBBGIK97zEFz+kDj4dGiyovyLA/t+6yo1dMVAkqYepmMtQOvpCiyeqe0ZsYfPVlyVgxkrUEZWwZT5bgDSxSSb5Q3nIRyxic3RcbwRXXpZJ/67LtetjsEZgrQsNtBqb3gG5aO/pgfNcD0gpSmdqz+BgZSKCcTBmpc2Ai/Dfb+zMjsHyxFP57ev9jMGigvxJuU+0agLEd90KlrSeceyb3PASl1naleRRKFRjdfjfMHHgc+sYXBMdiViycHXV8o7kZmCdqaRzYy2WGREVhld73ccCM9xu8qEcOIdzPoQYN7RvgnudE1rnaPm4jPaPN5SBukYW+rZeofrRl3uBY77Ix12QcpPseXtlLU3A+Uiax7Vhzs1yurXsI8oWSUjkuM/wamNoFfePbmxt8kTK+Ipna7TSERAK41zDmOLglX6y/Zn2z2f7OvTGNu96Vk9DP5Pq/53WIfGyKIt7GZiNvOXOvQRAEQTgJRfoiCIIgPEFMQIjm4gRJMcRIEKQ4+FCkKOwSbiuXMIHyuWMv118z4fLcXd/ApSQc7YlQ8/Mhv7Bzcc5RLfhz2it6FmFtrvhyH4sAmV24bozIlM+XYPrAY1Bu6wLU4Kg+Lyskao5u9j4zehjbeS8c+/o/wvLNG6ERWN/t6CXFchW23f0uuHXjGrz41J8pKx/hCAJz3Mi2Q9Da0RtGTGrrHoT23hEwYYRRM95Xi2Jv4tKlcHv/bW3dDJobREzsegCW5+8K+wwzVKzBFJWYoWuupQAnXvkiOEXgnkJjOuz27+6h6fAva+cmb0E+X4rQ2tUfGvAv37pRf4+dDbzBo7aKJyvPGcOam+dxdDhfL0HTiJuXzxyFtbXVusyjo38czh17CXzA68j0SEh1NrM5pjJ+/4BlPtO9n883iZTe0lLwpBHj2hFH+/rC8NzB8L7z2oVTUG7rDl8TKQn0JuatrKDJYzHZ29zhb4BzR18K9/C9o/Phe/IRf7HhaVsSDuDAPoAgCIIgPIGMvgiCIAjCMUSEb9FGImnyRXBoJ5kVIdJdEHl1Y9EmmBLytYunoVCshF7aZaNCeI2SpnF7ovD2oqUZGXpU3/sKm2/ZvNY1NA23lq5De88wFMqttouFBwRbqHTo72uszyzc//6mazd7L/ICFrFRQV2pi9fzt+PoUHqN9JrerJ/kctA9PANu1Q3uPqBf8RP38+tgs5drzIrjvs1RaBGY5wjcNB0zXjZjcwP+odl9cOzFz4cGEYVSK/RNbAdfoPkwRd1FzWUW9vAsIsTZt17Y9J6mqNQOzuGJ647uR0JeZcyhzezBJ+Ds0RfDOa9/atd6lEO8x1ECWz/jmfcszh321jrDgwj5/KyyHUTOE7mWltD5EpvjarB5Lmr9dM2xZmbQIZcDsX7EHDLZJNP7doVj0Yo8wkCWzLnc8NyBhvfK1U6otHXD4tULMUXLcL9yjuy2FckRzdRLpLPLxHyy2zcJgiCIDBh9ffu3f3vs52yh/M3f/M1UaWCC53kIgiAIwgmQX5g0xQlFYcgEScKQamc/uK7gOXPgcVi5dRNy+YKYkMmasjje8eHsnJOhJiIBs5FKRtWfmMFFe++wqtIQyTWu/Lu2x61M/uSN3HdorScIAg+218lELDo9iXa4oj1rZ0HfnwRhhtcsAuatm4tQau2INXJ1TlHYr6Yy3CfxVF7P8CxcPHkElm8tha9Zf2VRYrnwbLwSclQ6emB8530N73UNTcGpI1/FWaXUbVGRxvmbTWXzyLwdWL6FEbqu8W+ADc0dgFK1A5auXYZKRy90Da5Hbe0d3QaXTr8ZOvlytTP42F7G8KTqRPtAZox8FI4Nl+tscs/DcPLVL8Plc8dgdWUZ0OBulXrhdM4XovdyitZv/NsAgiAIgvAGtEZfTz/9dOSmil0A8Wy44tLABO/zEARBEIkTqnNVJCL8klKAwHRRLxNdzND6SIrCblBp64Fyezf4QEuhqD8TD/eXfu6ZfXwmdTih/Ec4j6nLSNpvZBtMkUf1oiE6gGtVtObX8wlFmr7dnyO9XTZ53/X9XXT9yJ6b3a4PgiBECXDveSXmJBaZdXN01qHZ/aECXTTunvtcX8eMgKiKmCL77KF3wJVzx0PZXEffWCojDALXGFB6tg/EIkZ0DUzCxdNvgJPUnpW1ZSbkcAb77KbxMbztELz53Gca3hvbfg+gwdJ4JvkvZzuonOJiIg33jMxteZ/t7eYOfwNcu3B6Sx92HUzrGN61MnCrziPTz1Zbo747MeGURrDchXIrjO+6H24uXoUXn/ozcIogm/OZGahOiY1k4axEEARBYAet0VeUkEfK4zNiASVtugmCIJTNqKITsLtVr6Ds6QR0a/4JGO0/ElGnedtW2ntgau8jtHdqhubpzM5eWkC5OOq7Dk/zonN9qbUdFq+e11ocwk1cOm+67AVSGIfaJTWm7thpL4cHLXoAeBRxuwan4OKp1xve6x6aBtQglovGkqW50vn10MUy+wvGPqS7TKb3vErzE0pqDU19iObRN7YAK7eW4NLpt+DmjavgJvjGlitgm5eY4nrv2LztYqBG5Twi5EDAkejUzRjbcS90DIzDpVNvwKUzb9kuDoGUtp4haO3og+uXz4avK23dofEpgXtdwjjn8KOu7CyCa+fAOMBzTT50QuzgcjvyQxFwso2M0zlse3VR3J6jCTRO53xHty4Jpo2AyLPqmj9oXiIIgiCybPSlYoNOm3yCIAjCDxIOy03WzMQ1UGSd5fwuRuFg/+ROOPPG8+A2+OrV5GMOzx2EfLEMWQbj2CJw0Dexw11vwgS+866lqQbVpYAThhdrxvsKbtnKmgNli/ainCV6hrd6jI6DKcFhqiOmIHz5zFFYXV0OX3f0jUKxtT1VmpFRsRA9N2EQbM2esh9idsbmTyMRBH5YJCUW7Yv9Pfvp3936BQemisysy1l5TpVkvs5oLxt2g1wOOvvHoVCsoDL6InmyGP0TO7TOnS35AkzvfxtcPX8ylIO1dQ+F7/HlAfbI/DwXWTFUny7JdWmLk8125+oDHnaOmGh+NvIVSEBRQdIUIcrRKIKyJeJCGbE+Zkbqzhe5reh4VDx+Zde/zMiVCIIgiGwbfVGkL4IgCIIbFw9JLpbZKGrqZ3B6L+QLZbh28TRcPnu0yTccFWhkCRoqFisH+fiIDPSVnU5Tae+Gwek9cPr152BtbRVcI76tkPc/SaGuWjl6QPuJ1FWYnfnCV5y4nMpIN4ub09u6B6Hc3s2dVktLAYa3HURVea0dvTB315Nw+dwxKJRaQ6/s3Jd1luYas0o4t59RwaM6s5eL1AWRLb8jz03gBeO+BmOZUmEnAo5IqupSMtN2zimMNuBb/06OUiOMd3NAlgn8a/8opWgy5vCWqH06u0OSNvoSINeSh47+MS+UQd1ev0VQ3CYJMixnzsIbcUAu52S9SqHhOR2al+LmUeE+4NhzSxOYf34j41Gy/PjmCmzlwY9oGzLn0d4Q2e91h/rCvw9QQkYekyAIgsBNDhw4kG38U5EGpj+CIAhCFWuZOUziEzZZ8JQluIayNbdvfAEm9zzkrqJwRtDSv33ZcwWezRUxOLlPlp1HFD3qwNRu2PHgN0LX4JSaBAmN+BGZSzVszsqKsojK+VnLfKk0TUuXTJjIRrdO7D/tvSMwuedhoT67/YEPQrVrALBRqnaEyoBdg5OQa2mxXRz3SegTowt3b3mPGdz5ipP7YKIOmxewtrvuPML0qf9a9zaMJi9RSC7nxFkll2uBnpE5pWlmUoHYCQJldwHrThwaqbT1RPyEN1+8ERhc7A8ullkHA1O7wohtePG4nUzvAwLd858u7N2xEh4h0fY+y2C8I2tyaC33be7Nj1nby8U9L7uDENnPVdq6oW9sITovB/tDFhDt81Hflx07Sb/L2pgkCIIgzIJZchUqYDf7U5EGpj+CIAiC4F3TiAxyu90zI1TKynMqRHvfQD/1UJ+p0ZIvQL5YttoahHnihcc0PoiMQPtk9HQPz4QezUXwzaAqcr72af8rE/wh4UcsmlqptaPhMp5d4BOunEfQHybEiXjcYqkKA1N7IKvEyawyI8+QgOrGIaL6cYa69+TeR6BQqgj/Li/xG8IfWLTf1o6++utSpR06B8atlokgVKNWsdPSwkIOi2PqxrDsysHu5MT9deRezrPNnOLHKRQrKB0yxZORtla2LllwbIC4KZw+oztcdCEi2qijbxTGdtzLnUyxXIXZu55cv1c3UXcWl0qn+zVCsuLAlCAIgsCJmLaFQb7/+78fRRoEQRCEK4hHfiKIO9DBnCC8g+Z5L9rBiQtjhcR5lIui3N5NXsNSYmpfmNSfaX+aom4d2Mtlxbufnqit6pMkHFCwuf3VfLEEMwffDlfOHQ/nyfaeETjz5vNqikkQCpm/9z1S0SG6BuSigxEWzwwB8ud0cN104dyXlb1c1Fo9On84NN4RhSkJRxmKudDuafY3WTnfRTs2WP8Pc/wwte9RuHr+JKyuLofG+1fOHkubKV6wlU2gH7I+69ywzBA25xTdebsg0yGSsNeG1H/E9qxtPUPcdZvL5WFy78OZ2dP4gdvzKY6+hqEMhAzjOx8QciJXbutC0ufUkRm5hWgdRFXL7fZnv1G6n/CsXxEEQRC4IKMvgiAIgnCNyHtUU4fHpHwUlsPUI7ktA80Esv17cGZvdMh2pQIXE53IjoAI+6WZ6wJZ1f3TyfpwscxpiHjccrUT+iZ2CCc3OL0HLp85mr5chFLK1S64ce3ilrZSvlXKqY+EZGJPqXyuIs00PJic0j1ZPzJ/GcvRjPlCCbqHpgEnivuhJ/26Du6jhNr9u4TBVxDkwv0fM2qM+IJwmjG5KUyrSeq+9V1iHWrXO1BdCMOMe8a230OjyUUC9ZHpeaJ72VhL1EfjpfUwayg/z7nUhZyVxQg6EbURBYdQTtQak6V2ZM86MLmL+/uzd70jvLNwjsilXc3Y943EesnaOQjZ45KshXAB886Y1A7U4W0H4fjLz9RfV9p7YPHK+ejssU0UBEEQRKYQv4kkCIIgCCKT3BEqrXknlMJs1IK5bDpQ2U+YYkn/xE5wDlWCMbxDjsgSzl7+m2fu8DsjPaBHwS5pWzv7tZWJkKdnZBbyhXL9dbFchY7+ZCU3UVifKVXauTy22vCkj3n/Z3wvR3VBZKAPqL3wjEvL37qkS+NsMbX3Eai0dytNk9ZejMTPWeXWRkVKtm9Ml1vg4Jrm7rmR5u1oxnbcC/ninTMRgR+pM6PMfODxvtjJddjFMiN63pGFwx4dTyLDIOjNNSCVJZ76lplfjO1T3N3K+UdMP2F7MxbZhsgAKoa+zJzD/Ru8DoSdPN9tiFZESFaha3UXdW5z7DHMEV8xPaPbYGTboTC6ec/IXCivjbvry5r+FkEQBIELkqAQBEEQRAYvAWsH0bSKJN5cbJJRgpd0D8+42R8jiHwW3c9I48Mx/OnzWUN0vhqc3rsezZD9LnJ+UFM2QpxCqRVmDr4dBqZ2h+00c+DtkC+WtFTl6PZ7IJ9fT7tYqsLI3KF0CTrYb2wYtBFRqPec7NylqyhB1tc0DysgDciqw6fzlDIU1QlzUBJlqK0FakqFVam2MvundjakyfaPWYP2ci7InkyXBCGW6mB49gC4uC/QuYf3/nxgDapXUZhSaFxEYtpLJ9PSUoDWzj5ATcK9zOY9PYtUnTXcUH7O9hzH7hK6hqaysd660B01QecqBURFBST5mHdnVr7x4ug8uBnt/XfN+T7VOzYP0/sfg9GFw+S8hiAIgkBN3nYBCIIgCILgpC54UHcoZx69mEfhG9cvNRjKXDjxGgrhhrMCZSIFGtqcBLHuQW2Wou4UtgOBem3LtbQoSYfQR6m1HQan92gfqNWuftj+wAfg5o1rUKy06buARD03I75UyphBGl2AK1R2Qj3mPCozOscMftWhG8p/CFDa7H71oSystV2DU1CudsH1y+egtaM3lNUdfeEfbReLwOKIh7DAGpr9L4vq3Dk4CaafPVMy+SjFXtk6QDYnZKotDdcJW7+ZUqhXbSYY6Et2r88cFy0vL4X/HprdD7mcWRln3LwtM6cPTu2BxUvnYGXlVth+7Jma7eV468uEXEV5Hi7sx+0EsjNOdNs60EYESvondsCVc8cb3itXO+HGtTu6JRjJlIzahTlYKRlq2yhY/1bR7hnpO6rnA1/OigRBEISbkNEXQRAEQVjF/sFucu/DcPylL8DStcvQ3jcC/ZO7Ioy+amW1f/jPlqDu9n+z8syRFy86nl9lmvbaJ1OX9836QYLXtdH5w3Dspc9vicBza+k6oCCyb8u1a6b6A0F4R4pISLlcaGRGEISLZHvtjjrbaT3zIT5bZeqsSxjFyDkhI8oiOgidMrV1qUnMhKJwJteurDxzVp4Tb12yiDF9Y9thdWUZ2roHoaVQBCfJ6l7O5THkcNFtwOQwG14ZyNCfBpq/9z1w/co5KLV2QLFcBVepnd1YpLL5+94Li5fPQ6m6/kxkwI8Pp+dnrPh+/gsyUviE9aXS0QvVzn64dulMPZohi3R5/OVn5EuncE1TPbaDYOP67hge7RWMz2Gu1V3k/KvYoYWLiESnlmz3JEN+ku8TBEEQOiGjL4IgCIJIQffQNJ76kzyrs4gQU/serb9evrXuYU89eIUJfOHbCSK2FyGsHjVjzvXx0TU0BZfOvgVXz58MX/eNb4el65eVG32hiWLgmmA6Ccf7XybaSIuyDGF3bnawjzowVWTqYg279/2MMTC120Ku1DZUd4SKYSI1z9Hw04+j+3nUSicunPsQVx9hhug5Wb2iaXvvMLgCprkFRVkQFEGUzJ2rAuT1hqEfa35WZsza3uPOPMcDM4JAOXcb6k9u3yHhG3NEBvcPiGHRGCf3PgKXzx6FlVtL0NE3BkvXr0R824O69OARvCfw7F7fAzoHTEanJgiCIIhsQ0ZfBEEQBCFIudoVGgxUuwdgaO6AsfqrCR5I+KindgkcZO6S2QGqXf2o+0GS0k2uJQ9Tex+Fm4tXINdSgEKpAm88+3eKSkoQNgmcnYeZJ8jzx19peK97aKbuLdJ37O3l/N3v0AWdA5AChZWIVkzxrHt4Bkwh9yx+7v+l19PARcukmO84rfxnDjoHy1MoViBfqihsDcIGtJdDRKBugXJ2bsvyvnXNTB040Teki4js2ZAVJ3u41QBOjE2CkCXL63vGyLQexW0ZTGQdNHk/au7fmEZLvtDgCHlpMcroCxMBakcPKsnaeTqyz4q2EU+14W12q/Mfq+tav2P3vUz3ws3nVD1PEARBEIR+yOiLIAiCIATZdvc7YXV1JfRslBqE58HIQypvWVUKEyLTUlxxqGVhtwW0GDuLDrIsjL9NW88wnDv2csN7pdaO6LrRXGUsMpbrMOFfWIcuIasn7NkYytplhWqwrh29Y/Nw+cxb9eii4aVAudV2sYgkwxSV/Un1XOWCUQHO4ageR9chjF6pZcdcZ/84FCvt4SrKDGqL5Sp4h5vdjPAE7PvtaJmO3nKz89bK8k0wAYuCs7a2Gv57YHo3qjbBVBYVRCsLKs4I3zLszNkqi8/J+qXOrVulvSd0ILS6slx/r61nyNl9rn4oKm9DbVA/ySQq292ITEaUqOdzYP0mosjeZi7r8zNGuZcuWDTBUqW9wTCptaPPWt0MTO3Wmr5XYBinGMqAuTxe4e682Du+AGffeqHxvdFt0T+I3MrJ1cHc3e+E65fOhvcO1e5BQI+qcXR7vUiuN4T7eYIgCMIbyOiLIAiCICRQYvCVHbkDesioAD9ahCBI5SpMkaRYboObN67W3xua2Wcs/1BZ79ZNCHI5GJzeA+W2LkAN0na0h3sVkrULVx3zmWt1WK52wra73wVXL5yEQqkKrZ3Rl66E42jom7lcHlZX7yhg1hQzCRzQHAfhPi6qdkxQrLTB0Kz+veNmBZoGA3uRR3VrCdMOXf4SLjA6fxiOvfT5hn7bM7oNzrzxnJH82T5y8fI5KLd3h/tKUzAjECLDQk8LETsJe3L/wak9cOLVL9Vf90/sjP6Be8EAImGyQLdxsNIRkZ19aFaeM94A4fTrX2t4j90D3LxxzVqZiA0YMCLM4laOyNZ0ObztILz5tc+sO/FtycPQ7H4r5WCyMubwzrdIv6kdCKtE8Tzn5Pkua3OwojZy2RiWzSvnj79Sd1TC7syYs01miGWCQqkVekZmjeRFEARBEEQjdEtFEARBEEQjuoRZDsrICPOQovC6Msn0gcfgzBvPw/LNReganIKO/jG4ce1SZK2ppH9yZ5inVeF25H1Bsw8QXS4QBMFNvliuzzXZg+atNIzMH4KjL/xj/TW7uMfcl1y+PCTkaOsehHyhDMu3btTfG5jc5V11sug6R59/KnSgwfZoPJ6LhfaWLipZqCg/tudGVhxvURlNyYBhSvfwDCxePQ8XT74OLfkiDM3th0KpAqYotbaHfzqpdg3AtYunG94bXTgMLhDd1jSgCa4ORNXEot5PbA89li9duwStXf2hB3Pf9vXM6dTV8yfrr4ulKhRkIsTSlNMAq8N8oVSPbM6otCF3UkLDPnN0D03DheOvwq2bi+FrZkTf1jMcKg9nAltr3Zq/azQ51iTE0N+X23tHYPv9HwjvVZljy5Z8wVg52b5xau+j4RzLIozlWsw5EnZmLCKez6LBX7dOGqxJQLok63Kx6f2PwbmjL4V9s3d0PjQyjTb68s04UswoVbWhamIftF09BEEQhNeQ0RdBEARBeCOUwHF6dNIjJOJLe98UChJxsPvogF0KbFYoEx1baQRu9oV1usH/fNJzKf5HE8PHKdC3NlKJj+3tJAHq/WLX0HToffriySOQL1bCiEYthaLS8hEpoDkujJAwufdhOP7i5+Hm4tXQeL9/clfd8+bWOnOz0pixJYuyt3j5PFQ6ehojfSE/PyrN09H2I+y3uZOykw3z3OjC3TC87RAEQc7L8yMz4C0UK3Dx9Bvh6/aeYejoG5dKS1n9WJEPmWlbt2Vf/vV/Yp1Ke3f4lzSWXZ3Ph2cPwJErnw4dFTAHVCMLdzkzn6ssp2pDWZYe2/ufeOWLt1Nhr3eAPTLkdIETV/q5TkV+Fh165uDb4fKZoxC0tEBn/4SkQYRbMKP+LPUZo9jcy6XO2uV9KBEHkxdXu/qtjPFStSP8cwOJ53dhXlReRAeemRDDaTkEQGtHL7TuvM92MdwgyKiBLUEQBOElZPRFEARBEM6wZlWQhuoCG1FRjJHFZya8vnRkisFL1y/XX3cOTEil47rSjer2c+W5CdV9J8pLGfUHHq+fBG+nw+N1cXB6T/jnFlkZj/ifkynx1zya12Bef1Vfus4d/oZQib02X6+uRhh9uVuV4X6umbGXirGf1T1NqaI3epEO4trKbUMOIglmJOAtQQBjO+8Loxiura1CqdpZn8/xn8FNlA97HagHf7sjfP6M1xl22P534f73hdHM2H6ORVCWae+s7tni6BtfCM8Di1cvQLWzX/lZg8ABl7In4uHBDL9YVMN04xnvXp8Z7DMHJcvL61H3Nkanxj5vYS9fM0j5mSCQsKZwftF4FxD1PZn5T8ecydaQK+dP1F+z/ZzJiG3iz2dXh8g4kT4bxJ6fR2bp3JocpTMRVTd4t3JGMN6+jnUngiAIAic52wUgCIIgCIJPAblvQqFHyIwf4DdDimiY0CHt8ESC4slj1BjfeX9dqbW1ow+G5w76+7AG2aik4w5Za++sPS8+Wtt7w6gU7LJudL4xqqLXXYB3/0dd1FLFE+I1K1e3LDLNZgYmd2lpAB6FWdWXi86dbSQUEpy7cBeAeYKutPU0vNc/udNaeQgz+NynXYbN4WxMMmV9fgMIakspnFi7stG2WTduy9rczox3WeRWblmSUP+I+W7qqgk0pauO1s4+6B3dRgZfWVm7DMydNucUF9cGFuFn+sBj0DU4Gd63ju9+IIzkSmjCha2cj2V3dr+NqSx+USi3rv9DxIDfwTleNyyyOYsOye6Q2P312I57bReJaIB0Seyjf97I5baeUfvGFiSzNzvPuSALIAiCINzFRY1AgiAIgvCHCEFaz8gcLF46V/dCx5STq50DhooUaLpzxXy4dUHAjLn+DPQ/DY+Pu0+K4OZzVNq7Yf7e98DqyrIeQyXXLyoky985MAknXv5igyJ8z/AsuIsL8zPhGrN3vQNWV1f8jk6BHdfnaAmycoHuwnN29I+FCpjnj78aXt4PTO2CSkejkY0O/Nl7GiLD1TW55yE48coXYenaZWjrHYbBKXuRDanfEkTzkeEkjhYbvzdxw+UgCGw4sP/39VyjGtr3Eb6tacxwnzmeEwLD2HfyjpXwkyDTEd6k9wKGqqVvfDucfeuF+utiuRoa86vC9TknTfmZ8dzMgcdhbXUVghzFUvAWVMawxGZKre1QqnbCxVOvh68LxQr0jM5RRREEQRCZB63R18c+9jHIGt///d9vuwgEQRAEokMsU0i+dOYtaMkXoLN/oh423pqQzcJlR5RAVXkdkEyHIDaMB7MDopnBl0njP/uofah8sQR9kzvgzBvP14Wg7PKH8Bwvx4ZeMBp8uX6RShCuwPYZI/N3weDMvvDi3vp84OPQjzw7+vewOuZuplwysftB8AM6bKfDvzFDONZtoo7mQcajVHoNzTuEmn7gjtGUK+XU2Q5+14EusrV2edZHrD2OH31ms6FJ+N4E3T8QGZ5TLMmdBqf3wsqtJbhy7jgUK+0wunDY2P4r+g438KoLuGLwla09ibpzho+Gq+Jyatx1MLb9HugenoHlm4vQ1j0E+WI5+TkDDXOWKM6chQmCIAgXQW305Y5AWA1k9EUQBEFspFhpg/6JHdyVom7dTJtOttZvwgG86ZJZMoQSIGNnBh7YZU/XwCTcvHENqp390FIo2i4SsYGsnXMJ94y7ossnUW7cj2oG3PdmmZ77mHMNgiAUgHB8OzefZasKCUl8VEhyiag9Mva9PWoyppzoKzbGAMazhRdkpVqV9Z87c1hW14isKZkT67DI6RdOvAYryzfD1/l8CXqGZ6l6HDtL5HJoVQUJznMgcxY8tuNev9YfmTXa0Uflhva9AlUl2Bk2Dq3IKzGVHWzNn34UmDO8bOseBH/wfcIiCIIgTID+JJcVYREJqAmCINJRqrR7Nv/TgU8XLS0FWFm51fBepaMX0HJ7L5SZvYKjz5mVPasvuDCe0pSR/bbc1hX+uQ6NLYIgCMdwYI11p26yUZe+nold2G9ykfY56Jikr24JeyBvOicV/FyQqWRlzGbkMY2QlT6DCCfnP4JAZfxNTueE6sV0OyByWDq9/zG4cOLV8HXPyFwYrdpVsupYodzeDflCCZZvLdkuCmF7epGKgOOAA2HaixOJeDj/495CEARBEAShgJwLF+S+/xEEQRBiDEzt3mLwxYSTmSFwNH2pNU99qO3hbQcbXpdaO6C1s086PcIUjW3OLpL4f2k4ZLsmovaNWVdoyPrzewE1oYIqpEp0CkvrD7cSg2PrI4ELmo/kao1wGGq+hrP1ZsodPspqcDd6LtdiuwjZJvBrjTa2rjuw/8zKXR7t5QhhGaTU0EA0nhAVhXALclTlMo4OfER7kUp7N4zM3xX++eB4LhOsbV3rR+YPQxCgVxnMPLQ/J5LJmANhRWRpL+fdPGLrjjVDfYYgCILAC/oTHFswff4jCIIgxBmY3AV949tDY6/23hGY3PsICTEQgV2g1D08A6Pzh6Gtewh6hmdhet/bUJc5a/sFXqETG/uYIvyh7EMYy+RJm3gnHCUIgtAGzZdZ9RhMEPwE4nu5tPs8mpq0MDS7P9FpD6FwHESllMtB7+i2hvf6J3ZQ1RNoGJrZt+W9wZm94CwZl70QGsiYLJjAQK3PZWM+UybX3ThWPak6cjqHQ+6P8q7JZzK87nYOjMOOBz4IPSOztotCqJYtB5rnPVTTFKrCWKW9Z2TLe5W2HvAOznUyWSa5ZnT9zxcr4IseEob9khC0tyIIgiA8Ar3RF4boWjrTIgiCIEBKgWV47gDM3/semNr7CJRa8Rh++IBzh3QJekbnYHr/22B0+91QKLeCG/jfLiKPmWvJw/SBx2Fweo/uEuEnU3tKVR6FCVNkzXBVJczA3cY8UCjZXBcz3l+Qz+ckw0hdgZDt58zI8/s4tlQWD/mjqiVTD1t3zMH2LyzSFDuvDU7vhWpnP2SVZkZwphieOxjKzboGJmFk2yEYbGJkQ3gsN0O+rnQNTkGxXK2/rnYNQGtnH7jAZoNKpiCLfh0XJep5ED1n79h8YtsYBVHd2AF/nyEIgsAAyckJjLQUitDa0Xwv7t0+1xDkACwZ6lt66BqahpaWgj8OVlLSPTQNhRhDK53r8uYzK3NkbCYyJs3bInWgay6KdKRA6ypBEAShgDwgx7TwI2mBrZVn4/ea/Saq3CTMIQiC0Ed25lg7h3U6hNo3diQaKZQqoZemaxfPwNULJ2MqjwRczkFtRvgMZ/9mRq3LNxfh4qk3UmQlPv8xY2h20Xv98lk5AzSC4CCL+8ogoL0c4eg4WdNcdiSPSaSHtTszNGLGXqxdmfGXl3D22b6xBbh14xpcOv0mLN9aAtPyA9q/EXo7mfzkzc4bs4eehCvnj4cGou09w87MF0NzB0Lv3OysxM5MfRMZPidZ3Kf0jMzB+WOvwMrKrfB1LpdvaghmjMzcSWTMOBHLXpxQD2fbMsP54y8/YyQv9PjyHNjO2gRhFNqvuIp2pyMyEcYlykR6DvqHd75Ygqn9b4PTr38NVpdvQffwDLT3DoNv8Pa/YqUNZg6+HS6deQtOvvplo+c4FuF85dYSXDl3AkqtHaEjaqv7j4zvfdA7byIIgiAIH4y+Dh8+rC3tM2fOwOuvvx5uqJoZcW02GqhUKtDe3g5tbW3hXy6Xg6tXrzb8bWRjFC+WVi2f8OJ7eBhGR0e1PRtBEARBeHXoj7q/zeDBvK17MFSCWV1dqb/X2T8O/iHh+QZBd8iO0SlGKKKI68TO6RkeW0wBcnzn/TA8dwi+/tk/Mpr3xO4H4cQrX4Slq5egrWfIoDdABBN6BugcmIAr5443vNfWPWStPN5zex5jl4wskt6tpev1j6je1dStK5BimXCNgRu4Uk5z5FrcMN7QDVNoGpm/C4a3HYKv/e3/ip3DaH4glPY9lVF3NMkImTIa87rtGkwuNzC1C3yGdz5iMsnjL37eSiQDtq+e3v8YnDv2UiiPY1G+mCIdYWev4voa5v09A0/7rPnRlqrpHpkNjXwvnX4L1tZWE+4DqO4Iwh5uyYZQQvO/h/WS/t5UZl9Q+w1zaHjqyLMNnw1M7hZOjxCntaMXpvY+QlW34ezYP7GjudGXxvFeu9+t6QqbwlheTs+PG/HlOQiCIIgsgdbo67d/+7e1pPt7v/d78LM/+7P1jc5mYy/2+u6774YHHngAtm/fHv4NDg7Gpnn+/Hl44YUXwr+nn34aPvOZz8Dy8nKD8VfN8OvcuXPwzd/8zfC93/u9ofEYQRAEQdiBhOCuwYRDLKrVyde+Er5uaSlA38QO8A0pIXJWBDLeCNAIojmbo0sx2vv8c5ghPGcF5ucEFklxYtcD2tLPAjaUHxO5rZDU0TcGxVIVbi5dq0eg2qi8qvJiiJTGGuuCKf+/+bXPhApj+XwJhmb3gW9Qm3uIbofCGjJgiujMi+vGPHzcUxB2EO2zUfMiyr0CwUfMXikz8gkCsm5s31IohpHOzrz5dSv7wkpHD4x13Kstfb9QuN4ItGncfJi2b9Bcq+/+gdhQH7mWUFl3ZP4WPP/3f9CkarKzl4scs445YiGIzY41GYVyNRMVE+e4ktZVQpiEvVz30AxcOHEEbt64WpfTldu6tFS077Jonucj+RJe8PfPjOzljLcD9nYnCIIgXCYz0rubN2/Cj/7oj8Jf/MVfhK83WtOzf/f398OHP/xheM973pNo5LWZnp4euP/++8O/7/qu74KLFy+G+fzWb/1WPaIYg/13aWkJPvrRj8LnPvc5+NjHPhZGDiMIgiAITMKB1OmjF164S//kTmjvHYGlxStQ7eyHfLFsu0g4MNznSpX2sA02R2KzBX6BoTgil0yRz6+4WvKFktoEiS0w739HvvLp+mtm3Mouh7IOM8jJDtm4YLA9b7fkCzB94DE4+9YLsHLrJnQNT0O1a8BwKfxbu3jo6BuFHQ9+IyxduwTltu5MR8MpVtoheyiMvIIdFc+UIonesfkwomFNmatndM78Xs7DZsW6rjlLNrY9hEpuK2qSLIjAwuDMvtChxIlXvrTFgQsP3ign+rgO+vhMgmRRAX5420F4/av/v4b3RhfuhqzDZCiiBhQ6yBfoLsiX+S/R8MHEHJy9Ka6+dznxyhfrr7uHZyLHeKaQ7Q9r8lGxfWZ47gD4Ib+RnygK5VaYu+tJuHrhVOgsorWzj6vdyRkdoblrWt3LET7IoqnPEARBEPbx+zR1G2Zo9Z3f+Z2hIRbbtNUMvth/8/l8GHXrr/7qr+C7v/u7hQ2+mtHV1QX/5J/8E/i///f/wg/90A9BtXrHO0wt36eeegq+5Vu+BS5cuJA6P4IgCMIcI9sOUXVjuFzN0oXEJoETuxBiHrFIycceQ7P7G14zw5SuoWlAh4fCSmWXQUle6IYbDY3K1U4oVshZg27aeoZCD7rM+IQpjk3tfxvki2RsR6ild3QbVWlocNMWRp0a33U/tPcMU51oZLMyK1MmYRfd/hp88e3fu4emM2bUKkPg3R6waZtruKRle4m5w98Qnt+n9j4Cw1vO8RTRkBcy6GpaKaAGvGOVwA2TP2yWlzEnQXWM6AlnSTBHxK0RbF/bNTRFlaQQ9cZwlsZr4Mi+hQzc6n2u2jUIbd1D9aqptPeE9xCEfN+TXS8HZ/Y2vO4ZmfVGhtA3tgBZhzkBKlcbDb+GZ/EaifhE3/gCTOx6IBxTTF4wOn/YdpGQYHbR3jzHNbt3dRU2ttsQyNqjnWUGSvZP0evbnfeZsVfnwHjoNJVF2UsqB0EYh0OG7p5cNDDyHJjrRahsuh4Db/UQBEEQHuB9pC9mYPVv/s2/gWeeeSZ8vTG618jICPzKr/wKbN++XUvezKCMGZK9853vhH/5L/8lvPDCC2H+NcOvV155Bf75P//nYUSwUomUGQmCIDDDlNM6Byage2QWsIJFaTJK0If58E8gQka4bLhvdfSPhZdB50+8GkYMGJjabSRyAI8Q3Xeat7X652eXrCz6zdXzJ0ODr7Ed9yrPg9jAhnbtGpwM/7wmUD/HRX8nO/MDD4ViBXoRK5fgUZ7FUg7CSTi7D7v4Z0aHJ156Bm7dXIQsg2fs68fkvr3U2hH+EQRWyKOwHK0dfYBW7sX+Z8BrPZM/jCzcBcdfegbW1lbDOukf35H4uyytN4RZqG8Bcln4mnMG/MkFMZ9lFsYnMyqa3PswXL90Nlxfqp0D3hgaaRmbGp1t9E/sDGVY1y6eDo3vekbmwHWYjJ0ZQjQz9tDCGu4+OLXvUTj12ldg6fqV8L6pd3zBoXXFbdhdP/sj7OkXMKcBzMj46oWT4etSpR26Bqecr0PmYI2NZ8zR41AYQxBEatR0Tm8iTxMEQRAEkSm8N/r67d/+bfjUpz7VcHhhF6pjY2PhZ8PD+r1sMOOy3/md34Hv+I7vgOeee67B8OurX/0q/MIv/AL86I/+qPZyEARBEPLsePAbUQvpQkjgT2SWQM7TZoox0zM6F/4RmgjSXz6lUTpiiuiTex6qRwhuUhDUUTQI36DbNRVM7nkYqp394fj2ok1tzEEq95rUrQmmaNM/HkZ2PPHKF+Hc0Zf8rxND/T6H+Nxqai+HAdfLTxiG5Dl81QRBY0SrDLcjU/5mipHLN280iUpN8w9hGOpySsnnS3iNW4UMW9V2jFyL9yoN9tggX2CROFhEDsKuojCTR3cPz4R/vtTBtrvfBWhAsPculCqRjuaMnCXpaoNo7HRGYWsNk9Vfu3gKVpZvQVvPkBHHmqKIjMXwzhix4+B4uZxMpNSU5TDS6ezP9caeg+6r5chUvUX0I2erILAalZcgCIIgbIIjJIgmTp06Bf/1v/7XLQZf5XIZfvmXf9mIwVeNtra2MKpYb29v/b2a4RczCGPGYARBEAReAZh5g681tJ64qk0u/SptPWAWOoD7ioxwJTMeDqOeMyOPH+1RWGd+GapcAnGor6i3qX+K0NE3isTgKwYscw6SYniDsxdncojOTWytzeUyrsCpeMyh9gKPZZ4jBOBpM2pXbcolNGbqDEzugumDj4d7OqyYiPK12QBiq8GXIWjYZ4746ITUIVTSN5EcuU+MQKucTIdsom98+5b3eoYNK1JTt/YYVZG+NKRJqEOl3yLakxOeY+OegUWSbO8dga7BSZQGX6K4clejXZckdTW4UY+EXXSsy96s9Z48RrIMIgYEbRm5JiAoG0EQBOE+Xht9ffzjH4fFxcX665qn/u/5nu+BhYXkEOmqGRwchB/6oR/asjFZXV2FX/3VXzVeHoIgCMIvdAsjautX//iOLQfVwdm9GwqiMFM6+GYvtDy1ubtCNGz9g/oSEhD3J0fgu4gjQbEPeHOxRGQbGa+JGen6wgoggZxRBPMYjpWmisKRjg2iK4DmS8IHMnfWT8ngzN4waqsvkaldgeZbIl0/ycgmTyHMsKnc1gVoUXZmjU+nd3QbFMvVBiOwQrlVUd4Zg4ahRmgvhxppPWEEg8ZEGRA8JmGYuHs/DP2eMEOztpZyOkh9hsgKnvR1Tx7DaETDZtD2nyAIgkCAt650b968Cb//+7+/RTDT2toK3/md32mtXO9///vhYx/7GBw7dqwh2tcnPvGJMDIZMwwjCIIgiNiDp4RSnEpK1Q4Y23kfnHn9OVhdXYbesQVo7+GJnpkhaQKhnrj+ndj3/e57rniQEyPgFtT5+fzZAsVluq/Pm62qdYcAR3QII2RsfBNy0FqefpwMze6Hzv5xexFfONc6Hw0iIqH5jxBQuqN5ECFr7p5roo1pTZeEILIO/6DrHp4JDZtKrR045pKIIuSCFu7vpnkOtqfddve74NqlM1AotYb1QhDKUKXzyRW1VU1ehFrindJgbjTMZSO8BPV4IGRAI5dT2bd8P//68hwIWVtbBd/wSb4Ye56M3crpdyBcKCY43aP1kyAIgtAIkh29ep566im4du3alihfjz32GFSrd7yDmYaV4b3vfe8WQSB7/alPfcpauQiCIAj3tUtMKgp3DU7CtnveBQv3vQ/6xjdHz/RHmIAC8hiTKQEWBnIteP1CqFJ8oT5DuERUv6foEP6B5dI1ao5UOXdmax6mzVwidAmWmtbOPjQGX9Y8bRKEIVAo4xMowbKXMwONg+yxpr7v0za5TmtHH5SrnfjXGIPlY/JJ5mjOXr0gbwuFkHxJAdnpLt63Sbb2c37AZYBJxM718uus53Xv8twe6Ywp4JfTx/SLSGdPTlcakTkapjC/+27U2JQ5B1TaewDz+h7k+B2VyMKiULMz/EZ6x+bVZkIQBEEQEXgrtfjc5z7X9P17773XeFl4yxBVZoIgCILI6mVEpNIx9gtwQhqptpXuDn73IxlhWC7XAh19o4CVpsatdLlAZJWNY9zv6Swz+LiXI7IH7dNjK0dbnTmjVOHjOc7HZxKFqkAKUk50uV9nKZohDXCCuomRkebIfqJ5Od02yHKl7gmEkKEJbiRtUfzczxFEErQWZgbs+x7kxcMKyZck6813w1UNtLQUYHjuoO1ixN6FiJzv0pwFJ3Y/CJ0DE2FUambwNTSzPzk/muQIgiAIBeB145+Sl156qen7s7OzxsuSVAa2iWCb8KgyEwRBEFkk5qBKhlAEwT1eeD52hUjBk8LoV8PbDqKI9KX7WQmCwElWLqdkvGcaRWE56BKD4OlXpGjpIwIGERamPupzRMoepKgCkw34aR21iMy2FMtejiCMjwnq+5nZyzVzxkQgh2N8ZkMUU4f2V4Q1+URG5J6Ee5CMREklggs0l8u5UXZhvRk6oxBZXJc16lfMHXoSStUOFHokGAz4C6UKTOx6IKIMbs+rBEEQBG5wr8QpeP3115suol1dXWCbzs7Opu8fPXo0VHKjxZ8gCIKQumkTODzqVKqONtbQlqXneChwikW8o9DeKT2Tex4Ow9DniyXwRlBHcw7hOVkxkMoSuhXoUPWZDF16oKp3I8i0bTb6g+ie1UflhGZ1QE5NiMySteUhA/M5/r2cf+sKgQOSBUdVDHhHM7mclFzWgboheTMhsi5Hn90c6OxEQztiMG6ldZXQQtxZIkNy2qygQtbmo1zSRfjaIWMCJkVzVkOkr6xu5QTkTJWOHnACxGsaRZcjCIIgVGBfaqGJK1euoBXSbizDRoHgysoKXLt2zVKpCIIgCNcx5bWEILRCHs6t0NrRi97gi0HzHEFsJFm5hC7m3AL9HGdfnEK4ANl8Gao05ERelKt6Vg/rjHAGVfcLdNGPHBkbBgR3TyEGyoHmWQkkUH/IDCJjn+YJwncy5+AlA9C8RRBE46TgXX0oM2xNGU2IzpNEqu6namzSVs7PqK1N5jlVfYZkuQRBEAQGkGsUyXP9+vWm71+8eBFsc+nSpcjPyOiLIAiCSCRKjobAA51y/JOnEjpIECJHCnKc61+BRmkltsrg90IXfTkQuPO4RLZQpUBAFxKoKZRaoz+0FB2CILBCBqoZN251XLGO+i/tSdQoCrs9DryMbinxKEHQEvUBOIvDRSfUEqvcRP0kOxENsTsqkYI6cOYETC6vy4RWSC5HZJXIdT8ra2SG1gWhvZyFaslMnyOQkLE9cEbw88xKEARBEHfwdqUrFApN33/ttdfANq+++qpwuYlswIwVT506BadPn4bLly/D6uqq7SIRhGNk+2COxysSlnL4gVdKU5oEulkXAuMZ+/ohQR1B3IE8iuGhb3x7w+tiuQ0q7T1a53Nn2j9DaxSRYl9G/SQ9jtSh2L7VjWeKIkt7dELjWZ/6kVNgGfdZl5EQ5iFZTXYQaWuai3CAZW3ykQa5DNWzF+BZz2jcEtTnCHORuGT2Cqj2eZF+QRGVkUAvl0PVp4lUezk6/xAEQRC+kwdPqVarcOPGjS3v/+M//iN88IMfBJs8/fTTkZ+1tbUZLQthnqtXr8JXvvIVePnll+Gll14K//vWW2+F79+6davhuy0tLdDb2wsTExOwsLAA+/btg/vvvx/6+/up6QiCQHwh0RylwhIS1PmLTNtmRQ6n8zmx1aGQkB5b4YkGHLFLcXI93KgoTOuiVQan98DKrSW4cvY4lKodMLJwWO6yFMleji5FCON9jtbyiIoJvGvTZp7Tqf19gDZ8BOHCvE17PCIVGXNKpQIf9zhN5xELcx/NZ2rJmtM5ZVC1eQf2ucXHdYVAgmTXovXDPXTPc/zp03wmUKnJ38nansRg93Ft7Y2O2OzWcySRleckCIIgiEwZfTEjmbNnz9YXdPZfduj85Cc/GUZTam1ttVa2P/uzP2soVw1myFMsFq2Vi9DD0tISfOELX4Cnnnoq/HvuuedgZWWF67fseyzqF/v7/7N3H9BxVGfDxx/tqvfei2W5V4yxcaH3FiAQQk3ABMNLJ+GjBQIJKRCS0OIECBAghFAcCLyhhJqAARtMTLeNwRg3XGS5yLJlySrfuZN3FbWdbbM7M3f+v3P22Du7mrk7Oztz57n3ufe9996TRx55xFg+adIkOeGEE+S4446TtLQ0vjogzpx3cxj+6EyWCrfxz9JiOG3fwzZJMRwnjvsNW033z2feUTj4KdE7+wXeFNZMT/wOEsLnT5bq0dNiX1Hcv69YW/84ryKcw4QEflh0fMT1lMOAJHCA3iMKU2fT937VUonvyeW2Dk5wWIcrjh/txP88ZwNOc4jjdZk6nts6Csf3HBd2ggznJST4+KPOFmS/aHifPvh5LugonHEuDWAzBtbUjlMG1gQAIJ60TfoaMWKELFq0qOcGNnBDpmZTevjhh+W8886zpVzPPvusrFy5sicJrfe/aiYn6JPoNW/ePHnhhRfktddeMxINrfT+++8bjzvuuEPOPfdcOeOMM4xZwQB4m47BR3hxeKbIcezHfujEs1GnsLJBdjZv6rMsv7QuqnURrIMWrPq5cXnQjs/nnXs6T127PTZquptnucPgfP4Uy3bNoN91NKcDV5xCXFFIRMOia1hYCfxwJps6CoeN0w9cc9BxHtTh/Bc0pmhyWNC5HDrEI5jhRj+OqcsBCcax7yFWxeRjXU3QPDPrgoReqm8SX2K//Ze3kzjjfj3zWFsfAMCZtI1czJw5c8CyQILV3XffLcuXL094mZqamuTmm28OGkycPn16wssE6/32t781vssLL7zQSPKzOuGrNzWb3S9+8Qs58cQTbTmmATiLl4KyXgrUeU0iO4G77TiKphOFE+SW1EhqWlafpIbimlFRfdbBjg87vkdPJSvAwXrNDuH0EwHCO4c45tzilHIA5uhklxhFVcMHLCupHRNlQgTnFx3xW4x6x1n7RcB2Ws6AEwznc/Q5Htgdnk5u1fV8oOvngoWoyzlFRnbBgGXZRRURr8dTdTl4UHeCEnDgZIPG5YI2VfD9w6k4Nr0gJT0z8j/ivAUA8ABtIxczZsyQzMzMPslegf+3trbKBRdcYCTMJIpK/FFJQIFt9p59LFCuQw89NGHlQfz8+9//lh07diR0Fy9ZskS+9a1vycsvv5zQ7QKwR7Agm1OSvoInphCAgT28GpiOaGSvOO4jf3KK1E86yEj0UrN+DdnjQMnILYxuZZaV05vHBPRCB2v9OKUuB2sx0iYSIZ7Jv6kZ2VJUPaLneVpGzqCJYNGf5xiRF4AenBN7cEo54BUMQhJsx0TwW3TM+cPKe1Z3fCZ4kUXHZrcT6wDeVDZ0Qp/nKakZkldSY/IXzm7DpI0Vdhx18IakJL/dRQAcec+q/bU3KbZ25uTkNImnqpFTBywrHTIu4vU4vk7OAGAAAAto26MoKytLvvGNb/SpoPRO/Fq5cqWcfvrpCZkdaePGjXLWWWfJBx980CcBLVAmtUzNTFZTYxZ8AkInFl566aXyv//7v+wqwKucfhMbBcffmCeC5waMjGLa+SiPE9d1wg6W8OmCBhnVWbhi2CQjaJeVVxL1ejw1ojA0lsAZDfl9uIpjvi9G90TCjzmHHPsIqmLYnjJ8ypFSP/FAGTbliOhG2vTad63NR3XZPVMCWHX/RQK/e+tlwWem1rC5zUvnbUSP48QzHHPPaiE3xFXhTK5rW9BYTlGl1IyZITmFFVJQXm8MQOfzJ0e8Hi3rcoBHr+9e34fBBxBOckRdKCHbdNZXEl8eSySJ6vfmsN9o3Fj0Mfv35SgftofEU37ZEKM+F6AGoMvIiXwAYepyAAAviDza4SLnnXeePP3009Le3t6TbBVIsgokfp144oly9tlny7nnnivp6emWbr+rq0seeeQRmTNnjjQ3N5u+9+KLL7Z023C+oqIimTJliowfP15GjBghVVVVUlJSIhkZGbJ7927ZunWrNDY2GjOHvfPOOzJv3jzp7Ow0Xad6/Yc//KGxnunTpyfsswD6c0cQILKb2DgGf7wSNEF8RBWnozFOO1bMaMi5CF5qyOHS6zJBznE+n4ca4DhoPcek8ZmOlu74Ladn51uynnCW/d8LMW8PieFPSWVXR8VbnXKcrLCiQTav6zs4YH55fcTrITYBnZklqjqtkyv6SknLiHiXeCq5Vfd7lHB+nx7rKGwZ9puj5JfVGY9YOOYcx3UV8cCpHpqeX7gXAay/t6kbv580rloi7a3bJbe4WvLLh8R1N/v8fmOb7a0tRuJ+NPewBtoZAAAeoHXSV2VlpcyePdtIuupd0e+d+LVr1y6566675KmnnpJZs2bJUUcdZSTMxGL79u3yj3/8Qx566CFjJrHeM4z1/7/69/jjj5cJE/pOOw89qcSuY445Rg4//HAZM2ZM0BvQlJQUyczMNI7hiRMnGomJq1evlnvvvVeeeOIJ0wY2lTD2//7f/5Nnn31WCgoK4vhpAFglOSVNOna39VmWkpZl8hfhj86k7ciF+sUkgehZ8Nt3S1DcLeUEEnEcO+q6DEvEvXMJhwycyuvXdw99fMd0ooNlimtGic/nZ49GoU981+vnQZvPHWoE4ebGNdLR8Z/YXGHlsOg6mMT7e+x2zn2zNskQsAgJ3E6WU1RldJzr6uzoc/2OxmADlUQyk0SvF6PaPuIjLSvPW7s2Locfx7SrMMM90O83wTnMyVLSzfqMBBNBAn9U33+MxwzHXAS7it+nFf7TR9fsDaIFq2I1aoCv8oaJkujvKC0zJ+Z1xBPt8gAAJ9A66Uu54IIL5O2335ZFixb1Sbrqnfil/r9+/Xq5+eab5ZZbbjFmX5oxY4aMHj1aRo0aFTIJbNu2bbJkyRJZunSpLFy40JiRSSXe9E7wCmyz93OltrZWfvSjH8VxD8Bufr9fDjroIDn99NNl2rRpUVcya2pq5MYbb5TDDjtMrrzySmlqagr63k2bNsmvfvUr+cUvfhFDyQEkSln9BFm7bGHP8+LqkcZoJhEj6KMlrwUP6DhkITePNhqk7JHMghPfY0mT6C/czcU/cTg7GYJrMeAMweJHWXklsmNbY89zvz9F0rLzPHWec9J5KmhHZweVMR5U3CIjp1DyYhxN35US+NW6r3NP7BXUlNQM6Wjf1ScWUjVyisSTmslw2NQjZceWDZKSnimZeSXRdRQOcr/qmPNBFMeT+45B2ILDxNFUO0Pt2H1k7dJ3jPNrdmG5lA4ZF93KdDwn6PiZIpBTVBn9SPoeZzZAq9epuk/vulxJ3Rhxg0jaHgCdOOZ+xSZO//wqCSIju1BaWzb3LCupHR3VuiK6v/N4HQlOZkOiotsF/T3rVZ+lLgcA8ALtk758Pp8x09epp54qq1atCpr4FXje2dkp77zzjvEIyMrKkpycHMnOzjb+r9bZ0tJiPHbs2CHNzc1Bg3zBEr7U86KiIrnnnnuMGZ2gn9TUVPnmN79pzDanErasss8++8gjjzxiHNNbtmwJ+r6nn37a2HZ9fb1l2wYQH4VVw4xOJS1bNkhGdkHUnaa4iYXOzALRTukkbxenN0jEu9E8mk5odFyDHvQKxsdTad1YcZQktzakWtiY5vSPioQOZMB1OTKqw9zOj+dJd3dXz3OnzbQU9Dt1ynnOIcVws4rhe4p3WXQA0VF4UMlpGVI+bJKsX/6BdHXsloKKoZJfHv/4turwnl8+JKZ1cD2D3kzqclxYI9ovKrl1d3trn2XZ+WUSTzlFFTJyxnHGbF/+5BSLZ8EJeoMbURmRWCr5LzOnSIqj7DTuZtads8KIyznl/ieB1L1pecMesu6L9417VuM4i3J2wYQfAx78vuAdpgOMevzYj3TwVdWXo7Wlbz+teN+z1o3fV77+/N/StmObZBdVSGn9+OhWNMh3re99rK6fC1HhcPAEr/cZAgB4g/ZJX0phYaE8+OCDcvbZZ8uKFSv6JF71Tsbqn6AVEEjwCrxvsPcEuykKlgBWXFws9913nwwZEltDIpw5s9cJJ5wgl1xyiVRUVMRlGyqRSyUMnnbaadLR0THoe1QC4+OPPy5XX311XMoAwPoRFdUjLEHbI7x0E0tkRlvaBpdjp2/gfaBAJ2ZAT9b8lhlReHC5xdWyq2WrtO/6zz18ckqa0VnYDRxTl/PQ9Qbi8mNO7+TXYOd5dd84XM1Is3WjpGflS2ZesbjFoOc5zjnwqN4dy/RJmLDmc+SX1Uleaa2RGOemAY4cU5cDEs3j1/JI43Wl9ePk68/e67kO5JXUGAPCJaKcYSV8ma7DQ+c5TY7rYHUMNftbrMeD5/W+XdPjcLFMUfUIIwGio71VUjNyXNOuEbTe6ZLyY3D+5FR2TQhu+Y0mXJD9UlQz0qjLdXV19MTp0jJz41oUVVdUiV9hc2lfEo5FhHegRHFshfwjTc6DXj+fe/3zAwA8wRNJX4pKvlEJMCoRZ8GCBQOSvAZL/uqv9/sC7w23Y0bvbY0aNUp+//vfS2VlmJ374Sq/+c1vJD8/P+7bmThxopx88snGrF/BPPfccyR9wVv07vPnmkCYU8qhC5/PM9W16I8frx9yOn7+iEa9ZxYZnUZJBGLlT0mVoXseIlvXr5Curk7JLxsiqRnZrtixburUjPCpUa1hfq+mT7JD4qjOJPHuUGJ3XZ/7SmiPanLoc4Bj40tJruxAh+hnw0GIupxjf6vOlFtcYyRBbG/6WtIychIym2F8E/iDvTnepUEs+N0ifgNM/efHr5IK3ZZYGPe6XJj7l9+ntbw9S3UvzDRtaay3Ya/DZPumtZKSlim5JTWu+d3S/uBGoY8tvtdwdqM7fqPx4pZzVKyIywEAvMBTrVC5ubnywAMPyOWXXy4pKSlBZ/nq/wgIvK9/Yliwv+n9XrVM/Ttr1ix54oknSPjSWCISvgIuuugi41gOZuPGjbJ8+fKElQeA7g0SdiTzeCMAYaa8YaLdRXA8OgrHnmzjtCCYtxKFvPRZYSkaaoNKScuQkroxUlY/XtIyc1xz3Yp3w4uTzq1eunaXUZcLzTuHg4cEHVJ4kHcmeeazQg/WXa/DuS577zhy9TnBKXW5IOVw9b61idpnpXXj7C6GC0R3f+Pl2auzC8qkYtgkKawaJj6/X5zHO/Ubzo0eZNFx3N3dZcl6YINgdSWHtZF4tcO2VVRiddnQCcaAYAiBYysySSLpWXlG+0N++RCH1uXiex6JdT2JOJ955Zyprl15JbXiJdHU36nzB6HZLblXfvcAAG9zduQiThf42bNny/PPPy+HH364sWywJK1QSV2DJYX1X0fvv50+fbo89dRTctVVV0lqKlOIwxqFhYUyefJk0/d8+umn7G5AM0GDEh66ifXKDXtOYYVk5pfYXQxHMO0I4pHjwaqhc1VDRG/F1SOdNwpYJB1/gn58rxwX7pZT5MHZj5P0S+CBNZzSuSR4XTOqlXmW358ipUPGSVZ+qd1FcQjOWYj0PGdyAvFM/R+eucfV5pDu9nRMyil1OVg3w1ftuH0ki7hcSF6J00Ycs9RwBqzB4od0oHQ4j/8+E4XfgR68fj3TzYhpx0hp3Vi+1zB4/Rym4+cPPuichvesHj93l9aPE38KfVBDSoo2zuPt48t14v19eSPsCQBwuGTxqOrqarnjjjvkyy+/lAcffFBeeOEF2b59e59KW7SVt0CAPz09XQ4++GA588wzZcKECRaWHvivmTNnyoIFC4LuklWrVrG7gFi55F7eOUEHp5TDvTJyCqW8YQ/JzCsWn889I4S5jkcDM2X1EyQ1PUt2btskGblFUljRIE7jrZFSvXnOVI1O6virGjFFvMayRkQPj4quKy0bXT2c0Ko6CVOPC5c3r4VeNOhAA3z9jublWViASDllMBUdOy3aMTNE/cQD7S6GozDoCCK9Z43rucgx7SBu5t19mMjrpNuuyZa0MbrrIzs0LufinQh3Itlh8N2iZQZ/BGW3JR7k4n2bYNWj9jYGm0vNyLa7KC7BseUFzqnLAQAQP55N+goYOnSo3HjjjXLdddfJm2++aTzeeecdIxksmkbtmpoamTp1qpGIc8ABB0hmZmZcyg0ElJWVme6MrVu3srMA3XhoRGG3NYpZ2VE4u8D8/O493S5IeIyzJGs+v3p/YeUw4+FUkdTDvXqecLsx+59EMoSl+B38d08kubcu55COwm7YhU6n6uUkfEW2v+ANTqm3J+Ja4ZTPCgcjoU47/O7hWR6/5kUcl3PBDVdEMxq6/ft3efFhHwZH0I9j4nKIDPdVgAvvWZ1SDhcIsquyCsqMAV49KYrj2DnHvrPoNrgLdTkAgBd4PukrIDU1VQ466CDjobS2tsoXX3whK1eulMbGRtm0aZPs2rVL2traet6flpYmRUVFUlJSYiR7jRgxQrKzGUUBiaWOQTPqWAbgDRF1lozn/XvQmAnBFCT8oPO2bm831MU1gMshFzOSIWJH5xINBetYJ0mubXwJ2qFR80Y2GhEjPGfpfTh4UmSztgY7T1hVGsAN50UO+P/uCvfuC8cnMbt31yaei49DO7ghiQk23Oe4/HfEPR1C69bmeA/wfKwxzt9j2LE92liR4Lic4+9jHMYVdQQGEAbgQa44PwMAECOSvoLIyMiQ8ePHGw/AyQKJiMGo5EQAHhH3m1h3djaGJswOP+I33u4oHMW5r7hmlGxZv6LvsuqRYg/OrZ5j2fWaY0c3cW9gjzGhls6ciBeOLQ1Hq3Zz2YFQEtmBgHtdV3FMZ0k6uSAeSOCPgnsHwAjaMdwFZUeYvPxVEpaD0+tyQKJ5+ZrgMY45z3HMIeHHnEcOOo/H5B1zjgMAII642gEut379etPXc3NzE1YWQAcpqRkDllU0TBI34CYWXuWVjsJe+ZyJGFU0LStPcooqe54nJ6dJQWVDXLcJWM7bsXstz+fOGYXOKeWAZzjm2Icddblg5z4v1X3hUR7viOGp6xmnM2iPg1w/3XG9Z3XOvS8Q51mbPHiou+E+Lug9KPem8Kiof7e639PqWF9x+mdK8tBnjbUtyQXXWyeJfn+xn13F4YOke35WXQCAJZjpC3C5Tz75xPT12trahJUF0EF++RDZumGl7G7baTxPTc+W3JIacQOnNJZaGmRyxkeC0xtRHXLsO0+3J2b6iuaco86XtWP3ke2bv5aO9l1GAlhqepbYg+MXcexc4kUu/kkl+eI7Lk9KWqY4BY2SsASnQT1mbXX7OcRBRYF763JOiecgTME6BMe5Lgc4VfTnMCpzOifwu4fby69BfTrhLPrsuic7eJBTBtb09u8T8RNNGyvHom6ccp4DEi7UPYvr72mipFl91v33pgAAhEbSF+BiXV1d8sYbb5i+Z9SoUQkrD6CD5NQMGbrnIbJt4yrjpjCvtE6SU9PEFZx+E8voTIjboeXwYz/uo4Zr+PmtCDKG2C8+v1/yHJHUq1dAVfeAsaOwb90rKf6NriW1Y6Rx1eKe5z5fsuSWVCe+YUTHaxQsR2Ochrx0jfLQR4XF958cO9qJ+/Us5nMr9TLEcPhx0vJW3ddLdbmgXPA9wVacF/Xj+AR+TkuAM7ihLufweigDCEOH4xjOY2Uba1pGjrS1bu+7LDPPsvUDABAtbZO+fvnLX8qCBQsGfe2aa66RqVOnJrxMgNXmzZsnmzZtCvp6bm4uSV9AFNRsLyW1o10XyHB8gwQQL1HH9ujA4FQRTW9PbBeAJixN+qobI7t2bJWWpnXiT0mTqlFTxeePMQREYxpiYXpt52I+6C5zcV01orpcFOec7KIKkeXv91mWkVNoybqBxAn9O/HMACeaYNR0eBbX1SC7Jckjs7YmRfz5U9KzoigVvHYcOn6fuPd2DcFHY3LGvnFKOeAZXCs8hPOLPvguI9xdXFs9wcLvuXzYJFn5cd9JGMoaJli2fgAAoqVt0pdK+FqyZIlRcevd0aCyslKmTJlia9kAqzzwwAOmrx9yyCHiIwkEHuLmTnFu6FwSdsc9S4MmBGDQcwBynAT9lej3O4mscwngzcYFSzrUO40ln8nF50QL61D+5BQZMmF/6ezYbSR7Oa5Ry2HFgc08fjzoWJeL6HwexfkpPStPsgvKpGXLhv+sQpKkdMi4iNcDRMWqfsJ9ficangc8eN52StKX4+p90J6WdZm4cv7+ine8QdXl0rPyjYFKAiIZhM/KY86z50yvfm5L/fd34tnjaDAu3hdxr8tpGMqFi5gef+793drBDXXfoAMIO+SeNdi1wtI6ngu+J92vq/Fj/T4Jeryw+13FynNcTmGFFFY2yJavvzSeF1YNk+yCcsvWDwBAtLRN+lq3bl3PjUwg8Uv9q5JgCLxBl1m+5s+fb/qe448/PmHlAWA/K69vWfmlsmPrxgHLnEKbQB0s4SZJnP0AAMvgSURBVJW6XaSfU7eEkOC/+/g3DgDOpNdv3FsSN2urSv4CnD5AB9ds/VhRDw11XNSO21e2rFsu7a0tkltSYySBAe5CXU47HolNwKNMx2Li2Pd2An90m6gbv6+s+2KRtO1oNmZxLa0fL47CYQ1L7nk4kLzaUTg5JV06du/qsywjp8Cy9QNW8koba1Ae+vh6ftc6fiZYTstjH+EnvEb+/av22qqRU6WsfqJxmklOSWOHAwAcQdukr5aWlkGXT5w4MeFlAazW1tYmP//5z03fo471vffem50PT7Gis6A7gl3BRpqxruzFNaNk57ZNPbPtFFUNj7nTMJ05AcR2EnHO+ZnzGWI7gKyaHqL3Kp3z+0D0HDPSJpBonMM8PWtrtPUqdX+q7luBxLO+LqcNCz6Tm+u18Ujgj451nVwARP+bs+rtTq/LRftBUzOypW78fhZsB7F8D4AnJcW/LlcxfE9ZvfjtPsuKa8O7f6XOhoTjPkG7/RUsMdk596xAYnFt9YZ4tLEmp1qZ7KVjMBgAkGjaJn35/X7p7OwcsLyurs6W8gBWuvXWW2XFihWm77nsssts2+nvv/9+TH+/Zs0ay8oCeImVN7G5xVXSMPlQadm8XtIycyWnuMqydQNeDLDHVZK3z3FBA7VePy7g6Vlz4E6OSfpKyPmTc7TncMry1j7TbMZZIO51OS6L7hJ05GCH1OUSQfP7bQZ6GQzXdi+JZNZWfi/uRMdXC/T5nehyXfT2ud7K30Veaa20bt8sW75eLkk+v5QNnWC0tQJ2oS0BVkpOzYjp7xNSf9Tl0gyLvtsQf8TxogfNY1UAAGid9JWZmSnt7e0Dlufk5NhSHsAqr7/+ujz00EOm7zn88MNlxowZtu30U045Jebf77BhwywrD7zD6wHLSDqXhLOvMnIKjYedaHxEOJ0N6FzgDZwPgH7oUO/ac0WwMpomsfJ9Q2Nu+N0ijvfmQc997HVoLpxrO+dHz17PMnOLZWfzpj7LHDUgE3VTWJHw6NFQvhvimJbM9OX8j+n67yk8Hv2hmbHoeu319kgdfztWJvCremHFsElSPnSiccwR94gffouxC358mp/n2Pe6z9oanM/vl6Kq4dK09vOeZcb5LlbEQAbZJe69rjoJ+9EbPDUYEwDAs7RN+srPz5etW7cOWD5YIhjgFmoGrCuvvNK047s69q+99tqElgtAgiU5O+iTkHI446PCKTgePIFAHQDdJfl82nXQcUr9FE7HcaIdkgGgsXhc29zcOdZyLq47WHnPWlo/TlZ++HpPR8rU9GzJKawMsyCWFQNAWLq9XZdz8XkbiDdiIs4UrO4dj/YHp8T6AHjsHJ6AuFzFsD0lPSvfmNUwK79U8srqwv9jC3etq78nJDCGFupvgtQNNA+wRDLDsxtwPgAAeIG2UYYhQ4YMWjnZtKnv6IiAW7S0tMj5558/aDJjbzfccIOUlZUlrFwAnMPxCREE3RAnyakZ7Fsv0DuuCojXg/FwQV0OiEm35dd4RhTWfURhKn/QG3U5DVkY98oprJDa8ftKXmmtFFWPkPpJBxmjqQOOPGdxydYuRu74a5TzdyE0E7LDrzbHpDYfRNvzMxC1KK7tjq8P2MQNSQWJ+O5UUmth1TCpGjVV8suHOHC/OK081nLe/na2sqHjBy6rH7gM7kayPQDAC7TtUdTQ0DDo8qampoSXBYhVZ2enXH755bJs2TLT95100kly1FFHscMBr3JMcMcp5YBOfP7gE9RmF5aLPzm1z7KiquEJKBUckQzhmHMfkOjGGBpddeP0pC8rGxJplERvPh+d2HU7z0fSuYTzAbygbOiEAcvKG/awpSxwT+eS3OIqqR07UyqHT5bU9Kzwy0FcDgnm9PuY+NMvLtXd1RVzXc415yLiip4T1bHJceLtupxLTmeA5Uj6ci++O+gsinpZVn6ZZOYU9TxPSc2Q/LIhFhcMtqPODgDwgOC9Z11u2rRpct999w1Y/u9//1uOOeYYW8rkFS+99JLxcLt99tlHjj/+eHGCG2+8Uf71r3+Zvmfs2LFy/fXXixM89thjMf39mjVr5E9/+pNl5QG80ijlrc5yXvqs3pNfWidbN67seZ6WkSNpmTmmHYVrxs6QNYsXSMfuXZJdUCaljM6knwjPcd46J0J3qvFh64av+iwrrBwmuvH86KFOOW9ZWgyHfCY4+vednl0gySlp0rG7rWdZTlFlgkqGuKBzCdBHQVm9bF77hexu22k8T8/KMwYvcVwdALElOHgp8YVjFr2QwB/pz8cF53zqcpzn4FEWDDyS5N56jOPPz+4dFwbQTJILqnJdFqwjnicdK8/Dzv8+YD81c/qQPQ6U5sbV0tXZIbnF1ZKSnumeOkDc6FW5cHxcTq/dDQCwibZJX1OmTJGMjAzZtWtXTwVN3ZSoxJkbbrjB7uJp7bPPPpO///3v4na5ubmOSPq68847QyZRlZWVyZw5cyQ1te8sJ3aZNGlSTH+fmfnfmysg4Vx8Q+/4m1ggTOXDJkn7rh2ys3mTkfClErpCySmskFEzj5eujt3iT3HG9dB2mnXQcP3IwUCPyI/Z4pqR0ty4Rrq6Oozn6Vn5xsh00IvVs0MAbrrGVwzbU9YsWSDd0m3M4FpGAr+rRdYxxKUd7oAIqI4kDXseKtsaVxvJEbmlNeJPTmEfaobzFrx8H1NY2SCbv17es6y4ZpStZUJsPD8gC/QW1URfqp+J2Sq5d4lpBzsEbaxAXypG523uPZ9Rl+P+HAOpOFxBxdDIdo3L4tNeP28TlwMAeIG2SV9paWnGjF5z587tc1Ffv369vPPOO7L33nvbWj4gHA8++KD87ne/M31Pfn6+/PGPf5TKSkbBBrzOMR2Fg8Q+aPhCuFLSMqRh8qHSqRK4IugIp+p8kSR8EfTWpNHVjniry4K8cL+MnEIZuuchsm3jKuM8V1A+1BiZTjeeCcg7fnYIj3wPcJT88iGSVVAmbTubjXMeyRAur6u6uexAKFHWV1Til0rkh76C1+WSnHGMeqWuDVtUDJ8sKelZ0tq8WbLySqSoegTfhItZMjuExzscQi+l9eNl/fIP+ixjoBL9OCcuByReSmqG7G5v7dOmr5ZhEG64rSIuB8CDqMsBALxA68jFmWeeKb5+HeBVh4mbb77Z3R0n4AkqYVEdq2aysrLk3nvvlWHDhiWsXAAcwEudN4J2itbws2IAOvuij4h/95wnnCA5jYZBq2TkFEh5w0QpqR0tyalplq0XzuH8BgnOq4hFd1iJ/9kFZdQBvdZRmFMLANdxegJ/AtC+hn7ULIaldWOlbvy+Ulw7yjmDkyE6HvqNM1AdwpFfNqRP8kNySrrkl9ez81wqaPsi7Y7QWKj+caX14/o8L6kbQ33O4wn8AOA61OUAAB6gddRdJcKccsopPTewgQDO0qVL5b777rO5dEBwzz77rFx//fWmwZeMjAy55557ZMKECexKAI7qXEJDqQX7kIAEEMHvgp7CTpZTVCl+f98Z+1TSkqdxyMZ5/7p3B2t5/dfwIwEIjcG2oDNiHgh6bJDkAp15KAkIbugozI0mEtuOpgYoGbrnIVI6ZJyRCDF0z4MlNT2r90r5SjTglDZWwA6FlcOkfo+DjPNc3bh9jRkOMThiAg7DJdhztGxHs4tmt/me6kvC7wAAPEv7yMUPfvADqa2t7XOBVx0Pbr31Vnn88cdtLRswmFdeeUWuuuoq6eoK3qiSmpoqc+bMkSlTprATAYu5OVBHgEMfdJJEZAY/b3VrFqmLuNHVvadz7Ub7rh23j6SkZRrfYW5xlZTUjRUvS8vIsbsIjuWV61+w+qZTOgonpE5Jg4T3xOPn7ZFzhu4dhYPeg8fxPMG9M9yCY9VlvFS/8dJnBTzIK/fm8Kbc4uoB9yB5pf/tSxJMaka2lNWPl/KhEyUtMzeOJXQ3N9dfnRKXA+Ij9LU9u6DMOM/lllS7+rdsGRfvAqcn8HN8wS3c3HfMizyVwM89OwB4lvZXu+zsbLn77rslNzd3QOLXT37yE7njjjukvb3d1jICAa+//rpcdtll0tHREXSnpKSkyO233y777LMPOw5AX5EEYO24CYwiJkIgBcB/TwhBkiRofHK87MJyGTn9WBmz74lSN34/8Sf3nfnLa4qqRw5YVjl8si1lgcNwPtMH3yUsOV7obAsHoAE5IbincZmguarObm4jxhbRzkICBhbSbbAirXjp+h/0nM6JQFf+lFQpqvlvbM7nT5biQWJ1luA4chV+9wDCPFk4fkeRwO8Fzj8OXfXZXfC7RmhOj8sBAGCFZPGAoUOHykMPPSSzZ8+WxsZGI2CjHmomJZUQ9uKLL8rVV18t++23n91F1cLFF19sPBCZ+fPnG/tt9+7dQd/j9/vlV7/6lRx88MHsXgDOvYlNSFBE78ALjUuwhGb9MxxzjkNUjHswvyduP0NKy8yRimF7yoYvP5Turi7JLa2RgooGu4sFB3D8eY4EfsSATr3eEtGIwjSqw2UKq4bJpjWf9V1WOcy28ujGzfEQp9Tl3LwPATiD02eHQPjo9D248oY9JLe4StpbWySroExS07NiOqxIrNYEdSjAozS8f/JQAj/XYAD/PSFoeD4HAKAfz/S6GzVqlDzxxBNy5ZVXysKFC3sSv1Sw88svv5TzzjtPKioq5Pjjj5cDDjjAeH9qaqrdxYZHqGPy/PPPl7a2tqDv8fl8ctNNN8mRRx6Z0LIBruKVAFaQz0mnDgA6i/QcF89AP40IiFVxzUijc3BXZ4ckp6axQz32+wzWgY66HHSWkV1gdxHcp9vb52c3nM/hTakZOZJXUiPbGlcbz/3JqSR9wUBdDvCoJP2SbdxcdiDca3ZWfqnxAJyWwB8c52ZELzk1nd3nISTwk/wBhHWu0Kxu4fS6nG77GwBgD62TvubMmTNg2dSpU2Xt2rXy9ddf90n8Ug+17K677jIeakYlNUPYkCFDJDs7W3JyciQrK8tIvImXiy66KG7rhnO9//77cu6550pra2vQ96jj9MYbb5TjjjsuoWUDPMkF/cqcfjNI5zwA8Tm5uOAEDUTA5/cbD3gPHeioL+pOzWa47otFPc8zsgslPTvf1jLBAajLuVJGTuGAZWkZOeJlKk5bPWa65DcNkd1tOyWnqFJSM7JjXWuEy53J6fGquMe9HHOec0o5oBVv/LwRQNIXEBkuvVoggR+6qBg2SdZ98X6fZcU1o2wrj1u5ub8D7Q/QWhx+mm7+vSOMupyOX69jYpAAgETTPunLLDgTuNHp/Z7Aso6ODlm2bJl8/vnnkigkfXnPRx99JLNnz5adO3eavu+6666Tk046KWHlAuBwrm10jeLG00s35gCiG50p6HmCEwU8hOPdXZxel+N4QoyKqkcY/25vWmskQpTVT2CfIvKRNqnKOYI/JVUKKxpk87rlPctKh4wTr/P5/JJbUm13MeAwSXEcMNASnFcRpvzSOtm6cWWfZQXl9ew/D/HW7BB6JF4H409OsbsIcBOHh6vsu2fV43yga+f2wsoG2fz18gHLvKygokGaN62VHVs3Gt91yZCxxozV8FCCqNPbH4AY+JNTE7f/XPBzRy9uOD8DABAjrZO+wh3FonfyV/8EsESNgOGKG0NYavHixXLOOefI9u3bTd931VVXyRlnnMHeB9CD0Zn0l5aZa3cRoAXNgvpUlwGEda5w/snCrR3oouoQ4oLvA/GJcRXXjDQewH8PjODHC5ytcsRekplXLLt2bJOcwgrJLiy3u0jQWpJ3klsBByfwq47CXV0dxnNjRsNMOgp7Ce0P+vD5k43f8Pamr3uWkcSZODol1ngC96auVFw9Srau/0q6ujp7BuhQy7xMJfzW73GQtLe2GMkRyalplpzPvDKzsw6sqct1u/rczTVY7340qenZ0r6rpWdZflmdrWWCMxCXAwB4gSeSvgbrPDDYTU7vZf0TwOKJ4Ln3fPbZZzJr1izZtm2b6fsuu+wyOfvssxNWLgCK8xthHN9R2Pm70FHKG/aQ9cs/6HmenJwmOcVVtpYJcFOgjsA9ALdxegwgIedVjeqLGdmF0tqyuc8y1akOiJVunWlodHX37EUFFUPtLobeNLouevn8HKw9KeHJrRxPEcnKLzVmQegtt9jbM/mpRN+GvQ6T7ZvWSkpapuSW1JCkHRX31uWc3v7AoAGRqR49TdZ/8b7sbG4yznkVwybF6ZvxMi6+OuDc4k5pWblSN2F/aVqzrCd5XS3zOnU8p5G0H6+9K07n9LocEOv5rXbsTFm9+G1pa90uWXklUjFsz9h2qlcTvx3eVhlNDNszNPvuAADh80TSVzQdqpjhC/GyfPlyI+Fr69atpu+74IIL5Pzzz+eLADAQN3BaJXkUV4+U3W07pXnjaqMjRcXwPY2R6AD0RUdhALqg0VUvZUMnyFcf/avneUpqhuSV1NhaJsBdHegiXW5JYeK3bsDD1CjynkBcTitl9eNlxfuv9STz+XzJUlg5TLwuPSvPeMQVvyXnsuK7cXsfNI2qi8kpaUbiF5CQ497F91pean/QbZCZ7IIy4wFYysXnM+rZ+vNUcssgMnILZcS0Y6Srs8OY2RZQ/P4UdgQAQHueqPkwKg+cYsWKFXLmmWdKU1OT6fvOOeccufTSSxNWLgDu4taOwlyPg+wXn08qh082HoCXZpKx7Bzi5oYXAJ40WOdJXTuWeKH+l1NUIbVj95Et6780OtOV1I2hoRER8Uojva7nOSCuXHYZLaoaLptWL+2zrLRurHaf2Zec4tKBklywc22gZr2pHjNdNn/9hTEIk6rLpaRn2l0swFbJqemyu72177KUdNGRF+5ZAYSJ8wGAsM4Vzt9NurUPe112Ybm0bF7f8zwju1D8Do9LJEr8E75c8IP3KDXw4rbG1X0GY0zPKRBHs/LcTL0VADzLE0lf3NDACVavXi1nnXWWNDY2mr7vu9/9rlxxxRUJKxcAF3J8nI7gB5BIKWkZntjhSRHOgEenDQBO7kCXW1wtzZvW9CyrGDZJHIMk24jlldYYDyDSxkjVMK06m3tC0HNLsLdzXwnv0GU289SMbCmpHS2Nq5YYz9Mz86SwarjoRiUGqZmgVJJQQOmQcbaWCbHJL6szHgD+o2L4nrLq07f67I7KkXuxewDdBytzfNtjfHlpoBJd7j8AK6hBvLRD0pf7rsEmqkZMkZUfz5NdO7ZKWkaOVI/e2+4iwak8VJcrb9hDdrVslbbW7cYMX1Wj96Y9AQDgCVonfU2ZMsXuIgCGr7/+2pjha/36/46+MZhTTjlFrr32WvYaYCM3dCxz60xflnLB9wTEK4C1fvkHPc8LKxu0nE1EjazduHJx32W1o8UxOAUBiFHNmOmyac1n0rajWXIKKyS/fAj7FPAAlRSwY2ujdOze1ZPwqZIHvNBBww332gCsuWdVCVFqhpiMnMLg5ziXU7O1p2XmSuv2zZKVXyIFFQ12FwmA07i4KpdTVCXZBeXSsmV9z+wCOYWV4iUkRADe++0ES/riXhbQR1n9eNmw4uM+y4qqR2h3nmNgfL2oAXaGTz1SOne3iz8l1e7iQCPdLr5pNX4Xex8tu3ftkOS0DG3jjwAA9KdfD9FeHn74YbuLAMiGDRuMhK+1a9ea7o0TTjhBfvzjH7PHALg+UGdlAwiNKYgrh/+WBlNcM8qYnn7H1o2Snp1vdKbTUWFFg2xbv1La23YYz/NKayUtKy/CtTi/4QWAd6mE3dK6seJdnKPhTar+NmLaMUaSgEoW8MqMrWa/ezd0lgEQeccL9dBZks8nxTUjxZFIsgUQI5/fL0Mm7G/UWdU5JSO7wDjvAdBbek6BeIUanMA4x/2f1PRsx3ekd3rbMOAGhZXDpblxrbS2bO4ZgFLHe1cGENaT069TQKKpvmQ6nsMBAPBs0hdgt6amJpk1a5asWrXK9H3HHnus/PznPye5AUB4COwDng5eqdlgdJ8RRgXoGqYcLju2bBB/Sppk5ZUErSeRHAoAANzEn5wi2QVl4jXU2QDTH8jgiz2YFOnFz5wonIcBRHTO8PkkM6+YnQZ4SFZ+qZH81L6rpc/spjoqGzpBVn08T7q6Oo0ZvsobJlJXAjwgOTVNGiYfKrt2bJXklHRJSc/UdJAN7ySJcp8LjqO+g00CAAC9cbUH4mTr1q1Gwtfy5ctN33fkkUfKzTffLD5GyQMQJkZnAuAFySlpxgxfALzTObaoerjsXLxpwMx/kaCRDwCcSXWkC/JCoosCAFqjPgwkVnrm4DPTd3uosy0AfeoQQyYeIBu+/FDaW3dIbmm1FFYNFx3lFFbIiGnfMGb7UrMZRpX4AcC1ie1qtj+d5ZcNkfVffthnWW5xtTgGsUC4hNviK5m5RZKcnCYdHW09y0z7WnDLCgCA6wRpbQcQi5aWFvne974nn332men7DjnkEPn1r38tfr+fHQ5YwCsNqWq0vf5S07JET+4KpABw0HmC0wfgOjlFVZKWkdNnVLqi6hG2lsmLEtOQ5Y16O+BVxTWjBi6rHbjMlMsa1QEAgP7KG/bo81zNEJFdVGFbeRBH1EXhUWmZOVI7bh8ZNuVwKa0bG3mMyEW3cSlpGZJbXEXCFwDtkkDyy+vFn5zaZyCmkroxtpYJQGKSWitHTRGf7z99UFPTs6SsfoLnd70uA6cCAKAw0xdgsZ07d8rs2bPlk08+MX3fAQccILfddpskJ/MzBBCZtKw8ycwtlp3N/50Jo6yBm3UAAOBu/uQUqZ90sGxZt1w6O3YbI1KmZ+fbXSwAQITULI1bvl4unZ27jeep6dmSW1Lj2s4yAAAASnH1SOlo3yXNG1dLSkaWVAyb1NOhDi5FXRQcRwAQ3vWym0G83JTUWr/HgbJp9WfS1dkhhZXDjBmA9DR4XJF4I6w4jtwor6RGcvapkPbWFknLzDUSwQAAgD7INgEs1N7eLhdccIEsWrTI9H2jR4+Wq666SjZu3Bj3/a+SysrLy+O+HcAJvDJChwpS1U3YTzatXiq7W3cYnefySgfvQOeohlIaUIHE8koDTJK3rwmAjg2SpUPG2V0Mx9X9EntKT8T5k3M0oLO0rFwZuuchsnXDV8asjQUVDUZiLwCEpGXsyBmfqdsrMQIgjlSHOZXopR4AAACAU2XkFErNmOnRr4DbR8C1VDyeATUBANATSV+AhVQS1/z580O+b8mSJXLkkUcmZN9XVVXJa6+9lpBtAUic5JQ0KR860bO7nNGZgNAycgsH+e0wmhMA3TmjU62OqH/BaXTvvO72z6calsuz97C7GIC7aZkABQAe4fK6nNdx/wsAAAB4hGfDb9yzAgDgNvT6BABoo9uKm1I61DiLZwMsQOxyi2uMBNHeKkdM1m7X2jOjFycnAAAAABbdXRCLAgBohw6E8I5g8Wlv1vG8+JkBAM7D9QgAAAD6IekLAABokIABoD+f3y9DJh4g2YXlkp6ZJ2VDJ0hBRYN3dpQnG9UBwAKcPgEAAAAAAAAAgJPRlgEAAAAPSba7AAAAWCW7oFyaN63psywrr4QdrGUyGBE8OGR2QIfLyCmU+okHitZI7gIAANBPt/51dQAAAESBWCAAAIg34lKAZzCoNQAAcAtm+gIAaKO0buzAZfXjbSmL03XHM1BpYT4WARYAUZ8/XNIBJK7nY8CL3PHTd7Tg9S92LpBY1BG8Xk+Elwx+THKsAoB7eWGwKQD/h9sLANAPsaP47VounIAj0EcBAAD3IekLAKCNjNxCqRo5VVLTsyQlLVMqhu0p2QVlEa2DIBMAAHAdj3YuyS8bMmBZQXm9LWUBAKulZxewUwEAFiIBBUik5JR0djjged4L2DFoAQBd+JNTJD0zr8+yzNxi28qDCPv2eO8SjHjQPPkzt7hqwLKcwgpbyuIFJNkBAKyQbMlaABiqq6vls88+Y28ANiqsbJCCiqFhNS5k5ZXIjm2NfZdFmCQGAEAsaAgHoldUPUKaN62Rrs4O43lucbWkZuSwSwFoISOn0Ohcsmvntp5lFcMm2VomDIYkCsBK3B8B0EVaZo6kZ+XJrh3/rcsVVjTYWiYAgIt1c+8JJFrFiMmy8uM3jPYHf3IqcTlAV5ondwVTUjdGmhvX9JmlunTIOFvLBAAAzJH0BQDwbAeR4trRsvOTJunu7uqZGSIlLSPOpfOCBIyo5M24CwCHoCMi4AyZuUUybPLhsr3pa0lJz5Sc4ip+n1bwaAMX4MT6xpCJB8j6Lz+U9tYWyS2plqLqkXYXCwAAwBJeGOW6dtw+snbpu7KrZatkF5ZLOQn8gKaIo/x3V7AvAOgju6BMRs/8prTtbJa0zDz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+      "text/plain": [
+       "<Figure size 1600x640 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 663,
+       "width": 1726
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "class WindowMean:\n",
-    "    \"\"\"Feed a stop rule the mean of every k losses instead of raw increments.\"\"\"\n",
-    "\n",
-    "    def __init__(self, monitor, k):\n",
-    "        self.monitor, self.k = monitor, k\n",
-    "        self._means = []\n",
-    "\n",
-    "    def __call__(self, approx, losses, i):\n",
-    "        if len(losses) % self.k:\n",
-    "            return\n",
-    "        self._means.append(np.mean(losses[-self.k :]))\n",
-    "        self.monitor(approx, self._means, len(self._means))\n",
-    "\n",
-    "\n",
-    "class Shadow:\n",
-    "    \"\"\"Record where a stop rule WOULD fire, without actually stopping the fit.\"\"\"\n",
-    "\n",
-    "    def __init__(self, callback):\n",
-    "        self.callback, self.stopped_at = callback, None\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
+    "for (w, label), color in zip(horizons, [\"C7\", \"C0\", \"C1\"]):\n",
+    "    t, z = signed_z(stage2, w)\n",
+    "    ax.plot(\n",
+    "        t,\n",
+    "        z,\n",
+    "        color=color,\n",
+    "        lw=0.9 if w == 1 else 1.6,\n",
+    "        alpha=0.5 if w == 1 else 1.0,\n",
+    "        label=f\"{label} (w={w})\",\n",
+    "    )\n",
+    "ax.axhline(0.0, color=\"k\", lw=0.8)\n",
+    "ax.set_xlabel(\"step within stage 2\")\n",
+    "ax.set_ylabel(r\"standardised block contrast $z_w$\")\n",
+    "ax.set_title(\"The same trace, three horizons: what a stop rule is allowed to see\")\n",
+    "ax.legend(fontsize=9);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9163ac60",
+   "metadata": {},
+   "source": [
+    "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
+    "contrast has unit spread — which is what the $\\sqrt{2/w}$ scaling is built to\n",
+    "produce — and a mean indistinguishable from zero: a single increment says\n",
+    "nothing about the trend.\n",
+    "\n",
+    "At the two epoch-aligned horizons the spread collapses far below what\n",
+    "independent noise would predict — and the control row explains why. The\n",
+    "shuffle is done once on disk and replayed in the same order every epoch, so a\n",
+    "window of exactly one or two passes averages over the *same rows* every time\n",
+    "and the batch-composition noise, which dominates the per-step view, cancels.\n",
+    "Widen the window to one and a half passes and it does not: the control sits\n",
+    "at roughly twenty times the spread of its neighbours on either side, even\n",
+    "though it is *wider* than the one-epoch window. Nothing but the alignment\n",
+    "changed.\n",
+    "\n",
+    "What survives the cancellation is Monte-Carlo noise and the parameter drift\n",
+    "itself, and against that much smaller yardstick the drift becomes visible —\n",
+    "the ratio in the last column rises with the horizon. Two lessons sit on top of\n",
+    "each other here. The horizon is not a smoothing preference, it decides whether\n",
+    "there is anything to see at all; and with a fixed replay order the useful\n",
+    "horizons are the ones commensurate with an epoch, which is a property of how\n",
+    "the data was shuffled, not of the optimizer.\n",
+    "\n",
+    ":::{admonition} Why a naive rule does not merely stay silent — it fires\n",
+    ":class: warning\n",
+    "It is tempting to accumulate per-step evidence with a one-sided statistic such\n",
+    "as $S \\leftarrow \\max(0,\\ S + (\\kappa - \\max(z, 0)))$, stopping when $S$\n",
+    "exceeds a threshold. The rectification is the problem. Under symmetric noise\n",
+    "$\\mathbb{E}[\\max(z,0)]$ is a fraction of a standard deviation, so any\n",
+    "allowance $\\kappa$ above that value makes $S$ climb at a constant rate *on\n",
+    "noise alone* and cross any fixed threshold after a fixed number of steps —\n",
+    "whatever the data is doing. A rule built that way announces convergence on\n",
+    "schedule rather than on evidence. The fix is not a better threshold but a\n",
+    "wider horizon, plus a second yardstick that asks whether the remaining\n",
+    "improvement is negligible relative to the reduction already achieved. That\n",
+    "design is under review as\n",
+    "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this\n",
+    "notebook shows the observation problem it exists to solve rather than shipping\n",
+    "a second copy of it.\n",
+    ":::\n",
     "\n",
-    "    def __call__(self, approx, losses, i):\n",
-    "        if self.stopped_at is None:\n",
-    "            try:\n",
-    "                self.callback(approx, losses, i)\n",
-    "            except StopIteration:\n",
-    "                self.stopped_at = len(losses)"
+    "How much would an online rule be worth here? The retrospective answer is a\n",
+    "benchmark you can only compute afterwards — which is exactly why the online\n",
+    "version is needed:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "9af360af",
+   "execution_count": 19,
+   "id": "fdd425a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:07.479507Z",
-     "iopub.status.busy": "2026-08-14T08:44:07.479446Z",
-     "iopub.status.idle": "2026-08-14T08:44:15.171817Z",
-     "shell.execute_reply": "2026-08-14T08:44:15.171380Z"
+     "iopub.execute_input": "2026-08-16T07:45:11.458430Z",
+     "iopub.status.busy": "2026-08-16T07:45:11.458291Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.461504Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.461012Z"
     }
    },
    "outputs": [
@@ -1828,135 +2064,115 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "raw increments: would stop at step 371 (loss there -122)\n",
-      "window means (k=50): would stop at step 4,650 (loss there -442, final -339)\n"
+      "stage 2 smoothed reduction: 0.60 nats; 99% of it reached by step 1,631 of 2,500 (65% of the stage-2 budget)\n"
      ]
     }
    ],
    "source": [
-    "loader2 = DataLoader(\n",
-    "    parquet_source(data_dir, columns=columns),\n",
-    "    batch_size=1024,\n",
-    "    shuffle=False,\n",
-    "    total_size=\"auto\",\n",
-    ")\n",
-    "model2 = build_model(\n",
-    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader2)), loader2.total_size\n",
-    ")\n",
-    "stream2 = StreamAdvance(model2, loader2)\n",
-    "stream2.prime()\n",
-    "steps_per_epoch = len(loader2)  # batches per epoch, torch-style\n",
-    "\n",
-    "k = 50\n",
-    "min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass\n",
-    "raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))\n",
-    "win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=min_windows), k))\n",
-    "\n",
-    "with model2:\n",
-    "    advi2 = pm.ADVI(random_seed=RANDOM_SEED)\n",
-    "approx2 = advi2.fit(\n",
-    "    20_000,\n",
-    "    obj_optimizer=pm.adam(learning_rate=0.02),\n",
-    "    callbacks=[stream2, raw_shadow, win_shadow],\n",
-    "    progressbar=False,\n",
-    ")\n",
-    "assert (\n",
-    "    raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None\n",
-    "), \"a shadow rule never fired within the 20k-step budget; raise the budget\"\n",
-    "win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules\n",
+    "# t99: the first step at which a trailing average of width one epoch has covered\n",
+    "# 99% of stage 2's total smoothed reduction. Retrospective by construction.\n",
+    "kernel = np.ones(steps_per_epoch) / steps_per_epoch\n",
+    "smoothed = np.convolve(stage2, kernel, mode=\"valid\")\n",
+    "total_drop = smoothed[0] - smoothed.min()\n",
+    "# \"valid\" convolution starts at the first full window, so index j of `smoothed`\n",
+    "# is the trailing average ending at stage-2 step j + steps_per_epoch - 1.\n",
+    "t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch - 1\n",
     "print(\n",
-    "    f\"raw increments: would stop at step {raw_shadow.stopped_at:,} \"\n",
-    "    f\"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"window means (k={k}): would stop at step {win_stop:,} \"\n",
-    "    f\"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})\"\n",
+    "    f\"stage 2 smoothed reduction: {total_drop:,.2f} nats; \"\n",
+    "    f\"99% of it reached by step {t99:,} of {len(stage2):,} \"\n",
+    "    f\"({t99 / len(stage2):.0%} of the stage-2 budget)\"\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4f8959b8",
+   "metadata": {},
+   "source": [
+    "Two honest qualifications about any stop, this one included. What such a rule\n",
+    "detects is a *loss plateau* — a necessary signal, not a proof that the\n",
+    "posterior has converged; the recovery and predictive checks above are the kind\n",
+    "of independent evidence a stop should be paired with. And a plateau in a noisy\n",
+    "loss can only ever be established relative to a horizon: at any finite step, an\n",
+    "improvement small enough is indistinguishable from none.\n",
+    "\n",
+    "## Where the pieces live\n",
+    "\n",
+    "The streaming stack this notebook exercises is being contributed to\n",
+    "pymc-extras. The map is by role; each pull request's current state is on\n",
+    "GitHub rather than frozen into this page.\n",
+    "\n",
+    "| Component | Role | Form in this notebook |\n",
+    "| --- | --- | --- |\n",
+    "| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size` | Imported and used directly |\n",
+    "| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, while [#635](https://github.com/pymc-devs/pymc-extras/pull/635) develops the longer-term ADVI API | The `StreamAdvance` adapter above, owned by the notebook |\n",
+    "| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks | The observation problem it solves is shown; the implementation is not copied here |\n",
+    "| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | Quasi-Newton alternative returning a Gaussian proposal from a short optimizer run, with `pareto_k` as its own veto | Not run on this target — see below |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aeaa364",
+   "metadata": {},
+   "source": [
+    "### Choosing a path: why the fast one is not taken here\n",
+    "\n",
+    "ADVI is not the only way to fit a model on a stream. Streaming Pathfinder runs\n",
+    "a short quasi-Newton trajectory on minibatch gradients — a few hundred steps\n",
+    "rather than thousands — and returns a Gaussian *proposal*, importance-corrected\n",
+    "against the exact full-data log-density, with the Pareto-$k$ diagnostic as its\n",
+    "own veto. It is the right tool when an approximately-placed starting point is\n",
+    "what you need, for example as an initialisation for MCMC.\n",
+    "\n",
+    "It is not run on this model, and the reason is a decision taken *before*\n",
+    "fitting rather than a result:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "8dd9d84a",
+   "execution_count": 20,
+   "id": "790757c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:15.172812Z",
-     "iopub.status.busy": "2026-08-14T08:44:15.172737Z",
-     "iopub.status.idle": "2026-08-14T08:44:15.347242Z",
-     "shell.execute_reply": "2026-08-14T08:44:15.346865Z"
+     "iopub.execute_input": "2026-08-16T07:45:11.463087Z",
+     "iopub.status.busy": "2026-08-16T07:45:11.462987Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.498108Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.497436Z"
     }
    },
    "outputs": [
     {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1600x640 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 663,
-       "width": 1768
-      }
-     },
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "free parameters in the unconstrained space: 154\n"
+     ]
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
-    "ax.plot(approx2.hist, lw=0.5, label=\"loss\")\n",
-    "ax.axvline(steps_per_epoch, color=\"C2\", ls=\":\", label=\"one full pass (rule arms)\")\n",
-    "ax.axvline(\n",
-    "    raw_shadow.stopped_at,\n",
-    "    color=\"C3\",\n",
-    "    ls=\"--\",\n",
-    "    label=f\"raw increments: false stop at {raw_shadow.stopped_at:,}\",\n",
-    ")\n",
-    "ax.axvline(win_stop, color=\"C1\", ls=\"--\", label=f\"window means: stop at {win_stop:,}\")\n",
-    "ax.set_ylim(-550, 900)\n",
-    "ax.set_xlabel(\"step\")\n",
-    "ax.set_ylabel(\"negative ELBO\")\n",
-    "ax.set_title(\n",
-    "    \"Batch noise blinds the raw rule; window means restore its eyesight\\n\"\n",
-    "    \"(y clipped to the descent region)\"\n",
-    ")\n",
-    "ax.legend(fontsize=9, loc=\"upper right\");"
+    "from pymc.blocking import DictToArrayBijection\n",
+    "\n",
+    "n_free = DictToArrayBijection.map(model.initial_point()).data.size\n",
+    "print(f\"free parameters in the unconstrained space: {n_free}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f710b13b",
+   "id": "41c2f3ea",
    "metadata": {},
    "source": [
-    "The raw rule fires a fixed ~80 steps after arming with the loss still far from\n",
-    "its floor — a false stop that a fixed-budget run would silently avoid. The\n",
-    "windowed rule stops once per-window improvement falls below a quarter of the\n",
-    "window-noise scale, here capturing roughly 95% of the total loss descent at\n",
-    "under a quarter of the step budget. Two honest qualifications. First, what the\n",
-    "rule detects is a *loss plateau* — a necessary signal, not a proof that the\n",
-    "posterior has converged; the recovery and predictive checks earlier in the\n",
-    "notebook are the kind of independent evidence a stop should be paired with.\n",
-    "Second, the $h/\\kappa$-window delay after a plateau is the rule's *nominal*\n",
-    "detection lag; clipping and the adaptive scale can move the actual hitting\n",
-    "time.\n",
-    "\n",
-    "## Where the pieces live\n",
-    "\n",
-    ":::{seealso}\n",
-    "The `DataLoader`/`parquet_source` used here merged in\n",
-    "[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698). A\n",
-    "`Trainer` that wraps this notebook's callback pattern\n",
-    "([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the\n",
-    "convergence monitor that grew out of the stopping section\n",
-    "([pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733)) are\n",
-    "under review.\n",
-    ":::"
+    "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
+    "at most a few tens of parameters. This target is hierarchical and an order of\n",
+    "magnitude larger than that, so applying it here would be using a method\n",
+    "outside the range its author documented. That is an applicability decision,\n",
+    "not a prediction about what its diagnostic would report. The ADVI results\n",
+    "above stand or fall on their own recovery and predictive checks, independently\n",
+    "of this choice."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7fe849e3",
+   "id": "a86a2db6",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -1968,7 +2184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d5d2bf6",
+   "id": "ef9e4cd7",
    "metadata": {},
    "source": [
     "## References\n",
@@ -1980,7 +2196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4906211f",
+   "id": "a98e518a",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -1988,14 +2204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "a4bfa41c",
+   "execution_count": 21,
+   "id": "91a9655e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-14T08:44:15.348613Z",
-     "iopub.status.busy": "2026-08-14T08:44:15.348529Z",
-     "iopub.status.idle": "2026-08-14T08:44:15.360535Z",
-     "shell.execute_reply": "2026-08-14T08:44:15.360148Z"
+     "iopub.execute_input": "2026-08-16T07:45:11.499940Z",
+     "iopub.status.busy": "2026-08-16T07:45:11.499792Z",
+     "iopub.status.idle": "2026-08-16T07:45:11.523090Z",
+     "shell.execute_reply": "2026-08-16T07:45:11.522484Z"
     }
    },
    "outputs": [
@@ -2003,7 +2219,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Fri, 14 Aug 2026\n",
+      "Last updated: Sun, 16 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -2022,6 +2238,7 @@
       "pymc       : 6.1.0\n",
       "pymc_extras: 0.13.1.dev4+gf8c50e5c6\n",
       "pytensor   : 3.1.3\n",
+      "scipy      : 1.18.0\n",
       "\n",
       "Watermark: 2.6.0\n",
       "\n"
@@ -2035,7 +2252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0300a50c",
+   "id": "5f5309be",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 0554d5b4b..7e9776827 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -26,27 +26,28 @@ myst:
 
 +++
 
-Financial tick data outgrows memory quickly: one month of Binance aggregated
-trades for a single liquid pair is roughly 28 million rows, and a modest
-38-symbol universe (four majors over six months plus a one-month tail of 34
-alt pairs) already exceeds 450 million rows — far more
-than `pm.Minibatch` can hold resident on a 16 GB machine. This notebook fits a
-hierarchical hurdle–Student-t model of *next-event price moves* to a dataset of
-that scale by streaming minibatches from disk with pymc-extras'
-{class}`~pymc_extras.variational.dataloader.DataLoader`. It tries to teach
-three things that only show up on real data:
+Exchanges publish per-trade archives — Binance, for instance, distributes
+aggregate trades as daily and monthly files — and a multi-symbol,
+multi-month slice of such an archive does not fit in memory. PyMC's
+{class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a
+disk-backed reader, so it cannot help once the array no longer fits. This
+notebook fits a hierarchical hurdle–Student-t model of *next-event price
+moves* by streaming minibatches from disk with pymc-extras'
+{class}`~pymc_extras.variational.dataloader.DataLoader`, on 300,000 synthetic
+rows that are generated inside the notebook. It tries to teach three things:
 
 1. **When minibatch VI is even valid.** Two acceptance gates that most
    time-series models fail — and a model class that passes both.
-2. **The full workflow at scale**: causal feature engineering, an on-disk
-   global shuffle, streaming ADVI with `total_size` rescaling, and an online
-   stopping rule.
+2. **The mechanics, end to end** on an archive small enough to rebuild here:
+   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
+   what a stopping rule has to be able to see before it can fire.
 3. **Honest reporting.** Mean-field variational inference produces some
    numbers you should trust and some you should not; with known ground truth
    we measure which are which instead of guessing.
 
-The notebook runs in about a minute on synthetic data with known ground
-truth — the streaming code is identical at any scale.
+The notebook runs in about a minute. Everything below executes on synthetic
+data with known ground truth, which is what makes the recovery checks
+possible: nothing here is a claim about any real market.
 
 +++
 
@@ -113,6 +114,7 @@ import pymc as pm
 import pytensor.tensor as pt
 
 from pymc_extras.variational.dataloader import DataLoader, parquet_source
+from scipy import stats
 ```
 
 ```{code-cell} ipython3
@@ -133,11 +135,21 @@ warnings.filterwarnings("ignore", message=r"Numba will use object mode")
 
 One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:
 the return $y_i = 10^4 \, (\log p_{i+1} - \log p_i)$ in basis points, and the
-move indicator $m_i = \mathbb{1}[y_i \neq 0]$. On real per-trade data roughly a
-third of all transitions are *exactly* zero — the price simply does not move —
-so a continuous likelihood alone would put zero probability mass on the single
-most common outcome. The fix is a hurdle: model *whether* the price moves
-separately from *how far* it moves given that it does.
+move indicator $m_i = \mathbb{1}[y_i \neq 0]$. Two consequences of indexing by
+*events* rather than by clock time are worth stating before the algebra.
+
+First, a large share of consecutive trades leave the price unchanged, because
+prices live on a discrete grid and many trades do not cross to a new level. A
+purely continuous likelihood puts zero probability mass on that outcome. The
+fix is a hurdle: model *whether* the price moves separately from *how far* it
+moves given that it does.
+
+Second, each row is a transition between consecutive observed events, not a
+fixed-duration return. So $1 - \pi$ is the probability that the next event
+leaves the price unchanged, and $\sigma$ is the Student-t scale *conditional on
+a move*. Neither is clock-time volatility: converting to a clock would
+additionally require a model of event arrival times
+{cite:p}`engle1998autoregressive`.
 
 $$
 \begin{aligned}
@@ -153,21 +165,24 @@ y_i \mid m_i = 1 &\sim \text{StudentT}(\nu,\ \mu_i,\ \sigma_i) \\
 \end{aligned}
 $$
 
-with raw covariates computed causally (from information available at trade
-$i$): the trade sign $d_i$, log notional $q_i$, trailing 60-second activity
-$a_i$, and the most recent previous nonzero return $\text{ylag}_i$. One
-precision: $q$ and $a$ are then standardized with per-symbol constants
-estimated over the full sample — a fixed affine map that changes the
-parameterization, not the conditional law, and is shared by every arm below;
-a deployed system would freeze these constants from a burn-in window. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
+The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an
+activity-like $a_i$, and $\text{ylag}_i$, the most recent previous nonzero
+return on that symbol. In the generator below they are drawn directly rather
+than engineered from a trade tape: only $\text{ylag}$ is produced
+sequentially, so it is the one covariate that could not see the future, and
+$q$ and $a$ are standardized once over the whole sample. On a real archive
+both would instead be trailing statistics built causally from the tape and
+standardized with constants frozen on a burn-in window — a construction that
+is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
 the standard way to encode intraday periodicity smoothly
 {cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
 back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
 $b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
 so a thin symbol borrows the global intraday shape where its own data run out —
 that is the hierarchical payoff we will measure. They are parameterized
-*centered*, deliberately: the familiar non-centered trick exists for data-poor
-groups, and at $10^5$–$10^8$ rows per symbol every group here is data-rich.
+*centered*, deliberately: the non-centered trick pays off when groups are
+data-poor, and the generator below gives every symbol at least several hundred
+rows.
 The degrees of freedom are
 shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
 the floor of 1 (rather than the more comfortable 2) leaves room for tails too
@@ -176,20 +191,22 @@ meaningful exactly there.
 
 The reported estimand is **event-return dispersion on the event clock**:
 $\pi_{s,h}$ together with the conditional-move 90% half-width
-$\sigma_{s,h} \cdot t^{-1}_{0.95}(\nu)$. Quantile-based dispersion stays finite
-for any $\nu > 0$; the familiar moment-based alternative does not survive this
-dataset, as we will see.
+$\sigma_{s,h} \cdot t^{-1}_{0.95}(\nu)$. A quantile is the safe choice here: it
+stays finite for any $\nu > 0$, whereas a moment-based dispersion requires
+$\nu > 2$ — a constraint the model does not impose, even though the generator
+below happens to use $\nu = 3.5$.
 
 +++
 
 ## Synthetic data with known truth
 
 Notebooks in this collection do not download data at build time, so the
-executed path uses a seeded synthetic generator that mirrors the real corpus:
-same schema, same hurdle structure, same heavy tails, twelve symbols with a
-deliberately thin tail so shrinkage is visible. Because the truth is known,
-recovery is checkable — and the likelihood, priors, and loader configuration
-are line-for-line the ones used on the real corpus.
+executed path uses a seeded synthetic generator: the schema an exchange
+archive would have after feature construction, a hurdle at exactly zero,
+heavy conditional tails, and twelve symbols with a deliberately thin tail so
+shrinkage is visible. Because the truth is known, recovery is checkable. Every
+statement below about what the fit recovers is conditional on this generator;
+none of it is a measurement of any market.
 
 ```{code-cell} ipython3
 n_symbols = 12
@@ -199,11 +216,11 @@ counts = np.array(
 thin = [10, 11]  # the two symbols with the least data
 
 truth = {
-    "kappa0": np.log(0.7 / 0.3),  # ~30% zero-move share at the baseline
+    "kappa0": np.log(0.7 / 0.3),  # logit(0.7): see the annotation below
     "c": np.array([0.25, -0.15, 0.10, 0.05]),
     "lambda_a": 0.35,
     "lambda_q": 0.20,
-    "alpha0": np.log(0.05),  # baseline sigma ~ 0.05 bp, the real-data scale
+    "alpha0": np.log(0.05),  # conditional-move scale, in basis points
     "g": np.array([0.20, -0.12, 0.08, 0.04]),
     "beta_a": 0.18,
     "beta_q": 0.12,
@@ -275,12 +292,35 @@ for s in range(n_symbols):
 
 print(
     f"{n:,} rows, zero-move share {(m == 0).mean():.1%}, "
-    f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp"
+    f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp, "
+    f"corr(a, q) {np.corrcoef(a, q)[0, 1]:.2f}"
 )
 ```
 
-The zero spike is the single most prominent feature of per-trade return data —
-here is the synthetic version of the picture that forced the hurdle:
+Each constant above is a setting, not a measurement, and it is worth reading
+them the way one would read a specification for a simulator:
+
+* `kappa0` = $\operatorname{logit}(0.7)$. If every other term in the logit were
+  zero, the move probability would be 0.7. The harmonics, the covariates and
+  the symbol effects all shift it, so the *realized* zero share is the number
+  printed above, not $30\%$ by construction.
+* `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional
+  location: the next move is generated partly from the previous nonzero one. It
+  is a synthetic dependence parameter — note that the classical bid–ask bounce
+  of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial
+  dependence, which this generator does not encode.
+* `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding
+  everything else fixed, sign $+1$ versus $-1$ differs by 0.04 bp in
+  conditional location.
+* $a$ and $q$ are constructed with a target correlation of 0.3 rather than
+  orthogonally, so the fit faces mildly collinear covariates instead of a
+  textbook design; the realized sample correlation is printed below.
+* `ylag` is generated per symbol in sequence, so row $i$ sees only the most
+  recent previously generated nonzero move. That discipline applies to the lag
+  construction; the standardization of $a$ and $q$ still uses full-sample
+  constants, an offline simplification kept for readability.
+
+With that in mind, here is the feature that forces the hurdle:
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
@@ -351,14 +391,13 @@ print(
 )
 ```
 
-On the real corpus the same check is part of the ETL script's validation: the
-head of any shard must already mix all 24 hours and nearly all symbols —
-asserted on a sampled shard at build time and spot-checked on others, rather
-than assumed. Note that the cell above is the pedagogical miniature — it sorts the
-whole table in memory, which at corpus scale would need several gigabytes for
-the keys and permutation alone. The published ETL is genuinely out-of-core:
-pass one hash-scatters rows into shards with bounded row-group appends, pass
-two sorts each shard independently, and no step ever holds the corpus.
+The printed check is the one to keep: the head of any shard must already mix
+every hour and nearly every symbol, asserted rather than assumed. Note that
+the cell above is the pedagogical miniature — it sorts the whole table in
+memory, which is exactly what one cannot do once the table stops fitting. The
+out-of-core version of the same permutation is two passes: hash-scatter rows
+into shards with bounded row-group appends, then sort each shard
+independently, so no step holds more than one shard.
 
 +++
 
@@ -368,7 +407,9 @@ With `shuffle=False` the `DataLoader` passes source blocks through verbatim —
 one block per Parquet row group, in a frozen column order — which is why the
 shards were written with `row_group_size=1024` above. The dataset size comes
 from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is
-the number of batches per epoch, as for a torch dataloader. The model reads
+`total_size // batch_size`, the number of *full* batches — which, as the cell
+below shows, is not the same as the number of blocks an epoch yields on this
+path. The model reads
 one `pm.Data` placeholder; everything derived — the Fourier basis, the
 integer symbol index — is computed inside the graph, so advancing the stream
 is a single `set_value` per step.
@@ -381,17 +422,38 @@ loader = DataLoader(
     shuffle=False,  # the shards are already globally shuffled on disk
     total_size="auto",
 )
-print(f"N = {loader.total_size:,} rows -> {len(loader):,} batches per epoch")
+
+# Count one epoch instead of inferring it: on the verbatim path a block is a row
+# group, and the last group of each shard is short, so the block count is not
+# total_size // batch_size.
+block_rows = [b.shape[0] for b in loader]
+steps_per_epoch = len(block_rows)
+assert sum(block_rows) == loader.total_size == n
+n_full = block_rows.count(1024)
+print(
+    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks per epoch "
+    f"({n_full} of 1024 rows + {steps_per_epoch - n_full} shorter), "
+    f"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}"
+)
 ```
 
-One deliberate loader semantics to be precise about: on this path nothing is
-dropped. Verbatim pass-through streams every row, and the last row group of
-each shard is simply shorter than 1024. The `total_size` rescaling copes,
-because PyMC scales the minibatch log-likelihood by `N / b` with `b` read
-from the batch actually installed, so a short block is weighted up by exactly
-its own size. Gate 1's equal-inclusion clause therefore holds for the full
-corpus, with no tempering correction to track. (Only the shuffle-buffer path
-drops a trailing partial batch; this notebook never uses it.)
+Two things that assertion pins down. First, **nothing is dropped**: verbatim
+pass-through streams every row, and the short trailing group of each shard is
+simply a shorter batch. The `total_size` rescaling copes, because PyMC scales
+the minibatch log-likelihood by `N / b` with `b` read from the batch actually
+installed, so a short block is weighted up by exactly its own size and every
+row enters the epoch exactly once, with no tempering correction to track. One
+precision on Gate 1: the replay is a fixed permutation rather than fresh
+random sampling at each step, so what holds here is equal inclusion *over an
+epoch* — single-shuffle SGD semantics — not independent uniform batches.
+(Only the shuffle-buffer path drops a trailing partial batch; this notebook
+never uses it.)
+
+Second, the printed comparison makes the earlier caveat concrete: because the
+short trailing blocks are streamed rather than dropped, an epoch yields a few
+more blocks than `total_size // batch_size`. Anything that needs a true epoch
+boundary — the stopping horizon below, for instance — should count blocks
+rather than trust the division.
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -509,6 +571,12 @@ partially pooled effect families on the symbol plate feeding two linear
 predictors, one shared tail parameter, and a single observed node whose batch
 dimension is whatever the placeholder currently holds.
 
+The loop below is the whole streaming adapter: a `pm.fit` callback that pushes
+the next block into the placeholder after each step. pymc-extras#710 proposes
+an interim wrapper around this same data-advance lifecycle while
+pymc-extras#635 develops the longer-term ADVI API, so the twenty lines here are
+deliberately the minimal version a notebook can own.
+
 ```{code-cell} ipython3
 class StreamAdvance:
     """pm.fit callback: put the next minibatch into the placeholder after each step."""
@@ -608,9 +676,11 @@ Notice also what we did *not* print: z-scores against the truth. Some would
 exceed 2, because the mean-field standard deviations in the third column are
 razor-thin — a z-score against a point truth mixes the sampling noise of this
 particular synthetic dataset with variational overconfidence along exactly
-such ridges. This is the first appearance of a theme the notebook returns to
-at full scale: a mean-field width is usable only where seed replication has
-failed to disqualify it — replication can veto a width, never certify one.
+such ridges. The stance behind that omission is a risk-management one, and it
+is worth stating plainly: material disagreement between independent-seed fits
+is enough to withhold a claim about a width, while agreement only removes the
+instability warning — it does not validate calibration. Replication can veto a
+width; it can never certify one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?
@@ -629,7 +699,7 @@ b_truth = b_truth - b_truth.mean()
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
 x = np.arange(n_symbols)
 ax.errorbar(
-    x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3, label="posterior (mean ± 2 sd)"
+    x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3, label="mean ± 2 mean-field sd"
 )
 ax.scatter(x, b_truth, marker="x", color="C1", s=60, zorder=3, label="truth")
 for t in thin:
@@ -638,19 +708,96 @@ ax.set_xticks(x)
 ax.set_xticklabels([f"S{i}" for i in x])
 ax.set_xlabel("symbol (shaded = thin: 1,800 and 900 rows)")
 ax.set_ylabel(r"$b^{(\alpha)}_s - \bar{b}^{(\alpha)}$")
-ax.set_title("Partial pooling in action: thin symbols shrink toward the group")
-ax.legend();
+ax.set_title("Symbol effects against the generating values (thin symbols shaded)")
+ax.legend()
+
+order = np.argsort(-b_sd)
+print(
+    "posterior sd of the contrast, widest first: "
+    + ", ".join(f"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)" for i in order[:3])
+    + f" ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)"
+)
 ```
 
-The thin symbols' estimates are pulled toward zero and carry visibly wider
-intervals — that is partial pooling doing exactly what it is for
-{cite:p}`gelman2006data`. On the real corpus, where the thinnest symbol has
-67,000 rows against BTC's 203 million, the same mechanism is what makes the
-tail of the symbol universe estimable at all.
+What the figure supports is narrower than the usual slogan, so it is worth
+being exact. The posterior spread is widest for the symbols with the least
+data — the printout orders them — which is the hierarchy expressing that it
+knows less about those groups {cite:p}`gelman2006data`. The point estimates
+track their generating values across the board, including the thin ones.
+
+What the figure does *not* show is shrinkage in the strict sense: that would
+need an unpooled per-symbol fit to compare against, and this notebook does not
+run one. Nor are the bars calibrated intervals — the paragraph above declined
+to treat mean-field widths that way, and that applies to their ordering too.
+And whether pooling improves held-out prediction on real data is a separate
+question again, which nothing here tests.
+
+What do those parameters mean as *objects*? For one symbol, holding the
+covariates at $a = q = 0$, the fit implies two curves over the event clock: the
+probability that the next event leaves the price unchanged, and the 90%
+half-width of the move given that one happens. Both are built from
+global-plus-symbol sums, so neither sits on the translation ridge.
+
+```{code-cell} ipython3
+s_idx, hours = 0, np.arange(24)
+Bh = hour_basis(hours)  # (24, 4)
+# .dataset: idata.posterior is a DataTree in arviz 1.x
+st = post.dataset.stack(sample=("chain", "draw"))
+
+
+def pick(name, *dims):
+    return st[name].transpose(*dims, "sample").values
+
+
+logit_pi = (
+    pick("kappa0")[None, :]
+    + pick("b_k", "symbol")[s_idx][None, :]
+    + Bh @ (pick("c", "harmonic") + pick("b_ph", "symbol", "harmonic")[s_idx])
+)
+log_sigma = (
+    pick("alpha0")[None, :]
+    + pick("b_al", "symbol")[s_idx][None, :]
+    + Bh @ (pick("g", "harmonic") + pick("b_sh", "symbol", "harmonic")[s_idx])
+)
+# transform each draw, then summarise: the median of a function, not a function of medians
+zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)
+half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick("nu"))[None, :], axis=1)
+
+t_logit = (
+    truth["kappa0"]
+    + 0.30 * z_truth["z_k"][s_idx]
+    + Bh @ (truth["c"] + 0.15 * z_truth["z_ph"][s_idx])
+)
+t_logsig = (
+    truth["alpha0"]
+    + 0.35 * z_truth["z_al"][s_idx]
+    + Bh @ (truth["g"] + 0.12 * z_truth["z_sh"][s_idx])
+)
+
+fig, axes = plt.subplots(1, 2, figsize=(9, 3.2), layout="constrained")
+for ax, post_curve, truth_curve, ylab in [
+    (axes[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), "P(next event does not move)"),
+    (axes[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth["nu"]), "90% half-width (bp)"),
+]:
+    ax.plot(hours, post_curve, color="C0", lw=2, label="posterior median")
+    ax.plot(hours, truth_curve, color="C1", ls="--", lw=1.5, label="generator truth")
+    ax.set_xlabel("UTC hour")
+    ax.set_ylabel(ylab)
+axes[0].legend(fontsize=9)
+fig.suptitle(f"SYM{s_idx:02d} on the event clock, at a = q = 0", fontsize=11);
+```
+
+The half-width is a quantile of the *outcome* distribution, not a credible band
+for the curve: it answers "how far does a move go", and it stays finite for any
+$\nu > 0$, which is why the notebook reports it instead of a conditional
+standard deviation — the Student-t variance exists only for $\nu > 2$, and
+nothing in this model guarantees that. Under this generator, the fit recovers
+both shapes; on real data the same two curves would be estimates, and would
+need out-of-sample evaluation before being used for anything.
 
 +++
 
-## Does it predict what matters?
+## In-sample posterior predictive adequacy
 
 A recovery table checks parameters; a posterior predictive check asks the
 model to reproduce the data features it exists to describe. The `CustomDist`
@@ -722,197 +869,190 @@ ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
 ax.legend(loc="lower right");
 ```
 
-Two of the three statistics sit inside their predictive 90% bands; the median
-|move| lands at the band's edge (0.8% outside at full precision — the
-3-decimal table hides it, so we say it here). Note also that this is an
-in-sample adequacy check: the evaluation batch was seen during the fit; the
-real-corpus scores later use shards the fits never touched. On real data one
-further caveat would surface
-here that the synthetic generator cannot show: real price changes are integer
-multiples of the tick size, so the continuous Student-t's residual misfit
-concentrates at |y| near one tick — visible as small stair-steps in the
-observed ECDF that the smooth predictive cannot follow.
+All three statistics sit inside their predictive 90% bands. The heading is literal: the
+evaluation batch was seen during the fit, so this is adequacy, not
+generalisation. A held-out split is the next thing to add before any of this
+is used to compare models.
 
 +++
 
-## Knowing when to stop — and how a stop rule can lie to you
-
-A streamed ELBO trace is noisy — each value is a one-batch, one-Monte-Carlo
-estimate — and eyeballing it does not scale to overnight runs. The stopping
-rule originally proposed for PyMC in
-[pymc#8384](https://github.com/pymc-devs/pymc/pull/8384)
-standardizes each loss *improvement* by a robust running scale estimate and
-feeds it to a one-sided CUSUM statistic {cite:p}`page1954continuous`, arming
-itself only after `min_steps` — which on streaming fits should be at least
-one full pass, since stopping before the model has seen every row once is
-never evidence of convergence. The trap this section demonstrates was later
-confirmed on real ADVI traces, and the revised implementation under review for
-[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733) is built on the windowed idea shown here, with horizons
-that grow over the run and a second, practical-negligibility yardstick.
-
-:::{admonition} The minibatch trap
+## What a stopping rule has to be able to see
+
+A streamed ELBO trace is noisy: every value is a one-batch, one-Monte-Carlo
+estimate, so consecutive losses differ mostly because the batch changed. That
+makes "has it converged?" a signal-detection problem, and the horizon over
+which you look is the whole game.
+
+Write the standardised *block contrast* at horizon $w$: average the $w$ losses
+before a point, average the $w$ losses after it, and divide the difference by
+the noise scale of that difference,
+
+$$
+z_w(t) = \frac{\bar L_{t-2w:t-w} - \bar L_{t-w:t}}
+              {\hat\sigma \sqrt{2/w}},
+$$
+
+with $\hat\sigma$ estimated from successive differences. Positive $z_w$ means
+the loss fell. Averaging divides the noise by $\sqrt{w}$ while a steady drift
+accumulates linearly in $w$, so the detectable drift shrinks like $w^{-3/2}$:
+the horizon does not merely smooth the picture, it sets what is visible at all.
+
+```{code-cell} ipython3
+# Stage 2 only: after the last planned optimizer change, which is where a
+# stopping decision would actually be taken.
+stage2 = np.asarray(approx.hist[6_000:], dtype=float)
+sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0
+
+
+def signed_z(losses, w):
+    """Standardised contrast between adjacent blocks of w losses."""
+    csum = np.concatenate([[0.0], np.cumsum(losses)])
+    t = np.arange(2 * w, len(losses))
+    older = (csum[t - w] - csum[t - 2 * w]) / w
+    newer = (csum[t] - csum[t - w]) / w
+    return t, (older - newer) / (sigma_hat * np.sqrt(2.0 / w))
+
+
+horizons = [(1, "per step"), (steps_per_epoch, "one epoch"), (2 * steps_per_epoch, "two epochs")]
+# control: a horizon that is NOT a whole number of passes, to test whether the
+# variance collapse below is really about epoch alignment
+control = [(steps_per_epoch + steps_per_epoch // 2, "1.5 epochs")]
+for w, label in horizons + control:
+    _, z = signed_z(stage2, w)
+    print(
+        f"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}"
+        f"   |mean|/sd {abs(z.mean()) / z.std():.2f}"
+    )
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
+for (w, label), color in zip(horizons, ["C7", "C0", "C1"]):
+    t, z = signed_z(stage2, w)
+    ax.plot(
+        t,
+        z,
+        color=color,
+        lw=0.9 if w == 1 else 1.6,
+        alpha=0.5 if w == 1 else 1.0,
+        label=f"{label} (w={w})",
+    )
+ax.axhline(0.0, color="k", lw=0.8)
+ax.set_xlabel("step within stage 2")
+ax.set_ylabel(r"standardised block contrast $z_w$")
+ax.set_title("The same trace, three horizons: what a stop rule is allowed to see")
+ax.legend(fontsize=9);
+```
+
+Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised
+contrast has unit spread — which is what the $\sqrt{2/w}$ scaling is built to
+produce — and a mean indistinguishable from zero: a single increment says
+nothing about the trend.
+
+At the two epoch-aligned horizons the spread collapses far below what
+independent noise would predict — and the control row explains why. The
+shuffle is done once on disk and replayed in the same order every epoch, so a
+window of exactly one or two passes averages over the *same rows* every time
+and the batch-composition noise, which dominates the per-step view, cancels.
+Widen the window to one and a half passes and it does not: the control sits
+at roughly twenty times the spread of its neighbours on either side, even
+though it is *wider* than the one-epoch window. Nothing but the alignment
+changed.
+
+What survives the cancellation is Monte-Carlo noise and the parameter drift
+itself, and against that much smaller yardstick the drift becomes visible —
+the ratio in the last column rises with the horizon. Two lessons sit on top of
+each other here. The horizon is not a smoothing preference, it decides whether
+there is anything to see at all; and with a fixed replay order the useful
+horizons are the ones commensurate with an epoch, which is a property of how
+the data was shuffled, not of the optimizer.
+
+:::{admonition} Why a naive rule does not merely stay silent — it fires
 :class: warning
-Successive losses are evaluated on *different batches*, so the per-step noise
-dwarfs the per-step improvement by orders of magnitude. A rule that
-standardizes *raw* increments therefore concludes "improvement is
-indistinguishable from zero" immediately upon arming, and fires a fixed
-~$h/\kappa$ steps later regardless of whether the fit converged.
+It is tempting to accumulate per-step evidence with a one-sided statistic such
+as $S \leftarrow \max(0,\ S + (\kappa - \max(z, 0)))$, stopping when $S$
+exceeds a threshold. The rectification is the problem. Under symmetric noise
+$\mathbb{E}[\max(z,0)]$ is a fraction of a standard deviation, so any
+allowance $\kappa$ above that value makes $S$ climb at a constant rate *on
+noise alone* and cross any fixed threshold after a fixed number of steps —
+whatever the data is doing. A rule built that way announces convergence on
+schedule rather than on evidence. The fix is not a better threshold but a
+wider horizon, plus a second yardstick that asks whether the remaining
+improvement is negligible relative to the reduction already achieved. That
+design is under review as
+[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this
+notebook shows the observation problem it exists to solve rather than shipping
+a second copy of it.
 :::
 
-The fix is as small as the trap is subtle: apply the identical rule to
-*window means* of the loss, where averaging beats the batch-resampling noise
-down by $\sqrt{k}$ and real improvement becomes visible again. We run both
-against the same fit, as non-fatal shadow observers, and let them disagree:
+How much would an online rule be worth here? The retrospective answer is a
+benchmark you can only compute afterwards — which is exactly why the online
+version is needed:
 
 ```{code-cell} ipython3
-class CheckLossConvergence:
-    """One-sided CUSUM on robustly standardized loss improvements (pymc#8384)."""
-
-    def __init__(self, kappa=0.25, h=20.0, halflife=200.0, min_steps=1000, z_clip=4.0):
-        self.kappa, self.h, self.halflife = kappa, h, halflife
-        self.min_steps, self.z_clip = min_steps, z_clip
-        self._lam = float(np.exp(np.log(0.5) / halflife))
-        self._prev_loss = self._prev_delta = self._scale = None
-        self._S = 0.0
-
-    def __call__(self, approx, loss, i):
-        current = float(loss[-1])
-        if not np.isfinite(current):
-            return
-        if self._prev_loss is None:
-            self._prev_loss = current
-            return
-        delta, self._prev_loss = self._prev_loss - current, current
-        if self._prev_delta is None:
-            self._prev_delta = delta
-            return
-        abs_diff, self._prev_delta = abs(delta - self._prev_delta), delta
-        if self._scale is None:
-            self._scale = abs_diff
-            return
-        sigma = self._scale * np.sqrt(np.pi) / 2.0 + 1e-12
-        self._scale = self._lam * self._scale + (1.0 - self._lam) * abs_diff
-        z = float(np.clip(delta / sigma, -self.z_clip, self.z_clip))
-        if i >= self.min_steps:
-            self._S = max(0.0, self._S + (self.kappa - z))
-        if self._S > self.h:
-            raise StopIteration(f"converged at step {i} (S={self._S:.1f})")
+# t99: the first step at which a trailing average of width one epoch has covered
+# 99% of stage 2's total smoothed reduction. Retrospective by construction.
+kernel = np.ones(steps_per_epoch) / steps_per_epoch
+smoothed = np.convolve(stage2, kernel, mode="valid")
+total_drop = smoothed[0] - smoothed.min()
+# "valid" convolution starts at the first full window, so index j of `smoothed`
+# is the trailing average ending at stage-2 step j + steps_per_epoch - 1.
+t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch - 1
+print(
+    f"stage 2 smoothed reduction: {total_drop:,.2f} nats; "
+    f"99% of it reached by step {t99:,} of {len(stage2):,} "
+    f"({t99 / len(stage2):.0%} of the stage-2 budget)"
+)
 ```
 
-```{code-cell} ipython3
-class WindowMean:
-    """Feed a stop rule the mean of every k losses instead of raw increments."""
+Two honest qualifications about any stop, this one included. What such a rule
+detects is a *loss plateau* — a necessary signal, not a proof that the
+posterior has converged; the recovery and predictive checks above are the kind
+of independent evidence a stop should be paired with. And a plateau in a noisy
+loss can only ever be established relative to a horizon: at any finite step, an
+improvement small enough is indistinguishable from none.
 
-    def __init__(self, monitor, k):
-        self.monitor, self.k = monitor, k
-        self._means = []
+## Where the pieces live
 
-    def __call__(self, approx, losses, i):
-        if len(losses) % self.k:
-            return
-        self._means.append(np.mean(losses[-self.k :]))
-        self.monitor(approx, self._means, len(self._means))
+The streaming stack this notebook exercises is being contributed to
+pymc-extras. The map is by role; each pull request's current state is on
+GitHub rather than frozen into this page.
 
+| Component | Role | Form in this notebook |
+| --- | --- | --- |
+| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size` | Imported and used directly |
+| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, while [#635](https://github.com/pymc-devs/pymc-extras/pull/635) develops the longer-term ADVI API | The `StreamAdvance` adapter above, owned by the notebook |
+| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks | The observation problem it solves is shown; the implementation is not copied here |
+| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | Quasi-Newton alternative returning a Gaussian proposal from a short optimizer run, with `pareto_k` as its own veto | Not run on this target — see below |
 
-class Shadow:
-    """Record where a stop rule WOULD fire, without actually stopping the fit."""
++++
 
-    def __init__(self, callback):
-        self.callback, self.stopped_at = callback, None
+### Choosing a path: why the fast one is not taken here
 
-    def __call__(self, approx, losses, i):
-        if self.stopped_at is None:
-            try:
-                self.callback(approx, losses, i)
-            except StopIteration:
-                self.stopped_at = len(losses)
-```
+ADVI is not the only way to fit a model on a stream. Streaming Pathfinder runs
+a short quasi-Newton trajectory on minibatch gradients — a few hundred steps
+rather than thousands — and returns a Gaussian *proposal*, importance-corrected
+against the exact full-data log-density, with the Pareto-$k$ diagnostic as its
+own veto. It is the right tool when an approximately-placed starting point is
+what you need, for example as an initialisation for MCMC.
 
-```{code-cell} ipython3
-loader2 = DataLoader(
-    parquet_source(data_dir, columns=columns),
-    batch_size=1024,
-    shuffle=False,
-    total_size="auto",
-)
-model2 = build_model(
-    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader2)), loader2.total_size
-)
-stream2 = StreamAdvance(model2, loader2)
-stream2.prime()
-steps_per_epoch = len(loader2)  # batches per epoch, torch-style
-
-k = 50
-min_windows = -(-steps_per_epoch // k)  # ceil: never arm before one full pass
-raw_shadow = Shadow(CheckLossConvergence(min_steps=steps_per_epoch))
-win_shadow = Shadow(WindowMean(CheckLossConvergence(min_steps=min_windows), k))
-
-with model2:
-    advi2 = pm.ADVI(random_seed=RANDOM_SEED)
-approx2 = advi2.fit(
-    20_000,
-    obj_optimizer=pm.adam(learning_rate=0.02),
-    callbacks=[stream2, raw_shadow, win_shadow],
-    progressbar=False,
-)
-assert (
-    raw_shadow.stopped_at is not None and win_shadow.stopped_at is not None
-), "a shadow rule never fired within the 20k-step budget; raise the budget"
-win_stop = win_shadow.stopped_at  # Shadow records raw steps for both rules
-print(
-    f"raw increments: would stop at step {raw_shadow.stopped_at:,} "
-    f"(loss there {approx2.hist[raw_shadow.stopped_at]:,.0f})"
-)
-print(
-    f"window means (k={k}): would stop at step {win_stop:,} "
-    f"(loss there {approx2.hist[win_stop]:,.0f}, final {approx2.hist[-1]:,.0f})"
-)
-```
+It is not run on this model, and the reason is a decision taken *before*
+fitting rather than a result:
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
-ax.plot(approx2.hist, lw=0.5, label="loss")
-ax.axvline(steps_per_epoch, color="C2", ls=":", label="one full pass (rule arms)")
-ax.axvline(
-    raw_shadow.stopped_at,
-    color="C3",
-    ls="--",
-    label=f"raw increments: false stop at {raw_shadow.stopped_at:,}",
-)
-ax.axvline(win_stop, color="C1", ls="--", label=f"window means: stop at {win_stop:,}")
-ax.set_ylim(-550, 900)
-ax.set_xlabel("step")
-ax.set_ylabel("negative ELBO")
-ax.set_title(
-    "Batch noise blinds the raw rule; window means restore its eyesight\n"
-    "(y clipped to the descent region)"
-)
-ax.legend(fontsize=9, loc="upper right");
-```
+from pymc.blocking import DictToArrayBijection
 
-The raw rule fires a fixed ~80 steps after arming with the loss still far from
-its floor — a false stop that a fixed-budget run would silently avoid. The
-windowed rule stops once per-window improvement falls below a quarter of the
-window-noise scale, here capturing roughly 95% of the total loss descent at
-under a quarter of the step budget. Two honest qualifications. First, what the
-rule detects is a *loss plateau* — a necessary signal, not a proof that the
-posterior has converged; the recovery and predictive checks earlier in the
-notebook are the kind of independent evidence a stop should be paired with.
-Second, the $h/\kappa$-window delay after a plateau is the rule's *nominal*
-detection lag; clipping and the adaptive scale can move the actual hitting
-time.
-
-## Where the pieces live
+n_free = DictToArrayBijection.map(model.initial_point()).data.size
+print(f"free parameters in the unconstrained space: {n_free}")
+```
 
-:::{seealso}
-The `DataLoader`/`parquet_source` used here merged in
-[pymc-extras#698](https://github.com/pymc-devs/pymc-extras/pull/698). A
-`Trainer` that wraps this notebook's callback pattern
-([pymc-extras#710](https://github.com/pymc-devs/pymc-extras/pull/710)) and the
-convergence monitor that grew out of the stopping section
-([pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733)) are
-under review.
-:::
+pymc-extras#722 states a measured operating range: non-hierarchical targets of
+at most a few tens of parameters. This target is hierarchical and an order of
+magnitude larger than that, so applying it here would be using a method
+outside the range its author documented. That is an applicability decision,
+not a prediction about what its diagnostic would report. The ADVI results
+above stand or fall on their own recovery and predictive checks, independently
+of this choice.
 
 +++
 

From 3f43203583f3c33abd3396ba9d6b67a4636b3e02 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 03:54:19 -0400
Subject: [PATCH 32/42] Credit Page (1954) where the notebook describes his
 statistic

The citation left with the executable CUSUM copy, but the mechanism
paragraph that replaced it still describes the one-sided cumulative sum.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/variational_inference/streaming_tick_data.ipynb   | 6 +++---
 examples/variational_inference/streaming_tick_data.myst.md | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index d2af56a2c..32fdbd7fe 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2026,9 +2026,9 @@
     "\n",
     ":::{admonition} Why a naive rule does not merely stay silent — it fires\n",
     ":class: warning\n",
-    "It is tempting to accumulate per-step evidence with a one-sided statistic such\n",
-    "as $S \\leftarrow \\max(0,\\ S + (\\kappa - \\max(z, 0)))$, stopping when $S$\n",
-    "exceeds a threshold. The rectification is the problem. Under symmetric noise\n",
+    "It is tempting to accumulate per-step evidence with a one-sided cumulative-sum\n",
+    "statistic {cite:p}`page1954continuous`, $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
+    "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. The rectification is the problem. Under symmetric noise\n",
     "$\\mathbb{E}[\\max(z,0)]$ is a fraction of a standard deviation, so any\n",
     "allowance $\\kappa$ above that value makes $S$ climb at a constant rate *on\n",
     "noise alone* and cross any fixed threshold after a fixed number of steps —\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 7e9776827..60e17a459 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -969,9 +969,9 @@ the data was shuffled, not of the optimizer.
 
 :::{admonition} Why a naive rule does not merely stay silent — it fires
 :class: warning
-It is tempting to accumulate per-step evidence with a one-sided statistic such
-as $S \leftarrow \max(0,\ S + (\kappa - \max(z, 0)))$, stopping when $S$
-exceeds a threshold. The rectification is the problem. Under symmetric noise
+It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
+statistic {cite:p}`page1954continuous`, $S \leftarrow \max(0,\ S + (\kappa -
+\max(z, 0)))$, stopping when $S$ exceeds a threshold. The rectification is the problem. Under symmetric noise
 $\mathbb{E}[\max(z,0)]$ is a fraction of a standard deviation, so any
 allowance $\kappa$ above that value makes $S$ climb at a constant rate *on
 noise alone* and cross any fixed threshold after a fixed number of steps —

From f78eac28146a51c5f892682e5e35cc5fd2852986 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 08:56:38 -0400
Subject: [PATCH 33/42] Give the two linkless references in this notebook a
 resolvable target

gelman2006data and hoffman2013stochastic carried neither doi nor url, so
sphinxcontrib-bibtex rendered them as plain text while every other entry
on the page links out. Both verified against CrossRef metadata (book DOI
10.1017/CBO9780511790942) and the publisher's own listing (JMLR v14).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/references.bib b/examples/references.bib
index 22443313c..13499419b 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -333,7 +333,8 @@ @book{gelman2006data
   title         = {Data analysis using regression and multilevel/hierarchical models},
   author        = {Gelman, Andrew and Hill, Jennifer},
   year          = {2006},
-  publisher     = {Cambridge university press}
+  publisher     = {Cambridge University Press},
+  doi           = {10.1017/CBO9780511790942}
 }
 @article{gelman2006multilevel,
   title         = {Multilevel (hierarchical) modeling: what it can and cannot do},
@@ -453,7 +454,8 @@ @article{hoffman2013stochastic
   year          = {2013},
   journal       = {Journal of Machine Learning Research},
   volume        = {14},
-  pages         = {1303--1347}
+  pages         = {1303--1347},
+  url           = {https://jmlr.org/papers/v14/hoffman13a.html}
 }
 @article{hoffman2014nuts,
   title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},

From 97ffbfd47b3a85df248620d2c34b68fd64b17faa Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 20:04:55 -0500
Subject: [PATCH 34/42] Fix the recorded objective, the batch geometry, and the
 provenance
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

An external review raised two blockers; both reproduce.

First, `approx.hist` is not the full-data negative ELBO the notebook said
it was. PyMC normalizes twice: MinibatchRV scales the observed logp up by
N/b, then OPVI divides the objective by the same constant because
`scale_cost_to_minibatch` defaults on. What is recorded is the batch's own
log-likelihood sum plus (b/N) KL, which scales linearly with b — measured
directly by alternating 1000- and 400-row blocks and reading the ratio of
mean recorded loss: 0.4126 against the 0.400 the batch sizes predict.
Multiplying by N/b restores the full-data scale (0.9998 against a full-data
reference over 1200 steps). The notebook now derives this, plots and
analyses `hist * N/b`, and reports 188 nats of stage-2 reduction rather
than the 0.60 that the batch scale produced.

Second, the shard geometry made blocks ragged: 288 of 1024 rows plus eight
of 636, which put a deterministic sawtooth into the loss and left
`len(loader)` matching neither the block count nor the yields. Ten shards
of 30,000 rows with row groups equal to the batch size give exactly 300
equal blocks, `len(loader) == 300`, and a loss series whose only structure
is optimization and noise. The per-step standardized contrast now has unit
spread (0.99, from 1.07) and the epoch-alignment result survives on the
clean series: the spread at epoch-aligned horizons is more than twenty
times smaller than at 1.5 epochs.

Third, provenance. The stored outputs were produced in an environment
whose pymc-extras metadata predated the merge and whose PyMC violated the
merged package's own floor. Everything here was re-executed against
pymc-extras at 8db1880 (the #698 merge commit), PyMC 6.2.0, PyTensor
3.2.4, and the install note now gives readers that exact command.

Also corrected: the CUSUM warning overstated its case (the recursion sits
at zero while improvement exceeds the allowance; the real failure is that
it cannot tell convergence from improvement too slow to resolve); the
fixed replay is described as cyclic finite-sum optimization rather than
the unbiased N/B identity; the hurdle alone does not prevent sufficient-
statistic collapse; the nu floor is a statement about support, not prior
belief (P(nu<=2) is 4e-6); the row-scale generator arrays are released
before fitting and the in-memory preprocessing is labelled as such; the
shrinkage claim in the model section is withdrawn; and the predictive
check is described as one batch, one step ahead.

Cold runs in the pinned environment: 11.0 / 10.0 / 9.6 s, peak RSS 619 MB.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1317 +++++++++--------
 .../streaming_tick_data.myst.md               |  259 ++--
 2 files changed, 864 insertions(+), 712 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 32fdbd7fe..8c2569aec 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f42fa55c",
+   "id": "a3e74acf",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,12 +18,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e148f2cf",
+   "id": "a836a490",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
-    "aggregate trades as daily and monthly files — and a multi-symbol,\n",
-    "multi-month slice of such an archive does not fit in memory. PyMC's\n",
+    "aggregate trades as daily and monthly files at\n",
+    "[data.binance.vision](https://data.binance.vision). Widen a pull across\n",
+    "symbols and months and the feature matrix can outgrow the RAM of an ordinary\n",
+    "workstation; at that point in-memory minibatching stops being an option. PyMC's\n",
     "{class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a\n",
     "disk-backed reader, so it cannot help once the array no longer fits. This\n",
     "notebook fits a hierarchical hurdle–Student-t model of *next-event price\n",
@@ -36,9 +38,11 @@
     "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
     "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
     "   what a stopping rule has to be able to see before it can fire.\n",
-    "3. **Honest reporting.** Mean-field variational inference produces some\n",
-    "   numbers you should trust and some you should not; with known ground truth\n",
-    "   we measure which are which instead of guessing.\n",
+    "3. **Honest reporting.** With the truth known we can watch the posterior means\n",
+    "   land on it while the mean-field standard deviations stay far too narrow to\n",
+    "   cover it. Which widths could be trusted in general is a calibration\n",
+    "   question that needs many simulated datasets; this notebook fits one, so it\n",
+    "   withholds width claims rather than certifying them.\n",
     "\n",
     "The notebook runs in about a minute. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
@@ -47,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8f56a48",
+   "id": "23d07b3d",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -77,17 +81,21 @@
     "likelihood with per-cell means and variances collapses to per-cell\n",
     "$(\\sum y, \\sum y^2, n)$, so one linear scan computes the exact posterior\n",
     "inputs and \"streaming inference\" degenerates into a glorified `groupby` —\n",
-    "valid, but theater. What provably breaks the collapse: a Student-t likelihood,\n",
-    "a mixture (the hurdle below), and row-level continuous covariates through\n",
-    "nonlinear links.\n",
-    "\n",
-    "The model below passes both gates, and each ingredient that makes it pass is\n",
-    "also substantively motivated by the data."
+    "valid, but theater. A hurdle alone does not rescue it — an observed Bernoulli\n",
+    "plus a Normal component still reduces to $(n_0, n_1, \\sum y, \\sum y^2)$. What\n",
+    "does break the collapse is the combination used below: a Student-t component\n",
+    "with an unknown $\\nu$, and row-level continuous covariates entering through\n",
+    "nonlinear links, so no fixed-dimensional summary of the rows suffices.\n",
+    "\n",
+    "The model below passes both gates. Each ingredient that gets it there is also\n",
+    "the standard choice for the feature it addresses, which is the argument for\n",
+    "it — not the synthetic data, which was written to contain those features in\n",
+    "the first place."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b638b51e",
+   "id": "38d7bcfb",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -95,27 +103,37 @@
     "\n",
     ":::{note}\n",
     "The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into\n",
-    "pymc-extras after the v0.14.0 release. Until the next release, install\n",
-    "pymc-extras from `main`:\n",
-    "`pip install \"pymc-extras @ git+https://github.com/pymc-devs/pymc-extras\"`.\n",
+    "pymc-extras after the v0.14.0 release, so it is not in a published version yet\n",
+    "and `pip install pymc-extras` will not provide it. The outputs stored in this\n",
+    "notebook were produced against the merge commit itself, which is also the\n",
+    "install line to use until the next release:\n",
+    "\n",
+    "```\n",
+    "pip install \"pymc-extras @ git+https://github.com/pymc-devs/pymc-extras@8db1880\"\n",
+    "```\n",
+    "\n",
+    "That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull\n",
+    "PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the\n",
+    "bottom of this page. Once the module is released, the plain\n",
+    "`pip install pymc-extras` above is enough.\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a1cfdc90",
+   "id": "fba94140",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:52.448992Z",
-     "iopub.status.busy": "2026-08-16T07:44:52.448587Z",
-     "iopub.status.idle": "2026-08-16T07:44:55.456777Z",
-     "shell.execute_reply": "2026-08-16T07:44:55.456026Z"
+     "iopub.execute_input": "2026-08-17T01:01:18.953070Z",
+     "iopub.status.busy": "2026-08-17T01:01:18.952945Z",
+     "iopub.status.idle": "2026-08-17T01:01:20.497978Z",
+     "shell.execute_reply": "2026-08-17T01:01:20.497557Z"
     }
    },
    "outputs": [],
    "source": [
-    "import json\n",
+    "import gc\n",
     "import logging\n",
     "import os\n",
     "import tempfile\n",
@@ -137,13 +155,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "08cb2965",
+   "id": "0cbf2089",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:55.458842Z",
-     "iopub.status.busy": "2026-08-16T07:44:55.458466Z",
-     "iopub.status.idle": "2026-08-16T07:44:55.464082Z",
-     "shell.execute_reply": "2026-08-16T07:44:55.463372Z"
+     "iopub.execute_input": "2026-08-17T01:01:20.499226Z",
+     "iopub.status.busy": "2026-08-17T01:01:20.499080Z",
+     "iopub.status.idle": "2026-08-17T01:01:20.501979Z",
+     "shell.execute_reply": "2026-08-17T01:01:20.501634Z"
     }
    },
    "outputs": [],
@@ -163,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8a21458",
+   "id": "c94473fe",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -173,11 +191,12 @@
     "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. Two consequences of indexing by\n",
     "*events* rather than by clock time are worth stating before the algebra.\n",
     "\n",
-    "First, a large share of consecutive trades leave the price unchanged, because\n",
-    "prices live on a discrete grid and many trades do not cross to a new level. A\n",
-    "purely continuous likelihood puts zero probability mass on that outcome. The\n",
-    "fix is a hurdle: model *whether* the price moves separately from *how far* it\n",
-    "moves given that it does.\n",
+    "First, prices move on a discrete grid, so an exact-zero return is not a\n",
+    "measure-zero event the way it is for a continuous variable: consecutive trades\n",
+    "can and do leave the price where it was. A purely continuous likelihood puts\n",
+    "zero probability mass on that outcome, whatever share of the rows it turns out\n",
+    "to occupy. The fix is a hurdle: model *whether* the price moves separately\n",
+    "from *how far* it moves given that it does.\n",
     "\n",
     "Second, each row is a transition between consecutive observed events, not a\n",
     "fixed-duration return. So $1 - \\pi$ is the probability that the next event\n",
@@ -213,16 +232,19 @@
     "{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
     "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
     "$b_s \\sim \\mathcal{N}(0, \\tau)$ are partially pooled {cite:p}`gelman2006data`,\n",
-    "so a thin symbol borrows the global intraday shape where its own data run out —\n",
-    "that is the hierarchical payoff we will measure. They are parameterized\n",
+    "so a thin symbol is pulled toward the global intraday shape where its own data\n",
+    "run out. They are parameterized\n",
     "*centered*, deliberately: the non-centered trick pays off when groups are\n",
     "data-poor, and the generator below gives every symbol at least several hundred\n",
     "rows.\n",
     "The degrees of freedom are\n",
     "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
-    "the floor of 1 (rather than the more comfortable 2) leaves room for tails too\n",
-    "heavy to carry a finite variance — and the estimand below is chosen to stay\n",
-    "meaningful exactly there.\n",
+    "the floor of 1 rather than 2 leaves the support open to tails too heavy to\n",
+    "carry a finite variance. Be precise about what that buys: under\n",
+    "$\\eta \\sim \\mathcal{N}(5, 1)$ the prior puts only about $4 \\times 10^{-6}$ of\n",
+    "its mass on $\\nu \\le 2$, so this is a statement about support, not a serious\n",
+    "prior belief in infinite variance. The estimand below is chosen so that the\n",
+    "question does not arise at all.\n",
     "\n",
     "The reported estimand is **event-return dispersion on the event clock**:\n",
     "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
@@ -234,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a25e5a85",
+   "id": "402fcf59",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -242,8 +264,9 @@
     "Notebooks in this collection do not download data at build time, so the\n",
     "executed path uses a seeded synthetic generator: the schema an exchange\n",
     "archive would have after feature construction, a hurdle at exactly zero,\n",
-    "heavy conditional tails, and twelve symbols with a deliberately thin tail so\n",
-    "shrinkage is visible. Because the truth is known, recovery is checkable. Every\n",
+    "heavy conditional tails, and twelve symbols whose row counts span two orders\n",
+    "of magnitude, so the hierarchy has both data-rich and data-poor groups to work\n",
+    "with. Because the truth is known, recovery is checkable. Every\n",
     "statement below about what the fit recovers is conditional on this generator;\n",
     "none of it is a measurement of any market."
    ]
@@ -251,13 +274,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8919c387",
+   "id": "c12ebcd5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:55.465905Z",
-     "iopub.status.busy": "2026-08-16T07:44:55.465740Z",
-     "iopub.status.idle": "2026-08-16T07:44:55.469946Z",
-     "shell.execute_reply": "2026-08-16T07:44:55.469398Z"
+     "iopub.execute_input": "2026-08-17T01:01:20.502928Z",
+     "iopub.status.busy": "2026-08-17T01:01:20.502874Z",
+     "iopub.status.idle": "2026-08-17T01:01:20.505629Z",
+     "shell.execute_reply": "2026-08-17T01:01:20.505272Z"
     }
    },
    "outputs": [],
@@ -299,13 +322,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0da47767",
+   "id": "819fa9a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:55.471261Z",
-     "iopub.status.busy": "2026-08-16T07:44:55.471154Z",
-     "iopub.status.idle": "2026-08-16T07:44:55.765237Z",
-     "shell.execute_reply": "2026-08-16T07:44:55.764565Z"
+     "iopub.execute_input": "2026-08-17T01:01:20.506458Z",
+     "iopub.status.busy": "2026-08-17T01:01:20.506399Z",
+     "iopub.status.idle": "2026-08-17T01:01:20.656446Z",
+     "shell.execute_reply": "2026-08-17T01:01:20.656067Z"
     }
    },
    "outputs": [
@@ -373,7 +396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7faf9ea",
+   "id": "8a4f6dad",
    "metadata": {},
    "source": [
     "Each constant above is a setting, not a measurement, and it is worth reading\n",
@@ -393,7 +416,7 @@
     "  conditional location.\n",
     "* $a$ and $q$ are constructed with a target correlation of 0.3 rather than\n",
     "  orthogonally, so the fit faces mildly collinear covariates instead of a\n",
-    "  textbook design; the realized sample correlation is printed below.\n",
+    "  textbook design; the realized sample correlation is in the printout above.\n",
     "* `ylag` is generated per symbol in sequence, so row $i$ sees only the most\n",
     "  recent previously generated nonzero move. That discipline applies to the lag\n",
     "  construction; the standardization of $a$ and $q$ still uses full-sample\n",
@@ -405,27 +428,27 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d6139ddc",
+   "id": "5299a5f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:55.767008Z",
-     "iopub.status.busy": "2026-08-16T07:44:55.766851Z",
-     "iopub.status.idle": "2026-08-16T07:44:56.568182Z",
-     "shell.execute_reply": "2026-08-16T07:44:56.567661Z"
+     "iopub.execute_input": "2026-08-17T01:01:20.657401Z",
+     "iopub.status.busy": "2026-08-17T01:01:20.657343Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.041312Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.041014Z"
     }
    },
    "outputs": [
     {
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X6+kLq77EuetCO/SauumLv0YI6PU0T1u0lN6rRl1fgvXFLR2BTQAAAACAd+k3cJ8+ffwCZSo/pcBQsPm99LtYv8PVkSh//PGHXdZv1mBUGUbzBDnlv1QmURkIoTqRlXXlDs5oriIN1o1EHYMada9tDxa4cwJkyi5Q8Er0e1sDTBXAi0Qdx+5AmbIuLrvsMht4ClZpRx2KysbS6P1ACjA6ZRgVaHSo81HTTITq/ygJbaemrVBGnKhDVxkRKmUWDWUDurNzlCWSCMqoaN++fUzHqfo31OnrCDVwWhkc7kCZggPK7gv2npUppKwyZZTpNbSsTBxlvkVbQUmDuPV5qrP+/vvvN0ceeeRuHeXuDJhBgwb5BTA0MFrPCyz7ps50BTc1nYZT7k0d/ir/puMukQKz/JIdLFM74gTRRYFmlZhLdOaaE8xwqHxfMO7gaGB5QTf33zR/o5s+I31WoiCT2sV0BpuScZxpgIG7DKP7uFGbHiyLLlR7XFJOGVtlfKntPu2003YL/s+ZM8d3X+/DHShT4Ev9sGoXA5+nYKf6Xp0Auqa10X689957gwaQAgNboYJl+hzcbZdDgcpQwTJ9Fu7jLfD1HI8++qjvvvp9ddxpqqRQAyIUtNM1WNnY0Qqcc0+ZigTLkMmSVoYxG+kL8DPPPBPVY0Olb0dLX8x1MXGi+uHoi7Ya7NmzZ9sv95GoEVb2nbZRNck10g0AAAAAADfNBaMOO4dKW4UbIKvSiipfqJH6zpxiCqjoecFK4mnU+pNPPmk7NJ3OQo2CV4ZP4OP1e/ehhx7yLStYoTJYkarJKBCkTJ5IlCWn96vsIXWCiuY5UxWWcCXnlJHmLoWl7Xn66afNUUcdFfI56iBWNpnetzsQKc7ca+pADQyWJSMTRUFEfWZOiS5lCEQbLHNvnzKlIs3jE61TTjklpserw9rd2XzYYYcFnZNImSGPP/64b1nBxyFDhoQMfKlz+frrr7eZGPfcc48v40zHSbTlKtVxr2CPgnmBc2SJ5vXTnGai406lz9zHiaYZ0bEZSJ3dOoZUXUlzKDmfn449BRuDZTaVlLLf3Nur7MxkUYBFwUtnXje9T3Xwax8mkj53d6C3devWNrswGAXlFVRVP93y5cttQFlz87kpSKQsHmebNSdhYDvkZMQqEBNLdmKiJes4c8+xpdJ/7mCZ+h6V0ZtsOt/UBivIHardd45ffZZ6H+5jW32+hx9+eNDnKRta2Vhqo51pf7QfVZoy8PNU26Jj1iknqmCZPvdg3NdYZfQ5wbjA7Eq3KVOm+JX7DTZPmLKYdcw6dF3WNScc9Q/rpmt2NFllzmANd4ayBsAAmcx7ObMeFm8ALFr6UqAvXu5AmVJqNRpPIx70RUU11DVqw10nWCModEGK5ouk0oD1Q0ajBAEAAAAAcFPlFHWgujv/lFEWibIlVL3EnTHgzhIJNreUMhocKk+o7B33HOTO3Druzm09J9RIfbdoAmXu4Ijm43GP3le2WzjqlHXPh3bNNdeEDZS5qVNX5djSSZ3g7myncePG2XJlkaizVp2xjtNPPz3qztVEUhaEssUc6pBXKc9gcyrpeHY6mMuXL28DtdFkiCljzt0ZreM5lv4hHbvBAmWBAs8TBeqCBTDcNHeSu4yn3l+o+aFKysm2dM7XeCsTKQtTQQnn9sgjj9i2RcEIZWQ6QSW1IeoHCww8xUvH7csvv+z3f+Gy8RQw0nntUCaRymu697mymJysKmXB7bXXXn6v5/TVKYMrmmzYZPLqcZYo6iuNpt3X9rsDTgrohQqUOdReu48FUSA82HXQnZjgLpkYyP03lTx2BoAo0OUu7RnqOXqtwOwuJ1M48DOMlq73sUxr5B7coozycHP7AV5HsCyWnVWqlGnYsKEdQaIvS5q0WNlmSk8NbCzjoS8L7ui/UtyV9qzRVQpw6cucLmTKJtMIMHcDpjIR7nrCgVQOQ+sS/QiIZyJNAAAAAEB20tw67t+Wmgcm2rJhyjJSx3ewkfPBnHjiiTbDyaGydO7SVsoscf9G1mP1nGRQhomySBxOllkwyhpwB9P0vi+//HKTadRJ7FDnceB8XpGyyhRMcH9+qaJyX+5Aq0odvvjiiyFLX6p8okPHTzTBVoc7UKD5x9QhHA1tizJPIlEgeMyYMb5lZTNGO7WHMn7c52bg3HnxUMaee54uBcvipRKkysZ0biqFp8/G6dzX51hYWGgztZS9lEjqxL/ppptsUN5dwi5Se6L+OGfqE7VFPXr0sNmGynbV35x2QMHawNKtasucoIyCgtWqVfP7u/6mNlJBLAXfdf6554hLJK8eZ4mi7VOwLBru7dfzdI2LhtbvzjbW/nQfTw53gF3B1cAsYtHzfv31V795JJXJGOna6f5/XbMCjylxTxUkTpZbMrjbBb2uu7QskGkIlsXg2muvtaOsNGpJFztF/NWQKesrloh7OBpB406HVoOnAFiooJZGPWi73Be+UKUilbHmXLQ16ivSiAkAAAAAQG5yl4BSR2LXrl1jer57zh79znVnYgSjwaDu8m4fffSRraiijmN3kEOd0ZpTJpncWSEq/xguu8opS+ZklKQjuypeGhDsnoNOA2zDZU6p09Xd4a7nJyKIEguVv1TGlrP/NdeQ5hbTAONgNL+Pu0M7liwLCSyzNm3atKiep5KW0WRiac489/YpOzHaY0nZge5MGp1vKjGXCE6Wl/u1kk3BSPW9aWC6O5s0EdR2uOesUp+bM5dYJMqYVVaYMt5U7k99d8pWVOaYAsYqcaeKT+7AhQIyTluqOdHUFxcYOFRmp0oYav3q/9O0KeprVHAqUZ+j14+zRFHgKJoSpDrG3AFJfTbRHtvKSnUPBtEx6sy76RZYGjFY4EslC53PQ0Hi5s2b+80/Fuw56tt1X5eClWCUwLYwWAZcogTuu8B2A8gknpqzTCnLKj2oE19lDPRlSyOzconqg7vTgDViQQ1mOEpTV0q3U9dWF2N9AQisq67G26khq9FNwYJq7oum6nk7j1Emmy7WAAAAAIDs5x7trs7+wJL/7mBKsMBK4Ch6Bcs0t0m4DkjNQabsDacEo7skohMQUem8eMrAqRNv3rx5tryVyjuq1GLg9rs7+pzf2cEEBkw6duxoMpE6+lU9x5kTbuHChbbMV6hOWM1D5y6zpeemkvo1lHmkz88J5j766KN+GRmBAufRUQDDnf0Q6XgODNpEU6pSgs3VF0xgZ3usc1qpbNrkyZP91peIwJaCCm6J6KNTVpa216HzXZ+pzicnu0ztxwMPPGADUgqCuoO5JaX56oqKivzmqFKZR1WQivY8UblGzVmlecu0req/U3BGAQ7N2xSYleecUyoLqkw0rcPx6quv2td32jbNs6c2UsemSl8qYKjAnubI01xZieDV4yyRwbJU7Ae1NRrM4V6fAm5umotQ/bnOOaTA10UXXRSynKITJFO7qypmznMCaf+7S/+6g2uBbY+OTSczVAMcNCensp81wCFRSR/B2gWn7xnIRGkPlika/s4779iLgb6QuWlURbAJENUIOSNBVC82mZOLptpXX33lu6+LqJPmHY5GgSjtWxOUii7WKpmhUW2hRDOiQF8cnYmKtR0EywAAAAAgN7hLr6kzW/NAxUODYiNR1RZ1KPft29f3um79+vWzj4mVMqFUIUZZHIsWLYrpuc48RMEEZsu5M9IyjTJeFIh0BtCqzGKoYJm7BKM6g6Odoy0RdEwoUOaey0fHizvTI5jADJh451uK5niWSIOfHe456yXWTD136dBg6yupwHNQQe14heur+vnnn83AgQPNjBkz7LKythScUmAiXLA9EgX7VaLTTcH4bt26xbwuZZZFOt5E55MTKNF7cAdOlemoAK9ooLv6RZU16wRrtW36PwXuNQWM098XL68eZ4kS7TES734IDN6G2g+aVkfXHfnpp59sJqyCtA53MMxpb9u3b2+DpxrIoeNH54D7uud+jgJeoeZn0yCCm2++2dx2221+gTbddMxpnjMN8DjggANs8C+e4FngABZtO5Cp0hYs05dVfdGNNFluMGpclJYsOqndZQszmUaluCfI1SiEaNKHndR+98VTI1ACv4CoHnfv3r3DrkdfjJ2JTvVF35lINdJknwAAAACA7JHo+U3c5QrDOfXUU+1oe/eofdHv22jmfgr0/fff29JpKptWEuHKwAVmnVWtWtVkqurVq5sTTjjBlr8UzRelDuDAjAF18ruzBjWwVlkzqaBsCn2W7qwQzbemEnaJCm5FK3A+oFCinSc+8HwLNu9aOIGVhRJ1/gYe/4nMRglGnfcKEl1//fXm66+/9mWpqFSiMsNKYsSIEWbQoEF+/6c+xWgGp8ebGCDKOLvuuuv8/q4qTk520A033OALlDkD57V9ev8KCqs9VIKBAi/x8upxlijRvp9U7QdlfTnBMmUaKgisYJizrAzCwGCZEiJ0HkyYMMEXHHMHy9zZaCp3rMBaKEqs0LYpw9FdxUyDQMaOHWtvonVoqh6VElVZUHcGZDQCS3m6M4+BTFMmXSPUNIFkSSesVHqsgmQqC6GbGptw6faZQjW33fQeo6XHqjFzygUErssJlgVeoAOpEXWCZXvvvXfExwMAAAAAso87GKC5TwLn2omVfo9Gy92p5x4gGisFdpSFFNjh36hRI9s5rU5sBTOULaNR+I4vvvhitzKS0Yi1g9Frzj33XF+wTJ+BApaa9iFw6giH9tlZZ52Vsu1TwEQDg93zjjnzssd6TKmcXjzzy7nn5AvHfVwlU7g55uIRuI+CnZvJeE191sreckptaqC9phuJNbtMpec0UN+9fzSIPJoAa0npte69917fAAEFvtzBGAWhFcQXBZo1QCBUBanBgwfbZQVcEhEs8+pxliheO98Cs3MV6HKCZcrwco4RZey55xjTZ+0Ey/QcZSY6gWN3kkWo7F83lV5UBqWyK3UcBSstrEwwXfd0UwU3lQdVAke0Aq+x8ZRKBnIuWKYT8KqrrvILlOnLqU5cZS/pi4/SriM58cQ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",
       "text/plain": [
-       "<Figure size 1600x700 with 1 Axes>"
+       "<Figure size 800x350 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 723,
-       "width": 1750
+       "height": 361,
+       "width": 869
       }
      },
      "output_type": "display_data"
@@ -450,7 +473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc9bc408",
+   "id": "666a1baf",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -471,13 +494,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "de936b5d",
+   "id": "7c5755a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:56.570202Z",
-     "iopub.status.busy": "2026-08-16T07:44:56.570052Z",
-     "iopub.status.idle": "2026-08-16T07:44:56.878662Z",
-     "shell.execute_reply": "2026-08-16T07:44:56.877604Z"
+     "iopub.execute_input": "2026-08-17T01:01:21.042327Z",
+     "iopub.status.busy": "2026-08-17T01:01:21.042251Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.201500Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.201088Z"
     }
    },
    "outputs": [
@@ -514,11 +537,13 @@
     "\n",
     "key = splitmix64(np.arange(n, dtype=np.uint64))\n",
     "order = np.argsort(key, kind=\"stable\")\n",
-    "n_shards = 8\n",
+    "n_shards, BATCH = 10, 1_000\n",
+    "# Row groups are the loader's batches on the shuffle=False path, so the geometry is\n",
+    "# chosen to divide exactly: 10 shards x 30,000 rows, 30 groups of 1,000 each.\n",
+    "assert n % n_shards == 0 and (n // n_shards) % BATCH == 0\n",
     "for i in range(n_shards):\n",
     "    part = table.take(order[i::n_shards])\n",
-    "    # Row groups are the loader's batches on the shuffle=False path, so size them here.\n",
-    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"), row_group_size=1024)\n",
+    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"), row_group_size=BATCH)\n",
     "\n",
     "head = pq.read_table(os.path.join(data_dir, \"shard_000.parquet\")).slice(0, 10_000)\n",
     "print(\n",
@@ -529,32 +554,68 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26523892",
+   "id": "2e8a3c25",
    "metadata": {},
    "source": [
     "The printed check is the one to keep: the head of any shard must already mix\n",
-    "every hour and nearly every symbol, asserted rather than assumed. Note that\n",
-    "the cell above is the pedagogical miniature — it sorts the whole table in\n",
-    "memory, which is exactly what one cannot do once the table stops fitting. The\n",
-    "out-of-core version of the same permutation is two passes: hash-scatter rows\n",
-    "into shards with bounded row-group appends, then sort each shard\n",
-    "independently, so no step holds more than one shard."
+    "every hour and nearly every symbol, asserted rather than assumed.\n",
+    "\n",
+    "Be clear about what this cell is and is not. It is the pedagogical miniature:\n",
+    "it builds the whole table, the whole key array and the whole permutation in\n",
+    "memory, which is exactly what one cannot do once the data stops fitting. What\n",
+    "follows it — the inference — is genuinely disk-backed, but the preprocessing\n",
+    "above is not. The out-of-core version of the same permutation is two passes:\n",
+    "hash-scatter rows into shards with bounded row-group appends, then sort each\n",
+    "shard independently, so no step holds more than one shard.\n",
+    "\n",
+    "So that the fit really does run against disk rather than against arrays still\n",
+    "resident from the generator, the row-scale objects are released first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9646b7df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T01:01:21.202499Z",
+     "iopub.status.busy": "2026-08-17T01:01:21.202431Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.236039Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.235699Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_x1npjld2\n"
+     ]
+    }
+   ],
+   "source": [
+    "del table, key, order, y, m, d, q, a, ylag, sym, hour, logit_pi, log_sigma, t_draw, sigma\n",
+    "gc.collect()\n",
+    "print(\"row-scale generator arrays released; the fit reads from\", data_dir)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1b71a43a",
+   "id": "ae464a8d",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
     "\n",
     "With `shuffle=False` the `DataLoader` passes source blocks through verbatim —\n",
     "one block per Parquet row group, in a frozen column order — which is why the\n",
-    "shards were written with `row_group_size=1024` above. The dataset size comes\n",
-    "from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is\n",
-    "`total_size // batch_size`, the number of *full* batches — which, as the cell\n",
-    "below shows, is not the same as the number of blocks an epoch yields on this\n",
-    "path. The model reads\n",
+    "shards above were written with `row_group_size` equal to the batch size, and\n",
+    "why the row counts were chosen to divide exactly. That is worth doing\n",
+    "deliberately: on this path the loader hands you the row groups it finds, so a\n",
+    "ragged geometry gives ragged batches, `len(loader)` (which is\n",
+    "`total_size // batch_size`) stops matching the number of blocks an epoch\n",
+    "yields, and — as the fitting section shows — the recorded loss picks up a\n",
+    "deterministic sawtooth that has nothing to do with convergence. The model\n",
+    "reads\n",
     "one `pm.Data` placeholder; everything derived — the Fourier basis, the\n",
     "integer symbol index — is computed inside the graph, so advancing the stream\n",
     "is a single `set_value` per step."
@@ -562,14 +623,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "4ec1cde5",
+   "execution_count": 8,
+   "id": "3f4c588b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:56.880407Z",
-     "iopub.status.busy": "2026-08-16T07:44:56.880221Z",
-     "iopub.status.idle": "2026-08-16T07:44:56.960781Z",
-     "shell.execute_reply": "2026-08-16T07:44:56.960176Z"
+     "iopub.execute_input": "2026-08-17T01:01:21.237036Z",
+     "iopub.status.busy": "2026-08-17T01:01:21.236983Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.282356Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.281967Z"
     }
    },
    "outputs": [
@@ -577,7 +638,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N = 300,000 rows -> 296 blocks per epoch (288 of 1024 rows + 8 shorter), conserving 300,000 rows; len(loader) = 292\n"
+      "N = 300,000 rows -> 300 blocks of 1000 per epoch, conserving 300,000 rows; len(loader) = 300\n"
      ]
     }
    ],
@@ -585,59 +646,57 @@
     "columns = [\"y_bp\", \"m\", \"d\", \"q_std\", \"a_std\", \"ylag_bp\", \"hour\", \"sym\"]\n",
     "loader = DataLoader(\n",
     "    parquet_source(data_dir, columns=columns),\n",
-    "    batch_size=1024,\n",
+    "    batch_size=BATCH,\n",
     "    shuffle=False,  # the shards are already globally shuffled on disk\n",
     "    total_size=\"auto\",\n",
     ")\n",
     "\n",
-    "# Count one epoch instead of inferring it: on the verbatim path a block is a row\n",
-    "# group, and the last group of each shard is short, so the block count is not\n",
-    "# total_size // batch_size.\n",
+    "# Count one epoch rather than inferring it. With a divisible geometry every block\n",
+    "# is the same size, so the count and len(loader) agree — which is what the rest of\n",
+    "# the notebook relies on.\n",
     "block_rows = [b.shape[0] for b in loader]\n",
     "steps_per_epoch = len(block_rows)\n",
-    "assert sum(block_rows) == loader.total_size == n\n",
-    "n_full = block_rows.count(1024)\n",
+    "assert set(block_rows) == {BATCH}\n",
+    "assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH\n",
+    "assert len(loader) == steps_per_epoch\n",
     "print(\n",
-    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks per epoch \"\n",
-    "    f\"({n_full} of 1024 rows + {steps_per_epoch - n_full} shorter), \"\n",
+    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch, \"\n",
     "    f\"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4cd2cf91",
+   "id": "d3a07796",
    "metadata": {},
    "source": [
     "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
-    "pass-through streams every row, and the short trailing group of each shard is\n",
-    "simply a shorter batch. The `total_size` rescaling copes, because PyMC scales\n",
-    "the minibatch log-likelihood by `N / b` with `b` read from the batch actually\n",
-    "installed, so a short block is weighted up by exactly its own size and every\n",
-    "row enters the epoch exactly once, with no tempering correction to track. One\n",
-    "precision on Gate 1: the replay is a fixed permutation rather than fresh\n",
-    "random sampling at each step, so what holds here is equal inclusion *over an\n",
-    "epoch* — single-shuffle SGD semantics — not independent uniform batches.\n",
-    "(Only the shuffle-buffer path drops a trailing partial batch; this notebook\n",
-    "never uses it.)\n",
-    "\n",
-    "Second, the printed comparison makes the earlier caveat concrete: because the\n",
-    "short trailing blocks are streamed rather than dropped, an epoch yields a few\n",
-    "more blocks than `total_size // batch_size`. Anything that needs a true epoch\n",
-    "boundary — the stopping horizon below, for instance — should count blocks\n",
-    "rather than trust the division."
+    "pass-through streams every row exactly once per epoch. (Ragged geometry would\n",
+    "still lose nothing — PyMC reads `b` from the batch actually installed, so a\n",
+    "short block is weighted up by its own size — but it would cost the clean\n",
+    "diagnostics below, which is why the shards divide.) Only the shuffle-buffer\n",
+    "path drops a trailing partial batch, and this notebook never uses it.\n",
+    "\n",
+    "Second, a precision about Gate 1 that matters for how the fit should be\n",
+    "described. The permutation is drawn once, on disk, and then replayed in the\n",
+    "same order every epoch. Each row is included exactly once per pass, but the\n",
+    "batch at step $t$ is a deterministic function of $t$, not a fresh uniform\n",
+    "draw — so the individual updates are not the conditionally unbiased stochastic\n",
+    "gradients of the $N/B$ identity. This is single-shuffle, cyclic finite-sum\n",
+    "optimization, and it is the arrangement the epoch-scale diagnostics later in\n",
+    "the notebook exploit."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "0ed5ceea",
+   "execution_count": 9,
+   "id": "3c899c43",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:56.962480Z",
-     "iopub.status.busy": "2026-08-16T07:44:56.962321Z",
-     "iopub.status.idle": "2026-08-16T07:44:56.970474Z",
-     "shell.execute_reply": "2026-08-16T07:44:56.969832Z"
+     "iopub.execute_input": "2026-08-17T01:01:21.283360Z",
+     "iopub.status.busy": "2026-08-17T01:01:21.283305Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.287368Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.286996Z"
     }
    },
    "outputs": [],
@@ -735,7 +794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1102ea5f",
+   "id": "243c580e",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -745,22 +804,24 @@
     "the hurdle's two logs are written with `softplus` —\n",
     "$\\log \\pi = -\\operatorname{softplus}(-x)$,\n",
     "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$ — which is exact and stable at both\n",
-    "tails. The exact float comparison `value != 0` is safe here because the zeros\n",
-    "are *structural* — a transition either changes the price or it does not, and\n",
-    "the smallest genuine move on any listed tick grid sits dozens of orders of\n",
-    "magnitude above the float32 flush-to-zero threshold."
+    "tails. The exact float comparison `value != 0` is safe here because the zeros are\n",
+    "*structural*: the generator writes an exact 0.0 when the hurdle says the price\n",
+    "did not move, and every nonzero draw is many orders of magnitude above the\n",
+    "float32 subnormal range. On real data the same comparison is safe as long as\n",
+    "returns are computed so that \"no move\" produces an exact zero rather than a\n",
+    "rounding artefact — worth checking once, in the feature code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "949c1f1f",
+   "execution_count": 10,
+   "id": "fdb67df2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:56.972036Z",
-     "iopub.status.busy": "2026-08-16T07:44:56.971904Z",
-     "iopub.status.idle": "2026-08-16T07:44:57.252875Z",
-     "shell.execute_reply": "2026-08-16T07:44:57.252010Z"
+     "iopub.execute_input": "2026-08-17T01:01:21.288298Z",
+     "iopub.status.busy": "2026-08-17T01:01:21.288243Z",
+     "iopub.status.idle": "2026-08-17T01:01:21.446228Z",
+     "shell.execute_reply": "2026-08-17T01:01:21.445769Z"
     }
    },
    "outputs": [
@@ -776,370 +837,370 @@
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-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
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        "<g id=\"clust1\" class=\"cluster\">\n",
-       "<title>cluster1024 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M808.01,-8C808.01,-8 854.01,-8 854.01,-8 860.01,-8 866.01,-14 866.01,-20 866.01,-20 866.01,-94 866.01,-94 866.01,-100 860.01,-106 854.01,-106 854.01,-106 808.01,-106 808.01,-106 802.01,-106 796.01,-100 796.01,-94 796.01,-94 796.01,-20 796.01,-20 796.01,-14 802.01,-8 808.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"834.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024 x 8</text>\n",
+       "<title>cluster1000 x 8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M809.5,-8C809.5,-8 855.5,-8 855.5,-8 861.5,-8 867.5,-14 867.5,-20 867.5,-20 867.5,-94 867.5,-94 867.5,-100 861.5,-106 855.5,-106 855.5,-106 809.5,-106 809.5,-106 803.5,-106 797.5,-100 797.5,-94 797.5,-94 797.5,-20 797.5,-20 797.5,-14 803.5,-8 809.5,-8\"/>\n",
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        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
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        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
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        "<title>clustersymbol (12) x harmonic (4)</title>\n",
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        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
-       "<title>cluster1024</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M719.01,-114C719.01,-114 943.01,-114 943.01,-114 949.01,-114 955.01,-120 955.01,-126 955.01,-126 955.01,-223.82 955.01,-223.82 955.01,-229.82 949.01,-235.82 943.01,-235.82 943.01,-235.82 719.01,-235.82 719.01,-235.82 713.01,-235.82 707.01,-229.82 707.01,-223.82 707.01,-223.82 707.01,-126 707.01,-126 707.01,-120 713.01,-114 719.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"933.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1024</text>\n",
+       "<title>cluster1000</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M720.5,-114C720.5,-114 944.5,-114 944.5,-114 950.5,-114 956.5,-120 956.5,-126 956.5,-126 956.5,-223.82 956.5,-223.82 956.5,-229.82 950.5,-235.82 944.5,-235.82 944.5,-235.82 720.5,-235.82 720.5,-235.82 714.5,-235.82 708.5,-229.82 708.5,-223.82 708.5,-223.82 708.5,-126 708.5,-126 708.5,-120 714.5,-114 720.5,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M846.01,-98C846.01,-98 816.01,-98 816.01,-98 810.01,-98 804.01,-92 804.01,-86 804.01,-86 804.01,-52.5 804.01,-52.5 804.01,-46.5 810.01,-40.5 816.01,-40.5 816.01,-40.5 846.01,-40.5 846.01,-40.5 852.01,-40.5 858.01,-46.5 858.01,-52.5 858.01,-52.5 858.01,-86 858.01,-86 858.01,-92 852.01,-98 846.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M847.5,-98C847.5,-98 817.5,-98 817.5,-98 811.5,-98 805.5,-92 805.5,-86 805.5,-86 805.5,-52.5 805.5,-52.5 805.5,-46.5 811.5,-40.5 817.5,-40.5 817.5,-40.5 847.5,-40.5 847.5,-40.5 853.5,-40.5 859.5,-46.5 859.5,-52.5 859.5,-52.5 859.5,-86 859.5,-86 859.5,-92 853.5,-98 847.5,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
+       "<!-- lambda_q -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"831.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"832.5\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"704.17,-195.28 714.05,-191.46 703.94,-188.29 704.17,-195.28\"/>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M70.32,-279.62C80.08,-266.01 93,-251.9 108.5,-243.82 159.96,-216.95 512.22,-199.93 705.75,-192.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"705.75,-196 715.61,-192.12 705.49,-189.01 705.75,-196\"/>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- beta_q -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
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-       "<!-- lambda_a -->\n",
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-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"158.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
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-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
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+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
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        "</g>\n",
        "<!-- tau_al -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node4\" class=\"node\">\n",
        "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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        "<!-- b_al -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
+       "<g id=\"node21\" class=\"node\">\n",
        "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- tau_al&#45;&gt;b_al -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>tau_al&#45;&gt;b_al</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
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        "</g>\n",
-       "<!-- tau_sh -->\n",
+       "<!-- nu -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"307.62,-345.73 217.37,-345.73 217.37,-288.23 307.62,-288.23 307.62,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M276.02,-288.06C285.17,-272.34 298.82,-253.89 316.5,-243.82 380.68,-207.25 574.34,-194.69 705.27,-190.39\"/>\n",
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+       "</g>\n",
+       "<!-- alpha0 -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"366.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M383.46,-279.74C391.57,-266.33 402.59,-252.33 416.5,-243.82 463.84,-214.86 602.13,-200.66 706.37,-193.9\"/>\n",
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-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
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+       "<!-- theta_r -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"466.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M483.87,-279.66C491.98,-266.4 502.89,-252.53 516.5,-243.82 548.29,-223.46 635,-209.05 709.56,-200.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"709.92,-203.49 719.44,-198.84 709.09,-196.54 709.92,-203.49\"/>\n",
+       "</g>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"574.5\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M596.25,-280.34C605.88,-267.17 618.27,-253.19 632.5,-243.82 657.52,-227.33 687.38,-215.69 716.29,-207.47\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"717.12,-210.87 725.86,-204.88 715.29,-204.12 717.12,-210.87\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"682.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M702.03,-281.13C710.3,-268.37 720.75,-254.46 732.5,-243.82 739.38,-237.58 747.07,-231.77 755.06,-226.43\"/>\n",
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+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"782.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M797.09,-278.66C802.05,-265.99 807.68,-251.61 812.96,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"816.16,-239.52 816.55,-228.93 809.65,-236.97 816.16,-239.52\"/>\n",
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        "<!-- tau_ph -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node11\" class=\"node\">\n",
        "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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        "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
+       "<g id=\"node23\" class=\"node\">\n",
        "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_ph&#45;&gt;b_ph -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.03,-393.28C1511.35,-385.5 1510.64,-377.25 1509.94,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.44,-369.08 1509.09,-359.42 1506.47,-369.69 1513.44,-369.08\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1629.97,-394.25C1627.17,-385.52 1624.16,-376.15 1621.25,-367.07\"/>\n",
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        "</g>\n",
        "<!-- theta_d -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"882.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
        "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M373.96,-279.73C382.07,-266.31 393.09,-252.31 407.01,-243.82 455.59,-214.19 598.02,-200.09 704.48,-193.54\"/>\n",
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-       "<!-- theta_r -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M474.63,-280C482.95,-266.62 494.13,-252.58 508.01,-243.82 540.98,-223.01 631.18,-208.53 707.9,-199.57\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"708.08,-203.07 717.61,-198.46 707.28,-196.12 708.08,-203.07\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M867.9,-278.66C862.94,-265.99 857.32,-251.61 852.04,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"855.35,-236.97 848.45,-228.93 848.83,-239.52 855.35,-236.97\"/>\n",
        "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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-       "<!-- lambda_q -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"566.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"566.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"566.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M588.21,-280.29C598.02,-267.11 610.61,-253.14 625.01,-243.82 651.46,-226.69 683.07,-214.8 713.5,-206.56\"/>\n",
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-       "</g>\n",
-       "<!-- beta_q -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"675.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"262.5\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"751.45,-228.55 758.2,-220.39 747.83,-222.56 751.45,-228.55\"/>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M262.5,-393.28C262.5,-381.58 262.5,-368.79 262.5,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"266,-357.45 262.5,-347.45 259,-357.45 266,-357.45\"/>\n",
        "</g>\n",
        "<!-- tau_k -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_k -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
+       "<g id=\"node19\" class=\"node\">\n",
        "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_k&#45;&gt;b_k -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>tau_k&#45;&gt;b_k</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.2,-368.78 1279.07,-358.24 1273.38,-367.18 1280.2,-368.78\"/>\n",
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-       "<!-- nu -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"824.14,-345.73 733.89,-345.73 733.89,-288.23 824.14,-288.23 824.14,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
-       "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M790.42,-287.93C796.37,-273.32 803.81,-255.02 810.7,-238.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"813.8,-239.76 814.32,-229.18 807.31,-237.13 813.8,-239.76\"/>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- eta -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"779.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"779.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M779.01,-393.28C779.01,-381.58 779.01,-368.79 779.01,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"782.51,-357.45 779.01,-347.45 775.51,-357.45 782.51,-357.45\"/>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1388.5,-393.28C1388.5,-385.5 1388.5,-377.25 1388.5,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392,-369.43 1388.5,-359.43 1385,-369.43 1392,-369.43\"/>\n",
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-       "<!-- alpha0 -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- g -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>g</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"982.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+     "execution_count": 10,
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@@ -1153,7 +1214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31e6bf78",
+   "id": "b28e807d",
    "metadata": {},
    "source": [
     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1170,14 +1231,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "1fe8fe12",
+   "execution_count": 11,
+   "id": "c0ff9878",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:44:57.254681Z",
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-     "iopub.status.idle": "2026-08-16T07:45:08.244311Z",
-     "shell.execute_reply": "2026-08-16T07:45:08.243635Z"
+     "iopub.execute_input": "2026-08-17T01:01:21.447266Z",
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+     "iopub.status.idle": "2026-08-17T01:01:47.343004Z",
+     "shell.execute_reply": "2026-08-17T01:01:47.342417Z"
     }
    },
    "outputs": [],
@@ -1214,52 +1275,72 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e904b6a",
+   "id": "ee7879b6",
    "metadata": {},
    "source": [
-    "The two-stage learning rate is not decoration: at a constant 0.02 the Adam\n",
-    "updates near the optimum are larger than the posterior standard deviations of\n",
-    "the sharpest parameters, so the mean jitters around the mode instead of\n",
-    "settling into it. Annealing was adopted after measuring exactly that on the\n",
-    "prototype."
+    "The two-stage learning rate is deliberate. A constant step size that is\n",
+    "comfortable early is too large near the optimum, where it keeps the\n",
+    "variational mean bouncing instead of settling; dropping it once the descent\n",
+    "has flattened is the cheapest fix. The loss curve below is the evidence\n",
+    "available here — the visible band narrows after the change.\n",
+    "\n",
+    "Before plotting it, one property of `approx.hist` that is easy to get wrong and\n",
+    "that changes what the numbers mean. PyMC normalizes the minibatch objective\n",
+    "**twice**: the observed log-probability is scaled up by $N/b$ so the gradient\n",
+    "targets the full-data model, and then the variational objective is divided by\n",
+    "that same constant, because `scale_cost_to_minibatch` is on by default. What\n",
+    "gets recorded per step is therefore\n",
+    "\n",
+    "$$\n",
+    "F_t = -\\sum_{i \\in B_t} \\mathbb{E}_q\\left[\\log p(y_i \\mid \\theta)\\right]\n",
+    "      + \\frac{b}{N}\\,\\mathrm{KL}(q \\Vert p),\n",
+    "$$\n",
+    "\n",
+    "which lives on the scale of *one batch*, not of the full dataset — and which\n",
+    "would move mechanically with $b$ if the blocks were ragged. Multiplying by\n",
+    "$N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks\n",
+    "that is one constant:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "e6e6de0e",
+   "execution_count": 12,
+   "id": "1989271c",
    "metadata": {
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-     "iopub.status.idle": "2026-08-16T07:45:08.544935Z",
-     "shell.execute_reply": "2026-08-16T07:45:08.544314Z"
+     "iopub.execute_input": "2026-08-17T01:01:47.344331Z",
+     "iopub.status.busy": "2026-08-17T01:01:47.344259Z",
+     "iopub.status.idle": "2026-08-17T01:01:47.458892Z",
+     "shell.execute_reply": "2026-08-17T01:01:47.458582Z"
     }
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    "outputs": [
     {
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       "text/plain": [
-       "<Figure size 1600x600 with 1 Axes>"
+       "<Figure size 800x300 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 623,
-       "width": 2119
+       "height": 330,
+       "width": 1048
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch\n",
+    "loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE\n",
+    "\n",
     "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
-    "ax.plot(approx.hist, lw=0.5)\n",
+    "ax.plot(loss, lw=0.5)\n",
     "ax.axvline(6_000, color=\"C1\", ls=\"--\", lw=1, label=\"anneal: lr 0.02 to 0.005\")\n",
-    "ax.set_ylim(-450, 1200)\n",
+    "ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))\n",
     "ax.set_xlabel(\"step\")\n",
-    "ax.set_ylabel(\"negative ELBO\")\n",
+    "ax.set_ylabel(\"negative ELBO (full-data scale)\")\n",
     "ax.set_title(\n",
     "    \"Streaming ADVI loss, y clipped to the plateau (early losses are \"\n",
     "    \"off scale).\\nThe visible band is batch-resampling noise — \"\n",
@@ -1270,7 +1351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "856ee17a",
+   "id": "172243ba",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1278,14 +1359,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "aced8ae4",
+   "execution_count": 13,
+   "id": "0f52f918",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:08.547818Z",
-     "iopub.status.busy": "2026-08-16T07:45:08.547664Z",
-     "iopub.status.idle": "2026-08-16T07:45:08.931103Z",
-     "shell.execute_reply": "2026-08-16T07:45:08.930210Z"
+     "iopub.execute_input": "2026-08-17T01:01:47.460052Z",
+     "iopub.status.busy": "2026-08-17T01:01:47.459978Z",
+     "iopub.status.idle": "2026-08-17T01:01:49.469909Z",
+     "shell.execute_reply": "2026-08-17T01:01:49.469472Z"
     }
    },
    "outputs": [
@@ -1319,87 +1400,87 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
-       "      <td>0.5299</td>\n",
-       "      <td>0.0052</td>\n",
+       "      <td>0.5396</td>\n",
+       "      <td>0.0048</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.3174</td>\n",
+       "      <td>0.3077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
-       "      <td>-1.6986</td>\n",
-       "      <td>0.0036</td>\n",
+       "      <td>-1.7077</td>\n",
+       "      <td>0.0032</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>1.2972</td>\n",
+       "      <td>1.2880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
-       "      <td>0.3384</td>\n",
-       "      <td>0.0050</td>\n",
+       "      <td>0.3609</td>\n",
+       "      <td>0.0051</td>\n",
        "      <td>0.3500</td>\n",
-       "      <td>0.0116</td>\n",
+       "      <td>0.0109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
-       "      <td>0.2094</td>\n",
-       "      <td>0.0047</td>\n",
+       "      <td>0.2263</td>\n",
+       "      <td>0.0054</td>\n",
        "      <td>0.2000</td>\n",
-       "      <td>0.0094</td>\n",
+       "      <td>0.0263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
-       "      <td>0.1733</td>\n",
-       "      <td>0.0030</td>\n",
+       "      <td>0.1555</td>\n",
+       "      <td>0.0036</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0067</td>\n",
+       "      <td>0.0245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
-       "      <td>0.1435</td>\n",
-       "      <td>0.0032</td>\n",
+       "      <td>0.1216</td>\n",
+       "      <td>0.0034</td>\n",
        "      <td>0.1200</td>\n",
-       "      <td>0.0235</td>\n",
+       "      <td>0.0016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
-       "      <td>0.0213</td>\n",
+       "      <td>0.0184</td>\n",
        "      <td>0.0002</td>\n",
        "      <td>0.0200</td>\n",
-       "      <td>0.0013</td>\n",
+       "      <td>0.0016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
-       "      <td>0.2505</td>\n",
-       "      <td>0.0012</td>\n",
+       "      <td>0.2469</td>\n",
+       "      <td>0.0017</td>\n",
        "      <td>0.2500</td>\n",
-       "      <td>0.0005</td>\n",
+       "      <td>0.0031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
-       "      <td>3.5092</td>\n",
-       "      <td>0.0249</td>\n",
+       "      <td>3.5104</td>\n",
+       "      <td>0.0219</td>\n",
        "      <td>3.5000</td>\n",
-       "      <td>0.0092</td>\n",
+       "      <td>0.0104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0 + mean(b_k)</th>\n",
-       "      <td>0.8299</td>\n",
-       "      <td>0.0113</td>\n",
+       "      <td>0.8563</td>\n",
+       "      <td>0.0097</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.0174</td>\n",
+       "      <td>0.0090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0 + mean(b_al)</th>\n",
-       "      <td>-2.9950</td>\n",
-       "      <td>0.0058</td>\n",
+       "      <td>-3.0037</td>\n",
+       "      <td>0.0065</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>0.0008</td>\n",
+       "      <td>0.0080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a + mean(b_ba)</th>\n",
-       "      <td>0.1956</td>\n",
-       "      <td>0.0055</td>\n",
+       "      <td>0.1692</td>\n",
+       "      <td>0.0058</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0156</td>\n",
+       "      <td>0.0108</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1407,21 +1488,21 @@
       ],
       "text/plain": [
        "                       mean      sd   truth  abs_error\n",
-       "kappa0               0.5299  0.0052  0.8473     0.3174\n",
-       "alpha0              -1.6986  0.0036 -2.9957     1.2972\n",
-       "lambda_a             0.3384  0.0050  0.3500     0.0116\n",
-       "lambda_q             0.2094  0.0047  0.2000     0.0094\n",
-       "beta_a               0.1733  0.0030  0.1800     0.0067\n",
-       "beta_q               0.1435  0.0032  0.1200     0.0235\n",
-       "theta_d              0.0213  0.0002  0.0200     0.0013\n",
-       "theta_r              0.2505  0.0012  0.2500     0.0005\n",
-       "nu                   3.5092  0.0249  3.5000     0.0092\n",
-       "kappa0 + mean(b_k)   0.8299  0.0113  0.8473     0.0174\n",
-       "alpha0 + mean(b_al) -2.9950  0.0058 -2.9957     0.0008\n",
-       "beta_a + mean(b_ba)  0.1956  0.0055  0.1800     0.0156"
+       "kappa0               0.5396  0.0048  0.8473     0.3077\n",
+       "alpha0              -1.7077  0.0032 -2.9957     1.2880\n",
+       "lambda_a             0.3609  0.0051  0.3500     0.0109\n",
+       "lambda_q             0.2263  0.0054  0.2000     0.0263\n",
+       "beta_a               0.1555  0.0036  0.1800     0.0245\n",
+       "beta_q               0.1216  0.0034  0.1200     0.0016\n",
+       "theta_d              0.0184  0.0002  0.0200     0.0016\n",
+       "theta_r              0.2469  0.0017  0.2500     0.0031\n",
+       "nu                   3.5104  0.0219  3.5000     0.0104\n",
+       "kappa0 + mean(b_k)   0.8563  0.0097  0.8473     0.0090\n",
+       "alpha0 + mean(b_al) -3.0037  0.0065 -2.9957     0.0080\n",
+       "beta_a + mean(b_ba)  0.1692  0.0058  0.1800     0.0108"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1458,14 +1539,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e34676d9",
+   "id": "dcff8666",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
     "global coefficient and the mean of its group effects are only *jointly*\n",
     "identified: the likelihood constrains their sum, and the prior only weakly\n",
-    "splits it. The identified sums in the last three rows land within about one\n",
-    "hundredth of the truth, comparable to the other likelihood-identified rows.\n",
+    "splits it. The identified sums in the last three rows land within a couple of hundredths\n",
+    "of the truth, the same range as the other likelihood-identified rows.\n",
     "The same translation ridge exists for every global-coefficient/group-effect\n",
     "pair in this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
     "$g$/$b^{(\\sigma h)}$, whose sums we spare you) — so raw split rows like\n",
@@ -1488,14 +1569,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "1b6ecfa0",
+   "execution_count": 14,
+   "id": "7bb77309",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:08.933238Z",
-     "iopub.status.busy": "2026-08-16T07:45:08.933081Z",
-     "iopub.status.idle": "2026-08-16T07:45:09.217971Z",
-     "shell.execute_reply": "2026-08-16T07:45:09.217439Z"
+     "iopub.execute_input": "2026-08-17T01:01:49.470864Z",
+     "iopub.status.busy": "2026-08-17T01:01:49.470790Z",
+     "iopub.status.idle": "2026-08-17T01:01:49.582460Z",
+     "shell.execute_reply": "2026-08-17T01:01:49.582048Z"
     }
    },
    "outputs": [
@@ -1503,20 +1584,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "posterior sd of the contrast, widest first: S11 0.029 (900 rows), S10 0.022 (1,800 rows), S09 0.019 (4,600 rows) ... narrowest S01 0.007 (55,000 rows)\n"
+      "posterior sd of the contrast, widest first: S11 0.045 (900 rows), S10 0.024 (1,800 rows), S09 0.022 (4,600 rows) ... narrowest S00 0.007 (90,700 rows)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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//bLLa4Rhw4aFV155pe3xyy+/HDbeeOOGfiZJUvfHvKpPffBpwe92ePetoiUxwe/5exy4e/qDT5PlUP5ZkiRJkiRJrcmgXQPMNNNM4bLLLguNNGjQoILH7733XlWvzz5/4MCBodGWWKIwa+HDDz9s2GeRJLWOZ8dPCKMnfJ2xTkCuXGlMpH9PA3ejJkwMz304IQwZMEcnf1pJkiRJkiQ1K8tjtqhskK3c/HdZ06ZNC2+88Ubb45lnnjksvPDCodH4HB2Zp0+SpPa4dfS4igG7YqUyeR7Pb1vOqMIsdkmSJEmSJLUWg3Ytav755w8LLrhg2+NRo0aF//znP7le+/rrr4f//ve/bY8HDx4cevZs/K40fvz4gsdzzz13wz6LJKk1fDZ5Snho7MdlA3YXD1w2bL/W0OTfcoG7B8d+lCxPkiRJkiRJranxkRY1zEYbbdT2f+bR+dvf/pbrdffdd1/B42aZN+7JJ58seLzooos27LNIklrDBxMnhanTywfs0hKY/FsucMdyxk+cVJfPLUmSJEmSpOZj0K6FDRs2rODxjTfeWPE1kydPDrfeemvbYzLsNtlkk9BoY8aMCffee2/B79Zbb72GfR5JUmuYOGVqroBdqlLg7ospUzv180qSJEmSJKl5GbRrYWussUZYZZVV2h6/9NJLBQG5Yi677LIwduzYtsebb755xfnsHnjggYKfL7/8suRzP//88zBx4sSqvgfPP+qoo8KkSV9nJwwZMiQsvvjiVS1HkqRqzfvks7kDdnkCdyxPkiRJkiRJrcmgXYsj2BU77bTTwv3331/0uSNGjAgXX3xx2+M+ffqEww8/vOJ7HHDAAQU/H374YcnnvvPOO0nZToKD48aNq7hsAo077LBDePHFFwuy/44//viKr5UkqSMm3/946H/3g1UF7CoF7lgey5UkSZIkSVLr6d3oD6DGZ9sRSLv00kvbstYOPPDApLTk0KFDwzzzzBPef//9cPfdd4fnnnuu4LWnnnpqp8wb99FHH4VzzjknnHvuuWHw4MFhpZVWCksttVSYY445wswzzxw+++yz8OabbyZz2L3wwgszvJ7P9Y1vfKPmn0uSpNSUx54JU24vLMucN2CXSp+XzdRjuT1m6ht6r7OaK1ySJEmSJKmFGLRTOOKII8Inn3wSbrrppra18fDDDyc/xZDJduSRR4Ztt922U9fe9OnTk0w6fvLo379/OPnkk8OWW27ZqZ9LkqSeywwMPebqH6Z/MqFdAbtygTuWy/IlSZIkSZLUWiyPqdCjR48kO+2CCy6oOD/dMsssE6677rqw3377dcqam2222cKgQYNCr169cr9mzjnnDHvttVf405/+ZMBOklQXPQfMHfoesmcSYMNNK6xYdcAuxet4PVgey2X5kiRJkiRJai1m2qnNsGHDwiabbBKeeeaZpPQkc8pNmDAhzDXXXGHBBRdMSmauuOL/OhWrQZnNWL9+/Uo+l3Kbd911V/jyyy/DyJEjw2uvvRY++OCD5HN8+umn4fPPPw+zzDJLUiqT51I+k5++ffu6JSVJDQncTXt9dPjPbPOE8K/Kc7GW8p+1Vgt9Bi+ZZNgZsJMkSZIkSWpNBu00Q9bd6quvnvzUymWXXVb1a5i7buWVV05+JElqVgTY+Jn11Xc7tJxZ+/R2DjtJkiRJkqQWZ9BOkiSpg2bt3SsM6Nc3TJ42Lfx30tQwdfr0iq/p1aNHmL1vr9CnZ8/k9ZIkSZIkSWptBu0kSZI6aJdlFkp+MH369PDchxPCLaPGhYfGfhymTi8M1A1daO6w3aD5wzfn7Z9kuEuSJEmSJEkG7SRJkmqMQNyQAXMkP59NnhLGT5wUvpgyNczy/9l4s/VxzJQkSZIkSZJmZK+RJElSJyFAZ5BOkiRJkiRJefTM9SxJkiRJkiRJkiRJncagnSRJkiRJkiRJktRgBu0kSZIkSZIkSZKkBjNoJ0mSJEmSJEmSJDWYQTtJkiRJkiRJkiSpwQzaSZIkSZIkSZIkSQ1m0E6SJEmSJEmSJElqMIN2kiRJkiRJkiRJUoMZtJMkSZIkSZIkSZIazKCdJEmSJEmSJEmS1GAG7SRJkiRJkiRJkqQGM2gnSZIkSZIkSZIkNZhBO0mSJEmSJEmSJKnBDNpJkiRJkiRJkiRJDWbQTpIkSZIkSZIkSWowg3aSJEmSJEmSJElSg/Vu9AeQupKpU6e2/f+TTz5p6GeRJEmSJEnS1yZ88nn48r9fhSlfTgmT+3zlqukEn0/4IvSe1DtMnDZTmFLjdez269rbr6uYc845Q69evRr9MaSSDNpJVZgwYULb/0844QTXnSRJkiRJkiR1EZdeemmYZ555Gv0xpJIsjylJkiRJkiRJkiQ1WI/p06dPb/SHkLqKSZMmhXfeeSf5f//+/VsulfqDDz4I22+/ffL/ESNGhPnmm6/RH0k5ue26Nrdf1+W267rcdl2b26/rctt1bW6/rstt13W57bo2t1/X5bbruiyPqWZneUypCn379g1LLbVUy66zyZMnJz+Ya665TCXvQtx2XZvbr+ty23Vdbruuze3Xdbntuja3X9fltuu63HZdm9uv63LbSeoslseUJEmSJEmSJEmSGsygnSRJkiRJkiRJktRgBu0kSZIkSZIkSZKkBjNoJ0mSJEmSJEmSJDWYQTtJkiRJkiRJkiSpwQzaSZIkSZIkSZIkSQ1m0E6SJEmSJEmSJElqsB7Tp0+f3ugPIUmSJEmSJEmSJLUyM+0kSZIkSZIkSZKkBjNoJ0mSJEmSJEmSJDWYQTtJkiRJkiRJkiSpwQzaSZIkSZIkSZIkSQ1m0E6SJEmSJEmSJElqMIN2kiRJkiRJkiRJUoMZtJMkSZIkSZIkSZIazKCdJEmSJEmSJEmS1GAG7SRJkiRJkiRJkqQG693oDyCpvl599dXwwgsvhDfeeCN8+umn4Ysvvgj9+vUL/fv3DwMHDgzLLbdcWGWVVcJMM83U7veYNm1aePLJJ8NTTz0VPvjgg+R95p133rDggguGoUOHhuWXX76m36lVfPTRR+GZZ54J//jHP8L48ePDhAkTQo8ePcLss8+erNtlllkm2Xb8X81lypQpyXH30ksvhbfeeis5Jr766qsw66yzhrnmmisstdRSYYUVVggrrrhi6NWrV9XL/+yzz8Jzzz2XHN9vv/12svzevXsnx/Xiiy8eVl111eSnPctWCGPGjAnPPvtseO2115Lj8L///W/o27dvmG222cJiiy2WHHusX7Zltdvt9ddfD6NHjw7jxo1Lths/HNssn+235JJLhm984xvhm9/8ptuvHb788stk23HssR3ZdpMmTUrOm1yXll566eS4W3bZZd3VmxDHB+fOkSNHhv/85z/h888/T9onc8wxR3JssN247s0yyyyN/qjK4FxGm+Xll18O77//fnJemz59enLsLbDAAsl5k3Pboosu2uF1x3J5H9qdnE95b653nJPnnnvuZF9ZbbXVwsILL+x2yrk+OWe++OKLYdSoUcm2mzhxYnKccezRZuHYW3nllUOfPn1cp02G4y29X0jbLBwPHHscA+m9HtfA9vr444+Tez2OO+5JaM/Qbkn3D9pEHN+qDu0T2vMcf++8805y7PE77hfmmWeepM3C/QI/3AO2x+TJk5P3YPvR9uTayr4x33zzJdts/fXX71A/QHfRndqPfPa//vWvSZuK88PUqVPD/PPPn1wbv/Od7yT7Vndj+1FSd9BjOq1ySd0aQbSbbropXHvttUlnRiUE8dZcc82w5ZZbhk022SS5Ccvr9ttvD7/85S+TG7hSaNyeeuqpyQ2jKvvnP/8ZLr744vC3v/0tCf5UQkfYxhtvHL73ve/l7qDiBo4bEgI+6c+///3vgvfbfffdkxs5VXfDd8UVVyTHHwHsSuacc86w7rrrhuHDhyf/lrsh5yb7z3/+c7jnnnuSm2+2YTkLLbRQsg2///3vJwE9Vfb444+HSy+9NDz99NNJJ2Y5PXv2TDowv/vd74att966bADvvffeC3vuuWdynOVthtHJvdtuuyWvs5M0X4ci580//vGPSadXJXReMKhku+22S7ZjLZxyyilJp1uM7fftb3+7JsvvzmhLXH311UmgvBLaKN/61rfC5ptvHjbddNOkDVPOYYcdlgT/2oM20U477dSu17YKBqZceOGFybWJDs5KllhiibY2Cx2I1bZv//CHP4TLLrssOZ9WwqCmq666qur3aRW0+a677rpw/fXXJ23CSggkrLPOOsk1j/NnqbbFhx9+GI477riafEbe7wc/+EFNltXd0Ba85JJLwqOPPpocG+XQviTwQ4f9tttuGwYMGJDrPWi/cJ/H8V2p3Unw7sADDwxbbLFFVd+jFRH0pL156623Ju2XSgiycE+2zTbbJNe/PAjUcD/CPlLunoQBaXvssUey7VqxvVmP9iNtfwKm6T0318133303GRyRWmONNcJ+++3Xoe/C4Oxf/OIX4ZZbbil5Pea8zXngxz/+cdWDD1ut/Rgfr2wz2vj8yzZkgER8T3fBBRd0aEBZPd5DUvMzaCd1c9woH3roocmNXHvQeLvvvvuSBnylG4Fjjz02/OlPf8q1XEZ88nw6MFUaAZ/zzjuv4o1xqSDCySefHHbeeeeSzxkxYkS4/PLLkwAd27Ccn/70p2WXpUJkhtA5TEO7PehMoTOymAcffDAccsgh7dovGEVLI58gkEp3XDKwgM6N9uAm8De/+U0y+KEYMhc222yzdi2bQQ8cs26/0uiwPOaYY3J1fBVDB+OvfvWr0BGcW3/0ox/N8PvTTz89bL/99h1adnfGaPAjjjgi2YbtwQj4O++8s2zGORlXvE97MPDhpJNOatdrWwHnzJ/97GfJgJX2YNvTUZwHnVc8n0EV1aDzcvDgwe36fN0ZHcbcL+Tp6CyGoE+p45YA4EYbbRRqgQDTmWeeWZNldRd04hJIu/LKKysG60p12p9zzjlJx305Dz/8cDjyyCOTzuRqr6lnn322A8ZKeP7555P1SkC0vYFstn05bDPOl4888khV9yHch3bHLKxGtR9feeWVcPzxxycBGCqulMN9wrnnnhs6ck7fe++9cw1oSc/hDIAhe7Arqkf7kft6sphpf1Ty97//PamYUq16vIekrsOh9lI3RiYOQTFKYcYYYcyIMMo6UMaEjn8y4yiDQ0OH16U++eSTXCOluYGOA3YEjBg5TbYQpYm4EbnrrruSGxMQIDrrrLOSUhzt7bzu7hhxmW2sM5pqgw02SAIvrLuZZ545adQRGOJGjEBRiht3grbl0JGSzQRRxxGU4djL3vSx3dZee+2kFC03B4yApEwJjW7K1MQjLMuNgmW52YAd+wPHGzdb6Yhptu0DDzyQlHdJUeaFz3bDDTckx6ZmREZAdgACAxg4p1Hel7I4DDzgvMn5lY4sbo5TnDPj82glbDtGetJBwohdshfIBCIzmmVTfirFMU62JEEhMjNV6Iknnkg6/ePrFtcjAqgEaygXy/qlfB7XJbIpuS7Fx1OerNhyGD3N9U3V4fy3zz77JOUwY2SMb7jhhknAmn2eaxvHHscFbZb4OkenDedV1d+NN94YfvKTn8wwgIH2JtkHZHvTZuH6xfmSbUcHZjxqvFyVhhjXTTKP4/YL77XWWmslP5xTKe9G+4j9ifZR3mW3ItYnmTUM4MpWblhvvfWSjCk6Bulk5vzIQEDOnXHwu17r14DrjM4444wkQzJGG5NA6UorrZS0CcmY4lxJu+Khhx4Kb775ZsFApUpBCtqODBaLAw0cz9zDUb6bwA7B+n/961/J/V68fNpTPJfPqUJcxwisxNnfZEEOGTIkybQiE5mBswTdaFtQApiBCnEbp1Kbhe17wAEHJPca8Xtwbub45nzJ+3OuZFul2WWU3CfTi32rFbJ56tF+5LWUbexsbMP999+/IGDHOZxsQO41CNRz/8K9RHru5l/2E37X1QYG1qv9mE6T0Jnq8R6Sug6DdlI3xqjLOGBHgI7sEcq3lUIwjZInlA/iBi0PyjbGN4vcXFAaiY6T7Aj13/3ud0mmAY0mOmpOOOGE5MaEjmp9jRulX//61wWrhIY2wQS2YzGMDORmnJIQlPSoZqQ7N/M0bJmfi5sSfuiU4cZb1WG/JhMj7gBhfRLYZl8vhht2bjhuu+22cM011+QeFZnedHNsEQwsZt999w33339/su+kN+J0ptB50tFsou7ojjvumGEAwsEHH5x0XJQrFcyNO+dN5ozIM9KdDhBKivFDqeBSpVAZcUkH29FHH922/eioZtuddtpp7fqO3RU324xgjjtcmFeHfX3QoEFFX0MHDQFWgthcnyoNdMiDDLs0E4Fza3syYlsR5ajiDheOEco1Uf6r1PHB+ZaBCRx71WZcpfvHQQcdlPv5zolWHOekbIc8WTtk+5eaN4vMEgYOUbqd7Le8JUs5njhu44AdnXLsKwQGs3bcccfkNffee+8M7Sr9D/cGccCOIA8Zk3ToF8PAH86zlOjm2IsHjBXDPkD2eXvKw8ftFAI/lO7X1whIZwN2lA8la7JUoIV7LzqGyc76y1/+kusaRbWNOGBHxz/n7GJZKQT3+BtVHVIc45zLCX7of1jvtFnicx/BBQb9sH6LoS1K++Lmm29OtvvYsWMrrs7zzz+/IGDHfST36dmymrRHae/S7kwH+zEvIlmSlPvuzurdfqRNQ98H990EZvmX+3a2Sy3Q18IA0njQKFl02cGaBPa4FnOfmAYduV/kXrQrqXf7MZ3TPO0z4Yf2BYH1WqnHe0hqfpbHlLopRoCRkZWOYGZEFaOgqxmhevfddyeBBkr0lcrIoXOaRn48aoybtGHDhpUNJsY37zvssIOdzxncNFGWNEU5NRrgeXETR4cLk81z81wKHWZsQzoiyRyKsR3jmwfLY+bvQGG0X4pjh0BQ3vlCCJwznww3gQTQiyG4x0hIgoOMos6DG3aCe3FAiQ43RtCrsKM5Lml64oknJhkI1QTc6QClU7PUAAkCtGznSmWHsyOA43LCBIP4HaPp9T90MnDeizu/KNeXd44Ktgs3/mzD3/72tx0ui8kxTzAhLrNqeczi6IRkoE/aYUYnCx3KpQYjFENWKuuX+XpKdbJly2OSicI2V8ewz7Pvp2gD0lFcbl7WGJ2dP//5z5PzGR1t5dAuiYMBzIXHds/zXuxfZJ20QtZINYEx2vEp1g1tjLzz/nGfwX0C8yZxTaolgr7x+XOrrbZK3keFQem4s5pAAiXi8mKwH20W2j6lSuATuGHgYLyP0D4lQ6sc2k/M0ZZiP+M41/9wb8CAy9QiiyySHEt5y91xPqOtwuBZgqLFkLFDOyQdyMl5koES5ebBI4jItkorSNCHQNlAqoR0V/VqP5JpR2YzwRgGIcTIouQ+raPlMemTYful93vch3K/V6ovh/2I4BZZsim+A9VbuoJ6th8Z7EDbvthgJO754gzj9paurMd7SOo6ejb6A0jqHIyYiksOcaNbbUkZGgaMwCzXKcwIpThgR6OpXMAOjOCL6+PTOVCL7IbughuruB47nfNk2VSD0eZ0bMWN/2K4QeTGIRuwU/vFwdY00y1vwA5sC4JEcQdoFmUaf//73+cO2GH11VdP5lqIkRWmr5GZHAfsOD4qHUNZjI5m2zDHSCl0AlQTsEvPrXEnCyO0Kamq0sfeUUcdlbvDJd0uP/zhD9s9hwgdMYxITxF88GY6f4dJPMKdDOJqOlxAVhBZsmbD1RdtzXSUfoo5i/MG7EDnFJ35hx9+eNnnkYVO6fC4fCMZIHnfi2xpA3blz5s77bRT7oAdWPfMM8dAv1q3hbPVHgjQ6mvp1AYpyr9VkzkMAjFU6Nh8881LPodgQoxsx0oBO8QD2Iotp9Vljz0GWVbTZuB8RpC23Hx2BE3jyisEgsoF7EAJSM7hKQY6MJdyd1av9iNZjly3sgG7WmIewniAJv0u5aZDYD/KztXblbZ3PduP6RQJnake7yGp6zBoJ3VT8WgpUBe/PejcIGhUSrbTv9QozWzjkBFd8c1AtsOnlVGiKL7BovHW3rmr7DCuv7gcCZgLodbbrr3bdZNNNplhNJ/Knzer6XhO8ZrOyIBj3piYJVLKH3vtve619/gi2yjN4CKwTuaC2nfstfe8SfuiMzvDNCPmPo7nX1l00UWTn8449ihBFpfyo8wfWSBq/LFXqnR7e5HJFc+ZR2mwSsGGVrzmxQM0aSOUK+Pd3mMvO19h3kGg6fzNKQZoxp+31dXjfoF512LDhw/PtUyy0ON7T7L5unOp70a3H2uFPpUHH3ywoB+Hgdt5BgZyjk0988wzFee5bBa2HyV1ZwbtpG7cidLZjUjKu5FplyK4t/766+e+GSg3wq2V1WPbqX7br5nKF2bn+zHDtWsde9nsvO7cgdLR7cf1qJpR0h3FaHZG+6bHPGXd1D3Om2qO8ybzaTFfb4oqAdm5k9V9rntxWUVQnrE9g2i6s3ptu+w8vdVcW+NBFAbs6n/dyw7OY562vFU/4moezKlc6/K3zaSR7cdaolwiJThTXCPJnKyEcyvTqpTq42lmth8ldWcG7aRuKtvY7IyMjHfeeSeZgDnOCMs7wp0bgZlmmqntcVzepdVl16HZNF17+1Eyr1nEN3KIj0HV57zZEUwQH1twwQUb9lma/dgjoFmvUcIc42eddVbbY8oZzz///HV57+7C617XVa9rHuWA4zYnmeMGcbrndY+5tJ5++umCAELeDKFWUq9tR6nw9hzjBNrjY5bScx6z9Tt3Emwl2BZnXVUTGMy2Y7rzvXqj2o+1lt1G2Qod5QwZMqTsspqV7UdJ3ZlBO6mbyt5gUae7s0tJMGlzXpRviefM4KauqzaQay17U0vZB8sYdh3ZsmBM3t4ssvvRAgss0LDP0hW23SOPPFLQ4dRIjHp96KGHCkYCWyqsOY495q5LO8ZWW221sOOOO9blfbvztmM+XfZ5NT/mbI07zcjg7oyMjHKdkXR2X3PNNUmGK3N6MbfQmWeemcyzlh2sovrfL1SLua7jrCzmG3IgROXz5rPPPhvGjh1b8+2x7rrrFjzOm4FDGyquCPDtb3+75p+tK+vsNgtTLcRZktWWTs0O7GPe5+6qme/d6tU3k33u6NGjQ1dg+1FSd2bQTuqmsjdG3MiddtppBRP1dlS2MVdt1kf2+dmGZquaa665CkqS4PDDD+/WN0vdSbZz46abbgrXXnttU5QFikuLdWT+jO7qG9/4RsEo5M8++ywccsghM8zn0ggXXnhhePvtt9seb7vttmGeeeZp6Gdq9mPvnHPO6fTSy3Qup8FUOsS4zppJUL111lmnYL1xvWO+si+++CJ0JoKtf/jDH8Lpp5+eXGcJ+PC+6b7TLEH7ZsZ+nx1AcNxxx4WXXnqppu+TXR7VHQjI8V5Dhw4NP/vZz5LrLXMvEXi6+uqrk21Ke5hMWIN3+e4X2O9//etfJ3MjNQJBhttvv73gd9/73vca8lma3aBBgwrupQiQ0WYZM2ZMTd+HuemYpzUuwffnP/+57Gs43n7xi18UBID22GOPmn6u7tZm+c1vfpNcj2olOy/9559/XtXr4zklu/t9eiPaj50hu42y0yKUw7kkbod1le3dqPajJNWDQTupG3c+r7766gW/u/7668OGG26YNERfeOGFDs+HlJ0Pq9pRsNnnO7/W1/bee++CdUNn/dZbbx0OPfTQcNddd5mV2MS23HLLMN9887U9Jlh3xhlnhM022yxcfvnlYeTIkQ0J4JFx8PrrrxfczNPRqcLO5913371gldA5RWfVSSedlARnCOTVE/vLUUcdFS6++OK23y233HLh2GOPrevn6Aq+//3vF4wkZ5T5wQcfHHbYYYfwu9/9LinpXEtk95DNkyLgQ+emqrf44osn5Q6zgwyYY4WAyzPPPFPTQUfx8X388ceH6667LjlHEvAhEHvZZZcl+w4deQSFusqI80b5wQ9+MMOxwXG33377JQGYbGnf9oiPXzroOL653rL87Jxb2RJ9V111VfJ53nzzzQ5/ju4YtFtmmWUKfsf1ZtiwYclgkX/84x91zXp9/PHHC7LF5p13XtsqVdwvvPLKK+G73/1uUqaZoEOtgtWnnHJKMo9kijbIBRdcMMPyad+SYcfx9tZbb7Udrz/5yU+S87y+xjqKB4oRKOd6xP0egw4ImnT0foHjJ8V9fzz4q5Js0KY736fXu/3YWT766KN2983w/eecc862x1RAKndtbfX2oyTVQ4/pzTD0X1KnzQlBY7NU2UnKGdH5y2hlMruoZR6XrKzkRz/6URgxYkTb43PPPTcJTOR19tlnhyuvvLLtMaOkmWhe/8MoMToPS+HmmW234oorhlVWWSWsvPLKuecUzIObcTpsUj/96U/Dzjvv7ObJ2RG81157lQyMc5POtlthhRWSADvHXmeWquQcsNVWWxVkjB1wwAHhyCOP7LT37Kq4sdtzzz2T7ORievbsGZZaaqm27UeJNv7t3bt3h96Xzu24Y5QR0ZzDs50knGM5FueYY44OvV93xSh1Or1KITuR7ZUee2y/ueeeu13vxTGUlgmj05vzdTyqHb/85S+T0fMpMrq23377dr1fd0dnE+vm3//+d9G/s24p38S2o83CtuNYrDazkRKm2QyCSri2nnjiiZY+raJNV6z0N+dNfth2tFlmnXXW3NtgjTXWaMt8ZNAJZTnTDmiWs8UWWyTL5frKeZPAAUHYuNOR6yxBPioaqHBwCO27Upk4rO/0mpfeL2RLktUK7RIGp8VBKQeplMb+zYCRUiUrOT9yb5de91ZdddVkG1ZbKhG0Ibn3e/DBBwvmGyTjj2srgQ4GOMSBPH5Pm4UgsGbEOYrsyFLBEYIobDeOP86ZHHtxIK6SI444Iik3naKE8K677pprWzOwL8645RpMEL+7qmf7sZinnnqqYOAg7X36VqrBgO14HsNXX301OUbzYnB33AbjXmi22WYLza5e7cdyGCwRDwyiL6B///41W3693kNSczFoJ3VzlEghSyPvZMKUUaDjg5v3SiUVWG5cHoWRuRtttFHuz3beeeeFSy65pO0xmSzZLJdWxg3cRRddlIz4z5MVyQ34+uuvn5QRqkUGlUG7juGYY14dAi950OlP1sBOO+1U0wY4+xEBITow41GJjELs169fzd6nO5k4cWI49dRTywbNY9zQ0iHFtqNTpT24kSyXzcBymTtt8ODB7Vp+K/nrX/+adCzmKW3IDTuDHoYPH56UHM3bkRl37hDIpSwfnThZBu2qQ0fhMccck3tONAI3m2++edJmWWKJJXIH7QjycCzR2UwAh443gj0E8xhRz/szOjvLdkp5ZLTRyUh2WyUMdKCsFcfdd77znYqdZwxQKlaykSAE7RX2hWLXYYLr8eA1ztU8X4WYP5mAWZyRXw7tCNosHHvVBBHKIdhDdmucFUEAj+NUpbG+fv7znycZQXkyY2j7cb/GwE6C4dWio5hS0AR7S+Fauv/++ydBV9ua5XG9ITCdNyOZcyHZeAQpCKiXc+uttyYDTlIcS7T/swOMsn71q18l1UGyCNpVem1XVo/2Y2cG7QhIpX0G7QmybrrppgWVBR5++OEuM59oPdqP5Ri0k9QZLI8ptcAE83Qmnn/++cmcI5U6RShJQyOdDhTKaJab0yL7t2ozTbLP72i5zu6GjmDKYTJCkgZlpcwabtq52eAmmeBBV6lF310RZKGziZJAjJCthI4ybpIpxfj73/++Zp+DOUXigB03cQQS7EQpjXVD2UM6O7hprpTBSslMAnx0gHHMlspu7gg6n/fZZ59ksANBRZVGmRxGrzPCvFI2CAUnnn/++eQ45bpXKlshRsdaXBZzt912KxqwU/XoRKEsGO0QOu8rjRCnk4bn0/FCJ3KeYNEtt9ySlLolk5wgBZm1ZCJTSol/yXqg85uMLILpMbZ7sWCe/ocMc9ohrNNKc27ShmQ7MO8cnZ6U9Sv33GLtUbL3rrjiiqIBu/Q6zMCneD/i85ULNrQqsg7ozCdjko7oSshy5BjifMvxWoviPXfeeWdBwI5sCAN2lREsINBwxx13hG222aZiIIc2BPM+EiAgqDZu3Lhc24fXMd8hZQMrHUNsR4LjnE9rPb9ld7PWWmsl5yUGAuXZ3zlXUp2GY49jphwC63FQnXtDArzlPPnkkyWzprv7vXpntx87E58n3j7VZNh1h76ZerQfJanezLSTWgzzjDBfBJ1OjL6i8V6uQbb22msnHR7FRo8xKpAb/BQ3Z9WUP8mO4qPRu8suu1T1fVoJN8Bst6effjrpwH/ttdfCJ598Ujb7h21HVkF7mGlXW4xc5EaYUiMce2RzlBsRTaCWkkIdwTxNlOOLWZ6vegTl2HYcfxx7dFaVKiMGspRZ9wyayOvRRx9t2x+48eY9mQ+GEe2Mvo33FUpd0aFSzQTzrYzjjfPmc889l5QKKlU+BwxsoeOMoEOespgEDej8LNVJaqZdx1DmkFHTHAdsxzfeeKPs3CQET8n2qlU5JzpxmK8tDtQRCLr55ptrsvzujCAbxxzHHvMo02YpNycSgyMICJSqFBBnEMTtSKpDVEKGJMHaFMc3JchVvlJHmnFKkICSXOUG8tFpzaASBpy1FwEnztEpy+a3D8E1jjt+CJj985//LCiZl0WAnc7r7NyGMa6bBPji0myUbmRgE8cmGcuUx6RtS/lMghlxIIDjjQEuqow2Ju0+7hc4HqjYUS4oTnlUBj+UwgAU5mbNZlRRDSQOTH3xxRfJQF+OY7Zl2iZK35vtWG5wRXdU6/ZjZ2fa0T5Jtx3nYq671aAfJ573kGtALUuAduf2o5l2kjqDQTupxdF44WaODmmygoo17sjaYgLyLAIKcUYQnZOM6MvrjDPOCNdee23bY0b+Ue5D+b333nvJTR2jAvnJjhLjJppRmKVGoZdj0K5zcXNMZwo3FHT600GWlXfuiWIYcU1gPb7Rp6QtmZjqGNYpHVd0Zt57773hsccemyEAW2qOs/ZgcAWdznEnGKOxyQQ0Y7J9JdhYlwRKue4xF0aMjg6ydyjdV6nzi/nq1ltvvZLvZdCutggakJVMJxpZ6ASDigUPqC5Qy8FOLDPOcOW6Wq6DW6WzVGmz0KnPuZPrYIzgN8cYpRfLzWmXPpdOzjwlyThXx9dSysvlLX+s/6F9Sccn9wuUxi9WzeHAAw9MMlTag3uR+B6AEracoytljSkfgmkcB/fdd1+S4ZoNwDIAhfNasXkmGaRE+T8GEqWYg5zsvlLbh7J6lKqLj9lq5z7X/zCIi2sdbU2OPa5JWZXWLQP2GEyWNXDgwOQekXMx19b4PpKBtLSR0m3IPSXHfyvrSPuxHkE7sjbjah8vv/xyVWU7ac/G+xf3qTPNNFPoDjq7/WjQTlJnsDym1OJoyDGyiDmv6Ci59NJLZwjwMKK82M15diRSPPF4HtlRn8VuFFXeggsumIwyp1FPaZVspiOZeGTbqfnQ0bHmmmsm5dnovGQOtWwAhhuHchldpZAFxKjmOGC37777GrCrEUbT0tFBOUxuzunoys5lx40h857VAgE6RsHH78E5md+pepQaJpuHDsf7778/Kd8VZ4cQgKVEXLGAA5kfKQaplAvYqfYY6U/JSkaykxHAwB86m2P33HNPQYC7o5jPhfJJsbjksPKbb775kgwPji8GGhEIiNFxXKrDLNtGZD/I2xnJ/IVx2a+887bpa3TcDhkyJCmNSOCAdme2bDsZ4MUCCnnEmZDgmDNgVzuLLbZYcrwx//jdd9+dtD9jZBBdf/31RV/LvWEcsKNjm2thue3DtZH3iq+ttHMt71097rcp98eAIYKu/JsdEMYAoXKVc2jvkFmXfR1VQAgWEdxJA3a0cQkeZYOuBO1aXXvbj/XS0b4Z5vVNcc3sLgG7RrUfJamjDNpJKsCcLtdcc01B8IAGKCOSsrKl35gPrxrZ51eqHa/KHYt0djG5fIzOlVrMNaLOQ939HXfcMSkNFuNmmdGc1WAULGVy4lHUlNo8+uija/Z5NeNcQMVKS1Waa6QalI778Y9/XPA7Mu3U8fXKPIRkpWbLU1FKJ0bJqLQDhM6rE0880dXfYGRfMf8cZdpiZC/XEnMCZ7NW1DGU3GKOQDJ2YgxAKja3TLaNSJsnLzoe4wATndtkr6h96NQnC4RBK/G8QWw3ggrtqfqRvV5+73vfc/N0Eo4lth1zBlY6b3IPGJcDZttnSy2WQpA3HkzIQEI6xdV+DFSgZHO29D1B10rBBgboErDlnmCBBRaY4e8E9JhXm+At1R2y9+nZOV5bXTXtx3rJ9s1QkScvjs84+72aEv9dUb3aj5LUEQbtJBXN6siOfi52I5CdLLtYeb9y4rrw3PQzT5M6hptpRlLGKJNhB2PXsP7668/QOVysfEcpPJfyVHGHJ+WmmC9SnYsR52QgxJj/rpbIFok7WpjnpNy8lsqPUeVkAZU79uIySHTWMAqdOX7K/WQHvDAoJv77iBEj3Ew1yDjPzpVU65HS2X3D4652KNscZypw/SpWqn3ppZcueFxNyS9kMwbKzW2jfKjUQdZVR489An1xRg/bOpu9rtoiQJMtZUoGara6A5lY2W2TzU4pJztHJXODqeOGDx8+wzkxz/0CgRimt6BEKlU5aIPQLmGAJ2UDL7roora50LPLow2q9rUf66UjfTPZ52aX1R3Vo/0oSR3xdZ0QSYow+pLRR6m4Pnqpxhx1z/MaP358QdCOUZ/VdsCoOLYLo8bim2w6m4vNEaPmPPa4cU5l50soNx8MJTDjUZJ0ppHJQDBXnS87ap3OZ7I52jupeTEE7caNG9f2mOPckkUdx8CRVVZZJSlVm+fYY/RyNSOYU4y+jkdgOy9a5xx7xdosHfHll18WPLa9UjvzzjtvUrovLr9X7NjLHitxGa884udzTezfv3+7Pq9mPPaY26kjx142a9wsu/pYddVVk2MhrsbB9otL0XK/FiuWoVVO9vnZ5aljx17cnsh7v5BaaKGFkp9S/v73vxc8Xn311dvxKbu/atuPnV35Izt4MO88ktmBhtmgcHfV2e1HSeoIM+0kFUUWQSwufxOXNopLZdCBSeZHHtkbAWr1q3ayI8rjuVzUtbZdsWMvi5HQlMuJ54lkPhHmuMjzenXOeRO1Xv/Zkm7ZOYVU32NPrbHt3nzzzRkCTeq8c2exNku2nfivf/0r9/KZZy0O2s0zzzy2i+p4v1AOg1Aef/zxggywrbbaqlYfT2WwrrPbK/s4u32Lla4tJ/v87NzNas7rHm3NOAi13HLLhRVXXLFmy+9umqX9uM4665Ttbykn+9zssrqrZtl2klSMvbiScnVQDRgwoOjzmD/t1VdfbXt8++23J/XdK+F5MWroq3Y3WtkRfqW2n5pPnG2AbMmVLDJW99prr4JtTonNCy64wGyQBm87Muxq2UHFfGrxe7Bss+w6b/tlz5uHHXZY2HXXXata5m233VZQInOPPfYoCD509zlDmmXbdVTceZmWBVRtMP9qXHmh1PYjI4TybC+//HLy+O23304GiuWZD/mRRx4peJyWf1P97hfKnSOZNy214YYbJoMC1fkohxfPf0zWXXZAQvYxJTTZXnFJ23KypW4JmKsx9wvVuOGGGwoqdzDvtvJvi0bdd3M9JCud4xSvvPJK8v9KVR24v6Bcaop7C+akbAXNsu0kqRgz7aRuig6mbCMkL27g/vjHPxb8rlTDjdGw8Yjom266qWLJIsr4xR0ozI1gyY3CEeF33HFHQbmaavzhD38ouAmno4ua7aqPW265pd2lNXjd/fffX7ZsR7bM0J577llQLpESLZdeemnRrC+VR2fwE0880aFtH6v1DS/zjsTHNsEfS59+7frrry/oZKoG16V//OMfZbcfo8zJYK3mJxuUo9xQ/PeBAwe26/N2N8yvM3LkyHa9lmslHf+ddezdfffdBXOckJ1idYDCgUK0/aZOndru9Ru3G2efffaSZbni+ZbZ7ldffXXF5XPO5NwZIzCk/7nzzjvbVeoXEydOLCiNWe2xxzakzRqzNGZ+o0aNCn/7299CrdosBMWzpX8JAsw///wF83nmfc/JkyfPsH0NmH/txhtvLKiQUQ0GOmTbq+XuF6pB/8HFF1/c9niJJZYI2223XejOOrv9WE/bbLNNwePf/OY3FV9z3XXXFWTFbrnlll0m46yZ24+S1FEG7aRu6sknnwxbbLFFOPXUU3OXrASjJ5mgOq6RT1AuO9F8inlI4sYhQYRTTjmlZMCJCc5POOGEglG1ZOZZvrGwA+yYY45JbpAI4MTrKk9pi3POOafgd5tvvnnu16vjCHiTOXrhhRdWFbzj2DjyyCNnKOG11lprFX0+c5lREvOdd95p+93yyy+f3JzF85Gouk4QgqD77LNPePbZZ6taddz03XzzzRWPPTqxrrrqqhnmyMqzX1122WUFv3Pkc6FLLrkkDBs2LJmPleOpmoESRx11VMF1a+WVV86VwaPaYE7c4cOHJ+0DOqKrQRng+HglkF3s2GM7jxgxIjkG83rsscfC8ccfX/C7XXbZxfnQIpMmTQonn3xy0slHAKea9UsWwGmnnVbwO9qbBEaL2X777ZMO5Dgb5MEHHyz7Hr/61a/asg7SgWK2i75GO5N1/vOf/7xgAFCe7X7ccccVBPxmmWWWqgKizN8bt2GY/8yAeH4ffvhhOOigg8Juu+2WBHCqGex33333hd/+9rcFvyt1XHz3u98teMx9XqV7Sz7LGWeckWTEpphHslVK7uUNFG2yySbJdqgmeEfg9PDDDy841zJvOUHXUm3bbLZ4ucxZKgIQkE+vp6effvoMJQS7m+7UfqSNEmddMhC43Pan/cVgz/g8vv/++4euoh7tR0lqFIN2UjdGY57GJzcEe++9d9KpXOomi8Y5JbwYxUynVozSe+UanwTd4hJtjNqljBhlV7KNqu9///sF5TTJCtp666078C27LzqzDjzwwKQEKaUOaVTSSVKq/AzBVgIO8UhBSjwccMABdfzUAjd8bDMyabiZ49iic6XUzTdZCgTZCbbH6BDLjnpOl7/ffvsVdEQOGjQoXHnllXYm1wCZwNz00glNgI3jq1jwnMwSOh05B3KzGN+0c24rNi8PyznrrLPCBhtskHRWP/XUUyU7CDje+Swcw8cee2xBlh03ld/+9rdr8XW7FQaOMFiFdfOTn/wk6dAvlf1NZ/Pll1+ebOf4Rp+yXyeddFIdP7XSY4N2ymabbZZ0QhMEz5bei48Nsj122mmncMUVVxT8jQEv8Xy78YCYH/3oR8mgil/84hfhxRdfLHpN5TjmmD/xxBOTtlPaeZkOVDr44IPdYEVwDDHwhHMbgTLObaXmv2I+urPPPjvZfgxAibPsjjjiiJLrl2Aex2Zamo995pBDDkk6HLPzfY4dOzb88Ic/TK6LKV5HW8mBYoXYTgQOCLgRBKKTt1QAj/VMiXuub/fcc0/B39gW1ZRsvvXWWwsecw+St+yiCgfs0f4nuMbgHu63imW+cm574YUXkvYE2yoO+tCGpN1TDG0Qyn3H11kyIskUi8+PKd6fwU+///3vyy5H/xuAR8CcNgvtSIKp3BcUw30EWcPcL6RlglOcF0tVXiAgSDuVQQ8cu5RCzKJ/4LzzzkvuyeNjn/vQVqmG013aj1Ra4RiPcW3mHB8fr2kmLOeO+FzANaCrzdvb2e1HSWqUHtPbW39NUlOj0UnArhiydwjmzDnnnEknMHNhMdK12A0eN4CMQio16jlFQIlGX9wBRlmFJZdcMpmbgs6TbBCP0c40qrpaw7Cz0fin0VkMIx0pdck6JZhD5wmjWIvdVPAcGuiVGp8EWIsFDdgn4hHQSy21VDIKOovRfGeeeWbOb9f9EZgmkFMM64/jj9HGdJIxQpNjo9ilmM4NbrKKIZsym3VF6b5q5zfjxu6iiy6q6jXdGWXaGL1cDNmL6bGXdoJQQqhYxhydXwT74pJSKbZ7dj4sOlooo8j2o0OL8ygdORzbxbJW1l9//SQo3N1HPleLEfzFguN0orDtOPZYv3RacD2igyaL6xbZAdnyQu3F9TMuTcSIdTrOVIj9mezkYmircJ3h+KBjhg7NUscGWTosp9hcksWurQRvCMTNMcccyWu4ltJ5GQeSUuxDlJAyA7MQGeWlMsJpO1Kim/Mm1xvaLKXWL+dYrmt5Oohp39LOzb4X8/YQ+OPYLjbinoEwZKjra7QzsiUuU9wr8MPxwXWJ8yvbr9gglh122CHJwMobdGNf4HiNM3r++te/enxVgcD47rvvXvRvZMvQ5uTY47rGuY02S7ESgNyPUWqWc2Epjz/+eDJYLHve5bijzcP5mfYQ9w3ZebXTgUZcDw3Kfo3gW1zZJsWxkLY3uV/gGCGQVqqMLcE+7sFLYRAKmUgptgEZy9x/c1xzvszOKwoGrWSDP91VPduPtFHiktspgqlxMJZz77LLLlt0GQw8qjT3J4NnCCxmr7OUZaftQ1Arey0maEsQuSupR/sxRRnUc889t+jfnnvuuYLzK+2iYiVGCfTGx2Mj3kNS1/H1RFSSuhVGwHLTxCixbKcyvy92QxWjYcpockpk5KlpTv1vRjPTIZI2/AkCMpq61PNpFBqwmxE32QRs/vznP8+QGUmHPzfdleYrZKQ7I/3ydC5yI15pHkKwLYttz3I3+a2IY4aAKqWKsoFwbrorlZ9i+3MclQrcotjIZjIzq0WnjkJBdhwjNMmMzJ4jCWyXOp/FN/cEZMjuoJMzL4K2HOuVyk1xs02Qnc9opsiMWDeMtCWTIMaNOtelYp1SMTr8ycRaY401cm871QYVAejsJcsg26lMp1KxIE+MgNC+++6bdCoXy04uhYFLo0ePLvscOlBpU7Fv0IGqQgTJOPZos2QDZXSM0UEWl8grZs0110xKbNL5n8euu+6anA/pIE3LyvFepa6DfEaCfOWuq62KYBv3CWR1Zzsy6Zgu1jkdozOU7EiyFqrB/hK3ZdgHDIhXh7kfCUKzLhkEFuM8yrmt0vmNzl3anAQIyll77bWTQQuUC47vQdhnmNOrFNoq3NOQsWXArhAlCBm8SqZkPHiP/zOgj59yuP/iXm/o0KGhGrSJyu0bBIO43rVSmcB6th+pOPToo49WfF6582+pyjsxKr1w7Tv//PPbzu3cy2SzNMGxSeC31GDRZlbP9iODlPJsO5SaI32llVZq+HtI6jrMtJO6OUayckEnMEM2HA30UvONMBqJbB1qupNhx+P2vB8joCmtk+3gpuOLYB0j0CyBkw8dUMypwzZkpGSpkimMdCWrkVFijNxkO+bFpPB5gnblbhoZHa1CBH3oBGPbcRNIQKZYNivoLCFgtOmmmyblULmBKIeOymuvvbbDq5ygXbHRnq2O7cT5kmOPkeyMhM6WXksxIpOOMzpN6PyqFMROS7jQyZX+lDv+OA9z88WcQ5yXDRpUxihobni57tE5wej0YtmsXJPIMOC6ROcU5888g1SqYaZddQgeUCaYY++ZZ55JgkClSizSGcU8nnTYEIypNAiIzhzmhuRaSqcZx3WpuSXZD8guZ/Q9c0fG86ipNEo2c+xx3WMdlyoLzfplnTJKnGPvm9/8ZrtWK4EK2pwELbLVHMA2pE1LZ2Q1AylaEcHPdNsxmr9UNgLIOuG6xDWJ9duekocECylRG1cQaKUgQS3RrmBdpm2WkSNHFi2BCDqlOS4o306bhf9Xg2ABZVEps0jbtljbiAAA2Ty0ZxnIZDC28nksvV9gOxIgKjWfOVlfq666anLNo+pCnkEqbCOmvuCaStu22L0k24x7R+4hKX3aqmVM69F+pPwk5Rk74qGHHipa/abUdZn5EzluswEs7gM5ThkIw37VlXVm+zH18MMPJwG+jihXSade7yGp6zBoJ7UYOqNpkNJoowOLYA+jlbkBjyctrlXQghsROgJYPjcarXoTUCuMvmKd0snPtmTb0fCktF6lEqalcJNYqmMmDxr8BP5UuaODwB3HA6PLKW3I9uOmq9oAOTcilUZ95sEIaEZPqzIyJOmApvODm3W2HZ3A3LR3dPQ4nWvsF/zLsc124VzJaOdiJTZVHUYWc7ywbrmBJ9DK+q3HNYnyQ3EGJaOx83a06OtR7nQysh05Njj2ODY6uh5ZNm2h9Phj+ewbBMYp6ViuRJLyYd3S6cmxR1Yj7QWOOTrxq8mIzIOMhA8++CDZlpybOXfS9lT70C7kfoFtyP0C24ttxyCjjq5Xjj06VuPOcDLtar1PtLL0eEgHBXHe5NzG/UKtBqew/dhH0vMz9yG8B+0i3k/tDz6wXjmX8X8G8qVtFu75Oortxf0kP5yXuQdZfPHFrb5Rp/ZjuQEtebXnfMm2pg+B8wJ9CFwj+emu593OaD9yzFC+siMY3FluIFg93kNS12HQTpIkSZIkSZIkSWqwjg0NlyRJkiRJkiRJktRhBu0kSZIkSZIkSZKkBjNoJ0mSJEmSJEmSJDWYQTtJkiRJkiRJkiSpwQzaSZIkSZIkSZIkSQ1m0E6SJEmSJEmSJElqMIN2kiRJkiRJkiRJUoMZtJMkSZIkSZIkSZIazKCdJEmSJEmSJEmS1GAG7SRJkiRJkiRJkqQGM2gnSZIkSZIkSZIkNZhBO0mSJEmSJEmSJKnBDNpJkiRJkiRJkiRJDWbQTpIkSZIkSZIkSWowg3aSJEmSJEmSJElSgxm0kyRJkiRJkiRJkhrMoJ0kSZIkSZIkSZLUYAbtJEmSJEmSJEmSpAYzaCdJkiRJkiRJkiQ1WO9GfwBJktSa3n///XD++ee3Pd58883D2muvHbqSSZMmhVNOOaXt8brrrhs23XTTTn/fa665Jrz++uvJ/7fYYouw1lprhXrpDtuto0aMGBFeeOGF5P8zzTRTOPnkk0NX1F2+R7OpxzHSqHOP8ps+fXp47bXXwksvvRTee++9MGHChNCrV68w++yzh8UXXzysvPLKYckll+zwKv3qq6/CI488Et56661k3+vdu3eYf/75w7LLLhvWWGON0LNnzy7xHmqu8/W0adPCP/7xj/Dcc8+Fjz76KHz22Wdh7rnnTrY7bY5FF120Ju/j/qvO8Morr4Qbbrgh+f8CCywQDj30UFe0JKlLMWgnSZIa4tNPPw233HJL2+Oll166ywV/Jk+eXPAd+vfv3+kd53SinXXWWUmH2hxzzBGOO+64UE/dYbt11FNPPRXuvPPO5P+zzDJLlw12dZfv0WzqcYw04tzTjOjwf+edd8Lbb7+dBJTGjBmTrJvUhhtuGDbaaKO6fqYpU6aE6667Llx//fXJ5ylnhRVWCHvvvXcy+KJaX3zxRTj33HPDbbfdlgRUiplvvvnCXnvtFfbcc892Bdbq8R5qrvM1AwJuvvnmcOmll4bx48eXfB5B56OPPjp861vfatf7uP+qM3HdffLJJ9vOwSuttFLYYIMNXOmSpC7DoJ0kSVIXytw49dRTk4Ad9t1336SzXlLtEJw488wz2x7T0bfxxhu7ipvE448/Hi677LIkUDdu3LjkvFgKGRb1DNqRhXbAAQeEV199Ndfzed4Pf/jDcNddd4VzzjknzDzzzLleN2rUqHDQQQclgcpyPvjgg3D22WeHBx54IFx00UVVXS/q8R5qLh9++GGyzV988cWKz+U5u+++e/jBD34Qjj322Krex/1Xna1v377hsMMOa9s3f/azn4V11lkn+b0kSV2BQTtJkqQu4p577mnrTJtzzjnDLrvs0uiPJHU7EydOLMhim3feeQ3aNZHRo0cnGRTNGOzdY489wptvvlnwezI8yLQkgEgW3rvvvhsefPDB5N/U3/72t6SDmWBkjx49KgZWGLDx73//u+13ZF5tttlmYamllkoyDcnIZpm8H55++umkPNwVV1wR+vTpU/G71OM91Fwo37rrrrsWBGnJnGTfHTJkSJLZ//HHH4fHHnssPP/888nfCZj/9re/Tcp1Hn744bnex/1X9UIG8wUXXJCca8nIJoN0t912cwNIkroEg3aSJEldANl18TxZBOxmnXXWhn4mSWokggXMrbXYYouFJZZYIpk77i9/+UtDPgudw3HAbrbZZgs///nPi2b6nXDCCeHaa69N/p5mTj/00EPhj3/8Yxg+fHjZ96EkchxM++Y3v5lkuDHfWDabaf/9928LDhLovPDCC8ORRx5Z8bvU4z3UXI4//viCgN1CCy2UtDkGDx5c8DwCs+yrZIj+97//TX538cUXh1VWWSWsv/76Fd/H/Vf1whyiZIKm879S8nW77bYL/fr1cyNIkpqeReclSZK6SJYdHaTo3bt32HnnnRv9kSQ1MFh1+umnt/200nx2ZP5cffXVSTlGMo///Oc/h0suuSQJBqyxxhoN+UzpPGCxUgG7tDOZeeAOPvjggt//7ne/K/s+TzzxRHj00UfbHi+yyCJJplM2mIZBgwaFq666KsmQS11zzTVJOctGv4eay7PPPptkTabYnmzHbMAuRXCOAEg8hyH7+9SpU8u+j/uv6m2bbbZJBlCAORrjLHpJkpqZQTtJkqQugE7q1NChQ8N8883X0M8jqXEI3G+//fZtP9/4xjdaZnMMHDgwrLXWWkkmUKVSkvXywgsvhC+++KLt8TLLLJNrLr299967YB67l19+uS17qRgymmInnXRSQcAsiyxEMuHi0q8E2cqpx3uouZD1GTvwwAOT7NVyVltttbDtttu2Pf7Xv/5VMcvV/Vf1RlYdZTLjfT3NbpYkqZlZHlOSpHZ65ZVXkjldKCFFJxsj5/v375+MPKcDlY6sZsf8JMytM3bs2GSeET7/iiuumASFyOQo5T//+U94+OGHwxtvvJHMg8L8anSkbrDBBskyauH9999P3oNyZ7wfo/xZt4zwLteBWAlzsNAx+txzz4WPPvoo2XbM1UIQ7Fvf+lZYeumlQ7NhP6NTOB453Kz7LMslA4b5Q1i/s88+e9L59+1vfzvMP//87Vomcya9+uqryWdnf+A96IiZa665wvLLL5+UbqtFqVCOAT77mDFjkvcYMGBAsk44HtKR2rXAMUPGwdtvv52sI74f32XxxRdPsojmmWeeLvE91DzHSHf6PF3RuHHjCh5zTsqD89gKK6yQXI/S6xPXPrZBFtvmmWeeaXu88MILJ8d0JQR1KVnJPHRp1jZZicXU4z1qoV7XhHocJ40+XxPAiOeIJBBOCcE8dtxxx4LMpbvuuqsgQBJz/y3fJqC9S9uMLNUvv/wy2YfLbYeRI0cmxyrP//TTT5P9kflXeR3zaDbLgIb2oKrESy+9lJRrZa5Qzou0VWkj0ValzVoN2sw33nhj8n+O30ceeSRXKVdJkhrJoJ0kSVXgxvHWW28Nl19+edLhXg7BL0oYMhKZ4EiMMkT3339/22MmRqejqdqb2iuvvLLt8brrrjtDiTQ6/+J50DbffPMkKMBN/tlnn510rKWdbDECZCeeeGLYcsstC37/+eefh3POOSfcdNNNRV9HpxnznZA90F7MjXPmmWcmwcRipZYI2O25557JSPC+ffvmXi6f94Ybbki2HZ1kpdApcNhhhyXrqlk6PW6//faC708nYb332UroNGLfYE4mMi2yWJd0nNCRS5A3Dzpk2df++te/hk8++aTk8/r06ZN85oMOOigssMACoT3B6zPOOCPJEii1z/HZjzrqqA51ov7zn/8M5513XtI5V6qMGOuJzlrmhFp22WWb8nt0V2QHkS1Ch2mM+ZtKnTMIDHCurPcxQjnGdJ6eUuf/SteBzjhmWxVBpFixoFsp2edml5WizRBniGy88cYF5QlLYRAAWVEMFACBAQJeBAsb8R4d0ZnXhHofJ81yviaIwcCo1JJLLpl74AhtBtp96XqhrCrtxGIB01bef0vtW/z+F7/4RdIW55weY5/IBu1oz7EfMn8mQd5SCPzSRiaoSlZ2KT/72c+S7QUCfZXKrvM5ab+kCJIfffTRFY/Z3//+922PmWOuVNCNeyPWE22lchgkuMMOOyRzO5cbYJhaeeWVk/NAOriCNrVBO0lSszNoJ0lSFUGfH/7wh8lNa96sph/96EdJwCQd4ZlidHY8Opmb87POOquqbXH99dcXLIOR7ll0NsXPIYuMEeEEvcrNOUNnEjfidIrtvvvuye/oXOB1o0ePLvk6Om6Y14Rln3DCCaFaZMDts88+BR1IWZQgo8QSHQeUjMzTOco8Fvvuu2947bXXKj6XwBbb+Y477gi//vWvGz5hPR1QjF6PO+fzdFLUep+tFGilI4bOv1LobLrtttuSDhzmyik2P1KM/e/OO+/M/T3pyL333nvDRRddFIYMGZL7s7/++uthjz32SPb5cvscc039/e9/LyhTWg2CdZdddlnFskysJ7Iu6PwkKJM346Fe36M7IziXdtrGOG+UOnfQSZ8naFfrY4R9Pj63k4VQKmhX7DpAJk+tj1muF7/85S8LfkdQohUsuOCCM2QV5ZUNCJfK2kqz8VLVnOfIvon3bZZVLCBRj/dor86+JtTrOGm283V2X6XsbF4M7qFKQTog6KuvvkrWQ7G5JVt5/y22b7GfsG+VOlewX8XIOGNQ3OOPP56rzXvqqaeGP/zhD8mArVL75JtvvtkWhGO5lYJ27I9PPfVUQcCauTnLBXlpv6bfnQBqqQxcAogcP3lwH8I903XXXZcc4+UCk+nn3HDDDZOBe2lwsFRwWZKkZmHQTpKknBj9GQc/uPlkZC4jOOlk46aRG3M6d7jZ52Y47azN4oad16ZlqBhpTcdv3tKSBMfizisyclZZZZWKr6P0EsErgmp0tjDSl04N3pffMXqdDL4UN8WUjCTISDAtDdjRubHmmmsmpXj4ztz0P//8822vo4NpnXXWCeutt17Iiw5fRganATvWK8vgPfgbHQV0XqUdGXQM8V24aWdUfSl0itEREW8Hth2dSquvvnrS6U6nKUESyvHEHfh0qDD/RbnldzY+U9ypU6wzrB77bCl0JLFvpJ2a7E98RrYbnSLs4+wf6Xajs/K0004L5557btnlUno1xTbiM5ONyshuMiz53IyEp8xROsqf/YR9gpHoeUp9MpqeDqe445SAKJlujDonYMtz6ODh+/HZycKsNpuPADadujGWz/7HstgOlHh77LHH2oJDdDpzTmCbVSqHWq/vofbprGOk2T4Pr407plspaEfJNjKj0nnt6PxnME6ljGU61+OskvQcV0x2wEw12fnZ4EN8na/3e7RXPa4J9ThOmu18Hc/FiHiOxTyyA5vIVi7WTmn1/TdGO5f9Mm3b0X6nTU2GHO3RuBx6Ggxl0BwD22K8jnY8+yTLTNvJ6eAgnk/7d8SIEUXvL3jPNGhHmXyCr1SbKIbPELfzwb5PaVUyB0uJS6+yjot9DgaJZQN2gwcPbmsjcXxz/8JnZN2k7SSOE86zlYJ2YJ9Mg3Z8F87RZGJKktSsDNpJkpRzvglKp8XzvJDttdxyy5V8DTeVZMPRgVQMN9Jp0I5ybGR2USYzD4J83MCmdtppp1yv4zvQ2URpGTqWsp+fUbynn356WykbboYJ/JBFQMcRo2l/9atfhbXWWqvgdZSgYtlxtiDLryZoR3CMziNu6MnWY3682CGHHBKefvrppFQUHZ2gA4FRxAcffHDJ5Z588skFQSgCkHwHOlljhx9+eJLhxChgOv9AIIsyRLxnoxBMjNGJ0ah9thhKtLJPsXzW66qrrlrw9/322y/ptCEgm3YOMvKaDqtyo9gZGU35ol133TUJ3pbqlKGjinKqaQlRPsuPf/zjXJkJ7BtxlgsdYHyHRRZZpOB5xx57bPI9+RvHbDVzKrI+44AdAXsCGXQ4Zx1zzDHJqHGCdenxTbYdn4uSZY38Hq2AjnQ6H+msj89ldKaX6tzLU6K3s46R9mq2z9MdkLFBubb0vEMQnk5oBn6UQ5nquDRiuefHAQnOj9UEcbKZgHmCHp31Hu1Vj2tCPY6TZjtfZ0tvcv6rRvb5BO2KafX9N8a5gX2GLEXau9k2dVrdIsU+EAfsyJzjddlS6bSFaRfTZiXABeaFox3BMrKy70sgq1TQjvZwtoRn+ppSQTva0nGWerHvSYCR+TDjgDz3HeUGqBFc5D6FzL+8GIAYo51r0E6S1MwM2kmSlAM3pfEcboymLhf8SEf40jlfKmtp2LBhSRAsHWl788035w7a8dwUHTlbbbVVrtfRAcVIXrLTGJmbRScYnVt0RKVZV8wtx2haRl/T6bXMMsuU7PDmdTwfjHhnZDxBijzowOD9L7nkkiQbrNRN9xVXXJHM05HOO3XppZcmj4t9HwJeZA+mWN98BzrhiiFQyPIoCZpu79/85jdJ6dFqR+nXSjyymYy/UnOB1GOfLbVP0YHECOZSHWSM5j7++OOTzsoUHarlOjYpJ7nEEktUfH86eOj4poOOUlDpdx85cmTZOeHYT+OA6FJLLZXsW8XKrZLtRsYF+yedwdnMhFKYP4XPFs//Q3C63PxEnBc4Rr///e8n24+MEY4JOuga9T3ai2zgeJR9Z2B91WpumnQ5DAqIg3YcN8XKDzf6GOkun6e7IBuK/T3NnGOuKoLvzPGaPebpkOeY/tOf/tT2Ozq+S13LOQ8wECO+llUzp2s2IFGsPHY93qMjOvuaUI/jpBnP19m2U6W5b2O0w9J5whq9bzX7/htjW9J2pz1aqk2XlsklEEm7IUVbnH2G9kQxBJcZsMUggnQAGucZ2snZwBVtPo6XtMIFx0mpQYBx6VA+WxpULFZSOkXmX1wSnOMli3uFdCAeKOleqaIEgUWOO+6Z8mTZgWM5/tzZrEFJkppN5Vl5JUlSMmq+PdlOKBXsoTMhnq+KDqVsSZxiCITFN5tbbLFF2SBAFlk8xQJcKcp5fe9732t7TBYAN92MKi8VsEvRSRCjTE816FQoFbCLO9DpyEox8pc52EplOWVHrpcK2KUo/clcMym+e1pSpxEoA5qicydvB0Vn7LPlyj9WGtG+7bbbhjnmmKPtcZplWkqeztkYHThxma4///nPZZ+fnbOPTtdK8yOyX1CyqZrR9OnIdLYbnfR5jlU63eKOM75LqXke6/E92ovzFOUSO/OH82ZX0BnHSHf6PN0l245O+DRLnGsH2c1kwzAQhOsPHdJcJ5lfKQ3YEZzh73GAPysbsKl2LqZsllaxAFA93qMjOvuaUI/jpBnP12T4xSVZaTvkDdzxXadMmdIU+1az779ZZGjmGYRFNlk8vx2ZnKUCdvGxcsQRR5RtD4PAdhwgI8iWnUsvFQfn+Axp2fgxY8aUHOgVv4bnF5tjMNtWzQYWK+27lUoQx+KgPRmhaRldSZKakUE7SZJyyN7ExqWNOoIgFR12qZtuuqnia7LPyVsaE2TwkJlSSTZwxk1xnvfJvq7aDvU4WFYOo2vjeeaKdcYxSj6dqyP97ltuuWWu5VOiLA6OtaezrxaY8yYO1lQTTOusfTaL0eybbbZZxeexveLAIcHnuCxcRzFaPF5+PD9hsXJe8b7BaPM8cwXSwZV3H6XDPi6LScc9WRV5DR8+vO3/dIoW6wiux/dQ9zlGOvvz0PFPeeX4p9WwDsjWJpt93XXXbev8pzOcedUI1L344ovJMc1xyHFORupJJ51Udu7UbACBOdCqkX1+nqBHZ7xHPVVzTajHcdKs52uWz7xolQI8xbCfF2t7Zbn/zrjPZAe5lXL33XcXDPYjwLwjIocAABqqSURBVJ8H2eEcA6n777+/rUJFLN4HmQMynmMz3ndfeeWVtscMOojn0S6VUR//nudn5z8s1laNs+5qLW5Dc4xWk1UqSVK9WR5TkqQcssES5jn72c9+lnR2dASjROnMf+ihh9rmqqPzrlQ2DpOnM/dditHXlUbcZoNqeTK1st+XYAMdWJUw2pyftCQPQae8yOIrNZdGsY5Rvks6ipcRs2m5obiDLu44Y+6KvCNy+a4sP+1woJwOJaCqmR+lFv79738XLZfUyH02i87KvNl/caYEGWh0BMUZCh3FfIUp5mAshblhsvtGXhtttFGyH1UKpvD+ccA17cDPiw5dAvppaSkyLrOfsx7foyMIkvM9OlM9Mga70zHSmZ+HzJaOlBHtLpgb9bLLLquYOU9nNUE8rpfMC5udZzWWnUsq7/ZLcd4nUJCWTKYt0Yj3qLe814R6HCfNfL6mHHM8OInsLj5fuaAiFQ7ScuixYvOeuf8W4liPA2qlkMEWD7iivCTzPufBsch+k1ai4LikHZEdXJedZ452b/a6zaCDdN/jmKJiBZ8lrabBa7LnftrN8RyDxUpjpsuLkZ1MWfpqzz95ZNvQtLErlY2XJKlRDNpJkpQDN5uUMGJ+GpBBw80oI16HDh1adRme2M4779wWtKNUC0G5XXbZpeSI2zQglmbqVWPJJZfM9bzs98n7uvS16Wek4yqvaudKolMhDdrRmUBWH2UFU9nRwiuttFJVy+f58Shh5t2od9Aumx2Xp5OnHvtsbODAgbmfmy0DljcgQVYK659OVzpZGMnPsRLPlZJ2iqbiuW2ysvtGNfsegWGCy3FnVDHZDns6N0t1GscjzeP/00mbfsdiAfB6fI+O4HiMj8lWVY9jpCt/nu6C8xSDbpjTLA5kEfyh7PJ8882XPIcBIFy7XnvtteQxQT4ysJgT74ADDii6bOaxqhQYKYfzSDzHabGMl3q8R63U+ppQj+Okmc/XnKepwnDvvfe2tan233//pDwo5dLjAU8EY6+88spw/vnnF11WsUFn7r+F8gaKOEd0tB0bl49nedmgHft5do465qiOxW3hNMhHG5PBYNm/F3tN+vxSJSsJ3L3zzjvJ48cffzxsvfXWSXbpJptsUlC6taOyy+rMrD5JkjrKoJ0kSTnQCXHkkUeGU089taBTnh9GszJqlk45frghzjsSFuuvv34yajXNqrr55ptLBu34W9xJxHx21ag0d0oqO8I17+uyr83OdVLOggsuGKqRnZuOsj6x7BxgleayK5YFWW559ZCdb6OacmWduc+2d9/Iln+rtH989NFH4ZJLLkkyAKrJ2gSdx6y/Yh3H2X1loYUWqmrZPL9S52m2M+iRRx4JHVGsw7ke30Md15nHSHf4PN1FNmBH9vgvfvGLkh30nBMIinCuIEhy7rnnJsGNPffcs+J8W9VmsWXL4mWXV6/36KjOuibU4zhp9vM1mfhULUjfg3XFnHvsl5Q2JAjJOn/22WcLSmAyeCwu215svbn/Fpp77rlzbZNsu7PafSZvO5aAGuV7QfYcx0q8T8cBuDT4tvLKKyfblSoXDDAjgB7Pex2/hufx/GIY2MB5kGzjdNAS++GPf/zj8NOf/jQZoMdceASWme8uT9WPUrJtaOe0kyQ1M+e0kyQpp1133TXplMuOGObmlk4MyrkwsTzlhOh0o+xVdtR30Ytxz54FGXOMhC0298qoUaMK5rXaaqutqh7JHs+fV4/XVaNUSdC8z88GNbKPq80sy3Y8VTtKvxaymQ7l5jyq5z5bj33j0UcfTUb+M2dOtZ2zqTjrI5ZmH9Zq3ygmzoithWLbpR7fQx1Xj/NnV/483QGZcnHAjs7ya6+9tmxGDaWxr7766oKAxq9+9aswduzYGZ6bPbazx34lec4V9XiPZr0m1OM4afbzNcu/4YYbksBINthINij7N/t5GrAj++7www+fYd6/YhUB3H/b197NtjurbSfnbcfGJTIJwsX3IATK0yoBBNjSoB3t0ThrL618USxox/PKtV+ZI4/gcDY7lcEMlPS85pprwhFHHJHMvcj90u9///uqM4HTz9ze84EkSfVmpp0kSVWgtOB3vvOdpGODAMeYMWOKdq5z88rP5ZdfntyIDho0qOxyKT9EmZn0BpKMuuz8NiNGjCh4vNNOO3WrbVdtsCg7t0s2OzA7f121y8+OlM87H14tZUcFt6eTorP22c5E2TOCifH3pbOFcmJ0glPKic4r1k+8XSjtRaditZ2xHd33isku89BDD61qTsJKI+br9T0kVUYgKUaWc56ybswXy3xizIEHznlkLfH6WN++fcO8887bVjKZjJnsPK7lvPfeexUzdurxHs16TaiHrnC+Zp8l2Mz8ygSUKS+a/Zy0tdZZZ51wyCGHJO3UuPxiWu4wq9X33/bqaDs2u8+Uasdm57WjLUh2Wzb4xrEWZwnyuvT44jWUtMTbb79dMPigVGnM2KabbpoE5W688cZw5513hjfeeKPo89KKEQw6I5M5/Zx5ZNvQ1VSvkCSp3gzaSZJUJTqH6FDjh0nin3766SRriZ+33nqr4LnMs0bQhPnEynXYU+6FEeSUfAL/Hn/88W2jarnRjEfxU9IwLkPTHVQz/12xUevZEcXZx9VmDWQ/T3vLR3ZEdnR6e0v5dMY+25ko0xV3ruywww7JiH465MqhkydPB22x+YeqkWdfymYbUF5s3XXXDbVUj+/REXS8FZvrppY4b1JiWGoUSrrFWfB0jG+wwQa5X7/xxhu3Be3A+bkY5p6K5zmlpPbSSy+d6z2y2XulBmXU4z2a8ZpQD81+vk6RTUX2HD8Evqj+QLYdASDmZWR/iAM3L774YsHrswPOUq28/7ZXR9ux2eeXascyX/MSSyzR1h7kuk1QNptBlw3uxcE4ymqyj3D+y2bdZV9XCpl2zKXID3PssUzaqZxfs3MCs2333nvvJMiXd47AbBu6M0r4SpJUKwbtJEnqgEUXXTT52W677ZLHTKROcI1SLmmHDB0IF154YTjttNPKLmvnnXduC9oxOpisqDSb7q9//WvBfChxOc3uIhs8qoSRvLEBAwYUPM525rF8ypHl9eabb5ZdXj1kg2aUKWqmfbYzjBs3LumoSdH5nfdzZOcMKiW7r7Avrbjiiu3e9/K8B53HtQ7a1eN7dMTzzz8fbrnllk59j8UXX9ygnRqKjnGu2XG2UjWlD7NZtHRWF0PwIQ7okQWVNyCRLbldKiBRj/doxmtCPTT7+brU4JNKAReCKilKcZP9WEyr7r+13GeqbSdX046lTHq6fDLZ0vkf4wBcNmOO+ebYRwju0n6klCVz18Wv4e95g2rZ9i/zdqdzd3Ne5B7pt7/9bVtgls/485//PFx55ZW5lhkHdNNgpSRJzcoJDSRJqqHFFlssHHbYYeGqq65KSvWk7rvvvoqvXX311Qs6FyiRmaJcVnwDTBmZ7obOlWrEHSyUuMl2zAwePLjgMZ0Q1Yifz8jzlVZaKdTbwgsvPEPnZTPts53hlVdemaF0bK33oey2LDaHZLlOn2JzTmWRDZudj6nW6vE9JJWXLc2cLa1cSfb5peZ+olM9Fmf3VZJ9bqlydfV4j2a8JtRDdzxfE6T517/+1faY7LzsHLqtvv92RC3bscWWF4uDs+m8x5RTT0uqc17iPiVG2zhe5wTryDyOA6f8vRbzQxLE+8EPfpBk1sWVDMgKzJu1mh0Q0QwlUCVJKsVMO0mSOgHlgei0T0vDffzxx22jVsshsy4dQU5HFT+UyIxvgLfZZptuOQ8DHVKUWWKUbiWUeKSzKF7f2Y5ORgBT+ibNgHjggQdybQMw2jhePvMOUban3tj2BO4oA5R+rmbbZ2stmxlBOa48KJ2UHVVeSnbfuOeee8Jxxx2XdEBVctddd+V6D8rXMkp+/PjxyePHH388+XxLLrlkqJV6fI+O2HLLLZPP2JnKdUK2V3beHzohpVI4BuPj8NNPP02yoil7nceoUaMKHpd6HZniXPu/+uqrtgz8k08+uWSQJMV5Jw5ecUySbd2o92jGa0I9NPv5uj3I0I+VqwLRqvtvR1CGNC5bSRCOtnKeYBOBrIceeqigPVmurD7BNfbF9HpHAC6e54+2ebFykgT72JfT1wwdOjRpP8bLrSW2y0YbbdQ2lyIlORnQRju9krgNzT5Bpr4kSc3KTDtJkjpJ3PHGjXB2NH4xw4cPL7gpJsOOjLu405i5XLqrSy65JNfzLrroooLHW2+99QzPIYi3+eabtz3+/PPPc5fQ+fWvf13wmEBpM4zOJ1gZl2Frhn221rKdQnmzC88777zc70FGYZytSidYPGdkKV9++WWSkZgH62/XXXdtezxt2rRkXqZaBoDq8T06YtVVVw3bb799p/60p+xWJelcou2de0qtJ57Hi2P8j3/8Y+7X/uEPfyh4XGrgCudGOqtTEyZMaOu4Lufqq6+eIZheSj3eoxmvCfXQ7OfrahGgueOOO9oeU/6ZuVtLadX9t6O4L4jPLZQuz+PSSy9N9p24nZwdkJINEC677LIF2zeek7ZUmdT495TEfvDBB0v+vVayAxviKhHlxPPiEZhtRBtXkqS8DNpJkpQDWVrVlCaio4DMmhSjOUuVvMp2FqfzN4B57eIOPUasDhw4sNtuM9ZzXAq0mLvvvrugo4uOhjg4F/v+979fUJbnsssuq1gu6bbbbisY1c42qaYcV62tttpqBR02r776alPts7WWHS19ww03VAx00Tn1t7/9rar3IaAWZzicddZZFbMyTjnllKrWKfsf+2fq4YcfTjJpGRlezXxZnAca+T1aDR2AzEuWisu/qXxG1EknnVTw0yrfZdiwYQWPL7744oIO4lIoQ3znnXcW/G6TTTYp+fxDDjmkoOP9nHPOSQZzlEKne3xNJft3l112KfuZ6vEezXhNqIdmPl+TaZb3faiKcOihhxYEy0499dSKr2vF/bejyF6MA9e0Uf/yl7+Ufc1jjz1WEIgkOLXbbrtVfK+4JOhrr71WUNa7VLlQMgEXXHDB5P9kOMbZl2TFVrpnYf1ns43LmTRpUrj//vvbHrNu8sxNxz5AFnSxtrUkSc3IoJ0kSTkw+fnGG28cDjrooGQEPZOul+v42HPPPQtKOsUjZSvZeeedC7LDKLMVl8/srtKbfjqoGCGfzSjjRv2KK64IRx99dEGHHZ2pxUr2gFHDe++9d9tjOhT222+/ZE4M5uyI8X7nn39++NGPflTwex43ojRmar311it4/NRTTzXdPltLlG+KO3kY7X3MMcfMUCINdDDyt3PPPbfoHIDlrLjiigXHE+uHIBud6NkO4Q8++CCZ94/OsmrmQSHgS4dhPJr7d7/7XdJ5ls79Ugyj4x955JHw4x//OKy//vpJB3Qjv0crYr2mmNvnueeea+jn6Qq4Xt1yyy0FP63yXcj6jM8/BNvp+B8xYkRy7cri7xdccEE4/PDDkyzcFKXlsvNhxgYNGlRQgpAsUN4ne11gmWRBHXDAAQXngSOPPLJiyeN6vEczXhPqoZnP1wTiyAQ8/fTTwxtvvFH0OXymX/7yl0nwkX049ZOf/CTXum7F/bejGPhz7LHHtj3ms7KPU5kizqQD55rrr78+HHzwwQVt3AMPPDDXgL84K471k7YbaWOXK10fB/Ti0ph5suw4nhl4RxuU4Cn7WCmjR49Ovks8kIbX5sm0i7MG01KqkiQ1M/PBJUnKiewYRm6no7fpoOAmmIAOGUmM4GRkfTppe1yCJQ4cVbLCCiskN8d0oGTLwZQbgd/V0QnE6GHm8aMzgtG6dF7yvek4oOwO2WAxMuDizMRi6PSKRwzTGUsHE516lHJi+xEYpVM+GygkgNrI0pjpKGb2Mzor0g4OOmSaaZ+tNTqkCDamHWlkojBnCtuLjkuCr++8806SdZg+h87GRRZZJPzmN7/J/T4EgF966aVknwPzz7FuWU8ch8x5Qicw88ik2XF0ztNRljcjgU6rM844Iwn+pp1o7Gt0UM0555xJ+dM0q4tOUOYvZFtXk41Xj+/RaiiPlp4zpkyZknT6UgJxscUWK5hTdNZZZw0nnnhiAz9pa2JgRzpfZCw9T6bIyChWTpFtyFxXtUKnMWWV99hjj+Qakx7PHPdkU7HvkMHD8cc8UcyZms67FWc3c66o5IQTTkiCKn//+9+Tx3Ry77777mHppZdOstI4z3DNS+dCTTFYYLvttsv1ferxHs14TaiHZj5f8x7XXXdd8kP2Em0B2gvptWnkyJEFAS6yBtlXqhnk04r7b0fRFmWfSQO4fH7OgZdffnlbO5n2HO3kOJsMG264YRKYzIPsMwYZcc2Lrb766mUrLxC0y5b5TX+fB/sUbVt+0gw9thVtJM7V7H9k42XP7wz2++EPf5jrPeKgHcstNzhCkqRmYNBOkqR24mY/e8Oftc466yTBobzzLaToIM4G7ehIaES5wnrhu1G+cp999gn//Oc/kwBaXJoni06sPOWYWPdkKtFBm3Z4gEBgdu6NFJ0WjOalzFIz2Gqrrdrm5yHgw0jmuOxiM+yztUQn0/HHHx/OPvvstiwURpA//fTTRZ+/2WabJc8lU7IaZMIxzyEdWnR2VVpP3/3ud5P9qNogDR2adMgfd9xx4e233y7YB8vt46DDtNL8OvX6Hq2Edc5cSOk+R6ci5+TseZnOP9dj/VEmuVJZQRDEKVZSmMyRWgbtMHjw4HDttdcmWTFxuTcyfeLSw8WsvfbaSXBv3nnnrfg+nJspv0lgggytFEGKYhlSlIhmkADBorzq8R7NeE2oh65yvibYXW7+QILQBJnJBq9GK+6/tcCcuASzCEKnwdxy7WQCqmQckrGfd+429k3OY/F+mSdjLs98d9UgyFou4y7NWmV+v7iUdSmcKx566KGC80N3vp+SJHUPBu0kScrZgcvoZ0bmUpYlW5Imxo0gN6rcLFOesD0YIX7mmWe2labh5nuHHXbo9tuKTqCbb745mYuDf7MZYCBjgVHplBHLi23C+mQdEhhkNG+xbdi/f/+kA4oR/c00d+DWW2+dlFKjo4YfOqEq7Q/13mdrjQ40stDIXinWMUsnG6O/GYHO8dJeBF2YI4mSUmQXkK2RxeeghFlHSoauuuqqSSYpGSIEg8iiKFY2L83eYoQ6mbXf+c53SpZ/bcT3aBXMiUQ5Xjp+KY344YcfNvojqY7eeuutgscbbLBBrtdxjDHnKiWJOc4pPZzNWklxfuY457zL8uO5zirhWnXRRRclZZApuctgjmx5Rc7rlIAja7o98zfV4z2a8ZpQD814vmb7Ub2AeX+z+3+MDKhtt902KfOZ59pUTCvuvx3F+YHSnbTtqEZBECouUZqitCeDAAgK016uFuekaoN2888/f1hyySULBlLEc92VQ5ubKhq0Van8kGYqF8MxToYc+ynt4njO6nJo88frqtEVNCRJyqPH9EqzOEuSpAIETQgmMZ8KZWj4YeQ3I1S5QaVDo6NZShMnTkw6EtKbzHXXXTf89re/rXo5fLZ777237TGlpPh8ldA8iOfwIYA1ZMiQXO9JYILMgjQIVyq4Vumz8RkYEU0pMdY1mWXMVVKLOWoopUUHAR3xfFY6dxjBvNxyy+UekQw6Y+OSQMyh155OkjwOPfTQtvXFumLuj0bss+3dp0BpLUo8pehY5TNUQmYh5eT4/HSysa3oEMpmpbBs3iNFp061xyKvZ12xX7D/LrrooslPjLlx0k5W9pf2dAARROW9+G6sU77X7LPPnmTkUYKxmg78Rn6PVsCxQoYkHZKsz7isIfsX+1m9j5Fqzj31OmbpbL3rrrtmyIjurEy7Yh3WeVXa38k2jueSZG669pzb0+OcEojsO3Qyc5xz3qVMXzXXm3Lef//9ZB8lO4WAM53oXC9rOR9rPd4jr864JjTi2tas52vWL1l//Ms65nrE52J98G+tdff9tyP7VilcA2jHcm4h6MX+xv5PidWO3IO8++67BaUk2fZU+qjUJiEwFg+0ozRttZl2tPvT/S5tq1IKlIFMHOPMb9meQDFl8imn2572syRJjWLQTpKkJsQI/bgUEqOFmyUDSo3BKHEyCFJ33HFH0lEvSaotzrWcc7Heeus13bxokqTKmLOabL50TmFKzTd7Jq4kSciXTy5JkuqKckkpRuTnLc2l7ouSQJQ8Sl1zzTUN/TyS1B2RHUdZyxTlmCVJXQ8laNOAHdmNlByXJKkrMGgnSVKToYQLJW9Su+yyS1LKRzriiCMKMu0o9yRJqh3mm0w7eddZZ52knJokqWv54osvkrkjU4cffnjuefAkSWo0r1iSJDWRUaNGhdNOO63t8ZxzzllQElGtbeWVV24r60On8uWXX97ojyRJ3crf//73tv8fdNBBDf0skqT2Z9n95z//Sf7PvNybbLKJq1KS1GU4p50kSQ00YsSIZFT/1KlTw9ixY8MzzzyT/D/FvHZ77LGH20htxo0blwTuGEHcp0+f8Je//CUsuuiiriFJqoEnnngijBkzJvTr1y9sscUWrlNJ6mII1hGkmzBhQlKt5LbbbgvLLbdcoz+WJEm5GbSTJKmBjj766HDnnXcW/du3v/3tJJPKUi7Keuqpp8I777yT/H+llVYKyy+/vCtJkiRJLe/NN99MBkJiwIABYejQoS2/TiRJXUvvRn8ASZJUqG/fvmGHHXYIxxxzjAE7FbXGGmskP5IkSZK+tuSSSyY/kiR1VWbaSZLUJBlTvXv3DvPOO28yb1n//v3dLpIkSZIkSVILMWgnSZIkSZIkSZIkNVjPRn8ASZIkSZIkSZIkqdUZtJMkSZIkSZIkSZIazKCdJEmSJEmSJEmS1GAG7SRJkiRJkiRJkqQGM2gnSZIkSZIkSZIkNZhBO0mSJEmSJEmSJKnBDNpJkiRJkiRJkiRJDWbQTpIkSZIkSZIkSWowg3aSJEmSJEmSJElSgxm0kyRJkiRJkiRJkhrMoJ0kSZIkSZIkSZLUYAbtJEmSJEmSJEmSpAYzaCdJkiRJkiRJkiQ1mEE7SZIkSZIkSZIkqcEM2kmSJEmSJEmSJEkNZtBOkiRJkiRJkiRJajCDdpIkSZIkSZIkSVKDGbSTJEmSJEmSJEmSGsygnSRJkiRJkiRJktRgBu0kSZIkSZIkSZKk0Fj/BwL/B0R1hifFAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1600x700 with 1 Axes>"
+       "<Figure size 800x350 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 723,
-       "width": 1793
+       "height": 361,
+       "width": 886
       }
      },
      "output_type": "display_data"
@@ -1558,7 +1639,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9a6a249",
+   "id": "5c70ef01",
    "metadata": {},
    "source": [
     "What the figure supports is narrower than the usual slogan, so it is worth\n",
@@ -1583,28 +1664,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "28f5a537",
+   "execution_count": 15,
+   "id": "b8e79b8d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:09.220265Z",
-     "iopub.status.busy": "2026-08-16T07:45:09.220131Z",
-     "iopub.status.idle": "2026-08-16T07:45:09.501613Z",
-     "shell.execute_reply": "2026-08-16T07:45:09.500965Z"
+     "iopub.execute_input": "2026-08-17T01:01:49.583622Z",
+     "iopub.status.busy": "2026-08-17T01:01:49.583554Z",
+     "iopub.status.idle": "2026-08-17T01:01:49.689477Z",
+     "shell.execute_reply": "2026-08-17T01:01:49.689043Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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uv4GPG/sktxv4mBhl0fQjyKYGAOEWZTbr9qNylfilRW/sLu5QG+/sKVGd3anbZuQqxnJwHonJbFbUBacZk+Tk/HKD3zbcB0rU9MizirnmUpnieQ/fG5ksFllPnncw26MfdpNJt+ZNVL219VTVtLho/XbKcOZMAohIlklj5di/xGu5tbFJc2or9WFSYAuvLMovI/ARAAD0WoEtFREBmgMbvQU3Gt8fHgTZmRsAdIR5TLaUlOizTlZjgxYU7W+37Pmt++RwuRh8AAAAAADQZZxut+ehfaAy42M0LsX3/RAAQPdVX1/f7v7jjjuu0/sCAIg8rt0FUl37f2uaLUttP7PU7IwUT1ARAHRnFpNJv58yQhePzOxwG0v2leu6zzd5AiCbeYIfLz5T5nEjA2rDXXBAtr8/L3dTU4f7gZ7NMm2CzHm5fus9OmKUtvZpvbCAMVvylmk5So6OCmMPAaDrWCbl+a1zVm15wO1tr67Xzmrfn3MAAAAiVa+8Y+sr2LA5ALLlBgDdlSkqSlELZvmtd/HenbK42wY47q9v0qKCwCcWAgAAAAAAhNqa4iqVN9kDrn/SkP4sJgcAPZi3Z28DBw7s9L4AACKPa/0Wv3U+Tx3Q7v75WYFlWwGArmbMe7tm/FBdPW5Ih9tYXVKtaz7doKoW92SMLH7RPzpH5lEjAg42tz32H7ltgd/XQWT5+JijVWexeC1f2j9dr2a1/Tm9cGSmppFlGUAEM2cMkCm9v886o/ILZAkiacVi5nkCAIBeqtcEPh5JQGN7x4ZyA4AjYTlmqtQnwWedQQ31ml98oN2yZ7YUysVrEQAAAAAA6CIL80uDqn/SYN+TBQAA3VtiYvtZe1NSCDYBABw55zrfgY/7Y+O0K6Ht36J4q1kz0vtyCQD0KJeMyvJkf2x/+X//NlbU6cqlG1Tc0NRqAe7oK86XeURgQZWubbtl+9d/5XY4OtgL9EQ1Nof+uGKbbt5WrL+PGNVunUUDMnT7mAlGpG6r/SOT43Vl3uBO6ikAdB3L5LG+yxubNK++KuD2FuWXMuccAAD0SubeluWxO24AcESvb9FRsh5/rN96F+/ZKXM7AY47qxv02f4KLgIAAAAAAOh0TU6XPi4sD7i+MTFqeFJ8WPsEAAgvbwGOjY2NDD0A4Ii4isvkLvK9sMqy1LQ2ARiGWRn9FGPpNVNoAESQM4an689H5crawTlou2oa9JMlG5Rf23BonykmWtFX/kCmIZkBteHauE22p1+R2+nsUB/Qs3xTWq2LPvxGHxaWeb5/O2OQvkrud6i8PCpaN+dN1B1jJshhbv23NcZs0m0zchXN31wAvYBlUp7fOmfVBD5vc29to7ZV1R9hrwAAAHqeXnHXNtwZG8n6CKCrWY+dJiX6nvQ3pKFOc0vaz/r49JZCVgMCAAAAAACdbtmBCtU5Ap8UdyLZHgGgx8vJyWn3fnR5eeCB8AAAtMe53ne2R8Oy1AHt7p+flcqgAuixFgzqr78dM1qxHQwm21/f5Al+3FpZd2ifKS5WMT+7SKbM9l83D+f6ZpPs/35DbperQ31A9+dwufXPjfn66dINOlBvO7TfbTLp7lHj1Gg264MBGfrh9GO1NG1gu238fMIwFjUD0GuYMgbINMD354yR+fmyBPG308j6CAAA0NtYFeHuuuuuru4CAISdsdqekfXR8cYin/Uu2bNDS9IGynXYSn/ry2v1VWm1pqQlh7mnAAAAAAAA31oY4EP6UdVVinE5dWLWJIYPAHq4sWPH6oMPPmiz/8CB9hfuAwAgUK71W32W11qs+qZFRqpmRqDQzPS+DDSAHu3ogX314HF5uu7zTaqxB595sbzJrqs+2aB7jh2jCal9PPtMCfGKueoSNT3wpNzFB7P7+eJctVaKi1X02Sd36P+AruUqLpV5QP92yw7UN+nmVdv0dWlNu+X74uJ1yfRZKo6N89r+MQP76uwR6SHrLwB0dyaTSZbJY+VY+InXOpaGRh3fWKMP4gObt7mooExXjRviaRsAAKC3iPjAx+9///td3QUA6BTWWdPkWPy5VFfvtc6w+jrNKTmgjwdktCl7Zss+Ah8BAAAAAECnqbU79Pn+ioDqXrx3p2aVFUu7Nsk2cYwsk8bKnD1EJnPHMhkAALrO3Llzde+997bZv3z5cp155pld0icAQM/nrquXa+cen3VWpvSXo53PEMcO7KdYqyWMvQOAzmEELD46Z6yu/XSTJ5AxWEbA5DWfbtRfjh7lCaQ0mJISFXP1JWq6/0m5yyt9NxBllWVMTke7jy7ibmqS/a0P5fx0paJ/8gNZxo5sVf5RYZnuXLPDb0Ctr6DHfjFW3Tg1m0AdAL2OZVKez8BHw1k15QEHPhpZmjdU1GpcysFFCgAAAHoDZoUAQIQwxcTIOn+m33qX7Nkpk9vdZv/yokptqawLU+8AAAAAAABaW1JYLpur7T2Kw8U7HJpR/r/MkNW1cn66SrYHn1LjTX+T7b/vyLltl9wuF8MLAD3EqFGjlJ2dfeh7Y4V6t9utzz77rEv7BQDo2Zwbt0t+Pl8sS01rd/+8rJQw9QoAOl9ucoIemztWGfExHTq+yenSr5dt1uKC0m/fs/dLVvQ1l0jJPoIsoqMUfeWFbYLm0L05N+9Q012PyvnJSskt2V54S+6GRk9Zo8Opu77cod9/sbVDWURbunFqjlJjo0PUawDoOUyZ6TKl+f68MaK0VGpnPqc3i/L9Z2EGAACIJAQ+AkAEsR43Q4r3voKaYXh9rWaXFrVb9syWwjD1DAAAAAAAoLWF+d9OoPPl6PISRbvbCWysqZPzMyMI8mk5P1vN8AJAD3LZZZd5gh1bqqys1JtvvtllfQIA9GyuDVt8ljtl0oqU/m32x5hNOmZgvzD2DAA63+DEOE/w4/A+vuePeONwu3Xjim16bee3c0vM/VMUYwQ/Jsa3PSAuRtFXXyLLyOFH0m10Ind9g2z/fkO2R55tncmzqkb2NxZpa2WdLv1onV7fVXzE5/rR6CzNyuBvLYDeyVjwyzJ5bNuChHhZjpmq6KsvVuIfrtKQIP5mf1hQKlcQgZIAAAA9HYGPABBBTLExss7zn/Xx4j072s36+FFBmfbWNISpdwAAAAAAAAeVNdq0urgqoOGYW3LAbx3L+FEMLQD0IKeffnq7WR/vu+8+2Wy2Lu0bAKDncTscBzM++rAuua9qotpmmjp6YF8lRFnC2DsA6BoD4mL0jzljNbZfYoeON2aU/L+vdrZaQNucnqaYqy+R4mO/rZgQp5hrLpVl+OBQdBudwLl2sxrvfFjOL75qv3zZGj3w6hLtDsH8oSvGDNKVefxsAOjdLBPzDn7RJ0GWY6d5sijH/vnXij7/e7KMypbZatUJg9su0uJNSaNd35TWhK/DAAAA3QyBjwAQYayzZ7S+ydyOnLpaHVvWdlU2I3fCc1v3hbF3AAAAAAAA0uKCMs99CH/inA4dVe47M6R52CCZ+iUzrADQg1itVv35z3/2BDy2tH//fs9+AACC4dqxR2ps8lnn89QB7e6fn5XKYAOIWMkxUXrwuDxNS0vqcBsPr9+rh9btOZSx3Zw1UDE/u0iKiZaSEhVz7Y9kHpwZwl4jXNw1dbI99bJs/3xBqq71WfeXm9cr1uno8LlSY6P0wKwx+nHe4Daf+wCgtzENGqjoX/xIsbf/WtHnnSrLyBEyWVovvrJgUHCfSxYV+H5uAgAAEEkIfASACGOKi5V1rv+sj5fu2SG1k/Xx3b0lKmlgRW0AAAAAABA+C/cG9lB+ZlmJYly+QyQtk8eGqFcAgM40efJkXX/99YcmUDdnfXzppZf04IMPcjEAAAFzrt/qt86y1LQ2+6wmk2Zl9GOkAUQ0I6vtPceO0dzMlA638ezWfbrry51yNgc/Dh2k6J9eeDDoMaP9wHJ0H8bnLMeadQezPH65PqBjshobdNku39mUvTlmYF89d/xEHZXet0PHA0CkMe55WbKHymT2PmV/RFK8cpLiA27zo8IyOVxt534CAABEIgIfASBSsz7Gxfisk1tbo2PKStrst7vc+s82sj4CAAAAAIDwyK9t0IYK36vKN5tTcsBvHfOkvBD0CgDQFS677DJddNFFbYIfH3nkEf3mN79RXV0dFwYA4JPxd8O1bovPOnviE1QYn9Bm/1HpyUqMsjLCACJejMWsO44aqVOGtg0CD9Qbu4t104ptsjkPLlBlBHCYB5A1t7tzV1bL9vh/ZH/6FamuPqhjv7e/QH1tvjMqtxRlNulXE4bpnmNGKyU2qgO9BYDe7YTBgf9drWhyaE1JVVj7AwAA0F0Q+AgAEcgUHyfrnKM7nPXxtV1FqrY5wtQ7AAAAAADQm32QXxZQvTinQ0eX+84MaRo2SOZ+ySHqGQCgK9x444366U9/2ib48a233tIZZ5yhN954Qw4H96sBAO1z7y+Wu7zS5/B83k62R8P8LAJ2APQeVrNJN07N1gU5GR1u48PCMl2/bLMaHM6Q9g1hyvK4bI0ny6MrgMzIh1uX1FdXTj1aldG+F11vNqxPnP41b7zOz83wfKYDAARvwaD+QdVfVOD7+QkAAECkIPARACKUde7RUqzvG5CjaqvbnUBY73DppR3+MyoAAAAAAAAEO+lq4d6SgOoeXVaiGNfBLALeWMj2CAAR4Ze//KXuu+8+JSQktAp+zM/P1+9+9zvNmzdPd999tz7++GNVVvoObgEA9C7O9b6zPRqWpw5os89iMum4zH5h6hUAdE9mk0m/mDBUPx07uMNtrCiu0s8/3chi2t2Yq6xCtoefkf2Ft6TGwDM2GhrMFt2fM1q/mDRDe+MTAzrm9GED9NT88RrZt212ZQBA4AYlxmpMv8BfS5cUlsvu5xkKAABAJLB2dQcAAOHM+niUHAs/8Vnv0j3b9UVKf2MmSav9L27frx/kZijOauESAQAAAACAkNhaVa89tY0B1Z1bUuS3DoGPAND5LrnkkrC1nZqaqtraWk/gY3OWECMAsqSkRP/61788m6Fv375KTk72bDExgWUg6SijH08//XRYzwEA6DjnOt+Bj1XWKG1I6ttm//QBSUqOjmLoAfQ6xvvbH40epD5RVv316106mHc9OOvKa/WzpRt0/6wx6h8XHfTx7rp6uSuqZB7U8eyTaGdcXS45P1kp+9sfSjZ70EO0um+q/jpqrA7ExgVUv0+URb+fkq3jB5FBGQBC5YRB/bWpoi6gujV2p1YUVWlWBgu6AACAyEbgIwBEeNZHx5IvpCab1zpjaqo1o7xUK1PTWu2vsjn05u5inZfDjWYAAAAAABAagWZ7jHU6dFS577qmoVkyp7SdwAwACK+VK1ceCkoMFyPY0XB4AGSziooKz9YZ/Qj3OQAAHeeurpF7T6HPOstT0+Rq57V8XhZBGgB6t7OzB3oC125dvUPOFu+1A7W9ul5XLl2vB4/LU2ZCbFCv3U0PPyt3VbVifv5DmbMGBn1utOUqKpH932/KtSs/6OGptVj1SPYovTswq82i6d5MSO2j26bnKiMhvAvRAEBvs2BQqh5Ytyfg+osKSgl8BAAAEc/c1R0AAISPKSFe1tkz/Nb74Z4dxgyONvuf37pPDpcrTL0DAAAAAAC9icvt1gcFZQHVPbqsVLF+7klYJo8NUc8AAB0NCgzH1t45WgZBttzC1YfD+wEA6J6cG7b5rbPssMVfmyfKzMlMCVOvAKDnOGlImv4yc5RizB1b7KOgrkk/XrJeO6rqA6rvqqhS0/1Pyr2/WKpvVNMjz8pVVNqhc+Mgt9Mp+wefqun//t6hoMfPUtN06fRj9W7GoICCHo2/oVeMGaRHZ48l6BEAwiA9PsYTXB6oT/aVq9Hp5FoAAICIRuAjAEQ467yZUnSUzzp5NVWaVtF24mFRg00L87nJDAAAAAAAjtzXpdUqabAFVHduyQG/dSyT8kLQKwBAR7UXiBiKrT3eghLD1QeyPAJAz+Bct8Vnuc1k0qp+/dvsn5KWpH4xvp+fAkBvMSujn+6flacEq6VDx5c22vXTpeu1vrzGZz1XSbls9/9L7pLyb3fW1KnpoaflKqvo0Ll7O1fBfjX97XE53v5QcgQX9FIZFaXbxkzQjWMnqywmsIyd6XHRemTOWP04b7CsHQyWBQD4d8KgwLPT1ztcWnagkmEFAAARjcBHAIhwpsQEWY/zn/XxUi9ZH5/Zss+TkQEAAAAAAOBIBLq4UqzToaPLS3zWMQ3NkjmlLxcEALpQV2ZaDGemRzI+AkDP4LbZ5dqyw2edr/qmqsFqbbN/flbgE4kBoDeYnJakR+eMVb+Ytq+Zgai2O3XNJxu1sqj9wAvX/mI1GUGP5VVtC6tqZHvoabkrqzt07t7IbXfI/vaHavrr43IX+F887HCLBwzUpdNm6aMBGQFleTTMy0rRcwsmanL/pA70GAAQjOMHpQY1uX9xftuEFwAAAJGEwEcA6AWs8/1nfRxfXakplS1W1vuf3TUN+nQfq+sBAAAAAICOs7tc+qggsIfvR5WXKtbl8lmHbI8A0PXCmW2xO2wAgO7NtXWnZHf4rLMsNa3NPuMVfm5WShh7BgA906i+CXpszjhPVr+OaHC6dN2yzfq4sKxtVsIHnpKqa70e6y6rVNODT8mxdIVcewvldgaXvbA3ce3KV9Nf/i7HB59Kfu6fHa4kOka/HztZfx4zUVXRgV3nGItZv58yQncdNVJJ0R0LjAUABM7d2KTkDVv03ejAE1V8dqBCdXb+dgIAgMjFp1EA6AVMfRJlnTVNjo+W+836+GXflDYruj29pVCzM/sx2QMAAAAAAHTIFwcqPav/B2Juif+V6gl8BICuFWhmRgAAwsW5fovfOu0FPk7s30epsR0L6gGASDekT5wenztO1362ybNIdrDsLrf+8MVW/X5qtk4bNsCzz7FsjVRX7/dYd0m57K+8d/CbKKvMQzJlHjZY5uGDPP+akhLVm7mbbLK/85GcS7+QOvBx7O2BWfp79ijVWn0vmt5STnK8/jwjV8OT4oM/IQAgYO6GRjnXb5Xz6w1ybdouOZw6Z/IEvZOUEdDxTU6XPttfrpOGtP38AwAAEAkIfASAXsI6/1g5Pl3lc+XTiVUVmlRVoa+N4McWNlTUak1JtaYNSO6EngIAAAAAgEizML80oHoxTqeOLvNd12RMfEvtF6KeAQCCdc011zBoAIAu5Xa5PBODfdma2EclsXFt9s/PSg1jzwCg50uPj9Hf54zVLz/bpM2VdUEfb+QgvGPNDtXaHPrByExFnfVduatr5Vq7OfBG7A65duz1bM1MqX3/Fwh5MBjSlJkuk8Wi3vJ3r+nef8q9rzjoY/fFxumvI8fqy37B/f07N/v/s3cf8G1Vd//Hv5K8Z2zHcWxn7z0IJCQhJISwU/Yus1BGWW0pLe0fHqCUtk8LPF2MllFGyyhQ9kwCZE8gkL2H997bkv6ve4OMHVvDtuQhf96v162te4/uPT4idqVzv+c3ULdOHmpWfAQA+J+zuqZZ2HGfdFS146H7DyhkWqo817j/zpLMIoKPAAAgaBF8BIA+wlj9zjb3WNk/X+dxAtCultUem1d9JPgIAAAAAADaq7rRrhU5JT61Pb64QJEOz5UhbdMn8iIAQDci+AgA6G7OjGypvNJjmzVJRyqNHW1BWssFYAEArSWEh+qxEyforjW79GVheYeG6M9bDqm8oVE3ThissKsvVP3TrxypYtVBzqJS2Y3tiy1HdoSFtq4KGRsdlC+nxWpVyKzpanjz43YFUP+bPkRPDx+tWpvvt4j2CwvRvceO0gmpLDoGAIHU8NFyj/dxWsoqdIGtUa/affsdvja3VBX1jYoNIxYAAACCD/8PBwD6kNCT58q+elOrqo+7YuL0/NCRWpOULFnaDj5uyC/TjpJKjU+I6aLeAgAAAACAYLA8u1h1duN2K+/mF+R5bWObNsEPvQIAAADQWzkrqmRJiJOzxH0Yx5z3PMrkxBizkhkAwLuY0BD93wnjdM/6PVrp44JWR/vnziyV1zfqZ9OGK+y6S1T/5L/l2HvQP8Nf3yDH3kPm5mLpn9CyKmTqgKCpCmmbP0v2r7bJcTDTa9vDkdH637ETtS2+feHF45Ljdf9xo9Q/MqwTPQUA+MKY5/AUfDScVVaoV2MG+nS+RqfTnItZPKztBWAAAAB6M2t3dwAA0HUs8bGyzZnR9DgnKUl3TzpGNx5zvNb0H+A29Ojywq6sLuglAAAAAAAIJh8fLvSpXbjdrtlFBR7bWIyV/JNYcR4AAADoy2yTxir8/p/owA+v0DPDRmlnbFyL4wVh4dod03Kf4aT0pC7sJQD0fhE2m35//BidPqR/h8/xxv483bdxj+whNoXdcJksQ9MVKM7CEtk3faOG195X3R/+rvqnXlEwVX0MvfwcyUOQ0y6L/jV4uK4/dna7Qo82i0W3TBqiv8wbT+gRALqIddggKT7WY5vB+w8o1PPtnC0syfRtLgYAAKC3oeIjAPTBqo/OrFyFLDpBtkHpWr/ka5+f+1lWsQ5V1GhobGRA+wgAAAAAAIJDSV2DNuSX+tR2ZnGhIh12j22o9ggAAADAYLFY9KHdpv8MHakXh45U/7pacyGVOUUFyoqMbHPB15PSExk8AGinEKtV9x07SnGhIfrPvtwOjd8nGUWqbLDrd7PGKPzmK1T31+fN+1YCzTo4VcHEOjBZIWfMV+N7n7Y6ticmVn8YM0l7jloMwJtB0eH69czRmpjoOXwDAPB/oN2s+rh8vfs2JeW6KNShl+p9q3G0Mb/MnJNJCA/1Y08BAAC6HxUfAaCPsfSLU/jt18o2YbSGxkVpQTsm+JySXtxN1UcAAAAAAOCbZZlFshsfKPhgQYH3G94IPgIAAAAwOJxOfZZV1DQYheERejdtsH45+Rj9bdT4VoM0PiFaadERDB4AdIDVYtFPpw7T9eMHdXj81uSW6vZVO1QZEqrwW6+SdeLorqmmFWRWjh2nfc2qGtdbLHp62CjdNP34docezxjSXy+cPJXQIwB0E1/mO84s9b2KozEXYxS2AAAACDYEHwGgj7t6bHq72n94qFB51XUB6w8AAAAAAAgeH2f4NikfZrdrTnGBxzaWQamy9qdCCwAAAABpa3GlCmobfB6KhelJDBsAdLLS7g8nDNZPpgzr8Dm+LqrQj1ZsU7EtROE3fl/hd9+skHNPldUIfsTH9ojgo33nPtX+5q+q//dbaly9SY7sPDkdDnW3mka7fvvFPv1y0z79bsxE2WXRtth4/XDGHP1r6EjZrb7fBhoVYtX9x43S/ceNVnSoLaD9BgC4Zx0+WIqL8ThEg/buV4S1dTV7d5Zk+h6UBAAA6C1CursDAIDuNT4hRjMHxGtDfplP7RudTr28J0c/ntrxD7MBAAAAAEDwy66q1TdFFT61nVlSqEi73WMb23Tvqx8DAAAA6BuaV3v0xUnpLKICAP5w6ehUxYWF6Ddf7DUrS7XX7rJq3bh8m/56wgSlpqXImpbSdMxRUibHgYwj28FMOTNzJHvHQoeWlP6yREe1+3mO/YflzC+S3djWbz6yMyJc1qHpZpDSCKkYXy1RkeoMZ02tLJG+VSLeXVqlezfs0cGKGvPx3tg43T7tOO2I6yeHxfcwjGFCQrQenDlGg2KoggwA3c1itZpVH+0rNrhvVFKmi8KdevHInwCvviooV0FNvZIjw/zWTwAAgO5G8BEAYFZ99DX4aHjrQJ6uHZeu+PBQRg8AAAAAALRpSabvNyIvKMj12sY2bSIjDQAAAEBOp1OftiP4ODo+SoNjOhdQAQB858yhyWalwHvW71a9o/3px4zKWt2wfKv+csJ4DY/7LpxoTYg3Nx0zyXzsbGiQIyOnKQhpfFV5ZcCqPRqM67RSWyfHrv3m1jxY6QpBGl/NoKUPVReN6pH2VZvU8N4yhd30fdlGDHHf1unUq3tz9beth9Rw1Dhvi09o189lxCOvHJOmGyYOVmg7qkMCAALLa/BR0hklBXoxItmn8xl/LYz3SpeMSvVTDwEAALofwUcAgGYkx5mrum0vqfJpNGrsDr22L1fXTxjM6AEAAAAAgDZ9crjQp5EJs9s1p6jAYxvLoIGyJlOhBQCCQXV1tT755JM2j5177rnqLj21XwCA1naUVCm3ut7noVmYnsQwAoCfzU9L1P/NHa+71u5UdWP7qzLm19SblR//fMJ4jU+IabONJTTUDAa6woFGENDpqgr5bRDSmZkrOVpf3wgjtpcRSmwz+NhW27xC2Y1t3VdHdkQaVSEHfVcVcmh6q6qQjvxCNbz8jhz7DpuPje+tP7/R/DmPVlzboAe/2Ks1uaXqrP4Robr/uNE6bkB8p88FAPAvq/E3Li7GY7A/bc9+RU1LUbWPVZCXZBQSfAQAAEGF4CMAQBaLxaz6+It1u30ejVf35eryMWmKCrExggAAAAAAoIW9ZVXaW17t06jMLClUlN3usQ3VHgEgeJSUlOjuu+82P5fuSQHDntovAEBr7an2aCD4CACBceyAeD02b6J+vHqHyuob2/184zk/WrFND88ZpxnJ3kN5xv9XtyT2kzWxnzRjsrnPWW9UhcxuWRWyoqpDFR+duQVmdccOqamTY+c+czvSWaMqZHJTENJZUaXGj5dLDY0twpONH69Q6OKTW5xqfV6pHti0V0W1DeqsuQP76d5jRykhvHW4EgDQ/YxqwbYp42VftdF9o+JSXRzh1HO+1bTQluJK5VXXKSUq3G/9BAAA6E4EHwEAphPTEjUsNlIHK2p8GpHy+ka9fSBfl41OZQQBAAAAAEALn2T4fiPy/II8r21s0ycwwgAQhIyKLS5tBQ67S0/tFwDgyO/o9gQfR8RFalhcy4pbAAD/mZAYo7/Pn6jbVu1QQY3v1XhdjGqRP161Qw/NGmPet9JelrBQ2UYONbemqpDFpbIktL+6oa/VHn3iPBKktBubqypkGxqXrpJt6gRZB6eqweHQk9sy9K/d2Z2+fJjVotsmD9VFIwfyngYAejhj/sNj8FHS6SWFei7M90r2q3NLdP6IgX7oHQAAQPezdncHAAA9g9Vi0ZVj0tr1nJf2ZJsfvAIAAAAAALgYN5h9klHo04BYnE5Nrijz3CZ9oKzJvk/oAwB6D7NiSw8MFvbUfgEApD1l1cqq8r0aF9UeASDwhsdF6an5kzQoOqJDz693OHX3ul36zRd79fyuLC3NLNT24kqV1jW0WJTEF8b/j7cmJZgVtNrLrBbZ1RxO1b/0tjLKKvXDz7f6JfRoLHr+7MLJunhUKu9rAKAXsBrh/dhoj20G7tmnuFCbz+dckV3ih54BAAD0DFR8BAA0OW1If/1je4bymq/C53RqVnGhJpWX6pnho1uMVn5NvT48XKizhw1gFAEAAAAAgGlLcaVyqn27Edlpsejdyy7ULZGSffM22b/eIVVWt2hDtUcACF7GTcw9MWDYU/sFAFC7qj0aTkpvf/UwAED7pUaH6x8LJprVG3eXtfxsxxd2p/TuwYJW+6NCbEqLDldaVLjSoiOUbnwfHdG0LyLE9xBIl1Z8bAdnVq7ee+4d7Rg0vNPnOnf4AP1kyjC/jgsAILCMsL5tynjZV29y36iwRBdFWPRMg2/n3FRQpupGu/l3FAAAoLcj+AgAaBJqtery0Wn6v28OmoHH2UUFuvrwPo2rKDePf5Y8UPtjYluM2L92ZemsocmycRMIAAAAAACQ9PHh1jepeXLK0AGyJcTINnaEnBeeKce+Q7J/tV32b3ZIFVWyTZvIuAIAAAAwg+nLMn0PPg6JidDIuChGDgC6SFJEmB4/caLuXLNTXxdV+OWcRmhjb1m1ubUlMTzUDEGmNwtDuoKRAyLDFWL1fUGT0IsXm1Ufze1ghlRVo65y+f69eic5TcXh4R16fmyoTb+aMZJKxwDQSxkLQHoMPkpaVJyvZ0J9W9ilweHU+rxSnZSe5KceAgAAdB+CjwCAFs4ZlqytKzbpwn17NLbySODR5epD+3TfxGkt9h2qrNXy7GI+PAUAAAAAAGp0OLS0nTcij+sX3fTYYrPJNmaEuTkvOlPOQ1myDmBiHgAAAOjLjEVRnBVVyhgyWIcra31+3sL0JCr4AkAXiw0L0V9OGK+71+3W2rzSgF+vuK7B3LYWV7Y6ZizgnRIZ9l0oskW1yAglhIe0+DthGz3M3Fxhe2dBsRmAdBzINL86s/PNRcT9LTc8Qo+Mmdjh0OO0/rF64LjRGhjVsecDALqfdeRQKSZKqnRfNTl1zz6FT0pSncO3v0UrsksIPgIAgKBA8BEA0GLS0PLh5/p/WXltjsr8wjwNr6rQgeiWVR+f35mlk9ISmTgEAAAAAKCP25BfptL6Rp/bnza4v9vPEyxWqyzDB/uxdwAAAAB6o8bP1sqx77AGSnoiNk5rkgZoTVKy9hlzlm7eTxgWDmIRFQDoDhEhNv1xzlg9sHGvlrRjgSx/szudyq6uMzcVtD4eabM2hSFTo5pVjfx2X9SApCMLcs08skC4s7ZOjsNZTUFI46uqO1cV8s20wfrH8DGqCWn/bZxWSddNGKRrxw0yQ54AgN7LXBRyynjZ13zhvlFBsc4Oc+o1H9eCWZ1bokaHs13VjwEAAHoigo8AgCb2zdvldBN6dLny0H79esLUFvt2llZpY36ZZqb0YzQBAAAAAOjDPs4obFf7Uwf3D1hfAABoj8bG74L7zUP5VqtxOzEAoLs4K6vk2J/R9Hh8Rbm5XXdwr1kha21Sst5PHaS9MXEtnpcWFa4x8VHd0GMAgCHUatUDM0crNjRE/z3g+T6U7lJjd2hfeY25tcWoCGlUhkyNbhaKjE9U+tw0pZxygsIsFjkLipoFITPkzDGqQnq/dkZklP4wZpK29EvoUN8HRoWZVR6n9W/59w8A0HvZpk/wHHyUdEZpoV6L8G1epay+UVuKKzSdvxUAAKCXI/gIAPjuj8JpJ8r+xRaPH8IuKMjV81UjdSg6psX+53dlEXwEAAAAAKAPq220a3lWsc/tJyREa0hsZED7BACAr6qrq9vcHxoayiACQDeyb98jOduevBxYV6vzsjO0JT6hVfDRqPborro8AKBrGFUIfz59uOLCQvTcrqxeN+wldY0qqavUtpLKVseM5VFSosK/DUXGKG3KdKXNPl6DQixKLypSTFaOHAeNQKRRFfK70lw1VpveTB+i54aOVL3N1qF+nZyepLuPGWGOKwAgeFhHDZOio6Sqtj+jMgzbf0CW8Uly+vheZ2V2McFHAADQ6/HuN4CrwpaWlspmsykuLs78CgA9nTUlWbZjJsn+xVb3bYyqj4f36TfjW1Z93FRQrm3FFZqYGNsFPQUAAAAAAD3NypwSc6V8X506ODmg/QEAoD3y8/Pb3B8dHc1AAkA3sm/d7fF4o8WiDYmtK54sTE8MYK8AAL4yQug3Txqi2LAQ/XXLoaAZOOMTsJzqOnP7sqD18XBbpNJGTFL6pGM0sKpKmYdyZJFTm+MTVRPSsVs2I2xW3Tl1mL43bADhfgAIQhabTbap42Rf86XbNtaCYp06xamPG30MPuaU6PYpw/zYSwAAgK5H8NFPioqK9M4772jNmjX65ptvVF5e3nTMCD0OGzZMxx9/vBYvXqxp06b567IA4Hchp54o+5dbPVZ9XJifqxeGjtThqJZVH1/Yla3/nT2WVwUAAAAAgD7o44xCn9saU/KnDEoKaH8AAGiPXbt2tXjs/La6WGIiwRkA6C7OhkY5duz12Obr+ARVhrSszpsSGaYJCS3nMQEA3euKMWmKC7Xpd1/uN0ODwa7O7tCBihpzMyV1bgGwMfFRenDmGA2Li/RPBwEAPZJt6gSPwUfDOWWF+jh6gE/nO1xZq0MVNRoay98PAADQexmFu9AJ9fX1euSRR7Ro0SL94Q9/0KpVq1RWVmZOhro2o/rj3r179e9//1uXXXaZrrzySu3Zs4dxB9AjWVMHyDZtouc2xofSh/a32v95drEOlFcHsHcAAAAAAKAnKqtv0NrcUp/bH5scr/6RYQHtEwAA7bF06dI2q9OkpaUxkADQTRx7D0p19R7brElqfcPvwvQkKmEBQA909vAUPXT8GLNyIXx36ahUPXPSZEKPANAHWMcMl6I8hxTHHDrcrnOuyC7uZK8AAAC6V5+o+HjHHXcoLy+v1f7o6Gg99dRTslo79mFKcXGxbr75ZrPCo2vVV9ck6NFcIUjDxo0bdeGFF+qBBx7Queee26FrA0AghZx2ouxfbfPY5uT8HLPqY2ZUdIv9L+7O1v8cO4oXCAAAAACAPuSzrGI1NvuM1JtTh/QPaH8AAGiPTz75xJzvM+b4jPm85nN9w4cPZzABoJvYt7asxtuWNW1U0DopnWq9ANBTGeH00fFR+vM3h7Q+r1T1Dt8/T+prEsJDdO+MUZqbmtDdXQEAdBGLzSbblHGyr/vKbZuQgiLNctRrvdW3xSVX5JToyrHpfuwlAABA1wr64OO+ffv08ccft5igdE1YXn311R0OPVZUVOj73/++Dh482GoC1HUNF+PY0cfr6ur0y1/+UrW1tbr00ks71AcACBRrWoqsU8fL8fUOt21sRtXHw/v1+3GTW+z/6HChbpgwWAOjwnmBAAAAAADoIz7OKPS5bajVopPSuBEZANAz5hHffPNNPf/8824rg02ZMqXL+wUAOHLPhcNL8PFAVIxyIqNa7OsfEarJSbEMIQD0YINjIvXwnHGqabRrb1m1sqtqlV1dp+yqOmUZ31fVKb+mTvY+nImcOSBe9x07Sv0jfQu1AACCh23aBI/BR8PFlcVaHzfQp/NtKapQSV2DEsJD/dRDAACArhX0wccPP/yw6fvmAUWbzaYrr7yyw+e99957deDAgRahxuZhx+aO3u96jrH/N7/5jYYOHarZs2d3uC8AEAihp89XnYfgo+GUvCNVH7ObTSjanU79e3e27pzGKtgAAAAAAPQFedV1+qqg3Of2cwcmKDYs6D+aBoA+Y+nSpVq2bJnP7aurq90eMxYNDaT6+nrV1NSoqKhIhw4dUllZWYs5xKMXOzUWUD3++OMD2icAQNucWblylnh+n7G6zWqPSbK6CbMDAHqWyBCbGVZvK7De6HAor7r+20Bk7XehyG8DkkaAIxjZLBb9aNIQXT46lb9nANBHWceOkKIipOpat20mZmZKE3wLPhp3sK/OKdHiYQP82EsAAICuE/R3l3z66adN3zefsDzhhBOUnt6x0t2rVq3SRx995Dbw6G5FWFdbVx+MrbGxUf/zP/+j999/X2FhrNAEoOewpg+Udco4Ob7Z6baNTU59//B+/XHspBb73z6Yr+vGD1I/VgkCAAAAACDoLc0sMifOfTG2vEyXxzjlrKmVJTIiwD0DAHSFnTt3mlUTPc2PtcU1v9b861tvvRWQPnq6fvM5xObHjH1G6DExkSrFANAd7Ft3e22zJqn1jbsL0/m9DQDBIMRqVXpMhLlJ8a2OVzfalVN1JBSZZXytPhKOdIUka+wO9TaDoiP0m1mjNT4hpru7AgDoRhabTbbJ42Rfv9ltm4iCIk2or9H2sEifzrmS4CMAAOjFgjr4aKzYumvXrjYnWk8++eQOn/fRRx9t+r6t0KO7yo/NKz02XzE2MzNTTz31lG655ZYO9wkAAiH0tPmq8xB8NJyWl60Xh4xQbrOqj3V2h17dm6MbJw7hhQEAAAAAIMh9nFHoc9sLczM05qss1X68TNZxo2SbPkG2SWMJQQJAkHA3R9ZVz28PX4KaP/jBD7qkLwCA1uxbd3kcluLQMO2MaxmESQgP1dT+cQwnAPQBUSE2jYyPMre23leU1jd+F4psFog0ApK51fWyd+F7D1+cNTRZd04druhQW3d3BQDQA9imTfAYfDRcVl2qe30MPq7LKzXv6Qy3Wf3UQwAAgK4T1MHHLVu2yG63t5q4NB4vWLCgQ+dct26dtm/f3irg2Lz64+jRo3XWWWdp1KhRZhXH7OxsLVmyRGvWrGlq63qu63sj+HjFFVcoPr71ClUA0F2sg1NlnTRGDg8rqoY4nbri8H49fFTVx9f25eqKMel8KAsAAAAAQBA7WF6jXaVVPrUNcTg0r6jgyINGuxxbd5lbg80m6/iRsk2bKNtkQpAA0Jv5WvXR0yKiXamtBU5dX0855RTNnTu3S/sDADjCWVYu5+Fsj8OxNilZjqP+bixIS5Sti/+WAAB6HuP/zxtheGObmBjb6nijw6n8mm8DkdXNQpHG1+o6FdU2dGmA8+7pw3XakOQuuyYAoOezjh0hRUZINbVu24yvrfb5fLV2hzbll2luaoKfeggAANB1gjr4+PXXX7c5eTlu3DglJ3fsw4LXXnut1T5XeNH4escdd+jGG2+U1dpyVYxLL71UK1as0F133aXy8vIWzzHU1dXpgw8+0GWXXdahfgFAoISePl91HoKPhtONqo9DRyov4rsVhCoa7HrrQJ6+PyaNFwcAAAAAgCD1cca3QUYfHFNSpIj6+tYH7EYIcre5OU+fr9AzT/JvJwEAXaY3VXx0F7g0+jBt2jT9/ve/75a+AACMao+e5yZdwcejLUxPZPgAAF6FWC1Ki44wt7bUNtq/DUS2rBTpqh5Z3Wj3yyhPTIjRgzNHKz2m7X4AAPouS0iIuVCkfUPL++AtaSmyTZ9gVoRMTExU6Hsb1eDw7fO0lTklBB8BAECvFNTBx4MHD7Y5cTlx4sQOna+mpkbLli1rMfnZPMB47bXX6uabb3b7/BNPPFF/+tOfdP3118vhcLRatfbtt98m+Aigx7EOSZd1wmg5tu/xWPXx+4f369ExLX+/vrwnWxeNHKgwW8swOAAAAAAA6P2Mz0U/ySj0uf1pxd5DkrbpHfvsFgDQM/S2io9H9ykkJETf//739dOf/lTh4eHd1hcA6OvsW3d5PF5vsWpTQlKLffFhITomOS7APQMA9AURITaNiIsyt7beN5TXN5rByKymSpHffZ9bXadGLwu6hFktunx0mn44YZBCjiquAACAixFuNIKPlvSBTWFH64D+TcejJc1IjtO6vDKfBm1lTrF+7hwuazd+/gYAANARQR18zMrKanP/mDFjOnQ+o2JjbW1tq2qNhoSEBN12221ezzF79mxdcMEF+s9//tPiPMZXo0JlXl6eUlJSOtQ/AAiU0DPmq85D8NFwRm6WXhwyQgXNqj4W1Dbow8MFOmc4v9cAAAAAAAg220sqlVlV51Nbm8OhuYV5HttYUvrLmjrAT70DAHQ1f1Rr7K6KjyNHjtQpp5yiiy++WGlpad3SBwDAEc66ejl2H/A4HF8kJKnW1vJ2l/lpiYRHAAABZ9znFx8eam7jE2JaHbc7nSqoqW9WKfJIKLKsrlHRoTYNjonQhSMGqn9kGK8WAMAj69iRCr/3NlmTWy760ty81ESfg4+FtQ3aWVKlCYmt/34BAAD0ZEEffGxrZdiOBh9XrlzZap8ruHjRRRcpMvK7sI8nN954o1577TXz+6P7t3XrVoKPAHoc69BBso4fJceOvW7bhDqdujzjgP48ekKL/S/uztbiYQNkY6UgAAAAAACCysftqPY4o7RIEfX1HttQ7REAeqdx48bpvPPO87l9VVWVPvnkk1YLhBpfzz333ID21ajoaFRyjI+PV//+/TV06FCz/4mJiQG9LgDAd45d+6WGRo9tViclt9p3Ujq/ywEA3c+4N2ZgVLi5HdP6zxUAAD6zhIbI4iH0aJiXmqA/bva8cExzK3KKCT4CAIBeJ6iDj7m5uW3uT07u2KcKa9eubTNIaTj//PN9Pk96eromTJigbdu2tTrfjh07dPLJJ3eofwAQSCGnz1e9h+Cj4aycTL1kVH0Mj2jal1FZq8+yirRoUH9eIAAAAAAAgoSxev2SjCKf259Z4j0kaZvWcjElAEDvsGjRInNrz8KlRvCxLb/73e/82DMAQG9k37rLa5u1RwUfY0JtOm5AfAB7BQAAAAA9T0pUuMb2i9au0iqf2q/MKdFNE4cEvF8AAAD+ZFWQstvtamhoML83VoltLiam/WW68/LyzIlY1/lcq88aRo0aZa4I2x5z5sxpc//OnTvb3TcA6Aq24YNlHTvCY5swp1OXZrReQeiFXdmtfhcDAAAAAIDe64v8MhXXHfn81Rubw6HjC/I8trGk9JcldYCfegcAAACgN3I6HLJv2+2xzc7YOBU1W4TVcGJqokKtQXv7CwAAAAC4dWJqgs+js7esWtlVtYwmAADoVYL2k9+amhq3x2JjY9t9vi+//LLN/UYAcsGCBe0+38iRI1vtM0JB2dnZ7T4XAHRl1UdvvpedqaS6lm+OjRWF1ueVBbBnAAAAAACgK32c4b2Co8sxpcWKqKvzWu3R+KwVANC38LsfANCc81CWVOG5UsnqpNYLpixMT2QgAQAAAPRJ89La935oVU5JwPoCAAAQCEEbfKytdb8iRUREy9X/fLF58+Z2V2/0ZPDgwW1O7FZWVrb7XADQVWwjh8o6ZrjHNmFOhy7NONhq//O7jlTNBQAAAAAAvVud3aHPsop9bn92mfeQpG36xE72CgAAAEBvZ9+6y2ubtUnJLR5Hhdg0M6VfAHsFAAAAAD3XmPgoDYgM87n9SoKPAACglwna4KPD4ehQNUhfKj42X33WZrPpmGOOaff5YmJi2txfUVHR7nMBQE+r+nh2ToYS61tWcviysFxbivgdBwAAAABAb7cmt0RVjXaf2tocDs3My/XYxjIgSZbU1lVbAADBKSQkRGlpaUpNTW3aXI8BAH2bfetuj8fzwiO0Nzq2xb4TUvsp3Ba0t74AAAAAgEfGPe3zUhN8HqUvCspVUd/IqAIAgF4jREEqOjraY/AxKirK53NVVVVpx44dLQKPTqfT/DphwgSFh4e3u3/urm9cCwB6MtuoYbKOGirH3kNu24Q7HLok44CeGDmuVdXHh+e03AcAAAAAAHqXjzO8V3B0mV5arPC6losjtVXtsflnrwCA4JaSkqJPP/20u7sBAOhhHIXFcubke2yzxqj2eNR7h4XpSQHuGQAAAAD0bCemJuqN/Xk+tbU7nVqbV6pTB/cPeL8AAAD8wRrMwUer9ciPd/RNM7m5nlcYb6vaY2NjY4vAo+u8Han26KkiZfPzA0BPFXL6Aq9tzsnOUL+jqj6uzCnR/vLqAPYMAAAAAAAEUmVDo1bnlPjc/oKKYq9tbNMmdLJXAAAAAHo7h5dqj4Y1SS0rxUfYrJqd0i+AvQIAAACAnu+Y5DhFhdh8br8yx/vcDQAAQE8RtMFHT1UVDx8+3K7zrFq1yu2xjgYfa2tr212pEgB6CuvoYbKOGOKxTYRZ9fFgq/0v7MoKYM8AAAAAAEAgfZ5VrHqHb4u32RwOzcjN8djGMiBJlrQUP/UOAAAAQG9l37rL4/Fqm02b+yW22Dd3YIIi2nFzLwAAAAAEo7B2LgqzJrdUjW4K+AAAAPQ0QR18TE9Pb7OC4jfffNOu8yxbtqxV1UiXGTNmdKhvxcXF7QprAkBPYvxODDljvtd252ZnKOrbirkun2QUKruq7fA3AAAAAADo2T7OKPS57YyyEoW5WQCuebVHd5+9AgAAAOgbnNU1cuw95LHNhoT+arC2vMXlpPSWQUgAAAAA6IscRSW6PPOABtTW+NS+ssGuzYUVAe8XAACAPwR18HHChAmt9hlByJUrV/p8DiMkmZmZ2fRc4yYcV5hy5MiRSkpK6lDf8vLyWvXLEBsb26HzAUBXs44ZIeuwQW6Pr0waoNunzVR1SEiL/Xan9NIez9UeAAAAAABAz1NUW69N+WU+t7+osu3F35qzTZ/YyV4BAAAA6O3sO/ZKXqqNrElKbvE43GrRnIEJAe4ZAAAAAPRMjoJiNSxZpdo//l11D/xZI1es1cKCXJ+fvyLH+xwOAABATxDUwcdx48Y1fe8KLRr27dunTZs2+XSOV155pc39xrlmzZrV4b7t37+/zXMaVSoBoPdUfVzQav/y/gN0/YzZunfSdO2JjWvzue8cyFNxbUMX9BIAAAAAAPjLkowieb4V+Ts2p0PTcrI9trEkJ8qSluKXvgEAAADovRxbd3s8bpe0/qjg4/ED+yk61BbgngEAAABAz+EoKFLDJytV+4cnVffgX9T47lI5M74rQnF6aaHP51qZXdJUtAcAAKAnC+rg4zHHHNPmfuP/qD366KNyeFkx8MCBA3rnnXeaApNHO+GEEzrct9272/7gfsiQIR0+JwB0Neu4kbIMTZcs0lfpg3TtjDm6b+J07Y1pO/DoUudw6tW9VH0EAAAAAKA3+STD9wnzmeWlCq2p9Vrt0d1nrwAAAAD6BqfdLvv2PR7bbI1PUFloWIt9C9OTAtwzAAAAAOhZGt9dpsb3lsmZ2XZlx6HFxepXX+fTubKr67SvvNrPPQQAAPC/oA4+Tp48WcOGDTO/N26gaV718auvvtJ9990nu91YG7C1yspK3XnnnWpsbDQfN3+uoV+/fpo3b16H+7Z58+Y2b+oZPHhwh88JAF3N+D0WdtFZCr/7R3JcdYEOxMT6/NzX9+eqsuHI71gAAAAAANCzZVTWaFtJpc/tL64s8drGNm1CJ3sFAAAAoLdzZuZIXhZNWXNUtcdQq0UnpCYEuGcAAAAA0LNYvcyrGHelH1/cjqqPOd7ncgAAALpbUAcfDWeffXaLUtyuAKPx9fXXX9dFF12kd999V3l5eWbIsaioSG+99ZbOO+88bd++vant0c9fvHixQkJCOtSnPXv2qLS0tOl8zY0aNarDPysAdAfrkDRZUwdo7sB+GhkX6fPzKhvsenN/XkD7BgAAAAAA/OOTjCKf29qcDk3KyvLYxpKcKEv6QD/0DAAAAEBvZh06SLrvx/rL2Ilak5isOmvr21jWJA1o8XjmgHjFhHbsfg0AAAAA6K1s40dJNs+3/p9S6vt8zspsgo8AAKDnC/pPgo1g4zPPPKPq6uqmEGPz8KMRbvz5z3/e6nlHBxKbV2e02Wy64oorOtynzz//vM3zWq1Ws0olAPRGxu+zq8am676Ne31+zst7c3TxqFSFe3kzDgAAAAAAuo/xWenHhwt8bj+7qlyhNTVeqz02/2wUANB7XHXVVepLjL9Xzz//fHd3AwCC2vpau/47cJC5RdgbNaOkWHOK8jW7qECVISHKiIpu0X5helK39RUAAAAAuoslMkLW0cPl2LnPbZspRQUKc9hVb7V5Pd+2kkoV1tSrf2SYn3sKAADgP0EffExOTtZtt92m3//+9y1upGkefjw65GhwtW2r2uP555+voUOHdrhPy5Yta/HYdY3Ro0crKiqqw+cFgO62aFB/PbktQznVdT61L6pt0PuHCnT+iJSA9w0AAAAAAHTM7rJqHaqs9bn9JZXeVwg2go8AgN5pw4YNfSa87pobBAAE1qdZxU3f19pCtLr/AHOzOJ3qX99y3tFmsWheWgIvCQAAAIA+yTZpjMfgY2hjo6aXFGt9UrJP51uVW6Jzh3P/JgAA6LmsfWXl2YkTJ7aanHQ9bmtzHTc0f05SUpLuuOOODvfl8OHD2rx5c1Po0sV4PG3atA6fFwB6ghCrRVeMSWvXc/61O0uNjtYBdAAAAAAA0DO0p9qj1enU+MxMj20sSQmyDEr1Q88AAN3JtbhosG4AgK5Rb3doZU7bi6c4LRYVhEe02HfcgDjFh4V2Ue8AAAAAoGexThrrtc1JpUU+n29ltvfFLAEAALpTnwg+Wq1WPf7440pLS2sRdjR4m9Bs3i40NFR//vOfzfBjR/33v/91e2zmzJkdPi8A9BSLhyUrIdz3ycasqjp9muX7G20AAAAAANB1HE6nPsn0/X37gtoKhVRVe2xjmz6B6lkAEATcLS4aLBsAoGtsyC9TdaPd5/YL0zt+vwYAAAAA9HbWxH6ypHmu0Di3uMC48d2n823ML1VNO96TAQAAdLU+EXw0pKSk6OWXX26q/GjwdVLTaB8bG6vHHntMM2bM6HAf6uvr9dprrzWdt/mkqc1m07x58zr9cwJAd4uw2XTZ6PZVbXhhVxYraAMAAAAA0ANtLixXQU29z+0vqvC+MrBt2sRO9goA0BN0d0VGKj4CQHBozwKpxg0uJ6YlBrQ/AAAAANDT2SZ7rvoYW12t0ZUVPp2rzuHUhrwyP/UMAADA//pM8NEVfnz11Vf105/+VHFxca2qOzbXfP8pp5yi119/XSeeeGKnrv/ee++pqKj1h/ZGAHL69OlmuBIAgsEFI1IUHWLzuf2esmqtyS0NaJ8AAAAAAED7fZxR6HNbq9OpMRmHPbaxJPWTZXD7FkwCAPRM3VlpsSvOCQAIvAaHQyuyi31uf0xynBLCQwPaJwAAAADo6WyTxnhtc2Jxvs/nW5Hj+/syAACArhaiPiYkJEQ33HCDrr76ai1ZskSrV6/W9u3blZ2drerqaoWHhyshIUFDhgzR8ccfr5NPPlmjRo3q9HUdDof+/ve/m9+3FbRcsGBBp68BAD1FTGiILhiZohd2Zbd5PNThkEVO1Vu/C0e+sDtLc1MTurCXAAAAAADA203In2b6Xn3ltIYq2SqrvVZ7JEwCAL1fW3NdgdbW3w9v/XA9p/lz3T2nO34mAOjrNuWXqaLB7nP7helJAe0PAAAAAPQGlsFpUlyMVF7pts3CsmI94+P5VueWyO50ysZiYAAAoAfqc8FHFyPguHjxYnPrCkao8q677nJ7/JhjjumSfgBAV7l0VKpe2ZOjeoezReDxzJxMXZ5xQG+mDdErQ4Y3HdtcWKGvC8s1tX8cLxIAAAAAAD3AutxSlbfjJuTzK7yvCGybPqGTvQIAdLdbb701YOdevny5tmzZYgYVXUHEtkKLxr7BgwdrxIgRiomJadpsNpsqKipUWVlpbocPH9aBAwdkt9vdBiiNcxr7hg4d2mXzhgCAIz7L8r2qiPHbe0F6IkMHAAAAoM+zWK2yTRwj+9ov3Y5FekmJkutqVRAe4XW8Suoata24UlOSYvv82AIAgJ6nzwYfu5ox2bpo0aLu7gYAdJmkiDB9b9gAvbE/T2EOu84yAo+HDyi5vs48fknmQb2VPli1tu/+FBkVIh8h+AgAAAAAQI/wUUZhu9oPq6v1eNyS2O/IKsQAgF4tEMFHI6x4//33a+vWrU2hR1dA0RV2NIKJ3/ve9zR37lyNGTNG0dHRPp27vr5eu3fv1saNG/Xuu+9q+/btrapBGtc4dOiQvvrqK/32t79VSkqK339GAEBLjQ6nPs/2Pfg4tX+sOf8IAAAAAJBsk8Z6DD4aZhfl6520IT4N18rsYoKPAACgRyL4CAAImCuGJ8u6coMuOXxA/b8NPLokNNTr7OxM/WfwsKZ9q3JLtLesSqPifbthBQAAAAAABEZVg10rc0p8bm+sAhx3wdVy5BXKvnmb7F9tlzM7r1W1x6OrbAEAkJGRoRtvvNGszNi8oqPr+wULFuiWW27R5MmTOzRYYWFhmjRpkrlde+212rdvn55++mm99dZbTW1c11uzZo0uuugiPfHEE5o4cSIvDgAE0FeF5Sqrb/S5/cL0JF4PAAAAAPiWdexwKTREanD/vuq08mKfg48rckp0y+ShjC8AAOhxrN3dAQBAcDJudEz445O6Ze/OVqFHl0szDijcbm+xz6j6CAAAAAAAuteKnGLV2R0+tz91cH/zqzWlv0JPm6+Iu29W+D23KmTxQlnSB5rHbNMIkAAAWsrMzNRll12m/fv3N1V5dIUQjQqPTz31lJ588skOhx7bMnLkSP3ud7/Tq6++aoYhjw5b5ufn68orr9S2bdt4uQAggD7NLGpX+wVpiQHrCwAAAAD0NpawMFnHjvDYZlxBgSLtvi04c7CiRocravzUOwAAAP8h+AgACAhLcqIskREe2yQ21Ot7ORkt9i3JKFRWZS2vCgAAAAAA3ejjw4U+t7VZpJPbqL5iHdBfoaeeqIhf3KTwe2+TZUian3sJAOjNKisrdf3116uwsLBF4NHY5s2bpzfeeMP8GihTpkzRyy+/rAsuuKBF+NHYqqurzSqUubm5Abs+APRVzpIyNWTk6LMs34OPkxNjlBIVHtB+AQAAAEBvY5s01vNxh0MzSnx/77Uyp8QPvQIAAPAvgo8AgICwWK0KOfVEr+0uyzigsGZVH41aEv/eQ9VHAAAAAAC6S0ldgzbkl/rcfuaAfkqMCPXYxpqcZAZJAABw+cMf/qCDBw82/X1wVXycO3euHn/8ccXExAR8sEJCQvTQQw/pvPPOawo/uhiBzHvuuYcXDAD8rHHtl2r849/15IplumPPdh1XXKhQh+dq8wvbWGgFAAAAAPo628QxXtucUtqe4GNxJ3sEAADgfwQfAQABY5sxyaz86ElSfb0W52S22PfuwXwV1dbzygAAAAAA0A2WZRbJ3jL74dGpg/sHsjsAgCC0detW/ec//2kVik9OTtajjz6q0FDPgXp/e+CBBzRu3Limx65+rV69WkuWLOnSvgBAsLNv3WV+Tamr1XnZGfrjli/09ppPdf+2zTo1N0vxDa3nCBeke55vBAAAAIC+yBIfK8vQdI9tZhYVyHrUgl/ufF1YobK6Bj/1DgAAwD8IPgIAAsZisynk1Hm+VX10fFf1sd7h1Ct7c3hlAAAAAADoBh9nFPrcNtxq0fw0bkIGALTPU0891eKxq9rjz372M8XHx3f5cIaFhenee+9tVfXRePz3v/+9y/sDAMHKWVImZ2Zuq/1RdrsWFObpV7u26voDe1ocG58QrbToiC7sJQAAAAD0HrZJnqs+RtbWanx5qU/nchgLgeX61hYAAKCrEHz0UWVlpYqLi5WTk6Ps7OymDQDgme3YKbIkJXhsk1xfpzNzslrse2NfniobGhleAAAAAAC6UHZVrb4pqvC5/by0REWH2gLaJwBAcCkrK9PSpUtbVXscOHCgFi9e3G39mjFjhqZNm9YUfnT1b9u2bdq5c2e39QsAgol9626vbdYkJbd4vDA9KYA9AgAAAIDezTZprNc2C0p9X/ByZU5xJ3sEAADgXyF+Pl+vl5eXp7Vr12rr1q3atWuXsrKyVFBQoMbG1uEbY8Jz+/bt3dJPAOhtVR8bXn7HY7vLMw7o/dRBarAeyeRXNdr1+r48XTMuvYt6CgAAAAAAlmQWtWsQTh3cn0EDALTL8uXLZbfbm4KFrmqPixYtkvXbz4e7yxlnnKHNmze32v/ZZ59p3Lhx3dInAAgm9q27PB6vtVr1Rb+WQceT0qkwDwAAAADuWNJSZEmIl7OkzG2bBSVFemzoaJ8GcV1eqertDoXZqK0EAAB6BoKPklnJ8e2339Zbb72l3btbrjDoWtW1sxwOh15++WVzIvdogwYN0sKFC/1yHQDoiWwzp6rx4+VyFrt/cz2grlZn5GbpnbTBTfte2ZujS0cPVISNyhEAAAAAAHSFTw77vupvbKhNs1P6BbQ/AIDgs2nTpjb3H3PMMV3eF1/74K7PAADfOevq5Nh9wGObTQlJqm82LzgmPkqDYyIZZgAAAABww1hQzDp5rOwrNrgdo+SyMqXVVCs7MsrrOFY3OvRFQZlmD0xgzAEAQI8Q0tcDj//4xz/06quvqra2ts2Qo2u12aO1NxBprFBrVJJctmxZq2OxsbFatWqVwsLC2nVOAOhVVR9PmaeGV9/z2O7yw/v1wcB0NX67qndJXYPeO1igC0cO7KKeAgAAAADQd+0tq9Le8mqf2y9MT2LFXwBA+//e7N3b5v6hQ4d2+2gOGTKk1TyhMSfors8AAN85du6T2lgourk1SQNaPD4pvWX1RwAAAABAa7aJYzwGHw1ziwr02iDfPn9bmVNC8BEAAPQYfbYO9ZtvvqkzzzxTzz//vGpqapqCjMYEZvPNxTju2jrq2muvbXp+8/NVVFS0GYgEgGBimzVNloR4j20G1tXqtLysFvv+tTtbjQ7/VN8FAAAAAADufZJR1K7hOXVwf4YTANBuWVlZbS48aiwU2t2a96H5nGB+fr4aGxu7qVcAEBzsW3Z5bbMuKbnVYisAAAAAAM+so4ZJ4Z6L75xaUezzMK7IKenU/fIAAAD+1OeCj/X19frFL36hX/3qVyotLTX/j1nzoGPzQKI/wo7NzZgxQ9OmTWtxzeZBTAAIZpaQEIWccoLXdlccPiCbw9H0OKe6TkszCwPcOwAAAAAA+jbjM8tPMnx//50cEarpyXEB7RMAIDhVVla6ncPrbg0NDW6PVVVVdWlfACCYOB0O2bft8dhme2y8isPCmx6PiIvUsLjILugdAAAAAPRultAQWceP8thmZEGBYjx89tVcQU29dpXyWRgAAOgZ+lTwsbq6Wj/4wQ/0zjvvtAofugKOR1d8PDqg2FlXXnlli8eusOWaNWtUUlLit+sAQE9kmzVdln6eb4pMra3RaXnZLfa9sCtLDlYQAgAAAAAgYL4pqjAXH/LVpVEWOZaukqPA9xWCAQAw1NbWtjkQRUXtqzwcCIWFhe3uNwDAO8fBTKmq2mObNVR7BAAAAIAOs00a6/G41enUzBLfF8BcmcM97QAAoGfoM8FHY4XWG264QZs2bWoKOLYVeGy+z98VHw0LFy5UePh3qxS62O12rV271m/XAYCeurJQyCJfqj7ub1H1cV95jdbklga4dwAAAAAA9F0ft6Pao+GUnCw1vrtMdQ/+RbV/eFINn6yUo6D7AysAgJ4vIiKizf27du1Sd9u9e3e7+w0A8M6xxfvv+NVJA1o8Pik9kaEFAAAAAB/ZJo42qvF4bHNaue+LWRJ8BAAAPUWfCT7++te/NkOProBj80Bj88BjVFSUTjzxRP30pz/Vk08+qZdffln//Oc/W7TrjMjISM2dO7fNMKVR9REAgp1t9nQpPtZjm7TaGp2Sn9Ni3/O7svwaRAcAAAAAAEc0Ohxalul7aHFYdLjidu5teuzMzFXje0YI8q+q/V8jBLmCSpAAALfi4+Pb3L969epuH7VVq1Y1fd98XtBmsyk21vPn2gAA9+xbPQcfcyIidSA6punxkJgIjYyLYkgBAAAAwEeW6ChZRwz22GZ6Qb5szu8KUniyq7RKedV1jD8AAOh2IeoDPv/8c7322mstAo6G5o/T0tJ0zTXX6KKLLjLDic1lZWX5tT+nnnqqPv3006bHriAmFR8B9AWW0FCFLpqrhjc+8lr1cUlKquyWIxn9b4oqtLmwQtOT47qopwAAAAAA9A0b8stUWt/oc/vLwh1SaXmbx5xZuWrMypWzokphF5zhx14CAILF8OHDlZ2d3TRP55onM0KHeXl5SklJ6ZZ+1dbW6r333mtzIdT09HRZrX1mPVkA8CtHfpGceZ4rzK9JSm5RmWRhepJfFqYGAAAAgL7EOmmsHPsOH7XTKuuoobJNGqvMwYNk35zh8/lW5JToopED/d9RAACAdgj6GbqGhgaz2qNLW6FHI+z44Ycf6qqrrmoVegyE+fPnt+qPwZjk9XfIEgB6ItvsGVLcd6u2tmVQTbUW5ue2qvoIAAAAAAD86+MMzzchH23eUe/X22KbPrETPQIABLMJEya0OU9mt9v1pz/9qZt6JT399NMqKytr0S/jqzGnOHEif9cAIFDVHg1rkga0eLxwUBIDDgAAAADtZIQbTZERss2YrNBrLlTEb+9S+K1XK2TB8Ro5Il3JEaE+n29ldjGvAQAA6HZBH3x88803m1aNbR56dH3/4IMPmlt4eHiX9SkhIUEDBx5ZAePoVQr37NnTZf0AgO5iCQtV6Mlzvba78tA+WZvd+LI2r1S7S6sC3DsAAAAAAPqO2ka7lmf5PnE9sV+Uorfv9twoPlbWYYM63zkAQFCaN29ei8eucKHx9a233tLSpUu7vE9btmzR3//+d7fVxebO9f55NgCgbY6tnt8/VNpC9HV8QtPj9OhwjYmPYjgBAAAAoJ2sKf0V9uMfmGHHsKsvUMgxk2SJ+q4gkNVi0QmpiT6f74uCclU2NPI6AACAbhX0wccXX3yxxWPXxKnx9fbbbzerPXaHcePGtVjF1mXfvn3d0h8A6Gq2uTOk2GiPbYbUVOuk/JwW+16g6iMAAAAAAH6zMqdENXaHz+0vDnPKWVLusY1t2gRZrEH/0TMAoINmzJjR5gKhrjm8u+66S5s2beqy8d2/f79uvvlmNTQ0mI9d84guEREROuWUU7qsPwAQTJxV1bLvP+SxzYbE/mps9v7hpPQkt0F0AAAAAIBnthFDZLHZ3B4/Me27hWe8aXQ6tT6vjCEHAADdKqjvPjGqJxpb87Cj6+usWbPMSczuYgQf3U2uAkBfYAkLU8jCOV7bXXV4f4uqj8syi5RRWRPg3gEAAAAA0Dd8klHoc1vj1uM5udle2xnBRwAA3P6dsNl06aWXtlgg1PW9MYdXU1Oj6667Tm+88UbAB3H58uW67LLLVFhY2DSP2LxPxr7vfe97iouLC3hfACAY2bfvlcXRekHo5tYkJbd4vDDd9+ojAAAAAID2mZEcr0ib7/GBFTnFDDEAAOhWQR18XLlyZdP3R68Y+/Of/1zdacSIEW3uP3jwYJf3BQC6S8gJx0nRUR7bDK2u0vyC3KbHRg2Kf+32fpMlAAAAAADwrLKhUWvzSn0epmP7xyls6y7PjeJjZR0+mKEHAHh09dVXa8CAAS3m8IygoStsWFdXp3vuuUdXXnmldu7c6ffRzMzM1G233aabbrpJZWVlreYRm1d7vOWWW3g1AaCDHF7eP9hl0frE/k2PUyLDNCEhhvEGAAAAgAAJt1k1K6Wfz+3X5JSo0cuCNgAAAIEU1MHHTZs2tXjsmiydMWOGJkzo3lXH21oZ1uifMbkKAH2FJdzHqo+H9svSbKXt9w8VqLCmPsC9AwAAAAAguK3ILlFDOyarLwxzyFni+fNL29TxsliD+mNnAIAfREZG6le/+lWLCovNuaovbty4URdccIF+9KMf6YMPPjADkR1lt9vNCo8/+9nPdNZZZ2np0qVNc4eGtqo9GuHIlJSUDl8TAPoyZ2OjGrbv9dhmS3w/VYSGNT1emJ7UIoAOAAAAAPC/E9MSfG5b3mDXN0XlvAwAAKDbhCiI7du3r80PxefPn6/uFhsb2+YEblVVVbf1CQC6Q8iJx6nx09VSVY3bNsOrK3ViYZ6WJw80Hxs3Zb68N0e3TR7ahT0FAAAAACC4LMks9LltqNWi47KzvLazTZ/YyV4BAPqK008/XZdeeqleeeWVpnmy5qFD1z4jsPjZZ5+ZW1RUlKZPn67x48dr3LhxGj58uDnnFhMTY25Wq1WVlZVN2+HDh7Vjxw6zauRXX32l0tIjlY5d1zo69Oh6bHw15hN/8IMfdNPoAEDv59h3SFYvgfXVSUeq/7qclJ4Y4F4BAAAAAOYMTDArJznasZDmMcnxDBwAAOgWQRt8NCYos7LavhFn5syZ6mnBRxeCjwD6Gkt4uEJOmqPG95Z5bHfVoX1a0T9Fzm9vPPnv/lxdPTZdcWFB+6cMAAAAAICAKatv0Po8z9Ubm5s9IF62JWvlsT5kXIyswwf7o3sAgD7if/7nf1RQUKBly5a1CCG6CyYa82irV682t/ZqXtGxrSqPzfdNnTpV//d//9epnw0A+jr7ll1e26xJSm76vn9EqCYntX0fBQAAAADAfxLCj7z/+rqowqf2K3OKdceUoW0WIwIAAAg0Y8GGoGRMfDY2NraatDQkJ3/34Xl3iYiIaHN/dXV1l/cFALpbyLzjpKi2fy+6jKyq1AmF+U2PqxsdemN/bhf0DgAAAACA4LM8q1j2oz439eS8UIecxZ6DkrZpE2SxBu1HzgCAADAqNBoBwzPOOKNV2NHQfF/zKpAd2Zqfo/m5m1/T2Hfcccfp6aefVmRkJK85AHSQ8fu03kvw8VBUtLKiopsen5SeJCs30QIAAABAl5iXmuBz28yqOh2oqAlofwAAANwJ2rtQPAUIk5KS1N0qKircTvACQF9jiYxQyEmzPbapt1g1sLblm+dX9uSottEe4N4BAAAAABB8lmQW+dw23GbVtMxMr+2M4CMAAO0VFhZmhh9vvfXWpnmyowOKR1eB7Mjm6VyufZdeeqmeffZZxcZScQwAOsOZky9bieeFU1Y3q/ZoWJieyKADAAAAQBeZn9a+92Arc0oC1hcAAABPgjZl56mctt3e/SGZsrK2P+Rn9VgAfVXIibOcZcoPAAEAAElEQVSkyNZVH+usVr2WPlSXzZqn1wYPa3GstL5R7x78rgokAAAAAADwrqSuQV8UeL4Jubm5KfGyfrPdc6O4GFlHDGH4AQAdZgQfX3zxRY0YMaLNkKOho9Ue3QUnXeccOHCgHn/8cd1///0KDQ3lVQSATrJ7qfZoWJM0oOn7hPBQTe0fx7gDAAAAQBcZEhupoTGt79d0Z2V2cUD7AwAA0OeCj54ChKWlpV3al/b0ISYmpsv7AgA9purjguObHtdarXp1kBF4PFGPjRqnovC232T/a0+2Gh2OLuwpAAAAAAC922dZRbIfyX745NwQh5zFnoOStqnjZfm2ShcAAB11zDHH6J133tG9995rhhE9VXr0lafKj/Hx8brjjjv00UcfaeHChbxwAOAndd/s9Hi8NDRU2+P6NT1ekJYoWzt+twMAAAAAOsf4bGxxlM34xqf2W4srVVRbz7ADAIAuF6IgFRUVJZvNJofD0Wrys7i4WKmpqepOu3btavV/II1+dne/AKA7hcyfpcY1X+jthGQ9lzpEJWHhXp+TW12vTzKKdObQ5C7pIwAAAAAAvd2SzCKf20barJqSmeG1nW3axE72CgCAb/+m2Gz6/ve/r0svvVQffvih3nrrLa1bt06NjY1NQ9Te8KMrPOl67owZM3TOOefo7LPPVkSE7yvbAwB8+J1bXiFbRrbHNusSk+Vo9nt8YXoiQwsAAAAAAea02+XYnyHH1l2yb92liwqK9fExs7U3Ns77cyWtzi3V2cMG8DoBAIAuFbTBR6vVaq4Em53d+gP1rVu3auLE7r0RZ9OmTW3uT09P7/K+AEBPYYmKVMR9d8i+P18lWw75/LwXdmXp9CH9ZWUlWAAAAAAAPCqsqddXBeU+j9K81H6yfLjWnNB2KzZa1pFDGHkAgN8DkIsXLza30tJSM/y4fv16c55v7969qqmp8ek84eHhGjFihCZMmKCZM2dq9uzZGjCAG7QAIFDsW3d7bbM66bvfw/FhITom2ftNtgAAAACA9nPW1Mq+Y++RsOP2PVJ1bYvjC0uLfAo+GlZkFxN8BAAAXS5og4+GoUOHKisrq9WKrxs2bNAll1zSbf2qq6vTli1b2lyJdvz48d3SJwDoKSwhITp3eIqe25mp8ga7T885UFFjvqlekJ4U8P4BAAAAANCbfZpV5DnEeJRzQhxyFpd6bGObOkEWq7XTfQMAwJ1+/frp9NNPNzeX/Px8FRQUqLCwULW1teb8myEsLMys4piYmGgGHI3NWDAVANA1ar7Z6fFGlHqLRZsSv5vTm5+WqBB+TwMAAACA3zmdTtX9/nE5S9wviLmwrFD/GDzcp/NtyC9TbaNdESE2P/YSAACgDwcfJ0+erDVr1jQ9NoKGxv+JM/bV19ebE5/d4Z133jGv7+pP8wDklClTuqVPANCTRIfadOHIgXp2Z5bPz/nrlkOaPTBB4TZuYAEAAAAAwJ0lmUW+vz8PsWliRobXdrbpExhwAECXc4UaAQA9h9O4D2L3AY9tvuqXpBrbd7eqLExP7IKeAQAAAEDfY9yfbh07UvZ1X7ltM7CoWP3ralUYHuH1fHV2hzbml2leGu/jAABA1wnqdMiMGTOavjcChi6lpaV67bXXuqlX0j//+c+msGPz0GN0dDTBRwD41iWjUtsVYsysqtPLe7IZPwAAAAAA3MirrtM3RRU+j8+Jqf1k+XqH50ax0bKOHMqYAwAAAJCsVr0wc5beH5iu4tC2F6Je3T+56fuYUJuOHRDPyAEAAABAgNgmjfXaZl5xgc/nW5lT0skeAQAAtE9QBx+PP/54xcTEmN83DxoaIchnnnlGtbW1Xd6nd999V/v3728RxnRVfZw/f75CQoK6CCcA+KxfeKjOGda+1br/uTPLvIkTAAAAAAC0tqwd1R4N3wt1ylnkeQLbNnW8LNag/pgZAAAAgI9K7U79KyxGfxw7SRfMXqCbp8/Si0NGaF/0kfs2DGsTvws+npiaqFDeTwAAAABAwFjHjpBCPd+bflpFSbuCj45mxYgAAAACLajvSAkLC9OiRYtaBAxdcnJydN9993Vpfw4fPqwHHnigRZXH5s4+++wu7Q8A9HTfH5Mmm5vfmW2ptTv0ly2HAtonAAAAAAB6q6XtCD7Ghdo08fBhr+1s0yZ0slcAAAAAgsWK7GLZv70tw2mxaEdcPz0zfLSuO3auLp11oh4aN1kFEZFN7RemJ3ZfZwEAAACgD7CEh8k6ZrjHNqPz8xVhb/TpfMV1DdpeUumn3gEAAPTx4KPhqquuavHYVV3R+PrOO+/oueee65J+VFZW6o477jC/Nu+Hy5AhQ8yKjwCA7wyMCtcZQ/q3+ybOTfllDCMAAAAAAM1kV9VqWzsmouenJUhfb/fcKCZK1pFDGWcAAAAApk+z3C+2khsRqSUpaU2Po0JsmpnSj5EDAAAAgACzTRrr+bjdrhklxT6fb2W27xUiAQAAOstz7eogMGHCBM2bN08rV65sCjw2Dz/+7//+r/Ly8vSLX/wioJUeb7rpJh04cKDpui6uvtx4440Buz4A9GY3TBhshhmNao7uWJ1OOZqFyR/efED/WjRFIdagz/cDAAAAAOD3ao+G74U65Sz0PHFtmzpeFpuNVwAAgCCSkZGh5cuXa+vWrdq7d69yc3NVVVWluro6RUVFKSYmRoMHD9bIkSM1Y8YMcx62X7++GVwqLS0156C/+OIL7du3zxw7YxHc6upqhYeHKzo6WgMHDtSoUaM0adIkcxFcY+x6GuN13rhxo3bt2qX9+/crPz9fxcXFqq2tVUNDg0JDQxUREaGEhAQNGDDAfO3HjRun4447zvwecCmvb9TG/HKfB+SE1H4KtzGXBwAAAACBZps0Rg2vem5zZkWxVvcf4NP5VuQU6+ZJQ/zTOQAAgL4efDT88pe/1Lp169TY2Nhm+NGo+rhlyxbdfffd5qSTvxjXe+ONN/Too4+qrOxI9TFXlUfXtY2vxsTQ+eef77frAkAwSYkK1zXj0vXktoxWxyxOp87OztAFWYf1o2NmqTIk1Nx/oKJGr+3L1WWjv1s1FgAAAACAvqw9wcd+YSEad+iw3C9BdIRt2sRO9wsAAHS/+vp6vf/++3rhhRe0fbv7is8VFRXmlpOTow0bNujll1+WzWYzw49XX3215syZo75g7dq1ev7557VixQrZ7fY22xjhR2MrKCgw56HffPNNPfjgg+aivVdeeaUWL16ssLAwdRcjpPnKK6/ogw8+UHZ2tse2RujV2Iz57oMHD5qvvUtaWpr5s1xyySUaNGhQF/QcPdnKnGLZmy0C7c3C9KSA9gcAAAAAcIQlPk6WIWlyHnb/GcAxBXmyDBsrZ7MCFO7sL69RZmWtBsVEMMQAACDg+sTyeSNGjNCPfvSjFpUWDc3Dj5s2bdLFF1+sW2+9VR9//LE5edNRhw4dMie7TjnlFN1///0tQo+ua7qEhITooYce6sRPBwDB7/LRaRoUHd5i36DqKv3p6436yd4dGlJTpZv27Wpx/KntmSqqre/ingIAAAAA0PMcrqjRrtIqn9uflJYo59fuQw+m6ChZRw3tfOcAAEC3MioWGsE1Y4FYT6FHd4zg3+eff65rr71W11xzjQ4cOKBgZfxsxs9obJ999pnb0KMnxhgbi/aeddZZ5th3NSO0+rOf/UynnXaann76aa+hR2+M5//jH//Qqaeeqp///OfKy8vzW1/R+3yaWexz2wibVbNT+ma1WAAAAADoDraJYzwej6yp1fiKI/e7+2JlTokfegUAAOBdn6j4aLj55pu1efNmLV++vCmAeHT40eFwaNmyZeYWERFhVn8cOXKkEhMT3Z7XWAnTCDaWlpaaEzlffPGF8vPzm85tcAUdmwcvXde98847zZU9AQDuhdus+snU4bpzzU7ZnA5dnHFQ1x7cpzDnd7UnFudm6dMBqfoy4cjqsFWNdj229bD+59hRDC0AAAAAoE9rT7VHw+JQh5wFnm9atk0dL4vN1smeAQCA7tLY2Kjf/e53+te//uXXSohnn322fv3rX+u8885TMDEqNv7P//yPWR3THw4fPqzrr79eV1xxhRmENBbLDbTXXntNv/3tb81KlP5mhEDffvttLV26VPfdd5/OOeccv18DPVtlQ6PW55f63H7uwARFhPB+AgAAAAC6im3yWDV++LnHNqeUF2t7nG+L1KzMKdZlo1P91DsAAAD3+kzw0fDII4+Yq41u2bKlzfCj63tDTU2NWQXS2Fyat3d9feCBB1pco3m4sXllx6NDkMbXSy65xFwRFADg3QmpCbowXDplzXqNrSxvs82du7fpumPnqNZ25M/b+4cKdO7wFE1JimWIAQAAAAB91tLMQp/bJkWEavShQ/puqaG22aazmBsAAL1VbW2tbrrpJjOo6G9GMNCoHnnw4EH95Cc/UTB49NFH9fe//z0g5zaCp/v379cTTzxhLswbCMbiv0YY9eWXX1agVVVVmZUfd+zYoV/84hct5ssR3FbnlKjB8d29Et4sHOR+8WkAAAAAgP9Z0gfKkhAnZ0nb914aTiwu0J8HjfDpfJsLy1Ve36i4sD4VRQAAAN3Aqj4kJiZGzz77rKZNm9YUdjw68Oja5wpGujZ3mrdpfs7m522r8uO5556r+++/vwt+agAIDo7D2bpl6RK3oUdDem2Nrj24t8W+hzcfkN3D73EAAAAAAILZ/vJq7Suv8bn9wvQkWWrqJJuHj46jo2QdNcw/HQQAAF3KCCbecsstAQk9Nvfkk0/qb3/7m3o742cIVOjRZc2aNbr11lv9Vk3yaPfee2+XhB6b++c//6mHHnqoS6+J7vVplueK8c2FWy2aMzAhoP0BAAAAALRk3MNunTjW47AklZRqYE21T0Nnd0prc0sYZgAAEHB9bpmF2NhYvfDCC+YEz9tvv91m+NHQfP/Rx5pra5XKo9s2P7/xvbGC6o9//GO//UwA0BdYBqfKNmqoHLsPeGx3YeYhfZ48UDvi+pmPd5VW6e0DeTp/xMAu6ikAAAAAAD3H0syidrVfNChJYdPOknPxQtm37JJ98zY5du6T7N/VgLRNHSeLzRaA3gIAeoOrrrrK43FjLuz555/v1Dl6El9+nt7ECKOtWrXKa7v09HSdddZZmj59utLS0hQZGamKigrt27dPGzZs0EcffaTKykqP5/jrX/+qESNG6Mwzz1Rv9MEHH5g/gy/zz6effrpmzpxp/rzG45qaGmVlZemrr77S+++/r+zsbI/nWLlypX7729/6feHc5557Tq+//rpPbePi4jRv3jwdc8wxGjlypOLj4xUdHW2+zuXl5dq9e7e+/PJLs69GZUdvXnzxRY0aNUqXXnqpH34S9GTVjfZ23ex6/MB+igrh/QQAAAAAdDXb5LGyr9rosc2C0iK9Ehnl0/lW5JTotCHJfuodAABA2yxOT+UMg9w777xjTiCVlpb6FHJsr6PP2b9/f/3ud78zJ4wAdM6uXbt0zz33tNj3m9/8RmPHel6RBr2bo7BYdb97XGpo9NjuQFSMfjhjthqtR6pTxIWF6PVTpyk+PLSLegoAAAAAQPczPpO85JPNOlRZ61P75MgwvXPGMbIetdibs7pG9q27ZP9quxmCDLvxctnGjQxQrwEAPd24cePaXBi0+SKgO3bs6PA5ehJff57e4sMPP/S6OGu/fv3085//XOedd56s337G3hYjDPfEE0/o2WeflcPx3QIJR4uJidFbb72lwYMHqzc5fPiwOQaewp02m03XXXedbrzxRvPndMcYnzfeeEN//OMfVVZW5vG6f/7zn80QpT8cPHhQZ599turq6jy2MwKOt912my644AJFRXm/sdEIwP7nP//RY4895jUAaZzv3Xff1aBBg9TbMBfpu6WZhfp/6/f43P6B40bpdG6MBQAAAIAu52xoVO2v/iDV1btts3/gQP1g7FSfzhcdYtPH3ztWoR4+QwIAAOisPv3/NIyJHmOC75prrlF4eHhT4NFV7fHoqo/etPU845yhoaH6wQ9+YF6L0CMAdJy1f6JCF5/std3w6kpdcXh/0+Py+kY9uS2DoQcAAAAA9Cl7yqp9Dj0aFqUntQo9GixRkQqZOU3hN16uiN/eJevoYX7uKQCgNzLmwJpv/jhHT9qCjRFWe/DBBz22MaoVGgE9IwDnKfRoMIJ+d911l5566imPYTkjOPjAAw+otzH67Cn0aPzMzzzzjO68806PoUeDMZYXXXSRObbDhnn+/1HGa+Stkqav/vSnP3kNPU6dOlVvv/22rrzySp9CjwajoqUR+HzzzTc1fvx4j22rq6t9qpqJ3svpcGj5oQKf24daLTohNSGgfQIAAAAAtM0SGiKrl4Uth+XnK7qxwachrGq068uCcoYbAAAEVJ8OPhoSEhJ09913a9myZfrpT3+qIUOGtJrUPDrQ6G4zNH9ucnKybrnlFvPcxsqoxiQQAKBzbPNnyTI03Wu77x/er+GVFU2P3zyQp50l/rlZAAAAAACA3mBpZlG72i8anOS1jSUyQhabrRO9AgAEi44uJOrpHD1pCzZG+KyoyP3/N0hNTdVzzz3X7sp8J5xwgln5z6h+6M7KlSvN+dLeYunSpVq1apXb4yEhIWa1y9mzZ7frvEbVy+eff14pKSlu2xQWFvolKJibm6uPP/7YY5uRI0fq6aefNl/7jhg6dKhZ8dOYX/fkvffeM38uBKfaPQf149df1wPbNuu03CzF17uvGmKYNaCfYkJDuqx/AAAAAICWbJPGehwSq8OhWSW+zy+tzClhiAEAQED1+eCjS1JSkm644QZzAsiYfDGCiosWLTInenxd+TU6OlqzZs3SjTfeqJdfflnLly/XbbfdZgYgAQD+YbFaFXb5OXLaPP8JC3U69fPdW2X9NsRu/O/Dmw/IEYQrdQMAAAAAcDTj88qlmb7fYJ4aFa6JCZ6rFQEAcPTfGio+9g75+fl66aWX3B43QouPPPKIx0CeJ3PmzNGtt97qsc1f/vIX9RZ//vOfPR6/4447dPzxx3fo3AMHDtTDDz/ssaKm8VoVFPheQa8t77//vhwOh9vjxvWNipBxcXGduk5iYqI5Xp7Cwo2Njfrwww87dR30XDkbtijKbtf8wjz9ctdW/XftZ/rrV+t16eEDGlJdafyxaNH+pPTEbusrAAAAAECyTRhtrEbmcSjOqvQ9zLgyp7ip0BAAAEAgsJReG0aNGmVuP/jBD8zHNTU1ysnJMScFKysrVVdXZ07QhIeHKzIy0pzQSUtLM8OTAIDAs6YOUOipJ6rxw889thtfUa4LMg/ptcHDzMdbiiv14eECnTV0AC8TAAAAACCo7SytUlZVnc/tFw1KCsrqVgCAwPHH3w3+9nQNo8pgQ0OD2+MXXnihZsyY0alr/PCHP9Q777yjAwcOtHl8586dZuXHefPmqSczFrbdvXu32+OjR4/Wdddd16lrzJw5U+eee67++9//tnm8vr5eL7zwgu68884OX2PdunUejy9evFhjxoyRP0yYMEFnnnmmGbZ0Z82aNbryyiv9cj30LOE797Z4bNR+nVxeam43HditDQlJ+vmUY48cs1g0Ly2hm3oKAAAAADBYYqNlHT5Ijv0Zbgdkcl6ebMPGym7xXl8pt7pee8qqNaZfNAMMAAACgoqPPjDCjSNGjDBX7jSqQJ511lk655xzdPrpp2v+/PmaPHkyoUcA6GIhp5wg50DvFXWvO7hHaTXVTY//uuWwKhsaA9w7AAAAAAC615IM36s9uoKPAAB0tNqja/PHOXrSFgyMEN1rr73m9nhYWJhuu+22Tl8nNDTUrIToyb///W/1dN76+OMf/9iskNlZxnmMMXPnP//5j/naddSOHTs8Hjfmuv3J2/m2b9/u1+uhZ6jNyVdiRYXHNoeivqsqf9yAOMWHuf/vHgAAAADQNayTxno8HlZXp0llpT6fb2WO7xUiAQAA2ouKjwCAXskSEqKI75+j2kefkcXDDSgRDod+tnubfmqsJmuxqKSuQU9tz9RPph6pAgkAAAAAQLAxghrLsop8bj8oOkJjWYkXAOCjW2+9tUecA75ZsWKFysrKPFb+S072vsigL0499VSlp6crKyurzeOrVq1SSUmJEhJ6ZsW34uJirV692u3xIUOGaOHChX65VkpKilkl8e23327zeGlpqVkh8+STT273uWtra1VQUOAx7HrssUcq8PmLUcXSCHK6qyyal5dnBjmNayN4HF73tQZ7abM66bvfLwvTWWwFAAAAAHoC26Sxanxnqcc2Z1aU6Ot+iT6db2VOsa4bP8hPvQMAAGiJ4CMAoNeyDh2kkAXHy/7ZWo/tjikt1lm5WXo/9cib69f25eic4QM0Ii6qi3oKAAAAAEDX2Vpcqdxq3ysEnTI4SRaLJaB9AgAED4KPvcu7777r8fgFF1zgt2sZlRCNyn+PP/54m8eNUNxHH32kyy67TD3Rhx9+qMbGRrfHzz33XFmtVr9d77zzznMbfDS89957HQo+VnipwDdgwABFRETInyIjI83zugu9GgtzGP1KSiL4Fkws23Z7PF4REqIt8UeCzsa/nBPTfLthFgAAAAAQWJaU/rIkJ8pZUOy2zfFF+dLgkT6db0dJlfJr6jQgMtyPvQQAADjCfzMzAAB0g9CzTlJDYj+v7W7et0v962rN7+1O6eHNB8yJdgAAAAAAgs3STN+rPRoWDeIGdAAAgpHdbvdYwdAIqs2YMcOv1zzrrLO8VqDsqbz1zajQ6E+zZs3yWG3TeO0cDke7z+vtOYmJgQmfeTuvp1Apep/68kql5ruvLGpYn9hf9m/DwsckxykhPLSLegcAAAAA8MRYDNM6cYzHNvFl5RpcXeXzQK7KKWHQAQBAQBB8BAD0apawMEVffo7XdjH2Rv1kz3ZjWWHz8RcF5VqW1b4bQQEAAAAA6OkcTme73u8Oi43UyLiogPYJAAB0jy1btnis/nfCCSf4verzqFGjlJqa6vb4hg0bemQAzujTxo0b3R5PT0/X8OHD/XpNo3rk7Nmz3R4vKyvT1q1b233e+Ph4j8cDtSikt/N66xd6l/3rv5HNS5s1SQOavl+YzmIrAAAAANCT2CaP9drmlDLf55tWZBN8BAAAgUHwEQDQ69nGDJf9+Ole280tKtBJBblNj//8zSFVN9oD3DsAAAAAALrO10UVKqip97n9KYOS/B54AAAAPYMRMvTk+OOPD8h1PYX5KisrtW3bNvU0RsCwqqqqQz9TZ8yZM8fj8XXr1rX7nBEREYqKcr+wRUlJYG5E9HTeyMhIs18IHnXf7PR4vNFi0YbE/ub3xruNBemBqTQKAAAAAOgY64ghUqTn9+oLS30PPm4qKONeTAAAEBAEHwEAQSH6vNNUGx3ttd3te3cqvuHIDaD5NfV6bmdWF/QOAAAAAICusTSjsF3tT42yqeHtJXIczJTT4QhYvwAAQNfzVi1wypQpAbnu5MmTPR7vicFHb30K1Fh5O+/27ds7dN6JEye6PZaXl6fa2lr5U01NjfLz890eHz9+vF+vh+7VUN+gwZme59e+jk9QZUio+f20/rFKigjrot4BAAAAAHxhsdlkmzDaY5v0ggLFfXuvpTcNDqfW55Uy+AAAwO8IPgIAgoIlMkIxl3/Pa7uEhnrdsve7VWj/vTtbhytqAtw7AAAAAAACz+506tOsYp/bj4qLUuq+A2pctlp1jz6t2vv+T/WvfyD77gNy2u0B7SsAAAg8T6G56OhoDRs2LCDXnTRpksfjO3bsUG8LPk6YMCEg1x0+fLjH6owdHatjjz3W7bGGhgZt3LhR/q4uapy3I/1B77P3i+2K9PJ+YU3SgKbvT0pP6oJeAQAAAADayzp5rMfjFqdTJ5T4XvVxZU4JLwIAAPC7EP+fMjgYK10aE0m7d+9WZmamuUJlcXGxufplXV2d2SYiIkKRkZFKSkrSgAEDNGTIEI0dO9ZcsTIhIaG7fwQA6HNCJ49TyaRxit36XbCxLafm52jZgFStT0pWo9OpR78+qP+bO04Wi6XL+goAAAAAgL99VVCu4jr3N5wfbdHgJNk/aHbTe1mF7Cs2mJuio2SbPFa2GZNkGzuSFwsAgF6murranON0xwg9Buoz8REjRng8vmvXLvU0xpxwZ36mjrJareZr4S6keujQIbOaojEn3R6LFy/WE0884fb422+/rXnz5slf3nrrLY/HzzjjDL9dC92v/CvvlUjXJCU3fb8gLTHAPQIAAAAAdIRt/Cg1WK2Sw+G2zZmVJfpgQKpP51uVU2Iu0mnjPkwAAOBHBB+b2bRpkz744AOtXr1ahw8fbjVYTqez1b62JgSNfaNHj9YJJ5yg733vexo3bpw/XzMAgAfJl31Pxb8+oKhvQ+ru/HTPdl0bP1fVISFam1dqvumex8QrAAAAAKAXW5JZ2K72p8aGyXnQTSCiqlr2dV9J9fUEHwEA6IWM0GNbc5suQ4cODdi1Y2JilJiYaC4q25aMjAz1NJ761L9/f7NCZqAYi+u6Cz4ar2FWVpZGjRrVrnMa7efOnWvOe7fl/fff1w033KAxY8aos4y+f/jhhx6rPQaqYia6XqPDodSDhzy2ORAVo5zII5VMJyfGKCUqvIt6BwAAAABoD0tkhKyjhsqx+0Drg1ERsk0Yrca0QVKZ+2Bkc2X1jfqmqELT+8fxQgAAAL+xqo+rr6/XSy+9pFNPPVVXXnmlXn75ZXPlTGMS6eitebDRFXhsq53D4TBXKn322Wd13nnnmZsRqPQ0uQgA8A9LbLRs55/utV1KXa1uOPDdCs5G1cc6u29v0AEAAAAA6Ik3IH+W1Xa4oC1j+0UrZd9Br+1sU7lJHQCA3shTtUfDoEGDAnp9T+c3ApFGRcqeorKyUqWlpT1yrHx5Ld256667FBoa2uYxYz77xz/+scrLy9UZxmt5xx13uJ0HNypa3nnnnZ26BnqWXdv2Kbm21mOb1c2qPS5MT+qCXgEAAAAAOso2aWzT95bkRIWcNFtht12jiIfuUthVF2jMCdPVukSQeyuzfZ+rAgAA8EWfDj5+/PHHOuWUU/Tggw+aFR5dwUVXsNHd5uKtjet8O3bsMCd0zj77bLOqJAAgsOKPn6bcYUO8tjs3O0NTSo+80c6urtO/dmfz0gAAAAAAeqVN+eXmSrq+OmVQkuzf7PDcKMQm64T2VRcCAAA9Q3a258+7jSqGgZSUlNSp/vWlsfJ2fqPiY0eMHz9eN998s9vj+/bt0/XXX6+cnJwOnd+YX//BD35gfnXnmmuu0THHHNOh86NnKty0zWubNf0HNH2/ID0xwD0CAAAAAHSGdco4hZxzisL/3y0Kv+c2hZ53mmyjh8lis5nHkyLCNCkxxufzrcwp4QUBAAB+1SeDj8aqnbfddpu5imVeXl6rsKO7So6+boa2zrdnzx6zquRvfvMbNTb6fhMSAKB9jN+9g64+XzUhIV7b3rV7m8LsdvP753dmKrvK8yq1AAAAAAD0REsyC9vVflFCpBx7PVd8tI4bJUt4eCd7BgBAaw899JA+/fRTc84OgVFS4vkms+Tk7yqyBcKAAd8FnzrSv67krS+BDj56ey06M1Y/+tGPdOGFF7o9/vXXX+vcc8/Vv//9b5+rcBr/bp999lnzecYCwO4sWrSIao9BxuF0KnHfAY9tikPDtCM23vx+fEK00qIjuqh3AAAAAICOsCb2U+jJc2VNSW5RHKi5E9N8X9TmcGWtDlXU8GIAAAC/8Z4ICTIZGRnmypWuCo/N/0+aK7R4NHf/R6655s89+jxHX8OYONq6dauefPJJ9evXr4M/CQDAk9Ckfio99URFfvCpx3aDa6p1zaF9+seIMapzOPXnbw7pf2ePZXABAAAAAL1Gg8Oh5dnFPrefmBij5H0H1eBo+/NQF9vU8X7oHQAArb344ov617/+JZvNpgkTJmj27Nk6/vjjNWPGDIWFhTFkflBaWurxeGxsbEDH2dv5vfWvK3nrS1xcXK8dK2Oe+sEHH1RkZKT5787d+X/961/rT3/6k0488USzQuPIkSMVHx+vqKgoVVVVqayszFzk98svv9SKFSvMfZ6cdtppevjhhxXiwwKV6D12HMrVqPIyj23WJiXL+e39EQvTPVd+BQAAAAD0DvNSE/TY1sM+t1+RXawrx6YHtE8AAKDv6FMzDQcOHNA111xjVnk0NK/G6GvQ0V3btp7jant0FUjj8ebNm3X11Vfrn//8pxITfV8JAwDgu+GnnqBdm7ZqaH6+x3aXZBzUe6mDlB0Zpc+zi7U+r1SzUgimAwAAAAB6h/V5ZaposPvc/pRBSbJ/+pnnRlarbJPGdL5zAAB40NjYqG+++UZbtmzRP/7xDzP0OG3aNDMEaYQhp0yZIqvVyhh2gBFU8yQmJiag4+rt/D0p+FheXt6jx8rba+mN8W/onnvu0Zw5c3Tfffcp382ciTEO7733nrl1lBGw/PnPf67LL79c3e2rr77q1PMzMzP91pdgkbn+G43w0mZN0nfVXk9K5z4IAAAAAAgGw2IjNSg6QplVtT61X5lTQvARAAD4TZ8JPhqTZz/84Q/N0KMvgcfmx4z9SUlJ5mqbrs3hcKiiokKVlZXm16KiolbnOvo6zQOQxve7du3Sj370I3N1zdDQ0AD+9ADQN1msViVddZ7qHn1K4Q5Hm22KwsL0f6MnmKFHl0e/Pqh/LZqiUG6oAQAAAAD0AksyC9vVfmFSjBw793tsYx09TJbo794rAwAQCK45M9ccWl1dnTZs2GBuf/nLX8xqc8cdd5wZhDS2cePG8UL4qLq62uNxY2wDydv5a2pq1FN4q14Y6LGKjo7ukrFauHCh5s6dq//85z96/vnnlZGRIX8xxuiSSy7R9ddfr/79+6snuPTSSzv9M40aNcpv/entjN/TMbv3eWxTb7Hqi4QjYccx8VEaHBPZRb0DAAAAAAT6M7wT0xL00p4cn9pvKapQSV2DEsK5Nx4AAHRenwk+3nnnneaqjG2FHo8OPIaEhJiTPsaqlxMmTND48eN9WpV0x44d2r59u1auXGlOyBrhSHcBSNdE7tdff62HHnpI999/f4B+cgDo25KHpGrdrGM1de2GVsfeH5iuJ0aMVeVR4fODFTV6dW+urhiT1oU9BQAAAACg/ersDq3ILvG5/dSkWCUdOKQGu+cKkbap43k5AABdovk83dFzeEYgbfny5eZmSEhI0KxZs5oqQg4ZMoRXyUM1TU+M+dBA8rboa0NDg3qKvjRW4eHhuvLKK83ts88+04MPPqisrKwOny89PV0//elPdfLJJ5vVHhG8duaVaFKh5wVXvkhIUq3tyL+Xk9KTuqhnAAAAAICuMC/V9+CjUaJidU6JFg8bEPB+AQCA4Ncngo///e9/tXr1ao+hR2Nfamqqrr32Wi1evFiJiUdWIvRVv379zAlWY7vuuuvMypJvv/22nnvuORUXFzcFINsKP7766qs688wzNXPmTL/+3ACAI6ZfcKr2bd+l4WVl5uOciEg9PGaiOQHrzjM7MnTa4P5KjgxjGAEAAAAAPdba3BJVN3oOMTZ3yqAkOVas9NzIItkmU1ELABB4zefszD9BzRYUbauNMef20UcfmZvBmNszgpDG/JwRhhwwgJupfA3LBTrM5+383sKGfWmsbDZbl41VfX29Xn/9db3xxhvaunVrp89nhCbvu+8+LVu2TOeff77mzZvnl36i59mzcauGOY1bV91bnZTc9P1Cgo8AAAAAEFSmJMUpLtSm8gbf5qRWEnwEAAB+YlWQq6ur0yOPPNIq9Ng8iGisonnjjTfqww8/1FVXXdXu0GNbUlJSdMMNN+jjjz/W97//fVmtR4b66MlaVx9+97vfdfqaAIC2hYeFqO7Cs1Rvseq19KG69tg5HkOPhupGh/625RBDCgAAAADo0ZZkFvnc1viEcmFKnOzb93huN3ywLPGxfugdAACeuebrms/bNd+8tcnOztZbb72lX/ziF5o/f7650KhRwW7JkiUqLy/v08PvLSznLWzXWd7O35MqPtq9VcIOgrEy/r289NJLWrhwoR544AG/hB5dKisr9cEHH+j66683w4+uCq0IHuY9FTv2em239tvg44i4SA2LowIoAAAAAASTEKtFc1ITfG6/Lq9UdXbPC+gAAAD4IugrPhrVFIuKilpUW2weghw0aJAef/xxjRkzJiDXj42N1b333mtOtN5+++1N1R9dE7KuvuzcuVOff/65FixYEJB+AEBfN33qGP367O/p/dJan5/zUUahzh2Roun94wLaNwAAAAAAOqKm0a5VOSU+t5+eHKd+BzJUX+/55nnrlPG8IACAgDLCUevWrTODi75We/Slzf79+3XgwAEz4GUsSjpu3DizEqRREfLYY49VRESE+gpvVQodDke3hgkDXUXRn8FDbz9LTx+r/Px8/fKXv9SqVasUaNu2bTMXB77kkkvMa0ZGdl/47ZVXXunU8zMzM/XCCy/4rT+92e6SKk3Jy/XYZmdsnIrCj/yOpdojAAAAAASnE1MT9dHhQp/a1tod2pRfprntCEsCAAC0pefMKAXIa6+91mICtHnocejQoXrxxRc1YMCAgPdjxowZ5rWMipJHBzGbT74QfASAwLn++PFa8slm1Tta/v715OHNB/T8winmikUAAAAAAPQkq3NLzIljXy0a1F/2NWu8trNNJfgIAAis3/72t+bXjIwMrV271tzWr19vLiDqryCkESYzQljbt2/Xs88+a4bHpk6dagYhjW3atGk9Knznb6GhoZ2qCNlZ3s7vrX9dyVtfujv42JmxMv6NXXHFFcrN9RxaM0yfPr0pJJyenq5+/fopJiZGFRUVKisrM4OAmzZtMkPLX331ldfFiTdv3qznn39eCQndc4Oj8fN0RlRUlN/60ttt2bxTZzXUe2yzOum7ey4IPgIAAABAcDo+pZ9CrRY1+Hj/5cqcEoKPAACg00KCeuXB3bu1Z8+eppBh89CjMVHxxBNPdEno0WXEiBH605/+pGuuucacwHL1x9U/Y5VNY9IoPj6+y/oEAH1JWnSErhqbrqd3ZPr8nL1l1XrzQJ4uGjkwoH0DAAAAAKC9lmQU+dzWZpEWDoyXfcsuj+0sg1JlTWL1XQBA1xg8eLC5XXzxxebjXbt2mSFII1i1ceNGVVVVdSoIaRx3tWloaNAXX3xhbo899phZ/dEIeLkqQk6YMEHBxFuo0xiPQOpNwcfuHitv5+/oWBkVVY1Feb2FHk899VTddNNNmjhxYpvHjeCisQ0bNkwnnHCCuW/Lli36+9//riVLlrg9r/Hv+dprrzXDj8x/917G71Bv7yEMa5OSza9DYiI0Iq77Kn0CAAAAAAInOtSmGf1jtS6/3Kf2q3KK5XAOl/Woz/QAAADaw6ogtqaN1ctdAcjbbrvNDCJ2NWMC9fLLL2+aZG0+IWuEITds2NDlfQKAvuTKsWlKjQpv13P+vu2wSuoCe2MDAAAAAADtUdVg19rcEp/bH5scr9jDWVJNrcd2VHsEAHSnsWPHmguIPvnkk+ac2SuvvKI77rhDM2fONMNfriCja37NFXRsvvipu+OuhUhdW01Njbko6cMPP6wLLrhAs2bN0u23366XXnpJBw4cUG/nrVpddXV1QK/fPLTalsjInhOMio6ODrqxcjgcuvPOO83wo6fz/v73v9df//pXt6FHdyZPnqy//e1vZvVWT/3bsWOHfvnLX7br3OhZ9pfXaHJOjsc2eeER2hsd21Tt8eiQOgAAAACgd3PW1Krxy62qf+EN/b9lS2U5ajEydwpqG7Sr1PPnHgAAAH264uOXX37Z9H3zD9cTExN1xRVXdFOvZK6YaUzUGqt3Hv2hv9HnU045pdv6BgDBLsJm00+mDNPP13lfndalosGuJ7Ye1q9mjAxo3wAAAAAA8NXKnGLVOXybWDYsGtxf9vXrvbYj+AgA6ClsNpumTZtmbjfffLPq6urMeTSjIqSxbd++3VxUtKPVII9uU1ZWZlavc1WwGzBggFkN0tjOO+889TbeKux5C9t1lrfz9+vXTz1FXFxcjx6rjlRLfPbZZ1vMlR/NCBL/4x//MEPFnWGEhgcNGqTrrrvObeXKZcuW6c033+yV/44gbdhxQOdWV3ocijVGtcdvf78uHJTEsAEAAABAEHAUlcixdZfsW3fLseegscqSud9Y9mZsRZl2xvn22c7y7GKNT4gJcG8BAEAwC+qKj3v37m0xgemq9rh48WJzMqe7GMHLBQsWtJpwdfUZABBYJ6YlaNaA9t0o8M7BfG0rrghYnwAAAAAAaI+lmUU+tw2xWLRgYD/Zv9npsZ0lpb+sA5N5IQAAPVJ4eLhmz56tn/70p3rttde0fv16PfbYY+ZipyNHjmxXtUdf2uTl5entt9/Wr371K/VG3sJy5eXlAb2+t/N3JMwXKN76YoRiA6miosKvIVEjSGlUTfXk/vvv73To0cWolnrfffd5bPPII4+ovr7eL9dD16r+2vN7CMOapAHm1/TocI2J91xtFgAAAADQsznLK1X7u8dV98Cf1fDGR3Ls2t8UenQ5u6rU5/OtzCkJQC8BAEBfEtTBx6ysrDb3z5kzp8v74q0PrsnUzMzMbusTAPQVxu/cO6cNN2/89Ilxo4ukhzcfkKON0DoAAAAAAF2por5Ra3N9n1SelRKv6KwcqcJzNSGqPQIAepOYmBidfPLJuueee/Tee+9p1apVevjhh80KdKmpqX4JQvZmxkKsnhQUFAT0+t7On5CQoN4yVoWFhb1qrP773/96DFNOnTpVF154ofzpoosu0uTJkz3+jO+//75fr4nAO1heo7Fu7rlwqbbZtLnfkX9DJ6Un9frfnQAAAADQ58VGy1ld43EYZhf5/rnS3rJqZVfV9vlhBQAAHRe0wcfq6mrV1dWZ3x9dWXHMmDHqbqNGjWpzf3FxcZf3BQD6oqGxkbpsdKrnRk6nFuTn6ukv1iimoUHbS6r03qHA3gwCAAAAAIA3y7OL1diOhXkWDeov+9c7vLYj+AgA6M369++vxYsX66GHHtKnn36qJUuW6MEHH9SZZ55pBsfchRxd2gpC9mZG+LM7w3zezp+enq6eIi0trUcHH9s7VkalUk9uvfVWBcJtt93m8fgbb7wRkOsicD7LLtLX8QnaGx3jts2GhP5qsB657WRhuucQMQAAAACg5zM+L7NNGuuxTUJRsVJqPYcjm1tF1UcAANAJIQpSNTXu/w9VUlKSeurKoZ76DQDwr2vHDdJHhwtUUNvQ6lhSXa1+smeHTijKNx//aP8u/WHsJD2+9ZAWpCUqLixo/4QCAAAAAHq4pZlFPrcNs1p0Ymo/Ob7xHHy0JMbLMsjLAkEAAPQigwcPNjejEp1h165dWrt2rdatW6eNGzeqqspzJeTebtCgQR6PZ3mp4tZZmZmZHudJo6Ki1JOqh/br10+lpaU9bqx8eS2bM/673r59u9vjsbGxmjNnjgJh7ty55lhWVla2efzrr782Fy4ODw8PyPXhf59mFmn3sFF6YdgoDayt0eyifM0pKtC00mKFfhsQX91/gPk1JTJMExLcByQBAAAAAL2HbfJY2Vdv8tjm1PJivRjh22JNK3NKdPEo5qAAAEDHBG3Fx8bGRrfHbDabultISNuBGYfD0eV9AYC+KjrUptumDGu50+nUmTmZen7j6qbQo+HM3CzNKC5USV2jntqe0fWdBQAAAABAUlldgzbkl/k8FrMH9lNUboGcxZ6fY50yvkXVKwAAgs3YsWN1zTXX6Mknn9SHH36oK664QmFhYa0qPwYLb2G5Q4cOBezaRvCtuLjYL0G+ruKpT0ZFxurq6oBdOyPD/ZyD8d9meyo+fvPNN7Lb7W6Pz5o1y+08dWcZ5505c6bb4/X19dqyZUtArg3/y6ys1e6y7/67z42I1JvpQ3XXlGN17pyTdP/4qVoyIFXrE/ubxxemJwXl71IAAAAA6Iuso4dJYaEe25xS5vsinV8UlKuywf19/QAAAH0y+BgZGen2WEVFhbqbu5UuIyIiurwvANCXnTooSdP7x5rfp9ZU65FvNunnu7cpxt76jfbP9mxXpL1Rr+/L1Z6y4F4NHAAAAADQM32WXSz7t9VVfHHyoP6ye6n2aLBNHd/JngEA0HMZobXly5fr97//vc455xwtWLBA//73v9XQ0CCn02luwSY6OtpjmO/AgQMB+7n379/vNYTa04wZM8bj8X379gXkusaiuMZr4c6QIUM8znsfLS8vz+PxESNGKJBGjhzp8XhOTk5Arw//2Vrs/p6KqpBQfT5goB4aP0XloWHmvoWDkhh+AAAAAAgSltBQWcd5fo8/KDdPUR6KFDVnzGutzS31U+8AAEBfE7TBx6ioqKYVBY9eWfDw4cPqbu5WUY2JienyvgBAX2b8jbhz2nANq6rUPzet1oxS96tQp9bW6LoDe2TU5n14c+BuCgEAAAAAwJ1lmb6voBtus2peaoLsX3sJPsZGyzp8MIMOAAgaRqBx48aN+stf/qLLLrvMrEJ300036fnnn9euXbvMsFmwBh6bmzhxottjVVVVOnjwYECuu3XrVo/Hx48f36vGyrB9+/aAXNcIPXqqJtnesSot9XwTYUJCggLJ2/lLSkoCen34T251nc9tkyNCNSmR+xwAAAAAIJjYJnleuMrqcOiEdlR9XJHj/r5MAACAPhl8DAkJ0YABA9o89sUXX6i7ffXVVy0eGxOrRvgmLS2t2/oEAH3V6PhozZw8Ut/Ee5/wPz/rsCaWlWhzYYU+ySjskv4BAAAAAGAorm3Qpvwynwdj7sB+iigsljPP8/tX25TxsliD9qNiAEAfsW3bNj399NO67rrrzKDjVVddpSeeeEKbN29WY2Nji6CjMSfn2lya7zt6UdXeyluY75tvvgnIdbds2dKpfvWlsfJ23vaOlRFo9STQi/DGxsZ6PF5ZWRnQ68N/wtrx/mBBepKsQfJ7EwAAAABwhG3iaMnLW72zKn2v4rgmt1SNDqPkBAAAQPsE9d0sQ4YMaXOl1o8//ljdyejT0qVL25w0HTp0aLf0CQD6uhsmDtHTk6aoxmrz+ofz57u3Kcxh11+3HFJVg73L+ggAAAAA6Ns+yypSe6aEFw3q773aozF5PbXnVV0CAMCXSnkvvfSSbr/9ds2aNUsXXnihHnnkEa1Zs0Y1NTVNQUfX4qPuQo3N9zV/jrFYqXHOhx9+uNe+GEYA1JN169YF5Lpr1651eyw6OlqTJk1ST2P0KSoqqkeNlcH4b7s9wsPDPR4vLy9XIJWVlXWqf+g5jh/Yz+e2pw/pH9C+AAAAAAC6niU2Rtahgzy2mZCbI5vTt5mryga7WWwCAACgvUIUxKZOnaqNGzea37smNY2vX3/9tbm667Rp07qlXx9++KGys7Ob+tN8gnXy5Mnd0icA6Otiw0J00XHj9Y+cHN2xd6fHtkOrq3TFof16dvho/XNnpm6dTGgdAAAAABB4SzOLfG4babOaFR/tL2330jBC1tHDOt85AAACLC8vzwyJGQE042t+fn7TsaMXQvW1YmPz5yUmJur444/X7Nmzza+DBw9WbzdlyhSzwp+7KnurV69uNVfZWXv37lVOTo7HMGZISM+bog4NDdVxxx2n5cuXt3k8MzPTDNsOHz7cb9d0OBxmUNeduLi4dodEvVV0LCkpUSB5O78RfEXvMDw2UjMHxGuDl4rzC9MTNSnRc6VPAAAAAEDvZJ08Vo6DmW6Ph9bWaXJ5mTbHJ/h0vhU5xTp2QLwfewgAAPqCoK74aExOtcWYwPvNb34ju73rq3RVVVWZq826m0B012cAQOAtHpqsPRMnaEuc91VsL884oJGV5Xp5T44Oltfw8gAAAAAAAqqgpl5fFfpeoWdeWoLCysrlzMz12M42eawsNpsfeggAgH8ZlemWLFmiX//61zrjjDO0YMEC/fKXv9Tbb79thiB9rejYXPPnGNX9jHPefffd5jmNANqjjz6qiy66KChCjwabzaa5c+e6PW6M4xdffOHXa77//vsej5944onqqebPn+/x+AcffODX661fv14FBQVujxuvnfEatkdqaqrH47t371Yg7dy5s1P9Q89h/C799czRGhgV5jEc+eMpLKICAAAAAMHKNmms1zbnVJX6fL6V2SWtFjADAADo08FHY0XW2NgjqwseXV1x27Zt+u1vf9ul/TGub0yeZmVlNT1uPvlqrBA6evToLu0TAOA7VotFd04foT+Onah6Lytchzid+vmubXI67Hrk6wO8IQcAAAAABNSnWUVqz1TwokH95cwtkCLDPbazTRnf6b4BAOAPdXV1ZgXChx9+WBdccIE5z3f77bfr5ZdfNivteQs6tnXTVPPnGBX9jGqDd9xxh1555RVt2LBBTz75pK655hqNHev9Jq7eavHixR6Pv/HGG367lrHorBEidceo9Hj66aerpzL65ilo+NZbb5lVGv3lzTff7NRr15ZhwzyH0DZu3Kj6+noF6t/wpk2bPLYZOnRoQK6NwEgID9ULJ0/RZaNSFdJs3iwu1KZzhw/QswsnKyXK8/sNAAAAAEDvZRmYLEuS52qOxxbk+Xy+7Oo67afIBAAAaKcQBbGwsDCddtppev3111tMerpCkC+99JLZ5he/+EXA+9LY2Khf/epX5sq0ruu7uPp0zjnnBLwfAADPJiTGaPqEEXq+IF8/PLjHY9uxleW6KOOQXrFYtTy7WAvSkxheAAAAAEBALM0s8rltdIhNs1P6yZaWqIiH7pJjz0HZv94h+5adUkXVdw3DQmUdNzIwHQYAwIeQ3DfffKO1a9ea29dff62GhoY2Q4xtVXE8eoHRowOQVqtVEydONAOUxjZjxgyFh/e9gI5R1TIuLs6soNmW9957T3feeaf69+/f6Wt98sknTQvAtuWEE05QYmKieqqkpCSzyuKKFSvaPH748GF9+umnWrRoUaevZVTb9FRBMj4+vkPVMY1gYb9+/VRa2na1hZqaGvNnCEQA1Xj9jfCjO0a/CD72PvFhofrx1GG6ZfIQ5VfXq6rRrpHxUbJ5WUAUAAAAAND7GZ+3WSeNkX35erdtYkvLNKi6SplR0T6dc2VOsfm+EgAAwFdBXfHRcN1115kTm4a2wo/PPfecrr/+emVmZgasD7t379YVV1yhd999t8X+5pOx0dHRuuyyywLWBwCA726eNETvjxipvdFHqgZ7cu2hveYb9z99c0i1djvDDAAAAADwu7zqOn1TVOFz+/lpiQqzffuZaEiIbONHKezS7yniwTsVdvs1ss2fJUtCnGwTRssSFsorBgDocjfddJNZffHyyy/XX//6V7NKnFGFzlWd0fwb5qWiY/P9rm3EiBHmnNzf/vY3rV+/3lwc1Qj1zZkzp0+GHg3GIrAXXXSR2+PGuP/lL3/p9HWM0Oqf//xnj22+//3vq6cz/vvx5E9/+pMZ2u0sY6xcQd+2XHzxxeZr1xGzZs3yeNz49+HPypWuRYCN83rrV1shZvQOoVar0mMiNKZfNKFHAAAAAOhDbJPHem2zuLLE5/OtyPa9LQAAQJ8IPg4fPlxnn312q8nQ5uHH1atX66yzzjIn9SorK/127eLiYv3mN7/R+eefb65S2/yaR/fjmmuuMVdbBQB0v4TwUF0/eZj+d+wk2eV5Ej7c4dDPdm9TblWtXtyV3WV9BAAAAAD0He2p9mhYNCipzf0Wq1W2UcMUdsEZCr//Jwq97Gw/9RAAgPb5/PPPVVVV1SK02Dzo2FY4qq2gY2pqqs477zz94Q9/MKv0vf/++7rnnnvMinyxsd4Xtusrrr76aoWGul/swAiIfvnll526xlNPPaUDBw64PT5mzJgOVTBs7u6779bYsWPdbsbxzjL6aPTVnT179uiZZ57p1DU2btyoN9980+1x47W66qqrOnz+M844w+Nx42d4+umn5U+PPfaYDh486LFNIKpMAgAAAACAwLKOHCpFel5QbH5Joc/n21ZSqcKaej/0DAAA9BVBH3w0/OIXv1BCQoL5ffOJ0uZBxLq6Oj3xxBOaO3eu7rjjDi1btsyccG2v8vJyc1L1xhtvNCfG/v3vf5srXB4denT1w/hqrD5rtAcA9BznDU+RBg3Uq4OHeW07raxE38vJ1Au7spRVWdsl/QMAAAAA9B1LM32fMI4LtWlmSrzXdmaoJDKikz0DAKDj2go6Nq/46NI86NivXz8zPPXAAw/ok08+0aeffqrf/e535iKoAwYM4OVwIyUlRZdeeqnb8TEqGP70pz9VXl5eh8Zw7dq1ZvDNk9tvv71XvD7Gf4u33Xab12qNRkXRjjDG+Gc/+5nHiovGa9WZ/55PPvlkr89/9NFH9fHHH8sf3nrrLT3++OMe2yQnJ5uBZAAAAAAA0LtYbDbZxo/22GZgXr7iGnwPM67KpeojAADwXYj6ACP0+Pvf/14333xzqwCi67HreyMAaUyUGpuxf/DgwRo/fryGDRtmrgwbExNjbkbbiooKs0Kk8XXfvn3asWOHcnJymq57dMixrcdRUVHmxJKnVVYBAF0vxGrRz6YN123FFZpXmKfBNdUe29+4f5fWJvbXn745qD/OGddl/QQAAAAABDdjgZ3tJb4v0LYgPUmh1j6x3h0AIAgcHXI0NJ/Hi4yM1HHHHafZs2eb27hxfPbaUUaY77333lNJSds3lhlznNdee61ZuTE9Pd3n865evVq33nqruRCsO3PmzNEpp5yi3uLUU081/3szAp1tMX7Wm266yQz7Ge18lZGRoR/+8IfKzc112yYxMbHTIdGwsDBdd911ZijYHePf2E9+8hPzvwvjZ2mryqo3RmD2T3/6k/nfjDdGf4x+AQAAAACA3sc6aYzsX251e9zidGpheYneSkrx6Xwrs0t0rlGYAgAAwAd9IvhomD9/vu6++2799re/bVo59ujw49EBReProUOHdPjw4Q5Nzh5dXbL5PuNxSEiI/vCHP2js2LF++ikBAP40rX+cFg5P0R8qJumvX2/w2DbaWBF7zw79MjxCa3JLNGfgkUrDAAAAAAB0xtKsona1XzQoiQEHAPRKzefuFixYoOuvv17Tpk0z59PQefHx8br33nvNyo7uGAu9nn/++frFL36hc889V1YPiykYi8M++eSTeuaZZzxWL4yOjtb999+v3sbo83nnnafq6rYXRTT2G2E+47/TG2+80fw53THG54033tAf//hHlZWVebyu8RrFxcV1uv9XXHGFec3du3d7DS5+9tlnuuGGG8xKkb4EII3nGdUi//GPf5gLA3szatQosz8AAAAAAKB3sk0YrQarRXK0XsTM5YxK34OPG/NLVdNoV2SIzY+9BAAAwapPzRReddVV5oSpUf3RcHT40bXv6AmdtlabbUtbE0HNn9s89GhUeDQqPS5atKgTPxEAINBumzRUF2UX6820wTovO8Nj29nFBVqUn6NHv47UscnxCrNRYQMAAAAA0DlLMwp9bpsQHqIZyfEMOQCgV3ItVGp8Xb58ub766ivNmjVLxx9/vLmNGDGiu7vY65111llat26d/vOf/7htU1paql/+8pd67LHHzPbTp09XWlqaIiIizLDj/v37tX79en300UeqqKjwes1f//rXGjp0qHqbYcOGmX3/2c9+5jEA+Pe//10vv/yyzjjjDM2cOdP87zQmJsYMRmZnZ5v/Hb///vvKysryes1LLrlEZ555pl/6bwSGjbnoiy++2G140+Xrr7/WLbfcYr7Oxr+5GTNmaNCgQWZY1gh0Gq+7Edg0KlZu3LjRfP3z8/N96odRtdXohzE3DgAAAAAAeidLVKSsI4bKsfeg2zajsrMVMniMGj0spOVS53BqQ36Z5qcl+rmnAAAgGPWp4KPh6quvVnp6uln90ZikaavKo4vrmC8rW3oLSTa/TkpKiv785z+bq9QCAHq2/pFhum78YD1VW685RQVKqav12P62vTt1dUKSXtmbo6vGpndZPwEAAAAAwedwRY12l3m+Ub25k9KSFGKsuAsAQC/VfL7OCFotWbLE3AzJycmaPXt202bMt6H9jIqCmZmZWrNmjcd2Rhsj1NcZRphu8eLF6q2+973vmUHPxx9/3GO78vJyvfrqq+bWUXPnztU999wjfxo9erT+7//+T7feeqsaGhq8tjeCmm+++aa5+YMRdnzkkUc0duxYv5wPAAAAAAB0H9vksR6Dj7b6Bh1bXqJ1/ZJ8Ot/KnGKCjwAAwCd9shSVUWXRmLAxJpCOrvTYPORoHGvv1lzzc7qOGxNkxrUJPQJA73HJqIEakBCrR0dP8No2vrHBDD8+uyNTedV1XdI/AAAAAEBwWpJZ1K72iwb7NpkMAEBP0db8XPP9zefgjApz77zzjlmNcMGCBWaFPaMinxGMNIJn8E1YWJgZ5DMq+wXS9ddfr9tvv73Xvyx33HGH+bMEkvFaGBU2jdfG34x/K8brbVSh7EpGpci//e1vOvnkk7v0ugAAAAAAIDCsk8Z4bXNOdZnP51uVUyK7m2JDAAAA6uvBR8PgwYP1zDPPmBMu48ePbxFcbD7J2tZka1vcPcd1XmPC6l//+pf++Mc/KjGR0twA0JuEWq366dRhWp+UrE8GpHptf3JBrqbn5eqvWw51Sf8AAAAAAMFpaWahz22TIkI1rX9cQPsDAIC/hISEtFpY9OgFRY/ed3QQ8sCBA3r55ZfNcJ1RAfKCCy7Qww8/rNWrV6uujkXpPImMjDTnSS+77DK//0dthPceeugh3XXXXQoWxs9i/ExGBUN/M14D47UwXpNAOfHEE/XGG29o6tSp6gqTJk3S66+/boYuAQAAAABAcLAmJ8mS0t9jm6l5ucaN8z6dr6SuUduKK/3UOwAAEMxC1McZ1R+Nbd26dWYlxqVLl6qqqqrpuCvA6Ev40dC86qMRcDzzzDN13nnnaeLEiQHoPQCgq8xK6aeT0hP1WMM4HVdSpISGeo/tf7Jnu67pl6AvRqRoRnJ8l/UTAAAAABAc9pdXa395jc/tT05Pks3HzzABAOhuGzdu1KZNm7R27Vpzjm7Hjh2two7NGcfc7TfY7XZt27ZN27dvN0NkRkDNCHnNmTPHDEVOmTJFVmufXQ+2TcYY3X///TrppJP04IMPKiMjo9PnNBaCve+++zRy5EgFmwsvvFDTp0/XAw88oPXr1/t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       "text/plain": [
-       "<Figure size 1800x640 with 2 Axes>"
+       "<Figure size 900x320 with 2 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 663,
-       "width": 1823
+       "height": 337,
+       "width": 911
       }
      },
      "output_type": "display_data"
@@ -1661,7 +1742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba8f36f0",
+   "id": "73366b8c",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
@@ -1675,7 +1756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41c52262",
+   "id": "650a4e87",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -1690,14 +1771,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "b64ae39d",
+   "execution_count": 16,
+   "id": "9ad6dd92",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:09.504396Z",
-     "iopub.status.busy": "2026-08-16T07:45:09.504244Z",
-     "iopub.status.idle": "2026-08-16T07:45:10.806762Z",
-     "shell.execute_reply": "2026-08-16T07:45:10.806119Z"
+     "iopub.execute_input": "2026-08-17T01:01:49.690583Z",
+     "iopub.status.busy": "2026-08-17T01:01:49.690501Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.481943Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.481578Z"
     }
    },
    "outputs": [
@@ -1731,24 +1812,24 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>zero share</th>\n",
-       "      <td>0.315</td>\n",
-       "      <td>0.331</td>\n",
-       "      <td>0.308</td>\n",
-       "      <td>0.355</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.321</td>\n",
+       "      <td>0.297</td>\n",
+       "      <td>0.345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>median |move| (bp)</th>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.036</td>\n",
        "      <td>0.042</td>\n",
-       "      <td>0.041</td>\n",
-       "      <td>0.038</td>\n",
-       "      <td>0.044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q99 |move| (bp)</th>\n",
-       "      <td>0.324</td>\n",
-       "      <td>0.340</td>\n",
-       "      <td>0.281</td>\n",
-       "      <td>0.411</td>\n",
+       "      <td>0.298</td>\n",
+       "      <td>0.329</td>\n",
+       "      <td>0.265</td>\n",
+       "      <td>0.407</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1756,12 +1837,12 @@
       ],
       "text/plain": [
        "                    observed  pp mean  pp 5%  pp 95%\n",
-       "zero share             0.315    0.331  0.308   0.355\n",
-       "median |move| (bp)     0.042    0.041  0.038   0.044\n",
-       "q99 |move| (bp)        0.324    0.340  0.281   0.411"
+       "zero share             0.300    0.321  0.297   0.345\n",
+       "median |move| (bp)     0.039    0.039  0.036   0.042\n",
+       "q99 |move| (bp)        0.298    0.329  0.265   0.407"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1805,28 +1886,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "4ed037dc",
+   "execution_count": 17,
+   "id": "1c03bb10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:10.808353Z",
-     "iopub.status.busy": "2026-08-16T07:45:10.808213Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.082254Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.081409Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.482948Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.482882Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.590689Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.590248Z"
     }
    },
    "outputs": [
     {
      "data": {
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tKjSNA634cMQRR8Su4FXPkz6bpQz2FJNOjrWyhwo94nQyqWJ/nUjHLSCiwhtannbMddddV5T3lV4PfWaL2VUkrvsRBVU0OfPoo49aHKjgRIP9Dz/8cOi3rfeVAjrF+k6bNm2a+86qpCKxIO6++243wB71YCeKZ5VVVrENN9zQ9/XD/r5Wp6oPPvjA9/UPOOAAX9fjeBlhCWt1Tx1P+B1zQMepYGrAgAGBnzKN88Tt/BYoFY0H3nLLLUW7vwsuuMB1Kwibjkd0XKFCuDj44osv3LhtFCtlluKxaMGFOJ3LV7PbbrvNnb/HJYD5yCOPuFBT3MZnH3roIResifp9O378eDvssMMIrxSBVgdWsSoQFvan8Z1nYS6scmlehDA0/LrrrruKWkyuTp5RLALFuHa86Tzr6KOPtpdeeqlo96fFkznfjgeNF19++eVFvU+NY+kcX+PVxfC3v/3NjQWC/TEWIrgCAGhVGF1Ounfv3unr6ARBLfPiWtCg1SR++ctfBj5p1upsDz74YGjbhWCrQ2qy6+OPP47V06jJRw0mxiXcpJX8dAL3ww8/WLXQ6qdRr+zhl/aVmgyOS1tvrZSm1bjQsZU1brrppqKuGKLVOOMQvirVfkQrd6j9s1r+xok645x33nluRdIwaVWqqDuttDQBplWKqj2sceedd7oBVr22qCx+wx6iYoMwJ0GeeOIJ39ddddVVfYVwOF5GmMLoHDhw4EDbfvvtQ9ketE0dmnReGga6zQJmN954o91zzz1FfSpUlBF2EYA6UqszrY4R4kShep37xW2Mr7NGjRoV+YrG6Jibb77ZrrnmmliMqRR6/vnn7eSTT45N5xV1q9cqssUqalRwTscncewyXGmuuOKK2HaGR3lhfxrveRbmwirXVVddZd9++22pNwNlSIvAXXnllUW9T82rhB2UYVw73jRvouP6N998s6j3qzGNe++9t6j3iZZDZaeddlpR51Q1bqT57GIvTKH3mzpLVTP2xyhEcAUA0CIVHYYxiKECh85SAacmXuJMgQKtpP3ZZ58FWjEH8ehcoYl2nRTF9WT9pJNOsokTJ8ZiYuG9996zanHDDTe41Q/iTCfxWgG51MX4OsHX5DQ6VqynTivFptX+77jjDqvG/YiKGPSdHdf9l4pf1NnplVdeCeX2Xn/9dbf/KgUdF91///1WrbS6rCYCUZl23XVXX+c2XrDrhRdeCG2fESS4sv/++3f6OhwvI+wwqboGBbXHHntYbW1tKNuE9o0cOdL69esX+Kkq9iQ0EEePPfZYSe73f//7X2gr106ePNl1gdUxTlzHtbVaa9xCNR2lVWa14BFKT0Ulf/zjHy2udI4Rh/G4d955xy2uU+xwj8LQClUg+vmJM888kxWpEQj703jPszAXVrk+/fRTe/zxx0u9GShTeu+UIrytgPKTTz4Zym0xrh1/33//vb388ssluW8tAhiXrprVSPuXc845x80XFPM+f/Ob39jUqVOtFC699FKrVuyP0RzBFQBAi3RyEEaqedCgQZ36/YcffrhkE7h+JkGV/vazqteHH35IwUZMaFJt0qRJFmezZ892XYgUmCrlgLlO3qtpH1jMjhhBJw8VXgmjS5Zfem/oPYL23X333UVbfbI5FVu8++67Vm37kauvvrrk4a6Ofo6DdqLRMYm665Sy20dcVnstRaGOVgJD5VInyd1339339YOETQqp4NRv4Lqurs723nvvTl+P42WEScciYRwLbbPNNqFsDzq+D9xtt91CKXb/8ccfS/K0ayJ0jTXWKMoFiKu77ror8G3onENjVKWa5O8oLch0xhlnlOz8O4hqX4EzLtRh4rLLLrO4e+SRR9yl1O/ZhoaGkty3Fs949tlnS3Lf1eSLL76w++67r9SbgTLF/jTe8yzMhVX+QkvleDwM/OUvfwnlSWBcG+0V0quzEEq32GcYndk7Q693WIvM+VGtc+jC/hjNsSwdACDSk8Hll1++w7/71VdflcVkUKH333/frr32WpfK7myr5yBUCLH99tvb+uuvb4MHD7YBAwa4QhKtOKtJIgUcNLipSWRNKnz++edudQptrwI3WNiO8bnnngv8dPTp08et+Dt8+HBbe+21bYkllrBu3bq5IMGXX37pTn7U7SBIFyOFa9SO96KLLirJy1fK0EwpgkJaRa6cBnP13rrgggvs+uuvL9lzhvhTOEJdzVTQkEwmq2I/okGvMFo9a5+uYs1NN93Uhg0bZv3793edF7TCsArIX3zxRfvXv/4VqBObijg1aHP77bcHKkTTdz6KSysyqXhPnzFUNnUr8duNTauca+Uov11bPDqm9EvnD9p/dQbHy+VFq7MXq2heq1vvu+++vhZxCKpr16620UYbBb4ddH4fEkZnNYU9t912W55+oAQ0BqbCjL59+wbqID1hwoTA2zJw4EDbc889bZNNNrHVVlvNjaUpZKvzIo2BaSztH//4R6CuLvpe1PnVsccea+VC47qjR4/2fX2ND++www42YsQI97wuu+yy7vhT57Ra4EDBI70H9Dx/88037hxWK3HrNdW5ZDmNhUVJx+2nnnpqKOd4G2+8sW233Xbuz2WWWca91+vr693CFQqla9xC5wpBVrPWqq36LHVmLqaSaLxc4zVBz7XiqkuXLrbWWmvZ0KFDbcUVV3QX7UOXXHJJ69mzp/u55oU07qb3lj7jmmvTvND48eNdh98wxm61P/3Zz37m9tVAR7E/jfc8C3NhlS9IPYL29z/5yU9ss802szXXXNMdV2o+XMeVOm7RcevcuXNdLYLmKHUsqeN4db5Xp91qLs5FcHofffzxx+6cxi/GtdERWlT50EMP5cmqguMeHZdqTAvFx/4YLSG4AgBosevJW2+9FfiZ0eC5BjE6SkX5YRW1rrzyyrbzzjvbVlttZUsvvbTbFt22Bk8++ugje+aZZ9wkYBgrgekg68ADD7SVVlqpUxOnfqjQ7PLLL2+zyEMTlLrod1dddVU3oOTRQJImI5Vc14TFSy+9VLXtL/VeUPeBIDRod8IJJ9gRRxzR4sSY3nu6aOLwxBNPdJM7f/rTn3xPAqtQUu81TVIh2rboQbseeJZbbjnbaaed3OCuOlBpX6TBWrW9VXGA9kUqHAmjBapuS59tFScArVGI8cEHH3QTzZVOk/VBA7E1NTV22GGHuX19S8Xe+kzroiDp8ccf71Ywu+qqq9x9+zFmzBg3mbTjjjt2+rraj9x6660WFh3D7bLLLrbuuuvaUkst5b7nvEmo559/3hWS+ek6V6nfG19//XWot6mCIx3DqfhGRSkqbtIlkUi499d3333nClEUStZxpQqeCM5ET58HhQJ0PtFZet30Xb3ffvv5vn+9xk8//bTv6x9wwAGd+n2OlxEFddwISue5KtBDcangVkWRQYtPVEQJYHEqANPYwdZbb+2K21WQrEVpVPz1+OOPh9I9U8cSKpL3Ezz0wof33HNPoG3o0aOH64Si45KW9uUaR9Flyy23tJNOOsmuu+46++tf/+r7/tRNVx3nND5XDjQm7/c8S4vqnH/++a0Gk7SAhZ5zndtpjErHts3PKV9//XUbO3asOzdVoVi10vvGb5dDj8Zv9XpovKA5vQ4q/NQchsLx+nyPGjXKd8BX4/sKFd98881WjTTOqjH3zi4uFmcrrLCCGxvSd8IGG2zQoWNfhVh00feH3lty+OGHu2M3LYCgTg5BjsN0fqjuNrvuuqvv20D501hpZ7A/jTfmwip//MXv8cwWW2zh6hF0XtLWHLmOK3WcrcW+mo+Dvvnmm+64UrUICkkHCekiXjQ2tPnmm7sFTnTupjkyLZyqxTy1wJvmqsN4vTUO7je4wrh2ZdBxreYpN9xwQ7ev0T5Hc2Pap6hzRtBzNtFtaa5tyJAhoWwz4kuLP4ZVA6T9oOZvtUiFFprWPLrqEXVuqloM7b9YaDKH/TFaQ3AFALAITV6G1dFBRQ0d9dprr4XS0l0TPmeddVaLRVlalbVfv37uBFcTeToB0crzCnAEnfT9/e9/bzfeeGOHr+N34k8TMDoxCzKorCIfXVS0rIlQPf7/+7//s3//+99uZZSOOu200+yYY45p9ec///nP3YG5H1oZULfvV+/evdv9HaXpg4QFVMCsyZ711luvwwUImnBXR5ZTTjnFV2hKgzwKeIXRPQAt034hjFWEtb9RWOmoo45yJ64tvUdXWWUV915XIcoll1xiTz31VOD71ftDK4Og/Oi7SRO/Ks7R4Jv2MRro1cm8CtK1n9Z3ZRiDvSpkaK1IqJIoLBhk0FIhUAUcVTzWEfqsH3LIIW4/f9xxx/leGfiaa65x99nZFSy17wpjdRodS/32t7+1ffbZZ7HJaK2sJnr/6PFp8kqrIFczFX0ELd4rpOMKHS+oOKU1XpGT9hsaFBUd02g/oZBrZ4oa2/ru0e0EKfxRiGudddbxfX1NesWR3v9a0diPJ554IlBwRQWEfvctKk7UhGJncLyMKIQxoVisrjJY/JxW3z2afAsi7LAnyovOddo6/tDYYJDVDzUJrcliv0oVbjjooINc59eWxrP0/a3VP/XcnHvuuYE6kIiKx/wGV3T8r64dfimQrbG0ji7+o2Nejd2uvvrqrsusn/NhFQ6oW7bGS8qBVqf2Q2MJOpfsbEFxIRUBbbPNNu6i8wCNkWlhBR3DKjBfLZ/zMM7xtPCQ3rsdHXfReZPO6U8++WTX0dUPLYwTtwVtVMCjsS6NcajwVa+bzl011qVVtHUO+/LLL4eyIvt9991nRx99tCtuK1caB9K8lb4TFFYJi8artN/XQm+nn3666/Djl4pROxJc0ee0tcU1NGekuSO/NG+kMXW/guw/oqT5OnV7joo+bwopBH0vnXPOOR3+ffan8cZcWOXze1ypY2/NIWmeM8j4gRZl0kVd7FRormMVfY8oKF0uIRbGzhenxRoVzlZYpTkdh2rsXK+xFisIOv6jc9df/epXvq7LuHZ50/ysjls156oFGAqpE6HOL375y1+6fZXqp4KMU4jOowiuVDYt9hBWjZXef6qpbC3cqbCVvvv0vaexLL81c5WC/TFaQ3AFAOBMnz7drXyjg7WwBgu0ykJH3XHHHYHvTwPOmuDp6EmFfk8D6CoYD7Jynxf40SCsVsLqyISpnm8/hYxBQist0eSZNyF54YUXuolDFTx29Plua5A/SMt4r6g/KlotXquMBVkd8pZbbllsZcKOUIGpCg00qOOHVlXXKjVhTl6FQQOZKrJVQYVO0lR0r9dRYSgFMzRAqglJTb7qT79dZ6J29913B16xXivb6XY6Wqyr50pFFFqR6Oqrrw503yokUBitsNNSqen50PtC+xkNJunxahJZ7wEV2WuCZOLEiS6UoZWX/Owfy5kKcc4+++wWC5kHDBjgLnreNHmpgTPtP/ScBaECAQWcNBFeqfsRfdcGGYDSQKgGUjoaWimk1VR/97vf2bHHHuvrmEorsKgrg4oVOkr3E0boTsdGOiZqayU1j8LA2mdpf6NJ63KZbAqbjmHDKLLRcZOODVTg5LfITJNCuuhYQe9fHS+0p63jraArD6lrT5THc6Wy1157uc+4nxCy9uOaqPVbFKuiQb9UqKSOPR3F8TLiHFxRESRKQ6t3Bw2uhPEeQPnSMU9bxwdvv/124OPZctpH6PlQWL4jBbj6HY076ngxSPdkFRH5oddGxzJ+6dxWiwv4KQbRuav2HRqL83sM9etf/7pTncFLxe8+Ut2gg4RWWqLX6he/+IW7qIP33//+d7eqbaV/znWOF2RsUIXnF198cadfD42fqcOQxof8rsyqLqxxCK6o0FVFhnrvNC8001iPLgoi6xxFXWY0ptCZcFRLdH6m8dggi2GVij4z6varcaQoQxV6j11//fXuPaaxWD807qzPR3tzP+oi25oghdCiQtlKHGvwPhtRUFhJwdEgNJ6gzk6dCQ2xP433PAtzYZXP73GlCsWD7qub01joT3/6U3dRmEHHlA8//LDFHWPni9L8heYq27PRRhu5ehMd62k+0q933nmnQ8cdzTGuXd60v9B3VHsLXujYRIuI6jy/M8Ha1sZJ1AkT8aHAtGrjVDegfYrGlHTso/o2LYagMMhHH33k9hMvvPCC6/jUlieffDLwQjCi800tXtnRujBt+5FHHunqGqoR+2O0heAKAFQhddnQIJaK0bSSsiYdVSDpp/iqrcL+jg5gajDt+eefD3R/Kmy98847fU1+jho1yn788Ue34pBfGhhUgaxuqz2d6WpSKOqJd61+oiJZXVQgXMn0WgUpMtUqIX5CK4UT7lqxUKtm+6FV/+ISXNEAuYru1QqzJX379nUXDS7suOOOrtuMCjYfeeSR2K1uppPcjkzAt3cSrdU9/Kwwr+4s2i9rXxaEBnPiEFzRwIEmXbXiiQqqW1sxRe8DnbRrwFr7Un0uwug+Uw400aqwQVuTuYVUfPD444/b4YcfHniAQ/cbl+BKFPsRBXOCDECp6MfrZOGHBtJUUNbRMGhL+/nOBFd0LPfNN99YEEsssYSbUO5IaKXQyJEj3USoumtUGx0v6TMZlN7fWhVqk002CWW7hg8fbg8++KDbl6ooBeHS66V9kVYI7Cx9z2mAXAVcfo5TtEKTH5rE6WynF46XERV1lAuqVB0RkAslBhXGJCFQCVTUftlll3WqAFRdj9QdsSPjj20VsGlsUsWPnfGXv/zFgtBjDbKCqYInOhby08VaY4DqxqDxvLiL67ixivbUhUWrhgZd8CXOdG4bpKuoukaqM5HfEJHGz3Ru7Xe8RoXKChmVsqhf80Iam+zo2Lk6uz700EOu6Gz06NGB7lsFsArMBFlQqxS0f1IhVrHGa1XYp+Ipv/sozSmGvcAaoqOwkb5Dg+67tfCdFvLoKPan8Z5nYS6sOsT1uFLHS+o4fsIJJ/jeRhSf5uU6ElrxaJ+kxZ+CdFlTTZMC3ToP7gzGtct7sUcteNHRLq2igJSOd4IsWus31I1oaP7trLPOarV+wgt861xy7733dv+mBRE0L9pa8DLIeX5h7UBHQyuF84m33XabO8cPujBpOWJ/jLZ0fKlFAEDsaeJRK1W1d1FBtVZl0SCjVm7XQXyYoRVRsaYGyjpCRV9Buy9ogKOzJ60eTSSpRZ8OGoPQAGBHHoffAeJirkoa1cpOcaDXKMiJiToCHHzwwYG3QwNyfin0ovBZqWkVOAU9Wis2b6vQTIPsWoEwTlQAEXSQVCsRBVnZUAUAHenc1BZ1owijIDDowLOKuTWo0NpkSmuFtVoxrBoK4DWRr8G3joZWPHo+NcAxaNCgQPevARytRFKp+xEFV/zSSnUqmAjjsSnM5odWoFWxSUeFEfbS59Xv/kdF+NVYtKDQc9BjaB2HXnPNNaGFVgrp86GAF8IXZPUtvxMoOv6rr6/3dd0tttiiU8XmHC8jSn7fx827fqE0wiiqVGc8ALniCm+CvbPXCzJup06Jn332WaeuozEodWn2a6uttvLVzbKQujboHMsvjVHEtftvOY0bq3i2s6GncjvHC3KscuaZZ3Z4TqStLq6au/H7+Q66KE8QGte78cYbO73gk4Im6jYzdOjQwItL+F0oqpSKFVrxaOxaY19+Be2+h+JRJ16NnwYdu9KCQ50N1LE/jfc8C3Nh1SHux5U6vleROuJP83d+6hq0yOLaa68d6L4nTZrUqd9nXLu8qXZNtTh+Fgct5vsM0Y036HxSl87WTyjEoqC1uoU3p8Uw1ZEuiBVXXNGd7/uhcy91Za027I/RHoIrAIBIOnccffTRnSrCCnqQ2JkVHlpbZVwDuEGoa8sbb7zR7u/5ba/71ltvuVXTEIxeoyBtabVakQbTgtLKRxp09jvYGLRLUVCnn366u2gAvFIEKcLwCugUogt6Qq5JiCD0/vjf//5npaJ9srp5lHJ1x3KgFTk0iOJ3gEMThkE9/fTTVon7EXUe0eqPfqmDSGcmAttamUwFJ3515vgo6PGBut7ss88+gW4jaCvuchTGZ0j7AnXoiUolF5WVerKuswPnhUU+n3zySaev98QTT5hfBxxwQKd+n+NlxD24omNmlIbf8Yyw3wNAudP5xmmnnebruhqTCrJghnz//fed+n0VNwZZqV2rhIdhp5128v0doLFAja1W6n721ltvDdRdGjn//ve/A3XV3XnnnUN5KnffffeSjW8GDfgrNO+HAj8q5vHbrSYuY13lQN8jq666aqDFcBB/77//vh1zzDE2b968QLejLkZ+OvSwP433PAtzYdXB73HlXXfdRScULHLcoDCBX34D2X7PXRnXLl+aT/WzuIdovj1IGFzHS+pGhtIu+KlxDXVbCdvLL7/sFnkIQvUMQTp7al+oUHE1YX+M9lROpSEAIDbUdloTNR1dBeu9994LdH8qxAqj/bsmVoIWwKjTQXu0eojfIl2tZqEDdk6c/AsS+ND7LMjEYXNBVlcvZXBFK7gHWeUyron/oIXfel6Cdm6S7bbbzrVQjnpfFAVvhcTOrOpejRRa8zPh17xgRwG4oAM1lbgf0f4xyACU34HRlgwfPjzy/bxaC0+ePNmCdosKGiDSiqrrrbeeVQsdiwVdoUchtKDBaZSGiqn222+/onVdUaHl2LFjfb/PdGzRGRwvI0qNjY2BbyOM83+ULjQUxnsAKHfqVB2kqMLPCqTNF98p1rGBHueWW25pYS2W1NlODoVKvQhMlF3Fxo0bZ4cffnhZhHPiSl2Yx48f7/v6e+65ZyiLHUmQcNrnn3/e6a5KYRUanXLKKYFuY9iwYe55LNexrnLS0fm7lnz99dehbgvCpy7O6o6sjmlB6Db8LNTF/jTe8yzMhVUPv8eVOo5QlyV1MQta6Ivyt8MOOwQ6/wx67vrDDz906vcZ1y5fqoMKEmJfbbXVijpOgnBpYVd1aYqC37k1zzLLLBNKoObQQw+1asL+GO2pbfc3AADohI033th+/vOfd/j333nnnUDPr05edt11VwtrtUOteB1kpZmOPJ7a2lpbeuml3YrwnaWW3r///e/thhtucMWwG2ywgVslXSvvDBkyhJVnO9gePcjgShjBBI/flbqDPo4gVHBw/vnnWyVO5gQNhKkQPwwqHtd+7e677/Z9G0H3rX6pANtvF5Fqoo4eYaxWrWKn119/3ff11ZVEE4nF7sgQ9X4kyP5RHdiCDqQXWmGFFXxfV8FeFXa2VyAapLuMqLBGkw9h2GWXXeztt9+2aqD9rCZ6gw5ShrEvQOn25ddff72l0+lOX/df//qXnXrqqR2eiHnyySd93Y8XxutskT/Hy4iS3o86rw0iyKr/CCboayd897Xd1QLVIWhh9qBBgwJdv7PjH0GODbTgQljF/N5Ymt8AeanG0or12mp8QEWGGiPWap7rrLOOrbzyyu68NEhQqlroHC9I15ogC1e01MFVnxu/5wAKMK200kpWTFpgRaH5MMa6Ohv0bx761zgvnaCjC4J3duVzFJcWuNEc7fTp0wPdzsEHH2y/+c1vfF2X/Wm851mYC6seQY4rP/74Yzv66KPdInvqpqbjSnXrUj2C/i1ohzSUj1Kfu2oOszMY1y7foJ3fzo2lGidBeDR+8dOf/jSypzToPLrOdcP43lMwR7VmM2fOtGrA/hjtIbgCAAiNJsUUqOjMit1Bi6uVnA96ElLoJz/5SeTBFVHYxE9wxaNCVnVUKOyqoOddq+9oMlIXDR7pTxXghvkclTNN9gXp8BNkVcfWCqT9mjJlilv5IYwJwc444ogjAm13XAXdF/Xu3dsF98Ky7bbbBgquaHVFnfSGGbRqj+7rqKOOKtr9lbOwApfbb7+9CzX4XbVa+8SPPvrINtxwQ6uk/UiQ4IQmYMIU5HHqdf3www/b/e7RaxiEuqT079/fwqCuDldddZVVg6ArGeu4TUUAKF8Koiv0/r///c/XcZwKCzt67PDEE09YkO6UncHxMqLWvXv3wOEHOnaUTn19fSgdE4BqprG6tddeuySrJ3s6sx9WgXKQMcw4nWNNmDDBhc+DdpuMUtD3hle0/Le//c1dPFqwwhsz1kWhBoVaNLau72YEHxsMc9xYRTEaZ1O3er/jIgral+NYl8JuKogNEo54//33Ky64oqLNiRMnuos+49ovf/fdd278Vxf9XMEr7xIlVqOOr2+//daNuwYNF+2111524YUX+r4++9N4z7MwF1Y9VIsQlPYnjz32mLsUntMrTO7VIXjHlgpbaZFQVA511Ntmm20C3UbQucDOnLsyrl2+1M0iaIftYo6TIFxBO3e2N4+gupmgdTth0Ht8q622cgvbVTr2x+gIgisAgNBWAbvzzjs7Xfj4xRdfBLrfNdZYI9D1w749td7WhFJ7z8OIESNCX0lTE68qgtPl5ZdfXqyoXgNU6tCiAuVNNtmk6Cvsx4Ha2M+fP9/39R944AF3iYtPPvmkqMEVnUztt99+Rbu/Ygq6L9JEf5grDIWxb9NjCjts1d6EFqsnt09BwqDtigv37dqvv/rqq75vQ8GIYgZXot6PaAV2TeD79cILL4R+bBF0P9/e5/izzz4LdB9hrt6nSSoVPQX5ri0XQZ937QfCCgyhdPbff39fwRXRKsIdCa5MmjTJFVn6LfpSQWJncLyMqIURWpgxY0Yo2wJ/q5gHRYE0ql0YQY6gY3qdKWrWqtxBXHPNNe4SB/PmzXPHOhrHjistQKSCm7C/61TUrmJ+XQopxKPCQ513anxBXUO0mnY1Cvpe1yqxlfJY/Iz1hPX49Z7ccsst7dFHHw001hV0dfBS01yPFjvQHJLG/T744IPAXV/jFGRGNIEidVrR/GDQwtErrrgi0FwH+9N4z7MwF1Y9dEypuY6gC1+19D3ghSlbmv/S+Y6OK1WLoFA23VnKl94/WjyvXM5dGdcuX2HUMxTzvYZw9zNaYDHK4x6/nUyjmEfX462G4Ar7Y3QEwRUAQGDDhg2zm2++2a2E1VlBB1HDLi5VEaEmR4JMAugxtVeIuPPOO7sVyYs12aBAzbhx49zFm0xSobK2Q6uhVUvhZND3WxwP+ItJk4bF7vBSLHHbF+kzOWDAAPvhhx8CvT+KGVwp9kqO5UpFKGHfXpDgStCJqrjtR7SyYNABqHLbzwddRTHMoiRNQqlIPkh3s2r53gizSxdKR90aBw4c6KuQ++mnn7ZRo0a1O/lXzG4rwvEyotanTx/3fR2EVpdGaQTpuuCplvEHIMqVj4MGwLLZbGzHnqIW9+BKbW2t7bTTTvbQQw8V5f40Nq3VR3VRsFrU0VvHuXvssYcLQldLsWElvdeLfUyvVdaDFjWGOXZW7LGuMGlRtPvvv991TAo63hMVVqOOH3Xd+cUvfhF4kRWN2/7hD39w30VBsD+N9zwLc2HVZbfddgs9uNIWb4FNjXt65//qWL377ru7kGvQ/QvK79w16AI2nTl3ZVy7fIURDCjmOAnK57gn6DyCvsfCHMsOa3HTuGN/jI7gqBAA4JsCHmo7fdppp/memAh6oKiVO8KkExoV1AZZRVSPqb3VEzUBqJWLnnnmGSsFrUivQmddtHrSLrvsYkcffXSoafE4ClogVYlFQ51RzK4MxRa3fZGokCJIcKWYBX1qF13p+4+wRBG4LKfCz6j3I9W4nw9ayKAuKWHS7VVDcCXoe419ZmXQZOs+++xjt912m6/CEnV52mGHHdr8Pb+rL6krl47xO6sa96MoruWWW67FVTk7Y/LkyaFtDzrfDS6M90ApaOxj3333Lcl9A4VWWWWVwE+IFqQplkr7Li2Hx3PooYcWLbjS2nPkdZ1eccUV7fDDD3eB6DCDCXFUDu+NjtI4gQqvihU6CnuRlnIb6worDHLHHXfYrbfeGvsutqxGHS/qqHXMMce4TkNBF1i58cYbQ9nXsz+N9zwLc2HV5cADD7SbbrqpZKFDBTIff/xxd1l66aXtkEMOcce6vXr1Ksn2oLLPXRnXLl/l9l5D+dQNxG0OXR1vqwH7Y3QEwRUAgC9abe3cc8+1oUOHBh5UDSKKgQ21kQwSXOnoYzrllFNcq3eFSEpJ96+VnJ988kmXaD/nnHNcoVslUueZSlLsx9NeIKucxXVfVMrH1Blq951MJot2f+Us7JBT0NsL8n0Xx/1INe7ngz7moPua5qpl0inoPrZfv36hbQtKa//99/cVXBGtat1WcOWtt96yL7/80tdtayVDP6uMVeN+tJIMHz7c7r33Xqv0Y6FirhaKRb/7Jk2aFPgpiSL0D5STMMbcirlS8Zw5c6ySlMPj0YIXGqN97LHHSr0prhPLxRdf7Arqzz//fNeJpVKVw3ujM+P9Cj+oALoYwu5iVG5jXWF8zk4++WSOcdFp9fX1dvzxx9vbb78d6NkbNmyY3XLLLYFXKvewP433PAtzYdVFq8Qr3KZgWqkpNHXttdfa3XffbWeeeSYLS1TJuWsxwwSVNg5caY+nrfGNMOYp6ehUfrR/CKOzU1uYQy+NStt/VdrjiYtEqTcAAFA+NFi23Xbb2T333OPalQcNrXgDq3EqtgzjNjv6mFZeeWU766yzLC4ymYw98sgjbtVoTZZUorivVlbsz0+pO0XESRz3RUGLv4v5/qjk90bYwn6vBL29efPmWTFF/V4p9n4xah15PEFXSwv7PVmswphyP6bo27dvaNuC0tIq1AoL+PH888+3OeCpcLlfWhXbD46XUQ6Fjer60djYGMr2oOPGjh3rxg2CqpaV5YAog97FXDii0o4NyuXxaGGhOO0vp0yZYscdd5z98Y9/tEpVLu+NOI6PVPtYVxAffPCBHXTQQYRW4Cug9qtf/crGjRsXuGPS7bffHupCNOxP4z3PwlxY9dEx3Prrr29xoS4sOtbVJZ1Ol3pz0IYwvhsSieKVpfL9U57COpdggc3ynFuLurNr3ObQo6gpiiP2x+gIgisAgHYPnLbaaiu74IILbMyYMXbzzTfbiBEjQnvWgnYb6dq1q4WtW7duga7fmQKaww8/3I4++miLk8mTJ9thhx0WuF10HFVacVMxJyBramoqenX4OO6Lgt5mMd/vlfzeCFvcBjgqbT9Sjfv5oI85rNUUo7q9Sv3eqJaATzV1XfE7aP7MM8+0+LNUKmVPPfWUr9vVKlVaMdWPatyPorjWXHPNwLehz87rr78eyvag45599tlQnq5K7uQJFOucsJjFPxwblIaC7rfeeqsts8wyFieaG7jhhhusEvFe96+ax7qC+Oabb+zII4+0GTNmlHpTUGZU6H366afbCy+8EOh2VlhhBbvzzjttiSWWsDCxP433PAtzYdVHRcE33XSTC6rFyaOPPmrnnntuqTcDEc9hcO5a+ce0QYU1V6Y56HITxgJBYYQzSqUYxz1Bj0uD1g5W6xw65wPoCIIrAFDFlDrXgZEmwoYMGWIbbLCB7bTTTi5IccUVV7juG+PHj3er7RxyyCG25JJLhr4NQRPUUZywBV2Rq7PF5mqHe/HFF0dS+O7X999/b6eddppls1mrJFEn9outmK+PBg2KObhUbu+NKFYNKPa+qNTtoqtF2K9L0AGToBNVcduPVON+Pmj767CPpcp1gLTY77U5c+aEti0ovZ133tn69Onj67r//Oc/W/z3l156ya1AWMwgTbXuR1Fc6667bijHA+pYhOJRd6inn3468O2oe0D//v1D2SagXIVRTFHMggyODUpnpZVWsoceesg23nhji5M//elP7li10lTaez2sIqhSPHflNNYV5Dzl1FNPtenTp5d6U1Bm9N757W9/2+oiGB213HLL2V133WUDBw60sLE/jfc8C3Nh1Uk1Hvfdd5+rA4mTxx57zB5++OFSbwZaEcb4XTFrCyrt+6daxrXDeo+UYx1LWOdsGrctR2F2/GtNXV1drObQqyWQxv4YHRGswgUAECuDBg2y5557zsqJJiGCHJzNnTs31O0J4zb9TKyoHfzmm29u11xzjRtwjsOJ6GuvvWZPPPGE7bXXXlYpqiXBXq4njqUUdEK0UvZFflX6+yNMYYecggacijlwUIz3STHf93GhxxykKCPoe6gY+8NKPIZlRdXKez/ssccedv/993f6ulooQJ0Ol1566Q4FWjoSkAxy/M7xMopxPLDKKqvYxx9/HOh2nnzySbcIRNAAJzpGhdNhHDNsuOGGPOVAmeHYoLR0jHjvvfe6BabU6SQOHbI1bn3JJZe47oDlWBzU1nu9WhZiCFvYxTdBx87KoUjmH//4h7355pul3gyUoYsuusgef/zxQLehsMpf/vIXF16JAvvTyh4/r/a5sHKmRXd0PPmf//zHfv/739tnn31mcaDaiF133ZV5RgTGuSvKTViB+3Kdly3GcU/QBUWZQ/eH/TE6onJGFAEAVXkwGkV6POgK2H5XxFHXm+uuu85N+h122GGxWIVU3XYqCcX1/lV6QVjQ90YUK+eXal/kt4MXSjN4VE6TOsXYj1Tjfr5nz54V9Z4sF0H3sayqWnkOOOAA36tqKSzefDDc74IEWjnRb/eXat2PovhGjBgR+DamTp1q//3vf0PZHrT/nXXLLbeE8jRtt912PN1AmeHYoPQUDtGx5rPPPmtXXXWVDR8+vKhdd1qiQkdtTyUJem5dzeI2rlAOBcx//vOfQ7mdZZZZxg455BC79tprXRjmlVdesbfeess++uijNi8jR44M5f5RXFdffbU98MADgW6jX79+duedd9qKK65oUWF/Gu95FubCsOOOO7paBHXR22abbUo+/6sFnui6gjBw7opy09jYGMrtfPvtt1aOivH9E7c59LCDMHHF/hgdQXAFAFBSQVf0+eKLLyxMs2bNsmnTpgW6jWWXXTbQ9VdeeWUbNWqUjRkzxu6++2478sgjbejQoSUpDNckxocffmiVQhM5QDnsi8K4Td7v8RR2yCno7VXaRGLQ7+ByNGDAgEDXnzJlSmjbEsXtVep77d133w1tWxAPa6+9tjtm96N5dxUVAfodwPYboPFw/IBi2HnnnUO5nZtvvjkW3Uor3eWXX24zZ84MZaW1rbfeOpRtAlA8HBvEh7pI7LPPPq4Dy+jRo92q+yo8LNXiR347BMZVNY4nhIWxrs53ug+6yr3CB5dddplb8OCCCy6w3XbbzdZcc023P2B128qkAvM77rgjcAGZFqtbffXVLUrsT+ONuTB4wegddtjBbr31VnvxxRddOFrdpEt17F9px5UoDc5dUa2dK99///1QbqcSqdNgEF9//bWFqVrm0NkfoyMqe+lsAEDsBR3AnDhxYmjbEtbthdVeWwnzTTfd1F28NPcHH3zggiQff/yxK2r/8ssvXYI+nU5bVBSg0aRHJRg8eHCg61988cV20EEHhbY9iI+47Yv0uVaQLohBgwaFtj0IT9ghJ30PBLHUUktZpQ2EKOjp93vxuOOOs9NOOy307YrS0ksvbRMmTPB9fR1ThOmTTz6xaqBjinHjxvm+/uuvvx7q9iAe9ttvP3vvvfd8hcV1LOEVj/idrF1hhRUCd7LgeBnFsPHGG7vg5Q8//BDodnR+rJWlVcSLaGjV07AKSLSSK4WUQPkJOragItstt9wytO3BwvPAgw8+2F28sQYVq+i4UgXxGivQJYoOwZ6XXnrJnXtXShdevdcVKPBbDKOiz2oV9lhX0NuL+1jXCy+8EPjzf8899wTqmDF79uxA24Diuuuuu+z6668P3IlIXRTXWWcdixr703hjLgzNLbHEEm5cxRtb+eabb9z4po4rJ02a5L6Xv/rqK9cZJSo6jtUYUdBFulDdGNdGuUmlUi68EqRjpLq2BA3FVzKdOwWt2dG5U+/evUPZnrDn5OOK/TE6guAKAKCkVllllcCFKmEK2l1EAyp9+vSxKGhVfBX46FKoqanJJb01aKTBI52YaEBJgzxhpPQraUVwDcjqxM/v8xJ2OAGVsy9SobY+i3V1daFsT9B9m4oGVLyK+Al7PxJ0gCPogE3c6DM4ZMgQ+/zzz6tmPx/0sx7mIJkmtaql+GHVVVcNdH0ds02ePDnw4B3iZa+99rKrr77a17GmCsPPOOMM+/HHH+2VV17xdf/777+/BcXxMoq1sqdWhVbBXVBXXnml6+JRqtXmK9l//vMfu/DCC0O7vcMOOyy02wJQPOoMHYTOsQiuFOe8UJddd911kX+fPn26O+9QiEXnyXo9NNYbxiqf6hD46aefRr5yfzm816dOneqeaxV+ViPGujrHb0DKc8kllwQKrci0adMCXR/F89BDD9kVV1wReLz0xhtvXGxuMSrsT+ONuTB0ZGxQF3VkKaRAtFeLoIvmRXVcGVbBtG5r22235QWCb4xroxS0GHHQ4/IgCyMrFK8aFbRMNQM1NTWBurbrfHejjTYK5SkuxzoEP9gfoyMSHfotAAAisu666wa6vjcwEpZnn3020PWLsVpRS4POmpjUJPDPfvYzGzVqlD3wwAP26quvusHo9ddfP9Dt+y3+jeuJ49ChQ31f388K2qiOfdH8+fPt5Zdfjs2+SJMPPXr0CG17EG5AMsjgSHNa9aqURUiV9nkux3bKa6yxRqDrv/HGG66rWxxWDS0nQY+vJIyCbcSLVl3aeeedfV33ySefdN8PTz31lFtpy89x7siRI33dd/Pb4XgZxXD44Ye7AEtQKhI99dRTfX1uwpbJZKxS3H///aE+rxorKVahHIBwabEDdbb0i7G00lKQQvvg3Xff3U488US77rrr7LnnnrP/+7//cx1Hg44dhd1po5TWW2+9QNcvx/GEsITd3afSx7q0er1fCqyoi10QOu+spDmfSvbEE0/YBRdcEHiBq2uvvda22morKxb2p/HGXBj86tWrl6211lq2yy67uOPI3/3ud/b000/b888/b6effrr169cv0JNbSceVKA3GtVEKXbp0CbwgYBBhdcqu5O+uoAsIjhkzJpRt0XlYtXRqZX+MjiC4AgAoKU2caeA0CBV5hUEtaMeNG1fyIsawqLPIjjvu6EIse++9t+/b+e6773xdL0gRUpSrAgR5jd5++23XDhKVZ/nllw+8SnNY+yK9/7W6caXsi7AoraT/1ltvhfa99c477wS6jTXXXNMqTZD3v77zFOQoJ0EKzEWdIUaPHh3KtjzzzDNWLYYNGxZ4QPrhhx+2mTNnWlwFPUav1lWeDjjgAF/XUwfF8ePHu8IUP1S8NHDgQAsDx8so1mpnOl8Nw9ixY11nkDDDwZ2hFUePOuoo9zkudwoCqfvTRRddFOp+/Pjjjw/ttirZdttt50LJfi6V0tEmaKCtWo8/ohbk2ECFbA0NDaFuD4JTYf9pp53mgopBwit+xo3j+jkPOk+hws1qpfCuPuth0OsbtDAozmNdWvxIx1t+hREE1qI6pey4wlhDxxe2OvvsswOF47W6tLq1hHXe1VHsT+ONuTBEsar6sccea4899ligsUm/9QilwvdZPDGujWLr2bNnoOsHWaRZY9L/+9//At1/NVh77bUDXf/f//53KNvx5ptvlt13XRDsj9EegisAgJInnIO21VPRXxgrhf/1r3+1dDod6DZ+8pOfWNxoMvC8886zrl27+rr+vHnzfF0vSDFnlEWcQVYk0yQBqxZUrqCfX520hhFsevzxxwN/BoKuvIdoaWXTsEICQSYvtZ9ebbXVrNIEff/rM1hOVl11VRswYEDgY6kwVlhVt7dqoeOqLbbYItBt6Bjr0ksvtbgKGsyZNWuWVaNNNtnErYLrx0033eSC0sUMzLSE42UUiwobVEwVBn2XnX/++YHP6TtDhdi33HKL7bnnnmW/WpqKKO+44w7bddddfQfoWrPZZpvZDjvsEOptonJx/BFPQY4N1IUhaFdZREerZgcJvvkZN47r5zzoPIWCK9Uc0gprrEsdrYO+xkEX+IhS0HmsJZdcMjavlV9B9wFxXgAkLC+99FIo3Q/VrSXIonZ+sT+NP+bCEIXlllvOfvWrX/m+flhd4Yslrse01Y5xbRRb0OPzIPOql19+uTU2Nga6/2qw6aabBrr+p59+aq+//nrg7QhjLr6csD9GewiuAABKbqeddgp0/alTp9qtt94a6DamTJlid955Z+AVY+O6mlfv3r1dYasffgt/gqzW9/HHH1uUxYRBCnxVzFOqFck0+XnXXXexckJEgq48pmIvtcYOQgUdf/zjHwPdhj57W265ZaDbQLT+8Y9/BJ5k1b75wQcfDHQbKsjwG2qMM7X8VTcMvx599FGbNGmSlYJeV93/I4880uHrqOBXBaFBC0NeeOGFQLdxzTXXBJ5QLze77LJL4NtQIPahhx4KZXta8vnnn/u+bpBjOZk4caJVq/3228/X9V555RVf11t66aVt6623trBwvIxi0fe1389LS7Q/PfLII11XuihpJXB9V++88872hz/8wZ0HlCMdd2ilt4svvtgVLl199dWBVgBvSW1trY0aNSrU20RlC7pSZTUff0Rp++23t7q6Ot/Xv/7660tWTDF79mx3/++++25J7r8cbLDBBr6v6+ccMM6f8yDneCo+VKC1VD744AP3nV4q6uQa5PzTc++99wbuJKBxobgK2jEoaFGvxiPvu+8+K6WgYw1Rzh3FwWuvvWYnnnhi4O/Ns846y376059aqbA/jTfmwhDH48piLkQSBsbO44lxbRTbMsssE+j6zz33nKt36yx1udJ10b6gixCKxs2Ddr0st4Uzg2J/jPYQXAEAlNxuu+0WeFUKhU7Gjh3rOwzwm9/8JvCKaHvttVeHf3fChAn23nvvWTH5LSbt3r27r+v179/f/FLXirfeesui6kCjFXH9mjFjhl155ZVWTCqEUkcgDSartXvYxUTIUdgjaNeCf/3rX7678qhzhlaJDlpopzBgt27dAt0GoqX9yI033hjoNv7+978HLtbYfPPNrVLts88+gYoItCJhMSdKdJ8asNp9993tnHPOsa+//rroAYrLLrvMd6BKKyirA1C10fdynz59At+OiosUWAr7PXX77bfbz372M9+3EeRYzutEls1mrRrtu+++rli7WEaOHGnJZDK02+N4GcV0+umnh7IvLVylTmMMCrGE/V2uY7i//OUvbv//29/+1r755huLOxW8qWhbxxY6x9YK2yrgPuGEE9xqcwcffLArXtRji4JWW/W7iAaq0xJLLBHo+up4gPD17dvXtt12W9/XVzH7zTffbMWk/ZruU6GbP/3pT747SpeCxpWKGYoMsgCBn4K9OH/O1XksyDzFbbfdZp988okVk+YYTj75ZHdO8Pzzz1up6Bw0aDGPOtiNGTOmose6gi4go4KnIK666iq3cFIpab7J75yTF5Kqr6+3SqR5w+OOOy7wd4CCL0cddZSVEvvTeGMurHrouCmq8/2wjyuDfDeUAmPn8cS4NoptpZVWCnR9HfedccYZndp/ak6WhYI6boUVVgi8ALXG1f0uAKA6RNUdqBaomrA/RnsIrgAAYtE+cY899ghcjPHLX/7S3njjjU4fJJ5yyik2fvz4QPevCa1DDjmkUyugqaDt5z//uTuxiHqFcnUI+eyzz4o68BJ0dYEwCvhbc9hhhwUqJlS3BK2sGzUFeK699lq3+u0ll1xi3333XeT3Wc30OQ5jFTIVnf/nP//p1HVUVHfRRRfZk08+Gfj+tV9B/N1///2+O1yoG0jQfZC6dKios1LpO1bFVX6NGzfOhVqjHkRSEFGFLSqm0v35/a5Wp4V+/foF2hYVkx1//PGdnvxXe2QVHVcjrRSsgt8wCnz03aECkqDFF3rPakJSgWp1AQsSzFZBWZCiGrXP1nFMtQ3GioKwOn4rBu3P999//9Bvl+Pl8qTvrzXWWKPoF60w7pfON8OeaFMQ87zzznMdUe65555AHTNV4PHUU0+5glAV92gRg7gFVrTCXmuvzTrrrGMbb7yxKzg/6KCD3PiHCri1Il+Q160j9Hyp8A4o5liSip4feOABnvQIBB1rUIgkaNfQjp4vawxN+z11tA3a7bQU1M1Sx5I6llaX8Kh1djw9aAglzp9zHZfsvffegeYoVCz+5ZdfWpQ0jqjv8sMPP9yNf2ghiTgsGvDf//7XHn74YV/X1TzAhRdeGHgbtCBInGmsKkgHK3XL++qrr3xdV69NZzr8RkldQ/3Sfl2FX6Xq5BUVdZLR/iNosEjf1zp3KTX2p/HGXFj1uPvuu91x8aWXXlqUcG2Q48qgQZBiY+w8vhjXRjGtvvrqgW9DCzTr+K29oKHmDzWPqIWCoq4vqzSdWYS6rQUg/dQAaQ49qkWb4479MdpSvOUfAQBowy9+8QsXBgiyIqrapOvA54gjjrCTTjqp3RXfVNSjYpYwWthrRTM/nRpeeeUVdxk4cKA7WNYqRCosiWLC0+9A/oorrliS1QU++ugjt4K8LhtuuKEtv/zyrkC0vVXvll122XZf+0GDBrlJLL3n/Lrlllvsxx9/dIWmvXr1srBoVQWtjqeV1zURW43FnqWkAJo6OGl/4pcGCrQP0sTxWWed1e4kvlbKU7Heu+++a2EUh6211lqBbwfR0/vk17/+tQstqJiwo7Ra9tFHHx14layNNtrIBg8ebJVK3xfqNHHTTTf5vo0nnnjCTRarG8ZSSy0V2rYppPDyyy+7/byKOvT3oPTduN9++9kdd9wReGJJ+y5191pvvfXa/F0ds2l/ecMNNwTuWlfOdNyprmhhrOCs51Pvu2OOOcYOOOCATq1ePHnyZFdYrSKUMI5tvUCEViIK0t1Jx0sqqFLxuFY0UnGKuoK11R1E4WLdb7lTmEQB9aiNGDHChgwZEvrtcryMYlKBqDqlhF1Ip6I+TSgpbKJzSh3/DBs2zH1m9N2u/ay+Q3WurIk/nd9p8QBd7/3333crmOtPzsn87UMUoNR3CdAZ+mxqjCVI0aYKnxXo2mGHHWy11VZzC+Zo9V6t9NcaHZ/ofYvWaR+qc9fXXnvN19Okonq9Ngr/aSX4oN23CymIp+IBfY8oWF8JdM7/5z//2W699VbXQUKLPmnBgzC7lHmF0kECRX7Gf+P+Odc8hW7bbyGQjiUOPfRQu/zyy904XZi0OIA68mg84fvvv7c4UqBAr6/mOToTRDj22GN9BzI8en032WQTizOdC2s7/Z63ayxG73+da3dmYS6tDqyi5bjQviPI2IW6Fuv7SO+ztdde25Zbbjm3D2jvOVl55ZVjeXz6xRdf2JFHHhl4vHeLLbawAw880O0roqLglebqOoL9abwxF1Y9NG597733ussGG2zg6hF22mknX3UNbdGxiY5d/fJbj1AqjJ3HF+PaKCbNtWtxBp0HBqH5Yp33a65X57l6H+u8SvtWzf/p5+qkPXXq1NC2vZqonk9dyIMsHqhzMdV26LhdQaP2Fv5TYFRd299++22rVuyP0RaCKwCAWNAEkwr0gq68pwklFW1q1bVtttnGTQ7pREGDLwoE6EBexXdaiVoTc2EVxwZdwUjbpe3WRQdv2m5NSmqiRRNvfulERsWnKmT0q73C1dYEbbcos2fPdiuBdWalNhWBa+X59ijZrkntIIWmf//7323MmDGu249OJP0GWDQpp0ItBVYUVgnaCh4WaEUfdRz4/e9/H/hp1CSyCpA1YaSVMjWBppCa9lP6bGoVUL0H33nnndAmPhWUQfnQ/kerY2oST2EnFVG0VeSjfaFWUgm68p4o6FnpVPyvYpMgq6L/73//c0FHrRauQIffVb/UMUuB2dGjR7t9vb7foghQaFX5oEEYTW6ri8hmm23mCgDWXXddV1ik4x2tgKrnU92C1CEqaFFJJdAxplY30mczDDomVIGTin01kbjpppu6gg511FEQUvt6Dazq97T6srr4acVVv9162qMwZJDgiuiYuzPH3XpOX3rpJSt3Oh5VUCfqjnk6h4oKx8soJnXcVEhE+7WwaVJJXVaDdlpFx+ic56677iq71VIRHxpP8huO8GhSuDMTwxr7euihhwLdZzU499xzXTjX78JDOq9VGOPf//63G0tTwZzfDn8qANBYmkLS+jOMBQHiSOFJjRXqomJhjRVrzFjna/qs+O0orddC4+NaqMHv2KjGMNTdq9I+5ypsV/BE32V+6RxAnRM0XqzuB34XqtK5n873tPCVxhA1lhh32j+ow5vGzNVZtr1uuHpc6kIdRgGWFjCJYyihpfdikNCG9gcah9DYQXsLJum9qEXNFHiKE401aMwtCBXwaS6oMzQOH6SzbFT03RjGZ0BjKVF32Nb8pb57O4L9abwxF1addFyhizoUrr/++m7+UseVOlYJEizXsYoW6AsyDuq3HqGUGDuPL8a1UUzaj2o+OigdD+q4UBeEf9yj8SwtRBj0fPf2229351eaQ9cCIzo+1ni4N3erOQ6FjHROXKljVZ3B/hitIbgCAIgNTWhookKrnAalCTcdDOoSNYVWwlyVREWIf/vb39xFdKCrVWFVtKjVRlT8rvvTpIRWkfIGknQgrBUGdX11cNAgkQpkgx4Mb7XVVr6upwEvTajG9WBchYRKxCvYE4QG4bSSnApWFZbSwJpeLwWmevfu7cIsmmTW66NCZa22oOtogl1dZSZMmBB4BQaESxPKWrUtjJXJ9P5XkbouUdPkrN+CAZSOBjg0yXr//fe7wQ1NFGj/pH28AioKOakLxjPPPBPaapr6LlGBUKXTKuoqrFIoKAh9typE8Mc//tEFSxUm8FZr1z5e+3pR1xG9ZtrHa7+ufYj28yrA1cqJUdP7Rivl3X333YFvS99bmuyuhPBAMSiApqCiBiPDou8PhZ10KSUVxgXpUFfNFDLSKk5RTjIo0BTl/pzjZRSTil816aNj2rA6R6H4tF/SghwdXYUZaEmQrh6Illa1135agfkgVHx/xhlnuIL2bbfd1hXKDR061AXmdX6l0LzOlbWwi87HdH6l7qOFY2lhjB+XG697py7ed6eK5TQWpHFjnaOqWELjxurMojFjjc2qs5ieS52rfvnll26MQau1Bj1P1bmC3wLwuH/OFQpQsCfomO2//vUvd9FrpPEejSVoAS+9973XSGMJXoGL7k/F+Hqf66Lub3EdW2+POiDpsSvQrzFzdUnXe1PzNnqsemyau9F7Mgx6Pg866CArBxpbCnqereCAzgX32Wcf9xzrPabjML1ftOiI5of0OxpPjOMiWXHvjFNsChNWKvan8cZcWPXS+L+OCXVRR3UdM66++uru2FLzR+qGrXFBFfrq+8U7rtTCfPpe0XeNFrVSCEbzn/reCUJBt3LsgMnYeXwxro1i2nnnnUMJriBa6vKphWeDLGzsUc2G5uPDmJOvdOyP0RqCKwCA2FCR7tVXX21HH3102QzUKtShFc6jpCCKLqWw6qqr+l7hRKEarRKuJHlc6bUbO3Zs4NW9ih2WQrQ0AHvttde6Fcw1eVwOtFKlij5QvubOnetW5yjGCoinnnqqJRIJqwY77rijK6y67777At+WJv/1fRHGd0aUk8EqDKnGIq5S0irH1113nWvhrcK6SqICI+0vNJmJztMKTrfccktk5zZ77rlnoNUQO4LjZRSTihn/8pe/uCBmkI5pKA0Vt9x6662uwAUIYrvttmN1yRg788wz7fXXX3dF50HNnDnTLRyiC/x349ClFNSVtFI/51qkQmODWqQgjOCIF0SpNhpX1UJlukRNHbT9dkMvNi1co4CexgKD0PiDgoRBw4SloPCagopRdCRGvLA/jTfmwuDR8Y6O78M4xi/2cWUpMXYeb4xro1i0SIEWkQijgx6iDVDovPEPf/gDT3ORsT9GS6qjWgkAUDa02lTQldGLZfDgwS5oUw7t5/065phjAl1fK37FmV47vYZalRAopFXqLrzwwrJ4UrTakTpB+F3lEtXXrniXXXaxanL22WfbRhttZNVAk/7nnXdeqTejKmll+WuuucatPFdJtOr15ptvXurNKFta9XrEiBGR3b5CtlHjeBnFpg6jCpxqpU+Uj+HDh9tDDz1EaAWh0AIqCkIhvgWO119/fajdp1F+9BnVQhGV/DnfcMMN7be//W2pNwMdXKVdIaNyodDKwQcfXPIOoaX+Ltltt91Kug0oHvan8cZcGOIwx3nggQdaOWLsPN4Y10Yxj20PPfTQkj3h5RLgj4MjjzzSdRNGcbE/RksIrgAAYkfBFa2OHmdKzGslWLXIrVTrr7++7b333oFuQ8XRcW/t26dPH9fCUQWnQPMVfrSSaJz16NHDrWpM+Kq8OjOUcuDqkksusWocMNTnZN1117VqsOuuu5ZsokeTTOoAUa20wppW6inl5zwK6oaI+IVLtE9TcUExcLyMYtM55AMPPOAWtkC8KbCpjm933nmn9e3bt9SbgwrC8Uf8F/PRuKi6Z6M6KdARNLRfDp9zdYE7/fTTS70ZsVfKc2Dd95VXXll2i0gce+yxJduH6lwyDovaqGis1AEaFA/703hjLgyldMopp5T1eEI5HNNWM8a1USyHHXaYC7MVm0IY2o+i4zUDmsdVjU2p3ifViv0xmiO4AgCIJa0WHteDNhXR3HXXXRUddFDhqQ7Yg3aT0cRROaxMp7aQ99xzT9GK/1BeA45nnXVWLDsr6XN62223uVUqUT5U8NC9e/eS3LdCK1r9vxoptHPHHXdE2vkgbsdRxe4yo+/86667rupXWt9pp53sxhtvdCuoVlKnpu23377Um1HW7wl9Z5djt5VCHC+jFN/dt9xyiwtFVFogsFIMGzbMHnnkEbf4SLkVi6I8iueGDh1a6s1AG9QZSwvBqFMWqouKzRXar5bPuQIG55xzjiUSTKm35oQTTrAll1zSSuHXv/51WY6N6hyxFN2+tSCbxixUsFVqWogp7gvYIVzsT+ONuTCUws4772w//elPy/rJZ+w8/hjXRjFoTrDYdVGqV9PCjZU0H1ms85Brrrmm6CF6LdKlsYVqxv4YhRhlAwDEkorER40a5Qov41SkokmQhx56yFZddVWr5KTz7bffHlqnlB122KEsJiCWXXZZe/DBB90gGVDoqKOOsuuvv75kKy+0ZMUVV3Tv14033rjUm4JOUlHG1VdfXfSCh1/+8pe22267WTXT95tWAy+H76SgVIBw8803u2KyYlCxqgKvm266aVHuL+623XZb+9vf/lZRIR4F3+LeRS/On8ewOxHpmKQU+3SOl1FsGgtQKELn4KutthovQIwm966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",
       "text/plain": [
-       "<Figure size 1600x700 with 1 Axes>"
+       "<Figure size 800x350 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 723,
-       "width": 1623
+       "height": 361,
+       "width": 811
       }
      },
      "output_type": "display_data"
@@ -1860,18 +1941,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3fdd2f6",
+   "id": "e0cf22f6",
    "metadata": {},
    "source": [
-    "All three statistics sit inside their predictive 90% bands. The heading is literal: the\n",
-    "evaluation batch was seen during the fit, so this is adequacy, not\n",
-    "generalisation. A held-out split is the next thing to add before any of this\n",
-    "is used to compare models."
+    "All three statistics sit inside their predictive 90% bands. Two limits on what\n",
+    "that establishes. The heading is literal — the evaluation batch was seen during\n",
+    "the fit, so this is adequacy, not generalisation, and a held-out split is the\n",
+    "next thing to add before any of this is used to compare models. And the check\n",
+    "is one batch, one step ahead, with the observed `ylag` held fixed: it does not\n",
+    "simulate a price path forward, and it says nothing about the hierarchy, the\n",
+    "hourly curves, or the covariate responses, each of which would need its own\n",
+    "stratified check."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "efaf1839",
+   "id": "56fc9024",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -1898,14 +1983,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "22fdcafc",
+   "execution_count": 18,
+   "id": "5ce513b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:11.084946Z",
-     "iopub.status.busy": "2026-08-16T07:45:11.084812Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.089832Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.089263Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.591765Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.591692Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.594653Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.594307Z"
     }
    },
    "outputs": [
@@ -1913,17 +1998,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  per step (w=   1):  mean z +0.001   sd 1.07   |mean|/sd 0.00\n",
-      " one epoch (w= 296):  mean z +0.010   sd 0.03   |mean|/sd 0.36\n",
-      "two epochs (w= 592):  mean z +0.028   sd 0.02   |mean|/sd 1.45\n",
-      "1.5 epochs (w= 444):  mean z +0.063   sd 0.59   |mean|/sd 0.11\n"
+      "  per step (w=   1):  mean z -0.001   sd 0.99   |mean|/sd 0.00\n",
+      " one epoch (w= 300):  mean z +0.014   sd 0.02   |mean|/sd 0.57\n",
+      "two epochs (w= 600):  mean z +0.032   sd 0.02   |mean|/sd 1.80\n",
+      "1.5 epochs (w= 450):  mean z +0.044   sd 0.46   |mean|/sd 0.10\n"
      ]
     }
    ],
    "source": [
-    "# Stage 2 only: after the last planned optimizer change, which is where a\n",
-    "# stopping decision would actually be taken.\n",
-    "stage2 = np.asarray(approx.hist[6_000:], dtype=float)\n",
+    "# Stage 2 only, on the full-data scale: after the last planned optimizer change,\n",
+    "# which is where a stopping decision would actually be taken.\n",
+    "stage2 = loss[6_000:]\n",
     "sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0\n",
     "\n",
     "\n",
@@ -1950,28 +2035,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "01e4ae61",
+   "execution_count": 19,
+   "id": "fe273c5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:11.091219Z",
-     "iopub.status.busy": "2026-08-16T07:45:11.091087Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.455463Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.454838Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.595574Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.595502Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.719300Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.718996Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xLto/1/6RYkOCDEyvTPethFfFnyixMO4xYlYxPw8Gc5n80lhJRdE1dlIRkzASvoqNezQ21JxVjxnUnpjL+oVLsRy/60Quj6l1IsW7+O2ete2225og5OW6qs+JYkkV/xfUurvGoSpOpGYiYT5viBb7QuHRZ14NTDTXVXfmuPZO1QFVRVoU3x2GPMZs6Dp6xBFH2AKuSvwKMuGrMj2fio/QHrzLdTjpSPoqgTIAVSHQqxhVOfFLC+jKEAwqOAflLfBrsU4ZvmGcEJRcoxNfFAGT2uRU4IaqF4TVPrsYPW9aGNeGkDaoskaLuEpMUjUGFnTDoQ0ubaYrcDbMC7Pr4FCVkRUsE+QxKphDnYoUrFfdgEKBYGEEZWlwpoz8KIKH1anigAMOsIHRUX6W9Nppcq8gEAVWIP20wXbllVfayj15oYmhFnRU1SeqBSqdn7TpropIakGfBZqEKqhDAeza4I+SEth0DlRFWSAIWZrH+KECBho/xlE4IG9jGs6dyabPgN6PhR2ig6SFYy3khr04rs+SAkxVhTGIAhsu12ptYif9Ndf5n+JR6aT1YAX46lxeWP0vSvqMqaiMAv4V5J1UCu7Qe11zoaApyU5zSq3XB03jA62zRBEEg/CoS1r9+vV9B1P7TeDQOd7vNddv4pbmEuoUlrakLwW8a71CFbPTdI5I2pxSVaNPO+00M2fOnLgPBwmwdOlS+9nSXp9LAmtSaR6toA2N+xE/FfzQ/Et7Y3Gs8+scqIKmmoNOnjzZpKEQgrojBFUsQjEpOv9rHp6nfZa0rD0iHup+qaJbqvAeZZCoEmCVjKpAxjDmv3nG/Dy7mMtEuzd02WWXBV6ktdRYBTVZ0JgxiPnJn/70J98/6/L4foPGFSfmt8CN1xHej44dO9rEL1d5ua7qdzvvvPNsh7owfk8VhtY81m8SH5KBfaFwKcFU1ysVNs5yocy8xWwollmJbYpZinpePnLkSHP00UfbPIwsFucg6atEavOqtu/ewKhy4pcGyFpYjKv6Z14X69SxJ+yAOZ1w1XZYE5KwKGBEm5zff/+9iYs2s5VRq8SdrNBJW4vegwcPjvtQMk/nQy28qdJ10gZgeh8o8U8dbsKixIpiHdxUiVuDlzC7R2hgdM4554RW0VD3q2oKSm6OetBZ+VysgDFVLUV66bOS9aCb6jz11FN2Ehd2hz5t+qoSkTbdFfCRBerioUmoSwXzIALa1bnt4osvjr2LHNItS/MYv8f117/+NfJxRR7HNJw7k03zE60jhb3hqoQyVQIP8z2tIhcKMI27yrqu1drEVgXFJC7i6ryvcxCVHdNJ19O//e1v9vMUd1EhfdaUVKXxRNLWonVsmvcpKS3s9SEVFHnhhRcCuz+NE7QuGve5DO5q165tOnXqFHkwkEsQkd+f1X6C3+vKxhtvbNZff30TNXU7VpB62s4RSaWxnsZiSS34gehpfqEE8ZdeeikzT7+6KGocHeY+C0qn9X3NZeNcWylMPjvooIMS2wlXFcK1BhTWc6V5+AknnBBJl7U0iWvtEfHQ+o8CJ7VXHme3SxVo6d69O3EpAWF+ng/MZcKjgrA6J8W5r+5RYSUFXvfr18856UvxuX588cUXvgtw+l2vUYyGHjdtHd3zdF31YkrDnjvrM6AYIXWnR/qwLxQunWe0vpD1Yh15i9lQYT2t1cQZ47t8+XI79tCajBILs4SkrzJ07tzZbvxq07BY4pcqSekkNH369HBeLVQIlIxysW7UqFE2qz9omlQoCEIBI0lIlPESd9SJJAtB4tdee61d8Ee0gYuqgpGkABlNSKPoYqfPss4VhQMYdfaJoguX2p+q3XTQVCFQQW2qppCE11TnSVUBuPPOO+M+FPigwXx1XfHyQpNVJVmHFYisSaKCfbJULUidcZRUHHdwrUftxJVMnOTOCkiurMxj/FIFeo2Pol5YyeOYhnNnsr322mu2A1dUSZkqUKHKjGF0FVTSSdK6Vr388st2Ey1JXS903tcGIuOHdJo1a5a9fiets4Su8xr7J2nDWN11opz36XqqoheudI7UOAHZEUcFaL/Vn11+1mWj2OU5clmvV7fjtJ0jkk7Vr5XkE2dAFpJFwRvqBBdmIbyo6H2tdbiwi2ihtD3t3r1726TaJKyteJT8rMSnpCX6ah6ugKqw9/81hlAgNYlf8a49Ir5rhJKC1ek+CbSHpb0sFYCEf8zP84W5TPCefvppG4uQpLGBxpHqtqHYSL/jyCZNmpg2bdr4Hh/4KcKvYlsu+7l+13pc1pdckr7ydF3V+1CxhO+//76JqhGD1m3oCpou7AuFS5/DCy64INYmKVHIW8zGhx9+aGMHkrIm//XXX9vukSoOkxUkfZVp5513touZtWrVKpr4NX78eDsA0kkf4dCg+vjjj498sU7B6joJBL3pqMq8SaMBs6ptppk2vOkIFF8AYxgJSH7oOKKqaKlgYm2k6pqgBRQtpES50X7HHXcEWtFVG1EadCYtqM37XdV9E+mhrgZXX3113IeRGJ9//rmtfh80LQBGfe4Jmz7rN998c6ICCeSdd96xwfp0/EJe5zF+KdhFifFRyuOYhnNn8qtsqlBG1IVW/vnPfwZakEKL8CeeeGJiA6m8RPskJFlpvKDzEEWi0kkBxnqvJ+VaWqybh8YX48aNS0QlbnUbiJI2ynR+cw0UTvs6KIINcvGT9KXCLsOGDXP6LPsJ/HDpLuZa/blcKnjQv3//1J0j0kLXAc23gMIxqN4TaQ9q0Brq1KlT4z4MGGPHSwoiTiKt/ej9npS1HyUsqAu1344S5Ro5cqQN2kpix+s8rD0ivrm65sIu4+EwaL3v0ksvtZ3+UD7m5/nEXCY4SoJXkfek7l8PGDDAJn/FsY7hJ5HKtSOLn8dUrJnfAl8NGzY07du39/WzebuuqvhX1N2xFcNz6qmnJqpYIarGvlD4nnvuOdvkIMvyFrMxdOhQW4QlSYnnogQ0FYudNm2ayQKSvnzYY4897CDUaxtbmPgl6qygilJUHQuHOjcpYDJqeo0VvBAUndBV5SupdGz//ve/TVr961//ivsQck3PfxICjtQSPkraWNFCihI61aI56iAGdeoLihLYkjy4vvXWW+0EAMnnJUQmdXExLo888kig3Si//fbbxAQ2B0UBo/qsJ5W6SGrzHsjbPMYvVYYbPHhw5I+btzEN587k00ZSHJ2+FUweVNEZFZvQBmDSE82V+HXOOefEvpiuRf1vvvkm1mOA/01lBU+OGDEi0U+hki+1Fq2KoXGKeqO8cO3niy++8P3z6kymwDJkS+vWrU3Tpk19/ayCa8rd21FiqGsHTz/BQH6rP2tPq3PnziZKAwcONGk8R6SJfldVawU8CrJQ9fK0UuIGewDJKVrzxBNPmKSP3c8999zYA1VVAE+JR1HPQz/++GOnIOosiGvtEdFTgqOCCZO61qLPvzojKugR5WF+nl/MZYIpNqexf9xr4TV56KGHzP333x95x/Io11w8WlMud61IxYP9Fg744x//aFZdddWyfy5v11U9x5rfxEFxlI8++mgsj43SsS8UPsUP3n777SbL8hazof2R008/PbHFaCZPnmxOOumkTOT0kPTl0wEHHGArWhUOlvV3LxFMgf+qApulwFv8d5I0atQo56fi9ddft60bk06D3DQuRmljXsFdiI+CGG+66aZcvgS6Nrz88suxPLY2QIPoNKkOaXH9DuVQct2YMWPiPgyU0GHmhx9+4HmqolJtENU+FaCYpHbQQVCF9GuvvdYknYKpCKhCnuYxLuKosJ+3MQ3nTpSSdO6acKaxizqVKYksLQEDfjeRg6AkHFWNRDppXSzIQg1hmjRpUizBpUnRr18/35uLSe1YATcuSU3a2FcASDmCCDAvN5hI40e/yZ5bbLGFady4sckLv+eINFIwAfuSqHxuefPNN1P5pKg4ZVSdklC1Dz/8MNCCg2HSfFeJX0Hsk7kEdqkQXhwUQJrWz3sQ8tLdE8bceOONsSd4lno+out76Zifg7mMf+pccf7556emCK8KVfopzvKHP/zB1K5d29djlrvOE0TSl64F5cYuunQX89sJLU/XVSUjXHzxxXbtLy5p+ZzmGftC4VPBXiXhZFXeYjYUs/i3v/0t8TGL3333nbn66qtN2vkbCcHq0aOHfaNef/31K7p9eYlf+lMfXrXlVHLP6quvzrOWEUqqULV4v1TdXwNIV3qfdenSxey8885m2223Neuss45p1KiRmTdvng2+0uTjtddec5oQ6H18wQUX2Eq9a665pkmLN954w+nn27RpY7p27Wq22WYb06JFC9OsWTOzxhpr2MmjFuq1aaoNdT3P48ePNz/++KNN9FSXlSS04a1Vq5bZZJNNTIcOHcymm25q/673R5MmTezrqPORbprM6Bym94yCgyZMmGDPWx988EEgXShUTe3777+3lXXzJM4BjKrEvPLKK+aoo47yfR+q3tKnTx/nY9HnZffddzc77rij2Xrrre3nSO3EVV106tSpdqNQx+pSLUa/b69evewAdLXVVnM+ZoQjaW17PRtuuGGF8+T6669vz5NrrbWWqVOnjj1PavFI53x9rjXhVMtfnevff/99e+53pfvSZHbXXXd1up9LLrkk8u6CYVJlDXXlCKILihZ/d9ttN/vneuutZ4PadO7QgqAWk3WtUjC4S3DsNddcYytn6T0FZHkek7brQd7GNJw7UQrNYZUEussuu/h+wpRA5bLOEFfAgNZP2rZtG/ljE3SdXhor3nPPPSZNNLdQoKfLmkBavfXWW3Zjqdz1SwV8uAQLrL322mavvfaya7Mbb7yxnXNoDVFzSs1ntI6ooGM9htbdVM1ViTqqeqiuiQiXgl00TvNDwS5ad48qEMjPfbhcj12qY+fpHFEunQM0Z2jVqpVda9pggw3sWpPWIurWrWvXmhRYpPGB5gBag9f+gtbQtSavQh2uybsK9tO84q9//Wtgvxf8q1evnmnXrp1NtNR1YqONNrJzSr0v9G96T6gSu94TuqmTrtYfte6oxHOdF4IYT6rzrOa1aeOy16e5teY9uhZoHqC1X+2h6rOoz5mu0d5+qub2es5VuExze12rCcT7L619KIA4Tcl3ej1VXCfrVcOrcuWVV9rrfIMGDUzeJHUvCsHSnk4Qnex1Pdhnn33s56V9+/b22qzPja7FipvQ2EwxMprDuXTlVnGWOIsRpQnz83gxl0k3xSDqnJMWGmursJyKNmodrVQ6T2+11Va+isBrbU7jfcWGlPr/g4gH0ZyunGQsl/UlP0lfebuuqihQEK8rsot9oXTEdnfs2NGu+eiaoPVXrbVpT0ZxHVpz1VqazuNa89FnXjfF5ym+O+y43jzGbChmUed6V1o33XvvvW0xP/1dv69yJPT7at1ce6Avvvii01qpYqa076NrVlqR9OWoZ8+e9k102223FU380sabOi/8+9//9l1tAKXTZpo+kEoyWXfdde3roMVdBTVp8BhEe75BgwY5BUsqSdB10VEBhxrIbr755iv9mzaJdMLTc6AgDwUhupxYtUmoDHbdR1r43fDWpENVwKoLvteEUzf9380226zCpEmf+bFjx5qPP/7YdkjT+y6qBCAlKWjTbqeddjLbb7+9DUQtZdKlm36Xli1b2snXYYcdZv9Nv8Odd97pHMyninbaXEkSnRc0GNIgwdtk1WdSG3k6T4RdwUSLCAceeKD9jGpAps1FbeTq3KLBmCsle/oN8NJ7WBtCrgPP/fbbzwYuN2/efKV/0/tNty233NJ2xFTwjR5Tk30/NKgbMGCAvR4D1VEghSZDOk/qOtq0adManzBNCnXTOVYTRZ07Dj74YPtvCgBQwITrZFTnSZekr3feecd2EA2Sfl9dE3SN02dV4wrddP3zEp81rvA6a2qsEeSGqirHuk4IFVCjVtRK4C42VlKQh87Hhx56qA241DhHv48futZfd9119v0AZHkekyZ5HNNw7kw/b56yxx572IVVFe7QNUaJ3brWayEziMBDzRf8Jn0pQSGo653WyDTeULKEFuU1L9P1WeMMrUMoyFXH6pJQ6dHzpopqQWxeIt2fsVIpqFUbHUEFt2psuueee9rxtZKENBfRtV3vdY1F9V7Xez6Iz7iCW/fdd187pk8KrT3pvKO5mNZB9Pvrs65AC22w6DlwpWu+1lS8+Vqp/K57KUBfYwRdt6ta8/fmkzq3af1pu+22W+mcqvU33XSOT0sHxTTxW+FYylkfVBKPn4CjylRsRnPhYmPPJAQC5fEcUUpSiY5bx68EcyWVlHL+0LqEEtA09/PWLZRUoQCEe++91wwcONDpGqS1JpK+4qMEI2+vRsFues1romA43TRO8Pb9TjjhBLsO98QTT9igNpfrxIgRI8zw4cNtAay00DzI7/rgDjvsYPf6dE6p7vOr51yfQ71OhRRAo/O6xmdal9Hzl9duqtofDqpLjdbXNd/VOVPXOr3fNf7VmFjBn1q3UpJuUGtgGmP57fqZZno+FZeSlzVA5IvOz64V87U2cPTRR5vTTjvNru1WpnOTbhqjqcC3uk7ecMMN9rH9UBFJ7SN269bN6bjzgPl5tJjLZIe6fOpcEwSNjxVPoTGb9tF1PtT8VXMRjc0Vl6B1zCCKiOv+NM9RTG256xl+12CUXFpq0lcQxX3KvR8VpVBSgh+Fc8lS5e26qnmG1lyCojUg7W9pnq39PH1+vKIi+qy89957thg/0oN9oeSP+xTffNNNN9mkr+piA3XTGrMafyg+sPA1VuKX1gsU56Y/g/6c5i1mQ3tbrjGLeq169+5tY7nV7KTY666b1lvPPPNMu+an8Yhf+n21NqX3SRqRhRQADVz05ldHr2KJX7qIaxNYiWEIhxaKFYyhhfzKNLjShERtXnWC00KvC68jkk4k5fK6b7nQhOeMM84oOVhFCUAaNJ988sm+g6Uef/xxu0moSoRpMHr0aF8/p0VwVeb1S6+JEsF0U9KNBgVK/nr11VftxU0TtKApSE6vjTKQg+woqI2QTp062edEmzp+Kfj10ksvLXpBjoPew+r4UHkTT5S1fuSRR9pg26uuuirwx1YingKBFXxVmQajSgRTdRMNTlwy0r/66iubSKbAonI9//zzttubX3qd9dx5yYOlUIC7Kp1qE9vvoFcD5u7duye2I6EWLap7TV2qB+hc41LFXYk8WafPuxaaNBYIcsCuSZaSY5966imnxAJ97pUg4CcoU4FlQbaE1sLUKaecYg455JAqryleEIqC0bWoetxxx9nNeS2wqZK0gqhcApNU4bp///7O3Xh17Sn1uqhAc3VEOPvss+3r4YcCEvIaSFAOzYlOOumkKv9dQbMKTPBDE3zdv1+lJMzneR6TNnkb03DuTD/NRXQuKDbnViLYQQcdZItUqDKYOkG4UOCiX1rXCqKwiTpg6Pct1hVagZm66RyodQzNp/V/XQMNtYGgcYq6eiPa67urhx56yAY7u9A8oJxxwgsvvBBIwqGCiLUxU6yzhpc8rMBwXW+00aRNDdfH1dxCmzMXXXSRSYLDDz/cJjIUG2tpzVJrWgpO0e/uN3G68PxWbkKH3zVEzR2U9O36/jjggAPsTXsI2pzTeE9VjoMInsF/NwMVVO5nbKbXo9T1NX1+gwhQ9/YPwk76UnCfxhdJkPRzRFXUxUtrTSoSEeR6pMaC2rRW8JKuW37HXRo3aqO/pi6nehyNy6ryr3/9yz7/fijgSRW0/fLbATkuGmtoPU3XdAWUBEUFmLRWovea9gW19u+XCsqkKelL72M/NMfQWMjP/khhMqrWPXXTHEyJ2lr703OoQNFyEsCq6zipa41LYo72HbSu6bImVR2tIWnN1JVeC71/tV5SLFle1wAVQ9KYWUm/V199te9OnZWLsCqJNilatGhhA5gUWKQ5r86TGj8oMFRzAM19FXQWRCEI7XNqrFpK4TsgTZQc4BJAqeuqusGXWgxJ56y//OUvtuiX9s78jkdvvvlm+5hpG99Ejfl5NLIyl8F/aU9ewe9BULyS1hOLxY94wdaKT9Oe5T333GPuvvtu58KLOq8fccQRNimoVBqjay/ND62jlLrvF1SxcO1VKnawlLgFdW3xOxb0E6OQt+uq5jZBFDJWsoW662nfrnLsrnfu0vtMv5/Oi9qzzgutOVQ3l9M8RTe/gpgnVod9oWhojODn3KO1hQcffNAp1kUxIIr10+3444+3sdyKddN7S8ma2g9wkbeYDV2zdJ5zobVpxV5qjaTUfbXbbrvN3HLLLXYs4odi0h544IGyE8+TIhdJX+VUcvVLg1oFdD/yyCNFE7+UZa7OTGl9oySZBvT64CsAuaZBl6rdapLy7LPPOj2mFvX9XED8nmg8WhBXQki5VDFbmxwacGrBvFyaqPXt2zeURJig6XM4a9YsXxcQl4SvYnTxVTKWbldccYXdIHUNVBKdV3Sf2mxSYGlY9Dh6v2lDS4mDfmgSowqSxTLP45hcaKO5fv361f4/JdCokqzO50FRMode+5oSJ1VdQAGNuqb4pQUDbRaWG7yha5VrVRMFpJcTHF24uKiFGQ1c/QygZ8+ebZNTtTCQRDVtnrrQops2RNNKkzIlmOqcpvOwgh61EamJlhZcVOVagc2q1qSJls5HpVIypRaVdf9h0ntereQ1ofBDiVuqGFssIbQm2ghWp8CgAq30GfaTQKwFOnUr002TWCUMa0HETwVcTaxcFoiVdKHxSrnjf10bNDFUgI4qa/uhcyhJX9XTRL26ybrLpqcXIJImaZrHhE2JpLoWaCykIFddC/R7a9FP13lvo0/nS10Tqtv0yOOYhnNnuilZRuP/mq5dKi6iOYXeX36DIESJBBpjlTtGVSCaru+u9t9/f3PjjTeWXBhEQXGa22gz0HXco3WNpCV9KSjV69itwD+Nh3VN00aD1m90/lMAoBbq9WdQnaeivL67UKcS13UUzTm0JlbOuoQ2jlypsqq6y5VaaEPrO1p70caMaxdw3Y/Wc+JKavfGddr0L5bwVpn+jzph6fzmkliqcU65dD4slzq2KEAqSLoGaN1Kt/POO88mqup1DLqrcx5pzP3000+X/XOalyq5opQ5XlCBQF4Aktbwa6J5q9+uP1p/ibuKZVrOEcXO7X//+9/teCLMfUetr2htQ+cDv1SMsqZASV0nqrtWuFxH9BqnbY7uhz5LChI59thj7Rw2LNrj0/6ixtJ+P/t6TygwLS38Bh9q3uCS8FVVQIkKBuqmsYPWZlQIrBTVfQ5cC1uogFeYnzMVX3AN5NWaq+6n1OQ0zYe0zq5Cbpo3ulCXBBXmjLu7pd6Pig/RuaJy5z+vEISSRZWYrLUvrf/57fDg0ZqUnneXAllZE+TaI+KLAXHp4K7PnxLaSw1ML6T1BCVVqDiRn30vjd1V4ENJNqga8/NwZW0ug//SGqLffe1CKnCr5KtS53pad1QCmGIY/XZsEv2sAtX/9re/lXVOVrKRn0La5azfBNXpS+MyxcyVEr/lsr5U7pg3b9dVPU4QBS20x65Ywuq6ShfGCWpOo9dGY/w8dI/WZ7O6OaprUfKw15nYF0rumE80hgk6zkVrFurYp5uKsqggn8ueZN5iNnQudhmHqFuZHtdPkfxzzjnHTJw40RZo8kPXQBXL0XkrbZLReiUEejEUOKFBhpeAFTYFhqkqt5fsJYWJXwqOUTA/gqPs/1ICJQtdfvnlzh2rRo0aVfbPaLHQbzataEFcVS9dNiX0HvVL718teiad325aqrAWJlUG1IRDFwzXrn8KCNQFL8yEr0KqNKjjj6OSfFBUGUbnipoSvgoDL4Na7FIyhAK1Sz3v6H2ic1vU5ygtno0dO9b3Y+65555OHae0cOcnqdWjCTqbMOmhIF+1GdZCpCoIKWBOwW06F2sMp41QBTPoe/p/6mal96iqvqgKek1BwtrcV9B82AlfhecMl+Q+v+dJBS670rlOFbM1cQyiY6QCsbR4paA6VUcph5K2XSod6TVQFRG/52+NJ1Uh1y9tCLucR5EvaZrHhEkLcxobq0iK5iraHNDmowJeNIZS8IX+jzYK1WFbC+mqeqxAtaqei7yNaTh3ppuSC7UBXuq1S0Gl+swUq45eDj/dwjS2cg3469Kli622Xm4naM3nNO5xrVKuTdMgujcFQfN5BeG9+OKLdq1HHR+1YaVNL72+qrSrRFJVx1VAhsZWqjKnDfCwEqySRuNCBRy4UBCC5uMKQiiVqtkp2M+FxqXa3Cl3E1OvvYJct9tuO6fHV+CinySXoOicpjlcKckcHl3/XQPRFRxe7rqgn3VEjVNKXV/y+/xpc02d6t99993QHicvXAK9Sw3wUZJeUEp9TJfgo7iD39N0jiikuYr2SHTcURSaVHKPS5G6JKzJ54HmskrmCDPhy6NxRTnBkJWNGzeurIJacUvqXp/GeZrzqwpyOeextFGwk2vhIY1tVXzBTzcyBRArScqV5lxx0hxea1naP6ic8FXVmpIS/5Wc5EqJia5z+CwIY+0R8VDnPpfOr0rQ3m233Xz/vAr2qAiBX64V//OA+Xl4mMtkVxDnFq09l5rwVUhJTCrm4jo/1r6DCluXSvEMftdOtS6gong1UWyknz2UONeXyl3rydt1VWv+pbz2Nc3JteZfSsJXIcWSX3DBBU6PjfCxLxSdpK73KG5HxdK1FuLnM5vHmA013/BLayTaD/WT8OVRnKPfZFJd65977jmTRplN+tJCjTZgVIlHL46CSo477riyNvn90AdPGZdVJX55G7V5yN6OqhpkOYGSouQVlyA+b3OkXBowu1DbRtfJkipk+12gVJUHZTMnnd8FbJeWxX7OTy6UCBElfcZcNhhcK9IFQYGUSnwslSZpShwOgrLoy82E16Z+ms5RWlhRN0tXStRRcKMfU6dOtQNQJJ9eY22cKcBHAbzlLh6q6k9N137XgGA/kxFV/ojyPOl1fXClDedjjjnGBE3VWLWxrGDlcjqXuVQDU8C0axUOVZZSpw0/NL53DYZAPqRtHhMWJfG+8sorZZ8/dR3RZkJVz0XexjScO9NL10glc5ZLiUEKRnKhzsbl8lspqzDYTeM/vwlrCsIKokq56+8RBFWw1Jih3DVKzWlPP/10u+6Ydepu5Fr9UmN0zR3KrTaqRDxXKqpQzhpEIRXAUNEwlw6ocb/X1aXgwAMP9PVzLmtmer+UO9bxs46ogAiXxJEo1xBhnPaDSqmwrPddEB2kPOqePW3atFADgcLeI8vSOaKQHrvcxHVXfrrCJ2lNPg+i3qvZY489nMYII0eONGmR9L0+jTWjSPaLi5LaXMc7Whspd2+sctVmFRhzoWKsc+bMMXHQNUMFn1TYrhz6jCtJqdyCapWpc/T7779v8iystUfEw2XNV3uGZ5xxRiDrOX7XtYYNG0bRwBowPw8Pc5ls0l6SazcqFag85ZRTfP+89il0vXUdsyjhuhwuY8xS1lO0HhRkXG8p60uKkfj66699dzRVJ9Ny5O26qjGhKyWB+J2fqKCFS2EfhI99oegkfb1H/KxB5y1mQ+dgdbJ0KYzr2tm1fv36du7uV1rj+zKb9FW4MKY3x0EHHWS71QTRhrE6SspRQIEGtlUlfiEYPXv29D2YUgVlF6VsvBYLWPFLC8Jbb721CWKB2SU46M033zRJp+AYP7766isSRqrhkgAVd0cJtQL1k0SlhAVXChxWZbioz1HlVuxUS3GX6tFqoevS5cij4Hc/7b/TdI7KOy0AqeOha+X6JGrTpo3vnx09erRZtmxZ2RNGV3/84x9t9a4wgx/K6RTpMlZSAqG688QdVMV5CFmcx4RBgT/qChhEh8G8j2k4d6aXqkb7Ddj0u2Dr91ygqpY//vij02OqUrvfJJjCYG/XrsjqNBsnde7WLeqg8TRRYKIS/JYuXer7PrQGq8QpFT8qh8bkCnB1oW7Drl0ftI5x5JFHOt3HiBEjbPBH1HQN9JugqfmDS9CGn/Obn3VEbUree++9Zf8c4kvIUDcHvwEkNXVg1Xy+lMrIGveWullcStCW38AuJWEHsc+Ql3NEmteaVF02TV2dUHoigsuY2rWbaRr2+lRlOKrk7CxzXeNUpWaXLujetdO1Ir7GbW+//baJw6GHHup7HU+BXqpY7VoM9rXXXjN5FdbaI+Khzhx+A+G9DhvlFmCrqruAAin9Usc5VI35ebYwl4lmvOYag6qiW67r1Frrdg1SL/f86NLBvJTCPaUkacn6669f0v/74osvalxfUlMLv4kQ5T4febyuuhYQ32KLLWzsuevnDcnEvlC0/M7RdN2LOwa5OnmL2Rg0aJBxEVRx+v3228/3z+pamMb1cyIMQqDNJ1VgUhBOscQvuFNg1IknnuhUUdulGt706dPL+v/KbP3pp598P56fypfVBZ+4VLyYP3++STJV1vM7KVVyjoI25s6dG/hxpV253XgKTZ482cRJmeE6L5ernO40VenSpUvJE/3Kj+2yuFLuOUptil0+20k5R8W1eYfSqfOqyyJvViv7Kkmh3M+tS1JDYbC5n/NjGBSM4VKNTMm9Qf0uLkFkCohPUiclJE/a5jFh6NixYyBdjIvJ25iGc2d6qcDETjvt5PvnXbsSl3sucK0OrrnN4Ycf7nQfQd2POqi4rM+4UBEeVa9E1TQeVHCo3w1uzyWXXGKTBMulTrozZsxwDi4MgmvSl8RR2b9Hjx5OcyPX81u5r58Ckv24++677XwqyuqSMJF3ttK4sqYO26XOY1XUrdTxYU33OWHCBBug44eK8Lh2EszTOSLtXaTiXpdHONK8VxPFNVrrcpozaBxEIVb/AW+uQZGa+/jtgF5ot912M+uss45zt6+oKcnatdia1g38FLQsVG7XjKwIc+0R8XjnnXeczulJWfPV74GqMT/PFuYy4XNd99tss82czmkeFaXQmC3K8Zo6ovodawZZaEdFPkuJT9D6Uk3duF3iJMpd98rbdVXraBMnTjSua/6uCZJBNXxA8NgXilbjxo19x9UdffTR5oknnrB/T5I8xmy4zG10HW/VqpUJQsuWLX3FZouuhWmco5H0FWJG6l133WU30SonfsEEkkjhd9IvGoj5/bBLuUlBqsjgIoiJlsdvVwFR0I0qBSeZWv/6rXSoAUGfPn1sFQxVIb/zzjttFXAl7S1evNjkmUswgJ47VRaNi98FBtcq9KKuj36vIS6PH+U5SudTdQtKwjnql19+IeAqwVTZ0iW4Oelcg6bKqTatltCulXl1rXPtlBEktX2uqcJVVGMlVZVymVCreyiQlXlM0NT977rrrgutw03exjScO9PLNXBL1yoX5Z4L1OnEhT5bLsGphVTpTPN+F66/j99Ah8suuyzyx00TndNOOeUUO9Z1rS7717/+1fcxuAZ3unR6rNztSxVEXbj+PnGc31w7bpZ7fmvevLnvx3rmmWdsl1DNdW+44Qbz0ksv2Q3iuMdbVRVgURVMv7e0c6kAXdNmbanVn//whz+UPNas6TFdNpBdnosknCNcPrOSxM9nUtaakI/3RZreEy6fd3VhVMEdrUVfeOGFZsCAAbZIjKr3sk9fM+2Jup4vlfQVBK3VlNu9Nwlj4j322MM0bdo0kGRpF1pf0uuZJ2GvPSIeLmu+Cip1LRwQ1Jqv5ot5jzmpTl7m53nBXCZ8rmvcrmOsIMd+U6dOLavThq7zfmMDfvjhBzNz5sxqA/dHjhxZcjxY27ZtSy6yH8Zaj+KRy036ytt11aWrmShuZPfddzdB2GuvvQK5HwSLfaForbfeer7jsWbPnm33WnXeO/30021zDxVMV8FPl3gzV3mL2VCMfU3JzGElphWz4YYbmjzF97lFSaDGRaV77rnHZvYrUUYDLRaTgxHE5EPBln6rO5e7GOMyOFDAiCpsxJ0tXThxDPJCEwYFxfitduq9vqokUlhNxAuw1YRDt4033tj+qcmM62ZzGJSgp8mqAjM0sFEFR02Uf/31VzsA+u233+z/0YDDtYJ2KTRpdn3v+b2o67XyI4jjdUlwUdc6v+/jKM9R6jgSROvuIM9RSfxM5p0mFGeffbZJkqVLl9rzo86TqlSh86Q+c0pSnTNnjj1P6rPknSvDHsOVU21a40rXc3dQHQiSspCiykhB0Zhd1cGqW/Ct6TzUvXv3wI4H2ZK2eUzQDjrooEC6qVYlb2Mazp35PRe4JI/6ORe4bor5LYZR1WdL1RA///xz3/ehz45r0Hm5jj322FjmxGmhMflJJ51kN9VdnHrqqU7d1FzPq9qoWGONNUxQlEBWapBBEgJctU7mWljC9XNSboVHHe/zzz/v+/E0T9Q5svJ5UkG+heuHuml9V+MN18RVlE9r2VqX0DpAuZTUVV2n3lKDcpTwVWqlcwVnaz7apEkTp8dMWtJXEOcI1zFQ2FVgdU5Q9Whd17Q2r0IOWmvS66m1Jq3LJ3WtCeHR6/7999/bm94fWoNU8pX3nliwYMGK94Sf81RW3xOuye+i53ngwIH2Vrh37+0beddoXZ8VrBnkmkCauY4hGzZsaJOdg6L55EMPPeRUqVqftSA6j0UdRK3iwup05pKwqUCsoCpop0HYa49IX2LDVlttFeixuMxZNQ5UQccg97OyhPl5vJjLpIs6F7kW3g5yzV5xUVrvcgl419paOYWxtb7xxhtv+HosrauouFxVCVGl/B4ao2kuofWemrrE17S+pOuD32udYharWj+qSt6uq64FrbQfVe5zXF3ReiUnI1nYF4o+MVx7JS6fTXVQVCMP3Ty6Dim2wlvz8W46T7p2EK9J3mI2tM7gErOYpGvJsBgKxbpihzFkWiB+4IEHbIVZVRYj8SsYQZzo6tev7/tnyz1puVTR0kUqiM2NoKShIpiCbN56661A73PZsmV2k1a3Dz/8cKUNDL1GHTt2NNtuu62d1Lm8v1xem9dff9189NFHdiKapFamrpW6/XKpMOK6AaSkLZduXS6BYlGeo/Szbdq0MUmRhnNUHu28886BdM9zpXO4zpNDhw61C2uuQaVBKuecXWo75apoPJq0BG7Xz+72229vkoLzELI0jwna4YcfHur9521Mw7kznbSw7DouUqCi3+D1cs8F2jhzKaoiQX+2dH8uSV9K/I96A+OQQw6J9DHTRGPb448/3hapcXH00Uebc845x+k+XN8bYbzXXUT9Xg9io8Z1Pa3c4JKw5kUKqtftiy++WKmz++abb2622WYbu46oNcywNxzx37Xb9u3b+9rA02uodeFi3SIURK4OGjXRhrNebxV3U9JSTec7BdtpzUJdQoJM+tJjx7nPkMZzRCn0Wddak/YMVLnb9XoWpCTtD+SJxtp6P7zzzju2u1SS1oni2qfxe87SWCjojpN6DrwkvMoUHKRzlc7Z2udT8LnWUfPGdQypsU6Qz1sQY2z9TlElWWj+F9RatcYfO+64o3n22Wd934cCYaMuepLltUfEc11VcoNf7733XqLWfMeMGUPSVxWYn0ePuUx+x2vaW9CYLSha71Jgvc5xfmmNpRzldrcqNemr1DUXr8iB5g39+vWr8f9rP6Oq9SUlC/idv5db3CeP11XX2JpSu7mVQomCigVU8RckB/tC8Yz7gl7v0bqvXkvd3n///ZWSgtq1a2fXe1RcRTddu4KSt5gN199Xe7mu+7lB0fhD7500FWtMz5GmmBIHNMA76qij7EkljwvEQdLmqEt718Igqaiocl9WKGA+6TQ5U2UETZiioG4w2tD12jFrQV/JXzoOVXMLquJDVQF4L7zwgnnkkUecqj9ndTNx00039f2zderUie2xJcjBZXU0cHGpEpg0WTrfZkmcXY8UDP3mm2/aaqAK1kpq19VyzpOu12JVTXGtjh20LH120zBWQjzSOI8JOhnftbJ/dfI4puHcmU5BBVvrXBBFArveZ67jp6A3AV2Ke8Tx2VGQnrr+oPi46bjjjjPTp093enoOPvhg07t379jHcUl7r2vzuLpuQUk8v7l2Siv3fKWxiYJSyg0scVnHU/Vf3QYMGLAicKBr165mv/32c15LQvVBMH6SvrwOPcUCPEoNBNJnw0tW0ppxKcXKdN/Fkr6mTp3qOzhHSYZx7k2l8RxRnSFDhth9P/0ZdnemPCT4ZIHGmP3797ddpZKU/JfmRMB99tkn8CCg6niFH1977TX7tcZQ6lqw77772gCcNAWAZGlMrNdB3TJd5gz6fEaV9KUuU0Hu77nOCaIuBJHltUfEQ+PfpI61/MjSem7QmJ9Hh7lM+rmeS7QWFnQ8kq7DLklf5f5O6mTqtyNqdQXlyk360p+lNIBQp2fNbYqtjXixhlEkfeXxuuq6d6zYmqDovaL131K6wyG/c+C07Qv5Xe95+OGHI3s8dcf84IMP7M1bY1bimeK6u3Xr5tz5PUtj/FI+D1n6fZcuXWp+/vlnWwgqLVZOH0coFNyhDaAWLVrYgV7hDeUvlgaxORlVQoUSj3RiyApNAJJu/fXXtxfkuKgyhio4XnXVVXZD6LzzzrOV1IL2yiuv2M1/BTYlOeFL4po0tmzZMrakL53v00AT3CxN6l27ECAcCmqKg6rqHnDAAebss8+2i3dJHneV8zl0vRYnqfpSFj+7Oq8m+b2G+KRtHpO2a0EexzScO9NJm5FBULGRKLiOO5ScFnTCk+tcK+o1mrjGwkmn83bPnj2dz2XalLnmmmucr7G6hrgmnwW9DuCypuGJMiE6iPNbHMHU6hIXJ60Z/vvf/7bvZR1LKQlBKJ9LBWh13Srn+5Wp6nOxv/t5zCgDgZJ4johq/FOdESNGmCOOOMJ2qVSF6yTPQZJ8bFmiooD//Oc/7Z7Ugw8+mNiEr7C63YWpR48ezvs0LhQk9dxzz5mTTjrJ7Lbbbubuu+82c+fONVnnOl8KI1AmTXNA1wC9yly7cGQpRqEmzL2zKQ2xKeXI0npuGJifh4u5THa4nhuzMl7zu9ajtbhi43oVyfj6669Lug9vfUeFdksdr1WVUOa3o7vWUb3ks1Ll8brquj4eREHXMO8PbtgXim/ups5bcVFi3LvvvmsuvPBC06VLF3PFFVc4dUHMW7xbln7fNP4+JH1FaL311jNvvPGGrRDWp08fuymk6oqNGjWK8jBSL6jnK6oggvnz52dqc00bWGnw97//PRGbwEoAe/HFF22Xm4svvjiQ50+TT/1+anOZlotOXMHvXhVdvy3N43rsKKXlM53X3ycLlIirSpxRn3sV+KmuAS4VpZJ6nnTt6qHW1UmTpYAJvf+0SACkfR4TtK222irU+8/aGKCU34dzZzo1bNgwkPuJar7rOu5wrY4WxlxL6zRZOv+lkQJoNVb/6aefnO5nl112MTfddJPz/F00fnPtGB/0OoASvV0/61FeK4I4v8WxlqeAclU4TgIl9Jx22mnmqKOOMj/88EPch5Mp2kj2mzjgGpRTGIBTatKXApCKjQf9BgIlIekriHNEnHMhrdvce++99pzx5ZdfmjSgIE34hg8fbg488EDz0EMPpS6hKg1UlVoJV0mgINRbbrnFJvc9++yzJsuyOAeMokt2WIUgXIOyf/nlF5MXzL2zKY9rvnnG/DwczGWyh/Ga2zqH1oCLdftSh/jFixfX+POVE71KXesptqajuAK/awwa+5Q79s7adaiU38f1dw56zT+M+RL8Y18oPhdccIFJAu0ZP/bYY7Yon/YbS7kO5D1mI0u/bxp/H5K+YqBN5H333deeuLQRoM3c119/PY5DSaWgBj+1akXz9l+4cKHJkrT8PmqHm5TBgTdpfOaZZ8xBBx1kfvzxR9/3M3v2bHPMMceYV199NdDjy6p69er5/lnXKuFpSfpKy2c6r79PFkTdVUoTsNNPPz3SVtBRc00oWnPNNU3SZC1JinMRsjCPSdv1IGufu1J+H86d6ZS2c4GqWyZtXuT6HCog18+ifZa6rMZJm6wnnniic3EGFdK6/fbbA0sSCuI6ksQA1yivj0H8/kEk8PlJrtNmXpK6qarL0yGHHGIGDx4c96FkhhK+Onbs6OtniwUCqTLypEmTSlpfLEz62nLLLUv6XFcVgOS305cK8sSd3JjWc4QXJHnJJZfYYo5ZKvAHNx988IH561//WtK5AP6dcsopZptttklU8YKLLrrI3rJ6PnAdPyZxDhjlmDjo3z9tRU/ixNw7m/K45ptnzM+Dx1wmmxivuRe3KZaAVWqhne22265CPFmp3baKdXVXBz6/e31+fv+sXYdK+X2StsflEseI4LEvFB91a+zZs6dJUrLT/fffb4444ggzY8aMsn42bzEbWft9F6Ts90lntFgGtWzZMu5DSI2gBj9RBUhFGUgUhTRNAJQcpUCiJJk4caJtS++nNbUC00499VTzzTffhHJsWbTGGmvE9th169Y1acA5CmFTpaMoKeH3vffeM1mmyWbWFpI4FyEP0jaPSdv1II/nkTz+zlmQtnOB6/vMb1eVsOdaUX1+tPkb9Xg4yRRwePLJJzuvayjw9z//+U+g7y/XMbaEkTTk+n6P8loRxAa4awEevzp06GC7d8TRaay6z8vf/vY3M2TIkLgPJTP8BgOpQ8a4ceN8JV+p8nNh4RUlLZWafFb5MaZPn+67mFjcXb6COkfENRe6+eabzdNPPx3LYyOZNJZR0SnX4DGUNr666667TOvWrRP1dKnbV+/evU0WuY6Lw5gDut5n2sbEQd5fXtZaJE1zb9cu0548XIdY/8wf5ufBYi6TTYzX3AvcFEvAKva9Yip39iq105cKSFQuhBZ1R/c8Xlddf+eg4w7jjGPEytgXipfi+/bbbz+TtDU/JaOV0/0pb+fWrP2+i1I2r05ntBhyLaiNvag2CJNUJTaoSjBpcv7555urrroqlE0Gv6ZNm2Z69epV9nN52223mS+++CK048qiuIKF0hSQzTkKYWvYsGFkT/Ljjz+ei06Irp9b1xb2YcjauSioTVtkS9rmMWm7HmTtPFLKXCFrv3Nezp1pOxe4vs/CqI4VRKXyqNYIlOSX1vN20LQJcMYZZziva2yxxRbmvvvuCzyYMohzahgBla7v9yjXw4JYg4nz87L77rubhx56yAaMJGnTWWuI5VaWRPCJT5UDf0oNyikW+NOpUydfj+m3y5dXOTVuQZwj4ljrVWGhBx54IPLHRXJpfKuk3LQFBKRZ06ZNzYABA8wee+xhkmTgwIHmqaeeMlmTxTlglGPioNdqXItABBHEmBZR7kUlZQ0siXs9Qcva+mfaYm3iwvw8GMxlsiuL4zW/v5Pf9Q512Cpcy9WY6auvvirpZyt39lp77bVLTj6rvJ7kN+lLiUN+OiLn8bpau3btRK35s5aQLOwLxUsF0m666SZz1lln2b8nxffff2+uvPLK3J5ba5qv5u33TRqiDoCQpaXbT5Ydfvjh5uWXXzZ77bVXrElAlTftX3zxxZL///jx482DDz4Y6jEhnzhHIWxBB2NWZfbs2bYyex64Vv/Rc5U0VDQCsq9Bgwah3n8exzScO5GG99m8efMCOxZPOdXVqtrki6qbT9jnvrRQ53IFR3/44YdO97Ppppuavn37mkaNGpkkXkfCeL+73mcer48utttuO/PSSy+Zk046KTEdkn/99dfczHXD1r59e9/BwH6TvioHAlX1vaqqihYGTblUf05C0lcaKejr2muvJTgXFSgJcMKECTwrEdP474477jB33nmn2WSTTRLVPcN1fpI0ruPHvI+Jgw4KdQ3KzlowVlbm30El42Xt/FMMc9r8Yn7uhrlMtiVxzd71Pv3+Tn4L/FRO8qq8BlNd7MuWW2650vdL7fZVuLazdOlS3wXSdI70M87L43XV9XcOoghh2J8/+Me+UPxUCPDMM8+0HdW33357kxQvvPCC+fzzz0v6v3mL2cjb75s0bqnMAGqkAAFlImuwjvi0bNnSdsr64YcfzKOPPmqTwNQ6OU7333+/OeCAA0reSAxiAVgT0J122snssMMOplWrVqZFixb2ParvV5cQp4HVRRdd5Pz4SJ40bYIgnVwr55TT5UvBcK5U9VMTyS5dupjNN9/cbLTRRvYcWVOXho8//tgcc8wxJg2f21mzZpmk0XMcxOsHIL/XgzyOaTh3Igqun60wApBcN8WiPF9ENRZOeoW0Cy64wLz11ltO96P1i379+pkmTZqYsDYp9HopQS0pVdYV4Om6npimavdJoXPEeeedZ0488UTz5JNP2i4eWk+M03PPPWcTJ1U5GP5pjV5BOH7OR4VBOeq8Nm7cuJJ+rljQz1ZbbWUDCmoKCtf56Msvv7TruJWPoRxaA1533XV9/WzeDRo0yPz444/O96Pri94Lu+yyi2ndurVda9L5WeP56qrYTpw40XTt2tX58REcfW7VFTIIqgSv13frrbe2n1ONcfSeqKkj0dFHH+3U+S/tunXrZp83nct1nR4yZIjT+M2V1jPV7eu4444zWZHEOaDrfUY5Jg46iDNNCW9xS9P8W52wg7iPJO71BC2Pa774H+bn/jGXyTbXortZGq917tzZxrr56aSoAj9ekZxS11w6duxYdB6vAj+ldAEufJxvv/3W9/Pmt7hPHq+r+ry4rNsnbXyPYLEvlBxt27a1DTG+/vpr89hjj9mxTNxFLu677z6bZFuTvMVs5PFakiTpWfkAUkoB4uuss46ZMmVK3IeC/68Kfckll5gLL7zQTuDeeecdu0H23XffRZ6YN2rUKPu4GrTUtJGoKsMuNOlUsIoqFRPwg0I6P5GYiix4+umnA+kMqbbRSQ+k22CDDZx+XpPkpFl//fXNpEmT4j4MACmWxzEN505E9T5zDULSeojr/RRyDb4O8lhQPW22X3rppbbwjgslKyjhK8ykBa3f6f5dxqTq0q6N/6Do/lzxfvdvrbXWMieffLK9aQ3v7bfftt3qhg8f7txxoVwqBPXqq69GVmgkyxQU4yfpS+eGqVOnmvXWW6/kQCAldBRbX1AlZiV5qHhMTbRuraQvFS8bM2aMibLqNYJZa9pjjz3M+eefbzbccEOe0gxQwMmcOXOc7qN58+a2wJ6Sl+B/3Lb77rvbmxIe3n33XfP+++/bPT+dq+Oo/pylpC/XtecgxrBB36eu31EJOijN9f5cg8IRjiDmE6NHjw6sY1iSMaeFMD8vH3OZbGO89j+NGze2MXcjR44s+3ksXN+p3OG9KlV19OrUqVNJPz9t2jQ7tlUxGJeO7n7XevJ4XW3WrJnTPDHoGBZiYpKFfaHkUcE03S6//HLz0UcfrYjtHjt2rK8EXxdaa1Iyl8ai1clbzEaUayxYGUlfQAS0ieM36UvBIupeguCrfWmT36t+oUoKmgQqCUuLpJpk/fTTT3bgH2bgqAYHNSV9aXLpUulBwa933nmn2W233RJTrRrJsdpqqzklpu63336mT58+gR8XUA5V2NY528Vll11mjjrqqFScJ9XlwIUqRf/888+JqvStsVKpi6mVKYjugw8+CPyYAKRLHsc0nDsR1cKtNj3Urcmv77//PtDNRN1fnJviKN0///lP5yATdb1QdT91cA+b68aM63uzMiUauVDRHwr/BKNNmzb2duqpp9puIlo7VDVe/alEVK0jap4VRMX+6tYQSfpy55IApc3lAw44oOS5q6o8VxckVErSl/dY+tPvpjZJX/7Mnz/f9zqFR0XYlPDlV9zVbLGy9957z+lpUac3JbI3bdrU932wV7NykOdBBx1kb6I1gW+++caOo9SpU9foCRMmhFpxWWOC6dOn22DCLHCduwU9JtY+bRDJllEJOunN9f60VobgqaNI3B0e/AS3p3VdyqXQ1ymnnGJ69eoV+HEhPszPa8ZcJvtcx2taw9K1KMjk8DjX7LXu4ee6OGzYMJtArevMF1984bTWo+PXmLOUtWUleynpy28H5zXXXNNsueWWvn42j9dVxcSMGDHC989r/TdIfos6ITzsCyVTnTp1zM4772xvMnv2bLv+othufY682G4l04aVDKa9IBUC3Geffar9f3mL2XBdY3nttdfMJptsEtjx5A1JX0BE3aX8ntg1MdKFyXXxENXTZFaTs8oTNE3wJk+ebDeFNFhQYoE2jDSIUAcuV6V0W3HdXFaFQZeEL1E1WWT7HOU3QDroDTzAD9fz5N577+2U8CWqKhuVVq1aOd/HkCFDzMEHH2ySdB7y65dffrHPv4I8AORb3sY0nDsRVcEUdaZw6a6l+bO3KB8E18Aml88OSnfLLbeY/v37Oz1ljRo1Mg888EAg499S6HFc5hZBB91p88oF7/XwzotbbLGFvRXS+q2KayhwRmuIuun8p7W/IALN1WEM7pRwoaB8BeeXS+cHJX2VWom5qurPNSWEVX7dFy1a5Lv6sxK3S602jeLBX35ts8025txzz3V6WlmTTx6XcYIC626++WanhC/hfVFzsJZu6gJWOYnS2+fTTQFCukZrzy8Iuq9dd93VZIHr2FvPrc6fKs4TBNcxtj57CqqNStDrS65Bpkkq/Ja1OUHc53I/3WvTSOcSFYHxuy6VxjVflI75eXHMZbLPdbymNSytO2633XaBHI831o5rHVPF3vv27eur86aSgerWrVtSkQElIHTo0KHatZ5Sk74Up1FqolllnTt3tus9fuTxuuo6Fwgy6Ut72BRySR72hdJBCa9K8q1c4EyF+HQN0r6Mzm0qAKRzu4oBBdEZWes9NSV95S1mw3XvUdcSkr78I+kLiIAG/U8++aSvn1V1DW1AbLzxxoEfF0qb8GgCoNuOO+644vtK+FKF3fvvv9989dVXvp/KUiZSGoz4pYleEFWAg9r4QnLPUUoA8fve0GLIGmusEfhxAaVyOU/KscceG/sxlEMdIrX46JJ8/PDDDycq6Wvrrbd2+nkFU+6www6BHQ+AdMrbmIZzJ6L8bLkkfQ0ePNicdtppgW2K6brvorqNWQTj3nvvNXfffbfTfdSrV8/ej9+qpX5stdVW5oknnvD989qwV4CAktWCoM+OC97r0VLBLlXL1a0woUedErV2OGDAAPPSSy/5vn8ljqmapTY34UbBQH5eCyV76DNeaoBLdYldHTt2tOvONW08a9NaAXt+k77atWsX2Dkpb1zXeY4++mjfQVge1uSTRXt16jjkl4JS1JnChYLD/CStwpgGDRoUTdrW/OLFF1+0hQZckrSD7u4UJ9cxpNZWVAk7qMIfb775pnMAn+YWUVGlcQU+6z2XhO6/FIIIh2tSo8v1RHS+cu0+mZd1Kdc1JKRT3ufnzGWyT5089f52uZ5o3TGopC/dl2uXFZcxqIrulLLGUtVaz+qrr17ynlx1/1fH8fzzz9d4P1rj0dqSziN+17Vc5O266joPV3JeUJ3x8jR+SxP2hdJN52XN+3UrXIfQGpquT/fdd59Th71Szpd5i9nQnoPf6673++65556BH1deuO04ACiJNnFdWxoiWRRs361bN/PYY4+ZAw880Pf9qAJwTdRpzK8WLVo4V3HT4tfHH3/sdB/I7jlKAzjXTbe8cwkACaIqRRa4nCc1EVH1ZVfayI6KjlkLDy40iYrymGui30dVV/M8VrrjjjvsoqPfmyrXpB3nQ7jK25iGcyei4jpWUhU010qfnldeecV5A9l18R3VU+BMnz59nJ4mVU296667nNfTyuX6eLqWvP7664Eciz43rsHDvNeTM8bddttt7efimmuucbqvUtYRi7nwwgud5hpZ4zc4ZuzYseaNN96wa6U1UZcZrctWt7bcvn37kh5Xm9N+A75dA4HyrJTq3NUptZtbdfwWtEjCHF0Ji1njt6u0J4iASu3TLF261Pl+UPF8ffLJJ5uBAweatddeO9JrtMtaaJh7A+r03KRJE6f7ePnllwM5Fv2Ouva6CGLtvxwaJ7zzzjuB/f4qQOpaQA7Bc03q01q+EiT9evzxx3O1P+jyOdb52W8nFWRPEubnUWAukw+uYxztrbuusxeu2btQEfy11lrL988rwd9v0piSvkrt6FzTPL+6ju+VP6OlJIeFtdaTt+uqkhNcqBDzu+++G8ixDBo0yOSZa3GksNaa2BfKpoYNG5qDDjrIPPvss2b77bcPdcyXt5gN7eG6rDWk7fdNGpK+gAhsttlmpnnz5r5/XoP9oCZbCH5Aeumll9qLmR/z58+v8f+oYoRfTZs2Na6UFOBS4RDJ16lTJ6euFs8991ygx5M3pVYOKsZv9Z+scTlPagFRFd9cK6a5Vtws12677eZ8H1dffbXTcxf0JqlL8IsmhYsWLQr0mBA9zodwlbcxDedORCWICu0PPfSQ831oQ0eBTS5U2d+1MAqqpmBZjTFdCxzcdtttthtG2tbvvI66pSSE1KR///5OP68NpsKO9UiGww47zGkTuZR1RNTM5fyiDoRBJfyUGgz05JNP+j6vxHEuzQrX9RLXhAmtxw8dOtSkdY6uarpZ4/qeCGKv5tVXX3W+DxS3wQYbmLPOOivS94fLZ0zUfTIsu+yyi9PPqxCCaycjb53GdQ8kqI5jcXxWtT/r+jq7Brmi6s+vggj9UgJvqQHmlemzdc899+TqpXH9HKdtzRfRyPL8nLlMPriO15R45Jpc7xXI+eCDD2Ifr/ld/1ACU6nX5JrWcZS8VmohCb/7HOrypm42LvJ2XdWaf7NmzZzu46mnngqkI3DeC+4ndQ7MvlC2KaZbsd1hjvnyGLPhci1R9zR134U/JH0BEXFpSahgcmUdx+WTTz4xt9xyS2yPn3Ra1NUA0I9SqjK6VOoKIpj/P//5j/N9INkUHL3TTjv5/nkt4nz00UcmLurK8cADD5i0UuUhv0aPHh3osaRVEs6TUSdn77XXXoEkq1122WUmLApQKidpWL+TywJPnBudI0eONFdddVVsj58VnA/hKo9jGs6diII6lbgGiqlLtjaAXRPHtDHmYvfdd3f6eVQf2Ni7d2+ncbEK69x4441m1113je2p3mOPPZx+/rvvvjNPP/20030MGzbMvPjii073oYSTxo0bO90HwuESVLZkyZJAjyXP17WWLVv63pAsRSkJXaUmffkNJlQgRRCdhfLKtXuG63pTv379Yg8kdZmjqxK267gtaeJ+T4wbN46krwRfo/10YHP5jMn3339vwtKtWzenn1cHo5tuusnpPubOnWtuvfVWp/vQcxxHIQR1Aih1zFBTQQnXrm3VdR6Fm/XWW8/p55944glf16KLL7449jFC1PQ+LrVLbjGKs9F+WBx0fdDjP/PMM7E8PvI5P4973JqEuUweqEBs7dq1ne5D67CunXOuv/5658+D69jTJelLMQYzZ84sqVBZKR2ywl7rCaK4T96uq4qpcX3eVIzhvffec7qPm2++OdHXjii4zoHDjI9jXyjbNt10U98dJUtd78lbzIbL7yvqvBuXX375xXb9TXLn3uqQ9AVE5MADD3T6eS2OT58+3URJFQaOO+44c/TRR6eqPe+IESPMN998E+lj+h2Yl9KJoG7dusav8ePH280VlwodfiuNIV3U0tbFFVdc4fReK5cCCRUYfcghh5gzzjgj1clPLpV/FQwYRMXMtHM5T2pBbcKECU7XypdfftnEUX02iEW9l156yX5+g15gevvtt83+++9vpkyZUvLP7L333k7Vfe677z4zZswYEyWNN84++2zTvXt3884770T62Fnkcj7UuZBqMMjjmIZzJ9KypqGxxvnnn28D9/wYPny4ueuuu5yOQQlFBxxwgNN9oOqgRr2+foJdCzdf1SVsn332SfV1xNvA9TtHnTVrlrnooouci0q4fmbzQkEdug5HWcTDJfjKdWMc//OnP/0p1KejlE5f2267re3KFxYFI7msl+Sd63OnJGC/lCgfRJdUV67dylSlNmuVgeN6T6jbn8ZJLmOtNNJ7qJyCUq5c1kf9dB13/Yypm1ZYYxglSrlWw9e68wsvvOD7Pa9iZa774grci+NaqPGegqhdqDDR+++/73Qf22+/vdPPo3qbbLKJ01Ok7irlFAzR5+If//iHGTJkSC5fGpe5uj6Tl19+eaTXUT2m4iz23XdfO8efPHlyZI+dVszPg8NcJh/WXHNN525fipFQ0LNfAwYMcE6EUQJSEAVrtt56a19j8lJtueWWJa3LlbIe5CKoju55u666JifItdde67sLsdafBw0aZPLOdQ4cZvdz9oWipTg7FTeKkt9zVqnXlrzFbGy++eZOBWPVhMa1gGa5lOR1ww032GRzFdlxLZQQF5K+gIi0bdvWaXFVQR9KwCqlwoQLVfFQNeGDDz7YHHPMMbZaQdoo+1jH37NnTztwDrtSgl4TvwORUga0LoNetf70u7mqTUgFvSIfVEld7cb90mfg1FNPtVVcw6TKUuoSoEm5AqOV5JnnaoA6v6mqn9/g2axwXRx4/vnnfU8IevXqFVvAxfHHHx/I/egzddJJJ5lp06Y535cCsk888UR7Pij3/vQ6ugSnagxzwgknhF5JWq/3W2+9ZcdJGm9ogS7qTm9Z5VodNYhgEKRf3sY0nDsRFSUmNmjQwHnx+LTTTit77Dpq1Cg7VnGtEKvzgyqdI1jqkHjWWWc5L45rs/bQQw81SVi/69Spk9N9aPNXa3jlrhWpmt/JJ5/s3BWvadOmtgADavbbb7/Z67A25B599FH7ddhcCmu5zn0RfJBMVZ/BVq1a1fj/dF3VOSeNv2MeuH7e/CY6aJyk62oSKuO7ztHvvvtuu06UFfpsuxZI0rXej9tuuy2XQf5KftQYXoGoUQS+RH2NVldWl2RCjRlvueUWmwgSNAUqHXnkkc73ozG+ElvKXXu98sorAym0pn3iuAwePNg89dRTvn5W65tB7M8qKBbhad26tfN96HXWPKQmOgceccQRsRQgTArtxSjBwSWoUElzYZwzK8f0KGCza9eu9vGiDmZNM+bnwWEukx/HHnus832o86QCoMvd61Z8hUvCmEdF6FWszZU6cYXZ7bzUZK5SO33FvdaTt+vqTjvt5LvLj0edfP3sH3/++efm3HPPdXrsrHBdZ1KHN62thIF9oWgpHl0FIM8880wzdOjQ0OOt1Cnd795PqeOqPMZsuK65aD4c1me6cnyCriG6lvTt2zfSAtBhcOvzCqAsCtxwSaLSBeivf/2rnXBttdVWgT77CjJUVQQlfEVZOS9MGhTotvbaa9tq3grgCPp58yo4+215XUpAqv6PMtz90qaPqvLpeShn0nH66aen/iKH8qqqK5Cyd+/eTsF+GtCphbtLsHVlWihQx7mBAwfaJMYkBD0EXQ1QVRv90ob/nnvuac9xHTp0sNWQVGVIC1vVadmypVOViSRxfb898MAD9vkrJSircFNdn5k4E0y6dOlir2tff/21831pfLL77rvbjUNNJNddd92yglq1if3ss8+aTz/91DmRTZ91vwnb6vaksdI///lPe+0Lkl5zBW3p9wwiQQ7BV0dVUoASaHRT1XwF9tevX7/Gc936669P14QMyeOYhnMnoqDAdAX9aYPNhTYD99tvP3PppZfaxdXqaJ6txdd///vfvufchZSYjmCpy6YS+VRwxoWqyGns5prsVFM1PnXLLYWuI3qvuvjll19sdUgF7isAo6b5mYJhFdyqn3Olx8vKXC8qChjQ86+ODJpfa8NRQRRBP49ae9UGkx+a55czT0PNnb40bgxjQ7mcqs5KMvX7nqgJSV9uXOcB+rzrOtC5c+eSf0ZrDaecckqo18Mo5+gqdKF1JlWf32GHHez9Kaisps4DSoRJYpKrgpOUoON33KOEL80xtWZVKp2j/vWvf5l7773X5JXmzaq+q1vHjh3tXp+6N7l2oSr2+XN5nv2cM3Qd2mijjezer1/33HOPDdbR+EVBahor6DNWXSfJ2rVr28etyV/+8hc7H9Nn2S+t8SqIS4FEF1xwgf1811SM8pJLLglkvVvziy222MLESR0QNJfW3kM5a+2KJ1DnDRfNmzcPPeg474IILFfxFM1D1BlPxXZ0n/oc6/s6L6nQ7SuvvGK7vqW1CnlQtM5/1FFHOXWBVxyMEuyvuuoqs8466wR2bHpttMemvSPtleX9tXLF/Nwdc5n80JqCunxrjdaFxnxffvmlPT/WlNSsoujXXXed70InhTQ2DLIImNZBXOJuqlPquEqdT5RcFEbcpT7brkkzeb2uao1XYy3FBbkWCtHcRp8BdZerKbFCn6077rjDef8kK1zXmTS/VOKdmm7svPPOZtNNN7XrR5oDa35dFc3JSllXZ18oWoqb0L6Ybopf1Dqq4nw222yzQB9Hn0Wtx0UxrspbzIb20W6//Xbf6xc6v2sP9fzzzw8sCdyjWE7F46ibmObVWULSFxAhTTDUHrDcqmaVT8CHH3643SzTALycAPVCGngrsUcTHnXDynJLeQXMaOCumxa5dVHUAFCTMpfqjLoAaiCvxVa/apoEiJI4VF3FL3WiUcCPkr/atGlT7f9VRQo9T9ooYtKRP5qcquOES/csLQZp41VVzXWeUiC934otSh7RRoYm8jNmzDBZFURVZw1WtfFdDp27/F5DkkbnSdfgAQX39+nTp8aq/pqc6Zx86623+q7OGyR1NtK4IIgqSjrvq4Ju//797YKurpXaGNeCqxYnFSir/6PP55QpU2xAgjbgdQuq25kWZjSJ7devn9N1T4lrCijX6+o34VvXRJ3TlECusdsPP/zg+5gQ3flQFXpURbecSrpKYFCFL2RH3sY0nDsRFW2maLHaNeld4wgVGdEivgI227dvbzcFGzZsaDeNNddW8qU+Mwp4C4KC7JQQjGApCDmIBFa9r3QLk8b5pc6ZNC5QgQWdu11oPHnTTTfZAGIlOSrwX5uLGl9rXU7rVbpWaeNh4sSJJgha94qzo0HaqfiREjV0a9SokU1S0LxICUIunQJ1vw8++KBTIIXOldVtXqM8WhNWII5LoH0QSV/6v3pvBE3BO2EUP8uTUtbtq6N1Go13tEmvZIyaqHOHiv1pTSMpFGShpGWXACutF2lMp1upFGRz3nnnmaRR4IE+Vyrk4VKVWsk4F198sV1nq47WofT+cR2PZInm6bpdffXVNrhV12ntu+p1cUnU1tqfEo1cPn9+zxlae3W9Fo0ePdreSqWEuVI6xyl4TnNArZm7UlCRgkL1mikRVMUYVKhSa+2a/+n9rvXXoLoD6nOmJLO46Rz497//3X6OVVW6pm4KQRaCUAwBY8dwKRFVAabldnsoRjEbuqHmMYLWDrSu5JcqyasLnhLttYbsN9Fc1wwVi3n33XfNO++8E0nX6Lxhfu4fc5l8UWdV7WG5FrXROHv//fe3cReKa9S+k8ZrGmdrbKKYQp1DNY4MKrn17LPPtskYSS9+o7lgqcneGn9prUdxmEEL+vfL23VVcZOKg3F9/ypuV585vR5eYW7tb2ktTntnej7fe+89u87jWsghaxRzpP12l/ecKCmwnKYbSkxRzGxN2BeKjz4rSpDUTdcfL7Zb517t0/g1fvx4G1OnPecoxlV5i9lQUSGNQ7QG7pfOyVr/1DlTa1BK6KyukFF1a/FK7lJjFV2D9buH3Y0yLiR9ARHTZo4GHi6V0bRIPGDAAHvTgqICV7Txr+B9XegUKKXNOJ28tRiiCZgygXUhUwcE3VQtLagA7TSZNGmSDdb3kqgUDKPnTtUElJmtjQZteCgAR5tu3kaRnksF9+vn9dzpgqjJhutkQEFENdEmiGvlWU06VLVCXVy0yazfWcENuvhq0qH3hiZOSgKhe0l+abFClQdVwd9v1QFRUsjdd99tA9sUGKVBuN5zqh7pnaP0WPpc6Vyo95wGrarc5Z2fxowZE3r73qSg2qI7XQu1iOJybdX7UO2LdV7WwpQmblrIVAVfBR8rCFOLM6+++qo9ZyaFFpFUdfWRRx4J7D712fPGC3FQJQ8Fvmrs4kLVMXVTwrOupToPKahPm+s6F+kar/OVzkXeWEmvs/e7q9o6FRmjpYAd14AyIK9jGs6diII2YFVtS8FqQS3iu1ZWLIXGiUkI+MuiLM/ZtH6nDmRBBPApeVGBrrqFTRssmsPAndYBNf/TzduU9uYUGgsocVVriApm8LrLajNK4weNCxQMo400JX9rU801ibWUNUSUR8EZYSR9lbPOo0CgMDqOab9A677wT59xfdZd1oCU4KsgNs0jVK1Wc14Ft2jfwSuoo70inWc0d0gaXU8UWKBK1vgvBZ24JH3Jk08+aQPjtF+jABYl12l+6a1PqbiSlyjnMp/NMgVt6H2pmwKCtJakAlZKoNI+nz67SrTXNVrXb12j9X/0fGrPVHtimosoAET7Yq6fPwX1aK/R7zXj+eefN0mlACMlwwfRgVDrfXq+dQubEp5qKoIZJSV8ap1aQYQKYNK1QONIFdDQ517r0LoW/PTTT4E8ns4pKhSHcOl6rtdz0KBBPNURUffj3r172w6CrnM9FWhRcUdd27XHqLmexn9a+9Kar2hup/Gc1nu1f6RzodZ8FUyYpH3CPGB+Xh7mMvmieaaSbTTeCIKS8INKxK9Ou3btAh+vaD4QRpctzTXKSTrQGD8NSV95u65qjqi4GhVBDmJOqgTIUoppYOW1UBUESSr2heKnPRXdlKTpdUj3Yrv1d82ntd6j2G4V4dD6pT6TWlfTeqtiuzXH9pIDXRJ/FAeiWLNy5C1mQwUvdSun4Fcxw4YNM6eddppNolWxIK1J67qu+Hb9vrpmLV68eMXrrN/XK1av31cFNpNQuD8K7PoAEVNS0bXXXmsrewVZ2Q7+6EKvWxy0mVdKNrgGK9qoVyayC13IC4NVgKoSSHr16mVuvPFG5ydIA+dyK2zkkSYlStoNYuM0rzSRU0KraxClgqyU2KVbmijw+quvvnLqaJMkWvxTpR0l4QUxCY0zgQ3lb5QrsYYq1ghC3sY0nDsRFQUs6zythea0UCcArcUA5QbvatP70ksvTc0Tp0AJVeJFOBQw8sEHH9hb1JS8c+CBB0b+uFmnYJkgAj0KaQOynCBzBSOF0XFM8yq4U+dfJZTkuXvHrrvuStJXAVXZvf32250rxSoYQwVGdIM7rR8qmEW3OCjI1i8ljCiIKKnVhxVEpHXaww47zAYTpUHbtm0T2S1Qz58KAegWNlXHDrJrBqpfIyHpK1qa8yqxU8WRg7h+KBFbN6QL8/OaMZfJF61hqiCCChKmgQK3//Wvf/nq5FEdjas7d+4c+LW53CLOYRR99n63oOXtuqpkDO1tzZgxI+5Dya3ddtst0Ulf7Asli2L4fvzxR3uLgxJZlYRUjjzGbCgXQgm8KoToSkWWVTBLNxRXq4rvAwiR2rueeOKJPMc5p1bJpVLbzjgFPdlGsun9pvbGiA7BW+6OO+44W+Ujj+dJbb4rCKncyWaSbbvttraKDvK5UQ4EJW9jGs6diMpVV11lq1umgTYs1cUV8KNHjx5OQbxRUpU/BXggmxSspWq0CJaCcILuhqXxmAJxyj2OpFd/zitVgFZxkjyvye+///6JOI6kUKXyPfbYI9Zj4PVIFiXvaszol9Zz1fEtyZTMfMUVV5i0vB7qMJDnzrcKUlRwGaJLjlbF+bio43AeXXjhhbaTKhCHtMzPmcvki64Ht912WyqSvhXPoeBwdegNQxhFcNSZqBzqTBL0a+F1MQtDnq6r6jqWpiJvWR0/l9M5Lw7sC8FPbHeeYzbUdU1FspTUjfCR9AXE5PzzzzdHHnkkz3+OW2yXk+Shan9xVWhVW9CTTz45lsdGfNQVQ5MtROOII47I7eZMUBR4HFdA5mqrrRZYB0+/1L1CFcrXXnttkxXakDj33HPjPgxEbK+99jLNmzfneUdg8jam4dyJKGjc2rdvX7Phhhsm+glX0ucll1wS92Eg5dQprmvXribJ1Dn6vvvuy3Vwa5ZpE/ycc86J+zAySQE4SpiMMxDI789Up2nTpqlJzk66Jk2a+N7cDyIQTntIcVt//fVJoK9E52QVYIqDxiQKHEFyaE1Ye2gu0lAkVOvuSTgnVUfBRffee2+sCTgSdEJ5uY99/fXX2/0KRHe97tWrV2znn3bt2pk80nVYn/cOHTrEfSjImTTNz5nL5I8S9dVJuG7duibJlHATZrHGMOLqyi3Wo2JAQc/bwowXzNt1VY0aXAp3uFDinorr5JmKK6lYY9KxLwRdqzp16uT7ichbzMZWW21l/vOf/yR+HJIFJH0BMbr88svN8ccfz2uQMxrEq1V1uR1pNKBU1YmoNwjUcjTpwXwInjaFlIVPVfxoaGP47LPPjujRsuuCCy6wyU9RU6XTrbfe2iShiufDDz8cWmWsOCjp+KKLLiq7UjrSS2OPPFW9QfjyOKbh3IkoKNFcCeebb755Ip9wbZwp6ZMxBIIYm6hbwJ577pnIJ7Nt27bmwQcftME8yB6tHSpwN0tdnZMm6KAZP127gu70pd8pzk7oWaOkrzgCqk8//XTTrVs3kwQKZqdS6/9o3S2O4k8ad2t8y+c7OTQ+DKK4p7ozJr3IgJecpvX3JL4Hte+qIghJWKM/7bTTbAJ2HP72t78l4jnIG3WA3GWXXSJ9TAXK6r2W9wIODzzwgOncuXPch4KcSOP8nLlM/mh9QYlfUceVlULr9CrQFnayh+IlguzGp4ICfsZ2Qa/1hN3RPW/XVSUfRt3dTPsM6si30UYbmbzT3HK99dYzSca+UL61bNnSXHnllc73k7eYDe1LKIk4rM6U+K98vJuABC8M/OMf/zDXXXddbJUBEX31n/vvv99XBwslXt18882RVYnT4+jxdthhh0geD8mj85ISFFWxKi8D0Dgdc8wxuepEEgZNHO64445IA1E0QTv00ENNUmjh8amnnjJdunQxWdGzZ09zzz33JL7NO4Kz++67p6LCE9Ijj2Mazp2IgpLtH3vsMbPjjjsmap3ljDPOsHNZqpsjyOuINmS1QZOkIFfNHx999NFAAxmQHHqvXXXVVakIAk+zIINmVKnWT3KQEqmDLN4SdiBQHq8BKiIRZWd1rREmqTiUun1de+21iboGxu2EE04w++23X6TrfUryViAekkH7ZkrCC4qKPvrZN4zjva9zYpISQXUNffzxxwPvnOkyT/73v/8deXXrgw46yM5XEA/Feuh6GQUVFL7ssssieayk056ROtGzl4CwpXV+zlwmn7QmoLGRAtaTQmNHjY+OPvroSB4vyHURv8lbQSZ9aZ8jirFunq6rOj+qI03r1q0jeTy9htqnDrNjW5pobaNPnz6Jj5VmXyifNKfXGlxQ8Wl5i9lQ8rBiFpNaNDYL8hHtBCTcwQcfbJ577rnA2/sGKS/BkWFXFFFAnNpZ+qVKYTfddFPowWsKUlCHL7U1Bk499VS7MJTkAVkWzlH6HfS5I/HLTfv27W3liPr165swrbrqqrYCkCZoSaPJoiqbagMiiZW8/Nhpp53MSy+9ZHbbbTeT1fPQokWLfP9snTp1Qn/PR613796mR48ecR8GMiZvY5o8nDsRP401VNhEXRqjDmorthDfr1+/RAVJI1sBPueee66teBpVMF91azaai2hjOmtjQPxv41sFTRgPh0/r8UFdv7bZZhvfa7ZBBgMRQBK8Fi1a2A6nUSR+qRq/5sNJs88++5hrrrkmsoJ0aXDDDTdE0gl0iy22MA8//HCkiYeonq7P6qAQ5PxH3QM0lwkyCTjMrkbPP/985JXxizn88MPNwIEDbWJkknTs2NG+R6JKjtOeks7RiI86L2uuGGYHZl2DVUxYN1R8XpQEp33BuOfq1WENNL3SPj9nLpNPm222mY1BPOyww+I+FLve8cILL0S6TxVk0pffZCvFrGgNN6j1pqDuqyZ5uq6uueaadq6t5zdMmjfeeuutkawfpIk+W3feeWeiCooUw75QvmguH0bidN5iNtTY5JlnnrHr3EleS66V0jiVdB41kEGtWrWy1XlVCSopFTcUvKVqharapsVp+KPNflXd0sVMk+sgNliVUR7WJp8uvBrAaOMG8Gy99dbm2WeftV2NmjVrlognRhuhWqjSgsMVV1xhskCLNXfddZe58MILEz+5TTItHj7xxBOhbZLrvacqR3/9619NkhcftOn9yiuvmL/85S+Z6HShDgYKbNVnpG3btiYJtEinrlQK9NE50sXnn3/u+2fV0aRx48YmS5RYqSrHem7D3CxH/uRtTJP1cyeSM+449thjzYsvvmgLh0TdBUJjaFU01+MT6I4oujpoc0bvuag22z36bOkzpkAJzUXouJJN2vjTa9ytW7e4DyUXVLE1qEJsLlWXg0r6UkBfUvYWsrh/o/X9Dh06hHL/WgdUMajzzjvPJJU63auoXZs2beI+lERQ0IICt1RcJKwgAXXu0V4NCV/JoE5c6lCg9aowKpJrb+7pp5+2QeVJDzzRsT7yyCM20Wi99daL/PF1Lu7fv78tepbUfRQFG6tAitZ8wnTggQfaYMksrP9nJdZDRWCDpvU9JaAr3gDF7bzzznaurr2SpBRDVHEidXDV+fK0006L+3CQ4/k5c5n8JixqrKYxU1jz2OpojOg9ftTrFEno9KWxWVDJRHHseeTlurrWWmvZMZbWO8KaNyluSXuiKP4+0z5xp06dEv/0sC+UbVpXUNFHJYJq7hWGvMVsqHC51rm1np6UZLdatWrZvSDF5A4ePNheV9IouWl0QA4pWENdvw444AA7eNbA74svvoj0GDTg1IRBJ3RNhJLeSrUYJappAvnZZ5/Z29dff+3UPcNlQKBNOS3ABj2J1cRS7xFddFWhZdmyZYFcbHWsp5xySuSBS0gHnQ/U1eiII46wm49qx/rdd99Feo7UouT2229vF1cVTJP0zU8/9Dsdd9xx9vyhDSItJv/4449xH1bqqIuLzo9KXNbE7Pfffw8kCUWb7n//+9/tAlAarLPOOubyyy+3QalaAFMV1l9++SX0x9XGppKHFXAWtK5du9pJoSZhCjb68MMPA7kOljMZ18KTxkqqyBJEYMG8efPseMWPLbfc0pxwwgkmq3Qu1HtJ1xwtNER53UF25XFMk8VzJ5JH6wkKfP3222/tRtlrr71mFi5cGNrjKXhOayja6NOYB4gyaEIbQEcffbSda2iMMn369NAeT+s1e+21l712aeyH4Gi9Tuuv3hril19+aX799dfIn2LNNXV9VOXDJHTNyButgWts5MolcSuopK8gA5tQfEyrsbQ6eahI3vz58wN5mpTQq64dSa6i7VGwoNaWXn/9dfPkk0+aoUOHmqVLl5q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to2NmZqZMmzZNdu/e7fK3tq2ay8ih/aVzhzbSqEE9ySsoksySuvLzz7/I/v37HR778ssvS7NmzeTiiy+WQCQkJEifPn1kxIgRpnpmy5YtTRhRK4OtXr1a5s2bZ/5vTyt03XjjjfL888/LuHHjfHo93fbPPvvs6gCaBiG12peGIgFYX0V5mQntV1VWSJNWnbjTZjjwGQgIOw0PJSYlseRjTPaerSbwpTTYop2+m7XpIvUbN4900yyEk1IsK8p3/EyqnENgABDLdm9e7jBeUpQnRbmZ0rBZ64i1CYgq3HAz5lVVVkp+9h4pLymSxi3aS536/v0mgijCve+Aavo9ji3wZfsMW5C9Vxq3aMdSAvD3adOPG4UBAGKDpUNfAAAAVkDnEsQKDbjYV2yZPHmyCb8gOjz00EMyevTokL+ObgPXXXedS+CrTp06ctttt8ngjomSlJjo8LeBh50uxSWlJhj19ttvO/ztySefNGGtIUOG+NwWfc4pp5wixx57rLRo0cLj44qLi+Wdd96R5557TsrKyqqn6/+1zT/88EONz3fnrLPOktdff716n9BApFY90/Cd1VBxzD9UuLLmjw+VFeWybfnPUpiXacbr1m8sPQ+aIHXqNYh00wAgKMpLS2THmt+lMDtD6jZsIl0GHiwNmrRk6caItA2LXKZl7FgrXQYeEpH2xDL7TlbxjOWAmjcQ9hMgFpQU5hL6imF8L+cvzlFWlbbhL9m/e6v5f3JyPelx4JHc5CLWsbsC1Qpz97ksDb0BHaEvIF5xkgSAeOLY0w4AAACA/+L4O5W0tDSHKl+20EvQxfEyjhWzZ8+WRYscO5MmJSXJCy+8IOedd55L4MumUaNGcscdd8jNN9/sMF2rZP3rX//yOiCjAZKJEyfKl19+KZ9++qmpNldbYKt+/fpy2WWXyauvvmrCafays7NNtS9fdenSRQ455J/Os5WVlSZYFkx0sASiT/7+PdWBL1VanC+5/19RBSFkrewgENXSNy01dxDW6xDtIJy65o9INwkBKi0qYBkCAADEKL4fBP5RWpRfHfhS5eUlkpmWwiIK4AgTzhsAcJM0AL6orKyQ4oIcU+ERAAAgHhD6AgAAABCw9957z4RzbAYPHiz9+vVjycahF1980WXatGnT5IgjjvDq+dOnT5fDDz/cYdqaNWtkzpw5tT63U6dO8sUXX5iQ1oABA8RXGtK68cYbXaZ/8803Ul5e7vP8Tj/9dIfxmTNnSn5+vs/zARA70lOWukzblbIkIm0BgFDI3rPNYbykKE9KCnJZ2AAAAACAiMpK3+w6bdemiLQFABA6ekOq9fO/lJS/vpMNC76W4vxsFjcAALA8Ql8AAFjs7n3c1Q+IoDitMqFhmK+//tph2jHHHBOx9iByUlNTZd26dQ7TGjRoYIJcvrjmmmtcpn3wwQe1Pq9z585+hb3saTWy5s2bu1T7Wr58uc/zGjdunKkiZlNYWCjff/+9+b/eeS4vM132p2+RspKigNoMxDLr3cHVau8nsLsr692Uc/elcbdNwOIqyksj3QQgaljv2gYAAESzhHj9UQJwh2txv/AZBkCsSduwqPr7yLLSIm68h/jF15AAEFeSI90AAAAATzIzM2X16tXmXx2SkpKkZcuW0rp1axk2bJg0btw4ahdeUVGRrFixQvbt2ydZWVlmXEME2v7+/ftLly5dQt6GPXv2yKpVq2Tnzp3m9Zs1ayZt27Y1FZjatWtnqXWmP0hUVFbKttTdkpGZLbn5hZJfWCTtN2RLs+YtTPWfXr16SceOHYP+2rp+NQyiYZeMtG1SWbxfmjVpJH16dJY2rRyDI4EoLi6WlJQU2bx5s+Tm5kpBQYFZvhooadq0qXmPXbt2Ddm6rcnvv/9utnV7kyZNCns7EHkLFixwmaYVvnQb9cWQIUOkR48esnXrVod579271xzHQqlOnTqm4td3333nMF338REjRvg0r4YNG8phhx0ms2fPrp721VdfmQpgqesWSM7eHWZacp360vPAo6ReI9+WEwBEq6LcLNm6fK5UVJSZ8Rbte0jnAWN8ng+dTgAAAAAAAEKFnsJWpN+n7duxztyMKTEpWdr1GCLN2naJ/rCdx/mwnQL4R3lpiZQU5rpU/gIAALA6Ql8AAETY7bffLl988YXHvx911FG1zuOM44+Qs04cb/5fWFQkI0aPlbKyvztYqhtuuEEuv/xyr9rzr3/9Sz788EOXTut//fWX6QhfGw36jB071qGD5r333itTp071Otjy3nvvmc72a9eu9djRMzk52YSIDhnaU8YO7y9JiYlRUe1I1+U333wjS5YscVgHzjScc+yxx8rFF1/sUlHG121Gwz5z586tHv/pp5/k9ddfl2XLlrl9fmJiohx00EFy4YUXysSJEyVQvq6zU045xQwaWAqGOXPmyEcfzJDFS5ZKYVGJ818dxjT0pdvnySef7HN4w9nChQvllVdeMUGUyspKt4/p3KGNnHrMYXLAuLMlIcG/O25qWOTzzz83waqatikbDcTo+tVjx5FHHhmWcOS3337rMN67d2+vg41a+WjUqFExe8yCow0bNrgskgMPPNCvxaTPsw996X7222+/yWmnnRbyxd6hQweXaRkZGX7Na/z48Q6hr8WLF8uOrSmS+/+BL1VeVixZ6ZulQ2//lhUARJvMtI3VgS+1f/dW08GkTv2GEW0XAABBQxUBoPbdhA66AIAoxU1mYGV5mbtk95YV1eOpa/+Q+o2Pk3oNm4S1HVwLAgjJsaXKfb+MmLhu4LskAAAQgMj3jgYAAEHVsEEDUyHF3h9//BFQlRYNZWglI2/oazl/6aEVU7yhoaGjjz5annjiCVmzZk2NX55owEo7zj/7xidy80MvydqUbRJJGrQ67rjj5O6775Y///yz1nDOjh07TGBIgznvv/9+UNqg1byuueYaueqqqzwGvmzBCV12V199tVx55ZWmUpW//Flnd911l0yePFkWLVokgYauNDym7+HX3xe4CXy52rVrl3z66acm0PPQQw/59bqlpaVmPZ9//vkyf/58j4EvtTM9Q559a6ZccsklZj/yhbb13HPPNevp559/9irwpbQS0g8//CC33HKLvPzyyxJqtiCOPQ1xeUsDWrF6zIIrd8cTfyvsuXuehvnCQcOszrSqnj9Gjx7tMK7b2w//cw1770td79f8YUH86AML0JCXN9OCtt0H6zdXOiYD8EFFWakU52fTYRQAogZVIAAAAHZvWuYSkMjc6XrDPi6tAACxigB/Dfy8GTUAIPoR+gIAwIKcAwsaAHLXgd1dyGTbNvfhKW9DGM6P02opPXr0qPV5L7zwgqnus2fPHvFV6q4MeeCZGfL7olUSiS8Tnn32WRO02r59u8/Pz8/PlwceeMBUFqqoqPC7HRpE0mDRrFmzfK6SpRW/9u/f7/NrBrLOUlJS5KKLLjJV0fzx9ttvm+drZTF/6bL3VUlJiVnOGhzzhVbpuvTSS71exzt37pRzzjkn4GBcOKxatcpl+/El9BWrxyy4l5OT4zLN32pzTZq43nVSw6Xh4O543qZNG7/m1blzZ1OR0d7CJf/cZRMAAACxJ2vXZlk3/wtJWfS9pPz1nZSVFEW6SQAQMXS2AgAAiB4lRXku0wqyMyTaeboZE9eaAAD4du4EAFhTcqQbAABAvNOAQf/+/as7y6enpzv8vVevXlKnTh2X51WUl0lZ8d+hlebN7DrUV1XJwQcfLM8995xDIEgrLB166KE1tqWmkIT+7brrrqv1/WiVK3vaFm/CQxqccpacnGwqpOg82rVrZwIzunx+/fVXU8XH/kve8vIKeebNzyUxIUEOGXGAhMt9990nH3/8scv05s2bmyDLoEGDpFWrVqZCTF5engk7aVWkrVsdKx3oPDTgcMyIzn6147HHHjPr2KZFixamAle/fv3M/zWUs2HDBpk9e7ZLJR6dPm3aNNMGd9taqNaZVq66+eabJTEx0VRJ89ZTTz1lqqS5075NSxk6sJd0bNdKmjVpJB16D5eComKzvFevXm0CSlpxzF933nmnqTBmo+Ggww8/XHr27CnNmjWTXds2yIpli2XhsrWSnVvg8FwNcGlYTZe1N6/jfCxQAwYMMIGq7t27S9OmTc3yLigokNzcXNmyZYtZl/o+AwkQ+spd5aUDDvBtH/R0zGpWy/MidcyKVRry/PLLL802sm/fPnNM0kprerzSQNOBBx4oI0aMMMugXr16fr2Gu+d5E+Bzx93zNOSn23dSUpKESnZ2ttvtevDgwX7PU5+blpZWPb5yjVb1OtHv+QGIZtxBL7j4wQpA9NHvY3ZtXGzulq5KCnMlY/sa6dh3RKSbhqjAuSuWJXA3ZAAA4lr8hUus9j2W1d5PbKDDOQAgWnBOAgBYGaEvAAAiTEMJtmDCzJkz5Y477nD4+6uvvmqqhDjL2ZsqO9b87naeQ4cOlUaNGpkwiH0AorbQ14IFC1w6Oth+4NCwjFZGqqlii3bG18o7NVXwcbZkyRITIHI2fPhwefjhh91W3LnyyitNJSANxmjQxUab+soH30ifnv4Fp3z1+eefuwS+NDxx4403ysknn+wxNKHL9KeffjKBsczMzOrpr7/+urSuO0VGDOnnUzsyMjLkgw8+MP/XAJWGiq655hq3r3/XXXfJ888/b16rsvLvDmq26jkapLr66qtrfb3grrMqsxyGDRsmHTt2rPW1f/jhB7eBr759esmZxx4swwb2dpje/+BjpU79htXjGn7TZT9jxgyff8hbsWKFqfSlNCBzzz33yKRJkxwes3dbVxnVv52ce8oEef2j72Ten47VfF5++WWZOnWqCQF6omEn+2CZ6tKlizz++ONmGddGw6MastNtMxydlfTYYE+PPe6OWTXxdMw6dmSXqDtmaRW2o446SiLlxYeuk7atW/j13Hfeecft9qKDVrbSbe+1116T1q1by7nnnmsGd9W2aqLHQGfOQVNv2R8f7cOiGoj0dRvzxRdffGGCh/Y0QBvIa2q4W49fNnn5BZK+N1M6tG0VUFsBAAAQfrkZqdWBL5vMtBRCXwAsV9Fwf/oWSa5bX9r1GCz1G7t+3q+Vl/3m6ZQFRFb8hVwA/IP9H1G0ebE5AgAQsARC8ABgWYS+aulArZ069+7dazoqaidfvdu8dibVTtQNGjQwlTvatm0rXbt2ddvBEUD8/CBS8ccSqVixTipT07XkUKSbBJvkZEns0kGShg6QpEOGx83dWrX6j1YD+vnnnz2GI9xtx/YVb3Qe48aNMwEZpZVV9O8TJkzwuuqOLu+aquboa2oIybkqkb6uVv2pW7eux+dqRZoPP/xQzjxtsmzfubt6ekFhsbz+4Xdy5GlXSCjpNcKDDz7oME2rL2moon379jU+V5fLxIkTTTWks88+W3bv/qf973/5kwwf3NenbdU+nHDvvffKlClTPD5Wr2FuuukmE7D617/+5RJIOumkk8x1TSjX2XnnnScbN26snq5Vqh544AHz+jXR6zENjTk79thj5c6brpQ9m5ZIbbTq2RlnnGEG+6o73rAFvjSApeu5U6dOHh/boH49ufqCkyUnN1+Wr93s8F5//PFHmTx5co3VmOxp9bU33nhDunXr5lU7teLYiSeeaIaioiIJNa0aZa9Pnz4+H2s9HbNqCn1F4pgVL7QK2H//+1/55JNP5OmnnzahTF+qV7rbRk4//fSAty37MFioQl96nHF3LDrzzDMDmm/fvn1dpm3evovQFwAAQIxW+gIAK8vJSJW0Df9UwC7MyZC+Y06UpOQ6EW0XAAAAwodgPgAAUY4beABAXCH0ZUerAWjHT62ssG7dOsnOzvZpYWrFh4EDB8qYMWPksMMOk169egV7fQGIQlUVFVI24wupWOq+YzIirKRUKtdv/nvYtE3qnHuKJCQlSTzQ4IJ9gELPbdqZvWXLlm4fv2HDBtPR32bIkCEmmGQLUCg9T9YUoHAOlmn4QyvGePLLL7/I1q1bHaZpGElDBjWFh2w0cH339RfLVXf8W0rL/glbLl290VST6tmzp4SKhnDsAzUNGzY01bNqC3w5hyOeeuopOeecc6qnpe7KkMUrN8jIof19btOpp55aY+DLnj5u5cqVprqcfQUdDWXddtttIV1nL774opxwwgkmTG8/39rW2dtvv+1QCUqNGDHCLMPcjB3iq5pCW55oAEvDMN48VwNEF5w+SZY/8KLD9N9//73G0JcGCu1pGMrbwJczvUlBKGng0LlSljcV27w9ZuXmF0jTxo2i5phlBVplTsOPWgGtsLDQfOZx3q9sdN1qtS+tMnf88cd7NX931ei08pxWFtRKhN7SYJdzFTn7in2hohX8nD8HahA20NCXu2PGrj2ulcwQZzx8Ec+P6YhHbPcAAADRY/em5Q7j5WUlkrN3h7TsyO+eAAAAcEKFLiCyCH0gHrHdAwDiUNyHvrTD4AcffCBfffWVpKamOlQO8JVWBNPqYPPmzTMdI/v16yennXaauat9qDvcAoicimVrCHzFiIolqyXxgH6SPHywxAPnajW2qjjHHXecV+GHQw45xGUezlVx7GmHfg1O19QGZ++9957LtFtvvdUEqLzVvk0rOXnSofLJN79UT9PTuJ7f7777bgkFDc/Zh6XUtGnTTAUoX2lAQpeT/fL/a/l6h9CXN9clGuS4+eabfXptffx3333nEL7S93XDDTd4DHAFY53pcpo+fbqpDGb/Hp3XWUVZqRTmZkq9hk2krDJB3n//fZcA1r///W+fgiSB0rCaVmjzVpeObaVn1w6yZUd69bQ1a9bU+BznAE40V5PVUJDz9tmuXbugHbNWrd8qY0ccEDXHLNt217+/76HMQBTn/xNySk72Lbis288RRxxhBt12df9z3me2bdtmlpXu35s3/1OZzhYGvf3228161ZBlbQ466CBz/LCvQKhV9WbPni2TJk3y/ESn7WjGjBnmtd3RsFoovPXWWw6hQVt4UysRehNqrYm7/SIj07ebjAAAAACIAXR4hAWUFue7TCvI3lND6IsNH0Bs0d8e9m5bLSWFudK4ZQdp1bmv+R4Q4JwGBJOv14hcUwIAAACAJ3Eb+tLOtM8//7x89NFHpqOzc2dVf7/Us5/P+vXr5ZFHHpEXXnjBdAS/6KKLJDk5bhc5YFkVv/4V6SbAx/VlndBXzV989u3b11Sh1ECyjXbq9xT6cg5HaPhBO6n37t1bNm3aZKZphafdu3e7rWa1evVqycnJcQlheKKBgEWLFjlM0/ZqpR5fTTxsuHz23TyprPxnmcyfP19CRatS2Qel1BlnnOH3/MaNG+cQYFmzcZvP8zjqqKOkVatWPj1HH69VkL755pvqaVrhRiuAuQt3BHOdadUcrfhVUVHhdp0V5e2Xbct/lvLyEjO+NbNS8vMdO5zotlxbxa1gV43wZz337t7JIfSlAZuaOIe8VqxYIeXl5VF5HanHA2f+Vspyd8xauW6Lx9BXuI9ZNvoaesOIcFr184c+P6dt27byxBNPyDHHHFNrYKl79+5m0AqAGq7UG1jYh7b0/9dff73MmjWr1oCn3uzilFNOkY8//thh+oMPPigjR440VcZqo8FIrZzoiacwWKDHdV1ezvQznDdhwNro+9bAoH3b9+133P4AAG7Q1wMAAAAAEGQ71syX/P1/f7edl5UuVZUV0qbbQJYzgPhEtRQAgFXwm5II97IAAMsKX1mEKDJnzhw59thj5e2335aioiIT1NKQl/2gdLovg3I3D+08/dRTT8nkyZNNp10A1lJV/HcgAbGhqsgxqGN1Y8aMqbEyjo12Ql+8eHH1eKNGjWTYsGHm/86d3T2FqbSKmD3t3K4d/D1Zu3atlJSUuASX/Am2tGjWRAb07uYwTcMeWtEzFJyDTxo88re6kercubPD+N7MbCkoLPJpHhre8oe7wNby5ctDvs40kKJVzjyts4zta6oDX+r3eXNd5qHXVuH88karqQ0ZMsTn57Vr7Rhy0aCbczUve86vsXPnTrnnnnvMdWu0cQ7iqUAq3Dofs1Zt2BI1x6xY06NHDznppJN8qlCln2HOPfdceeWVV8zysKdhPP385A2t5Ofu+Tpv3Z5rsmTJErn44otrDHYF+667GnTVCof2IVQ1atQouemmm4J6DLFXVPxPsA4AAAAAAOuhtxWA6FNaXFAd+LLZvYU+HAAQtQilAQAAAEB8hb40gKV3b7/66qtl7969DmEv29+dQ1y+zt9+sA+A6fjmzZtNR0e9ez4A60ioXy/STYAPEho4dri2OueqNdrZfseOHW5DPoWFhQ4d3W1BHud5OFfX8TRdgysaxKipkouzAw5wX9HHG727dXQY13PvunXrJBSWLl3qMK5BJQ0g+TtoVR1nufn/rA9vDBo0yK/34u557tZNKNbZ4MGDPa6znIxUh79t2LzdYTwxMVGGDh0q4dSxY0eXIIs3Gro57rgLS9lXMHMOh8ycOdME7PRaVm8iUFlZKdHAueKdcm67L5yPN3v3ZcvujKyoOGbFE12GN998s8v0d955xyUY5U6XLl3klltucZmun4eOP/54eeuT72VtynbJzS+Q8ooK2Z+TJ/Pm/WoCVlOnTjU3zVC6Tp0r36l69YJ37bVx40YTUrPfnlS/fv1MZehgVthz3jdK7KqpAQ7oFwkAAAAAABASJYW5LFkQIgG84XW/ueB8oe1PPz0AAAAAiBfB68EW5bRz4m233SbffvttdSDL0wfHQO8cb5ufc/UvpXesf+ihh0zoTO8mDyD2JR0+Siq31Vy1AtG1vuKJc/jBFnTo2rWryzR79pVybGGK8vLy6uo4zudSrf7kHIRy99r23FXh6tWrl/irc4c2Xr1GMOze7XgXSA0LrF+/PqivkVdQJB28fKyuH+d16i0NZ2iQyb6qTmZmZljWWe/evb16DZWdW+BSXa1x48YSTs2aNfPreclJrvdZqKmKkVZB0+vERx991GG6rpfXX3/dDE2bNpUDDzzQBJW0wpX+PxKBJXcBoKSkJL/n5+64sXLdFmnfpmXEj1nxRsNX7777rqSlpVVP0zCWVsXS7a02F1xwgWzYsEE+//xzh+m67L+du9AMtdEKdx9//HF1CMxGt/9g0OqCWlXMef49e/aUt956y+993hPnfaOiIjrCm0ER3OJrcaOKdBcAAEDMokMiAAAAAPCZCQAQfvzGWhN+uAcAq4qbSl933XWXfPPNNw4dPp0retVU9cubwd18nF/HVvXr1VdflRdeeCGMSwBAqCQdOEiShvtf6Qbho+tJ15fV2Z+T2rdvLz169Ki16s2CBQscxseOHVv9fw3W2Fdk2rdvn+nIb2/JkiWmI7+nEIY7ubmud1MMpCN/o4YNXKY5d+QPVmUjd9WNgq201HMoyJmGfQIJrTuHp/Ly8sKyztw919M6yy8sCtrr+sufKl/+uvDCC+XWW2/1WGVI18W8efPkueeek2nTppmg0znnnCPvvfeeZGW5VsYKFXdVvZyPBb5wd8xauW5zVByz4o1u78ccc0yty74mjzzyiFx//fU+H5/0tW+//XY5++yzpaDAMfCp2rRxDfn6KjU11QTTMjIyHKZrgPbtt9+WVq1aSbA5nzvq1Q3fMQUAoh438gViGj+yA+wPAAAgQgK8oTGc8P0EEHX4zgEA4MtZI55wUyoAiC9xUelL79L+5ZdfOgS67DlP1zuw9+nTRwYMGCADBw40HU+bNGliOpDqv/o47Qydn59vOtxu3rxZ1q5dK+vWrTP/twXLnOdrP13/r6Gvfv36yYQJE8K8RAAEU0JSktQ5/zRJ7N1dKlaul8odu0T+v7oIokBysiR27ShJQ/pL0iHDA67mGIsf3rR6jVYzsVm4cKFUVlZKYuLf2W89n61atcqhypBzBSadx7Jlyxw6/ffv399jkExDSEOHDq2xXe468jdo4Brc8lb9enW8eo1A5eTkSLQJZLmphg0bOlTY8rTcgr3O9HW9eQ1VVOQY0PG6qpWX+0nwBO/1NMw1fvx4efHFF2XWrFk1hqm0qpUGmXR48sknZcqUKXLNNde4XcbB5G79BxqKdD5mrd64zeGYVVhUHJFjVjwaPXq0vPHGGw7T0tPTfZrHFVdcIePGjZNnn31W5s6dW+vjDzjgAFMZWT+LuQuh6naglf4CodXLNPC1Z88eh+k633feeUfatWsnoeC8D9erVzckrwMAVkKnDgAAgBgQX32q4oaVfksBAAChQ4dzAAAAAAg9y4e+NIT11FNPuQ18OVfj0o6FkydPlhNOOEFat25d43ztOwIefvjh1f/ftWuXfP3112bYsmWLQ8jL9jq2adp59e6775aDDjpIWrZsGdT3DSC8dJ9OHjvCDEC00eo177//vkMlpTVr1lRXwtEQmAZG7B/vTAMU9hUq58+fLxdddJHHyi8jR470WKGoptBOUZFjNSdfFJe4VsbyOhgUYGUjDYt88sknQZn/qp8/9Pk5gSw3VVhY6NVyC/Y6c37dml67QYN6kl9QFNJAXzTq2bOnCXFp8OWXX34x+6sGuzQU5elHFF0nb775pgnY6L+BBmRq4q4aUqDBSOdjlq73Lanp0rvb3+9j9YYtETlm2WhQ6NJLL5VwKs7/J5R559VTpWXz8FS6c1dRy59Kcvo566WXXjLLTtfFnG8/lf05+ZJXUCiVlVXSvGkjGT5mnBx51FEybNiw6udpGNX59bp06RJQ2FRDa+eff74Jftnr2LGjvPvuu+bfUCgtLXU5XjZrEvxzFAAAAAAAVukAbPtNFQAAAKHDTZcAALAOvkcBAOuyfOjrX//6l5SVlTkEr5R9CEw7Dt55552mkkKgtJPg5ZdfbgYNfmkn3b1797oNftk6xT7xxBPy6KOPBvzaAAB4qtSiVSwrKiocAg+20Jdz+EHDEs60E75WDLKFdDR0oh3Y69ata85lWvHSnrsQhrOmTV1DC1pB019a/cdZ8+bNJdi03RoOsQ+dRLr6lwagAukEodXe7Gll03CsM+cKPjWtsyaNGjiEvpxf1+o/SOg6OfHEE81g2+a0ktXixYtl3rx5snHjRpfnbNu2TS677DKZOXOm2VdDwV1AZvfu3UE/Zq1at6U69LVi7aaIHLNs9LPF+vXrJVLKy/9ZLqEW7EpueuOMU04+Wfq0cK1aN2DsqZJct57DtA0bNrg87sADD/T79TV0poGvnTt3urRLK3x17txZQsXdftGmZbOQvR5inbXPaYhnbNsAAAAAABhkWgEAocTNE2paOGx7ACyC390AIJ4kioX99ddfsmjRIofAla3Klo12hP3222+DEvhydtJJJ8n3338vZ511ltuO2LZ2/e9//5PU1NSgvz4AIF5VuQR1Bg0a5DDtjz/+cPt/TwEKDTmNGjWqelyDFMuXLzf///PPP031ytrm4cxdlUut0Omv1PQMl2ktWrSQYNPzt/N8NUhgHwILN31tf68l9HkaYqmtelMo1tmmTY7hnZrWWfOmjR3GtUqPc1gtnjRr1kzGjRsnN998s7mW/OGHH2TKlCkmLGUvJSVFPvvss5C1Q9dX48aNXfaHQLg7Zq1cv6X6/yvWbY7IMSseuavqFehx1ZeA5ooVK1ym+RLQcxf42rFjh8P0tm3bmgpfXbt29Wu+vry+s7atg3+OQozhe3gAAGISd0tFjRdzAVY2AgCrVGoDgGhm9RsJAjHJ07UHuysAgHMDAADWDn299dZbDuP21b20Q6xW2LrhhhtCVvlANWrUSO6//365/fbbHYJf9l+UaxWDGTNmhKwNAAA4BxqWLl0qJSUlphO6fWind+/epgO8O84d7efPn+82NNamTRvp27dvrQvdOdShVq9e7ffK2rw9zWFcz7kDBw6UUBg6dKjDeFFRkaxZs0Yiyd9l567d7tZNKNbZqlWrvF5nfXt2cRjX0I4txAORHj16mAq3jz/+uMvimDVrVkgXUb9+/RzGt27dGvRj1obNqVJaViZZ2bmyM31vRI5Z8WjLln/CdjWFP0Plm2++cRjXz21HHXWUz/PJyMiQCy64wFS/c173WuGre/fuEoll2b1zu5C/LgDEDnpvWBHBGCA2sK8CiDjCRQCAKEEYDAAAIHZx8xIAgJUli0Xl5OTIb7/95vKDpS14dffdd8sJJ5wQtvZceOGFpk0vvfSSS+Ux/f93330nd9xxBz+wAkCcc67So5wr0vjTMVLDDy+//HL1uAa+Fi9ebDrCe1vtxvlvCxYsMOFp/dfemDFjvGqThnvq1atn2mIzZ84cuffee90uh5pk5+bLupQdLiGY5s2bSyjosvjpp59cgjXOYbBw0mV33HHH+fy82bNnu0wbNmxYyNfZvn37ZMmSJV6vs0F9u8tXs/4O7dh89dVXcuihh/r0ulZ34oknyhtvvCHr1q2rnrZhw4aQvubgwYMd1qWtCptzBTBfOB+zSsvKzT6+PzcvYscsm86dO4d8mTpb9fOHEgnz5s1zmda/f/8QvVqVSyh048aNDtOOPvpoadKkiU9zzczMNIEv5zBi69atTeCrZ8+eEg7r1693GE9MTJAeXTqE5bUBIKaFuwMw2TMAcYjOEAACPo6E6iJKrwWdfuetpSEIIkLBsKoE8eG4EkocswDA6bDIgTG45xmWJwDEpyj5vAMACDrLVvrSwFd5eblD0Mv278SJE+Xss88Oe5uuvfZa0xncvj32nRGXLVsW9jYBAKKLVoh0VlhYGPB8DzroIGnQoIHDNK1241zxpqYAhVbC0aoo9hWetBP79u3bvZ6HvTp16sjo0aMdpmkIzTlM5Y3Zvy2RCqdw3NixYyVUxo0b5xJy+uijjyQ3N1ciRZdbVlaWT8/Rx2toy56GroYMGRLydfbxxx9XX6t5s84G9e0mTZ3CHt9//70JGMGRc4glL88xKBVs7sKOzgGXYByzVq7fIivXbYnYMSveaBXIuXPnOkzTzzCHHXZYyF9bPyc9/PDDLq99ySWX+HyM08CXfUVL1apVKxP46tWrl4SL8z7RvUtnaVC/XtheH7GFjtcAEAf43RUAAAAAAFg5HBS0+RJeAgCAUyQAwLKhL61e4u5OZNpB+/bbb49Im7Qdt912m1dtBgDEp2bNmrlMS01NDfh7z7p168rw4cMdpmm1G/uKN8nJyTJy5Mhaq+/YVFRUyH//+1+Xx/gSoJg6darLtMcff1yKioq8nsfefVny5Y+/O0zTU/+5554rodKpUyeZPHmywzStbKRVOyPVUbu4uFiefPJJn56jj3de1qeeeqrZXkK5zjSo9dprr7lcJ9W0zurVrStnnn6yw7SysjK59dZb/66Gx93KqjlX8GvZsqWEkh4XEhMdP1Y4V3Hzlbtj1qr1W2T1+q0RPWbFi9LSUlMZWZeZcxjPPkgXKlqtzvmGGCeddJIMGDDA63lkZ2ebasspKSkO01u0aCFvv/229O7dW8JFqyNq6NDeiIMGh+31AQCIFEKsQGygkgsAAAAAWF1V6L7X4Tfa6ORLxVwAAAAAUc+yoa81a9Y4jNuqax1xxBHSsWPHiLVLO0oOHDiwuj321q1bF7F2AQCiQ58+fVymzZs3LyjzHjNmjMu5cu/evQ7Veho3blzjPJzDET///LPDePfu3aV9+/Zet0nPy85ViTQQdNNNN7lUgXInJydHHnrmLSkpLXOYPnxwP9OWULryyitdwlFa8eqee+4xgQl/7Ny5U9746DvZkbbHr+d//vnn8umnn3r12E8++cQ83rmS15QpU0K+znTZOYfExo8fX+s6O+uMU6Rp06Yuofkbb7zRr2UerVXCHnvsMdm0aZNfz9X92vlGAv3795dQ0hCNc7WvhQsXBv2YtWVHumTl5EX0mBUrtIqVv9u3Blivv/56Wbp0qcvfbrjhBq/n4xwYq1ZV84+HGsh64oknHP7WunVrE/D0llZdvPjii2XDhg0ulQx1/loFLpx0WTofo0YPd62Qh3jEHUoBAABiFpdyQMwgiA4AcHNy8HTSYGEBkcLuF+Tl6X6Bcm0MwBo4aXhE5hcALMuyoS+tiuLuDpVHHXWURNqRRx7p9kPVjh07ItIeAED00A7pzoGamTNnmk7qeXn/hB38UVs1G2+q3QRjHvb0XP3www+bSpz25syZYzrsb9++3eNzV6xYIeecc45s3bHLYXqjhvVl2tnHSqh16dJFHnjgAZfpGro688wzTbjEmy9NNWDxv//9zwShjj76aPn+l7+k1IvwlD0Nn9mue+699155+umnPYagdLr+/b777nP52+WXXy5du3YN+Tpbv369w3QNcmlYrjbNmjY1gSjna7zvv/9eLrnqRlm5brPLc5zXgYbOdB1p1aDnnntOotFnn30mxx9/vFx00UUmnJeZmenV83Sbmz59+t+Vz+zoew0152tsDblo9TmrHbNihZ439HiiYUzdN7XSlDchLd2XtNqfPseZbpO1VVZzbsMFF1wg3377rTnO1UaDjtOmTZNHH33UYbpWdNMQmAa/vKGvdckll7jcBMQW+Ap1CNId+6qatrYMHhje4BmA8KFain/obGBN7A8AAE/KS0tkf/oWyd69TSrKHW/mBNhU0YkLgJXRGRNgvwIAAAgA35sAQHxJFgvSzszZ2dmmY4FzRa0DDjggom1z1wZbO/fs8a+qBwDAWk477TSHKifaEV87wWvYRSvSNGnSRBITE6WivFTKigvMYyYePkImHV5zZ3ytNKkdzfUc6c7BBx9ca9vatWtnQmlbtmwJWoBCq2BeddVV8uyzz7pUCtKQgVb70UFfW5fF7t27TfWzZcuWuXQO1VP+ZeecIG1aNpdwOOWUU2Tz5s3y2muvuVTv1ACVVhcdPXq0CRnosq9fv74J7+mwbds2Wb16tWzcuFHKygLr3NKmTRtTgeuDDz4wgZ+XX37ZhIU09NGvX7/q9a5Vb2bPnu02RDRo0CC57LLLIrDOEuT+++/3uhKrhot02b700ksO0zembJYHntksHdq2lKEDeknH9q2laeOGsikrWYpKyszyXrt2rSxfvrx6ees+Ec3++OMPM2hAr3fv3qa9vXr1MpW19Dig61rXq+6P8+fPd7tfjhgxQo477riQt/XEE0+Up556qjpwppXcfvvtN5k4caLf89T326RRA8krcKwKF+ljVqzQynvffPONGRo2bCgDBgwwxyINdur206hRIxPM021IA1K6/3r6PDJ8+HCXMJY3/vzzTzNoFcFhw4aZ19fqanm71pq/5+QVyL6sHFm+dpOk7spweb6GSx955BGf1tOMGTNMwNTdvG6//XYJ9OYd1113nc/PmzVrlsO4Hic1zAYAqB0/WAEAYD3lpcWyZelPUlL09w2uGjRuKT0OPFKSkutEumnwV5RUReHaEQAAIHoE69qMm0UB8PZYwU3IAACAlVmyp1lhYaHHv3Xq1EkizVOn5oKCvzvuAwDi29SpU+XLL7+UlJQUly8p0tPTzeAsO6f2Kir6BYcGcX744QeXvzVu3FiGDh3qVfu08727AIV2qNeAkz80QKTvz7nykoZzNDiiQ22SkxLlyvNPlkNGHBDWL35uvvlm6dChgwlEOIe3du3aJV988YWEwx133GFCXUuWLDHjWVlZ8tFHH3n1XA2GvfHGGyaYEc51pq+ngQ5fQ0nXX3+9CbI9/vjjLlWt0vdmmeEfn0us0/eo4UAdfNG3b18TxNKQaKhpIFX3f/uKRj/++GNAoS/dNw/o10MWLP07IBRNx6xY/Hykxwbb8cHXoJPua/Xq1fP79fW4sGjRIjN4S0NpGoD2tVKzht3c0bCrt1XzPNHgnK90v926davDtJNPPllEAquEBwAAAACxKicjtTrwpYrysyQ/a7c0a9tFIo+yK5EIg4W8I6/OP8DvgAEAABDq6qzRcSMBAAAAAIhGoe8BGgElJSUe/6Z3uY+0Bg0aeKxQBgCAnic0gDN27Nigf7HqqVrKyJEjva464qm6jlaKatq0qfjr6quvNgGVtm3b+vzczh3ayD3XnS+Hjx7i1eOrKitl18bFsu63z2Xjn99IXqZrkM7XoJ5W2dJqOIHQSmBjRxwgbVo08/m5devWlddff93ngIQGOt5++21TPSqc60wrV7355pty0kkniT8uvPBCs59osMlfrVq1kmjUunXrgJ6vYSmtQqfbpFa6CpcpU6Y4jM+dOzfgmxoMGdAzao9Z4RKpOxjqdvjggw+aqnrhXk6HHXaYfP311z4fz6KRVlpzrmA3ZIh35yoAAAAAsYe70NdOv5PzZhqiTCz0wY2SimOwvgQConGI4CiAeMS1lSVxzWw5VDwG4pibYzqfVwHAuixZ6aumKhVFRUURD355CqVpZ20AAJQGNTQQs2bNGlOZa+3ataZSSF5enqnY4qmSSW08hb48TXdHq4Vp2MK5Db7Mw5Pjjz/edPJ/77335Ntvv5V169Z57CyjbdBKP4cM7SWHjugvST5UM8pMSzGDqigqkx2rf5P+Y0+RpGTvK1050078GrJZvHixqbC1cOFC2bt3b63P08CUhlJ0+el737bYMSDgC73GefHFF802owGwVatWeQwFHXTQQSY4dfTRR0u415kGkk499VRTaSkQusw0GPL999/Lxx++L8uWr5CSUsdqa866d+9uAiX6+hq+iEa6/nTfnzdvntmeVqxYIfn5+V6F2CZNmiRnnnmmXxWJAjVhwgTp3Lmz7Ny504xr4Eu3CW2Pv4b07xnVx6xo9fzzz5uqa3/99ZesXr1atm/f7lIVz1N1rWHDhplKVMccc0xAn0/0uKA3tfj1119l6dKlkpubW+PjmzRpLOPHHylnn312wAHaaKHb3MyZMx2m6XEXsKFDMAAAQOyiUxUQlh0NiOnP/GUlhVKnXkPzfTxiHQekeME1HgAAQAwj2AoAsDBLhr5qCnXt3r1bevZ033k0XLQNvlQAAwDEL61Eo4M72bu3Seq6BT7Nr0uXLrJhw4aA2tS4cWMTSAkVrXZ1ySWXmGHfvn0muJSZmSlZWVkmJNSyZUtTgUaDCU2aNDGVukqK8nx6jfRNSx3GKysrJHv3VmnV2f+qUTYjRowwg9Kg3ubNmyU7O9sMGgDQUIUuQw3H6DVJKKpNaVhDB73m0OWXlpZmwoLNmjUzITMNqAWzApSv6yyY9Afz4447Tg4+sL9sXTVfNm1Pk8z9uZKbXyiFRcXSqdcQadGqjVneWl2sTZs2Xs/7gZsuksHjHatX+WP8IQfKtXf92+99X8M6ug537Nghu3btMgEwvZGBBnJ0W9L31K9fP/MeI0nX9QUXXCAPP/xw9TQNQAYS+mrXpqV89vK/HKYlJ9eTAYedGjXHrGikx/qOHdrLESMHSGlhntRp1FL2F1VJenq6CaNqIE9vRKHbkB4XtJKXBiL79OkjiT4EaGui+71WQdRBO7ls27bNhM/S0nbK5lULpKKiQurVrSstmjWWju1ay6QzLpX6DRsH/LrXXHONGaLBzz//LBkZGdXjetzV4xUAwAf0q0Mc08+pZcUFUrdBEzoKAwAsjos+WEtxfrZsWzmvOvTVfcgRUr9x80g3CwCAqO54z03SAAR8zOFmC3GDcwYAIB5ZMvSlHY+1c6d2ZnS+c9b69esjHvrSChjuLkJ86YQMAEA83G1Og0Ljx48Py2uVFNZchcYfPXr0MEOktG/f3gyWWWe1bPJ16iTLgN7dHKb1HX281GvYVGKZhnA0xKNDtNOA12uvvVZd5U7DVlqtzBaERPh2iB2rf5f8/babTWyXTv1GyoABR0ZkFehnMtvxsLKiXNZ0dK3y50u1xljx9ttvO4xfdtllNValjm3csTqouAudl4upynSk00qtdRsEHhoFgGiSv3+PuZ6rKC+VuvUaSfdh42L+cw0AAJH8Hp1PrQintA2LTOBL6b863mv4RFZCTOMoAjgrLS6QqspK870cFQ3jHN9nAwAQPQg/AoBlWa9n3f/TTrHuEt2//PKLRNqvv/7qMk2/BImFjrwAYDWWvPuHFd8TECMseUzx8eYLV1xxhcO0V155JWLtsQQ/tqnSony7wNffdm9aLtEgXvaRJUuWmMCjTadOneSMM86QaBO8H+PjY70ielSUl8mWpT/JpsU/yIY//ye7Ni6Om+MLAOvT41na+oUm8KVKSwokfdMyiWocghFAR1E9rwOw5s3J4uGmZYC7a7nC3H0O05zHgWBKIJCGMNOg1851f8qGBV/LxoXfmBuWVFZUsB4Qwo2Oa0oAHBMAt6dIvncBgLhi2dBX//79XTqz6Zess2bNkuzs7Ii1a/Pmzabzn7vOdc5tBgCEjnYo2bFmvqz77XPZvGS2lBQEv8oTgPCho3f00GBL165dHW54sHbt2oi2Kd4U5ma6TKuooCNlOL366qsO49dee63UrVs3rG0ArGx/+haHTnOZaSmm6hcAWEFZcYEJwtjLy9wVsfYAoVBRVipbls0xHUXX/T7TnMutgw6JAPsT4ObsENUd1qO5bUCYsTvUqiB7r+zfvbV6PHffTjPAguco9gdLIiAAwMo4xgEArMyyoa/hw4e7/YBaUlIizz//fIRaJfLUU09Vt8f5g7N9mwEAobV78zLJ2bvDdILXDqN6FzIAiJ6qObGrTp06cueddzpM+89//hOx9gCRqPJlX2F62LBhMnnyZIuvCI59CK/0TUtdpmXsIGAMwBoqK8oj3QSEAp8VHezbucF0FlVVVZWmamd5aXFsbXtR3XkfiDFB2p3osAwgdDjvu/vckpeZLoU5+0zlp1gT3UHM6LZ7ywqXabs2LIpIWwB4xnEO8YawDwAAsDrLhr4OO+yw6k639v/qh5oPPvhAfv89/J37P//8c5kzZ051O+w7BTdq1IjQFwCEUdauzQ7jxYU5VPuKUXxhiZo3kPAuH7bHv40fP94MNnrtvWDBguAs4zj7gd3T+7Xatmal9frEE09U/z8xMVHuueceAqEhoPtA5s6NpkLEzvULpaykKBQvg7AIzv4fcx3F4aVwnx+scz4CgGi2d9vqWr+rAwAgJlnsOzvA03cwmxfPkm0rf5HNS2fLznV/Wu77atRcndqZ3mQVAICoxb0rYVVcggNAXEkWi2rXrp2MGDFCFi1a5BCy0qGyslKuu+46eeutt2TIkCFhaY/e6f1f//qXS2c/W7smTpwodevWDUtbAADulZcVSz1pGjuLx8MPKFbqOA8gdmnQZdCgQdXj+/fvj2h7gHDIzMyUsWPHmkF17txZDjjgABZ+CGSlpciulCXm/1oloig3U3qPPNYSATuu5RCX6BwGAHGvvJQQP2A5fE0NwDJi//umYNqfvsXcTNMme+92adWlnzRs2iqi7QIQ4+j7AMDiCMkDAIBAWDb0pc4991wT+rKxD34VFBTIBRdcIA8++KCccMIJIW3HjBkz5PHHH5fy8vLqAJqz8847L6RtAAB4gx9tACBYOnXqJNdcc03QF2gCx2pEsVatWoVku4crW+DLprggR4rysuhcAiBAfCYEECKEW704BFvkGEzIBfGoKlqqnHt6PDsmooBuz1Y51yHu7d6ywmUZZGxfI90GHx73ywawkni+OVlpUb7sXL9QSvJzpFGLttKp/2hJSq4jlhC/qxUWEc/HpmAqKciV/Xu2SkJCorTo0FPq1m8U6SYBAIBaJIqFTZo0SXr37m3+b7vbt+0HAh0vKiqSW265RaZNmyZbt24N+uuvXr1azjrrLHnkkUdcAl/21ccOP/xwGThwYNBfHwAAAABiAp1gEQTFeVQUBBCo6PnBuLS4QNLW/yVbV/xs7qIOACFDB3RY49QJRJ3KygopKy6MdDMAICwqK8otsqS5uEE8Cmy79xTU9zXAH01Bjh2r50tB9l4pLy+RnIxUSXe6CR2sraK8THau+1PWz/9Stq2cxzV9rKjpEBIlhxdb/+VoCLZuWfaTZGxfK3u3rZatS+dIeWmJWAJ9DqJmOwMABJ+lQ1/qvvvuq/6/u+CX/v+PP/6Qk046SW666SaZN2+eVFZW+v16paWl8sMPP8iVV14pZ555pqxcubI63OXcDlWvXj256667/H49AAAARNE3dUCwsWkjhHy/a3qU4zvsGMWKQzBY63hWVVkp25b/LFnpmyU/a7e5s3D27m2RbhaAQPBju+VEU4dEANEnd1+arP/9C1m/4CvZtOhHKSspinSTECacHxC/Yu37Ha7lWAyIGWH+HUOv24rysxym7d8d/BvJhx7HOX/t3rTMrPOy0iLJy9wlO9bMD+qaQfyKlt9l9XeH8rJ/Ql6lJQWSs3d7RNsEf0XHNgUACI9ksbiRI0fK1KlT5b333nMIW9mCWLbgV1lZmXz33XdmaNGihYwYMcJU3xowYID06NFDGjdubIa6deua55eUlEh+fr7k5ubK5s2bZf369bJ27VpZtGiRmW57DXdhM/vX16BZ165dw7xUAABALHvsscfMgNrF84/sRXn7zZ2ZykuLpWXHXtKiQ0+xgnhep5YQJV9mA1G9P7CbAHa7SXzuEPnZe6SkKM9h2u7Ny6V5++4RaxMAAEDUq4qeAL9WBqioKDPj2ml479ZV0qn/qEg3DRH+TjPWIjEAAMSjCrsgBOKTBmLsFebuM2HAOvUaRKxNCFCCRT5sBolW+HK2d9saadW5r8SMOP3tCAAQ3ywf+lJ33HGHbNy4Uf7666/qkFdNoaysrCyZPXu2GZwlJyebx1VUVNTaGcVd2Ms2Tf89+eST5bzzzgviOwUAwHLfNwDwQ0V5makOUV5eUv1ldFKdetK0dSeWJ4Bq9jdGAYBIItTtqDA7w2UZ6Z1lAcDKEugKD8TvF9Uh7qwV7hsJ5O/fIxXlpS4dRwl9AQCiDl8PIyiCdK1FB35EeRiQ0FeU8HisoKMUYPD7PwBYVqLEgaSkJHnppZfkoIMOcqjwZWMf/rKv/uVu0Ipg5eXlHv9uPw/7edvmb5t2zDHHyEMPPRT2ZQEAAADry9mzvTrwZV8dArHIUyUgvrgGYBUczwAAgHUQZEZ84preXoXTd3IAAAAxKeDfoWLzGjHcNwwIP6u/PwCIH9Y/ZwEA4i70pRo1aiRvvPGGTJgwwaXCl7IPbtn+5s9Q07xsfzv33HPlP//5jwmjAQCiCHczA2AR+ft3u0wrKcyNSFsAAACsorKi3FRuKC0uCOnrEBgAAADgGgoxisIDiFcW+Y2VjrMAACAe2fejBgAA0SlZ4kiDBg3k+eefN+GvZ599VkpKStxW/Krpixx3Fbxqe6zt8c2aNZN77rlHTjjhhADfCQAAABxwA5u4kWCVX4/jFJ3YbdiOQ4PlCiB0NEC/ddlcKSstMuMdeh8orbv0Z5Ej+PiBHYgNfA8BBHF3sliV8xhtNgDEFY7VLAewP1gdxzkAAADAUuKm0pe9adOmyTfffCNHHnmkx+pentg/3pmnql9a0eu0006T77//nsAXACAM+AYP+Gd3iL79oby0OOR3i7Ty3SjjLTRk5XUZj+8z6FhuFj2esT8A0Shjx7rqwJfavXmFVJSXRbRNAAAA8P4zck5GquxY/bvsSlkiZcWFLDoAAPzCjbfghK+zgcjit8L/XwwcjOJJvPUZAQAg7ip92evSpYu8+OKLsn79ennzzTdl9uzZUlT0d8eN2oJf3l5EamWvE088US644ALzegCA6BZr1WP40gKILWUlRbJ95a9SlJ8ldeo2kC6DDpFGzdtGulmIUXyRCQBAeO1P3+IwXlVVKbkZqdKiQ09WBQDA/gTB0gCqd4fo2R9y96WZwJdNfma69Bl1nCQkxuX9URFS0bPdAwBghVNi+K8prX0uj6ZrdAAAAADei9vQl03//v3l3//+txQWFsovv/wiv//+uyxcuFDS0tJ8npcGxXr06CEHH3ywHHbYYTJ27FipU6dOSNoNAACA2LJ78zIT+FJaJSJ17QLpd/BJAd1sAHCHbSqOcPwAYBUx2tegsrIi0k2AFdH5BvGKa9s4F6MXA4gZe7ascBgvKcqT/P27pUmrjhFrE+INxzlYW6zdWBNA9CGMBCDe6e8NZUUFUrdhE37v9xPnEgCAlcV96MumYcOGctxxx5lB5eXlycaNG2Xnzp2yd+9e2b9/vxQXF0tpaan5e7169aR+/frSunVradu2rank1adPH2nQoEHEViYAAACiV/ae7Q7jZSWFUpyfLQ2atIhYmwDASkHDaGkHAPf4sc0H9AcFACAi+EwRoeu8quBUOff4+KoqKS7IcZmcl7krJKEvqrMDAAAAAHyRn7XbVKeuqCiTuvUaSfeh46Reo6YsRPiM77YAwLoIfXnQpEkTGT58uBkAAIgldKZEPIrVzhSVFWWRbgIQXh531djch+GqsqJCKivKJbluPRaPn7iWA2IHdzJHaDYsQsyIrKrKSklITGQ11L6kWEYAACDyEv75Pqk4b7+UFOZKo+ZtpU79hpFuGRB2fE/j38eXQH9jDf1vtH/Pv6y4UHIz0yQpuY40adXJ/ItIrhcAwaLXcWkb/jKBL1VaUiDpm5dJ9yFHsJBR28bDEgKAOELoCwAAGzpWIVrxQd3C6NAJP/H9Hcx2EF0bQubOjbJ78wqprCyXpq07S5eBh0hiUlKkmxW9omv1AdEpyo5zAGD1OwqnbVgk5aVF0qxtV+nYdyTXcgAABOlGLlF9g5cobhq8t3fbajOoxKRk6TF0vDRs1ppF6AFhCCAa9zPPjy8typctS3+SstIiM67hVq2Ck5jI7w/wAz/Px7Sovq72kx7jSosLXCpTh1Ws9Zez3mYAAECtuF0jAAAxi0+xADg+AIgO+mPErpQlJvClcvftlKxdKZFuFgAACAu+n4h1Wql1+6rfpLQ4XyorK2T/7q2yL3V9pJsFIG7OD0E6j1T52OE41jq1AYAHFeVlkrF9rcO1HddyAKxEP6PaAl+qIHuvFObsC82L8RUHgDCrqqxgmSOI+K4DAKyK0BcAAAAARCsL3q3MrTh5m1amVb6cpW9aFpG2AIhFnAhYHgAiKWfvjurwvs2erSsj1h4AiOlLxHj5LgeIVRYMfGrF1qqqSodpORmp4W2EVZYrx3DAbneoCu9+U8NsbJUM7WVsXxOc1wUAWEh8fR6nei0AxBdCXwAAABYtAw8A0YdjrVWVFRdEugkAAADwU2lRPssOAAAgRjkHvgAgKoQhDAawTSHox5yQioU2AgCAaEXoCwAAAJYVr2G+eH3f8YV1DPcKcjIkK22TlBTmsohiFvs38M/ewP4AAPEiwSrVIQBEENeOwcVxGQCAuMBvitbEeoVlWe9zXzT07Ujg8x8AAFEvOdINAAAAAAIWBV+EAUCk7d22RvZsXWn+n5CQKN0GHy5NWnWIdLPgEecuAADgdHVQWSmlxQVSt0FjQlAx2iEnGjrqALEWxg/5fsNuyYIDANeTD8sEiPnrzdAE1C1/EyqOf9zbALAEix+rA8H9WwDAsgh9AQAQq3cu4TMsAFie5X9cirP3GUqVlRWSsWNt9XhVVaXsS10XttAXnVsBhEP2nm2SuTNFEpOSpU23gdK4RbvILvgQfYTkmArEp8LcTNm+cp6Ul5VInXoNpfuQI6R+4+ZheW2uxwGE6OAS6RlY6/cAAFGvICdDCrMzpEGTltK4ZfswvjLfrQZvSbIsgWjDfgmAYwV8OGnEl3h7vwAQ5wh9AQAAIK5UlJdJYmKSJCQmRropQNx1Bo/VdseCorwsqawod5iWv39PxNoDAME+PxRk75XUtQuqxwtzMqTP6OOlbv1GQVvYnKcARFLa+r9M4EuVlRTKrpQl0vPAo1gpAOAlruWCjVCcXzx9tuE7Mcvbn75Fdq5fWD3erucQadttUETbBACB4fccAAAAAIgWlg99PfTQQ/L++++7TE9KSpLvvvtOunbtGpZ2/Pnnn3LRRRe5/dtjjz0mkydPDks7AABxgB8PYVlVAVfB0U50OXt3mOoQ7XsNk5Yde0lE0XcCQLDw+6tIgkUOqqxLwK09W1e5XNtlpaWYazqPu1NVlSRY5dgAi4nN7ZLO9KG9OUlxQbZL2BUAEAx8yAIQehk71jmM79u+Ttp0GcDN5xCT+D4F7lRWVEhB9h5z87nGLdpLUp26wd7wWPAAvDhWsJCAmlDVHACsy9LlDUpKSuTrr782X0g4D5MmTQpb4EuNGTNGhg4d6rYt/8fem0BJlld1/jeX2vd9732nuwu6oWkQ6VY2QYH5M6LI4AygiCAijgKOzaAwKqiIjDbDqs2MIwfwsAgCA9JN71tVde1VWVW57/ueGRkZy/uf36uKrMjMeBFv+S3393vfzzl1qjIq8r377ru//S5f+9rXtMkBAAAAAJDWXb+x3hYaH2gnzytSIT9PPeeepfm5GaPSAVCTtGxc4zAPAABAxfGhevDD5FD3pWEkLQMmAMBVxDoVAACsRdFczNY5nq1yAwCSkZ2dXPRzoZCj/Pycc4kgAADpRCRf6jj5KLWfeIQ6Tz9BLUd+TLlsxs25j2vPAwAAwGEwZgEAQJpwOujrhz/8IU1OTvoZfUt/BPX19fSe97xHuzyley6V59ixY3T+/Hnt8gAAAFgCzlcAcJreC0eWfSaqQwAAAAAmcO4wHACHQXsFAG0E6MCVjTnM8QAAAFQaHTA+ALdBlW07Qd8EwjI7PkTTY/0LP2czUzQx2KFHgUH76K4sITWDdg+cRcJ0e2q0j/qaj9JobzN5xZQlaLKsT8WZDQAAgDTifNBXpRLkd911F1133XXa5bnnnnto//79Ff/v+9//vnZ5AAAA2A025ABITnZ2CmoEALiBZZvxAADAEzhiAtgasAc41gIAuMNl/xrOYCCNdg8AiAmaMAAVGWg9sewzERhhNOkZ2isAQCLj/e3UfvxhGu5qop5zh6jn3LPQr4VgPQYAAMBlnA36yuVy9Oyzz1Y8+Hzd615Hpnjta1+7bGErfn700UeNyQQAAMAtsIgFAARmvQMAAIepQ+QbACkmYO6DOREAkpsa1hmqwDwGAJAWRLb0ZRnTZY0vGKeALir4HwAAgFNgTAW+HVxWQ7FYgE4AAE4z0LY4uHWsv43yuaymu2PPFcS0nEpzNqxXAQDAWZwN+jpy5AhlMpllnzc0NNCrX/1qMsUv/uIvLvq5FJR27tw5GhkZMSQVAACAi+CgDgAAADewyQtsAbYqT5PQJUgjnqXOTlhDglqmw9y2ASjBwVTRpQLgPMOdTdT0xHf8P0MdZ0yLo9ARikOnCtgBswAAcARrViCl0rShQQ5rSABSiaoztPm5mWWfZaZGKT2gUwUAAAC443TQ19IDdrEIPXjwIG3evNmYXDfddBPt2rVr2edCvkOHDhmRCQAAAADAWXCYDgAAAABgMZonc5g7AgAsAkGFAACbnO5mxoeor+Uo5fNZ/09/63GaHhvQIh8AALid2KcOa1kAgBNgjatL0bpuBIBuYNwAAAAAcBtng75aW1srfn7rrbeSaW677baKi9Xm5mYj8gAAAAAA2I6tBwF1yJgE4mKpzVsrNwBKQHsAAAR1D+gfAADA2r4aXTgAFdvJYPvJZZoZaD1xqdnIaTiomgxYzNcxDgBgBbaeKekCY2q6UNce7Gxnrtg/+jkAQCrBHA8AAIDDOBv01dLSEhhwZZogGdra2rTLAvgxOztLAwMDNDg4SJOTk1QsFk2LBADgCharAMhoSE5q0ZUDCZCeQ5m0PCcAAABFBPrdw4EYALlNDXM2AAAA8RCVvpYyOzkMdYIUgXkUcJ06cgO0Vaupc8UOLdsDUNxscH4EAAAAAAAAAESNriqho6OD6ios6K+77joyzbXXXltxkYqgr/QwPT1Nx48fpwsXLtD58+f9v7u6uvzPc7ncou82NDTQtm3b6IorrqAbb7yRDh48SC996Utpx44dxuQHADAH5xEAAAAASCOunOkjCBIAc80Piykg3ahSUgkIYxeQACphAwAwvgAAAAAApBnXNksAAErAPqQSKvlZAwAAAIAXTgZ9icpImUzGn4yIYKryScnGjRvJNJs2bVr0c0lOUdUJuEk2m6XDhw/T008/7f85ffo0FQqFUL8rvieqfok/4hr//M//7H/+ghe8gN70pjfRG9/4Rlq1apXiJwAAAABAKLDJCDQBp3SwFDjJAsAEHIylyqcDB6EAAPRBAABgwZwS+3VAK5YubgAAAADLUHYmgqEcACtRUhUQ/QGwEJxbAQCAuzgZ9DUzMxM64MoEQTJUkxvYGej12GOP0Q9/+EN66KGHaHZ2Vur1jx496v/5n//zf9Jv/dZv0dve9ja/KhgAwNBiHwDAEgTGAAAAAAC4v3YLnPNZuvTDHBZUtIslyc0AADxBHw5SSdI5l6VzttQ8D1iOY3MyjF1ABlirOAbGMmDCjmB3moCiAWAHmiWIbTswHgAASBNOBn1VC65Zu3YtmWbNmjUVP0fQlzv8/d//PT3wwANa3unw8DD9xV/8BX3729+mv/mbv6Frr71W+T0BAAAAAACQ60SCDUmQQmD2APAj6IAM7RXENSnXjMdLyXMCEALYPQAAgCggGSEA3Ki8hklbW03b84IgPOZrI9gp9AaAI0m20J0BAAAAIAH15CCNjcGxbJlMhjhUgKpEsVjULgtQw5EjR7QH8Z09e5Z++Zd/mf793/9d630BsBLnN7Bdf770AUeicFoCAACQdgcBtzJtAwAASOkaj8WYCtI3x4LdAQBc2BNFXwYAAAAAIA3sTwAAAOAKxigAAAApxMmgr9WrVwf+3+joKJlmZGQkstwAhK1y93u/93v03e9+FwoDAAAAbEBV5ij4uLgP3jEAAAAAQLrAQTZQhNaMxgAAYGCTAwm1QPqS4oC0gX4OAKAe9etGPn0ZFzkAAACAOGCvFwAAXCW4JJbFrFu3jlasWEH5fH7ZgWVnZyft37/fmGwlGYLkBuli27Zt9KIXvYhuu+02uuGGG2jfvn20Y8cOWrNmDeVyORofH6ehoSG/ctgzzzxDjz32GBUKharXFP//x3/8x/51XvKSl2h7FgBsInjDEBt4AAAAmJEWR5GUPGYJONYCAIAcp44Fh8rA8dKzc6BKy/gP3AEmC2SA4DMAgCPwcVgGABgHazvAHpyZAwvmRJhaAQDQjwCgdqwFAABgBU4GfQn27t1bMbjqxIkT9NKXvpRMcvLkyWUOKsLpb8+ePcZkAvoQgV2/9Eu/RK95zWvolltuCXT4FIGLa9eu9W354MGD9M53vpO6urroS1/6En3jG9+omilOBIz94R/+If3bv/0bbdmyReHTAAAAB7CIBdXMA/YBACvQJgEAAACQJpxbjjj3QHbMnxEMZRdY8wBgsPlhnNKlZySzAQAAAEDZ5ADKsAjMmQEALoM+DgAAgMvUk6McOHCg4iD+xBNPGJFnqQyVNsOFzMBNGhoa6FWvehV95StfoQcffJD+63/9r/S85z0v8qGIsJGPf/zj9OUvf9mvElaN4eFh+uu//uuEkgPgKNh3BAAAAABwDtuczpB9DQAAUkwMhyiMGwAwA/uLANRsEImdrWQ5EMMROa7i5OgfAAA4gi4OAH5EnbNZdh6gHfRzIG3EWffV/J1L/4/2BCzCtvNyAAAA4XE26Ou2225bNpiJw4UjR45QX1+fMbkOHz5MPT09FQ87RNUn4BYrV66kX/3VX6Uf/ehHdP/999NLXvISKROrl73sZfTP//zPNat4fec736G2trbE9wMA8CTo0ByZS0AqibnR5m57cfW50kewcy/eMQCu4u7YVJm0PS/gDewRAGCs/8H8HgCQYpybg7n2PEAOsAsAAEe86GMzHIlTZAcpXac6NzcFwDHQRkGax6hlQA0AAJAqnA36ev7zn19xsif+/Q//8A+GpCK/QlMQd9xxh1ZZgNrKXm9605voxz/+sV+ZS0UVt6uvvpq+8IUvUGNjY+B3CoUCff3rX5d+bwAAAADIoY6QZQcAATZmZXQo6E8AAEDVATAOkkEC60mJ8tLynPrnXZgng9SQsm4EAAAAP7DuSwj2JgFILYHrVln7clgsAAAch0U/h7kcAAAAwB5ng77uvvtuWrNmzaJMM6VqXyIIprm5WbtMjz/+OD388MMLcpRnwBEVm5ZWJwP28jd/8zf0iU98gvbs2aP0PgcPHvQriVXj+9//vlIZAHAsXRUAAKRvAw8AAAAAAFwCczNgHs7OppxlA8nAu5VH+tb5aXteEBeOVUE4ymQFmA8AAAAAQMUUA2sLAIAi0L8AAAAAwAWcDfpatWoVvfzlL184rCw/tMzlcvSHf/iHNDc3p02e4eFhuu+++5Z9Xgr+etWrXkX19c6+jtSxefNmbfd63/veRytWrAj8/8HBQWppadEmDwAAAMCJtG7gwWEPAAAAAE44jsKhFICy9gBlAAAAALHmlADoBHYITAC7gy4BAABIGlKxprCVtPqFgKWkzQ7S9rwAAJBunI4yeutb37ro5/LqWufOnaP3v//9ND8/r1yOyclJeve7300DAwMLVb6WUqtaEwBBbN26le68886qCjp9+jQUCEAZ2KgBAHChjpBRGNQg4HCh+lhmoV3hEAVIwULbB8iuD6SANR7gSlpsMy3PqZQgFaZItfZV3EnRywEg9jiAdgLA5daA9sAGJB0B4HJzQN8EbAB7DvHUhvYt2Qwxl+ODvHeBdnIZ+K0AAAAA/HE66OvFL34x3XLLLYsODEuBX+Lvxx57jN7+9rdTT0+PMhmampro137t1/ygm/KAr9K/xd933333gpwAxOFnfuZnqv5/Z2cnFAuAk2BjCfoEl1sD2kNqwKY6AMAV4Bh5SQ0YwwEjYI4AAACAdAr5HE2N9tF8ZhratRnM21MH1moA2IF9gfM8wJkSSBvVbD7w/8LukwX+esSNNuzLAQC4gukWP7BHAQAAIIU4HfQl+NCHPrRsU7o88Ou5556jN7zhDfQv//IvUu9bKBToC1/4Ar35zW+m1tbWwI23+vp6+uAHPyj13iB97Nq1q+r/j4+Pa5MFAJvBBj8AAACgeKzFBqybwLkEAGAMw94g6P+AkjkRqqUAANxhbnqczj/9PWo//jCde/p7NNJ93rRIwOE9hKD9fexFAACSgsAqAEDqQcADAAAAYAc4twIAAGdxPuhLVNF63etetxDoVaL855mZGfroRz9Kr371q+mBBx6giYmJ2PcbHBykv//7v6d7772XPvOZz1Aul1sUZLb0/qIKGKp8gaRs27at6v9nMhkoGQAAQDphkhUOziVAgVVBqYAXCOgDqkyrWKSBtpPUevRB6r1wxK8UAdJD+MQcbo2LmDsCAMp6BCjDNvDKFjHYcZryuezCz/0tx/W/E8AfrCetAsnzYuoHdg4cpw4RIXaCvglYAOYeshWKRWs8/UBvVts27D49pKyp4iwJAADSRSOlgI9//ON0/Phx6u3tXRR8VR74Jf7d2dlJf/VXf+UHa9122210++230/Of/3y69tpracOGDbRx40ZavXq1//3Z2VmampryA8TOnz9Px44doxMnTtCZM2f8Kl+le5Rfv/xn8ffNN9/sVyIDICnZ7OVD00qsWrUKSgYgTQt6158vImIMnhjooNnJYVq7cTtt2nWlaZGAw+DgAQC0HWAQZBt1kv7W4zTc1eT/e2Z8kOYz06ZFAg4DB00AAABALhODnYt+LhbzUDFwHjhdAcCbwKqA5GFrCeCMZ6FB4KwZROtZ5QC7AwBohkvOOQ7jLs5YAQAAAPakIuhr/fr19NnPfpZ+/dd/naanpysGfpUHZ4kAmiNHjvh/ltLY2Oh/RwR2VdvEX1pVrPwz8fOuXbvo/vvvp5UrVyp4YpA2+vv7q/6/CFgEAIC0Mth20s8qLBjpuUDZzFTAN7GLkWpUb6QFXB9BYkAGmakxGuk+568ztu67DkoFwHYYnO1wohTwVWJqpNeYLMBCOByWAiAJOJC7Dvor20CbBCBMQ9G89+pF/Q/sBwMAQnYvWFuClFPI56i+oXGRHxQANcEy1ywYu0KRy2ZoqOO0n2xu4479tGX3NarfDEgK+hYAqoIKvAAA4C6pCPoS3HTTTfT5z3+e3vWud1Emk1kW+CVYGvxViVwuV/U+lYK9yj8Xn23fvp0eeOAB2rt3r4QnA4Do1KlTVdVwxRVXQE0AuJi93TJxTSDG3VLAV4nB9qA+Ewq1GibtFxsoQDe5uVlqO/YQFfLz/s+TQ92048qbGb8IHm0VAAAA4A3Gy1S/Y7x+UG4OTNa6eoADKQBpJTgpUpr6QGArsxPD1N9yzHcY3rz7Ktqy+2rTIgFgBtumcqmaZycL9uo68yRNj/RRw4qVtO/Gu0yLBLS2By+d618ucgAtNtdx4hHKTI/5P0+N9gWbPcwilcjul5AQGMgHnRMAAKSJekoRd955J331q1+lnTt3LqvwJRCfLQ0Ai/pn6XVK1yp9fsMNN9A3vvENuuYaZIYAcigWi/Too4/WDHoEAIBUgk1ZoNvkuGyqwPbd30y+9PFob/NCwJegWMzTSNd5sg4mTcdmEHQKAA/QFi0fR1CdFRial+fns35GYfYEzk0xmbMb2zx0uYP2AABwHHRzi4Ih2o8/TDMTQzQ/N+0nnBvrazX5doAiUN0IpPWsa7jzLE2N9Po/53NZPwCsWMibFg+kDcw9JKsTCi3t4WemRhcCvkr0NT8nV+EAAAAAAABIIlVBX6Xgl29961t07733VqzwVR60VelPlO+Urlv6/Fd+5Vfoa1/7Gip8Aak89thjNDw8HPj/GzduRNAXAEvBPhYAKYJHg3emwiBgx0jPhWWf5fNZI7IAAAAA0sFUCTiO2DPubz1OTU98m849/T3faVgE8QMAADBE2VkhsBEuk0cucgDVTAx2UKGQW/TZYMdp+xSPPepLejD8HgBgyNI+rVgsLOv3ADBVoUtVQG7QmS6bSmRAGpnJkWWfiX7ONmCb9icNRL8jDwS2AgAAcJlGSiHbtm2jz3/+8/S9732PPvOZz1BPT8+ywK+gSXG1iXLQ79988830oQ99iF7ykpdIewYASjzwwANVlfHKV76S6utTF98JQKrBItYexFxhtOcCTQ730Mo162nX1beZFgkAAAAAwE5w6G41qg6mVa+NUNkNJCU7M0FDHWcWfp4a7aPGlauh2BQCBx0AAFDZyUK7QA2iOgSwEI19AuZ4FoL9JQBkNihoEzgDfHBcfRd6+ynYEdAC8hkBAICzpDLoq8TrX/96+oVf+AX65je/6VfgampqWvi/SkFgUTfu7rrrLnrb295Gr371q6XJDMDSKl9PPfVUVaX8h//wH6A0AEKCBbZ7cH+nIuCr98KRiz+M4ZDYaXibImANjAeAtAVfcJ+/AJDKdqO7WaIbcJ9L73io83LAV4mx/jb98gDAATemcgCAOHM0z9CcCP0O0AiCf9wG71eATtVGYLvADjRvlGFfDgArwZiWFMzlrARjFgAApIpUB30JVqxYQW95y1v8P2fOnKGHHnqIHn/8cf/f8/Pzka61du1aOnjwIN1zzz30ile8gg4cOKBMbgCy2Sz9+Z//eVVFCHt88YtfDGUB4OiqBw7B9rMQ8HUJZAatDjbqAChrD7aOZYFiW/o8wH6bNAjGNQAAcJ9a42NubpZceB7MAwAAIOUwqYoSeY3FQ2y+MHmvAOhDr83HSUAM5IE1TE0FAbBobuVKwjUAAGBJYCIUT0waNQtjBzhjBQAAkEZSH/RVzi233OL/ed/73kf5fJ6am5upvb2d+vv7aXh4mObm5vw/YgNu1apVfpDXzp07affu3XTNNdfQ1Vdfjc05oI1Pf/rT1NZWPfPvBz7wATLB0aNHE/1+d3e3NFkAAACkg9gHdNqcF+AcCQAAAACgaYoFgJvA8Tqd4L1bB5xOAOCHdsd+5+eozj8gABJBewEA8IdNECT39a8rwRfc9cwV6A0AO0BbRdU2AABwGAR9BSmmsZFuuukm/w8A3HjkkUfof//v/131O695zWvopS99KZlAVM5LggiovO6666TJA0DqnC8cf7xIQBepx/n2HkBan9tJ8CoBAACkmoQDYdRiD5hDgbQ5VAFQgkH/h8zxAIDkROzLHPDZBTZhZqwtFguUz2Zoxep1SF5r+F0AwBO0B6DQuhisM+Nhq9wApARr+5ZquPhMQAc4ZwAAgHSBoC8ALENUwfrQhz5UdYNk8+bNdN9992mVCwAAAAAuHmwAoAe0EQB7WdQgYBAARB9IFOtMb7vEQZ1DpKVLT8tzAgAirfHqLMiCPz3aT2P9bdSwYiVt33+jaXFSPEer/D3MiQBI3sd1nn6CCvl5WrVmA1118F6o1NG9HOytgrTiFYuUmR6j+voGWrVuk2lxgCLQx4E02/7EYAdlJkdp7aYdtHHHftMiMceL3I+gf7mMDXs4AAAAQNpB0BcAFjE9PU3vec97aHx8vOr3/uRP/oR27dqlTS4AnAFOSgAA3f0I+h0Q29bsNB44bAEAXGN8oJ3GBzpoxaq1tOPKW0yLYzF2jmsApG4exUCEtMHivQPgKDPjQ9R+4hHyvKL/89RQD61aD2dhZZj0H7N0DwWAJAgH1u6mZ/yAL0E2M0V9zUeh1Bo6kw66H2At/I23kM9Rx8lHaWZ80P95084r/ABXYABJ/Wf09a+aCaYrQSCuPEeaGWw/5f/x6T5Hu6653bRIQBGBQWnkoUA1AAAAABZA0BcAllAoFOgP/uAP6Pz581W/9+Y3v5le97rXaZMLAGAQbNSFUZL69wAAAABwoC5FQ64zcyBXniO9TAx2UdeZpxZ+nhkbEOkQjcoEkoJ2CcBSCrl5ymVnadXajVAOOzDmGMWZOWl6HQmHOk4vBHwJ5rMz/h9gcYAqGzGYCJIQZMOXrVDSSnZ20p/DlTM53K1XCJAqrKsO4UZXnWomh7oWAr4EE4OdtG7zTqMyAbdB8EW60T3HF3PxhYCvSwy0ntAqAwC8wWTuoho8++flAAAAQoOgLwAs4eMf/zg9/PDDVb/zvOc9jz760Y+Sab72ta8l+v3u7m76P//n/0iTB4BlWOyMEA7Xnw8AzmbvpbTfAapwxVEIAABcpL/l2KKfReZ0wA1JmYaD5nI4OwPxjcoK3Y31tVLPuUN+UITImH7geS8N+KYdzwMAAOVMjfZBIUywOXgQAK4UC3nTIrAFfQ4A5Q3CXm2IaoZLKQ8CA+mwAwBcBXM5AAAAAACwFAR9AWABf/d3f1czkGrXrl10//3308qVK8k0L3jBCxL9/tq1a6XJAgAAAHAAgTsAAJep0xz1gD4VcGF+btq0CM4DZzxgP561TljFQoF6zx9ZqIIjAluXZhgGEgkKeND03jG/AvKNykMFVCDHjhxaywIA3CVoLoU5ln1gHwJ6ACYbILQPAAjTVZjdQ9OK1mfC+hgAAADgTr1pAQAA1fnKV75Cn/3sZ6t+Z/PmzfSP//iPtHfvXqgTgBAEH7K4uAuQbnA4YydOvrfAR3LwWYFeLGgv85lpP8OmcBy2QV5gL3CkAQBI6EgAAJeYGumlYnFxhYjJ4R7ohxWOdVp1cC4BACQDa0IAQHpxbF4IALAK5+ZgOMdKDZF8Ehwz8/SBFwgAAAAA+0GlLwAY8y//8i/0yU9+sup31q1bR1/60pfouuuu0yYXAAAAAFIUsAbMYrlNDbSdXKgIsXL1etp7wwtNiwQAAIAjioY7a+dmtsoNnKJYyJkWAfjo6Q9Q7QYAAABQi3MO8QAAJ0DfBKobiJcsSUfk/a2g6j1MxlAmYjgsMADphUs/BwAAAADWoNIXAEz5t3/7N/roRz9a1UFpzZo19IUvfIFuv/12rbIBAHiAjXiQPjzt7Uieo7DmjTpsDKYHxtnwc9nMQsCXYH5umka6z1X+MmxWGdYGPFTDlUdy5TkAMEhpLierr8MaC8gmyDZdszUn5xspwzWbNKoztIfq+gFukvh1O2Yv6AfSg2OmCxSA/oAxaMAAgKDewe3+AXs4l6ljfMYKwEXc7o8SAdUAAABIIQj6AoAhP/nJT+jDH/4wFYvFwO+sXLmS7r//fnrRi16kVTYAnMDxQxZs1IF04tnp7Ol4fwTUsWA7jG1orK9l2WdTo31GZAEAAACAAeA4IRm+8z4AAACABdh/A9JNyuL5l82ymwI6AxcNAXoAIHL/CZWBNMz1YOhsCMrBY/E7sll2bli9hpMFzmUAAMBZEPQFADMeeeQR+sAHPkD5fD7wOytWrKDPfOYz9LKXvUyrbAC4DhZ/ADBB5kZMyGthIw0A+RTyOahVJ16aBAg80XECzEkBWNQioA6bwQFrCBXxtXHOsrlI0Jo0Te+hjpBh20pSZKMAAJAI9Jdug+EQzqW2gr4JXDQEFnqIuv5VVqWJhzoAAC6Ok5zHXWzLWUma9o4BAAAg6AsAVjz11FP0u7/7u5TLBTvINjQ00F//9V/TK17xCq2yAQCAlWCB6xbYr4igKigLAFDqEDz1VQvR5wAAjOOp7TMD+1KchAIgtQ0CM6ArAwAoonwNWTWwE8MIAEaBoyAAAAAl44ukSR6f8wcucgAAnPMnYiIGAAAAAHiDSl8AMOHQoUP0nve8h7LZbOB36uvr6ROf+AS99rWv1SobAAAAkFpkbfRx2TAEqYazAwefQztgA5xtGQBQhSptF+OAo6jKuOwCMYYyjH9gOZLaGKZWUsGYFs+uoDdgZO2naK7ivD2naU0eOSkEAAAA4A7Oz2kC8RyfS7vxfLrBvpzdsHl/EdegbOQGAAAAAAsaTQsAACA6evQo/dZv/RZlMpmqpck//vGP0xvf+EaoDICEOLMwDnwMR54vLCl7XMDcjhypgiP6ybnpcX/+sWrdJv9vwA27bAoAYP/YAAAAAACHcWWvTCdQ2SU1QBHABmCn8dQGvdkM+mf39/ABUArGABDNYKAvAIBGLE7MYIOM1gMdV620DgAAwGoQ9AWAYU6cOEHvete7aHZ2tur3PvKRj9Cb3/xmbXIBAIAL4JDOTvDezFIsFKjz9OM0NdLr/7xh21664taXUX19g2HJ0okzgcrAaJ8qbWuXvT1ylw8AwL1ZB81Dg+enagTE+A9ACg/OTc6zuPgBRJBjfm6GZseH/CQlazZsUSkVACAUWIsBYDdow6p0qG1tx+AV2uZcGvhuGOhSJziPA1aQsnYJgE+kOQQaif2Yf4fWzeUY6IwH0AMAAKQJBH0BYJAzZ87Qb/7mb9LU1FTV7334wx+mt73tbdrkAsB9sOhxCrv2HoCijZugA7qamz26ugPdDnwJbieCvUoBX6Wfp0f7aeP2fXJkA+YQdsi1ahuGZgkqNKhE9sFgtgF9AiCt2cjqn9DPAdhOvDYIgAKmxwao48SjVCzm/Z/3XPcC2n7gJugaAIuRtZ5F4DyQDRwJAQAApAFlcyhL9wowpwQgPWC+DwAAAACbqDctAABp5dy5c/SOd7yDJiYmqn7vAx/4AL3zne/UJhcAAAAA4m/0Rd4YZOhA3Hv+8LLP+s4fMSILqAI/0wEpgM3hh4S+Uxzc5uZmcYALAADAEEzGVOtlTAEM14ymGeo4sxDwJRhsO0XFYsGoTKkm0EZhu0CGHcgK4JdzGbb3A1bsj8CBHAAAgKtIG+OYr38xlgOt9oZFRaqwrX/J57I0MdhFMxND1smeCpjmAAYAAJAcVPoCwAAtLS1+wNf4+HjV7733ve+l97znPdrkAiA1uL7mdP35ogBdACbGYtNmVz43t+yz3HzGiCwAAAnY0/1oIzszSe0nHqH5uWlasXINXXn7y2nNhq2mxQIAxEH2HAt9JgAAsGd6rH/Rz4VCjjJTo7Ru0w5iAcYSANAAHQcOqMBZG1YyhmNiAJgT2BxguyDNXZjjD+744wEgE4yHy8nOTlHb0QcX/Ee27r2W9t14FwzPFOjTAQAgVaDSFwCaaWtro//yX/4LjYyMVP3eb/7mb9Lv/d7vaZMLAGAjWL1BDY4C0wYADQJYh87g1qBDlrAy9F447Ad8CcShRE/Ts2QEiwKCAeDeHlQfvgb2L8pu6yXOtDnSc4GGO5tofm5GmlTAsG0yHjfgAHFJD4zfkfMYUT3eNwCRqKuSahr95yKmxwao5fCP6dxT36PBjtPGxheM7ykCQxrQaW6eR8Nd56j16IPUffZpymWRcA4AULPngIoAAIvmEqlB57NWW7OXMdpzYVHC4NHeFprPXDxz5UCq7AMAAEDqQKUvADTS1dVFb3/722loaKjq9/7zf/7P9MEPflCbXACkjeAsflj8uQfeqVPEaaMeFxkDAhRssVH0j04g7C3cdq0JLGkLaW+nnjsOdOVkpseokM9RQ+MKYoEjepYBDmdA0rmSvLmWPQ1T9Gci0+bczIT/83BXE11zxytp5Zr1pkUDZaB/sxud6zhr1oxSSLJaSZOeAOAHxjV55Oez1HHiUSoW8/7PA60naOXqdbR511US7wJCE7jVi3EHLDUJgzYR0kGXC8JRuK/5Of/fIkWJqNh63YteS3VMniNd82+QKoz0U56d7U+CGMVCnvLzc9iPcwndbYhJcwBgKcPd55Z9JhLQ7bnuBXqVhTUZAACAFIJKXwBoore316/w1d/fX/V7b3nLW+i+++7DewEAAAA0orx6AzZmgWTYHH5VA5utTLDAVhjgeUXTIgAAWGB/nzk92rcQ8CUQWTfHB9qNygQAACB9IAgIyDEkOd/Xv4cS/35j/a0LAV8l+i5cDI4AtmP/WsN9PDtfO5c92JAxW70Xjiz6WaxfReAXYAYXuwLAQsb6WunMY9+kc09/j1qO/Dvlc1nTIgGQCmLtQ3AZ7iwfd3HGalD3FY2YRzIFAAAA8kHQFwAaGBgY8AO+enp6qn7vTW96E/3pn/4p3gkAFmRO7zl3iJoP/4j6mo9SsVAwLRKQgd37KAAsAQbtDHiVQIYZWX5YsAzHHgcAYAFsHIhrI6pBLPus7aQRWQAAIJ3wGxsAMAaaQ2RmJ4aXfcbOURjv1Tk4rmuU4uDj2v4O56bGTIuQYuy2HZAOdJ9vJOlTC7l534+kFAAxOzlMw51NxAnbx4z0gPcELMK1c2gAAACAKQj6AkAxIyMj9I53vIM6Ozurfu8Nb3gD/fmf/znV1SHaHgDuC87us0/RaG+zn3luuKuJ+luOEiewUVemC2wuWIp7m0KB7dK9RwWcYWxv6K/tNiIn3p8Dj+AkLtgWcMJWsMYCHGyTtR2iv1anBwd168TcEQAAFIEzQm5YPGZZLLpLaJnDw7cAAODKYOLZ3/+ND7Qvq3gz1HnGmDyA0rEvB2pj6V4U7A4AAAAA5SDoCwCFjI+P+wFfLS0tVb/32te+lj75yU9SfT2aJADcF8yiytfk8OKqfSM9F8gEcJIBwCIs3UgE/MFmL6hlC27OF4KeycVnBQAkImIfGNhnOtmXAlADmH0V3chXjptzNrfBO4MegE1Jl9DHxlQokAn0CZyHX/BFLDBmACAtMN3aNVMCuXPzGWKDrfoHdrcfEAuc9wOZIJkMAAC4CyJMAFDE9PQ0/cZv/AadO3eu6vde+cpX0qc+9SlqaGjAuwDAAor5nGkRAACR8BT/fzrARiND3WOzH0QyJPRlNoE+F4DyBpHS/iuljw0ASAlp7dsBcBo17drWtREcNAGwE7Rdg7o30d9jTnpJDXaOtYAZ0uzIs/4561wJgHUdhlXiQAr6EABUgLkcAACkCgR9AaCA2dlZete73kWnTp2q+r17772X/vZv/5YaGxvxHgCwBWwAAcATJpsZQQdkug/OcFCXHqIdIPFoJ8BegvsW9HFAkc2h3wLcbYXJHBSkBNgbcJ74fSrWwADYDdowAIbBPBO6BcBxMNcAACTvSKBDN2HyYnkcvwIAAACAOYg0AUAy8/Pz9N73vpeee+65qt+7+eab6cMf/jANDg4qfwciqGz37t3K7wOALTi/sev684HU4mbbdfGZ1IIAAHv0wVk2AAAAIApuzkMBACrmv8gVHU5PLoMxA4DK7V1L1YOgZEwB/U4dErzFVTSlBZl9uv7xLz3vKTrQDQAALO4W0S+mCrzvcmWYew/ADhv20rGXBQAAAAA7QNAXAJIRQVxPPfVUze+dPXuWXvva12rR/759++ihhx7Sci8ArAbrdPfApqWVGHGQUl6hCx0MAAAoJ1FXi36aJRFeCxysAVCLaGOzE8OUy87Qus27aMWqNYmulXona6xVwfKWAZ0At2wX/RwAwGFK6084fwJwmURrPMwbAGBJ2P1WWeMhl/1dLnIAANwjuL803+84s1+PPhwAAIDDIOgLAABACjG/YAYAuAr6F9VoyQzNCWxMgtC2Uu2/VAe2AqngvYAUwqY/CpRDr3yJnGUUHc72XThCIz0X/H83Nq6iq1/w87R6/ebqv8TlvRqEjW0rJi3P6apuxRoL7zAOsHsAlIPxxTDo59ICgtf0VxoEgDvOrQ/E87jizJ5GmNhjqD6dkZ1hDApDlPflpdLuXT8n5Y+tcgMAAABABfVKrgoAAAAAAACDTS02B1OebvmYPLfjFAt5mhjsotHeFsplM8QdNu2hEpxlsx0dutX9+hyxF9ZtEgDbUN2cUt5cxTynFPAlyOezNNJ93qhMAACLSXmfCgCoBDoGYCepS04F+O7tcJHDIux1/gYAACAPjAWAB2zmlBYBnQEAAEgjCPpiwv/4H//DtAgAAJAeGK6XvWKR+luPU/Oh/0ddZ560InjABnBoA7TbSlBwF8eOByTutztOPkadpx+nnnPPUsvhH9F8ZhpaBekFBxJAjWFBr4AFaZ/LjQ+0L/tstK/FiCxAH2m3+1hAZVbrb2Z8kHrOHaLB9lOUn8+aFcYmYPegmh1wWScyEcP0PtZYf5t/BiH6u0TX4vJejeJFny9Bb2yADQMAAFc8JsHXmOsA82BfTpOeMUcHLsKo6iQAAAC5OB309Zd/+ZdkA5/85Cfpq1/9qmkxAAAAGNzAEw4lQx1nKDM9RuMDHdR58jFnDpMzU2N+NRwAlNoa9w14izYMuWeo5fKuZ6dGaHqsf+Hn3HyGxvpatT0vNqFBREOK+h8BX+fR/lT0D2hT9lOHQwwgoW0H9iO6+78E91MxlxPrOrkX5D2epGlOC2zARVvh90zTYwPUduynNNrbTANtJ6nt2EPy+76UgX4OpNqOksx1FK1rRMKi7rNP+2cQbUcf8ivXAwA0rFMMd2PY7wIhjCToP6C8FBFqzlVtjqI4AYC1c0LgFBhT+RB4do6+IhkJ1qKc2kfa7ICT7gEAAKinkRzmgQceoL1799Kv//qvE1c+85nP0Fe+8hU4JwEAQMoZ7Di9LJggOztFq9ZuIFuZm5mg9mM/9QMh6usb6MDzfoY2bt9nWiwA0rG55EV3cJsc6qIVq9ambiMsLsOdTRX78l3X3G5EHgCCGr5o06pDOXX3G+inQEW7wMFGyjEzf4HdgRBWkhJ74yybfJS8i3SpkB1DnWfI8y4Hec3NjNP0+ABt2LqHbEZUo54a6fXX+hu27aW6eqfzQAKb8dIwVppDVC8UVb7K19RDHadp084DRuWygrTYVEoeUyXYqwKANzr2yIFDMBwXC/kcDbad9BMIr9u8k3Ze+Tys7wBwCiSBFf3czNiAH3y2fssuqm9odOKZpkf7qb6hgdZtFs/UYFokAAAAlmP/6FiDT3ziE7Rr1y569atfTdz4X//rf9HnP/9502IAyezfv5/OnTsHvQLAGFsOX+bnpq0O+upvOeYHfAmKxQL1njt00cFEZxWGtBzKpgUVfnWSbcSW/qWcqZE+6jjxiHLZszOTviNdfn6ONu++mjbvulLp/QADIrQvG9tOGnHhPdngBOiCntP0vkA6bIR7uxSVlWcmhqhxxSo1sjrsGTU/N0P5bIZWb9jiJysBIBm8+woucBzfhQPGUiYHu6oHffF7jEVkpkb9aj6FQs7/edPOK+iK5/2MabEAMATzBquYmYnBZZ8Jh2GgF+5rCmDf/AWoAe8aAINw72oNyicqtk4Od/v/nhkf9PcC91z3Arv1eQlRZRsJSiopJsILtORdGwU6Yk0hN09txx5aWKeu3bidrjp4LzU0riCbk6+0HX2Q5mYn/J9FwO5Vt9+rJfCrzuUDHQAASDnOB30Vi0X64Ac/SNu2baM777yTuPDlL3+Z/u7v/s53fMfGEQAA2IPWgKVaMD9kEpmEyxEBYNmZCVq9frMxmQAw1p485hnNFQsoDh/ERl0pEHRqtM/PzmRz9T84BQNgEOZzoPC48hwAOJTJ0rOvueayGf/wMJuZMi2KdQy0naTB9lP+v1et3UhXH/w5WrF6rZ6bM7YpwIwU2Uqidakz80N5jPY0LwR8CSYGOyl79e3SE0whiAHwImpfwGivn6HDF86vqykn7tsAzr1AMQepem4IY2ELXs0lNUARAA2Ke7sQlWJKAV8lhruaagd9MWduepy6zjxF2dlJv6rP/lte4ie0AiCV8Op2tCP6uPLEJLOTwzQ91k+bdhywVmkTgx0LAV+lgN2Z8QE/UbpceOsBAACAXOopBc752WyW3vve91Jraytx4J/+6Z/oU5/6lGkxAAAgvTjuCML5MFZsSgJQE042zEkWRYgNJtVMDHUtBHyVGGg9QTajLetcoAnGsM0U2DOogP/aA969ayaRxMbRPgBwFNUdnfmOdLS3GQFfMYPlSgFfAuFgMtJznnibiHl7s1E0ALQ7+jGZV472tSz/rLdZnwA81AAMt5fEDrOW2hE3R2GQTqKdE8FmL2rBc+99M5mX6Ap4v/DsD6n16IM0PTZgWhz7cc10XHuetPWrzN+fOANtOfLv1HL4xzQ51CX9+vkl56uu+JJ0nnqc5mbGyfOKfrLQ/uajpkUCthCYuE2FbfNoL67T3fTMss/6zh8hm+m9sFz+vgvPGZEFAACAOzgf9FUK/JqYmKDf/M3fpOHhYaOyfOMb36C/+Iu/WJALAAAAH5JscCnZHHNw/8ArFkyLAFyFS3sJ7AvSfeg6PdK37DOxkW8zdXWpWErpIx1NwX60HqQoQpGsYi442H6amg/9P/+wMjvrTsUdEbTP5SAYgCDU2yjfNlAeuGQLHJwpx/vbln021HnWiCxsQd9fTTkOtgvT9wc6EM50AHDEfB8IgAbbhJkDZtX8XGJqpJd6zh/yzxxEkruOE4/6iT5sxysW/Uo4Lu0zAqYw2fdnMyeM8Nx+tapTT/hVaWanRmhu5nJVF3m418/PZ6aXJbEaq7BXB0BaYNP/xUa+/MW4vmWMVYkk6QAAAJLSSClBBFj19vbSu971Lvq///f/0rp167TL8J3vfIf+5E/+xHdEEfKU/gYAAGDPgrlqtjz06SH0B+cSkM7NrtQ7yzs45dVW6QsAS/oZ0/evtYk+0n2e5jNTtGH7PiVjjLj+QNvFCoaZ6THKTI3RDXf/Urw1PxNVFnLz1HnmCZoZHaDGVWto/8130/otu0yLBYBiPOv6OBCPYiEP1QHpqOgrbF4HBxJDT0K3+fk5WrFqjRKR0oJwHJZ/UQdtFNi7NxS4dc/fTouFgr9+RPBGBJ0VCzTac8EP7Ni4fR+t27xT4RsCgG//c3G+yKCfS3BGmlRP/S3HFv1cLOb9Cqe7rr4tjjTEgXwu6yeWEkFsgh1X3EK7rz0o/0Y8HhcAS1jeYIY6z7i5blcMAh8AsAdxVthz7lmaGR+iNRu3mhbHapQkSa80BMF3EQAAnCUVQV/lwVVNTU30u7/7u/TFL36RGhv1Pf4PfvAD+uM//uNFAV8AAABA2oidjSUmGG9BWsEBg3rq6htID/LWDbCLtOI5Nx5GfZ7OU4/R9NiA0myRfc3PLfp5fm7ad8qwOUhKBLFNj/b7/85lZ6nr9BN000v/g96gV8dsN+3EG4eY2wBz8WQR1/mY8/ijby4XRAzd8FUnAHII6DPEPK7r9JOUz83RmvVb2GjbxvWVEucSkBo4j+u267W/+agfnCCqyhcKOdMiWYNYo04O9/j/Huk6R1fc+jLauGO/lnvbOAZUI3XtO9bj2qkjYasO5mNbRqXKOmJPK17QFw8mBjoWAr5KgSVb9lxDq9ZuMCqXvdjZhlM3jgReJ+BzBh3cxGAX8YWv3dc3mN6Xi4Huyndpm58px1J9MrCD3vOHaWKoa6G6apqe3XZ/OQAAAO6RmvT05cFWTz31FN13333a7v2Tn/yEPvShD1GxWFyU5bv833fffbc2eQAAIPV4bhzyBd+T7+IXziUglJ3ItOGEm0HSZAmQwzUHgTRhvNIXTAdIMSPPecPLzkwuBHzpPjyYmx6vcU9izUjPhWUZhkPpEoA4qGoPzNtZEDjUV0+9pqCvtMz30/KcQL+9iP6w+8xTfsBXqaIqdzi3B88r6rybxnsB64nq4MudCGJPDnXRcPc53/lLWcBX7CzfgaWLyDSiulcp4KvU9w60Xqy+LRXzjwq4VyNlYCR+kg7zYlgIb6X1Xjiy7LPB9pPa7s/BtoEDwIyYYv7FiGQH2qpTAwdx0O+CwRoriPHBDmIJX5Ux2ZcDAADgIk4Hff3Kr/zKoipf5YFf3/3ud+nTn/60chkeeeQR+v3f/33K5/PL5Cj9+4UvfCF97nOfUy4LAACAeIi+WmRTGx/ooEIuCzUmABt1wNXNLpA+gg4kQFzQvoEaeynPiAuSk8vOQI3AaQKDrTAPlaxoMo7xAH5gNzptuFr/wyCruWpEEH1uPlPze1Y79mgE+3JAv9Hpvp19c7m+C4urRoNwzFRISDI3u7zSDy/42iEAOipEFwuobJCU3Nxs4msAAGqDZEzmQBWccjB31ALjtSKIi5eqpHMAAADSRSM5zJ/+6Z/S4OAgPfzwwwvBXuWBX1/60pdo9+7d9Na3vlXJ/Z988kn63d/9XcrlcoEBXwcPHqQvfOELtHr1aiUyAAAAqES0jMI9Tc/QWH9bjS+6uxi127nEPR2CIBJW9FK8oQcHNPfARh2wBX8NSikm1Q9v/1wO4yfgAmzRPeosPHSFHTpOnDVpCrY8iqqq3qTU8QcOdEAZ1rYX83KHCWwFy6lrsG8uB1zHfH/CVazs7CR1nnqC5mbGae2GbabFsZpiIe+mkQAH9iEUn7EymWtWkuOi/6Ha+5Z8DNOAVxQBwiuII9iXA66TqK/h0U2zor6+Ucs+HPomAABIF06nNK2vr6fPfOYzdOutty4KthKUAr/+/M//nB588EHp9z506BD9zu/8Ds3PzwcGfN1yyy305S9/mdatWyf9/gAAAOQwn5muHfAFQgPnEpDe6hDaBdF8w/ShqzpEzcOsKK8aZgEU2SOXQ9ckqHmGuNfkq0/M5QA/PKMHXjhQs5d6XY7CDo+di3DteYBaApfNlf4jPc5tOvA8fQH8kfs59CMgBXM258Z/g9Q3GM5ri3cJmMGln6tE95mn/YAvwezUSMC3GMnPSBQ9QV/p6OcwBtfUkJb34KY+sGaVCc4fYpohcBbOczzAw5cEAABAunB+dBEVtL74xS/SgQMHFgV7lf5dKBToD/7gD+j48ePS7nns2DF697vfTZlMJjDg6/rrr6d//Md/pA0bNki7LwAAAPmM9jaH/CajxbZkUXLZjLSqDjqdS4DFeCmShdOzKsW9Q4+6OrNLKdc2eXHoakzxZCVeuvsf85k2Nd3LVvsEcgmdUFixvcAcnVOcjZW+AFCGOLfg2lw5T+UsnKvortoKAJvgQe3N1b7+gdM7H+w4Tc2HfkSdpx73k/JFCfqSv46Udz2scfmgdV/VwvmCTAr5XJVAL8A66AsAKaSgD2S8ZrVx7uEV5J4/2KgDkABPvi3osiHYagWdWD6GmDx/SFOFSAAASBvOB30Jtm7dSl/60pdo8+bNFQO/5ubm/CCt9vb2xPc6ffo0vetd76LZ2dnAgK+rr76avvKVryzIAwAAgEslHi92kJKRRbjiW+ZzWWp97ifU9OR36Ozj36LJoW6rHIUv3lDv7YDNm0GI7gJysjNhUxaEMyBstqqgUvsLv7Gtf9Jg44GF9kyb9qkIONseuMunmLr0BPAjIAKkEu17WuHvVxfWg45TN83YqUxFH4c1MAiPyflESipvGq3MKIeR7vM00HqCMtOjNDHURe3HH674nurrKjvQYS4HlBFk9ly6Eab9WdoTQU6PDVDHyceo8/QTNDsxnPh6hUKOZIOxEHDCRnsMvWblBGM1F4uMg1sZ6y1tyOwr+J9L6MSu/szUuxP2Nz3a769dszOTgd+r1xX0ZeHYCQAAID6pCPoSXHXVVfT5z3/er/xVKfBrfHzcD9YaGYmfaaipqYne+c530tTUVGDAl6g4JgK+tm3bJuW5AAAAqMXKjTpJDLafopmJoYWDhK4zT1ExYXYl7Y7CAGgHTiTpoc5C5xJs+qUWxzZ8cQgTnpnxIeo9f9jPlC4C+hPrXkUf55h9AjsdNGQmDpF0R3Ibvs/Hes3qYNtybi5S5R1xyLBqZf/MQG+m+yQxn5ubHpfy/nivV4GbWNjvpLW/ZUJf83OLfs5mpmj20hlFmLG4qCAgIhXAZh1u92bl4nzGqvqdifmbCFydHO6micFOajv+08DqhWFBpS/AFs/UDRT1MVy7dIb96GhvM3U3PeP/LaNfTeqLEhq24/ZlcA6WNnSfP7hrr6rneP0tx/x5Xe+FI9R8+P/5Qf7cKn0BAABwl9QEfQkOHjxIn/rUp6j+Ulb+pYFfXV1d9Fu/9VuUyWQiX7ulpcUP+JqYmAgM+Nq7d68f8LVr1y6pzwUAAMBN5xLTh0QiM8nSzEpTIz2JrgnnEmAfqAAGIlqMLkdhSzZ5gcY5gfaAhEU3IevncoweQdYcUBw0tB17iEZ6LviZ0tuPPZx4Lqa9aiunFwOcC8CQ8n3gHFizAmW2FbN/Mb03pIIoz8TZUVg1ImC/9chPqPXoT+jCoR9ST9Mzie3B8xgHtoJUI6uvC5oTutiXpgkROGEqIAK2ky5UvG9dDrA6HW0j6cnkvlzc+bckYYY6zy6qdCb6JxEUERZt1SEAqEbi9UcK5mApTlQizhx6zh2isb5W/2/xM7tKX2mwQVCGBe/bAhFBMIV8bpEfnUjWNNzVVPG7dZf80wEAAACZpG50ecUrXkH33XffosVl+b/PnDlD73//+6kQIXtER0cHvf3tb6fR0dHAgK+dO3fSAw88QPv27ZP6PAAAAIBO8vNzVjkKc8ooA9SSioODlGHrOy0/yE070d6hne/bNLa2k8TAUTgUQ51nFvVJmenRhSquVlTASat9uwxeKbAA6c4lAKjENd8yjBOLGO9v8+dvJcb622huaiyZilHpCwCzYI2jHO1VcPBO7QTvDWhgfKB92WciMCIs9Q0rJEsEgA3nDDzkiOJfkPbzh6U/J05UwnrNqtk+2bTLdMCnHwScmR7tX+YLMjXSW/G79Q0mA/jTOzYBAIDrpC7oS/DWt76V3vWudy0LzCpV/nr88cfpv//3/x7qWt3d3X7A19DQUGDA17Zt2/yAryuvvFLhUwEAAFDiKBw6Cx2fTQDVgU6jvS107qnv+X+iZKXjv1EH+CAxK2/YX5HVbDz+fYQJQvelVuHpCYiQaDvYsAYSjIi5EivIFzqhsIJn88wfPixlcqgr0TW9CAlyQl/TtKIAa9jYB/v+D8hCf0VDkJY+i8WaKKAvE3Kz6W+XElptTOVPQF/z0WWfDXacSnRNNftywbrHGhQAR2E8Ny4UcqZF4APj9wQUEFj8WsV+F2xLB/UNjVruA4AVcO52GCz1o8Jh/R80PukK4OegA2ALmmyFg0ly68846CQBdZqqtqI/AwCAdJHKoC/BH/zBH9DrX//6wMCvb3/72/R3f/d3Va/R39/vB3z19fUFBnxt2rSJ/uEf/oGuvfZaDU8FAABA/mKI28rW7Cp3dnKEes49S/Nz0/6fnnOHaHZimG8FHBw+ASZgs0WqMlkDR2HADS1OvKzbJeO5nIXzFK2VvuAobC8W2naJoP5S91yOm5N87KzFGh6jkJunzNSotECGouTgVm7vUhVpec4FtD5vynRr01zOAIV8smAGVKYGzsMlGZMj4yLr/UzTjsKl+0cJ6mZsF6zfNXsd1dAdVAtigqAvyTDug22hWiIT58aRIHuBHZWpQo2OtJ+xOma6IAZo1/ai4d2J9eXkcA9NDHZd2pMLf896TUFfAAAA0kWq08N84hOf8Ct0Pf300wvBXuWBX5/73Odoz5499OY3v3nZ74rfEwFfotJXUMDXhg0b/ICvm266SfuzAQAAIDkbjyarQzBkfKB92WeD7afoqoP3snQUBoCN86LOTJtpdLqUzNz0uB/QumbDVlqzcSvLiobOjTuOPY4JjNuE7f2O7fJrGh8Q2ArYtsF0NGFnkDVXHe9vp+6mZ/wghpWr1tFVz/85WrV2A81npmlmfJBWrd1IazdtDxKi8se61qwWjDtYU7in76qVyHQGV0V4VhYV0jiR0E607stZ0M8BTn2bWsda4+tlkIwIY4GuoK8F0Nc5BvoK6clSDLYRln2/Z0/QF9aDQJIlKf06j/UiBxl4IfqP+dkpali5ihpXrIr0u7LncjLHAs79IssxzwCc31Et8A75IfqjtmM/pdnJi0nQV6/bRNv23cCu0lflm5u7NQAAALWkOuirsbGR7r//fnrrW99KFy5cWAj2EpT+/bGPfYx27txJ99xzz8LvjY6O0jve8Q5qb28PDPhat24dffGLX6Rbb73V0NMBAACQgVanF0uZGu1zwlEYGyk24LF//7Aj+xDVHAr5eVqxao3/88RgJ3WdfnLhXe69/k7atv8GdkFfAJg4AOHSx0WRm8fBrzsocRT2eNsbkInH/5KBWWol3wdI6Y96zh9aqFozn53xE5Js2XMNdZx4ZKG/2nX17bTzqudFui5XbHZcAMzfn8dl3OUgg9soWa9We22c7R5o7+P0rM0izuVUJWOqEhCBFeriPbmR7nN+8qV1W3b587ikaA/6AoANGHPlqZKHLusbG1P1vABwIsr6GOcPixHnrR0nHqXZqRHfz2bXtQdpxxU3h9c9zliBKXAuoEZ/FjM92r8Q8CWYm5mgiaGuxJW+yn3MpeCe6gEAAFShnlLO+vXr6Utf+hLt2rXL/3lp4Fc+n6cPfOADdPLkSf+ziYkJeuc730nNzc2BAV9r1qzxq4S94AUvMPZcAAAAJC16wi62OC2kGC+oVTjQ8XBMAtY6w2m7F+w0usrU60xUhzj7+Leo6cnvUOvRB6mQm6ehjjOL+hX/50qyeHr6OfRx4KIhBHqohTAi34vXGFoP6So+J1zqpKoYh64gtQRVhUKwtylmxgaWOfWKytRDnWcWzcfEXE44FJtLVJKWdUBantOAGtKuWoMB/JwD9YRsUfq2y7+HcQuANMLVGbnn3LPU33qcxgc7/H+LeVxSEPQVE75DngM4qFwl+Vwc1FPC56yvrxz0hX0I9boHcRSsVr+pf38c53IB73y4+5wf8OV/hTzqbzlGuWym0gUq/n6xqCmA34IugfOehHFc1E3AM+m0AyREX0zvhSPLdDQ91h9FoRXBXA4AAEASUh/0JRABXyLwa8OGDRUDvzKZDP32b/82nT592g/4ampqCgz4WrVqFf393/893XXXXYleDAAAAAMkWi87uLGgAM4LWGxiuAqPthm0ISecUAv5nHZ5gMg2l6PupmcWDhBmxgdpqPMsZabHFqknN5+h/PxcaJXBiQ44TdChQ5W+9sKzP6D5uRnZgoT/atj4fQUHJzwPo72yKofRxx/OVVuBo4Rum4qdSwLk6Gs5SqO9LUrvDaI59IoMnIu+V8wvysjJpdIXzzECqHx/LBzuY5jd5HCPAjHctn/VDjkiwLXpie/Q2ce/eWlNW2Ab9OX6uwYSUW4qmm3RRSdEyYj1qOjPyhloPZG4fynK3mdNkoRn2W/otQv0wSmijq9jOOywMvn5rF/lsNp7C1o/6KtOzdOmgNuw6TNUJBA2EQwSWLlInZ7FufdSRnsuhP79OMlNqsLEpAAA9jXgxL5tgQmEUZ0aAABAfBD0dYnrr7+e7r//flqxYkXFwK+RkRF685vfTGfOXF6gLA34Er/7t3/7t/Syl70swSsBAADAaXHIwiknAK6HOLqDvhCsBVgRsV3OzYxTx4lHFLRn+/oH3c82MdCxzOEtKKNwlLFAW0CEFa/Ys+8wD0gnm5mqeNCnC87zBFN2LwJcRZVD8UdkU48yP9PnWHIJC+e7gFefrmPNJNpR5Yy1DlHH0RaS9e9BsnmynUtqChJFR3zaFlABz/fbcfJRGutrJe6Y2CMzcU+RkKT7zNOUz8358zLxbsYCgo/r6xvIJJzmA8BeotpRULsM/Bx2WkuhpIqc9OQwF0GlL5CIaiZfq39J1f6F/iAClxjpPk9NT3ybLhz6ITU/+8PI+wmy+zmMhUApjnULaC/xEOvXsPAOhtBt0I41INPEnKeg3VsI8zmpfJ85z6qzcQAAAMlA0FcZojrXJz/5yYWflwZ+FYvFhUCvpQFfjY2N9KlPfYp+/ud/PuErAQAAwIs6ZwOwTKC7OgTei2OobGbK23Dw9WcmhvysjkAvooKXCqQHRMg0TYxVbhPLVvTMX0Z7m8kYoYM2Gc3lFLbVuekJ6m855h+eisBXUaFoYrDT8FwuyFlIwa2AUarNzfkfnlaXL0rGWsAb7cGtANRAT/9Y/R4DbSd5ZCkHfkKFpTbRe+FIRc3U1ek5/uM/hgMgqGynwx1n9Qbvo7lcJkKCpSgOawj6gr2Faooa5xzG5zem729STo+fbgr5eeq78NzC/GludoKGO89Gsh3pFQ0B4ISBylQBNwz9TaOO9Qb7+GjrwAiJNXUnYwKAPZbM5Qxg634U1qwAAACSgKCvJbzuda+jD37wg4s2UcoDvyoFfDU0NNAnPvEJes1rXpPoZQAAAGAIEmDIPXRl7EBn66YAsIQa5jU12qtLEiCJwOoQCioaApAcz63DxcAYIYzltZidHF72We/5w+FVjz4OpJUa3cv83LTk+6E/M4X04NZar5JxdfFI8weYrFJFyHMWrnKdKvfIZWfJGOgPY1NnuNKXD/oGwIUqa8i2ow9SAQ70zsC6Ag7rPpG1cIAZkeamMK0KKlmslOHuc5H0D0fhmMAW46mN+XpsrL+NZsaHzArBeVuHM54uX5IgG+Zt25FFZN5WbSBw3g/dghA0H/oRzWemWSZKBwAA4BYI+qrAO9/5Tnrb2962LLir9Hf5Z/X19fSxj32MXv/61+t8bwAAAJRsUDIre+zgBoIKR+HqB698dch9o9w6mOsTgQjp0Zm+jTo79WNrG+Z72EBssLXNLmC5+EkoFHJsA/ittytg3dw66DqwxfSgrZ8r2RrmQCCRHUF9arFHwWN9rcs+q6vH8R+wp72YnGuJCnpTwz1Sr+n6vi+L5/P0BH1FvT8vrBCSFVj3Adf28RKBLgRYQvvxhyk7O2VwrlNn3bjFebxjHQzBV22pg7MNW73GskguHZT8w8OQmR5dlmDTaAJh1onmAAAAJAGnPgHcd9999KpXvapi4Ffp3+Lzj3zkI/TLv/zLiV4CAAAAxmAxlJ6NOuAkiTeiZG1kpXhDLG2bi5yr4Li4Ce4aKuy76jUtNYkothxlUz7N9Jw7FKr/UjGXi5KQAVgCq1fHShggkwjde35+bvmHnoVrVphzOt+FhvUP9zWWfejXZ3fTM8sCv+rqNB3/BdkP7ApIsS89a8jhriZyG37rYlXJ9rQFQwDADSQwcRBPT3Ar5mwgtB3UsTyTKhbzNBKxYl5a2ouNa31UMwSJsNDma6LzkXCemoip0b7Q41ba+3oAAADxQdBXFcewv/mbv6EXvOAFiwK/xN+lwfJDH/oQvfWtb02gfgAAANwJffiY5oVUhMW/5/ENhtBOmm0mJioOB2RvgrDZVGEiBudNwmiBICb7Oc+ylw3MAYfgJO1XyQE0lzEhBKO9zTTSc4FdpS8A2GBRe2YFk0CArjNP0nBnOGfuYkFuP5eWoPu0PKdSNKiQy3qVixzJ4fUcfc1HGVb64qUjYABn2ntUkNhCKnWuOArzbQ/ujM3m4DofnpseNywBT73YTBHBrTGBLbo8joz2thi7t9GkcybV7qnREc4fylWMfktHP2W6/7oMFzn4EPfd8HmnlUE/BwAAIAkcTn3YsnLlSvr85z9P11xzzaKAL/H3Bz7wAXrHO95hWkQAAACqCRvzleJFuAhwCOMkbGQBm97XYjfMN2IAqGan2KgrA02ZTT/HfYM7MlGeJ+x5ogkdBcVCGGo8fc3PmakOUQ3XbBcEE/NVy28vsLnKMAzgjyhTf8sxyueyNb/HutIXSGfVVtEvYTwEISjk5xf9XF/fAL3JbItoh2qRpV8mAefAHLoq4NhwHoR+q6p2yDWCbLLt2EM0PtBO6cSLpTPubYd3cCsAQAXc+6U4BPbBspMxSQz0sWH+BxLA9PV2n3061J62MkK2FSP9FNN3VgvZ/RwAAIB00UhMuf/++4kLBw8epNbWVj/YS/zZs2cP5fN56TK+733vk3o9AAAAAUTa3OHn3MZxQd17/jCtWLWWNm7fV/V7XhGVvgAA9jE+0Ebb9t0QKlO6Lkfhy329feMUkEe4QyZLd73jUGEOFDUoACx2FC4UMHcDALgzZo73t9H2AzdV/R7nAH44l4BKVmEntsptGQHZ1EuJDXXgonMiAKGA6WsgIBlTPqfj5sBVVIxbDMZCUfV4866r5F1QVaIDEFL9enSFdwJKlqATK9cPJit9WdJ3To/2+/ttYRKTFIsIbF3AxvagCy89zzQ7NeIHfl11+z26JQJJQQJhAAAAaQv6MloGucoCs6+vjz772c9Kvz6CvgAAgB+hx6IEmy6BGYYs260QmdNrBn15+hyHszOT1PLcvxNXbHu/LKjSzpLqU147tDM7JEuUqyz8WqOv+SjNTY/T/pvvrvld2cGttW0ngqJgh/wR76jS3ENRe5DVx7kwhmJcXk6dOIQtmHOYw9hpM/IqLCS2A2lFIzQ7uTDpb12jsMgJOGDezjjoC3ACbVSbpsP2v0qctDX/niZEP1fXwPZoELDGM2D3ci6MtQXD91Kn5ss6zx8AsKVZZqbHyBTof8MxMdhJm3ZeEU532OOHLdqEJHvV3pdEuJ+VSeekqDP8RbKZKeo4+ShdedvLawZ+FVEBJ97bwNig7uyQgW6nRnr9tlHfgIruLoDzBwAAAEmonareMGLyxOGPapkAAABoHFsYeV+IwKQLz3yfXCA7O7no56BNO13jXsepx3DICxh5W8FR2HbG+tsoPz9X83ucq0MAtzG5ruS8puU077MN03M5AIwQaN6we7lq5qtP6XM5m/tMLyXPyb1deLzbTCwcexzbkJ2oJDWgn7OC4P6Sx76csbWUhb7ILoK1tOM4ktzRyj7OATpPP0GjPc3EiXw2Q82HfmRaDKCJ5H0V2r/K9dvEYIf1/a6o9jU7PmR3MIRF+gac33+865hqG+HHBwfbh6JiJfAlAQAA4HTQl6iwwuGPSpkAAACkl57zh/zsRmnaWNLlXLI0CI0ddr4+Lbh4EOriI0XBykx3FZgZH7T7QIIxNh1OWYeLqlXxTEqKQ3h2v7OA/QpkTgcsUT2OcGmXlqF0Xq9oT5X1XC5l86Wp0T5qO/ZTaj36IE0OdZsWxw2421AC+YzsIzBXp2nnErHGG+k+r+VegC9h26aTe4EOwGWvpvf8YZrPTIf7si6RmejGehlN4bmoZy5ygLj0txxj1dcLeTLTo9rlsQlRdWVmYojy81nTojgEj74s0txU8fFnz7lnaaDtJLlAf8vxyz8E9D3FQl6fQMzBGkmfphf/bQa87/Sg4/wB/ugAAOAujcQcLhvKqmTCIAsAAAaIsk9XV288gCA6/MbOZYtYlB4H7AjKqu5JmSdio44hdWrWAQiGAEkQfQXrEEnP2ukHU7grzQsO4A+oAhbvNp5Ve0KcmBrpo9nJYVq7aTtt2LrHtDjAYZzZvwzRpwinLT2ioH+rlcSl48SjC3Pr2YlhuvbOV9GaDVu1vB9bkGZHrO2Rs2x2Itu5JMgOB9pO0MRgp9R7uYjQnzPjLAd4FPqK1K+KREnYN6ylzqIfBH7Di3+R6hsuujZgD5Y/hXyOJoe7qZjP0cbt+2nF6rVkM5i/lyvDkmvGvqeZ+WehkGNli7ETlqaE7MwktR17iHLzGX8s33fTXbRlzzXkHOXmZnTKynldqE4xuWyGxvrbiCNx+qIwa1HMiwG/roJz/yMX+/Ym7Hw30s8fWO/rAgAASF3Ql30TCgAAAPaSIKMwFlKRMgpLdBMGKQIbvcAGdFUzBEDKRq60+QvjDWXPgWdghsi22dC4Qv198jnqPX9I+X1sZbizifpaji78vOe6O2j7gRvJ3rmcx2L+GPR97fNQdEnqVRyUrEF2MERKXqbs5xzuOrcomYL491DHGbri1pcRB/TuAblnQ0rahXtqUpjbRs+aFQFf1cnnstR99mmaGRuk1es30/6b76ZVazdoeTc2kYo9d3EObttzShE32vl/LjvrBxBt3nUV6SQVNiiQ/JzCkbH9+MN+khKBmMddc8craeWa9ZQ6UmJCHElN+wVGGGw/5Qd8ldY3vReO0KZdV1K9zERZwGAAf/h+RaVP49RIb/UvxO7nPL57AbJFs3ksiCS6pyQh02D7acrPZ/w5OK/AVovfq4u2CiIl9kECYQAAAElQW75E0mDo+h8AAACMURh7nNYxQEe5ajtI5/tPLbrbuxP9i5zqayYCW0G6nJyteA/M2010+DyPyLbZfPhHlCZ0zeW6m56m6bEBLfeyDbGOEM4l5Qx1nCYuyF3neDzGHOf60ejBnmkBAfxlujA43k4MdCz/bKiL2KOqr5BV0IvRHMpluOsZ/RyP99p3/ojvxFks5v2giK4zT1Kq9eOlu12qbvPTI33kApPDPbW/lPJ5Oxe7n50YWgj4EojACK6VSlyBR1ICtD+z+gc6GR/sWLZvMjM+mN6XkILccmZIo0I0PfMlf825mXE997MMEcDfdvQhGh9o989oupuesWNfDljZJMf6WskZ2KxHUWQFAABchW2lrxe96EWmRQAAAOAsnoLFELIUh8+cjio4QOMGCpd9FeAInvbqEOkJNuYsG4d3wUM/LjvKxW0ffc3PUWZqlJwkaGiVPJcL0j0CvqrorFikQiG3rFoEAKqqyvW3Hpd7UUPDCa9xzKPx/naanRqxVH4gHYNzde3rhKC1VCUbN1i11dU2J39fzk096XYUFmsKkVBixao1xmRyAzsTCKlEVJRbam8ciFUVg9l7FP1pZtLR/YCEDLSdXPaZSFyy6+rbiAOBFZ5j2Jir8wVpMGu3TiBdpXhH0jRZ4Hs2ZDv6+1oVviTysW4MYjYm9J4/zDwo3Zy+RIXdUjXDEgMtx2nTjgPkHB6TOETb2vNSvPiJPURQoVxZ5OiyLmH/rmu/1XrbAQAA4EbQ1z/90z+ZFgEAAAAIf/gYZx2VcJFn6+LNVrldDL6YGR/yM1it27yTVq/bRGwwoRvzr8NZJga7aLSvhdKC9LYdGNAIo3USvNZwagpyFK74uVqlTgx2ps4QUNGQAUjSdxld4652eMiRnZ2ivpajtj+G2fVqwOUyU2M01utSFlPJ14vjCO4o2hwE2PR/9mDr/hbmcnwp+kH9jgV92dlMtPap4vxBVRcs5nLJAr5S+gJD4HlFv0Lf3OxElF9SKVKF+0X5qmc+qM9Z3GtHrs8bXX++1IL3WkM/xILA8Yjz++M85HHWm+E+ODszSTMTQ+6MD5JFmxkbWPZZNjNF/ImvCNbv12H6W46RS4hxrOawAFsDAADgYtAXAAAAoIrABbv2MzdFN+S+H4FFLAsGO07TQOsJ/991dfV0xa0vo43b9xFruNt2FdK6USg2zYUDBEeSZmcCIFl/oLFP8MSMQ9L9qgZbpbOfq4rn5thQLOZNiwBsxUhgv5x72tpekzLc1UTuYnZfwNkqkSmgWChQMb+42qJ6PFaX4Uha++nq2FedmjUwseQYDALh3Ueo08tobzOlDfnvuvL1ZseHKZ9HteV0kc5+RB+c9WuIKNVxAVBlhiHtTWYFResC+FX2wUwqENlMkG1iHmc3sfoW7v2Rl07BsrOTVW5d/d4sxxiGIgEAAHALBH0BAAAAEg6jsckObMIrFmm44+zln70iDXee5R/0ZQOBm0vpdRQW9uUMYV6jxg1GsZnpTJZ2BWrLzc3SxFAX1dXX06YdV1DjylVkmpHu8zTUcYZy8xnTooClcDwcYDyPAKZfQsDHnmdtRvXEB3TK23A6+4j5uWniSixbt/012i6/Awx1nqXBtpOBc3C794Zslj2+/Pn5Oeo48ahkUXjr0qk1OnAI1wL1I8ihcvquSB+8xzs9stnhKMz5PQHpsOn/OPUXjkZLSK94LvdyALhNhQZj4V6slDkz03HHJYyubewza2XwXvvIBi8+uTo8/TZV6XJ4lQAA4CwI+gIAAJBCmCzMmYgB0sfczAQVCoszk89MDFGaUb5hl9L2PjXa5/Dmqh45gp5XtOG2Yw9FvlpaEAFfLc/9O+Wys/7Po90X6Jo7X0UNjSuMyTQ7OUK9F46ouwEO2QLUkh67d7e/BamGixlykcOZ5+asUM6yAZPMTY9Tf8sxZddP07gbOD/z9OtloO0EzU6NUJqQnThE1nw7TW0AmICJfVVoL6hED4AbiduCxkOZ+1LB1+LSx5kWAACzYD4rQ4luVQxTb1Oeg/sC9j4TALagok/lNAbykSX8/icAAAAQlvrQ3wQAAABAMLEWxu5u1FUjrRuzQAae9k0d2GtCErR35bqPkX0vjD3p2kicGOigmfFBLfeykfHBjoWAL8Hc7ARNjfQYlWmw/VSIb+FQ05hzDqN5F8YekMB4oDxHQD+gTLMGdJ+WdumlosoXOyLbq+fuK4wp/2hvS5Vr2q6UABx9LGAYVe1F1nV12z3amRq1lttDhADiZDeVeSnGhuHqmKeDJKqTWB2GtX0BqeBdQ2+xbcfqvt5m2UEUtFTA4XExqWBsiN/P8dGdx0BOz7GxQz/QFwAAANUg6AsAAED6CDwPrJRS2OO74AvKdsVmYwJUBRskThLU/tAuU4SmLjifyxJ7DPZzlaow9Derq8wQhpkxg0F6yJAJNNgT0AneQXhNeXKys6dW50mfW6XeEjpIWvlKNQuN/n4R2dlJYzpzz2HAkedh/l7YVwOxDuhNpd7c6+fMBH1EncPW1LvC16Lqnad33r6cVLcroJYothXyu3HtVfzeYPvpgP8kqwnsz6THQ1iuKLCI6dF+6j1/OIZW1PgsWN8QQ1CncC6XtO/k1r7D9vWYw4AQVqJPSS7OqV18JiWo6t/V6x8VxQEAwF0aTQsAAAAAgLSBTQSQws0tbJ65ge3vkbH43A6fAG+U2IsSE4Rdh1MT9CTP4jxlx1Bhmc9M09Ron2EpAEgC+iTA7LBcxzjJeSwO6xSGthtBpYzfN2BBIZ8j1+DcR1jpjGWsH+H7HgHQUy5OU4U7ixjrbaGBthOmxQCABZPDPdR58jG5856yfkfHnEVXsGNtQSLIoTLoi/HajfP82hkYv38b4W6z2Kux753xeddu6gkAAEBlEPQFAAAgdahYHGIRXlEpQS+AJ5o3zlzdpABAB6LPTZo9L/EBVWATlp5qE7iCgrO/MPMPmXMUzHdSBioesYVrW5wa6aUO4VziFck5mOo8rbam9HlkT+Uc02Han9N2vVf7vtY9CpgLACzJz2ep8/TjNDNusEq14/u+FT9X6T9ds5KXp9ZR2QSc5yTaRWOsC4PMz81QZmo0uso85vrWaPuR+jjFxKtoZPsaxla5DWPt+w7PYPspZ8+eXX2u9FH+Hj1Nc1SZQZDElxT0cSWyM5N+VcPUIdWU02MvRtsi1AwAAEABCPoCAAAATJGizRcAZBy2xTqEC+PcUP0LEe8X9LFm5xIm/QsXOXSSxmcGPOzH6Ca95zlg+7bLrxGoii+G381g26mqAV9x+onEfVvkX+fjvOYynMcMzu+as96ABPB6kykB7QPG5kIfbLgf6G89xi7gK/K4bGXiCseCrkITY21Q9n51BZ3wth0Qlf7W4zTUccZaxcEeoRPfDjDvBQEEBrS6AJMAfgCAWQbaTvoBrjbDeRxPJBun5zJ5XM5l/HAtuQsAAIAF6i//EwAAAEh7BaokCzD5DoyxF9WcFtScF7rAKThvkPkwFw+kA9b9L/c27CyeW2J78W2L/TjCCugKVGZ2asTY2Ka8DcPs+ZG4aCteKrADHbbq34PpXAhtNZn2onwMwFhfK5Qgiwh9atJK9klAHwvSxHxmWlHAV62kcwpuuewWmgd3pvNG59ClZrxPoNS+5Hyf85zF5FzOaPv11P0O5/etE+hhMflclobaTytQtJe+8dDCRy0WC3b072F0K9nWcL4NAADpApW+AAAAAKCXNG2YVANqWK4Sz6PJ4W6aGOgg90jrC2f83NYkOGKsQxCJOmNGJ9OGYI+RtIU5T0irgl3JA7pUB3Sbmn7QRpkdxNy8qQrIkCoNjP2O6R/9JqhqHh6Pvh3De3pg/a49p2/Hfd400nMh1XMU0R8bDUwwQOz3ibkNSCFW7t+oJFL12Dq+78XW18pUbpfnCTYg/EdceAdiPlaxadds74ae3fD4MD3aTz3nDlEuO0ubdhygfTe9mOobGozKBAAAAJgEQV8AAACAjM00hdmMbMWFTRegl6GO0zTQdtJytcPu00JgH4fDMZBC9I/5jvS1lvYXcAIATmNnswQAAMAIzJUAMNj+IpWFNkBFP2GDjsJe9WAQpWt9ZZdGYGssrVm6PxGFQn6erMdj/g4ryMBCLteATkEa4dyVGM1rYHFwqyYRrB6HLBZdBV6xGOJLHnsb4mGTHGQIR8epx6hYyPv/Hh/soJVrN9Cuq2+7+J8sdLkUr2YfDT86AAAASXA+6OvP/uzP6J//+Z+Xfd7Q0EA/+MEP6IorrtAix9NPP03veMc7Kv7fJz/5SXrjG9+oRQ4AAACc4LgIVQ8WsZc1AcrswvN4Bnx5tm7gMbGvGmIUiwUa7jxLs5MjtG7TDtp+4Caqq68PcV3PSMZYHhuxCbBdflABzx7HD0Ng3gFSlW2WfVv3rL0nf90qwrXndu15FIMxVJOeFxztg5JKSLTdoOt4jMexsLmYFLXvXDZDY30tvoPJ5l1X0er1m8l60BcCjijun1I7l6uB6Nu6m56mqZFeWr12E+276S57+jm80hAqgpLkmly4IEq8B5tAGwFAWmuSNtdiHsBfAbXVbPk+dxzZws9NOD93WnCwOqjlZpXWNW0p4KvEYPupy0FfgXi835OGd2m00joAAAClhPCotJdsNkvf/e53/QF16Z/XvOY12gK+BHfffTcdPHiwoixf+9rXtMkBAACAzwFaWhfmtm+oAD0bNrqR1R5T265j0nv+sB/sJ5xL+luPU3/LMeJN7fcLGwDskBkMxqSLC5I7kUMTp/6bkyw65GP+uMC0oy+cAYCig0trgydB2EAdE2tMm+3HZtlVUcjnqO3og/6adajzLLU+9xOam5kga9CVOR2TOa2I5Dmjvc3U13yUpkf79d7cBpj0ZVH6VJOOwv2tx2hisNMfM2enRqjz1OO8x4MQsnGWn7NswHI0O5fClnWB6hDWgW6+gkqgFKXzAEfnFmzHmbQmeE0JpvsrnfeP0sZM6wUAAAAAKQr6+uEPf0iTk5N+pszSH0F9fT295z3v0S5P6Z5L5Tl27BidP39euzwAAJBWom0UhU4pTFzgv/DmKR/bDcSUUJoXOUtK7atWfzTW17ro5+Hucxp1ZofNoW8CAACwbGxgOp92ATa6ZTJ3TOc8RHZga0p0yOQ5/SCd4z+lpie/Q2cf/xaN9FwgLqSzPensa+Vfc3qsn7KZqYWfC4UcjTKyKZDOttHT9Az1nDtEw11Nfn83PtBBHEAfZ+f21NJ9OdHnzU2Pp2ve7TIqzsFcJ8Z8TW//51n4DnHGakqlYInasB6zGt3vL/B+lT53/XxbJmiHClWLwUGhdhVeGwAAAAAgGs4HfS2d5AqH4rvuuouuu+467fLcc889tH///or/9/3vf1+7PAAAALjjWbt5w1k2LpjUkbUBVlV1Fk+fzjoYOPpYblUkAakjTN+rzK4U2yVnsw/rW8L6IRyqqAbUgjEIugXVm4jt/RfaeE1Gus8vVL4R1XBEZWFR9ct+y5UzR7SzDYSezGmhPJDQ1r0vW+U2DgO95efnlgV5DbWfNiaPVbDJyl/pc4X7tF48O6t5WUPtwc5xDKQeBuOH/PbD45kAAJzON+T0C06vVeI8W2J1qNNn0neFeR1QgdV2ZbHooIzyvjHojFXHWGepOxgAAIAUB33lcjl69tlnKzo1v+51ryNTvPa1r102eIufH330UWMyAQBA+vB4bCC4vHGZ1g1b4ByyNget3mRkiDF94jWqUy3GBnW6lWi41a+FBlLNaa6Sw7m1Y4N0se3UA/qtOErTf21Z78na9uoyqhJZ4FXzIObrHWg7seyzsb6W5PI4j6fX+J1vZ9wfkLt8IIiZiaFln83NTjihsOC5lvtzOX7JuWo7qfEAyZhcpVgo0PTYAGWmRq1adxvvZ+xRlT4ssp9oyH0um9qZS/2cqKzpFYvEB4OO6VYF8Ce5IhNdMpOlIlzszjBs2l8MuedmJig7O2ntM4AyGL9C5X1ZjLV6OJkky412BgAAqaKRHOXIkSOUyWSWbZY3NDTQq1/9amNy/eIv/iJ96UtfWvhZyCcmuefOnaORkRHatm2bMdkAAABUQOE6McyCLzM5SmP9rVRX30Bb915Hq9ZuUCeQrUhbxDLesQAaSHEVHJV46XOqkb/ByFeHgAFeSm1FRUW0WPH7NX7J86jn3LM02nvR2XzL7qtp3413UV297fl3UmRrXAlqAg6+G+3PFNi/6BYj2NmGm2Ow76w0M06r1m6khsYV1s8RXWxHaSeXndV4t5gJgS6dEYAkmof+QusES1ZgCl5TGKVw6dNZ941MdKSXND5zNPLzWWo//lPKTI/5P2/edSXtv+luKfsodQ50Qlz6lkimbFhkEUDY33LMT8i0Zc81tPOqWy+vqZmoM2jNX21fAMTSLgu1TY32Ueepx6lYyNOKlWvoqoP30ur1m8lZUmCvKvfpas3lRH8wOdxDk0NdtGL1Otq+/0ZqWLGSbIf1HBbU3CfuPP04TY30+j9v3nkl7b9Zzlwu+VQupl1pM8fa543qRYh/D07zE06yAAAAALKx3dOoatBXpQ2SgwcP0ubN5hbNN910E+3atWvZ50K+Q4cOGZEJAABSh4os8ArWjdmZSWo99iCN9Fyg4a4majv6oH/oZUIWAGzdfMGmjllSuTGfwke2BfQHSrVL9ssmv2qrcCwpBXwJxvrbFg67qkvCWZ8AqAF2byciScm5J/+VWo78mJqe+DZNjfQRa8KsOSSvS/RV3FSA50gbZiya9jlslWvpnSuHvxfm8AC4FbBgDM5jAbOAftb9bpipHCrgKNOtDsYH2hYCvi7+3LHoZ9VUtR/ObSOM7fMWXwmFfI46TjzqV40TiSAG20/ReH+babFAihFjbO+5Q37AlyA3n6G+lqOmxbICzvshoSNRFDyCCPbqOPmof+4g+jjxb9ZzOROkRh8ei2DGyeHuRWdg44Md/jmZFaTFVFLVLrieP+gQBAAAgKs4G/TV2tpa8fNbb72VTHPbbbdVXGg1NzcbkQcAAADPBd9I9/mFjd/S5u/EYAdZ/2zYRLCa6dF+aj70/6jpie9Qf+txrZvHRhwJoz5f4Pf1ys7a6bIGJZvi+AzlMnGUrxa6D3twuBRDZyrsSrx3aUUxedi9Le1vdnJ42Wd9zc+RPSCjcHX1mLRDHtWoWMAl02ZK7Vw4J+XzFxOTFIsF6j1/WJNgAKQMxRUenZi3u/AMmrBlLs0N6C1mXxH2dzzVMqbU7lPaNzoxrqVUD33Ny4MfhBM7cB8V42w+N0fF4uUzVkGoNavR7R4TN7evr7AVEXw4Pzez7MzVCbz0mqnKSl+1+oSBtpOLfp6ZGKK56XGyYm5S1ZeEWGLj3Eo3lcbZfp3BrbGWrDUq6nE1SG2k9/lVJd6Rnqik4vV4JZcBAAAgD2eDvlpaLmfTXhpwZZogGdrakFkIAAB0EGURFf678he7hUKuYiCYynvqgO3GiG6xLNwYzOeyfpYwkV1TBCEOdZzhkxnR471Ri43gNCHZluzrKqx/UNFes7NTiwKvTW8OKxs7LRyLOPXTcd7L0oN9O5F8IAE7NJLtWlQV1h4MrLCvV73GSKud1tLrzPjgop/n56Ypl80olgoAxqSzq5DXp7rS13J/DOl65v7AQA8e6zkYl7lcJTnsrOBmSp8eu3caCQtF5kAhN29aBPb47aFmm1BfgbhSu+R2HiiSlXAmlL5s7P+Aj1csQhO1YBLAr6bvkn9Nz1tuUzNlVZWC197EHPYC6sHC/r6QXz5v475XLALEReKBtmMPGpVDZ/AZt/mZS1i5TgUAAGAVjeQoHR0dFTNqXHfddWSaa6+9tuKgj6AvAABIF7Gci7FIBAYZ72tbdigmMjZt2XONMZkAc+Lsa4l+rlZmPGO+JbVvjH7absThQ8eJR/zg1rq6etp30120ZffVZAM6bK+qUweDfexK8inVS9JLM9BZquSWDM+DsdoyjfY2U+/5I75TwJr1W+iqg/dS48rVWqQDEbB43eeVrxeU9sHJrh00PkgfN+x9ldFIwXOqyt4txpPszARNjfQquT5IseFbPJYAYD1R2p+NMV/cCbO3GP5iZC8qkh9aTBiTSKIGXesLYA0iqDdy24K5qAO6jakfKK4mKit9xSAVY3oE0qIP1vMNzrIRUc/ZZyibmTItBgvcaS/Mn4N5mwAAAMAbJyt9FYtFymQyFSe2GzduJNNs2rSp4oHx5OSkIYkAAABcxHNkI4WTLPaJpwsbN00yU6PWZUYsr2ihA+czlmvAxrZhD9BtLYY6TvsBX762vCL1ND0rveKX7vYivuNcu/LYRKkqkANjBghjIl7s+VAp4Esg+jtRuZU1jnVfiXGkOoSpcYnXuj0GlosPKiP64QvP/oCGu5rMvnzb2wcAqpHYRNK2dyTmoCKwVU/VYxt1aN9cjjvO7YEAwG2c4CJHSvsK6XLbqQaQMqzfz4mbDJhzA431TrzUvG/Ab90UZFc1A7642KNMOSJcinV7ZCma53TyMgAAAOZxstLXzMxM6IArEwTJUE1uAAAAEomyMFW6iGXkXCyZ6puQdjwDqIDKvYEwbS2G6RTy89R27CGaHhugVMFmAy6GHFxErwUyH2rSp15Gei4s+lkERkwMdemr9sVED8CBww4ZBPmiSjdUS/Xo2WeTEwMdCwFfJYa7z9Ge6+8gLVSTr4Y+ayYakNQe0+aEXZNUPnYqH9ppWDtJEdFg+6n4vxy1cEBgX8ZbR2Fwfl4mEejKYix02snNZ6jl8I9CZE5X3D9p7iOijD0qnbFitXfG/ant/Rf3OQlIZpuFQs6kBHzbT9j7smoeJoUJc07GSlkgBYg9uczkKM1NXUyWp3KMYzNWRqjeyC4ZkyV9BJt3HQm+awpwWWvAFA7qvi6ZGrTNvy3p9wEAAMjByaCv2dnZwP9bu3YtmWbNmjUVP0fQFwAApAysveSoUZaDJ16Ikwy0HK/+bnXttcC+4ihNLaqcaqQn2sRgYZpCbl7OhZid/cWyLzbmqEAQNs9mAThAsJZ8Pku24RUL1HX6ST8AN94F7FwrRLqfmNOksF0qeyfSL5uWd5OC5wyxflBilyls34tI+/MrQa5ObQ/CMEKKdDYx2GlaBAtguFlQC49535GeJlYd6EELsxPD1N98lOazQcl9vRSNlWl5TnVr9fTYin707Ot4TlZsbXvuQZqdGon1+9JsmnPbUJowldzCxPNwth0AjNkq2oVJMN8DAACQBCeDvhobgx8rk8kYD/zKZis72RSLi7MtAwAAcHsRHmuD2bN/LY5F7IIizL4Ixw5cgn4fATM2tvcwv8O3/cDmFmvDDTy7RWAgvllSr4D0gLmVkwx1nl1WnUwtqPSVWIOs26LtC2rdMtuoI8awbhua1zFBWdMdmfSyXxMyFw9Yipe2asXx5LAB9n1YKCQ+gwvqcGCeos8rP7oeuk4/oa3dVLsP73UYiAXeKWDA1EivaRHcwUibVldNV+nYh/6vpGSgqUKy++ujkKToUc3aCxQNAAAgPvXkIKtXrw78v9HRUTLNyMhIZLkBAABoAGsrkHL4Hwxyl68K7HXLB852yFm2UFguPqjAIpusVtXQrZcf2BYTPGa8YHy39AqigvevA70BX4yI4LCs9sBbTWIGzv1nqg7yWWNh1RMr1iHy51CpBnpLGepfuPV7DpyfnbNulQ55CedynPUGaoJ5rQV6llYBR85lQt8OfYNRZOsffYX7AQta7EBxf5ZeO9WUdJgZbJ+BqVjcYfs+U6wnFXM5VvNDk6Jw0gMAAAAncTLoa926dbRixYqKC+fOzk5DUtWWQcgNAADA0kWsJkfhMPdhsaDWKoKsjWQGemOPwgMJ0+oPyjQOu3CWOlX2LNtmUmKD2PS/rAnecJfPgqqtS67AmcAqmtL7ObmXSwWBgY/mlIk5k5tzdNbvVZVonJ8ZpJLq8w07K5lEmUOF/i6arhL9AzXgHURSlvOCK9ufYo+68xS2c1iucnEPbLBdb9rEt1xPJuBoWxxlAiCFbSN4LhFfDqwBlugA3R1QQPV1gGd1P8d2jWMU1TpJuqZB8i0AAADycTLoS7B3796Kn584cYJMc/LkyWUTM7H5uWfPHmMyAQAAANrAhgRf8G6U6RAbcS7aoS1yAj7vzJQjV7UKYLJuIY7rdOrXs+e1OwmU6mvBmvHKdWochka016D3qv9922xfntWiyR7PZNqObjuMcj/0iQuKUPU6AJDXFmGnjEEfklyDXOZyAVSSQ2XVjziPzURVNdH0TuGgDS6bnOUVvaS2GRUOrHw6H5NjRrg+h4+urILLXCB1pEDvHrP+yHOgTaG9lhQBnXHAS7l92yavBWCNCQAAIAnOBn0dOHCg4gLoiSeeMCLPUhkqZbgSMgMAANCBAselWIkp9VQH4wYWsZf0gEoMyyzDbTQ7aHLRZyI7Z/IM7GWy0C4AcAU0KV6odJZkRqAzq0mjTDy3RYOKTXpMXy4wufTgwD6Kev9cT3epr4DPHcmazsTm2AS5OIJUfcovHACYoaP9FfI5mh4boFw2Q7ph1eeCYDAORAZjp3yg06gKC6G7tDufMwb2LkWL5KwcXB7NpK1a3t9gDpxu0vH+0/CMaqsXs5kLpOi8FAAA0oazQV+33XbbsoFYDKxHjhyhvr4+Y3IdPnyYenp6Kg70t9xyiyGpAAAAJEf/4s3ajQVLxU4D3G2KzSYJMG6HnG2Bs2ys4aw3z5aswTIlCbgFl/cUKAYq8ZjVf9zLycp4zcQ+GbefOoejg9Trl0mFJU/3e2WcUVjVjcw3VeAosce7tNtk2OdnMM6yIIQeuO/9AKCmDXhOOZJlpsbo/NPfo7ZjD9G5J/+VxvpaJbZ3TbpSmUQPY4JT2D5u6ZDfv4enUQ4VsRBotwBYjeq+Dn1EJG1V/CewD2fsXutjeA6/b3ufLV2EeE+S27YzfQUAAIB0B309//nPrzi4iX//wz/8gyGpiL785S8H/t8dd9yhVRYAAEgtVTMbMxDECRhkiAbpUlrCzYzg9u/JuY6DKg9DrH5Vl67i+EeHki2lLxsoJriqT2ZylNqO/ZS6zj5V+Tsp3+wN2w/F0ZMq3bJ5ZwFy2O5w5QSBAUG83w0b244DZ9GZZm3k+L7D9V/85AbApnYWDj1y5+ZmKTszqW7OxrC/CCUT1+oQ/NSpEPfm2apkt1knsudy5X3ZYPspyueyFz8nj/qaj5JXLCoQzk79yw1usVMH6ZlTqA2w5GeT7r+npXZZLBYoXTaHAP40koY+ODmadRThyDh8H4/3HFYfMttEWtpXKtZNxvDce1cR2gULeSPCRea09D8AAAD00kiOcvfdd9OaNWtobm5uYUOlVO3r61//Or3lLW+h6667TqtMjz/+OD388MMLcpRv9GzZsmVZdTIAAAAcUOcozGStaYDUPjh/PXjmDkZd3/TgsrlkB3boykqb1S2zhSqyDa9QoPbjD1M+n417hfS2hxAU8jn/74bGFUqub63ebJUbpHOuI80PPaUJQmItc5nIDpKD/t45qrXPWvOSpecZnJ9NyNp34QiN9Fzwf16/dTddeevPUn1DoxtzsWogUYnTGG+DNrcZJqLLmidNDncv+rmQn6fZyRFat3nHkhvySW4iG8w5ucN3zqAdTW1KZ5sIulflz+PJNTHYRX3Nz1EhN0+bd19Fe6+/k+rqnc1nHa0PtqSfBpahyq48N+chMpgeG6DJoS5auWY9bd173bL1alw469D6ADntumWqB6CImmVbgQ39HROR6rAeAwAAZ3F2Z2TVqlX08pe/fGGALx/oc7kc/eEf/qEfEKaL4eFhuu+++wIPS1/1qldRfQo2qgAAgAM6Dj8mh3uo/cQj1Hnqcf/A1ZQcHGG5+AaXwLtxBk99Oxb93EDbSZoe7Zd+bauxXHxg13gtZAsT8GV7uxLy5+ezit/JEkfh5qN09vFv+X96LxyxXoessVW3Nspt2lEYKCGtB3jxEq94tR0VbWzbwHE89WOOZruP1s6iy5aZHFkI+BKINev4QAelD/Rn1oExSJ1qtVeHUHC/WsG5OqvhSHg+pe+Eafene56JeW2KCBUoRNY8Q35+jrrOPEm57CwVi3ka7W2m0b4Wo+IBoBQX5oCqH0F7PkNP6bjs+5Ec+6m/bhXnEB2nHpN+PwDA0kbIXCMa5eN87m07YXQrf52G9wkAAGnC6Sijt771rYHZKM+dO0fvf//7aX5+Xrkck5OT9O53v5sGBgYWqnwt5Vd/9VeVywEAAEAPIshLBHtNjfTSxFCXv2mXy2akXBsHdRV0YmuVEpNrb6z7lytDlk48hw9tyhjqOEMdJx+lwfZT1Hb84sGEbDj2d6E26iQ3MI56UEEaNpiNZ2W3OONl0xPfobNPfItajvy77/gRqb3EaEPTY/003NVEnlf0/4x0n68c4JqS9gkkocFejI4ZnmnZZZX0CrgOm7VChf+oYzY+o290hjTMz4BDlJnrYMfpZf/dc+7ZCr/joo3rdy5Jy5pVJtBZRaXYff0EYK9AxXvk+76BzagNAEj8fcb9XFRGe1v8/bhyes8fNiYPcAMO868gGbD2DqE7zmN7DNsaaj+96JnE2UN2ZlLKtVljZCrnmA51IfU8U2s0k/xLSmyH1a7FoZ+TdcbqDjHaQSh9pVmnAAAAkuJ00NeLX/xiuuWWWxZtmpcCv8Tfjz32GL397W+nnp4eZTI0NTXRr/3ar9Hp06cXBXyV/i3+vvvuuxfkBAAAYJCKi9jolxnqOL3oQKJQyPmZ6ELdryYeP4fEiiJUk8G8fIDvoQc7oJLlKvE8P+hr6QGFfSAAhyuiotNYXyuND7RTIZ8zLQ4Ii4IxRPQ3XaefpHzuYqDX7OQw9bceV5Q1/fI/B1pOLPvv/pajFX7F7UECVXDi6QdU1xoAqVt3yHxWznrjLJtGp3z9th3xfknks+gd5+ZmUjuWp6p/dRAXbTIxltp0lLYY9rv67MNOnasiPe0yLc9pCC/FfbMXva1lZybIDdQ4CmO+5yCu9REq0J6MSW2A3uzUyLLPpkb7yKZ5TNK+iHvwC4/qwYq+C2JRp9qvoFZlZ5kv2dJ1tivPh7kcAACAJDgd9CX40Ic+tGywLA/8eu655+gNb3gD/cu//IvU+xYKBfrCF75Ab37zm6m1tTXwgLi+vp4++MEPSr03AACAGihxFL58zcnh5cHEEwMdy38lrbsvKX1sTrC0PeabL0lxafOmkJunYjG/6LPcvJxqhj6cdRVGNsbys2z7SxCVMVuO/Ji6m56hrjNPUduxhxD4pbACTuTrarbv2YmhhYCvEiIgUHX7mJseW/b/c5UcTpTpg3tb5S6f+wS3XZPvhqtdeFbO5aKN2fEOnGcmhvxA/oqVDFMWIOKE3I7hWtWTJH2I+F0t83imth9edwbkZ6ozZ+U2DfRWvS9ioh/2+2+655RJ76dS3BiyLbJBz2I70IYjetD5GE7bjkXP5tZSgP3+PfpMrdqmtGHDmZQ2ks590oTT4zGIjeG9wtS2x7BU8M0W5w8iwas4+08LRuyk0i1TPJ8GAADXaSTHEVW0Xve619EPfvCDRZW2ygO/ZmZm6KMf/Sh96Utf8qtyvelNb6JNmzbFut/g4CB9/etfp2984xs0PDy8rLJXidL9xf1Q5QsAANQiqoTk5zO0cs0GZY5DsRZv2BeQpHxZiuTsyLmYzNSYn+1w7abttHLNekoNVmzyeu7LoTqRFRsdAq1ceu2TQ100Pze98HFmapRmxgZo44790u6ROqzoO4ODTAFgS9SYSfJwzqLiNZT6OC+985A4a1wRQCsCrEvsuuZ22nnl8yhVKM7a7AQxVZHPZWlquMdvnxu376fGlavIJHBilKRHq8axkJK63txdfz4XSfE7Uz3+OjEWoNCXA9hhh/OZaRrpuUDFQp627L7aP4MAvNG9hgm8nytJQ0zKUn5vlgl/3GBiqIumR/po1dqNtHXf9VTf0GBYIhfeqayNORd0YRjWOuQsmzvjuzhXG+tvpfz8HG3acQWt2biVTOLEWkwWrsyVQtB34Yi/phA0rlhFVz//52n1+s3kPHa9JgAAABbifNCX4OMf/zgdP36cent7KwZ+lf7d2dlJf/VXf0Wf+cxn6LbbbqPbb7+dnv/859O1115LGzZsoI0bN9Lq1av978/OztLU1BRNTEzQ+fPn6dixY3TixAk6c+aMX+WrPNirdP3yn8XfN998s1+JDAAAgDpGe1uo9/xh8rwirVm/ha46eG+kDRJ2Dl/MxIkDO51axnDXOeprfs7/d319A11528tp/dbdpsVyANVOn7D78KqyW1fyN6/t1kdUei8cWfZZ34Xn5AR92QBz+3d6DGeue1ZAVclV6HmUy87SilVr5SalqPFu6ky761ttO55TfdJQ59lFPw93NtGOAzdTXX09uUzYeVr5nnHie0q0Hc7jcEk24VjSdvTBhQqZw51n6Zo7XmU88EsO9oQ8sXfCCQq6TFgRLcZvxb5f1atycRS2sC8B6e6H1AK7l6vNFOnTS4ns3uUEim1HH6L57Iz/83h/mz+XW7NhixoZU8DFeYH54B1T8xPO/YW1TuiWis1FcUuT4MxODtMVt75MpwgGbdFe47G2vXJNICxFn5a8E0vETM4lP9hikdqPP0yzUyP+zyPd5/1gm7QE8VdrZ5znJDr6UJHYYcXqtb5/kWpEZa9SwFcpQZiwxX033UXub8GEsDMHxjQAAADmSEXQ1/r16+mzn/0s/fqv/zpNT09XDPwqD87KZrN05MgR/89SGhsb/e+IwK5qC81yx4BKAWC7du2i+++/n1auXKngiQEAAJQOqEoBX4LM9BgNdZyhBhXORp6eX3JiMwKL2Nh6EBt1g20nF34uFgs02HE6etBX0L0NvhsnbLvawYMbj8cHHFLH1Jvu23lu9A8Yt0LpSOfBq/J7ibV7GIdZ120jcEhz/LkVIyoYdpx4lHKiEvGqdXTl7S+PkeGQ3ztIa3tgoecYwUnZ2clFPxfy8/7h64pVa0iGjJND3TTa15L4WsA+w58a7V0I+BJkM1M0OdxFW/deR+nA03tNacnUmXRymnHxuUPN09x7bPtA5Q6VyjVz1+LF84eFAHol/YsXo/071uDDPo6D/bsuJoe6FgK+SucPw11n6cAtLzUql8ykDPGFMHv7izJ4TAL49d3KaqyJ1U7XCx0oO2MtVf0Szuky9kNqgT3VZNoD9ttZlGRMLNEtVgw9iEDWUsDX5blcE12xSVNwawqRNU9VZffZ2Sk/EHB+bpoaGlfSFc/7GVqxeh2pRCSOWIo4K7Au6CspRvsyayaiAAAAIuJ2+tYybrrpJvr85z9Pa9asqRiUVR6YVQoKq/Qnl8tRPp+v+H/lv1/puqXPtm/fTg888ADt3btXux4AACBNTAx2LAR8lRjuPhcxw6G6hVjchbtwxhtsP0VjFRbL3DfzOMN2A7GMuZlxKhRyiz6bGR/UJ4Dpg1VmTI8NUNeZJ6nn3LOLHBudt2fj2QiZACctBroGodQnGq2lKpQxp4nVN4X+HUWK9dLVJu0dP+LJ3dN0yA/4EghnOpGkQp5EtupSHZHtCypMzMzYAHWeepymRnrVKlrpfBeGEJeec4dCfQbM2GSiMVdis8B4ZRr0cfG0Br1ZrQJVSzfP853Hzz7+LTr7xLf96qm2o8vWk9/Hwiz+FiRjGu1dnrhhfKCDTJKZHqVzT/5r8vYV4myDre3UwlKxQbz5urV2yoRcdrZigiY5pPfdBCfEjKaTQPtWqNqZ8SFqP/EItR3/KU0O90R+ntDrbBPVqbG/lToq+Q+J4FbAox1VH8PVdHT9Lcf8gK9S4rfyapeq9hKX+ujZSl2MwCkTcznMDQEAIF2kJuhLcOedd9JXv/pV2rlz57IKX4JKwVtR/yy9Tulapc9vuOEG+sY3vkHXXHON9ucHAIC0IYKj9KFpI9fzqP3Yw8sykaUZLGLl6cyoLhNugCR1GI/6+5nJUT8rkjj0FofhbUcfpPx8cJ8DO1X3LuITJxBEzWW1Xo8tqXlQu2HzmtgIAkB4vIuZNYXDWjkzE0PRtYgmUEElntXXF7YRdw4U5/AxLHFkGmg7Yf3cV6r8nsWOwjHev0p7jA8De2QSbAWW6jbhPgLHl+MZkC9ljoSibxSOdN1nn6aR7vMLFZ6k7MtZrMvE9qb42VW1h+mxfj85m0jSJRzo+lqO0uzEcETh1FU88Jzr57jJA3QhEqeI9jU10meh0hnvdeu4F8OqLuJeIz0X/GR+/PBCBL+gL0yoWnM6lRQYBeQxn5n2z3pF4qLp0X4/idHs5OUqSXqRZAeeqOwz6Qd6iOrrylBqtrqCbNyD576cHFx+Nh9Fpjo53L0s+Fn0EbJZ3NYcf1dJSVe3BAAAQDKpCvoqVfz61re+Rffee2/F6lyCoCpf5ZtNYb6ztGrYr/zKr9DXvvY1VPgCAADjiyV7V1HiAHmpo6ht2OzAYB5skGgnwF6Hu5oWZSkSQabjAyJ7lv5DG+Fk1HL4x9Rx8lGamx7XsEmq+hnRR6jDTt2azggm7VDI4YzCF+W2U/ZkFcES35RsJI6dujj/M2IzNa/vsZ5K2trH+SiyYWFHvReO0NnHvkVNT3ybRnubrV8iiAA2vlhsg7a0EZerQzN8tFr9arGQ94MPCvmclLsZ2a9TmjXdRUIk1IGuEulhuPOsH/AlAr/EGN57QV7VViCBoNeqyO4HWo4v+0wEgdk+7xX7i33NR/1AtsrfId5Ifd/cH1YOJZst95fgyEDr8jZnA8EVYyJeJyX2qBoRCFGt6rqsuZLzzukOYbptmb6/DajSkdiHKxbzl+/jFWmo44zV439+PkOtzz1IQ51nyVrYqp+tYJfHLoZDj2hXIrhyYlBDxTGV+x2eO2OB2C+sLARfG3dCXo1wX9cBAACITyOlkG3bttHnP/95+t73vkef+cxnqKenZ1ngV9CGTrVNnqDfv/nmm+lDH/oQveQlL5H2DAAAAORTsd8PubiO5/Tp8CK06rM5/Ny2L85jvprO00/Qhq17aPPuq63bQIh7gDc+2LHss7G+VgkSRZRjoMN3MvKZIt+h78aXvCHaRcQ7i6CHIJ2J/lLK2/csd6Dj/QB6kTXOQaWJ9Rc3M2xmaowyU6O0btMOWrVuI/Fx2Av63NNqQC6291JV9Es/BXwp/PWEk3lP0zN+Nm5hQ/tvejGtXr9Ziqzu6l0/7SceoXWbd9L2/TdSXX3M/FDOrG30PsfEQIcfwC8QTiY95w757yLaHFWl7fB9r0r74CiJ64tFv2Jffn6O1m/ZTY0rV1GaSb1jI6MmI9aGwqlGJA+qr2+kK257GTWuWFVlwqhOeBE8kJke8+eUK9es5xOUruyFMTIEII9LbaR/ScCBqP6+57o7qL6hUb2JBAz5Yv6wduM2Dfty+m2be5Ci6NuWIqpDqOrrdCAqQvRdeM4fP5TiuWcP7PYQ4uhLVh9Sp6/NScVaE1ucmLjyVyRWTVHSFvUpXyT0Y4sBG7S9b5WCZz4QUewFbd55Zfx9OQvtYGZ8yK8WX5jP0pa919C2/Tdqr4YmzlgrVcXZuH1fhKuo8yWJ8zsiOUXN65o2el8IBjI4CU8/janRPr+aXkPjvbR+626rbK5mexHXrTCHFonR+puP+sFuYn2494YX0qq1Gy0cr9FWo6mrrKBI5EPumKA/BQCAVJHKoK8Sr3/96+kXfuEX6Jvf/KZfgaup6fIGT6UgsKiTrLvuuove9ra30atf/WppMgMAAHBnA1vZhhqPx9OHpc9r1A4l33pisNP/I7ICbdt/A6UWT/9eqtgsLEdUHJsY0pApqxKyTbpKG5HSfya8RNwgGmn3cQwdfWJcXY71ttLK1etpw7a91NC4gtLCaE8z9Zw/5P+7rq6eDjzvpVRf3xD8C9abKgL4VehKOC2UxiURQNh56nG6/sW/uGS/xXrjSTxRENku6+oaIlxJrs6mRnr9P4XcPO2+9iBZieJxRNU4VSm7+HDXOXIe9mfU4R12us48udDPrVi1lq55wSsUOnpb0F/y9C2xmrh9vl+h5ZLDvggqFQ78B27Rn5RupOfCQl8n5pRX3PoyPrbMRAwX9zytQKKuctkMrVq7gZTjBVcoEH+ys1P2zuVi4lcy9DxqWLGSWBElgJxZRcPR7gvqA74kgP4OaLe5uBOHlA/NofW26GvpXVSE0Rf6PzWaN4lIKCn+iMQd+258EaXBpsVZZvuJhxeqzYj1q9hT4UK15JcggS5Czmel6tniNXKc/p5zcl7xXgfaTpoL+tKMqBoo9sQE+dwcdZx4hG64+5eY2Kq97SIM0uZKcfzGMZcDAACgmFQHfQlWrFhBb3nLW/w/Z86coYceeogef/xx/9/z8/ORrrV27Vo6ePAg3XPPPfSKV7yCDhw4oExuAAAAYWC+MHZ7LR1MWp9bBso36rxEWduFY3fcoK9Qmy/MN2Z9h22q13oQl5vPLPtsdnwo8gZwJPECvyznGUubYaXDHtcRtjE/O+U/7+oNW1hvyEdG0rOYPkibn5v2HbpFZYKrDv4c1TeED8yIDJN+TtjlYPupsp+LNNR+hnZdc1vAL5D9MHoGozYfkJUwLsL5dGnW+OzsJK1et0naPZxA2yuv/m6HOs+YcxQ20P8JWxzra/PVsmX3Nb6ztm7nqUoOtaIijtIqhZqzJnNFxrsW76o84UIuO0ujfS20+5rydpQuvbJ00EzbK7jE7OTwsj7PD46IOf+Im5V8sG3JnLLjDG0/cKNiR+HwV3caTf296fUaDyTpIOG7EXO5Xdfc7ta+Qo01q+hThN42776K9t14V6A9cnGQ1y5HjPvZEPAlW8/zmWl/Hrdmw7Zlez5cbEd5/3zpOV3uP5yoSMvcHm1uL2wo1yHUKVe1VRQqbFecz0yPDfhJz9Zu3lE9+ZmiNiL2UXdf+/xUJJ4b729fdgYoEpVs3LFfryAIsuAJ0/EkTeOcqnnT0r0qu4j2/svPWEs+HWKc0XV/YAgFr0kk+Jkc6qL6xhV+VVB2SW8AAABoJfVBX+Xccsst/p/3ve99lM/nqbm5mdrb26m/v5+Gh4dpbm7O/yM2PFetWuUHee3cuZN2795N11xzDV199dVOb4YCAEAqCbsowxo7gkqhLPMbbGregShTbw6Pub1qtPs6tb+g2oHGKxT8SjDGKpZVIcwzRrEZr1iknnPP0lh/m/+zCCq68vZ7lB3q2eBcpPQ6CZmZGKLZiSELs9BF119+fm5ZUGlmerTGXTymjksxbiDW9QrtTmQyFc6Jw938qvkI3dYp7ksKuSylF0mB00HZZg32l6HaJZP+XJCbm6XW5x70s2yWqjpe96JfqPIbfGTX+g45P3asvCvhK3iFYbjr7LLPRP++OOjLYhzLKOxmpfdo1xXrj8r/QUrIzc0s9LPlDj6eF5AsRvtyJeENOfaRktes/veLRZqbGacVq9ZR48pVCYRzGE+X8dT+PeE865ajcOVnnpsaW+REJ/ZVqq/VdTdY+fdzb0/dY3FYM9h+mgbaTvj/Xrl6HV0tqrauXhdDtjBi2fAO6wL3MQZaj9Pc9ASt37KLdl51K9XVV05+BuS1a1GRe3q0n1at20Rbdl/tdB/CXT5eRA9snRzuocaVq2nj9v1qE5pZitirbH3uJ5SZHvN/Fv3clbfdY0RXIlnH2o3byHXEOUuYJJc2BNtwli0NCP2LfQaRXHPdll3q5nFGiGFb2Jdj8RqCEHbKmYr9GfzljCLmBa1HfkL5fHbhHOuaO16J+RwAAKQYBH0FKaaxkW666Sb/DwAAAKAGdzcBZR7QiEzUwrlk9brNAU4LtgYVxNmoI6VgYzq5/oL2Up3SreJnGeo6qyXgS53jZfivzk6OLAR8lYKKJgY6aOu+69TIZimc2s9A28nYQV+JAncTO6RGCxAJctjm9C5kt+s4vxtXHx0nHuWb0dDT8L6X2pesqZx+T3GJl+LdtqwmQLdi/C0PRBD/HutvJf7xgRJsBZW+pFEsmEx6AcIinJTz81ktwSpKxiLPk5dgwytW+U8ptwh/P/l3YxN8yH5cjyDf/NwMtR/7qV+tVaxn9t7wQmfWqzLbq6xrcXd4r2rbmkUXVc2W0nPuEG3YuodM40Q1oZrwttWwiPnBYNvJRX3ecOdZv69zglhJcCp/3HX6SZoe6y8L4C761Wjcg0/A5lhfK3U3PbPws9D7ziufZ1+iLy/Fcy4GjveiOnXr0QepkJ/3f96wbS9dedvLjc+XuSHOo0oBXwJRiWVmfMDXV7QK6rXuxNxmdRLZBr2UxrubP8eUe1svXPW9SAH8pxaSQdTXN9JVB++ldZt3UFpxei3C9NHCJFIs/6618xqG8NxDKT9kDfqKtyzZkqg8vWL1umVztNHeloWAr1KC1pnxQdqwzfzeBwAAADMgBRMAAACwgB2OwiGuTKyJ8NyzE8N07qnv+hnWmp74tp/ZMN3I2c3iuWlkWCZJDrQ8N5c0IcmuJgY7Q9yLzOMlF66USbicnvOHllyOw8OaBjqwYrxRYqtBB+hcbCKeHGwDvsKSUP91dencihLzr2CnEFk2bbBtcGmWCcbgwdbLDp92zJ9LRKzYEqUqqW0vVhah33eIijoWB4hGspWSbAydB4WDsthP6D1/OJEOnXac4YzHM+BTVPWrNJaYRna/LZ5TBHyVrt174YifnGnJTUHUqnsx+6Iwv2erE3dUnWRnJitWOZN1/aQouV+tS1ZMyu5YA130OMmDEMcH2pbpaKTnAqWZoPlOKeCrxFDn8oq37LGsfxRV6JYGgS0bg8up1e8kGHtGe5qp++zTNNJ9PrhybFJYr7l5jR/l3xFjn2iPPU3P+tW8liL6tFLAl0CcsWZnJiiVVNGtcJ5eSl/zUTKBuXWnmqRzQZeNmnSOTXAruqraOpKi/3BfE+OiWLOWKBbzNNxl4RwlxfMdO5C5t1ttHuXpEsN6xJgg5qUiMQU3oo5XYg7S9MR36NzT36MLz3zfr9BaznBXU6gkOAAAANIDKn0BAABwFjlOlVg9m6S/5djCgUSxWKC+C88FZlazDk92hSkNm3jYJ5SWyUktZqRwzpFEA/n5yxVGAFnqaO+m/qKPKXzfUei+KY6dKYvfN6lP9fe21RFVaXUIWe+cb1O0Ai5zGdFGlMoSuEzW9/xyni9Ov13+O+Gc6US1B+E8t3LNOtp1zUFauXrdov8HdgRGCZsTDo8bt++PXbXVBYKcS/T3fwqqaMSay8WTo7vp6XDJSkwguV8a7W1ecvkiTQ330ObdV5H1ePKd88d6W6hhxUradc3t8fYvMayEh+dwEwLP6dtFQeU8Kuy4VshdDoQAl5C0Xuc6JzRGDHufn1vs8CmYK6tEpIuB1hMLjqWiYrZwRA0e45gEZVhNtDVr5+knFpJljva10IGbX7JonrZ0Llf6bFFFQyf1mJxqweRBOqs5/kDVZaRvnOCy7+gSIih96T5HpQBYa4nk0gT7Ur7GkJRAWOn1U0J5hb/K8NZl6V2Lv0VihVIlL5F0SQSdX3nbzyY/Y3XkHBYAAMBy0pleGQAAAJANFuFKmJkYWvSzWOjmc5fLV8vE5s1Wr1ignnPP0tnHv00tR/6dMlNj9tmx5/iGKpcsdFIu7UiGO0U6irIpqywzKqiKCCIe7W2h/pbjy8YZUH1MrJ6FDqQmozDJrfQlbQ5m2zik2vm9WNR8UOhZNReP7ITowHrvsj14EcpD2P/cSexxpPsc9bce9ys0jg90UMfxRxa1K4yLFWB+nizep1K4V3SvKp+C6hAe77Eg7v3YBnyFJOn8oKoDrGJMrqGr6W18sMOv/DafnaHM9Bh1nHyMctlMnLskktG/gmtjd2B1iHrmY6FnpMKo68geJ5xrL8tw/fk0E9deJJ4ZVFt/qJpHLa0kMNx9LmLbMR/gaQrVwY8iAK8U8FUiVCVaOAHzhvmaOi1JwZT0LzHGEe79nDq80P4RIHzbmh7t95OVTI8NWKo23v2G6vWHqf4g/H09ba+qesCXPcxnpmh+bmbRZ5PD3bV/cUk7d39dCwAAoBxU+gIAAJA6ai1MRVCRODhuaFyhWBCHF19Vz7FwOHtRRZ7UjQ0RwCDI5+ao48QjdONL38h+09w1fNu2sl1HlTmCo7BryH6/VtqLIjTqovvMUzQx1OX/e7iria649WW0cfs+chl5QTWaHXGj3s/Tq6PUHromfWxX5icS24Ow9UI+RzPjg7Ri1VpavX5z1XlcUNsQNin+r7/5KI31tfqVJnZf+wLatPOA//8uzw2r6aQiQheVfidutmbZRJHDS2kAv0ou6UJk1ixnbnaCMpMjtHbT9kvfs786UvXbRb+fNCd8Rf1VdnaS0gybQEWPid2ndj1WHrzq+VVDRIDSuk07/LlDTQzMJ8Rerch+LJzUVq/bTPtvuZtWr9tEXBjqOLOsrYkqGruuvs2YTPYRtT2mN4Cfg0MXBxmqUtJVsegHfI/3t9OKVWtoz/V30LrNO/mNi4xwec3ohm0TC9i0HWX5FpgnR7wk3+zE8LL/Wuo4zC1wWrQxp/uZGrbj8l5y5GcLtAPm5w8qYSSKLb4koRLYyXzHus/HYhlF5bYlzkLL9zn3Xn8nbdt/QwLp0kNNG5KVQFgnTMTgTqw5y6J3XF3RxUK8wFXWSXAAAAAoB0FfAAAAwCXEIaBwIBCHgGKzcedVz1NbAjy1q+lkG3XpXsRW1slQ59lFP+fmM77T8Potu6yxyTD3rtoeY4su02HCY6Fb5YdmQX7CXDYqtRHueWcnR2h67KJj2oZtexe9H+0H5Ja+I5l6EgffpYCv0rWFg56WoK86++cMQe9Cd/uPdD+VslnapszP5Rx27kiQ0a/rzFOUy876P4sDV3HwGgcR7CUybwsKhRx1nXmS1m56g+/kCBbbYeqasHjgasGETMYa7szNjC8EfYUbj9Ki17Q8p+Xq0ZigB5gh6nsUgeKlecOKlWvo6hf8PK1au7HmGCqCxC5WPPMoPx+nohVFlrNUySIzPUqdpx6nG178iwmvqtbmRZVI+c5knNupXtmirin0B/CrvHaUZEycbSYhIR5NBF8KB9dSorT2E4/QTS/9DwvJ/kLtN7Fud/LGhMvfxXqdM1VtVklRmugXFc6rUyM9fsC22AtXeS8X+7hQ52Sl78RsribOWIVd9J4/RJPDPbRy9Xrad9OLaM2GrVplkDkXUDuvsK8frt5+A/aHrQ3gF+ufLM1MDPq2LBJ32Xn+wEWf4fx35qbH/QQlK9esX/Kf9jyHtnZYFy5Rifi5atAXznCUUN3XBfbsAqHmCAnfNc5YAQAg3SDoCwAAQPoIWEMJZ4XM9Nil73g00HaSNm7fH+6ScRZmitbtrjjqsMkWKBxm8znqbzlGc1NjtG7LTtp59W1UX99AnJnPTBMFBX3FthH7Dht0oiYojR88HII9KzbqJga7qOv0Ewu/t+OKm2n3tc8v+yoHXdpzsDM12ke5uRnfYWDZ4U6oi3g0PdqXyBlP9UYqG5uIEdzJRva4xJrKWf7MlbA486UVg22ATka6zy8EfJV+3n7gJlq5el3QhQKv33v+sJFKE9ozq16+aMxfjOpcQvpQHsAfbD82kdieqv56uHm9mJ+AFDmFGHo2mX1n4D5LmD5cYlVUJXMoy/owdXihKzvm5mYXAr78n+cz/hxk7w0vrLlH1vrcT2h+bpp0MdbftqxqX3Z2ilat3bCwpyuc1sQzb7/iJtqy+2qyugqE51F2ZoLq6hsWnlG9SJ4TfTL7iqaeuj7RuncYh4jP2HvhyKKfi4W8319s3XttrOul4R3BgVA+fkVuBu083hlmtLWjuEfHqUf9ypyCxhWraNc1t4e9WWQ7tKFNRabsmVQ931L96tjfHOw4tTCfEwH8HScepRtf+kZn+xwxTxVJmYR2t+y+hlatu5RUgbPN6hbN0gD+uZkJPwGFCGwV7LzyeXrlShmF3Dy1H3+YZqdG/J+37bue9lx/50LfwcmXRAkx+oyggMqSzZav/92EcT+bNIFwDHuQMpfgPHYFwVzkpGPa8gB+L3lwNQAAAGtA0BcAAABwiYWArzImh7uhH9mUO8l5Hs1Nj/lZ3tZs3Lo4kEr3BkKV23WffcrPQCcQG4vigLiWE0zt+/EJFLx8feY7IEaIoJMY2ew4bBZF3vBzxFFYh+5FNuFFP3c20c6rbqX6hkvLMOis4iZkJfv1K3EOdvj/bmg94Weg150N1BSjPZczU8dC2Jms5snGZlXIwXBctqDy5tRInx8YX8hlacvea/0+LvKhKxu7Mkd5BcISY70tEZyjLlNJ7yIbKlhMkGORjU5jYed94nt1XAJWDek57PsNbQeM1qxcCHQumZ/zE+uIyoYbtu3zswmbcPBTfeCtrH3FCMiP/n2eBqayX7axz5dpp2P9whF1MSM9F2rud4m9Up0BX0EU8vML85yu008uPJdYO65as2GhKqO896/HXsSeY8fJx/yK4QIRwLbvxruorr6exxiumaBn5u0kXnlfw4o9P0eo9tyiauvl79npKDw51E0TQ520YtU6P1lI48pVZBMicYIIMhbnLOs27aCt+65n3qbVEb2v0LsvHzWAXyRNLHciFw7mY73L5xupQZVZJ33dBtrb0gozwk5mxgdpfVDiSu5UWZ+JirhtRx9caAsi+OvaF77GT+yUxrlcEIH9fpCKlKku2oWHO88uCp4Rtr1m47ZUzsuUPs+la4v2Uwr4Kq1Xt+697mKFtdAySEykY0MbTumcypY2yEEGa2zZ1vYUoVuKvQZCOwcAgFSDoK8qDA0NUXd3Nw0ODtLo6Chls1mam5vzB91Vq1bRmjVraNu2bbRz50664ooraPPmiwsLAAAATAhcNDNxFHZ6MV07S4043Os68+SCk+vajdvpqoP3UkPjioX/r3wBvXoTGYxLAV9RnGBARJhscrmwUVdLRuGglZkapXWbdtKGbXvkXdvpPu0Sof1/PX/NsLSClNCRCDAWDg1hnUukZvZn/I4WZBMblRWeuRTwJSgUcn4g1L6b7iLXEYdaPecPsZkrVbdZDvbl+c5PQ51nF6rrgSTqrP5OhQN/56nHqFgs+D8Ptp/yq/CVKitwy7I/MzFEmYkRP9HAus07F/3f+ECH73RW39BAO664xf9/Ye8LQboyiNifl/Qa6RYs2qEpItpb5IMxJlUAPL3BIzbMb3VT3s7CVYEl4wins56mZ2h2csQPgth/04upceXqmr8X6f2XvhrQtNpPPOKvQQTTYwP+39sP3Bj++kASnuZtr4iOyUmqV2BfrqQIpXMNgXDQ5YSY+y99/4Mdp+mq2+8hrlSzV1GFqBTwJRBVMbbsuebi/DWU81DMdhTm95Q6dkb8PvO5nBJK+g9cxlv2jAnlrd7vywv057bGEmcposJICVFR6doXvlpe0JQGB0JRgU1Uoy71eWKuuvvag8QdDrZQvdKwAvki9jeVqoaUO+vHulfKCGdnNRZfIapDTI320WDbKT/YvDwYVie5uRm9N9RkYyKxwrLgx75W2nX1baF+38og2DhJ59jP5Srfb36J3Yo2u/QcECTTcTl9LUeXfSYC7Q4876VVx0XxuTirEH2dF3PdKxvR34p9sZVrNtCKVWuIA6Wz7KiIvQSh35mxQVqzYQvtuubggn9POomSlKMY+zKx5qIa9+XAghLL1KlGn1bOFQAAAEgDQV9lnDx5kp588kl65pln6OzZszQ+Hm2DY8eOHXTLLbfQ3XffTT/7sz9L1157rbw3BQAAwDlS60B36bmF4215VQOxKTox2OFnaOKkH1G5wrVDQg4HlPHw7A12ZGLPYhNW/BEM0Rnae/2dEp2QeTyjLmK/U4+fXdjIaF+LmaAvzRupfReeI04EO5eosuVo1xVZ/oe7zi0cnHSefmIhAKnmnWK1RwfbsBetHS51FhY2eznoi0/W9NHeFuo59+zCz3uuu2Mh2EDMSbvPPLUwPyoFIwgnmC17rqa917/Qr6wgw2akzcEcND3d84ioB2NcxuxiIUedpw/T7PgQrd6wxa/8EZqaj8DjGcPDQF4mdlEKxB0faPeraG/edaUfhFuiu+lp3yFYMDXS6/eHV9z6s4oOiCtfsxTwVaKv+bnUBH0JBz+RsCA+McabANus6ijMx5ztr6YeUhaRZGg+M02r122qONeIdetQwai2v+zFlftEYpuliL7OSjyPupueWfbxQOsJuuaOVxJXxDpMBG7oJHgMC0pgotnu4wROJ/wFe/d7DaK9P0x+P9EflJOZHqXMpcB+GbIpr4paLC4EfJUY6jxjRdBXItXK2gpIZLPy9r24rI91VzjTgoZb5+cz1HHiUVb7doXcvD+XEMneNu04sGg9a4yYe8UimG4p4kzOD/pi0nR0EtRfiH3X9CFvbZ9YEi79eIx+uLzSWlA7FUlAxB+diPX9eH+bH8y+ccd+WltWAW5uZoLajj5E+dycP9fae+OLaOteNf6kkfb6hP5i7A32Nx/1EySX/HvEM19528+S88hoN1Wv4Vk9p4l1Xsa8L1JHsucONYYiLgwAAJwl9UFfY2Nj9NWvfpX+9V//lbq6uhJNLERFMFEd7JFHHqG//Mu/pBtvvJH+43/8j/TLv/zLflUwAAAAPFCyeIx1SfcWsVEyAi09oBT0nDtUFvSl+8CB//vQtTEjKpuJALwVq9bS9gM3hcoCbxLh2Cg2Sk33F+K73DenSgFfl3+Os+lt/hnD6FlkHxcVf/yqM3uuWVatRWmAR8iN8mr9nDi4mJ+dYpOFLg5xdCzGELXNyOQuZ5h7L354cdDNaeyo+k51dg0B9xKZGxd9zSv6gUnKxGDe58cjvKNwZnKxA7+gkJ8PmYFa7/sYvlT9rfznbftv8PscMTZWaie+/fS2+NVoxThiBYEq0GCrodpDJT17lJ2ZoKlh3c7Z3E+9KutTrJdK1T9yIxnqPvtUjAPnoAD+CN91IdNmtWcLqdNqc7nc3Kxf4VXVOmWpY0nrcz+hbGbK/1lULrz2zlf582ARnFsK+Cpf7519/Fu0as0GP4h+9frNVmUaFboVFSJEkIGoXLz72hf4lRo5BvCLRDeVAjhMzefsnLvEkbmOvUOvqDLaffZpvx9ZuXodXXXw52jV2g2S71L9+VQ79juHZzawN6wQce067u+JSmv9LcdIO5ZmthZ6FkkmxNgsAj437bwi0u9yrKisk9DjWFVfSq/mXE7sNQ+0nfATLZSvcTmQnZ1c9pmwqYpBX3HG/Tr1VS5AfIJsVvQPKuZ5wdfk4ZTsZrCrFzroLu7aS+x1cQr4Ev1C27Gf+kGspSo+Ys9OsHHbPlqzcSvxw0Xb40FQu9a9lrVz7awbz9Cla+/tLT0L12EvHScfXaiKPdzVRFcdvJfWb9m1IE9pf1DILBIhbd59FdXXh9zLYrZOKgV8lRCJWMTeJItqXxVMonT+MD83HfOams5YVeDFk8P3eSkWw++3xsVwXyvWfWJ9tWrtRmnPGqqadNLnXtrOMWQBAECqSG3Q18zMDN1///30ta99jebm5pYNqHE3Scqv09TURH/xF39Bn/3sZ+k3fuM36B3veAc1NqZW5QAAoB03N/zdeS+1KmhVO8Ba8kV5wqWFKhs8wgFQbEyWmBrpo+te9Av+3IhjqXCRGbTvwlEqFvO8szPptNMI78nPKhY1o17MDUKdCDvuPPnYQn8hsnyLDfakhH1Gcd/gt1B2jYDrlTsAckBs1ousu8Kxdv2W3QuBElJZUAW/fsYNorVPVPQzi8n+NNSBRJQAWCaUAiFK5OYzvlOJOIhcGgyxlN7zhwOCviK2K/F9Wdm9HVrifuFwAAEAAElEQVTnCGdK4dCju91EHseY2HMp4OvyzwPU0BD2QL26ozBHu/KDR7mtQxY5ClfW2cRgF3WdeVL6XC7oHU0N9yzq58Qcf6y/7VIm8cq/IxyYZ6dG/LXfDXe/PkDH6oJtktB56nFf9pKjiVjP7Ln+DmLBEpX1N6sOhAhcnEX7PMm94lypihyiKpxwkBLj9Na910u7Z4AgZAIRjNl77tBCHzE/N+M7gR245SXJL67DuYQLNbqb2YlhWrNhq7QqalKxaCyuZjvCQapSNYtY147Yn3EOWqw2b5kY6FhUdUBUHVbs4UhpJGxbCuoP+1suVy/QSWznSqnNgW/bSkqoNQUTR1z29+LStXCRw7L2xOXMoXxfoxTwVVqrlgI2xH7RVbffQ+u37pZwJx0JkRjIYGzcjbovx2SObms/7Dwh53Kak84FiTU3PbYQ8OV/zSvSSPe5haCvpVWRxX7HhWe+T5t2XUnb9t1AK1atkZdYM8LYU/0sOxrFS0Ff4v7q1rXRryvWrJ2nH+dTDbzKO635vmOpNfr+oPANEntW4gxNVK3bvv9Gso0wNij2jNpPPOLPO8T5ypW3v5zWbd4p5e461jUiOZBYs4q/5e4rAAAA4E4qI5AefPBB+tjHPuZX5aqWCSeOw0j5dUrVHsbHx+nTn/40fec73/GDwA4ePCjhKQAAAHAi1uaFx6/6loSb+U55MiqBVNuoy89naXq0jxpWrFLuEBFmPjDe3+5XEmloXEnbr7jJ39gS8q/bsis4q1EcsTUcOi6twDY3M06zE0OSNjmCCTXvWvKVXDbjVztIduNkv774UunYsQ9+Tj7PL5wEy+UU1X+yM5O0at1GskGXfReOqDl8jfmKRACayNQmEBvj4kBi51XPoxSdhTtL1H4r0C79WBZ9fUBa+lvT1HK8rDW/DB3AzxzhmG0PyXUr5rB9LUdpcrCLVqxeR3uvv1NZVmUxpx/uWFyFLSrCnkRQcrVvVIRTEJEmG619cFzJUZiMIOyw98JhGu/voMYVq2jPDXfSxu37tMmWNLhVVDzR6Ug30HZy2WfCSc4P+qqBCDYRQTZrN26TozPFTWs+M70Q8FViuPscn6CvJcTOIqxqzVrFLlWMz1GrQwhHBZHlv1RVRTiabNunOPDLAMKxZGlF3/GBdjlBX2CBluf+nVav3URXPf/nKjvRWRgME2fvTCUzE4PJkzDFJWAuxz2BSXnAVymZlai8GYrSMwQFDTJ5xrAkHnckPG/Q+YOJgC8RsC+CxYuFnF/5Ye/1L1wIWi3k5qnn3LN+YIQqHSetHOTyekskFxNtNX6VMpmB87orqgfs6yjZt451YEauEa0vt7ydXeonhtoXj41LbU2s9+QEfcnDtjE3DLr3a21NxqRfx/Y/t+x2VP57XIJYlwZ1leYPtfbjRGCr2IO/7kWvTV5lqKSXKG3LXfNaQJxpywr4klGJUP9cLlrCGbG+F8m2Sut837YVBh+aPCvsvXBkYf9R7NH1XXjOT4SdmFCPFKO9LqH16IMVq0A7M08EAAAQSKqCvsRk5lOf+hT94z/+Y8XNy6SL82rVwsT/tbS00Nve9jb6oz/6I/pP/+k/JboXAAAAZsQYQ5QHK11y2Bzva6PGlatp93UvWO4oJ/ueUTIC1VrEBug0NzdDHScfo1x2NrJ81W8XL5BFOCF1nX1q4edSYIRgzfotdPXzf54aVqwkXgQ/qwjyWooImFEd9BUHcdhqiuBMVRY6XsiSjdEjlmdUKyE2dRcHfSn1FA7+rxD6zteohKgKEUgpKHeIEw755f1aqSqLqqAvztm6k2KsSolUR2FGDV02cZ7NZX1YaS82jMGGZQnRD4nA6ZHu8/6/RTbH9hMP040vfSPV1zdIX9tMDHZIWROJA64VKytnRI083jBwovXHCy8d1UVEtlVRTVQEHYn1hsgcKpxbhaPtaG+L/5357MXD5pt/5v9LvK4K/R7DVncNOKiXEugTiWTvLn9pDijjsqrncqX5alxcnmsmcURR18d5kT4Wew+VnDxD3YnVeF+d+E7jsvQQzrmEv05rt+e52Qka7blAu665nbQj0adTy7tgmpxK1vpatz1Hud/SqsQykzEpe25j717PXE4ZAWLl5map5+wzC+9WzIPXrN9KW/dd5/8skiFMDHW5Mb+xLBhMZM0Xa5HktuLZ2faYTwXU9XHh7j3e30aZ6TFav3mXXwlDG+znaHJhU50lBLX2VfjPrxmMB7rnbNw7upQie/9ONUnatlgLTI/1S/PhiTYv5KG/0ITKgbL4S73nD9nTT9S0I6mHvRU/npuZWPZZtXVIcjnIGOI8YtHP02N+cr1a52AcnntmbDDyPgIAAAB3YFIzWT2FQoE++MEPLgR8lVflKlXkKlH6v7h/SpRft/R/uVyO/uzP/oz+9m//1oAWAAAAsELxRpSoPCUcNkVmErHoE4dTwnlfKVEyl9T6VsBB2lhfq/SAL5mVsZZuDiwNlmC9kebZdXMRAGgMyw6oXXUUjoIUR47Q/TZvXVQKEhbVvJqe/I7/p6fp2YV1TJDTsshOLQ5cpR9UGmpb/a3HeVTzUeaP5RlvR7FkCPRl49HGuPd7sZCo22r2IjKViyyBIuOlSsfnS4Iouq7q71eNIA742JPSHy4NRJ4eWeyEH5el8snqd0VGRuHUHYk0T+VqVPEL9V1JiIoFoiKVmFOI6lgisFzQ1/zcEtGKNNbfKvfmMqpDaHe6jBZAE1pGmfMvSddS7bwsxiFRJdAapzsdc7ka95A5l+Myh1L1/ms9X12dpmO5eLmNDGZN95QE/SytrHT5btHux8NuIwT1aUFOXz07OWLs3laSsG3rYmZ8iLqbnvEzpotKnTpJWrVVNyLhwVKZe8ocVMcHOrTN5bSNURYg1sKtz/2ETRWRmnOXWnM5idHI1syhFSLWrKKPE2ewHaceW0iekzgBWLluTQaDa8CqgFWdaD4fEfYkzt37zh9JcBGZEjHff1F4zYr9tLLHlnBhBl0Rl6RzSRGJ0aTdL0qhL0fGE45JVvkkndD4jh2o2hpUCTrSNSI8U9y5EAK+AAAg3aSm0td9991H//Zv/+b/uzzYq5ykVb9Kv7/0+ksDv8TPX/ziF2nlypX0O7/zO7GfCQAAgN2Owsq49Hy95w9XdJTbccXNhgQryZEs6EscgmqlhriZ6cVZYJYiDn627LmG9OO4nUtCOAgLZ3PlzjvsN9XUOApzQsbmNQ8nryRUll84Wo/1ty0KGt644wBt2LanZsDrzqtupV1X3yZNNlOHvUMdZ0Jt5hqr1sWEJI7C3InVvu1+ZGOZNvPzc9R58jFtBxP2993mEVkP12/bUz3LYZx9NIMOPtydGZXaLYNqZgIxBx8faF/02VDnWdp97fMrfl8E6HBo24v0ZPnYV4tIOtali4BuQ2Sd3bg9RHb9uosVo3vPH/HnNavXbqKrDt7rO9aJCunV5nrh5oGe3vcgM6DY+rWjPc4latcUnrS2LcPZBUTFS+W9244+SKvWlldmV9COHHAIrTVXMzE3KjmKz04M0ZoNW2nr3uv8qq2ZyVFqO/bQwh6CGKevv+t11NC4olzgGDeUJ7t/OSZzubmZxfPcqARXMY51MSMIm1mzcSuZptSOsrNTft+k3s48aftyKuw5+Jr6+zhOiIrVS4O8BtpO0rb9N+gVJN3b1JHRUznVk34NIfecqD5SKPj9pOwKJP3NR0NXV5apX+77csAOOAVmm+7QL68FzAwOC/cX67QQSYX8aplTo7Ru807atPOKaPewOlGip3cuxzyA31Rl6sD7VvEjl3Xv8vdS7f2k3R8BAABAyoO+HnjgAfrOd75TM9ir9HlDQwNdf/31dPPNN9Mtt9xCV199NW3YsIHWr1/v/y2+NzU1RdPT0zQ5OUktLS105swZOnv2rP/vSpXESn+XB3599rOfpRtvvJFe+cpXatYIAACAtLLUUU4+8jKXcNlscJEg3XJ3ilYlX9uxn5JXKEQ+kKi4mcxbhXL0yugZhROaqBQjgvc2bN3jbw4Hfnfp+4rlXBI2aJWRkkIgsi4vZaD1eNWgrxLDnU2048pb5B021qm19WqbpMNdTeQmMRyFQ254X75MCJsXvytpk1rJeMCq2ZoTZuGwIYxTUo33GXToKjJXKgn4iuqLlLCvjmOH0qruRXYMS3ZfUSFDOIDsuuo22n7FTSSDQj7nO4iqJvDZA+yXR3C7osGQ0fxEvJeomVZ1zq+SJirRTfWgoNrPIvMQ2dSBtKhsvuvq20MFG5YCvgRiHdH01L/6/161ZgNdefs9tGrtBikyzZusTO2Zd9zl1EYCMdUtqmwnnpvjhhIMPl7UMU1U4PYDpUMtuxZ/SVTWHbnkyLv9wE20Ydte4oao/pqZHnPHUVj5WMhhvnoRUbFV/BGIZEJinbf3+jtppOf8ojEgl5319++27r1Wj2BVp0aesXGK+967FgLstPnIj2jPdS+QFnhYmfBtc7TnAuXmM5HlqSanpAuZ+d1l1yIeGJqriD6tUgVyGaSqn0iZL3NP07O0ceeByOdfXWee9IOnBWs3bPOTljSsWJlYHt/nbaRXSsBXjRtV/JiNM7vGxEi2tW8xtxvpOufPbcUZ4KYd0ew3EQmTzmlHWeBK9F9h07ZCBpuKZM97srO0bd8NVeZwZhEyrt203bitxOqXAn8lXQH8YjwVa7/6hsYafb/k9WHEs/ZF/5+wLdvQFwAAAIiH80FfIgjr05/+dMWAr6WVvUSQ1xvf+Eb6pV/6Jdq+vfqEbdeuXQv/fvnLX77w797eXvrud7/r/2ltbV0U5FW6T+mzYrFIH/nIR+iOO+6grVvNZ68CAIC04DFZxLJe+MaUTeYzcckorGoT1iab0fIuYmUelbNZMTs5HOPebm/mV4dPVihR0VBUpRIMd5yl/be8hDbvurLyl2XLx7gPj8p8drlDqshoG4ZiMU+5uZmArNwWpRSOgYpMW9z7iqpZ6HRXtmABd/l4Zi0c7b3Yb+uCvcO5Je9XBEr0tRyl9Vt30+r1m5OJUSxS+/GH/WALU0StMqa9P1I4zwibyVK1HJHRKktY5xJyBInzGYMHyoNtJ0MFFgQhHNV7Lxymqw/+XGJZZidHqP3YT0knom37DgxVkjFoHxM9jXJY1B6NOzJd6k9rrWW47FtykUPoTbUs1a7ffeZp2rrvusjJv0RQbKnNzYwP0XUvfE3FuRwbPYclyHy5JLpScrswCTqW/ora5y4FfJUQlW/2XPuCRdXkS4jAnKRBX+Hfo2fV+UPi9xTYnavdl5O9LzbYfjp54OHsJO294YWJZVEeBLGEuZkJf597fnaKNmzfR3uuv4PJXE5nnxrjvMzFqq1eehPTWUUM3YpzLHGWEoWZiaGFgC/B7NTIxSDqiHPCSvRdOOIHMhiMTIl4i3Tas45+bnZimKbHBmj1uk3+GCT6OPFZ99mnF77TdeoJWnXXa/3v6JAtdHVq3QH8iuzQWHCGoXa1dJ7V13yU+luO++fMB255SbKzB0XP1H32GSnX0T82B23MUWoQ69D+5mNUKORo8+6raN8NLwqusFXkM9e2x1sCAACAbniGyUvkT//0TymXywUGfInP9u/fT5/73Ofo29/+Nr397W+vGfBVjb1799Jv//Zv0w9+8AP6q7/6K9qxY8eyTc9yOSYmJuiv//qvY98PAABANbivVrnLp4jSOFhjpQoHXQMEbCyI6jdhK+CUsmsKp5KB1hO+g8lw1zka72/3nYWtIzALXX3E6hDkDLqzwwdRyM0vBHxdvLtHQx1n+B9yGpRDnQ7qpMnGP/NUHU2N9tGFZ39ATU982z+MYGNbEgnc8NZ4iCYcdcoPFhcLouKO9gRjl6okCYc6YYPCAUArC1O5Ojsc6Ng4B2m4nwabrDbWhkXYbKyge5lEHW4c6Oov2zwTZ+iILK/aGie7qSfhec1VhwgkaVB2RMd5rsiw4enR/mQyXNKZyIYtnAmkEOI9TI300fmnv0dnH/smdZ15iryA4Db9NmtofRyyj1fW79W4bOB+goQ2V/5MSZ/Phj7OXuI5tod1zM3Pz/nBD35l+7L3KP7tSoVrDpW+jFZ0NF0VtYoNB+3/Ll8fmmrbnoP9i6GqrXGS2lV572ErJQkbEwFiovrNssDDngtVg/xDCCgdIWO1ZxNtt+PEIzQzPuhXFxvtbV72XKHk01mVhkvbUSZH9evWBSRZkNMPh5/L1dyXMzCXi6YD7ucA8vvR8YGOUNfIZTP+vm/bsYeW/V/P+UPxhfPKztY0J+UKq6cgG1K1dpO2bg/DpUdgcZ50SQQRRNj63E9ooO0EdZx6jPpbjvmfD3WeXfJ1j4aXfGaMRVVbGehSoX3Gum6EuZxMuadH+xK9DzFmzc1cTFpS5UtkijBjqui7xb6i6GNjXUfFXI5LUhRD7zQ3N+tX2szns77uhQ9TuW9J7fcTfa4ipV9i8noAAADwxOlKX88++ywdOnRoUaWtpYvHd7/73fQ7v/M7tHJl8hLYS3nDG95Ar3jFK/zgr69//euL5CjJIn7+3ve+R+9973vpwAGN5ZABACDNKFk88ll5sVmkJ4S7c4mKCi/VxTD7XoVTuajsUIvRngvUe+HIws+DHZczdK7v30VX3X5vxazWttlt1OoQqUDnK/Q8P3v+UsSGcNg+JY7NhW6HmqshKSGCifMP1JKHcNLoPPnYggOJcJhbtWaDlOyaqghjtyKj8FDHacrnsrR1z7W0ZuO2oIsFf17jNlEykwn9CgfFaldLC+KAXzgt1Tc00q6rb6MN2/b671Qcfk2P9S9858rbX04btu7RIhPbTJtRnZGSmpHJuVngI6mXqdpYG5YRzVnTIzkKB6pQ7/tW+i4jxO9zmrdo7VNCT/n46CeQUDFfhhyFHWZ8MJwzX9jgjSBEGxWB4GJeUMoaPz7QTitXr4s8Jta0Z4nVytlUiDXUhgPbnJAnaRsK8Uxh+y4u+3LqxiKONpP8+vOZKeo6/aQfOFAJkZRp/813x7t42fNPjfRS+4lH4ooZ4ZaSdO7JT1IiHGSDb6fAVhbi98NP5tI6lwvfz2nWj6JxMarjfMCXyRZEsNfkcE/g/xfzeapfGVz51AQdJx4NfEciIcrSINKgZCtsKn1pdEq2CfF+6uoalOsh9L4c82RMbiZMkzCXm5uh3uMPB87lZCDmcTL7k4EQVbdFMMTUSA81NK6iDdvEHnbUSl/x5at4uWKRes49S6yoZD+K+0YR3FVuzyKBjTiDmBzuXvbdicGu+OuIqIR9bAfGDh8Ze2lejD0+ierrOXfIP2dMSjYz5V+nvKpckr5OC57n78WJ6tyiPTU0rKCrnv9zgd8NvIzGtZvyOWVIm1b9zOJsdek9RHXdLbuvUTh3krEvJ0Mv2KMHAABXMZ8KTSEPPPDAop/Lq3s1NDT4FbZ+//d/X0nAV4l169bRxz72MfqjP/qjRc7h5QN4oVCgf/qnf1ImAwAAAKZw3oeKu0lW+j0ZGxZMDl15YVbGMFnP+1uOB//+2ABlpsfceEeR90lqRkOwZ2H+GnjYy/sQUY58IQ9UbXihGpx/Yx1yMncUzkyNLssYLLJrioyI4uBSFkJ3YbO412K2ahWoi47CbUcf8jONin6+8/QTflY+k84C0yN9lMvOBv4/G+cSVeOSd/lQSgRSC4dCYXsdJx/zD/6FI2cp4OuiGEVpmTZr9fU1/y/O9xTjguOQDUTRs953cvFeou/OTI5Sfj576XPJ441nsgpZ2DZZ+itC1JcyFAbfV72rhLmcZzC4NZYOwkR9yWwPdQ4n3zBzb5FIpVpG+MmhroWAr/LfYb1mYzI2m1q3qayqG+2ZLKnaGhjXz8OOYqFQdFEhRqWTsKAUbGqSwIp5muY5Y31tftIUE2NVcLIL4o2UjOee3Lkhl3HR0ip68QKuKTHVAr64Mj83Hfh/uQhVA6vZrJKg+ghBpiYwNhcIOh/RNXfyQq7jmM+VkDCkMiJwQPVcTnZ/IRLjiTlo0L3mM9PUcvhHflBI5+nHqeNk5UDY6siVeWKoy6+Mq5caZ6wG+lZx5rBUhqBz/GVjUNI+purvV/EzWTQfZDKXS6gLc3tdcm1utKdZyjspBKyzoiAqqAYF3ctGtJu+C0cX2rCoICiq5wV9t8qFFAince0Y64yVlJKdDQpE1OPrUmuuGpgsi/kcDgAAgFmcrfQ1MTFBjz322LLNglLg1Uc+8hH6pV/6JW3yvP3tb/dl+tznPres8pj49w9+8AP6b//tv2FzAwAAJKJ1MRRrDeveYi3UM3l2OZcE2pGMDMymUNg2ljqbLUVkCVv7vJdqlUkNVTJzV/zYtueL4aDGvU+T/A7YP29SovRvEvtCW6voiUApUYlJVDNct3lHYjsVATz9rcFBtFEQwWPVKkSIgLV8bvH/D7SejLHhLa9NzFQNVEsPA60nlulfVNRsaFxZMbBaDhHeY03fEh5zuUhljVRK4VfEk3XPeId1xucjhu4vnEvajj3kB02KcWbvDS+sYr9M5nJKblcrgL/C54ymO3z6lJABshqpZp+6bReOe2oY6qxc7UGQnZ0KfZ3q9lDDEUFiVSQldsnQuaTkRFdygNy27wZavX5zoKwiQFmsKdRT/cFFkPTs1EjFOSev/j/ZCzQ+L6qEBJGqBYnKQqwhlyZDUYl4VwOtx/0qZY2r1tCe6+6IvA8h+31X65cv3VDq/fxLMnMUjqrTpW1ZZRvMZzN+NYqK/zef9atli/tXSzajFU7dUZS2xUnuMNi55biY+FM5qQkkWI6hChGJvrxigVauWR8tgN/w+aGRNXSkc9Nk+uFph56UeZZyFOiuWAg+GxbBVeWBbGIfu3Hl6iDhSAd9F56r/gWFSedsDeDntC8n7E34kXCSSQks+7lgOL2PwfbT2tZH2ZnJZWesgYmUE+zLxbEHncnfwui7vFiGKjnCEBhsJcM/LdQj2dW2AQAA8MLZoC8R8JXP5xeCqsr/ftWrXkVvectbtMv0/ve/n5588kk6fvz4InkEIyMjdPToUbrjjju0ywUAACA5Mp1f1KL4nhIXsWw27JVV8Yh4Xd9RWI0o7HRuQduO7szogG5LB3Y8fOcjIyULXdhfqbFnu3xT026kBmpZrBZx0CQcv9ZtvifxtWQFfJVnrgxiamR5huSlBxRSxgmZfQST8Ur1oVGlrJrTowO0efdV6m4a6ZFqBPBzeU8pCMgOj8pn5qvPoY4zfsDXQubN5udo5ZoNcp8v4vhVLBSo9/whkoHzgeglZLfZqvM1r6o9DXWepfr6Bud1H6uCn0NzXGuIonLNYx+PyoLV8NQHHR99yM/yLBABCNe96BeCHYVlO5fUmgMF2E7zkR8ll8NRVPf7RseVEP1DST5h2/rw/MQXYuwVCIfhjhOP0MYd+yNfx3mYBPCrrGYYlq6zTwX+3+Rwt/+HFwlflIT5V7z+R9fv6LwH775CVzKmmpdks5+iRo6FwAxPVO444ifUEmzcvo8O3PIzait9lV87QaU1IXs6972qIFMfUG0kBttPhU5UoGsfN+j8Qwsef8MSZ34m1ybV3vfs5DCdffxbF4O+GOksEVK20mIEBDEdJ2T0A9NjAUFXCoiyztGpc1Ghu6fpGTNyMLWtWhS9QuK2GS1JelAQ8OW5KAAAAJCaoK/Dhw8v/LvcobKhoYH+6I/+yIhMQo4Pf/jD9Na3vjVQZgR9AQAASEzIxZ84oBcO6qvWbqS6+vrFl9CwSVYr4MFbuqg2tYFUpaqRre5szmyCmkS6M2PU7LgeDXWclixD6LtH/JwHMjbGwrcdr2rlnu6zT1PDCh5Z02UEcsntUxT2rBpMdGqkl2ziYruIksWZRztnM44ZEkMEGnDuTwfaTtLc9HjN6p/SqVadVcntZGZWtMgoZdxCc18y2tey6GdRmWJuZpxMMtJ9zs90HIyCbJu1Dgu1VzPzmGbarE4+nyV+JHswl5IRpI6EVVYWPvczU0iSafGFA+RgMpdTxsXnEw7CpYAvQSE/T2O9LbR+6x6FAREO6jZw3CJrYbOesYTeC0cW/SzaVWBiEw6qFcOqkj6VkeOXF71tSpnLWY6qti81GVMEWNhiytBd4VdULK14LyUBZgoTtMVEBBaUAr4Ek8M9NDHUSes27agsjoQqmGGrZFSj/cQjNDM+SI0rVhFrXFyGoltMrsLA+QKXBCZyrh62basaa2s9n/AbqdWnmdBRCZ1VhxehLCixTpqOsdZkToK5XNR3KwK+RGVFbZTHrVcLZirbhza2ntAVwF/zu0Efe34Sw4bGBL4kLs6zAAAAuB30dfr0YifYUjb9e+65h/bu3WtMLhHUdcstt9CZM2eWHaifPXsxUx0AAAC1KFk8WnbAJZxxhy6VNRcHFFfc+rPUuFLGAYBXc9MhjP5ZZaFT5FzCc9PNYNauiPcWB45jfa1kisiOkZJVO9zV5LdjndQKdNTZZmNtdGvcqKumi8z0qPlMgro2A2PYhNNOxw48WzVH1Np9QJSN7hrfZTOXMzNuLg3WV9G/Vu9nvcjZW03Cc86VjKhjrghI9Q/4zJ3FmyOiXMEBUXLvU62io3qnzSjJLgy8WAbV+disRZcRxxHS0zpHkTaXYzhtcmGemmhNFCfYWWNVl1jB2Irbuli3L0VUKwoM+pKwZuXbf8WnWjKmS//QI4fQrfL4fc+8PkNexTQikVkkzIusnMrt32NTzVFKMqZF13DnpSYO5KmzZ1/OxXEqOXWa5lZe5HlMX/NRGZeK3qeEtEPV+z2Dbcv3t3rPHaLr73qd8blvmKRk81HHSkn3jl6NMyZG+5Nq97Z/3Wh2X45HO6K0j4Ua9j9c3LO3br+pii0GyWTWfu23mer6k/d8IjAyMFGKjr62SjCTDmuPa6e6xqBa/V/zsz/0q5zX1ak7CwYAAGAvzo4OXV1dFSehr3jFK8g0P//zP19xwtHZ2WlEHgAAAGYwtSmSm5v1nXFLi8mZiSEar5plXu8zNR/+ETU9/m2/Eo5ORNa+icGuxAfYKsEG6EVy2Qy1HfspmaXO6LvrbzlGprDiQKISS+RO/E4s1MNQxxk1mZVl6MJCfaYRLu1fhRxh+gRdGSTDZNpUd3PPPed3LlWNDLeNjhOP0PzcNJkkXFAhT3TLbKS/9TTK4cWotLzk0JXLmMQCT5XtxgkAsGRscIhIFXqrOZXr7OcUt19TFU/CE66fU3d7zxI9XcLF/t7FZwIa5ylM9qqjBurD7u3AlnV+WtHoKFwp4IldUIbifmU+M11RN+arU/OdR3Sceix0VRHdc1Ed6x3u+10L+3KMx+TA/kW3yCruFyvRnZl3VV/XQEbha6IBqNs/TXOfFQfO/ZvWoKJacxIlagqRoGPpx4beV+D5g67q1KUq3gFyiIAvVnNuAAAArHAy6Gt+fp7Gx8crTuhuvfVWQ1IFy1BylBoY0FhWFQAAUo0ZR2EdcoRhuPvcss/6Wo4aEK0usIJTPp8lE3SdfmL5QU7CzYbAg4uo2c1kvhQmvgFx7y0qfBXy82RSLtmO7tw3IX1qicj8GbAxdjG4tVplkcUYcvKAc4kBlSfPKCy7D+N62NR65N8pn8sa77vr6nHoWolsZqqiQ07UwJLwxPj9CDYjux0Ix6SRngvEAp5N3Op5TiQCH4WJB03gWkWGfGGrQ9j1vqv2F2H0JvFxrQkIrkKi8Z15XxHsQKfKQUnVGFz5qtF/xZBziUpH4SgVnSxpr8oCSJi3V+144b+jdR0U+VY6+x29LOg9wr6uyncV9dpFT08SFdbEOkIK80sS+vPS0BDlWkz70djVmw3t80Tbl6ua7UEa2ekJKhRypBWe5hRAuLlcvKAmves3FYikozpwYWwHpteQvKj4jKYeO8zYpFC2NLzvRUhYm19eK0TQXZx5D9P5nzUk2Y6Wqnq17zF4zurxaOuB20zJ53KR1hMK25M1iZ4AAABExsmgr9nZ2cD/27dvH5lm7969FT+fmZnRLgsAAID0oS1YptbileE6U2wsDHc1Lf4satbSpch0onF4I60Y4hCxpHNZlekSEfBeU5HN1tJDFxlBX6HbPeP3vbSPU+mwG8cmXN2EFIf+2dlJ4oenrR1JdZxR0cZCXDMzPUbjfeVjkKGgr7p6o85JnPv71ud+4lcEXUwKxmYGRLMLfbqPbq8xZbNh+KqRQVK7g2PE4DNtmTaDRTCPMrniO8Y6icvjg8Znmx7t9zP9m5bDxJwmbn8qJVEJE93GDlDvPs98TmqvfmvZ7EhPeN1bida2oXZiWDOgUwsxAs6Xfh7rndjdBkUgRLGQX/4fCStxBu3hxbKJKAFIlr8PFkRsB4nmCpH8T2tVh+Dx7tWtYb1Y/y0juNWK5IA1mBkfdO6Z3DsH5CsnH93Kv9/CuBkhOFnZWFtDn3X1TrqSJibwfSR8TRzPRdUl0nOy65Iyx5KbALr6tVSMzWGuOTncQzxgcP4AAAAAxMTJmXo2G1wdZO3atWSaNWvWBFYoAwAAIAfh7Bno2K5iQ6DGNefnZthsTAQ5CkshRBYh7oeBM+ND5D7mNuoq3WN8oIPaTzwS/hoGDk6EjN1nn/b7FeGIxHEDVj3VHYX1+pZEv9kyu4klL+/+yxRS26SDTauQz1HbsYfICapmFOYcxCGPRdVRjYhRZY5VlOhcUjXjIN++MDefofGB9sUfGvIBWv595oGPCZmdSMMcugoyX4miyis1nUs0d2pRg8+WH4rzaweq6Ws+6s8rWPcJllQOqgb3PYtlSKsOIe+5hZNE5+kn4skR+6ZkD5oqfQXfn9jSe+EITY32aenjtAWGR7mDof58oPW4tqocquHjWK7wACKSM7RCfYSes5U+lywLl1cdAaGbrjNPRfwl4osXI5mUoTY6NdJHQ51nyGV0zmHZVG01ROBaVtNcbm52gjpPPU4dJx+lVGOPyaRcdxGDW1PgcB/chzIxak+xL0nZfYL/j4kujO/rMNSD0ZgvhvqISqJ9Oc/CdfFius48SWN9rZc/MCRn+POHWFcvv2DV+zth0wAAALTjZNDXihUrAv8vk1ma8ZlPUNrKlSu1ywIAAC5SLBSo7eiDmu9aK1tK0d+EF8Eil3/DzCKuvr5B2bWdXJgm3GwIrpYT8bo6fFEMbKyIDfy+80ei/pYiaSrfS2Sa9jeh+tt8R8uepmciOzPq3FxTfq+Ay4/2XmB9ICMna3qK+8KqJA8EvKwz+x2FlyICUGZCBkNw3YivLZ9s560a12OiJ1OZNqvNgSXcPNy9JASYqWKg9YSCgxpLMNQ2hjubqOfcoQh+qjzacBRslDkIDpW+fBkiZ7VPLl/Ya3CdyxWLeeo592wkecM8s5TsvZe+qjw5hgNBZSYJdMQP8a6jtMHpsX7zVebD4DlYnTrEQxVyWRrtaY5UVUsnAy0h53IG3l+0sYiZgVVhqPNstF/gPC+q4VTlMhWTzikk6nxJ574cZyaHuyk/PxfBPkM8tJQAKweUG/Dc85lpZ4Jjqu6916rooLF6hCqWtxVDdhvWUTjW0iXcM00MdVEa1iByBdFxC3N9aai5jgXzIS77uEoTlQTG73ts3lmwb0PK4Vj9qkyKyL9hUeXd8f526m85xlK+KO1Fl62YqdpaO9hJMNimI+GMJ2euHacvDHXr0kEZqQP9OAAAOIuTQV/Vqnn19/drlSWKDEEVwAAAAERjaqSXspmpwP83tfEiNuEnh7rNb9SFCvqKKVuETWW+lZK8cBteod+fvOeUZbucHB1EIEQ+H1yltSKeWedxUfWrWCnDvg/zDG1S7Kbys0wO91DvhcPEFp1273ms2lmizUDNz6H2MMlPUUi66T3PuF1IdC7RmlHY9valCBmVvsLOac8+/i0/GNpmktoRj8Nc/Sx9btEvOJUxPfC16nZGUuFcUv0wUVRrW14Z1YhHfbTPY9+myvUYjzMTg50Rg3w1P4u0qZbB/QLPnrEh8limybZz2Yx2OeLp3nOvOoRX+/Ey02PUc/4Q5bKzxJHM9GjIb/Ltq9kk05COx76aDIdkRP6+hkIfuiD7Ge1tpsH20xV/RwlVHJa1VIewmOxs8NlVnLFe6jmL4kAxHf3H0nsMd5+LEERgoh+uYzeXq/WeVOzLLbpm2DUhs2FT9lzOmf1PRx4j9LNyPfq2ZU/T6QD+GlFfXKgTviRh5nLqnoPr+x7pueCfhS9DVXVq5XrQFSiWfI+p++zTxJVIOmFi26rHgWrXn8/OsG3rupIxlX8bAAAAiIqTu66rV6+m9evXV3QabGpqItOcPbs4a15pErNjxw5DEgEAgFuM9bVov2fYBWnfhagVjRT4u4faqAOLlFZRlSEX4YGJNr1Ub/QX8jk/s3S4rEwlzOisUMhV3ZCSAkN7CKKa7Y72tvjvliNSqkNEqnZgzzvl5SBvwamoS0T2EzbvQHcRxc4l4X9JuhxJ7isqwCS/9rJ/VL5XQcK9NMFpziVLlkBncQNzJeEwns9FDODXia73b9XwFeQo3EIDbSf0SRExO+6yMUjhq50eG6CBtpPElWX9fTVdaO8WrGoMoZUmHExsyqbPORiC05zS2BzBU6kXPvMe9u8pctAkOQmnuXK6A+qiEPwsg20nKT+va24erOswQV9x1i6sndRTCNv+Y4lcU0MVnLOZEL1iHlOda6L95COLzh649Qky5trcnknt5FfRddOKI/0Dl7WiiYCmsb7WZeerpvr9urowCYTV0XfhOTr35HeJI11nnqSi5OR7MpNhRgtAIisQVcpdGR+r9XG13l2Ud2uk0leIJETVf0kjQecSmvZNxXna+af/jVqPPqjlfgAAANzCWa/vAwcOVJzwPPzww2SaRx99tOIkXsgMAADA7Y3PcodIVZsTtRb8bIK+LCkpHVzpK9zvy820Ke9SJhEbJu3HH/YzS2emRqP/vo2KsFDkuA/DNVv48o06iwI8DFDqu8K0NyltsqRbO4YGhTC3sSRjosz2w1xNNleHsHKMXUroSkV6n9UJ3abZ6azWY3r22Eu1dzbc2aTRiS7amKLT8UcEvw22nyIXCPUOJVRXK91HbdVWPSxtI/n5OWp97ifkAtXaf+3+nHl/b9V4ZNa5xD6CgoFteue6nJ/Dk5ubpc5Tj9OFZ74f/ZcZ6z5wvqBZZBVzqIVretW/Mz7QpuX8odqeuY75gN3rq7BrVv26UK5XA/0HnwACGYW+4jsKR9K9Z/Y9Bj3L9Gj/4nWassDwGv8dWIlIbhCAK/AJyJa5N83lmS4iAr7bTzzMsi+OOmcPXhPZPO6H00UhP08dJx5hsS7Us7fjVU12JD35qiREMrypkV7JV5Wgb9lNRMJeoSzmpsfCf9lEVxEpGEuTgBIDyELfMl7UF++kc/FuWvM+gmwmfPVnAAAAoBwmXt/yuemmm5YtSsRg/uMf/5jGx8eNydXS0kKHDx+uuEhaKjMAAADHD/wMiVlfXzs7U9yFfpjfs9spIwbSNkY9Z5xLZidHaHZyOP4FGB9IBKvWs7/fKunC0xjoyPiAv5bDpBXvNAyaH4Or/ThNhHGKi6OMmvblWV8dQkr1LQfnaYH24pnQlST9MvItidYveOztjFMVNROI9zk91q/pZtH+Q0qfy9j2IrHsMfg4CrvIxGAnzc9Nh/syc1XqC4bQ71wShvnMkvdoSA6VjsIu7rm5+Ewc6Dj5qF/B0DlHI9iL8arMYl7BJumcC2iyaZf72khrVu5qqPqeNJ6ZqLjXomerkqikq0mtHFID+OuU6cEqFPUvLvdbUelvOepXLHeBpIlY5Qmi8trBF5+ZGPLP0HUIUjWAP8RcLs1tsCC7sq7UY9HoCZyiXd1EAD9vW4ukE01zudrzXzMB/JrFqHLfsHO5GJd2Zf4GAACALc7uut55550VJxPZbJbuv/9+Q1IRffrTn16QZ+kkp1xmAAAACbBmHWVG0Lq6eqPPNDXcQ+0nHkkW9KOR4E2RcO8PAQzLGe1tjvky4v1a+lC8UVc76osnWjeFPfvtdeE9eqF1Kw6jes49S70XjlB2djLeRrwD1SFcJmpWNNn3KfuC1PtdvCZZRGVhi0VkFK6srnQH8SgnRnvkflDrKgt6r9nHahGmSkbNcIeuaMO1dVbStVZcmMtd0pmoJNDd9Iw/t+VL1CQlCZxLpJqSmXGg+fCPaG7aXELAWsjP6O7IeMtp3qC6sommdzY3M0GZKNnSTc7holaHCNjH1T5vULJm9UI+i6axuEr2/1DnD57l/YFEqr3TqFVbi4UCFXLzlz5WrC+m72Ppc3OomCKLJMmYovWDBvblItw+8vdkE9T9OWRrMom69rcBDs8k+vuBtpN+IP9Y/+Uqn9aTgn3cWs8y0nPecDvxy7YauG9aq6ob9CWpJjeD6tTcklEG4snaw5UiTbhreUzOiVQZlOeorQEAAABE1OiqFn72Z392oZpW+d9iAvXVr36V7r33XnrZy16mVaZvfvOb9OCDDy7IUV7ta926dQj6AgCAhAhH19mJYZqdGNKvS4s2p4MOXcWBROIsnCHUkJvPUG4kQ/8/e38CJslVnfnDJyJyqay9q/ddarWk1gpCSKCFxXjYjGwYgxk8GDBjsOczYM+YBzP2mDFjbI/NM17AgI09DAbhwQZj/uw2CCyMFiQhCUndUqul3vfu6tqrco2I7zk3K7OzlsyMiIzl3hvvT0+qqiszI+4edznvOSQry/PQ9vzaldo7U7DLqNOOlUPm6GRer+u5fSR7GNDea/oyQ+EIjdSP7b2naWChLoavPLPx5OFHv0eOU/coPXPuOFnZXKQpBCF54PbTZzsYdXm5l37IlSc3BNFXo55UNryQ77nYTEgk6Vh2E4oLu1alyVMHRYSIVNCt/hSyhejWdlv3K1Uw4gYh0NQDukK4z3WTHxhJddGyl3R2VqNCu/PTZ2XJTxTP5iXXbFMmdq0inNBsueL59e/INpeTpH6Uocd25LcdhtpeEm56K6Le+UTmtUL7iIbypjnSeWmE+e7YJ2AoHGZBe+b80Sfp3JG94pxndNMlwZz/+TFa7fhhI8Hxw22eFxZnJ8i2qyQzoRl6u/rM5WSbo3kX/IQQHUKn55Vk55FxlW1c4o2TTz9AU2ePkm6kYi4niwCo7VkqorYGKMyILhvgupF3lfj7ovyial+qrw5XCa9su81JIp9reY70lczY7tXpXLCL934JAAAAIJWir40bN9Lzn/98euihh5aIrPjlOA79+q//On3605+m66+/Ppb03H333fTBD35wxSFsI10vf/nLKZeDUSQAAPRidHjksbuViR4V7QZzsOvyIZjVq+irkQKtNl97zYsRkpGjJmUqNosVsortsOm+2oZY4E2yUL03JbtRF5uhMCXRhzwKoOanSWaWO6AIox4vnDjQFHw1Bb4V/wLf+NqPpLhyj/fxGaK68RuXBBmIE6qqdvlnD6+9X5w0JLhYcTm1SkmIXPuG1oSalLAuFMv4wRGaXFd4Ep6fOufzqwo0sKBpdFVS9nT5WEzP4vYeY8PrsysvoURFhYq3PLtiP+D4vvtoZvyE+MvQ2i1+b0SxEMN9uG0Kg2kNBTjt20PI5drVo3ByffHCyWeaoq/kokNEaVyi3zgnTeQmmQyjQIcilKAM+Uw6grbSdNDRTcAvg6FwELGRl1u25l2Gug6Ljlnx7ozpzKHHmv/mqC+maVGkyFoHLglnXIcf+1ch+tKJTvPTcA2Fu07mQrtXmwR4/WA0tw8Y6Ux+o/SkcENrlxeOP02zE6co3z9M67bvoTTbRPQi+JJivtQGWdbhkcz7G+UugYCf20CnPEY1l1ueBlXx1T685DOEvdggbTZK56hJjQuJ7BP4KJOO6et2HT/3CfFaQZBdwB/tM6h1zUqJkXp7CwAA0BhtRV/ML/zCLwjRV4NW4df8/Dy97W1vow996EN0xx13RJqOO++8k/74j/+YarVaU4C2nLe85S2RpgEAAHSHDZO8Cr6kMRSWzQudiA6RjeTaKtPr4j7MBXXUG2mqbLDKkE6/bV2GNPeMRFkQBxJu0obCEhWIXzgfXscmLxGcXJcmTh+kUIjY0Dw246dFAkV8k3i86GksU0ncKv28p43oq0V4Gfa1VWJ5vYT1DJ4ZPykiOfLckI3n1u+8mtJKeX7at+BLxj7sL2KovGn2TDMLkuTF7RyFckVfXv4FHeokFryV09zEmabgi5m9cCrg7fwZIEh5AO1SD2Oc5O2yU/10sweRPW+B0hdRnrr0g0ijQ0hfTwEISavI4lYWdM5PnqPC0Bpas/kySgp166mRbnnTr7XBvdfiT/zZ6vX5Lm87ipqVawu35+udP/bkquNegKv1lJbkr18fY6fOHgkk+JJ9fO64Lu1q3OvnPl3GUkmKKal1ert2srzPBVvryGEoHCbtz3H8XYfHubOHHm9GZl6YvkDS9reIYadUveVJ3salxf5bz8+YuAT87f4enYBfWzputQRv09HPSyQd55bfW/ZxwddeaEzr1W77Ugk7EE6eCPflpM87AAAA1dFa9PXKV76Sdu/eTQcPHmyKrVqFX8Vikd73vvfRl7/8Zfqd3/kduvTSS0O9/969e4Wo7PHHH2/et/Fwb40+9uIXv5iuvjq9xkIAABAGJ576oY9PR+GdKch3ElrwtY0OEYKhsI6L2A6bnqrARsp8GOLYVRrbejllcn2kIhc3NyUuewnaReLiwISNS6I0oAPtSx2s9LZ5bN89woBaJwOTdgZ0oY87SRgdSzB+e/fC1+bPgQy6UkBIdXvm2UebzxI25Dl7+InEvIDGFqmlDYHFICog0ViQ1BiTeNTWhjg99YfPPsquByFP/esunX7mYZLaEUHjK7GMcwqNAyKp3stQljWRLAYQiaWj7VwujEhf8pVzr4TV788c/LGIUM2w4zDf4k5NyjMUXInrXxYBf6S36zKXi8lQuJMRf2SGwinshl6jtpbmpmNIjSK4Lp1KYm4rS7SQWAyF5ZhTykYo5aLjONfu2ewzsw3BV4PinISR/DBfDKEIYzp/6J4QKfblot0Dad832+0NSuu8R2bcmB0ISxONM5xzVj5jLc5NkszIOJfrnqbw0yFT5LjuUVtT6EQGAACANmgt+mJ+93d/l9761reK31cTfvHv9913H/3Mz/wMveIVrxA/X/SiF5FpBtuQrlQq9L3vfY+++tWv0t13373kfg1af8/n8/Tf//t/DyGnAAAAUr1pHgJLvNCleGO6q0f5Ln8Pep+waVy/UppvRqVoGLmMrN8e6b3TTHAj7DDbQ1RtSzLRnU8PkdioWx5Bw2Mxx1Tfrc4xdIA9yAcRfMXexYSxtc/Pr/5GOMlxHOGJuasBpizzFNk8Ctu9i750MQ72JgZ2fR0wlouzIaYq3cQpbvXbptsbaXUTvZP0yORRmNPSqW7YUDgSQ0JNxrjlZRdGn6qWFkjmOWmc6Pgs9GJAF+bYrIpxSXJr2zZzOQj4I12DNARfDUrzAQQS+g4P2hB4Lhci0YmuXI/OmOJ6pnUwFA54xp5WQqmnBOYvgdKNcXS1gvRR5r2skfzcJ4GKarmn5/trKOCX2RlY6HnSeN0VNT2focRY9NNnj/r7QsKOriKlmQU/ApFkBPxtv+PYZFiLZqbowy1F5vZYiUbzDGL63FGqVco0vH6bh++13iaeeVlczym7WqFDj36XSvNTJDt+nDF2muN0m//4Kfuko7Z6n0vKdca6otwwzgEAAJAQ7UVfN910E735zW+mz33uc0s9YiwTflWrVfrmN78pXmvWrKHnP//5IvrWVVddJSKADQ4OilculxPfL5fLNDc3RzMzMyKS2P79++nJJ5+khx56SPy9cQ+mcd/WSU3j/u9973tpx44dMZcKAACkm2i80AXwXJLUIradV7VQjUs02HwNazGflIBhMd0TJ59d0eanzx8npXEVHF/c5CPAhJcA2b1URejVWZfNPdfH2OXNoXCIyGj665/x4/t7aNfytrMojUvYyJVFylpHEIo6aqsTQtRWHWnb9Hwckqni4S+u4UMTgW6oczl5h27/JFy9UXoN1tGALqw8J1I2HW7Ztg2krwpDpac1UZhrocjXrG6K9yvVzHugfUzJ67lj+hLOU6/PWZmfp3Hsy3lMCWk/mXN7i+Spllg3avyPF8u/3vMzZPE+/upF1/qIFz9l3mlvItRnSNdIX0kfQDQ+JZeAHxHQ2hWXPGNF1G1X5jlSUlQrRV+fV2YPthd8NZNIVV++7ynqxwotAZQGPI07Rv1zRx//Ps1Pnxd/On/sSRoYWR/kht4/Gkj1FU+9zYyfCCb4SuCZc/zJ+9WL2hpJ/2u9plcBfwTJ8HTfdvZyYURtDVAOkaDvGR4AAKQd7UVfzG/91m/RgQMH6MEHH2yKvDqJsiYmJug73/mOeC0nk8mIz9ltPGi3TthXE3s1/sY/X/e619Fb3vKWEHMKAABAKdykrrv6B6rlIo0fu48WZi5Q3+BosFtLtHkfvacrN5B316YRY0xF1TVaSiuK1F+sByd+yyRgGfo63O1yiB/V4WJzDi3LRrzbY58NUFfS5L1nXB9jlxtvuWAPUupnaRDvj16ZnzznWfAlSxklkY6OngDTcCiekOFnaM9WSdpu4khcDLKML5HQyFuXPEYXnaJNelb5u4gOEaZPkua1KX14MhROY8FoNIYYIT3PYkx71AaxntcnEWW5q3fmCI1L9HyOyZQnmdKSjuyfPfS4r8/r2QeW5U0GUU6HCJH1vW8zdnG/rnh5pnWLohsZQeyEYxhIpBG5e7+p90/G5Wyt633CT0dr2/Ae6Sv0ZDQSEHAuF8IittWJdNrnHRLTsY1K/ZyT7IzPV2Q40gCP5R9LG+o8l2vveAPnD4sFsbxgeq6RhenxpuCLcewazU6cpkiReO10Yv8DpCM9RYXzdZ7lJlv3nqdycrXBxhjH+3NBo1fLlicAAAD6kQrRl2VZ9Jd/+Zf0zne+kx555JEVYqzWqF/dJkAcEawTyzer24nAXv3qV9Pv//7v95ArAAAAQYniYEQpz5RtbsvRNRoL2UppLtyLK02PeVqcGpQXZujEUw8Ir0RDY5tpaO2WAElJuRe6QMYNvXF07w98fb7tJnm4ob7Cu1aE94/2EJqv7U+Q6bghWw5L3l3CExkqnFGN8xIu3g8R2x3qhTHGnX72kZ7TkYa5HBuQTJ09sup7fBDY+w3QT1YvFjWMsKWfyzWJL51+xpaoxzkfiYj28lIYl3SeN7czFI7j/krgoY00xHteDYV7T9LFfXYf3wpypwDf8XsLddqG77qTxKNw9Pf3+rGk0gkDOl+lFUJ7CuUaIbYXv5HLpSPpPi77XC7h/YfYRFWdokN0ELcaVmjhIfSho+2n14daAvsT0o5ZsqZrdcoLsyHtTQQTKvm/T0RjnFrVtipwxuTz2Rz3uMX3k8TIPSlq1TKpilZzOR/OmKJNh/83nUVxK87awq+LiVPPJHDb9o0gSeGfvHPMiAkz20lEbQ20zStXXfMZ68mnHxJnsJlsXw82cwAAAEB0pEL0xQwMDNCnPvUpet/73kd33XVXU+S12oH0cgGYX5ZPjpaLyX7hF36Bfvu3f5vMgKpwAAAAvSLX4jFO6n6bItyk0bFoe86T0RTVleanxe/T54/T3ORZn+nwl5DlEU2lQsY0RU2Im1ZBvYbHRdIeR9t7AnSF+HLi1EHxz2ppPsBNKT346aah1Lk+hdtzH5BskzustHUrl1qlRMkiU7m3T0u5OCteUXkUbo6hMrdDv/h9Xvi4RmIknJ5eI0HFOVfxLYYMGuVXpemtDM25g36/U1n34mXz4o3Vx9/4RdKS9LolKuMSWfPVNWJojI0lGuOS1mvKayDfKSI7j3HF2QmamzhD+YGRgFeXt/2FTojG69KQouqLnTj7u5HueYCIDtHu/IHXrE3Rl6vVMzZRRESO3sY5jixx6JG7lkSYiIQ46tDtLapEIGd9PXDuyN6QoraGk57FG/X2ftT3v/jBaNPR9rbRnbFqOc5pmCWVOfHUDynXP5R0MlKJ5/4d2x5je5F+aXZq1fcWps7TseP3iPNVK5uLOH2a4NWRagiRv2NZK8Qxpqf0uRGqo5mko7YmXYkBbWlmL5xq/l6x59RuvyqdVwEAAPBFakRfTKFQoI997GNC/PXRj36UyuXyqtG9Ok1+lkcJ68Tya4+MjNAHPvABuuOOO3rMCQAAAFk3zV1ZvDMpfLAmG+1Fct4yyfMBFpo0BF8N7FqFosX1L7BSpN4S3ygKQLyRviIqH7/e+5M6/G3zZ/ZAd+iR71Kt2ouwRL22F0v0p1DbN6m/ydlL2+9gHCUDnUSVPV/bz6GWLAYQkiRjuadNnfIUBm2fW1oaCktegbKnL4io/OIHer5HfHRZs8bmoMF/OcxcOEnnjz5ZF/MFKMfkyz5GElsHRlvGsdShxgJ+eZ5nGrSTHuqg4YhkOTPnT9Dk6UO91ZO8WQ9M9/Jw4zOgC6ttaVhPsiPzOts33bISV9TWTv3Bjf45JPNY75cwo672gm/Bl6R10Gt5svBLRcI1FE5izujfUDipcaBd+kLZl9MQ5SOcroq6eZo8c5j6h9eRcsTe35OzJVlyiJWATcuxffe0/c7xp+5v/l6r9Rg5Tv7uEqODHRbw914gleKceIVDwD3yCO/thTOHHqOhdVtJT8I7z4p8DuVV7JjYQODGM6eVdL0EAABAbVIl+mrwS7/0S/SKV7yC/tf/+l/0ve99b9XoXu1D1XYXhC3/fCaTode+9rX03ve+l8bGxkLJAwAAgB6I0qNwVw/w0R+6dlscR7l41uIAfXkdtq1T73kNK3qJ7J5cQTwkH+nLm+gs6vEgyGFhb4IvjTbn/DiwUCTP1XKRFqbHKd8/RH2Do+0/GIvuuVdDYYqVMIz7QzFQ8mEYEcn44qo/73FtGJe0KZneyzYEQ+Gjj3+/bZS29CFX3/F0IBpmkpMyTPMs4E/WULhT+jh6co93JS3w1Ya8GZckQkubPHf4CZo+d4yyfQO0efcNpDJxzg/YmczZQ4/Hcq8w10JRrKtkm5cFodf1qi7lsIK2/l5iFvCrsJb0TLBnfWlhmmrVsj57I6rRLHcfTucialP1665+bbtaoeLcRKQCTO3oZCgsi/OtFNHq4V8pYmwD0RsKk5KsGOMC7bm6+vVrn877Vr9EOGWh5Vw5AAsz46QacdZd61wqXDzO5eIQ8KMrxIfrMVJuj2tWPnd4+odfi+FQLPrG0+uYf+LJi+JEpQizbJOYQwS5Z0Jj0fF99yVzYwAAACAEUin6YrZv306f+MQnaP/+/fR//+//pe985ztULBZXFYAFnYByZK+f/umfpre97W3ifgAAAORAGtFTYpG+ory2m6LoEB4vYBiJHGg32qK/OY0i9adIMv3ppLxlam7yLM1NnA7lWpGPnZFrz/wbCod6eyUbYgDjOE8nEtQzs+On6Lh9P81NnvH9XRZ7HXnsbrLtqvj3psueS6pS9wirYNtyUypslmzeE45HYbnylGjkpiXX6H0uF6bgS+VnUNphI9huHvSjq19vxiWx0cG5RjzRnCgVxGeY33CE4+cb9Q9fOPE0nTu6rzlW8rzOMM1IUhl1mR194t/oyhf+NCkH5zlU25IuF0vcUUkjGZoPBNrkr9u+nBFLWcxOKCoCCJnDj36XcoVBUo5Yu4MRSf9rPDe7jl0JB23tFB2CxcpzZ84KB2kicmta8dg+5qfOkZXJebhcMlHjgzxHlYjaCkKJshlL20hqTtnuz44tHDJMnz9Oub4BKi/MhHZLlQmj34exLxfqONmuDWD8iQ5Xg/sFGuYizDjaa5iFGfC9ZKNs6jpmFecmSUW6Ovb2WF98Tug4XdZaEde997aVTBuM0jmjLP0qOgEzAACApEmt6KvBnj176MMf/jAtLCzQ3XffTffccw898MADdPLkSd/XYqPqSy+9lG655RZ60YteRLfddhtls9lI0g0AAKAH3AgX4d3OXOPwztQVSURvyuD2vKAO5VCD0+HnMo17StHmwsZNZZovnDhAp5552MOdEva02Xw/ysOI9m/NT56N7r6pohHpy8tne6/raqVIU2ePBPru+WNPNgVfzLnDe5N9TvVwi8M//h7tuuEnKS7C23xW/9RVpjlM0HrpepDj494ylUfvrJ6XyVMHPUVHE3M5eKQPBRY7zU2cUXMqF0KihUj58e8LY9eIb5Xs9T3SdpxxI0ykJHmP4znhZ+8hscPoxduefvbRFfPC9t+RvxK9rNm0p1t06qijtnq+vPztyTcK9BG/tBujZi+cpFxhwFM9hjGXO3/0SYrBUphkpzQ/TeUFFaPXyl+24RFT1NYAZcrC7o7P+a43TVM9Eh169LseP9l7dIhguEvG6tLcFGVyfZTNFzr0uejrUK/9jBDz7aP/dFsfRL9+cKWu63Zr+tmJ0+LFBBJ8adt+Q3DGFNa+nKuJkbWW577dcNOTlZjqN6nxhp8hctjLRM/0ueOLa1YF57kQt0pT5n4j1p546oGukeSj7//JOmPScz4FAAAA1Em96KtBf38//dRP/ZR4MbOzs3TgwAE6ceIEnTt3jiYnJ6lUKlGlUt/Eyefz1NfXR+vWraMNGzaISF6XX345FQq8kQkAAEBu3CR36qTYTIv8HjotpN0OnjazOY/OXZM4dG3cXj5jvzR6lgmjbM8d2evxZj3fqs115RICtCvT40/dH+VNSQu8ZKPZTeXNc+NwaGb8ZOjCl97orczOHalHuogDx67G5107xP4ji6c0WcbDBixg4v7Ac5TC0JqkkyMN7drL5JnD4uUpamuCczkpD117mMpxFBzTSud25NnDT3QXfFH0gic5xq72Di0q5XmqVcsR3VWGvIeBj3x4Ghd6L5fizASd3P8gTZ07Surj9mxgoSaevXt0v1K356YbseG51xvoMiToTq9zOS9tMgZ4nsZrLysjgaPIHrflZChPv4Q1T+a1lpdxPlJxa5e8LDGilSw6RE+Cr5UJCPFaGpDgPgnP3Y/8+G4qzk2If2/YeU37D8eRzLQ2jTDznUjU1qUiQp9fifWBefzJ+6K+sV60qaezBx8jp1btaphev4YbioB/6syRwOPcucNPCDHf0NgWGly7mRJFkr1xFbGrFTGfS2Iu14za2uXaS87QJZvLhXp7jR7Y7Z5bnSLgLrtAMmdege4Zh2NNfdpGnHCELy+Cr1jKOM1VuKRs01wQAAAAoiKdVhYeGBoaohtvvFG8AAAA6EUkmyYNMUS3axsxLKaT3AjRcN3azpDh5IGH6NSBH/V0jSjRabO0wfT54yJChCzG/nHWF2/UeTd+daXw2i6Lx89Q76hN2wvPs6vXz0hDHMLnHu8xN3mG4oLHFR5be0WpNtCOIHmQLNv8jJx/4nzSyZCPMCKuItJXaPCchl+qEcY453V8j3xM7Xr9uAa3NgYRe++J8JaSDdyRYsS6LmQj7onTB31+y3/aZI/aCrxGh4h4j8RjX9dx30RHwqinpEVK7JiBhbF2tUyDY5tobOvlq34ObVJ+T/3H992beD0lfX/Z0qEiy8uu5/k/+1NIxFC4/mPi5DNNwRdz7ug+6hsYbfMVqL6SwkvZ8z4hC2PGj+/v+VogIBquWdu1F9uu0plDj8U2lyvOTohXEHifoiEUmps8S2vmp3pOD/BHGM+58vwMHX7sX6laXuj8waijUHW1JdHccSrnX/E8RjHvku38oe2zPpZtOf2ehd7obc9+dvyUN8FXVG245ZrprUMAAAAgeiD6AgAAAGIk6QhHM+eOR7zI1m8B36m8vJWlEc5GnevXo3vj9mpvnC7n6GPfT9xYJwn8ROKJ+pC/a7uP62Ay6eFG4QNYL22ED0AP//h7ZJgWSYsGh0NpIsz5hzwHYOqOAzqObVHBnvLlEza2S49s6QSBiKi9yXbYmny/Svr+EiXdTWsbUKOvxJbnrhEdul9n5vwJOv3MIyJinxLtSUNnTFoa2bhqrx8c26bj++5rRqSenTgtIgwA9eAIH1761ZJIW6GyeG8ZurabzFinzbgWQbkk6XSOIxovp9ROEKGAMyZ1cXsut8OPfJdKC9NR36r7NT3XYURr5+b9Mc6FVKBKz+UqxbkVkaHaRptN7fijBhdOPdNd8BUlDQfCfpwyRjXOiblDMu2V741TvdXqI1kHwtweKsVZMq0sZfMFT9+JMGGppNc+WS3FvxfXFs9RW/WrbGnWrLCfAAAAbYHoCwAAQPqIcvEo+cI06kWmlgdqPWbJMMyE2oWGdbHo/U9JemwDru0jIkbk0SGST4fYANe0jcsEe62UGWkPh3R8FnYjjXnWdN7THFs1ylrvB6Ys+kpWaFian6ZzR/ZSrVKi0Y2XkJXJJtzupRx95Z7L+fh+5AL+rhF4ko30BbyUnIeyM+pCAy8fVWpeHUtwCIXKQyJqlTId4wg4Xp6ZCY9zeqNj3tWO9DV74WRT8NVgYfZCeqpPFkIYFzwJIVJk3JXusTZsFC3LAG1AqXmnYvTaJ+cmznge56J+rnrNi5bjkI5ZctWey5WLs4ndG4TLhRMHJLEl8eFAINIxISkPQKQfIURtTWQvyr3oePbo3h+IuQCzdtsV7dMTSzp1bCTRF8vyfYfO14pawe/1GxrWtY7zUwAAAFIB0RcAAIDUEXW46o60bNRpeSDRQKu8yXMgEcRY1Jd32bjqLYV2wr2WvQyRvvx6uIx8oy6RDXCdxjYNyiWQM/8YPArruEktGxHP5eqRnUK/BUgK5aND1Ojwo9+jWrUk/s3ehUc27Fj9w2i3iY3tXd93fAj4oyJBb+WrjrmJTOWSz3soeMgHjx1P/uAfyZQ9aivQhvPHnvS89xF9X0xx29Iw62HsqSU5/icaTaANBjbmIiXpfbnW+tVm7rMaOmWt17wkGKkjwJdAUnQp+5nx45QkwfZT0aDUQJ4zVp939T1viutcQOvnezu0ynO3vLS2ueTEZ5GjVZ2qev5Qr4OpM0eagq+GQLJvYLTNN1BvsiKcfyX5HHEDXD+q5iRLM5UlHQAAALQCoi8AAADpI+LoM51YujmMVZ4K9LzpYYRzIFGcm6D5QFF3oLCSgV43Qf2IvqIm8Y26BNFmMzvk52By5aJJfWiApzYQYrtD5NII0bBb9dxexFwuOcHOzPjJpuCrwfS5Y3o/pzTE11xOlqitEYP2GkMZuw7ZdvJRl9reFoadStGtvhamx0kavC5ZYWSmBooL+A3DVO8hrSE6zTu6jl1xbQVjDAXBGk705Ya2qeaatfWaKT5/aM2ULnPVcCJ9udpH0ASdSetcLsq2j3M9mcrSTUTc2mhfJw88tOK90vxUmy9FnSp9nn++6THfjl2N7V4ebhDx9QEAAID0AtEXAACA1BHNJpYrlXOmxNBgE2bFRlKPeWKhXxibU0ceuzvY/X0cSMSxwZvajboecRw/kb6i2hh2parjRNqSJs1XlwOyYG0gjhOJ6G+RelxpQseRfmiYpzDmcgkaCgcT/YPYUSFq62L/7joPiGtsS2QM1c+ATmkkrYPUtg3VjUtarul9vaNfXTfHeo3asatsdAigE0muB1bip09E5zpdl72lRFh+/NBjWSZWF4G2UWI4f0hr2+xatvKvWZvX93oOoWFd6zSH0/c53IHY6g/tJEr8nKtH0b9jicrLaUnKAZAW41zIjjVdV6FyiSOdqpRFuHSb13R733F8RPqKoIxbr+m9PetX1+r0ZQAAAKrix8UcAAAAoAeRrrP8qL70Q8tDlp6jQ4jwEBQ7jXvCC10KPW3KcnAFL1HSEvaYpN/Q3yMokLSUsZbznub4oE/eeo/aaiTqadPXXA4HStLi59A1sv7X7N769G/faJL1NB8ex5P39JZvT+LWmjyGwskPtwnmT5ayDZNQokMkaCjsxxmTjvWXxrlc0lFbYW+uHr22mcSGDknHLEmTFTW9zj8cW6JxLvF0SJI/TQhjHpbkXC4qAVBPoIlGX8RulA6EZRDwg1AJoZpUcVQSy5oVzV59W5JU74HomCcAAAAyAdEXAACAFBKFR2FvC9PWzWEdDYW1RNGNukb78uURDE1Sj426iA2Fu49dMXgWF17oort8hxsncVMFUKRcYmo3em5Sy0UUZbzkmqhDzVA10pcrrXGJjGmSHdeHAV3Sz5G41qlJ5FOfNXj4HoVBZMWbIkPhZNesmMvpSxhjVJLRITBv0gNfEXAi25dz/V8/Ued30aLPvC5l4zME/NIixTh38QZAJ8I4Y/XlSCdk/O6BYcsMhNnmIj1mxWArFUk6EJYMzPPbFUx4UVujsZeTqT3JkRa0ZQAAAFEA0RcAAADl8bspJc0mlizpCBMds9SjYIuNOxIxLnGDHDDEVYE49VhR8iFu1EU1xsm2MZVEeqR5foA6qI8U40bazz2PL5E5FE5yrNFvnOs90pc60SHiQb82EgeOI4GAX7q6ky09CuHqUReyzq1lW/eogm1Xlat71HWqvDFRcsg2l5Nxfik/jo8xLqr21hyzulxfZwG/PoRddurURRztE20zWLn4G+f81Yn/9KnTpkNHy3JQXMDvYy4X1/iDdUwcbShCB8I+rh1tXSc0xmg5h+w1aqubqANhf1+KxbNm9PfQED8C6cifV16vr2NdS5IlOCACAAB9gegLAABA+jbqoogO4fHQNY5DdWGsnNgCWZJVrEQkVx+BVF9AUhL3zuTn8o33I273yRjgu1oYEISd9qTKIsiBRCwHogq3jSgJs53I0/9kSUeIaJil3vtksgJ+w8DWnQ44EkX66nr95tJWQ+/t0jw/QB1J6wPtZPVi6VJf/tZnCY9zFz9IutHMu055C0XzhUhfoFsbCdNresK05AVG4XKiy75cIBRKatqQ6fzBa5tWqu17RMdxO5SorXDGBHrE8RUtLuJz9a6mJDGd6yel+cI4t3q5JBLpK8hX9HtO6YJTS3Yu19o20txO0px3AAAA8QDLEQAAAMrjd7M3yoVWt2v78QjWW0LgqVumslTFuETHQ7JUHkhEHnnGm0fhyDe10FxReGgD6SXK55WPa+v43GyO4RrlrdfnkYjamkB5BBK2alRvqY70FVV0CI9zuajT0bw8HuTylJ1KPmMwzmlB9M8rj4bCUTX+RB/H+s0FXMWjQ/gCY5wWYojI9oGDCPOjnFeivYZbnioSKN2K5jUFJO+oJEiEKw3ncjp2EVfxuZyv9MdUgao+NxLEt4A/kkBfXvfljFjqOrF9OR2ab+jbcm5C41wg1RfQ4vwhypR0v35jLonzAQAAAMA/GZKUt771rZQm2IjoM5/5TNLJAAAAJZEh0lfzmpIYCmOB3FvphX1Qn4ToS9nD4tQSnkfhqNubPG1LDa9nUuLq4mkzyIFE9JWIZ7Ci4JBBX1Qd5xbTjUhfatDteer6MKCLnK5dIvpn5fmj+yjfP0xxI888tkfCjg6R0CRX1jmTrOnSiij35cTvXr8TfjJABKg6l2sQl6d+P+jyPIzRaZYvQ2GJijdJ53eR3dd16/WlcjtekXY18yKtoxKV20aC+BvnJHH2hqpWgjCeF/6iNIUL1od64CbYhnw/C2PzH5ysYwxt9uiYXvPiugrZksRwxqpT25BWwB9Be1tSbV6dzpF+LNmfTDKDEu5FAQAA0Fv09eCDD8YXtjhhmhvUAAAAAo6jinhrZZYM91FGqYju0p5uj82Y1sJI1gudrzlGPA0H854YvNAlSQARrF9OPv0QmZn4lzJaHvAp7GlT2meNrOlKHDe0uoyi7pv9W4Lqm504TRdOPpPMzTVsvz23F470lehczs+H9au/NHrajPr55nU+E+W8Z27yrHiBlM9JA7T1WPKuSfGmrVxa24b3diJ5poKgY5YUF3352QPTZnzXEF/OmCKrR59RW31+1A8XThygoXVbork4UGcdL+mQhbE0hjVrNCFwfF9fz7oO4MhAdlzF7QAkDPQFIj5jNaIa57xd02jZDI50jzCx9qpfRwmlnhQ5l4nnXFaNspCt7GWyJenaTvh9sVfipst+AAAAANBZ9JWWBzGMngEAIH7vTJFG2Op67ZaD/oiSUS3NJ7cBruFzW9mNukZ0CHhxUYMuTcRfpC9X+36+MDOezI2X5D35cgjFIFJxAzqgB/7bUJT9z48BXXTpOHXgR5FdG/gnSU+biPSlCt0OXX2sWROeyzXnBupOdTogi6dNyUBZLC+QZOohRaRhzZr0Wk8vI+gw1qyKeFeOKZl6tY9w6NZG7FrVz8V6T1DH63e7vRvLvlyie3OKO1Jd0QeV7ZIBEh5LpK/ob6EkYZZ9lM6Y/FxfwzmfnrZR6jqdW7y7z8+r/YzSFZnEEF3HrtjmOcmMN1qOc2GcsyZSH5jL6UTyTudcX+3d0HR/UMsxDgAAgFRIL/qCKAoAAIAKhsIXF29u4s+1+enztP/eL1MS6Gk04PZcJokYCjfSLV+gLxD5gUS0XpG69fOmkRk2teQlZPFasoeu8oG2H30biqSMXf/X1nHeo2P7DWMe5tfJRGLoV33akLjXdHFhv0IADRuULlnSJR9BiMNOWMNnYTiEWS6SRIdAXStBKI5KElyz4oxTD2QwFPa6L7fsW6QbDQNBpQm5WlTam0B0CADSRzhzueT25WTcK8Y6xj+O3zYUpQPh7gr+JZ+OJA0u2y6oM3+QnV77fmJ1EUTzFcM4p9LcViaStiVZ0o49t+lo6zrVbUn5RTMAAABlRV+6LzRw4AMAABT/wb0swSEiTIjvzcuw0OGx7YY8F3GTjoIj24peh0YSP2pG+kJdq4DSkb4CpB3GJQnihnhwL8s+gSzpAJ0JoZqSGeckjfTFyVLcs34SuBJE+mquP1M8dOlz0Otqsf8e7L661KFe+K5LzauxODdBp595JJmb6zg/VXnN6pvYQn0Bn2e1au3LuXqfsTentCrnbWnae85LUnbCmMullkieqxKNV2qPLxKi6lwOzUArZBLwd9V8LfVoojHqZi78qK0J7csFU31Fj0RzArkIsVwiX7J2HejiIZGmhPYLAAAg5aIviKIAAACEvdkbzeawR0+brQfIWm9YaJS3UA4kEiiPxXv6mUvhMCs5OELfwvQ4FYbXrlpnfqJDRDe2uKnr/svR01hGXa/p0o5ZkiZLZuSIFtcM9UXpRsf892pA5ybTRheTjbmcGnSbI8hgXHIRbx6FtZz3aELodZNYXaON6YJfAX+U+3LLf+2SEIqK8RNPUxJIu0ZKOE9Jir7kfJ7KmCa5SdprerTXBYmPC1KOE+qOiXKOu8nj6mQo7PNzfqmWF2ji5LPJrONb8qTLvC6UuVySe8d+2lls448ebSNOlNqXW/LJCOs6sehS+rVfdZyMLAMC/tQiTZt1o8vfgQe+QeWFmWhu0PHm8d8SAABAupBa9IVNOQAAAOoYCnvbHDGki7oUNljFrlYmUrVRICW86XTwke/QyPrttP2a21YYeDu1mnpzdC2HAz087LXWodIGdDiQUIpObc1vhNIo9wp0MagIjIbZVzWiYbMtIqqWFvgR8Ec2xjW1rRp29BQb0IWB46oTKTyW9pvmPtKBydOHyMrkqG9wVHIDOsajobCO40AjSzplLYyorYnuy/kw6sT4kxjFuUk69Oh3ad22K2l4/bYV7zt2VZp69OU1Xcs2pWOeekUlo+1YwkPEcA+98DtuRT+H8qzgjywFJw88REmg5/xUzX05qetEwiRp5ajEJXKNKAo5wNmqhucgUvapxJ3OkTJgzZpk4dfLX4ngGR6jU0c5HiQi+NJ2jAMAACAT0oq+3v3udyedBAAAANpG+opgoeX1kgqswXtBx42eXvPE30/EUDhIujWsP9WYPn+cRsdPrjAw8WNEF7WhcPcBL/qNOhCmeE1tT5t+2ntcbRJtf3WOPv59Gtuym9Zuv5JM01paZr6fk+HXZZCxU8d5TxOd8qbqOBcgamtc9aa/I4vwcWw/oppk+1/zOabTOKDxMzoUYSuitmrfTsJg8sxh8dp6xU00tnX3kvfsmncxRFRzqCWOLTQcv9JMGHtqiRgKN9qhhM0RfWR15qfOidfuG19JheGx4OLWhPfldH+O6dF+3VDrLKkyCZRuaL6Sww1xPRB1m0te85UcWoxx4ePXYViooEpSGrU1ur1PaeYySSVDlvxLpflSqUyiT6sQNpGhWLlEDwvSx4/vpy1XPJ8GxzbJ51gziLM1HccDScAZHgAA6AtEXwAAAJTHv4FSBMYlDZFD14WpId+mXqhomKee6ympSF9e2ySQjWP77qVrX/ofevBCF02d+95c1bHtuTpqvsIwFE7m0FWk3W/6YzEu0aWhhEulNE9nDj1GtWqZNu++obc2FGURu342yvWrax0P0sIwoEt2PgWBlSrUKmVhVJ7NF1a852eci669+RNz6TgeaENrHSos+pI2OgSafkdOPfMwDazZQPn+4R4ifUlSyJIkI1y0zFTvJDiXw/NUPU4d+BFd9vxXBHfGFFE/vCjMD/AdHVE5ayGnPckIOH6Jo01q3e4jwu9cLnIBf5qjtmpIKAJ+RaK2MnFEYEHbX6VMXIdKc1OUHxhZtQ78CCIagpOkHWtGC98j4TFUoyFcpblY7w6Eo0hJIjdRknJxlk489UO6/AWvISuTlXdfrsvltbZfglMqAAAAEWNGfQMAAABAuo2USNeQfi6u82KWlGX5ZnkYm+fJeBRe8QtQBG4vyw+xZDAuablBajfqdDlMCzsfyR1ouNrUSZpgT3TVcrE3L3SJqr40F+DoOIaHIeBP9OBWvoiGYCWzF07R/nu/TPvv+//o+L77VoxrUkSHaFy+62SO9EWTvLXWYSgCfpWMU2BckjjcXlj41YpjJx/pK1g0Y00GBd3X44qKW5t1oWOdaM7C7IUVf/M3l6NkWWxzWo4HjAb5WjEfd1UV8Af5ThwCfvXbSBQc23cPHXz4O1ScnVzxnt+5XPSGwogO4ascZCeEbCS5ZvXzPNX22asIzzz0LTr4o29TaX56xXv+BRER4kfAH1GbYoFcsmJKxQm7XhIbO/zfN5bzBwylHalWijR15nBvY1wUgb6WXBSVCAAAAEQFRF8AAACURwpD4eZBfuePtXqX0nLzV8Ms9bzRxoFoktw4lbFOdDeYD4H56fNL/m37OXiNamxpGI149bSp5Rjn3+NoKjxtJlnXroQehXVs+yEzeebQkn/7fk5GUcZBDONQ1UrgKm8o7OdLUaUGeCv+egVMnTtKM+dPBPcoHFV0CL9tSsv2FG6ErMRYknSFo7ZKalyCuVx35ibOkF2r9mBckrC4tZkMhceBruiTN2WdMQVB6zapFsvnbq1jXnfk2JdDe1Kpr7vKOmMCarEwMy6iQ/TicC66qkd7EqWg4VwglLkcxCnLCkS/dhIWxbkJOvTIXVRemFnydxmi4DT6Qrc+sXQciKauJ88cpqN7f0BJoOU4p2qeJBXw63L+HiXT548v+bdr21KtH7z2CWX7jgrAHgsAALQFoi8AAADKI4WhsOdNCL3FLtiEWb1Mkjh49bp5vOQ7cWzUYfPGE6UWj5tcZn4266KP9IUNOOVprUNXXUPhelOXsT3KmCa5mDp9uKe5nCzPEp3nPXrlLQQBfyJGdP7nckAeijMXJI4O4Xp8X7+2J8vzo3c0ifQlqXEJ8IZdLQcUQ0TljGnxB9qIfqg6zskcbUnGNElGeVmECD9zucjr3KsBnYZzOZ3zpaIYIkhbl3JMTBml+SmanzrXkxgiyrmc+DXV45yGeVL5/EHcHN6YVMOuVWj8+P7gbUiSuZy+aJj/Hus0qfmRns/RdCDlXC5QW9avDWK9AwAAIGog+gIAAKA8fg/uI93A6LaQ1VvzpeVGZShRcJI4eG168seBhIq0Rvbiwwhf41ZE/dC/J2H9xgNdaK1LVSPgXBTV+g31FUO7RNPvSrk4u8Rzuv9nbQSeNj0+N1ujtuo570GeVpQJ/5dopC+55nLiGaL7miYEai1iCMaXgD/haCSNtqfjeABk85qONqYyrWOVHNEhGtdOfu2cKBrmSdU168V1tw9nTBgXpaHY4ozJv4BfjmiG2tLMvj7l0Os4l5wYIpCCP4J0LLuDhs/CsJmdON2boXDka1av5xCkHzrmKQSS3CeR8bkrY5pko1Kc63Gci0LcGuCaGle1yu14edp7zUuiwlZhvmTINc/ieyBKka/zh1a7Em9lHKBewrxB8+yLNCT6aI0AAADSDURfAAAAlEelSF9LNk00PnxSeaNueb30vHnFUZoSM6KTEz+bh2ml9RBLtgg4nq+v8DCgvdHAkmwo6jW9fuNk7gtCoXVs83uoFWVfTHvUVk0H794vkeBcTul5dYoxzKVbro7jUxARBRBzaUPrc1BZJyUyR4fAHDOGuZwjRbRzHUl7/qWcy0k4pqCddKc0t1T05Weci7x8u9rP6WxAp0fGwh4XknYaIV/1qd9GomZhenzJv2UwFPYzdnJ0i/Fj+2lumXhNN3R5Xqu8Zq3fPKLPgliftb6itkYVAaexZu02D2jd89GxUWmYpV73kVodJCaSbl8Cq5iczoGulOampHKsuZgQT5fXeQ9Pxj0gAAAAepFJOgEAAABA7JG+IlhoeT5Mbdk00XERC1bHdW01PPSjScppQCfLwb1HT7o6b9Qtybsum3YhZCPZQ1d/GYijXerZ9sOntd0kdqi1GqmP2pp0BUgIC/gTmMsF8XaIAyV5WP5sdHxE+kp+jpH0/SMk8bKVdC4ny3rDE5jLySn6kqANBRERajzeaTUnCEPAn8Q41wz0lfLoc4piVyvS9amLafCaluTTDNoRsugrqedwoIAlcQj4o7+F6izMjIsxpRHN3n+EkWTFrYce/S7pipbz0xCylOzesYR1ImGSpN+Xk+n8wd9mL+mGjuNcz1FbE96X4/mA5yzEUX36NZFIWCJm9XtWHtHYwv3b0LSfqwaccAMAgL4g0hcAAADlkcK45OJJPqUZGQ7AQ0fx6BBpb5NaRPry2waj7oeeN37R9uQl5OgQCR1IcN+QcuMYbd9jMfVgKJxgGbdulGs572mgUd7CqCfM5YD/drfcuEQej8Ke+7dG40ADKecNQVhSN73nKXGPwr6+E0VCEriHBjgtgujW3xMfX3x54tewsnXMUgjrzWTGOYkrQ+KkydrukjbE9Le+CeAITCG0yJerR3SIQHPrGOpPmzl/xFTLC4H3PCLph7pHtvGKDmPcMsKozySfw37ae6rbrmQsF7P6ifRVv0AkIQ2X/kxrO9JwnOt1gZXU2YMbINJXPO1TxzYi2Rlr1HSNaOjxcwAAAABYASJ9AQAAUB7fm70RLh67bf42POfVP0waol+mevfO5CZULgGiQ2hYf3p4Tfd3cB9dPXqM4KVzM9Ikb2EfzCfnJdH1XyexOBTWpKFETOvYJsU45zVC5pIDMP3qWsv22+tcjv9LwLikGTlTxoMvCZMkG9PnjtEz8zM0unEnrdtxlVQHr17nclqOB4t9asm6XHFCEbZKZMjeDRiXyMOScU2q6Lt6jl3eSXv+VyeJ53BjfJZyLod24nufQ45npVfhPqUCOftWMHp9LiYX6UufOkgjdqVM1DcgZ3+SLT2gNxR1xuTbcU6syJgmySN9+RR9ybInJks6IkHKvhVUwN/j5ewEz1j9RgSKQ8CvctuIkWN776GR9dtpwyXXyuNA2LOvOYcqxTmy7SppR2vZoikDAACIAIi+AAAAKI9vL3QRrK5mxk/SuSP7qDC0JtWGwk202ozpNS9uMgevQYw146o3jYwspYz0FTUe06PjYYSWeVI40tfi3Uk6JEyS/OOcDAZ0DXyppfVDtmdOQKrlItm1CuULQ72P3awvTaJcJK4KHZ+HUVCan6Izh6bIyubkGOf8etDUZDzQkSV9UFEDuouiWkmMEkCPjkokGOOCeOJHe1ICdcWtHl35AzUifckwzqEpLZaDq9VcTt0xLhixPHs1aCNx8OzD/0L9Q2tp0+U3+G5DUbS5Jf0CA16jILQglHEuweewjGesWMcoOpcL4lhTk3FAd3oW8Cc1l2sG+jKTuT/omenzx2n2wmnadNlzfH0v6rlWt+fUsw/9M9WqpUjTkHpgjgUAANoC0RcAAADlkeVQ6+zhx8mysunelNUxS4oeSMjthQ50Y0nUG0nq0Htf8B9lThkkqQvZvEwl51E4yBgdfR2eP/Zk5PfQgdZ2k1y0uNWem50/58vrIUiMp+//qlgj9A+vCyWiTzLjXIC5nC7PKc04e/BxUgntDey4nxiG2uvxJVM5Nder9Rv7H7fiqDel20aKHZUES0Py6Q4bGeoifBQe58TNfXwUhsLSsKLNyNS3uqRF9z1hLeaqLXUTijMmKQzZvaJB/WnEwuwFOvLjf6X1O68mqUhxM8FcTsa5nHwNcmb8RNJJkJ7lbUYWWxL/bUq+9tcreoxzbqjtK/HzMSNt9acXjlOjiVMHSQ68Ob/RWfClxXoVAACA1ED01YULFy7Q+fPnaXx8nIrFIpXLZTGJzefz1NfXR2vXrqX169fThg0b4qkxAABIKXa1QqWFaeobGCUrk5X2UKtbCOr5qXN09vATNLrxEtIRLGJXLRRy3AQ26wJsumGjTlKv6TIdRnhoJxff129TS8cxTmXjElnrY/bCqaSToATSRYfwGiHTkL8N9oIueWqMbQsz42FcLZFnceN5KludVMsLdOrAj5JOhlLUamWSg8U2lfLDee5T6st39Yj0tRjry/d3ogZzOW/UKiVybJtMy5JrzZryMU5HVHXG5Hl9AZRYr8pQj400dEtLeX6aTjz1Q3LsWkwpAz0RhjOmhJ7DgcbnGLrSqWcejv4mGsGG5lIYCi9pG8mPucmhX97DiWiYvMMwmbhw4kDSSZCeFc9GCdaJvh1raov6+VtRl26IjmBj5OK6ws9OKaK2ykhpfoqkQv1uHpw05x0AAEAsQPTVwszMDD3wwAPitXfvXnr22Wdpfn7eU0GyAGzXrl10zTXX0M0330y33HKLEIQBAADonZnxk3R8373i8CGTzdPO615M/SPrmu9LZVzigXNH9tL5o0+SYWoYKl2CTdOw6dUwRJixyWDM7gn96k8Pr+mqtJ+lpN2gWGZaDYRCiQ6RVBvlNiZhdAjgjVZBtAzPyeLcBBVnJvzNz3RsTxpmqVfEuKGIR2EZjFGBCnRpJw3BoY5jnCawETc7lLEyOTJMS1kDOtHG5NN80dTZI9HfRANOPv0gnX72Edp65U2SrFkDiKV1HOZ0HLtDEX3FP84Fi7YUT/2xIAh0hp1HnH72URoc20QDI5I5/ezSTCbPHCat0WCc43MunsuZVoby/cNKC/j9f0P9+tMRdu6SPK0R8FLcTlKc9U4kuXfspz2Kzxrqu5nRgWqlKJ61/JzN5PqkOH/w71gz5eOhAo5wGG5fvQ7eibXPxfZl+Bm33JgcdhmGDtP+1CGrI8N4CdcGIziYjwAAgK6kXvTFE45vf/vb9OUvf5nuvfdeqtVqgRZQHAVs37599OSTT9IXv/hFMk2TbrzxRnrd615Hd9xxB+VyuYiqEAAA9Ic9yTfCmteqZTp7+HG69LkvS9z7TS+wQYxry7PBGDbpXsgvw00mOsTF28tl8MRe0+E53W+kL0n6k8YRvDzj6tO+yguzwlA4FI/CCUb6wvNGXWSMaPjsw/9CQ2u3pHyjXJOBLmSUmcsB4KUteW5SmrY9DbJ1Yv8Dzd9HNuzo+XrJGj/5FPDrUIGaCRBP7n+QhtdvTzopVCnN0+HH/pXWbd/T9bPnjz0lIuFoL4wAyc7lJDZ2kmX9JTvjx/fTheNP09Y9N5MUYF9OG84c/HHz99GNO3u+XmLnY0GGN6xvAYi4k5GW845korbiPEx1Dj36XeFMeMd1L5Js/qtH305z9tnBM78MMmjdjj09798necYq8CH6imN9y86zgeJgzg8AAABERmpFXzzp/sd//Ef65Cc/SSdPnmz+rRVf3gwWv9+4hm3b9NBDD4nXn/zJn9Db3/52eutb3wrxFwAABDAgWe5lbmF6fOn4K5F3JqA+jWc5CyIcuyraYG/XS2ijLsCBhIyGKGllduI07b/vKzS25TIaGJXLo3C6jc/18LB39Il/Ez/Zo/Dohp0KR/rCxrE2EQ0lEvB3Eya37hOoPA4AH/BeTxJzpCDtC00SeGggXtoziyfY4Yqe6GW4NX3umNJRW30/S/HslQ520iRLdLS5iTPi5cfYXjeCRZeSmzDm3InuHcsX6Av47FMTJ5+Vosw4HRx5LO3rUN3yP3X2aM/XSOz8AZG+QMhzSj77LQyNpbpctTyjC8PpXJKCHV/p17D+FIf3tlhAIoPoq2lv4Gt9oF+b0mmc47ywU5leaTi/jp2m5stPpC996g+AKNBtvQoAAEA+Uin62rt3L/3O7/wOPf3000setqtNZL0+jPm7y7/f+O6FCxeE8Osf/uEf6Hd/93fp9ttv7zkPAACQFgzTIsMwl2zG8cYHb4yxsXrj30AOdFnEnj38BJ0/si+UjcfkDEvcAJG+9Kg/XWDBK7fFsS27SQY894eG8aaG7UmXMa4BP0snTh9UfpwDaiJjpC+v/YYP8ioLszQzfoJ0Hed0G+9UPIi+GJUJdQHC4fiT91OtUu7aplqjSIF0oJIjHZ2MgwCIBB3nDSHkKdm9YzhjUp2F2QskA6WFaXr2oX+mfP9I0kkBkqHSXE7L5xQIjYOPfEdEMlZpnzB0NOkjLLSpVUqU6xsMZQ8/iblcY+2JNaj6sJM3y8pKEUW2OHOBCsNrPX9Hy/anyTgXJsk99xp1IVekL6A+OF8EAAAAoiN1oq+//uu/po9+9KMiEhdPMtoJtfyyWpSw5R7Ajx8/Tu985zvpLW95C73//e8ny7IC5gIAANIDj6VWJke1amnJ3/f92xepf2gtbbvqhVJFhwDqb/RwWxNev0LKS1KGJUHmNNiok5OJU3J4FG7SpW2deuZhqu27N92HsykjybpGdAh1EeJqx6bh9duVMlCy7arW0SGAfPibn6k/FwfRcvrZR1JfxDh0liw6hO9IX1GlBgAgExzpo1Kao8E1m8NxyJTAOAdnCiAKeJ+5ODeR8sKFcwyZorb6/womcyD6SMYgeQ7c/zWxh5ovDFGuwMKv3khk77gZJNyHgB9jnLTIcl45P31evLqJwwzTpLVbryCdQX+Rp336i/QVZUqA6tTKRbIyLLJFQxEkOC/w1a8BAAAoRWpEX7VajX77t3+bvva1rzUXD40H3GqCrV7g6y2PINZ6rzvvvJMOHjxIH/nIR2hwsPdNDgAA0B0ru1L01fCyyd7KM/m+RNIFOqD4Oj7MzbWkN+p8bSYoXm8gWo48djdt3v28rgZXHKEMpIukxNf1NRcGLlXhedyxfffSms27kn9WgovAOEEeEOkLABDXcJOU+Jo1X77ncpj7AZCWSB+MaWUomysoOs4FGK8wFwcgkq6lO8lGMwQARIXKgggWfDHl4qx49Uoye8fqlj9Qvz+xc9oLJw6QjsABrTwEc1SiVl8C8XLgwW/Q2Jbd6W4mij1vAAAAqIdJKcBxHPqN3/iNpuCrIcLqJM5iGu/7eXW6TuM9/v2+++6jX/7lX6ZSaaWIAQAAwFI40lc72LNlZaH3DWMQEljDykNjow7RIUDI0SHmJs6kt0yXbNRhwGuWRJJRmnwHh0C9ycbU6cNUq2BdDEAY45VqBgwAJCuoRPlfLJKk5nJC9eXvGxjnAPDUR3RZ9zh2LRxD4SQEEQGiQwAAvHQt9ClZCDQvw5gIQPj9KgWoFLXVIETWkBEVnc7xWkhLMMxJCCIagvCYOPUs1Wrl1BYp5nIAAACiJhWRvv7wD/+Qvv3tb6+IuNVg+d/43zt37qQ9e/aI165du2hoaIgGBgZEZC7TNGlubq75OnbsGO3fv1+8Dh06JKKKrXbd5cKvRx99VIjRPvGJT8RcIgAAoF6kr07UquldNMoGDl0lxE+gLxwoAdClO+E0YvWxI6noEFwjqBPV4TpcmB5POhlgkYt9Cn1LFjA/AwDoGh0CUVsBiKRnoVhXLRU3sXtizQpA2J0L45w8BHBUgucUAKH3KxA1qBMA0Kc0BY6xAAAAAACUQ3vR11133UWf+9znPIm9rrzySrrjjjvEa/PmzYHuNzExQd/61rfo61//uhB1rSY0a0Qb45//+q//Sp/5zGfobW97W895BQAAXclk8x3fb42uCBIGh67SUF6YpVPPPExzkymOygRABDTm8iD5SF/CUMTvcwdntFJimBa7rkw6GYBBH5GwLlApAITbtdCn5MH1L2zFngMAQBVgQAdAtF0Mczo1wVQcgM5dBOsdtcct1B8AEXU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UkoVbOzMVIUIZMmzakanQpZmyEFxcFI5uQqUBABKBRd38HH6s0k/7qgU6bPPoVB+c2OENz4FYDLLRrNFmq0IXnIwQavD+CAs6eJzj5zmLZQ/XckIo8oPyoBDkAQD8wXPga7NFeml+lq7JFld1MMHOFnjOwesZ7mWXZMq0wao132vAYs6Lc401vtLxOiGCN8Q9WATG1+f9C/4bw9fke9elpyTm7byHxM5rrB7WTLz3+lS1j07bWTEe8XXHzFr9uuTSM7U+mlg211qOQa4Q/Lbuz3LaWFjLjnUuyVTohtwCjZo14RyEhWaPVPrpvJ0RgjQW3fI8kZ18HLFz4jOcbx73eE2TWRTtb7UqtMWqip/rrRpl6VpaqBDlZ8s0bNfIdF2arJp0wC3QU7kheiI7RKeJBXkGbTYrdHm2LIS5fL2jrkU1h8isOkKkMrhQpkumZmjH1CyNzJfIqjlk2C6ZrRUMgATMWRkq2PX+6YeKYdCZvgJdyOUp5zg0XKtS3nGoahhCNDVQq9H6SnnJ51mgeXhgiM7n83SiMEAly6SqYdJ8JkvjuTwdHeC/rRwfLBZ28Z6Eaa74+5ZikTaWiuLf85kMDVWr4veJXJ4cwxBjHPdlhoVdZ/sK4u9dcd16ubgulSyLaqYphJ0s8Lx+eoJ2z83SplKJthYXaF2lRH02O/EIl4lsTqSVU8/pnsjlyDYMKpuWKOOiadGaaoVyji3SNVKrkuG6QqxWcJIToB7tH6Az/+6l9JZ/t9opHQAAAJWB6AuAsDtVl0ha1cXJrc6iLwBAtBiwjAAAAACAohjiuBCHagCkFfZ4y69WeHnDh/r8en5uoWks9Wiln35c7RdCMPbAm4ShExuTsidgNmjg3R42xGIjBDbUujpbFMYPj1UK9GBlkPZWC8IDOEgHfAjPRnubrKpoJ9yO2cD+lJ0TQgg2zmGjZo44wcbIbMjDhi9Hajmadi0R6W69WaXLsnVDZTbCeazaL6LkcTtjL/JsMPN0tY/KMPJbAhtDctm+MDdPV2RLQuQwtBiRCEQP5nJAtAODaK1l01profnsBjH0P6N+qMsG4fy6JT/vS6THQjw2smSBjfi5GNGEIxOwwSU/f9gYi+c3C64pnln8XJte/C4E+emBV+0sSuA5Lz9r2WB3q1UVbcTXdQyiTVZNvDrB4vhdGZ5jTyz5e20x2h2Ly9hQuRfjZwBAo1+a5PqMxgC8wYJWFkHw67U0JUQjHHWQRRIslF0uOuG51Gr0GTbdkCuK188PTIhIZE9WC+K5/J3ySMc16p5sibaYleZ4zcJ7jozBa0t+9rPAlsUnDTEaALLD/WdbpiLmpzxPtRZFlBzJj8VEPK/lqJ4lMsQ8mffvdlkV2mBVaLjLPgH3SRZhNYRey99r0NfjWQY7prkmWyJqMdNa6QK7zpCQ1vcO77021go/S1OrfoYjnj1RKYhoYSzSyhlOPRKx4QqHHTyejBg2zS6OHyziGjKcjg48bgxpbTjE/gLyeSpR3XEA//868ZqjN9Kc2DPjEbRrFMq1RLR9mKaJXy0sir5M2yGrZFOmVCOrVCPDccnJLO4/syCvxu/XiG/DPzMLNTIdhzILVcrOs7Cj/a3tnCUim/E1+eWahhCcOdn69TPl9mIQ2zRoqpCnuWxWrOUyjiMiqrmuS2dzfXSsf4C2LSyIKEgs6pnPZWmi0Eeb5uapv2ZLL27iKE4czYnzcTbfR7PZLI1VyjRaq1Kf5dKZ/gLVHIMumZ2lbfPzNFCtUdk0hZCvXRQ3fr9mGEIcZLpEk7kcHRgcpoODQyIqXMm0qGyZNJ7rE6Ik0YxdEn/rt23xXXvxxYKlq2en6TlTk7SltNAUHPE1BuwajVYq4m+HBwbpyeERmsrmhBhoMpujQ4NDNG9l6AUT5+m5U5MiGl7escVPvg8Ls1ig9czgEN29fhM9MzRM05msEFJZTl2smHFdUeecxplslnbNzdHlczNCWMTpPN1XoNN9/XSmUBAiLbdTp3Rd2lgu0YZSiS7k83Sqr7AyFLlHbMNcNYor//14/4B4hY5hUHGZDS5HFjzen1n1fiy2GqlWhAjt0oU52jk/J+qSowhyNMFBe1HYu1i2Z/MFOtPXR4cGhmg8nyfLJVHGLPI6n8vTgaFhms3mgqV9UbDGz64FK1OvJ9cVwjxuCwW27TVIiPY4T1znm0pFumJuhl48N04D5SqdtfL0yOgY7RsepazjiLbEYrLdczM0Wq2Kv3GENG4z/U5NjOHFbIZOjg3Tda96Kb1pOxyiAACAjkD0BUDIrBalqpWFhWgPQefnOx/0FQqFSO8PAAAAgODAgA4AoD2LG9sAANDNWOqm/IJ4NWCPuH89t54O2fUD/zBhAQkbs7KAZ4dVEZ5vWUjSzUCEuTm/IF5siHVPZYj2VvqEMQTDXr79CsHq0liDBgxbmJWsN2viJxtas3HLtGJG15dY7Cm5KiK7sQdzjuT2aHWgaZTGBmiyssmsCuOiKzMluipbFEYvq3mH9kpD3Lga1+eK4rUcNnZmD8zsoZk9wbNRNB/g8u/84rbBojP2isytgr1UPyWhUIwNtVnkxiYLA4ZDg6YtjIfY+/RJO0fnnIw4qNhoVcWLy56jgVyWKQtDHs4rt3/2IM1GRr3UA+iRlM/lNuy8huanz9P81LmkkwKAf5GeGFG9c/sqUy4WJ8+5phjL+Zl0wbboocoAPVgZoGMRzNGi5IbsPD03xyJ+R8xJHqkM0P6WqAcc2YrzyeU3atjC0HXWtcQzSaW5WKdnM8+BL+eoM1ZV5JXnEzwPlmlNwGkCAITrVDLFUzlat+1KqlVKNHXuaOT3MkMaw9jBxQvy8+L1HwYmm9GJ+FnEOxaNaGFexblzjilEHrxufLgyQGcdtR0G8x4Ki4OrZAhxHe/l8DOOn+ccEbVuNt8eft5zuQ0ajrgGi9397FPws5TX5Lz347iGqAueJbC4nqPeBinfLNXrc3umIq7B6XqmmhfOkHiviOdj9R2A4HAZ8Xqbf7I4kFsOR9lkp0sz7MiGXLEnxn/nsmTBYLey9JIvr+lmh0scpeuFAaJ5p13cms0VqFpZub+0HBZMib1X6my/NmxwWcpVntxnexq5FjeVHNMiJ2tRdSiAqIPFRVWHzKpNVrn+YsFJrZClymCW3GyHaMuuS9nZCmUXqk0hmMvRlgayQiwmulqLMId3EkuLv7M14h5xiUEaNzbQeMtlTzmuEKRlyjUhUDrk1PcHB5wa5ao2Dc6VKWfXKMvpzho0mckuRl4zaIQjwVWr4hkzaeVoNpOhA7lB2psZohnbEFHX1hg1GiSOQudSX5ZojGp0gbIi72sqZbo0U6EtBZsrSOSLsfMWVfvrteWyoG4xXyzcG3RMylD9GZajTHPtdVGmMkYlGqMFl4QTEha9cRQlHvMdy6TiZI3OzBv0TH6IDg0Oioh27Lyk8eIULKwS9boxxvJ+9WbTpjUmRzGu0nYWjJo1IXQ8a/fTEXeQThpufczlNbBZH39Z8MWC07xZ37fPuxZNVfuoWMvTgGvQMNXoyKY1dHDTmBhPOR11cWp9/OMxd7X1JAu/pnIr19RPjK4Rr0AYhhCk8SsNsLCKy5BfT46MrvIBl/ocFvmZImpYO7iNcIRrFsQyHN2Q5xv8HOa64/OOfsMR7Yx/clTFSfeiCT6fM3G75q2FxjcZ/n1uUaCYMQ3aYFZpq1EULYOfx1eOlGj3Vo6gPSQ+P8jO4WyibbV5cSbE84wh06DtVoHGzKxoSzwecipb98ovJZeu3jQWYskCAACQCYi+AAiZkZH2Hpa8iLJ6pdv1R0dXmdgCAJQRgwAANCflBnQAAP1Ju3FJ2g+dAegFjmjx34ZP0xPVgjBOYq//bOjEh6d80MqGIw3B1GpGPOy5d6NZpUHTEUZAbCizedGIxq8BSTtDrNf0z9Gr+y76juXxjg19Wj3PLp55C6HLMTsnDgDZEIwPDzltXmChDIun2BiJjbX4gI+9dbMRMv+NDyA5z+zZlw2zVzvgXs4Oi6OXleiabJF2WmUhNDpcyzejgCw4ZtOIig/BWXzDh/GcB44CwnXA/96RqYhy3taxXM+v+Asfmn+lOEo/KA96Sm+7ug4Km4I9L7cgDmXZ8PnKbCmUdhGGsfNVWRadNcw7vMGGWvw6b2eF8VldRMhGCoaoU84vX3to0UM2G8M9vujhmQ29/Bi5cV1wu+BoINzm2SCe2ySLKPdkSiJCCAsDemHErB+Og+RJ+1xu7bYrhOgLgLTChk9jxkVREIvJXlWYES82dOJoAWwUzJFNjtby4hnBT/WGgVvc8PPuhfl5MW8R6SdXzFGuyJTYFnEJLAATmFmqsDEkPznbPA6LriGel8y8Y9KztbyY27HgnyN7xiW+5vLlPDXmtJvNqjAqtBef+TyHO+NkaM6pP5v5c7szJbouV2w7z9l65c108ukHY0k/ACAJ0n3OuGbzLjp/7ElSmUZ0oqDwvsNVZn2N+brClIhazlHWT9RyIloSPy/mF9eTy59nHN2anx6Nv/N6cp1Zo6M270AYNGLUhAF0r4KkdvD9eX3JzzM2lOZIRGwkzc90XqMsf27zPgNHO2V4PczOevhvXAaNtfDySEF2i6iOL8fG3LxTw9HS+G/83B0ybZHfUaNG/RxSpgPsyIXnRFwubJjNaeA5BM+V+HcuPxZKsMiJr8v347X6arTmkaM0nRFCLDZOd8W6/8jivhDP1HgewNfi/Y0rMnXjdBZq1NNULwM/8P3OLd6P7/Fkta8ZEZbLhkXj/D5Hi2rd07kmU6SdmbJobzz/4qidnI9TdlbMn7gsOEtrWDhhuEJkX6/fWtBgNKlfs17ynJfSMw99K1jhAT8NjZycJV41vwGODIOqw3nxCsqq/cM0qDaYEy9m1+LKoP7sF7Hwmh/ld5ZbDjaeLCOLr21UpZfR0ki8K2msDXnc72uK07rB4+GYiHhpe3rusRMOgcXC3Dp963N0yXqiS8RdV78zjzeT7EDNMS+uZ7tEEGah47BZ9pgToq1U7bpvy2I6FpOJcY5c8Yw9bOfpsUq/2NPnvVoWkHHaOGm8z877qzwmZhbLi8fbA7U+z+nSGS4nfq6xQ7EZ1xTO5hriZX6Gc3nzmpifSVyuvM///Ny8aEd8lvEEz3vsuhixLha36ZJFIbRwBLgoQOQ5AdeHF+djq81DeoXrnQXa/AIAAAAaQPQFQMyir5mZmUjLvNv1u6UPAOkJe6UEAAASkXYDOgBACkj5XG7n9S+mM8/+mErzU0knBQAl4QO25+SK4rUcPsxj45PzTlYIWPjwlMVHfOjKgqwkxjj+Z584crxI41CZxVH8CgKLuUZXKQOG55IVPtRsMVQ+a2fox5V+YYjD3qtZQMfvs4dIPsy8PFNaYVTUrpyXc0X24qHjuu17aHBsEx157G7feeJD95/rn6SfLUwKT9hsQN3wVs71zsZQLHRjQy32xMoHtvx3cXi/6DWVD85ZeNbweM4CpmO1HJ1nY2fXEkK4kmuK9sBG0pxlFnntzpSF8E6nRxSLsOpCLG9tjPP/ovxc899Vt24kxmV81mZjNEsYi7EBIB+es5EY/z5s2qLMEXkrTWjUUXpYtwMAVsIGupsL1VWNndk47XgtJ8Tox4RAqm5g1YhIws9n9sU+3eIduxtsjHVpplyPyOGyML0mDI75OsJDvFml5+cWhEG3HyzToBxPLDvAgqlCI3qLRXR5y3yI88tG2Dz34DnKt0ojwgi6Nforp5XnOizUZwN5FmxdavHcsCyeqzyfZUM/NhBnOAeua1DGcJuRaXk+yDE/wxyS1my6lApD8AgOgM5gHgNa4XVcu6jTzuLeQsP4nJ853QROrY5qjtRy9GS1QI9XCzTeEu2Knci8ND8jhE68pmQRFsPPtcYzm/+2v9on9jB4rsD35mc8f7edIGq15yHvM2xqEcht7u5fRuzZbOxBVLccTu9lLfOEXmjNI4vVLlm23ucy9XYh/4eQfL/GHtYlVKEbcwsdBWL5DnMU/vvWTFW8IiHF6zXek7MyAaJWAaApPBwIpyUJRzPm5+2IiJpXhwVvY9ZCx7F0NXgf/BwV6IlyRkRh5LUw/+RnblSCaz/wWpyFWOw4juE1cVj0Gza9IDdPP9E3K5ydBB3qx6ziRYcvXegkDlxOih89AAAAYgaiLwBCZmys84HI+fPRegLtdv01awKG/QUAAABA9GBHCACgOWmOXMp5HxrbTGfox0knBQAt4cNTs2Fw3DDCTeEYx9NJNq5phQ2WXlmI1glRWPBhKguQRFiQFliodW2utPrhvfAMexH29Mk0vJcD/7Cn9IYh/s5lhmODiLqVamAonPw4D4BqsLEzRxPlVzdYCLWv2kePV/vpnJ0Roig2HFu76J2bxV0sXudoVSMRifp7nc9xfrcJI+b6fPSW/LwwzmMBF89RWrf+2HP4aoZkVxA8eQMAootAD4DXPZaGExu/kZtZwMUG1fz6j0Qi2hY7EmGRl5coZSzWWk2IBtRgefS0uEnzOCfDviQAIDrqolmHNhqzS/7O4mx2PMJOR3g/l52q8PqTI1/z+pkjM7IwjB178Zp0a6ZC28UZSkU843nU5ojV7Cat4UyFRxN2v8bzARaCc0SyZ2p5EVWShWa8G9+IonVDbkE4H1sOi7g5Sna/4Yp08br4rJMR6eB7cFqmFucHPHfgdDciV3NeOA+NaJA7Ogi/QZvGAgAAQEsg+gIgZDZv3tzx/fHx8UjLvNv1t27dGun9AYiaVG9WpTjrAKSFNB9GNOgbGEUEHAA0BuMcAEBrcJiW7jU7AGkA4xw26ACIEI7wcXN+QbwSW68a0QydHMGrF8/hsYAxHgD9kW3cSQQUQtyMmbZ4ARAHqd+XwnwOgNRNY1i4xU67drb5yq5MhW7Pd75sp+c0C792Z8vi5QcWcfOrgYjIbdWWROFsFYMPiRjXdZJy6gcAAADIDqxKAQiZbdu2dXz/5MmTkZb5iRMnOkYh6+/vj/T+AEQO9uJx+AqAxqTdazpHwFm/46qkkwEAiJI0j3OLeU/7WA+A7oYlaTYuwfgGgP6gnwMAgN5gnAdAb+CMCQCgPdh7BwBoTNrncplsH41tvizpZAAAAACJke6ZAAAJiL6OHj0aWbnPzc3RxMRE4LQBAGQnvcaDAKSHdPfzwTWb0l4EAGgPDMgAAFrD8xgYlwAANCbtxiVYrwKQhvUqNqYAABqT9vVq2vMPQApI9flDmvMOQFpIeT/fce1tZFqZpJMBAAAAJEbKT+gACJ+BgYGO4qrDhw+T614MXxsmhw4d6vj+lVdeGcl9AYiXdC9iAQB6k+rDCCbl2QcgFaR9nBOgDADQldSLIeqFkHQ1AABAtGCYA0BjjFTvzaU5Yi0AaQH9HGcwAGhPiudyAAD9wVwOAAAASDcQfQEQAddcc03b9+bn5+nIkSORlPvevXs7vn/VVVdFcl8AQLxgIQ+AxuAwAgCgOWk2oAMAAP3BGA+A7qRd3Io9OQA0B1MZAIDuYF8OAKA5OH8AAGhN6udyBsoAAABAqkn3CR0ACYi+mMcffzyS+z7xxBM9pQsAFUjzRl2a8w5AWkA/53EOYx0AOpN2Q2EAgOZgzQYA0B2McwAAjYGwEwCgO2k/f8A4D4D+4PwBAKAzaZ/L+cU0M0knAQAAAAgVWFsBEAE333xzx/d/+MMfRlLu999/f9v3BgYG6Nprr43kvgAAAAAIBxxGAABACg5jcCYDgNYGZKk+eE1x1gFIC6ke43waC/cNjNKGnXDCBoBSpH2MS3n2AUgFaR/nfJDNFWjL5TcmnQwAAPAF1uwAaE7a53KGv/277VffQmkEzwIAANAXiL4AiIDrr7+eBgcH275/7733kuu6od7z2WefpdOnT3cUomUy8GAAgBakfB0PAAAAAHWBuBUAoDVYqwEAdCf1xiWGLwOLTK4v0uoAAEQg6sQ4h2YFgMbAANRXYZGZyUZXGQCASEjz+QPGeAD0B/3cV2FhfQ8AAEA70rvaASBCLMui2267re37Z8+epYcffjjUe37jG9/o+P6LX/ziUO8HQGKk/dAVAKA1aT6MaGxU+tmsHFq7JdL0AAAiAHM5AIDW8Dwm3WtWHDwDoDfo475KK7J6AABEBNuEpbrvpjnvAKSDtJ8/1JfsGOsA0Bp0cQCA1mCQAwAAANJMynd1AIiOO+64o+P7X/rSl0K7l23b9JWvfKXt+xzh61WvelVo9wMAAABARGCfzhfrd1wdVU0AACIi3YbC9byn24gQgBSMcSnu4n7Ht5H12yNLCwAgKlI8yDXwOp9FUQGgIOi4AADdwTjnvaTq/wEA1CLd4lY4owJAd9I9xvk/f0j3mTQAAAAdSfdMAIAIeelLX0rDw8Nt3//6179O4+Pjodzr29/+Np08ebLt+7fffjuNjY2Fci8AkibVG+zNBWmKywAAzUn7Rp1vMBwCoB7YYAcAALDI2NbL073GB0BBsGb1XWDRVAQAIBLSLuBnMDcDQG9g+OpjkE/58wAAZcEaDACgM5ifeJ7Ppnvem+a8AwCA3sCqFICIyOVy9HM/93Nt369UKvTRj3605/tUq1X6yEc+0vEzb37zm3u+DwAAAABiINWbTw28lwEMUQBQj1QbCmOIB0B/jJR7AhdzWR9zOcx9AVCPFA9xfkn18wAAoCR+52ZDY5sjSwsAICKwBvM3R8N0DgDlwDoMAKAzqT5j9T2XRfRDAAAA+pHymQAA0fK2t72Nstls2/f/8R//kR555JGe7vE3f/M3dPjw4bbvX3HFFfTiF7+4p3sAIBUp3mDHJiUA+oN+7rfAUvxQAABoP3b1DYySlclFnhwAQHhgLhek0DCfA0Al0m5c4ksQkXYhMADKjnHot15Zs+UyyuUHIq0TAEC4wPGGr9JC8wNAMbBe9Re1FmcPAAAAAAAAqEW6T+gAiJiNGzfSm970prbv27ZNv/Ebv0Fnz54NdP3777+fPv7xj3f8zK/92q8FujYAQF5wKAOAxqTd6NVn/jEeAqAehpnebQi/Rr+5wiCt235lZOkBAESAcB6Z8vmcL1BWAKgG1mAAAK3B1MRXGUDYCoCCpHy96mcuW/9sussLACVJ+Tjnh427rqfC0FjSyQAA+CDNZ6wX8TjOp3kql9Z8AwBACsBMAICIec973kNr1qxp+/7p06fp7W9/O508edLXde+991761V/9VarVam0/c+utt9LLX/5yX9cFQH6wOgEA6AsM6PwO83gmAKAaGOf8lhXGOQDUAn3WVxGguABQDxjQ+SyviOoBABARBtas/osMAKAQaY+CI8C4BYC24OzBZ3kt/gcAUIe0j3N+xiyMbwAAAHQEuzoARMzIyAh94AMf6PiZgwcP0s/+7M/SP/3TP5HjOB0/Ozc3R//7f/9vesc73kELCwttPzcwMEAf/OAHA6cbAFlJ9SI2xVkHIDWkeYwLQKqfCQAoC/otAEBvAzocJgIAdAZrMO+CEJQVAOqBfuu7wKKpCABAdKDb+isujHMAKAYGOV/7kiguANQDcxN/xYWBDgAAgGZkkk4AAGngNa95Df3whz+kL3zhC20/MzU1Rb/1W79FH//4x8Xnb7jhBtqyZQv19fUJodehQ4fogQceoH/+53+m2dnZrvf8vd/7Pdq5c2fIOQEAAABAlKT9EBEbbwDoT6rHuSB5T3FxAaAkKQ/Q53eMF/6EDYNcN7IkAQDCJs1zOb8gaisAIBXjVjzPBZ43uoRJIwA99yVE+vJTWGhwAKiGkfLzh2AFlnQiAAA+gC2FjyVozOObTGtWtBMAANAXiL4AiAmO9nXixAm67777On6OP/PJT36yp3u9613vojvuuKOnawAAAAAgAbC57tMLHQ4jAFAO9Ftpy4oNf1y3c+RpAECXfpRmxdcimMsBoDepH+fEMI+xHgBtwXrVZ3HFOB7yveApAIAQuhLmMV7ncvV5L8oLAJXAehVjFgDak/Z+7iuYYbzCVsM0yXXs2O4HAAAgnZhJJwCAtJDL5egTn/gEveAFL4j0Pu94xzvo137t1yK9BwAgKVK+gAcgBaTe06Yh7wFO6usGAPSl3seRxTHLj4FNrOOciS0iAHrvSDAKC1BoaHgAqETajUt8AsNqANQi7YbCjSisvr4T03MB61UAQutN6S5KP2NWzEWV9mcQAKH18bSvWf2cPaS9rABQEPRbP4UV9/0wpgIAAEhxpK+PfexjlDbe/e53J50EEDGFQoE+9alP0R/8wR/Q5z//+dBFZb/7u79Lb3jDG0K9LgDSgYWS5zLoH15HCzPjkVcJACA8sFHnu8Bia36IgANAWH0Jm96eyyrmEwnTsMihWqz3BADohk8jYRi1AaAccIbhp6wQtRUA5WA7YRdrVl8FFhN4/oA40XkfGPtyfgssznshoiEAPXcj7DNJi87PVgDiBOsi4apEykaHM1YAAACUdtFX2jadIPpKB9lslj74wQ/ST/zET9CHPvQhOn78eM/X5OhhLPi67LLLQkkjAEBO/D4X+0fWUf/wWho/8TTFATbrAAilJ6EYJfVCJzwK4zwCgBA6E8Y572UVc3mhbgAIoRv5j45AafecHlNxYb0KQFh9Kd1jXJAoOHGBcQ6A0FRfKEofxRUXMG4EcbFh5zU0OLaJDj36XS0LPe19yd9cLmYBv2mSa+MAAsTQ1sgglzSd78i5VIsN30KIGPcxsV4FcbJu25Wx2UjFjqR7UnIS8x6eTHUjU1oAAACkQ/TVwHU1XWwuQ9aDQhAdL3nJS+iWW26hr33ta/TZz36W9u/f7+v7pmnS7bffTm9729vETwDSgqxeOwAOJAAIA8wJ5R3j034gDkBYpHou18i6pOt/IW4FAPTYkeTs37FhyD3PTsk2M0iYNZsupYHRDXRi/wOkJWkf53wRd6Qv1A0AYfSjtJxLr4rE4wjGOBBzi9O3wDXOWiROXWIsMESHAHFx1e0/K9arM+MnNCx0eZ10pB3UC4iLsS2X0diW3dqKvlJ9xir5uINxDgAAQBxIL/pKwwMx1QcIKSeXy9HrX/968Tp69Ch9//vfp71799Kzzz5LZ8+epfn5eapUKlQoFGhwcJC2bdtGu3fvpuc973n0ohe9iMbGxpLOAgDxk4LnQlgLeXFITfGBAwkAQmDxIFFbL3thE2ekLzx/AAirM6EkvRdWrAc4ELcCAHQe5+pjnB3LvUC66RtcQ1YmR7oC4xJfhRW/gB/DHAC99iQoInyVFkdGi2kuByclIC4037fC3o+8YJwDIIR+pPkY3hVD8rkc1qsgLnQeClI+zolx3lcZxHjGijUrAACAGJBe9KW7ICr1i07QZOfOnfTWt74VJQIAUJeUbzAAEAZpN6DzOzeOVQyBjToQE9uvvoWcWo1OHnhIyzJP9xrYkNsLHcY5ABI4dNQPP/OzOMc5GJeA2Nqa7mOA7vkLO/+xOipB1FYAeu9IfDCNcvQx8MRWWBjjQKzoPN3BXM5PYcVbXmmvGxCv80ld25vIl6Z584ivuo11iMN6FcTW2rQuam3Hbw2QaZxDOwEAAH2R52nTKWx6BK8w09DLtQAAAIDogKEwAMohziMwR/ReXjAuAZqi8TAg06a3EuDgFWjIhkuuJcvKkp5oPIBHJciPy6Mwnj8AhNSX0j3OyezUBQJ+AELoR4aJcU5Sh0yYywEQUl9K+ZpV6qitWLMC0Hs/whjnu8TiAmMcAKF1JhSln7LCGSsAAADNMFWI9BXFqx3dRF2rfd+PEMxPWgAAAIBlD5zFnx7LBQcSQFPyhSHSF2zU+TEijDU6BA5dQXytTe+y1jx7KgNDYRAH2Xw/bbz0Osrk+vQscHGOiIFORnQb4zA3lRydDTB0zlvoXtP5iYA1K9CPwuBY0kkAaSTlzx8QH8LWQ+c1HfqS96KCgB8A9Uh9oC95n1+67csB2fdt5O0LvQJnTBKDeTYAAIAYyJCk3HTTTZFd+/z583TkyBExEWqIrlonRcuFWIVCgYaGhmhwcFC8TNOkubm5Ja9WWoVffK3Gffjn5s2baevWrZHlDQAAdAeLWHnLC3UD4qAwNEa7n/9KevIHXyK7VtGu0NGPfJdYjLfSa4PYtDLk2LWkkwHaold7ayXNRuqNMd7zWB/z4VSa6waAsEh9P/LrPTNGg0rd6obncjquh4D8YM0q79oQRnQgLnbf9Eo69MhdND99XrtCF+e5Gq/Hwx7jxedjKi6McQCE1JckncfEiqRloNuaFYBkkLN/x4s/x5pxPRcwxgGAvhQ39ZOHGM9YIW4FAACQZtHXnXfeGcl1v/CFL9CHP/zhVY2tGsKsm2++mW677Tbas2ePeG3cuLHjNScmJmj//v3i9eCDD9I999xDtVptyQKpIfy6cOEC/Yf/8B/one98pxCPAQAAAFHBC1iX4osoiUUsiKWdYcMeLG0QsaHbGGdZWYi+JAXGF6DZFuBRWKm5MPBGKuZyKciiiug2lzNNi+ykEwGUMiIND93zp7KfEr3GOQASIUYRk6zIOl/HGAdibG16F7b2c9UQiVEMoeua1XGwapURnc8ftI/WGHrdwrEm0BOdx7nU42d+FnMzwJoVAABAqkVfYVOpVOi3fuu36Jvf/OYSgVfj9/Xr19Mv/uIv0h133NFV5LWcsbExuvXWW8XrP/2n/0RTU1PiPp/97GebEcUY/lkul+nP//zP6f7776ePfexjInIYAAAAP6R3gX5xk9JHdAg3zugQVmz3AkDbTfvFjaplgWdThg8vdIiA01N0CCAvOh9IYNPbb3kh0lfgtmZZ5CKiobxoOs7pPH5HgZjLxRUdQjMxBOZyICnSPM75XX/GXVa6jXNAclI8FoBW4otOrZ0YwsrAGZPMaDzGpX2+IPNcVre6EeMcRF/yInFf6Bmd8xY2oqiMWAV5cNIGQK99Sa/5QrTw2QME/AAAAPQiFTMBFlq9/e1vF0IsFng1BF/8M5PJiKhb//Iv/0K/9Eu/5FvwtRqjo6P0H//jf6Svf/3r9L73vY8GBgaa7zXu+8Mf/pDe9KY30eTkZM/3AwAAAIIwtvmyUAtOt4NXICmab9bXjcj0zmOo9YuNusDAUFhmNB8DNB/HvSGpFzoNPQoDED/pnsv5nssiAk5gMJeTF2HGpPF8B8YlfsRf8T4TdJvLAZAEaY8O4XvMinUup1e9WFY26SSAdujV1LTvS0HwOs7XywpzuaBgzSozGo8DGOMkrl4Da1YQU1OTthOEg+75UxjMswEAAMSB9qdALLD6r//1v9LDDz/cFHs1hFdbtmyhL37xi/Te976X+vv7Q783C8pYSPbVr36VrrzySnHP1of8s88+S//5P/9nIUoDAADgkTSvYY1wvzM4tqmX1Ky8FYxLQJzoOhbomq8IicsYR7eNOgNiCJBU24MXOj+lFe/dMM6BONubppOexr4j8FVqsRSXbutVzOVAco0vxWOc5HnXbZwDIBnk7ueyjXXxCp31EuRBDCE3OrW1leicN7XRbc8U45zc6DrO6ZqvSMsrziKTfE0NgAqk/ezBj9O5uJ266DaXAwAAICfaP23uvPNO+t73vrfE6ILFV9u2baO/+7u/oz179kSeBhaXfe5zn6NrrrlmhfDr8ccfpz/90z+NPA0AAKAL2KzzvpDv+rmQNwR0W8Tqlh+gCjFvsOtwwBDT5qZuY4KZySSdBNAO3ccA3fPXCckPY7Qb5yBuBSB+fA9z8RkK6yaGwFxOYkSTlvuZ3wtpNy6RWgisWd3oNjcFimDo15d8IXPWNasbE5G+pEX3M0jM5fxEpo73rEa7NauJ8wdp0XmY0zlvURDz3AprPBCfrYHOg4HOeVMcrdsdAAAAWdBr52AZZ8+epT/7sz9bsnnFoqu+vj76+Mc/Tps3b44tLYODg/SJT3yC1q5d2/xbI+IYC8L27dsXW1oAAACkhc6eS8I+vNLtQEK3/OjCxXmdnpsmOHSVt7x0GxMsGJeAhMDBnvdHmBjjMM4Fb2uajdvaoOcU7iI42PM1P4t1LqeZeAAGdCAxMM5J+yzTbZyzMtmkkwBSiO5ij2ieCZjPBQECfsnReSjAXE7aMtBtLodxTnLk7AYhELPjDdkIENUmtvmvOOrQp250G7OBOujUjwLh23kwxQYcUQIAAIgDrWehn/70p6lYLDb/zQIrnvy84x3voCuvvDL29GzcuJHe9773NaN9NXAchz75yU/Gnh4AAFCSVC9ijXA/HnqkL73qBotykBQwMPFXWnGh2wa+acHTpqzo7oVOt/mCTuO7buOcgUhfciN3d9C2n6cZ3YSgmMvJjN7jAMY5ecUQuu1jWZlc0kkAaVzX6ZovDZ4JwoRZo/kc5nISo/k4oO34Hdkxa5znD3rVDcY5edF5TVfvR/rmT3V0On+AkxKJ0ex5mrr8ecGQtKxQNwAAAGJAnxn1MiqVCn3xi19csTnS399Pb3/72xNL12tf+1ratm3bimhfd911l4hMBgAAAHTHCGVhGfYBgk4bdQwMhSVF982SmKO6SAdnX9IDGYxxAITWmVCUshqXaGRAx8C4BCSCkfZxTt686zaXMy29xB3aIW9X6J1Uj3Fyo984h0hfIBl0M7qPfP0ZY3HpVDe6CXX1Q5+2tgKN+pFuYw725UB8jU3ncUDnvEU0zsXUHoSAX6M1K84e5EZWW4Mw0KkfxdMO4EAYAACAXmg7E/jhD39I8/PzK6J8vexlL6OBgYHE0sVp+Omf/ukV0b7439/73vcSSxcAAAD9Dhi6b2aELPrSzVAYB69So9Mhf1o2ISNBnEfEVGaatTkcSEiMIY6/SFd0zlvoZRC3cYlmh0W6zU31odGudR0LdM1XRMRpXKLZmGCaiNoqK7quVdOSP295N3xE8I00SXqPcxmMc3Kj51iQ5jEuELE7TtdnnMO+nLzovm+lUz+Knngd9OlWNxjnZEffuVya53P1Naif/CMKTlDgpAQkRnqHOOkLIM3PHwAAAPGh185BC/fff/+qf3/hC18Ye1q8pqFdmgEAAIBIwlWHvObU7UBCt/wARdBc7NGdsMWt4aGdAZ0FAzqQDKl+vvocsuI3FNbL0zgE/CAx45IUz+XCd1QSHro9fwxE+pIarccBGDD4KKuYb6fZOIc1K0iDgb90+DaUjnHuq1m9YIwDSaH1PDXs/Mc9l9Pu/AFRW2VFa6N0nfMWBRjnAmNlMMbJSpwR7JJAt72faB1rxvvM020uBwAAQE60fdocOHBg1b9fdtllsaelWxp4gsGRvtqlGQAAwNIxE8i5IaDbBoNuhs8AKIPvcT6m6BCaPX+sgIeuuXxyUZNThV7NLT15UxzdxjnM5eRGt/bWRBwqJ50ItYChcDBgKAySIu2Gwn6fYbGWl2bPVoxzkqNXc9N/jqoLGtVP0DFOtzMYKYnZODR2dM6b4ujWv+GMSW60HueAnGgWhQ3rVZAU2JfzV1pxottcDgAAgJxo+7Q5cuTIqguG0dFRSpqRkZFV/37ixAkh/gIAAOB/YZaOxa1/r+mxbp5ptFHHwBOLnOi0Idze+1TSqVCIOL0zabZRFzQ6BKJKRI/+45xefckfi3XrtY7F5zDOBQXGJXKi/RiHiZy/+VmHzxYG19Dmy24Ip2I0rBvTRNRWaYFHYY2R00GJrh6FYUQHEkHzuarqXvR1ms8FdVKCdS7oue2lfZzzITroNuZs3n0D9Q2EZ3uk254p5nIgCTDG+XdSEmsUHI3GOYxxMqP5XCflczlfY1zMZaXTGAcAAEBetH3azM7OSrvIa01Dq8jLtm2an59PKFUAAKA4EozvMoar7vh2yGXWzbhkbPNldMULXiN+qoAZUBCR7x8OPS0gTX1e13x5w++4FKdITreIMUEPJGBgHBcajwVt+rlOxluqopuhsG75AYoghjKMZ/7LLMB7KSfoehWAntF2La6BGEIz4xIY0YEkiN2JmpSEl//1O66mOFmz6VLasPMa0hnd9iflRPMxIPVjnL+y6vxMiPeMVTUwlwOJoLkTltCBIEKZsgPgYtND25O2/8maLgAAAFqh185BCwsLC6v+fWpqipJmenq67XsQfQEAQBewTvJBt+gQRrzGJYYhBFHZvgFSAXjblBXNBwEYCvsoqri9M+nV9gKLvmBgHAN6tTXPfUnBPrbj2ttp+1W3RFpWsnnaVEmcF3Qul8n2hZ4WsBrqtCV/6JqvJEBZpkncoRMqPSuDoNu6SCdgKAxibW+6jnWaZisyOjwTsvl+6hscDfl+nd8eXr+dNu66nvqH15Guz1Psy4Eo2562Y3tPGLHt23Vb42VzBdq8+3m0ZvMu0vn8QYUxHMhNuseyIHmPsbw02k9IdzvTK+Kdcuict9DpLATecMm1NLhmY3h3w349AACAGND2dDibza7690OHDlHSHDx40He6AQAAdEbrhXuDkLMYeqSvLovYxv1MRbxRBk0nFvMRY+i9mdrJo7CKeY4j8l27chlau0Vs1oV3o+7ln8sPUL4wRCoQNGKXAdEX6Bn1xrJ29A+tJSub02a+2m0OM7J+O137E2+iS5/zE6T3XE7uegJy08noS8W5nG84/z7yWS+rmByVaFb8WHeCBBtf+7d062jLkTx7uo0LOkaHsKxsqAZFIAq6zU2A95KMwomJkUoRrEqRvnicU524I3XGjW7zhWCEWb8hir66jF1mJkvrtl8ZexTFoJgBxwM/e62gBzQd5yBsXSwHSeezOs/RAIgLjHM+xjhDrrkcAAAAEAbaPm0GBlaPIvLAAw9Q0jz44INt3xscHIw1LQAAoM8CTs7NqyQ3YuuHU71fxyteD3FVWewGPfyCIAL02PCCvSch67ZdSVe84DXU1z/i41s+8ihheVx6w8voihfeQYNjm0hbj8IBxWJxoYXhguZe6NqKIVTMc1exQs83iPDawedoshuZ9Ry1NZOVdmxj4R2/tEDBLt8zKo5zkdNp7hv/s2H9jqto067nCCG/9KA5yYvmddOxX2KcS7Q8uq2FBkY30NUvegNdecvPkAroIBxghtdto217XiAic+y+6VVUGBojLdC0v6fdgK7reYJvYVeYEXAMzUSj+uzLFQbHRLpyfQMiIjpQGE3H9kjHRCP+fW01xrjgkQlldxzKYx2QmQ79FmOcP+dVhkm7bvjJcGunyzjH66ZrXvxG2nrlzSQ9Acd/f2fmIAj6r9uwLxdqewh1Lqd72wMAACADauwIBGDHjh00Pj7efKDyT9d16bvf/S4tLCxQf39/Ymn72te+tiRdDdavX0+5HDzXAABAIFKwfgp7gyL062lnKGymV3AgMfpv1GlEwI0t3xtinQxyEtpcU2IcCCr6kvBQmQ9JSgvTwkB7w6XX0on9yTv6AEHaXnrGd89jU8xF4tm4RPO5nGwGxiyiXr/zarGnlcn10cmn2zsSAmkQg+qHTIehw+u3U//wWiovzFDlzGGSG3nKDcjdrsNH57x53I/w6vQokig3vc19LAnF7SqtPYPAbWDN5l2tf0kwNcATnQyFXVe56NTFuUlyXSeaG8QohvCDEmvWgGUjo0O9rXtu0kfQmgI67f3U7WlIa/yJW41Yz6u87svJOA6E6oxJsjlg38AoFYbWULVcpOF1W8lxbDpz8MdJJwsE0kKoN8Zt2HmNmMvNXjjl6fP+15+dzlfN8AX8hjfBaFDRaLwEPANXIm86oO+6W+89Rw/4yL8h6VwOAAAA6AVtnzZXXHFF83c2jGkwNzdHd955Z0KpIvr6179OR48eXZIu/smTsiuvvDKxdAEAgDK08zak8cK9FzqWS9gbAh6vp8ZGXfADCVUOXJRH0y7f0aOwqpt4fpIdehZD3KjzcS0VjEuCbjzKOIbvuvHlwmM9R1nrH1lHqlNva4r2dy+Gr+3ypmCWlR2XexbwqzHXCToWmxm5jEt4js1ir2y+UPe+mmBn4UgVYaHr+k3ky0jHmOE3/22+0OX9MMvM0OqANmgaRzbsIJnJ5goa3F/vvq7lmjUhhtZuCXW8gaGwnKjwTAEtdJhvqzbGDa7ZSJc9/xU0vN7PHD7AeqNduRgROJ3zKrpVYM0atGxkE0PUkadvjG3Z3ftFEl53R05M0fniYPNlN9BlN74i2TPWEJ8N3cauRlpkjPgXqmNNyc5WeD9u21UvpEuf+xO0dtsVys0H2qHrONf5bEWtPHMkzY27rg8WXS6MdhrBMZXnNash1ziwGkHHAtnPj61MsoEK1my6tPeLaDJOBxXwpwKv2fQUtRWiLwAAAGoh/65nQG677bYVf2tE+/qrv/orOnjwYOxpunDhAv3RH/1R20nWLbfcEnuaAABANdobCqu3gGXvTGObL4vuBl3KJOxFv+lxA062Dfswy0f2jTotULCv+6PTQaFaeQ8+xng04GhEzu148EqJoIRRV8CykfFQmcubPdbXD5LV6iepo8MGu5oHzT7FDb6v3tl4JJPNxzt2NcZdReY7wT0KyxUBQ6aDOpnSIi0aGdDFQaexv6NQOGJUMBQOPpeTbwxn49y+gREaGttMO69/SaJpGRzbFNKV9O3vnecLhnIRcKLdl2v+b1XWbNpF2Xx//AJ+FdarPQgbwhSph4KhdDdJHWquS9thJH+PhAT8Mu5drSCoobCEzphkWifKlBZZ6VhGihVffnAk9L2xJNuTbgL+oGOxnOJWoAws3NXlWSDDVA77cqGvrWU8W+HI1Lw/wo5pLnnOS8lK8HwktP0KTYYB/3nTOeNRROYK2UlJlzkaiyq37XkBbbni+aHuBwIAAEgXauwIBODWW2+l/v7+JWKvxu/FYpF+9Vd/lcbHx2NLz8LCAr3rXe9q3rMR3asB//7yl788tvQAAIBuqHYgyxsW7J2pf3S9ny+FaigcOl4jfUm4maXyRp0qxjrho1af90wnL7jKZTnqBHcXtob6bPAxnqpw8Bp07DAkNC5preakD/RCMTD04nlMR1Q8jDWC5tEIpUx2XHt7qJ4XEelLVuOSZe1Awa6SNvSZyxFt3n2DEON4Juw8GsnUnYwGGKHN5STM26Zdz6HLb/4pYVgSyIO1ZPORpOejkaORtnXrVS+gNVt8iL68OB5Z+oWub4fZXrpdq+k4RZE2GnROVhgaI7kwlN7Dbocq7ShYRAM9nDHFkdzO7SC58lJiXy5g+cg4l5NqPyeMuVzEDn4Sp1PUVsUyHiy9Ps8MYnSu6V3Ar0Y9BT0Llu0M2XHsZX9Ro/y7okk2dBa2xjEmd7UlCXO80eyMNeh4JWPe2BkTRwi+5PqXUP/w2mTndoo846TdE1as+HgfeNNlz434LjE6Su86bzSFyHLt1sspk+sL774AAABShXyzyZAYGBign/7pn26KvZhW4dfRo0fpzW9+cywRv86dO0e/+Iu/SD/+8Y+XCNAaaeK/cWSy7du3R54WAABQHl3OXOPYsOjqaDPcaYB3Q2G5NuzD3HRL4jBi3Y49lMlE580QyBXtQCmayfWxkR8kjx3tSxKKDqGAGDNoe5LRW7JM5S2jx2X55j+GHmNcnAevcaVBNwF/wANU6URfK6pFvb6yajvT9AC30zpPtXFubMtltG77HsrkCj6/6dOArl2ZdXhmRL0Gl9EAIyykG+OY1rpJeGwIba9Gre4e2txbtXEuloiCsfpi0mvsCupdXLY12YpxRdM5kD50mMspVndNMbuvuZnkbdSruFWRNWsQZFyPy/T81+1ZGL+hsDx12fN6Mqzrx+l4U7Xyj2hdLZvTOddxpJkPZHOFSKPb6T+XU+wZEcceapd9zLCf8V77jxJ1xY5HA8zLZNyXk2mdE1ZaZJqfho5G5w+bdt9Aa7deEeCbXseSbp8L+/zB+9ilxDgHAABASrR+gvzKr/wK5fP5JQ/y5cKv17/+9fTRj36USqVS6Pd3HIfuvPNOIT574oknOn72Pe95T+j3BwCAdKHWAjY2Y7VO5RJykXneqFPFgC7AppLIW4u4Ow4GRtbT7ptfTVsuv5HShEwbkKHSKcKPYnkOvLEYktP0RJ4LTeMSBca5gO0JBxJdCzakq6jV371jtB+/Vcyyz4iC4ddruN6nvR4yqGJAFzSd0o1z8U4tO6PYXCQRuoqYVMJI7ZxfRmPa8CJ9yTdPlWttF0ZaZMpPFOgj4PebZL959NS2E4gOoQpB52TSOSqRaowDvUWHUKwuY0pvuzLjv4cdzdDrOCzdmm41gm6byjhPlahvhNLmxCXkyVOcKDmfk2z+F+kaT7Hq0WUu566I9JU0YTUExRpUCp0xxWbbEKMtidf0K7EvR0agdCaRt+7Ogw15+koYUVslmptGQec+q1beA0XY9ZNHD2vSsNesXt/XvZ0CAACIDr1OgZaxZcsWeuc737kkstZy4ReLvf7yL/+SXvWqV9FnPvMZOn/+fM/3nZ2dpS9+8Yv0Mz/zM/SHf/iHND093Yzo1Xrvxt9e97rX0fXXX9/zfQEAIB20P0RUiwCeNgPeo/MnYvRc0tyok2vDPkwjOj50dWO3zDUomy/Q8HpvEUOT3CjlaAG9ol5fDzHSl2p5D5LeQKJW+Q5xVPDOFLQ9yeY1fQW6RIfQlY7Fk56y8+7R0suBR3yGwk1P8YoYFAcZi3meKls/dl15PAqDdEVtDdbWQjYuSQgV5nJBi03K9bhE4xrG2B7LSKK69EbEjko8PBfC3ZeTfK0Wg6Fw6BE3IogOoQ9ylXNopNxQ2HcePXlOjx811qxwxhRNserfT6ON9EVKEXju7nVfrluBGGkcu2JwxiSZ2MORTPSFNaunQmrzd1KKOOo6fvGIRg6EjWDpTCJvm3Y/l6xMTolxJbQ9WYnyFDo6rVk1qyc//VuJcQ4AAICUSHjKGy6/+qu/Svfddx898sgjS0RXDcFV429nzpyhP/qjP6IPf/jDdNNNN9Gtt95KV111Fe3Zs4fWr1/f8R4s6nrqqado//799NBDD9EPfvADqlarSwRejXu2/pvZsWMHfeADH4iwBAAAAEhJDOtXT4bC/JmQIlN53YQJY8O+b2CEMrk+mps8S1ERZIMrycOIdvdmoz72RMditLHNl1GtWqaZ8ROUBEoYTyZNR0+iam58RbZZ7MVbUkJFpsRGXVDRl4yGwglsaPN4tlyIEd611ezr3utHFwG/BGkWNq0xCvh9fq4Tub4BGlq3lS6cOEBSib4kFLbK51G4N5Q7ePSLVmKIRXzb/hrhfTbuMjMkjqAQ0lic1Jq1r3+ExrZdTqcO/KjjuJD0szWUNatYhija372gTTTDGNaMMZdJ93WoWnVkBJmXJdAOB9ds7Lg/CQG/Wggppm6GwpGnO8boEIaagoFOkcv8OpBLYs+Ro0PUamVSgXDmYOFGqZONznlTLd/hCuhXuXzXIkliX64xzskmRgprvAo0B4xzXy7B8aFh4xYG2o5zOo1xi3nxM841nbYJO0hPX4jVUZ93Z3gKnLFyaQeYcyax58jnMENrt1BxdpKOPH73KolSrG90Rbf8+HHGRKkgtGeYGAOTmcuFcd9svp+q5YWerwMAAEAt5LbYCwHTNOljH/sY/fzP/zwdO3asrfCr8W/btumBBx4QrwYDAwM0NDREg4OD4ne+5tzcnHjNz8/TzMzMknu2RhZrJ/jif69du5Y++clPUn9/fwwlAQAAetD+0FWtFWyQg4iLB7Vh5TVsN3TxeWcyrSxd8pyfoL13/z1FRoByFht1cQf66nIQwt6bRtZvF3MPjgh2fN99pDZq9fUgm0HtxjkZD2Ey2T6qVUuhptfv+CjjQXUYBxIs0pw4fZCiIuiBeNyHrn0Do2RlsjQ/3XtE5lhEX6FtNMvX36P3Jqxinn1GEjAisC4JEa9ztHCeRwZtufzGiEVfRsC+He9kbv2Oq2jy9CEhzJc9OkS40UhU7PMpy1ugPuT3O50FF4lFbVVBwB+QJAzoLrn+pTQwuoHmp9qIIpa0m6QF1Zr03wjRZowLghGBGDjE4tStboJE+koCNp4b23q5mM/NXjjlQcCfXD1lcwUx5wzDgYlu7a2JyJcuzpgSjg4RyVyuq4e7+v8VEH015px+nXzE7YyJ63Djruvp1DMPtx07pDLM1nVsiis6hGrlFyC58Y9LEUWHEP1ObtFXUAFu3HNAHlcdp9b2fZn25QSq9dOY6RRNWbkxLhY8ONcM605+nEQpMpezsu2jZ8nkWJP7hJXLU//wWun7hlTzSlmR0C6iE5lsvu3ZV8MRRrTOnuJbs/ppv2G09Uuufwk989C3er4OAAAAtUjFbGlsbIz+9m//li655JJVhV6tgqyGKKz1xeKu06dP0zPPPEOPPfYYPfroo3TgwAE6deqUiPK1/PON67Req3H9xj3XrVtHn/rUp0SaAAAA9I5yXpRl8LSZlKFwSBt1UW9AKRMdwuieXo6KxoIvHQhf/AiCYmVytOGSazp8IuI+6uH6cR9INL3ohWAoPDi2mbZc8XyKDMMQnnpVOJC45Lk/IcrC04FvTGNDtOJMjce3eDVM0RN1XmIuK9NQ4zA1WgF//NtkLG69/Oafoh3X3Lbq+ys8N2MOJDWdngMyHaCrsMYOPQKPj/JXITpEUOLOG3scHVq7WayV2+0FyNQ3wkiL6DsS5Sl0Oo1zEk70OFJ8WwLXk0enR54+l5BHYUXGKxZU+S+D+L0xsbMlNraR3VA41z8k1ZirGqqVXaD0+v1Ot/laQmUWxnxnw85rhGg9MgyDsn0Dvr+WxDx1bOtuuuIFr6GNl16/+gck6hthPAtV6+tpzl/08+5uG5lhC/i9R21VRRChwpx2dNPOjmuG5ftyya55XCnXXFKh0flDfON1m7OuKArSo/hCBWdMXD8sjPf9vQTz1v6M1dCr3cuTnUjo1Ddlm+flC0O0ZvNlHT6R8PlDQk7S6x8NwVF6JktrNl3a83UAAACohfwz5ZDYvHkz/cM//AO98IUvXCHyaif+Wv4K+rnGZxt/37NnD33hC18QPwEAAISEXOvXaBaQQQ5dO7598bkVBt0Wpo08q2JAF2ShXT9oSSbUV7u6XOEtVLG+kjq6eQGXiCtecAf1Da1p/4FmciNKt1zFEf7BpEFkWdkwkrP65Q2Dtuy5SXpxK6eTnxtrt14uDG7kr2OJG6YUdPC0qWDZ+U+z4b+0uhlWh/ls6HYtxaooyAFqUsbSLNAf2bBj1feWe1NXsa8sQfHkd6dDv5RsLscMjKxv/+ZieiNtc+LS8Xkn9ZqXMMaCDZdcK2V/TdI40FO5GvE5sFj9/vLVmWy0q0fZDEsYNk7v7zDO1fuon3SHuy9X/0gy0SFUYfPu51H/8Drve4mJNMPON10p4KfkEGeW8vVVmQh7v1wOfK5DZY1g7qNuwpjv8DUidcZEFOj6sUdtXSzzXGGQBtZEKIILCf36bxSoc/7Ac7Whsc0dPhDtOasfJ3Bh4Gcdqso5KzsBkb0fW9k8XXbjK2nndS+W/4wVUzmtxrjuBHCC2vys1/2vbucB6gr4x7ZcJpxzRAmvVQfHNvn6TpLjd7s9gxXRXJPsKsr10wRQyJbksue/Upx9tSXQVM7fPl7XuVqoR6zhzjM9Xiic6wAAAFAG/U6BOjA8PEyf/vSn6b3vfS9ls9mO4q/WV4PVxF2dvrM84hf/fPvb3y4EX1u2+PNQCAAAoDkYtykKxRYzMSS3m6Fw2AnxeiChige6IAvtJDbqum1UrPQonGBfwaZDb96ZJBvnMrl8NOmNU8AQEaEZ20WZfsOg4bVbhSFkJtthw1WCSF9+njOxHQpHGelLrq4eKh3LR8J88zhWGOwkbk0+0aEal8RoKBxHXw0m4E9gm6xLUawwLkmSMKtNgv4TCR2HObnyPLrxEtp8+Y2JpqFbmYQ+VnhcH4exZs3mCiJaaVRw2eT6Bn1/z1Pk1IjwMsbG1U/aC5fCcN7gwaBAS0KOztcjbHzVOTJ18GdRuGNTjPtyEtWPV/L9Q3TZjS+na17yRk9GwzL2veX7ckmmUZxd9nh7Gcs4XNQxoJMmErqEnubD2qOPsr3ztTmS2OjGncoYCrfbE5Qq0qRq/TQBujoXkogd172ICsNrE01v12jeCQn4w9i/iqP8us7HJYEd3Q2v2+pNwJ/kXA6qL499Vh2nczwXaHvmFttRVzvnVWGXmfe9klAid5oWbb3y5p6v0/4GdTucS65/Ke289kXCcaXXdMVOl2eVTOcPYawfvNlIqUun/iHbOGdlsvE6tlx5g7A+FDqhjHM+xlUAAAD6INEuXDzwZOKd73wnffOb36RXvvKV4m/tInd1EnWtJgpbfo3W795yyy30T//0T/T+97+fcrk2nkQBAAD0NL6rhT9PS5EnI4xLefa0qcb0I6ih8PK5QdKsPJDQA203MMR5RPsNdqUIMC779c7U8T6hG1v68c5kSv9sE6VjmuLg9arb/z1tvPQ6ScdwQ8rnR9TzDvXmNT5QyLhk61UvEN6sQ68nr9/rcEjdfD/UuZw8fSwMMtm87+8YhrUYAUEeVkaHSNTVZoL3VoOO45hkxbftqhd2FADF8izqFLGh2xjo91Z+5nJhzDmMqMdVg7ZcGSA6RMzzqdZyl8kbvUzzStVQZb7QqOOk59XdPaeTmgJ+MihfGIrtfo17dv+QUY+AIFEdr/CanjChrbl0XbMqJODvRpD0+v1OnA7nPF3PCDEaVrhT0VWvz+W3/epb6fKbXi2ixHr6WpKir3blKlHXgMd8L2XUYb4gUV2yE412IpwmgbflJMpo0OgQIcz7WFQXNWNbdgtBhNcxrk689dP67FNhrYNxrpfCI+kEXzuuuT3ZuWeM61U/11PBsWajfrhPDq/fRiMehfxt51MxOshpxbKylO8fIWkIxbGmZJ095c41OxL1WXs3UVnokb59XCu0SF/hXAYAAIA6yL9qjYht27bRRz7yEfrGN75Bb3zjG2lwcLBrZC8vL6ZxnXw+T695zWtEZC+OMLZnz54EcwwAAHqg2uFqsgZ0Xj4Svxc6WQ9UwvAqI2MUs+XGJSqUf0dUT39XOuVPwrwnKN6Qsi0vpik8Y7sovVsZge6VZHQIb/0/Jk/W7Q6AQ/JCpy0dy0eufLNnxjWbLg3XgM5n+/B2j2Q8CqsAC/aWH2LKWQbdPG3KZSgM9Bnnlju9WuUTjQ/6uagW8/XwjEsoUgZHN9L6nVf7eibJHrU1tqgkUUb6EhcibenkBVw1uo+DKz/v9/pd7x9npK+An10NM5OlS2/4SZINLtN6BAR5WOE1PennZNL3V3lPWLWia07l/Cbcp0OmdpHQQ44A6ed6oUX6iljA36BvcFS8ZN+XkynSV2FoLLq06D5OKrRm7U7Ee3MePhvnGUXrvcI4ixwc20SDazZS1Ayt3Swcznneo0uwGXp5fiR6LiWZoyop6bjGM6RzxpTJ5dsnq5e2FlpWExLwK+BYc8W9vEYxS9iWZN32PUvKd+22KxIXokUzr5Srv4eKVnO5ICh8/rBkLhfWOKfXuS4AAIDuJGixJwe7du2i3/u936Pf+Z3foXvuuUe8HnjgATp06FCgSB3bt2+nm2++mW677TZ66UtfSv39/ZGkGwAAwDJUXdhFukHl0QNubN6gwjRkibi+A3p1SWSz31DHUDhUIYOmXb6T0ZeUIqdOBEquf6M3GSNqhLHBFrZxTFjtKckDCW+GwnGkpP1mbCjjXMiRTWSiU/lIN8Z5nNdEPyfplow4DYUlqyMPbNvzAjqx/wGqFGepVi17i9oqnaGwZgJ+BduR75lMO8NX1eouDj8lXSMahn8/L9u9ShgK81TRNGnTrufQxkuuo4nTB+nUgR91/Vrs0bZa6tBiY6YUCJeU6+t+6WTcL9EY760eok6vhzKJdS7nfzxs+30yKJsviMiqXuZYoeCpqORpg23ncgmnMen7S49WBnQB0hv23CuhMkvamDbIc8q7oXByRn2hRFALibZz2pCeqzqPlZ3mSKrNY6NOb9zOmPxcK7R1nWJ1/v9n7z+AbEvuwn78e+7cOHdyzvnNvBz3bU7a1WpRlvipCMYFIjoUuGxRQIGNKeNAKMDCCKqgyohkY5NMMJigwB8ktKy0SqtdbX4bXs553rw34V995t375s7cc053nw7f7vP9qEb7buru07m//Q1a2GxMt3ILsBLKLGme8+b+AcNaE18G5RtD7jZQsd+RdfDLn4Fc2sblcpvoHBiHYuUJuH7pbOg0r713pMm39PfNcrUTbly71CTnwIuxpRPn9nIJ5dVa5iRnTBYNyJQ8N8b2JgiCILSTeaOvGsViER577LHwj7G4uAivvPIKvPHGG3DmzBk4e/Ys3LhxA5aWlurfZ5G8ent7ob+/PzT2mp+fDyOGEQRBEBqJ1Mtx7EBjQFE4TDspXLUlj8IuUO0a8KIObHgUZv1Kl8K0c2NdBoce0eiFgCDKlQ4DG9EhDAo6ObOyqSiMKgpRpKKwQwPYBkkCfPYZNo+lStv09v6Pd8AlOr5U298w7mPSUqp2wOyhJ8J/f/3v/wiWl+OVkm3PMyzC3LljLze81zeBJ2q8WiNDT+fL8LH8eDbtUVs50lfrNCJ5b1jrl2rmQ7Xn7Wap1/+dy/ErCltU1mXGIUyZ5Mq54/X3ekbmrMwNkZG+PBm/OnGujmL3nuAVSfuYhnkjHAMrKnJVkIa6vDDuL7Y4Y7JZRHbWCjI6F3jqjIl55j939CV75U0SCyouA2+/c8KAf2tmKKO2buxH0ZG+zI+NSGdMCsri6/zmquyHJ3KqRKoiBQCMqDBuXV/x8D2fzTJVu/rh0pm3Gt6rtHUDJjC2GSqQjtmm1GRQCveewr9J2K9h3P+6vpfD4JyARUyNippqipZ8sfkHqvqc2103Qw8n56hEqY6bLV0SVfOT4/M0QRAEIQ4ZfUVQqVRgz5494R9BEAThhNWXFQMUk2gRrCk9w/pzoGR1XfNutFEhjQtkyupra+YjfbV29cO1i6c15+JPf2sgyRjCIUxE5Fv/r8UyROarSrnEjKJws9c4I30ZVg6PLQspl8jVm1vzmP1n4VC+UGmEg8mwUgcBTuWnjX2JKW1eOHkEVleWw9fFchu092z2tunPOPKR2D4UGQEsZ2XPnoiMcomM0lissrDi/s5rc6vMa7qaZFTmZd6jcLAlAuPxl5+BpWuXobWzF4Zm9xsuT8Kap6TPKXY+gYjYaMS6vfNqQPvZIbErqK0zsUhfavY86NocoRMJTGv8unw8ZZtha3PluPN8w3MHoWd0LtLoy4iimsn64jAiq5VHlTEExv5uMzqE3v2TGNHrGL42Q4dH9w+i5VV6Vr2dv7X7Bxeczm3JivdAbq8fDs7s3WL0NTi7D884UbCVc3a8q3CIi+yZ62Mislw1x0Ry8jneb0Z+gqy+ZNDufHkDLfkC2r2caD2YaHudBvy+bwcTHWsiI3Y+MuGnJKlObFUZRe4kCIIgJCGjL4IgCMITok9juZY8rKzcAkykuxDlF9QZv3j1jIldD8D5Y6/A0uJlOH/81eQfIBSklDd5obNqnEeCOt6KingXX/9SXiQp/UhxY2AZROpfyaUrRzSK9OnHvEZiFNLojZ7jMsRydAiFbtM98BdMiwABAABJREFUxo1L1+SxX3vf5rqq2KOdY96exQn4Ll0tKgqXWjtg7q5vgAsnXg0VFfvGFiC3OSoPzqEigTcPIrA1CSL73cqKJYVwm3OvFo/tCVlyG7nn8M+rW+oHvwE/I18sh+ds21CkL+mKi/4oTlnMijOm2wpy8V5CJJPmHG98FudyZUibFtK9tytOQBozjc9zdXVzRDW7da9sfXWvCwkoCkd+CJhgBl/q5zgJpWLJtcFXwyhuJPuT+aitG+VyAXT0jcHls0fr77V1D9lRXtatKIxsvKtEZszadVRic44xe8ea3H8Dt+Y5B2FyudGFw3Dy1a+E0Vr7J3eE8xyeezqVVl+egnBfEo15uVjzUhg0IBGY5/DTWNZ8scL3K5q/Exxr+n5HlZ64OsI3z8Wju7x8Bl8G93oNZyv3DPgJgiAIHJDRF0EQBOEUUQezuAMbRqOvO+c5kUOYWi9064dKlYdAkwZm+i9zan2nb2J7+O/LZ47B8q0bCcVil1/2FIXHdtwLR7/+VP11LpeH7qFpfB5zHMkDn3cmx6IZam4j4wp0AqgT1KlJRqUXOptjD1MUosh6UFE9fk5vzl3YJEfYkkpU6LexnknTlENyjCkd/xbmknyxlLiXY5Fea1G2zNFYF6XWdmuRb3hAu+/Agsxejo29zfrgCMCgXKK8DJzpqVCgM71n4s2PlAPrFRZRkSoaw/fzqoRBQC4Ha1sMXwyRuJfSayhl26NxZNZp+2h9X4usr1suTmtHH1y/fLbhvbbuwYbXVucH2sJ5pSicVB4j5Y3tzzEGdJpRpUyrU4bBzp4byRdKTuzlhucOwMqtJbh++RyU27pgZP6Qh4rCuMa62ejUgM/oK3aaEdyb1qPq8H6dY551OdKX6Uhl2PaNEfSMzIV/GGH3z76eNVWB9fzVlPqUpEFXg9dRCWcUVScxPFYwR/rC1/l1R5A17BjbgfsHqyTu5USTEzlnJu8VVa6rQg6EFZ01aV9AEASRPcjoiyAIgvCEeKMvfBg6cSfqtig8xPokaA6aX8QmKQrbYGO9dw5MwM3Fq3Dh+KvQki/C8PwhO/3fp75gnLiLtgCfAp1iT5tCv6nfXeBTyHExOkSx3KhsgpFq18AWJYPNnjaNEWWErqjPeXshESNfj1f6x6dcUi+v9aZSu5crt3bCjeuXwEcK5SrcuHYptp/1ji3AmTeeA8xYnx/Y/JfCyYFXZ4aI50uOEIjHIDa+PwXibRbO86raWLWTEoFIX8ouXXPo5gLT0SGwjvno/TrO8jpxXg2SxgI+61bt/RNb/99QHmXzHDoHUIF1Y4hXv/g3De8NTO0GPKwpXqczZtwa6YzO0nmV10GIQJvH72WTMhL9TBT+tLZES5bKTp/RWrFcDY1EG96rtPMVy7LRF7sjmTn4dvBbUdhzkow1o+rbVqAv22eNxKnW1v0DfuNWVxDuR9anGesF8G6OswX/+NN9bo37zI5czgVaOxv3cgymk5HkUM581FacRBvw+9NHdBFfR8jOrOjnBHv5q+vrtuuQIAiCMA2d4gmCIAg/iDnLYDT6SnWI4/VCl2zxJV8Gz2lWdyzqAvYLEuYdanB6D2x/4AOw7Z53bfEmvE7guICmrrEAPrLeh6IEcggVhbV4kwoUfU9xHxFRkkEUkSqKzf1ms4dhNGyod+Ypr3dsfsNHOegbX9j09cALL3S+wtosco2IiYqBckylivSAV5g/OLPXW6UQplgX9zxMSbdQqgB6DMxzUXWlNKKtt1NdnAErwjnOomfQen2YLAPvWVqVMq2JY9FtWjijQ7g4f7umXGJfYQGXYgkGBfVoJI0nZSJKRH6s1oBf4MuqMgVM2FAQ29gfKh29MDx3EPL5Uhi1Z3T+MJSqnVbLJPIZcbuGBPuRrchLyeW029ahAZlqRWHO6I3YjSGm9j665VlaCkWu3xq/60I6ZeiM9OWzsrHs+oDXUQkCLBXPdtQ/1XI5twgynT12Yp1ExsxlVsdUpO8oicYWtt033KE86b+FUiu09wxveT9fKHs5f7t2xxrOAz7v52Lmsqg50K6+XKyXKL058zjysNVXlMnl1CRDEARBuAM+LXiCIAiCUCz4t3mIZZFTbt64quYQJiWoM2f4hVIhW3F0iERYdapUhuUiQOj9kKQL8nUn/mGoyLByC/CBoB+Yvq+43fdzgSrlEj0P0DMyuyVtdknhAkOz+8PoXkvXL4X/Lbd1bfqGIaMv3coO3s6jcs/FxtQqxHtI1NcGEXOvib7G0Q9UzxMd/WNQaeuBxavnE/JN5xnQhtJO1F5uat+jUGnvCRVzlRs28YBwuEfPcWthn0tXRQgfWCHxl8r4DPjVj1O1B1yVcwVrm0KpCsu3lgyebc1ZfbGIqFy/8naPIQbVg3TFSX2OVl4U6n4IOPfQEn3QzphM3yaB8TmFa70M9ymm5XIbsw9CxySbnZNs+pKJgjSVT4Z1oyh/X+fReEXhKAcmgVvlVZw/xr6AsUw1xnfcB6Vqh3SZ0a6paBSF/e9DqUgYsygdlWiIyMe9p0u6Yw0/UttX2ntH4Mq54zH5KTZwNNjXy23dAHDEWH5esram7Ezi7Ty3flEv/isL0amT2qDe1jrnubgMtOzzzDlF0cn0/rc1XRvzxXK8XpBC5wRCIKxWivSVqvLiPow0NjQ7w/EhNeZ5dOBEklM5QHijcSvcy6F3kEAQBEEoB6mEhiAIgiAikLhctWX01T003fQCLwSJ0ErlIbBYaYdCseK8oI7ROTix5b1SBX+kL8J9wj4kpieM9tJVXlCnJv31z11VoKslpL78nf3jMLztkDP9qFm7tvcOQ9/49iYGX2bL0fx98igcX3Fx/Tp6zNrsn9GKfbX/6JtnQnF9ojKO+vwL5VbnPVIyJRlej8Js/1oz+CJ4UKRc4utllETUKrRniATjV/35q98LDc5yRjNUtO7oPANvjtLKIqKOzN+lLT/f0Bq11WOPwvERIOI+Q7iXM7AWcc0Bgf49UNNsHeyjZY6IWettituA38QeyLYhUNYU6Gzva5PyFy6fUB+RMwZ2wTgh6TyeIuFUUXBMn8Vt928bkb5QakYrIrZPY9vLcUSG1r6X43FUonie6B2b5/qesoiGBvs7tyyfHGtGVw3b59JeLsU8F/ORjfuHJLmbCV8Rhtc8H84iTPZWam2u/8OMvpJAKwc2THQ9kGFrKkMfhLokVsd9glGUzbJh1iUhCIIgcEO7SYIgCMIpZIRPNoy+uodnYHT7PYnPIeNRWOg3At5E0sLyGprbry5BABiY2g02hEx9Y9u3vF+oVPmF7ibJmHemehoIn1s3UWMfrYBYsr2F+4khZSaxuRevdyY2r8oqiKDtaxsJ/PAo7CtxfShWMRGlgZG8YSv33MTzNVu2GKoMIjQ8AEtzYHLXlvcjIxpuaQ+zeznROjDjsT+ifVnV0DwXX3dxl4iOVZ728tZ1+MzVS1vXILR29HHM07gvXZmDGWbktZme4dnQwJ9IhjwKSyKpcG9HgY7nO8LWOoK/SzjTKT/zBTA0s4/vm8jnuWZOpSodPRzl0VIc94hsF3tR0FwBq0KYFEaiysV/qHo/2Wz/414dBsLRqQlHZZQokXMehNbxj+AYlZnH43+jeI5gzs56hqGjb8xLReFKGOkrnrh1o61nCHBg2dhbWZs5tq/hJSY6VfzdhPl1pa73EeskTy5NJfVlMTI1ZuLqmMfoK9difk3FKJOOWsdoj8dVezGfRIxnT/ZyPpVB1XrunJyCIAiCSI2d0CeIOX78ODz33HPwwgsvhP8+efIkXLp0CZaWlsK/tdueZdii+YlPfMJ2cQmCIAiOw4wNoy+mSCbrTUoVfAc8tQXpGpyC00e+BkuLV0AnOg+vk3seahqhzZtIX0YUhUm4oMc7U5SgzmK/s+hpkw9Lgjpl0SHMtm0ul4fV1eXIz0eaRAjDhql+F6kojKLf+wdG5RJza11g3miK49mUKgor9uA7vusBaO3s41ago3HbtFIinRtgVxK3ToyyRHTUarCG6jOryNzIE5VC7VwbhHu08Z33wYtP/Vn8Nznz7R3dBlfOnYCbN64a28sxBcCRhcPN8wuf737ovnAKbi1dh2MvPt3wuRWDMKRjXbdHYV9JjLaM0lFJggFH4JjhGUcEBWYAev7Eq1rzrfUFXfuo4W0Ht7xXae9VFulMLZLGgxqJbJc1dW3vLaFuq/hejtW5cUdgCWcqqfGpun0VJ9c/uQsunz0GpjB9VmSRXK9dPB1r+E8kKQoraLMwCT/nuuS9D777B+XRDMMfBeq+p2FdZPu5y2ePxn6no3cUTr/+NS4HpRdOvGZ8XW8WubClUOSKdBa1nk7vexs8++nfBeWIVoHt6cH3vZglrBhEJJ2v05y5kNoGMpnWuWMvgwlsnFuSjL7YedWG3lLmnDF5Pk/WIyA3u1eLeHaMd6xpzlu8/SRMH2l/UBehGOfzEQRBEPqg3SRAaOT1x3/8x/B3f/d38Oabb26ppJqhl+xGc3V1FV566aWmn3V2dsLw8LBYqxEEQWSZSGdHuIy+aqj0ziSRe/yncQqJKciXKtqNvnRRrnZGKoi0FEqJv+8amoLLZ+IvY7KNuv7mrYJ2jOd0Ge90VtHdRDwXHorLMDJ/Fxx/6QtxhbpdNFUK8WCUjv5RuHjqjS3vV7sGQiXh7pHZyN8Ozx6AE69+SXmZVEZ+U4qoEhjBsfeIVjxB64VOe5vbUS7hIbXCz+1is/6g0uaLrQmdA+NCl64ryzcdC4BgwoA/xvNt2vw9nyZ9UoSu9QPRZ1K7duOuz2Yy4zqa+gJTgou7qGfzc3vvbVnz2hocf/kZWFtbDaPm9E3siPwdiw505fyJWEVjrxA8cwknj7zvSiPhpMS+o5Kot02dG8xkI1rfmM+sgzN7oaOJkSpTgI4tSpCDvvHtcPHk65LneVM4vpfzHFZ30gYEip1ZpKY2LweajfhjHRuo7W+V9h7o6Bs1Z/ilYT8XV79t3UNNjTPYPq7S0QtDs/vBOFjPNySXU1tvCX3T6v1DnOgfa//UxMb1qdzeHTofvX75bOxvqp39TecV3fuBqPmKGXxEGbTV9nI3rl+CrBOOuTVm/qZxbxH4W3drsBrxGa4zq5Y5THGaqucKdtfIdDFuXIsa50HD/ufWzUVwiaj7h2KpCqW2ThhduBucwIQD4agxl7G1XQqZyKU271htOkrnumJVWwima3bl3PHkAqnKl4YMQRBE5si00denPvUp+JVf+ZXQ6Cvxon7DQp/0vc3kcjn4t//238Lzzz+/5bOxsTH4m7/5G6H0CIIgCDFsGH0lej+uX7oKnMJkfpM5Qze9z9U3tgBnj77Y9POugcmm3uu0l0uwXk0od6A1QnKAeAMmfN6Z4vuT3r7G05eV9vdgPToCj5IY7wURU65lHjlXV1eaZ6ijDmPWkMHpvXDp9FuhYnCNid0PckWFKLa2Q0u+uNV4wlMKpVZ9fU6TUTYKJJ8LZURDU5EzMXWFDc+sbq0394DNxhUbs0WOaK6M6f2PwZEvf0pHwQS/r74I3JkweRimPomRsD0DwbnMZqWqzlvifBv3ucriaalmPlmySkTS7Bmdg2r3ANxaWoRytQvyxRhHJprWNazGBTr3Wt7u4+rK/bFfQHNmTex7Uu2kXv6itL8ENvbX6vv7wOSupu+35AuRv6m09cDQ3P5Yz+qd/RMojL6MzBGxUVvT5u+GfNq0zFu1MwsvjIE19HeW3tiO++D5v/8Dpek2z8z8ms7kb6fKbQ1RZIfnDoRGEATfnZ+KvWfifsd1Z0wSDtSsyuViEW0o9XOSyvOOSFos76l9j8K5oy/CzRvXoKWlEHl3mZAQqGZs+z3QOTDR9LPB6T2hInTt/oE9c3vfuiPOgcndUCi3ojf6Muc8IiKSZwb3YELEXatJGr6mL5JkRFhT9w+RH2owIsvlQhn71z/7fxK/y853bz3/ueQ0TUfcjamSYrmt6fsL978PrIJy3tA75rJKpANhb/ZyquVo6nUx2Nkt2uhrY9Zq2iTL/Z0gCCKrZNLo68iRI/DjP/7j8MwzzzQYcek8yH3Xd30X/OAP/uCW948ePQqf//zn4fDhw9ryJgiC8IvALS900ZJFM1mjFOKAAsMAXc8Vn+7A9J5QKHLtwunQI1PvyLbQkx6LAnbnEqO5YBGNRyqLyiVq88DZt+14Z8IpqJPZW0vtx+MucBT3E6ZAxi5TX//K38YXiXPdYW23/t1mRl/mL/CKlTYYXTgMJ1/5MqyurYSRJDp6R8EquqOLSNLWMwQXTxyB5eWl+nusvki2ytM+URok0b/LteCL9CUjSFfePy0aCLb1DqtRwlBubxKf4OYID+w5NhtBRF0S5zmivprAjAG/PaNu1/Hqkk16fglwGvDXEw3M5YfgzFJq7Qj/CMMehe03vR4kx4/NM2vyuNbYWFzKI3YM4JVFtTM8z23eyzGP6fP3vbehHJEKf5qKilHxNl4mga+8qIgzOndsjywll7NkvMCXYCAkc2ORJHBGMI1fL5nzpROvfBFWbi5Be/8o9I7OGy3dljIh7NuMXNACPSOzcP74q/X3QuM4hHMyLuTmMRvRIe70PfUGCbz9et0ZU5IjAbAGM4gfmNod/ptFbW5q9JXkoFTDA3QPz0R+xiK3bjv8Trhw8kgoj+sZmdtqxGnYiFq4DowY4uQaHPNt+lR7/i4joxMSmm8G+uY5tr6XKu1w49rF+nssUt+dL0TovqRytsD7G/X3qDz3rGzcr64sx36va3AKVm7dDCM550vlppFeWTpsz9fcwML8WGGOlzbrg8TNiVixf/9AxNddLtrpiGMOhPX3A470ldqQBeGcxMZ9s0irm7+bPkPbupAEQRCEDTJn9PWHf/iH8JM/+ZNw8+bNpsZevNG+RHnnO98JP//zPw8nTpzYktcf//Efk9EXQRCERrAaRKBQbLEkUGFGBW8+91nnBD7sEmVodn/De5WOHq7fju96AF770ifUFwpZHa3TvEyFUsV4SVwj3oAVoXem2O4X6O3Tlro+j9I/d5skXWDqiA6RUHFMENk1NI1u/sUGE5CP734Ajn79KVheWoRqzyAMTu0JL6zVtJGv9R+vKBzVP60ol9QUyhKM1HRfgOmJUpEeZuR47uhLMQoISSTUryaYMgnzfnz53LHQ0DUqkkRTsjQvWvJ86wUSxphW6zRWfw5BW1usG2YotXT9cvyXWPC7GOUcPXWop07CsmYoREnUhbiaNkMwdpAZ8GNUQDDmpMRCmpwZq0oITMLOq0zx79KpN8JIEMPbDtHepGmzRLTL2hoEOUUGf57OddJzg83tXGSUMQ6DiZQRE2w/e1z79U/shOuXzqY4s9qh0t4NMwceF/4diyzBHObcuIYzQg5Tyu4ZnpWLRNSMIAjXgEK5CouXz0O1sz+UU1y7dEZJ2l7PcZER7d27YxU31hHPIflzlZrCaX6aYDSy5YM0xiTpKFU7YGh2n/F8nYI1S9QyrMxPiafzXNwdQ5y8U/M8x5xXvPHs34UOcdkd48i2Q8k/Cuzf8eo6r/L2P7a2h84WAeDZT//u1i+s4bs3HJ4/BMdf+HzoMLJc7Qr3pLZBKc/XWSaEj2v7+aKi5PpOGJXVggF8a3svh9EX37rDoqSeOvJsTEKipSMIgiBcJ1Or+s/8zM/Ab/zGb2wx9tpo6NVssU8yBOMhl8vBt3/7t8NP//RP31EeCwXxa/CXf/mX8BM/8RNQLBZT50MQBOE70YeyOMVEix6FJS5QFGaeeEi1pYTW1jMcXsQpuYRDBvPesnj1QsN7oWd15AfuSlsPLF49r6zfMw+Dp1//2ob3cqEhyfGX1yOtEpGVJzXP2SPB06aRvAUvNFPnl/wVoXUHoWK9dL6IFYW7h6bh5o1rCr08B9DWPQAL970v9EjIjILRXp4gQlYhzMZeLl+saNTE4DVuxasEXCxXQw/kbK1fXb4VjgOpiKaGn5H1wa6hqfBP+LdY1uGIOhvbfg8cfeEfFWXRPA8V8rFaPfo6X8oY8Nsktl/LFFfkN4HZehEdwwNTu+Ct5z8X+51EZWiEbW4cpFUQubcIPH/wtCT2aYyOStSfr3nnrXWP8ebWBZF5TtX+2vTaxpTo2PzM/nzbyynNQjKSC1GrHwlFYYz1qrlIXM9scS/U3jscGk+xqBDFShWOvfj5Ld+ptPeE8+H1y2ebpKCp7JqSrXb1h+stVqMv1f2BjcdwTdjkyAXjmQsVkkqvKO9Y1z/UnHnyXGfUKYBMXSQUD+eYMR3qK8AZxSUXwMrKqht7DkwkGDE1f1ufcVNNnsrW6e0PfABuXr8Cxdb2hqg70W2KQV5muL9J3R3hGhOd/ePQ1jUIKyu31qOacTo0ZNHfOgfG4cQrX4IsEOlEC1l7YmR9XyZWf7rlcoVSK9xaut7w3vDsgduZx/xQayRDnpTMOknf2D68bcKceySkmrJUBEEQhGvgdMujAWZs9fGPfzw8UNUsudm/N77eaAS28U8V7373u5tusK5fvw5PP/20snwIgiD8Bp93JhlQCGWj77D15Xcbphw/te9tMLXvUZja+wh0DU6CLzCPU5svwVh0MBRtHjNWZBRmYjKBvvHt0N47Ej53S74IYzvurRtFEKoVb/X2rWaXukx4l/g7zZrCPIJfPcJhdUotsYrCYRIayq9LZ8UBQbzKMm505NEwt6nIw/T6bJI4g/QYb8O8F2NpDLYbi5K7YxQUWVypG0ilvzG9t9icX0ffKMzd9STM3/ueusfNtGmiwLABq3BUKBMK9ZFlCsMaqcvHS2QU0rDWad0qS+pnKr6oY47gTbNrcAqm970tPNcwo0qZ+ULL/khXd8HaDXWhczx6XJfxRkxxxk0W5HJJ48+E4amtvqDwrMzkOmnzMk7U3IykrEb2v7FK4fqzd5n4sWHR8CCWiPOBlNZs7b8iv41RoFatkBeTXTNaO/vCaDIs4nNT1jybA3Q8D9Y5Q/fZCsmaYbL/xX6G8I41FR42r1uOBN3GxD0IG48jC4e3vB/KJlTl7+s8x86eNh3yxsDuOMptXVvvOhTeP2y8t+L6vq1KUdb/cDqibCkUQ6d5uu+1TLcxc5igDp19z8/5Lc2ZNdDcF5mx40bjJKZH0smh/yXdP1WcV7UgcGbldg6alIzf/Z0gCILIaKSv//W//lcY4WvjQhcV7atarcL+/fthfn4eurq6wvc/+tGP1o3E0tDf3x+m/aUvfWnLovsP//AP8OCDD6ZKnyAIItPEHGYKpSqYpqVQiv9CCkGd2I9Sf0FDnuvkWlqgvWc4/PelM29pycMGxUobjO+6H04f+RqsrtyC3tH5UCn6+qVmHkPTIy4MiVG2V8S6oVchNOhbuXUzDBmv7GJQUGDtHDFGD7aE7209Q7B07VIYJakGU5jQ5WlT9DmlFXI0wt0/bSgKayQx2oVHRCmuqhmnlvotBGjbULdyCVPoZ+Pt6oVTkMvlYWT+EOST9nL1wukrF09/0uJRWM6lpuDXJY1JbIK8qCoV6s1EMTBstBjkYG1t1UA++PZysSiOQKPS6F82OmQqgq37YPbHEI6kF6aFsM1jUOn87A6BU3sLFesqyrGuitjjX5yisH1FJ7XtFCj6nmKPwgKR2Hj3DcxJ05VzxyXyIkSZ3P0QvPG1v9cetTX1HFU3CkqXDNa9XGykh1hbOn0VUqq0w9rqKtxcutagVJdYLt0yVJ50jc8VGZ6bsM/Lof8QDUaAWz8gpOc4e86YZBHV3xG+d0jaqwWqx576/WniuEM4d+g5lzpGEITOFyptPbB49Xz4VrnaCe19o+L36cSdakVrwG+RODmmDbmcyvlCW9n1JJwvlrDasNXpGpiExSvrc1JaWP9i55ylxSv199jdHI/T2SxTl+UI2nrr3ssx3biZg2+HS6ffCMdl58AEFEqV+EKFH+nQfxNIP5wCLenfdA9xyjlM3NURBEEQLuG90derr74K//k//+ctxl0bX+dyOXj88cfhwx/+MBw8eDB8XePYsWOh0Zcq3vGOd4RGXxthZfjc5z6nLA+CIAivkTi3lFrbQ88zG4UQ7BB19cJJJUXanFZLS6GuGJZ0kEzjpSnt9+I9NJsl+kBqWhlUTX7s4r3h8n09ccgMG56VebMiRKpOQvCluWsxgdP0/sfg9Otfg1tLi6HSF4t8oCczhV8OL11TlmdjcjYMDm1FKkOE2vpW2SH0ZmFaUNs3thAa6J5+4zl7njbjfqf7QiJfCOc5Nsex6JTMMH1D7hGFCvS3L5feLo4xHfVMowt3w7EXoyObY9mL8hEgL4ZapfFytQtuXLvY8HZbzzDcuNL4niss3Pc+eOEf/lh/Rgj3cljhG/8ajCEMzjtoDHN50rURiUkQpuR249olJWkVijXFg82oqV+31jf9/cRGdIhSa8d63nGKzQiwFdGQvy3VyzV1Y9yRhaqImIoNAJspDxXKraHxkIvMHnwCXnnmr9BGwdG9nxvbeS+8+bXPwPKtpXA9HJo7oCknfXcVOOfN6PnCqrEcqvkPq1wuQkFdQR4Y1zZVyD6bnaitfF/S2lw80VO1LAAq5+Lke+Kso8qx5sTuB8O1WkmZgnXHmtMHHoOr50+E+mTMiSq7b/V5jlJB7HyFtu7wnlndmCNcKGMjI9sOwfGXn2l4b2ByN1y7eBpQYEBth43V/smdDU62+ia2K5EfZWGejBqbkQ5LDRjwMyMv5mAT09y0bsCf/C1lBPxpsnW+d2wezr71QnySifqG+GX6BEEQhFq8n/n//b//93Dr1q1Igy8W0etP/uRP4GMf+xjcddddDQZfOmDGZRupleXFF1+EixfdVJwhCIJwgfGd90G1ayD0DtM9NA0D07uVpc3Sau3oC//NFIXHdt6X6C1FTtig4zdIhB5IiuGkYFTNfYRaT5sZEKbpI0AbwW5sx70wvf9t0D08g6MfcAjOTF9IbMyPCat5lDEiBbO3/+cOLpU1PdFCVEeVSwzlGRsBgikmgi2PwkH9UqLR4CvuFybqjCOPbA09T1GzmVPtNb0W1bNGPl8KvXlSn+OrP0wwA77N6xYz4k+kZvAu4w1d7AcxRcBVl8IeyHWUX0GazdqfeWDFDHPk09q5LndRQbHSDpW27ob3ekbm1MxxyPttOuQiU+lWQKhFkd9I7+g263N1XPTHDQWxAlOm4sHF6BD41xQze7nR7XdveX9weq+yPLzdFEoZ8Afa64Pda2y//wPh39zhd0KxXL2TvYE+JYvSMmh4HJ+iyWze16BEqaIwXgV51MTMV7FGrxYM+O9krtDgtp6WIkcPqqO2pkG2HFjK7xJR048Gw1amEM7O6uwMf8fBJrVZUvuI7j+075kk9xu1PiVWPsXPoqlu4u5FswBzrFrTLWL0DM9CuV3TXg6xY02mbzBz4HEYnN4Dk3sehoEppr+VjT7gnTMmSfk+hjOrzS43NLsfpvY9GhqCsjEgBYIqJAiCIMzidaSvz3zmM/DMM880hHav/Zv99/3vfz/8x//4H6FYNBeBYmJiAjo7O+Hy5csN77MyvfDCC3DvvfcaKwtBEISLyF5iMg+/TGhQ48ZVdYa2+UIZZg89Acs3b4RGXxsPzZHCqcBfRWETgrqsCP10YLdPKiy/aS+yXOHVVeQTHYEv8n2040H2olFh/hb7NVOmYhEEmGc2Jqw/9dpXt34p6a5HR3QInXWCWFcmPA8a6A7K6tf4HGduvMisQya80EXmnRiZR2+98URuVZ+p4PtJn8X9QHG/UzEGoxT/UFxAxaE4OkR770ho8H3hxGvhGWdgatdtBZOU+diInGkwv7iLV1t7OaYkxC7NTx35at2Ar38i2ThdqlxYFQmsbH7wRNbeTO/YAlw+ewxWV5brxk75YjlTUVtZuZgn9mMvfj6UD7Fo7eyi/dqFU8ryyGRk6qg1VLNySffwbNiHL556I4xeywyXmeOS27mr6071iIHSRW1SBjvzRLmtO1zrr5w7nvBN1+QQ5s+lyupC8V6us38CLp1+s97GPSOzofEs1rUpkQD32Vhrvdb2z7lc6KhE4IfaimQlaqsmXBwTI/N3wfGXvtDgKKdraBoWX7kAmNFhEKEnDzt9oliqws2la1rzkI1YaDOCQFIERt0RPk3OEfW8ZI7ignveWl4uzoHKQVgFsf1eUZuZbnsmlzr9+tcM5CRXdzbHgk1D5uQIMggHCKdBna4zq4o6YTJ2prfE5FG5fD50TIS+rhX3h9regjm3YH868vCSuv26oC6J1WhQSi8WxcZ2GOgrPn+b8i3WXqEjq55huLl4NepbcQng3MgQBEEQWvHa6OvXf/3XG15vNPh67LHH4Kd/+qetbBgXFhbg6aef3pL3q6++SkZfBEEQkggfVDXM/3JKUlJaJtxfS1TIQYNCJRwd5UCetAqUGuV5GEZ8/t73wIuf+1P9GcWOSzudSHoeUXk5GfddpGOL1Vvv2Hz4x2hq9MWRhjNoKyrOOvBxnjNWbxLTnFWPwpGkUAQMVM0BiDwKS45V1ArDpgjwrQ21PFiEZPbnB6Y0heOystffmdFeR/8Y3Lx+BVq7+iFfKK2XSPFeTgieSGLG5zjB/JhuiWkDRgUN09rRC9sOvxOuXjgJxXIbVLsHAT3Mfl+xQQQzCmKRjDe/nzppn9e2mMisceiO2sr2isxQeXjboTAvvr2jIWPghHy0rOscyj+h4eOuB+HK+eOhEyv2mhlBbv1RUlb4+juLhlAzdK7R0TeK5mxpwgic5cEiGDNP0UxxiI2LQrm19mG6tKUiDqjAVH7iysAmjA5iSXRUIpKUyI845jcdzaZ0/4rYe1EMzGCfje2LJ49AvliB4bkDoaMHHagb62uGHfSkSdz8HMccEDCDiNe++Am9GUmOSxtyuXJbV/KXQqcD+gx1nYp4Kr2/QPQMjmDkvIepbylio1GHTtbHbVT92bl/NbpPFH6MZIdF1ve6nsLW1kpHT8N7VM+qKte/OZT/btqxZw90/86O3F7qLCF5vsEolyMIgiD04q3R15kzZ+Cpp55q6qWGRdv6hV/4BWsL3/bt20Ojr80cOXLESnkIgiCcQuUByQRRZzMDHkN5BM96okPofzY3CbBoCou9L4Nn7cnGSbFcNZZXpCdtx+pV++WXY/XRnJjoU4i9M5VbO+HG9UsN71XaGy8n/Pco7JsxmEFjCECoXKJdGU4WW/OAWoVTQ1kpxM1LbJXzj0y0KsKcN2ZZytXO8I8fSQN7XgU6DkVhr41nLMMMnnoqcwZywtmGUfOcq1FbTRHrGZ0pglneIzdVeE+Sywm0lfCcZCNiqwDMKKizfzz895XzJ7xRFC61todGXiyiIYMZPLEIh3iKak4ux/o5qw9CpOri9h+YDNWTSTPHrDtRTcyg9mUtZYjO0O2jn4q9BkuDGXqxKKW2zxhCGIkA61B9bCA0WjdQ9vj+Eq34r3sv1yz66MDk7lqx4ucqgXzE6zhZLmclEpjIZw7K5Qql24bqWDFwx+qOQ1eESEXz0mSorgxLujEbz8yJG0PRtNUkw4yknNoLOYOJPUnE3oLak8+oCJkuiXQ0WRPzW9K6gKTPRcqqk62+9BSIIAiCQIurGmmJ/O3f/i2srq6G/2bRvWr/ZQv2Rz7yESiV1j3a2mB+ft3b/2Zee+0142UhCIIg0iN3eDZ1+PL0EOhquRFDQtHYyjHZENoUa3WgPDKDhKFTnGBTdTSAhv/GfYeT2jklOjkchrmb6Z/a1fC6Z3i2HjFEOcqqYM3QhYSSxM0rgBrzYixn2Ko7OkQscZfBwuNUbR0rn+dSIF8O1eVPn15H39iW91rbe/HvP5XqzyF/Vsy2reH6oNYowFYkWe3zC4eisB4fJeoSteKBV2ezqFbiUY3Svb3wBwrS9oA4ZYi4oYwyaqs8vHv18HuJNlOGlEOTfitqiFSfwwEl4zsfgLHt98Dg9B6YOfgEtOmMaCijP66ZeAN+N8ejsXNPzDwXF1lI5xk+0VA+afza2ttrUqBzfi+nsy6w7+UcMeCPc8qmj8DM/Bwjc4w1KNIsl2NG6L2j28I6yOXyMDizd0v0ky1lkmgjPWNewzxn0MDDthOCqChvpUqj0Xr/xA6NOeKrAzMG1QifWwFcZzBsiO49leZd/z8ckINayLpcTsUch3FtU4Zv9zaan4dnHjXaXzQ4uPe6vxMEQRDZivT1hS98YZNHsnUhzuzsLDz55JMWSwbQ3d295T1WvnPnzlkpD0EQhBegPeCqV47gFvJhqxNs5TGMLuGsunRVKhq5qUCCAfLgp7xGwWmQzptdg5OQL5bh6vmTodfwrsEpcAIjF0Y42ywJU0Lh5DUr4oLTptFXFKmqjHcvx/NVk31O/aUeFqO1jbT3jkKxVIWbS9fq42Ngeo+2caIqaojK8hmZEwy3vbHLrzgjWoT9PR4M5cVhDBEJczCGzLOqv0TXNToDfo8v3GX7tU0ZgbAxk2HQ9BWPFIVrUcy6h2ecKKvxcYK7GnBj2FCdmwCnl3lnML4eOF5fKVC5P/bnzLUxyoCBfNYPrOK/M2DAPzJ/FwzO7Fs3/GrZKAdMMmzVWCgeuRySLid+LrVsGBwDK/Pk3ofh+EvPwNK1S9DeNwoDU3sgS8hGTMGMSQN+GYcWOPczEvO1IqeaMmmJgbG+EaDBgF9m7EVG/jQil6O+oUcuZ69e9cwlgbpnV1m8NM6YZH9LQ4YgCCJzeGv09fzzzzddIB999FGwTXt7o2eamlHa1atXrZWJIAjCFWSEDM0+wym8M+/NCrsiWhbaCc0YMuI1HXnaGIjztumax0FT3pmwKt0gRVU/Yp7St3pLz5BHYY0K3va8UZox8khUNrAQ6UvmctPEnMyTh4651uj+EOFelCkXTR94DM68+XVYubUUGra29w7DrRvXAQNGlNcRtosrzxQbWQPjxiTg+UzhJSp35jLppQf7+XgdF8qI7+J8a1I613fH2kjhpBFgjPSV0NZCbS7atOH3486rDnqh34wT86busipSrFS5lYsZc6nnORmlQQiciagkZ3yHJwKzurbm+C2P13TD0QyFnzlOsVZXm+LrKgbRf2ZVspezNp7NyOVkPjdlmNySL6BSpueSy+loN6koEMIf3M4K56RUau2A6f1vM5KXaB2Yib7EJaxJmQd4SWzUwti7RIQGEbW3paYEzh9xnEnX9RnBOi6dKbzFlXnDUjlZlMqlxSuQSQwao0n9Jnb+QNKxJXW4yBk3QRBE9vA2DMPRo0ebLnwPPvggYDP6qnHt2roXaYIgCEICJGcxG55wojNP+DgU5CGpOCwehZFUh170KwqjVGhNgcmLL788+NWMskR+IaAoo0PZgyMtpf07vCmJu4Ty9rjm/nyCLNKXEsW9AO8cZ1VRWIMChsi6gnYMYN3LKaJYaYPRhcMwsftB6Ogf06z0pyZdYwqVKfORu8hDOAfIgLC72/Poa1O5DGFDIKm3LCnP+OZRuK1nCMZ33m81mmGscp1vUVu5+0lg2jLSqNIMIYllhXpl85xIOiryNBYdIu4jO+MkeX2OVxQWM25VaWiQ+KE0WM+RXqJo7DHnt2ptvjQb0FroYibWYpaH1D5Bt1xOwkjDlOFN7JlZ9R2rpFFeukjGNJ+KY2asynyGG1N7ubhxaVFnQwI85UJSDmf7vm3UyxyUpBSxbrk6xxWKFZg7/A3a85GXG1isVxOGzFJ5176ioQyShmlRnyT9kiAIgsgWnmhNNHL9+nVYXFy8I0TcwOjoKNimra2t6ftk9EUQBMGD6otHS9w+6Om+dLUhEJT1riKZGTgDlrJG2nwZvJwi4utOUAFIuwDU5XDyrvdFHcXXqSiMwd1fFIqLplW5JEw78LOvJ3oki5r/LCoK26xbV9pVgvrez99H1Edknbm1l8OjuGD/EhPtZbZUuRQaGih3UhIY3vsgbVfDKHXCgHWsIHCww7wJm1EUTrhOihzOOXtKkdJKE+l/k6RYLeT0BB01AzN3rhh11bWqdNXOl7jaRVlEbAPEjss4mSrKsay3TN7u5zWT5XpT++wq13cEGDNsDbxYV6TvWDf9NgvoiCBE+HHHanw9MjXNJRlqYiRySdM/v4VnVgvzhMm2GJjcBa6gxxnTGtJ5TvgDFYlro9jKgj+YMEhP+hzpPBeFtIpYIHgPGXP/YHBtjXeMKq53sC6L4C0dQRAE4QsIJTTpqRl8NaOnpwdsE2XchVpJkiAIAj3xHteySbLXD7QCzhrYyycA/v6ZPWU8fsw9j2t1J6dcZ4gET5wuCPBRXqibxlTkGvHEULWZK0p0ccpwVi9kDUVDUK6IYli5UM+cr7j8uKd9Reg3HLIZ/UnX3GpqjxU7z2Hc52nwdmmqL6ZIFHd6RIzcXL9BBMpxyoPRcosrWWOM2lpva1fbPBZ1Bm1JY8LZMYMRQ3bOadtMKgq6Q/0kbt8Z+5nODUHC3XFiFByB+hdyzlH/Tkz+gQPRp6KyoE2e+nowIOOzZYib9sxqzLA1ScbkEDJ1JqpMH+ZhYS+kVH6YVH6a6yTq2kQW/oxV431N5l5QMgqiKeTmBHXpuj5P9Ixug1Jrh7oEEfcVl5CPUJnx9UF2zxkADM7s3RKdrFztVFMuyYfXPr/wjFcke7nI800aQzKCIAjCS/DdtilgeXk58rNisQi2uXz5ctP3y+Wy8bIQBEE4h9SZxeJhUcflE6/nEi5FYCSHQMn6aO3sU1sMLPUhgqgSppEIAzrrUUZIknLLG7ipnGUdKS90IsnfVjSKnYPFi5CYlmr7BJcUwx1GxkNeS0sBql0DVtYL4fZPq7iHwTN5TP65fAHQgURRGM3eJVL/O0k5xh2xFJZ5OTYqkiAdfWPiaamqBpF0cFQ9J4qNOy32XXMGt4aUmbQ8jmcOvHQ8DtY2jCyXnQlHjaJwYLecYfYBvvXeqTUkJYkiQENnZyKhGfTPP/FjLnDzzGKq2DFzWfLviK3VYrhihPcdFvZySM6VNpAxpOoanIz6UE2hmiZtqY2M5IvzbCgX9cCI5Q3HFxSu3ynSkt1vR8m+iZi6lnCCIYwJZ4uerkexRuexz2yzPtTdEUo5Zkg6nzguEy+UKjB31zdAvkD6mAoaQWViEe8Gzu7lMNx1xdVf7+g89IzMQj5fgnJrJ0zsftD+QRbB/ILmjjWqHEl1hKAOCYIgCLPY33FooFKpRH528eJFsM2lS5eavl+tVo2XhSAIwjViDVZEDjTWzz7igjI8B84kTCgarecxON3okQY1WJrP8mWYO/3YDqxvB9giCMW2Gd72dKKvJeiWaPEUmmnhn8CaG+RgZP6uGG9zuIz10vf3ALoGpxSVJiaXICfn4SyXg86BiYb3Nr/2dk1Iqq/A1b0Ch+EuIVilYnVZ7eyH3rF5JWnpj6ynYg8U2B+3Mc9eqrSDDeLb2q7Cswt7ubjoENgjW7iIyj4RrSykLAsvkY3aoj/SlxmDW+WOGUI9YTc7nd+R0kQJ0MpNRT8TzETPd6MTUZAGZz4SisIoz6faxyfHmU6p8TfNN6n2phgQaMJiuQ0mdj0ALYVS86R0GwIJRztO73TOxPnHpwiedyITCpR5zZGzptKpM16pvtLRC5W2HnUZugjCfm/M0aGX9SxuMId3TsBRLrz1wz+Oci0tUO3qB/Sg38qZkMu5abBlag9VK6dM9NKWfAFGF+6GHQ99I2y7512hc20T5dYjn1BT7vWorkqSqiVo+LcBVNq6t7zbkrcfEIUgCILQh5dGX62traiNvl5//fWmQtjBwUFLJSIIgvAbmxcVZqIqSRIql4DTMGGEO4ZfuCtbRpjVN7YgnpaKaDQGftPw+9ttZ8IgQsbzNor5RJmAVORZeC52tVhDZEeCn1FmDz4BOx74IHQNTVnxzi43z6VXLmEeD7uHZ9Klk5iPvACdXUb0jMyFhip949theO6gumLFXQYnGPhJec8UKFn8p5g2c3LlGJzZ69AltJ601a3j/Oks3PtemD7wePTlDzIP425Fh4ie5+KeY2juwJb3mMdNm+i+dK3Vh+m9rPp5Ass8nAEMKI6rmW+iHXno28spVlKIyUdGUc6ut2PLin1JytU6yiFlsxtkSgHQKkZkO7jaBavcSlghLaZeu4dnIdeSb/huZ/842ERqfAb8v01uV/H10DhrbvVPKwTmE5vc/RAs3Pde6OgfE3aooWZdkh87RvOUysa9/h7dB8zknvix0uVbPrHo/Xayod/Uvkcp4pdYZYN20EakksfUuWF9XRefN6zqkiQ5pZEqm6P9RGffV9TGrp2BlToFQLLmcSSuMe2YXFFEIbVEbLkCa7LC+j2u6XVVQoUgqj6S+lWh3AodfaMN7w3N7EsuI0EQBOEsXhp95fN56Ovra7r4vfzyy2CbZ555Zst7rJwjIyNWykMQBOEUCC8lzCmKCObHUVc6yp6kHC30G44LiYGpXcoVhl1CtA1VXJK1dvTB2I57oa1nSEmZ0M8bt3/fO7pNuwAq9kICW70mCadkvbnzthfH10wrTbmhpIWwHxmCt33K7d3QUrhtCBE5HG0qHOjLd3T+sLVL/riorXe80B2GmYNvh+G5A5DLtRgvY0OZ0qwHIvNcYCEioESHkt3LtXb1u6NYgmX6VBBpsFhpS9ib53BVBMIzYBSxfT7mOdi+emOECKYk3NY7rKxU8Z8GyvZyYoawyZ+rnOO0nFEsRFPQe9bC7JBAbdlEo7lqJ/BjjpORB+lGrfdoHmWRjd/mmugkyiGbX9yPJeUQjvRdrVisg4HJXU3f1xp5KbBTScbmkdiordG/YU5UmNJ8e+9IeL4Z33U/tHb2m1HUxCafcIhYD/nBnYhTKsmskdnaGv+zp7nXUqXQLWrA74ijEh5jTSsYy1YwKgaHMwc0d2EprpTyxVIoayV4q1rduhvl1NRE1FaRvoumn/Mgo9hv/fHk5OoCSaWL/qijfmT6cWTzWW/ADCFe11F3P9jkR+n1PwKj5bRrFK8Guw6iaoXw43w2vvP+cE/RNTgJ4zvvg57ROdtFIgiCIDSCYAXVw9jYWFNB+NNPPw02YWX64he/2HTTMDdHiy5BEIQ0WA9josriSrOOv5BYD1eNpd6CVN9XJhTQWB9oBAaR96F8dVgotcLsoSege2gasYBcj8CXRZabu+tJ0Iv+yxxhAkf7vO38TXtY8xQb/YjPo3UOl0fhlPnWo63kcpEGvSpYrzf8Y7MRvYpEqcBQhpQw472pvY9AR9+Y7aJkhv7JnVYjqMqkoyJPsxFdBBUEgyAcC+xybv6e98D8Pe+G8V0PyBvQ+3TxqsXTpkZF+I1J3S47c5ihFPenfhTIGitzpS3h8l/FeDMyz8UY6VtFRl/s9hyr9bwRBmDDc57RUxaE/QE5KtqBKc6xyMc9o9siMkmQBxvGunxIgPj6iX8OFpWanW1mDjzeYMyfnjXJMtuWy6lOLjCyjm/GLWdzOuSMtvtRYP6sIrOvSFmeeEdwKAT9qgsikHeC0rNCwwE8dSMhq7E9Vh3GRt219wzDyLZD0Dex3arcghsVeRosNjZjEmm0lzfAbRCcGhfLjeTOWNEYausejHH4p/EMY/6Ktf57dibUS5DsvMKVvVyau49A9Iumnl8+nzR7WhZ1nDlKZ/dLXYNT0mUgCIIg3ADD7b0Wdu3atWVxZEqVn/vc58Amn/nMZ+Dy5ctNlTz37NljqVQEQRBZxK6ghwTwVJ8KOxM6cCiIKrx03VDJxdZ2BSWKzUxKqGNLWT5uLpOp99B7JmenrhupmFLcvZ2WcxdDm3G8+NKIGNjxeBRWGjEgfTqp++WG32vdo8QmHedRHd+FRD3SmNbq4lD6cX1Oun0h0dGP3+hL39gI1Chyc6y7+XwJeoZnndvLqVEuMTNW0nhjZp+XWtuh1Nphdr+hWpFS+NJVRVp24Llg75/Ykenzv9JobSrHhc69nNQdfUoDfg5jLObtNS3yymDBFkWfUqXd6nqTLm/e9kpxrjCMbB9s7ehVXhbvSdnmLJIUMyrqG1+IySJOTgLmUdTPjUQhiYtAbdDQyJ4c6/Z3eX5Tj/6A8/n5ZUXJ6+fE7gcVZupwfaWFs68wGYFsWvacprnRrmnqp3eToTHbyxWKFbCFOScp8YbUJtcAmXsQlLJW14mqUoG+wKKD9o7NSzlrUNbnBJJRM7cGoaEbznOeZecmSeJ/oTZXeY+p705Uph9j3XNnC742qLT1hM4wJvc8ErleR9+xghVUyadG5u8CneA10pQY0yZkgtamDXUZkwNhgiAIYjPItDjUcejQoaYL4Jtvvgmf/vSnLZUK4Nd//debbsZaWloaykwQBEE4JOiJPVwHCi8kahepIl9PVixEgUZnPgRnpXL2hYax5kr7pC1nwyPrfehYAVeMYmKoTLrht6ECfd+ohhJylimc51qs5V3/CvZOGmeIFNzxhOYMPkYuE3aWZkdROO2lpKmxEheF1OqeREY5O8WFBHd925rnLPRBJ3B038yMvNg+oW98O0wffByKlTb5PJI/1IKJOYoprLXki+kTYgbswsYkmhWIJZOROrPKRHCLqRelba9jDK8lr1/MqHXm0BMKM9U3Hny8RC5Xu8SitjqrKJz8e+bplSmfKI8+x8nQ7P7Q+JiRy+Vh2IDCn/o5TiajJGNgJPOcpFyua2iaTznfY8wqe+dgYHIXzzclPxMpi8Sany7DcB4rt3amTys2GyR7fhFsLV3YZW6pufN8nf3jMVEKCJV9hp3JeCIk6HTGJKWIrsAZk5H5JymPGGOWwZl94Vhg635re69BY8goOWJO/3mHywBWuBgc2Srsg9xp+T6nq3x0y2PVyl5FTZ4sEmqxzCGX1DadRH+oPzoP4DOICDCOf31lsS1n9QLOZx+a2x86K8m1xOgJaHSoYcOosPb7clsXzBx8e6q04jOSK6fN6NDRTgwNDqYAi9FvlicQgiAIQhXeGn3dd999kM/nt2wUmDDn137t16yU6dlnnw0jjdWijtXKw17ffffd0Nam+YBLEAThAXFeX7RH3ZAgWrE5Z+Awm3SBo1i5JJGMH2KRKA0k9ckkOoc4vIOniHCAUzBuUoEnpu5iytHa2RdetLb3DIeXr9P73gZ5ZZ42JQWIxto67gLMUBHSZJhQT6FyZGFdOTKbSjnmlSAb+66o8rOaCwnRdjIS+cJi2k3rw1TUHoUecZUqbdS/AOaQWaMC05eurs1xKtuweUIsSilbS1hkhHK1U4EROphHheJeUpStXEu4l2LGX6nyMeGN2RQYxhOGMsSyxh0Jh3f8EXGI9YdKe4+lKERMQGVaUZjvSyxCw+yhJ7ZEauDOJsjJGfAH6+Ng4f73w9yhJ2H7Ax+A9t5hqTI0T15cKfKO0ZdGpTJLisIm586WfAGm9j5qLD+viWg3ZmjSPTwTynom9z4cyn+Sk9Ipl5OyflQyzvPFcqwhNfMob0XJOvLIqGgsrpkzbpU6s+p2YBCRugpEDECUtSf27a1WeVPzt2vON1hE9dGFu/n6rmOR97CQpn7Yms/OzDsf+hDM3vWOUKnZxJ4jcu+ZKtIXp4w4aV9v/I41DhoTTqI68rpY5lq+GplEcHsvp9MYolZ3gmtEcNu5AHNOshFmtGKCqHKxddFGvlu/yN8BWB1q67+ikaGwTM+2nTFJpJlmf89k6xudJQgb/LjqjGnD73XO4U46KYkgzV6Od/7i+Z7K84NctO0U+bk40REEQRCp8dboq7u7OzT82mxcxfjyl78Mv/Ebv2G0PNevX4cf/uEfjvz8ne98p9HyEARBuIsfBxdTl67Jdl846jO1cjuOx7B06Db38MyLY+/ItpSeeRxorDhsetxMuKhgkb2m9j0aXr7yKAHpRlqoqaiOlXtN18Aah+YOU1ZduO/94AI8z+Ma4h5gdZVEc8YcP1fh1RqVAVNKTCgK28Oe0g7BVXtCdSqjCK8TLcaPSb+/XQeDM3tjv8eia87f+x5YuPe9KBSFXXEKseV33A8U4PH2WctTsDFi9wmayu/c/GlB4Z0ZRgxM7Q7PRtHFyvmlXGJqn5Wk9JqQB/PyXOnoCZWGbZNKCYe7Lu0ol8gVR9hSsf7Pald/tg2/BMev6Dxeam2Hse331B39cKVlZC4LrKxdcfPH1L5HoHtoOlVEw9jo1JY2bcmylqi9VE1RWJ0hF0e2cmmpwrFtknIQi+XW+3HzBpq/5z2w7fA7YceD3widA+MNnwmPR0WRBcV/kk7lZn3uSa+2kzjekpyh8OxdkJxH7sxxdlFZH2nSinOY2vxtHO3oIib2X3oN+MVR2V8KpQqUWjuaftbWM5Q6fVk5aEuhGO4l2T6SGa0wGSIzBENx/yBinyfSP2rfTTCaEmn/iT0Pws6HP8SRN3eSG35C85Z1IvoXcyzEouWxMcNkcXz9UKM82ML9Aw8sUmpyOXLp9nKW7h/iHUGpvXsQomaEFdffaGohCIIgHMNboy/GN3/zN295rxZl6+d+7ufgmWeeMVaWH//xH4cjR47U89+44eno6ID3ve99xspCEAThJ9HK/c0OcbYvJtIdYjk9l8ge+m0IUlJ55XMEJJdhon2SCeiYN7Na9KhCuZUnNcBE6ktXjrZjwkxsWJ/nNI/r+vPh6m5ixCgKb1y7mHJkQVXkNuRzkdaLcxOGLGrcXpqPDrEhw6hnGJzeAx19YykVZMPQrNGfWUPmQkKmvLV5S+Vejr8cXQOTYbQnLSCZWnSOc9vrqlnlkhyuqGyKPG32js5DW3d6BZL4rGLmOcfQfkYL7OwDZNPsn9y55T1mWMQ/AbrQLxBrCnNWITu/MsMItm8Jo+VGTpn6FPpZ2sKObVLmW4uAo5v19UGd8YCJtdWW13RukKwZWPY52UC/QZGMwpVWDCnusXlwbMe9ML3/bXryifwM5/jRL29PON/q7GsSSZerXVveC51mCe4V0pJlheU4JVQWNYo5nBNIDFChQCyXL1WgWK6mTEfTnaBNReGoddPEnWIoosXU1/xZo3AROCbjU1NekfnATOQxgImdD0DP8Gw653Mpxi3Ll0XGZg6hBiZ3QTYivnEgWJ1oztqEMWedTObOoviN77wPipU2jfdrGjFwF90xMB46KYmbo9DVi0ZS3zWrK4nCtAiCIAhCP0hOCXp4+9vfDrOzs+G/a8ZWtX8vLy/D93zP98CnPvUprWW4efMm/OAP/iD8+Z//eUMZGDXjr2/91m+FUqmktRwEQRDe4MmZy1R0CFwXEtF0D81seY8Jdr1pcI0IC38EPfy09w7D1N5HhKJHxSuXgHkU5hn1bIVSa+h5OVQsTJuHqEKcxWGiXJFI6DeB8vHB5UkwQwLXLMKMXBsQjvRlqX8YyJcp30zueSj01Ng5MKFpX4JvnosCy6WryDzHFIj6xrdv7edqSiL4PnGnhlQpa0RFrYn5DaIISybqiEWGiFYCVnUBrvDS1liEJMvKJbGPKVYH8/e8Ozy76Dqz5gt3jGqY9+c0kUQIdYxsOxQ6KWGGXqMLd/P9CNvZSoFxa7mtG4rlNvcdIEhlLC6DkPGaLkyAb031Va7oNWtq13B1Bvx+9iOUUeMT5BKRhsxSBhHInl0hQ7P7t7wnptDtb93YJn6OEDuzqlh3ZeYsJVG6ggCGtx1KnU5CJuALaYy++PuJO/XFnmmzY0IRR4Wu71mNYtFxxe0PzROYqTcWbWt0+90wc+BxMUNg3n2wY/1cu2PNmnMkhU4p+H8jLpOp9gxueW9dLseZp7Lmd6sfSSHqFEGmSpCNx7TzA8/v2fzEnJRsf+CDMDSzL+pbiWlIllAmO60YdQpuPIKmwrOKqJ4CQRAE4T04tJQ08iM/8iNbDK1qC+ni4iL8wA/8AHz0ox+F69evK8/71VdfhW/7tm+Dv/iLv4hcxHt7e+H7vu/7lOdNEASRNdB5MEUUrjr1d0SzlZAOsOhR60Ze6+TzJegdm1dcMkK5AoYVT652Ll156B6eCQV17EIiFZGPaGcukxV2mar36L4rXu6pvY/C/N3vTjGniuXHvNi6pPzkPEG0B8dcLh9Gf9msKLzxLMmXhVvjtOnvE+agVJ4aYyJfuDYWanOc3svipIhBevLWGf1kQyabE3eAIDvlMHC2EupPyC6B5SP8uDX/yezlRCKdcT23YNXkWgraDLFKre0we9c7QgMjdkE/te/RMBqrcXSOhzU3PRMz2QFzUsIir21pk4ifRa/fqowhxNJRoqAcBKGBvvZ5QVT5Z/1DwIbUeXVNbN5O/B6iemkRdGCz5dnwPAp+sugrAVFfT0ZcGQyrAnF9npNSwuRJH+dzxzl6YgbiLfli6LSL7ekqHT3mC6Kx3kxEckDz7Dr3cla69nqmHX2jML7zfvlUkiJ9Se9NLJ5lA5V7uTX151vBfs3uPmMSk0qzBpNrlyrt4b/Zf9lrjI4EsYFyPYu1+XLT6stoNGxFd6y2+8adeU7h/X3Dd7gTAww0M9Zne7sosMpb3UadIaDO8SWXdtry8NyxrpMvliCXL8gVw6dubcKIOjGPAH2lsojmkWDcwxAEQRDakXOP4RAPP/wwvOtd7woNr2qRtmoRttjfysoK/Oqv/ir8wR/8Afyzf/bP4N3vfjf09KQT8r7wwgthmn/9138Nq6ur9fyaRfn60R/9UWhtbVXwpARBENkAnXcrScGBGS90MUZnCd6btJCQ1cjCYegcnIRbN65BW88wFEoVuHrhlJrEeVPJwME4ss0VXpTGhqVHLjhpikC/YH2IGY/oKYfwBwaIEtbmtI8nrvRllC01FXtk/i44/tIX6q+Zp8LOgUk48/rzUYXRUo4szHPNCM9fEZ8xQ83a+Sw1yqpXNCE32lU+EiSC+UTBXk48yg9XooABWe+rTqGrqhX1CykDBhnF/dQEKD1tbvpBmsxiPgOcBJa8bdbrytDZoZ6UvGFKsVwl5yQWkekPosrPru6Va8pgzKnE+M774K3nP6crI3CPwLJH4QTla4E6ZYrgHf3jcPKVL8HyrSWJNKPzYjK4SlsPLF49X3+ve2iau2yEvrksdh6TUYQPLJydFBm2Cnw7RUZY5W/iSM1zlh9xcvdDsLJ8E46+8I/KHUCxPsQMxPsndq47pLndp0zLqp2TjStc+8XvJSTmOWt3hDllcxyLdBOVx9raalJKSRnF/BLfHQOa6BBSTl+20t4zDJN7HoYzb30dTr32VeXlKVU7YP7e94T7RGbcyrhx9aLyfLKOmbEiL7fQgepzss46XJ+PBY1MrBt3RZ1ZdTsaup1vQr1gkZMwecfY9nvg9OvPheth3/hCaNR/4eQR20XLDkrPSTj6VQ0T/ZwrGliiDElu/2NzHEc7TzGxl+M48+moGsk0e0e3wbljLzc4rRV10kQQBEH4Twa0cgB+8id/Ep577jl48803mxp+sX+fPXsW/st/+S/w0z/903DXXXfB4cOHYWZmBqrVamS6R44cgYsXL4Z/p06dgmeeeQaefvppOH369JaoYpv/zf77vve9LzQyIwiCIPwl2ntNCk+bKfPe9CU0sLWxrXtwy3uEXaTawHa/3vKjlIrCxgdKvLGmC8heuqqNOKenzmTT7BmZC41aL558HfKlCgzPHYCWKE9ehCbilX/UeIBNL6S2okTX8PvkS1H5sRXvIRfjum/3QoLjkgfJ2sAi5TGj59XV5fp7XQOTVsuUPVT2BRz9qg7CuUHGE7iUkqMS1lB7RY1VunGh7TmLqGqNwzLv82J9bRfcy7kanbqxnjnmmph5KslQLlbR3nZ7y0RtFRpTgv2JK9pGIBTNkBliLV27BGfe/DqoZmL3A6GhxY0rF6DaPQjDcwdh8eoF5fn4hlHjckR5mI/aamZ+CZ0YRc6jkb8Cq0TUby5IoygcWFnri5W2UKE3yegrDRgMRTKLielPSSYW7kM4xhGLbnv+2CsNchfRcsiOV6uKwkl3rJqj4SU+u+g2IJeTdxrImVfN4IvQRORxS6GRLLZzlWJneVojUyKrOvMG/HaM2k318e7hmfBPmRNHCbTma0OGpTtql4E5kztTnbokPHI5A+XAKZcL8J7LVOuSpKz/odkDYRrXLpwKz8VMLrcxwAhBEARBZMboq62tDX7lV34F/uk//aehgVYzwy8Ge80ifzHDLfa3kdoiuvG/LILYZjYuthvT3fia/Xffvn2hMRpBEAQhCFIvTCYFdUq+z+FVXRoTTeFYe7uA2guJHKq2Q3dBIkuk4YHF54uckg1FM0xOTF1SKSc39lxDs/vDv00J8yaQKn/xDJGg7LnlhKJaLyARKQ4aM2CKLadbxmAoFClDPWE7ynibybW0wNDc/npEw3yhDP1Tu0KlZF/AclkuroQan1rTd00YZ4roCacPSQEosFSOpItBlUp067/Dqfh853NTxUDS7xzY93AjUafCF+O22i3tXs6QokQ4L8Q43cCIFq/pIu2V9FUhuxnuwyPIGltwR0FG2t5uE1GpMfOYlNKdhcbDso9Oi6lz82Zklbxqa4O2+k8ybK19zv6rQlGNw3hZKxkVy8kQbUQu/vDCZxVrCuA5Zb+PeuZKWzfMHHw7XDr9Rmj8tbJya2s6KSJ9oSVyL2dgTk6sr1AwpzC/2n+MXLIayINoxpb7IUHUyeUC/Aa1kvmIRvSyvldNcDqnS77EHfVU6PybUrbB2RYNDgq525vmvdQgvb93UY4bucfljfSlyBBI2fxnwQEWXyab/tu8IPrLcSezxHvWkW2HGt67tbQYnRqycUQQBEGYIRNGX4zZ2Vn4+Mc/Dt/5nd+5xfCLsdn4i4dm39u8oG42+GKvFxYW4Fd/9VehVCIvOwRBECqxJ5TjMa7a/DYCzyV0EPQD7YYgMkq/uAQMqcebsPFF2uxw1Z9JJWGhutNWT/IXHDbWIVbXa2urxvN1EamxJSo3t+TBXIVRYrN/K0ehIYm5uVK9cgl32cMoN4CHhHL3jm6Djt5RWFq8Aq0dvZBryQsYfZk3JBJPW9MFu7BbaNFySFwAYtuLOGLY6h2a+0HSXMjGhtq1AVm/lgXb+ExEUXmllRkEo7YqKK9MGmn3NQ2KwmnSSlLMl0w6qkw2vdVKRYcQjgIcaOpzdufnpr/xZY6VQnCt8kWR26Dy53oa6ZPgyiY8gymSAxpaswOFZ1aR/nnnu4Yahzyc26kjhWmaiIKjIg9TEY+F8wwAKu3d4R+L/nnqyFebJZSQhGw5oxTqJZNTkI6Jc36tvuKi4Qr1F+fOckRzxO9FW1oKsLqyDB39Y6H8NBFsso3AnTxdul9NQvdezmaaBHZUtrm+PZtgljp+1DylVHK5dGmjHK9R20gDd6y26kNlvj6tawRBEIQaMqX1sH37dvi93/u90ACsWZSvzQZgGz9vxubvbU6rmcHX3XffDf/jf/wP6OzsNPDEBEEQ/iHj9QTjQUjG06YNxSE55DzTuObZzDv0y+9uf4SnT3L/GkN4eaRzmc06S/R2JaEojAnesdLWPQh9YwsiCRPIFYWdiA4hq/Dr+Li0pVxivD5TpFkot4bzEjP4Isyi8qwQ26/S9rn6zwODxhBmlbF1KiDqIMqIQaq8Ij9BWh+ioLxMJywb8MuMn7Rzq5l5ju1/Iuc4i2NBykiptv/VbeAaLyCRzF8y6o8n867zBPaMIWyhxAhDLExsioxw1V26Ma9ZiS7xK+brUm7/GjkoU5cnImHILgrPn9jGamA4kl2kDVaipnDCx8jqVdf9g+XnpIiqyNDsiKy1vRe2P/hB2PHgN8LE7ge5ZKaJZwgFCOknWDOolcoo5qOoKJGRvwCb5FJFp+b5TsD3XXeWBueJMjDGgtpxjKtjpY7aqkwvIt1eTvnvNGJkXajnES3LFClHPl+C7qHp6OyQ9WuCIAjCT3BosBpkfHwc/vf//t/woQ99qKmR10aDrY2GW81o9t2N39+YJvvv933f98Gv//qvQ1tbm5FnJQiC8JKoc5KMF07fFYW5LofcqTPnweKNVPRCMLaL4FP20qM/p0jxz7AinzIkLt7T1ZneSzZptGWjoF94PJfbnk+Eo6ipKK6UjlJaAwVDogEXu2qkfpGEonDCBYNQAbg/l/2uKgJv8tO23uiuojUPBqsrc1wiOI3BdEQMFVPMNvP8tXo2t7cIcI8rzGfWCGTazorCjIwBktJ5Tn6usT0faSHyzCqzNqwJ9kWOOtcxN3jYjIQ6Ywgr49yhuSVU+kK6Z4skqhvUFIV1lzswL4O0LSsi7INtPKZWFOb6fXpjTOl6w1Xd5s75dTFezN2ZSqNtDudP2kA2ppwkiD6TMuOdlkIxdVrrHznaVobW8di5AWnVRTuPyiEpF9KKS2BzP1O3d7DUf1Gg7vypcy9nw5E2V9spkMthXQNiHbRHnq+x9Hf+Oi1W2mBsx70wvvO+hCT1txPWvkAQBEHoB8sKapRqtQr/6T/9J/iN3/gN2LFjR2SUL1E2R/2qpbtnz54wutdHPvIRyOfJ2zVBEIR5Ar0Hrdi7yMCiF7rwxsHLw6CL5dYV+UhZXSgdE7E3EvrSjvxJfN0Xy9XY7wRBi3ahp9T3TKUjlbVsfxdREE4shFSHw6Y0oHqsuzh/qiDOmUf8D0V/YGn8IrmQ4MsnZo605cE57lLKpsKfpeFqYp7YXH9Y516MmKgrbHk4Y9h6J0eht30y9FE6f0glhbWS3Rif+VJFfaKBW/1b3Xxhei9nyngyJ1kO2x3BUkTvREVgtv8FR9lU8Czv5YQfHdc+SyJxO+UxGR0iciqLOBvqbtM1g/OcUD0HmTJOVyaXy8J8GWcgI/ZBXCbgE+b2oGk/t6AorOOOlRsO5WzsfdGCsr1Oql0DkA34jKB1ZaEjT9F5LtXYEt6z4RwPcns5ka9aNOpyJZKnYbqHZ7a8VyybDyoQuUaobB5sbW36/iHQVM7AIV2SVHu5QGiei+7ThuVydN9BEARBKCCTRl817r33XvijP/oj+OVf/mV44IEHGgy1mhlxJf0xNkb8Onz4MPzSL/0S/P7v/z4cOHDA8tMSBEH4AjIBgCR1IxKh6BAq8xdPjIWq7p/YoSXtLJAvlqFUaW94r9LWA9iJFawHbvSBpPK05Iswueeh6N8buUBMJjoam03jrqhLQ5OXrhqNDEUxZFCcTpCMa3wmYrm4otEhVMx/NrzQGe0XjnVBpWuDsJJagkMa0xcShCaEb+Ii3lbXGfB4WVR06So6XjXNiaLKJcr21AlzT7O1zqTxJ3oluQwxOL1ny3vlahegITBg+G/L4Nq4comkshva4SpjwI9lrVNfqTSv4kZ0vEs5MEFmwG968kgzBuKeSzRdVWMxWS5hZ55L2sthkw+LQnMp7jpFZ9ya8v5A1fMkRoeQzAejoUSaOQ6tczXHo+qYgN1Rb2z7zv5xKBQrZttRdByt2d2PqEDJnYeh81eou4fwLtX8Xi5QeAeJtd78gzmp7Rmerb/OF0rQOzYPWIjc90vdaea8clTSsBdMk5bxvZwBbOqSGNcTMZwfQRAEkUko7BQAPP744+HfyZMn4ZOf/CT8/d//PXzpS1+CS5cuCVVmsViEXbt2waOPPgpPPPEEzMxs9cJAEARB6CNaiGez1i16oQs4nl2gbsptXdA3vh0unX4Lbt64KlEeiYbwTJA4PH8I3nz2M7C6ugz5fAmG5vbDkS9/Kl2iwnUkdhEvavAQl0fyZ5rgqKP23hGY2vsovP7Vv7Vu9IVNKU1KWCtZZ2E/VHjRoFaImeANyhKYysMMWRevnge0yNSVsKKwrfYwfSEhmZ/khXXzzwIUFxJS84xS+1A7DgRMMrpwNxx78WnbxXCW2L2chNdOG+tO6vXcdJkj9eHsDEIryuLs96mjMUheYid6PI1Zb5w4s+rLr7WjL3T2cuHkkboX9baeIbjx2kWHFaYFDfiVRW0V/X78D/ond8KZN57n+31kUgYU9Ux4g7ZqwF/7MWcePNEhEJ3nXCwfBuzPc6IOnGzI5RQkYbTcgorClseJ3Sg4lvouomHHlHEL5SosXjnvbpR7U6g8f3q2PvHI/TfWhfTa42S92ZvjuNYeF6vUYdg93vSBx+DKueOhk83OwUl46XN/BpnaE1rpcyrOknZl+VKG6pbHd32e01Z3CXeeEoaoqM6PnEVh+hqjO+6BN579O8DAyMLhcG67tXQd2rqHYOmamN6qVjC1r+Fn6xyYgEun35T+ffgVFRFEsbaBlEg95/D6jbQdCIIgiExARl8bGBoagm/7tm8L/xhHjx6FV199FU6cOAGnT5+Gq1evwo0bN2BlZQVKpRKUy2Xo7e2F4eFhmJqagvn5ecjnqUoJgiDsRJWxd7Ay573mtvBNMFw1T5rO4Wix23uGYceDH4Sla5ehVO2EtbVVcBkZT65WFIUT87z9eSA/XnmM49ajyqYR5En+zgYG2zkxCg5yuMeEKv1TjXUyOLsXXv9Ko+EkUxa+dvE0ZAUl4zHOG2V0xvqVSxRd7oobd+kHnSdCIeWSwNGohfx5lFo7VJXGDwwME539W85QMqVhK5L9gEtREKT7gEzUD5WPj6kqA76yMeWS5eUlwABTKBrdfk9oYLS6shI6fjl/7GXAQ2Ag0pcdZbak3/SOzofK5FfPn1Si9NpSKEYUJP53KM+eSaQ456fPO1DbVxCtFxjXLh4Gp/fCqSNfbZDTXTl/AlAQWadrSKOv86OiPBieCescGLXW6a+z2l4OZ72YoiVfhN7RbXD0hX/k+4Fj9WXbkCy6fwd+yYg2GnRxPFuuJa9l72FtfZfaP5tYF+4YREQPBfXGECqNHdXeJ+Og2tkf/ilDWAxvRDAXWwJFmfB/U8U5WfDMmmN6dxKykrB9ooqLtZtHDflci7rE0qAlSZX3D2kvxwT7uOZ+xMrS1j1Yf23H6CvinCkTRc+ZM2t8wzIDPBaJ7cybX098nlQ6Wkl7NVmP3xbnPy3RF7l/avqO1WBFO3auJAiCINRBFkoxjI2NhX8EQRCEC4gpENv2uFUTbmotR6xi9Z3LCsIs7EKu0tET/ntl2QGjL6lAX9j6lVlF4ejvJ2nQJSm+CpbDogQPm7BWBebny8C58caE3kxJ+NxbL4bl75vYHr6fzuhL3fMFjigK20D0QkJ+PMRd/sj9zJqRhoNe03Ej2Tc2f9XBMSi8zxAdEDHzWNJZQfwzPfAqva2uLEf8XkZJAtlFpmGMjCVr+oS42qHU1gn9faNw4pUvoVEu8crYFnvEC86GZXPc9L63wWtf/ARcu3RGbi8XNBobNv9KotWXFNrPpjL2xDW5nMCccMfZC655ZAvI5rnW9l64fuUcYICdV3MtLaGhV7naBQNTu+H5v/8Ds4WwvMamHY93fm9OUTiUcIucv5hMPMhJO9tCF+0haS3TYRSjMLpNkhMsOSw0hgMRp5jB/uWzRwErkYaqce3pyl4u5hE6+kZDp1hx+32+8RokGvBLKwInZm1VMKf+/oH3cQKe9UZmI4pn3iBMrAfi81is0zQrUVsD42nkC2W4eeOa4nzcGntp5jmefoJNPmYPqgeq9uR+UGnvgZ6R2XCOOfPG81t7Ec95lcv+SI9cDiNGo7Yqmu94nEA35Lv5fSkBpvhPCIIgCL8hLSWCIAjCHxw68LhpEGFBwdNBowfsKBXiulLvgVmhU+T4Tul1CJ3SiaZ+pmrchxGFtPRRXBdumLxwsucfmtkHOx78/2DHg98Ig9N7wH3MK5eE/UixEUjf+PbkPJMzSfYonOrZYrxwYkTisqBmRCg0bhF5YbZ1EYxpnsOMyvaJj6KZOnGJn+QSP5/Y/WDM54HZ6V5ZW7hnwC83XgX3vpLpYT27eK9ko+pSfW1Nqq54L+PVtod41NakfHO3PXwXSq1Sv99MS6G50ZdxWZYRRe7AqlwuWWHH8zkAAAamdwMWWHuwsxEzohyeOwAt+YKKRMW+Luo1fQ1v/+Ynvo46+sagvXck+teCUQ5YO7PoS+rBOV6TjNtE1gjr+xIreuzp5K3W62wDPSNzW+aSjZEi7CO+TxfdyylBShez+Y9YRIixHfdB58AklCrt0b9vmLcD+b1c4E5/dcsZkz9GJDKMLtxtuwjZw8BYFdofJN09jC1w5CcYnbooe2YNNMiZ7CDjQNi+ISrvBgrQENdniM2VJVG/Fs6sMuMg7jfM4Is5V2Dkcvnk59HYnZLvSWLuX7Uifp+EQW4hq0vi2npCEARB+IX9FZQgCIIgRMBo9BBzqFN5IXHHE4nQj6IKFv95GlR6LonOhPubXYNT/KnSAV0J+AR18eUJPTOl+L3aywhPQPMsti84DICwuMxzOvvDZ6gq8RvhQF8IG4R5tJ/YGV5MRNGwL+F4hHypIlcQlQr8prYVNi8kuAy+1F3Q64An2khyInjGFfPOvREWIcI4gvUhpSSHqM55ae8Zhrm7nmz+oagxhBYlYbxrhDUFIw1zUvo0FRpPKnMfL5suESI8BSqoXynbx+gf9U/sSJQhNSqX8CgKR8xzuqLCWJz/Ir3ryigKCxqpcXlX17CXUxtxUoHCkEvrnwlsV4eq9hDqAjGy8yAHA5O7YHzn/dA9PKNsvOYljVul5hPN9xVJ+/m1VbmIZmlJjg5pxYLLfJ6hAxsc5/JCqQJjO++DQrESKqQyI7CoccXPmlNyOWtE9IHBmb2hgTFrm9lD7wjbpunPBaNDREVt5bD6Al/OuCbkcvXxGjnNO+bAaiOb2y52K+/qQ/Ki9/lqTsCUgWx9ZbLSntG5hJ/nhPuR/F5OQoHfiF5DDGuWxp4nYzu6nfQ8n3G5HKp2wlQWc/U8tfeRxPEorEsS5YAGVXurQr3hn7CzLUMG/P7vmQiCIAgMkNEXQRAE4T82D1eRl8E5A55e1CqXuApTkPIZ/cKDNb2K3JZhntI7ByaUGmlGKQonK2EECcoidrwzySiLSwvqZJQkjM3xZjv21n4Qs54Y8ZDvuIBTso7E+z+S5920DuaLpVC5JMqrMN+YvfNsUUoqySnEKfvhqzv185ygojBPpBC3qk0KTH1jZNuhev9nygejC4fBaUQVVx1V5hLdyzHDZdl5Tgrd9S2xBhrpA7fzyEqk1PizExlP2Ip+sgVVXUM08k/E95kTm8GZfUry29gHoxToksaGvEdhfGP6zrPI/FbEqFPHXg7XOp1EFmSPOok9k0aKj9y5+p3c8xBUOnpCpbfB6b3KnkfWIdPtDAEVa4rXulgC/nFrcy+HqI0wlYXRNTgJC/e/H3Y89I3hedXGfBBpICkVecWGzDPNviLe6L6jfywqAaHsowz4kxWScfVXLlQa8N/5daq8G1MKlM2xWtrHwSbHjugcpzKPpM9sML3/MShW2mPlaDJrUb5YlipPbP0gqzs9e7ka6Q3Sa3XpzXlO+O6d2FpV6tYvvXtUdXKe4bmDjfNRZDfiyfPOd4rltohvqDUuu/M7fP3cSNTWxOfWZcCvcE/gyxxMEARBKKN53FGCIAiCQEqk4IlHkaLhB4H/h1ieQ6CIgQXnd7EdPDErOmAUsKggtg9YUQTIxV5IRHo55/h98+8HkMu1wOrqipjgPlEXTLDuEHpT5/wx5/c4vqCjChAOW6koLpkkQOHNXwtxisK3FedY2QqVKiwtXkmY55KfIS9rDBEkedpEOMAURm2982NlGuV6IuVY8Rgr/aFRym1dsHDf++DmjavhxZypPT0fEfUktUTEKJfYaI/A/HmAOQa4dXMxXcYKyuGeAX/t94E9m/RI2yo8c4kL5cLHmjPtIZdG8990D003rsuKDDykIxoGauvEzDlGh6KwIgSjtt4B3/nPReVKexhQNsQWsTOiPJW2HmjvHUn+OZJIX1gVRZMVhd0xyLVT1qg8RQx88c3JQdCiJjHLS47yCDmaSCtHEf19S4oIOLEfOeIYxvT52vh9F7b6xlYexYj3b9Hvy8xjuOTnUXsxJjur1V//1C44/tIXmv9eog9Jz3MxZ6x4HRN7JDoM1VQ8Lc/NnSSe9cbpqI26CBBttZ10Opf8cMXWdjk5GdI1WcZYOVWdi+qtBUjOrUjbjyAIgnALBDdbBEEQBKEAxw5I6QQH6S8cbQswueH0lhN/6aIgwwwTe7EqoXBmxat+jDFEKUKoltqjsIQSnWw0F+1CcJnLdYNzcmzkIBlvXpJ91Nq86tj65z2K2kPVuGZRvrgU5ATzK5Qqkl7oJMeX9n4uMVdIrA3CykoxCjcbCiKQXtp6VNcOInMmNuUiNqZKrR3qlMUVRYPxWXGbf7ypU+wvlFuFf1PPL1IhWDQhsAeycafeeYDC+SytAjj2uk5bPpWKujJlEV577ezlUisKC+73IhXoErcd/lwnGXkWDpsuoba3YX/giIKWSwiP95h5zGikkDuZWjCGkJDLJTh4is0PnULwWjpFYe1kfEII9YQx1wGismHf92qZQzellULpdWM/y7XkpeYDbPKVVBFCUshklNWDbDIImkFILoehwC5gYg+utP+mT2dj3+gd3Qazh95h3YA/XqYJGYr0lczNpWsJ9YK0wjjhHituP6ZZIuvUzP0af+IKk+I1LuLZy22Uy+ULTb9z68Z1rjRUOVGyuTdEsy8VKQdvtQcZqj+CIAjCOP7c0hEEQRDZwISg0wB3BJz6yhzWB6YqsWYP4ft2B1MjIy1SSoVLmQsJKS90tfwVCk1twSKdySB2gchhJKfUU3btP4rbIa3yk0P9wiZMAC6zT3DFo3BaNs5z0V7X7ryfL5YjEgrkNV9FlaMN9f2k+tC7/wws1YPSWzF1aRG1So2oa8hsuw1M7Yr9XGacMm/F6sGmQByN/Nym0KATXdTBtGlHrycuyTJcQ3Qvp6QtZNJQGB2C5xlyLc3PayvLt/CvUU3TF1fsq+9/tdrGxCduev63td74PsehfD4JL9sq82jy5XRZmZLLJRZE+w/sKwoH/EXn70vmxogVZ2OieSKcMlDjiFwu9R3Uxr0cjywoYvwtLy9x59P0Y4esvU3c+9XrWdG6mvxdmzK+2Mt38BvN87iMT0VkY463n5fbuiJ+L2H0FXX/wIOoEVPk1GdoL6fSgL8+byV/dXVlGbw4Q6VMSrh/ej8nmpmbdJ6jpcqp0FFJmmdbXU0alwjl6ZIoc6oYm8mWf2z5HNuaSxAEQRBJ+K4FTRAEQRAoPZRIXUgIPEd7z3D8QVmnojKdiz0j7kZC4hIwdf8QTyCt52O5Cwlx5RJdEXKsIHhRLZdF8uWFU3WWJmoIagWI1DcuYBcLisLrKSlKZ3OqavYlUQp0icpmcTZf7H/W2xvXhUSyQQRkA4f6hQz6L5ViokNwGHliIG6OYp5/u4dmpH+vI9JXpHGXrXpdszHHCTyr4nqJHFO4uvUGAiHDJGzjE/18iXqfbDcKjsxezienOrmgxYhDiOSI3sgN+L0sj0dE7eWQ1Tm//E2dHF02OsR6hsIfaCVp7kmcm7Qb3lrKVzOb+2Pq6K6ZQl2dqIpioB0TCuccfS1JgR/b+pBqbTChKJxYX6blt2TAj8Fpm5FxhG5tCbjmaFV3D2kN+LGNofQG/HrKXWnr1po+NuLGrovro06EZatx1efKVo73zBo1v8g4KsmLR6eWnscQzn9mZIwcOnEyVWOyOhG2HUEQBGGX5rHfCWEuXLgAn/vc5+CrX/0qnDt3Lnydz+ehu7sb5ubm4N5774Vdu+I9DxMEQRA84DvUyByu01xIxOVXKFZCT1qj2++pfTspNcBN+vLhVkpSUP8ImxCd4DxteSR+Lxfpq/YfZPWXsg/oupxfXV2JL4NsPWLrv4m4Vl7HWMtGe4iulbLzPNY1WWovh+VZ3Oxy+i5dnZvDJQg8iQ4hQkyWMwfenmigJWX0lSbSl2gVIey2Ji9doz9FWDHaIjTh1YBI2w7KzgJrnisKc0dC5fmeRiMQaRGTvfGsUiFHPPPk53ZmrktEsQKNUwg+oIm9VGweiBok0BdNPl2kL98UhfkRmZNuLS0qy1e4jhW2BdZ2VUbKx7O+l7JhwG9BRtTQD3X2SWlFYdUFSZ9BujrnVOy+vV+MnxuxawqrwLXy4jaMSp+NmvYQk7emzSwwa8DvWh+35Kym0tGrvFrSy/htyJ1pjhOoLDf2klb2cuK/L1baYPHKedGMwDlUOklXzPq8pLBOa47XzVziGciDIAiCwAgZfaXklVdegV/8xV+ET37yk4leZKanp+F7v/d74YMf/GDabAmCIDKLnDd6mweeIPayWuRSMelwyNLc/sAHVBTPPWKb35eHVIRofcRsb+Q8nAbueU035J0psW4seZSV8mYuK6gL+CP9LF4+V/sRmKA+Bytuh7SCP++VU1RhSlHY0eYw2Y+ivQTGCNdt9vPECwmBsknNp0kXpRrqJipJlVmJtCnNc5uaQUyZP34d12gYEJWjwkvX9t4RKFU7ktOWUS4pVUA5guVQ1w4yob5kjXsFzrdGL0F9Amd9dfaPw6Uzb+F7dEdsvrBE+uLICZwjYS8nNgfd7lAiHqBjvQmzzwWytyFPUbBXoHmes06l9kg2jJMk1m/FcjmWd9JZ3aSiMEa5TPp1IfmZbt64evubJg6QeAnbPxuPSmiXienHzYjy9gz470TBMTT+a2lh0xNG2S/E6egbg8tnj6Z/PgPjFdvewsYdVr5YTpOh2PuWWVtVb8Cf1GaVth4YmNq9/l1D9dK4f9cxecr+Gme/qMHbPn3j2+HC8VdhZeWW8bLE1qElo0bjSIyj7qHpLUZfbd1DcPXCyZhsArUyHIvzoom9HM/4wbbmEgRBEARk3ejrb/7mb+D8+fNNF+0PfehDkEuxifj4xz8OP/dzPwerq6tcyrCvvfYa/NiP/Rj84R/+Ifz8z/88DA4OSudNEARBbMatw5gphZwk4YseQRaui16hg7pb3Qgt6ASkFhToZJRL6n3VitKOYjaUVVd/CCQ8PTuFgHdjlZdCyknZ/Er7j8QYEvWYray8gsmkNXRoEK5rnGuUzmO258QU+av0Xu7U2iCJ98+IUrkkbu8TZKIPFcttBhXkA//OqxyPtLqynC6PyLyT65n1iWaiXCvnGFZeEf0HfN0FugYnYXThHv1GX1KsZWLdaowOEfUle3OD7jqRGbsmlEv4lK3U10302QDsQHu5VMg44sEWec2KM6YURl+qHAnYPMdsnK/RyWkVEK206MIc51h7yMxBkUNIv1xOBVJjJqU8X2bM8hjANslI7PtJZTISvTIqa717ubbuQWgpFDmWVdxjmr8PBs4+Iw+Tex4OZRBNjb4UoXbdN9AeQoZ/5vdyuZZ8ZmTMSWudynK3dvTBxK4HQmdX3MYjSOtNmDhZiePP2D+xE4Zm94VGX0pwvD6kZERpI33x3LFuer97eBbOvPE83Lq52OBUK87oy7mzhKZ9JO+8KCdP4chf1pEzQRAEQSjAa6Ovq1evwkc+8hFYXt6qRHD//ffDN33TN0mn/VM/9VPwW7/1W/UNgsiG4gtf+AJ8y7d8C/zmb/4mTExMSJeBIAiCwIK6SyDU5XD2cOpqufnQfeEifHGYdAmWth/JdHNwS7lEFP2Xbuki00j1IU7l0s15bSoEZAGpC36CH9Gqxd7v0oyXTd8plqtw88Y18fxlPNhbXMsjjTck1gbRscqUEXK5vHlFD1tGDyqSQbzvY1FAV5Zv4qpDR2yQlF66CngUrrT3NHjbbOsZgqvnT6YoCFhBLmqr/sLeunG9lllUISAL+LCX6xndBrmWCIcMSh/NDUVhKVyJ9JWk04BtAeFxIqJxrmHOYLAqFprEpX5hBA7j5PR5qEtKTR68Xw6sOmNKIno8a65wmb2crGFroMHYxdkpYHPB0xn1YKdrcAounnod0OHINtlG1FZmkLR8a0ksn8QBaak/y0w9aQz4E/V0czC6cDdnWjKK5aKOYu58wwoezHMdfaNw6fSbdvaqCo1nXY+yfOcNQIXttVyp3CJpfsvloFBuVZfflgw4DRZR9QFUhZGC3dWF6O7LEjJcXUY3yuE+sqo7szJZ6vT+x+DUka+Gd4FdQ1NQ7RxIyB6psZHUfki/LHO5ZlCn7P4hvj+T/IsgCIIwgaHbQDt84hOfgFu3boWbyI1/jA9/+MPS6f7e7/1eaLDF0mIbqtqmanM+zfKtfffEiRPwXd/1XXDhwgUlz0oQBJF54rzwBPgEdbYFiDrrROWz8RtzxAgRbdd1HDZ0qg1kakzhzLNIX8nldNOjsMSvE79RKFago38cjIJ1LhGNDuEaSvXeZBJbszLutM3VERcsMh6FR7ffs+W9roF1Y8woWNqRig2hQRg4Q73OAn3pF0qtWozoZLF2YYF1/hWgtb0Xuodn1KzvlvdyVvYXFvZy7DmH5w5CsVStX54Pzezn/XHUB5F5ocNAmW4uCRoOc7BRTmsb/qiOyOdiHNUpbxhnZZ+sr9Ii91FcSq+Nv2WGrc6f9VMg9Sxr/OOQ7eNK1U6OcvD3F1Rr8Ob3A/zziLtIGPyYiA5hRLndDWdMGEntNIFjvqk5YyLCiym01cAzznpG5mCsiYxHR2lEseIcQWq9TXtmTVaE3zwuS60dMhmJ/ybN7zT24VwQ4XRCAaPb74ZiZWP0b0XPz12P6Ky3PUGR3N7AeIidu5Vlb+4cIrsv6Z/c2fC60iZ+ht1UEsDI2qoGZzVIZGPCa7rKYiuIdKji+0YwVCQTd6xKCGycn5LvWJu9X6p2wMTuB2Fq36OhEwaOjCQLCPYINBjwc8LluNTkHavKvBBORQRBEIQZ/Lmla8Jf/uVfNlz41xbPmZkZeOihh6TSPHXqVBjlq5mxV42N+TX7Xu31sWPH4Cd+4idSPydBEESmUKVMbejwptI7k44LLpSCKdWIKNAgPh1HKmTZbENH+o8dT5viyiVJ/c9aRCuZqWdDmVSOq3y+FCpgzx56xx3PZQ6O5wYCx8vvDHJrqDNe6Dj7S9Tz8MyTm/tktWsAhmcPQL5QDsfjxK4Hk71EOqhcEq1sI68ozNNenQPj0JIvOB9RQ8VeE/szJsGitc0cfHsY6UsJotURM49F1q3WMSeeti3lkmpXP2y7512w/b73w7a73w2V9u6U5RC17lHVDmvmL7o52pl5MDVJY/1jmleYAb8r+w0Z7D6b61HUdJ6/Rubv2vJez8issnIYJdBlKCfXn8rVLpjc81A97/g2wTQf6cKPZ4yKwCu6V42W7ciUKjITuc+4kq79XiAdC3u5XEuKiMmiEb20i+VSRvrSMDezft81OF3/d6anFUxlkaS9d8SI0qUUjuyT+R0+RDkCEa//obkDW97rHd2WkL0HHfY2uvosi7jRPTTNXw6P6tR7xw8WjKXks8DWrwIrz9M/sRN6x+ahVGmH9p5hmNjzIGd2onOt3fq2fgdl6+5ZEennYezPiah8MnXtxlYuNab20m7elanb/yaluZlqZ//tbwdq69SR+ZEgCILwkxRSb/w888wzDRv8msHVk08+KZ3mf/tv/w0WFxfDdDYfvjbntfH9jd+vlYP992/+5m/gH/7hH+D++++XLhNBEARxO3KEgNcUpcQlr0NQJ3KIVBl1S6W3QkO4KfjYyujCYVi6fgUWr5wHJ9CpXKJR8JJKGWzTWGdKwi0tBVhZuVV/r617CK5eOBlTAECJlHKJpKAuaZ7oHZ+HvvGFzT+Syku2DNYuchUqBWR2HnZJCKuprFzG6Nx6KgH0TWwP/2osXjmX+Jv4rHGNLxuXOCxaWrmtC3o3z3VKoqOlU0KyBrbyCMIMhUxc+imNtotp7raoXFJTGBZVGo6uP2z1qqE/OT5edZY/LtKl0H4bYR2bmjOk+qUN5anAhrwlJ+VYZnB6L5x+/Wvh64Gp3VAoVeD88VeV5oOV2rOoPt8t3PveTZEh4spgOtKtnfnDB2VoFs29a2gazr71ghNKoNjqPPUcJ7GXZnXADFmunDveEB1i8aq8bNWlOVBnH2BGJR19Y9DWM6Q+cUxKu7zHZ1WGnx4R7VwE/EXUWDT293xpsb0ciyh+4cRr4eu27kHo6B+Hc8delihn7WNLe4V4C37lc3Jcfs3O//HO+FTOOwHOeQJZcZQj7KBHV0E2ZqHfaYSSfsa5T92yl+Pc3jKHaCPbDgHE27M2y1HobduodCBs+yG5I/laKadb/cIPmb4tZ0wS931R+woZHZM082vSb2ufC1drgK7v6T5fs/TZuTX5i0ozVZhYLUl845ogCIKwi7dGX6+++ipcuXKlbly1cRF829veJpXmiRMn4E/+5E+aLqgbjbomJydhdnYWSqUSHD9+HJ599llYXV3dYihWe/2f/tN/gj//8z+nhZogCIIDO97o5VlbTRDUWSt3LV+c9aZMcIC0X4g8B/O03TMyByde+RJYQUq5BJcShA0FOnY5yAwhTh15dv11riX0SBdr9JVYTtz9WVkfcOcx8QSH8MBVGlOiW761hK9D2PaymJIt81+a51Gypio0SjG2xkcr6a9/KlKO2/Uf85PR7Xc3KJgkp+/mpJnJi4oAYV3IeHK14cjBUqQv+QwF39dNyqitumBemtezUnqzqvRrXEkpVfAkvPOYbYiGeS7GwHDz64GpXdA/sSP8DXtdUxqOzki6gGAPvvpQkGTM96Mj+mlRysLmdMmDeW7urm+IlucIP5+J+tCfh9AY4o6AE/F2rgVkGJrZBzcXr8LS9ctQKFZgeNtBeO1Ln+AohphCKEblSl37386Bia2RIq08vnoji+SvRZ+dMnm+NYQre7nUkZJlnBEFAYxtvwf6xhbCe0jmQOjG1Yvxv1E9YC22D5rodIGbjjVF0sK4zsmg30FsoG4ei91a45XLiUfYMovwHs9QXas1+kLsyMfwXMJtgCbYP1lk76wS59AqEkf2ctxngjRRqxQMARfPHVL9JiXM4cvwtgPQUijezizu224aavmyPyMIgiDE8dbo60tf+lLTxbS/vx/27Nkjlebv//7vw/LycoPxVu3f7G/Hjh3wH/7Df4C9e/c2/O7UqVPwsz/7s3XDrtr3a+U6cuQIfPazn4UHH+QLR00QBJFpZA5IFg+/SgV1SUKRQOKw58lZ0EUBh7VnVHkn7koHklFoVnCByDylt3b2hwomrR294cWr2X5sr33kn0Xigtt572zpyh8+vytC8xiY8tVbz38OnZKIKwZ1vGPOtrKMT+s1mstqk3UqlVdgtq4RdzFb+ya5iJ36yiqVdlqjLyTKYC45MOExIElIIPEr3cOzCUngqxcdrO/lBL5vvF7S5Wd77yFn9ejefJ+mX4jMkVj2P1sJhOVyUmuD8v7MIn3hnutU9GdnZEcR5PMlKJRb1SVownDIlSrnViCWm3uYHI4Z7K0s34SWfBFyLXLGY/VyOFOxjXUmUm6l+wwpRymEcgI8ezm5vmBhL2fFoEJ+j5V057ApI3CNVMrVhKJGcK/f6HwOE+sauv6tWi6nuwoD9/uyrFwuaQ2zKWtrkFFHGYEgj07NHFgpPRvyEAh8SXv1BU7cscoa0/vgdM6GgZWVO5uYx5na+0gY9ZsrGUEHHqnPRgjbgSAIgnAPZKdFdbz44osNr2tGVvv375dO84//+I8bFvuNUcR2794Nv/M7v7PF4IsxODgIP//zPw/f8R3fsSXqWI0//dM/lS4XQRAEcVsIJHRGUugRTUK5RFd+3GnIXLoG7h1WhQUsiMruMnHCEdcU4NL+vq17MIzWJnT5Koh2QZ2UJ0KXx1KSQBSZwBR7XXOUb/bgE9DZPxHxc4XrNfa6whCBNERjPSW1gaULCam1SaJMcp5dcV2AWsGXR1XWjw1UiCN7uS3vo7nUxGXcldrwT2Du4ukfpdYOGJ47AB39Y/VfZZrw8W0bRjmA7KW6K1UrqKzR5IuAGav7FtOGhwjaAt0+EVlxMqEojKzSU0fASaEMxgy9CqWKmMGXqKIwgnEfV2dSipZC51Az9yym4Y8OgafMXuLIXk7lPBcdVS5VFvFpa/qdQAYxn+lQFFYj82DfVSlTTLp/kMKT8wQqhOsK60Rmrs2xr5WY9h9WDEhUPj6+qlTWP7sGJ2F81wOQaSKrKnBvCrTidE7JZg5QIrUdMqmyjrTevC87QRAEoQNvjb6OHTvW9P35+Xmp9L761a/C8ePHw39vNtzK5XLwMz/zM1CtVmPT+JEf+RHYtWtXw+9rhmOf+MQnYGlpSapsBEEQBOD1WpSgXKJdkBg4KhhQ6jTZ8WesdyEsSqwbM1d3Scefp07F/Kg6TucJWJlwy6GunEZQJzwvygiZHQTjxZMqWgolp9Yjl9tChzG693UXsZerr78yXgNt1QO3ngeudhKrL1xlRxHlRkbHE109piwPsj7N7bncatRWPSLj3rF5mL/n3dA3vp1jrpHZ9/N+zYTx5ObXkYcncB5TY0zG4NqGdonG+oiMWmWoDTB7DTe7/7XkmMKiQ5t04OsXMvOI9vVaQ4Sl5h8FDm7lHL3KtjgnOltnnKj0YJ9+DAfOz3PKUWggaWUvp/EcEv17U30oLh+3+rFZg4i4+y636k0GjHt8jBYFaqO24hqraZ/N9L4kOkIg9r6Mw5mpyjQa0jPddzdlF21UzVeugak90JIvQBaI3Gs7ErVVhrTn/YZIyxrnmnp5hKsV37yI48waKpPpSVc3Dq1pBEEQhFowrKBGjb4WFhak0vvbv/3bLe/VjLcef/xxmJ2dTUyDGYf9q3/1rxp+X2NxcRGeffZZqbIRBEEQeO8j+CJq2AOfQucmUno1dPb5mn5XWabaFZWx1XvqC4k0XiMzinSdJTYVgr5lrAi8guR1JxIuw57BjGAZoTJeZBaqyrrpNUdf0doWDgqhzRvKcVtmNf7XOQL+fufsM+rZHxmJgKjTgN/CM+cMG/CrVEJVgsx+fuNeTuLMoiWiNFrSK+Jg3su5pBTVFAt1q7PO1lzdy2nuRnHPbNvpQXLEDuRjLHKrsEWDzt95RDWi9YF4jeCFW1EuShHNuFwO2V4ubdQgzU401CqFuodL/UIO205M3JgDI/s7t4gneZ7jGVNJRnKJ/RWlowosa0OzQmia69BNK+gKJIUqWY2Jed+KoxpV3236e9PGPoJzGcK5z+ReztRz2jHkztY8Z0D4IpwtZpmnXGTMlL9Piw4naroJ1O/l4ufFQGjtlllz/T8DEgRBEJhBIA3RZ/TVbJGdmJiQSu+zn/1s5Gff9E3fxJ3OI488An19feG/N5fv61//ulTZCIIgskT0AcriwSruUKdDkCF0iEy6xBHKmPNbiA65mThw47tww+aFLr03bENb5sThamf+kzL8Mzr2AkPJ6Xmm1HWViXmOUEUapVeevpoY4VR6fbB0WeXApRSqfVczkBfPKNYuBLGv4xxwlida0S7wwqGFNWzNgzrrBUufILSvedjX8TtwPlua51Hh6Rv9hGXGGZPSdVJHlbrXTG6AUCSt/tzFlXjjf0V+40p0CIz3D6KkrXNr0akdqmOXy2wrYqgrezkr1xdYFIXdweTaENt3A9wGD2kNr29/6AmWHkSm2ZFdsaZ3doNELdGhvoyjzgLzcmKZvAxFRbMlL+eZx9HJ8m0jE/03bT9qMGCSNzJO+o50ObV3EfNjVxk6nCwieTSCIAjCTzCcFLRw7dq1pu+3t7cLp3Xjxg147rnn6ovzxk1UZ2cn3H///ULpPfzww02FlGT0RRAEYZAAiXK1yIVAkiI1Yu9NtlivE7efsXYRZE+5yn1F4bQeZ809jynjJeQXEorq23w/tCnwx6vMmnbuUqrcEbh04RZYNPpyZ71xrly1/iwwPyV7YdYxHt1Yx5vR3jsMeMFVry4Z8KdVvOXxGonb22yGFegsppUtRR6NWDe6sp1/WgJUe7nk/orPUELleUI+rWiZhMi85eIcF4JMRiRMvd3tPIfcHsUvxW0U0Vxit86BP3Wm8lHwVYtZxxMp03UZteuVI3u5tArjG9/X2EcSFYlTRiwzCpqxhKUc6QhyLf6fWQOX2lB/mYTOIYaManTj0nkq9bgTkKWqbZ+Aq/7N9Ak370JQIBgZMXZsWZcL+umoxKXuHBnR26guiUN7XBfLSBAEQRgFya5DLcxIq3YBt/kiTsbo6ytf+QosLy83pMf+yza0Dz30EOQEhec7duxo+v5rr70mXDaCIIjsIe8ZxbWIGipIukTSUm88F1e8SaVVGELaL6RA+CwyCrhWxipvnoFd5RJp4VbgT3+VuXRR3aeSymDP23HU+xg7gOdgrHLOfmA72kXSPBe9bwGvokP43yEVz93cylM56B1bAKykNUI3G7ETm7jQDeWSO54cRdsa39wnr3RYqwNwEqm+Etl8AmdWznHaPTQNGDG2P5bIpm98+5b3eke3gauk2cspaScXx7bh/S8ORUIDZRBxYoCiTtKjaj9hZF9iJAtzisK5GEV0PdjZt6tE235epgqwy6249YHj9uyOKLPKYPnRbMixbIx1rjGrZCwhH4/NiOgD2tbTQMxYSEc5jPTBTeXOtbQoKc/Q7H7IKlGG+jIG/HH9yqX9CDo5I/Y9SdS9tIRYTkkZHKqvVLDH9ORR9beZuA4Q5iiXaX8TOc/pNDJWrn9hr/Njce4iNm7sOgVyfT9AEARBqAHHCqqY69evR35WrVaF0/vyl78c+Rkz+hJlZmZmywaCCS0vX74snBZBEETmQHh2iTsIqr2Usq2ojbDyFZe5WBbfJ1iv00BznoodCttAqaBOJ0nFtDYG3Y/2poQApzFz2L891i1R9nDMaYeiRjRz8a4pXZ59ibULCZzzhm1DuaR60WPArzIpbg261GlM7H4QSq3ijn60Y70PxSBj32JhjU/vUdiU+DOhnJb2R1KGfyYvXS0YRGK7FOVRgGCGSiPzd0FWYf24WBGf47uGpiFfKNVf51ry0Ds2D9jgnee4HAsF+Az4bZ4P1TpjklzTY7fAuOajrQicSxU8YzNDTfyItqHgOJHodnHtoWoNFFLe43bGFFU3SK6yBcerzf1G+ugQQpmBcVDNnet3/NzfNtyfUcloERXFFLzzwMZ20noOUqworEpmFleu1bUVJXnw5ocanVHg4gysObMtltuga3AKsIJNDmAdDQ65tP1eODvBPRtCR3Rm9wtuj43UOgCOP79RZKoK8dUI15jb8syBXQfCSPtr7LyL4ZwfmKrPAHU7EQRBEH6QBw+5efNm5GcsYlexWBRK70tf+lLkZ/fccw+I0t3d3fT9q1evCqdFEARB4CY5OgQd+JQQK0jgTwZldAjLisKlaofwb+KFNO4pAmAX1GEUHMlHLUvwjonhUbFdCoc2X45IzS1Tae9R01ex9YFYAlQRSGWxOc8l1ZnIJXJNGSZeKZYvrTv5mrug16kUE7vWcuTLDCE6+kbBSRDOKVov3KQiKfEOjKifm4raGn+p55JimWydJT2j9vncoToW7ReuGnypNJ5me7lSpR2WFq/U3xuY2h37m0KpAtMHHofzx14O1/Se4TkotYqfcdGQpj7dGx5KQL3/dXDO0klLvogyEt+d87ai9jLR7Dr37Ta8ppv2AC5cXHxjWbbO1O7V8NWLDkT2+Gwfw/YmLmFd5mjjbsRnQ8akfDAOWxSOdKLP+GtanDGp9MbEb3jI5ADN984B1/oxe+gdkC/ecbiBDluGPTJ9WEJ5HrdjTRyTC5Zy2InmYwbuOrbgACoS7P0Ce/kS293GXs7Qbzb+nKeduPJIt1ezPQeIkObORsUda4ofGF2HXGpTgiAIwgxI3KOppVKpSEUBi7qYZkZfdcWNDQvwyMgIDA4OKivflSt3LqwJgiAIwUMNUk+bOpRLVAgl68/vgKBIh9CwGUOz+3FGh0jymK2wLw/O7N3ima+1sx+UYkJoxvubTW9HK+iaivSldjwq896s+lIKO7Jl163DHOltkz/j9t4RMA6irtDWOwyFYuNZbHjbQeF0TOwhRPPg/T7PeI7eawkVSW4+xTh32FYuSaoShFUmQy7Go7BTF/JxIHwOneNdJan7AJq6t2UMZiNqq5JFI30akWnrS/pOHgFnnfoetVUdrA4n9z4S7mmZ0jSLCDQwuSvxd+VqZ2g0N7pwN1Q6tjoBQE2gcC+nojhYou0IzBeFUhVBoK9AzXwroLSLCd7yzBx4HIqVNkBLoLu+1bWbqshrykgZBdB4ZKQMG/BL5ZUUnVrDuoTPOI1jfQ5yMLJwGHASGEoucM/ozBDGHJXItrXVqK24+4BQnSJ6lmblZlGRm36Xo/07BydxG3yZQGH7olPwDjRGktNCjPyl6dsBPu/9pbLBy0fwki1zF8LI5K4hMzdhX8dV7cU2/l7vHO7eXg6Dcxe1RmI897YG6htfkxIEQRCG8DLSV1tb9MXQpUuXoKurizutF198MfxN6Klnba3hvwcPiisIMvL55tV+69YtqfQIgiCI26y5GOlLIc0Oj7Y9rafNK60gmTOBse33QPfwDOBGf1v1j++A1ZVluHzmaKhoMzx3QK2RlQeCOpQgq2/jfSDSqA+nEZ0ssZeuHELzfKEEg9ONhp1ZW9uZUcnUvkfh5GtfgVs3rkPn4AT0jMyJJ4RvyOHcl6isO81zStyclXwpZbdD4FegSznHcZbF9hzNo6SGuYxCex8r+w5HFIVr5RHULYlJEKxhsJ1xnR0kzj7pD62olP+2oMxTrRqYo5apvY9AVnF2L2cRZiTY0lKAlZU7dy88xoKxKJ23EJydY39ixoiXRe0rt/Hf29lA1V5O2MGHjCIUsrHqXKSvSHAqirKzFJPnbqS1o1cuMZmI0pFJ4TEwVJJ0SkXxHQ9+I7TkC5Blmhn2xjmACUG8TZYhSsbUMF606glj9SwUGN7/Bg6fWfXBxuOK9K/dHawo5XXoipRyL2d6DCmTy5lheO4gnHjliw3vDU7t0ZOZlHED0opTOgv4/oxqYOcy5ljpxrVL9ffKVYzneBmdpCDdemdonCifT5UlJ+7sCMX+KlA8/hE8EkEQBOE/WCTlSmlpaYFyudx0k/DGG28IpfXUU09FfiZr9HXz5s2m79fKTBAEQTgS7p2HBOUtocOsSpm5uvvbrUlbOKDnchF27EE6RWNUmBC65HIwNLMP5u95d6hIV2rt8MI7U1pMKZegEG4pIgjSee5DO6dbFKhGzlOc5Vm4731Qae8GlBjo+7X5iikSsvlt293vDJU9k9qTRTzcDHZlxNQRSG0oSSFGR9RWkWqIrrOacUmAu/45y5d2jssUonUSt1/DfOGmsDzG9nIWohxwsWbDaQKuPoQZF9dGO2Tj7JkIzxk8LqpcBj0K51paYGL3g+G+ns1tXQOT0D+5Uyotrmgjog6ZfJkCYh/R8YdcU9yPY9IZnNnqqEXOSBFZnafdy22WMemeUyKdC4mmA0ZgTrs2y8m7hqY15dZsjlOYOqI9pFB0CK7lwZZKBp46ZUZvmx3vDc7sQ7cH1OoEL8V9iorxoX6M6W+fXIQzZX0I1pFMlUr2A917qshoTDz9xuZxbQ33GiM3/+OZu5XUHZL1Xdj5gqF26B6ahkrbnajkXYNTUElpwC/SZmkN263De/8QoUvCXVcu1IeBMg7N7q/Pa8xYmL2ORZEuCRsj5dZOfXs5TmtRPgP+FPpsAe8X7BqfqSDV+Uj4bB4ntLKkr0cQBEEQkjig4SxHT08PnDhxYsv7r7zyCjz88MPc6Xzyk5+M/OzQoUNSZWORw5rR2toqlR5BEASBl+6RGbj+4rmG9zoHJlKmqlK5152Dv8zFDxMWOK9ckoRDwhtGVHsUihW4dXMRqXKJ4egQkR+709YmyxrVp3wb+1HegHmf055xq9vtMDS3H9782mca3huY1uRlcSOiYyjyXi5Qp1yioi0dmsfqqDRwdsVYWquyePM8ohRLXFr7EkH4KKx+O/rG4PLZo/X3OvvH43+T1ruvVk+blo3YkiIjONSfzY69wEK59T/f1r4e3S/WVh1ZHwjr2Ha8kjjGbClSJozptp4hWLjvvbC6upIcUcQwvpxb8UUJdZCAKXzOwMUTR2Bp8Ur4FtunVdp7xJOKqfOoPtc3vh3OvvWCQHED7wz4XR2v3cOzcPPGtbDv5IuVUI7hfUQpC00RLVcL+IyCXUXx3mNk/i6otHXD4tUL0NY9CJ0Dk7Hfb8kXwWk2zX98joU0dnDJ+djm/Nfa3gv5fAmWl5caDCLwgHNtkCl2znvZnP7nGJ0/DMde+nzDe8PbDqrdyylqD6N3eIYNn10zYmopFGHm0Nth8fL5cA9XqnY2tI8/Y5ADjY/qhANgB84gLJr69vvfH+7lmCFWvliK/b6yfXIAMLX/bXDu6Itw5s2vg1dOIkXA1R3SgWBukx1fSbopvEaEaO5rCYIgCKfAISnXwMLCQtNL0M9+9rPcaZw/fx6++MUv1g9RGw9TXV1dMD8/L1W206dPN32/Wq1KpUcQBEHcPtMgPNd09I+HxiwbBZv9Ezuk0tJxeYhfUMhXvpYWfy+ya/sZbEK9ddT1ycHZfZzPqNLTJqfHViTKJcKoGt8yl/uyF8i132GM6mh6vtyUX3QUHPvKnzqxrTjDlOx6R7etGxEHORiY2g2tHX3gU32aHFd49x2B/CWO4kfa0h6qvMwjJ06BznnW7BokRXpqvs3Y9ntChSkWAYX9d3ThbsBGakVhQ8olSeW0pXQitZbKlgmpQQjmiIZ410ZFeLxPVQpnP0ijXKKkrzmoKLwRVQZfqjzEh23i6BywuT+5qkBng7ixWChVYPbQO8LodNP7H4PxXffLyaQkosuxKGNcUcVUdlnO/o9GLod0vLI+NTSzD7Y/8AGYO/xkaEgjnVZNSU3oWXHWCy+8z+r1Xi4wuxb3js3Xz6FJ9dc/sTU65/BsY3Q7HkqVdhQVkk6Gy1GuhPQTo1Pb6s9xy1YuB2M7763fAZarneGcpys/sXQCuTpD6vwvWn4UoJfhcxF5TRgo1QnYGImG3SMwBxSqZXnNYHOrNhyRyyWBec1m62O1qx/KbV2KyimQhoV6sdEW0Xes7p7L6yguf1L75ItlaO8ZTjT4UioXXFs/L7PIYukdbKcpR4rnURG1tT62BeVS2qNlR3/EFR1NaVGQ1Q1BEARBKMbbm5gdO3bApz/96YZFmm0kPv/5z8PZs2ehry9ZUe8P//APYWVlpf7bjf89fPiwdNneeuuthte1NPv7+6XTJAiCyAqxxhsiZ2xDh7d8oQTTBx6Dc0dfgtWVZegenpHy0CqNSs8fab2m6/TOFOW9FJGn99REGcEoigYjhYxcK6K83UPTUO3sh+MvPwNXzh1PXbRNmab8uZn+kdyWdpTFk2Bz2uKV8w3vGTWKcdwggj9SV5RxqyMP6ihsfDEvxIMz+8J/8ygzsovcq+dP3klDwuuz+LhO0Q948zJw2Rj13DbnOZVGlXf6gbrnEaobxNNFaiVsxM+m3BNvzMM2i9DQMzKb6E12fOd9IgUwH7XVkb2camzOfUYj4QTmnz8ybY1VHh1xA3f/1OWYwzWYAv3VC6fs1z/HvkSngRUWZT2fjBi1tJcFJypx5yTbMov0rCWsHYqeb+3O3iwp8upGmCzt2qUzDbLoUmuH1NrPDL/Yefa1L30SbMwd0edBHHOP9j7gK0LVk06Wb2ue41ofbHWTlPnKGXKoe9hye3d4b3Dh5JHwNYsS1jU0LZ5OWxdUuwbg/IlXQQ+cz8yzl9M5p0g794h4f81gVJEHPwjLN29AodRqfm+RkXk+15Ii0hc5+whhBhAzB98OVy+cDPcvbF+VdJ/QNTAJF0+/saEd8tDWHWco1rw9Bqf3wq0b1+Hy2aPKDQai1ldeuTmeMYvv7oEPS4agWnVJzBNpwO+RTAvlsyjrE2ua5SicpVjVG+kr8dnq9bnmTJ/REx0tUJiMvf5EEARBEDLgkJRrYNeuXU0Pm7du3YL//t//e+Lvl5aW4H/+z/8ZecC77z4BhZhNvPLKK03fn5yclE6TIAiCwAu7aGcK42M77g0vllITZEP4JkJUpK+wTjx5Royo9t5XrLRBR+8oZDU6BFYJUFI7s+iFG+cfdhHP5d0LKdL9RfMld7xHYQc8aRoUKjdbazv6x1IravN6r2dGFxvnjZ7ROSmPnqbYPMdZUbLHjEqlhTVzBsGuXQ5Gee/XcyHkLywyIVOA2via7a/U0rx/9E/uTPNzrXsxLIrCSs9xCucxtnfbDPO6T6RkU7PGRW0lBTn8sP0lhnGhe11Ma+xuK6Ih0qM0R/mYzMqPvXT8WQlvucVQ9xybI84Uy1XpaFb9U7sajLWZwm98X0l7vgjEZYJp7fcNR/pSN9Z86ftZNYYL+KNDEPpaIQhgdPs9sO3wO2HmwOMwc+gJabnzyMJh2Hb3uyLvku5kCjj3cgrKhXkvkQRb68L1EtEzGL9/lJKnCBj2mHT+YgFT8s7QgH9gIryP4LlPYHs55kipxvDcgUgDvNh88wWY3PNQOF9iw7RcLjbScsZBaRSkC97o1AHyunO8yXpHtqmPTJh4ZA2sys1wzjX2ytSSL4JtYttEpmoMnoExOkslCIIg7OJtpK8HHngAqtUqXL9+vb7Q1SJ1/dZv/RY8+OCD4Xei+OhHPwonTpxoiO5Vo6WlBZ544gnpsj377LNNF9/xcX7vfQRBENklw4cahUrHztfXZkFdZKQv8AaUglgd3vt0PGZaoy9TyiXSxbTbN9gFVkuhFEY2KrW2p1SGRNjPkYz5SIWE2+cVrKRebySebWBqN7z+5U/XldCYQLmZQrsu2nuGYe6uJ8MxUWxtD73RXr98tiH6FyaM7gmkDZgsKgonOdazPW8FbubFW29+GH2tGatYZuDFlDtYtIdCqQLltm5QDTNiPX/8lS1OLthlbanaCSde/iLcuHZRbaZBygswQ3s5NMZlgvSOLcCVs8dheXnp9ut5yBfLUmnJzInW51FDREVtlYkI6hLKns16FQVhVMTB6T3w4lN/piF13nXRbiSRuHnOeTmTSH9W+qiuGvAHW5SEI+cznuIgPtMmjTuZ+mbG8kdf+Mc7ryd2So8hdv7cds+74fqlM+Her1ztBHyk65O5wLQSumB0CJ/2MvVHyfAdTIajQ6hCdT9h6bFIXSKGtUuLVxreY1FzwnSqndDa2QdXzp8AG1iX4ca0TTqD4XSYHkdx+TWrh9jv67nQ0pBmch6RhkYcY9p63+YB6RrG5qX5e98Di1fOQ7HSHsrzYkmSVfPKwAKDdbfl99jawh8D9vocpqLoWh/f/D1QtBFmgPo8KoSy6lN4X9HaHuoRXDr9Zj0ydc/InHA6ttaZzfsC2/dVSftsjOc1ZpS8sQ/UHApjQWQvl1rOja95CIIgCAfx1uirXC7DO97xDvg//+f/NBhusb+VlRX4F//iX8BHPvIR+NZv/VYole54hLpx4wb84i/+Inz84x+v/65GLQ1mLNbX1ydVrgsXLsBrr73W1Jhsasq+t1CCIAjCMJYP3iKHWO7vqlQI5qwf3ugr0RkBYvAKGmUUaFMLmzR7M2z+e0OKwsmWBWi7YFv3YPiHC9QDWxhSLuGH9cXxXQ+ERglMmMwU6GSV12VhyigbFVKqnf1hxM9zb70Ii1cvKM8vzTy3dS7XN3YwXjgk0Ts6D+ePv7olmhuWehBTLjG/lxMhl8vD6uoyeEtgzqNwR5/6yKk1mCFrsVSFm0vX6u8Nzuytz78DU7vgzec+qzTP9JG+DHVq2Wwsz42V9m6Yu/udcO3CKSiUq6ESZGqUPJN7a0YcuTxFh3Cbtbpx7eY50GwxbCsKp/w8szhcMZzzOVtr2Zl1ZeXW1s9cfv6NKHyM7uEZKFe7QucglY5eaO3oTZUei3zC/pTsizQ0V2q5nOFIXz6dZ50jpbMHncRF+nLC2CEGb+bp2wzO7oM3v/aZ+ut8vgSdg5N3vpCkLCt1AcH7Rbt9xbe2RoFHa0NUpC++foN/HtTd/9OsBWyNYdGlseLcXi7wbLzq9odn7a4CyR2rq/3CkXpn9Tu+4z7oHpqB5ZuL0NYznGzcmpyqhvpQ6aRQqzcmyd+BVca23wPFchssXj0frnf94+mMvoTHbeT3dUVtxTsmCYIgCPfx1uiL8aEPfSg0+tp80GaL/82bN+FnfuZn4GMf+xjs2bMHent74dKlS/DFL34xjA4Wxzd90zdJl+mzn41WfNm9e7d0ugRBEJmBzkdihloWLvJtZBb1nKsry14I67ASep0rVuDWzcWG6A/pMCdY4fXOZE5R+HY+opdDWerizTxtKm8fw96M0xq3Zqn9BegcGA//MMGijbG/Zz/9u4AJJyLTWOznLHIRM6C5fPZY+DpfKEPP8KxkajzROVRdVuBAZO7M5VpSGH3hrofYPXzg1qOydpo+8BiceeP5cA/YNTgJnf245lt785y71hDssr1rSIEzKpk5CdE8plNJIipqa5gnZkVhjirJwpm7QYHP5ONKeRTW56Uc777RUKOoHqqhbokGA/7In+utpyijL67oEBlUFK509IR/RGMto5h7RBWFvVoG1e3lrCifqpznAhGnc2tolXFT54t/et4CO5+OLhyGCyeOQL5YgoGp3aFTKAykMQpR0ocS7wvF5OIurN8oDUiS+oHS6UJMLtc8Ca8WOufPs8lzgdw4z0IdK5PNosaRh1G5ZePci0bJ5UIDfh33NsSdqsvloL13OGWNrOGYU9Ls5XjKndo5gVjdKNvLJSTDzlJDs/vACDRWCYIgCM/x2ujr0KFD8OSTT8Jf/dVfbYnaVXt99epVeOqpp5oahm3+N/vvrl274PHHH5cu06c+9amGMtTo7++HkZER6XQJgiAyDx3eJFEvFDFxgcmbBzP6cp7a9iXA1z5sLzMyf1cYzYEpn+ULJRiaSRDYWAg7z6scsrbKo0CXRU9dBi9vbz/i+v47ZVKerQu5SOUE5IrChFfe+7UT4JtfQ0+Eux6Aq+dPwvLNG2GUowZPhAL14rr3b6UEqr2+4q1bH9udRboZ3X63McOnyL0c9/zF0bcUtBPWvYezfRBndUo/QGYN+JXpDpjrx+tGyCsN77V2KIiAFwfn/OHsePaOAPf6ENjJI5XndBe6diYMfpJlT7X2FHNKlvMj0hf2sa8S9EU3X0Cfo0P4SM/IXPgnhUSbcs8PPHs5jX0KbX81vldCWg+JqL3b4470xWPA78I5RXuzO1AH0vuawDHnIdnYt+tvX78qLHIvh/45DTqK8QWN6zyXLkmA79nw93M7sD2OHt0kqm+CIAhCHzgk5Rr5sR/7MWhtbW1YVDcac9UMump/G9/b+Jvav3/4h39YuiwsgtinP/3phjRree7fv186XYIgiCzh24FU6fM0TUqhgY2Vqk+X6epKo6IWwTwJ926pBhatS5aO/jHY8cAHYfbgE7Bw3/uh0h7vpbhvbGHLe72j2/SOGd6fWL6Yqo9HQUGQb/OiOJ4+/6Z+4LVyiQ/P4DBb+hA1R1Plbxbtq2dkttHgCwF65gA7ntqjlEu8QVH1sWhzmSPwY53xYs3mREgR3PEoELztGmXAjzdyUnYZ3naw4XWxVIW27sEN7+D2KMzr5VpPfxeLDuEeamUGYZt4Uzdp93JuKcm6TEeTCK3MsUUdHX0ycOucIB4Fwp9xrLa+au/7sS+MNuD3vf1pfk5DIBW1NTKxVGW5nYiCNLJIvLMo8eRwtkNkpC9v+o1ew5b4iJAKseBY0xe5nC2ZNy5dEuTPqrF8sc6YaLuztU4yBm/fS7WX87piaRDxgH29IQiCINzA+1vlwcFB+Kmf+qn6642GX5uNvzYbhW2EffZ93/d9cPfdUZ6Mk/nrv/5rWFxcbJrHwYONl8kEQRBEBBqVV7BTXztUCLyQC/VUsbrKF+krC/2nBlNW79yk4DE4szdVmi2FIrR29kGuJVkJo61nCMrVrju/zRehR8DoS2ukr1SCOhUESo3PVAnHTXppRDEWkSrxRF8aBkYjH5jGCS+hmOGYB/iVze2NARRzA1pEPOzbqMdAgXIJB05MFWrOMWzf1d4zLG1Arx0tuiWBG9EhZD1tah+bTgwQ72lpKTi630nbPzE/W3O6h2dheO5gGN2rc2ACpva/DU2UGetnVqSyJNN7nLj8RMuiZ5+r0LBVZC+XQtEV9zyYBM5xEScX7BqcajgP9o1v15yrY9EhIsexW20tgz9Guurnv1wuJjqEy1MYI2PtntTPdY4DnvVOpwws8dkzMM+przeJOkvoB7baIYi63+MpjgN7OZVje/Mda7FchWKlHUzAzqmb16S27iHxhATqI3BsLxfpCCUL653MI0ba7+tzwqp2nktpwO+D1VcGunahtB7swTbY93LR9s0RHyjq+thlOqrn/0yvMwRBEIR1DLkbscuTTz4ZGn79u3/372BlZWVLpK1mbP7O+9//fvjX//pfpyrH7/7u70Z+9tBDD6VKmyAIghAjMwcuK7q9dg65TMjNa+TlJurqb2zHfeHFwNL1y6E3XxatyxRMqDq9/zG4eOp1WFm+CV0Dk1Cqdtz5gsWxaVsglSyoS/Kc6xMCz4Tm8desKApnZj0jtLH50tWmgoe1vFOMI7ExqNCAXyNm5pVAqQd/3MavWyPJp2V85/1w8tUvw+KVC1DtHkhtwI9+HAduKJeQgpx0xRmvz+SIFAry4Izaup4n5jkse7C26xtfCP+wYf3MKh1NHvfexyoiVYOoGpv1BR5HQE6ywXmkL4xtvyeUB968cRU6ekeh3Naldf3lrruo5dmQ4a38HOcfQv0At28RZcTt5bjOo7b6j4V8Kx29cOvs0Yb3SoaMIaxhJDqExf4QqSisyHkdpk2OKAHyvbyAg7soZ0w87WNTLmcj76G5A7B0/QrcuHYRCsUKjO28z9g+ge25+yd3wKkjz94x4J/QbMDviFxOGh/3eBYi3KMj4NvLOft8Gp+FnRGvnj/Z8F6lvQdMMbpwNxx78emG9/qndiHRJUHiQJiwA3LjNoIgCCIbZMLoi/GBD3wAZmZm4Md+7MfglVdeCd/bGN0rSvBSLpfhB37gB+C7v/u7U+X/1a9+Fb7yla/U89uY7/j4OMzOzqZKnyAIIitEztuunm+dEiQFqOuNCet8NPrScWHBLgW0XwLEkC+WUivQiQguuSN9rdoW1BFeXwCnJFZRGLOM0al1zmHS1DOWNgocKCPGizmPqiaVMqfLly0S/ZtF+xrdLh+J3rXL56i9HO++wNjlvXQ2mg2QDA6Pel2r9BLsCbl8hAG/TxO5ThDN8yb78eb+kUq5hKLGp0ZaUTfWgYs/c0AaA3438KmtctA1OGkyRzcUhZVHbfWnzxgD8V4xNpohon0KhrVvYGoXXN5k9MUMJAjOvhLlVFGB4Y2007nME+3oUuo8J9kOus8hkXs5nnxdngYlqpVF9tp29zvh1tIi5Itl47KOgandUO0ehKVrl6DaNQClVhyONaNAIwvCUg5kUdmsyKUstEXU3cPq6op1BzvYxg+LDH36yNdgeXmp/t7gtDmnc+ysevnMW3Dl/Inwde/ottCZMQpnHSn2ckqGWtIWIitnUOE2zki9EARBEJkgM0ZfjL1798Kf/umfwv/7f/8P/uiP/gg+//nPw82bN5t+d3JyEp544gn48Ic/DH19dzaPsvzar/1a+N/NhwW22XzkkUdSp08QBEFkAXGBU9TB/s77yA+4geDF6y2dhSFMkCiMkhHUBY54Z5JVLtE9jtMKu0WeS0YRXLGidHR6Uskl58dZzljlEqdvVQnbbFGgs2lg7+rFKy8ahqrRSxzdUVt9jQ5Rx/P+rbOf8CYZqRhlyqNwRtpYOZqN3rQTpIraGv7c462cjOIMiz5z5dzxhvdMRqdGTRrlEgVIK864Mpy5we01XWmaInK5NEZfiJXs6mTFoDnxcfTJ5SJ/bijSVyK+tbUyLCjtqpyHOdPKQnQIVbBIEMNzB+H0618L5e29o/Ph/g4PNqND2F7vkN4/GEbtXkkiLev9QMNezoVDrYb5ulCqgC2qnf3hnxNjxvRamcJ4tjEZv+Y+M7hXZ2srK7aLgA7mcG5q/9vgzBvPwcryTegenoH23mFj+bN99+TeR+Dm4hXI5fJQKLcCFpiRoFYS9gi1ecz+nnIz2MqziUg/LbJzVoLjl1jnTwRBEASRjkwZfTFyuRy8+93vDv+Ywderr74Kx44dg+vXr4dRvbq7u2FiYgIGBweV5Xnp0qXwQPj44483/fy9732vsrwIgiCILJPtQ2K8QUQCmA/Yt4U2mREu69AT5o30ZdnoS/3lKZI+IyN4FCl6RsZGqjnOaZALipHDM6+gUaCLwbc1UOl8j71uBJRj00SHwHfJtYFa0ZA3FWZSG20ZGid3+vWa95GDnSfAtJfDPTnYWIOZ1/Kr507U+y6bA/omdoDP8NZzs/HsmxJulhAdX9jbOs2ZFfNahblsetBgiMgbnTUmkopJovLDPgaVoqDOa/Xo7Hl+U7nTyuWcrQdJ+sYXwqgMbA5NZ0jiBtHzQ+P7a6tporamKQd/GlbAWq7E8mEvuJiDuyi5nC/zV7KTVD/g3ncJtWvUd/n2yWjuHzzpy8pxvV5SztGrq8vgPurbsNLeDRO7HwRbsDmqIYphw4eJv1ZZkMbXHPdQWs+TyserInmHYbGJb2s3QRAEQYiQVc3BkGKxCDt27Aj/dNLZ2Qm/9Eu/pDUPgiAIQvQS3sGDoMxh2Wa0DsNk1yCCUEV7z1aPo23d6hwBJKLamZCD+HKBqKMuoi7GmLIAamMHAr2Am3fckRAdp8KoT+3iu0dhn9rKtbpDE+krE2ezQPihMO3/dJYl7ryaPYOCeFo7emFs571w9s0Xwzbpn9wJ5Wqn7WLhZfO5AeGkgrFM+gjEnl9g3uGfKwwYzig24HeBbPVjOzC5R/fQNFw4eaT+Xs/InLn8k/aMiPYs2kh6xmZjH1G96CxLVNqrKyuQa6G9XNM6y+Vo5tyMZhlu0l7B3Nk4O2CaA9OSa4nYy3E8I91PIEJHn0wd6Mvs3JPpiF62Ixk6SBi5KYUhD+GNB2Gur6VZ71TIf5MN/HE4UsEGyXMIgiAInyDtaIIgCIJwDFuHcl7P9SrLJ3IAJ6MvX1AvVIu8UNjUV/PFUoNySehRfnwHmme3NfZTX9jZmrNkhb6OeXTG7oUudb2R3ox+Mi7sdx4dkRFtzdtpFIVprkBDgFG5xJRHYcWPrmzv4ahxuN69l8IzK6/xdKQBP59yCWpi60Du2boGp8K/zBOYVqBz8+xnPF+VenRIz5l3MCSXc2EezMi5Sc9ejj/NkfnDkC9WYPHKOWjt6IP+qV1gHsE6yEjfECdwRKHZb7kcgUvplUfZV1f5WOQ16bR1V5nxbYCYQX78fi0Qf1Ck60ZkpC/0+1VOPHmMtMjMA+mdMbl7XiMEqx7p/BbH2sqKP86YHKx/LWishrW1FFFbVYC0iV0dQ/K6JPHve7N3IgiCIFBCRl8EQRAEQXgBGX1lQyAsJSQR+Mnowt3Q3jsCS4tXoL1nGCrtPWAKF4XhupS3ItsZcRVJG8elbHd2IeE63vZ9R9isKEztoZHaPBE5xcmMBXPjR6Z8Qgb8KYy+MF8q1cuWGS+L6p8n2oB/0+uItdhUHSf196jPvboETHwUj541Cc5+t7pCisLOoWVOCfQbHtpcb3xZ63QYJvlSN6mjtuLHq/U6DYH+KCNDs/sAY8BS4XnUwS6D/UyKkdCAH/F5lDBEYGIvIT/WhmcPQO/YfHIOHu1LjMCqS6bKEvqBrXaI3MvxlMcFA/7oBd50QdwjbR0hqWPxsYWj3DJgn88jy6fQKJG3DljUVsItdPRv3nPQ2qpmo6+EZ2MOUmJ/nmSN5Aux9vtNPkQ+JxIEQRCECBTDnSAIgiAcEUqoVWatuxmR/63053rqjYy+iOj+wr/lZR74OwcmYGByl5DBV+poWFxkRFCn7HIAHEdAUdiJS1VCK5z9vbN/fMt7LMIhXx4GBpWlvoz9ErQpaw6X3XY0JsI9OPu5mf1YDEjHow2jSCVzE87qVA5+o6+UDUHbVPPnXWtjJyOD1gVsBVxrSWHAb3sN93D9x4QrZ5ZkRT9HDfiRF891sCsK84y/2O+4PD9jIDAbHYLdZ2y+J6t2DUDfxPaUMhe9EwlmRz4hjqxjTYlyQNSkTaMiffmyl3NlP+KyMYSZ6NRcOQq+n+2+G5X+nXZ3s96EorZyTWF46yEr8xsvKs9nm9PqHNh6x8r2WqpoyReg1NrR9DMWCbtQijf6srZ+O7APaIquoUNDkiAIgtAIadQQBEEQhHPweyxSK+SppWUnmk0SzTwV9o5u05onoQN8FxLGUD1GVCWXVlDnmLBZpYc5E5TbusBvPWFHBcVY2NRve8cXGi5Zi+UqdPSPbf6RocIRpuFdD22tm2mUS1y4VMrM5Wuirqy+iHG6Fei0kZW+EUXGn391dcUJBTlC8zoZmBhD6cpdLLfh3UMYyrd2NnFGxrAFveXO5RqVzMVwYB50tdkdj2ZoFF/3JGuazy4K9arTn5kCO5G+HJjCCBzw7fvlI5awSE1949s3/CbX8Fo1zioKK5oq2J5Qal+IdL2JMgz0XZ7l7t5eFpk+mzJHQ32IdAKS8X08y7AWGvAj3swJtFn25rMINPbzrsFpaMkX72QFAQxO71Gax/C2g1uib3b0jcLE7gflE6WuoZTICINU0QRBEIQB0tzCEARBEIS7ZPxg64xQT6CczIsh86Rz7eLp8DXzaNg9NAOuU784c6XN0sLpVFdM+cANwYtseZwZzyIIPZOHzx/B6PxhOPbS5+uvi6UqVLsHYe3Is1bLRbhDtbMfpvY9ChdPHoGWQik0mOaNlMk115DSuvl5y6M1YPNFljcGoonjwp821GcMwZmm5TkocT4NbBnwg6Pt79fYiGJ15Ra4DrZzVdbYukeTb49KW3S064GpXdLpZorAfU/8suVLs5fDvJW7gxuyJd3IKbeDE/SMzMr90JHnU/EsfvZ3eQMbN6K2EqiMWw2cWQemdof3ZEvXLoX/FXMYlm48ZBKZupGVE2luh1yQYi9HIAKfAb/KSF8Dk7vg9BvPNZXJ9Y7OJzh2BO+58+wK7li11leAK9IX4RY61kPOJPPFEkzvfxucefPrsLp8C7pHZpVG+mK09wzD9gc+CDeuXYRytSuM/uXg0NJMZh6UIAiCILZARl8EQRAE4cqBVOpCyIJwN7AnyGSK7BdOvAYryzehs388Mvx5s98S/uJK+7b3jcZ/wY3HQETgXfG7h2dgZeUWXDr1BuSLZRjedsiZ/h2PD8/gTh22dQ+Gf4Rc/aVBRyQXo9FGZLISiJ6YLtIXOADNddI1F7nWBagifQ1M7wYf0a3c66fyMP9zFUpVqLR3w8VTrwNKKGqr8jpJ0wBtPUNw9fzJhve2Rm2Vp9zeHSqUMMWSGvl8CcZ3P8C3f4xc9v0c52pws26atWnQ4reicFb6sY512YW1nikK94zMxZ8tAkefO/DPyzmm8RhGbXXjQCqFz89mA64zq4KIOiT7S1F/Ig0QBKjmIyGayuVSGOa44AjM1bZCQNp+vtnoK0167I7s/PFXYfnWjfp7XYOT0De2HUrVDjf2ZsLwl9v1SPJqoyfyGvCvOF9vdVzt4oLYHsvMEfXErge05sEMvZgTT1/qzJX7JYU5CpaDIAiCIPghoy+CIAiCyHJwCKELjNo/MAi+gkivwr2j24yXhvAkOoRFSpX2UIEPI7gu95u0Jf7mVQa7eO2f2BH+ZQZfLls2zXO4xpWZ+XZoZp/RohDg3HxZrLRFftY1MAkXT78R82sHxpTpeyVbWNx3NXP4UChWjOTd0TcKre29Cd+yVTfpxofQmpXU/r71d0FG5u+C4y99oUHxiSmY5/J5KFc74ca1S1bLR+A/BzOv5huNvlgf6h1baEwrhVfzmjOd00eehaXFK9DRNxZGhkWvlGC6fCrzE0kK+XYnl5O/bnT6fIR9fBBcsLkv7jwSj4d9wFq/dq8u11ZWkMuu3KtT3dhUekWvVC5cNWqex/Q+QLQPqNoL19sf6bk5jdGX1b0c57iKbHffpsmk51H5vJzN3lIsKcuS7ddmDz0BF0+9AbmWFuganAodJGrBt76B6DltyRhYNMzTr3+t4b2hWc57q6z0B8LxdrdTYFx7XH11kLiXI/kQQRAEYQB1cZQJgiAIwirOnbgtV4v5+sq6Bxl5MAlJDKBBGIK97zEFz+kDj4dGiyovyLA/t+6yo1dMVAkqYepmMtQOvpCiyeqe0ZsYfPVlyVgxkrUEZWwZT5bgDSxSSb5Q3nIRyxic3RcbwRXXpZJ/67LtetjsEZgrQsNtBqb3gG5aO/pgfNcD0gpSmdqz+BgZSKCcTBmpc2Ai/Dfb+zMjsHyxFP57ev9jMGigvxJuU+0agLEd90KlrSeceyb3PASl1naleRRKFRjdfjfMHHgc+sYXBMdiViycHXV8o7kZmCdqaRzYy2WGREVhld73ccCM9xu8qEcOIdzPoQYN7RvgnudE1rnaPm4jPaPN5SBukYW+rZeofrRl3uBY77Ix12QcpPseXtlLU3A+Uiax7Vhzs1yurXsI8oWSUjkuM/wamNoFfePbmxt8kTK+Ipna7TSERAK41zDmOLglX6y/Zn2z2f7OvTGNu96Vk9DP5Pq/53WIfGyKIt7GZiNvOXOvQRAEQTgJRfoiCIIgPEFMQIjm4gRJMcRIEKQ4+FCkKOwSbiuXMIHyuWMv118z4fLcXd/ApSQc7YlQ8/Mhv7Bzcc5RLfhz2it6FmFtrvhyH4sAmV24bozIlM+XYPrAY1Bu6wLU4Kg+Lyskao5u9j4zehjbeS8c+/o/wvLNG6ERWN/t6CXFchW23f0uuHXjGrz41J8pKx/hCAJz3Mi2Q9Da0RtGTGrrHoT23hEwYYRRM95Xi2Jv4tKlcHv/bW3dDJobREzsegCW5+8K+wwzVKzBFJWYoWuupQAnXvkiOEXgnkJjOuz27+6h6fAva+cmb0E+X4rQ2tUfGvAv37pRf4+dDbzBo7aKJyvPGcOam+dxdDhfL0HTiJuXzxyFtbXVusyjo38czh17CXzA68j0SEh1NrM5pjJ+/4BlPtO9n883iZTe0lLwpBHj2hFH+/rC8NzB8L7z2oVTUG7rDl8TKQn0JuatrKDJYzHZ29zhb4BzR18K9/C9o/Phe/IRf7HhaVsSDuDAPoAgCIIgPIGMvgiCIAjCMUSEb9FGImnyRXBoJ5kVIdJdEHl1Y9EmmBLytYunoVCshF7aZaNCeI2SpnF7ovD2oqUZGXpU3/sKm2/ZvNY1NA23lq5De88wFMqttouFBwRbqHTo72uszyzc//6mazd7L/ICFrFRQV2pi9fzt+PoUHqN9JrerJ/kctA9PANu1Q3uPqBf8RP38+tgs5drzIrjvs1RaBGY5wjcNB0zXjZjcwP+odl9cOzFz4cGEYVSK/RNbAdfoPkwRd1FzWUW9vAsIsTZt17Y9J6mqNQOzuGJ647uR0JeZcyhzezBJ+Ds0RfDOa9/atd6lEO8x1ECWz/jmfcszh321jrDgwj5/KyyHUTOE7mWltD5EpvjarB5Lmr9dM2xZmbQIZcDsX7EHDLZJNP7doVj0Yo8wkCWzLnc8NyBhvfK1U6otHXD4tULMUXLcL9yjuy2FckRzdRLpLPLxHyy2zcJgiCIDBh9ffu3f3vs52yh/M3f/M1UaWCC53kIgiAIwgmQX5g0xQlFYcgEScKQamc/uK7gOXPgcVi5dRNy+YKYkMmasjje8eHsnJOhJiIBs5FKRtWfmMFFe++wqtIQyTWu/Lu2x61M/uSN3HdorScIAg+218lELDo9iXa4oj1rZ0HfnwRhhtcsAuatm4tQau2INXJ1TlHYr6Yy3CfxVF7P8CxcPHkElm8tha9Zf2VRYrnwbLwSclQ6emB8530N73UNTcGpI1/FWaXUbVGRxvmbTWXzyLwdWL6FEbqu8W+ADc0dgFK1A5auXYZKRy90Da5Hbe0d3QaXTr8ZOvlytTP42F7G8KTqRPtAZox8FI4Nl+tscs/DcPLVL8Plc8dgdWUZ0OBulXrhdM4XovdyitZv/NsAgiAIgvAGtEZfTz/9dOSmil0A8Wy44tLABO/zEARBEIkTqnNVJCL8klKAwHRRLxNdzND6SIrCblBp64Fyezf4QEuhqD8TD/eXfu6ZfXwmdTih/Ec4j6nLSNpvZBtMkUf1oiE6gGtVtObX8wlFmr7dnyO9XTZ53/X9XXT9yJ6b3a4PgiBECXDveSXmJBaZdXN01qHZ/aECXTTunvtcX8eMgKiKmCL77KF3wJVzx0PZXEffWCojDALXGFB6tg/EIkZ0DUzCxdNvgJPUnpW1ZSbkcAb77KbxMbztELz53Gca3hvbfg+gwdJ4JvkvZzuonOJiIg33jMxteZ/t7eYOfwNcu3B6Sx92HUzrGN61MnCrziPTz1Zbo747MeGURrDchXIrjO+6H24uXoUXn/ozcIogm/OZGahOiY1k4axEEARBYAet0VeUkEfK4zNiASVtugmCIJTNqKITsLtVr6Ds6QR0a/4JGO0/ElGnedtW2ntgau8jtHdqhubpzM5eWkC5OOq7Dk/zonN9qbUdFq+e11ocwk1cOm+67AVSGIfaJTWm7thpL4cHLXoAeBRxuwan4OKp1xve6x6aBtQglovGkqW50vn10MUy+wvGPqS7TKb3vErzE0pqDU19iObRN7YAK7eW4NLpt+DmjavgJvjGlitgm5eY4nrv2LztYqBG5Twi5EDAkejUzRjbcS90DIzDpVNvwKUzb9kuDoGUtp4haO3og+uXz4avK23dofEpgXtdwjjn8KOu7CyCa+fAOMBzTT50QuzgcjvyQxFwso2M0zlse3VR3J6jCTRO53xHty4Jpo2AyLPqmj9oXiIIgiCybPSlYoNOm3yCIAjCDxIOy03WzMQ1UGSd5fwuRuFg/+ROOPPG8+A2+OrV5GMOzx2EfLEMWQbj2CJw0Dexw11vwgS+866lqQbVpYAThhdrxvsKbtnKmgNli/ainCV6hrd6jI6DKcFhqiOmIHz5zFFYXV0OX3f0jUKxtT1VmpFRsRA9N2EQbM2esh9idsbmTyMRBH5YJCUW7Yv9Pfvp3936BQemisysy1l5TpVkvs5oLxt2g1wOOvvHoVCsoDL6InmyGP0TO7TOnS35AkzvfxtcPX8ylIO1dQ+F7/HlAfbI/DwXWTFUny7JdWmLk8125+oDHnaOmGh+NvIVSEBRQdIUIcrRKIKyJeJCGbE+Zkbqzhe5reh4VDx+Zde/zMiVCIIgiGwbfVGkL4IgCIIbFw9JLpbZKGrqZ3B6L+QLZbh28TRcPnu0yTccFWhkCRoqFisH+fiIDPSVnU5Tae+Gwek9cPr152BtbRVcI76tkPc/SaGuWjl6QPuJ1FWYnfnCV5y4nMpIN4ub09u6B6Hc3s2dVktLAYa3HURVea0dvTB315Nw+dwxKJRaQ6/s3Jd1luYas0o4t59RwaM6s5eL1AWRLb8jz03gBeO+BmOZUmEnAo5IqupSMtN2zimMNuBb/06OUiOMd3NAlgn8a/8opWgy5vCWqH06u0OSNvoSINeSh47+MS+UQd1ev0VQ3CYJMixnzsIbcUAu52S9SqHhOR2al+LmUeE+4NhzSxOYf34j41Gy/PjmCmzlwY9oGzLn0d4Q2e91h/rCvw9QQkYekyAIgsBNDhw4kG38U5EGpj+CIAhCFWuZOUziEzZZ8JQluIayNbdvfAEm9zzkrqJwRtDSv33ZcwWezRUxOLlPlp1HFD3qwNRu2PHgN0LX4JSaBAmN+BGZSzVszsqKsojK+VnLfKk0TUuXTJjIRrdO7D/tvSMwuedhoT67/YEPQrVrALBRqnaEyoBdg5OQa2mxXRz3SegTowt3b3mPGdz5ipP7YKIOmxewtrvuPML0qf9a9zaMJi9RSC7nxFkll2uBnpE5pWlmUoHYCQJldwHrThwaqbT1RPyEN1+8ERhc7A8ullkHA1O7wohtePG4nUzvAwLd858u7N2xEh4h0fY+y2C8I2tyaC33be7Nj1nby8U9L7uDENnPVdq6oW9sITovB/tDFhDt81Hflx07Sb/L2pgkCIIgzIJZchUqYDf7U5EGpj+CIAiC4F3TiAxyu90zI1TKynMqRHvfQD/1UJ+p0ZIvQL5YttoahHnihcc0PoiMQPtk9HQPz4QezUXwzaAqcr72af8rE/wh4UcsmlqptaPhMp5d4BOunEfQHybEiXjcYqkKA1N7IKvEyawyI8+QgOrGIaL6cYa69+TeR6BQqgj/Li/xG8IfWLTf1o6++utSpR06B8atlokgVKNWsdPSwkIOi2PqxrDsysHu5MT9deRezrPNnOLHKRQrKB0yxZORtla2LllwbIC4KZw+oztcdCEi2qijbxTGdtzLnUyxXIXZu55cv1c3UXcWl0qn+zVCsuLAlCAIgsCJmLaFQb7/+78fRRoEQRCEK4hHfiKIO9DBnCC8g+Z5L9rBiQtjhcR5lIui3N5NXsNSYmpfmNSfaX+aom4d2Mtlxbufnqit6pMkHFCwuf3VfLEEMwffDlfOHQ/nyfaeETjz5vNqikkQCpm/9z1S0SG6BuSigxEWzwwB8ud0cN104dyXlb1c1Fo9On84NN4RhSkJRxmKudDuafY3WTnfRTs2WP8Pc/wwte9RuHr+JKyuLofG+1fOHkubKV6wlU2gH7I+69ywzBA25xTdebsg0yGSsNeG1H/E9qxtPUPcdZvL5WFy78OZ2dP4gdvzKY6+hqEMhAzjOx8QciJXbutC0ufUkRm5hWgdRFXL7fZnv1G6n/CsXxEEQRC4IKMvgiAIgnCNyHtUU4fHpHwUlsPUI7ktA80Esv17cGZvdMh2pQIXE53IjoAI+6WZ6wJZ1f3TyfpwscxpiHjccrUT+iZ2CCc3OL0HLp85mr5chFLK1S64ce3ilrZSvlXKqY+EZGJPqXyuIs00PJic0j1ZPzJ/GcvRjPlCCbqHpgEnivuhJ/26Du6jhNr9u4TBVxDkwv0fM2qM+IJwmjG5KUyrSeq+9V1iHWrXO1BdCMOMe8a230OjyUUC9ZHpeaJ72VhL1EfjpfUwayg/z7nUhZyVxQg6EbURBYdQTtQak6V2ZM86MLmL+/uzd70jvLNwjsilXc3Y943EesnaOQjZ45KshXAB886Y1A7U4W0H4fjLz9RfV9p7YPHK+ejssU0UBEEQRKYQv4kkCIIgCCKT3BEqrXknlMJs1IK5bDpQ2U+YYkn/xE5wDlWCMbxDjsgSzl7+m2fu8DsjPaBHwS5pWzv7tZWJkKdnZBbyhXL9dbFchY7+ZCU3UVifKVXauTy22vCkj3n/Z3wvR3VBZKAPqL3wjEvL37qkS+NsMbX3Eai0dytNk9ZejMTPWeXWRkVKtm9Ml1vg4Jrm7rmR5u1oxnbcC/ninTMRgR+pM6PMfODxvtjJddjFMiN63pGFwx4dTyLDIOjNNSCVJZ76lplfjO1T3N3K+UdMP2F7MxbZhsgAKoa+zJzD/Ru8DoSdPN9tiFZESFaha3UXdW5z7DHMEV8xPaPbYGTboTC6ec/IXCivjbvry5r+FkEQBIELkqAQBEEQRAYvAWsH0bSKJN5cbJJRgpd0D8+42R8jiHwW3c9I48Mx/OnzWUN0vhqc3rsezZD9LnJ+UFM2QpxCqRVmDr4dBqZ2h+00c+DtkC+WtFTl6PZ7IJ9fT7tYqsLI3KF0CTrYb2wYtBFRqPec7NylqyhB1tc0DysgDciqw6fzlDIU1QlzUBJlqK0FakqFVam2MvundjakyfaPWYP2ci7InkyXBCGW6mB49gC4uC/QuYf3/nxgDapXUZhSaFxEYtpLJ9PSUoDWzj5ATcK9zOY9PYtUnTXcUH7O9hzH7hK6hqaysd660B01QecqBURFBST5mHdnVr7x4ug8uBnt/XfN+T7VOzYP0/sfg9GFw+S8hiAIgkBN3nYBCIIgCILgpC54UHcoZx69mEfhG9cvNRjKXDjxGgrhhrMCZSIFGtqcBLHuQW2Wou4UtgOBem3LtbQoSYfQR6m1HQan92gfqNWuftj+wAfg5o1rUKy06buARD03I75UyphBGl2AK1R2Qj3mPCozOscMftWhG8p/CFDa7H71oSystV2DU1CudsH1y+egtaM3lNUdfeEfbReLwOKIh7DAGpr9L4vq3Dk4CaafPVMy+SjFXtk6QDYnZKotDdcJW7+ZUqhXbSYY6Et2r88cFy0vL4X/HprdD7mcWRln3LwtM6cPTu2BxUvnYGXlVth+7Jma7eV468uEXEV5Hi7sx+0EsjNOdNs60EYESvondsCVc8cb3itXO+HGtTu6JRjJlIzahTlYKRlq2yhY/1bR7hnpO6rnA1/OigRBEISbkNEXQRAEQVjF/sFucu/DcPylL8DStcvQ3jcC/ZO7Ioy+amW1f/jPlqDu9n+z8syRFy86nl9lmvbaJ1OX9836QYLXtdH5w3Dspc9vicBza+k6oCCyb8u1a6b6A0F4R4pISLlcaGRGEISLZHvtjjrbaT3zIT5bZeqsSxjFyDkhI8oiOgidMrV1qUnMhKJwJteurDxzVp4Tb12yiDF9Y9thdWUZ2roHoaVQBCfJ6l7O5THkcNFtwOQwG14ZyNCfBpq/9z1w/co5KLV2QLFcBVepnd1YpLL5+94Li5fPQ6m6/kxkwI8Pp+dnrPh+/gsyUviE9aXS0QvVzn64dulMPZohi3R5/OVn5EuncE1TPbaDYOP67hge7RWMz2Gu1V3k/KvYoYWLiESnlmz3JEN+ku8TBEEQOiGjL4IgCIJIQffQNJ76kzyrs4gQU/serb9evrXuYU89eIUJfOHbCSK2FyGsHjVjzvXx0TU0BZfOvgVXz58MX/eNb4el65eVG32hiWLgmmA6Ccf7XybaSIuyDGF3bnawjzowVWTqYg279/2MMTC120Ku1DZUd4SKYSI1z9Hw04+j+3nUSicunPsQVx9hhug5Wb2iaXvvMLgCprkFRVkQFEGUzJ2rAuT1hqEfa35WZsza3uPOPMcDM4JAOXcb6k9u3yHhG3NEBvcPiGHRGCf3PgKXzx6FlVtL0NE3BkvXr0R824O69OARvCfw7F7fAzoHTEanJgiCIIhsQ0ZfBEEQBCFIudoVGgxUuwdgaO6AsfqrCR5I+KindgkcZO6S2QGqXf2o+0GS0k2uJQ9Tex+Fm4tXINdSgEKpAm88+3eKSkoQNgmcnYeZJ8jzx19peK97aKbuLdJ37O3l/N3v0AWdA5AChZWIVkzxrHt4Bkwh9yx+7v+l19PARcukmO84rfxnDjoHy1MoViBfqihsDcIGtJdDRKBugXJ2bsvyvnXNTB040Teki4js2ZAVJ3u41QBOjE2CkCXL63vGyLQexW0ZTGQdNHk/au7fmEZLvtDgCHlpMcroCxMBakcPKsnaeTqyz4q2EU+14W12q/Mfq+tav2P3vUz3ws3nVD1PEARBEIR+yOiLIAiCIATZdvc7YXV1JfRslBqE58HIQypvWVUKEyLTUlxxqGVhtwW0GDuLDrIsjL9NW88wnDv2csN7pdaO6LrRXGUsMpbrMOFfWIcuIasn7NkYytplhWqwrh29Y/Nw+cxb9eii4aVAudV2sYgkwxSV/Un1XOWCUQHO4ageR9chjF6pZcdcZ/84FCvt4SrKDGqL5Sp4h5vdjPAE7PvtaJmO3nKz89bK8k0wAYuCs7a2Gv57YHo3qjbBVBYVRCsLKs4I3zLszNkqi8/J+qXOrVulvSd0ILS6slx/r61nyNl9rn4oKm9DbVA/ySQq292ITEaUqOdzYP0mosjeZi7r8zNGuZcuWDTBUqW9wTCptaPPWt0MTO3Wmr5XYBinGMqAuTxe4e682Du+AGffeqHxvdFt0T+I3MrJ1cHc3e+E65fOhvcO1e5BQI+qcXR7vUiuN4T7eYIgCMIbyOiLIAiCICRQYvCVHbkDesioAD9ahCBI5SpMkaRYboObN67W3xua2Wcs/1BZ79ZNCHI5GJzeA+W2LkAN0na0h3sVkrULVx3zmWt1WK52wra73wVXL5yEQqkKrZ3Rl66E42jom7lcHlZX7yhg1hQzCRzQHAfhPi6qdkxQrLTB0Kz+veNmBZoGA3uRR3VrCdMOXf4SLjA6fxiOvfT5hn7bM7oNzrzxnJH82T5y8fI5KLd3h/tKUzAjECLDQk8LETsJe3L/wak9cOLVL9Vf90/sjP6Be8EAImGyQLdxsNIRkZ19aFaeM94A4fTrX2t4j90D3LxxzVqZiA0YMCLM4laOyNZ0ObztILz5tc+sO/FtycPQ7H4r5WCyMubwzrdIv6kdCKtE8Tzn5Pkua3OwojZy2RiWzSvnj79Sd1TC7syYs01miGWCQqkVekZmjeRFEARBEEQjdEtFEARBEEQjuoRZDsrICPOQovC6Msn0gcfgzBvPw/LNReganIKO/jG4ce1SZK2ppH9yZ5inVeF25H1Bsw8QXS4QBMFNvliuzzXZg+atNIzMH4KjL/xj/TW7uMfcl1y+PCTkaOsehHyhDMu3btTfG5jc5V11sug6R59/KnSgwfZoPJ6LhfaWLipZqCg/tudGVhxvURlNyYBhSvfwDCxePQ8XT74OLfkiDM3th0KpAqYotbaHfzqpdg3AtYunG94bXTgMLhDd1jSgCa4ORNXEot5PbA89li9duwStXf2hB3Pf9vXM6dTV8yfrr4ulKhRkIsTSlNMAq8N8oVSPbM6otCF3UkLDPnN0D03DheOvwq2bi+FrZkTf1jMcKg9nAltr3Zq/azQ51iTE0N+X23tHYPv9HwjvVZljy5Z8wVg52b5xau+j4RzLIozlWsw5EnZmLCKez6LBX7dOGqxJQLok63Kx6f2PwbmjL4V9s3d0PjQyjTb68s04UswoVbWhamIftF09BEEQhNeQ0RdBEARBeCOUwHF6dNIjJOJLe98UChJxsPvogF0KbFYoEx1baQRu9oV1usH/fNJzKf5HE8PHKdC3NlKJj+3tJAHq/WLX0HToffriySOQL1bCiEYthaLS8hEpoDkujJAwufdhOP7i5+Hm4tXQeL9/clfd8+bWOnOz0pixJYuyt3j5PFQ6ehojfSE/PyrN09H2I+y3uZOykw3z3OjC3TC87RAEQc7L8yMz4C0UK3Dx9Bvh6/aeYejoG5dKS1n9WJEPmWlbt2Vf/vV/Yp1Ke3f4lzSWXZ3Ph2cPwJErnw4dFTAHVCMLdzkzn6ssp2pDWZYe2/ufeOWLt1Nhr3eAPTLkdIETV/q5TkV+Fh165uDb4fKZoxC0tEBn/4SkQYRbMKP+LPUZo9jcy6XO2uV9KBEHkxdXu/qtjPFStSP8cwOJ53dhXlReRAeemRDDaTkEQGtHL7TuvM92MdwgyKiBLUEQBOElZPRFEARBEM6wZlWQhuoCG1FRjJHFZya8vnRkisFL1y/XX3cOTEil47rSjer2c+W5CdV9J8pLGfUHHq+fBG+nw+N1cXB6T/jnFlkZj/ifkynx1zya12Bef1Vfus4d/oZQib02X6+uRhh9uVuV4X6umbGXirGf1T1NqaI3epEO4trKbUMOIglmJOAtQQBjO+8Loxiura1CqdpZn8/xn8FNlA97HagHf7sjfP6M1xl22P534f73hdHM2H6ORVCWae+s7tni6BtfCM8Di1cvQLWzX/lZg8ABl7In4uHBDL9YVMN04xnvXp8Z7DMHJcvL61H3Nkanxj5vYS9fM0j5mSCQsKZwftF4FxD1PZn5T8ecydaQK+dP1F+z/ZzJiG3iz2dXh8g4kT4bxJ6fR2bp3JocpTMRVTd4t3JGMN6+jnUngiAIAic52wUgCIIgCIJPAblvQqFHyIwf4DdDimiY0CHt8ESC4slj1BjfeX9dqbW1ow+G5w76+7AG2aik4w5Za++sPS8+Wtt7w6gU7LJudL4xqqLXXYB3/0dd1FLFE+I1K1e3LDLNZgYmd2lpAB6FWdWXi86dbSQUEpy7cBeAeYKutPU0vNc/udNaeQgz+NynXYbN4WxMMmV9fgMIakspnFi7stG2WTduy9rczox3WeRWblmSUP+I+W7qqgk0pauO1s4+6B3dRgZfWVm7DMydNucUF9cGFuFn+sBj0DU4Gd63ju9+IIzkSmjCha2cj2V3dr+NqSx+USi3rv9DxIDfwTleNyyyOYsOye6Q2P312I57bReJaIB0Seyjf97I5baeUfvGFiSzNzvPuSALIAiCINzFRY1AgiAIgvCHCEFaz8gcLF46V/dCx5STq50DhooUaLpzxXy4dUHAjLn+DPQ/DY+Pu0+K4OZzVNq7Yf7e98DqyrIeQyXXLyoky985MAknXv5igyJ8z/AsuIsL8zPhGrN3vQNWV1f8jk6BHdfnaAmycoHuwnN29I+FCpjnj78aXt4PTO2CSkejkY0O/Nl7GiLD1TW55yE48coXYenaZWjrHYbBKXuRDanfEkTzkeEkjhYbvzdxw+UgCGw4sP/39VyjGtr3Eb6tacxwnzmeEwLD2HfyjpXwkyDTEd6k9wKGqqVvfDucfeuF+utiuRoa86vC9TknTfmZ8dzMgcdhbXUVghzFUvAWVMawxGZKre1QqnbCxVOvh68LxQr0jM5RRREEQRCZB63R18c+9jHIGt///d9vuwgEQRAEokMsU0i+dOYtaMkXoLN/oh423pqQzcJlR5RAVXkdkEyHIDaMB7MDopnBl0njP/uofah8sQR9kzvgzBvP14Wg7PKH8Bwvx4ZeMBp8uX6RShCuwPYZI/N3weDMvvDi3vp84OPQjzw7+vewOuZuplwysftB8AM6bKfDvzFDONZtoo7mQcajVHoNzTuEmn7gjtGUK+XU2Q5+14EusrV2edZHrD2OH31ms6FJ+N4E3T8QGZ5TLMmdBqf3wsqtJbhy7jgUK+0wunDY2P4r+g438KoLuGLwla09ibpzho+Gq+Jyatx1MLb9HugenoHlm4vQ1j0E+WI5+TkDDXOWKM6chQmCIAgXQW305Y5AWA1k9EUQBEFspFhpg/6JHdyVom7dTJtOttZvwgG86ZJZMoQSIGNnBh7YZU/XwCTcvHENqp390FIo2i4SsYGsnXMJ94y7ossnUW7cj2oG3PdmmZ77mHMNgiAUgHB8OzefZasKCUl8VEhyiag9Mva9PWoyppzoKzbGAMazhRdkpVqV9Z87c1hW14isKZkT67DI6RdOvAYryzfD1/l8CXqGZ6l6HDtL5HJoVQUJznMgcxY8tuNev9YfmTXa0Uflhva9AlUl2Bk2Dq3IKzGVHWzNn34UmDO8bOseBH/wfcIiCIIgTID+JJcVYREJqAmCINJRqrR7Nv/TgU8XLS0FWFm51fBepaMX0HJ7L5SZvYKjz5mVPasvuDCe0pSR/bbc1hX+uQ6NLYIgCMdwYI11p26yUZe+nold2G9ykfY56Jikr24JeyBvOicV/FyQqWRlzGbkMY2QlT6DCCfnP4JAZfxNTueE6sV0OyByWDq9/zG4cOLV8HXPyFwYrdpVsupYodzeDflCCZZvLdkuCmF7epGKgOOAA2HaixOJeDj/495CEARBEAShgJwLF+S+/xEEQRBiDEzt3mLwxYSTmSFwNH2pNU99qO3hbQcbXpdaO6C1s086PcIUjW3OLpL4f2k4ZLsmovaNWVdoyPrzewE1oYIqpEp0CkvrD7cSg2PrI4ELmo/kao1wGGq+hrP1ZsodPspqcDd6LtdiuwjZJvBrjTa2rjuw/8zKXR7t5QhhGaTU0EA0nhAVhXALclTlMo4OfER7kUp7N4zM3xX++eB4LhOsbV3rR+YPQxCgVxnMPLQ/J5LJmANhRWRpL+fdPGLrjjVDfYYgCILAC/oTHFswff4jCIIgxBmY3AV949tDY6/23hGY3PsICTEQgV2g1D08A6Pzh6Gtewh6hmdhet/bUJc5a/sFXqETG/uYIvyh7EMYy+RJm3gnHCUIgtAGzZdZ9RhMEPwE4nu5tPs8mpq0MDS7P9FpD6FwHESllMtB7+i2hvf6J3ZQ1RNoGJrZt+W9wZm94CwZl70QGsiYLJjAQK3PZWM+UybX3ThWPak6cjqHQ+6P8q7JZzK87nYOjMOOBz4IPSOztotCqJYtB5rnPVTTFKrCWKW9Z2TLe5W2HvAOznUyWSa5ZnT9zxcr4IseEob9khC0tyIIgiA8Ar3RF4boWjrTIgiCIEBKgWV47gDM3/semNr7CJRa8Rh++IBzh3QJekbnYHr/22B0+91QKLeCG/jfLiKPmWvJw/SBx2Fweo/uEuEnU3tKVR6FCVNkzXBVJczA3cY8UCjZXBcz3l+Qz+ckw0hdgZDt58zI8/s4tlQWD/mjqiVTD1t3zMH2LyzSFDuvDU7vhWpnP2SVZkZwphieOxjKzboGJmFk2yEYbGJkQ3gsN0O+rnQNTkGxXK2/rnYNQGtnH7jAZoNKpiCLfh0XJep5ED1n79h8YtsYBVHd2AF/nyEIgsAAyckJjLQUitDa0Xwv7t0+1xDkACwZ6lt66BqahpaWgj8OVlLSPTQNhRhDK53r8uYzK3NkbCYyJs3bInWgay6KdKRA6ypBEAShgDwgx7TwI2mBrZVn4/ea/Saq3CTMIQiC0Ed25lg7h3U6hNo3diQaKZQqoZemaxfPwNULJ2MqjwRczkFtRvgMZ/9mRq3LNxfh4qk3UmQlPv8xY2h20Xv98lk5AzSC4CCL+8ogoL0c4eg4WdNcdiSPSaSHtTszNGLGXqxdmfGXl3D22b6xBbh14xpcOv0mLN9aAtPyA9q/EXo7mfzkzc4bs4eehCvnj4cGou09w87MF0NzB0Lv3OysxM5MfRMZPidZ3Kf0jMzB+WOvwMrKrfB1LpdvaghmjMzcSWTMOBHLXpxQD2fbMsP54y8/YyQv9PjyHNjO2gRhFNqvuIp2pyMyEcYlykR6DvqHd75Ygqn9b4PTr38NVpdvQffwDLT3DoNv8Pa/YqUNZg6+HS6deQtOvvplo+c4FuF85dYSXDl3AkqtHaEjaqv7j4zvfdA7byIIgiAIH4y+Dh8+rC3tM2fOwOuvvx5uqJoZcW02GqhUKtDe3g5tbW3hXy6Xg6tXrzb8bWRjFC+WVi2f8OJ7eBhGR0e1PRtBEARBeHXoj7q/zeDBvK17MFSCWV1dqb/X2T8O/iHh+QZBd8iO0SlGKKKI68TO6RkeW0wBcnzn/TA8dwi+/tk/Mpr3xO4H4cQrX4Slq5egrWfIoDdABBN6BugcmIAr5443vNfWPWStPN5zex5jl4wskt6tpev1j6je1dStK5BimXCNgRu4Uk5z5FrcMN7QDVNoGpm/C4a3HYKv/e3/ip3DaH4glPY9lVF3NMkImTIa87rtGkwuNzC1C3yGdz5iMsnjL37eSiQDtq+e3v8YnDv2UiiPY1G+mCIdYWev4voa5v09A0/7rPnRlqrpHpkNjXwvnX4L1tZWE+4DqO4Iwh5uyYZQQvO/h/WS/t5UZl9Q+w1zaHjqyLMNnw1M7hZOjxCntaMXpvY+QlW34ezYP7GjudGXxvFeu9+t6QqbwlheTs+PG/HlOQiCIIgsgdbo67d/+7e1pPt7v/d78LM/+7P1jc5mYy/2+u6774YHHngAtm/fHv4NDg7Gpnn+/Hl44YUXwr+nn34aPvOZz8Dy8nKD8VfN8OvcuXPwzd/8zfC93/u9ofEYQRAEQdiBhOCuwYRDLKrVyde+Er5uaSlA38QO8A0pIXJWBDLeCNAIojmbo0sx2vv8c5ghPGcF5ucEFklxYtcD2tLPAjaUHxO5rZDU0TcGxVIVbi5dq0eg2qi8qvJiiJTGGuuCKf+/+bXPhApj+XwJhmb3gW9Qm3uIbofCGjJgiujMi+vGPHzcUxB2EO2zUfMiyr0CwUfMXikz8gkCsm5s31IohpHOzrz5dSv7wkpHD4x13Kstfb9QuN4ItGncfJi2b9Bcq+/+gdhQH7mWUFl3ZP4WPP/3f9CkarKzl4scs445YiGIzY41GYVyNRMVE+e4ktZVQpiEvVz30AxcOHEEbt64WpfTldu6tFS077Jonucj+RJe8PfPjOzljLcD9nYnCIIgXCYz0rubN2/Cj/7oj8Jf/MVfhK83WtOzf/f398OHP/xheM973pNo5LWZnp4euP/++8O/7/qu74KLFy+G+fzWb/1WPaIYg/13aWkJPvrRj8LnPvc5+NjHPhZGDiMIgiAITMKB1OmjF164S//kTmjvHYGlxStQ7eyHfLFsu0g4MNznSpX2sA02R2KzBX6BoTgil0yRz6+4WvKFktoEiS0w739HvvLp+mtm3Mouh7IOM8jJDtm4YLA9b7fkCzB94DE4+9YLsHLrJnQNT0O1a8BwKfxbu3jo6BuFHQ9+IyxduwTltu5MR8MpVtoheyiMvIIdFc+UIonesfkwomFNmatndM78Xs7DZsW6rjlLNrY9hEpuK2qSLIjAwuDMvtChxIlXvrTFgQsP3ign+rgO+vhMgmRRAX5420F4/av/v4b3RhfuhqzDZCiiBhQ6yBfoLsiX+S/R8MHEHJy9Ka6+dznxyhfrr7uHZyLHeKaQ7Q9r8lGxfWZ47gD4Ib+RnygK5VaYu+tJuHrhVOgsorWzj6vdyRkdoblrWt3LET7IoqnPEARBEPbx+zR1G2Zo9Z3f+Z2hIRbbtNUMvth/8/l8GHXrr/7qr+C7v/u7hQ2+mtHV1QX/5J/8E/i///f/wg/90A9BtXrHO0wt36eeegq+5Vu+BS5cuJA6P4IgCMIcI9sOUXVjuFzN0oXEJoETuxBiHrFIycceQ7P7G14zw5SuoWlAh4fCSmWXQUle6IYbDY3K1U4oVshZg27aeoZCD7rM+IQpjk3tfxvki2RsR6ild3QbVWlocNMWRp0a33U/tPcMU51oZLMyK1MmYRfd/hp88e3fu4emM2bUKkPg3R6waZtruKRle4m5w98Qnt+n9j4Cw1vO8RTRkBcy6GpaKaAGvGOVwA2TP2yWlzEnQXWM6AlnSTBHxK0RbF/bNTRFlaQQ9cZwlsZr4Mi+hQzc6n2u2jUIbd1D9aqptPeE9xCEfN+TXS8HZ/Y2vO4ZmfVGhtA3tgBZhzkBKlcbDb+GZ/EaifhE3/gCTOx6IBxTTF4wOn/YdpGQYHbR3jzHNbt3dRU2ttsQyNqjnWUGSvZP0evbnfeZsVfnwHjoNJVF2UsqB0EYh0OG7p5cNDDyHJjrRahsuh4Db/UQBEEQHuB9pC9mYPVv/s2/gWeeeSZ8vTG618jICPzKr/wKbN++XUvezKCMGZK9853vhH/5L/8lvPDCC2H+NcOvV155Bf75P//nYUSwUomUGQmCIDDDlNM6Byage2QWsIJFaTJK0If58E8gQka4bLhvdfSPhZdB50+8GkYMGJjabSRyAI8Q3Xeat7X652eXrCz6zdXzJ0ODr7Ed9yrPg9jAhnbtGpwM/7wmUD/HRX8nO/MDD4ViBXoRK5fgUZ7FUg7CSTi7D7v4Z0aHJ156Bm7dXIQsg2fs68fkvr3U2hH+EQRWyKOwHK0dfYBW7sX+Z8BrPZM/jCzcBcdfegbW1lbDOukf35H4uyytN4RZqG8Bcln4mnMG/MkFMZ9lFsYnMyqa3PswXL90Nlxfqp0D3hgaaRmbGp1t9E/sDGVY1y6eDo3vekbmwHWYjJ0ZQjQz9tDCGu4+OLXvUTj12ldg6fqV8L6pd3zBoXXFbdhdP/sj7OkXMKcBzMj46oWT4etSpR26Bqecr0PmYI2NZ8zR41AYQxBEatR0Tm8iTxMEQRAEkSm8N/r67d/+bfjUpz7VcHhhF6pjY2PhZ8PD+r1sMOOy3/md34Hv+I7vgOeee67B8OurX/0q/MIv/AL86I/+qPZyEARBEPLsePAbUQvpQkjgT2SWQM7TZoox0zM6F/4RmgjSXz6lUTpiiuiTex6qRwhuUhDUUTQI36DbNRVM7nkYqp394fj2ok1tzEEq95rUrQmmaNM/HkZ2PPHKF+Hc0Zf8rxND/T6H+Nxqai+HAdfLTxiG5Dl81QRBY0SrDLcjU/5mipHLN280iUpN8w9hGOpySsnnS3iNW4UMW9V2jFyL9yoN9tggX2CROFhEDsKuojCTR3cPz4R/vtTBtrvfBWhAsPculCqRjuaMnCXpaoNo7HRGYWsNk9Vfu3gKVpZvQVvPkBHHmqKIjMXwzhix4+B4uZxMpNSU5TDS6ezP9caeg+6r5chUvUX0I2erILAalZcgCIIgbIIjJIgmTp06Bf/1v/7XLQZf5XIZfvmXf9mIwVeNtra2MKpYb29v/b2a4RczCGPGYARBEAReAZh5g681tJ64qk0u/SptPWAWOoD7ioxwJTMeDqOeMyOPH+1RWGd+GapcAnGor6i3qX+K0NE3isTgKwYscw6SYniDsxdncojOTWytzeUyrsCpeMyh9gKPZZ4jBOBpM2pXbcolNGbqDEzugumDj4d7OqyYiPK12QBiq8GXIWjYZ4746ITUIVTSN5EcuU+MQKucTIdsom98+5b3eoYNK1JTt/YYVZG+NKRJqEOl3yLakxOeY+OegUWSbO8dga7BSZQGX6K4clejXZckdTW4UY+EXXSsy96s9Z48RrIMIgYEbRm5JiAoG0EQBOE+Xht9ffzjH4fFxcX665qn/u/5nu+BhYXkEOmqGRwchB/6oR/asjFZXV2FX/3VXzVeHoIgCMIvdAsjautX//iOLQfVwdm9GwqiMFM6+GYvtDy1ubtCNGz9g/oSEhD3J0fgu4gjQbEPeHOxRGQbGa+JGen6wgoggZxRBPMYjpWmisKRjg2iK4DmS8IHMnfWT8ngzN4waqsvkaldgeZbIl0/ycgmTyHMsKnc1gVoUXZmjU+nd3QbFMvVBiOwQrlVUd4Zg4ahRmgvhxppPWEEg8ZEGRA8JmGYuHs/DP2eMEOztpZyOkh9hsgKnvR1Tx7DaETDZtD2nyAIgkCAt650b968Cb//+7+/RTDT2toK3/md32mtXO9///vhYx/7GBw7dqwh2tcnPvGJMDIZMwwjCIIgiNiDp4RSnEpK1Q4Y23kfnHn9OVhdXYbesQVo7+GJnpkhaQKhnrj+ndj3/e57rniQEyPgFtT5+fzZAsVluq/Pm62qdYcAR3QII2RsfBNy0FqefpwMze6Hzv5xexFfONc6Hw0iIqH5jxBQuqN5ECFr7p5roo1pTZeEILIO/6DrHp4JDZtKrR045pKIIuSCFu7vpnkOtqfddve74NqlM1AotYb1QhDKUKXzyRW1VU1ehFrindJgbjTMZSO8BPV4IGRAI5dT2bd8P//68hwIWVtbBd/wSb4Ye56M3crpdyBcKCY43aP1kyAIgtAIkh29ep566im4du3alihfjz32GFSrd7yDmYaV4b3vfe8WQSB7/alPfcpauQiCIAj3tUtMKgp3DU7CtnveBQv3vQ/6xjdHz/RHmIAC8hiTKQEWBnIteP1CqFJ8oT5DuERUv6foEP6B5dI1ao5UOXdmax6mzVwidAmWmtbOPjQGX9Y8bRKEIVAo4xMowbKXMwONg+yxpr7v0za5TmtHH5SrnfjXGIPlY/JJ5mjOXr0gbwuFkHxJAdnpLt63Sbb2c37AZYBJxM718uus53Xv8twe6Ywp4JfTx/SLSGdPTlcakTkapjC/+27U2JQ5B1TaewDz+h7k+B2VyMKiULMz/EZ6x+bVZkIQBEEQEXgrtfjc5z7X9P17773XeFl4yxBVZoIgCILI6mVEpNIx9gtwQhqptpXuDn73IxlhWC7XAh19o4CVpsatdLlAZJWNY9zv6Swz+LiXI7IH7dNjK0dbnTmjVOHjOc7HZxKFqkAKUk50uV9nKZohDXCCuomRkebIfqJ5Od02yHKl7gmEkKEJbiRtUfzczxFEErQWZgbs+x7kxcMKyZck6813w1UNtLQUYHjuoO1ixN6FiJzv0pwFJ3Y/CJ0DE2FUambwNTSzPzk/muQIgiAIBeB145+Sl156qen7s7OzxsuSVAa2iWCb8KgyEwRBEFkk5qBKhlAEwT1eeD52hUjBk8LoV8PbDqKI9KX7WQmCwElWLqdkvGcaRWE56BKD4OlXpGjpIwIGERamPupzRMoepKgCkw34aR21iMy2FMtejiCMjwnq+5nZyzVzxkQgh2N8ZkMUU4f2V4Q1+URG5J6Ee5CMREklggs0l8u5UXZhvRk6oxBZXJc16lfMHXoSStUOFHokGAz4C6UKTOx6IKIMbs+rBEEQBG5wr8QpeP3115suol1dXWCbzs7Opu8fPXo0VHKjxZ8gCIKQumkTODzqVKqONtbQlqXneChwikW8o9DeKT2Tex4Ow9DniyXwRlBHcw7hOVkxkMoSuhXoUPWZDF16oKp3I8i0bTb6g+ie1UflhGZ1QE5NiMySteUhA/M5/r2cf+sKgQOSBUdVDHhHM7mclFzWgboheTMhsi5Hn90c6OxEQztiMG6ldZXQQtxZIkNy2qygQtbmo1zSRfjaIWMCJkVzVkOkr6xu5QTkTJWOHnACxGsaRZcjCIIgVGBfaqGJK1euoBXSbizDRoHgysoKXLt2zVKpCIIgCNcx5bWEILRCHs6t0NrRi97gi0HzHEFsJFm5hC7m3AL9HGdfnEK4ANl8Gao05ERelKt6Vg/rjHAGVfcLdNGPHBkbBgR3TyEGyoHmWQkkUH/IDCJjn+YJwncy5+AlA9C8RRBE46TgXX0oM2xNGU2IzpNEqu6namzSVs7PqK1N5jlVfYZkuQRBEAQGkGsUyXP9+vWm71+8eBFsc+nSpcjPyOiLIAiCSCRKjobAA51y/JOnEjpIECJHCnKc61+BRmkltsrg90IXfTkQuPO4RLZQpUBAFxKoKZRaoz+0FB2CILBCBqoZN251XLGO+i/tSdQoCrs9DryMbinxKEHQEvUBOIvDRSfUEqvcRP0kOxENsTsqkYI6cOYETC6vy4RWSC5HZJXIdT8ra2SG1gWhvZyFaslMnyOQkLE9cEbw88xKEARBEHfwdqUrFApN33/ttdfANq+++qpwuYlswIwVT506BadPn4bLly/D6uqq7SIRhGNk+2COxysSlnL4gVdKU5oEulkXAuMZ+/ohQR1B3IE8iuGhb3x7w+tiuQ0q7T1a53Nn2j9DaxSRYl9G/SQ9jtSh2L7VjWeKIkt7dELjWZ/6kVNgGfdZl5EQ5iFZTXYQaWuai3CAZW3ykQa5DNWzF+BZz2jcEtTnCHORuGT2Cqj2eZF+QRGVkUAvl0PVp4lUezk6/xAEQRC+kwdPqVarcOPGjS3v/+M//iN88IMfBJs8/fTTkZ+1tbUZLQthnqtXr8JXvvIVePnll+Gll14K//vWW2+F79+6davhuy0tLdDb2wsTExOwsLAA+/btg/vvvx/6+/up6QiCQHwh0RylwhIS1PmLTNtmRQ6n8zmx1aGQkB5b4YkGHLFLcXI93KgoTOuiVQan98DKrSW4cvY4lKodMLJwWO6yFMleji5FCON9jtbyiIoJvGvTZp7Tqf19gDZ8BOHCvE17PCIVGXNKpQIf9zhN5xELcx/NZ2rJmtM5ZVC1eQf2ucXHdYVAgmTXovXDPXTPc/zp03wmUKnJ38nansRg93Ft7Y2O2OzWcySRleckCIIgiEwZfTEjmbNnz9YXdPZfduj85Cc/GUZTam1ttVa2P/uzP2soVw1myFMsFq2Vi9DD0tISfOELX4Cnnnoq/HvuuedgZWWF67fseyzqF/v7/7N3H9BxVGfDxx/tqvfei2W5V4yxcaH3FiAQQk3ABMNLJ+GjBQIJKRCS0OIECBAghFAcCLyhhJqAARtMTLeNwRg3XGS5yLJlySrfuZN3FbWdbbM7M3f+v3P22Du7mrk7Oztz57n3ufe9996TRx55xFg+adIkOeGEE+S4446TtLQ0vjogzpx3cxj+6EyWCrfxz9JiOG3fwzZJMRwnjvsNW033z2feUTj4KdE7+wXeFNZMT/wOEsLnT5bq0dNiX1Hcv69YW/84ryKcw4QEflh0fMT1lMOAJHCA3iMKU2fT937VUonvyeW2Dk5wWIcrjh/txP88ZwNOc4jjdZk6nts6Csf3HBd2ggznJST4+KPOFmS/aHifPvh5LugonHEuDWAzBtbUjlMG1gQAIJ60TfoaMWKELFq0qOcGNnBDpmZTevjhh+W8886zpVzPPvusrFy5sicJrfe/aiYn6JPoNW/ePHnhhRfktddeMxINrfT+++8bjzvuuEPOPfdcOeOMM4xZwQB4m47BR3hxeKbIcezHfujEs1GnsLJBdjZv6rMsv7QuqnURrIMWrPq5cXnQjs/nnXs6T127PTZquptnucPgfP4Uy3bNoN91NKcDV5xCXFFIRMOia1hYCfxwJps6CoeN0w9cc9BxHtTh/Bc0pmhyWNC5HDrEI5jhRj+OqcsBCcax7yFWxeRjXU3QPDPrgoReqm8SX2K//Ze3kzjjfj3zWFsfAMCZtI1czJw5c8CyQILV3XffLcuXL094mZqamuTmm28OGkycPn16wssE6/32t781vssLL7zQSPKzOuGrNzWb3S9+8Qs58cQTbTmmATiLl4KyXgrUeU0iO4G77TiKphOFE+SW1EhqWlafpIbimlFRfdbBjg87vkdPJSvAwXrNDuH0EwHCO4c45tzilHIA5uhklxhFVcMHLCupHRNlQgTnFx3xW4x6x1n7RcB2Ws6AEwznc/Q5Htgdnk5u1fV8oOvngoWoyzlFRnbBgGXZRRURr8dTdTl4UHeCEnDgZIPG5YI2VfD9w6k4Nr0gJT0z8j/ivAUA8ABtIxczZsyQzMzMPslegf+3trbKBRdcYCTMJIpK/FFJQIFt9p59LFCuQw89NGHlQfz8+9//lh07diR0Fy9ZskS+9a1vycsvv5zQ7QKwR7Agm1OSvoInphCAgT28GpiOaGSvOO4jf3KK1E86yEj0UrN+DdnjQMnILYxuZZaV05vHBPRCB2v9OKUuB2sx0iYSIZ7Jv6kZ2VJUPaLneVpGzqCJYNGf5xiRF4AenBN7cEo54BUMQhJsx0TwW3TM+cPKe1Z3fCZ4kUXHZrcT6wDeVDZ0Qp/nKakZkldSY/IXzm7DpI0Vdhx18IakJL/dRQAcec+q/bU3KbZ25uTkNImnqpFTBywrHTIu4vU4vk7OAGAAAAto26MoKytLvvGNb/SpoPRO/Fq5cqWcfvrpCZkdaePGjXLWWWfJBx980CcBLVAmtUzNTFZTYxZ8AkInFl566aXyv//7v+wqwKucfhMbBcffmCeC5waMjGLa+SiPE9d1wg6W8OmCBhnVWbhi2CQjaJeVVxL1ejw1ojA0lsAZDfl9uIpjvi9G90TCjzmHHPsIqmLYnjJ8ypFSP/FAGTbliOhG2vTad63NR3XZPVMCWHX/RQK/e+tlwWem1rC5zUvnbUSP48QzHHPPaiE3xFXhTK5rW9BYTlGl1IyZITmFFVJQXm8MQOfzJ0e8Hi3rcoBHr+9e34fBBxBOckRdKCHbdNZXEl8eSySJ6vfmsN9o3Fj0Mfv35SgftofEU37ZEKM+F6AGoMvIiXwAYepyAAAviDza4SLnnXeePP3009Le3t6TbBVIsgokfp144oly9tlny7nnnivp6emWbr+rq0seeeQRmTNnjjQ3N5u+9+KLL7Z023C+oqIimTJliowfP15GjBghVVVVUlJSIhkZGbJ7927ZunWrNDY2GjOHvfPOOzJv3jzp7Ow0Xad6/Yc//KGxnunTpyfsswD6c0cQILKb2DgGf7wSNEF8RBWnozFOO1bMaMi5CF5qyOHS6zJBznE+n4ca4DhoPcek8ZmOlu74Ladn51uynnCW/d8LMW8PieFPSWVXR8VbnXKcrLCiQTav6zs4YH55fcTrITYBnZklqjqtkyv6SknLiHiXeCq5Vfd7lHB+nx7rKGwZ9puj5JfVGY9YOOYcx3UV8cCpHpqeX7gXAay/t6kbv580rloi7a3bJbe4WvLLh8R1N/v8fmOb7a0tRuJ+NPewBtoZAAAeoHXSV2VlpcyePdtIuupd0e+d+LVr1y6566675KmnnpJZs2bJUUcdZSTMxGL79u3yj3/8Qx566CFjJrHeM4z1/7/69/jjj5cJE/pOOw89qcSuY445Rg4//HAZM2ZM0BvQlJQUyczMNI7hiRMnGomJq1evlnvvvVeeeOIJ0wY2lTD2//7f/5Nnn31WCgoK4vhpAFglOSVNOna39VmWkpZl8hfhj86k7ciF+sUkgehZ8Nt3S1DcLeUEEnEcO+q6DEvEvXMJhwycyuvXdw99fMd0ooNlimtGic/nZ49GoU981+vnQZvPHWoE4ebGNdLR8Z/YXGHlsOg6mMT7e+x2zn2zNskQsAgJ3E6WU1RldJzr6uzoc/2OxmADlUQyk0SvF6PaPuIjLSvPW7s2Locfx7SrMMM90O83wTnMyVLSzfqMBBNBAn9U33+MxwzHXAS7it+nFf7TR9fsDaIFq2I1aoCv8oaJkujvKC0zJ+Z1xBPt8gAAJ9A66Uu54IIL5O2335ZFixb1Sbrqnfil/r9+/Xq5+eab5ZZbbjFmX5oxY4aMHj1aRo0aFTIJbNu2bbJkyRJZunSpLFy40JiRSSXe9E7wCmyz93OltrZWfvSjH8VxD8Bufr9fDjroIDn99NNl2rRpUVcya2pq5MYbb5TDDjtMrrzySmlqagr63k2bNsmvfvUr+cUvfhFDyQEkSln9BFm7bGHP8+LqkcZoJhEj6KMlrwUP6DhkITePNhqk7JHMghPfY0mT6C/czcU/cTg7GYJrMeAMweJHWXklsmNbY89zvz9F0rLzPHWec9J5KmhHZweVMR5U3CIjp1DyYhxN35US+NW6r3NP7BXUlNQM6Wjf1ScWUjVyisSTmslw2NQjZceWDZKSnimZeSXRdRQOcr/qmPNBFMeT+45B2ILDxNFUO0Pt2H1k7dJ3jPNrdmG5lA4ZF93KdDwn6PiZIpBTVBn9SPoeZzZAq9epuk/vulxJ3Rhxg0jaHgCdOOZ+xSZO//wqCSIju1BaWzb3LCupHR3VuiK6v/N4HQlOZkOiotsF/T3rVZ+lLgcA8ALtk758Pp8x09epp54qq1atCpr4FXje2dkp77zzjvEIyMrKkpycHMnOzjb+r9bZ0tJiPHbs2CHNzc1Bg3zBEr7U86KiIrnnnnuMGZ2gn9TUVPnmN79pzDanErasss8++8gjjzxiHNNbtmwJ+r6nn37a2HZ9fb1l2wYQH4VVw4xOJS1bNkhGdkHUnaa4iYXOzALRTukkbxenN0jEu9E8mk5odFyDHvQKxsdTad1YcZQktzakWtiY5vSPioQOZMB1OTKqw9zOj+dJd3dXz3OnzbQU9Dt1ynnOIcVws4rhe4p3WXQA0VF4UMlpGVI+bJKsX/6BdHXsloKKoZJfHv/4turwnl8+JKZ1cD2D3kzqclxYI9ovKrl1d3trn2XZ+WUSTzlFFTJyxnHGbF/+5BSLZ8EJeoMbURmRWCr5LzOnSIqj7DTuZtads8KIyznl/ieB1L1pecMesu6L9417VuM4i3J2wYQfAx78vuAdpgOMevzYj3TwVdWXo7Wlbz+teN+z1o3fV77+/N/StmObZBdVSGn9+OhWNMh3re99rK6fC1HhcPAEr/cZAgB4g/ZJX0phYaE8+OCDcvbZZ8uKFSv6JF71Tsbqn6AVEEjwCrxvsPcEuykKlgBWXFws9913nwwZEltDIpw5s9cJJ5wgl1xyiVRUVMRlGyqRSyUMnnbaadLR0THoe1QC4+OPPy5XX311XMoAwPoRFdUjLEHbI7x0E0tkRlvaBpdjp2/gfaBAJ2ZAT9b8lhlReHC5xdWyq2WrtO/6zz18ckqa0VnYDRxTl/PQ9Qbi8mNO7+TXYOd5dd84XM1Is3WjpGflS2ZesbjFoOc5zjnwqN4dy/RJmLDmc+SX1Uleaa2RGOemAY4cU5cDEs3j1/JI43Wl9ePk68/e67kO5JXUGAPCJaKcYSV8ma7DQ+c5TY7rYHUMNftbrMeD5/W+XdPjcLFMUfUIIwGio71VUjNyXNOuEbTe6ZLyY3D+5FR2TQhu+Y0mXJD9UlQz0qjLdXV19MTp0jJz41oUVVdUiV9hc2lfEo5FhHegRHFshfwjTc6DXj+fe/3zAwA8wRNJX4pKvlEJMCoRZ8GCBQOSvAZL/uqv9/sC7w23Y0bvbY0aNUp+//vfS2VlmJ374Sq/+c1vJD8/P+7bmThxopx88snGrF/BPPfccyR9wVv07vPnmkCYU8qhC5/PM9W16I8frx9yOn7+iEa9ZxYZnUZJBGLlT0mVoXseIlvXr5Curk7JLxsiqRnZrtixburUjPCpUa1hfq+mT7JD4qjOJPHuUGJ3XZ/7SmiPanLoc4Bj40tJruxAh+hnw0GIupxjf6vOlFtcYyRBbG/6WtIychIym2F8E/iDvTnepUEs+N0ifgNM/efHr5IK3ZZYGPe6XJj7l9+ntbw9S3UvzDRtaay3Ya/DZPumtZKSlim5JTWu+d3S/uBGoY8tvtdwdqM7fqPx4pZzVKyIywEAvMBTrVC5ubnywAMPyOWXXy4pKSlBZ/nq/wgIvK9/Yliwv+n9XrVM/Ttr1ix54oknSPjSWCISvgIuuugi41gOZuPGjbJ8+fKElQeA7g0SdiTzeCMAYaa8YaLdRXA8OgrHnmzjtCCYtxKFvPRZYSkaaoNKScuQkroxUlY/XtIyc1xz3Yp3w4uTzq1eunaXUZcLzTuHg4cEHVJ4kHcmeeazQg/WXa/DuS577zhy9TnBKXW5IOVw9b61idpnpXXj7C6GC0R3f+Pl2auzC8qkYtgkKawaJj6/X5zHO/Ubzo0eZNFx3N3dZcl6YINgdSWHtZF4tcO2VVRiddnQCcaAYAiBYysySSLpWXlG+0N++RCH1uXiex6JdT2JOJ955Zyprl15JbXiJdHU36nzB6HZLblXfvcAAG9zduQiThf42bNny/PPPy+HH364sWywJK1QSV2DJYX1X0fvv50+fbo89dRTctVVV0lqKlOIwxqFhYUyefJk0/d8+umn7G5AM0GDEh66ifXKDXtOYYVk5pfYXQxHMO0I4pHjwaqhc1VDRG/F1SOdNwpYJB1/gn58rxwX7pZT5MHZj5P0S+CBNZzSuSR4XTOqlXmW358ipUPGSVZ+qd1FcQjOWYj0PGdyAvFM/R+eucfV5pDu9nRMyil1OVg3w1ftuH0ki7hcSF6J00Ycs9RwBqzB4od0oHQ4j/8+E4XfgR68fj3TzYhpx0hp3Vi+1zB4/Rym4+cPPuichvesHj93l9aPE38KfVBDSoo2zuPt48t14v19eSPsCQBwuGTxqOrqarnjjjvkyy+/lAcffFBeeOEF2b59e59KW7SVt0CAPz09XQ4++GA588wzZcKECRaWHvivmTNnyoIFC4LuklWrVrG7gFi55F7eOUEHp5TDvTJyCqW8YQ/JzCsWn889I4S5jkcDM2X1EyQ1PUt2btskGblFUljRIE7jrZFSvXnOVI1O6virGjFFvMayRkQPj4quKy0bXT2c0Ko6CVOPC5c3r4VeNOhAA3z9jublWViASDllMBUdOy3aMTNE/cQD7S6GozDoCCK9Z43rucgx7SBu5t19mMjrpNuuyZa0MbrrIzs0LufinQh3Itlh8N2iZQZ/BGW3JR7k4n2bYNWj9jYGm0vNyLa7KC7BseUFzqnLAQAQP55N+goYOnSo3HjjjXLdddfJm2++aTzeeecdIxksmkbtmpoamTp1qpGIc8ABB0hmZmZcyg0ElJWVme6MrVu3srMA3XhoRGG3NYpZ2VE4u8D8/O493S5IeIyzJGs+v3p/YeUw4+FUkdTDvXqecLsx+59EMoSl+B38d08kubcu55COwm7YhU6n6uUkfEW2v+ANTqm3J+Ja4ZTPCgcjoU47/O7hWR6/5kUcl3PBDVdEMxq6/ft3efFhHwZH0I9j4nKIDPdVgAvvWZ1SDhcIsquyCsqMAV49KYrj2DnHvrPoNrgLdTkAgBd4PukrIDU1VQ466CDjobS2tsoXX3whK1eulMbGRtm0aZPs2rVL2traet6flpYmRUVFUlJSYiR7jRgxQrKzGUUBiaWOQTPqWAbgDRF1lozn/XvQmAnBFCT8oPO2bm831MU1gMshFzOSIWJH5xINBetYJ0mubXwJ2qFR80Y2GhEjPGfpfTh4UmSztgY7T1hVGsAN50UO+P/uCvfuC8cnMbt31yaei49DO7ghiQk23Oe4/HfEPR1C69bmeA/wfKwxzt9j2LE92liR4Lic4+9jHMYVdQQGEAbgQa44PwMAECOSvoLIyMiQ8ePHGw/AyQKJiMGo5EQAHhH3m1h3djaGJswOP+I33u4oHMW5r7hmlGxZv6LvsuqRYg/OrZ5j2fWaY0c3cW9gjzGhls6ciBeOLQ1Hq3Zz2YFQEtmBgHtdV3FMZ0k6uSAeSOCPgnsHwAjaMdwFZUeYvPxVEpaD0+tyQKJ5+ZrgMY45z3HMIeHHnEcOOo/H5B1zjgMAII642gEut379etPXc3NzE1YWQAcpqRkDllU0TBI34CYWXuWVjsJe+ZyJGFU0LStPcooqe54nJ6dJQWVDXLcJWM7bsXstz+fOGYXOKeWAZzjm2Icddblg5z4v1X3hUR7viOGp6xmnM2iPg1w/3XG9Z3XOvS8Q51mbPHiou+E+Lug9KPem8Kiof7e639PqWF9x+mdK8tBnjbUtyQXXWyeJfn+xn13F4YOke35WXQCAJZjpC3C5Tz75xPT12trahJUF0EF++RDZumGl7G7baTxPTc+W3JIacQOnNJZaGmRyxkeC0xtRHXLsO0+3J2b6iuaco86XtWP3ke2bv5aO9l1GAlhqepbYg+MXcexc4kUu/kkl+eI7Lk9KWqY4BY2SsASnQT1mbXX7OcRBRYF763JOiecgTME6BMe5Lgc4VfTnMCpzOifwu4fby69BfTrhLPrsuic7eJBTBtb09u8T8RNNGyvHom6ccp4DEi7UPYvr72mipFl91v33pgAAhEbSF+BiXV1d8sYbb5i+Z9SoUQkrD6CD5NQMGbrnIbJt4yrjpjCvtE6SU9PEFZx+E8voTIjboeXwYz/uo4Zr+PmtCDKG2C8+v1/yHJHUq1dAVfeAsaOwb90rKf6NriW1Y6Rx1eKe5z5fsuSWVCe+YUTHaxQsR2Ochrx0jfLQR4XF958cO9qJ+/Us5nMr9TLEcPhx0vJW3ddLdbmgXPA9wVacF/Xj+AR+TkuAM7ihLufweigDCEOH4xjOY2Uba1pGjrS1bu+7LDPPsvUDABAtbZO+fvnLX8qCBQsGfe2aa66RqVOnJrxMgNXmzZsnmzZtCvp6bm4uSV9AFNRsLyW1o10XyHB8gwQQL1HH9ujA4FQRTW9PbBeAJixN+qobI7t2bJWWpnXiT0mTqlFTxeePMQREYxpiYXpt52I+6C5zcV01orpcFOec7KIKkeXv91mWkVNoybqBxAn9O/HMACeaYNR0eBbX1SC7Jckjs7YmRfz5U9KzoigVvHYcOn6fuPd2DcFHY3LGvnFKOeAZXCs8hPOLPvguI9xdXFs9wcLvuXzYJFn5cd9JGMoaJli2fgAAoqVt0pdK+FqyZIlRcevd0aCyslKmTJlia9kAqzzwwAOmrx9yyCHiIwkEHuLmTnFu6FwSdsc9S4MmBGDQcwBynAT9lej3O4mscwngzcYFSzrUO40ln8nF50QL61D+5BQZMmF/6ezYbSR7Oa5Ry2HFgc08fjzoWJeL6HwexfkpPStPsgvKpGXLhv+sQpKkdMi4iNcDRMWqfsJ9ficangc8eN52StKX4+p90J6WdZm4cv7+ine8QdXl0rPyjYFKAiIZhM/KY86z50yvfm5L/fd34tnjaDAu3hdxr8tpGMqFi5gef+793drBDXXfoAMIO+SeNdi1wtI6ngu+J92vq/Fj/T4Jeryw+13FynNcTmGFFFY2yJavvzSeF1YNk+yCcsvWDwBAtLRN+lq3bl3PjUwg8Uv9q5JgCLxBl1m+5s+fb/qe448/PmHlAWA/K69vWfmlsmPrxgHLnEKbQB0s4SZJnP0AAMvgSURBVJW6XaSfU7eEkOC/+/g3DgDOpNdv3FsSN2urSv4CnD5AB9ds/VhRDw11XNSO21e2rFsu7a0tkltSYySBAe5CXU47HolNwKNMx2Li2Pd2An90m6gbv6+s+2KRtO1oNmZxLa0fL47CYQ1L7nk4kLzaUTg5JV06du/qsywjp8Cy9QNW8koba1Ae+vh6ftc6fiZYTstjH+EnvEb+/av22qqRU6WsfqJxmklOSWOHAwAcQdukr5aWlkGXT5w4MeFlAazW1tYmP//5z03fo471vffem50PT7Gis6A7gl3BRpqxruzFNaNk57ZNPbPtFFUNj7nTMJ05AcR2EnHO+ZnzGWI7gKyaHqL3Kp3z+0D0HDPSJpBonMM8PWtrtPUqdX+q7luBxLO+LqcNCz6Tm+u18Ujgj451nVwARP+bs+rtTq/LRftBUzOypW78fhZsB7F8D4AnJcW/LlcxfE9ZvfjtPsuKa8O7f6XOhoTjPkG7/RUsMdk596xAYnFt9YZ4tLEmp1qZ7KVjMBgAkGjaJn35/X7p7OwcsLyurs6W8gBWuvXWW2XFihWm77nsssts2+nvv/9+TH+/Zs0ay8oCeImVN7G5xVXSMPlQadm8XtIycyWnuMqydQNeDLDHVZK3z3FBA7VePy7g6Vlz4E6OSfpKyPmTc7TncMry1j7TbMZZIO51OS6L7hJ05GCH1OUSQfP7bQZ6GQzXdi+JZNZWfi/uRMdXC/T5nehyXfT2ud7K30Veaa20bt8sW75eLkk+v5QNnWC0tQJ2oS0BVkpOzYjp7xNSf9Tl0gyLvtsQf8TxogfNY1UAAGid9JWZmSnt7e0Dlufk5NhSHsAqr7/+ujz00EOm7zn88MNlxowZtu30U045Jebf77BhwywrD7zD6wHLSDqXhLOvMnIKjYedaHxEOJ0N6FzgDZwPgH7oUO/ac0WwMpomsfJ9Q2Nu+N0ijvfmQc997HVoLpxrO+dHz17PMnOLZWfzpj7LHDUgE3VTWJHw6NFQvhvimJbM9OX8j+n67yk8Hv2hmbHoeu319kgdfztWJvCremHFsElSPnSiccwR94gffouxC358mp/n2Pe6z9oanM/vl6Kq4dK09vOeZcb5LlbEQAbZJe69rjoJ+9EbPDUYEwDAs7RN+srPz5etW7cOWD5YIhjgFmoGrCuvvNK047s69q+99tqElgtAgiU5O+iTkHI446PCKTgePIFAHQDdJfl82nXQcUr9FE7HcaIdkgGgsXhc29zcOdZyLq47WHnPWlo/TlZ++HpPR8rU9GzJKawMsyCWFQNAWLq9XZdz8XkbiDdiIs4UrO4dj/YHp8T6AHjsHJ6AuFzFsD0lPSvfmNUwK79U8srqwv9jC3etq78nJDCGFupvgtQNNA+wRDLDsxtwPgAAeIG2UYYhQ4YMWjnZtKnv6IiAW7S0tMj5558/aDJjbzfccIOUlZUlrFwAnMPxCREE3RAnyakZ7Fsv0DuuCojXg/FwQV0OiEm35dd4RhTWfURhKn/QG3U5DVkY98oprJDa8ftKXmmtFFWPkPpJBxmjqQOOPGdxydYuRu74a5TzdyE0E7LDrzbHpDYfRNvzMxC1KK7tjq8P2MQNSQWJ+O5UUmth1TCpGjVV8suHOHC/OK081nLe/na2sqHjBy6rH7gM7kayPQDAC7TtUdTQ0DDo8qampoSXBYhVZ2enXH755bJs2TLT95100kly1FFHscMBr3JMcMcp5YBOfP7gE9RmF5aLPzm1z7KiquEJKBUckQzhmHMfkOjGGBpddeP0pC8rGxJplERvPh+d2HU7z0fSuYTzAbygbOiEAcvKG/awpSxwT+eS3OIqqR07UyqHT5bU9Kzwy0FcDgnm9PuY+NMvLtXd1RVzXc415yLiip4T1bHJceLtupxLTmeA5Uj6ci++O+gsinpZVn6ZZOYU9TxPSc2Q/LIhFhcMtqPODgDwgOC9Z11u2rRpct999w1Y/u9//1uOOeYYW8rkFS+99JLxcLt99tlHjj/+eHGCG2+8Uf71r3+Zvmfs2LFy/fXXixM89thjMf39mjVr5E9/+pNl5QG80ijlrc5yXvqs3pNfWidbN67seZ6WkSNpmTmmHYVrxs6QNYsXSMfuXZJdUCaljM6knwjPcd46J0J3qvFh64av+iwrrBwmuvH86KFOOW9ZWgyHfCY4+vednl0gySlp0rG7rWdZTlFlgkqGuKBzCdBHQVm9bF77hexu22k8T8/KMwYvcVwdALElOHgp8YVjFr2QwB/pz8cF53zqcpzn4FEWDDyS5N56jOPPz+4dFwbQTJILqnJdFqwjnicdK8/Dzv8+YD81c/qQPQ6U5sbV0tXZIbnF1ZKSnumeOkDc6FW5cHxcTq/dDQCwibZJX1OmTJGMjAzZtWtXTwVN3ZSoxJkbbrjB7uJp7bPPPpO///3v4na5ubmOSPq68847QyZRlZWVyZw5cyQ1te8sJ3aZNGlSTH+fmfnfmysg4Vx8Q+/4m1ggTOXDJkn7rh2ys3mTkfClErpCySmskFEzj5eujt3iT3HG9dB2mnXQcP3IwUCPyI/Z4pqR0ty4Rrq6Oozn6Vn5xsh00IvVs0MAbrrGVwzbU9YsWSDd0m3M4FpGAr+rRdYxxKUd7oAIqI4kDXseKtsaVxvJEbmlNeJPTmEfaobzFrx8H1NY2SCbv17es6y4ZpStZUJsPD8gC/QW1URfqp+J2Sq5d4lpBzsEbaxAXypG523uPZ9Rl+P+HAOpOFxBxdDIdo3L4tNeP28TlwMAeIG2SV9paWnGjF5z587tc1Ffv369vPPOO7L33nvbWj4gHA8++KD87ne/M31Pfn6+/PGPf5TKSkbBBrzOMR2Fg8Q+aPhCuFLSMqRh8qHSqRK4IugIp+p8kSR8EfTWpNHVjniry4K8cL+MnEIZuuchsm3jKuM8V1A+1BiZTjeeCcg7fnYIj3wPcJT88iGSVVAmbTubjXMeyRAur6u6uexAKFHWV1Til0rkh76C1+WSnHGMeqWuDVtUDJ8sKelZ0tq8WbLySqSoegTfhItZMjuExzscQi+l9eNl/fIP+ixjoBL9OCcuByReSmqG7G5v7dOmr5ZhEG64rSIuB8CDqMsBALxA68jFmWeeKb5+HeBVh4mbb77Z3R0n4AkqYVEdq2aysrLk3nvvlWHDhiWsXAAcwEudN4J2itbws2IAOvuij4h/95wnnCA5jYZBq2TkFEh5w0QpqR0tyalplq0XzuH8BgnOq4hFd1iJ/9kFZdQBvdZRmFMLANdxegJ/AtC+hn7ULIaldWOlbvy+Ulw7yjmDkyE6HvqNM1AdwpFfNqRP8kNySrrkl9ez81wqaPsi7Y7QWKj+caX14/o8L6kbQ33O4wn8AOA61OUAAB6gddRdJcKccsopPTewgQDO0qVL5b777rO5dEBwzz77rFx//fWmwZeMjAy55557ZMKECexKAI7qXEJDqQX7kIAEEMHvgp7CTpZTVCl+f98Z+1TSkqdxyMZ5/7p3B2t5/dfwIwEIjcG2oDNiHgh6bJDkAp15KAkIbugozI0mEtuOpgYoGbrnIVI6ZJyRCDF0z4MlNT2r90r5SjTglDZWwA6FlcOkfo+DjPNc3bh9jRkOMThiAg7DJdhztGxHs4tmt/me6kvC7wAAPEv7yMUPfvADqa2t7XOBVx0Pbr31Vnn88cdtLRswmFdeeUWuuuoq6eoK3qiSmpoqc+bMkSlTprATAYu5OVBHgEMfdJJEZAY/b3VrFqmLuNHVvadz7Ub7rh23j6SkZRrfYW5xlZTUjRUvS8vIsbsIjuWV61+w+qZTOgonpE5Jg4T3xOPn7ZFzhu4dhYPeg8fxPMG9M9yCY9VlvFS/8dJnBTzIK/fm8Kbc4uoB9yB5pf/tSxJMaka2lNWPl/KhEyUtMzeOJXQ3N9dfnRKXA+Ij9LU9u6DMOM/lllS7+rdsGRfvAqcn8HN8wS3c3HfMizyVwM89OwB4lvZXu+zsbLn77rslNzd3QOLXT37yE7njjjukvb3d1jICAa+//rpcdtll0tHREXSnpKSkyO233y777LMPOw5AX5EEYO24CYwiJkIgBcB/TwhBkiRofHK87MJyGTn9WBmz74lSN34/8Sf3nfnLa4qqRw5YVjl8si1lgcNwPtMH3yUsOV7obAsHoAE5IbincZmguarObm4jxhbRzkICBhbSbbAirXjp+h/0nM6JQFf+lFQpqvlvbM7nT5biQWJ1luA4chV+9wDCPFk4fkeRwO8Fzj8OXfXZXfC7RmhOj8sBAGCFZPGAoUOHykMPPSSzZ8+WxsZGI2CjHmomJZUQ9uKLL8rVV18t++23n91F1cLFF19sPBCZ+fPnG/tt9+7dQd/j9/vlV7/6lRx88MHsXgDOvYlNSFBE78ALjUuwhGb9MxxzjkNUjHswvyduP0NKy8yRimF7yoYvP5Turi7JLa2RgooGu4sFB3D8eY4EfsSATr3eEtGIwjSqw2UKq4bJpjWf9V1WOcy28ujGzfEQp9Tl3LwPATiD02eHQPjo9D248oY9JLe4StpbWySroExS07NiOqxIrNYEdSjAozS8f/JQAj/XYAD/PSFoeD4HAKAfz/S6GzVqlDzxxBNy5ZVXysKFC3sSv1Sw88svv5TzzjtPKioq5Pjjj5cDDjjAeH9qaqrdxYZHqGPy/PPPl7a2tqDv8fl8ctNNN8mRRx6Z0LIBruKVAFaQz0mnDgA6i/QcF89AP40IiFVxzUijc3BXZ4ckp6axQz32+wzWgY66HHSWkV1gdxHcp9vb52c3nM/hTakZOZJXUiPbGlcbz/3JqSR9wUBdDvCoJP2SbdxcdiDca3ZWfqnxAJyWwB8c52ZELzk1nd3nISTwk/wBhHWu0Kxu4fS6nG77GwBgD62TvubMmTNg2dSpU2Xt2rXy9ddf90n8Ug+17K677jIeakYlNUPYkCFDJDs7W3JyciQrK8tIvImXiy66KG7rhnO9//77cu6550pra2vQ96jj9MYbb5TjjjsuoWUDPMkF/cqcfjNI5zwA8Tm5uOAEDUTA5/cbD3gPHeioL+pOzWa47otFPc8zsgslPTvf1jLBAajLuVJGTuGAZWkZOeJlKk5bPWa65DcNkd1tOyWnqFJSM7JjXWuEy53J6fGquMe9HHOec0o5oBVv/LwRQNIXEBkuvVoggR+6qBg2SdZ98X6fZcU1o2wrj1u5ub8D7Q/QWhx+mm7+vSOMupyOX69jYpAAgETTPunLLDgTuNHp/Z7Aso6ODlm2bJl8/vnnkigkfXnPRx99JLNnz5adO3eavu+6666Tk046KWHlAuBwrm10jeLG00s35gCiG50p6HmCEwU8hOPdXZxel+N4QoyKqkcY/25vWmskQpTVT2CfIvKRNqnKOYI/JVUKKxpk87rlPctKh4wTr/P5/JJbUm13MeAwSXEcMNASnFcRpvzSOtm6cWWfZQXl9ew/D/HW7BB6JF4H409OsbsIcBOHh6vsu2fV43yga+f2wsoG2fz18gHLvKygokGaN62VHVs3Gt91yZCxxozV8FCCqNPbH4AY+JNTE7f/XPBzRy9uOD8DABAjrZO+wh3FonfyV/8EsESNgOGKG0NYavHixXLOOefI9u3bTd931VVXyRlnnMHeB9CD0Zn0l5aZa3cRoAXNgvpUlwGEda5w/snCrR3oouoQ4oLvA/GJcRXXjDQewH8PjODHC5ytcsRekplXLLt2bJOcwgrJLiy3u0jQWpJ3klsBByfwq47CXV0dxnNjRsNMOgp7Ce0P+vD5k43f8Pamr3uWkcSZODol1ngC96auVFw9Srau/0q6ujp7BuhQy7xMJfzW73GQtLe2GMkRyalplpzPvDKzsw6sqct1u/rczTVY7340qenZ0r6rpWdZflmdrWWCMxCXAwB4gSeSvgbrPDDYTU7vZf0TwOKJ4Ln3fPbZZzJr1izZtm2b6fsuu+wyOfvssxNWLgCK8xthHN9R2Pm70FHKG/aQ9cs/6HmenJwmOcVVtpYJcFOgjsA9ALdxegwgIedVjeqLGdmF0tqyuc8y1akOiJVunWlodHX37EUFFUPtLobeNLouevn8HKw9KeHJrRxPEcnKLzVmQegtt9jbM/mpRN+GvQ6T7ZvWSkpapuSW1JCkHRX31uWc3v7AoAGRqR49TdZ/8b7sbG4yznkVwybF6ZvxMi6+OuDc4k5pWblSN2F/aVqzrCd5XS3zOnU8p5G0H6+9K07n9LocEOv5rXbsTFm9+G1pa90uWXklUjFsz9h2qlcTvx3eVhlNDNszNPvuAADh80TSVzQdqpjhC/GyfPlyI+Fr69atpu+74IIL5Pzzz+eLADAQN3BaJXkUV4+U3W07pXnjaqMjRcXwPY2R6AD0RUdhALqg0VUvZUMnyFcf/avneUpqhuSV1NhaJsBdHegiXW5JYeK3bsDD1CjynkBcTitl9eNlxfuv9STz+XzJUlg5TLwuPSvPeMQVvyXnsuK7cXsfNI2qi8kpaUbiF5CQ497F91pean/QbZCZ7IIy4wFYysXnM+rZ+vNUcssgMnILZcS0Y6Srs8OY2RZQ/P4UdgQAQHueqPkwKg+cYsWKFXLmmWdKU1OT6fvOOeccufTSSxNWLgDu4taOwlyPg+wXn08qh082HoCXZpKx7Bzi5oYXAJ40WOdJXTuWeKH+l1NUIbVj95Et6780OtOV1I2hoRER8Uojva7nOSCuXHYZLaoaLptWL+2zrLRurHaf2Zec4tKBklywc22gZr2pHjNdNn/9hTEIk6rLpaRn2l0swFbJqemyu72177KUdNGRF+5ZAYSJ8wGAsM4Vzt9NurUPe112Ybm0bF7f8zwju1D8Do9LJEr8E75c8IP3KDXw4rbG1X0GY0zPKRBHs/LcTL0VADzLE0lf3NDACVavXi1nnXWWNDY2mr7vu9/9rlxxxRUJKxcAF3J8nI7gB5BIKWkZntjhSRHOgEenDQBO7kCXW1wtzZvW9CyrGDZJHIMk24jlldYYDyDSxkjVMK06m3tC0HNLsLdzXwnv0GU289SMbCmpHS2Nq5YYz9Mz86SwarjoRiUGqZmgVJJQQOmQcbaWCbHJL6szHgD+o2L4nrLq07f67I7KkXuxewDdBytzfNtjfHlpoBJd7j8AK6hBvLRD0pf7rsEmqkZMkZUfz5NdO7ZKWkaOVI/e2+4iwak8VJcrb9hDdrVslbbW7cYMX1Wj96Y9AQDgCVonfU2ZMsXuIgCGr7/+2pjha/36/46+MZhTTjlFrr32WvYaYCM3dCxz60xflnLB9wTEK4C1fvkHPc8LKxu0nE1EjazduHJx32W1o8UxOAUBiFHNmOmyac1n0rajWXIKKyS/fAj7FPAAlRSwY2ujdOze1ZPwqZIHvNBBww332gCsuWdVCVFqhpiMnMLg5ziXU7O1p2XmSuv2zZKVXyIFFQ12FwmA07i4KpdTVCXZBeXSsmV9z+wCOYWV4iUkRADe++0ES/riXhbQR1n9eNmw4uM+y4qqR2h3nmNgfL2oAXaGTz1SOne3iz8l1e7iQCPdLr5pNX4Xex8tu3ftkOS0DG3jjwAA9KdfD9FeHn74YbuLAMiGDRuMhK+1a9ea7o0TTjhBfvzjH7PHALg+UGdlAwiNKYgrh/+WBlNcM8qYnn7H1o2Snp1vdKbTUWFFg2xbv1La23YYz/NKayUtKy/CtTi/4QWAd6mE3dK6seJdnKPhTar+NmLaMUaSgEoW8MqMrWa/ezd0lgEQeccL9dBZks8nxTUjxZFIsgUQI5/fL0Mm7G/UWdU5JSO7wDjvAdBbek6BeIUanMA4x/2f1PRsx3ekd3rbMOAGhZXDpblxrbS2bO4ZgFLHe1cGENaT069TQKKpvmQ6nsMBAPBs0hdgt6amJpk1a5asWrXK9H3HHnus/PznPye5AUB4COwDng5eqdlgdJ8RRgXoGqYcLju2bBB/Sppk5ZUErSeRHAoAANzEn5wi2QVl4jXU2QDTH8jgiz2YFOnFz5wonIcBRHTO8PkkM6+YnQZ4SFZ+qZH81L6rpc/spjoqGzpBVn08T7q6Oo0ZvsobJlJXAjwgOTVNGiYfKrt2bJXklHRJSc/UdJAN7ySJcp8LjqO+g00CAAC9cbUH4mTr1q1Gwtfy5ctN33fkkUfKzTffLD5GyQMQJkZnAuAFySlpxgxfALzTObaoerjsXLxpwMx/kaCRDwCcSXWkC/JCoosCAFqjPgwkVnrm4DPTd3uosy0AfeoQQyYeIBu+/FDaW3dIbmm1FFYNFx3lFFbIiGnfMGb7UrMZRpX4AcC1ie1qtj+d5ZcNkfVffthnWW5xtTgGsUC4hNviK5m5RZKcnCYdHW09y0z7WnDLCgCA6wRpbQcQi5aWFvne974nn332men7DjnkEPn1r38tfr+fHQ5YwCsNqWq0vf5S07JET+4KpABw0HmC0wfgOjlFVZKWkdNnVLqi6hG2lsmLEtOQ5Y16O+BVxTWjBi6rHbjMlMsa1QEAgP7KG/bo81zNEJFdVGFbeRBH1EXhUWmZOVI7bh8ZNuVwKa0bG3mMyEW3cSlpGZJbXEXCFwDtkkDyy+vFn5zaZyCmkroxtpYJQGKSWitHTRGf7z99UFPTs6SsfoLnd70uA6cCAKAw0xdgsZ07d8rs2bPlk08+MX3fAQccILfddpskJ/MzBBCZtKw8ycwtlp3N/50Jo6yBm3UAAOBu/uQUqZ90sGxZt1w6O3YbI1KmZ+fbXSwAQITULI1bvl4unZ27jeep6dmSW1Lj2s4yAAAASnH1SOlo3yXNG1dLSkaWVAyb1NOhDi5FXRQcRwAQ3vWym0G83JTUWr/HgbJp9WfS1dkhhZXDjBmA9DR4XJF4I6w4jtwor6RGcvapkPbWFknLzDUSwQAAgD7INgEs1N7eLhdccIEsWrTI9H2jR4+Wq666SjZu3Bj3/a+SysrLy+O+HcAJvDJChwpS1U3YTzatXiq7W3cYnefySgfvQOeohlIaUIHE8koDTJK3rwmAjg2SpUPG2V0Mx9X9EntKT8T5k3M0oLO0rFwZuuchsnXDV8asjQUVDUZiLwCEpGXsyBmfqdsrMQIgjlSHOZXopR4AAACAU2XkFErNmOnRr4DbR8C1VDyeATUBANATSV+AhVQS1/z580O+b8mSJXLkkUcmZN9XVVXJa6+9lpBtAUic5JQ0KR860bO7nNGZgNAycgsH+e0wmhMA3TmjU62OqH/BaXTvvO72z6calsuz97C7GIC7aZkABQAe4fK6nNdx/wsAAAB4hGfDb9yzAgDgNvT6BABoo9uKm1I61DiLZwMsQOxyi2uMBNHeKkdM1m7X2jOjFycnAAAAABbdXRCLAgBohw6E8I5g8Wlv1vG8+JkBAM7D9QgAAAD6IekLAABokIABoD+f3y9DJh4g2YXlkp6ZJ2VDJ0hBRYN3dpQnG9UBwAKcPgEAAAAAAAAAgJPRlgEAAAAPSba7AAAAWCW7oFyaN63psywrr4QdrGUyGBE8OGR2QIfLyCmU+okHitZI7gIAANBPt/51dQAAAESBWCAAAIg34lKAZzCoNQAAcAtm+gIAaKO0buzAZfXjbSmL03XHM1BpYT4WARYAUZ8/XNIBJK7nY8CL3PHTd7Tg9S92LpBY1BG8Xk+Elwx+THKsAoB7eWGwKQD/h9sLANAPsaP47VounIAj0EcBAAD3IekLAKCNjNxCqRo5VVLTsyQlLVMqhu0p2QVlEa2DIBMAAHAdj3YuyS8bMmBZQXm9LWUBAKulZxewUwEAFiIBBUik5JR0djjged4L2DFoAQBd+JNTJD0zr8+yzNxi28qDCPv2eO8SjHjQPPkzt7hqwLKcwgpbyuIFJNkBAKyQbMlaABiqq6vls88+Y28ANiqsbJCCiqFhNS5k5ZXIjm2NfZdFmCQGAEAsaAgHoldUPUKaN62Rrs4O43lucbWkZuSwSwFoISOn0Ohcsmvntp5lFcMm2VomDIYkCsBK3B8B0EVaZo6kZ+XJrh3/rcsVVjTYWiYAgIt1c+8JJFrFiMmy8uM3jPYHf3IqcTlAV5ondwVTUjdGmhvX9JmlunTIOFvLBAAAzJH0BQDwbAeR4trRsvOTJunu7uqZGSIlLSPOpfOCBIyo5M24CwCHoCMi4AyZuUUybPLhsr3pa0lJz5Sc4ip+n1bwaAMX4MT6xpCJB8j6Lz+U9tYWyS2plqLqkXYXCwAAwBJeGOW6dtw+snbpu7KrZatkF5ZLOQn8gKaIo/x3V7AvAOgju6BMRs/8prTtbJa0zDz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88cUxb0c1XF566aWm71EjfAAAAHdobGw0gsDBFBcXy5lnnhnzdvLy8ozk8WDUfZvZjKXh+Oijj+TDDz8M+vro0aPl6KOPllipBLiTTjrJdBTvp556KubtAADsM3ToULnwwgtl//33l8LCQld+FVzjnXmNV7Orqpm2gjn44INl0qRJEis1IJs6foP5+uuv5dVXX415OwAA/eoQOtEpTkAdAgD0pWYAvemmm6SioiKu25k4caIxA4iZ5557LqZtEIuIHPUIAEAsdKpH6IR4RORo0wDgJFomfe3YsaNnNq7+yVVmM8H0ZjaNaGtra0zlU6PmDeatt96Kab0A4DRvvPGGbNu2LejrxxxzjDHKqBUOO+wwqaqqCvr6m2++KVu2bLFkWwAAIL5U8NVssI1TTz1V0tLSLNmWCgRnZmYGff3555+PacaJv//976avn3XWWWIVlQhndi8bqiwAAMQb13hnXuND/f2sWbNiWn8k66K+AgDQnU5xAuoQAKCv/Pz8hG3roosuMp2dcuPGjbJ8+fKo108sIjrUIwAA0dKpHqET4hHRoU0DgFNomfS1a9euoK9lZ2eHtQ6zioBKKovFyJEjB13OTF8AvHazcOKJJ1o6NfRxxx0X9PXdu3fLP/7xD8u2BwAA4ufZZ58N+prqrHTCCSdYtq2cnBw59NBDg76+efNmefvtt6NatxqM5IUXXgj6uko2O+KII8QqQ4YMkcmTJwd9ffHixQS1AQC24hrvvGt8c3OzMWhPMLW1tcZollaZNm2a6aA9r7/+umzfvt2y7QEA4CQ6xQmoQwAArKJmIjW7Ximffvpp1OsnFhEd6hEAADeIdz1CF8QjokebBgCn0DLpy0xubm5Y70tNTTXt9BeL8vLyAcvUjGQrVqyIab0A4CRqdg6zGQxLS0tD3nRF6uijjzZ93awTEwAAcAZ1v/XJJ58EfX2PPfaQyspKV9QhVAC5sbEx6OsHHXSQpKeni5WOOuoo09fnzZtn6fYAAAgX13hnXuNVcrsaKCeYI488UqykEvjNOrO3t7fLggULLN0mAABOoVOcgDoEAMBKM2fONH191apVUa2XWERsqEcAALxcj9AJ8Yjo0aYBwCm0TPoym81LZSyHIysrK+hrZsH4cGRkZAy4KCgbNmyIab0A4CQff/yx6cjM++yzT8/5zyrDhg2TioqKoK+/++670tHRYek2AQCAtebPn28MimFWh7Da1KlTTQf+iHamL/VZzMTjs+y7776mr5sl5QMAEE9c4515jQ9Vz6G+AgCAdXSKE1CHAABYqayszPT1rVu3RrVeYhGxoR4BAPByPUInxCNiQx8MAE6gZdJXZmam+Hz/+Wj9Ewqam5vDWkdycrLk5OQMuo41a9bEVL7W1tZBl+/cuTOm9QKAk6gEq1BT38bD9OnTg77W0tLClM0AADjcO++8k/A6hBqYQ80gFszy5ctl06ZNln8Ws3pLtGpra6Wqqiro6++9954xIysAAInGNd6Z13iz+I2aacSsjhQtNfO7WcJ9qGMFAAC30ilOQB0CAGCloqKiqPpZhUIsIjbUIwAAXq5H6IR4RGxo0wDgBFomfZnN1GU260x/paWlgy7//PPPJRbBMsfb2tpiWi8AOMknn3xi+vqECRPist3x48eHnK4YAAA4l9m12u/3y9ixY+Oy3VB1k2jqEGZ/U1JSIuXl5ZLoz6IGG/nqq6/isl0AAMxwjXfeNV4NjmP2N6NHjzZNzoqWWueoUaOCvq7KxABpAAAd6RInoA4BALBaqP5SaWlpUa2XWETsqEcAALxaj9AJ8YjY0KYBwAm0TfqqqamR7u7uAcu3bdsW9jrq6uoGrEM9//jjj2Mq2+LFiyNKVAMANwp2rguc74YMGRKX7Y4bN8709SVLlsRluwAAIHYdHR2ybNmyoK8PHTrUmJUrHkIlk0Vah1i3bp1s2bIl6u3FItS6zeppAADEA9d4Z17jly5dOmgMPWDMmDFix2fp6uoyygYAgE50ihNQhwAAWG39+vWmr+fm5ka8TmIR1qAeAQDwYj1CJ8QjrEGbBgC7aZv0VV9fP+jyL774Iux1DBs2rM/zpKSknkC2WVA+lHfffXfQ5fn5+VGvEwCcRI0MuWbNmqCvq4SvwDnVaqozuJnPPvssLtsFAACxUyNLt7e3R32dd1Idwix5LZztxYL6EADAabjGO/MaH+r91FcAALCOTnEC6hAAAKt98sknpq/X1tZGvE5iEdagHgEA8GI9QifEI6xBHwwAdksWTQU7wUYyQmjvKarViKeBBAX1/+eff15OP/30qLLK33jjjT7JDoF1FxQURLw+AHAilfBlNlK0mkkxXrKzs6WwsFA2b9486OurV6+O27YBAHCDTZs2yfz5840ZjFWAT1231XVz165d4vf7JT093RjtqqKiQqqrq40ZHtS9kXr4fPEdNyTUdTqeAdlQ9ZNI6xA6fRYAAGKl03VRp89iNmCP2z4LAABr16414h2ffvqpfP755/L1118bg3iqeEdKSooR71BtseXl5cY1To3QvMcee8ioUaMSsvOoQ1iDOgQA6EfN9qz6UZmJ5nqt07VXp89CLAIA4IZ6RCSIRwRHPQIArKNt0tfIkSNjTvqaPHmy0amxd8KX+lc9f+CBB+Skk06S1NTUiMp12223GVOIB9bTO/lLdaYEAB2ECtSpDuTxpNYfLOlLLVczkWVmZsa1DAAAOJHq8DRz5sygr3d2dhozbTU3NxvX84ULF8rf/va3npmJDzzwQPn2t78te+65p3Z1iIyMDCkqKpKmpqaoyuakzxJq3ZF+FgAAYsU13pnXeOorAABdvPfee3LQQQcFfb2trc14bNu2zZj1Y8GCBT2vlZaWyqGHHionn3xy0PZlK+h03dXpswAA7Ddv3jxjsLpg1CB10XTWJhZhDeoRAAAv1iPCRTzCHPUIALBOfIdpt9Fee+3VJ6EqkGS1ePFioxNjONRobxMnTuyZrab3rDUqO/uGG26IqExz586VZ555pqcs/U2dOjWi9QGAkzuUmykuLo7r9lWH7VjKBwCArsxm4gxl69atRgLYqaeeKieeeKK89dZb4qU6hBodvLW11bLPUlJSIvGiRi/PysoK+jp1IQBAonGNd+Y1XsW47aqvELsBAFg9sne0Nm7cKI888ogce+yxMmvWLGNm9HjQKU5AHQIAYCU16LaZQw45xBiwO1LEIqxBPQIA4MV6RLiIR5ijHgEA1tE26UuNQq9GY+vfqVF10nv77bfDXo8K8PcWmJ1L/fv000/Ltddea8wYY0aNln/33XcbSWL9E9EC/H6/7L333mGXCwCcTHWKtqvxMjAyZyzlAwAA5j755BM5++yz5ZJLLgk6u6bX6xCh3hvvBDazfaVGNo8lARAAgEhxjXfmNV4l9QeTlpYmOTk5Es/4fUpKStDXid0AAOyg2pC/9a1vyY9//OOQ7b+R0ilOQB0CAGDl7Bzz5883fc/xxx8f1bqJRViHegQAwGv1iEQjHhEe4hEAvEzbpC8lWBLVK6+8ElHSV6BxO5Ck1Tvx66mnnpIjjzxSbr31Vvnggw+M0eA6OjqkublZli5dKvfff78cffTRcscdd/RkdfcOmgfWddRRRxkziwGADswq2Eo8Ow2Fs/5Q5QMAAOF58cUXjXumjz76yJJdplMdItR7c3NzJZ7M1q8GJlH3rAAAJArXeGde4806wMW7rhJqG8RuAAB2evTRR+Wb3/ymfPXVV5atU6c4AXUIAIAV2tra5Oc//7npeyZOnBj1ANrEIqxDPQIA4LV6hF2IR5gjHgHAy7RO+tpvv/36PA8kav3zn/8Me1rNrKwsYwT7/qOb9U782rBhg9x7771y6qmnyv777y/jx483KguqMeDXv/610SDQ+/2BsvQu13nnnWfJZwYAJ1CjQprJzs6O6/ZDrZ+OQwAAWKexsVG+853vyOuvv659HSJU+cJ9r5rVIjU1VdzyWQAAiBXXeOdd43fv3m06g4mKi8eb2WdpaWkxBlcDAMAuqn335JNPlo8//tiS9ekSJ6AOAQCwihpce8WKFabvueyyy6JeP7EI61CPAAB4rR5hJ+IRgyMeAcDrkkVj06dPl8LCwgHZvZs3b5bnn39ejjnmmLDWo5K+nnnmGVm5cmWfxK1AIlfg/8H0TvDqLfD3p512mjQ0NETwyQDA2cw6DSmZmZlx3X6o9be2tsZ1+wAAOJHqQDRmzBgZMWKE8aitrTUa6tTsVmlpacaIzioxuqmpyZjFeOHChbJ8+fKw1r1r1y65+OKL5b777pOpU6dqW4cIVb5w35uITtRWfhYAAGLFNd551/hQsREn1FdUHTPeSf8AAPdT15NAvGP48OFSVVVlxDrUIzk52ejwreIdatCaRYsWybvvvitr1qwJa93q78455xx55JFHZNiwYTGVU5c4AXUIAIAV1CByDz30kOl7Dj/8cJkxY0bU2yAWYR3qEQAAr9UjwkE8Ivz9ZIZ4BACER+ukL5/PJ0cccYT85S9/6Um8Cvx7//33h530pTpA/uY3v5EzzjjDaGgeLPErWGJXQO+ksN7vVVOIXn311VF9PgBwqlAjMauG1nh3ag818gMAAF6Qn58vBx10kBx44IEyc+bMsDsRqVmLFdUJ6sEHH5S//vWvITv1tLW1ySWXXCJPP/20lJeXx6UOEeoa76Q6hNlniXddSKE+BABwEq7xzrvG2x27CeeztLe3x70MAAB3Kisrk0MOOUQOOOAAmTZtWtizZJ100knGv59//rkxcM1zzz0X8tqpEr8uvPBCefLJJ2NKRtYlTkAdAgAQK9XucOWVV5oOrq3aNq699tqYtkMswjrUIwAAXqtHBEM8InLUIwDAGj7R3FFHHWX8qy7yvR9LliyRN998M+z1jB07Vm677baeRoPeiV791z3YI6D336gR5377298mJJAPAIkUqnEw3ue9UOsPFeAFAMDtxo8fLzfffLO88cYbctNNN8lhhx0W1ajR1dXVct1118lrr70mhx56aMj3q1mWr7jiirjVIfx+v7ilDmH2WeL9OcLZBvUhAEAicY133jXe7u8knG1QXwEA9KbaWFWCl2pb/ec//ynXX3+97LfffmEnfPWmZgT75S9/KS+++KLstddeId//1VdfyU9/+tOYvhBd4gTUIQAAsWhpaZHzzz/fSKo2c8MNNxidqmNh9zWL9gbnfSfhbINYBAA4VyLrEb0Rj4gN8QgAsIb2SV8qUP/pp58O+lAj3UdCjRb3xz/+UUpKSnoSuQLJX2YzffV+TyAJTI20/9hjjxnrAgDdhAqExTtYF2r9zPQFANCZmtFYzcylZutSsxZbobCwUObMmSPXXHNNyIbKd999V5555pmotmP3aNFWNvbZPYI3SfAAACfhGu+8a7zd30k426CjFQCgt3PPPVceeughY2Abq9oYqqqqjHV+73vfC/leNbP5e++9F/W2dIkTUIcAAESrs7NTLr/8clm2bFnImTkDA3zHwu5rFu0NzvtOwtkGsQgAcKZE1yN6Ix4RG+IRAGAN7ZO+AjfSgz3MErWCmTx5sjz33HPy3e9+1xg5rvdMXr2Tu/ongwXeV19fL7feeqvcddddkpmZaflnBQAnCFVZ7+rqivuNnhlmWAQA6CyaEa7DddZZZ8m1114b8n133nlnyOtxNNfoaNYZiVDrj6RTmdlniffncEISPgAAvXGNd9413u7vRKG+AgBwQrxDXROvvPJKOeecc0K+9/bbb49pOzpcd6lDAACideONN8q//vUv0/eMHTvWmM3TCnZfs2hvcN53ohCLAAB3SnQ9ojfiEbEhHgEA1vBE0pfVcnJy5Ic//KFRifjRj34k++67r2RnZ/ckdvV/qFHiTj75ZPnd735nJIxZnUkOAE6TkpJi6+hIodYfqnwAACC40047zRgEw8yaNWvkpZdeing3hrpGx7vBz8o6hNl7E9FwGWob1IcAAInENd5513i7v5NwtkF9BQCQSP/v//0/OfTQQ03fs3DhQvnoo4+iWr8ucQLqEACAaKiB4h577DHT95SVlcmcOXMs61ht9zWL9gbnfSfhbINYBAA4jx31iEQiHkE8AgDCEf95kTVWUFAgp59+uvFQmpqaZOPGjdLa2mrcBBYWFhqPjIwMu4sKAAkVaoSm3bt3x3X7JH0BABBfV1xxhbzyyivy9ddfB33PM888I0ceeaSldYj29va4BmqtrEOYfZZ414UU6kMAACfhGu+8a7zdsZtwPosbG+gBAO6VlJRkjBz+1ltvyc6dO03jHRMmTIh4/brECahDAAAi9eCDDxqDZJvJz8+XP/7xj1JZWWnZDiYWYR3qEQAAr9UjEol4BPEIAAgHM31ZqKioSEaPHi177rmnjB8/3pjhi4QvAF6UmZlp+rpZg6kVduzYYfo652YAAGKjOuBecMEFpu9RnaRCXZPdVocIVb5w3xvpfrH7swAAECuu8c67xoeKjTihvpKenh73MgAA0JsazPPMM8803Skvv/xyVDtNlzgBdQgAQCTmzp0rN998s+l7srKy5N5775Vhw4ZZunOJRViHegQAwGv1iEQjHhEa8QgAXkfSFwDAcnl5eaavx7sBM9T61QgfAAAgNieccIIRfDSbleujjz7Sqg4RqnzhvleN4K32j1s+CwAAseIa77xrvJrNw8mdz7Ozs0OOyg4AQDycddZZ4vf7g76+YcMGWbVqVcTr1SVOQB0CABCuZ599Vq6//nrp7u427bx7zz33RDWLZijEIqxDPQIA4LV6hB2IR5gjHgHA60j6AgAkPIDa3Nwc170eav10cgYAIHaqA9T06dNN3/Phhx96tg7h5M/i8/kkNzc3rtsHAMAt10UvX+PNBsWJ9+cItQ1iNwAAu6jr4/jx4y2NdyjUIaxDHQIAnO+VV16Rq666Srq6uoK+JzU1VebMmSNTpkyJSxmcfO1ViEVEvs+sQD0CAJzPCfUIOxCPCG8fBUM9AoDuSPoCAFjObNYPpbGxMa57PdT6CwoK4rp9AAC8Yp999jF9feXKlZ6tQ4T6LJs2bRK7PotqTE5KSorr9gEA6I1rvDOv8WZ1m7a2Ntm+fbvEy7Zt20xnNCF2AwDQKd6hW5yAOgQAwMzrr78ul112mXR0dJjO1HD77beHvObGgliEdahHAAC8Vo+wC/EIc8QjAHgZSV8AAMtVVFTY2ngZav1VVVVx3T4AAF4xbNgw09fXrFmjTR1CBRAzMzMt+yzxTGDbtWuXtLS0BH2duhAAING4xjvzGl9ZWWlbfYXYDQDAzfGOtWvXRrxOneIE1CEAAMHMnz9fLr74Ytm9e3fQ9/j9fvnVr34lBx98cFx3JLEIa1CPAAB4sR5hF+IR5ohHAPAykr4AAJarrq62vEE0EmYdzNWIXpF02AYAAMEVFRWZ7p7m5mbX1CFaW1ulqakp6rI56bOESraL9LMAABArrvHOvMZTXwEAILp4h5qxMlI6XXd1+iwAAOssXLhQzj//fGPm6GB8Pp/cdNNNcuSRR8Z91xOLsAb1CACAF+sRdiEeYY54BAAvSxZN7dy5U1566aVBXzv++OPFLk4tFwBYKVQFe+XKlXHb4Wq0ys2bNwd9nQY/AACso5KpQ40AGYmamhrb6hCrVq0yfT3SOoROnwUAgFjpdF300mcJtb1YUF8BAHgp3qFQh7AGdQgAcKb3339fzj33XGNwtWCSkpLkxhtvlOOOOy4hZfLStVenz0IsAgC8x4n1CLsQjzBHPQKAl2mb9LVlyxa5+uqrjYu9k5KrnFouALBSVlaWEdwLNurTihUrpLu7e9BzYay+/PJL09dHjhxp+TYBAPAqdT230pAhQyQ1NVXa29ujus7HYvny5ZbWIUaMGGH6ups+CwAAseIa7876SqjtxYL6CgDAS/EO3eIE1CEAAL199NFHMnv2bGMQbDPXXXednHTSSQnbecQirEE9AgDgxXqEXYhHmCMeAcDLfOIB6kIYeDiJU8sFAFYYO3Zs0Nd27NghX331VVx29CeffGL6+ujRo+OyXQAAvMhsdk0lPT09ovWlpKTI8OHDTTtARTOadjg+/fRT09fHjBkT0foqKyslPz8/6u056bMAABArrvHOvMarGInZgDyLFy8WOz6LKtOoUaPitm0AABId79AtTkAdAgDQ+77xnHPOke3bt5vulKuuukrOOOOMhO44YhHWoB4BAPBiPcIuxCPMEY8A4GWeSPpSjcTxmE1G13IBQLyTvgIjdcTDxx9/HFO5AABAZDMZm8nNzY14d44bNy7oa52dnSETvKMVqm4STaKU2WdpbGyU9evXS6I/S2ZmptTX18dluwAAmOEa77xrfHZ2ttTV1QV9fcmSJUFnYI2FWudnn31mOhq7mkUeAACd4h06xQmoQwAAFHVfN2vWLNm2bZvpDrnsssvk7LPPtmWnEYuIHfUIAIBX6xF2IB5hjngEAC/zRNKXU2fScmq5AMAKU6dONX19wYIFcdnR8+fPD/qa6jBkFtgFAACR+fzzz0OOYu2GOoSaPeyDDz4I+vrQoUOlpKTEFZ9l9erVsnbt2qCvT548Wfx+v+XbBQAgFK7xzrzG77333qZ1pA8//FCs9u9//1va2tqiKhMAAG6Nd+gWJ6AOAQDetnz5cqOj9tatW03fd8EFF8j5558vdiEWERvqEQAAL9cj7EA8IjTiEQC8yhNJXwCAxJswYYIxukIwb731luXJr1988YWsW7fONKibnJxs6TYBAPAydT03U1tbG/E6p0+fbjoj8ptvvilWe+edd0xnsZg5c2ZU61Wfxcy8efPEam+88Ybp69F+FgAAYsU13pnXeDvqK6HWSX0FAKBjvEO3OAF1CADwrhUrVsiZZ54pTU1Npu8755xz5NJLLxU7EYuIDfUIAICX6xF2IB4RGvEIAF5F0hcAIC7UyJBmjYUbNmwwRna20nPPPWf6+n777Wfp9gAA8LLOzk55++23QyaBR6qoqEjGjh0b9HU1I9fXX38tiaxD7LvvvlGtV80wqj5PMK+99poxg4aVnn/+edPXqQ8BAOzCNd6Z1/gZM2ZISkpK0NdfeOEFsZIaAOgf//hH0NdVWUI12gIAEE/btm2Tjz76yPJ4h25xAuoQAOBNauans846SxobG03f993vfleuuOIKsRuxiNhQjwAAeLkekWjEI8JDPAKAV5H0BQCIm2OOOcb09SeffNLSjufPPPNM0NfVDF9HHHGEZdsDAMDr/va3v8nmzZtNr7177LGH5XUI1VH4qaeeEqts375dXn755aCvFxYWRj3qtc/nk6OOOiro6zt37jTt9ByplStXmibVjx49WhoaGizbHgAAkeIa77xrfF5enuyzzz5BX1+1apUsXLhQrLJgwQJZu3Zt0Nf3339/ycnJsWx7AABE6sEHHzTaG8w6jw8dOlS8HiegDgEA3qMGY1Mzc6xfv970faeccopce+214hTEIqJDPQIAYCW31iMSiXhEeIhHAPAqkr4SrKOjo+f/SUlJ//0ifHwVAPRzwAEHSG5ubtDXn332Wdm0aZMl23rppZdMOw2pDkyq0zYAAIhde3u7/P73vzd9z7Rp06LusHv00Ucbs4YG8+ijjxplsMLjjz9udKoK5sgjjzQS2KJ17LHHhgzeWkWtSyXFBfONb3zDsm0BABANrvHOvMaHqq888MADMa0/knVRXwEA2EkNbvPQQw+ZvueQQw6JaRs6xQmoQwCAd2zYsMHoqG3WHq+ccMIJ8uMf/1ichFhEdKhHAACs4uZ6RKIQj4gM8QgAXkSmUYIF60yYkpKS6KIAQNylpqbKSSedFPR11Vn7zjvvjHk7u3fvljvuuMP0PaeffnrM2wEAAP9x6623hgzKxtJxqLS0VA499NCgr6ukcSs6QW3btk3uvffeoK+rgTpirUNMmDBBxo8fH/T1JUuWyHPPPSdWjLo5d+7coK9nZGQYgXIAAOzENd6Z13hV7yorKwv6+quvvioffPCBxOq9996T119/Pejr5eXlcvDBB8e8HQAAoqGSo1Tnsh07dsTUschLcQL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338krr7xigm+HDx8O6b2BAoDW/SqU/VMVFBTE9PEmHDTw3b9/f4/g4HHHHSdPPfWUOUYBAAAAdlGKEAAAAHGjVKlS8tprr/kMdGiH72effSYjR46Uc845R9q1a2eyGZYtWxax7dEsjnBn3rgyQ4IJJRvEO+Cxc+dOCRcNTGipQF+BLF/za/l67P0e7+fewbNYEmsd8rGwPbG2n1qDdzfddJNcddVV5nsZalBLWTONvF/fu3ev+7l17rlw7c/RPN4UlwaqdT6wtWvXul+rV6+evPjiiyF/VgAAAAAZWwAAAIgreXl58vrrr5u5e95991354YcfPDqUrR2pb7/9tslouPzyy2X48OFh7/TX8l7Wcl9aBq5fv37FWqc12ymQUDvlrRk54fwcNNtG5/n58ssvzfNff/3VfCau0oH63FeASufT0YwhDQjoXD+rVq1yz81kDYZpptkxxxwTtu1F8u2nLhpAmzBhgsdrFSpUMFlDWjpQs8k0KGede0zdfffdQTPavLPOrMeFcO3P0TzeFIcG0jVTyxq81HZfffVVKVeuXMTbBwAAQOIhsAUAAIC41KFDB/Ojc/jMmjVLZsyYYX7++OMPM1eONSNBy45pp/qIESPCug06Z5RmWriyKDSY06dPH3GCtqnzZoWyfFHK1dmhmTmuwJaWGdM5vBo2bCiLFy92l04rXbq0CSC4uJ5Pnz7dnaWlgS0NILhei/VsLcTHfqq07KWWR3TReaU00KXzgQUqhaiBJDvbo8E4zTzavXu3eR5qZpWd5aN5vCkq/Tw0Q27+/Pnu16pWrSqjR482wU8AAACgKChFCAAAgLim2RU6V8+gQYNMx7hm+2h5q/bt23ssp1khGmgJN83ycNGsI19ZKZGwfPly28vqHEfr16/3uc3h4F1yzpVxZS0pqBla3pkw1ve53jN79myPgEcsz6+F+NlPP/nkE49sp8cff1y6dOkSdH4vDdTazY607leu+ezsWrFiRUwfb4pCg5mDBw82QUzr9muZylDnIAMAAACsCGwBAAAg4TrQNUPk5ZdfNnP4uGin9uTJk8PeXosWLTw6cq2l9yJpzpw5tpedO3eux1w6TZo0Ceu21K5d25QMdHEFtPzNr+XrNV1W/0bW9ygCW4nJ6f1UM7asGUN2MwFD2c80S9Hl77//dmcrhrp9sXi8CZVmXg4dOtQjuK1z8WlQq2bNmlHdNgAAAMQ/AlsAAABIWDpnj9Xq1at9Luc9p08o8+O0a9fO4/k777wjTvj888+LvKy1czxcWrdu7X6speY0SyxYScFmzZqZ8m1q8+bNsmjRIo+OcJ2HJ9yZHcX5WyN+99OtW7e6H1esWNH2+yZNmmR72ebNm3s8d5XntMPustE63oRCM9xuvvlmjwCllj/VObXq1KkT1W0DAABAYiCwBQAAgIRVoUIFj+fepfBcMjMzPZ6HMj+OztFTUFDgfj5lyhSZOnWqRNrMmTM9gkD+aObIBx984JE1oZky4WYNXOm8OlpSbseOHe42GzRo4HPOIC1P5/Ltt9+a3yuS2VrF+VsjfvdT63utZTkDWbp0qXz88ce22+jatav5TrtodpJmVQXzzTff2C6/GK3jTSjuvvtuU/rRRYPXWnayUaNGUd0uAAAAJA4CWwAAAIgLGiyZOHGiHDhwoMjZFscee6zP5awdxWrJkiW229BO+IEDB3q8duONN3oEaOyWPNOSgaG44447ZNOmTX7/XzvVNXNCPzuX888/32/goDi8M7K0I9uazeVvLiNr8Gr06NEegYBIBLaK87dG/O6nWi7TZd26dUHLHWoGoZbSsxOYss4fpYEn6xxYI0eODBp4vu++++LieGPHqFGjZNy4ce7nJUuWlGeffTYiWaIAAABIXgS2AAAAEBf27t0rN9xwg3Tq1Ml0nmpHrs7j4ouWwdMgyUMPPeR+rXTp0nLGGWf4XN47k2DMmDGmPbs0WNSlSxf38+3bt8sll1wi//3vfwPOs7N27Vp566235MILL5Rzzz1X/vzzT9ttaok+Ldmm7502bdpR/798+XLp37+/R1ZXtWrVjuoUDxfdHuvcOVu2bLEVoLIGxKzvSUlJ8ShvGC7F/VsjPvdT73VqMEhLdHqXM9TnX3/9tfTp08dkUenccdYsrGA0kOwqr6k0c1Hb0kCZt59++snsvxpoq1KlSkwfb+x47rnn5JVXXnE/L1GihPzf//2f7fnMAAAAALvsX6EDAAAAMUAzHLTzVH80G6Bu3bqm81nncNEOdC0zNm/ePI8sJXXrrbceVfLMms3RsGFDmT9/vnmuwSAt16dz5uTk5HhkG2lGximnnOLxfg3CaGbGzp07TWe10kwPzVp66aWXzDbWqFFDypYta7Zr27ZtJtvEV2e3XQ888IBcddVVsmLFCtOprfNR1atXz3Qm62uajWHttNfOdu341sBBpGgAS9v21rZtW7/vqV+/vvmMrXMgKf1dcnNzw76Nxf1bIz730/bt25vvoWv/1DKG1113nQnINmnSxJQq1P1y9uzZ7v1S96VHHnlErrzyStt/dl2fZmBpMEvnmlJazvCzzz4z26n/r8E4DSppRpcrC0vL9w0YMMDjmOJPNI43weh8ek888YTHaxpI/+qrr8xPKAYPHixVq1YN8xYCAAAgkRDYAgAAQNzSjA8tqaU//mgg57bbbpO+ffsGXNedd94pl156qbuEmnYG6/w13rTD1VewQzvGX375ZXn88cdNFoprPRpcWrhwofkJpGnTpqaD3a527drJgw8+aMoRaluaoaU/vugcV5o5cdxxx0kkaWDLWoZMaUe+Bt38cWVmffHFFx6vRzLLo7h/a8Tnfqr7Zr9+/UxAzUWzpfTHmysQXJSswe7du5tAkwar9HdXGszT4I+v3/uxxx4zATvv9gNx+ngTjGaNeVu2bJn5CZVmpBHYAgAAQCCUIgQAAEBc0OyDa6+91pQ4s9PpWbFiRbn88stNwCRYZ7nSOWDeffddk9mRlpZWpG3U92kZNs1QuOyyy6R69eoBl9eAzxVXXGHafe+990z2Uih69uwp77//vnTt2tXnvFna+a0l1TRbJBJl/XwFtrwzTewEqHyVKoxkYCscf2vE336qAd6xY8eaubP8ZYXpMlqq75NPPpFTTz1Viuqcc86RCRMmSOfOnX3umxrQ6tWrl4wfP960s2PHDo//z87OjrnjDQAAABArUo54FxUHAAAA4oCW4VqzZo3JFNBOYX2uJc+0Q1hLloUyZ403LVO2aNEi2bBhgynldejQIff/NW7cWBo0aGB7XTqvzcqVK02pPS1BpsEmLbGn5cLsdF673H777SaI5eKdkaGfgZYb0xJvroCBZmToZ+IkDQjs2bPH/bxZs2bmdw1ES6RNnjzZ47Vu3boFzVpx0bJuWnrRmjFjt+RiqH/rGTNmeGShaOCwKJ+NZrKddNJJxf59nNieRNxPtUygvlf3TX2vljLUfaawsNCUILT68MMP3evW4FCrVq1C3k7NEtP5r3S+roKCAtOOBgFddF6va665xv38hRdeKFK2YLiON6HSv7GrLGJxaVA0EmVIAQAAkDgIbAEAAABxIFhgC0D8uv/++2XMmDHu5xok8pdVBgAAACQ7ShECAAAAABAlW7ZsMVlhLprJRlALAAAA8I/AFgAAAAAAYaJlJFevXm1rWS0XOHz4cPnnn3/cr5133nn8LQAAAIAACGwBAAAAABAmc+bMkTPPPFPuvvtuM1+aLzrV9dSpU6Vv374ec1NVrVpVevfuzd8CAAAACCA90H8CAAAAAIDQ7N+/X9555x3zk5eXJw0aNJCcnBw5fPiwbN68WebNmyc7duzweE9GRoY8+uijUrZsWT5uAAAAIAACWwAAAAAARMimTZvk+++/D7hMxYoV5emnn5ZmzZrxdwAAAACCILAFAAAAAECYNG/eXLp37y7Tpk2TDRs2+F0uJSVF6tSpY0oPaknCMmXK8DcAAAAAbEg5osW9AQAAAMS0GTNmyLJly9zP+/TpE9XtARCclh1ct26dKTu4fft22bt3r2RmZkr58uWlbt265l8AAAAAoSGwBQAAAAAAAAAAgLiQGu0NAAAAAAAAAAAAAOwgsAUAAAAAAAAAAIC4QGALAAAAAAAAAAAAcYHAFgAAAAAAAAAAAOICgS0AAAAAAAAAAADEBQJbAAAAAAAAAAAAiAsEtgAAAAAAAAAAABAXCGwBAAAAAAAAAAAgLhDYAgAAAAAAAAAAQFwgsAUAAAAAAAAAAIC4QGALAAAAAAAAAAAAcYHAFgAAAAAAAAAAAOICgS0AAAAAAAAAAADEBQJbAAAAAAAAAAAAiAsEtgAAAAAAAAAAABAXCGwBAAAAAAAAAAAgLhDYAgAAAAAAAAAAQFwgsAUAAAAAAAAAAIC4QGALAAAAAAAAAAAAcYHAFgAAAAAAAAAAACQe/D9wsetNp6fb2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 1600x640 with 1 Axes>"
+       "<Figure size 800x320 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 663,
-       "width": 1726
+       "height": 344,
+       "width": 859
       }
      },
      "output_type": "display_data"
@@ -1998,7 +2083,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9163ac60",
+   "id": "862a00fe",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
@@ -2012,7 +2097,7 @@
     "window of exactly one or two passes averages over the *same rows* every time\n",
     "and the batch-composition noise, which dominates the per-step view, cancels.\n",
     "Widen the window to one and a half passes and it does not: the control sits\n",
-    "at roughly twenty times the spread of its neighbours on either side, even\n",
+    "at more than twenty times the spread of its neighbours on either side, even\n",
     "though it is *wider* than the one-epoch window. Nothing but the alignment\n",
     "changed.\n",
     "\n",
@@ -2028,15 +2113,18 @@
     ":class: warning\n",
     "It is tempting to accumulate per-step evidence with a one-sided cumulative-sum\n",
     "statistic {cite:p}`page1954continuous`, $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
-    "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. The rectification is the problem. Under symmetric noise\n",
-    "$\\mathbb{E}[\\max(z,0)]$ is a fraction of a standard deviation, so any\n",
-    "allowance $\\kappa$ above that value makes $S$ climb at a constant rate *on\n",
-    "noise alone* and cross any fixed threshold after a fixed number of steps —\n",
-    "whatever the data is doing. A rule built that way announces convergence on\n",
-    "schedule rather than on evidence. The fix is not a better threshold but a\n",
-    "wider horizon, plus a second yardstick that asks whether the remaining\n",
-    "improvement is negligible relative to the reduction already achieved. That\n",
-    "design is under review as\n",
+    "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that\n",
+    "recursion behaves sensibly while there is signal: if the standardised\n",
+    "improvement stays above $\\kappa$, the increment is negative and $S$ sits at\n",
+    "zero. The difficulty is what happens when there is *no* resolvable signal.\n",
+    "Under symmetric noise $\\mathbb{E}[\\max(z,0)]$ is only a fraction of a standard\n",
+    "deviation, so with $\\kappa$ above that value $S$ climbs at a roughly constant\n",
+    "rate and crosses any fixed threshold after a roughly fixed number of steps. The\n",
+    "rule therefore cannot tell \"converged\" from \"still improving, but too slowly\n",
+    "for this horizon to see\" — it announces the same thing at the same pace in\n",
+    "both cases. Which is why the fix is not a better threshold but a wider horizon,\n",
+    "plus a second yardstick asking whether the remaining improvement is negligible\n",
+    "relative to the reduction already achieved. That design is under review as\n",
     "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this\n",
     "notebook shows the observation problem it exists to solve rather than shipping\n",
     "a second copy of it.\n",
@@ -2049,14 +2137,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "fdd425a9",
+   "execution_count": 20,
+   "id": "0e5dc504",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:11.458430Z",
-     "iopub.status.busy": "2026-08-16T07:45:11.458291Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.461504Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.461012Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.720391Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.720333Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.722437Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.722147Z"
     }
    },
    "outputs": [
@@ -2064,7 +2152,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "stage 2 smoothed reduction: 0.60 nats; 99% of it reached by step 1,631 of 2,500 (65% of the stage-2 budget)\n"
+      "stage 2 smoothed reduction: 188.11 nats; 99% of it reached by step 1,946 of 2,500 (78% of the stage-2 budget)\n"
      ]
     }
    ],
@@ -2086,7 +2174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f8959b8",
+   "id": "763f1723",
    "metadata": {},
    "source": [
     "Two honest qualifications about any stop, this one included. What such a rule\n",
@@ -2112,7 +2200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aeaa364",
+   "id": "0e20ce4a",
    "metadata": {},
    "source": [
     "### Choosing a path: why the fast one is not taken here\n",
@@ -2130,14 +2218,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "790757c7",
+   "execution_count": 21,
+   "id": "2ef45e27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:11.463087Z",
-     "iopub.status.busy": "2026-08-16T07:45:11.462987Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.498108Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.497436Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.723402Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.723346Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.739908Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.739496Z"
     }
    },
    "outputs": [
@@ -2158,7 +2246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41c2f3ea",
+   "id": "4f45ddf6",
    "metadata": {},
    "source": [
     "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
@@ -2172,7 +2260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a86a2db6",
+   "id": "0c3eb165",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2184,7 +2272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef9e4cd7",
+   "id": "2e33ded5",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2196,7 +2284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a98e518a",
+   "id": "216c5e2f",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2204,14 +2292,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "91a9655e",
+   "execution_count": 22,
+   "id": "c677643e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-16T07:45:11.499940Z",
-     "iopub.status.busy": "2026-08-16T07:45:11.499792Z",
-     "iopub.status.idle": "2026-08-16T07:45:11.523090Z",
-     "shell.execute_reply": "2026-08-16T07:45:11.522484Z"
+     "iopub.execute_input": "2026-08-17T01:01:50.740942Z",
+     "iopub.status.busy": "2026-08-17T01:01:50.740879Z",
+     "iopub.status.idle": "2026-08-17T01:01:50.759840Z",
+     "shell.execute_reply": "2026-08-17T01:01:50.759488Z"
     }
    },
    "outputs": [
@@ -2223,21 +2311,20 @@
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
-      "IPython version      : 9.16.0\n",
+      "IPython version      : 9.16.1\n",
       "\n",
-      "pytensor   : 3.1.3\n",
-      "pymc_extras: 0.13.1.dev4+gf8c50e5c6\n",
+      "pytensor   : 3.2.4\n",
+      "pymc_extras: 0.14.1.dev3+g8db1880d4\n",
       "\n",
-      "arviz      : 1.2.0\n",
-      "json       : 2.0.9\n",
+      "arviz      : 1.3.0\n",
       "logging    : 0.5.1.2\n",
-      "matplotlib : 3.10.9\n",
+      "matplotlib : 3.11.1\n",
       "numpy      : 2.4.6\n",
-      "pandas     : 3.0.3\n",
-      "pyarrow    : 25.0.0\n",
-      "pymc       : 6.1.0\n",
-      "pymc_extras: 0.13.1.dev4+gf8c50e5c6\n",
-      "pytensor   : 3.1.3\n",
+      "pandas     : 3.0.5\n",
+      "pyarrow    : 25.0.1\n",
+      "pymc       : 6.2.0\n",
+      "pymc_extras: 0.14.1.dev3+g8db1880d4\n",
+      "pytensor   : 3.2.4\n",
       "scipy      : 1.18.0\n",
       "\n",
       "Watermark: 2.6.0\n",
@@ -2252,7 +2339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f5309be",
+   "id": "bbfc96a1",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 60e17a459..ba07945b1 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -27,8 +27,10 @@ myst:
 +++
 
 Exchanges publish per-trade archives — Binance, for instance, distributes
-aggregate trades as daily and monthly files — and a multi-symbol,
-multi-month slice of such an archive does not fit in memory. PyMC's
+aggregate trades as daily and monthly files at
+[data.binance.vision](https://data.binance.vision). Widen a pull across
+symbols and months and the feature matrix can outgrow the RAM of an ordinary
+workstation; at that point in-memory minibatching stops being an option. PyMC's
 {class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a
 disk-backed reader, so it cannot help once the array no longer fits. This
 notebook fits a hierarchical hurdle–Student-t model of *next-event price
@@ -41,9 +43,11 @@ rows that are generated inside the notebook. It tries to teach three things:
 2. **The mechanics, end to end** on an archive small enough to rebuild here:
    an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
    what a stopping rule has to be able to see before it can fire.
-3. **Honest reporting.** Mean-field variational inference produces some
-   numbers you should trust and some you should not; with known ground truth
-   we measure which are which instead of guessing.
+3. **Honest reporting.** With the truth known we can watch the posterior means
+   land on it while the mean-field standard deviations stay far too narrow to
+   cover it. Which widths could be trusted in general is a calibration
+   question that needs many simulated datasets; this notebook fits one, so it
+   withholds width claims rather than certifying them.
 
 The notebook runs in about a minute. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
@@ -78,12 +82,16 @@ one is not about validity but about honesty of the *showcase*: a Normal
 likelihood with per-cell means and variances collapses to per-cell
 $(\sum y, \sum y^2, n)$, so one linear scan computes the exact posterior
 inputs and "streaming inference" degenerates into a glorified `groupby` —
-valid, but theater. What provably breaks the collapse: a Student-t likelihood,
-a mixture (the hurdle below), and row-level continuous covariates through
-nonlinear links.
+valid, but theater. A hurdle alone does not rescue it — an observed Bernoulli
+plus a Normal component still reduces to $(n_0, n_1, \sum y, \sum y^2)$. What
+does break the collapse is the combination used below: a Student-t component
+with an unknown $\nu$, and row-level continuous covariates entering through
+nonlinear links, so no fixed-dimensional summary of the rows suffices.
 
-The model below passes both gates, and each ingredient that makes it pass is
-also substantively motivated by the data.
+The model below passes both gates. Each ingredient that gets it there is also
+the standard choice for the feature it addresses, which is the argument for
+it — not the synthetic data, which was written to contain those features in
+the first place.
 
 +++
 
@@ -92,13 +100,23 @@ also substantively motivated by the data.
 
 :::{note}
 The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into
-pymc-extras after the v0.14.0 release. Until the next release, install
-pymc-extras from `main`:
-`pip install "pymc-extras @ git+https://github.com/pymc-devs/pymc-extras"`.
+pymc-extras after the v0.14.0 release, so it is not in a published version yet
+and `pip install pymc-extras` will not provide it. The outputs stored in this
+notebook were produced against the merge commit itself, which is also the
+install line to use until the next release:
+
+```
+pip install "pymc-extras @ git+https://github.com/pymc-devs/pymc-extras@8db1880"
+```
+
+That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull
+PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the
+bottom of this page. Once the module is released, the plain
+`pip install pymc-extras` above is enough.
 :::
 
 ```{code-cell} ipython3
-import json
+import gc
 import logging
 import os
 import tempfile
@@ -138,11 +156,12 @@ the return $y_i = 10^4 \, (\log p_{i+1} - \log p_i)$ in basis points, and the
 move indicator $m_i = \mathbb{1}[y_i \neq 0]$. Two consequences of indexing by
 *events* rather than by clock time are worth stating before the algebra.
 
-First, a large share of consecutive trades leave the price unchanged, because
-prices live on a discrete grid and many trades do not cross to a new level. A
-purely continuous likelihood puts zero probability mass on that outcome. The
-fix is a hurdle: model *whether* the price moves separately from *how far* it
-moves given that it does.
+First, prices move on a discrete grid, so an exact-zero return is not a
+measure-zero event the way it is for a continuous variable: consecutive trades
+can and do leave the price where it was. A purely continuous likelihood puts
+zero probability mass on that outcome, whatever share of the rows it turns out
+to occupy. The fix is a hurdle: model *whether* the price moves separately
+from *how far* it moves given that it does.
 
 Second, each row is a transition between consecutive observed events, not a
 fixed-duration return. So $1 - \pi$ is the probability that the next event
@@ -178,16 +197,19 @@ the standard way to encode intraday periodicity smoothly
 {cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
 back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
 $b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
-so a thin symbol borrows the global intraday shape where its own data run out —
-that is the hierarchical payoff we will measure. They are parameterized
+so a thin symbol is pulled toward the global intraday shape where its own data
+run out. They are parameterized
 *centered*, deliberately: the non-centered trick pays off when groups are
 data-poor, and the generator below gives every symbol at least several hundred
 rows.
 The degrees of freedom are
 shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
-the floor of 1 (rather than the more comfortable 2) leaves room for tails too
-heavy to carry a finite variance — and the estimand below is chosen to stay
-meaningful exactly there.
+the floor of 1 rather than 2 leaves the support open to tails too heavy to
+carry a finite variance. Be precise about what that buys: under
+$\eta \sim \mathcal{N}(5, 1)$ the prior puts only about $4 \times 10^{-6}$ of
+its mass on $\nu \le 2$, so this is a statement about support, not a serious
+prior belief in infinite variance. The estimand below is chosen so that the
+question does not arise at all.
 
 The reported estimand is **event-return dispersion on the event clock**:
 $\pi_{s,h}$ together with the conditional-move 90% half-width
@@ -203,8 +225,9 @@ below happens to use $\nu = 3.5$.
 Notebooks in this collection do not download data at build time, so the
 executed path uses a seeded synthetic generator: the schema an exchange
 archive would have after feature construction, a hurdle at exactly zero,
-heavy conditional tails, and twelve symbols with a deliberately thin tail so
-shrinkage is visible. Because the truth is known, recovery is checkable. Every
+heavy conditional tails, and twelve symbols whose row counts span two orders
+of magnitude, so the hierarchy has both data-rich and data-poor groups to work
+with. Because the truth is known, recovery is checkable. Every
 statement below about what the fit recovers is conditional on this generator;
 none of it is a measurement of any market.
 
@@ -314,7 +337,7 @@ them the way one would read a specification for a simulator:
   conditional location.
 * $a$ and $q$ are constructed with a target correlation of 0.3 rather than
   orthogonally, so the fit faces mildly collinear covariates instead of a
-  textbook design; the realized sample correlation is printed below.
+  textbook design; the realized sample correlation is in the printout above.
 * `ylag` is generated per symbol in sequence, so row $i$ sees only the most
   recent previously generated nonzero move. That discipline applies to the lag
   construction; the standardization of $a$ and $q$ still uses full-sample
@@ -378,11 +401,13 @@ def splitmix64(x):
 
 key = splitmix64(np.arange(n, dtype=np.uint64))
 order = np.argsort(key, kind="stable")
-n_shards = 8
+n_shards, BATCH = 10, 1_000
+# Row groups are the loader's batches on the shuffle=False path, so the geometry is
+# chosen to divide exactly: 10 shards x 30,000 rows, 30 groups of 1,000 each.
+assert n % n_shards == 0 and (n // n_shards) % BATCH == 0
 for i in range(n_shards):
     part = table.take(order[i::n_shards])
-    # Row groups are the loader's batches on the shuffle=False path, so size them here.
-    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"), row_group_size=1024)
+    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"), row_group_size=BATCH)
 
 head = pq.read_table(os.path.join(data_dir, "shard_000.parquet")).slice(0, 10_000)
 print(
@@ -392,24 +417,37 @@ print(
 ```
 
 The printed check is the one to keep: the head of any shard must already mix
-every hour and nearly every symbol, asserted rather than assumed. Note that
-the cell above is the pedagogical miniature — it sorts the whole table in
-memory, which is exactly what one cannot do once the table stops fitting. The
-out-of-core version of the same permutation is two passes: hash-scatter rows
-into shards with bounded row-group appends, then sort each shard
-independently, so no step holds more than one shard.
+every hour and nearly every symbol, asserted rather than assumed.
 
-+++
+Be clear about what this cell is and is not. It is the pedagogical miniature:
+it builds the whole table, the whole key array and the whole permutation in
+memory, which is exactly what one cannot do once the data stops fitting. What
+follows it — the inference — is genuinely disk-backed, but the preprocessing
+above is not. The out-of-core version of the same permutation is two passes:
+hash-scatter rows into shards with bounded row-group appends, then sort each
+shard independently, so no step holds more than one shard.
+
+So that the fit really does run against disk rather than against arrays still
+resident from the generator, the row-scale objects are released first:
+
+```{code-cell} ipython3
+del table, key, order, y, m, d, q, a, ylag, sym, hour, logit_pi, log_sigma, t_draw, sigma
+gc.collect()
+print("row-scale generator arrays released; the fit reads from", data_dir)
+```
 
 ## Streaming the model
 
 With `shuffle=False` the `DataLoader` passes source blocks through verbatim —
 one block per Parquet row group, in a frozen column order — which is why the
-shards were written with `row_group_size=1024` above. The dataset size comes
-from the Parquet metadata: `loader.total_size` is `N`, and `len(loader)` is
-`total_size // batch_size`, the number of *full* batches — which, as the cell
-below shows, is not the same as the number of blocks an epoch yields on this
-path. The model reads
+shards above were written with `row_group_size` equal to the batch size, and
+why the row counts were chosen to divide exactly. That is worth doing
+deliberately: on this path the loader hands you the row groups it finds, so a
+ragged geometry gives ragged batches, `len(loader)` (which is
+`total_size // batch_size`) stops matching the number of blocks an epoch
+yields, and — as the fitting section shows — the recorded loss picks up a
+deterministic sawtooth that has nothing to do with convergence. The model
+reads
 one `pm.Data` placeholder; everything derived — the Fourier basis, the
 integer symbol index — is computed inside the graph, so advancing the stream
 is a single `set_value` per step.
@@ -418,42 +456,40 @@ is a single `set_value` per step.
 columns = ["y_bp", "m", "d", "q_std", "a_std", "ylag_bp", "hour", "sym"]
 loader = DataLoader(
     parquet_source(data_dir, columns=columns),
-    batch_size=1024,
+    batch_size=BATCH,
     shuffle=False,  # the shards are already globally shuffled on disk
     total_size="auto",
 )
 
-# Count one epoch instead of inferring it: on the verbatim path a block is a row
-# group, and the last group of each shard is short, so the block count is not
-# total_size // batch_size.
+# Count one epoch rather than inferring it. With a divisible geometry every block
+# is the same size, so the count and len(loader) agree — which is what the rest of
+# the notebook relies on.
 block_rows = [b.shape[0] for b in loader]
 steps_per_epoch = len(block_rows)
-assert sum(block_rows) == loader.total_size == n
-n_full = block_rows.count(1024)
+assert set(block_rows) == {BATCH}
+assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH
+assert len(loader) == steps_per_epoch
 print(
-    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks per epoch "
-    f"({n_full} of 1024 rows + {steps_per_epoch - n_full} shorter), "
+    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch, "
     f"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}"
 )
 ```
 
 Two things that assertion pins down. First, **nothing is dropped**: verbatim
-pass-through streams every row, and the short trailing group of each shard is
-simply a shorter batch. The `total_size` rescaling copes, because PyMC scales
-the minibatch log-likelihood by `N / b` with `b` read from the batch actually
-installed, so a short block is weighted up by exactly its own size and every
-row enters the epoch exactly once, with no tempering correction to track. One
-precision on Gate 1: the replay is a fixed permutation rather than fresh
-random sampling at each step, so what holds here is equal inclusion *over an
-epoch* — single-shuffle SGD semantics — not independent uniform batches.
-(Only the shuffle-buffer path drops a trailing partial batch; this notebook
-never uses it.)
-
-Second, the printed comparison makes the earlier caveat concrete: because the
-short trailing blocks are streamed rather than dropped, an epoch yields a few
-more blocks than `total_size // batch_size`. Anything that needs a true epoch
-boundary — the stopping horizon below, for instance — should count blocks
-rather than trust the division.
+pass-through streams every row exactly once per epoch. (Ragged geometry would
+still lose nothing — PyMC reads `b` from the batch actually installed, so a
+short block is weighted up by its own size — but it would cost the clean
+diagnostics below, which is why the shards divide.) Only the shuffle-buffer
+path drops a trailing partial batch, and this notebook never uses it.
+
+Second, a precision about Gate 1 that matters for how the fit should be
+described. The permutation is drawn once, on disk, and then replayed in the
+same order every epoch. Each row is included exactly once per pass, but the
+batch at step $t$ is a deterministic function of $t$, not a fresh uniform
+draw — so the individual updates are not the conditionally unbiased stochastic
+gradients of the $N/B$ identity. This is single-shuffle, cyclic finite-sum
+optimization, and it is the arrangement the epoch-scale diagnostics later in
+the notebook exploit.
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -554,10 +590,12 @@ gives the term *observed-RV semantics*, which is what makes PyMC's own
 the hurdle's two logs are written with `softplus` —
 $\log \pi = -\operatorname{softplus}(-x)$,
 $\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at both
-tails. The exact float comparison `value != 0` is safe here because the zeros
-are *structural* — a transition either changes the price or it does not, and
-the smallest genuine move on any listed tick grid sits dozens of orders of
-magnitude above the float32 flush-to-zero threshold.
+tails. The exact float comparison `value != 0` is safe here because the zeros are
+*structural*: the generator writes an exact 0.0 when the hurdle says the price
+did not move, and every nonzero draw is many orders of magnitude above the
+float32 subnormal range. On real data the same comparison is safe as long as
+returns are computed so that "no move" produces an exact zero rather than a
+rounding artefact — worth checking once, in the feature code.
 
 ```{code-cell} ipython3
 model = build_model(
@@ -608,19 +646,39 @@ approx = advi.fit(
 )
 ```
 
-The two-stage learning rate is not decoration: at a constant 0.02 the Adam
-updates near the optimum are larger than the posterior standard deviations of
-the sharpest parameters, so the mean jitters around the mode instead of
-settling into it. Annealing was adopted after measuring exactly that on the
-prototype.
+The two-stage learning rate is deliberate. A constant step size that is
+comfortable early is too large near the optimum, where it keeps the
+variational mean bouncing instead of settling; dropping it once the descent
+has flattened is the cheapest fix. The loss curve below is the evidence
+available here — the visible band narrows after the change.
+
+Before plotting it, one property of `approx.hist` that is easy to get wrong and
+that changes what the numbers mean. PyMC normalizes the minibatch objective
+**twice**: the observed log-probability is scaled up by $N/b$ so the gradient
+targets the full-data model, and then the variational objective is divided by
+that same constant, because `scale_cost_to_minibatch` is on by default. What
+gets recorded per step is therefore
+
+$$
+F_t = -\sum_{i \in B_t} \mathbb{E}_q\left[\log p(y_i \mid \theta)\right]
+      + \frac{b}{N}\,\mathrm{KL}(q \Vert p),
+$$
+
+which lives on the scale of *one batch*, not of the full dataset — and which
+would move mechanically with $b$ if the blocks were ragged. Multiplying by
+$N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks
+that is one constant:
 
 ```{code-cell} ipython3
+ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch
+loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE
+
 fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
-ax.plot(approx.hist, lw=0.5)
+ax.plot(loss, lw=0.5)
 ax.axvline(6_000, color="C1", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
-ax.set_ylim(-450, 1200)
+ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))
 ax.set_xlabel("step")
-ax.set_ylabel("negative ELBO")
+ax.set_ylabel("negative ELBO (full-data scale)")
 ax.set_title(
     "Streaming ADVI loss, y clipped to the plateau (early losses are "
     "off scale).\nThe visible band is batch-resampling noise — "
@@ -664,8 +722,8 @@ recovery.round(4)
 Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
 global coefficient and the mean of its group effects are only *jointly*
 identified: the likelihood constrains their sum, and the prior only weakly
-splits it. The identified sums in the last three rows land within about one
-hundredth of the truth, comparable to the other likelihood-identified rows.
+splits it. The identified sums in the last three rows land within a couple of hundredths
+of the truth, the same range as the other likelihood-identified rows.
 The same translation ridge exists for every global-coefficient/group-effect
 pair in this model (including the vector pairs $c$/$b^{(\pi h)}$ and
 $g$/$b^{(\sigma h)}$, whose sums we spare you) — so raw split rows like
@@ -869,10 +927,14 @@ ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
 ax.legend(loc="lower right");
 ```
 
-All three statistics sit inside their predictive 90% bands. The heading is literal: the
-evaluation batch was seen during the fit, so this is adequacy, not
-generalisation. A held-out split is the next thing to add before any of this
-is used to compare models.
+All three statistics sit inside their predictive 90% bands. Two limits on what
+that establishes. The heading is literal — the evaluation batch was seen during
+the fit, so this is adequacy, not generalisation, and a held-out split is the
+next thing to add before any of this is used to compare models. And the check
+is one batch, one step ahead, with the observed `ylag` held fixed: it does not
+simulate a price path forward, and it says nothing about the hierarchy, the
+hourly curves, or the covariate responses, each of which would need its own
+stratified check.
 
 +++
 
@@ -898,9 +960,9 @@ accumulates linearly in $w$, so the detectable drift shrinks like $w^{-3/2}$:
 the horizon does not merely smooth the picture, it sets what is visible at all.
 
 ```{code-cell} ipython3
-# Stage 2 only: after the last planned optimizer change, which is where a
-# stopping decision would actually be taken.
-stage2 = np.asarray(approx.hist[6_000:], dtype=float)
+# Stage 2 only, on the full-data scale: after the last planned optimizer change,
+# which is where a stopping decision would actually be taken.
+stage2 = loss[6_000:]
 sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0
 
 
@@ -955,7 +1017,7 @@ shuffle is done once on disk and replayed in the same order every epoch, so a
 window of exactly one or two passes averages over the *same rows* every time
 and the batch-composition noise, which dominates the per-step view, cancels.
 Widen the window to one and a half passes and it does not: the control sits
-at roughly twenty times the spread of its neighbours on either side, even
+at more than twenty times the spread of its neighbours on either side, even
 though it is *wider* than the one-epoch window. Nothing but the alignment
 changed.
 
@@ -971,15 +1033,18 @@ the data was shuffled, not of the optimizer.
 :class: warning
 It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
 statistic {cite:p}`page1954continuous`, $S \leftarrow \max(0,\ S + (\kappa -
-\max(z, 0)))$, stopping when $S$ exceeds a threshold. The rectification is the problem. Under symmetric noise
-$\mathbb{E}[\max(z,0)]$ is a fraction of a standard deviation, so any
-allowance $\kappa$ above that value makes $S$ climb at a constant rate *on
-noise alone* and cross any fixed threshold after a fixed number of steps —
-whatever the data is doing. A rule built that way announces convergence on
-schedule rather than on evidence. The fix is not a better threshold but a
-wider horizon, plus a second yardstick that asks whether the remaining
-improvement is negligible relative to the reduction already achieved. That
-design is under review as
+\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that
+recursion behaves sensibly while there is signal: if the standardised
+improvement stays above $\kappa$, the increment is negative and $S$ sits at
+zero. The difficulty is what happens when there is *no* resolvable signal.
+Under symmetric noise $\mathbb{E}[\max(z,0)]$ is only a fraction of a standard
+deviation, so with $\kappa$ above that value $S$ climbs at a roughly constant
+rate and crosses any fixed threshold after a roughly fixed number of steps. The
+rule therefore cannot tell "converged" from "still improving, but too slowly
+for this horizon to see" — it announces the same thing at the same pace in
+both cases. Which is why the fix is not a better threshold but a wider horizon,
+plus a second yardstick asking whether the remaining improvement is negligible
+relative to the reduction already achieved. That design is under review as
 [pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this
 notebook shows the observation problem it exists to solve rather than shipping
 a second copy of it.

From 42be54ff2aa90de03855281a5c355cc337a836c4 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 20:08:59 -0500
Subject: [PATCH 35/42] Give readers the install line the stored outputs were
 built with

The generated dependency line asked for a released pymc-extras, which does
not contain the DataLoader, while the note underneath said to install from
an unpinned main. Neither reproduces the environment in the watermark.
The dependency line now covers only pyarrow and graphviz, and the note
carries the pinned commit on its own.

The substitution lives in notebook metadata as well as in the front
matter, and the jupytext hook rewrites the text file from the notebook, so
both copies are updated.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/variational_inference/streaming_tick_data.ipynb  | 8 ++------
 .../variational_inference/streaming_tick_data.myst.md     | 8 ++------
 2 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 8c2569aec..8446646ab 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -98,9 +98,6 @@
    "id": "38d7bcfb",
    "metadata": {},
    "source": [
-    ":::{include} ../extra_installs.md\n",
-    ":::\n",
-    "\n",
     ":::{note}\n",
     "The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into\n",
     "pymc-extras after the v0.14.0 release, so it is not in a published version yet\n",
@@ -114,8 +111,7 @@
     "\n",
     "That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull\n",
     "PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the\n",
-    "bottom of this page. Once the module is released, the plain\n",
-    "`pip install pymc-extras` above is enough.\n",
+    "bottom of this page. Once the module ships, `pip install pymc-extras` will do.\n",
     ":::"
    ]
   },
@@ -2370,7 +2366,7 @@
   },
   "myst": {
    "substitutions": {
-    "extra_dependencies": "pymc-extras pyarrow"
+    "extra_dependencies": "pyarrow graphviz"
    }
   }
  },
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index ba07945b1..cb9121246 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -11,7 +11,7 @@ kernelspec:
   name: python3
 myst:
   substitutions:
-    extra_dependencies: pymc-extras pyarrow
+    extra_dependencies: pyarrow graphviz
 ---
 
 (streaming_tick_data)=
@@ -95,9 +95,6 @@ the first place.
 
 +++
 
-:::{include} ../extra_installs.md
-:::
-
 :::{note}
 The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into
 pymc-extras after the v0.14.0 release, so it is not in a published version yet
@@ -111,8 +108,7 @@ pip install "pymc-extras @ git+https://github.com/pymc-devs/pymc-extras@8db1880"
 
 That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull
 PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the
-bottom of this page. Once the module is released, the plain
-`pip install pymc-extras` above is enough.
+bottom of this page. Once the module ships, `pip install pymc-extras` will do.
 :::
 
 ```{code-cell} ipython3

From 1843748a68d963fb0ba48ae8967379b8effa1037 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Sun, 16 Aug 2026 23:52:19 -0500
Subject: [PATCH 36/42] Surface the extra-dependency install line, and print
 the z-scores

Two defects, both invisible from the source and both visible on the built
page.

The front matter declared pyarrow as an extra dependency, but the notebook
never included ../extra_installs.md, which is the only thing that consumes
that substitution (conf.py maps pip_dependencies -> extra_dependencies).
Nineteen notebooks in the repo declare extras; seventeen include the
snippet. This was one of the two that did not, so the declaration rendered
nowhere and a reader following the page installed pymc-extras and then
failed on `import pyarrow`. graphviz is dropped from the declaration
rather than added to it: fifty-five notebooks call model_to_graphviz and
none of them declare it, so the repo treats it as assumed.

The recovery section then argued from a number it refused to print.
"Notice also what we did not print: z-scores against the truth. Some would
exceed 2" understates by a factor of three, and the reader can compute the
column from the three columns that were printed. It is now the fourth
column, and the prose reads it against identification instead of asserting
a bound on it: the raw split rows are not recovery statistics at all
(alpha0 reads 404, which is a statement about an arbitrary decomposition),
the identified sums are all inside 2, and between them sit theta_d at -7.1
and lambda_q at +4.9 -- cleanly identified rows that miss by more than
their own width allows. The withheld-width stance survives, but it now
rests on a printed number rather than on a characterization of one.

Re-executed in the pinned environment; every other output is unchanged.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 884 +++++++++---------
 .../streaming_tick_data.myst.md               |  45 +-
 2 files changed, 488 insertions(+), 441 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 8446646ab..256d78198 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a3e74acf",
+   "id": "8dd3d69f",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a836a490",
+   "id": "e976da26",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
@@ -39,10 +39,12 @@
     "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
     "   what a stopping rule has to be able to see before it can fire.\n",
     "3. **Honest reporting.** With the truth known we can watch the posterior means\n",
-    "   land on it while the mean-field standard deviations stay far too narrow to\n",
-    "   cover it. Which widths could be trusted in general is a calibration\n",
-    "   question that needs many simulated datasets; this notebook fits one, so it\n",
-    "   withholds width claims rather than certifying them.\n",
+    "   land close to it while the mean-field standard deviations stay far too\n",
+    "   narrow to cover the distance that remains — standardized gaps of 5 to 7 on\n",
+    "   rows that are cleanly identified. Which widths could be trusted in general\n",
+    "   is a calibration question that needs many simulated datasets; this notebook\n",
+    "   fits one, so it reports that gap and withholds width claims rather than\n",
+    "   certifying them.\n",
     "\n",
     "The notebook runs in about a minute. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
@@ -51,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23d07b3d",
+   "id": "21eb99eb",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -95,9 +97,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38d7bcfb",
+   "id": "02c02e2d",
    "metadata": {},
    "source": [
+    ":::{include} ../extra_installs.md\n",
+    ":::\n",
+    "\n",
     ":::{note}\n",
     "The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into\n",
     "pymc-extras after the v0.14.0 release, so it is not in a published version yet\n",
@@ -118,13 +123,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fba94140",
+   "id": "0fecb17f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:18.953070Z",
-     "iopub.status.busy": "2026-08-17T01:01:18.952945Z",
-     "iopub.status.idle": "2026-08-17T01:01:20.497978Z",
-     "shell.execute_reply": "2026-08-17T01:01:20.497557Z"
+     "iopub.execute_input": "2026-08-17T04:50:42.791169Z",
+     "iopub.status.busy": "2026-08-17T04:50:42.790950Z",
+     "iopub.status.idle": "2026-08-17T04:50:46.612550Z",
+     "shell.execute_reply": "2026-08-17T04:50:46.611732Z"
     }
    },
    "outputs": [],
@@ -151,13 +156,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0cbf2089",
+   "id": "2c52f1da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:20.499226Z",
-     "iopub.status.busy": "2026-08-17T01:01:20.499080Z",
-     "iopub.status.idle": "2026-08-17T01:01:20.501979Z",
-     "shell.execute_reply": "2026-08-17T01:01:20.501634Z"
+     "iopub.execute_input": "2026-08-17T04:50:46.615107Z",
+     "iopub.status.busy": "2026-08-17T04:50:46.614768Z",
+     "iopub.status.idle": "2026-08-17T04:50:46.620090Z",
+     "shell.execute_reply": "2026-08-17T04:50:46.619538Z"
     }
    },
    "outputs": [],
@@ -177,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c94473fe",
+   "id": "3fdb39ba",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -252,7 +257,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "402fcf59",
+   "id": "f81db8c3",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -270,13 +275,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c12ebcd5",
+   "id": "20ec934e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:20.502928Z",
-     "iopub.status.busy": "2026-08-17T01:01:20.502874Z",
-     "iopub.status.idle": "2026-08-17T01:01:20.505629Z",
-     "shell.execute_reply": "2026-08-17T01:01:20.505272Z"
+     "iopub.execute_input": "2026-08-17T04:50:46.621552Z",
+     "iopub.status.busy": "2026-08-17T04:50:46.621450Z",
+     "iopub.status.idle": "2026-08-17T04:50:46.626334Z",
+     "shell.execute_reply": "2026-08-17T04:50:46.625723Z"
     }
    },
    "outputs": [],
@@ -318,13 +323,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "819fa9a4",
+   "id": "22c4a193",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:20.506458Z",
-     "iopub.status.busy": "2026-08-17T01:01:20.506399Z",
-     "iopub.status.idle": "2026-08-17T01:01:20.656446Z",
-     "shell.execute_reply": "2026-08-17T01:01:20.656067Z"
+     "iopub.execute_input": "2026-08-17T04:50:46.627667Z",
+     "iopub.status.busy": "2026-08-17T04:50:46.627563Z",
+     "iopub.status.idle": "2026-08-17T04:50:46.929771Z",
+     "shell.execute_reply": "2026-08-17T04:50:46.929012Z"
     }
    },
    "outputs": [
@@ -392,7 +397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a4f6dad",
+   "id": "d548a752",
    "metadata": {},
    "source": [
     "Each constant above is a setting, not a measurement, and it is worth reading\n",
@@ -424,13 +429,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "5299a5f8",
+   "id": "f272231f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:20.657401Z",
-     "iopub.status.busy": "2026-08-17T01:01:20.657343Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.041312Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.041014Z"
+     "iopub.execute_input": "2026-08-17T04:50:46.931478Z",
+     "iopub.status.busy": "2026-08-17T04:50:46.931341Z",
+     "iopub.status.idle": "2026-08-17T04:50:47.709873Z",
+     "shell.execute_reply": "2026-08-17T04:50:47.709238Z"
     }
    },
    "outputs": [
@@ -469,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "666a1baf",
+   "id": "1374ef5a",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -490,13 +495,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7c5755a0",
+   "id": "fe16b1ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.042327Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.042251Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.201500Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.201088Z"
+     "iopub.execute_input": "2026-08-17T04:50:47.711705Z",
+     "iopub.status.busy": "2026-08-17T04:50:47.711564Z",
+     "iopub.status.idle": "2026-08-17T04:50:48.089907Z",
+     "shell.execute_reply": "2026-08-17T04:50:48.089270Z"
     }
    },
    "outputs": [
@@ -550,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e8a3c25",
+   "id": "c7555511",
    "metadata": {},
    "source": [
     "The printed check is the one to keep: the head of any shard must already mix\n",
@@ -571,13 +576,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9646b7df",
+   "id": "4228e2e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.202499Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.202431Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.236039Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.235699Z"
+     "iopub.execute_input": "2026-08-17T04:50:48.091297Z",
+     "iopub.status.busy": "2026-08-17T04:50:48.091177Z",
+     "iopub.status.idle": "2026-08-17T04:50:48.149743Z",
+     "shell.execute_reply": "2026-08-17T04:50:48.149155Z"
     }
    },
    "outputs": [
@@ -585,7 +590,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_x1npjld2\n"
+      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_ow6k9hf8\n"
      ]
     }
    ],
@@ -597,7 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae464a8d",
+   "id": "1039f285",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -620,13 +625,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3f4c588b",
+   "id": "62be3cb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.237036Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.236983Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.282356Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.281967Z"
+     "iopub.execute_input": "2026-08-17T04:50:48.151346Z",
+     "iopub.status.busy": "2026-08-17T04:50:48.151208Z",
+     "iopub.status.idle": "2026-08-17T04:50:48.231955Z",
+     "shell.execute_reply": "2026-08-17T04:50:48.231354Z"
     }
    },
    "outputs": [
@@ -663,7 +668,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3a07796",
+   "id": "4402f049",
    "metadata": {},
    "source": [
     "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
@@ -686,13 +691,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "3c899c43",
+   "id": "52923952",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.283360Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.283305Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.287368Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.286996Z"
+     "iopub.execute_input": "2026-08-17T04:50:48.233519Z",
+     "iopub.status.busy": "2026-08-17T04:50:48.233376Z",
+     "iopub.status.idle": "2026-08-17T04:50:48.240463Z",
+     "shell.execute_reply": "2026-08-17T04:50:48.239955Z"
     }
    },
    "outputs": [],
@@ -790,7 +795,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "243c580e",
+   "id": "e33fedcd",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -811,13 +816,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "fdb67df2",
+   "id": "ce22bfab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.288298Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.288243Z",
-     "iopub.status.idle": "2026-08-17T01:01:21.446228Z",
-     "shell.execute_reply": "2026-08-17T01:01:21.445769Z"
+     "iopub.execute_input": "2026-08-17T04:50:48.241886Z",
+     "iopub.status.busy": "2026-08-17T04:50:48.241789Z",
+     "iopub.status.idle": "2026-08-17T04:50:48.694709Z",
+     "shell.execute_reply": "2026-08-17T04:50:48.693785Z"
     }
    },
    "outputs": [
@@ -833,367 +838,367 @@
        "<svg width=\"1705pt\" height=\"483pt\"\n",
        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1700.77,-478.95 1700.77,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.4,-478.95 1701.4,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M809.5,-8C809.5,-8 855.5,-8 855.5,-8 861.5,-8 867.5,-14 867.5,-20 867.5,-20 867.5,-94 867.5,-94 867.5,-100 861.5,-106 855.5,-106 855.5,-106 809.5,-106 809.5,-106 803.5,-106 797.5,-100 797.5,-94 797.5,-94 797.5,-20 797.5,-20 797.5,-14 803.5,-8 809.5,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"835.5\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M806.12,-8C806.12,-8 852.12,-8 852.12,-8 858.12,-8 864.12,-14 864.12,-20 864.12,-20 864.12,-94 864.12,-94 864.12,-100 858.12,-106 852.12,-106 852.12,-106 806.12,-106 806.12,-106 800.12,-106 794.12,-100 794.12,-94 794.12,-94 794.12,-20 794.12,-20 794.12,-14 800.12,-8 806.12,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.12\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M945.5,-243.82C945.5,-243.82 1119.5,-243.82 1119.5,-243.82 1125.5,-243.82 1131.5,-249.82 1131.5,-255.82 1131.5,-255.82 1131.5,-353.63 1131.5,-353.63 1131.5,-359.63 1125.5,-365.63 1119.5,-365.63 1119.5,-365.63 945.5,-365.63 945.5,-365.63 939.5,-365.63 933.5,-359.63 933.5,-353.63 933.5,-353.63 933.5,-255.82 933.5,-255.82 933.5,-249.82 939.5,-243.82 945.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1087.87\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M946.12,-243.82C946.12,-243.82 1120.12,-243.82 1120.12,-243.82 1126.12,-243.82 1132.12,-249.82 1132.12,-255.82 1132.12,-255.82 1132.12,-353.63 1132.12,-353.63 1132.12,-359.63 1126.12,-365.63 1120.12,-365.63 1120.12,-365.63 946.12,-365.63 946.12,-365.63 940.12,-365.63 934.12,-359.63 934.12,-353.63 934.12,-353.63 934.12,-255.82 934.12,-255.82 934.12,-249.82 940.12,-243.82 946.12,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.5\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1151.5,-243.82C1151.5,-243.82 1425.5,-243.82 1425.5,-243.82 1431.5,-243.82 1437.5,-249.82 1437.5,-255.82 1437.5,-255.82 1437.5,-353.63 1437.5,-353.63 1437.5,-359.63 1431.5,-365.63 1425.5,-365.63 1425.5,-365.63 1151.5,-365.63 1151.5,-365.63 1145.5,-365.63 1139.5,-359.63 1139.5,-353.63 1139.5,-353.63 1139.5,-255.82 1139.5,-255.82 1139.5,-249.82 1145.5,-243.82 1151.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.12\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.12,-243.82C1152.12,-243.82 1426.12,-243.82 1426.12,-243.82 1432.12,-243.82 1438.12,-249.82 1438.12,-255.82 1438.12,-255.82 1438.12,-353.63 1438.12,-353.63 1438.12,-359.63 1432.12,-365.63 1426.12,-365.63 1426.12,-365.63 1152.12,-365.63 1152.12,-365.63 1146.12,-365.63 1140.12,-359.63 1140.12,-353.63 1140.12,-353.63 1140.12,-255.82 1140.12,-255.82 1140.12,-249.82 1146.12,-243.82 1152.12,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.75\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1468.5,-243.82C1468.5,-243.82 1642.5,-243.82 1642.5,-243.82 1648.5,-243.82 1654.5,-249.82 1654.5,-255.82 1654.5,-255.82 1654.5,-353.63 1654.5,-353.63 1654.5,-359.63 1648.5,-365.63 1642.5,-365.63 1642.5,-365.63 1468.5,-365.63 1468.5,-365.63 1462.5,-365.63 1456.5,-359.63 1456.5,-353.63 1456.5,-353.63 1456.5,-255.82 1456.5,-255.82 1456.5,-249.82 1462.5,-243.82 1468.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.37\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1469.12,-243.82C1469.12,-243.82 1643.12,-243.82 1643.12,-243.82 1649.12,-243.82 1655.12,-249.82 1655.12,-255.82 1655.12,-255.82 1655.12,-353.63 1655.12,-353.63 1655.12,-359.63 1649.12,-365.63 1643.12,-365.63 1643.12,-365.63 1469.12,-365.63 1469.12,-365.63 1463.12,-365.63 1457.12,-359.63 1457.12,-353.63 1457.12,-353.63 1457.12,-255.82 1457.12,-255.82 1457.12,-249.82 1463.12,-243.82 1469.12,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1571\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M720.5,-114C720.5,-114 944.5,-114 944.5,-114 950.5,-114 956.5,-120 956.5,-126 956.5,-126 956.5,-223.82 956.5,-223.82 956.5,-229.82 950.5,-235.82 944.5,-235.82 944.5,-235.82 720.5,-235.82 720.5,-235.82 714.5,-235.82 708.5,-229.82 708.5,-223.82 708.5,-223.82 708.5,-126 708.5,-126 708.5,-120 714.5,-114 720.5,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M717.12,-114C717.12,-114 941.12,-114 941.12,-114 947.12,-114 953.12,-120 953.12,-126 953.12,-126 953.12,-223.82 953.12,-223.82 953.12,-229.82 947.12,-235.82 941.12,-235.82 941.12,-235.82 717.12,-235.82 717.12,-235.82 711.12,-235.82 705.12,-229.82 705.12,-223.82 705.12,-223.82 705.12,-126 705.12,-126 705.12,-120 711.12,-114 717.12,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"931.62\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M847.5,-98C847.5,-98 817.5,-98 817.5,-98 811.5,-98 805.5,-92 805.5,-86 805.5,-86 805.5,-52.5 805.5,-52.5 805.5,-46.5 811.5,-40.5 817.5,-40.5 817.5,-40.5 847.5,-40.5 847.5,-40.5 853.5,-40.5 859.5,-46.5 859.5,-52.5 859.5,-52.5 859.5,-86 859.5,-86 859.5,-92 853.5,-98 847.5,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M844.12,-98C844.12,-98 814.12,-98 814.12,-98 808.12,-98 802.12,-92 802.12,-86 802.12,-86 802.12,-52.5 802.12,-52.5 802.12,-46.5 808.12,-40.5 814.12,-40.5 814.12,-40.5 844.12,-40.5 844.12,-40.5 850.12,-40.5 856.12,-46.5 856.12,-52.5 856.12,-52.5 856.12,-86 856.12,-86 856.12,-92 850.12,-98 844.12,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- lambda_q -->\n",
+       "<!-- tau_ph -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"832.5\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M70.32,-279.62C80.08,-266.01 93,-251.9 108.5,-243.82 159.96,-216.95 512.22,-199.93 705.75,-192.5\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.75,-196 715.61,-192.12 705.49,-189.01 705.75,-196\"/>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.66,-393.28C1511.98,-385.5 1511.26,-377.25 1510.56,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1514.07,-369.08 1509.72,-359.42 1507.1,-369.69 1514.07,-369.08\"/>\n",
        "</g>\n",
-       "<!-- beta_q -->\n",
+       "<!-- nu -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"158.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"90.25,-345.73 0,-345.73 0,-288.23 90.25,-288.23 90.25,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M175.27,-279.43C183.35,-265.97 194.4,-252.02 208.5,-243.82 250.39,-219.45 536.46,-202.03 706.37,-193.7\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.31,-197.21 716.13,-193.22 705.97,-190.21 706.31,-197.21\"/>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"829.12\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M58.53,-287.86C67.65,-272.06 81.3,-253.61 99.12,-243.82 150.53,-215.58 507.28,-199.13 702.36,-192.15\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"702.44,-195.65 712.31,-191.79 702.19,-188.65 702.44,-195.65\"/>\n",
        "</g>\n",
        "<!-- tau_al -->\n",
        "<g id=\"node4\" class=\"node\">\n",
        "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_al -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
+       "<g id=\"node20\" class=\"node\">\n",
        "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_al&#45;&gt;b_al -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.18,-395.53C1156.84,-385.58 1161.92,-374.73 1166.77,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1169.8,-366.16 1170.87,-355.62 1163.46,-363.19 1169.8,-366.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.81,-395.53C1157.47,-385.58 1162.55,-374.73 1167.4,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.43,-366.16 1171.5,-355.62 1164.09,-363.19 1170.43,-366.16\"/>\n",
        "</g>\n",
-       "<!-- nu -->\n",
+       "<!-- beta_q -->\n",
        "<g id=\"node5\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"307.62,-345.73 217.37,-345.73 217.37,-288.23 307.62,-288.23 307.62,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"262.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"149.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M276.02,-288.06C285.17,-272.34 298.82,-253.89 316.5,-243.82 380.68,-207.25 574.34,-194.69 705.27,-190.39\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.16,-193.89 715.05,-190.08 704.94,-186.9 705.16,-193.89\"/>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.15,-279.79C174.44,-266.21 185.76,-252.08 200.12,-243.82 242.53,-219.43 531.58,-201.99 702.72,-193.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"702.74,-197.17 712.56,-193.19 702.4,-190.18 702.74,-197.17\"/>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
+       "<!-- tau_k -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"366.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"366.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M383.46,-279.74C391.57,-266.33 402.59,-252.33 416.5,-243.82 463.84,-214.86 602.13,-200.66 706.37,-193.9\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.35,-197.41 716.11,-193.29 705.91,-190.43 706.35,-197.41\"/>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- theta_r -->\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.34,-393.93C1273.32,-385.49 1275.43,-376.46 1277.49,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.82,-368.78 1279.7,-358.24 1274.01,-367.18 1280.82,-368.78\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
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-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"466.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"466.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"45.12\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M483.87,-279.66C491.98,-266.4 502.89,-252.53 516.5,-243.82 548.29,-223.46 635,-209.05 709.56,-200.01\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"709.92,-203.49 719.44,-198.84 709.09,-196.54 709.92,-203.49\"/>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M45.12,-393.28C45.12,-381.58 45.12,-368.79 45.12,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"48.63,-357.45 45.13,-347.45 41.63,-357.45 48.63,-357.45\"/>\n",
        "</g>\n",
-       "<!-- lambda_a -->\n",
+       "<!-- lambda_q -->\n",
        "<g id=\"node8\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"574.5\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"258.12\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M596.25,-280.34C605.88,-267.17 618.27,-253.19 632.5,-243.82 657.52,-227.33 687.38,-215.69 716.29,-207.47\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"717.12,-210.87 725.86,-204.88 715.29,-204.12 717.12,-210.87\"/>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M279.08,-279.86C288.86,-266.29 301.76,-252.15 317.12,-243.82 381.42,-208.95 572.66,-195.92 702.32,-191.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"702.11,-194.56 711.98,-190.7 701.86,-187.57 702.11,-194.56\"/>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node9\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"682.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"367.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702.03,-281.13C710.3,-268.37 720.75,-254.46 732.5,-243.82 739.38,-237.58 747.07,-231.77 755.06,-226.43\"/>\n",
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+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M384.09,-279.75C392.2,-266.34 403.22,-252.34 417.12,-243.82 463.84,-215.2 600.04,-200.95 703.16,-194.09\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.34,-197.59 713.1,-193.45 702.89,-190.6 703.34,-197.59\"/>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- tau_sh -->\n",
        "<g id=\"node10\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"782.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M797.09,-278.66C802.05,-265.99 807.68,-251.61 812.96,-238.11\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"816.16,-239.52 816.55,-228.93 809.65,-236.97 816.16,-239.52\"/>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.6,-394.25C1627.8,-385.52 1624.79,-376.15 1621.88,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.24,-366.09 1618.85,-357.64 1618.57,-368.23 1625.24,-366.09\"/>\n",
+       "</g>\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_ph -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1512.03,-393.28C1511.35,-385.5 1510.64,-377.25 1509.94,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.44,-369.08 1509.09,-359.42 1506.47,-369.69 1513.44,-369.08\"/>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.12,-393.28C1389.12,-385.5 1389.12,-377.25 1389.12,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.63,-369.43 1389.13,-359.43 1385.63,-369.43 1392.63,-369.43\"/>\n",
        "</g>\n",
-       "<!-- tau_sh -->\n",
+       "<!-- kappa0 -->\n",
        "<g id=\"node12\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"467.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- b_sh -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M484.5,-279.68C492.62,-266.42 503.53,-252.54 517.12,-243.82 548.34,-223.78 633.22,-209.41 706.58,-200.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.77,-203.8 716.27,-199.12 705.92,-196.85 706.77,-203.8\"/>\n",
        "</g>\n",
-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1629.97,-394.25C1627.17,-385.52 1624.16,-376.15 1621.25,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.61,-366.09 1618.22,-357.64 1617.95,-368.23 1624.61,-366.09\"/>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
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-       "<!-- tau_ba -->\n",
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@@ -1210,7 +1215,7 @@
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     "The plate diagram makes the two-link structure visible at a glance: five\n",
@@ -1228,13 +1233,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c0ff9878",
+   "id": "6bf237a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:21.447266Z",
-     "iopub.status.busy": "2026-08-17T01:01:21.447197Z",
-     "iopub.status.idle": "2026-08-17T01:01:47.343004Z",
-     "shell.execute_reply": "2026-08-17T01:01:47.342417Z"
+     "iopub.execute_input": "2026-08-17T04:50:48.696767Z",
+     "iopub.status.busy": "2026-08-17T04:50:48.696607Z",
+     "iopub.status.idle": "2026-08-17T04:50:58.364906Z",
+     "shell.execute_reply": "2026-08-17T04:50:58.364289Z"
     }
    },
    "outputs": [],
@@ -1271,7 +1276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee7879b6",
+   "id": "cd70a74a",
    "metadata": {},
    "source": [
     "The two-stage learning rate is deliberate. A constant step size that is\n",
@@ -1301,13 +1306,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1989271c",
+   "id": "d07451f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:47.344331Z",
-     "iopub.status.busy": "2026-08-17T01:01:47.344259Z",
-     "iopub.status.idle": "2026-08-17T01:01:47.458892Z",
-     "shell.execute_reply": "2026-08-17T01:01:47.458582Z"
+     "iopub.execute_input": "2026-08-17T04:50:58.367076Z",
+     "iopub.status.busy": "2026-08-17T04:50:58.366906Z",
+     "iopub.status.idle": "2026-08-17T04:50:58.578190Z",
+     "shell.execute_reply": "2026-08-17T04:50:58.577637Z"
     }
    },
    "outputs": [
@@ -1347,7 +1352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "172243ba",
+   "id": "a5179e95",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1356,13 +1361,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "0f52f918",
+   "id": "1ce817b2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:47.460052Z",
-     "iopub.status.busy": "2026-08-17T01:01:47.459978Z",
-     "iopub.status.idle": "2026-08-17T01:01:49.469909Z",
-     "shell.execute_reply": "2026-08-17T01:01:49.469472Z"
+     "iopub.execute_input": "2026-08-17T04:50:58.580070Z",
+     "iopub.status.busy": "2026-08-17T04:50:58.579949Z",
+     "iopub.status.idle": "2026-08-17T04:50:58.808113Z",
+     "shell.execute_reply": "2026-08-17T04:50:58.807478Z"
     }
    },
    "outputs": [
@@ -1391,6 +1396,7 @@
        "      <th>sd</th>\n",
        "      <th>truth</th>\n",
        "      <th>abs_error</th>\n",
+       "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1400,6 +1406,7 @@
        "      <td>0.0048</td>\n",
        "      <td>0.8473</td>\n",
        "      <td>0.3077</td>\n",
+       "      <td>-63.7738</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
@@ -1407,6 +1414,7 @@
        "      <td>0.0032</td>\n",
        "      <td>-2.9957</td>\n",
        "      <td>1.2880</td>\n",
+       "      <td>403.8714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
@@ -1414,6 +1422,7 @@
        "      <td>0.0051</td>\n",
        "      <td>0.3500</td>\n",
        "      <td>0.0109</td>\n",
+       "      <td>2.1339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
@@ -1421,6 +1430,7 @@
        "      <td>0.0054</td>\n",
        "      <td>0.2000</td>\n",
        "      <td>0.0263</td>\n",
+       "      <td>4.8798</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
@@ -1428,6 +1438,7 @@
        "      <td>0.0036</td>\n",
        "      <td>0.1800</td>\n",
        "      <td>0.0245</td>\n",
+       "      <td>-6.8694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
@@ -1435,6 +1446,7 @@
        "      <td>0.0034</td>\n",
        "      <td>0.1200</td>\n",
        "      <td>0.0016</td>\n",
+       "      <td>0.4715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
@@ -1442,6 +1454,7 @@
        "      <td>0.0002</td>\n",
        "      <td>0.0200</td>\n",
        "      <td>0.0016</td>\n",
+       "      <td>-7.0816</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
@@ -1449,6 +1462,7 @@
        "      <td>0.0017</td>\n",
        "      <td>0.2500</td>\n",
        "      <td>0.0031</td>\n",
+       "      <td>-1.7951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
@@ -1456,6 +1470,7 @@
        "      <td>0.0219</td>\n",
        "      <td>3.5000</td>\n",
        "      <td>0.0104</td>\n",
+       "      <td>0.4756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0 + mean(b_k)</th>\n",
@@ -1463,6 +1478,7 @@
        "      <td>0.0097</td>\n",
        "      <td>0.8473</td>\n",
        "      <td>0.0090</td>\n",
+       "      <td>0.9284</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0 + mean(b_al)</th>\n",
@@ -1470,6 +1486,7 @@
        "      <td>0.0065</td>\n",
        "      <td>-2.9957</td>\n",
        "      <td>0.0080</td>\n",
+       "      <td>-1.2307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a + mean(b_ba)</th>\n",
@@ -1477,25 +1494,26 @@
        "      <td>0.0058</td>\n",
        "      <td>0.1800</td>\n",
        "      <td>0.0108</td>\n",
+       "      <td>-1.8818</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       mean      sd   truth  abs_error\n",
-       "kappa0               0.5396  0.0048  0.8473     0.3077\n",
-       "alpha0              -1.7077  0.0032 -2.9957     1.2880\n",
-       "lambda_a             0.3609  0.0051  0.3500     0.0109\n",
-       "lambda_q             0.2263  0.0054  0.2000     0.0263\n",
-       "beta_a               0.1555  0.0036  0.1800     0.0245\n",
-       "beta_q               0.1216  0.0034  0.1200     0.0016\n",
-       "theta_d              0.0184  0.0002  0.0200     0.0016\n",
-       "theta_r              0.2469  0.0017  0.2500     0.0031\n",
-       "nu                   3.5104  0.0219  3.5000     0.0104\n",
-       "kappa0 + mean(b_k)   0.8563  0.0097  0.8473     0.0090\n",
-       "alpha0 + mean(b_al) -3.0037  0.0065 -2.9957     0.0080\n",
-       "beta_a + mean(b_ba)  0.1692  0.0058  0.1800     0.0108"
+       "                       mean      sd   truth  abs_error         z\n",
+       "kappa0               0.5396  0.0048  0.8473     0.3077  -63.7738\n",
+       "alpha0              -1.7077  0.0032 -2.9957     1.2880  403.8714\n",
+       "lambda_a             0.3609  0.0051  0.3500     0.0109    2.1339\n",
+       "lambda_q             0.2263  0.0054  0.2000     0.0263    4.8798\n",
+       "beta_a               0.1555  0.0036  0.1800     0.0245   -6.8694\n",
+       "beta_q               0.1216  0.0034  0.1200     0.0016    0.4715\n",
+       "theta_d              0.0184  0.0002  0.0200     0.0016   -7.0816\n",
+       "theta_r              0.2469  0.0017  0.2500     0.0031   -1.7951\n",
+       "nu                   3.5104  0.0219  3.5000     0.0104    0.4756\n",
+       "kappa0 + mean(b_k)   0.8563  0.0097  0.8473     0.0090    0.9284\n",
+       "alpha0 + mean(b_al) -3.0037  0.0065 -2.9957     0.0080   -1.2307\n",
+       "beta_a + mean(b_ba)  0.1692  0.0058  0.1800     0.0108   -1.8818"
       ]
      },
      "execution_count": 13,
@@ -1530,12 +1548,13 @@
     "    truth[\"beta_a\"],\n",
     "]\n",
     "recovery[\"abs_error\"] = (recovery[\"mean\"] - recovery[\"truth\"]).abs()\n",
+    "recovery[\"z\"] = (recovery[\"mean\"] - recovery[\"truth\"]) / recovery[\"sd\"]\n",
     "recovery.round(4)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dcff8666",
+   "id": "9c1ffd53",
    "metadata": {},
    "source": [
     "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
@@ -1549,15 +1568,26 @@
     "`kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not\n",
     "estimates of their generating values; a sum-to-zero constraint on the symbol\n",
     "effects is the standard reparameterization when the split itself matters.\n",
-    "Notice also what we did *not* print: z-scores against the truth. Some would\n",
-    "exceed 2, because the mean-field standard deviations in the third column are\n",
-    "razor-thin — a z-score against a point truth mixes the sampling noise of this\n",
-    "particular synthetic dataset with variational overconfidence along exactly\n",
-    "such ridges. The stance behind that omission is a risk-management one, and it\n",
-    "is worth stating plainly: material disagreement between independent-seed fits\n",
-    "is enough to withhold a claim about a width, while agreement only removes the\n",
-    "instability warning — it does not validate calibration. Replication can veto a\n",
-    "width; it can never certify one.\n",
+    "The last column standardizes the error by the posterior width, and it has to be\n",
+    "read against identification rather than straight across the table. On the three\n",
+    "raw split rows it is not a recovery statistic at all: `alpha0` reads 404 because\n",
+    "a prior-dependent decomposition can have an arbitrary location and still a\n",
+    "narrow width, which says something about the split and nothing about the fit.\n",
+    "On the identified sums every $|z|$ is below 2. Between those two extremes sit\n",
+    "the rows that are identified on their own and still miss by more than their own\n",
+    "width claims they can: $\\theta_d$ at $-7.1$ and $\\lambda_q$ at $+4.9$.\n",
+    "\n",
+    "That gap is the result of this section, and it is worth being exact about what\n",
+    "it licenses. A z-score against a point truth confounds three things — the\n",
+    "sampling noise of this one synthetic dataset, bias in the variational mean, and\n",
+    "overconfidence in the variational width — and a single fit cannot separate\n",
+    "them. What it does establish is a direction: these widths are too narrow to be\n",
+    "read as uncertainty statements at face value. The stance behind reporting the\n",
+    "gap and stopping there is a risk-management one, and it is worth stating\n",
+    "plainly: material disagreement between independent-seed fits is enough to\n",
+    "withhold a claim about a width, while agreement only removes the instability\n",
+    "warning — it does not validate calibration. Replication can veto a width; it can\n",
+    "never certify one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1566,13 +1596,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "7bb77309",
+   "id": "13a007a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:49.470864Z",
-     "iopub.status.busy": "2026-08-17T01:01:49.470790Z",
-     "iopub.status.idle": "2026-08-17T01:01:49.582460Z",
-     "shell.execute_reply": "2026-08-17T01:01:49.582048Z"
+     "iopub.execute_input": "2026-08-17T04:50:58.809806Z",
+     "iopub.status.busy": "2026-08-17T04:50:58.809531Z",
+     "iopub.status.idle": "2026-08-17T04:50:58.987982Z",
+     "shell.execute_reply": "2026-08-17T04:50:58.987526Z"
     }
    },
    "outputs": [
@@ -1635,7 +1665,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c70ef01",
+   "id": "1cb4fdbe",
    "metadata": {},
    "source": [
     "What the figure supports is narrower than the usual slogan, so it is worth\n",
@@ -1661,13 +1691,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "b8e79b8d",
+   "id": "8bcb748f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:49.583622Z",
-     "iopub.status.busy": "2026-08-17T01:01:49.583554Z",
-     "iopub.status.idle": "2026-08-17T01:01:49.689477Z",
-     "shell.execute_reply": "2026-08-17T01:01:49.689043Z"
+     "iopub.execute_input": "2026-08-17T04:50:58.989754Z",
+     "iopub.status.busy": "2026-08-17T04:50:58.989629Z",
+     "iopub.status.idle": "2026-08-17T04:50:59.190567Z",
+     "shell.execute_reply": "2026-08-17T04:50:59.189991Z"
     }
    },
    "outputs": [
@@ -1738,7 +1768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73366b8c",
+   "id": "21f8ad81",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
@@ -1752,7 +1782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "650a4e87",
+   "id": "fcd6066e",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -1768,13 +1798,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "9ad6dd92",
+   "id": "4d7d6b18",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:49.690583Z",
-     "iopub.status.busy": "2026-08-17T01:01:49.690501Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.481943Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.481578Z"
+     "iopub.execute_input": "2026-08-17T04:50:59.192580Z",
+     "iopub.status.busy": "2026-08-17T04:50:59.192442Z",
+     "iopub.status.idle": "2026-08-17T04:51:00.813249Z",
+     "shell.execute_reply": "2026-08-17T04:51:00.812204Z"
     }
    },
    "outputs": [
@@ -1883,13 +1913,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "1c03bb10",
+   "id": "3209ddfe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.482948Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.482882Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.590689Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.590248Z"
+     "iopub.execute_input": "2026-08-17T04:51:00.815039Z",
+     "iopub.status.busy": "2026-08-17T04:51:00.814886Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.040810Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.040048Z"
     }
    },
    "outputs": [
@@ -1937,7 +1967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0cf22f6",
+   "id": "06db051f",
    "metadata": {},
    "source": [
     "All three statistics sit inside their predictive 90% bands. Two limits on what\n",
@@ -1952,7 +1982,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56fc9024",
+   "id": "7d688532",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -1980,13 +2010,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "5ce513b1",
+   "id": "bf579ff3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.591765Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.591692Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.594653Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.594307Z"
+     "iopub.execute_input": "2026-08-17T04:51:01.042829Z",
+     "iopub.status.busy": "2026-08-17T04:51:01.042690Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.047609Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.047210Z"
     }
    },
    "outputs": [
@@ -2032,13 +2062,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "fe273c5d",
+   "id": "515cad6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.595574Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.595502Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.719300Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.718996Z"
+     "iopub.execute_input": "2026-08-17T04:51:01.049191Z",
+     "iopub.status.busy": "2026-08-17T04:51:01.049067Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.303878Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.303222Z"
     }
    },
    "outputs": [
@@ -2079,7 +2109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "862a00fe",
+   "id": "1e97e4e3",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
@@ -2134,13 +2164,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "0e5dc504",
+   "id": "8d0899b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.720391Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.720333Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.722437Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.722147Z"
+     "iopub.execute_input": "2026-08-17T04:51:01.305788Z",
+     "iopub.status.busy": "2026-08-17T04:51:01.305644Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.308927Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.308378Z"
     }
    },
    "outputs": [
@@ -2170,7 +2200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "763f1723",
+   "id": "503c7809",
    "metadata": {},
    "source": [
     "Two honest qualifications about any stop, this one included. What such a rule\n",
@@ -2196,7 +2226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e20ce4a",
+   "id": "1068af23",
    "metadata": {},
    "source": [
     "### Choosing a path: why the fast one is not taken here\n",
@@ -2215,13 +2245,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "2ef45e27",
+   "id": "6fb1c55d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.723402Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.723346Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.739908Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.739496Z"
+     "iopub.execute_input": "2026-08-17T04:51:01.310400Z",
+     "iopub.status.busy": "2026-08-17T04:51:01.310292Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.340763Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.340230Z"
     }
    },
    "outputs": [
@@ -2242,7 +2272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f45ddf6",
+   "id": "5dda7204",
    "metadata": {},
    "source": [
     "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
@@ -2256,7 +2286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c3eb165",
+   "id": "8ee74a58",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2268,7 +2298,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e33ded5",
+   "id": "76ddfbb7",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2280,7 +2310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "216c5e2f",
+   "id": "fd3e44a9",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2289,13 +2319,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "c677643e",
+   "id": "e86e26dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T01:01:50.740942Z",
-     "iopub.status.busy": "2026-08-17T01:01:50.740879Z",
-     "iopub.status.idle": "2026-08-17T01:01:50.759840Z",
-     "shell.execute_reply": "2026-08-17T01:01:50.759488Z"
+     "iopub.execute_input": "2026-08-17T04:51:01.342328Z",
+     "iopub.status.busy": "2026-08-17T04:51:01.342216Z",
+     "iopub.status.idle": "2026-08-17T04:51:01.366379Z",
+     "shell.execute_reply": "2026-08-17T04:51:01.365773Z"
     }
    },
    "outputs": [
@@ -2335,7 +2365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbfc96a1",
+   "id": "668c6eda",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
@@ -2366,7 +2396,7 @@
   },
   "myst": {
    "substitutions": {
-    "extra_dependencies": "pyarrow graphviz"
+    "extra_dependencies": "pyarrow"
    }
   }
  },
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index cb9121246..ca4f1483f 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -11,7 +11,7 @@ kernelspec:
   name: python3
 myst:
   substitutions:
-    extra_dependencies: pyarrow graphviz
+    extra_dependencies: pyarrow
 ---
 
 (streaming_tick_data)=
@@ -44,10 +44,12 @@ rows that are generated inside the notebook. It tries to teach three things:
    an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
    what a stopping rule has to be able to see before it can fire.
 3. **Honest reporting.** With the truth known we can watch the posterior means
-   land on it while the mean-field standard deviations stay far too narrow to
-   cover it. Which widths could be trusted in general is a calibration
-   question that needs many simulated datasets; this notebook fits one, so it
-   withholds width claims rather than certifying them.
+   land close to it while the mean-field standard deviations stay far too
+   narrow to cover the distance that remains — standardized gaps of 5 to 7 on
+   rows that are cleanly identified. Which widths could be trusted in general
+   is a calibration question that needs many simulated datasets; this notebook
+   fits one, so it reports that gap and withholds width claims rather than
+   certifying them.
 
 The notebook runs in about a minute. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
@@ -95,6 +97,9 @@ the first place.
 
 +++
 
+:::{include} ../extra_installs.md
+:::
+
 :::{note}
 The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into
 pymc-extras after the v0.14.0 release, so it is not in a published version yet
@@ -712,6 +717,7 @@ recovery["truth"] = [truth[p] for p in scalar_params] + [
     truth["beta_a"],
 ]
 recovery["abs_error"] = (recovery["mean"] - recovery["truth"]).abs()
+recovery["z"] = (recovery["mean"] - recovery["truth"]) / recovery["sd"]
 recovery.round(4)
 ```
 
@@ -726,15 +732,26 @@ $g$/$b^{(\sigma h)}$, whose sums we spare you) — so raw split rows like
 `kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not
 estimates of their generating values; a sum-to-zero constraint on the symbol
 effects is the standard reparameterization when the split itself matters.
-Notice also what we did *not* print: z-scores against the truth. Some would
-exceed 2, because the mean-field standard deviations in the third column are
-razor-thin — a z-score against a point truth mixes the sampling noise of this
-particular synthetic dataset with variational overconfidence along exactly
-such ridges. The stance behind that omission is a risk-management one, and it
-is worth stating plainly: material disagreement between independent-seed fits
-is enough to withhold a claim about a width, while agreement only removes the
-instability warning — it does not validate calibration. Replication can veto a
-width; it can never certify one.
+The last column standardizes the error by the posterior width, and it has to be
+read against identification rather than straight across the table. On the three
+raw split rows it is not a recovery statistic at all: `alpha0` reads 404 because
+a prior-dependent decomposition can have an arbitrary location and still a
+narrow width, which says something about the split and nothing about the fit.
+On the identified sums every $|z|$ is below 2. Between those two extremes sit
+the rows that are identified on their own and still miss by more than their own
+width claims they can: $\theta_d$ at $-7.1$ and $\lambda_q$ at $+4.9$.
+
+That gap is the result of this section, and it is worth being exact about what
+it licenses. A z-score against a point truth confounds three things — the
+sampling noise of this one synthetic dataset, bias in the variational mean, and
+overconfidence in the variational width — and a single fit cannot separate
+them. What it does establish is a direction: these widths are too narrow to be
+read as uncertainty statements at face value. The stance behind reporting the
+gap and stopping there is a risk-management one, and it is worth stating
+plainly: material disagreement between independent-seed fits is enough to
+withhold a claim about a width, while agreement only removes the instability
+warning — it does not validate calibration. Replication can veto a width; it can
+never certify one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?

From eaa7a81836b391bc90bf4bf709341e7bdfa27e91 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 17 Aug 2026 01:44:11 -0500
Subject: [PATCH 37/42] Average the iterate over a pass before reading anything
 off the fit

An external audit raised the last-iterate question indirectly: stop on an
epoch boundary, it said, or show that epoch-boundary checkpoints are
stable. Stopping on a boundary (2,400 steps = 8 passes) moved the recovery
table enough to be alarming -- theta_d went from -7.1 to +2.8 posterior sd
on 100 fewer steps -- so the question was worth measuring rather than
assuming.

Two measurements settle it. Between the same phase of consecutive epochs
the variational mean moves a median of 0.11 and at most 0.58 posterior sd:
the descent really has flattened. Within a single epoch it swings a median
of 4.3 and a maximum of 41.2. The batch at step t is a deterministic
function of t with period one epoch, so the gradient sequence is periodic
and the iterate orbits the optimum instead of settling on it. The last
iterate is whichever phase of that orbit the step budget ended on, which
is exactly what the 2,400-vs-2,500 difference was.

The fix is the standard one: average the variational parameters over the
final whole pass, a full period, which is what makes the average cancel
the cycle. Every identified quantity then lands inside 1.2 posterior sd of
its generating value -- theta_d at -0.05, theta_r at +0.02, the three
identified sums at 0.14, 0.22, 0.23 -- against misses of three to seven
before averaging. The ridge splits are unchanged, as they must be.

This also closes the audit's other reading of the recovery section, that
one fit cannot say whether a gap is a too-narrow width or a biased mean.
Most of the gap was neither.

Also from the audit: Gate 1 now separates the factorization requirement
from how batches are drawn, instead of claiming the notebook's cyclic
replay satisfies a uniform-sampling condition; the disk shuffle is
described as a fixed hash ordering rather than a uniform permutation drawn
at random; the shard-head check asserts over all ten shards instead of
printing one; the memory-release cell releases the four row-scale arrays
it had left live; the plate diagram is described as an inventory rather
than as showing two predictor links it does not contain; the ADVI citation
moves off PyMC's total_size semantics, which it does not discuss; the
Fourier citation is narrowed to what Andersen and Bollerslev support; the
w^-3/2 claim states its noise assumption; the ragged-batch sawtooth is no
longer cross-referenced to a section that never demonstrates it; the
control/aligned spread ratio is printed rather than asserted; the
predictive check reports its margin on the zero share; and pymc.Minibatch
uses the func role its inventory declares.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1257 +++++++++--------
 .../streaming_tick_data.myst.md               |  228 ++-
 2 files changed, 869 insertions(+), 616 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index 256d78198..bed307444 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8dd3d69f",
+   "id": "8ab3ab09",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e976da26",
+   "id": "fa221894",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
@@ -26,7 +26,7 @@
     "[data.binance.vision](https://data.binance.vision). Widen a pull across\n",
     "symbols and months and the feature matrix can outgrow the RAM of an ordinary\n",
     "workstation; at that point in-memory minibatching stops being an option. PyMC's\n",
-    "{class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a\n",
+    "{func}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a\n",
     "disk-backed reader, so it cannot help once the array no longer fits. This\n",
     "notebook fits a hierarchical hurdle–Student-t model of *next-event price\n",
     "moves* by streaming minibatches from disk with pymc-extras'\n",
@@ -38,22 +38,22 @@
     "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
     "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
     "   what a stopping rule has to be able to see before it can fire.\n",
-    "3. **Honest reporting.** With the truth known we can watch the posterior means\n",
-    "   land close to it while the mean-field standard deviations stay far too\n",
-    "   narrow to cover the distance that remains — standardized gaps of 5 to 7 on\n",
-    "   rows that are cleanly identified. Which widths could be trusted in general\n",
-    "   is a calibration question that needs many simulated datasets; this notebook\n",
-    "   fits one, so it reports that gap and withholds width claims rather than\n",
-    "   certifying them.\n",
-    "\n",
-    "The notebook runs in about a minute. Everything below executes on synthetic\n",
+    "3. **Honest reporting.** With the truth known, recovery is checkable — but only\n",
+    "   after a correction the fit forces on us: the cyclic replay puts the optimizer\n",
+    "   on an orbit, and the last iterate misses the truth by several of its own\n",
+    "   posterior standard deviations purely because of where in that orbit the step\n",
+    "   budget ended. Averaged over one full pass, every identified quantity lands\n",
+    "   inside 1.2. The widths themselves are a separate question this notebook\n",
+    "   cannot settle on one dataset, and it says so rather than certifying them.\n",
+    "\n",
+    "The notebook runs in well under a minute on a laptop. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
     "possible: nothing here is a claim about any real market."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "21eb99eb",
+   "id": "42854ead",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -66,10 +66,17 @@
     "real work — and they are worth internalizing before reaching for `total_size`\n",
     "on your own data:\n",
     "\n",
-    "**Gate 1 — validity: the likelihood must factor over rows, sampled uniformly.**\n",
-    "No latent path coupling observations, no label shared across rows, and every\n",
-    "row given the same inclusion probability by the batching scheme (unequal\n",
-    "inclusion breaks the plain $N/B$ rescaling). This is exactly why the classic\n",
+    "**Gate 1 — validity: the likelihood must factor over rows, and the batching\n",
+    "must weight every row equally.** No latent path coupling observations, no\n",
+    "label shared across rows, and every row given the same inclusion frequency by\n",
+    "the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How\n",
+    "the batches are *drawn* is a separate matter that this gate does not settle:\n",
+    "fresh uniform batches make each step's scaled gradient conditionally unbiased\n",
+    "for the full-data objective — the identity above — while a fixed shuffled\n",
+    "order replayed every epoch visits each row once per pass, targets the same\n",
+    "finite sum, and yields cyclic rather than unbiased updates. This notebook does\n",
+    "the second, and returns to the distinction where the fit is described. The\n",
+    "factorization requirement is exactly why the classic\n",
     "stochastic volatility model of the {ref}`stochastic_volatility` notebook\n",
     "*cannot* be minibatched: its latent volatility path ties every observation to\n",
     "its neighbours, so a random subset of rows does not carry $B/N$ of the\n",
@@ -97,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02c02e2d",
+   "id": "5b9fb0f3",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -123,13 +130,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0fecb17f",
+   "id": "e758565b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:42.791169Z",
-     "iopub.status.busy": "2026-08-17T04:50:42.790950Z",
-     "iopub.status.idle": "2026-08-17T04:50:46.612550Z",
-     "shell.execute_reply": "2026-08-17T04:50:46.611732Z"
+     "iopub.execute_input": "2026-08-17T06:42:29.154802Z",
+     "iopub.status.busy": "2026-08-17T06:42:29.154626Z",
+     "iopub.status.idle": "2026-08-17T06:42:30.576941Z",
+     "shell.execute_reply": "2026-08-17T06:42:30.576439Z"
     }
    },
    "outputs": [],
@@ -156,13 +163,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2c52f1da",
+   "id": "9a25b4a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:46.615107Z",
-     "iopub.status.busy": "2026-08-17T04:50:46.614768Z",
-     "iopub.status.idle": "2026-08-17T04:50:46.620090Z",
-     "shell.execute_reply": "2026-08-17T04:50:46.619538Z"
+     "iopub.execute_input": "2026-08-17T06:42:30.578238Z",
+     "iopub.status.busy": "2026-08-17T06:42:30.578099Z",
+     "iopub.status.idle": "2026-08-17T06:42:30.580961Z",
+     "shell.execute_reply": "2026-08-17T06:42:30.580645Z"
     }
    },
    "outputs": [],
@@ -182,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fdb39ba",
+   "id": "449cee9f",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -229,8 +236,8 @@
     "both would instead be trailing statistics built causally from the tape and\n",
     "standardized with constants frozen on a burn-in window — a construction that\n",
     "is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
-    "the standard way to encode intraday periodicity smoothly\n",
-    "{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
+    "a low-order version of the Fourier encoding of intraday periodicity in\n",
+    "{cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
     "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
     "$b_s \\sim \\mathcal{N}(0, \\tau)$ are partially pooled {cite:p}`gelman2006data`,\n",
     "so a thin symbol is pulled toward the global intraday shape where its own data\n",
@@ -257,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f81db8c3",
+   "id": "40d16f8e",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -275,13 +282,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "20ec934e",
+   "id": "c9879503",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:46.621552Z",
-     "iopub.status.busy": "2026-08-17T04:50:46.621450Z",
-     "iopub.status.idle": "2026-08-17T04:50:46.626334Z",
-     "shell.execute_reply": "2026-08-17T04:50:46.625723Z"
+     "iopub.execute_input": "2026-08-17T06:42:30.582011Z",
+     "iopub.status.busy": "2026-08-17T06:42:30.581948Z",
+     "iopub.status.idle": "2026-08-17T06:42:30.584284Z",
+     "shell.execute_reply": "2026-08-17T06:42:30.584033Z"
     }
    },
    "outputs": [],
@@ -323,13 +330,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "22c4a193",
+   "id": "432bf099",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:46.627667Z",
-     "iopub.status.busy": "2026-08-17T04:50:46.627563Z",
-     "iopub.status.idle": "2026-08-17T04:50:46.929771Z",
-     "shell.execute_reply": "2026-08-17T04:50:46.929012Z"
+     "iopub.execute_input": "2026-08-17T06:42:30.585272Z",
+     "iopub.status.busy": "2026-08-17T06:42:30.585222Z",
+     "iopub.status.idle": "2026-08-17T06:42:30.737603Z",
+     "shell.execute_reply": "2026-08-17T06:42:30.737281Z"
     }
    },
    "outputs": [
@@ -397,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d548a752",
+   "id": "ae083a28",
    "metadata": {},
    "source": [
     "Each constant above is a setting, not a measurement, and it is worth reading\n",
@@ -429,13 +436,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "f272231f",
+   "id": "22556b3d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:46.931478Z",
-     "iopub.status.busy": "2026-08-17T04:50:46.931341Z",
-     "iopub.status.idle": "2026-08-17T04:50:47.709873Z",
-     "shell.execute_reply": "2026-08-17T04:50:47.709238Z"
+     "iopub.execute_input": "2026-08-17T06:42:30.738711Z",
+     "iopub.status.busy": "2026-08-17T06:42:30.738646Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.127100Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.126730Z"
     }
    },
    "outputs": [
@@ -474,7 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1374ef5a",
+   "id": "e2362735",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -486,22 +493,24 @@
     "stopping decision would be biased by construction. The fix is to shuffle\n",
     "**once, globally, on disk at ETL time**: every row gets a deterministic hash\n",
     "key, rows are scattered across shards by that key, and each shard is sorted by\n",
-    "it. After that, sequential reads follow one fixed, data-independent uniform\n",
-    "permutation — replayed identically each epoch, which is single-shuffle SGD\n",
-    "semantics rather than fresh per-step subsampling — and the loader can run\n",
-    "with `shuffle=False` at full speed."
+    "it. After that, sequential reads follow one fixed, data-independent\n",
+    "permutation — pseudo-random, because it comes from a hash of the row index\n",
+    "rather than from anything in the row, and replayed identically each epoch,\n",
+    "which is single-shuffle SGD semantics rather than fresh per-step subsampling —\n",
+    "and the loader can run with `shuffle=False`, without a copy through the\n",
+    "shuffle buffer."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "fe16b1ab",
+   "id": "e04b7efe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:47.711705Z",
-     "iopub.status.busy": "2026-08-17T04:50:47.711564Z",
-     "iopub.status.idle": "2026-08-17T04:50:48.089907Z",
-     "shell.execute_reply": "2026-08-17T04:50:48.089270Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.128107Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.128022Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.335821Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.335407Z"
     }
    },
    "outputs": [
@@ -509,7 +518,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "first 10k rows of one shard cover 24 hours and 12 symbols\n"
+      "first 10k rows of every shard cover all 24 hours and all 12 symbols\n"
      ]
     }
    ],
@@ -546,20 +555,21 @@
     "    part = table.take(order[i::n_shards])\n",
     "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"), row_group_size=BATCH)\n",
     "\n",
-    "head = pq.read_table(os.path.join(data_dir, \"shard_000.parquet\")).slice(0, 10_000)\n",
-    "print(\n",
-    "    f\"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours \"\n",
-    "    f\"and {len(np.unique(head['sym']))} symbols\"\n",
-    ")"
+    "# the head of every shard has to mix hours and symbols already; check all of them\n",
+    "for i in range(n_shards):\n",
+    "    head = pq.read_table(os.path.join(data_dir, f\"shard_{i:03d}.parquet\")).slice(0, 10_000)\n",
+    "    assert len(np.unique(head[\"hour\"])) == 24\n",
+    "    assert len(np.unique(head[\"sym\"])) == n_symbols\n",
+    "print(f\"first 10k rows of every shard cover all 24 hours and all {n_symbols} symbols\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7555511",
+   "id": "6ea61d1a",
    "metadata": {},
    "source": [
-    "The printed check is the one to keep: the head of any shard must already mix\n",
-    "every hour and nearly every symbol, asserted rather than assumed.\n",
+    "The assertion is the one to keep: the head of every shard must already mix\n",
+    "every hour and every symbol, checked rather than assumed.\n",
     "\n",
     "Be clear about what this cell is and is not. It is the pedagogical miniature:\n",
     "it builds the whole table, the whole key array and the whole permutation in\n",
@@ -576,13 +586,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4228e2e8",
+   "id": "613dd96a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:48.091297Z",
-     "iopub.status.busy": "2026-08-17T04:50:48.091177Z",
-     "iopub.status.idle": "2026-08-17T04:50:48.149743Z",
-     "shell.execute_reply": "2026-08-17T04:50:48.149155Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.336900Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.336843Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.373208Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.372821Z"
     }
    },
    "outputs": [
@@ -590,19 +600,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_ow6k9hf8\n"
+      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_0c_z9zdw\n"
      ]
     }
    ],
    "source": [
-    "del table, key, order, y, m, d, q, a, ylag, sym, hour, logit_pi, log_sigma, t_draw, sigma\n",
+    "del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves\n",
+    "del logit_pi, log_sigma, t_draw, sigma\n",
     "gc.collect()\n",
     "print(\"row-scale generator arrays released; the fit reads from\", data_dir)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1039f285",
+   "id": "a4333815",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -614,9 +625,9 @@
     "deliberately: on this path the loader hands you the row groups it finds, so a\n",
     "ragged geometry gives ragged batches, `len(loader)` (which is\n",
     "`total_size // batch_size`) stops matching the number of blocks an epoch\n",
-    "yields, and — as the fitting section shows — the recorded loss picks up a\n",
-    "deterministic sawtooth that has nothing to do with convergence. The model\n",
-    "reads\n",
+    "yields, and the recorded loss picks up a deterministic sawtooth that has\n",
+    "nothing to do with convergence (the fitting section explains why the recorded\n",
+    "value scales with the block size). The model reads\n",
     "one `pm.Data` placeholder; everything derived — the Fourier basis, the\n",
     "integer symbol index — is computed inside the graph, so advancing the stream\n",
     "is a single `set_value` per step."
@@ -625,13 +636,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "62be3cb4",
+   "id": "72db2140",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:48.151346Z",
-     "iopub.status.busy": "2026-08-17T04:50:48.151208Z",
-     "iopub.status.idle": "2026-08-17T04:50:48.231955Z",
-     "shell.execute_reply": "2026-08-17T04:50:48.231354Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.374203Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.374142Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.421480Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.421082Z"
     }
    },
    "outputs": [
@@ -668,7 +679,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4402f049",
+   "id": "0c579f99",
    "metadata": {},
    "source": [
     "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
@@ -679,7 +690,7 @@
     "path drops a trailing partial batch, and this notebook never uses it.\n",
     "\n",
     "Second, a precision about Gate 1 that matters for how the fit should be\n",
-    "described. The permutation is drawn once, on disk, and then replayed in the\n",
+    "described. The permutation is fixed once, on disk, and then replayed in the\n",
     "same order every epoch. Each row is included exactly once per pass, but the\n",
     "batch at step $t$ is a deterministic function of $t$, not a fresh uniform\n",
     "draw — so the individual updates are not the conditionally unbiased stochastic\n",
@@ -691,13 +702,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "52923952",
+   "id": "044702c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:48.233519Z",
-     "iopub.status.busy": "2026-08-17T04:50:48.233376Z",
-     "iopub.status.idle": "2026-08-17T04:50:48.240463Z",
-     "shell.execute_reply": "2026-08-17T04:50:48.239955Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.422539Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.422472Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.426456Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.426107Z"
     }
    },
    "outputs": [],
@@ -795,13 +806,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e33fedcd",
+   "id": "cad1f553",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
     "{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`\n",
     "gives the term *observed-RV semantics*, which is what makes PyMC's own\n",
-    "`total_size` minibatch rescaling apply {cite:p}`kucukelbir2015automatic`. And\n",
+    "`total_size` minibatch rescaling apply. And\n",
     "the hurdle's two logs are written with `softplus` —\n",
     "$\\log \\pi = -\\operatorname{softplus}(-x)$,\n",
     "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$ — which is exact and stable at both\n",
@@ -816,13 +827,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ce22bfab",
+   "id": "82de0750",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:48.241886Z",
-     "iopub.status.busy": "2026-08-17T04:50:48.241789Z",
-     "iopub.status.idle": "2026-08-17T04:50:48.694709Z",
-     "shell.execute_reply": "2026-08-17T04:50:48.693785Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.427381Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.427329Z",
+     "iopub.status.idle": "2026-08-17T06:42:31.575875Z",
+     "shell.execute_reply": "2026-08-17T06:42:31.575392Z"
     }
    },
    "outputs": [
@@ -838,367 +849,367 @@
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-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.4,-478.95 1701.4,4 -4,4\"/>\n",
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        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.12\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
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        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.5\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
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        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.75\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
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        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1469.12,-243.82C1469.12,-243.82 1643.12,-243.82 1643.12,-243.82 1649.12,-243.82 1655.12,-249.82 1655.12,-255.82 1655.12,-255.82 1655.12,-353.63 1655.12,-353.63 1655.12,-359.63 1649.12,-365.63 1643.12,-365.63 1643.12,-365.63 1469.12,-365.63 1469.12,-365.63 1463.12,-365.63 1457.12,-359.63 1457.12,-353.63 1457.12,-353.63 1457.12,-255.82 1457.12,-255.82 1457.12,-249.82 1463.12,-243.82 1469.12,-243.82\"/>\n",
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        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
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        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M844.12,-98C844.12,-98 814.12,-98 814.12,-98 808.12,-98 802.12,-92 802.12,-86 802.12,-86 802.12,-52.5 802.12,-52.5 802.12,-46.5 808.12,-40.5 814.12,-40.5 814.12,-40.5 844.12,-40.5 844.12,-40.5 850.12,-40.5 856.12,-46.5 856.12,-52.5 856.12,-52.5 856.12,-86 856.12,-86 856.12,-92 850.12,-98 844.12,-98\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
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        "</g>\n",
        "<!-- tau_ph -->\n",
        "<g id=\"node2\" class=\"node\">\n",
        "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
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        "</g>\n",
        "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
+       "<g id=\"node23\" class=\"node\">\n",
        "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "</g>\n",
        "<!-- tau_ph&#45;&gt;b_ph -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>tau_ph&#45;&gt;b_ph</title>\n",
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        "</g>\n",
-       "<!-- nu -->\n",
+       "<!-- kappa0 -->\n",
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-       "<title>nu</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"829.12\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"829.12\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"828.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
        "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M58.53,-287.86C67.65,-272.06 81.3,-253.61 99.12,-243.82 150.53,-215.58 507.28,-199.13 702.36,-192.15\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"702.44,-195.65 712.31,-191.79 702.19,-188.65 702.44,-195.65\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.86 76.86,-251.92 91.01,-243.82 142.52,-214.32 504.59,-198.39 701.38,-191.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.26,-195.32 711.13,-191.5 701.02,-188.33 701.26,-195.32\"/>\n",
        "</g>\n",
-       "<!-- tau_al -->\n",
+       "<!-- beta_a -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_al -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.43,-366.16 1171.5,-355.62 1164.09,-363.19 1170.43,-366.16\"/>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.78,-279.42C165.86,-265.95 176.91,-252.01 191.01,-243.82 233.97,-218.88 528.22,-201.61 701.39,-193.47\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.54,-196.97 711.36,-193.01 701.21,-189.97 701.54,-196.97\"/>\n",
        "</g>\n",
-       "<!-- beta_q -->\n",
+       "<!-- lambda_a -->\n",
        "<g id=\"node5\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"149.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M166.15,-279.79C174.44,-266.21 185.76,-252.08 200.12,-243.82 242.53,-219.43 531.58,-201.99 702.72,-193.67\"/>\n",
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-       "</g>\n",
-       "<!-- tau_k -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_k -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_k&#45;&gt;b_k -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>tau_k&#45;&gt;b_k</title>\n",
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+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M269.48,-279.86C279.08,-266.28 291.78,-252.14 307.01,-243.82 372.65,-207.95 568.97,-195.2 700.98,-190.66\"/>\n",
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        "</g>\n",
        "<!-- eta -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"45.12\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"45.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"461.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"506.14,-345.73 415.89,-345.73 415.89,-288.23 506.14,-288.23 506.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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        "<!-- eta&#45;&gt;nu -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>eta&#45;&gt;nu</title>\n",
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-       "<!-- lambda_q -->\n",
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-       "<title>lambda_q</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"258.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
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-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
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-       "<!-- theta_r -->\n",
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-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"367.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"367.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"464.51,-357.45 461.01,-347.45 457.51,-357.45 464.51,-357.45\"/>\n",
        "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
+       "<g id=\"node22\" class=\"node\">\n",
        "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_sh&#45;&gt;b_sh -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>tau_sh&#45;&gt;b_sh</title>\n",
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-       "<!-- tau_ba -->\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.96,-279.73C382.07,-266.32 393.09,-252.32 407.01,-243.82 455.13,-214.44 595.99,-200.3 701.63,-193.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"701.75,-197.17 711.52,-193.07 701.32,-190.19 701.75,-197.17\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M475.22,-287.78C484.46,-272.36 497.96,-254.32 515.01,-243.82 546.75,-224.27 632.12,-209.85 705.7,-200.6\"/>\n",
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+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"565.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M582.76,-280.22C590.92,-267.04 601.77,-253.08 615.01,-243.82 642.38,-224.68 675.88,-212.12 708.13,-203.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"708.65,-207.36 717.54,-201.61 707,-200.56 708.65,-207.36\"/>\n",
+       "</g>\n",
+       "<!-- tau_k -->\n",
        "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.12\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_ba -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1389.12,-393.28C1389.12,-385.5 1389.12,-377.25 1389.12,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.63,-369.43 1389.13,-359.43 1385.63,-369.43 1392.63,-369.43\"/>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- tau_al -->\n",
        "<g id=\"node12\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"467.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"467.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M484.5,-279.68C492.62,-266.42 503.53,-252.54 517.12,-243.82 548.34,-223.78 633.22,-209.41 706.58,-200.3\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.77,-203.8 716.27,-199.12 705.92,-196.85 706.77,-203.8\"/>\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"567.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M584.88,-280.23C593.04,-267.05 603.89,-253.08 617.12,-243.82 644.22,-224.85 677.36,-212.34 709.31,-204.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"709.74,-207.6 718.62,-201.82 708.08,-200.8 709.74,-207.6\"/>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
        "</g>\n",
        "<!-- theta_d -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"667.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"667.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"667.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"667.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"665.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"665.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"665.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"665.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
        "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M686.39,-280.83C694.64,-268.04 705.14,-254.19 717.12,-243.82 725.44,-236.63 734.85,-230.1 744.59,-224.27\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"746.15,-227.4 753.1,-219.41 742.68,-221.33 746.15,-227.4\"/>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"745.38,-227.31 752.35,-219.33 741.92,-221.23 745.38,-227.31\"/>\n",
        "</g>\n",
-       "<!-- lambda_a -->\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- lambda_q -->\n",
        "<g id=\"node15\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"775.12\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"775.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"774.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"774.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"774.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"774.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"811.13,-239.74 811.8,-229.16 804.69,-237.01 811.13,-239.74\"/>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M789.92,-278.32C795.22,-265.77 801.22,-251.58 806.85,-238.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"810.02,-239.74 810.69,-229.16 803.57,-237.01 810.02,-239.74\"/>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node16\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"853.54,-236.97 846.43,-229.12 847.1,-239.69 853.54,-236.97\"/>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M867.1,-279.01C861.59,-266.2 855.32,-251.62 849.44,-237.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"852.8,-236.92 845.63,-229.11 846.37,-239.68 852.8,-236.92\"/>\n",
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        "<!-- c -->\n",
        "<g id=\"node17\" class=\"node\">\n",
        "<title>c</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"983.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"983.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- c&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
        "<title>c&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M963.69,-281.02C955.43,-268.25 944.96,-254.37 933.12,-243.82 925.79,-237.28 917.56,-231.24 909.02,-225.74\"/>\n",
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        "<!-- g -->\n",
        "<g id=\"node18\" class=\"node\">\n",
        "<title>g</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.12\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.12\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.12\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.12\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
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       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x119c149b0>"
+       "<graphviz.graphs.Digraph at 0x12f469640>"
       ]
      },
      "execution_count": 10,
@@ -1215,13 +1226,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "975c2fa3",
+   "id": "c3631aa8",
    "metadata": {},
    "source": [
-    "The plate diagram makes the two-link structure visible at a glance: five\n",
-    "partially pooled effect families on the symbol plate feeding two linear\n",
-    "predictors, one shared tail parameter, and a single observed node whose batch\n",
-    "dimension is whatever the placeholder currently holds.\n",
+    "The plate diagram is an inventory rather than a picture of the two links: five\n",
+    "partially pooled effect families on the symbol plate, one shared tail\n",
+    "parameter, and a single observed node whose batch dimension is whatever the\n",
+    "placeholder currently holds. The two linear predictors live inside the\n",
+    "likelihood's `logp` and are not separate nodes; the equations above are where\n",
+    "that structure is visible.\n",
     "\n",
     "The loop below is the whole streaming adapter: a `pm.fit` callback that pushes\n",
     "the next block into the placeholder after each step. pymc-extras#710 proposes\n",
@@ -1233,13 +1246,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "6bf237a6",
+   "id": "a2e1b376",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:48.696767Z",
-     "iopub.status.busy": "2026-08-17T04:50:48.696607Z",
-     "iopub.status.idle": "2026-08-17T04:50:58.364906Z",
-     "shell.execute_reply": "2026-08-17T04:50:58.364289Z"
+     "iopub.execute_input": "2026-08-17T06:42:31.576988Z",
+     "iopub.status.busy": "2026-08-17T06:42:31.576895Z",
+     "iopub.status.idle": "2026-08-17T06:42:36.673358Z",
+     "shell.execute_reply": "2026-08-17T06:42:36.672820Z"
     }
    },
    "outputs": [],
@@ -1263,22 +1276,39 @@
     "        self._shared.set_value(next(self._stream), borrow=True)\n",
     "\n",
     "\n",
+    "class ParamTrace:\n",
+    "    \"\"\"pm.fit callback: record the variational parameters after every step.\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        self.mu, self.rho = [], []\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        mu, rho = approx.params\n",
+    "        self.mu.append(mu.get_value())\n",
+    "        self.rho.append(rho.get_value())\n",
+    "\n",
+    "\n",
     "stream = StreamAdvance(model, loader)\n",
     "stream.prime()\n",
+    "tail = ParamTrace()\n",
     "\n",
     "with model:\n",
     "    advi = pm.ADVI(random_seed=RANDOM_SEED)\n",
     "advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)\n",
     "approx = advi.fit(\n",
-    "    2_500, obj_optimizer=pm.adam(learning_rate=0.005), callbacks=[stream], progressbar=False\n",
+    "    2_400,\n",
+    "    obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "    callbacks=[stream, tail],\n",
+    "    progressbar=False,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cd70a74a",
+   "id": "eb0ea6e5",
    "metadata": {},
    "source": [
+    "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
     "The two-stage learning rate is deliberate. A constant step size that is\n",
     "comfortable early is too large near the optimum, where it keeps the\n",
     "variational mean bouncing instead of settling; dropping it once the descent\n",
@@ -1306,19 +1336,19 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d07451f5",
+   "id": "effafd18",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:58.367076Z",
-     "iopub.status.busy": "2026-08-17T04:50:58.366906Z",
-     "iopub.status.idle": "2026-08-17T04:50:58.578190Z",
-     "shell.execute_reply": "2026-08-17T04:50:58.577637Z"
+     "iopub.execute_input": "2026-08-17T06:42:36.674531Z",
+     "iopub.status.busy": "2026-08-17T06:42:36.674461Z",
+     "iopub.status.idle": "2026-08-17T06:42:36.783578Z",
+     "shell.execute_reply": "2026-08-17T06:42:36.783271Z"
     }
    },
    "outputs": [
     {
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       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -1352,22 +1382,129 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5179e95",
+   "id": "c1c9210d",
    "metadata": {},
    "source": [
-    "## Did it recover the truth?"
+    "## The last iterate is not the answer\n",
+    "\n",
+    "Before reading a single parameter off this fit, one property of the replay has\n",
+    "to be dealt with. The batch at step $t$ is a deterministic function of $t$ with\n",
+    "period one epoch, so the gradient sequence is periodic and the optimizer's\n",
+    "iterate inherits a periodic component. Two measurements separate that component\n",
+    "from genuine progress: compare the same phase in consecutive epochs, and look at\n",
+    "the spread within a single epoch."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "1ce817b2",
+   "id": "57c93a4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:58.580070Z",
-     "iopub.status.busy": "2026-08-17T04:50:58.579949Z",
-     "iopub.status.idle": "2026-08-17T04:50:58.808113Z",
-     "shell.execute_reply": "2026-08-17T04:50:58.807478Z"
+     "iopub.execute_input": "2026-08-17T06:42:36.784722Z",
+     "iopub.status.busy": "2026-08-17T06:42:36.784655Z",
+     "iopub.status.idle": "2026-08-17T06:42:36.791768Z",
+     "shell.execute_reply": "2026-08-17T06:42:36.791384Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch over epoch, same phase: median 0.11, max 0.58 sd\n",
+      "within the last epoch:        median 4.31, max 41.2 sd\n"
+     ]
+    }
+   ],
+   "source": [
+    "mu_t = np.asarray(tail.mu)\n",
+    "sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging\n",
+    "ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart\n",
+    "drift = np.abs(ends[-1] - ends[-2]) / sd_unc\n",
+    "last = mu_t[-steps_per_epoch:]\n",
+    "swing = (last.max(0) - last.min(0)) / sd_unc\n",
+    "print(\n",
+    "    f\"epoch over epoch, same phase: median {np.median(drift):.2f}, max {drift.max():.2f} sd\\n\"\n",
+    "    f\"within the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd086d04",
+   "metadata": {},
+   "source": [
+    "Read those two lines together. Between the same points of consecutive epochs the\n",
+    "mean barely moves, so the systematic descent really has flattened — the fit is\n",
+    "not being stopped in mid-descent. Within one epoch it swings by multiples of its\n",
+    "own posterior width. The optimizer is not converging to a point; it is orbiting\n",
+    "one, on a cycle locked to the order the rows are replayed in, and the last\n",
+    "iterate is whatever phase of that orbit the step budget happened to end on.\n",
+    "\n",
+    "That is not a subtlety to note and move past. It means the number you would\n",
+    "report depends on where you stop, so the fit is summarized by averaging the\n",
+    "variational parameters over the final whole pass — a full period of the cycle,\n",
+    "which is what makes the average cancel it. This is Polyak–Ruppert averaging, and\n",
+    "it is why the step budget above is a whole number of epochs. The same tail\n",
+    "average is what [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)\n",
+    "applies to its own iterates for the same reason."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7a5882d7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T06:42:36.792825Z",
+     "iopub.status.busy": "2026-08-17T06:42:36.792766Z",
+     "iopub.status.idle": "2026-08-17T06:42:36.795563Z",
+     "shell.execute_reply": "2026-08-17T06:42:36.795254Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "variational parameters averaged over the final 300 steps (one epoch)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rho_t = np.asarray(tail.rho)\n",
+    "approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))\n",
+    "approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))\n",
+    "print(f\"variational parameters averaged over the final {steps_per_epoch} steps (one epoch)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "150fb417",
+   "metadata": {},
+   "source": [
+    "Everything below reads from that averaged approximation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "385a4492",
+   "metadata": {},
+   "source": [
+    "## Did it recover the truth?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f507ce2a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T06:42:36.796446Z",
+     "iopub.status.busy": "2026-08-17T06:42:36.796389Z",
+     "iopub.status.idle": "2026-08-17T06:42:36.916634Z",
+     "shell.execute_reply": "2026-08-17T06:42:36.916251Z"
     }
    },
    "outputs": [
@@ -1402,99 +1539,99 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>kappa0</th>\n",
-       "      <td>0.5396</td>\n",
+       "      <td>0.5336</td>\n",
        "      <td>0.0048</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.3077</td>\n",
-       "      <td>-63.7738</td>\n",
+       "      <td>0.3137</td>\n",
+       "      <td>-65.2181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
-       "      <td>-1.7077</td>\n",
-       "      <td>0.0032</td>\n",
+       "      <td>-1.7012</td>\n",
+       "      <td>0.0034</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>1.2880</td>\n",
-       "      <td>403.8714</td>\n",
+       "      <td>1.2946</td>\n",
+       "      <td>378.5027</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_a</th>\n",
-       "      <td>0.3609</td>\n",
-       "      <td>0.0051</td>\n",
+       "      <td>0.3517</td>\n",
+       "      <td>0.0053</td>\n",
        "      <td>0.3500</td>\n",
-       "      <td>0.0109</td>\n",
-       "      <td>2.1339</td>\n",
+       "      <td>0.0017</td>\n",
+       "      <td>0.3298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lambda_q</th>\n",
-       "      <td>0.2263</td>\n",
-       "      <td>0.0054</td>\n",
+       "      <td>0.2065</td>\n",
+       "      <td>0.0056</td>\n",
        "      <td>0.2000</td>\n",
-       "      <td>0.0263</td>\n",
-       "      <td>4.8798</td>\n",
+       "      <td>0.0065</td>\n",
+       "      <td>1.1587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a</th>\n",
-       "      <td>0.1555</td>\n",
+       "      <td>0.1624</td>\n",
        "      <td>0.0036</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0245</td>\n",
-       "      <td>-6.8694</td>\n",
+       "      <td>0.0176</td>\n",
+       "      <td>-4.8789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_q</th>\n",
-       "      <td>0.1216</td>\n",
-       "      <td>0.0034</td>\n",
+       "      <td>0.1223</td>\n",
+       "      <td>0.0031</td>\n",
        "      <td>0.1200</td>\n",
-       "      <td>0.0016</td>\n",
-       "      <td>0.4715</td>\n",
+       "      <td>0.0023</td>\n",
+       "      <td>0.7391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_d</th>\n",
-       "      <td>0.0184</td>\n",
+       "      <td>0.0200</td>\n",
        "      <td>0.0002</td>\n",
        "      <td>0.0200</td>\n",
-       "      <td>0.0016</td>\n",
-       "      <td>-7.0816</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.0473</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>theta_r</th>\n",
-       "      <td>0.2469</td>\n",
-       "      <td>0.0017</td>\n",
        "      <td>0.2500</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>-1.7951</td>\n",
+       "      <td>0.0019</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nu</th>\n",
-       "      <td>3.5104</td>\n",
-       "      <td>0.0219</td>\n",
+       "      <td>3.5097</td>\n",
+       "      <td>0.0230</td>\n",
        "      <td>3.5000</td>\n",
-       "      <td>0.0104</td>\n",
-       "      <td>0.4756</td>\n",
+       "      <td>0.0097</td>\n",
+       "      <td>0.4224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kappa0 + mean(b_k)</th>\n",
-       "      <td>0.8563</td>\n",
-       "      <td>0.0097</td>\n",
+       "      <td>0.8495</td>\n",
+       "      <td>0.0096</td>\n",
        "      <td>0.8473</td>\n",
-       "      <td>0.0090</td>\n",
-       "      <td>0.9284</td>\n",
+       "      <td>0.0022</td>\n",
+       "      <td>0.2253</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0 + mean(b_al)</th>\n",
-       "      <td>-3.0037</td>\n",
-       "      <td>0.0065</td>\n",
+       "      <td>-2.9948</td>\n",
+       "      <td>0.0064</td>\n",
        "      <td>-2.9957</td>\n",
-       "      <td>0.0080</td>\n",
-       "      <td>-1.2307</td>\n",
+       "      <td>0.0009</td>\n",
+       "      <td>0.1427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_a + mean(b_ba)</th>\n",
-       "      <td>0.1692</td>\n",
-       "      <td>0.0058</td>\n",
+       "      <td>0.1813</td>\n",
+       "      <td>0.0057</td>\n",
        "      <td>0.1800</td>\n",
-       "      <td>0.0108</td>\n",
-       "      <td>-1.8818</td>\n",
+       "      <td>0.0013</td>\n",
+       "      <td>0.2236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1502,21 +1639,21 @@
       ],
       "text/plain": [
        "                       mean      sd   truth  abs_error         z\n",
-       "kappa0               0.5396  0.0048  0.8473     0.3077  -63.7738\n",
-       "alpha0              -1.7077  0.0032 -2.9957     1.2880  403.8714\n",
-       "lambda_a             0.3609  0.0051  0.3500     0.0109    2.1339\n",
-       "lambda_q             0.2263  0.0054  0.2000     0.0263    4.8798\n",
-       "beta_a               0.1555  0.0036  0.1800     0.0245   -6.8694\n",
-       "beta_q               0.1216  0.0034  0.1200     0.0016    0.4715\n",
-       "theta_d              0.0184  0.0002  0.0200     0.0016   -7.0816\n",
-       "theta_r              0.2469  0.0017  0.2500     0.0031   -1.7951\n",
-       "nu                   3.5104  0.0219  3.5000     0.0104    0.4756\n",
-       "kappa0 + mean(b_k)   0.8563  0.0097  0.8473     0.0090    0.9284\n",
-       "alpha0 + mean(b_al) -3.0037  0.0065 -2.9957     0.0080   -1.2307\n",
-       "beta_a + mean(b_ba)  0.1692  0.0058  0.1800     0.0108   -1.8818"
+       "kappa0               0.5336  0.0048  0.8473     0.3137  -65.2181\n",
+       "alpha0              -1.7012  0.0034 -2.9957     1.2946  378.5027\n",
+       "lambda_a             0.3517  0.0053  0.3500     0.0017    0.3298\n",
+       "lambda_q             0.2065  0.0056  0.2000     0.0065    1.1587\n",
+       "beta_a               0.1624  0.0036  0.1800     0.0176   -4.8789\n",
+       "beta_q               0.1223  0.0031  0.1200     0.0023    0.7391\n",
+       "theta_d              0.0200  0.0002  0.0200     0.0000   -0.0473\n",
+       "theta_r              0.2500  0.0019  0.2500     0.0000    0.0151\n",
+       "nu                   3.5097  0.0230  3.5000     0.0097    0.4224\n",
+       "kappa0 + mean(b_k)   0.8495  0.0096  0.8473     0.0022    0.2253\n",
+       "alpha0 + mean(b_al) -2.9948  0.0064 -2.9957     0.0009    0.1427\n",
+       "beta_a + mean(b_ba)  0.1813  0.0057  0.1800     0.0013    0.2236"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1554,10 +1691,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c1ffd53",
+   "id": "a8abeaf7",
    "metadata": {},
    "source": [
-    "Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
+    "The table is a selection — the scalar globals and the three identified sums —\n",
+    "and the figure after it checks one of the five symbol-effect families; that,\n",
+    "plus the two curves for one symbol further down, is the extent of the recovery\n",
+    "evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
     "global coefficient and the mean of its group effects are only *jointly*\n",
     "identified: the likelihood constrains their sum, and the prior only weakly\n",
     "splits it. The identified sums in the last three rows land within a couple of hundredths\n",
@@ -1570,24 +1710,28 @@
     "effects is the standard reparameterization when the split itself matters.\n",
     "The last column standardizes the error by the posterior width, and it has to be\n",
     "read against identification rather than straight across the table. On the three\n",
-    "raw split rows it is not a recovery statistic at all: `alpha0` reads 404 because\n",
+    "raw split rows it is not a recovery statistic at all: `alpha0` reads 379 because\n",
     "a prior-dependent decomposition can have an arbitrary location and still a\n",
-    "narrow width, which says something about the split and nothing about the fit.\n",
-    "On the identified sums every $|z|$ is below 2. Between those two extremes sit\n",
-    "the rows that are identified on their own and still miss by more than their own\n",
-    "width claims they can: $\\theta_d$ at $-7.1$ and $\\lambda_q$ at $+4.9$.\n",
-    "\n",
-    "That gap is the result of this section, and it is worth being exact about what\n",
-    "it licenses. A z-score against a point truth confounds three things — the\n",
-    "sampling noise of this one synthetic dataset, bias in the variational mean, and\n",
-    "overconfidence in the variational width — and a single fit cannot separate\n",
-    "them. What it does establish is a direction: these widths are too narrow to be\n",
-    "read as uncertainty statements at face value. The stance behind reporting the\n",
-    "gap and stopping there is a risk-management one, and it is worth stating\n",
-    "plainly: material disagreement between independent-seed fits is enough to\n",
-    "withhold a claim about a width, while agreement only removes the instability\n",
-    "warning — it does not validate calibration. Replication can veto a width; it can\n",
-    "never certify one.\n",
+    "narrow width, which says something about the split and nothing about the fit\n",
+    "(`beta_a`, at $-4.9$, is the same story in miniature). Everything that *is*\n",
+    "identified — the three sums and the six standalone rows — lands inside $1.2$\n",
+    "posterior standard deviations of its generating value, and four of those nine\n",
+    "are inside $0.5$. Without the tail average the same table showed misses of\n",
+    "three to seven; that difference was the orbit, not the estimate.\n",
+    "\n",
+    "Two things this table does not license. It does not say the widths are\n",
+    "trustworthy: they are the widths of a mean-field approximation, which has no\n",
+    "correlations to spend, and z-scores this small on one dataset are consistent\n",
+    "with widths that are somewhat too narrow, somewhat too wide, or right — sizing\n",
+    "that needs many simulated datasets, and this notebook fits one. Nor does it\n",
+    "transfer to the raw splits: no amount of data fixes a ridge, only a\n",
+    "reparameterization does.\n",
+    "\n",
+    "The stance behind stopping there is a risk-management one, and it is worth\n",
+    "stating plainly: material disagreement between independent-seed fits would be\n",
+    "enough to withhold a claim about a width, while agreement only removes the\n",
+    "instability warning — it does not validate calibration. Replication can veto a\n",
+    "width; it can never certify one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1595,14 +1739,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "13a007a9",
+   "execution_count": 16,
+   "id": "ede5a7d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:58.809806Z",
-     "iopub.status.busy": "2026-08-17T04:50:58.809531Z",
-     "iopub.status.idle": "2026-08-17T04:50:58.987982Z",
-     "shell.execute_reply": "2026-08-17T04:50:58.987526Z"
+     "iopub.execute_input": "2026-08-17T06:42:36.917635Z",
+     "iopub.status.busy": "2026-08-17T06:42:36.917574Z",
+     "iopub.status.idle": "2026-08-17T06:42:37.011959Z",
+     "shell.execute_reply": "2026-08-17T06:42:37.011565Z"
     }
    },
    "outputs": [
@@ -1610,12 +1754,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "posterior sd of the contrast, widest first: S11 0.045 (900 rows), S10 0.024 (1,800 rows), S09 0.022 (4,600 rows) ... narrowest S00 0.007 (90,700 rows)\n"
+      "posterior sd of the contrast, widest first: S11 0.044 (900 rows), S10 0.023 (1,800 rows), S09 0.019 (4,600 rows) ... narrowest S00 0.007 (90,700 rows)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x350 with 1 Axes>"
       ]
@@ -1665,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cb4fdbe",
+   "id": "10c1a2b2",
    "metadata": {},
    "source": [
     "What the figure supports is narrower than the usual slogan, so it is worth\n",
@@ -1690,20 +1834,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "8bcb748f",
+   "execution_count": 17,
+   "id": "60591480",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:58.989754Z",
-     "iopub.status.busy": "2026-08-17T04:50:58.989629Z",
-     "iopub.status.idle": "2026-08-17T04:50:59.190567Z",
-     "shell.execute_reply": "2026-08-17T04:50:59.189991Z"
+     "iopub.execute_input": "2026-08-17T06:42:37.012998Z",
+     "iopub.status.busy": "2026-08-17T06:42:37.012926Z",
+     "iopub.status.idle": "2026-08-17T06:42:37.115067Z",
+     "shell.execute_reply": "2026-08-17T06:42:37.114696Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PLfPFjTfeiAULFnjsDedqwoQJ6NKlS7H+YHLOJNk5mZmZ6ra8J8mYufXWW1UWhgyIO5KekK7LvJGBME8ksOmNtzKBjoNqZbXeopL94y3AIIODEnyW3l4y0Cr/OpL9/cADD6hJdL4M5tsGa2VbJTAh2U3bt2/3+H4kG62g0r4FDaLKObGQ486115v0nvLUwsCXksMl3X7pO+gtqCb7WjJ/JVNYMkhcB3JlYqGUvrzllltQXKX9+pJB1rhxY6cejpIpJZ95CUC5fq+4Bio9ZT36cx8V9+9ZlseYJxJ8k3K2BU0kkT5qknkm2VCeegEGwveCv96Ht8DjyJEjVeDT1/+/yoLsDzkO5LtcvuvltvS8TEhIUIEXT2VjpRS5TOYpj2OvNLe9spCgn0yy8KRBgwbqfEn2i3yXyGehtLITA1mgnM8QERERVQR+/zUiP7BtP55lRq3clmwDx0wAX/Tt21c9Vkp9rF69usy2l4iIiCq+unXr4v7771dBu8Iyf6Sknlyk/5oMPt51110YP358oecoMqHq1VdfVWUYPfV3dBywe+SRR4r9XmrWrKkGlmWw03EAWoIQrn2V2rdvr0oC+hp89FYasKAMTW/3yYBkWa+3qLyVhZOsVSkrZ/sby4CgVOmQsryufflkH/sa8JF+YM8995z9vcgEO8lSdbVx48ZiBR8lQG4rHyclM21BdBsJMn300UdFXm9pbL9kkrnS6XSqRPHVV19t39eyTx988EG3zJovvvhC9UcsbgZeab++fL6ltKDrd4hMhnQNPkrg6cSJE07LpPyxa38vf++j4vw9y/oYc/Xuu+96rI4j32OSLe7YI1eCv5KlWVCQr7y+F/zxPuQ15VhxfR05JiQg6evv6tIkr3nHHXeoigIyicfb97i8Xwnayv+5NnJdji9bkN7fx15pbntlIaWFPXn88cfVZ8OWeS3nGHJbJppXdoFyPkNERERUEfg1+CgzbB37jMiPIsfAY1FI3wqZDSknuFJ2SH70VbaZhkRERFR6ZFBRgoJS5lAGe30hGQ4SUFy5cqUqcWgwGAp8fLNmzXDfffepSg4FlT+UnkMlIdmMjsFHOR+SQIJk8LhmYxaFr/vFkbcBbseB2bJab1HI4Oju3bvdlktpQslYcSSZCBIYGjZsmFu27F9//eVT8FGCzFKGzzHzSPqUy2Du/v373bYt0JRk+yXw5imrTj6DroPzcv4uQRephOJIzu8lM7A4/QfL6vUlICcTJx2PQSmlJ9lQ8tkvqOSqZHCFhoaW+TZW5ONRfs9JL15X8ptPtlMmXrgGaqUXp0xKdX0P5fm94I/3IRNOpMS2a+BRgnRSZai8yPe2BKZsZNLx0aNHVWanZMY59tOUcsXJyclOz3fsZ1wVt71169b2bM+SKmkGaEpKisrKdSUBYfmeciRZvNK3VsrCl4VA2i+BcD5DREREVFH4Nfi4du1alaloKzUjmQGuitLbRUof2WbXSYkPBh+JiIjIGznHkOxHGcD65ptvVJk81xJ63ixfvhxffvmlen5hZPa/TLaSUomuZLB/wIABJf4jSal66VHpGLyQSV2Og2IS6LjiiiuKPLnLE8dBV1fesjxr1apV5ustCk/BAOGajeY4QCnBgGXLljktl30u57OFZRXJIKynkocySOsaYJDSmoGmJNvvrfSwBF0cJyI6kuw918+jZOAVJ/hYVq8vx4RMnJSSp44k2Ch90WzHrXy3eMo69Mc2VuTj0ds+kcxS14CdI7mvuP0ey+J7wR/vQ4KPriQr895770V5k6CdbJ+UJJbgvO33v6+TlavytktZdbkEgp07d3pc7u28QkrgyiSHxMTEUt+WQNovgXA+Q0RERFRR+DX4ePDgQft1+eFelEBjYSdr5f1DhYiIiCqGli1bql5YkoG4ZcsWNXi/bds2NXjsrZyWmDZtmspqLOz8RQb45TzHU/BRMmZKi2Q/OgYfXQe3ZKCuqCUrvQ2EFRSM8FY2zHFdZbXeovDWU61p06Zen9OkSROPmQpy3llYnyaZaOeJp6zX4mRSlLWSbL9rbzbHUpRF4am3mi/K8vUlK9E1+Dhv3jx78FEmKpw7d87tGOvRo4fftrGiHo/e3ouUKi0rZfG9UB7v46KLLgqIwGNJy28WFMApaxV52/3Z21AmgBf02SiL4GMgCYTzGSIiIqKKwu9lV21kVlxJOc44L6i3EhEREZGr4OBglcEiFyEZDpLpIKXsZs6c6XEgTkqwFTTw5k+XX365KnPmbQKWBCeLSkpHSnDVtZRfamqq1+d4uk8CGo4VKcpqvaUxMCwlD73xlCnm63lnVFRUkdYZaEqy/aU1CF/cflhl+fpSclD2jZQktDl+/LjKdpM+cZ5KrnrqA+fvfVQRjkdv+6SgXmpl9Zol+V4oj/chmZjfffcdbrvtNpQX+X6XILyn4F14eLia+BMWFmafwLNmzZqA6XkXKNsuZU6lolNpkP8vbT0zi8NbSdCCvjPK6vskkPZLIJzPEBEREVUU5fZr0/VkzaYo2ZDS58TGsYcKERERUXEmNUlmilxk8HjOnDluj3HNaCrv4Kn0oJMeYq769Onj1IPOV1L2z7GsvS+9rOLj492WtWnTxmmSWFmttyhk4NgTyVKSflLe7vOksKzHwoIXFUFJtl8G6z2R8qBFCcB06NAh4F5f9ouUT/7888+dlkvQsVWrVm7lOKVPrKdygf7eRxXhePS2T2TSh3z2y0JZfC/4433Ia7lmpklGvxw7EyZMQHmQErYygcfVXXfdhQceeMCtZ/KIESMCJvgYKNv+xx9/qCByaZAeo3///XeZfDZKO1u9Iu2XQDifISIiIqoo/Bp8dByoKWhmmK8cy5n5MghEREREVZOcd8hAmq+D+jKg7yn4WFC/rvIgfdymTJniNqmrOFmPNgMHDnQbVJOMAyml5lq5YseOHR4zLz31tSyr9fpKMlc8Wb16tcf1SunJdevWeRy4LMsMpuLyNIHP22S/staiRQuPy8eNG4fhw4f7tI6kpKRiZ4WU9evLer744gun/St9GiVY5ZoVO3ToUNStW9fv21gRjzFvwb5Zs2bhkksuKfC558+fL9Zk1LL4XvDH+5BA4yuvvKICmo5effVVtR2uPUZdSanx5ORkp2Xyf6RMXCmu3bt3ezxm7rnnHrfgnZS7dd328jz2ynLbKypvE5jk+JfvNVeyX1z7x1ZW5X0+Q0RERFRR+HWqlfwos5HSRCUhP5hsvVLkB4G3H45ERERE0qNNBotkwFYGggoatMzJycGff/7ptlzONwKtRJb0V7KVjbWJjIz0OXjhydVXX+22TPbXhx9+6LRMytR+/PHHbo+VmfySGeav9fpKMlo9ZXJMnz7d44CpBJccS2valKRcW1ny1N/TNbjgL507d/Y4MfCNN94osB+YBHYk6COZRjfeeGPAvn5sbKzb504Gl99//32fSq76Yxsr4jEmkz4iIiLcli9duhSffPKJx+9t+b5+77331Oc4UL4X/PE+5Htest7r1avntFzW/cILL6hMsYJIFtlDDz3kdJHXL4nMzEy3ZbI9Bw8edFqWlZWF//3vf17LepbHsVeW215RSSa3pyzeGTNmuGX5yb6SMvBVYb8EwvkMERERUUXh18zHfv362evjb9myRZ20ugYNfS276ngS17FjR9SoUaPUt5eIiIgqDwkOyICrXGTgVgadpeyVDN7WqlULGRkZaib7woULVUaRq969e3scAC1vd955p9p+x0HwkvRdatu2rQpe/vPPP07LJRNUyvxddtllakBNykxu3LjRY+aWpyBtWa3XV1J28tZbb3ULEMmg83XXXacu7dq1Q3Z2Nv79918VKPAUgL7lllsQiFyDEGLPnj147rnn0K1bN/uxK1lVck5elmRgddKkSXjnnXeclktvRClXePHFF9sDNBJ0kQmFknm0adMmeylJbz0KA+X1Jagofd9cs9YcSQZM//79y20bK9oxJvtEvs9ef/11j7/9Fi9ejEsvvVRNaJUMU/ktKd/XEgx87LHHAuZ7wV/vQ4LgEoAcP368U2aVfI8+88wzKgNSegP7i7fvZyldKtn4DRs2VMf37NmzcezYsYA69spy24tCsmbl2CgNcp5TEnIcy/+7X3/9tVsAVqouyEXOoaT87IIFC9R3U1XYL4FwPkNERERUUfi97GrPnj2xYcMGFYCUHws//PBDkUtXyYzRVatW2W+PHj26DLaWiIiIKivpSyTZkHLx1b333otAJBlYrllYJfX888+rKhWO/bXFypUr1cWbmJgYPPHEE35fr68kQCC9nlx7e0mgwVPvTFcPPvigGoQORJJJ99NPP3nMUpGLjQwWeyopXNpuvvlmNTArvdQcSSBt/vz56lKRX18GniUryPVYdiSZLQX19CrvfRSIx5hkc0qQz1NpU8lELIuyjmXxveCv9yHleyU4NHHiRKfsPcmQle9M+Z1dkkz4opAykhIElOCUIwmcu2Z/yYTjkJAQt8eW17FXltteFNIf1lOP2PJy++23qyxa116OMlnrm2++cXt89erVPWaRVrb9EgjnM0REREQVgd87XD/88MP26/JDW2YT+tr/UfoIPPXUU04/AGTGGEtWEBERUVl69tln0b179yqzkyWjSnpJFqWntmR5yaSygvqVldV6fSUDxl9++aXKei0KGWyWc1bJZgpUkilXGhkdpUWCHrKve/XqVaznywBtIL++rL+gCZC2rKHy3MaKeIxJFuGnn35a5PLGnspDluf3gj/fh2z3Z5995jahV0pgym/vZcuWwR/ke93xt35BHnnkETRu3Dhgjr2y3PaKTEoSS1awBBULIxmipT0RKpCV9/kMERERUUXg9+CjDNxJ+RobKVckJTQkC3LRokVOM8cuXLiAffv2Yd68eSroOGzYMKf+FVIm57XXXity5iQRERFVLbZzDSmBWJTSqV27dsW0adNUhlJVY8sgkWyDgrK3pMSrDDrOmjXLp6zAslqvr6SM5c8//6zKG/oymC3HgJTqffTRRxHI5LiWYIen8oTlpXbt2pg6darqQSf9SQsj5/RDhw7F559/jl9++SXgX99bP0chPWajo6PLfRsr4jFWs2ZNlc0nPeSk71xB5LtB+qwV9Lcor+8Ff74PCfpIkEh+HzsyGo2qP6j0CXXkmskmSmOCjWSRSslXCeh6O4ZefPFFFbQNtGOvrLa9opPKVfIdJVm23v6vlsCtZAJWNeV9PkNEREQU6DRWTx3vy5jMwrzvvvtKNAtTZprKj4OqOBhIREREJTsP2bt3L+Lj41XvJimrJmXCpD+PzO6Xmf4yyCYDscUdJJL+R9LXy1MJM1+za2QwT7bJkQQdilN6TEqDyeChp4xOX/rGSfWJ5cuXq95k0s9IzsNktr8MvA0ePFgFUIqjrNbrKxmY37Ztm6rGIX0+pWefDBLK69qOgebNmxe4DukT+t5777ktf/LJJ1VPNlcSzF6/fr1bNoRMtCsNUrJT+tJt375dHdtyu6DX8tf2y08O6Vko+/vIkSNqX8tnUQI0ksEnPbSkd5vcLgtl9frS1y85OdltuZTd7NOnj9+30R9/z6IeYyUVFxenPqMJCQlqn8jnVr63ZP/KhBLXgFtx9kNpfy+U1/uQUr1//fWXx8DZ5MmT1f9vUjJTehjLsWXTtGlTNcnXW+CtON/t8n+gTCSWCcWS0Wnr22fL+nrrrbeQmJjo9Dw5vgvqq+uPY6+str2ik+NFgthSSliC1zVq1FCfA+lRawvYS3l613L2sl/8PVGiPJT3+QwRERFRICqX4KOtD8VHH32Er776ym1grTAyMCgzSEur6TgRERERERFRZSd9RmUisI0EPadPn17ksrNErqpy8JGIiIiIAqDsquOPHOmXMHv2bIwaNUr1xSiMzMS86aabVHlWBh6JiIiIiIiIfOdagvWOO+5g4JGIiIiIiEqdHuVMylC88847qs+JlKGREiqpqak4d+6cKj0kpWGkjIeUt5FZc6VVCoaIiIiIiIioqgYfpZyoYxYkERERERFRpQk+2kjvkoEDB6oLEREREREREZUe6fcok3/lYiuTGRQUxF1MRERERESVN/hIRERERERERGU34fejjz7i7iUiIiIiosrb85GIiIiIiIiIiIiIiIiIKhe/Zz6uWLECDRo0QPPmzf390kREREREREREVMq6du0Kvd55iKlZs2bcz0RERERVlMZqtVr9+YLPPvssZs6ciY4dO2Ls2LG44oorEBYW5s9NICIiIiIiIiIiIiIiIqLKVHZ1586dePnllzFgwAA8+OCDWLp0KUwmU3ltDhERERERERERERERERFVtLKrroxGIxYvXqwuERERuPLKKzFmzBi0adOmvDeNiIiIiIiIiIiIiIiIiAK57OqOHTswdepU/PPPP8jOzvb6uHbt2qkgpAQj69Sp489NJCIiIiIiIiIiIiIiIqKKEHy0ycjIwKJFi/DHH39g06ZN8LYZBoMBgwYNwlVXXYXBgwer20REREREREREREREREQUeMot+Ojo2LFjmDNnjrocPXrU6+PCw8MxcuRIjB07Fu3bt/frNhIRERERERERERERERFRBQg+OpIsSAlCLly4EOnp6V4f16pVKxWElLKskZGRft1GIiIiIiIiIiIiIiIiIqoAwUebnJwc1RdSyrKuXr0aZrPZ4+P0ej0GDBiAxx57TAUkiYiIiIiIiIiIiIiIiKh8BGzw0VFaWhrmzp2rApH79+/3+JhXX30V11xzjd+3jYiIiIiIiIiIiIiIiIjyaFEBSFnVSZMmYd68eZg9ezZuvvlm1K1bt7w3i4iIiIiIiIiIiIiIiIgqWvDRUXR0NBo2bMg+j0REREREREREREREREQBRo8KIDc3F8uWLVNlV1esWAGj0Vjem0REREREREREREREREREFSn4uGPHDhVwXLBgAc6ePev1cU2bNkXz5s39um1EREREREREREREREREFODBx5SUFMyZM0cFHQ8ePOj1caGhoRgxYgTGjh2Lrl27+nUbiYiIiIiIiIiIiIiIiChAg49ZWVn4+++/VdBxzZo1sFgsHh+n0+nQr18/XHXVVRg2bBiCg4P9vq1EREREREREREREREREFGDBR6vVik2bNmH27NlYtGgRLly44PWxLVu2xJgxYzBq1CjUq1fPr9tJJWM2m+0lc8PCwlQAmYiIiIiIiIi/IYmIiIiIKie/Bx8TExMxa9YsVVb1+PHjXh8ngaorrrhClVXt2LGjX7eRSo8EHu+++251/YsvvkDdunW5e4mIiIiIiIi/IYmIiIiIKim/Bx8/++wzzJw50/PG6PUYOHCgCjgOGTIEQUFB/t48IiIiIiIiIiIiIiIiIqrIPR/btGmjAo5XXnklM+OIiIiIiIiIiIiIiIiIKqhyCz5K+c2RI0eqoGPbtm3LazOIiIiIiIiIiIiIiIiIqKIGH3v27ImhQ4di8ODBqswqEREREREREREREREREVUOfo/+jRkzxt8vSURERERERERERERERER+oPXHixARERERERERERERERFR5RdQdU+zs7Oxf/9+nD17Funp6dBqtahdu7bqD9miRQvodLry3kQiIiIiIiIiIiIiIiIiCtTgowQaZ86ciT///BNxcXEwmUweH1e9enV07twZY8eOxYgRIxAUFOT3bSUiIiIiIiIiIiIiIiKiACy7arVaMWXKFAwePBjvvPMOdu/e7TXwKDIzM7F27Vo8+eSTGD58OFauXOnX7SUiIiIiIiIiIiIiIiKiAAw+Go1G3HPPPXjjjTdUqdWiSklJwe23345PPvmkTLaPiIiIiIiIiIiIiIiIiCpI2dWnn34aS5cudVsufR07dOiA2NhY1KpVS2VCSu/HQ4cOYceOHUhKSnJ6/Mcff4zw8HCMHz/ej1tPRERERERERERERERERAERfPznn38wf/58+22NRqP6OEomY/PmzQss07p582Z8+OGH2LBhg33522+/jaFDh6J+/fplvu1EREREREREREREREREFEBlVyV4aFOtWjV8+eWXqvxqQYFHW5CyR48e+PHHH/HYY4/Zl0vZ1s8++6xMt5mIiIiIiIiIiIiIiIiIAiz4KOVT9+/fb7/9yiuvYPDgwUVez5133okJEyY4ZVNaLJZS204iIiIiIiIiIiIiIiIiCvDg47p16+zX27ZtiyuvvLLY63rooYcQFBSkrp8+fdopqElERERERERERERERERElTz4mJKSYr9enIxHR6GhoejatavHdRMRERERERERERERERFRJQ8+nj171n69Xr16JV5fVFSU/fqZM2dKvD4iIiIiIiIiIiIiIiIiqiDBx+rVq9uvp6enl3h9juuoUaNGiddHRERERERERERERERERMWnhx/VrVvXfn379u0lWpfZbMauXbs8rpuIiIiIiALDkfQs/N+2QziWkY3wEAP6RYWhf/1wtA6rAa1GU96bR0RERETl6MSFbPzftsPYcTodjWuGYHTTKFzaMAJBOr/mzBARUUUOPnbs2NF+feXKlUhISECzZs2Kta4///wTaWlp6rrBYEDr1q1LbTuJiIiIiKjk4s9dwF3LdyPDaFa3T2TmYPfpDHy99zjqSiAyOgz9o8PRq14Yahh03OVEREREVUjC+Uzcv3IPTmUb1e2dpzPU5fPdR3Ft8/q4qlkUQoP8OnxNRESlxK9TSLp164bQ0FB13Wg04rHHHitWr8Z9+/bh1Vdftd/u1asXy64SEREREQWQlMwcPLx6nz3w6EoGmeYdTsPT6/bjknkbcf+KPfjlwAkcTc/y+7YSERERkf8nqd27Yrc98OhIlkkActSfm/He9kMqO5KIiCoWvwYf9Xo9Jk6caL+9Z88eXH/99VizZo1Pz7dYLJg+fTomTJiAs2fP2pdPmjSpTLaXiIiIiIiKLj3XhEdW70VaVq66XdNkRMdzZxBqzLvtymS1YmPaOXyw4wiu+Wsbrl60VQ00bUg5C6PFwj8BERERUSUSd1YCj3twJsdU4OOyzBb8Gp+szg2fXb8fe89k+G0biYioZPyet37bbbdh9uzZOH78uLp9+PBh3HrrrWjTpg0uvvhiVZo1JiYGNWvWVH0dMzIycOjQIdUjcuHChUhOTnZa3/Dhw9G/f39/vw0iquS++OILzJ8/X32/TJ48ubw3p9Lu34EDB+Kpp57yeB/3PRFRxZRrtuDJtXE4eD4vgzEsNwdTN65GbZMRRo0GqyLqYUF0LDaH14XVS8/H4xey1UCTXKrrtaosa//oMPSLDkdEtSA/vyMiIiIiKi17TmfgwVV7kO6lOoYnMhXtn+On1KVbRCjGt4pR5fvZP5yIKHD5PfhYvXp1fPPNN7jxxhtx+vRpp1KqcimKzp0745133imDrSSi8vD+++9jyZIlGDJkiCrLXJ5SU1Nx4MCBYvelJd/2b8uWLbnviYgqEYvVipc2xWPLyfP2ZWeDgvFls1Z4cv9uGKxWDElLUZfk4BAsqB+LRdENkBYc4nWdmSYLlp04rS6idVgNFYjsXz8c7cJrctCJiIiIqILYeSodD63aiwsm3wOPruQ8Uy5NalXD+Jb1cWmjSATr/Frcj4iIfFAu38xNmzbFr7/+iq5duxZ7HWPHjsV3332ngplEVDmkpKSogFRSUlJ5bwoREREVw8c7j6gZ6a7+rB+LaR06Oi2LzsnGpMPxmL5uOV7fuQX9T6ZCZ7X4VKbru32JmLR0Fy5fsAkvb4rHkuOnkGEsuGwXEREREZWfrWnnVcajt8Bjr3q18V6/Nuhdr7ZP6zucnoXXtiRgzMIt+H7fcZzLce8dSUREVSjz0aZRo0aYNm0a5s6di59++gm7du0q9DlarRaDBw9WfSP79u3rl+0koqrp7rvvVj1pa9WqVd6bUuVw3xMRVUzTDyTh5wOeJxDFBOlwVfP6wP59QK7zwJAOQL/TaepyMigYi6JiVLDyRLXCJxlKn6AFR9LURafRoHNELfSPDleZkTIbXuOlrCsRERER+c+m1HN4bM0+ZJs9TzTrGxWGN/u2QohOp6pbHDh3AT/vT8LiYydhtloLXPfpHCO+2H0MU/Yl4som9XBDi/poUNN7VQ0iIqrkwUeh0+lUBqNcjh07hq1bt6rSq2fPnkV6eroKNoaGhiIiIgIdOnRQmZJ16tQpz00moiqiXr166kLc90REVDjJPPxgx2GP99XQ6/DOoPYIN2hhNuhhWrsZ1sQUj4+NyM3BhGOH1GVzWB1VlnVlRBSM2sILtsjA1Ja08+oiGZgx1YPV4NWw2LroGhHKPyMRERFROViXchZPrtmHHIvnIOLA+uF4vXcrBDmUTm1ZuwZe6NkCd7dviBkHkzE7IaXQUq0S2PztYDJ+P5iMIQ3qYnyr+mhfhxPKiYiqZPDRUcOGDdVl1KhR5b0pRJXGG2+8gdWrV2PEiBG499578eeff+Kff/5BYmKiCu63bdsWN9xwA1q1alXgek6ePImZM2di06ZNqldrtWrV0Lx5c1xxxRXo3bu31+cdPHgQs2fPVpnN586dQ82aNREVFYVOnTph5MiR9skE27Ztw//+9z9VdlUsW7ZM3e/o1VdfRZcuXZyWGY1G9Z5WrFih3pPcjomJURnSo0ePhsFgKHCf3HfffWp//PXXX2oCREZGBp5++mn0798fX3zxBebPn6+uT548udT2i6+vX9S/7aJFi/D333/j+PHjamJH69atMX78eHtPRbPZjAULFqjXk7K2er0ePXr0wK233lrgpI7i7GObuLg4/PLLL2pSicViUc+7+OKLcfnllxf43grb97Id8j7kuEpOTsaFCxcQFhaGjh07qsksTZo08Wnfy3Em7+3IkSNq/0hJ8Kuvvhp9+vQpcPuIiMjZ1pPn8eLGA5DhpLGJR3Coek1sC6+r7jNoNXi7b2u0qF1D3dYP6gXdwJ6wHjsB05otMG/eCeTketyl3c+eVpfzQUG4rtdAZOmK9tPlRGaOGoCSy7hmUXi8S1NmQhIRERH50aqkM3h6XRyMXgKPF8XUwau9W8LgZaJZVPVgPNCxMW5t0wBzD6VienwSUrI8nzvaSG7lksRT6tIlohbGt4zBgPrh7BNORFRVg49EVPokyCQ9FNu3b6+CTGvXrnW6X7KNZ8yYgeeffx7XXXedx3VIcObZZ59FZmam03IJuEnvVgkkvfnmmwgODna6X4JOEjA0mdz7L82bNw9vv/22CrpJQErWLdtpI5nPcnHk+vr79+/H/fffrwJHjiQgJev9/vvv8dVXX6FBgwYe94lkU0+aNAmrVq1yul+CpCI1NVU9rlmzZqW6X3x9/cI4rueOO+7AypUrne7fsmULZs2ahY8++kgFeyVAKX9v18fI32L69OmIjo52e43i7mPx888/47XXXnP6+2/fvh0LFy5U+yc2Ntbreyto3//2228qUO2JBBa//vprFbC86aabvO4zCVI+8MAD6j042rlzpyoF/thjj+HOO+/0un1ERJQv4XwmnlizD7kWK248moA7Dx1AllaHxzt1x+7a4Xi+Rwv0cOnbI6VQNY0aIKhRA1jHXgLzlt0wrd0C6+HjHndtWItGmD6yF9Ykn8HqpLPYmHYOOV5KdnkzMyEFnerWwqWNIvnnIyIiIvKD5Ymn8cz6/TB5KZs6PLYuXurZAnofKlzUNOhxY6sYXNsiWlXc+Gn/Cew/5zwe48m2k+nYdjIOjWqG4MaWMRjROEKVdiUiorLH4CNVSBtSzmL+kTQkXshGZdCgRghGNo5Er6iwMlm/BJgks2vQoEEq01GyDw8fPoxvvvkGe/bswQsvvKD6sLr2Ut2wYYMKxEjWmmSTSUBGMuokQPbHH3+oQI0E4SSL8v/+7//sz5MsPlvgUTIAJbApASpZz4kTJ1SQR7ZJsuiEZDTK7Q8++ABLlizBkCFD8Oijjzpti2OwSrL7JkyYoLZDSqPecsstqiyzZOHt2LEDX375JeLj43HXXXfh999/dwsACtl22SeSBScZfPXr11fvQ/4tTHH3S2m9vut6ZDvGjRunAp7So1KCgx9++KEqYS2BOvnb7t69W22nHAOSGbl+/Xp89tlnKiD3+uuvqyClo5LsYwmovvzyy7BarSq7VoKjsp8ks1WC0pJFKUHa4pAsR8lQlPfaokULtb/k/csxJ1moGzduVEFPyfj0lsE4Z84cte9lv0uWqmR+Hj16FJ9//rkKTr7//vsYOnSoWj8REXmXlpWLR1bvRXquCbcdjsfNRxPU8moWM97auQXrr74SlzSMKHAXaoKDoe/bTV0sJ1JUENK8cTuQmX+Op+/bHdHVg3FVs2h1yTabsTn1PFYnn8HuI8nYJ/NcfOjt+M62Q+gSEapm0BMRERFR2ZEA4XMbDnjt1ziiUQT+110Cj0Xrzy2BSplMJueYm9LOY9r+E1ibcrbQ5x3NyMabWxPw5Z6juKZ5fVzdLAphwd4rORERUckx+EgVMvD48Op9hTacrkh2nc7AP8dP4cMBbdHTJTugNEigRcqYvvvuu/ZyY5INOWzYMBVgkmCgBKAkAOjolVdeUYEdCfZItlrt2vnbJiUxpVTyp59+qkpkSlBTyngKCS5J4FGCXt99950q8WnTrVs3tS1PPPGECk6J6tWrq9Kv0uNVSHnWgkrBSmDLFhSTYF/dunml3YRktcn7kteQQJJk9U2cONHjPpHAmWuQ0xfF3S+l9fqu63nmmWec3qNkOkqwV4KNaWlpqjzst99+61TOVf4OISEhKjtTAr5S8lX2e2nsYylvKn9bedxPP/2kXsd2zElg+fHHH1f7pjgkkC2BUFfdu3fHmDFj1L6QYKi8X2/BR9lnUt5WsoEd39PAgQMxfPhw9b7ls/DII48UaxuJiKqCDKMJD6/ei+QLObgnYT+uO+7c77Gm2YRLTVlFWqc2JgpBV4+AddRwmHfsg3nNZlhSTkLbwfmcQGarSy/HfvVCkf37LBhrVMfOli3xW+262JCeA7OXU8R0oxmvbj6ozre0PgQriYiIiKjoFh9Nw4sb41X5U0+ubBKJyd2aQ1eC8zEZ25LxM7nEn7uAnw8kYfHRk16zLG3O5Jjw1Z5jmBqXqJIAbmhZHw1rViv2dhARkXeF57UTBRjJeKxMgUcbeU/zDqeWybolW01KUdoCjzYSFHrqqafsJTYlO85GsudkmZBAoWOAzUZKeUrZVCElPm0kMCckYOUYeHQkywvqF+iNZLgtX75cXZesPsegmI2UEJWSpsI1oGojPQKlpGhRlWS/lMbru5KM0JtvvtltufRkrFEjr7+WBNU89ZG09V6UQLFkMZbGPpb9Y1uXBPhsgUcbOQYlQFicv72QvpoFkWCvkMxKW3DblQRmPQWkJfgtQVUhfSqJiMgzo8WCp9fux8GzF/Bw/F63wKPQXz0ChouK10NXYzBA370jgh+4BSHP3g+Nl9JYlt0HgPMZMCSlotuK1Xhj0Z/4O/0EPoqpgUtiPfcz3pB6Dr8n5PWYJiKi8mE5dAzGuf/AvHU3rGYz/wxElciCI6l4oYDA49imUXimhIFHV9JbXEr9zx7RFTe3ikFNQ+FlVaWEv5wTXrN4m+pJeaKSVFYjIgok5Z75eOrUKdV3LC4uTl2X7BtPPeJ8yYbxluVCVNVJVldEhOeyZ5KVJ6U6pcfitm3bVHaakOsiKChIler0FkCU8pSS3WZ7vJA+hEJ6DErZUcmulFKvpUHKhdpeW4Jq3kiJULF3716V6SalRl3vl/dWVCXZL6Xx+q6kZK1rUNkxQHjw4EH1GE8iIyPVfpH949hrsiT72PZ+JZDnKeNTSDBTtklKpBZHbm6u6te4Zs0a1Y9S/t+Q1xe2Ur7Si/P8+fMeg8Oy3VLi1hNb/0pfe28SEVU1MrFDsgc3p57Fk3G7MCLlhNP9MtCku24kDP09/x9QVJpqzpNYHJnWbnZeYDRBu3knOm3eic5REegT2wgfBtXGOZf/bz/eeQS969VGo1qc5U5E5G+mpWthnL3Yfjv4sduhaey9HzwRVRx/HErBm1sS4C1d4Nrm0Xi0cxOvYxglVa9aMO7r2Bi3tInF3MOpmB5/AsmZuQU+R7Z1aeJp7DiVjp+Hd2YpViKiyhB8tPUkW716tX3QuCT69evH4GMVIWURpERpZct+lFlfVzapVybrlvKn3shJn5QJld6Pqan5mZdSrlNIBl9BWWqNGzdW/zo+V4I7Y8eOxezZs/HVV1+pS7NmzVTAqVevXiow5yko5AvpGWnbbluWmy3DzTHTTQJUQiYzSCBJ+vo58haMLUxJ9ktpvL4rW3ajJ7bt8/YYCcDZgo+2bNWS7mPb+5VjqiByf3GCjwkJCbj77rtV0LEw2dnZHo+zgvaZLSDsuD+IiCjfZ7uP4u/Dqfjfvp0YmpbstGvMGg2sN4xCjT55k1PKktVohDU5zfv9KScxNOUkBmk0mBPTEJ83aw3TfxNPZKb7i5vi8dXgDkXuM0RERMVn/GsFTPP/td/Wtm8JLQOPRJXCbweT8e62Q17vv7FlfTzYsXGZBR4d1TDoVDnVa5pH49/EU/hp/wnEnb1Q4HNOZRvV4+7vmDeWQ0REFTT4KIGI999/n4O7VCy9osJUrx4pUZpYScoiNKgRogKPZdHvUXgrfep6v2PWsS2DrLDymJ6ea+v7J6U+f/vtN5XdLEEjuUgZUimdKf0IpTxpUUlAybZ9vpbGtAXJHHnLfCtMSfdLSV/fH0qyj23vt7BjrjhlV2XdcsxI4FHK1l577bUqoC0ZnMHBwepHTFJSkjq2hLeyq0REVPxBpV/2HseLe7Zj4CnnyTUmjQY5N45BRO/Oftm9Upo1+LkHYYlLgEl6Q+6Mk5kjbo/TW624OvEogiwW/F+rvOoOYvfpDPy4PxG3tmG2DRFRWZPzctOfS2FavMJpueHyodz5RJXALwdO4IMd3icIT2zdAPe0b+iXwKMjmWR2ScMIXBxbF5vTzmPagRNYk3zW6+N/T0jGTa1jUDuoeG1iiIionIOPEoiQMowFDUgXZ1C6sIFuqlxsTaXJN1LSuCAnT55U/zpmiUlwxzHTzxvb/a4ZZnJSeeWVV6pLTk6O6icpffj+/PNPVaZTMp/lOePHjy/SnzE8PNze6/Dzzz/36TmllWVYGvulIijJPrbtH9sx5U1h+88TyZQ8dOiQCtz+/PPPaN68udfAKRERla5liafw8eZ4vLp7G3qfcf6Oz9Vqce6GMWjUu5Nfd7tGMvjbtlAX6/kMmDZuh3nNZljTTrs99sqk41gZUQ8b6kTal3295zj6RYejdZj3jHgiIiqFwOPcv2FassbtPsuR49A2rO/+HIsFxhnzoevVBbpm3qv4EFH5+zEuEZ/sOur1/jvaxmJS21i/Bx4dyWv3qFdbXRLOZ+LnAyew6OhJGC3OE5YzTRbMiE/GHe0KruRERES+8WvE7sKFC3j77bedlklvOMlekd5gUoavNHqgEZEz6cMnWWOegvRSYtNWZrNNmzb25a1bt1b/nj17Fvv370erVq087lZb6cy2bdt63e2SldatWzd1ueOOO3DXXXdh+fLlmDdvnlPw0ZeTUelfKZKTk1XAy7Wcalkrzf0SqEqyj237JzExUWUh1q/vPpggx6K3XpgFOXz4sL2krafAo5AsWyIiKl3SA+f1NXvxxq4t6HbWObCXrdXixPVj0N7PgUdXmtCaMAzrD/3QfrDEH4F57RaYt+4GHNo7PBG3G7f27I8Mfd5ERynh/+LGA5gytBOCdYFbkYCIqKJSQcRZi2BescHtPsO4EdAP6OnxefL9bV6zRV207VrCcMVQj0FKIipf3+49jq/2HPN6v2Q7Sv/FQNIstDr+170FrmoajVuX7nS7f0Z8kirZWtPAJBciopLy669sCTacP3/efltK4/3++++47rrr1EAyA49EZePMmTOYOXOmx/u++eYb9W9oaKjqx2gj12WZ+PLLL70G2CSbUQwbNsynbZEAY7t27ewTEhxVr15d/ZuRkeH1+d27d0eDBg1UAEuyJ/2trPZLICnJPpb9U6tWLadjy5Uci6dPu2elFMb2f0RKSorHUrrp6emYOnVqkddLRETeHUnPwjMrd+G17ZvcAo+ZOh32Xjsa7fuUb+DR9TxD17IJgm6+CoarL3O6LzI3B/fHO5cTTzifhS93e5+tT0RExWPLXnQLPGoAw/VXQj+ot+fnmc2qRKuNZc8B5LzzJXK+mwFLknPJbyIqv4zmL3YfLTDw+EDHxsUKPObOWgTjnL9h3hkH64VMlJV2dWqib1Re5SZH541mzEpIKbPXJSKqSvwafIyLi7Nfl2ygRx991J8vT1Slvfbaa5g9ezbM/2UASHnKTz75BNOmTVO3b731VoSEhNgfL30Zb7/9dnV9/vz5ePXVV+1BQTnRlMkEDzzwgLrepEkTjBo1yv7cH3/8Ua17z549Tj0P5bFr167FjBkz1O3OnZ37Qsl6xNatW+3ZmK6kLPPkyZPV4OL06dPxxBNP4ODBg06PkdeUXoWfffZZqQejSrJfKoqS7GPZP7fccou6/tNPP6njwFYKVY49OQblWCxuUFS2KTMzU23fuXPn7PdJWd+JEyd6PW6IiKjoTmXn4qFVe5FmsiKhRk2n+9L1eqwdMxL9+vmnx2Nx6Pr3gLZ1M6dll6WcQK9TzqW/fz6QhC1p+f+nEBFRyUgA0fjzHJW56ESjgWH8WOj7dff6XPPmXR7LZ1u27UHOm58j98dZsJws+kRGIiodMtbx2a6j+H5fotfHPNq5CSa0iin6us1mVULftGQ1cr/+BdmT30b2658id/o8mDZsh+XUGfX6peW2tp6Do3JumG3Kr55BRETF49cccilTaDN8+PByrfdNVJX07NlTBf+ffvppFfipW7euKqlpCwoNGDBAlUN1JUE2CSAuWrRIBRQlECUZcRL0kWxKIWU5P/roI1Va1Ub68klQ8+OPP1b9+aRsZ82aNVUfQFugTtYjQTpHF198Md555x31XSHXo6Ki7NmQEuTr0qWL/XHPPfccXn/9dcydO1ddpNeg9CqU7DfJqrNYLOqxklld2oq7XyqSkuxjyWqXzE8JNMsx8PXXXyM6Olr1HpXnSmak/C03bHAvv1QQCeaOGzdO9Q6WwK/sfynrKsexrYfkpZdeisWLF5finiAiqpouGM14ZPU+JGXmqMHi91u2Q7DFgktTTuCswYBFIy7FLYPy/l8OVPJbw3DjaOS88RmQLe8D+KdpC2wPcy4nLkNYL286iJ+Gd2KJrVIi/zfLxCWZFCQVC2TikCyT8yKZqFSvXj3ExMSgWbNm6hyRiCpZ4PHHWTBv2e18h1YLw81XQd+tQ4HP13VtB2RlwfjXSiDduVIOrFaYN+5QAUpd364wXDIImvDaZfAuiMgTCfx9sOMIpscned1BT3VtiquaRRdrB1oTU4Bco/Oy5DSY5bJmc96C2rVUL1jtfxdNgyjVB7w4OtWthW6RodiSll+lT5zJMWLO4VRc14LlnomIKkzw0RZEEP7u00ZUlcnAzksvvaQCe6tWrbL3zpNA0g033IB77rlHZbu50ul0eP/991Vw8ocfflA9Dm3PlUCUBHruu+8+FSR0dNNNN6nP+NKlS1XQU4KOchGRkZG44oorcPfdd6vXdyQBKsmUk+2U15K+gTYyaOVIekVKJpyU9lyxYoUKWNomOMh7kezqiy66CCNHjiy1/VjS/VLRFHcfS3lUKUn7+eefq8CsBGRl/8hz5PFPPvmkCk4XNfgoXnzxRXWcSNBXtuXYsWP2fqWTJk1S2bQMPhIRlYzJYsEz6+MQdzZ/0Neq0eDt1u1VqdUTXTrikWF52eiBThteW5VfNS1aAcP40Whfrx6sS3YAFudZ8xJk/XDHETzb3XNPYSpYVlYW1q9fr84XZAKSBB4dq194/ftotWjatKnqCz5w4ED069fPXr6diCoeq9GE3KkzYdnhXOIaOi2Cbr0Wuk5tCl2HxmCAfnAf6Pp0g2nFepUBhcy8SbN2FgvMqzfDvH4bdAN6wnDxAGhqcSIDUVmyWK14d9sh/O6lJKmcFT7TvTlGNalX/NdI8KEU/rn0vL6w0ttbBAdB27ShCkTqe3cp8oSE29rEYkvaHrflP+0/gauaRcFQzMAmEREBGmtp5qsXQvp8Pfvss+r6gw8+qAbnqXKTTCcJMokvvvhCZdyR/8jnTAIxkpn28ssvq2U5OTlITU1Vgz0SxJFAmq8ks08y3mQigQQRZR2Fka8YCTxKxlvt2rV9PgakP6wcP0Zj3qy32NhYpwkMjiQDz5ZVacvO8zYgmpSUZN+WgoKDso8kuCUDYJJdV1r7xdfXL4wv6zl69KjKMpBtcg302sTHx6v9J9kHBWUeFGUfO5JSq5JlK8+XLAdbJqht/0r/TDkOi7rvZX2STSHrl2PKtu3SC9IWCJagu16vL9I+k7+jvE/JymjYsGGh74+IqLKR/7df2XwQC444lyZ1nCH+8cC2CCnC+UN5Uz93jEZo/usd/PP+E/hw5xGPj323b2sMjOEkSV9JlQMpqf7333+7TRQrDpnANGTIEIwePVpNWCrKeSpVHvwNWTFZc43I/e5XWPbEO99h0CNo0nXQtWtZvPVmZsG0dC1My9YBOe5935UgA/QX9YF+aD9oqlcr1usQUcGBxze3JKhsQE9kBOS5Hi1weePIEu1Gy/EkmLfvVUFIy+HjgLHwiUyOgiffC239ekU+T5y0bBd2n86r0uXomW7NMLppxZ5UTkRUZYKPUnJHfkTKoLHMav3+++/99dJUTvjDMfCCj0RERETeTFuzE78eSkNKiPvgbeOaIfj6og6oHexeLaGiDaDdt2IPtpx0LrElwoMN+OXizupf8kwmhs2ZM0f1fJbqD2VFJp5JNY1rr73W6wQ0qpz4G7ICZz1+O905+BhkQNCdN0DXqlnJ159+AaYlq2BaudFrQEITUw/BT91TITLziSoKs9WK1wqYmKbTAC/2bIlLGkb4tD6rxQLTouXQD+wFTa0a3h9nNsMqwciDR/OCkQePAhcKmOhUPQQhrz/psQSrBDVNKzZA2/y/Uq0RdZy+J1YlncFja1wytqVdUI1gzLikK/RafqcQEQV82VXJNJFyi/PmzcO6deuwc+dOdOzY0Z+bQEREREREHvy1cS8Gz5qH3lodHurSCyeDQ+z31Qk24IMBbSt84FFoNRo816M5xv+zHZmmvP7Fjj1+ZGb/m31acfDahUwglR7Q0s/5+PHjBf7ma926NRo3bqyuSx9sqXwgF6lQIFU4pNKATEyVKg1Sol+qGbjOiZXXeOONN/DVV1/hrrvuUq0CJDOSiAKTRjIcb7sOuV/9DMv+Q6oUYtDd46Fr3rh01l+rBgxjLoV+SF8YF6+Aee0WwOz8Ha4f1Jvf3USlyGSx4uVN8Vh8LK+NjiudRoNXe7fE0AZ1fQ48Gn+eA/OG7TBv34Pg+yZCE+q5ApNGp4OmcSy0jWOBof3UeYI19VReIPK/izXttP3x2qaNvPZ+NMclwLxuq7ootWqoIKTqHdm8EfrFRKNl7eo4cM45uJl4IQf/HD+JyxqVLKOTiKiq8mvwUUyePFn1A5Gyeo888ojq21VYSUMiIiIiIio7m7bsQ8fpsxD2X7nz/9uxCQ917omzQcGortfi/f5tEFMjPxhZ0cl7eaRzU0xZuQMx2VnYWCd/tv6yE6ex6NhJjOBAk93mzZvxyiuvYO/evW77UoKLQ4cORd++fdGrVy91u6ikhP3GjRvV78QlS5Y49f2WLLjXX39d/W585pln1GsRUWDSSKbjHTcgd+rvMFwyENomsaX/GrVDEXTtSFiG9VfZUxLEgNUKTUQ4dL27lPrrEVXlHuDPb4jHksRTHu83aDV4vXcrDPKxXL0KPP4yN+8zK7eT0pDz8RQEPyAByML7PUumoiYqAtqoCKBvt7x1nE+HJeEYzAlHoY2t73svyfQLsEh51+155zWaOrVx7+jL8YhL8FFM2Zeosjpl8hoRERWN37vmSm+uKVOmqIDjsWPHMGbMGMyYMUP1JSMiIiIiIv86sGM/Gv000x54FI0zL+DuhP1qRvsbfVqjTbj3vsAVkQyAjTicgO+3rMXze7ejbo7zb5F3tx1CSmZOuW1fIJk/fz7Gjx/vFHiU3shjx45VAcGVK1eqwOTll19erMCjkH7Mw4cPx7PPPot///0Xv//+OyZMmKB6Q9vIb8d77rlHZUISUeDSBAch+M4byiTw6EhbNxxB48cg+Jl7oevaHvrLh6hMKU8sqSdh2rBNffcTUeFyzRY8s36/18BjkFaDt/u2Llrg8df5MK/f5rz85BlYkj2Xc/WFBC11Xdoh6KrLoO/V2fNrW63uwUfXx5w+h25/LUWTGsFu9x1Kz8LyE/kZlkREFMCZj6J58+aYNWsWXnzxRdWP7rnnnsNbb72FLl26oGnTpqhZsyZ0Xk4avZEZsO3bty+zbSaqiCTT+P7771cDOkRERESuknbsR93vf0U1s9lp+c7QMHzcog2e7d4MfaLCKtWOs+bmIveLn2GJPwwp4imXx/fvweQOXWVavXpMhtGMVzYdxEcD21b5me4nT560l0SNjIzExIkTcf3116NWrcKzFIqrQ4cO6vLkk0+q/pLffvstDh8+rO5LSyv+ICURlQ7r+QxAr4Omunt/YH/TRkUi6NZrCnyM6c+lMG/ZDdPfq2AYNRy6jm38tn1EFU2O2YLJ6+KwOvmsx/uDdVq827c1evl4fijnEMbf/swrlexIp0XQ7deVSk/YApnNMFw6WGVHWg4eUVmPHrczMRlPdUjHPerM0Nn3+xJxUYxzn0giIgrQ4KOQTMewsDAYDAYYjUZkZGRg1apV6lIc0dHRDD4SuZAMY5Y1JiIiIk/O7IxDte9mINjiHHjcWjscz3TshomdmuKKxvUq3c7TBAW59RfqezoNl6WcwKLoBvZlG9PO4feDybimBVtESNBRsg6vueYav/ZdlD6R1157LcaNG4eFCxfigw8+8NtrE5Fn1rPnkfPJVKB6NQTfexM0Ie6ZQoHEkpisAo/CmnISuV9Ph2HcCNUfkojcA4XPbzjgNfBYTafF//Vvg+6RtYsWeFy9yT3weNt10LVvVeZ/Ao1eD/1FfdRF9Y2UbMv/ApHmXXFARn6p1bZrN6JJ70E4nOt8bhx39gLWppxFv+jwMt9eIqLKxO9lV22le6Qsz6+//qoCj0RERERE5D+Z2/dC9+2vboHH9eEReKpjd1zWogFuaZ0fiKtsDNdcDrgEIO+P34fI7CynZR/vOooj6c7LqpqLLroIf/31lyq96s/AoyOtVosrrrgCf/75pwpGElH5sJw+i5yPvoc19RSsh48j98tpsObkBvSfw7jgX/dlsxfDcjypXLaHKJD9dfyU6n3tSXW9Dh8OaFu0wOPvC2FetdH5Dq0WQbdeC13H1vA3yVzURtaBvncXBN04GkE3XeX8gPMZePZ8isfnSvajrRIEEREFaPBx2bJleOKJJ5CVVbV/xBMRERERlYfcLbth+W4GDC69r1bVrYf/deiK3rEReLxL00pdWkpTozqCrrvSaVlNswlP7t8to2VOpcde3HgAJkvVHWxq0qQJqlevjkAgVXNatmxZ3ptBVCVZ0k4j98PvVdaQfdnBoyq4EMj0/bpD0yDKeaHZgtypv6sy3ESUJ8Nowkc78kqcu6pp0OGjAW3ROSK/F3OhgcfZi2FescH5Dq0GQbeOg65TYJQ+1rVtAW3bFk7LWm7ZjtYwuT12x6l0bDl53o9bR0RU8fm17KpkOb7wwguwOAx0SAmfkSNHokePHoiNjUWNGjXUj8qiYk87IiIiIqKCmfbGwzh1JnQuM7f/jYzGa206ok1EKF7t1RJ6beUNPNrIjHtdr84wb9huX9bzzCmMSjqOuTEN7cv2nLmAqXGJmNQ2tpy2lIiofFlSTuaVWj2X7rRcU68uDJcPQSDTdWgNbbuWMP7xF8zL1tmXSwlW4x9/I+jaK8p1+4gCxbd7j+Nktnt1ulAJPA5sh7bhzhUjCgo8muY4f97sgceJ46Dr3A6BxDDmEuTsO5g/+cxowrMpR3FzlHsvyin7En3O/CQiIj8HH1euXInk5GT7bSmd89prr6FatfJvUk5EREREVJnJYFDarwsQ5hJ4XBgVg3dad0CDWtXwf/3aIESvQ1VhuOoymOMSnAbU7zkYhw3hdZFcrbrTgFy/6DCfB96IiCoLy4kU5Hz6A5B+wWm5pn4kgu+7GZrQWgh0Gq0WhlHDYYk/AqtDuVUpB2lu10IFKImqsoPnMvFrvOdSxE93a160wOPcf2D6d63zHRoNDDdfDV3X9gg02vr1oOvXDebVm+3LGu07gE7h9bEjyHm8ekPqOew6nY4OdQL/e4+IqMqVXd2+PX9WccOGDfHmm28y8EhERERE5AfHNu1G2On8cnnij5iGeLt1B9QOCcKH/dsiPLjoFUgqMk31agi6YZTTsmoWM56O2wWNQ5DWbLXixY3xyDY798gkIqrMLMeSkPPxFPfAY4NoBD9wS4UIPNpo9HoETbwKMDjPwc/9eQ6s550zOomqEgkYvrMtAWYPFeZ71auNoQ3q+Lwe04J/YVqy2j3weNNY6Lt1QKAyjBgCBOf1tdbUCYPhlnHo38VzmXfJfiQiogDMfDx79qz9+ogRIxAUlPfFTkREREREZevCP6sR6XD7aLUa+LBFW5Xp+H7/NmhQM6RK/gl07VrmzXhfs8W+rMu5MxibeBSzYhvblx1Oz8KXu4/hoU5NymlLKxYZhDx06BB27dqFU6dO4fz586r9RmhoKOrUqYO2bduq/o06XdXJtCWqSCyHjyPn85+ArGyn5ZrGDRB8zwQ1eaOi0UZFqox346/z8xdmZCJ32hwE3T2+Uvc6JvJm0bGT2HrSPQCv12jwWOci9ADPNcK8K855mQYwjB8DfY9OAf0H0ITWhGHkMFhNJugH9YLGYMA4owk/HkhChtF54tnKpDPYf/YCWoXVKLftJSKqKPwafAwLC7Nfj46O9udLExERERFVWWdST6GBQ/sDMSO2MbRaDV7v06rKlxM1jLkUln0HYT19zr5/7jy0HxvqROB49fzBpV8OJGFA/XD2+ylAQkICpk+fjrlz5+LMGedMW1c1atRQk1JvuOEGdOgQuBkRRFWN+eAR5H4xDcjJdVqubdYQQXeNh6ZaxZ2souvXHebdB2BxCJJY9sbDvGID9IN7l+u2EflbhtGEj3Yc8Xjfja3qo0mo75MMNMFBCL5/InI++QHWEyl5gccbR0PfqzMqAtfPf02DHtc0j8b3HjIdpRf4a71b+XHriIgqJr+WXY2JibFfv3DBuWwHERERERGVjblnsnFj70H4JbYJMnR6nDEE4a+oGIxrHo1+0eFVfrdrQoJhuHGM034IsVhU+VWtQ/lVufbKpoNqsI6cZWdn45133sGVV16JqVOnFhp4tP0mnDlzJq6++mo888wzOHcuP/hLROXDvD8BuZLx6Bp4bNkEQZLxWIEDj0KyuFS57VDnHnbGOX+p/pZEVclXe47hdI7RbXlUtSDc1ia2yOvT1KyB4AcmQhNbH4brR0Hfuysqsutb1EeIzn3ofMnxUziSnlUu20REVJH4Nfg4aNAgaLV5L7ljxw5/vjQRERERUZVksljx+8FkpAWH4MvmrXFNn8F4tkNX5Op0GNeM1UhsdK2aQjeol9O+63D+LK45fthpWVJmDj7Y4bysqktPT8ctt9yCb775BiZT8QKzv//+O6677jqkpHDwn6i8mPccQO6XP6vyiY60bVvkZTwGB5f4NSxWK7amnce3e49jTfIZmCwW+JumVg0EjXeecAKTGbk//A6r0T0QQ1QZSenQ3+Kdq2LYPNK5Carpi1cWXVOjOoIfux36vt1Q0YUFG3BVsyi35db/sh+JiCiAyq7Wr18fAwcOxPLly9Xl2LFjaNiwoT83gYiIiIioSlmVdBopWfkZLFl6PfaEhqFvVBga1ap4PbvKkuHK4bDsOQDrybysPZNehyyd+0+meYfTMKh+HQyKqYOqTvo43nnnndi6davTcuntOGzYMHTs2BGNGjVCrVq11ERUCVQmJiZi9+7dWLJkCdLS0uzPkR6RN998M2bPno3q1auXw7shqrqsFguMc/4GXDK7tR1aI+jWa6AxlHz4KCUzBy9visemtPP2Zc1Dq+HJrs3QJSIU/qRr2wK6i/rAvGydfZk1KQ2W/Yega89yilS5ySSAd7YdgqfQf5+o2rjIh/Mb6e3srR+kphL1cx7fMgZrdx7EoRDn85JFR9Nwe9tYxNSo2NngRESVJvNRPPvss+qHZ25uLh577DGWXyUiIiIiKkO/HfQ8q1362JB7vyKDZMNoAG3Thqj21D043qm9x930+pYEnPFQqqyq+eGHH7Blyxb77Zo1a2Ly5MlYvXo13nzzTYwfP15NQO3SpQs6deqE/v3749prr8VLL72kJqTKYyIiIuzPP3z4MD744INyejdEVZdGq0XQ3eOhqRNmX6br0g5Bk64tlcDj4qNpuPGf7U6BR3HwfBbuWr4br26Ox1k/f6carhwGTUxeVpMmvDaCHryFgUeqEhYeTcOOU+luyw1aDR7r3NRrUNHGtHQtjFNnwmo2ozKzJKWi1tQZ+GbDKsRmOrcPM1uBn/afKLdtIyKqCPwefGzcuDG+/vprhIWFYfv27eqH58aNG/29GUREFOCkPPc///yDffv2lfemEBFVWAnnM90GekWDGsHoG50/wEz5dM0bI+j+iQh66FbooyLwXI/mqO6h9JgEHt/ckqBm/ldV8t6lv6NNnTp1MG3aNFWCNSgoqNDn63Q6jB07Fr/99pv6nWgzY8YMZGZmltl2E5FnWgnASb+2sFDoenSCYeLVJc5gOpdrxP/W78fzG+ORYfQeqJCM8mv+2oa5h1JUVpY/aAwGBE28GrpenRH81N3q+5+osjufa8LHO494vG9Cq5hCq2KYlq+DcfZimLfsRu7U3yttADJ31iLkvPU5LHviobNacU/CfrfHzD2cijSH6iJERFSOZVdtg8knTpzAHXfcgc8++wzx8fGYMGECWrVqhd69e6Np06YqM1J+iBaFzKItjxKuMjP3r7/+wtGjR1V/kpCQEERFRaF9+/a4+OKL1cxfIqp8pFRYUlISoqOj0aFDBwSyQNtWX7dH+kYtXrxY9X96+eWX/bqNRESVpYTe7LjjHu+TXo/aQma1V2W6lk3t16Wc1qOdm+DVzQfdHrfsxGksPHoSlzeORFW0c+dO9dvO5pVXXkGbNm2KvJ6YmBi89957uOaaa1QZ16ysLKxcuRKXXnppKW8xERVGWzdc9WtDrZoqG7IkNqScxcubD/o8OC9Bkde2JGDekTQ81bUpWtSuUeZ/MG39egiaMLbMX4coUHy5+yjO5Lj3Z65fPRi3tG5Q4HNNKzbA+Psi+23Ltj3ItVgQdNu1Jf6+CDQamXhmyZ8I0f9UKrqcOYVt4XXty4wWK34+cAIPdWpSTltJRBTY/B58/PXXXzFz5ky35fv371eX4nr11Vf9GnyUYKOUkN2wYYPXx7z44ouqzNDDDz/s08zfsnT27FlV5lbK3Tp666231I99IiqaH3/8UfUjuvLKK/Huu+8G9O4LtG0NtO0hIqqsLmzdg/HT56B6TCP8EdMQZ4OC1fJgnRYjm9Qr782rUEY2jsSKE6exIimvF6Sjd7cdQrfIUERXz9u/VcmxY8fs16Wv4/Dhw4u9LpmQ1LNnT6xfv97+e4uIyoemdsn6L2abzfh051HM8FL2uzBSDvLmJTtxQ8v6qqdaNQ/Z50RUdPvOZGBWQorH+x7p3AQhBXzWTKs2wjjzT7flUqa+sgUehf7igTCt2wpk5FdiuC8hDnd16wuLwwQ+2Z8TWzdAWLChnLaUiChwVb7/Hfzg33//xejRowsMPIrs7Gx8++23agbv6dOnUZ5kFvKqVavUNjteZFYxEREREVU+5/5aidpGI245chAz1q3ADUcT1PLLGkYgNMjvcxArNOl9NLlbc4QH6xGZ7Xz+fMFkxiub4v1WJjCQnDt3zn69c+fOJV6f9IX0tG4iqljBjYlLdhYaeOxdrzaq6bwPSZmtVtVP7fq/t2H5idPlWkWAqDKQ85R3th2CpyO6f3QYBtUP9/pc05rNMM5Y4LZcf+UwGIb1R2WkqRYCw+VDnJa1zEjHJSnOfR6zzRZMj0/y89YREVUMDD4Wo7TQo48+6tSDpFmzZnjqqafwxRdf4P3331c9ThzLrUq/snvvvdct69BfpGfa/Pnzy+W1iYiIiMj/TIeOoU5S/sz2IKsFqcEh6vq45tH8kxRDuNaKz04dx88bVqJ1unNgTPpq/lbMDJ+KLCIiwn69Ro2Sl0d0/A3luG4iKn2mFeth3r6n1Pq1SbBwyr5E3LZ0Fw6ne5/kHGrQ4bXeLfHRwHb49ZIuuCimToHrTc7MxZNr4/D4mn1IupADfwYdTUvXIueD72A1upeoJKpo5h9Oxa7TGW7Lg7QaPNq5qZpo5Ylp7RYYp89zW66/YggMFw9EZabr2w2aaOfS+rcfOoAQs/N3woz4ZKTn8nuCiMiV36c833bbbbjiiitKfb3NmzdHWTMajSrw6JgteP311+OFF16A1qHEwOWXX45bb71V9bW0lZLdunUrPvnkE/V8f5dblfKvNsHBwcjJ8d8JOwWOU6dOqePRarWqUrtNmjSxB8ePHz+uepV27Nix0NJa8liTyaTWUdDnTvq7pqamIjY21t77JzExUZXQMpvNqr9rgwYF9xMozdeWfkSyjvT0dPU+5f3aSG8h6UGYnJyMjIwMhIeHo3Xr1urz4kqymLds2WLvbyS9XiXA76hbt26oU6eOx++QuLg49beoXr26eg+eHlfc91HSbfX19datW6f2k/TqlRJrnuzdu1f9vR37OpZk39mU5BgiIqpK0hauQJjDbQk8LouMRpeIWmgVVvY9tCob86FjMP40G/XT8rJvnt63E3d174tcbX55sk93HkHvemFoEloNVYWcM9jI/+slJedjntZNRKXLmpUN49x/gFwjULsW9P26Qz+oFzQ1qhdrfYkZ2XhxU7wql1qQXvVq47kezVGvWt7vrKjqwXirb2usSjqjSlgnZXofq1iZdAYbU89hUttY3NiyPvRlWObRei4dudP+gGVfXq9f05//wjD6kjJ7PaKydi7XiE92eS5nfnPrBoitmTdBzZVp/TYYp891W64fcREMlw5GZafR6WAYcwlyv5hmXxaRm4Prjx3GlCYtnKpgzExIxq1teO5CRFSuwUcZbPdHoLAszJgxw6n3SN++ffHSSy95fKwMuEsm5MiRI+1Zkj/88ANuuukmREY6z5opS9ILMy0tTV0fNGiQChjI4D9VHRLwefnll7F48WIVZLNp2bIlnnvuObV82rRpKmgumbuezJkzB5999hkOHz7stFwGhaSXqDzX1TfffKPWfd111+Haa69VpX+3bdvm9Jg+ffrg7bffLjCAVtLXHjVqFF5//XXs3r3bfv8HH3yAESNGID4+HlOmTFGllCUg6Ej6tEpPQslqrl27tn25BGvvu+8++21bCWNH33//Pfr16+dUgvnTTz/Fzz//rD6DNjKzUPbB008/bQ/2Fed9eFPUbfX19d544w217smTJ6tMb09++ukn1d/Xsa9jcfadza5du4p9DBERVTWWk6cRGhfvtOz3Bo1g1mpxDbMei7dP9x2E9b/Ao2iaeQG3HI7HV81a25flWKx4cdMBfHNRhzIdFA8kbdu2RePGjXHkyBH75CTH7MWikPPUZcuWqeuyjv79K2cZN6JAYN64Iy/wKM6lw7R4OfR9ugJFnJsiE1vnHUnD+9sPIdPkvTxpsFaD+zs2Vpn3Wg/ZVQPqh6NHZCi+3Xcc0/YnqSxKT6S84ae7jmLh0TQ82bUZukaUrDelN7lTf4clPv/3p2nJGmjbtICudbMyeT2isvbFrmM45yEzL6Z6MG5qHePxOaaN22H8+Q/A5eOov3QQDCMuQlWha9cS2jbN7ZMRxPXHDmF+/Vic/K+qiPjlQBKub1GfPWqJiByw2UsRSP9GRxK4KYhk5Nx+++346KOP1G3JmJQB+UceeQT+IAGVefPySiNIlpUESiVYQ1WHZKtJwFuCbEKyHSVTTQLSkpk2adIkFYQsiGTO/vLLL+p6rVq10K5dOxgMBvV8yUSU41kyBiWr2RMJGs2dO1dl3LZv315lFUoQXy4ySCXbMGvWLBXsK+3Xlky+2bNnq4xDCe7Vr19fZSnXq1dP3S99UH/77Tf12rJvZNKADHzJuiU77/fff8f27dvVxANbKTHJyhs2bBj27NmjZufLc+R9OXLM3JPAowToJPtZyONln0sfI9k3a9euVRnUMllBAmnFeR/eFHVbS/p6hSnu9pTkGCIiqorO/b0aIQ4Dtxd0OjVAEhFiKLS8HXmmv2QgzDv3wXo8v7TqdccOY1XdKOypnZ9juvfMBUyJS8TtbRtWiV0pE6luvPFGNTFJfuvIRLbCfiN5IxOQbNURxo4di2rVqk4GKZE/ScDQtGqj0zJtxzbQhOdPuPTF6Wwj3thyECuSzhT4uNZhNfBSzxZoGlpwVmWIXof7OjTGiEaReHtrArae9J5FmXA+C3cv342RjSPxQMfGCAs2oDQZrr4MOe9+DTiUpM39aTZCnr6n2NmhROVl75kMzD7kuTrBY12aIkSXX8XBxrR5J4w/eQg8XjIQepc+iFWBZD/mvPWFfIGq2yEWiyq/+mab/OphEtydnZCCG1t5DuYSEVVFDD76SAa/pdyfTa9evXzK4Bw3bpzK2pJSkeKvv/7yS/BRAhvPP/+8/ba8ppSqrGys6ReK/+TgIGiCPP9IsWZccDvJ8lmQAZpgz0EQ64VM6fLttlxTq2zKn73zzjsq8ChBGckOc8ySk6Da3XffrQJB3kjgzxb8k4y1u+66y16KVAJTUkpYgmaS2SaZwDL73ZVkzrVo0UIF4R0/M5IFKAHxAwcOYOHChRg9enSpv7YEKSVDUrIOPWUWStlQ2S/Dhw9361O0evVqlfUo+08mHjz44INquaxHPtOSrSgBup49e9oz+zx57733VOBRBufkORMnTrT3UpB133PPPSqIJhMDFi1apIKsRX0f3hR1W0v6emW1PcU9hoiIqiJrZhZ0G7c7LZPA4wW9ATc2i6oyGXllUXYraMJY5LzzJWDOy+6Robqn43biju79kOMwcPfd3kT0jw5H2/DiZQBWNBMmTMCSJUtUNQOZaClZi3LepPMwmOmNVOGwnRPI//kPP/xwGW4xUdVmiT8Ca3JedSQb/YCeRVqHlEl9dfNBnMn5L3vSA/nfZmKbBqpMqqEI//c0C62Ozwe1x59H0/DRjiM4W0AftflH0lTw84EOjTCyST2PWZXFoW0QDcOo4TDOXpy/UEqx/jIXQZOu89objyjQSBaxBPM9DW8NrB+uso7dnhOXAOMPs+yBNhv9sP7QXzG0Sh7/2pgo6Pp1g3n1Zvuyy1JOYFaDRthfK3/ixrQDJ3B182gE63i+TUQkGHwsQhaho6FDh/r0PCkFKJk6EugRCQkJ6tKsWTO/lVvt0qWLGhSojLKffafYzzWMu1z1tfC43tc+BSRQWAz6ywbD4GUmWM6H37v90BPVPsrvy1ma/T4lwCMeeOABt/KcnTt3VmU1JQDpSW5urgrwiTFjxtiDbzaSgShBbSmDKdlnUr70rbfecluPPO7zzz936w0os+QlM1fKAEv2n2PgqLRe21Yq1FsAzVN5Txsp9SVBMgkKLliwwG0bfJ0E8Ouvv6rr8hl0LVEqg2sS4JP3ePLkSZVp6a2MaUHvoyz4+/UKUpxjiIioqspeuRGG/ya9CTM0+L1BY+g1GoxtyhLVJR140o8YAtP8JfZljbIy1cz3T1u0cRroe3FjPKYO6+gxm6Cy0ev16v/pxx9/HEuXLlWTw+RfqQAjE7ykAosncr63YsUKlfG4adMm++8WOQcsbulWIiqc2SXrUVOvLrStmvq06zJNZny44zD+OJRa4OMa1AjGiz1bolNd94mVvpDgxhWN62FAdLgqszrnsPfXO59rwmtbElT516e6NkWL2qUzsVc3uDfMe+OdSi1aduyDed1W6Pt2K5XXICprcw+lYs+ZCx5LIT/auYnbcmt2Dow/z3EPPA7tC/2o4VUy8GhjGDEE5k07gZxc+7J7D8bh4c495UtL3T6ZbcT8w6kqAElERAw++swWPLTp1s33k83u3bs7PV9KGpZl8FF+7EuJQtugvfRJk5KJVLWsX79eDerI3156LnoyZMgQVR7YMavXRgaBJCAmvAUohfQHlACgZAp6ItltrkEjm44dO6rAka3EVmm/tpRpldfwRWpqqupXJL2KzP+V15EArpB+k1Ly05Z56SvJAJCyq0IyHj2R7EsJgq5cuRLLly/3GHwsyvsoDf5+vcIU5xgiIqqKrCYTspetR373GWBZZBRSQ6rhktg6qBvC8tQlpR/WL6/86pH8c6erE49gZUQ97AjLL2l7OD0Ln+86hkc8DOxVNjIBSCoQSHl26W0vEyDj4uLwxBNPqN8i8rtH+kJKQFEGLeVcS849pXKBnF/ZSPUHKU3/8ccf+/S6UvmioN7XROTOei4d5u173bIefQko7DyVriZWHL+Q9/vGm9FN6uHhzk1QXV/yyRe1gw14pntzldX41tYExJ/zPkF4x6l03Lxkh+q5dnu7hiV+fY1Wi6DxY5D95mfAhSz7cuPvC6Ft3gjaehElWj9RWTubY8Rnu496vG9im1jE1HA8Y8xj3hUH65lzTst0F/WBfvQlVTrwKDShNVXZWdO8/EloXc6dwYBTqVgVkT/B78f9JzC6aT1WGyEiYvDRd5Kt6KiwPnmuwQVHBw/mz5wrbefPn3cqt3rHHXe4vT5VDbbjTII2YWH5vYhcdejQwWPwUUpdCum3I8E3uUh/EOH4b0pKXu8AGWjKzMx0m90uwU1vIiLyfrA5DjyV5mtLZmFh/vjjD3z99df2vpgFfbZkQK0obOuU5zVs6L33U9euXVXwUQbhPPHlfZQmf79eYYpzDBERVUXmzbsQcsF5dvuMhnnBr2ua1y+nraqM5VfHIOftLwFjXoapTPF7Om4XJvXohyxdfmGZ6fFJahC8fo2iTV6qaCTQaKv04EpK5cv9cvGlV7n04vaVTApj8JGoaExrNgOWvNLRikEPXa/OBT/HYsG3e49jyr5EODzTTXiwHs90a45BZdBbWDIopw7thBnxSfhqzzFk/Vf+2pXZKmUPk/DP8VN4rHNTDIoJL1HARFO7FoJuHI3cr6fnL8w1IveHWQh+ZJL6P4EoUH2266jKDHYVWyMEE7z0JdT36KTaAhl/mQvr6XPQtmyi+h1W9cCjjf6iPjCv3qT2jc3dCfuxrk4kTP8lfSRl5mDxsZMqe5uIqKpj2VUfZGVlISkpyX5bAjneygd5Eh0d7bfg42uvvaYyuITMMpZ+clQ1yQCOKCjwWND9tqw/Of4Lyj50fU3Xz4YvWbe2gGJpv7YELwsiM+tt5V2lZJh8ZiRQKINZcnItr79mzRp1vy0bsigkYCnCw937KDiqU6eO09/MVWHvo7T5+/UKU5xjiIioqpHvwYx/VsMxzLWtdjjiatVG67Aa6FiHZSxLizYqUvU8Mv3xl31ZTHYW7krYjw9atnN67IbUsxjNcrdEFACsZnNe8NGBTgIN1b2f+x9Jz8ILGw9gr4eyja694yZ3a1amGfZ6rQY3torBsNi6eG/7YSw7cdrrY1OycvHkujhVtvWxLk08Znj5StexDXT9e6iAg4316AmYFi6DYeSwYq+XqCxJprK3csWPd2lSYE9CXevm0D59L4wL/oV+cG+VBUx5NAYD9FcOh3Hq7+q22WDAoqgYuIZmZbLGZY0ioWPQloiqOAYffXDq1CmngW3p41gUro8/fdr7SXJJLFu2TGVxCQmcSLnVoKDKXV4r5LUniv/kYO/7JuTZ++CxI7cvggzeX/KhWwGLf4Iktr+9reynN94yxqRMlq0EVq9evYr0miXlj9dOTk5WPYnEDTfcoHo7yuu5ThS4/PLLUVy2Mq2+/g1CQor/o5iIiKo2y/4EBKc495WeEZuX9TiueTRnrJfBzHfp/WVJyC9nNubEMayMiMLm8Lr2ZQ1KMOBNRFSaLLvigHPOkx31A3p4fKyMf8xMSMHHO48gx0uWoaim06ry0qOa1PPb/zNR1YPxVt/WWJV0Bu9uO6SyjLxZlXwGG/8+h9vbxuKGlvVhKGYQxTD2ElgOHII19ZR9menvldC2aQ5di8pfXpsqFuk9/c62Qx7vuyimDvpGFzw5WmhCghF0NUube6Lr1gHmFRugiY5E0OVDsGbDQRjP55dmFkczsrE08RSGx7I8MxFVbQw++kCynxwVJevR0+MvuJTDKg2SYfXcc8/Zb19//fXo0cPzD4nSJAGcgpw5c6ZMX1/KQZTJemuW0XprFO3YKYmYmLwyGkePHoXFYvGaPSYlTT2JjY21B/U+/fRTvw5a+uO1pR+jyWRSWYny2dF5KJlz7NixUvkbSD9C+R7xllFoK8/qmiUdqCRLtLCgqi17lYiI/CPrn9Vw/J/saLXqWFs3EqFBelzSMD8YRqVDsgAM40cj560vVAk+m6fiduHWHv1wQW9Q+717ZGil3+XS13rChAl+f132tCcqGtPKjU63NU1ioW3oXnoxLSsXr26Ox7oU575vriSj/oWeLdCwZvlUTRlQPxw9IkPx3b5ETNt/AiYvlVAkePrprqP480ganuraDF2L8b2sCQpC0MSrkfPeN4AtGGsFjD/OgvapewrMHiXytz8SUhB31n3cUbIdH+7EYHlJyfhU0IO32Msu39K6AZ7f6N7G5/t9iRjWoC4nABJRlcbgow+kl1xJMqxs2U/e1lcaXn/9dXu5Vcm0fPzxx+EPgwcPLjSDrW3btn7ZFnLWrVs3+/G2atUqDBo0yG0XSa/HHTt2eNx1/fr1U4M6kvkrpUf79+/vt13sj9e2lTitUaOGx8CjWLBgQaHZmQWVY+3evbv6V4KcixcvxpgxY9weI0HJJUvyGpaX1YQBX7a1KGyler0FZyUouXnzZr9tDxFRVWdJPQldnHN/8pmxTWDVaFQ2Sgh7UpUJbWRdGEYNh3HmQvuyejnZ+PpcEtKvGqF6lFWFHknyHm0Tk4goMFlS0mDZ75wJpR/Q0+1x/yaewhtbEjz2ibORMoJ3tIvFTa0aqFKo5SlEr8O9HRrhskYReHtrArae9NzGQhxKz8LdK3bjisaRKgAjk3OKQgK1+pHDYJrzt32Z9cx5GH+dD8Mt46rE9z0FvtPZRny+O78qg6Pb2sRW+j7U/uLY73VYbITqRXv8gnMWdvy5TJWhPbAM+uASEVUULNztA6MxfzazKOqPa9tAu7f1ldTy5csxe/Zs++0XXngBNWuyr09V17JlS3Ts2FFdf+ONN9wy0eQ4lGNFsiK9Ze3ZSo7+73//Q0KC86Cmo9zc3BJnCfr7tW1Zid4CsPPnz8e8efMK7dN4/Phxr49p3ry5PQD5f//3f+q1XMsZSZ9WKcUsP1avvfZalAVftrUoOnTooP5duHAh0tLS3N6TTIYoKOu5tLeHiKiqM9cNx4s9+mBtnbzSTmcNBiz+r//M1c2K1i6AikY3oCe0LZ2zCGKOHkPnanpoORBNRAHCtCq/X6FSoxp0XfN71GYYTXhpYzwmr9tfYOCxSa1q+HZIB9zaJrbcA4+OmoVWx+eD2uOFHi0QVkhQccGRNNy1fBfSC3if3uiH9IW2VVOnZeY9B2A9VbYVn4h89emuI0g3uk/ybVQzBDe2rO+23PjnUtUL1rHVFBWNfBdObN3A433fxyVy3xJRlcYpqj5wLZXorUeeN66lCYtatrWw7C3HcquXXXYZhg3zX9NzCXwWRAIQzz//vN+2h5w9++yzqgyWBO9Gjx6tehs2btxYBYxmzpyJuLg4dTxKdqSnmZpybO3atUuVZpXnX3LJJSo7T0qVyrGXkpKCAwcOYOXKlRg5ciRefvnlUvsTlPVr9+3bF5GRkWpf3HrrrWrftGnTRmUiynH9zz//qM++t0zlrl27qn8lcPniiy+qTFPbZ1uu2wJsct8111yjMpOvuuoqVRK5devWOHfuHObOnYstW7aox912221qeVnwdVt9Je/j22+/VSWk5bocYw0bNlT7UoK28jq248of20NEVNUtTzqDZTVqY1nH7mh8IQMNsjKRo9NhYP1wxLDnYNmXX71xNHLe/BzIyYWue0cYxo3wa6l9IqKCWHNyYd6wzWmZvk83aP6bJL0l7Rxe3nSwwN6J4trm0bivQyOVbRiI5Pfs5Y0j0b9+GD7bdRR/HMqrDOVJwvksvLI5Hm/1aV2kjEX5zg8aPwbZb30OZGar0rVBN10FbQR/v1D523EqHfOPOE8OtnmsS1ME6ZzzT8wJR2FavFyVEDZv3Q3D9VdCW7fwfpDkbkTjSHyz9zhSsnKhs1pg1uTt692nM7Ax9Rx6ReVVjyIiqmoYfPSBa7AwkIKPkmEkQRhRu3Ztp0CkPxTWo84165P8S4I87733HiZPnqz6c77//vtO90tQTH5szZgxw608sK285vTp01UA+e+//1aBJbm4klLEEnwqTWX92iEhIfjwww9x7733qqzQr7/+2un+Jk2a4KmnnsI999zj8fkDBw5E7969sX79evzyyy/qYvP999+r0rGiVatW6vZjjz2mej9+8cUXbpnUkyZNwiOPPIKy4uu2+koC2BLsle8bCarKMWYj5XLvuOMONfFAAtz+2B4ioqrut4P5PbiP1KipLmJc84rRS7iik4E6w7UjoQnSQ9c5P5OIiCgQWE+ehqZaCKxZ/41jaABd/7zqLHMPpeD1LQkSe/AqMsSA//VogT4VZPC8dpABk7s1x8jG9fDm1gRV+tCT5SfOYNqBJExo5d73siCa8NoIun4ULCdSoL90kFP5RaLyYrJYVelhT4Y2qOP2+bXmGmH8eY4KPApLXAJy3v0aIS8+DE1w0VpNEWDQanFbo7o4v2Ap2p8/i/u69lbtD2zZjww+ElFVxeCjD1xLmErGUlGcP3++wPUV14oVKzBr1iz77SeffBIREXnltohsLr30UpVNJll2+/btU2VWY2NjVYZsp06d7ME1b9lmkmn48ccf4+DBg1i2bJn6NyMjQwUH69Wrp7L1JFgkvRMdSclX6XVYUM9PCfDJdjRt2tTvry2kJKqUDp0zZw52796tehDWrVtXZVgOHTpUfdZtmcSuwVnpE/ndd9/hr7/+Uv0NT548aS+p7LovZf9Lz0fJpty0aZPqZSmTEKQsq/x9vAVPfX0fhfF1W4vyepLxKO9LStNKdqoEseXvKe9HSv5OmzZN7Ttbiday2p7CjiEiospu/9kL2Oahx5WU1+pVr3a5bFNVpO/Zqbw3IeBICbdDhw6pShZy7iO/ieQ8NDQ0VP1/L/+/yzmDt97bRFQ6tA2iEfz8Q7DsOQDTqo2Svqcy9Q6ey8SbWw8VGHgc1qAunurWVAX0KpqOdWth6tBOmBGfpPqxZZnd2418tusI2ofXRNfI0CKtW9elnboQBYpZCck44CHQXk2nVT1OXZkWLYM19ZTTMv3Qfgw8FoPVbIZ51UZcunA5kJmlll2ScgKLo/NKsW5JO4/tJ8+jc0TRvmeIiCoDjZWFvQslu0gG2W0lBCWbb+fOnT6X55CAw3333We/PW7cONXnraQuvvhiHD2a10i6T58+mDp1aqHPkdKStjKP4s8//1QBkLIiAw133323ui4ZXxLYocAhx/TgwYPVYJD0JJTypURERFRxvL75IOYcdi8t92jnJriuhXtvH6KyJuX+pXqFTHwrqAe0kAlkI0aMUL9RPE1YoqqJvyHLfqBcsvWe27Affx1zDj7Y1DTo8ESXpri0YUSRypIGqhMXsnHb0p04k+Pe57FuiAE/DuuEuiHM9qKK6VR2Lq5ZvA0XTO69Hu/v0Ag3ufQjtBxJRM5738hgp32ZplEMgh+ZxEzeYn6n5rz1BazJ+SVv04KCcVOvAcjW5eX89I0KwwcDSjapnIioImLmow/kZFuyaiQzSkiGjpSwrF/ftwGd48ePO90urWCf44956bV20003Ffqc/fv3O92WspKOPS0feughlfVFlYeU5Y2KinJbLll+UjpTAo+ShSdBSCIiIqo4k+PSc01YdOyk233V9Vpc0TiyXLaLqi5pNSEVK6ZMmaIqF/hC+kdLmXa5XH311eq3ibSSIKKyI4HHYxlZ+MdL4LFbZChe6NEC0dXd23JUVNL/+JVerfDAyj1umZ6nso3434YD+HhAO+i1pRNotVosqj8kkT98svOox8Bjk1rVcH1L53FLq9GE3J//cAo8QqdF0I2jGXgswXeqYcwlyP1imn1ZZG4Orjt2GFObtFC316acxd4zGWgbXjqV8IiIKgoGH30kJYFswUexfft2n4OPW7dudbrdokXefz6lScpRyqWoJIPTkfS+o8pF+jrK8SvZuzExMap86JEjR7BgwQIcOHBAPUayU2vVqlXem0pEREQ+suzch/S5/6Jv3RisjKwHsyZ/kHNEo0jUNPA0n/wnPT1d9Xx2/d1TFL///ruq0CLVXDxNnCOi0vNj3Am4FyEFRjaOxLPdm0NbCbIdXfWsVxt3tmuIL/ccc7tPyiJ+ueco7uvQuESvYc3NhXH2X0BOLoJuvqpE6yLyxdaT5/Hn0fyMO0eSvSy9CB2Z/loBa5Lz4/WXDYY2hv/vloSuXUto2zSHZV/+uOz1xw5jQf1YnAwOUben7EvEW31bl+h1iIgqGr+PSnz00Ueqz5d48MEHcckllwTEugrTv39//PHHH/bbGzduxGWXXVbo86SvifQ0swkJCUHPnj3LbDuJXEkf0FWrVqmLKykhPGnSJNx5553ccURERBWI8d+1qJOahhdT05CcEIIvm7XC0np5E+PGNY8u782jKkR+78i5pGvgUXo7Sl9m6eHcqFEjNdFNq9WqQGViYqKa2LlkyRJVwcVGekTefPPNmD17tqrMQUSlLzUrBwuOuAcr9BqNCs5VxsCjzS1tGmDn6XSsSXafdP1D3Al0rFMLg2Lye9AXheVYEnJ/+B3WlLyKBKZ2LaDvwX7AVHZMFgve2Zrg8b6LY+uih0vvbzlGTX+vdFqmiY2GfvgA/plKgWQ/SvlVW1ZpNYsZkw4dwFttOqrby06cRsL5TDQL5fkNEVUd+vIoAWnLtjp37lzArKswF110kQrUSMlVMX/+fFUWKCio4L4AEvBx/EE9YMAApzKnJSE9FKV0ZlG8+uqrTqVX33rrLacMTsmQo8pFylitX79ezSSXz4x8VmTwp1WrVhg+fLjKhiQiIqKKw3LkOKwJeX2/RXRONnT/DXT0iAzloAb51Q8//ODUU75mzZp44IEHcOONNxb6W+n5559XvSHfffddnDyZN2B/+PBhfPDBB3jmmWfKfNuJKisp+2k9eQbaenXd7vt5fxJMjiUX/yPluqMqUalVTySw+mLPFpi4ZCeSMnPc7n95UzymDu2EBjXzMpV8Zc01IueLn4D0C/ZlxhkLoG3aENq64aWy7USufjuYgoPnszyW33+wk3MWr9X0X7lVi8NnX6tF0PgxLLdaSiR7VNevG8yr8xNQLk05gVkNGuNArVB1e+q+RLzUi+OuRFR1sB6Tj2Tm7tChQ7F48WJ7edLffvsN48ePL7AXz9dff+20bOzYsSgtxenNKIMBjmQmcmn1oKTAJDPM+/btqy5ERERU8Zn+Xet0OzU4BEsj87Idr2HWI/mR/N6RMqk2derUwffff482bdr49HydTqd+H/Xu3Ru33HKLag0gZsyYgYcffpjZj0TFZNkbj9wvf4a2dTPoB/SEtkMrFWA4m2PE7EMpbo+Xwow3ta4ak1JrBxnweu9WuHP5LhgdAzFSQtpoxuT1+/H1RR0QrPO9Z6MmyADDVZfBOPX3/IXZOTD+OBtBD97C/o9U6k5m5eIrDyWExe1tG6JeNeeJBKa/V8Ga6PzZ118yENoGrJZRmgwjhsC8aacqvSzkW+Teg/vwSOeegEaDv46dxB3tGiK2iBMciIgqKnbALgIp7So/kG3+7//+r8A+i1OmTMGGDRucAn2SaVaQb775BjfddJP9IgFOIiIiIiJhOXUGpm17nHbG7w0awazVIqpaEAbUL165OKLikP7xJ06csN9+5ZVXfA48OpJKHO+9956aNCeysrKwcqVzaTgi8p1p1Ub1ryUuAbnf/orcr6er29Pjk5Btdu/2OLxhXTSsWToVmiqCdnVq4pFOTTzeF3f2Av5v26Eir1PfvSN0PZ3LrFoSjqqgD1Fp+2jnEWSa3CuhNQuthutaOAcULSdSVK9HR5qYeir4SKVLE1oT+oud92vXc2fQ/1ReRTz59v0hLpG7nYiqjAodfMzJyS+TERxc9uVBWrRo4ZTpeOHCBVVOSEqwOpY/PX36tCpn+uabb9qXSclWX0oHSZ8TCVjaLsePHy+Dd0JEREREFZFp2TpoHMrlZep0WFA/Vl2/qlk09NrK26uLAs+xY/lZF9LXsbCJlgXp0KEDevbsab999Gh+aWEiKtokFcuevPY0NrpObZBhNOG3g8ken3Nz6wZVbhdf1SwKlzWM8HjfnMOpWHAktcjrNIy7HJq6YU7LTAuXwnKY4zpUejanncPiY3mlyl090aUp9P9N5BFWsxm50/4AHCcdaDUIunEMNHoWwysL+ov6QBPu3G/z7oQ46C15fwPpuZvioewzEVFlVKGDj46BOddyomVF+jxK30YbKb/62GOPqZKWV199NS6//HIMGjQI3333ndPzXnrpJXTr1s0v20hERERElY81MwvmdVudli2IjkWG3gCDVoPRTeuV27ZR1SS9xG06d+5c4vV16dLF47qJyHfmVZsAx2qi1YKh694RsxJSkGF0z5QaWD8cLWvXqHK7WKPR4OluzVSmmCdvbT2E+HMXirbOaiEw3HSVKq9oZ7Ei94ffYc1msIFKzmSx4J2tnjNzJZjeLdI56GX6dw2sx5KclumH9Ye2UdUos1wepAyzfpTzZKyGWZkYfSJvwpb03P1pf37VCCKiyqzCBh/j4uKwY8cO++3GjZ2bKZcVvV6PTz75BNddd53TcvlxvGvXLlWG1Wg0OvWKlPKsEpgkIiIiIiou05rN9h4yQoaQf4/NOwe+ODYC4cEG7lzyq4iI/KyhGjVKHrxwnFDquG4i8o3VaIRp3RanZbpeXZCj1+HnA84BCJuJVTDr0aaaXoc3erdGdb370FiO2YKn1+1XGaNFoWvWCPpLBzkts548A+OsRSXeXqLp8ck4lJ7ltiOq63V4oKP7uKiufStoHAKNmqgI6C8bzB1ZxnTdOkDT2Pm7deKReNQy5p3HzzmUglPZ+ef0RESVVZnl2K9btw7x8fFuyx17JMpjHEunFsZisSAjI0Ot499//7WXOq1duzaaNPFcr78sVKtWDS+//DJGjhyJX3/9FUuWLFF9SRw1aNBA3S9lWqOiovy2bURERERU+VhNJpiWr3datjwyGskheRkb1zR37u9D5A+xsXklf0VKSkqJ15eUlORx3UTkG/PWPcAF57EJ/YCe+P1wGs7k5E+StukRGYqOdWtV6d3bJLQanu3eHM+udy5VK45lZOPVzQfxRu9WKlPSVxJ8tOw76FRuVSoXmLu0g65dy1LbdqpapFTnN3vyy507urNdQ0RUC3Jbro2JQvAjk2D6dy1Mi5cjaPwYaAycrFbW5PvCMPZS5H6QXxUv1GTCzUcS8GmLNsixWDH9QBLu8xAwJiKqTMos+Dhv3jzMnDmzwMdIr0S5lNSYMWOg0+ngb7169VIXk8mEtLQ0dQkKCkK9evVQp06dYq1z0qRJGDVqlP12TEzplkJ47rnnkJ6eXmbrJyIiIqLSZ966GziXfw4nZvyX9dg+vCba1fFPCwIiR23btlUVaI4cOaImlspE0eK2w5CJpsuWLVPXZR39+/fnziYqItOqjU63ta2awhJZBz9tcS7ZbXNLGwb5xfDYCGw/mY4ZHnpiLk08jenxSbihpe9jJxqdDoabr0LOW184VSwwTp8H7eR7VXlWoqL6aOcRZDn2bvxPi9DqBU5CU8fjxQOg79cNmhrVueP9RLKgdV3b553D/6e62QRI73aNBjMTkjGhdQxqBzEYTESVV4Utu2rTunVrPPjgg+W6DVKKtX79+ujUqRPatGlT7MCjaNasGXr37m2/NGzYsFS3tV27dk7rlyxOIiIiIgpcVqtV9exxtCM0DPtCw9T1ccx6pHKc2X/jjTeq61IJ5v333y/2ur7//nucOJHXA2ns2LH8nUJURJZjJ2B1yLQT+oE9sfjYSSRnupf3k4krkvlIeR7s1BgdvEzk+XjnUWw/eb5Iu0obUQeGMZc4LbOePQ/j7MXc5VRkG1PP4Z/jpzze93jXptBrC8/MZeDR/1TvR50ORyIjcVe3PnindQd7T9hMkwW/xbtPeCAiqkwqZPBRgn0yy/bxxx/HjBkzij27loiIiIgo0Fn2H4I10bmk5YyGeS0HwoP1GBZbt5y2jAiYMGGCqgYjfvrpJxWAtLXH8NW0adPw7rvvqustWrTAww8/zF1LVESmVZucF9SuBWv71pi6L9Hj4ye2aVCkUqKVnUGrxeu9WyEsyL1AmNlqxbPr9+N0tnvp2oLo+nWHtnUz53VJ+dU97iVeibwxWix4d9shj/dd3igSXSM4iSBQaeuGI/jJu6C5fyLiatV2u//X+CRcMBbtnImIqCIps7Kr999/v/oh6urTTz/F33//ra7fd999uPjii31ep5RWrVGjBiIjI1V5UyIiIiKiys601Dnr8Xi16lhTt566PrpJFIJ1FXI+IVUSMjH0888/VxNDly5dii+++EL9e/vtt2P48OGoXt1zibfc3FysWLFCZTxu2pQXNOnSpQs++eQTTi4lKiJrZhbMm3Y4fzb798Dy5LM4kpHt9vhmodUwsH4497OLqOrBeKlXSzy8ai+sLvelZRvx/IYD+HBgW+h8DNqqvm83jELOG585l19d8C+0bVsw+Es++eVAEg6nO/dyFTUNOtzfsZHTMvOhY9DG1ofGUGbDvVRE2vr10BpAv+gwrEk+63TfeaMZ84+k4roW9blfiahSKrP/jaQMqVxc1a6dP9ND7pcMRiIiIiIicmdJSoVlT7zTst8aNIZFo1ElTK5qFsXdRmVq7dq1WLhwYaGPk773Mkk0LS0NcXFxeOKJJ2AwGFRbCekLKdVqZCBe+kImJibiwIEDyMnJsT+/Vq1aaNmyJT7++GN1u2/fvhgxYgT8TbZJgqLx8fFIScnLOI6KilLbNnDgQAQHB6OyqsrvvaIzb9gOGE35C7Ra6Pp0xdTNhz0+fmLrBtAy69GjPlFhuL1tLL7e61zCVmxMO4ev9xzD3e2dAz4F0dYJU+VXjb/OV7d13TrAMG4EA4/kk5TMHHzr4VgUd7driLoh+YkZlrTTyP1kKjQR4Qi6cQy0jRtwLweQW9vEugUfxb+Jpxh8JKJKy+9TYWw/XlwDkURERERE5ExTJwzxQwei2ppNaJCdhXN6AxZF5w0mDYqpo7I0iMqSBBJ//fXXYj3XaDSq58ulMOnp6fjtt9/styXQ5c/gowTePvzwQ9XWQ7bFEwmQXnfddXjooYf8VonnwoULOHr0KI4cOaIuct0xaCtB2quvvrpSvncqQl/gVRudluk6t8X6bDPizl5we3yDGsEYHhvB3VuA29rGYufpdKxLOed23/f7EtGhTi0MKELmqJRftRw8ov4uus7tuO/JZx/sOIxss8Vteava1TG2WbT9ttVigfGXOWoSgjUpDTnvfQP9sP7QXzmMge4A0aluLXXZccr5/9ntJ9NxKjvXKZBMRFRZ+D34+OCDD6oLEREREREVTBMchA9q18OeXgPR/2QqapqMyNHp1H3XNM8fdCKi4jt+/DjuuusulfFXEAnMffPNNyo78Msvv0RMTEyZ7Pbt27fj7bffVsFGySQtiJS1LUnwMdDeOxWdZX8CrKmnnJbpBvTElDjPvR5vatUAei17PRZEskJf6tkSNy/ZgZSs/HKpNi9tPICpwzohpkaIT38jyfoOurlkkwSo6lmVdAb/Jp72eN8TXZs5fY7NqzfBEn8k/wFWK6zZOQw8BpihDeqq4KPOasEVSYnQWyyYFdsYK5POYExTVjMhosqHRcCJiIiIiALU3jMZ2HU6Q0YusTIyf1Ciaa1q6B4ZWq7bRlQZSFDNNfhWrVo1XHHFFWjTpo26LZmb8+fPR1ZWXs+t/fv3q+f88ssvZdKfMikpyd4Hs6q9dyo60+rNTrc10ZHYERaObTuS3B4bEWLAFY0juZt9EBZswOt9WuGuZbthslrd+rQ9s34/vhrcAUHsu0xlIMNowltbEzzeN7JxpMqgs7GcOgPjnL+dHqOpUxuG0cP5twkwF8XUwZplG3HPwTg0zbyAswYD5jRoiGWJpxl8JKJKKaCCj9nZ2erHzNmzZ9UPIa1Wq0qz1q1bFy1atIDuv1neRERERERVwW8Hkz0ul6xHyaQgKmsTJ07EhAkT/L6j5begP7z44otOwbf27dvjiy++UD0sHUm50bvvvhu7du1St+V368svv6wyFMuSZDY2bNgQTZo0QaNGjXD+/Plil8GtaO+dfBN0zeUwNayvMp+sp89BP6Anpsad8PjY8S1jGCwrAimv+nCnxnh3u3vvzL1nLuD97YfxVLdmPFSp1H226yhSPWTd1jLocH/Hxk5ll43T5wG5RqfHGW4YBQ379AacqMwLeHvnFvvtMKMRXc+cxkatTgWcaxoCapieiKjEyv1bTQKNM2fOxJ9//qlmVZpMDk3SXX50de7cGWPHjlW9P9hngoiIiIgqs7M5Rvx97KTb8hp6HUYwc4X8RILcen25/2wsE3v37sWCBQvstyMiIvDtt98iPNy9l1tkZCS+++47lRVoK4U6b948TJo0Ca1bty7V7erSpQt++uknFWyMinIuw/bPP/+USvAxUN87FZ2mVk0YLh6o+rtZ9hxAfN0IrF2z3+1xoUF6jGnGsn5FNa55NLafSsffx51L24pZh1LQKaIWRjQqfjapNT0Dxj+XwTBqODTVfCvjSpXb1pPn8XtCisf7HujYGOHBBvtt89otsMQ5Z0jq+nWDrnXzMt9OKjptZB2cqheJuqn5JdWHpiVjU50IrE4+i0sbsh8vEVUu/plO6oHMzpkyZQoGDx6Md955B7t37/YaeBSZmZlYu3YtnnzySQwfPhwrV6706/YSEREREfnT3MOpyLU4l3oTUjKvup4VQYhKSrL85HepzaOPPuox+GYjVXkef/xx+22LxaL6H5a26Oho9OzZ0y3wWBXeOxWfRquFrkNrTDniPmlFXNcimv93FGe/ajR4pntzNKlVzeP9b25JQML5zCKvVz5/pi27kP36pypr1fjHX8XZPKpkcswWvL75oMf7pNz+qCb5memWM+dgnL3Y6TGasFAYRl9S5ttJxWfo3sHp9qC0FBgsFixLdJ/gQERU0ZVL8NFoNOKee+7BG2+8oUqtFlVKSgpuv/12fPLJJ2WyfURERERE5cW8ez9yfvoDG7a7Z67YSq4SUcnk5ORgxYoVTsG1kSNHFvq8yy+/HGFhYfbbS5cuRW6ue2m8QFaV33tld/h8FpYmnnZbXl2vxbXN65fLNlUGMuHnjT6tUM1Df8dsswVPr43DBaO5SOs0/b0SxikzgQtZ9gw28978MshUNX279ziOZriPkwbrtHimW3N7yX17udUc5+9gw/VXMoM2wEX26eJ0u6bZhF6nT2Jt8llkm4v2PUJEFOjKJfj49NNPqx8qrqSv45gxY3D//fdj8uTJeOKJJ1RviUsvvRT167ufKH/88ceYNm2an7aaiIiIiKjsmf5ZBcuGbXhn1XK8vWMT2p87Y7+vT1RtNPKSfUFEvlu9erWqrmMzcOBABPvQH0vaf0j1HhtZx5o1ayrUrq/K772y+2F/Itzz5YGrm0WrsqtUfM1Cq2Nyd8+lLI9kZOP1LQedsokLo+veEQgOclpm/GUurFlFn6BPlUPc2Qv4aX+ix/vuatcQsTXzy/KaN2yDxSVYrevdBbp2Lct8O6lktOG1kRztXNlgaGoSsswWbEg5x91LRJWK388+pUfF/Pnz7bdl1o70cZRMxubNvdckl5O4zZs348MPP8SGDRvsy6XJ/dChQz0GJ4mIiIiIKhLLkURYDh613+515hT+jorB7tp55RCvYeYKUanYunWr0+3u3bv7/Nxu3bphzpw5Tuu66KKLKsxfpiq/98os6UIOFh11L7kapNXghpYcLykN0o9tx8nzmOmhH98/x0+hU91auK6Fb/taWzcchtEXwzgjv/eq9ex5VX416IZRpbK9VHGYLFa8tvkgzB7i123DazgdV9Zz52Gc5VxuFaE1YRh7qR+2lEqDTkqvLsj/Hul3Kg0hZhOWnTiNQTF1uJOJqNLwe+ajBA9tqlWrpvpESPnVggKPtiBljx498OOPP+Kxxx6zL5eyrZ999lmZbjMRERERkT+YljpnEaUFBWNpZF6Z1ZjqwegbnV/ykIiK7+BB555abdq08fm57dq1K3Bdga4qv/fKwjh/CXJnLYIlNT/YKBlTZg+Zd1c2qYe6Ic4ZdlR8D3VqgvbhNT3e9+GOI9h5Kt3nden6dYe2VVOnZSy/WjX9fOCEynx0pdNo8Gz35tBr88ut5v46H3DJkA26biQ01VkZo6Jo0LcrzP+V0BXVLGYVgFyZdEYFoomIKgu/Bh8PHTqE/fvze9e88sorTmVbfHXnnXdiwoQJTtmU0vCeiIiIiKiispw6A/O2PU7LZjVoBJM275T96ubRahCKyJ9WrVqFjRs3BsRO37dvH/78889SWZdr0KxBgwY+Pzc6OrpCB+Cq8nuvDKzZOTAtXw/zsnXIefUT5Hz6A04fTsTcw6luj9VpgAmtYsplOyurIJ0Wr/Vu5bGMrQR/n1m/H2dyjD6tS6PVwiBZjq7lV6ez/GpVcjQ9C9/sOebxvptbx6Bl7Rr22+ZNO2HZtd+thK+uo++TSKj86UJrIjHGOUt6aGoyzueasO3k+XLbLiKiCh18XLdunf1627ZtceWVVxZ7XQ899JDqOSFOnz7tFNQkIiIiIqpoZDAZDrOds7Q6zKvfUF0P1mpwZZPIctw6qqri4+PVxM+77roLO3bsKJdtkADX448/rtp1uJYMLa60tDT7da1Wi8hI3z9f8li9Xu9xXRVBVX7vlYF50w4gJ9d+27L/EOYmnUWuh2yZyxpFIqZGfp84Kh31awTj5Z4t4Gk6UGpWLp7fcMBjFmpB5VcdWc+ch3HOX6W0tRTILFar6hea4+Hz26RWNdzWJtb58QcOOT+oVg0Yxo0o682kMqDr2sHpdq/TaahpMmJp4inubyKqNPwafExJya9nXZyMR0ehoaHo2rWrx3UTEREREVUk1swsVWrN0YL6DZBhMKjrlzaKRO2gvOtE5WHZsmW45pprcPPNN2PJkiUwm81l+npSWm7t2rW49957ccUVV2DevHmlWu0mKyvLfj0kJEQF4XwlLUHkOTaZmZmoSMr7vScnJxd4SU11z+Cj/M+FaZVzJrKlXUtMTXUv1yiBsZuY9Vhm+kaH47a2zoEhmw2p5/Dt3uMlK7+6ZgvMe+NLvJ0U2P44lIqtJ9M9fn6l3Kpk2jqSTFnDxKuBGnklVoOuuQKaGtX9tr1Uehr36wqjJv/vG2S1YsDJVCxPOqOC0kRElYF7nYgydPbsWfv1evXqlXh9UVFR9utnzpwp8fqIiIiIiMqDSQKPDpksEtaZ2aCx/fa45s6lDon8pUuXLqrUpgSFxPr169VFfouNGTMGl156Kdq3b19qr3fgwAEsXrwYc+bMwdGjR53uCwsLQ58+fUol+OYYyAwODi7yOiQAl5GRoa5LIDY7O9spKBeoAuG9FzYR2WAwqEpJ5M6ScBTWE87B2eVNmyEz3X0ywEUN6qBpKIMSZWlS21jV41GCja6+23scHevUVEFKX8uv5rz5udO5gJRf1T59LzTVAv+7hYouJTMHn+w84vG+a5pHo1PdWh4ngOilzGqrpjBv3gVdF+c+vFRxGGpWw97YGLQ8lj9RYWhqEhZFN8DeMxloX8f9709EVNH4NfOxevX8E9/0dN+bcHvjuI4aNfJroBMRERERVRRWsxlmKbnqYGVEFJKr5Z07y+BT6zCe61L5BR+lz+Idd9xhb3thqzzz5Zdf4qqrrsKQIUMwefJkzJ49W7XDMBp963dmMplUSdW5c+fiueeew8UXX4yRI0fi448/dgo86nQ6XHvttVi4cCGGDRtW4veUm5s/uC8cy4j6SgJkBa0zUFXl914ZmF2yHhFRBx9mex7Wmdja916eVDzSh/nlXi1Rr5pzz0YheUvPb4xHcmaOT+ti+dWql8X8zrZDuGBynzgQXT0I93RoVODzNbVqQn9RySfjUPnSupRe7X7mNMJyc7As8XS5bRMRUYXNfKxbt679+vbt20u0LplhuWvXLo/rJiIiIiKqKMxbdsN69rzTsl8bNnGa/U5UnmSip/RcHD9+PD799FMVZJTAoc2JEycwa9YsdbEFp2JiYlR2ZJ06dVCtWjUVuJSgpGTenT59WgUv5XkFBa4kw0MyKx988EE0b9681N6PbI8jX4OljnJynAMKFSHrMVDe+/Llywu8X6oaPf/880XersrOej4D5m17nJbtbNMaZ43uwYs+UbXRNrymH7eu6goPNuD13q1w1/Ldbn0ez+ea8My6/fhicHu38pneyq/K31j6eDqVX+3cDrq2Lcpk+6l8/HP8FFYmea7g9nTXZqiu1/l9m8j/WvTrgqz5fyPEkvc9roMVg06mYOmJ2ri3QyN1HkREVJH5NfjYsWNH+/WVK1ciISEBzZo1K9a6ZPatrbm9/Lht3bp1qW0nEREREZHf+nctXeO0bGdoGPaGhqnrdUMMGNKgDv8YFBDq16+PV199VfVh/PHHH/Hbb795rGgjAa0jR46oS3EDZKNHj8bEiROL/XuxIBIIld+QtsCbazDNF1Jq1EbW5ZgVGsgC4b1LGd+iZFZSHtO6LYDZoe+pQY/3DLUAD61Qb2ntuRchlY2OdWvhwY6N8f6Ow2737T6TgY92HsHjXZx7OnrC8qtVw7kcI/5ve36A2dGIRhFOpXqtubnQVJD/X6joQqqHYGfDWLQ9cgRbw+ri33rRWBlRD+kZ2TiUnoVmLJ1NRBWcX8uuduvWDaGhoeq6/Nh57LHHitWrcd++fepHr02vXr1YdpWIiIiIKhzLgUOwHs/rpecp63Fs0ygYtH49ZScqlGQ1PvXUU2pC6TvvvIOBAweWOPgmAae+ffvi9ddfx6pVq/DSSy+VSeDRU0sQCaYVpXSo/JZ1DMBVtBYgVfm9V1RWiwXm1Zuclh1v1QKHLO5ZMZ3r1kLXyLxxF/Kf61pEY3is54pcvx1Mxl/HThav/KpGA12PTlIjubQ2lcqZBKnP5ORXD7AJD9bjkU7554CW1FPIfunDvL7gVGllXjII4/pehMc798Cf9WORbsg7n2LpVSKqDPx69iL9JGT2qvTwEHv27MH111+PF154Af369Sv0+RaLBTNmzMC7777rNMN20qRJZbrdRERERERlwfTvWqfbiSHVsKZuPXsvqTFNo7jjKWBJhuKoUaPUJTMzExs2bMCmTZtUe4z4+HicPHlSZfd6IuVYW7RogQ4dOqhJqhJ4rFmzpl8DqOfOnVPXZRuTk5PRqFHBPbZs5LHy29SmQYOK1VuvKr/3isqyaz+sZ5zLc39ZO+//Cle3tOHfpDxIecRnujXHgbMXcCQjP0Bv8/rmg2hRu7pPmUy28qtSajdo/BhoG/NvWlmsTT6DhUc9B6If69wUtYMN9ozH3O9+BdIvwPjLXFgOHYNh3OXQBDEzvLLp3qYxMuLTAIvz+dKyE6dxW1tmsRNRxeb3qVO33Xab6hFy/Phxdfvw4cO49dZb0aZNG1x88cWqNKv8GJIfntLXMSMjA4cOHVI9IhcuXKh+7DgaPnw4+vfv7++3QURERERUIpbkNFj2HHBa9ltsE1j+6+8i5VYjq7HUFlWcbLqLLrpIXWykpGdqaqoKTMp1yY6Ux0VGRrr1HvQ36SG5d+9e+235feprAC4xMdFtXRVJVX7vFZVp1Uan2+ej62Glwf0z1Kp2dfSNyivbTf5Xw6DDG31a47alO5HtWCIXQJbZgifXxuH7IR1RK0hfaPnVoIlXS0NVaAzMeKwsLhjNeHNrgsf7BtYPt2fOyqQQ46/zYT2Rar/fvG4rNLVrwXDFUL9tL/lHTYMePSJrY23KWaflcWcv4MSFbMTUqBg9pYmIPPH7WYz82Pzmm29w44034vTp006lVOVSFJ07d1ZlfoiIiIiIKpzgIOj690DW2i0IslhwXq/HougY+93XNC+4LxpRoAsODkbDhg0RiCTr0tGOHTt8qsZje2xB6wp0Vfm9V0RSetGy76DTspnRnjPhbmkTqzLwqPw0r10dT3drhhc3xrvddywjWy1/p19raAv5O2lq+S8TnPzj891HkZzpXua6hl6HJ7s2tX92zWs2w7zR+btWUz8S+uED+KeqpGTCoWvwUSw/cRo3tMz/bUBEVNGUSwOZpk2b4tdff0XXrl2LvY6xY8fiu+++c+pXQURERERUUWjDayNu+GBc02cwvm3SAj81ao5sXd7cwJa1q6u+XURUNqTMqyMpF+urzZs3F7iuQFeV33tF5Nrr0RQSjOk13XsLNq4Zgosa1PHjlpE3IxpF4iovZdNXJZ/Bd3vzKoFR1bH95HnMPOhcyc3mgY6NUa9asLpuOZoI48yFzg8IDkLQbddBE8xqGJXVwPp1PA7QS+lVIqKKrFyCj0LKukybNg1vvvmm6vPhC61WiyFDhmDKlCnqef7sCUJEREREVNpkIOqcIQg/Nm6OGQ2bOGU9MnuFqOxIFR0p/2qzdu1apKWlFfq8U6dOYfXq1fbb0dHRqnVIRVKV33tFI33fTOu3Oi1bFtsIuTqd22Nvbt1A9QqmwPBI5yboUMfzmNXXe49jZQmCClK23Zrl3leSAlOO2YLXtyTAUwfkbhGhGN00r3+r9UImcr+bAZjNTo9RfT+jIvy0tVQe6oQY0Ckib9Kh1mpFl7OnccvheGw/mY5T2e7ZskREFUW5Fo/X6XQqg1Eux44dw9atW1Xp1bNnzyI9PV0FG0NDQxEREaEClJIpWacOZ/IRERERUcUngwn/HD/ltryWQYdLG3KQiagsSXD/yiuvVNV0hMlkwg8//IDHHnuswOfJY4xGo/32yJEjK9xEgar83iuc7Bzo2raAedse4L8egt/Xcc+oi64ehMsa8f+NQBKk0+KNPq0wcclOnM7J/9zYvLAxHlOGdkSjWr73v7WazTD9uxamhUuh69UFQddfWcpbTWXh+33HcTg9y215sFaDyd2aqRK8VosFuT/MgvX0OafH6If0ha5LO/5hqoBhEaHov24jhqQlIyI3Ry1bHhmFlUlnMMZLJjURUaALmM7V0gtELqNGjSrvTSEiIiIiKnNzDqXCZHWfB39lk3oI0btntRBR6brjjjswffp0ZGZmqtvff/89Lr74YnTq1Mnj43fu3Ilvv/3Wflsq8dx+++0FvsbKlSsxZ84c+22ZVHvLLbegKrx3KjlNaC0ETRwH69gMmNZtxepdCUisVsPtcRNaxkCvLbfCVuSFlNKUAOS9K/bA7PL//QWTGU+ujcO3QzqihkHnU+/P3B9nwXokMb8vYJd20LVpzv0fwA6cu4Af4k54vO+Odg3twWfT4hWw7HXuE6pt1gj6UcP9sp1U/gY0ioTJIfAohqQmY1liQwYfiajC4tkpEREREZGfmSwWzDqU4rZccoiubhbNvweRH0hVnXvuucd+W7L6br31Vvz555+wWPKyzITVasWiRYtU0NAx8+/ee+9FeHh4ga9x8OBBzJs3z37ZsGEDqsp7p9KjCa2JuB5d8FTjVm73hQcbcOV/ZRsp8HSJCMUjnRp7vO9QehZe3RyvPmeF0mlhTUp1WmT8ZS7LrwYwk8WK1zYfdAs8i9ZhNXBDyxh13bw3HqZFy5wfUKsGgm4dB42HEstUOcXUqoZtDRs6LRuWmoSNKWeRYTSV23YREVWKzEciIiIioqrAkpSKVUYgLcu9h0u/6DDE1gwpl+0iqookA1BafyxYsEDdzsjIwCOPPIJ33nkHrVrlBXr279+PEyecM1ekbOmkSZPKbLs+//xzFbh0lZLiPGlh/fr1ePzxxz2u4/XXX0dQUFCFe+/k2dR9eRlvrm5oWR8hDFAEtHHNo7HnzAX8edS9t+q/iafx4/4TqmdnQbR1w2EYfQmMv+V9XoX1zDkY5/zN8qsBanp8EvaeueC2XKcB/te9OfRaDSynzyJ36u9wagip1SDolmugqR3q1+2l8mfp0h44mJ8B2yA7C83Tz2F18lm2ZCCiConBRyIiIiIiP7GaTMj54Dt0MlvwZJ1ILIuIwpbwujD9Vy7vmubMeiTyJ+lZ+OabbyI0NBS//PKLfbkE3FyDbjY33ngjJk+eXKbbtWbNGp+yJA8fPqwunrz00ksFBh8D9b2Tu4PnMrEi6Yzb8poGHa5uxl5ggU4+a091a4qD5zMRd9Y9GPX5rqMqE653VFiB69H17w7ztt2wHMj/zLP8amA6lpGFr/Yc83jfTa0aoFVYDViNJuR+/xuQ6dwPUn/lcOhaNvHTllIg6dSlFRIXVFNBR5uhqvTqKQYfiahCCriyq7m5ubhw4UKRLyYTU9CJiIiIKLBZ9h8CsrJRLTcXlycn4u1dWxD+X2+XBjWCCx14JKLSJwG6F198UfU9HDJkCAwGg9tjZJncN2XKFLzwwgsFBvUqkqr83iuSH+I8Zz3KhJWaBs4prwgkO/WtPq1RO8j97yWFjv+34QBOXMgucB0arRaGG0cDQc6fU5ZfDSxSRveNLQnIMeeXsLZpXDMEt7WNVdeNsxfbe3jaaDu1gX5oP79tKwWWpqHVsSnWufTqkLRkrEs6g2yzudy2i4iouMr1LDU5ORlz587Fpk2bEBcXh1OnTjn1kSiKV199Fddcc02pbyMRERERUWkxb93tdHt3rdr4f/buAiyuM+sD+H8cd9cESEhC3N1T26bulnrTVHbbrW5ld7vtl3q3adOt69a9W2/clSQQEjS4u8Mw8j3vBWYYGAgkMMPA//c882SuzOXlDoG597znnFInZ+n5sjBfyGWi6yMR2cPcuXOlR01NjZRNWFLS2l8tICAAI0aMkDIE+2rBggUWvRGDg4N79brVq1fjkksuwenQaDR2/d7p1BgNBulvhWJCLGRqNfLqmvB7blmX/ZwUclwR07ufJxocgl01eHLmKPx5x3Ep4NhRjVaHB3an4O3F4+Gk7L7PH8uvDn4/ZJXgYGmN1W1/mxYNjaI1D0QeGQL9XiXQ1s9P5u8D9dUXSJmyNDyJ914/eRyQnmpaF9DchOiKCuwrrsbCEB+7jo+IyCGCjw0NDXjppZfw8ccfQ8+ZG0REREQ0DBj1eugTky3WbfE3l1ldEuJrh1ERUWci0DZx4sR+OTHR0dHSo6/mzZvn8N87nRpDygm0fPA1WlycoJw1Bf/zDugSqBIuGBkIL03XTFUa3GYGemHN+Ai8ejSny7a06gYpY+4fM2J6DEB1W351yjgoYvv++4b6j+jnvS4x2+o2USJ5sp95Iof4/y0PDYL23S9grK6F+sbLIHNm3+/hbvL4aJz4xQ1R9XWmdUtLC7GloILBRyJyOHJ7BB5Fc/oPP/yQgUciIiIiGlY3lNFgWVJtq39rr65AZzXGervaaWRERDRY6Lbvb33S0ATd5t1Y8PumLvsoZTJcNYpZj47qmtEhWB5mfcLRr7ll+Dy96NTKr37yA4yNPZdupYEtt/rc4ROoa+laHlN8zhNB587kYcHQ3Hcr1LdeJQUiicZ6u2FvSKjFiVhcWoyd+eXQGYw8QUTkUGye+fjiiy8iPj6+Sw+JuLg4qZyLu7u71T4TJxMbG9uPoyQiIiIi6l/6w8cslo+5e6KkreTq4lAfltkiIhrmDBVVMCSZy+0J3wVb9v8S/hTpj0CX3pfVpcFFZDU+Oi0amTUNyKhp7LJ9XWIWRnu5YKq/Z9/Lr/7wB9SXrxywsVP3NuVXYGtBpdVtD06J6rY/q8zFGYrYKJ5akogWDPpJ44C0FNMZ8W7RIqakBIfLajA9oPvfC0REwzr4WFFRgU8//dS0LIKMa9aswdVXXw1PT/7yJCIiIqIhXHI1Idlq1qOwJJQlV4mIhjtROhNGc2ZLvUKBDYHBXcpXXRsbYofRUX9yVirwzJxY3LApEbWdMuX0RuBve9PwwdIJPQaZrZZf3XlQ6hnH8qu2Va1twXOHM61uOyvcD/OCzb1/iU5mypgRSHb3wJhac+/QpaVFUulVBh+JyJHYtOzqtm3boNO1NlIW/v3vf0vBRwYeiYiIiGgoM6RmAg2W2Q1b2/o9+mhUmOjrbqeRERHRYGBs0UG327JK1G+BoWhUWM4ZXx7ui3C31qx5cmziffznjFGw1t2xsrkFD+9JhVZvreNnK5ZfHTxeTsiW3rPOvNRK3DNpBPQZ2dAfNWeyEfVE9AbdFWRZenVhaTF25JXC0GGCChHRYGfT4GNmpnkW0Jw5c7B8+XJbfnkaphp0euworMTGvHI06brW3iciIiKydcnV4+4eKGorubooxAcKmbVbj0RENFzojxwDaust1n0f0rXk6nWxljekybGJjLhbxnV9n4Wkyjopm070EjxZ+dWOjA2NMOT33DeS+s+e4ir8lF1qddu9k0fCs7kJ2ve+hPbNT9Hy40YYDd0HlIkEpVyG5oljLE6Gm16HqPxCHK+s40kiIodh0+BjdXW1RfCRaKCVNWpx8+ZE/HVXMv62NxWX/H4YJY3NPPFERERk45Krxy3WbWnLehSWhPrw3SAiGub0ew5ZLB/y9Ea2q5vFugXB3hjl6WrjkdFAu2FMqPTeWvNDVgm+zSzp8fWi/Kp81AjpuTw2CpqH10AR07pMAz/Z/en4E1a3zQvywopgb2jf+wqoaQ0Y6X7fDu1rH8HY2MS3hno0Y1Q4jnha/l5YJEqv5lfwzBGRw7Bp8NHb2/xL092dpaVo4H11osiigXtpoxZru/lgSERERDQQpF5M9Z1Krvq19nv0UCkwzd+DJ56IaBgz1jfAkG7u2yf8YCXrcRWzHockuUyGf8yIQYSbk9XtLxzORGJ57UnLr6quWAn1mmsh9/EawNFSR68n5aCwoesEdxelAg9OiYL+p00wZGRbbpTLAY2aJ5J6NDPQC9sDg6GVybHNLwB/HzcJL4yOw+aCih6zoYmIBhPL5gEDLCwszPS8ooIzNWjg5dZ1nU22q6gKadX1nDFKREREdim5muLmgSJnF+n5ghAfKMVNKKIhprCwEKWlpairq0Nzc98rj4SHhyMmJmZAxkY02OiTUgGD+WZys1yO3b7+FvuIiSoT2B94yHJTKfHsnFjcuDkRDTrLspw6oxEP70nBB8smwtdJ3W35VfncaTYaLQkiIPxFuvXytneOj4Bf2gloN+60WC/z9oD6uoukgDFRTzQKOeomxeFC/yDUK1UW9zkzaxsR5dF6LUFENJjZNPi4ePFiqFQqtLS0ID7espE60UAY6+2GDXnlXdZ/lFKAJ2aO4kknIiKigS+5eqRzydXWrEdhSQhLrtLQsWPHDnz99dfYtWsXqqqqTutY1113HR555JF+GxvRYKZPSLZY3u/thyaF5e2aG8aYJ3PT0DTSwwWPT4/BQ3tSu2wrbWrBw3tSsX7hOKgYuLI7rd6Apw5mwFr+2WQ/d5zvpoT2je8sNyjkUN94GWRuLJ1MvTN3RCB+Lanpsl6UXmXwkYgcgU2n2vj6+uKMM1obYe/ZswcpKSm2/PI0DK2M9IezouuP+R+5ZSioZ419IiIiGliG9GygvsFqv0cXpVwqqUTk6ERVm9tuuw033XQTfv7559MOPBINJ0atFobj6RbrtvsFWCzHebthOkt0DwtLQn1xfTfldY+U1+LlhE4lPHvB2NyM5rc/gyG3oB9GSMIHKflS9llnarkMj4yPgO69L4Amy6x/1UVnQx7JSQTUe6JvqFIm67J+SwGrCRKRY7B5nv+DDz4IPz8/6PV63Hfffaiurrb1EGgY8dSocMFIc3ZBO1HE5ONUfvAmIiKigSVzcUb9xLGoVyhMJVcL20quzg3ylkoqETkyUVb1xhtvxJYtW+w9FCKHZDieAbToTMt6yLCrU8nVVWNCIbNyA5qGplvjwjE70NPqti8zivBzdmmfAo/a1z+GISEZzes/hCG3sB9HOjxlVDfg/eR8q9tuGhOGoF82wlhQYrFeMX0iFPOn22iENJTKMc8I6Pq7IKWqngkVROQQbFp2VQgMDMTbb7+Nm2++GampqbjkkkvwxBNPYM6cObYeCg0TV44KxhfphZhUWY5rszPw/Og45Lu44n9ZJbhpbDh8nMy104mIiIj6kzw8GN/MnYuPPEMxvaIcxg73jpeEsuQqOb7169fj+HHL0sIjRozAokWLEB0dDU9PT2g0mlPq+Ug0HOgTLP//HPbyRq3K3NcvysMZC4K97TAysheFTCa1iblhUyLy67v2zH06PgMjPZylNjO9Cjxm5LSuaGiSApCaO6+DPCx4oIY/pOmNRqncqujD2dloTxdcVV4I/b4jFutlwQFQXX4uJxDQKVkc6oPdxV0rSmwtqJTudxIRDWY2Dz4mJCQgNzdXCj6+8soryMnJwfXXX4+YmBgpADly5Ei4u7tD0TY7vLcmTpzIC1Syyi83Hx8ei0doaevswGtyTuCZMRPQbDDii4xCrI6L4JkjIiKiAbM5vxxauQK7OpTR08hlUuYjkSPTarX47LPPTMsuLi546qmncM4559h1XEQO1Rf4qGV/vx2dSq6uig2FnFmPw46nWoWnZ8fi5i1H0awXtZvMxL2Mh/ak4P2lE+Gt6WEytfi56XxvraERza8yAHmqxMT2pMq6LusVMuCfQa7Qv/OD5QaNGuqbLoNMY55QQNQXC4N98DROSP1FVQYDZlaUYWlJITa7qRh8JKJBz+bBx88//xxfffVVl/Xp6enS41Q9+eSTDD6SVYbkDFPgUTijuBAfRUajwNkFX2UU4drRoXBV9S3YTURERNQb2bWNOFHTtSfQrEAvuCj5+YMc26FDh9DQYO5p+uKLL2LJkiV2HRORo1FfsRIl+xKgTM6Am16HHb7m4GOoqwbLw/zsOj6yn9FernhkWjQe35fWZVtRgxaP7k3Fy/PHQSm3XpJXplZDfeuV0L7xCQxpWV0DkHetgjy0tQ81nVx+XRNeT8q1uu3GcB+EfPU/aUJBR+qrL4A8gP+H6dSJam2T/NyxcPc+nFFcIP2dEBJSM1C+aCJ8nRjYJqLBi01maMhTLp0DqM2zARUw4uqcE9Lz2hY9vssstuPoiIiIaCjbnF9hdf2SUF+bj4WovxUWmnuHxcbGMvBI1EcyhQKKKXF4Z/oMXDh3Ce6aPBOlTs6m7WKibHeBJRoezgz3w5Ux1ksrHiitwWtHs3t8fWsA8irIY0ZYbpACkB/AkF/Un8Md0uVW18afQFOnLFQhwlWDq/fvg7Giusu9KMXkcTYcJQ1Vi0N84dmiNQUehaUlRdheWGnXcRERnQyDjzTkydzdoOzU2PvM4gIENbVmIXyaVgCtlQ+QRERERP1RctVaL6f57N9FQ0BjozmrNy4uzq5jIXJULQYDtuRXoEUuR6KnuRy3KKf5p0h/u46NBoc7J0Riqr+H1W0fpxXij9yyHl8vSn6qb7MSgKxnALI3xP0ikWW6v9QyuNjuiQAnIKV1gns7eXQElCuX9+r4RCezKMQbmwIsJyFMqq5EfFoeTx4RDWo2L7t644034k9/+lO/Hzc6Orrfj0lDh3LpPOi27wdaWmcJKY1GXJVzAi+OjkNpUwt+zS3DeSMse2sQERERnSpDbiGK3dyQXFXfZdt0fw94qG3+MZyo3wUEmD8/q1Q99B0jom7tLa6WKvJ0tjzMF2oF54sTpOzXp2aOxvWbElDcqO1ySp48mIGRHs6I8XQ9aQBS+8bHMKRndwpAtpVgDQnk6e6kQafHg7tTsK/EeuDxwpGBGDs1Cnq366D94Cugth5wd4X6+kulzGai/hDi6oSqkRGoS060yH70Op6KuiWT4KbidQURDU42/+0kgoQMFJKtyTzcoJg3Hfote0zrzi7Kx38jolDi5Iz/puTj3Eh/yEVDdiIiIqLTYDQY0Pz6f+HW2IR/evlhi38gdvkGoLntJhRLrtJQMXLkSNPz4mK2MiA6Fd1lra0IY3lusuz79sycWNy65Si0BqPFqRGlQEWA7L2lE3uc3NQagLwa2tc/hiGjYwCyAc2vfMAAZCfV2hbcuzMZRyvqrJ5Pf2c17pwQIT1XjB4Jp/tvg/aDr6E8Zwlknu788aV+NT/cH9v8A3FOUb5p3eKSQuwsqpLKMxMRDUacRkfDhmrZPEBpnnmmEtmPuZnS8+y6JmwrsN6TiYiIiKgvDBk50sx3pU6PRWXF+PvxBAQ0N0nbxDSnRSE+PKE0JERFRZnKre7btw9NTa0/50TUO016PbYVdr0ODXBWY4Ivgxdkaay3Gx6YEmX1tOTVN+PxfWlSb8KeSAHI1VdJZUEtiACk6AFZwIkkQmmjFrdvTeo28KhRyPGvGaMsMs5kXh5Q3309FKM6lbcl6geLQ32wyT/IYt3Y2hocSc7i+SWiQYvBRxo2xMwzxdxpFuvOKcyDf9vNwA9TCmA8yQd1IiIiopPRHzlmsZzh6oZcl9ZSaJP93KXsBaKh4oYbbpD+bWhowLvvvmvv4RA5BENFFfQJydiTW4YGncFqyVVW5SFrVo4IwMVR1suj7i6uwlvHck964mQaDdSrr+4agKxrC0AWlgzrk59X1yRlmGbUmPsad+SmUmDd/LGYYqUPp4zVtGiAjHR3RllYCCpVaov1Hkkp0kQWIqLBiMFHGn7Zjx3q7quNRlzRlv2YVFmH+LIaO46OiIiIhkLJVf1hy+Djlg6zlFlylYaalStX4rzzzpOer1+/Hr/++qu9h0Q06On3J0D79meY8vKb+GfSIcwrswz2nMESetSDeyaNwMRuMmPfS87H1vyK3gcgoywDkDLRv1c9fCdJpVfX49atR1HQ0Gx1u49GhddnRGOyX9fAI9FAEoHthWGt7Rw6WlBciP3F1nuSEhHZG4OPNKzIvD2hmDPVYt3Kgjz4mrIfzbXTiYiIiPrKkJkL1FiW6Nra4SbBYpZcpSFo7dq1uOiii6DT6fDnP/8Zjz76KDIzWyf4EVFX+oTj0r9qvSjPXYLplea+j6GuGozxas2WJ7JGJZfj/2aNhm83lRT+cSAdWd1k7VkPQIa3Lvt4SmVD5b7ew/LEJ5bXYvXWJJQ3tVjdHuyiwduxAQj795vQbd3Dyllkp9KrwRbrourrkJh0gu8GEQ1K3XeiJhqilMvnQb/7IKBvLW+jNhpwRW4W1seMwZ7iaqRU1SOWF3tERER0CvSHkiyWT7i6IcfFTXoe5+2GQBcNzys5lN27d+OXX3456X4qlQpBQUEoKirCl19+KT0iIyMxevRoeHh4QKns26XnnDlzcPbZZ5/GyIkGb8lVY26hxbrtfuZJKivC/Fi6kU7K31mNtbNG4/Ztx7r0eWzQ6fHgnhS8s2S8RU9Ca2ROIgB5DVo+/x+U5y4btoHHPcVVeHC3KF/ZtQxye8nLddNHwmP9BzA2NqPl619hyMyD6sqVUhCXyFZ9X4uDAlB6XAN/rTk71+VoMnTLpkEpF93liYgGDwYfadiR+3hBMXsK9DsPmtadV5iLTyNGokKtwUcp+Xhy1mi7jpGIiIgctOTqkdZslnZbOtxQXhLqY4dREZ2elJQUfP7556f02uzsbOlxKjQaDYOPNCQZEpMtlmuUShzxNAd8lof72mFU5Igm+Xng3kkj8NzhrpnmWbWN+Mf+dDw1azQ0CvnJA5CrLsFwtTGvHI/vS4OuUxC3nZg89m9PGVQvvQ1jda1pvT7+KGSBflCdvdiGo6XhTPQCXhTqi00BQbg8z/z5an5hPg6XVmN6oJddx0dE1BnLrtKwpFw+H0a5+cdfYzDg4rY/3OKDp2gwTkRERNQXhqw8oMNNKWFrh36PolQSERENb/ojlsHHXb4B0Lddm45wd0aMh4udRkaO6OKoQJwb6W912/bCSty4KREZ1Q2n9TWMRqNF0G0o+S6zGI/sTe028LjMVYlX0xKh+uCrLp/xZBEhUmUtIlsSLRw2BViWXg1tasSxhDS+EUQ06DD4SMOSKCWinDVZel6tVOGtEaPwcUSUtCyKbHycWmDnERIREZGjl1zNdHFFtmtrydUYTxeEuznbaWRERDQYGOvqYciwzAbe4Rdger4izJclV6lPZDIZHpgShbHe1vuEptc04PpNCfgsrRCGbgJsPf7MGo1o+eZXND37OgzFpUPq3fkwJR9r40/A2lmRG414sLYUj238A7Kk1K47uLlAfcOlkKms990kGiiT/TxQ6OODfCfL6wqnxOOn9H+ciGggsewqDVvKFfPR4OWBa2qVqFUoLLb9mF2Cm8eFwddJbbfxERERkYOVXD18rNusxyUhzHokx7Rq1Spcc801Nv+68g5VSoiGCv3RVBHNMS03yeXY7+1nWl4eZn5O1FuirOrTs2OxamMCqrS6Ltu1BiNeSsjC7uJKPDYtBn7O6t4HHr/+Bfpt+6Tl5lc+gOauVZAHWs+0dBTi+1p/NAcfdTPpfFRtDf6VnYKg8gqr2+WxUVBdsXLY9sck+xJ9HRe0ZT9em3PCtH5MWSmOV9YhzsfdruMjIuqIV3Q0bMn9fOB19mIsjjbfGOz44fzz9CK7jIuIiIiGRsnVLR2Dj6Hs4UWOm1WjVCpt/mDwkYYifYJlX+B9Pn5obpsIO8rTBSM8mCFPpybIRSMFIJ166O+4p7gaV284gq0FFb37ed2yxxR4lNTUSQFIQ3GZw75NeqNRyna0Fnh01uuwJj0ZbxzaYz3w6O4K1XUXQb3mWgYeaRCUXg1CnUKJXwJDcP+EaVg9ZTa25Pfu/zYRka0w+EjD3jWjQiGzcha+PlGEupauswaJiIiIOuuc9Zjl4oqstpKrEW5OiOINZSKiYc3Y3AxDcobFuu1+gabnK8KZ9UinZ4q/B95bOkEq9d4dkRn5wO4U/N/BDDTq9D0eTzF7CmSRoZYrpQDk+zCUOF4AUqs34LG9afg+q6TLtrllJXh//05clp8tlVztTDFnKpz+dgeU0yeyNDLZ3cxATxR5eOLCuUvwzJgJ2O/jJ/UO3lJQIWX2EhENFgw+0rAnZpcuslIKra5Fj29PFA/780NEREQnL7lqONKp5GqHG8qLQ314o4qIaJgzHM8AOgR7dDIZdvuYy1cuZ4Y89YMoDxe8t2QCrhoV3ON+IgB33cYEqUxjd2TOTtCsubabAOQHMJSUO8x71qDT46+7krExv+uYQxvq8WTSIQQ2N3XZJgv0g/rPN0B95XmQuXYf1CWyJSeFAnOCvNDSqUR9Tl0TMmsb+WYQ0aDB4CMRgOtiQyzOg9rQelH4WXqhNDuOiIiIqDvG7HwYK2u6Lbm6lDeUaQhrbm5GRUWF9GhoaBg0xyIa7CVXD3v6oE6lkp7Hebsh1M3JTiOjoUatkOPPE0fglflj4e/U+jNmjQhU3LT5KN5PzpPKkXYbgLz9GsgiLO+ZiFLzrRmQgz8AWa1twV3bj2FfSbXV7UWubiicOtFypVIB5Z+WQPPgaiiiI20zUKI+WNzN9QVLrxLRYMLgI5G42PNxxzR/D/g3N+HutOP4bM82uLW0oKypBb/klPIcERERUfdcnCGbNx2VarW0mOPsisy2kqtBLmqM8XLl2aMh69NPP8WcOXOkx0svvTRojkU0mBh1OuiTUi3WbfcLMD1fEc6+wNT/ZgZ64ePlk7AktGulp3Yi6PifpFys2ZaEwvpmq/vIXJxbMyC7C0CWDt4AZFmjFrdvTcLRCusZnhqFHM/NiUX05ecAnu7SOvnokdA8tAaqMxdBplTaeMREvTMvyAtKWdcmUqL0KhHRYMHgI5G4GDQa8XBmKj7euw0XFeTAp0WLi/OzpXMjGpF3NwuQiIiISB7oh0OL5uHi2Yvx50kz8J/o0UDbzYDFIb4suUpENMwZ0rKARsvAzs4OwUdmyNNA8dSosHbWaDw6LRrOiu5vAR4uq8U1G4/gt24mX/cYgFw3OAOQeXVNuGXLUWTUtJahdNLroDCaK1u5qRRYN38s5gV7Sxme6svPheqaC6G+4zrIAzghgAY3N5USMwI8u6xPqapHQX3XEsJERPbA4COR+CAtkyHAoIO6Q5DxkvxsuOpakFvXhK2cOUREREQ92JxfAYNMhiNePtjta76h3FO2ARERDQ/6hGSL5WPunijTtJZZneznjkAXjZ1GRsPlfsfKEQH4aPlExPm0Vmawpq5Fj8f3p+PxfWmoa9F1H4AM79RPsroWWtEDsnTwZFylV9fj1q1HUdDQGvSfXV6K9/fvxIX5OdKyt0aF/yyMw2Q/D9NrFONjoZw5iZPGyGGIvvLtnHU6rCguwNrEeCTvPGzXcRERtWPwkaiN6syFFufCXafDRW0fTD9MyZeyI4mIiIg6ExUSthV2veHmo1Fhgm9rCS8i6vvNcqKhQjE1DifGjUGFSt2l5OryMD87joyGk3A3Z7y5KA43jQ3r8Wbgb7lluGZDAg6VWfazNgUg77iuSwDSWFWD5uffgCGvEPaWWF6L1VuTUN7UAt/mJvwj6TCePhqPoOYm3JSZjvEyvXQeRrMsPjm4hcE+EJ+WVmWl47vdm/FIciLmVJRCczjJ3kMjInKM4GNNTQ1efvllrFy5ElOnTsXs2bNxzTXX4KuvvmIwiPqVPCQQskljLdZdmpcFF50OxyvrcaC06wdvIiIiosNlNahs7pohsDjEBwoGUIh6rb6+3vTc2dmZZ46GDMWokXh21DhcMmcx7pw8E78HhphuyCxlhjzZkFIux63jwvHG4vEI6SHjtrChGWu2JuE/R3OgM5hLlVoEIMM6ZUCK0sKa1gB7Z7aazL2nuAp3bj+Geq0O5+fn4IP9O7G4rNi03dmgx7+LsxDu1pp5TOTIfJxUmOTnLvWd13T4fzq2oADlNdb7nBIRDengo/jAcdddd+H888/HBRdcgNzc3G73LSgowMUXX4zXXnsNqamp0sVoZWUl9u/fj0ceeQQ33XQTmputN8QmOhXqsxZZLHvodLigwJz9SERERNTZlvyKk5ZCIqKTS0tLMz339Ozax4jIUeXWNSK5ql4qz33U0xvlbSVXp/l7wtfJerCGaCBN9HWXyrCeE+Hf7T4ilPF+Sr7UNzGntrVvomUA8lrIwoLMK12cIfOz/tnHkJaJpr+/hOZ3v0DLxp3QZ2TDqNX23zcEYGNeOf66Mxmh1VV49dBe3JN+HG76rpPD1Dod0M9fm8hexGTHbX6B0Es5kOYge9oull4lomEYfPzll1/w+++/Izk5GSNHjkR4eHi3Qcp7770XOTmtgR9rdu7cib/97W8DOFoabuShQcCEWIt1l+dmwVmvw76Sahyv5MwhIiIiamXIzoe+rh6brfSG9lApMM3f3EeIiHomJphu2rTJtBwdHc1TRkPGhrxyq+tXhPvafCxE7dxUSvx9RgyemjUK7ipFtyfmWGU9rt2YgO8yiy0yGGWuLm0ZkK0BSHlkaLclsw1Z+TBWVsNw+Bh03/8B7cvvoemBtWh65nVoP/sfdLvjYSgsgbFTlmVvibE9uesYbkpPxpsH92BcbXWXfYwuzlBdfQHUd66CTMM+qzQ0LArxQZVag4PeloF/5eHjdhsTEVE7JWzso48+Mj1ftWpVt/tt3LgRhw4dMi3L5XJERUXBx8cHR44cMWU8/vjjj7jsssswa9asAR45DReasxahOTHFtOypa8H5+bn4LGIk/ptagKdmjbbr+IiIiMj+xM037ftfwlBRjQe8fLDFPxBb/YNQp1RJ2xeE+EilzYiGii1btuCbb77psj47O9v0fOvWrSguNpe3683/o4aGBmnCacdJp2q1GpMnT+6HURMNDn/klnVZJ8pyM0OeBgPRd3SCjzv+cSAd8d20m2nSG7A2/gR2FlbikWnR8NKozAHIe26Gfu8h6Xl3DNl5VlYaYcwvgl48dh1sXadRS0FMeUQo5CNCIY8Mg8yz5/7ZokrV/q0H8U76cQQ3WWZomsycBOcLzoDMzbXHYxE5mhBXJ8R6uWJTQDBmVponuowqLEBdTR3cPNzsOj4iGt5sGnwsKipCfHy89DwsLKzHC8ovvvjC9NzJyQnr16/H/PnzTccRgcusrCxp+eOPP2bwkfqNPDwEhnGjID9mLvt0eV4WvgsNx6a8cuSMa0SEO3vQEBERDWfG3EIYy6ukAkczKsulR4qbJ9LdW2/GLQlhyVUaWsS112+//dbjPiIQ2TEYeaouvPBCll2lIeNETQMyaroGRGYFesJT3fo3g8jeAl00WL9gHD5OLcDrSbnQddOjcVthJZI2HMHj02IwO8hLWidTKaGcP6PHiSaiWkSvNGthSM2UHu3kURHQ/OVGq8d9d18ygn7fimdLi6wersLDHf7XXgh1bFTvvj6Rg5Ze/bgsANpUGdRt/3fFvyk7D2Hy2QvsPTwiGsZsOh378GFzvelp06Z1u19dXR127dplWr7mmmtMgUchKCgITzzxhMUMW52o2U7UT5zPXmyx7N2ixcqCPKnnwcdpBTzPREREw5z+cJLFcr6TM9LdWmfmuyjlmBnYekOOiPpm7ty5eOCBB3jaaEjQJyZja7r1oMuKMD+bj4eoJ3KZDNfGhuKdJeMxoocJ1+VNLfjzzuN48UgmmvW9K5MqyrOqrjwPirnTIAsNBLopz2qNzKtrGfsmvR7v/bYXM7/7CUutBB61MhkOTpmEoMfvZuCRhjyRRS+qr+zzsezhqjxkeb1CRDSkMx/T09NNz0UJ1e4cPHgQLS0tpuWLLrqoyz6izGpgYKBU1qepqQknTpzA6NEsh0n9Q5T50MVGQ5mSYVp3ZW4mfggJx0/ZpbhlbDj8nNU83URERMOQmGmvP3zMYp0oudp+I21ukDc0CpZcpaFFlEJ1d+9a+k6r1ZpaYqhUKqlqTW8pFAq4uLjA19cXY8eOxYoVK7BgwYJue4YRORJDeSW0b32GS2QyxHj6YLtfAH4JDoVWroBaLsPCEG97D5HIqjHebvhg6QSsS8zG1ye6L6X9eXoRDpRU44mZoxDj2X05U/E7XRYcAHlwADBnqrTOKDIc8wphyMqTsiKN2XkwVlov+bpN5YTNe1NR0dSC8iYtyptbUNeibx1rW7n7jg57eqPgnGW4aHYc/57QsDDS3RkRbk7YFBCE+eUlpvUjiovRWFUDZysBfCKiIRd8rKqqMj338Oj+F197aVYhNDQU0dHRVvcbM2aMqadIXl4eg4/Ur1zPWYzmDsFHnxYtzi3Mw9dhkfg8vRB3TIjkGSciIhqGjHlFMJZVWqwTPR/bLQ31tcOoiAbWVVddJT06e//997F27Vrp+ZVXXolHHnmEbwWRCD4mJEvnQWk0YnpVOUbV1eDHkDDTJBU3lU1vxxD1iZNSgQemREk/q08eTEdls/VqY6Kk8PWbEnHH+EhcHhMkZU92nrBV06KTsiVNwUPxb3Pbstob5RFuKA+IgrymDmNqqzG2pgpja6ul5656PT5pliMpz9zLrp3caERsnTlgWa1U4fXoWEw4cx4ujgnmO07Dhgjwi9KrX1TXoVGugLOhNTivMBqRvSMeY861rO5GRGQrNv202zGbUXwA6c6hQ4dMz2fM6L5uvLe3eaZgbW1tv4yRqJ18ZDi0MSOgTm/tLdqe/fi/kDBp9t+qMaG8YCQiIhqGOpdcLXByRqpb68Q6jVyGOW09kIiIaPjStwUf2+30C4Be1poVvzyMk1TIMcwP9sbHyyfhyYMZ2FVkTijoqMVgxL8TsrCtsALhrk5twcXWIKMIMHbXP7ILjQY7NQHS/5X24GJ4Qz0KnF2s7h7ZUAcXfWuQ5bfAELwRPQZ/mTcOZ4azpDENz9KrH6YWYJevP5Z1KEUsF9VaGHwkouEQfHR1NZdhKC0ttbqPKKHasTfk1KmtJRmsYZ9HGmhu5y6F9t/vmpb9tM2YWlmBPb7++OZEMa6LDeWbQERENMxLrkpZj20z/WcFesFFqbDT6IhsT5RLvfrqq6Xn06dP51tAJP5W1NbDcCLH4lxs920NqDgp5FJAh8hR+Dqp8eLcMdIk7HUJWWg2WA8mxpfWSI/+YpDJkO3q1u12z5YWbPYPxA/B4Tjm54+1s0ZjHv9v0TA11tsN/s5qbAoItgg+hpeUoKWiGiofT7uOj4iGJ5sGH0NCQkzPjx49anWf7du3m3qGnCzzsabG/KHGy4szzKn/KaIi0BgVAc2JHGzxD8JHEVHIdGvtdfNpWiEujwlmTyciIqJhxJhfBGNpRdd+j22WsOQqDTOzZs2SHkRkpj+aImarmJZFGbyDPq3ZjiLw6MxJKuSAZR0viQ7CVH8P/H1fGlKrG+w9JBz28pEe3hoV1s0ejcl+7GtHw5coebwo2Bvf1zehTqGEm95cKjlvx0GMPG+pXcdHRMOTTYOPcXFxpud79uxBbm4uwsPDLfb5+OOPTc8DAwMRFRXV7fGys7N71UOS6HR4XfYnPBl/Aj+ZY+KSiuYW/JxdigujzD2eiIiIaGjrnPVY6OSMlLaSqwqZjNksREQEfcJxi7Ow18cPWnlrVvwZYSwJSY4rysMF7yyZgDeO5eLj1AL0sqDqafNUK+HrpIKvRi396yMeGhVCXJ0wM8AT7mr2UCUSkyC/OlGM7X4BOLu4wHRCZKJlBIOPRGQHNv3rPGnSJAQEBKBEpHy3tGDNmjV4/vnnERsbi8bGRrz66qvYvXu3af9zzjmn22PV1dUhJ8dcxoSZjzRQ5CGBOEuhwU87LC8ghf+m5uO8kQHSzUYiIiIaBiVXD1n2e9zqZy65OiPAAx68+UVENKwZm5phSD5hsU7cCBZclQrMZl9gcnBqhRx3TYjEnEAv/ONAOkobtad0HBelHD5twcTWh1oKKLY/l4KMmtZAo0re2i+ViLonsn/FtcjGgGDL4GNdA/QNjVC4OPP0EdHQDT7K5XLccMMNeOaZZ6Tl1NRUnHfeeVLgsLa2Fvq2RtGCSqUy9Q7prjyruAHUvm9YWJgNvgMarmYEeCLWyxUpVfUW6/Pqm7E5vxzLOXuViIhoyDMWFHcpuSr1e2yzOKS1pB7RUFJfXy9dq9maq6sr3N1b2x0QORL9sTSgw72NFpkMe3z9peeLQnzYtoOGjOkBnvh4+US8nJCNX3NKoTcCSpnMajDRpy2Y6Cstq6WAIntkE/UvpVyGBcHe+LVZiwxXN6ks8Ub/YBz38MQ7TTrEufCME5Ft2bwuwXXXXYfNmzdj3759pnVVVVVd9hNZkZ1Lsnb0+++/m56PGjVKCkASDWR/g1Wxofjb3tQu2z5MKcCyUF9pHyIiIhq6Omc9FmmckOzuKT2Xt91UJhpqvvzyS6xduxb2uG585JFHbP51iU6XISHZYvmQlw/qla33K5aHcZIKDS2eahUenx6DB6dEoVlvgLtKwXsjRHa0OMQHP2WX4qZpc03VWYQtBRWI8+GkLiKyLZvXLVAqlXj99ddx4YUXWv1AIoKId9xxB26//fZuj1FWVoYNGzaYlseMGTNg4yVqtzjUB2GuTqZlhcGA0MZ6KRtyX0k1TxQREdFQL7naqd/jVpH12PZ5VpQ5ErP4iYho+DK26KBPspywul2U5wakUngzA1snrBANNRqFXPoZ56RsIvsSf2ecFXKLwKOwJb/CVEGQiMhW7NKRWZTQefrpp6XsRlE+taCgQPoFGBERgSVLliAw0Fy+ypoDBw5g2rRppuUFCxbYYNQ03Im+jteMDsHzB9NxZnEBrs45ITVXv27mfHyUUoBZgV72HiIRERENEGNhCYwl5RbrtvoHWUxSIiKi4c2Qlgk0m/vfGQDsbOv3uCTEh33riIhoQDkpFJgT5IVN+ZatInLqmpBZ24goD9ZeJaIhHnxsJ4KNPfV17M5ZZ50lPYhs7WwvJ0zdvwMBTY2mdcuLC/GbTI5jFXUY5+PGN4WIiGgoctJAuXweincfhm99PYo1TjjWVnK1vcQR0VA0depU/PnPfz7pfnq9Hl988QVKSkpM63x9fTFu3DhERkbCzc0Ncrlc6h+Zn5+PY8eOoaioyLSv6O941VVXwcmptdLIpEmTBug7Iho4+oTjFstJHl6oUGuk5yvC/XjqiYhowC0O9e0SfGzPfmTwkYiGTfCRyNFovD2g8HQHOgQfr8k5gQ2BwfgwNR9Pz4616/iIiIhoYMh9vFB75mJc1uKKqNpa+GqbTeWM4rzdEOjSenOZaKiZOHGi9OhJeXm51DajPfA4Z84c3HbbbZg1a5YUcOzOkSNH8M477+C3336TgpLi3zfffFMKVhI5GqPBAH1CisW6HW1Zjz4aFab6e9hpZERENJzMC/KCUiaDrlOZVdH38YbYEMh6+GxGRNSf+NuGqA9E/wKvlUst1oU3NmBJSZE0gyin1hyUJCIioqFlW2El9JAhzd0De3z9TeuXsOQqDWPNzc246aabpECiCDQ++uijeP/996UAZE+Bx/bsxnXr1kkPtVqNrKwsXH/99aio6Dpbn2iwM2TmAnX1VoOPS0N9pDYeREREA81NpcSMAHOFFi9tMy7Iz8Edmzeh6utf+QYQkc0w+EjUR64TYlHqb1ky57rsE5AZjfhvagHPJxER0RC1Od+y52M79nuk4eztt9/G8eOtpSZFtuO1117b52OceeaZePzxx6XnBQUFeOGFF/p9nEQDTebhhr3jxiLfyVlaPuHqhnxnV+k5S64SEZEttV+fnF2Yh693b8Ff0o9jYk0VDIeSpEx9IqJhU3ZVp9MhKSkJR48elWa5VldXw2AwYOXKlZgyZYq9h0fUJfvR409LgPe/NK2LaKzH4tIi/KyQ45Zx4fB3VvOsERERDSF1LTrsK6nusj7G0wXhbq03momGG3HN9uWXrZ+JRU9HUXr1VF166aVSIFNkP/7000946KGHpD6QRI5C6+OFfwaPQINfOKLq6+DRopXWi2vDib78WSYiIttZEOyNpwEke3hC0WG9S109jLkFkEWG8e0goqEdfBQ9Qd577z188cUXqKur67J97NixVoOPohfIp59+Kj0/66yzcMUVV9hkvETtPKeMQ84PPgjoUBLq2uwMbPYPwmfphbhrAvvUEBERDSU7i6rQYrDsmyKw5CoNZ2LyaGFhofRc9HfUaE6v9+mCBQuk4GNjYyN27NiBs88+u59GSjTwdhdVoUFnkPoBn3AzBxtXhPlCzpKrRERkQ75Oakzyc8dhoxE5zi6IaGwwbauLPwYPBh+JaCiXXd28ebOU2fjuu+9aDTz2ZMaMGYiPj8fu3bvx6quvQqttnVFIZMvsR6dzFlmsG9lQj4VlxfjmRDFqtTq+GUREREOkh5ehoqrbkqtLQnxtPiaiwUKUSG0XENDa2+50dDxGe1CTyFH8kWv978TyMP6dICIi21sc4iNNiNnhF2ixXnv4GN8OIhq6wUeRuShK8lRVVZ3S6318fKS+IEJpaSk2btzYzyMkOrmA6RNQ7OVlse667Aw0tujw9YlinkIiIqIhQPvZ/9D8j3/jsh9/xmW5WfDWNpu2Rbg5IcqDJVdp+BItM9rV1NSc9vE6HqPjsYkGuwadHjuKKrusD3HRYJy3m13GREREw9siEXwEsN3PcoKYa2UVDMWldhoVEQ0nNg8+ijI6DzzwAIxGoymDTJTTeeONN7B9+3ZcdNFFvTpOe/BR2LBhw4CNl6g7Mrkc8jMWWKyLrq/D/LISqfRqk17Pk0dEROTAxEW5sbBEej62phprTqQgrEPJoiWhvtJnWaLhyqvDRDxRmab9Gu9UHThwwPTc29v7tI5FZEvbCyvRrDd0Wb8i3I9/J4iIyC5CXJ0Q6+WKZHdPlKotS+M3H2L2IxENweDjyy+/jKamJum5s7OzFHT897//jcWLF0tlduTy3g1pzpw5pn137tw5oGMm6k743Cko8vCwWLcqOwOVTVr8lMVZRERERI5M3+mivEytwVEPc7CF/R5puIuKirIok/q///3vtAKPhw8fNi2PGDHitMdHZAuG/CLsSc21uo0lV4mIyJ4WBnvDKJVetcx+rGPwkYiGWvBRlFn99ddfTcv/+Mc/sGiRZd+83nJ1dUVERIT0vLKykj1ByG7Zjy3L5lusi6mvxdzyUnycVgCd4fRmfxMREZH96Dv1Q9nmFyhdvAtBLmqM8XK108iIBocxY8YgMjLStPyvf/0Lhw4d6vNxTpw4gfvuu8+UOSmu9ebPt/yMTTRYNX3+Ix74/nu8Fr8HV+acgH9z62TrEe7OGOXpYu/hERHRMLagrfTqDl/L4KNbYTGMldV2GhURDRc2DT7u3r0bBkNrKZLo6Gicf/75XfbpS+mqjhe6mZmZ/TRKor6JWTgNRW7uXbIf8+uasCm/nKeTiIjIARlKymAssOzhvMU/0PR8cQhLrhIJt9xyi0XPxlWrVuGVV15BbW3tSU+QqIjzwQcf4PLLL7eYTCqOodFYlgcjGoyMNXVAVp70fFxtNW7LTEN0Xa0p65GluYmIyJ5Ge7ogwFmNw14+qFUqLba1JCTbbVxENDxY/tYZYBkZGabnIuPxdD+Iu7u7W2RVEtmDXKFA7ZI5CPrf76Z1znodfLRafJSSjxW86CQiInL4kqvlajWOepp70LHkKlGrSy+9FH/88Qe2bt0qLTc3N+PVV1/FW2+9hblz52LChAnSpFE3Nzfp+q++vh55eXlISkrC9u3bpeWOxo0bh9tvv52nlxyCPjEZHe9qNCgUiPduzTJhyVUiIrI38dlrQbA3vj5RjN0+/jijxDzZqzY+Cb6LZtl1fEQ0tNk0+NgxQBgaGnrax+vYH1Kr1Z728YhO1bils5CzaSe0RuDDiChsDgiGQSZDRXUD9hZXY3aQuT8UERERDX76w0ldSq6Kv+2Cr5MKE30tqx4QDWfr1q3DnXfeKQUT24kg5ObNm6VHb8XFxeGdd96BWq0eoJES9a/mw8csyknt8fGHVq5AjKcLRnqw5CoREdnfwmAfKfi43S/QIvjolJULY30DZK78e0VEQ6Dsakft/Tw660s2pOj12M7Dw6NfxkV0KpQKBdKvuBA3TJ+HjYEhppuTwgep+TypREREDsRQUg5jfueSq0Gm54uCfSA/zQoeREOJk5MT3njjDdx///3S875SKpW4+eab8cknn8Db25xhTDSYGRubgLQsi3U7/Fp7aq0I87PTqIiIiCxN9feAi1KO/T6+aO6QyCM3GqFPSuXpIqKhEXz08WktPyKUlJSc9vGOHz9u9dhE9rB4fBR8nbvO0o4vrcHRipP3vCEiIqLBQX/YsuRqhUqNRJZcJeqRQqGQAoiiBOs999yD6Ojok54xUQ3ntttuw6+//nrKgUsie9EfS4PcYDAta2UyKfNREK03iIiIBgO1Qo7ZgV5oUiix39tyckzdQctqL0REDlt2NSIiwvT84MGDp3Wsw4cPo6ysTHquUqkwatSo0x4f0en+Mb9iVAheSczusu3tY3l4ad6Y0+5zSkRERHYouepvLrnqoVJIs4eJyLqAgACsXr1aeoi2G8eOHUNpaSlqa2ul6jfu7u5SdqMosernx+wwclyN8UkWN1TivX3RoFRinLcrQt0YSCciosFjQbAPNuVXYLtfAOaXmxOCmorLYDQYIOuQEUlE5JDBx7lz50rBF3HRGR8fj7S0tC5Bw94GZ1555RXT8wkTJsDV1bXfx0vUVxeMDMB7yXmoa9FbrN9dXIVthZVYFMIMXSIiosHMUFoBY16RxbotfoGm5wtCfKDkxTlRr3h5eUnXgERDjbGlBUjOsFgnbugKy1lylYiIBpl5QV5S+cPdvv5IcfPALl9/qQekV2QI/sNrGyIaKmVXZ8yYIT0XAcjHHnsMWq22z8d59dVXsWPHDtPy+eef36/jJDpVbiolLoky94SC0YgVxQVYWFqEl45koUlnGZQkIiKiwZ31KEquJniZJw8tDWUpPSKi4c6QcgJKEYBsXwawy7c9+Mi/E0RENLh4alSY5OeOGpUat02bgw9GxOCEmzuOVNSiutn894yIqD/ZPKf6L3/5i+n5oUOHpB4fve3/WF5ejgcffNAi6zE4OBgXXXTRgIyV6FRcPToE3holApoa8fTReDySnIh7046hoaoW76fk86QSERE5UL9HMSO4veSqi1KOGQGedhoZERENFnXxlhNVjnp4oVKtwSRfdwS6aOw2LiIiop5Kr3amNwK7iqt40ojI8cuuCtOmTcPll1+Ozz//XFretWsXzjzzTJx77rmYN28eKisrTfvW19cjOTlZKs8qMh1/++03NDY2mrYrFAo89dRTUKvVtv42iLrloVbivnBvTNn4K1z0rZmOXi0tuDMjGc86aXBOhD8i3J15BomIiAYZQ1kFjLmFFuu2+ptLrs4L8oZGwX4oRETDmVGvh/FoapeJKsIKZj0SEdEgtSDYG+sSs7us315QibMj/O0yJiIa2mwefBQef/xxFBcXY8uWLdJyQ0MDvvjiC+nR0dq1a7s9hugN+dBDD0kBS6LBZunEaOwNDMLkAnOm44qSQmwKCMJzhzOxbv7YXvc3JSIiItuQadRQnrsMxXsOw7esHFUqFY54eZu2L2HJVSKiYc9wIheapqYu/R7F1JSlDD4SEdEgJRIhRrg7I6vWnNgj7C6uglZvgJqTLIloKAQflUolXnvtNaxbtw5vvvkmDAbRIaH3XFxc8PTTT0sZk0SDkVwmQ8A156H2xbfgrtOZ1t+begyrPH2wKb8Cy3hhSkRENKjI3N2gOmMB7oM7tCVlCG1sgF7WmumokcswJ8jL3kMksrmNGzd2mSS6bNkyXHbZZd1u7w8dvwbRYFJ9MBEd69iku7qjyNkF0/094OvEqkxERDS4sx87Bx8bdHrEl9VgdiCvdYhoCAQf20um3nPPPTj77LPxzjvv4JdffkFLh4bt1jg5OeHSSy/FLbfcgsBAcwksosEoOiwQP8+ZhSXbd5rW+WubcduJVLzk7iL9UXdVKew6RiIiIrKUVdOITHFB7uyKfGdX0/rZQV5wUfLvNg0/ubm5poo17SIiInrc3h86fg2iwcJoNMKQmNIl61FYEe5np1ERERH1Pvj4UWqBaTm8oR7zy4oR+OZBGK67EPLIUJ5KInL84GO7MWPG4LnnnsPf//53HDp0CEeOHEFJSQmqq6ulD/ZeXl7w9/eXekVOnTpVCkASOYqF5y/GkWMpmFReZlp3fmEuNgYE4Z3jubh74gi7jo+IiIgsbSkot3pKFof48lQREQ13Oj02BIchzpCPmPpaU79HhUyGJSE+9h4dERFRj8b7usNLrUSVVocnj8ZjfnmpaZvuyDGoGXwkoqEUfGzn5uaGBQsWSA+iocJNrULzReeg8Z2P4WzQm9bfn5qEWz28cO6IAER5uNh1jERERGQmSqN3Jm4qi1nCRMORq6trl6ozHh4ePW7vDx2/BtFgkdmoxUvBkUBwJIIbGzCjshwnXN0wJ8ATnhqVvYdHRETUI3FdMy/YGz9llyLHxQ3oEHxsOnwc6vNW8AwS0dALPhINVQvHj8RnEybggiOHTevCGxtwbVY6nj3kif8sjINMJrPrGImIiAgoqG9CSlV9l1MxI8AD7mp+bKbhSbS9EI9T3e5oUlJS8PPPPyM9PR3FxcXSOhFcjYmJwZ/+9CeMHj16SI6rrKwMGzZskCoR5eXloba2NavP3d0dYWFhmDhxIpYvXy5VJRrONuSZs+MLnV3wg3PrRNIV4cyOJyIix7CwLfgoyoZflZtpWq8qq4ChuBTywOH9t56I+g/vohANMBFYnH3xchzLzMS4mmrT+stzs7DFPxC/jQzEWRH8w05ERGQvhux8wEmNLdXW+48vCeVNZaKhLj8/Hw8//DD27t3bZVtiYqIUmHv99dcxe/ZsrF27FiEhIUNiXBUVFXjmmWfw008/oaXF+u/Affv24ZtvvsGTTz4pBTofeugh+PgMvxKjoi3MH7nmdhrtVHIZFrHkKhEROYiZgV5Qy2VIdvdEqVoDf22zaZv+SDLkZ/AeJRH1DzkGkaamJiQkJGDbtm3Sxc8vv/yCXbt2SbM89XpzyUoiRzPSyw3Hz1gKbYcMRwWMeCAlCa8ezkRdi86u4yMiIhrOWr7/Hc1PrcecDz7FTZlpCGlssPiwvDB4+N1kJxpODhw4gPPOO89qgK+zPXv2SPvGx8c7/LjEdfa5556L7777rtvAY0c6nQ7ff/+99JrU1FQMN2nVDcipa+qyfm6QF9xUnNdNRESOwUWpwPQATxhlMuzwC7DY1nz4mN3GRURDj90/IVdVVeGrr76SSsiIix9xQWONi4sLJk2ahAsvvBBnn3021Gq1zcdKdDoumjseXxw+hsvTU0zrYuprcUZ6Gt5I8sdfJ4/kCSYiIrIxY20dDBk50vOg2lpcW1uL4+6eKGgrpTfZzwM+TuzjRTRUZWVl4Y477kBdXZ1p3ahRo3D11VdjzJgx0rK4Tv34449NATdRknTNmjX48ssvER4e7pDjEhmPN954I8rLzWVENRoNLrjgAixcuBDBwcFSBReRebl161Yp6KjVaqX9xGtuuOEG/Pjjj/D2Hj79cK1lPQrLw/xsPhYiIqLTsSDYB7uKqrDDLxAXFuSa1ivyCmGsrIbM25MnmIgcN/NRlCx5//33sWjRIjz33HNISkrqNvAoNDQ0YPfu3XjggQekXhPbt2+36XiJ+mNmUeQFy3DC1c1i/arsDOxNTEeqlR5TRERENLD0iSnig6lpuVGuwAFvc5nVJaHMeiQayv72t79JE2Lbiay+b7/9FldeeSWmTJkiPa644gqp7OjKlStN+1VWVkqvddRxvfLKK1Kfx3aif6Q41hNPPCFdb8fFxWHcuHFYsWKFVG5VTBju2O9RvHbdunUYLgz1DTianN1lvZNCjgXBwycAS0REQ8P8YC/p38Oe3qhVKrteHxEROWrwUZR0uf3226WeFKLUal8VFxfj5ptvxquvvjog4yMaKIvD/fHz7FnoWEQ4x8UVcqMRzx46AUOHm59EREQ08PQJxy2W9/j6QatQmJbZx4uoZzk5OcjNNc+YdyRbtmzBwYMHTcujR4+WrlFVqq7ZzmKd2BYbG2vRC3HHjh0ONy5xPS6yFjt69tlnERMT0+1rxPGff/55i3XiGD1NIB5K8nbE48Xtm/D+/h1See6Yuhpp/fxgbzgrzX8ziIiIHEGAswZjvV2hl8ux28eyx6P2iOX1ERGRQwUfRYP6zZs3d1kvLnZEmZc777wTDz/8MO6//36sXr0aZ555plT2xdpsTVFmhshRiNJFly+djm/CR0j9H98aMQq3TZ2NTFd3JFbU4cfsUnsPkYiIaNgwNjbBkHLCYt12v0DT8zgfNwS6aOwwMiLHsWnTJilTbtmyZXjkkUekgFTHjLrB7L333rNYvu+++3ps7yECfaIST0fvvvuuw40rPT0dNTWtwTMhKioKs2fPPum4xD4dA5TiGOJYw0FTWw+sEQ31uDbnBK7MyZSWV4SZM+WJiIgcrfRq5+sfSXoWjPUN9hkUEQ0pNu/5uGHDBotZliIYI/o4ikzG6OjoHsu0itmfL7/8sjSTs+MMzaVLl1oNThINRhHuztCuWIibEzOQ42JZgnV9YjYWhXjDU83eUkRERANNn5QK6A2mZTExaE+Hmb9LQlhylai38vLypNKc4tHen1AEq8Rj1qxZcHd3H1QnU5Q0PXDggGk5JCQECxYsOOnr5s2bh7CwMOn7FcS1qQjCeXh4OMy4OgeH23tI9obYt2PAsbS0tE+vd0T6+kYEFxRarBM3akVbjTlBLLlKRESOSZQNf/NYLvb7+KJZLofG0HpdJDMapesk5czJ9h4iETk4m2c+iuBhO2dnZ7zxxhtSmZieAo/tQcrp06fjo48+wl//+lfTelG29bXXXhvQMRP1t2smRKLFr+ss2SqtDv856phlq4iIiByNvlNJoYPefmjo0PNkSSgzWohOVVpamnTtdscdd0jBx0suuQQvvPACdu7ceUqtN/rb1q1bLUqGigCfXH7yy2NxXdoxGChKmIpjOdK4FB1KSwvWyrl2p3MGprJTn6ihKGtfApQd2mNoZXLs9fGTJo1qFHYpJkVERHTaRnm6IMhFjSaFEvu9/Sy26Y4k8wwT0Wmz6SflzMxMpKammpb/9a9/YdGiRX0+zq233oprrrnGIpvS0DY7g8gROCkU+OukEVa3fZdZjGMVdTYfExER0XBi1GphOG5ZLnCbX4DpeYynC8LcnOwwMiLHsmLFCmly6Pz586XJpdbo9XokJibizTffxI033ogZM2bg2muvxfr166XqNiJQZmtJSUkWy1OnTu31a6dNm9bjsQb7uCIjIy2W27Mle6Pzvp2PNRTVH2otudruoLcvGpVKLA+zvFFLRETkSKSJS22lV3d0uA4S9MfTpeslIiKHCT7u2bPH9Hzs2LFYuXLlKR/rz3/+s2nWZUVFhUVQk8gRLAjxkUocdCY3GLBufwr0HWbXEhERUf8yHM8AtOaAhx7Arg4X3UtCWXKVqDdCQ0OlyaHvvPOOVOqzPdtRBM26y6jTarXSvuvWrcNVV12FmTNn4pZbbpH6FB4/flxquTHQMjIyLJZPVomnp337s++hLcYl3rPRo0eblhMSElBUVHTS44t9Dh06ZFoW5VZFWdihrKVJi7Bcy4Drdr8AeKgUmBXoabdxERER9Yf2+5K7fP2hh8y0Xq7TwZBs+ZmEiGhQBx+Li4tNz08l47Ej0btiypQpVo9N5CjunTQCGrn5j3tMbQ1eP7QHK/ftx/eZ/JkmIiKyVcnVI14+qFaZywkuZclVoj4Tk0NFIPHuu+/Gp59+KgUY27Mdx40bJ82wt6ahoQHbtm3DM888gwsuuEDqEyleP5BOnDjRJSDXW8HBwT0eyxHGdeedd5qei8zTxx57zKLca2din0ceecQiS/Wuu+7CUJe+/yic9WJ6SivxbKdvABaH+kLVi3K4REREg9lUfw+ph3GNSo0jXpYJEoacAruNi4iGBpt+Wq6qqjI9DwiwTOc+FYGBgabnlZWVp308IlsLcXXC9WPCoNbrccuJVLwRvwej6mqxtLQI8Zv2o7LZ9iWoiIiIhjqjTgd9UkqXTJZ2EW5OGOluvXwkEfWei4uLNOn0wQcfxLfffitVwhHZjldeeSVGjLDegqD9urE/A3rWdLx+FH0LfXx6n+3s7e1tkdXZn9eithrXmWeeieuvv960LIK/4n3ZsWOHlJnaTjwXvSMvv/xyaVs7EVBevnw5hrq6Q5alaxM9vVGtVmNFGHsCExGR4xMTaeYGeUnPt/oF4oinN16NjsUVMxegYNFcew+PiByc0tYXn+1qa2tP+3gdj+Hq6nraxyOyh2tGh2BHWh7OLcyDAuYSU7cmH8VbB6PxwNxxfGOIiIj6kSEtC2hstli33c88qW1JqG+3GVpEdOq8vLykoJd4tFevEYGtt956Czk5OTY7taKsa1NTk2nZyanv/V1Ff8v2LMDGxkbpmKf7e8PW43r44YelUq0vvviiFKgU5VdvuukmKYPV399f2qe0tNQiGCkCnPfeey8uu+wynKqTlXgdLBOLtS06RGRb/lyKnljeGhWm+rPkKhERDZ3SqxvyyvF9aIT0aLe9sBKRnJBJRI4SfPT1Nc8OPHLkyGkdS6/X4+jRo1aPTeRI1Ao5bp01Bq/m5eOR5ETTen9tM0Zs3YUjo8Mwyc/DrmMkIiIayiVXk9w9UaYx3+Rnv0eigWMwGJCUlITdu3dLmZAHDx60CLjZgijz2rGvpEaj6fMxOr5GHEsE+jpOtnWUcYkg4rJly/Cvf/0Lv/zyi7ROBBvz8/O77CuCxn//+99P+9r7ZC1YRPbm2LFjYW/HDh5HbIcys8IOv0AsDfWBskPrDCIiIkc2J8gLChmg79Rye3thhZQwQUTkEMHHCRMmmJ5v375dKqUTFRV1Ssf6+eefpVmY7RcnsbGx/TZOIlubHeiF76fEYW9xIWZVlpnWryzMw/N/7EPc5ct4gUtERNQPjAYD9InJFuu2+ZuzHoNdNBjjxYoaRP0pIyNDCjSKgOPevXtRU1PT4/6RkZGIi4sbsDdBTGTtSH4KvfsUCkWPx3SEcYkMybfffhvvv/++RYuU7vz222/S+yjKtd5yyy0WJV6Hotp482RnIc3NHUVOzlgR7me3MREREfU3T7VKSnqIL7X8fJZQVouq5hZ4aYb233siGiLBx6lTp8LDw0O62BQXOn/961/x7rvvSqVb+iI5ORlPPvmkaXnmzJksu0oO7y+TRuKurPGYsG87XDrcJLjy0EF8O2k0Lh1nLn1AREREp0ivh2rFAlTsT4Rrbr7UAF2U0Wu3OMSHJVeJTlNBQYEp2Cge7ZNGuxMQEIDZs2dLjzlz5iAkZGBn2XfOBGxutizD3BudszVPN+vR1uOqq6uTAojx8fEWZXEvuugi6fpavCeiXKsojbtv3z588803UoCyuroaL7/8stQjUgQu3dzc+jxGUWr3ZGVXH3/8cdhTo06HiKxsi3XbfQPg76zGJF93u42LiIhoICwM9u4SfDQA2FVUhXMiW0uxExEN6uCjUqnEqlWr8Morr0jLx44dwxVXXCGVbpk7d26vSvR88cUXeP755y36PYq+FESOLtBFg5VTR+HN0mL8Jd1cDi60qRF7ft6MspFXws9ZbdcxEhEROTqZSgXl4tl4290fW1NzMaWqAvnO5kzHxaE+dh0fkSOqqKiQMhpFwFE8srKyetzf09NTCnC1BxtF30FbX5eKrL323oinEuTr+BpR6rRzxuFgH5fo29gx8LhgwQK88MIL0nvT0bhx47BkyRLcfvvtuP/++7FlyxZp/aFDh6RjvPnmm30eY1BQUI/bB0NGZeKRNEzoFMgVvYGXh/pCzp7AREQ0xCwI9sG/Eywn3QjbCisYfCQixwg+CjfeeCO+/fZb5OXlScviwvSGG27AmDFjsGLFCqk0q5jpKmZQihIxYkZmZmam1CNS9KDo3Jx++fLlmDdvnq2/DaIBccWoYFybNRqJJYWYUGMufXRBTib+u/kgVp8zh2eeiIjoNOkMRmwtqECVWoPNAcGm9b5OKkxkRgtRn3z55Zd47LHHLHoVdubs7CxVwWkPNoqSqqdSUrQ/ubu7S0HT9oCd6LfY2+xFkV0oeil2PJYjjWvDhg0W2YejRo3C+vXre+wxKSoYrVu3DpdccglSU1OldeIYGzdulHpGDjXVByxLruY7OSPT1Q2Php9ev0siIqLBKMzNCSPdnZFZ2/Y5wmhEbF0NYnemozHhIJxvvcreQyQiB2Tz4KO4cBLlWa666irTRVV7KVXx6ItJkybhueeeG4BREtmHSi7H/VOjsLakHO8c2AW1URQ5AMR85QXbd+Lg1FhMC2JGBg2MksZmfJRSgL3FVahsboFGqYBGLoeTQg6NUg6N+Ffe+q+TstPztm1O4jWKtvVt/3Z83nmd0s43HoloeDpSXoMqra7L+kXBPsxoIeqj+vr6LoFHkbk2fvx4KdAoHpMnT4ZaPbgqeIi+kh2vR0Wp2JiYmF69VuzbUUREhEONS5RQ7Wj16tU9Bh7biX1EBuQ999xjcayhFnxs0Omxw6iEu6c3JlRXSuW5RdZjsKsT4rz7XmaWiIjIESwI8UZmSiMCmhrx6uF9CGg2VwAwFJdCHsjyq0Q0yIOPwsiRI/H555/jgQcekMq1nIoLL7wQjz76aL/01iAaTKb5e2JcbCTeLyvGrZlppvVR9XX45qvfMWnNZQzYUL+q0erwYUo+vkgvRLOhw83DFnPv0YGikAEaRWvAMtBZjUuig3BupD/7rRHRgNqcb76x39ESllwlOm2igs11110nBaREyU57Zzh2JyoqyuJaNCcnp9dBvtzcXIvl/iwba4txJSQkWCz3pgVKd/uKCkVDza6iSvzqHyQ9vLTNmFdeimMenlgR5svPqERENGQtDPbBhykFKNU4Qd5pYpn+SDLkZzD4SER9Y7crQTEL8+OPP8bTTz8tzYrtDXHhKvpNvP/++9LrTqW5PZEjuHtCJH4cEY00N8tSSSvTUvDj3mN2GxcNLU06vRR0vOjXeHyUWmAZeLQRvbF1drnItEyuqseTBzPwxIEMNOkHPvBJRMOTwWjElvzyLus9VApM9fewy5iIhhLRNuO1117DxRdfLGU93n333fjkk0+kVhqDyejRoy2WDx8+3OvXdp5AK8qWOtK4qqrM7R1ET0gfn95XVvHy8rLoydjxWENFbp0500OU5/4pOAyZru5YEe5n13ERERENpHE+bvDWqGCUybDTN8Bimz6xb9UKiYjslvnY8UJHZDCKh5ilKS6WROlVcQFTW1srBRtFbwk/Pz8pQDllypQ+XRgROSo/ZzVuGh+JZ6ur8Xr8HijQGhRSGY2I+GkDiidEI9DN2d7DJAfudfZjdgnePpaL0qYWDDY/55Qis7YBz8yORaDLyUuAERH1hrG6BsaqGiS5elj93bcgxIeVBYhOgejleOaZZ2Lfvn2orKy02Cau63777TfpIQQHB0sBSdH7UTwCAwPtds4XLlyItWvXmpb37t3b69d23nfRokUONS5RPai6ulp6rtfrpV6RTk5OvfoaWq0WLS3m36Gurq4YaoKsfP4c5ekiPYiIiIYqhUyG+cFe+F9WKbb7BeD8QnNFBWN2vnQtJfPiZE0icpDgY0fh4eHS47zzzrP3UIgGBVF+8n/ZJfi8dASuyjXPFB9bU4Uvf96B6y5bYdfxkeMR/Zg2F1Tg9aM5yO4wo3swOl5Zj1WbErF29mhM8eOHWyI6fbo9h6H7aROCXF1wp5cfNgUE45iHl2n70lBfnmaiUzBx4kSsW7dO+pwhJpLu3r0be/bswf79+9HQ0GCxb2FhodQjsL3noCgL2t4XctasWXB3t6z6MZBEeVPRDqQ9I1NkGGZkZJy0hKrYPz4+3iK7cMSIEQ41roCAAFPwUUhMTMSMGTN6Nb7OJVvFsYaaFWF++CW7FHtLWs+Rm0qBv02NZslVIiIa8hYE+0jBx8NePqhTKOGm15m26ROSoVw4067jIyLHMjgbcBARlHIZHpg8Eu9HRiPXuXWWbbVShafGTMB6uSv2FA+9Ekc0cA6UVOOmzUfx8J7UkwYeZQDmBXlhaagP5gZ5YZq/B8b7uCHG0wXhbk4IcFbDQ62U+jQOJFGK9Y5tx/B1RpF0Q5OI6HTojxyX/nWrb8Al+TlYXlxo2uailGNGgCdPMNFpkMlkGDt2LG688Ua8+eabUvDx008/lcquzpw5E2q1ustrRFDtv//9L+644w4p+HjppZfixRdflAKYzc3NA/5+iK/XkSgXezKd97nkkkscblzi/ehIvAe99dFHH1ksi/dtKF6HvTx/LF5dMA5PzRqF78+eKpWiIyIiGupmBnhCI5dBJ5djt69lj0d9Quv1FBGRw2U+ElFXk/w8sCIqCM/VxuGCgly8EjMGlerWMkDPH87EJ8snQT3AASBybClV9XjtaDb2FJtnt/dkQbA3VseFI8azdyW0RFCw2WBAs7710aQzmJal53rry6LfZPtrGvUGbMmvQL2ua59HvdGIZw9nSt/HfZNH8uediE6JobwSxjxzsFHY5mfO1pkX5D3gEyqIhhulUimVZBUPEVwUpT0PHjwoZUWK4OKxY8ekkp/txHORVSceb7zxBlatWoW//e1vAzrGq6++Gu+//z5KSkqk5R9//BELFizABRdcYHX/H374QXq0E2Vkr7rqqh6/xnfffYfXX3/dtDx37lw8/vjjdh3XypUr8fHHH5uWf/31V3zwwQfSOe+J2Efs2/lYQzWYzkkpREQ03DgrFZgR4IUdRZVS6dUVJeZrKH16Foz1DZC5sgw5EfUOg49Eg9yd4yNxWUEFnvCy7HeaW9eEj9MKcMOYMLuNjQavvLomvHEsB7/nlvdq/4m+7rhjfAQm97HEqbgx46RQSI/TsSq2EffvSu42K/P7rBJk1DTg6dmx8HfumjlBRNQTQ0KyxbKoJJDo5W1aXsKSq0QDTvQUnDdvnvQQamtrpR6FIhi5adMm5OfnW+xvi6oHYkyPPPII/vKXv5i+3kMPPYTU1FTccMMN8PdvnfFfVlYmBQPfeecdi89Ajz76qNWMzs59L9tLqLaXVbX3uKZMmYKzzjrLIpD4f//3fzhw4ACuv/56TJo0SQoeCzqdDkeOHMF7772HP/74w+I4f/rTn6R9iYiIaOhYEOItBR/3+/hBK5NDbTRI62UGI/RJaVDO5N9+IuodBh+JBjkfJxVuj4uQsr86ey85H2eG+yHE1ckuY6PBp7xJi3eP5+HbzBIpa/BkojycpZ8vkfEoblbZS6S7M95dOgH/2J+O7YWVVvc5WlGH6zclSAHICb626wlFREOn5Gq7HX4B0MtaMx1FWaE5Qebej0Q08ERJ1aNHj0pZjiKwVVRUZLfTLoJwojTsyy+/LC2LYJ8I5omHn5+fKcjX2b333ovly5c77LieeuopKeAr+j22+/3336WHCH76+vqavoa1Ergi6Pjkk0+e1vdIREREg8/8oNZJmo0KJfb7+GJeealF6VUGH4nIbsFH0aPjl19+ga2Ji6yzzz7b5l+XyBYuiArE/7JLcLyy3mK9KFn54pEsPD93DN+IYa6uRYePUwvwaVqhVMb0ZAKd1bh1XDjOjvSHwo5Bx47cVEo8OycWbx/PwzvH86zuU9bUgtu3JUn9UM8bGWjzMRKR4zHW1MKQmWOxbruf+ffHrEAvuChPL3ubiHomSqqKYKMotyoe8fHx0Gq1g+a0rVmzBgEBAVL2X329+fO2teCeq6urlJV48cUXO/S43NzcpP6Nzz//PD755BMYDObPj6JEbudM1HZyuVwqC3vfffdJQUoiIiIaWvyc1YjzdkNSZZ103WQRfDyeDqNWC9lJKj8QEQ1I8LG8vBw5OZY3eGyhrq7O5l+TyFZEcOiByVG4cXMiOueyaY+mIkHfgIkLpvINGYa0egO+PlGM95PzUKXVnXR/D7USN44JxUVRQYOyv5lcJpOCoqM9XfDPA+lo0HUNpLYYjHgq/gSSq+pxz6QRUMkH3/dBRIOHPjEFHf941isUiPc2lzJnyVWigSFKhLYHG/fv39+r6zWFQoG4uDjMnj1bKulpS5dccgnmz5+Pr776Cj///LNUKrU9ICcCbiNHjsQ555wj7RcUFDQkxuXs7IzHHnsMN954I77++mvpvTp+/DgaGxu77Dd27FjMmTNH+johISH9+j0SERHR4Cu9KoKPu339ITp0t0/VlLXoYEjOgGLiWDuPkIgcAcuuEjmIcT5uuGBkIL7NLJaWPVu0uCM9GWeUFKIqNQmNk0fD2d3N3sMkGxElVX/LKcObx3JR2NC1FFZnTgo5rhwVjGtGh0gZhp2JUl7GgmIY0rKgWDTLriVYhcWhvohwd8b9u1KQV2+9D6QIuoo+kGtnxUrliYmIelNydY+PP7RyhWlyz/xgc+9HIjp1ubm5Uv/G9oe17DxrRo0aJQUbxWPWrFlwd7dfaXURvLvzzjulh8jWbP8eRJlTERg9FRdeeCEWLFhgkXE4GMbVUWhoqFTiVTxEYFP046ypqZE+D4r3QzxEoJOIiIiGB9Ga5/WkXFSr1Ejw9MaUanN7HH1CMoOPRGSf4GNMTIw0I7I3RH+PhoYGi3Wenp4IDg6WLnBEc3sxQ1ZcyIrSLx0FBgYiKirKtCzK0RANdbePD8em/HKoa2rxZvweeLe0lqvy0jYj/cPvMeGOq+09RBpgIki4s6gKrx3NRkaN5ax0a8SN9QtGBuCmsWHwdbIsi2FsaoYh5QT0x9KkB6prpfXy2CjIgu3/OzXKwwXvLZ2Ax/elYXdxldV9DpfVYtWmBKlc61hvBt+JyJKxoRGGVMueydv8zSVXZwR4SBnhRHTqNm/eLPX+y8uzXjK9s7CwMOl6sT3g2N6/cLARQT1xzXm6xPWteAy2cXVHBBn7e8xERETkWKI9XBDsopEmu+/wC7QMPh5NgVGvh6wfJkAR0dDW73dbbrjhBunRExFQfPjhh02Bx4iICFx11VVYsWKFdDHamZjdmZaWJpWZ+fTTT6VZmGK2p+hnIWZn2jtDh8hWPNUq3Dk+Ak8dzMBxd0/MrTDXXY9JSUPhoWMInjKOb8gQlVBei1cTs3GkvDVIeDLLw3yxOi4c4W7O5uzG4jIp0GgQj4xswEp/SLFd3k3wsfntzwCDAYqxMZCPGwW578BmDImgwAvzxuD1ozn4MLXA6j4ljVrctuUoHpoajXMi/Qd0PETkWPRHU6XfWe20Mjn2+ZgDHSy5SnT6srOzeww8+vv7SxmNItAogo7WrveIiIiIaPAQ99pF9uMXGUXY4ReAuzKSTduMDU0w5hdBFhFq1zES0eBn86nezc3NuPnmm3Ho0CFpWTz/85//DHUPjWrF7M4xY8ZIj+uvv14KXG7ZsgWvvfYaKisr8Y9//MOG3wGRfZ07IgA/ZJXgpdHjMGn/DrjqRfX1VoYvfoJhbDTkThq7jpH614maBrx2NAfbC80zzXoyK8ATt4+PkDIBRSNwfVIq9EmtAUdjhfUMwo7Eflg2r8t6Y7MWhqQ0MSMEBnFDX3wgDfCFfGxMazAyZkArUHcAAPRwSURBVARk6v4vfyqyN++YEInRXq7418EMNFsJmDYbjFKPyNSqetw5IRJKOSelEJEoCWRZcnWfjy8aFa0ff0UBwYXB5t6PRNQ/PDw8MGPGDFN2oyirSkRERESOZWGIjxR8LHZyRqKHF+qUKmz3C0DQjPG4jYFHIhqMwcdXX33VFHi87bbbcO+99/bp9T4+Pli/fj1uueUW7Nq1S8qEnD9/PpYvXz5AIyYaXOQyGR6YEoVVFXV4PSoWf007ZtrmU1+PrM9/RtSqC+06RuofRQ3NUk/Hn7NLYezF/mO9XXHn+EhMD/CEbucBNCcmSz0c0aLr9deU+XpBFhokZUl2zio3pGdJgceOjCXl0IvH1r2ASikFIFuzImMg8/ft18z0FeF+iHR3xgO7U7rtc/lpeiHSquvx1KzR8NKwDyTRcCZNmDiebrFuu5+5VOFkPw/2iyXqB87Ozpg3b56pjOr48ePZH5CIiIjIwU3xc4ebSoG6Fj3unjwTxrb7O2GVTbjVyj0jIiK7Bh9F38bPPvtMeh4UFIS77rrrlI6jVCrxxBNPSGVaxQ3yDz74wO7Bx9raWhQXF0Oj0Ug9OHrK5LTVua6urpYe4o+BmIHs6+srnTtyfCID7JLoIHxpNGJZSSEmd6i9HnjwCBrnTYVzTKRdx0inTvxe+yqjCK8kZksZfScT7uaE2+MisDTUx/ThT3/4mNTP8aQUcsijI6GIGy2VURWZjN19gJT6QvakRSfd6Jdu9n/TGshszYocBfnoEZBpNP3ys//+0gl4ZG8qDpTWWN1HrL9+U6LUB1LsT0TDk/S7qMPkCz1k2OVrLs28OJRZj0T94fLLL5ceRERERDR0KOVyzAn0wh955abAo5BX34Ts2iaM8Ght8UNE1B2bRqK2bt0q9WsUROBQpTr1rJTw8HBpVm1iYiL27duHoqIiKaBpSwkJCfjqq6/w22+/oarKspTh6NGjsXLlSlxyySVStuZAn1dxHo4fP47MzEyp54oob9uZk5MTxo4di2XLluGiiy6SgpHkuG6LC8eGvHI8NzoO7x7cBU1bTytRRq7qg2/gdP/NkHm423uY1EflTVr860AGdhf3XB7Vv7kJY/VazJk3GeeN8Jc+FHakGDeq++CjpzsUcaOkfeSjoyDrZZle1VmLIR8RLt3Q1yenA3WtfXu7Yyyvgn7HAekhgpyax+6G3McLp0tkNL48f5zU/1JkOlojMiNv3nIUj02LljImiWj40R+xLLl6yMsHtSrz5LDFIQw+EhERERERdWdBiI8UfOxse2EFRniw5yMRDaLgY1ZWlul5RETEaR8vMjJSCroJ2dnZNgs+arVavPTSS3j//fdhaAv4dJaamooXXnhB2ufJJ5/E0qVLB2w8d955pzSm3mRDipK34iFK14pem6tWrWJZJAflplLi7omR+Mf+FrwXGYPVma09+ASv6mrUP/cmXFZfDXmobYPydOq2FVTgqYMZqNJ2LZOqMBowrroasytKMbeyDCPragF3VzhdvQKyToFHQR43Cvj2t7YFGeQjw6XMRhFwlIUEnlJ5DJm7K5QzJgIzJsJoMMCYVwi9yHQ8lg5DVp5I2ez+tR7ukHl7or+Ino5/mTRCymxcG58BrZUMUdEb8tF9aVIfyNXjI6TekUQ0fMjHj0ZLQyN0yRlQG43Y5h9g2hbn7YZAF/ZHJiIiIiIi6o7IfBT3UvSd7vdsK6zEtbEMPhLRIAo+lpSUmJ63tLSc9vE6Btw6HnsgiWDjPffcgw0bNlisF+VWQ0JC0NjYKI2lPShZXl6ONWvW4MUXX8Q555wDWxFZpQEBAXB3d5eCjoWFhRbZkGKcTz/9NJKTk7F27VoGIB3UWeF++D6zGF8aI7GktAixdeYylIrqWjS99A40qy6GYsIYu46Tetao0+PfCVn4LtPy95izToeFZcWYVVGGGZVlcNd1CkrW1sOYVwRZREiXY4p+i8olcyCLDIViTDRkLv1bDkMEPGURoZCLJuNnLoKxoVHKtBSlWUVAEjV1FvuL8qvdBTxbNuyAISkVioljoZg/HbI+ZMWfE+mPkR6tfSBLGq1PwvgwtQCp1fX418zR8FCz9DTRcKGcNgG/+AbiRZ8UzKooxWEvc6bjEpZcJbK577//Hm+99Zb0/LzzzsOtt97Kd4GIiIhoEBP3UETvx85tbxLLa1HZ3AJvzalXNSSioc+md2FF2c92KSkpp328jsdwdrZNnWmR8dgx8Ojq6ooHH3wQF154oanPoygB+9prr+Hzzz839W976KGHpGxPUSp2oMTFxeGMM87AvHnzEBsba9F3UqfT4fDhw3jnnXewadMm0/rvvvsOUVFRuO222wZsXDRwRDDn/slRuHbjEfxz3CS8kHAAwU2N5u3aFjS//RlU562AculcNoMehI5X1uHxfWnIqWsyrVPr9biwIAdX5WTCU9fzRA0R7JNbCz7KZFBdeCZsRQQ3FVPipIf4nWcsKIZeZESKzMgTOVCMjen2tYajqdI+howc6PYfgeaO6yBzden11x7r7Sb1gfzb3lQcLqu1us+e4mrcsCkBz80dgyiP3h+biBzb5vxyNCiV2BwQbLGe/R6JbK+yshJpaa39o0tLS/kWEBERETmABcE+FsFHUZlrQlUlSj/+Ae5jRkA5d5pdx0dEg1fXWn0DSGQGthMBPJEVeKp2794tlVptFxo68Kne4uu9++67FgHP//73v7j88sstAn2i/OsTTzwhlTVtJ7IO/+///m9AxnXZZZdJM4m/+eYbrF69GhMmTLAYj6BUKjF9+nT85z//kTI3OxKB0s49K8lxRHu64PKYYBQ4u+D2KbOQ6GHZU09mBHTf/wH9gdYSxTQ4iJIV7yXn4abNR02BR4XBgJUFufh433bcfiL1pIFHODuJdGwMNiLwKcr9qlbMh+bu6+G09gGp5Ks1UsZkVq55Oa8Iza9+AGNdfZ++pq+TGq8uGIdLogK73Sevvhk3bU7ElvxT/9tDRI6jrkWHfSXVXdbHeLog3M02k9aIiIiIiIgc2YJgb9PzM4ry8c2uLfh3wgGEH06Ebne8XcdGRIObTYOPixcvNmVe1dfX44EHHuhVr8LOiouL8eijj1oE+8aMGfiykuvWrZMyCNuJ4OK4ceO63V8EAqdMmWJaPnjwILZu3drv43rsscf69P2LcU2ePNm0LMqybty4sd/HRbZzy7hwRHs4o0qtwb2TZuDXQMtMuIPevtgZ3DU7juyjoL4Jt29NwutJuVIQUmY0YllxAT7cvwN/TTsGf625RHJnstBAKFfMh/rPN8Dp/+6H6pwlGOxkzk6Qqawn2htSM4FO/RqN+cWtAcjavgUgVXI57p8Shb9NjYJKbr3Ea4POgAf3pOLNY7kw9NCjkogc386iKrRY6QfLkqtERERERES9E+rmJN1zFCrUGouJ8sbsfBirLEuyEhHZJfgoshNFALLdjh07cN1115nK7/TGtm3bcMUVVyAvL8+0Tix310usv9TV1eG3334zLXt4eODKK6/s8TVyubxLL5Ovv/4ag8G5555rsSx6P5LjclEqsH5hHMZ6u6JFLsfTsePx5sjWTLNsF1f8fdwkPLQ3DT9k2aY3KlknypH+klOKazYk4Ei5uTzos4kH8VhyIkI7lMztSD4uBqorVsLpiXvh9ODtUK1cDkV0JGQKhcOfatELUrXqYsDd1WK9saAEza++D2On3pG9cf7IQPxnYRz8nLrvPfDO8TypT6TIjCKioam7LOclIb42HwsREREREZEjl14VDnv5oE5hOblcn8h7ykQ0CIKP7Vl6ok9iu0OHDuH888/HzTffjM8++wwJCQkoKyuTsvFEdqTIcty3bx/efvttXHzxxbjllltQUFBgev3o0aNx0003Dfi4RdCzpcU8s+Pss8+26GHZnUWLFsHX13yTa/v27VIJVnuLjIy0WK6oqLDbWKh/iCbP6xfEYWaAp6h7iU8iovBo3GT8bfwU1ClVEMU5nzqYgQ9T8qUgGNlWjVaHx/al4R/701Gv01ts2+Xrb/U18tgoaP56MzSrr5Fq6Mu8PDDUyDRqKKdNgOaemyDz9rTYZiwsRfMrIgBpvY9jTyb4uuP9pRMx3set2322F1ZKZW+za60HfYnI8Yi/b/q0TDQ2a7GrqGtJ+Qg3J0S1zdolIiIiIiKik1sQ0lp6VSeXY3ene1j6BAYficg663XwBjj78c0335QyAkVwUdDr9VJQTjz6YsSIEXjrrbe69DccCJ3HNnv27F69TqFQYNq0afj999+l5YaGBhw4cADz5s2DPXUMpAodA8LkuFxVCrwwdwz+eSAdG/LKscOva/+79UdzUNncgrsmRELW2CT1DRzozOHh7mBpNf65Px3FjdbLTP8YHI7Lc7MQ1Nza+1EWGQrVymVQjI7CcCH384H67uuhfeUDGCvMAQNjcRma170PzV2rIPPsW/DV31ktZUA+dziz26zfrNpG3Lg5Ea8visMoT/4eJHJ0xpwC6feIzskJf/b0wTa/wNaL47a/c4tDffg3j4iIiIiIqA/GebvBR6NCRXMLdvgFYEVJoWmbIS0LxoZGyFw4yZOI7Jz5KEyfPh2ffPIJ4uLiTvkYZ5xxhnQM0e/RFjqXhp00aVKvX9uxv6K1Y9nD8ePHLZZt0TOTbEOtkOOJmaNwSVTXwGO7T9IK8ezOJDS98BZavvwJRr1lJh71jxaDAa8mZuOObcfgVFYB/7bgYmcB7s6QnbUIsmB/qG++App7bx5Wgcd2cl9vKQAp8/WyWG8sKZcCkKfSR0D8fxA9IB+YPBKKboLsdS16PLo3Dc16kR9MRI5Mf6T1842yqQlnFxfg9hOpFtuXhrLkKhERERERUV/IZTIsCG7Nftzn4wetrENIwWCAPsnyuouISPrdYa/TIIJdX375JZ588kmMHz++V68RPRRFGVOR7fjKK69YlDMdaCdOnDA9V6lUCA4O7vVrw8PDuz2WPYiM0y+++MK0LDJHzzrrLLuOifqXCLLcN3kkbhkXZn27wYD5v28CSiug33EA2tc/lmYpUf/JrGmQSnr+cSQdDyYn4r0DO3FjZteJB+dG+uOjZZMQuWQWNA/eDsXEMcM6K0fu4wX13TdA5tf6obadsbQCzeveg6Gyus/HFOfz4uggrF84TipP3F0G5BtJOac8biIaJCVXjxyzWLfNL8CU9RjkosYYL2Y4ExERERER9VV78LFRocR+H8t78iy9SkSDouxq55Kkl156qfTIzc3F4cOHpYy8yspK1NXVSTeMPTw84O/vLwUoRQahLQOO7crLy00lYoXAwEApENpbISEhFsvZ2dmwF/G93HPPPVIvzXZ33nknfHxaGwfT0CH+/9w8NhzeapVUdrJjl8e705Mxrcrc59OQcgLNL74N9a1XQR7ArJDTvfn99Yli/Hd/Mi7LysC5hXlQtfXYPKO4AJ+Gj0SOqxs8VAo8NDUay8J4vjuTe3tCc/cNrf0eS80/p8aySmjXvQf1XddLQcq+muLngQ+WTsADu1OQXGX+nd4xI3hxqC8m+rr3+dhEZH/GolKL3xnCdn9zFYDFIb7DenIH0alKT09HQkKC9DwmJgYTJ07kySQiIiIaZmYEeEKjkEtVo3b4BmBeealpm+F4OozaFsjU1id8E9HwZNfgY+fsQPFYuXIlBpva2lqLZS+vvt309vT0tFgWgdWBlJGRIfWWbNfU1ITCwkKp1+RPP/1k8fWvvfZa3HbbbQM6HrIvkfHlpVHh8X1p0LUFwY54eePsonyojQbL0pYvvgX1TZdDMWqkHUfsuMqbtHhh1zFEHzyC9/Kz4WSwLOOpAHBTVjp+XLwIj8+IRoCzxm5jHexkXh7Q3H09mkUPyJJy03pjeRVaPvga6r/ceEpBhEAXDd5YHIdbtyQhpVMAUvzveOJAOv67bCKclOLdIiJHLLnarljjhBQ3c69Y0e+RiPpux44dWLt2rfT8uuuu6xJ8TE5Olq4z2qvbiBYb1ohrvcWLF0vPR40axbeCiIiIyIGI+yQzAzyxvbASu3wDoEeSdJ9Lom2BITlDquZFRDTogo+DWcdAnuDk5NSn13fev/Px+tv999+PpKSkHvcRWaRr1qyRytiejqKioh63iyxWsj+RXeeuUuDBPSlo0BmwKSAYhU7OeOroIfi0aM07NjRBu/4jqC77E5Rzp9lzyA5nV3Yxjn2/EfdknoCbXmd1nwaFAgFRYXh5/hgo+pA9PVzJPDsEIIvLWle6u0J11fmnlb3kpFDgHzNicN3GBLQYOuYEA7l1TVh/NAd/ncwAPJGjBx+3dyi56qNRMauZaIDs2bPHIjjZXfBx2bJl0oOIiIiIHNPCYG8p+FitViPR0xuTq833ffWJyQw+EpEFBh97oXOwUKPRDOrg48mEhoZiwYIFiI2NPe1jnSx4Kfpjjh079rS/Dp2+mYFeeG1hHO7ZeRyVzToc9/DC6qmz8X9HDyGmvkN2r8GAls/+JwV7lOevgIxBsh41NjVj61cbMOHQEUzpGMjtQDTi3jxyJCZcdhamhvjzx7kPZB7u0Nx1PZrXfwBjXQM0d66CPNDvtM9hlIcLbh0XLgUaO/sio0jKkJrmb5m1TkSDl6GsAsZ8ywlR2/w6llz1kfohExERERER0amZF+wNcVUlpnFv9wu0DD4eTYFRr4dMwUpSRNSKqTe9oFRaxmj1en1vXtbt/qLX5UASvVji4uJMjxEjRsDFxcW0PT8/H6+88gqWL1+OdevWwdCpNCQNXWO93fDmovEIdmkNoJc4OeOuKTOxw7drQEy3eTe0b30KY2OTHUY6+IkPVLkbd6Ps7//Gon37LTNI2+ghww/BYfjs8otw9l1XI5qBx1Mi83CD5s7rWwOPwQHoL1eNCkGcj5vVbf86kIH6lr79rieiwZP1WKlS46int2mZJVeJTp1arTY91+msV3cgIiIioqHP10ltuo+yQ1Sa6ai+EYaMrhO8iWj4YuZjL3QM3AnNzc19Osmd93d1dcVAevbZZ7usEwFG0Y/lm2++wWeffYaWlhbpsX79euTm5uK55547pa+1devWk5Zdffzxx0/p2DQwItyd8dbi8fjzjmPIqGlEo0KJx+Km4JbMVFyVm2WxryEpDc3/fhfqW6+E3Nd8E3e4a8nMRcV7X8GvqrrbfTYEBOG70WNwy6LJuDywb31iqSuZu6v06E9KuQx/nx6DazccQXOn8quFDc14JTEbD02N4ttB5AAMnYKP4kLY0Jbp6KFSYJq/ufcjEfWNh4eHxSRGIiIiIhq+FgT74GhFHYqdnJHi5oHYuhrTNn3CcShGs40NEbVi5uMpBB8bGxt787Ju9+98PFuQy+UYN24cHn30USn46O1tDiT98MMP+PLLL0/puEFBQT0+AgL6L0uJ+o+/sxqvLxpv6n9llMnwZlQsno4dj5ZOZemMhSVofuEt6E9w9pJQWN+Mfx7Lh0u1+cNVR7t8/HHTtDnYsWwJXjh/HmYz8DjgjEYjWn7fDkNRaZ9fG+nujNvHR1jd9m1mMfYUV/XDCIloIBmra2DIyuva77HNghAfKFlCnOiURUdHW/R3zMjI4NkkIiIiGqYWhpjvKXfOfjQcT7fDiIhosGLmYy90DNQJJSUlfTrJxcXFPR7P1saPHy9lR95yyy2mdf/5z39wySWXQMZ+SMOGh1qJV+aPxd/2pmJnUWuA5degUOQ7ueDJpEPw1LWYd65rgPaVD6Becy0Uo0ZguPotpxTPHs5EXYsRE4JCcV6h+Wb3YU9vvDVyFE74+OLeSSOwckQA/z/ZKPCo+2kTdL9vh27rnlMqzXp5TDC2FFTgcFmH3qdtnjqYgU+WT4K7mn8uiQYrfUKyxXKdQol4L1/T8pIQHzuMimjoGDVqFPz8/FBWViZVdBHXDEuXLkVYWJjUnuLw4cOmfY8cOSK1d+gPkyZNwsKFC/vlWERERETUP0a6OyPUVYP8+mYp+HhFbib2+PjjQFAwHrn6DJ5mIjLh3dReEGVSRRZfUVGRtFxRUYGmpiY4OTn15uUoLCzsdvawvYgLedEbMj093VRCSTwXNxdo+HBSKvDsnFj8X/wJ/JTdmjWW6OWN1VNnY+3ReIxoqDftKwsLgjwyFMOBsalZ6h9mLCmDauVy5NQ24s1jufgjr9y0z4eR0TizuACZrm54a8QoHPT2RZyPOz6cESOVtiXb0P2yRQo8Smrr0bzufWjuvA7y0KBeH0Muk+GxaTG4esMRNOkte+CWNGrxUkIWHp8e099DJ6IB6ve429cfurZMRxelHDOZgU50WkSA8brrrsOLL74oLTc0NODHH3+0uq8IPopHfxBfk8FHIiIiosFFJK6I0qufpRci08UNF8xdipa266+V9VpMc+E9MSJqxbKrvdQ5YJiWltbbl3bZdzAEH9szIDvKzs6221jIfkQpusemRePqUcGmdYXOLrhjyizs827NHCnROOGFqTPQolBgqDLq9dAfTYH2/a/Q9MhzaPn4O7Rs2Il/bDyEy34/bBF4FMo0TlKQdvWU2Tjk44ebxoXjzcVxDDza8j1r0cGQ3Kn0W30Dml/9AIY8y0kfJxPm5oS7JkRa3SYC8zsKK09nqEQ0QIz1DTCkW/Yr3uYXaHo+N8gbGgU/7hKdrptuugnLli3jiSQiIiIiLAhuq+onk5kCj8L2At47ISIz3o3ppQkTJlgsx8fH9/alOHjwYI/HsheNRmOxbDBYZvzQ8Jq1dPfEEbizQ++7eqUKD0+Yis/CRuBv46fgh4pG3LPzOOpb9BhKJTsNmbnQfvkTmh59Ado3P4U+/ijQopO2y4xGuB5NhrGb12e6uiPE1QlvLB6PW8eFs6eYjclUSqhvvwbyqHDLDfWNaH71QxhyC/p0vIuiAjHD39Pqtv+Lz0B1c4dSxEQ0KIhJIzCYf0s3yeXY79Oh5GooS64S9Vf24/r166XWDTNnzux1BRgiIiIiGnom+7nDXdU1QWF7YYV0r42ISGDZ1V4SfU1ef/110/KWLVuwatWqk76uvLwcR48eNS1HREQMmtKmotRqR/7+/nYbCw0O18aGwkujwtr4DOiNgF4mx+vRsabtB0prsGZbEl6aNxY+TippnfhQ4Wi9Qg0l5dAfSJAexrKeZ2WtKC7EV2HW+1z+KdJf6u/opuKvUnuROTtBvfoaaN/4GIaMHPOGhtYApOaOayGPCO11+dVHp0fjyj+OoEFnGWQvb2rB80cy8a+Zo/v7WyCi0yDz94Vi2gTUHjkOF50O+3z80KRo/Z2skcukzEci6h/i8975558vPcSkxdraWqkVxZdffmnq8yj6Qd5111391vqCiIiIiAZnFTVxrfVbbpnF+rz6ZmTWNiLKw8VuYyOiwYN3zHtp4sSJCAwMRHFxsbS8e/duqUxpZKT1Mn3tvv76a7S0mLNlVqxYgcGgsrIS+/btMy2rVCqpByTRyhEB8FQr8ejeVDR3yCZpl1xVj1u3HsW6+WMRrJJD+5//QrlwFhRT4gb1yTPW1kEfnwSdCDhmWwberdHJZNjn7Yc/AoNFhFUqJdHOQ6XAQ1OjsSzMnF1D9iNz0rQFID+xLL/Y2ITm9R9Cs+ZayCPDenWsIBcN7pkYiafiT3TZ9ntuOZaElmNpKN93osFCERWBPH9/XO0ShCmV5ahTtk6MEWYFesFFOXTLhRPZk1wuh6enp/Rwc3MzrXdxcUFQUO/7LhMRERGR45Ze7Rx8FLYXVjL4SEQSBh/7MNP3xhtvxNq1a03ZXk899RTefPPNbl8jApVvvfWWaVmtVuPaa689aTZiRUWFaTkgIEAKenansbERzs59a+QrZio/8cQT0Gq1pnXz5s2Du7t7n45DQ9fCEB+8vGAc7tuVjDorZVZz65pw6+ZEfJifBueMHGgzciCPPwp5kL+UhSLz94Hc3wdwdRkUWZHaL36EftdBi9J83Tnq4YU/AoKxJSAI1Sq1xTaVXIazIvykEqsBzpZli8m+ZBo11KuvkkrnGlIzzRsam9G8/iOpPKtiZKfyrD0E4DcXVGBXUVWXbc/En8BkXw9T5i8R2d/m/Aqpz8g+X8sKDks4UYCIiIiIiGhAzAnygkImg75DmVW1Xo/Kg0ehPbAfqnOXQubpwbNPNIwx+NgHV111FT744AMUFLT2Edu6dSueeeYZPPDAA10CLKLc6po1a1BTU2Nad/XVVyM4OLjHr/Haa6/hq6++Mi2vXr0a99xzT7f7X3PNNVLg8LLLLkNY2Mkze4qKiqTA48aNG03rFAoF/vznP5/0tTS8TPHzwOuL4vCXHcdR1tS1193K5ONwzjFnhxmOHJceFpw1UjBS7ucjBSRbH63LcLNdYFLm5tpj4DHX2UUKOG4IDEGBc9fSEN4aJS6OCpJ6Avo6WQYkafCQqdVQ33oltG99BkNKh8zFpmZoX2sLQEZFnPw4Mhn+NlWUXz2M2k7B9yqtDs8cOoGnZ48eFIF1IhLBx/Iup0FcBM8PZslVIlsYP368NElTmD59Ok86ERER0TAgWhBN8/fAvpJqafn+lKNYWlIEZ4Me4k6KaIGjXDDD3sMkIjti8LEPRObi888/j+uvv96UNfjuu+9i7969uPzyyzFy5EgpE/Hw4cP47LPPLDIY4+LicPfdd/f7G1hfX4833nhDekyYMAFTp07FmDFj4OfnJ2Uy6vV6qR/LiRMnsH//fuzYscOiDKxw//33Y9y4cf0+NnJ8ozxd8dbi8bhr+3Hk1TeZNxiN8GoxZ852q7EZxpwC6HNaA/YdaR5eA1lwQJf17Y2p+xLYEa8xFpdJmZfW1E4YA+dft1qsq1SpsTEgCH8EhCDF3cOirGq7aA9nXDEqBGeG+0GjkPd6PGTnAOQtV0L79mcwJGeYNzRrWwOQq6+GIsZ6D8+O/J3V+OvkkfjH/vQu27YUVOD3vHLp54KI7KuwvlkqB97ZdH8PeKj5MZfIFkTAkUFHIiIiouFZerU9+Kg2GKTAYzt9wnEGH4mGOd6V6aNp06ZJ2Y4PPvigKQCZlJSExx9/vNvXjBgxAv/5z3+kHigDKTExUXr0lhjPQw89JAVOiboT4uokBSD/svM4Utpv8MpkeHHUOOS4uOL2jFQocPJypp3JfK1npBgysqXMtfbSrTIpa9J6KVdDeSX0BxKhF30ci8ugeezu1n3apFXX49O0QvyeW4aX3T0RVV+L7X6BUpbjQW9f6OXWA4pzAr1w1ahgzAjwZHabA5KpVVDfcgW073wBw7E08wZtC7Svfwz1bVdBMWrkSY9zVriflFG1taCyy7bnD53AND8P+DkzE5bInrYUdM16FFhylYiIiIiIaGCJajMvHMmSnu/wC8CKkkLTNkNaFowNjZC59K1dGBENHTYPPq5btw6///679FxkAp5xxhmD4lh9cc455yAmJgYPP/wwjh492u1+SqUSl1xyiRSoHKjAo+gHmZWVZcoW6w03NzecffbZuOOOO05aBpZIEP3tXls4Dg/uTsGB0rZSwjIZvgobgW1+gZhbXorQxgaENTYgvKkBQY0NUPbwMynz9pACRNYYSyuAxqZuMybbS7mKr2/MzrfYpD+YCJy5UOrV92lagXmsANaOmYAytQaNSuu/9kRm4zkR/rg8JggjPQZ2ogANPJlKBfVNl0P73hcwHE01b9DrpTKsvTqGTIYHp0ThcNkRVGt1FttqWvRYG38Cz8+NZYCayA4MuQWQBYoJAuYqE+3E9JSFISy5SkRERERENNAJCzGeLkivbsA+Hz9oZXKojYbWjQYD9EmpUM6YxDeBaJiyefCxuLgYaWmtmSjV1dWD5lh9NXr0aHz99ddS1qMIgGZnZ6O0tBQajQYBAQFSmVURpPT19e3TcUNDQ6XXdgwu9kT0oKyrq0NycrL0EIFIcS5EqVXxrwhKimCjl5eXFDAV5VVnzZoljZOor7XcX5o3Fn/fn4ZNHW72ljg547tQyz56CqMBAU1NpoBk+78RzQ0IbGhEkcYFh9ILEeHujBHuzghwVkPels0oBR97UcrVmqpd8VgDd+TUdw0u5bq4Wn2Nn5MKl0YH4cKRgfDUWA+IkmOSqZRQ33gZtO99CUNiCiCXQ339pVBMGNPrY4genyIA+be9HQKYbXYUVeKn7FKcO6Jr+WAiGjhGvR7N6z+CsUWHiz294e8XiJ1+AWhUtH6sneznzv68RERERERENiq9KoKP4nrsgLcv5laUmrbpE5IZfCQaxlh29TSJQGHHYOHpWrNmjfToCxFcZK8VsgW1Qo4nZ43Gc4cy8W1mcbf76WVyFDq7SI8DnbaJwKSLTofatrIM7VmHEW5OiHR3xrUn8hB5iuOrbdKirrIGUJ88uB7r5YorYoKxItwXqm7Kr5LjkylbA5AtH30DxaRxUEwa2+djLAvzxfJ8X2zI61re8cUjWVJ53kAXTuggshVRnhuifI/IcCwrkR6Xz1poCj4uDunbxC8i6tmXX36JV155xean6dJLL8Vdd91l869LRERERL23MNgH7yW3Vibb7hdgEXw0HE+HUdvSbfUzIhraGHwkoj5RSKUoR0qlWN85ntfnsycCk7Uqyz55zXoD0qobpMcR/whEuvqaMiajtI0Ib2qEb309FIa20g0d1ChV2OwfhD8Cg3HUw0sqx9odWduMrCtHBWOKnwfLZQ4TMoVCyng8HQ9MHon40hpUNLdYrK/X6fHUwQy8PH8sf56IbER/5LjFcoqbB4qdzH1EloSae/8S0emrr6+XKs7YWk2NuXw+EREREQ1OY7xdpapiZU0t2OUbAD2SoGjfqG2BISWjTxWoiGjocOjgY3OzubQiy4gS2Y7ohXfruHCEuTrh9aQcFDdq++3YpU7O0sNaxqR/U5MpKOmua8EJV3epprzuJJmLzgo5Vo4IwGUxQQh3Y6Nr6jtRkvfhqVG4f3dKl217S6rxbWYJLorquUw2EZ0+o+gbkpBssU70Hm4X5+3GTGQiIiIiIiIbEW2U5gd747vMElSr1Uj09Mbk6krTdnH9xuAj0fDk0MHHvLw8i9KjRGRb50T6S2VLRW337NpGZNU2Iqe2Cdl1jcitbUSzwdhvX0tkTBY5u0iPzoHJ7gQ6q6V+jheMDIS72qF/3dEAMlRUQb/3MJRnLeoxe3FhiA/OifDHzznmEiLt1iVkYXagp9RsnYgGjjE7H6iutVi33d/cd3Uxsx6J+l14eDgWL15s8zM7atQom39NIiIiIuq7BcE+UvBR2O4XaBl8TEyBUa+XqlIR0fDisHfjU1JSkJCQYFqOjDzVLnFEdDpEv8Sx3m7SoyOD0YiihmZTMFIEJ6VHXRNK+zFT0po4HzdcGRMsld5Tsp8j9ZRBtX0/Wv63QSoFIvPzPmkj9HsnjcD+kiqUNlmWX23UG/CvAxlYv3CcNOuPiAaGPsGy5GqWiytyXMx/fxaHsOQqUX9btmyZ9CAiIiIismZ6gAecFHI06Q3Y6ReAuzI6VKtpaIQhOQOKuNE8eUTDzIAFH/fs2YP09PQu6zMyMiz26Vg69WQMBgPq6uqkY2zatAl6vV5a7+npiREjRvTTyImoP4gAjMgCE4/Z8LLY1qDTI6dDMFL8Ky3XNUn9H0/p67VlvFw1KgQTfN35JtJJad//CobDx0zLLV//CsWYaMjcu8+kFxm0j0yLxl92WpZ9FOLLavBlRhEujwnm2ScaAEajsUu/RzGrtl2Mhwsi3Flam4iIiIiIyJacFArMCvTE1oJKFDk545i7J8bVVpu2i2pTDD4SDT8DFnz83//+h6+++qrHfX788UfpcbouuOACKJi6TeQwXJQKjPF2kx6dsyVLGrWmYKRUxrUtONldX0lXpQLnjwyQyquy5CX1hXL6RGg7BB/FbDwRgFRff0mPr5sT5I3zRwTg+6zWkiIdrT+agzmBXgyAEA0AY0ExjGXm8j2d+z2KbHciIiIiIiKyT+lVEXwUfg0KsQw+itKrDY2QuXCyKNFw4rBlV9vFxsbi7rvvtvcwiKifsiWDXDTSY1agZbZko8iW7JAlKTIkI92dpWxHN5XD/yojO1BMHAPFlDjoDyWZ1unjj0I/bfxJm6HfPTESe0uqUNRgGRQXP5dPHEjHG4vHQ8Hyq0T9qnPWY5HGCWlu5kx39nskIiIiIiKyj3lB3hBNaIwANvsH4c70ZKiNxraLOb10v0U5fwbfHqJhRFQqdDhKpRJjx47Ffffdhy+++AJubt2XyCOiocFZqUCslyvOCPfDzePCcceESJw7IoCBRzotqovPBjrNvNN+8ROMjU09vk4EvB+bFmN1W2JFHT5JLeA7QzTA/R6lrMe2IH+4mxOiPVx4zomIiIiIiOzAx0llaoNUq1Jjl2+AxXZ9ygm+L0TDzIClC91555245ppruqxfv349/vjjD+n5HXfcgRUrVvT6mKK0qqurK/z9/aFWq/t1vERENPzIPNyguuhMtPz3O/PK6lq0fP8H1Fes7PG10wM8cUlUIL46Udxl25vHcjEv2BtRDIYQ9QtDSTmMBZaljrf7dyi5GuIDGbONiYiIiIiI7GZBsDcSymul578FhWBmZRm2+AchbPFMzJw9ge8M0TAzYMHH4OBg6dGZp6enxT4ig5GIiMheFDMmQX8gEYbkDNM6/a6DreVXR43s8bV3TojEnuIq5NU3W6zXGoxS+dW3F0+AUt6amUVE/VdytUKlRpKHuTz3klBfnl4iIiIiIiI7Bx/XH82Rnu/z8cNFcxajSaHECoUzZskdsgAjEZ0GmzdKCwwMxKhRo7oEIomIiOxBZEuprliJ5v9bD2hbTOtbPvsf5A+uhqyHTHtRDvix6TFYvTVJ6mvQ0fHKenyUmo8bxoQN4OiJhmfJ1R1+ATC0ZToGOqsx1tvVTiMjou7U1dUhOTkZVVVVqKmpQXOz5USdk4mNjcXUqVN5gomIiIgcxAh3Z4S5OiGvvgl6mRx6RWvAcVthJepadGydRDTM2Dz4ePfdd0sPIiKiwULu4wXVecvR8tUvpnXG0groftkC1fln9PjayX4euHJUMD5JK+yy7e1jeVLT9dFeDIwQnSpDZTWM2fld+z22WRzKkqtEg4UINH7zzTf49ttvkZ6eDoPBcMrHuu666xh8JCIiInKwyd3i+uy/qQUW65v1BmzKK8d5I83XcUQ09DHfmYiISJRfnT8D8pHhFudCt2k3DDmWQQ9rbosLl2b4daYztpZfbTmNm69Ew57RCMW86ah3cpJORa1SicNePqbTwpKrRIPDhg0bcNZZZ+GZZ55BamrqaQUeiYiIiMgxnRPhb3X9TzmlNh8LEdkXg49ERERihp5cDtWV5wEKhfl8GI3QfvI9jDpdj+fISSHKr0Zb/aOaVt2Ad4/n8RwTneqHVR8vKC77E65bsBR3Tp6Jf8eMha6tX4i3RoWJvu48t0R29vPPP+POO+9EZWWlvYdCRERERHYU7emCWCvVnw6X1SK/rkl6bjR2blxDREORzcuunoxWq0VLi7nnVm9pNBoolYPu2yEiIgciD/KH8qxF0P20ybTOWFAC3cadUJ25qMfXjvdxx7WxofggpWumpFi3MMQHY73dBmTcREPdkbIalGv1KPf0xlFPb9P6xSE+ULT1fiQi+8jLy8NDDz1kcRNp2rRpWLlyJaKjo7F582a8++670vpzzz0X119/vVSeNSUlBVu2bMH+/fulbZ6ennj44YcxZswYadnHx5zhTERERESO40+R/kipqrdY56prQepvO+CXmwPFnKlQzppst/ERkW3YNVpXVFSEH374AQcOHJAuPsvLy08p8Cg8+eSTuPTSS/t9jERENLwol8+D/lASjAXFbSsUgErVq9fePDYMOwsrkV7TYLFebwT+uT8dHyybCE1bw3Ui6r3N+RVW14t+IkRkX2+88Qaam5tNfX4ee+wxXH311abtx44dMz0XAcUJEyZIzxcsWICbb75ZuhZ84IEHkJ+fj3/+859Yv3495s2bZ4fvhIiIiIj6wxnhfng5IRv6tslpl+Vm4aasNGgMBkiF+eVyBh+JhgG73AFtaGjAU089haVLl+KFF17A1q1bpUDkqQYeiYiI+otMoYD6qvPEHVTIoyOgeeh2qJbO7dVr1Qo5Hp8RYzUTK7O2EW8dy+UbRdRHBqMRWwq6Bh/dVQpM9/fg+SSyI9HX8ZdffjEtr1q1yiLw2BvTp0/HZ599htDQUDQ2NuKuu+5Cbi7/XhIRERE5KtEeY16Ql2m52MlJCjy2M6RnwVDOcv1EQ53cHoHHm266CR9++CH0er2tvzwREdFJySNCof7LjVDfdT3kAX59OmOit8GNY0Ktbvs4tQCJ5bV8B4j64HhlHUoatV3WLwj2gbKt9yMR2cfx48dRW9v6d02lUuG22247peMEBARIk1OF+vp6vPTSS/06TiIiIiKyfenVdrt9/VHbqV2a/kAC3xKiIc7mZVdffPFFxMfHW6wTF6pxcXEYMWIE3N3dpeW+io2N7cdREhHRcKcYGX7Kr71+TCi2FVZ26XEg5vn980A6/rtsIpxEOVci6paYCStz0nRbcnUJS64S2V3HDEVRTvVkfRp7mnw6Z84c6ZpOtOPYsGGDFNQU14ZERERE5HjmBXvDU61EtVYHrVyBTf7BOL/Q/NlRv+8IlGcslMr2E9HQZNPgY0VFBT799FPTsggyrlmzRirN4+npacuhEBERDRiRjfX36TFYtSkBLYbWHgftcuua8FpSDu6dNJLvAFEPWr7/A4aE45jp7YtqnwBs9wtApVojbXNWyDEr0FzGh4jso7q62vRcTCS1Rtlhlnt7b8juzJo1Swo+iv127dqFM888sx9HS0RERES2opLLpd6PX2YUScu/BYVYBB+NpRUwZOZCERXBN4VoiLJprapt27ZBp9OZlv/9739LwUcGHomIyJEYm3q+eSpEe7rglnHWsyc/Ty9CfKn5hi0Rdfo/pm2B4ViaaPiICeVluDftGOaUl1rMotUoWHKVyN6MRvMEG2dnZ6v7uLi4WExG7Ym/v7k8V2FhYb+MkYiIiIjsX3r1mLsncpzNnwvbsx+JaOiy6V2bzMxMi7I6y5cvt+WXJyIiOu2AiMjGanriZRhr6k66/9WjQhDn42Z12zOHMqHrlBVJRK0MyRmAtsV0OkShxp2+AaZlllwlGhw6TiKtq7P+d9HDw8P0vKCgoMfjdZyoWlZW1i9jJCIiIiL7GOPlipHubRPUZDL8FhhqsV1/6Kh0n4WIhia5vcryiOAjERGRo9CnZaH5mf9At3EnUNeAlq9+PulrlHKZVH5VI+/awyCrthE/ZJUM0GiJHJs+4bjFcqKnN6rVaum5Wi7DnEBvO42MiDqKjo42PS8qai2p1dnIkeYy4+np6WhsbOz2JKalpZmeq9v+zxMRERGRYxL9HDtmP/4eGAxDxx0am6E/mmKPoRHRUAs+enubbxS5u7vb8ksTERGdFv3OA1JPAtPy4WNdAiTWRLo7Y3Wc9R4Gbx3LRX2LyOkionbGFh30iZYXoNv8Ak3PRa9HV5WCJ4xokAQfXV1dpedJSUkWZVg79oJsL70qMht/++03q8cqLy/Hli1bTMvBwcEDNm4iIiIiso2zIvxNAYhSJ2cc8vKx2K7fd5hvBdEQZdPgY1hYWK/7fRAREQ0mqovPAlws+1lpv/gJxobuMzjaXRYTbC410kFFcwv+m5rfr+MkcnQGMfO1scli3XY/llwlGowUCgUWL15sKrt6+PBhq/ssXbrUtPzss89aZDgKtbW1uOeee9DQ0GBaN23atAEdOxERERENPH9nNWYGmkv1/xpkWXrVcDwDxupavhVEQ5BNg4/iwlSlUknP4+PjbfmliYiITovM3a01ANlRTZ3UA7I35VfvmhBpddvHaYUoaWzmu0PURrfnkMW5OOzpLc2QFRQyGeYHs+Qq0WByzjnnmJ7/8MMPVve58sorLTIcL7zwQqxevRpr167Fgw8+iOXLl2Pv3r2mfWbMmIGoqKgBHjkRERER2cI5kQEWE0sbFB0q2RiN0B1I4BtBNATZNPjo6+uLM844Q3q+Z88epKSwpjMRETkOxfSJkI+LsVin3x0PfcqJk752bpAXZvibZ/u1a9Yb8Oax3H4dJ5GjMlRWw5CcbrHu5w4zY6f5e8BT3TqRjYgGB5HVGB4eLj3/9ttvUVpa2mWf6dOn4/zzzzctt7S0YPPmzXj//ffx3XffoaqqyrRNo9Hg0UcftdHoiYiIiGigLQrxhquyNeDYpFBia4e2GoJ+3xGr5fuJyLHZNPgoiJmtfn5+0Ov1uO+++1BdXW3rIRAREZ1ys3TVZecCGrXF+pbP/gejVnvS1941MRIyK9t+zCpFWnU93xUa9qR+Hx2uOesVCmzzN1+YLgn1HfbniGiwkcvlePLJJ/HXv/4Va9asQVFRkdX9xD7Lli3r8ViiN+Qrr7yCMWPGDNBoiYiIiMjWnBQKLA8zX8v91qn0qrGwBMa8Qr4xREOMzYOPgYGBePvtt6UAZGpqKi655BLs3r3b1sMgIiI6JXIfL6hWLrdYZyyvhO7nzSd9bayXK86O8O+yXsRaXknI5jtCw5rRYIB+j2W/uI0BwdLMWEHWNmOWiAaf2bNn49Zbb5UeEyZMsLqPWq3Ga6+9hpdffhkLFy6Em5ubaXJOWFgYrr32Wvz6669YtGiRjUdPRERERAPtnEjzvZAjnt4o0jiZlmVB/jA2NPFNIBpiWu/m2FBCQgJyc3Nx8803S7Nac3JycP311yMmJgZz5szByJEj4e7uDkXH2s+9MHHiRFO5HyIiooGkmD8d+vhEGE6Yy6XqNu+BYkoc5JFhPb52dVw4NuaVodlgWVJkb0k19hRVYXaQ14CNm2gwM6RnS4H87kquTvJzh6+TZdYxETmes846S3oIzc3NUCqVfb72IyIiIiLHMsnXHWGuGuTVN8Mok+Gb0EgENTXij6BQvHj5Evg481qPaKixefDx888/x1dffdVlfXp6uvQ4VaKMD4OPRERkCzK5HKorz0fzM/8BdPrWlUYjtJ/8AM39t0Km7P7Pa6CLBleMCsEHKfldtr2SmI0ZgZ5QyKwVZyUa2vR74i2WM13ckOxu7pO6JIQlV4mGGtHfkYiIiIiGPlHt4pzIALx5rHUS9xfhI0zbfssrx5Wjgu04OiIaEmVXiYiIhgJ5oB+UZy3u0qdA98eOk772utgQeGu6BijTaxrwU3Zpv46TyBEYGxqhP3LcYt1PwaHiCtW0vDjUxw4jIyIiIiIiov5wdoSf1fU/ZZfwBBMNQQw+EhERnSLlsrmQhQZZrNP9vg2Gwp4/OLuplLh5rPVS4W8m5aCxPZuSaJjQHzwKtOhMyy0yGf4ICDEtj/N2RZALM6SIiIiIiIgcVYirE6b6e3RZn1bdgNSqeruMiYiGUNnVG2+8EX/605/6/bjR0dH9fkwiIqKeyBQKqK86D80vvAW093DUG9Dy/R/QrL66x9deMDIAn6cXIqfOsql6aVMLPkkrxE1je+4dSTSUKCaOgbGpGWVb98Grpga7fANQrTb3/Dgz3N+u4yMiIiIiIqLT96cIf8SX1nRZ/3N2KUZ7ufIUEw0hNg8+iiAhA4VERDRUyMNDoFw6D7oNreVWFVPHQ3XxWSd9nVIux50TIvHA7pQu2z5K+X/27gO8qbLtA/g/Sfeku6yWUvbeCiKggCAoCO5XUZygIu7t6x6ve+Defm4BFRmioIiyRPZeBVoKXVAo3W3Gd91PSZqTppO2SdP/77pykXNycvJ4Ek/Pc+7nue/DKjgZ4ceC69Q86EKDYTp3MKYUeKPd0WMo9DLYXvPVS20QBh+JmpL09HTs2LEDWVlZyMvLQ3Fxca330bt3b5x11lkN0j4iIiIico1zWkfgpU0HUGQya9YvPnQUM3rGQX/0OPTREfx6iDxAowcfiYiIPI3X2OEwpxyG1/AzYejZucbvG9YyDH0jg7HxaK5mfaHJjA93pOLBfu0boLVE7umP1GzkGs3Y2iJMs35U20iE+PCSlcjdFRUV4YsvvsDcuXNx4MCB097fNddcw+AjERERkYcJ9DbgnNbh+CXlqG1daEkJzk1NRs7z/yAgIwu+D98GfSwHoBI1daz5SEREdJp0Pt7wnXFtrQKP6n06HWb2bOf0tXkHMrD/ZAG/G2o2fjyQ4XT9pISYRm8LEdXO3r17MWHCBLz88sv1EngkIiIiIs81Pj66fMFiwfsbVuP2pF0q8ChMaze5rnFEVG8YfCQiInKhbuFBOK9txZQikoDkra3JLmkTUWPbl5OPLce0M4BFh5AA9AgP4hdC5OYpVqdOnYrkZP7NIiIiIqLq9Y8KQYz/qTIzOh3+jIrVvG78dwssZm1aViJPtedEPt7emqxKMB0vLoUnYQ4rIiIiF7ulexyWHc5GqdmiWb8y/QTWZeZgQHSoy9pG1Bh+3O981uNF7WPUDGEicl8y2/Ho0fK0WV5eXhg1ahRGjBiBxMREhIaGwtfXt9b7DQwMrOeWEhEREZE70Ot0OD8uCp/tPqyWf41thStSD5ZvkJML8+79MHTt4LpGEjWCNRkncOeKnbDeDZydlI5Pz+2JCL9Twfkmzi2Cj0ajEdu3b8e2bduQnZ2NnJwcmM1mXHjhhejbt6+rm0dERFRnltx8mDZsVfUgK9Mq0A+XJcbiq71pFV57c2syPju3p7o4J/Ik5uwTMP31D4wDe2vqfVj5GfQ4Py7SJW0joprJy8vD4sWLbcutW7fG+++/j44dO/IQEhEREVGlxsWXBx8PBAZjd1AIOuedtL1uWruZwUfyaEazBc9vSLIFHkVGYQnmH8zC1C6t4QlcGnzMzMzEp59+iu+//151XB117drVafDx119/xTfffKOejx07FldccUWjtJeIiKimLBYLTOu3onTuYiC/ALrQEBj6dKt0+6ld2mB+chZOlhg163efyMfilKPqwpzIk5j+2QTjH6uBP1bjxeBQ/NA6Dr/HtLK9fl7bSAR5u8U4OSKqxIYNG1BaWpYaSK/X4+2332bgkYiIiIiqFR/sr0psbMsuiwn8GtNKG3zcshOWwiLo/P14NMkj/Z2WjeO5hQgzGXHcpzxTTJHJBE/hspqPy5YtUzMbP/nkE6eBx6oMHDhQdXRXr16Nt956CyUlJQ3WTiIioroo/eonlP7fDyrwKErmLIKloLDS7UN8vHBDlzZOX3tve4pHXXwQSf0OCT5adc/NQe+c45oDMykhhgeKyM3JYFIrGTQqg0eJiIiIiGpivN0g69+jW8Jon/Gp1AjTph08kOSxvk9Kx5iMw/huzXI8vHMLup48odb3DA+Gp3BJ8FFmLt5yyy04caLsgNZWeHg4xowZo55nZWXh999/r+cWEhERnR59l0TtipN5KP3ptyrfc3FiDNoEVqyLJWkXvtuXzq+EPIZ570FYsrXXgQtjy4PvnVsEomsY670RuTsplWEl9R09jZQHkUdzbpcM9C0uLm6UzyIiIqLmZVSbSPjoywKOOT4+WBOuzfhkWls+YJXIk+zNyceGzBxMOpwCH4sF52Wm4d2N/+C+lH0YHNsCnqLRc1kdPHgQ999/v0pHJ3Q6nUqdetFFF6Fbt2547bXX8MMPP1S7Hwk+/vzzz+r50qVLcf755zd424mIiGrK0L+nSrtq3r7Xts60ZiNM/XvA0Nn5DVpvvR639ojHw//sqfDaZ7sOY0K7aIT5evNLoCbPtGaDZjkpMAi7g0M0sx7lGpGI3FtMTIzTQGRTlZubi19++QULFy5EUlISjh07ptZHRESgQ4cOGD9+vOq7BgcHe2y7ZP/z5s3DmjVrsG/fPuTn56v1gYGBahBwXFwc+vfvrx79+vWDj4/PaX8mERERNU+SAersluH4/XDZtc3i2FYYeqw8s4Y5KQXmo9nQR4a7sJVE9W/2vnT0O5GNhIKya22rFr06Q+9B90IaPfj4xhtvoKioSD339/dXy8OHD7e9LrVCamLw4MFqW+nkrly5ssHaS0REVBcSOPG+7AIUP/c2UFyeHrz0m/nQP3QrdL7Ob9ad2zocPcODsPVU3QOrAqMJH+9Mxb19EviFUJMm6YdNm3dq1v0S21r+p1HPA7z0qt4jEbm/zp072/pkhw4dQlO2fPlyPPLIIyqzjiNZJw8p+yH91+eeew7Dhg3zqHZJCl15vwQ5nZEgpDzke7b2v6XG56hRo+r0eURERERiXHyULfgoMx9zvLwRaiyrKS5MazdDP+4cHizyGDklpVh86Cj+ezhFsz4lIBBnDO0LT9KoaVclzerixYtty0888YQm8FgbMvJSRl2K48ePIy0trd7aSUREVB/0YaHwnjBas05STZYu/KPKoOXMXu2cvvbD/gwk51ZeN5KoKTCt2woYy2uYlup0WBLTyrY8pm0UAr0NLmodEdVGbGwshgwZop5v2LAB2dnZTfIAfvfdd7j55psrBPgMBoN62JNtbrrpJsyePdtj2rV+/XpMmDDBaeBRrkv8/Pzq0HoiIiKi6p0Z0wLhpzI8GfV6VfvRnunfzbB4QIYNIqufD2QiLD8fg+1m+Yr9vboj2MOynTVq8FFGZFrT8UhNkIkTJ1bYpjYptuLj423PDxw4UE+tJCIiqj+Gs/pDn1g2WMbKtHwNzIcqHzTTKyJYzYB0ZLJY8Pa2ZH491KQZHVKuroiMRo53+UzgSe3L0zgSkfubNm2a6sOVlpbilVdeQVOzatUqPPXUU5p10k/96aefsGXLFvWQNKRSJsTek08+qVKTNvV2bdq0CTfccIMa0GvVo0cPPP300/jjjz+wbds2bN68GRs3bsQXX3yhtg0LC6uH/0IiIiIiwEuvw9i48sw3v8aWD0wVlmMnYN6vnSFG1FSZLBbM3Z+BiYdTYD+UMN9gQJfRg+FpGjX4KPUjrGTG4+nW8rGvaSGzKomIiNyNTq+H95UTAC+7ywoLUPrDYlv9Y2du7REHLyd/J5cfOY6NR082VHOJGpQE3S2p6RVTrp7SPSwInVsE8lsgakIGDRqEO++8Uz2fM2cOXn/99SZT/9FoNOLxxx9X/1rdf//9ePHFF9G1a1d4eXmpR5cuXfDCCy/goYcesm0nwVbJ5GMymZpsu6SW5O23347CwrKsCtI/l33J7MnLLrsMrVu3Vp8jAgIC1Hct7ZBUsHfffbfKRkRERERUH6lXrXYHheBAgPYaQ1KvEnmCFUeOIzu3AOPTUzXrNya0R0KU5w3wa/S0q1bSkTld9vUhS0rK62kRERG5E310JLzGaGswmZOSYd60o9L3tA3yx8WJzmeAvbnlIMxVBC6J3JXxn42a5UxfP6wLKx/lehFnPRI1SdOnT1d1Cb29vfHuu+/i8ssvVyk88/K09YvdjQTZUlJSNANkZWZfZaZOnYpzzz1Xk33nhx9+aLLtksCl1Hq0+u9//6v2Zd/PdsbX11fNeB082PNGZxMREVHj6xgaiE6hAWULOh1+jdHGDUwbt8PCe//kAb5LSsOozDSE2A0yFKHnnglPVDaM0QUqm+1Rm9mQ9qlhQkJC6qVdREREDcHrnCEwrd6oaj5alc77DfrunaDzcZ7T/YYubbAwOQt5pdrZCzuO52Np6jGc17Y8aEPk7iylpTCt26JZtzimFcynrv0CvQwY3SbCRa0jososWrQIn332WY0OkPTJjh07plKCymxICWK1atUKoaGhthl0NTVu3DgVCGtI3377rWb5jjvuqPY9so2kI7X65ptvcOmllza5du3atUvNVLWSQOJVV11V5zYTERERnY7x8dHYs+Wger4kpiVuOrBHpaU8Gh2JlsMG8eBSk5eUU4D1mTn46LC2nNKmyGgM6t4enqhRg4/h4eX1q+xHWNbVzp07ne6biIjI3UiA0XviaJR8Otu2zpKdA+OyVfAeM9zpe0J9vXFdlzaYtbVincd3tqVgRKtw+BgaNYkBUZ2ZtuwCCooqTbkqqXb87dMTE5FbkH6b1PyrC0m/mpqaqh611bt3bzQkaZME4KwknWn37t2rfZ+kOpXttm/frpbl37S0NLRs2bJJtUsCnPYDgmfOnFkv7SciIiKqizFxkXhza7KqiXfM1w+vdOqObSEtYIqOwNyz+552+TYiV/s+KQ29co6jQ742O0z+mf1g8NDfd6PesYyLi7M9X79+/Wnta9OmTTh69Kh6Lul9OnbseNrtIyIiakj6Pt2g7xCvWWdcsgKWE5XXcLw0MRYtA3wrrE8rKMbsJG3tPCJ3ZlqjTbm6vkU40vxPpdYBMCnBeZphIqKGIHUL7dUmhajjtn/++WeTapfUeJw/f75tuX379ujXr1+t20pERERUX8J8vTEktoVteVHLNkgJDMLh/GJsOpbLA01N2skSI35JOQqDxYJ9gcG29Yf9AzBoWH94qkad+ThkyBA1SkFGWG7YsAF79+6tEDSs6SiGWbNm2Z737NmTxe6JiMjtyd8478ljUfzSB5J/vGxlSSlMW3fD6+yBTt/ja9Djlh5xeGzt3gqvfborFRe0i0JoJWlbidyFOfsEzHv2VzrrsVdEMBKtNT6IyK2EhYW5ZKBnVFRUg+5/9+7dmuW+ffvW+L2O2zruy93bJTNZ7etxnnXWWU63Ky4uVgN9q6sBSURERFQfxsdH4e+08jJrVouSs9A3kiXXqOn6+WAmik1mbAyLwI39B6PnyROYdDgFuvZx6ODnuff0Gj3t6sCBA7F27VoVgJSC9v/3f/8HHx+fWu3nrbfewooVK2zLEydObIDWEhER1T99m5YwDO4H06r10MW1UsFIQ/vyzADOSB28b/ceUbUe7eWWmvDpzsO4s3c7flXk1swHUwG5eW0yq+U8gxf+iiyf6chZj0TuS/pantjfSkpK0izHx2szE1SlXbt2Ve7L3dslNTmdBS0LCgqwcOFC/Pzzz9izZw9OnDihAo8tWrRAr169MGzYMEyaNAkBARwsQkRERPXvrNgwhPh4qVli9pamHsM9vdvBj2U6qAkyWSyYY5+5TKfD1tAw9fhmdMOWmnC1Rh/CeOedd9qeb9y4EdOmTatx/cdjx47hgQce0Mx6lBoWkydPbpC2EhERNQTv8efC+6qJ8L37xmoDj0Kv02FmT+cBRkm9mpqnraNH5G68+vVA6WN34r0OXbA/MAhLYlqixFBW3zHE24Bz27B2NxE1ruRkbT3lVq1a1fi9jnUUDx482KTatW3bNs1yQkIC/v33X0yYMAGPPvqoGiwsgUdr3c7s7GyVwvWpp57CqFGjNClbiYiIiOqLj0GP89pEVFhfYDRh+ZFsHmhqklakHVelkxwNiApB+xDPHtTX6MHH/v374/LLL7ctr1q1CmPGjFGzIBcvXozjx8unVufn52PXrl2qcyNBx5EjR+Knn36yvW4wGPDss8/WeuYkERGRK+mCA+F1Rl/oapHGrG9UCIa1DKuw3mix4J1tKfXcQqL6t+hoPr5tHY/r+w/Bu4mdbevHxUfD71QgkoiosZw8WV5vWfqTwcHltVeqExgYCD8/P9tybm5uk2qX4+BfmSF53XXX4dChQ5r19vuyHxB87733qmxERERERPVtfHy00/ULD2bCfOAQjBu0g6iI3N3sfXazHu1c1kE7cNATNWraVavHHnsMGRkZavSkNb3L999/rx72nn/++SrrZj344IOV1qcgIiLyNDN6xmNl+nGYTpWLtPr98DFsPZaLnhE1v0FJ1Jgk3f6PBzLKFnQ6lOjKg40XJTjvXBIRNRSTyaTqGVr5+vrWeh8SmCsqKss8UFhYqGYInm5txMZql2NQUvrnpaWl6vmAAQNUdqJBgwapfck+/vnnH7z77rvYtGmT7T2Sjah9+/YYN25crduYnu78BoyV/YBkIiIial66hgWiXbA/DuYWquXQ0hJccCQVY9ceRnFhARDgB0PPLtB5uySsQVQr+08W4N+snArrWwb4YqiTCQaexiX/l3p5eeGdd97Bm2++iQ8++EB1iGpDakz873//UzMmiYiImov4YH9clBCDuftPBXHsvLk1GR8M764G5xC5m01Hc22dR3v9IkOQ4OFpRojI/UhArboZftVxDAzKgNqgoKAm0S7H4KNsI6644go88cQTmmsJf39/jBgxQtV7fOihhzSZiJ588kn1Wm1rQA4fPrzK1729vdG1a9da7ZOIiIg8g1yHjI+PwtunMjz5G4246eDe8g0KimDevgeGPt1c10iiWsx6jMvPQ7CxFNtDWqjB2OKSxFgYmsH9u0ZPu2qfMvWuu+7Cjz/+qGpLSAejOtL5mjJlikrPysAjERF5GovZDOOq9TAfq3zE/41d2yLASZH1LcdysYw1EMhN2WY9OriofUyjt4WIyNns7MZ4T2N8Rk3e42zwb48ePdQMyMoGMcnsyaeffhodO3a0rZO6kLNnz651G4mIiIiqMjYu0ha0SPcPwKZQ7Qwx49rybAxE7upkiRGLUrIwJWU/3t60Fu9vWIOx6YcRBAsubBeF5sDl85O7dOmCl156CY8//jg2btyIzZs3qxoUOTk5quPUokULREVFqVqR/fr1q9PoTyIiIndnSkpG6dxfYElNh753V/jeUF4f2V64nzeu7dwK727X1mUSb29Nwdktw+B9mmnfiOqDugFuNCHHbMEfh49VeL2FjxfOaRXOg01Ejc5xpp59qtOasn+PBOxqO/vPle2S2pBZWVmadbfccosaIFwVqUF5880347777rOtW7p0Ka699tpatXH58uXVpl2VQCgRERE1T9H+vhgYHYp/MsvSVf4a0wp9csoHaZt37IMlNw+64NPLOkHUkBYczERAYSFGZJWVHOicdxIP7t6G5SG+CPWpfiKeJ3B58NFKUsGcffbZ6kFERNScGP/ZhNKvytOYmTfvhGnvARg6Jjjd/ooOLVXq1czCEs361Pwi/LA/A5c3g6LV5P4sqWkofuv/kNqhPdp5h2JvcIjmdUml42NgoJyIGp/M4rOvjWj9tzbs3yOpSU+33mNjtkuCj/YkC5GkVa2JkSNHqn1aZ0/K4OGSkhIVmKyp2NjYKl+vSVYkIiIi8mzSX7QGH5dHxeKOfTvhZ83eYDbDtH4bvEac6dpGElXCZLFgzv50XHjkELztMpMU6/XoOGpwszluvONDRETkYoaenYFAf8260h8WqzSszvh5GXBL9zinr320MxW5JcYGaSdRbRhXbwQKi5C4dQc+3LAaz27boHld6pcSEblKaGio7XlpaalKIVpTJ0+e1MwwtN9XU2iXZBey1759+xoHDyVwGRdXfg0in5ednV3jNhIRERHVxPBW4baSMwVeXvg7Utt/ZOpVcmer0o4jI7cQE9K0Wcs2xcUjsWUkmgsGH4mIiFxMF+AP73HnatZZDmfAtFobrHGsgdApNMBpTvnPdx9ukHYS1ZSlpBSm9Vs16/YGlc98HBgVirhgbcCdiKgxJSRoswukpaXV+L1Hjhypcl/u3i4JNlYVjKyO4/a1CZASERER1YQMuh7VJsK2vDimleZ1KVljPpLBg0lu6fukdAw7moGIEm3GsoBzzkBz0ujBx7/++gtJSUmN/bFERERuzTCkH3QtozXrShf8AUtBodPt9TodZvZq5/S17/al4Uh+7VO1EdUX05adatajlczhXRzb2rY8qT1nPRKRayUmJmqW9+/fX+P3HjhwoMp9uXu7OnToULFGby2YTCbNcn2knCUiIiJyNC4+yvZ8Y1gEsnx8tdckazfxoJHbOXCyAGszczD5cIpm/c6wcPTr3RnNSaP3En799VeMGzcOl1xyCb766iuOkiQiIpLZjwYDvCeP1R6L/AIYf/2r0uMjBdiHxFacrVBituC97drUDkSNybRmo2Z5Q4sIpPuVzXQM9/XGsFZh/EKIyKW6du2qWd64UXveqsqGDRuq3Je7t6t79+6a5dqmTXXcPiyM53QiIiKqf30igtE6sCzgaNbp8JvD7Efjv1tgcRgUReRqs5PS0Sk3Bz1OarODnBjUF156HZoTlw1R3Lp1K5566ikMHToUM2fOxLJly2A0skYVERE1X4bO7aHv1UWzzrj8H5gzjlb6ntt7xjv9Y/7roaPYeTyvAVpJVDXzseMw79HOvlnUsnzW44XtouHNWTJE5GLnnHOOZsbe33//XeP3rly50vZc9jFixIgm1a4ePXogNjZWM2MyPz+/Rp9x/PhxHD5cnt49JCQEERHlKdGIiIiI6otOp8O4uPLZj7/GaoOPyM2HeRczLJL7yCs1YlFyVoVZj1m+vuh/7kA0Ny7Pj1JaWqpmQ06fPh3Dhw/H//73P+zatcvVzSIiInIJ74nnAYayouqK2YzSn36tdPv2IQEqmOPMm1uSa51Kjai+Zz3menlhRWTZb1TG+F2U4Pz3SkTUmCIjI9G7d2/b8sGDB7F27dpq37d+/XpNGZF+/frVa/CtMdolN/JGjx6tSaO6ZMmSGrVv0aJFmuWBAwcy7SoRERE1SurVlIAg7AgO1bxuWruZR5/cxvyDWfApLMK5mema9Xu6dUWovx+am0YPPl5++eW44IIL4OdX8WAfPXoUn376KSZOnIhJkybh888/r3UKGCIioqZMHxUOr3PO1Kwzb98L0469lb7n5m5t4W+o+Cd9w9GT+DvteIO0k8gZi9lcoe7G0uiWKNGXBdTPiAlFq8Dmd8FNRO5pypQpmuVXXnkFZrNUqXVOXnv55Zer3IejkpISnDx50vYoKChwi3ZdffXV8Pb2ti2/++67KCqqul50Xl4ePvzwQ8066bcTERERNRTpP/aNDLYt/+qQetW0dRcsBYX8AsjlzBYL5iSlYXx6Knws5dfuJTodEs4bguao0YOPvXr1Up0nSQnz7LPPqpGSMvLS0Y4dO/Dcc89h2LBhuPXWW7F06VI1S5KIiMjTeZ03DAgJ0qwr/fHXSmsZRPr74OpODulHTnlrazKMVdywJKpP5t37YTl+UrNuYWwb2/PJCeVp/oiIXG3cuHGauoibNm1SfVBnWQNknWTpsa+rKOlLx4wZU+VnfP3116rPa33ce++9btGudu3a4bLLLtPMsLzrrrtUsNSZwsJC3HHHHUhLS7Ot69KlC0aNGlXtfw8RERHR6RgXX54954/oWBXMsTGaYNq4nQeYXG5V+gmk5RVi4pFDmvWb28YhsXXzzADlsrSrQUFBuOSSS/Dll1+qFC+333474uLiKmwnAcfff/8dt912G84++2w888wz2L6dJxQiIvJcOj9feF8wUrPOknEUpr//rfQ9V3VqhUi/8hkMVsl5RZh3ILNB2klUXcrVPUHB2Bccop5H+XnjrJZhPGhE5DZkEKwMiA0ICLCt++KLL3DNNdeowa+pqanqIc9lnWTmsZL3yHudDaRtKu2SYGPHjh1ty3/88YfKUvTtt9+qFK6ShUj+/eqrrzB+/HisWLFC8zmvvvpqg/z3ExEREdkb2ToCfqeyPeV6+2B1hDaQY9qwjQeMXO77fWk462gmYoq12UT8RpyB5soLbqBt27aYMWOGeqxbtw7z5s3DL7/8gtzc3ArF7aXTJY9OnTqpFC8XXnghoqLKcz8TERF5AsOg3jD+vRaWQ+UzDEp/+ROGAT2hCwqssL2/lwHTurXFsxv2V3jtw52HMCYuEkHebvFnnzyUJb8Api3aut2/xLa2PZfapF563qQmIvfSvXt3lbJUZvVZM+1IjcWq6ixKutLXXntNzfxryu0KDg5WaVSvuuoqHD58WK1LTk7G448/XuX7QkJC8OabbyIxMbFW/01EREREdRHobcCI1uFYnHJULS+OaYXhRzOwMTQMYUMHoMvwATyw5FIHTxbin8wcvH44RbN+T2gY+vUrz2jS3Lhs5mNlBgwYgKefflqlZZWRlJJ21WAoqxNkb8+ePXjhhRcwYsQITJs2TS0TERF5Cp1eD++Lz9euLCyCcdX6St8zvl00EkP8K6w/XmzEl3uONEQziWxM67YAdqmBS3R6LIluZbvgnJgQw6NFREhJScGaNWvUrL3Vq1ermXVV1TNsDCNHjsTs2bPVANfqyDZz5sxR/VBPaFfLli3V4N+a1m4cOnQovv/+ewwePLhWn0NERER0OsbHlU8+WhseiSvOGIa7+gzCF6FRKnsUkSvNTkpDaEkJOuRpJ9NlD+rTrAdhu+0UCF9fX5XaRR5ZWVn4+eef8dNPP1UIMhqNRvz555+q1kRNOmVERERNhaF9HAz9e8C0fpuqAel94SgYBvaqfHudDrf3jMedK7Wzz8TXe45gUkIMYgJ4UU71T2qOGVdrU67+FRWNPO+yVMBDYsMQy98eUbOVkZGBTz75RPXpJJWns5IcUufwxhtvRHx8vEvaKDUWpb8pAdGFCxdi3759yMwsS1seHR2NDh06qL6pBN2cDY6tjI+Pj5phaOXv7+8W7bIn7ZO6kbfccgsWLFiAf//9V31nJ06cUK9JpiEZJCzB0F69Kr8OISIiImoo/aNDEe3vg8zCEpj0eqT7lV1T/ZWWjZMlRoT4uG2YgzxcXqkRi1KyUODjg0sHD8fojDRMOpyCEGMp+p7bfFOuCp3FWdV6N7Zjxw78+OOPquN17Ngx23qpBXnppZe6tG1UkXxH06dPV8/fe+89RERE8DAREdWC+XgOTCvXwWvU0BqN5pM/6zNX7MTazJwKr42Li8LjAzvw+FO9M6ccQfHLH2jW3dNrANaHlf3df2VIFwxlvUeiZmnZsmW4//77cfLkyWq39fPzUyk/J0+e3ChtI/fEPiQRERE58862FHy+uyxVvL0H+iZgcvtYHjRyiW/3puG1LQe1Ky0W/CcqEHcM792svxW3S7tandjYWFUjknUeiYioOdCHhcL7gpE1TiOiOzX70VlSBxmJtfWYNgUEUb3w8UZJ3x4o0pddWqb5+WNDi3D1PMbfB4NjW/BAEzVDGzZswO23364JPEqGm3bt2qkZfe3bt0dAQIDttaKiIjz88MMqJSsRERERkb3x8eWpV+0tTM7igSKXMFssmJ2UXvEFnQ7n9+bg/yYxH7mkpESlVpV0M3/99RdKS0td3SQiIiK31alFIMbFRzm9AH9x4358NrKXStFKVF/0sVH47owz8G1ANM7NTEepXg/Lqd+Y1Hrk742oeXrqqadU302v1+OSSy5RmWp69Oihlu3t3bsXP/zwA7744gu1vbxv2LBhKmUpEREREZGID/ZH9/AgbM/O0xyQbdl5SM4tVK8TNabV6SeQml9UYX2fyGB1b665c+vg45YtW1TAUVKsSr2JyiQkJCAxMbFR20ZEROTOpndviz9Sj6HQZNas35NTgB/2Z+DSRKYkofpjNFvw84EM5Ht5Y36rtrb1Bh0woV00DzVRM7Rnzx7s3LlTzch/8803MXr06Eq37dixIx544AGMGjUK1113nao3KDUOhw8f3qhtJiIiIiL3Nj4uqkLwUWexYOOqzWiZlQZ9t47w6tfDZe2j5sXprEcAlyW2bPS2uCO3Cz5KR3PevHkq6JiUlFTpdiEhITj//PMxadIk9O3bt1HbSERE5A5M+w5CHxUBXWhwhdei/X1xQ9c2eGtbSoXX3tuegpGtIxDu591ILSVPtzL9OLKKKmamkDqPUf6cuUTUlBUWFsLfv/ajyHft2qX+HTJkSJWBR3v9+/fHhAkTMHv2bBW4ZPCRiIiIiOyNahuh6uuVmi1qeXTGEdx4YC9iiotgklJ7J04y+EiNQmbbrs44gUBjqRqIbRXt74PhrcrK0DR3Xu7SoV2yZIkKOq5atQpms3aWhpXBYFCd18mTJ2PkyJGqXggREVFzYz52HMZ5S2DatAOGM/rA56qLnG53RceWWJCchYO5hZr1eaUmvLUtGY8NYP55qh8/7s9wun5SQgwPMVET9/3336uMNA8++CCiopzX2XEmL69sRHqbNm1q9XlxcXHq39xc1igmIiIiIq1QH2+c3TIMfxzOVsulOr0KPFqZ9xyAOfsE9OEteOioQc1JSkd8fh4+XL8af0TH4sfWcdgdHIqL28fCS89SRy4NPlosFqxbtw4//vgjFi9ejPz8/CrT8Fx00UVqFGx0NFN3ERFR82Xasgsln88BSo1ly/9sgnnoQOjjW1fY1luvx719EjDj7x0VXpN6kBPbRaN3ZEijtJs815H8IqzJqJgev2WAL86IYYePqKmTftuCBQvw559/4o477sBVV12lBoVWJywsTP27ffv2Wn3e1q1bNe8nIiIiIrI3Pj7aFnxcFRmFXC8vBBvL7pEI07+boR/D9P3UcPJKjViQnImbjqTAx2LG2Iwj6rE6Igr9LhjAQ3+KHo3s8OHDmDVrlqrncfXVV2Pu3LlOA48tWrRQHds5c+aozu6NN97IwCMRETV7+natAb32z3fpD4vVzWFnBkaHYlSbCKevvbTpgKrVR1QXFrMZxrWbsXDPYTj7FV2UEAO9jqP9iJo6/am/OTKT8dlnn8XFF1+MTZs2Vfs+a2mMbdu24e23367RZ0m61d9++03zfiIiIiIie2fGhCLMtyzNZYnegGVRsZrXTWs3V3qPhKg+LErOgq6oGGPTj2jW61pGo8Wp3ya5IPj4zjvv4K233kJqamqF17y8vHDOOefgzTffxN9//43HHnsMPXv25PdERER0ii4kGF5jhmmOh/nAIZg2bKv0GN3RKx7+hop/8vfmFOCH/c6LYxNVx7wrCaVf/oiL/+9r3Ld7G7rnHJcpUuo1g06HC9vVPD0jEbmvK664AnfddZet7qPUYpR1jz76KI4fP17p+2JjY1XfTkj/Tt4jA0v379+P4uJitb6kpAQpKSmYP38+rrvuOrVP0alTJwYfiYiIiMgpL70eY9tG2pYXx2gzQVmysmHevodHjxqE2WLB90npKvDob5ZKo2XkWdsxQ3nU3a3mY5cuXTBp0iRceOGFiIhwPjuDiIiIyngNPxOmVethOVp+01dqQBp6dIbO16fCYYr298WN3dpi1tbkCq+9t/0QRraJQIRfxfcRVcW4eoP6199kwvj0w+iQl4tp/QerdcNbhfE3ReQhfHx8MH36dFxwwQVq5uMff/yhRpLLLMUlS5bg3nvvxSWXXAKdk5nOjzzyiEqjevToUWzcuFE9rGR7ZyPSJcj5zDPP2GZcEhERERE5GhcfhW/2pannO0JCkeIfiLjC8uyKpfN/h75bR+h4TUn17J+ME0jNLcTzR1I067e1ao0z41vyeNtxWY9OgozXXnstfvrpJ8ybNw9Tp05l4JGIiKgGdN5e8L5ojGad5cRJGH9fWel7rugQi4Tgslkr9vKNJry1VXvBRFQdS24+zNt2a9Ytii0fbTq5vTbtDRE1fW3atMG7776rHq1bl/3/fuLECTVbUWY1yoxIR23btsX//d//qZmMjpwFHlu2bImPPvoIvXv3bqD/CiIiIiLyBJ1aBKJjaEDZgk6HL+Lba163pGXCtK6sljhRffpuXzoGZh9Fm8ICzXr9sIE80K4OPg4cOFClXv3rr7/w8MMPo2vXro3dBCIioiZP37Mz9J0SNOsk+GjOPlFpWpJ7+2i3t1qUkoWNR082SDvJM5nWbQFMZttyiU6P36PLRvi1CfRD/6gQF7aOiBrSueeei0WLFqnZkN7eZfVMpAak1IKUGYtSG9JeYmIi5s6dixdeeAFnnXUWAgMDNa/7+vqiX79+Koi5cOFCDBgwgF8gEREREVVrfHx5qQ/pjyYFBmleNy5aBkupkUeS6k1KbiFWZ5zAZIdZjylBweh7BssHujz4eNFFF2HkyJGqviMRERHVjaSr8548Vo3wsyk1wvjz0krfMyA6FOe1dZ7e/OWNB2A0syA7VU9mKxnXlKVctVoeFYO8U0GIixKioXeSfpGIPIefn5+qAym1GocMGaLWmUwmfPHFFxg7dqxa75i6VfqBn3zyCdavX49//vlHpW9dvXq1SsX6zTffYMqUKRUCk0RERERElTmvbSQMp7qeZp0OHyV01LxuyT4B06p1PIBUb2YnpaN1QT7OzD6qWZ/Rvze8DQYeaQcspEFERNRE6VvFwHCWdoaIacM2mJIq1na0mtmzHQK8Kv7533eyAHOS0hukneRZLCmHYUnLcppy1VuvwwXtol3UMiJqbAkJCfj000/x2muvITq67P/9rKwsVQfymmuuQVJSktPBMy1atFCpW8PDw2FgJ52IiIiI6iDCzweDY8Jsy6vDo7AlpIVmm9Jf/4KlqJjHl05bfqkJC5KzMMlh1mOulxd6njeYR9gJBh+JiIiaMO/x5wABfpp1pXMXw2IuT4lpL8rfBzd1bev0tQ92HMLRwpIGaSd5DuOajZrlI37+2NQiXD0/p3U4wnzLZkASUfMxbtw4/PLLL5g6daotw43Mbpw4cSJeeeUVFBYWurqJREREROSBxtmlXpXMUB+0d6g1nlcA47LVjd4u8jyLkjNhKS7G2PQjmvW7OnVEWDAzuLht8NFoNGLz5s346quvMGvWLFUr5KmnnlIpeIiIiKhyusAAeJ8/QrPOkpoG0z+bKn3PZR1i0T7Ev8L6fKMJs7ZVPmuSyFJSAtP6bZoD8Utsa1hOpVmdlBDDg0TUTAUFBeGhhx5S9R2lhqMoLS3FBx98oIKTS5dWnhaciIiIiKguzm4ZhhDv8nSX20LDsDIiSht7+GMVLLnauuREtWG2WFTK1fPSjyDIVF5HVIb9txx9Fg+mOwYfMzMz8cILL+CMM87AZZddpgKOb731lqoVIoHIffv2OX3fr7/+qkbVyuPbb79t9HYTERG5E8PQgdDFRGrWlS74HZbCIqfbe+n1uK9PgtPXFqccxcaskw3STmr6TJt2AHYpa+RCe3FMK/W8XbA/+kaGuLB1ROQOunTpgq+//hrPPfecSqsqjhw5gttuuw3Tpk3DoUOHXN1EIiIiIvIQPgY9RrfV3g+R2o+aXFDFJTCuYO1Hqru1GTlIzi2skHJ1e2xLdExsw0PrbsHHZcuW4cILL8Qnn3yCvLzajTwYOHAgNmzYgNWrV6tgZUkJU8QREVHzpTMY4D15rHZlbn6VqUX6RYVirMMFutVLm/bDWEnaVmreTA4pV9eFRSDLr2wW7UUJMaqWGxGRnAsuvvhiLF68GJdffjn0+rJu559//onx48fj7bffZh+OiIiIiOrFFR1awmDXFT0QGIzfTg2SNQYHwfuKC+F13tk82lRn3yelod+JbLQryNes1509kEfV3YKPMnPxlltuwYkTJ+r0fhlBO2bMGPU8KysLv//+ez23kIiIqGkxdO0AffdTtQ28DOrC2mvkkCrfc3vPeAR4lacnsUo6WajSSRDZM2cdg3mfNi3vwpZlI/x89DqMi3cezCYiz1RQUICDBw9i27Zt2L9/v9MBpaGhoSq7zXfffYfu3burdcXFxXjzzTfVQNQVK1a4oOVERERE5Enigv3VYFh7n7brgHfbd8KD546GYXA/NWibqC4O5RViVfoJTD6snfWYGhiEPkN686C6U/BROqj3338/LBaLbVTs+eefj/fffx9///03Jk+eXKP9WIOPgvVDiIiIAO9J58HQtzt8H5kB7wtGQufrW+VhifT3wc3d2jp97YMdqcgqZGYBKudYRzTHyxurIqLV85FtIhDq483DRdQMAo6ff/45Jk2ahAEDBqg+mcxwlP6cLMu/kpkmOztb875evXphzpw5eOyxxxASEmLrF95www2YOXMmMjIyXPRfRERERESe4MaubTWDqzP8/PFd2wSsO1GAv44cd2nbqGmbk5QBiWR90L4jfmgVh4JTgez0fr3gzaC2ewUf33jjDRQVldWg8vf3V0HH119/HSNGjEB0dLQtJU91Bg8ebNt25cqVDdpmIiKipkAfHQmf6y6FPiKsxu+5NDEWHUICKqwvMJowa6t2lhs1XxaTCUaH4OOSmJYoPXUtNslhlCkReR6Z4ThhwgRVy3HHjh0wmUya12VwqcyAnDVrlkqtunz5cs3r0ne76qqr8Msvv2DixImarDhjx45V5TiMRmOj/fcQERERkecI9/PGNZ3LUq06emtbMkvLUJ3IvbH5BzPV85SAILzZsSsuOXME3urYFd1HV51tjBo5+ChpVqXuh9UTTzyB4cOH12lfgYGBiIuLU8+PHz+OtLS0emsnERFRc+Gl1+HevglOX/v10FGsz8pp9DaR+1HpVnNyNesWxpalXE0M8UeviGAXtYyIGoMEFa+//nocOnRIs14Gk4aFhSEgQDuIRWY+3nbbbVi7dm2FfUVGRuLFF1/El19+iY4dO9pmVL7wwgtqRuW6desa+L+GiIiIiDzRlR1aIsrfp8L6lLwi/HSgLIBEVBuLkrOQb9QOuizw8kL+mf0Q3iKIB9Odgo+rV6+G2WxWzxMTEzUjXq0kDWtNxcfH254fOHCgnlpJRETUvPSNDMH5cc7r9b208QBHCBL0nRIw94JxmN+yjUoxsis4BAeCgm2zHmtz/UZETc/TTz+NnJyywShDhgxRNRtXrVqFTZs2Yc2aNdi4cSP+/fdffPzxx7jgggvUOaG0tBT//e9/K8yQtBo4cCB++ukn3Hfffbbg5Z49e9TsyAceeADHjh1r1P9GIiIiImra/LwMmFZJaZmPdh5CXmlZlg1zWqatJBxRZeQ3Mjsp3elrlyW25IFzt+BjUlKS7bnMeDzdG1XBwcGaWZVERETknPnYcVjyCyo9PLf3jEegXX0EqwO5hfhun/OLLWo+ik1mfFoEvNKpOyYPHoFnu/RS630NeoyNi3J184ioAaWmpqpAo3jwwQfx6aefqlqPERERmu2kluPQoUPxyiuvqLqPBoNB1XVcv359pfv28vLCjTfeqFKxyj6tJCgp5TmIiIiIiGpjXHyU09Iyx4uNmPfvTpR8OhvFz78D8/Y9PLBUpbWZOTiYW1hhfY/wIHQL56xHt0y7atW6devT3p99fciSkpLT3h8REZGnsRQXo3TB7yh+9i2UzllU6XYRfj6Y1r3yEYKZhcUN2Epyd0tSjyGvtGz2UpHBC4cCAtXz89pGINjHy8WtI6KGtGXLFvVv3759cd1119XoPaNGjcL555+vnm/evLna7WNjY9Vsyg8//NCW3Yaj0YmIiIiotgw6nRpc7WhKchImfvcDTBu3q+XS+b/DcipDI5Ez31cyEP+yDpz16JbBR3uVdSZrMxtSaj3aj7QlIiKicqa9B1H0zFsw/vY3YDTBtH4bTJt3VnqILm4fiw6hFUcIFhjNeHNLMg9tM/bjgQyn6yXlKhF5Nmufq3PnzrV6n3X72mSoGTZsGBYsWIDbb78dfn5+tWwpERERERFwZmwLDIoO1RyKdD9/GOziEZa0TJjWlQ2yI3KUmleElenHcdu+Xbgi5QCCS8smvkX4eePc1uE8YO4YfAwPL/9iMjNPv8jrzp07ne6biIiIAH10OFBSqjkUJd8vqDT9qpdeh/v6JFQ6821dZlm9L2pe9pzIx/bsvArrO4UGoFsYU40QeTrrIM8DBw7U6n3W7Ws7SNTHxwczZszAHXfcUav3ERERERFZyexH+ylOS6NbYl+gtv9qXLQMllN1IInszUlKR2RRISYfTsH0A3swZ81y3Ld7G/4TGwxvu2ycVLVGPVJxcXG251XV/qiJTZs24ejRo+q5t7c3OnbseNrtIyIi8iS60BB4X1yW9s4mN7/K9Kt9IkMwPt55Db+XNh1AKdOSNBuWklKYs0/gx/2VzHpsH3Pa9buJyP316NFD/fvPP/9g3rx5NXrP2rVrMX/+fPW8W7dudfpcqQdJRERERFQXnVoEqvqPVhadDh8ldNJsY8nOgWnlOh5g0igwmjA/ORMT0lJhQNlsWV+zGedmpuP89ky56rbBxyFDhthuUm3YsAF79+6tsE1Nb2LNmjXL9rxnz54IDCyrPURERETlDAN7Qd9De4FdXfrV23rEIcjbUGG9FNr+dm8aD28zYVy2WqXtjVi+Cr6msnqPVgFeeoxp6zxITUSeJSEhQdV7FPfffz/uueceFVwsKSlLPWRlNBqxdetWPPPMM7j++utRWlqKli1b4owzznBRy4mIiIioOZvWrS189eWxhjXhkdgcGqbZpvTXv2ApLHJB68hd/ZKchZLiElyQlqpZv7NDe0SEsfSfW6ddHThwoK3m43//+98KndaaeOutt7BixQrb8sSJE+u1nURERJ5CBvX4XH4B4O9XMf1qXr7T90T4+WB6t7ZOX/t4ZyoyCoobpK3kPizHc2Bc8jd0RiOuOrAP//fvCvQ7fsz2+nltIxHoJEBNRJ7pscceg7+/v3ouNRmnTJmCPn36qBqNY8aMwYgRI9TyJZdcgi+++EIFHvV6PR5//HGVRpWIiIiIqLHFBPjiio6tylfodPggwSF7Yn6BGnhLZI1ZfZ+UjhGZ6Qg7VefRKmrUWTxItdToCWrvvPNO2/ONGzdi2rRpNa7/eOzYMTzwwAOaWY8ymnby5MkN0lYiIiLPTr/6S6XvmdQ+VtX0c1RoMuPNrckN0UxyI6U/L9XUC40qLsJJL2/b8qSEGBe1jIhcQVKnvv/++4iMjLStM5lMyMjIwMGDB5GWlqYCjlYBAQF46aWXcM455/ALIyIiIiKXuaZzK7TwKU/nvz00DCsitFl8JPhoyc1zQevI3fybmYODJwsw+UiKZv3uqCh06tLOZe1qqho9+Ni/f39cfvnltuVVq1ap0bIyC3Lx4sU4fvy47bX8/Hzs2rVL1QuRoOPIkSPx008/2V43GAx49tlnOZqWiIioLulXN0j61R1Ot/fS63Bf3/ZOX1uaegxrM07wmHso0/4UmNZv1axb0LIN9gWXpRfpGhaILmFBLmodEbmKpE9duHAhZsyYgfj4eKfbxMTE4Nprr1XbXXDBBY3eRiIiIiIie0HeXrihaxvNOqn9qCksUlyi0q8SyazHbrk56JJ7UnMwSocM4MGpg/KwfyOn7ZFRsn/++adaLigowPfff68e9p5//vkq08g9+OCDOOssTnclIiKqWfrVC1G0/22goLyeQcn3C+GXGA9dUMXayb0ignFBfBQWJGdVeO3lTQfw1eje8NY3+jgmakAWsxmlc7UzYvMMXvi4XXlqmssSWWCdqLlq0aIFbr/9dvXIzs7GkSNHVF/Oz89PBR7lQURERETkTia1j8F3+9KRml92L+RgYBB+i2mF8zOO2LYxrVwH8zmDoY/Q1oSk5uNwXhFWpB3Hw4e1sx4z/fzRe3h/l7WrKXPJHUMvLy+88847mD59uqoFUluSxueNN97ANddc0yDtIyIi8kS60OBap1+9rUc8gp3U9kvOK8I3e9MaopnkQqZ/NsFySPu9ftYuETmnarb1jgjG2LjytItE1HyFh4ejR48eGDRoEHr16sXAIxERERG5JRk0fVvPOM26z9p1QIlOV77CZIZx4bLGbxy5jTn70xFWUowRWema9am9e8DHyyVz+Jo8l01XkJSpd911F3788UdMmDAB3t7ldYQqIyNqp0yZotKzSqpWIiIiquXf3wGSfrVzjdOvhvt5Y3p37UW61cc7U5FRUMyvwENYCotQuuB3zbrkgED82Krs+/fW6/Bwv0To7TtoRERERERERG7unFbhKruTVYafP3461de1Mq3fAvNhbeCJmoeU3ELMTUrHhUcOwdtisa0v1uvReQwzb9aVy0O2Xbp0wUsvvYTHH38cGzduxObNm5GZmYmcnBxYLBaV2icqKkrViuzXr58KQBIREdHppF+9AEX7k2ucflVSlPx8MBO7T+Rr1heZzHh9y0E8f6Y2mElNk1FqXORqv+O3ErvAdCpLxfVd2qBdiL+LWkdERERERERU93sht/eMx01/brOt+yq+PcanpyLQdKoCpAVqQK7vtKt4mJsRs8WCZ9YnwWQ0YULaIc1rOxMSMDiyhcva1tS5PPhoFRQUhLPPPls9iIiIqKHTr45D6Rc/lK+0WGDOyobBSfDRoNPhvj4JuNHuIt3qj8PZWJNxAmfG8GKsKTNnHoVx+RrNupURUfg3vCzFamKIP6Z0buWi1hFRU3Hy5EmkpaUhPz9f1YIsKiqCj4+PKpsRGBioUrNKulYiIiIiosYmMx/PaR2OZYez1XKOtw++bZuAGw7uK9vAYIA+OhIWsxm6OpSKo6ZpdlI6Nh/LxZRDBxBRUqJ5Leo8znr0iOAjERERNR7DgJ4wbdwO87bdMPTtDu9LxkEXXDHwaNUzIhgT2kWrGZCOXtl0AF+N6g0fAy/Om6rSH39VNS5syzod3mlfNqNVkqw+3D9R1ckgIrK3b98+LFmyBGvWrEFSUhKysrKqPUBhYWFITEzEgAEDMGrUKPTs2ZMHlYiIiIgaxa3d4/DXkeMwnUqtOadNPCYcOaQG3na9Yhy6tW/Nb6IZOZxXhHe2paBD7klcm5ykfS02Bh26tndZ2zwBg49ERETNNf3qFRfAnNRLBR9r4tYecfjz8DGcLD2VkuSUlLwifL03DVO78CK9KTLt2Avz9r2adbPbxONwQFkw+rIOsegRXl4bg4ho6dKleOONN7Bnz55aH4zjx49j3bp16vHee++hTZs2uO222zBx4kQYDAYeXCIiIiJqMHHB/ri4fQy+Tyqr7Vho8MI1g4aqf3smZ+PDhFbqfgk1j3Srz25IUiWFrkveBy+7Wo8mnQ6tLh/v0vZ5Ag5hJyIiaqZ0IcE1DjyKMF9v3NJDW5Dd6pNdqUgvKK7H1lFjsBiNKP1hsWZdtrcPvoxLVM9bBvhienfn3zkRNT/Hjh3D1VdfrYKFdQk8OpOamoqHHnoIF110EZKTk+tln0RERERElbmhaxsEepUPepPAo9ianYdlR8pSspLn++lABtZnnVTPn+nSC/NbtrG9ln3WIAQk8l7I6eLMRyIiIqqxiQkxmHcgE7tO5GvWF5vMeG3zQbwwuCxVJzUNxr/WwpJ5TLPug/adUOBVdon4QN8EBNh1yoio+Tp48CCuv/56HD582Onr3t7eiI6ORqtWrRAUFARfX1/1KC0tVbUfpQ5kenq6ehQXVxysIsHMyy67DB988AF69+7dCP9FRERERNQctfD1xrVdWqt0m47e3pqCs1uGseyIh0vLL8asreUDHwu9vPBKp+74OyIat5zIQNfJo13aPk/B4CMRERE5ZSk1QuetvVQw6HS4v28Cbli2DeUJKcr8eSQbq9OPY3BsGI9oE1HYtSM2rNqMMzIz1PLO4BD8GtNKPR8bF8nvkoiUkpIS3HnnnRUCj7169cIFF1yA/v37o2vXrjVKm2qxWFStyPXr1+OXX37BP//8o9aJEydOYObMmZg/fz5CQkJ49ImIiIioQVzeIRZzk9KRUViiWZ+aX4Qf92fgsg4teeQ9lPQ9nt+QhAKjucJryW1aI+66cdCdGpBNp4dpV4mIiEjDkpuH4k++R+mXPzo9Mt3DgzGhXbTT117edBAlpooXcOSe3jpyEg907YN7eg3A/sAgzOrQFRadDi18vHBXr3aubh4RuYlPP/0UO3futC137NgR3377LWbPno1rr70WPXr0qHG9RqmhI++/4oor8Pnnn2PhwoXo16+f7XWZGfnyyy83yH8HEREREZHwMxgqLTHy0c5U5OXmo3TRMhhX/MsD5mHmJ2fhn8wcp6893C8RQQ6D8KnuGHwkIiIiG9PG7Sh67h2YN+1Qz+XhzK094hDiU/GCTEYJfrX3CI9oE7AuMwfzDmaq5+vDInBD/yHYEdJCLd/Vu51KRUNEJObOnWs7EJISVYKOffv2rZeDk5iYiK+++gqjRo2yrVuwYIFK1UpERERE1FDGxEWiY2iAZp232YxR+5NQ8vSbMC5ejtKFf8BSyOtST5FZWIw3thx0+toF8VE4M7bsngjVDwYfiYiISLEUFKLkuwVAfoHtiJTMXghLrra+o5DA1K2VjBL8dNdhHMnnxbk7KzKZ8PyG/Zp1MuNRDI5pgTFtI13UMiJyN7t27UJyclk9FJnd+Morr8Df379eP0Ov1+OFF15AcHCwWpb6kKtXr67XzyAiIiIiciwrc3vPeM26xLyTuD1pF/yLTtUozy+E8Y9VPHAekm71fxv2w1RUjAd2bUV0UaHttSg/b9zJ7E/1jsFHIiIiUnQB/vC+5Hzt0cgrQOmcRU6P0ISEaHQLC6ywvthkxuuVjCQj9/DxzlQ1S9WRv0GPB/q2V2kRiYhEamqq7UAMHDgQbdu2bZADExQUhNGjRzv9XCIiIiKihnBGTAucGRNqW94V0gJ/R2jLzBiXrYblZB6/gCZu8aGjWJl+AtOT9uD8jCP4ZN0qnJ+WKlFJPNgvEcFOsnvR6WHwkYiIiGwM/XtC36uL5ohUln5VRgne16c9nIWplh85jpVpx3lk3YylpBR7M4/jqz3OU+NKzYuWgb6N3i4icl9ZWVm253Fxzme815f4+PKR55mZZWmhiYiIiIga0oye8Zr7Gh8ldITJfoOSUpT+9he/hCbsWFEJXt10AAOzj2Ji2iG1LshkxAN7tuPZ7MMY2jLM1U30SAw+EhERkY3MePO5bDwQoE2pV1n61W7hQbgoIcbpEXx18wE1C5LcR8mSvxH48vsYkX5Eje6z1z08CJd2iHVZ24jIPQUElNfByctr2BHfubm5Tj+XiIiIiKihdAwNxPj4KNtycmAQfo1trdnGtHIdzEez+SU00XSrL248AHNhEe7fvU3zWqHBgIHjh7usbZ6OwUciIiLS0IUEw/vScRXTr85e6PRI3dK9LUKdpKdIzS/GF7sP8+i6CXP2CZQuXYmwoiL8d9dWzNq0Fu3y82yzWB/pl6j+JSKyFx1dnnZq48aNMBqNDXaA1q9fb3seE+N8YAsRERERUX2b1r0tfA3loZLP4hNRorMLnZjMMC5axgPfBC1NPYY/j2Rjxr5diCo5VcvzlPSRZyO0tTbNLtUfBh+JiIioAkO/HhXTr27a4TT9aqivN27rEVdpbcG/jnB0oDs4OecXGEzlyWO6ncyxPb+2cyskhnKWERFV1K1bN3h7e6vnaWlp+PbbbxvkMC1btkwFN6169+7Nr4OIiIiIGkW0vy/+07GlbTnTzx8/ttbWOjet3wrz4XR+I03I8eJSvLzpAIYczcTYDG35mf2xsejKWY8NisFHIiIiqnn61e+dp1+9sF00uocFVVgvSVcfXbsX27LLU+lR4zPuOQDfbbs1635q3RYHA4PQLtgf13Vpw6+FiJwKDQ3F8OHlqYief/55zJ07t16P1vLly3H33XdrAp6JiYn8RoiIiIio0VzdqRXCfMuzOn0V1x55BrssTxagdP7v/EaaEAk8WvILcO8e7UD6fC8vtLrhUnXvixoOg49ERERU8/Sr+c7Tr+p1OtzXN8HphYXUfbxn5S4cyivkkXYBi8mE7G8XaNbleHnj0/gO6vlD/drDxy69DBGRo+nTp8PLq+zGi6RdffjhhzF16lQ1W9FkN6O6ttauXYuZM2fi5ptvRkFBgW39bbfdxi+BiIiIiBpVkLcXbuxaPtvxpLcPvm3bTrONecdemPYd5DfTBPx5+JhKuXrH3p0ILy3RvJY6ejjCYiJc1rbmomKBpgb25ptv4rffflPPpaN53nnnucW+iIiIyHn6VUm1at6yq0L6VUPf7pptu4YF4ZYecXh7W0qF/ZwoMeLOFTvx0Tk9EeZblr6PGsfJv/5F8NFjmnUfJ3RAnrc3Lm4fgz6RIfwqiKhKPXv2xIwZM/D666/b1q1evVo9QkJC0LdvX/To0QOtW7dGq1atEBgYCD8/P/j4+KC0tBRFRUUquChpW48cOYIdO3Zgw4YNOHZMe24Sl112GUaNGsVvhIiIiIga3UUJ0fhuXxpS8orU8pw28Zh8OEUTvCr9eSn0d93AWXNuLKe4FC9sPIBzMtNwbpY2Ve7uVq3Qa8xZLmtbc9LowceMjAzs3btXPc/JyXGbfREREVHl6VeL9iUDBYWa9Kt+HeKhC9amWp3SqRXSC4oxd39GhX2l5herGZDvDOsGPy8DD3cjsBQUwrhoGXzs1u0LDMKClm0R5e+DWyup1UlE5OiWW26BXq/Ha6+9BovFYlt/8uRJlTZVHqfrqquuwiOPPMKDT0REREQu4aXXY0aPeNy/pqxsSZHBC5/HJ+KufTtt21gOpsK8dTcMvbrwW3JTr245COTm4c695d+bOOntjbbXX6L6NdTweJSJiIiobulXl6youK1Oh7t7J+DslmFO97X9eJ6qAWmyu3FNDSd59mIEFBdr1r3VoSvMOh3u75Og0soQEdXUtGnT8OGHH6JTp071etDatGmDl19+GY899hgMBg5OISIiIiLXGdYqDL0jgm3LC1u2wWE/f802pQt+h8VsdkHrqDor0o5jcXIW7t29HaHGUs1rh847BxHR4TyIjYTBRyIiIqpR+lW9dVSfTgevUWfB+8KRTrf10uvw9KCO6B6mnRVp9XfacbwiRb8ZgGxQeYfSELlhi2bdn5Ex2NQiHCNbR2BYK15wE1HtnX322Zg3b55KwTp69GgEBATU6TB6e3tj6NChePbZZ/HLL7/gwgsv5NdBRERERC4ng6pn9oq3LRv1enyc0FGzjSU9C+ZdSS5oHVUlt8SI/21IwtiMIxiSnaV5bWvbtuh/3mAewEbUpIe7F9uN5Pf19XVpW4iIiDw//eoFKMkvgPfE86Bv16bK7f29DHh5SBfc9OdWlW7VkaRljQ3wxTWdWzdgq5svCewe/upnxNkFeIv1eryX2Bkh3gbc06edS9tHRE2bpCk6//zz1UP6ZFu2bMG+ffuQlJSk6jrm5+erGo9S61H6af7+/ipIGRMTg8TERHTo0AG9evVCUJDzQSpERERERK7UIzwYo9pEYGlqWY3yZVGxuPLQAXTMy8WhiAgk/udCGDqyX+1u3th6EJacXMzYt0uz/ri3D9pNncw6nY2sSQcfU1NTbc/ZcSUiImpYupAg+N5xfY23D/fzxutDu+LGZdtwosRY4fW3t6Ug2t8HY+Oi6rmllLRqI+KOpGkOxLdt2iHdzx+P9mqHCD/7KpBERHUnwcWBAweqBxERERGRp7ilexz+PJwNo8UCi06HNxO7IsRYipURUXgvLBx9Xd1A0liTfgLzD2ZB7+OLr+IScN3BffA5NSA7+fxzMTjKeXkgajhNNu3q7t271Qhbq/j48qnQRERE5B7aBvmrGZC+BueXHE+vS8K6zJxGb5cnKy4qgff8pZp1mb5++CYuAQOiQnBBPIO9RERERERERFVpE+SHixNjbMtbW4RhZWS0KkUza2syS8m4kbxSI57bUJYG16zT4Zu49pjWfzD2BAVjfXw8zhx5hqub2Cw12MzHNWvWqNQ7jiQVj/029qlTq2M2m5GXl6f28ccff8BkMqn1oaGhaNeO05yJiIjcUc+IYFUD8sHVu+FYjl1GEN6/ejc+GNEdHUIDXdRCz7Ju7m/oV1CgWfde+06weHvjoX6JTDNCREREREREVAM3dGmDhclZyCsti0NYbc/Ow++Hj2FUm0geRzfw9tYUZBSWaNYdCAzGPQOH4IvhPXgfxNOCj/Pnz8ecOXOq3GbBggXqcbouuugiGAyG094PERER1Y05Iwum9dvgPe4cp68PbxWOu/sk4OVNByq8lm804a6Vu/DRiB6ICWAN59ORlFOAL4v1CA0MQmJ+nlq3NaQF/oiKxYxubdXITSIiIiIiIiKqXqivN6Z2bo23tqVUeO2dbSkY1jIcPpVkeqLGIdm0fjiQ4fS1W3oloGV4ML8KF2ny/2d07twZM2fOdHUziIiImiWL2YzS31ei+IX3YFy8HMYN2yrd9tLEWFzTqZXT1zILS3D3yl0qVQbVjcliwbMbkrAhNAw39x+MVzt2VUXV3+zQFZ3DgnBlR+fHnoioPpWWliInJwdHjhxRGWvS09ORn5/Pg0xERERETdJlHVoiNsCnwvrD+cWYuz9D3RcxHzrikrY1dwVGE55dX55p016/qBBMal+eNpc8aOZjQ/Ly8kLHjh0xfvx4TJkyBX5+HMVPRETkCiUffQvztj225dLZi2Do2A664CCn29/SI06lwvj10NEKr+07WYAHVu/G60O7wlvf5MdHNbq5Sekq9Ysw6fT4uVUcFse0hsnLgE/6tYeXXufqJhKRhykpKcHKlSuxatUqVXJDHpmZmZX24dq3b4/evXurx3nnnafKZxARERERuTNfgx7Tu8fhiX8rlpjbsHIjJv54CLrDafCZdhUM3Tq6pI3N1bvbUmA4mo3ORiN2h5T3LfwMejzaLxF6He+DeGTwccaMGbj66qsrrH/77bexZMkS9fy2227D6NGja7xPSa0aGBiIqKgo+PhUHG1AREREjcswoJcm+Ij8ApR8vxA+11/mNKe+XPg92j8RWUUl2JB1ssLr67JO4pn1SXhiQAfm5K+F9IJilfLFUYnBgCkdW6FLmPNgMBFRXaSmpqp+3a+//lrjWY1GoxF79uxRj9mzZ+Ppp5/G2LFjcf3116NLly78IoiIiIjIbY1pG4lv9qZh94lT174WC57bthFDsrM0g7N9broShq4dXNfQZmTT0ZOYu+8IZu3ais65J/F1XAI+j0+EUa/HrT3i0JplZzw3+NiyZUv1cGQ/ulVe79q1a0M1gYiIiBqYoW93mDZuh3nzTts6eS7rvPr1cPoeqYfw4pmdcfPybdh/srDC64tTjiLW31fNkqTqWSwW/G/DfhSazBVeaxPoixu7teFhJKJ6YTab8cYbb+CTTz5Rsx5PR3FxMebNm4eFCxeqAKQMXvX1Zd1fIiIiInI/MpB6Zs943Pb3jrIVOh32BQVrgo8wmsoDkF0SXdbW5qDIaFID169IOYhuuTlq3ZSU/Rh8LAtfnD1Ulf0h12v0nGYxMTEqZao8mGaHiIioaZPZjT6XjgcC/TXrS2cvhOVkWQpQZ4J9vPD6WV0R5eft9PXPdh/GD/vT6729nmjJgQysST/u9LWH+iXCz2Bo9DYRkecpLCxUmWvee++9Wgce27Zti+joaJXJxtmMyA8++ACXXXZZpSlbiYiIiIhcbUB0KIbEtrAtf9auA5ZEO0y+KjWi5MNvYNrtvA4h1Y/3dxyCd3oWpiZrU+H6mk2YcUZXplttrjUfZ86cqR5ERETkGXQhQfC+dDxKP5tTvjK/ECWzK0+/KmICfPHa0K64+c/tqki4o5c2HkCUnw/ObhXekM1v0k4UlyLzpyV469hRzOrQBbtCyjtCE9pFq84REVF9eOKJJ/DHH39o1kk5jP79+6Ndu3aqPIakYE1LS8O6detw+PBh23Z6vR5fffWVynyzc+dObNiwAQsWLMDWrVtt2+zatQvXXnstvvnmG7RoUX4uIyIiIiJyF7f3jMea9BOQvENmnQ7Pd+mp1o/OTNMGID/4Bj43XwlDZ86ArG9bj+Vizu7DeGfXVnhbLLb1cldp3/jRGBcRUu+fSU1k5iMRERF5ZvpVfW9tKnVJv2qct0SlBa1Mx9BAvDi4MwxOApRyMf/I2r3Ynp3bIG32BJ+t2IoLk/eje24O3tv4Dx7YtRVhJcUI9/VWnSIiovrw559/4qefftIEHV999VW1XtKw3nXXXbj55pvVvy+++CJ+//13fPTRRyooKZKTk3HdddehtLQUvXr1wtSpUzFnzhxV+7FHj/IU3fv378eDDz7IL42IiIiI3FL7kABc2C7atiwByP916YGl0Q5pPk8FIDkDsn4Vm8wq3epVyUnokK+9V7S0U2eMObtPPX8iobkHHyVVz5EjR1zdDCIiomarsvSrxj9WofSbn2ExVZzZaDUwOhSP9k+s9MLynlW7cCivYm3I5k5GW3ZfsRo+dsFdGW0ZZDTi3j4JCPFp9AQXROShPvvsM00K1blz52L8+PHw8vKq9G/C2Wefrbbr2bNsNHhqaipee+01zXYSiJQA5KRJk2zrli1bVmGGJRERERGRu7i5W1v4GcrDKiadXs2A/D2qsgDk/sZvpIf6eGcqfA+n4+qUA5r1BwKD0Os/450ObKdmFHx86623VBqe+iKd2KuuugorV66st30SERFR3dKv+lx+YYX1pjUbUfLpbFhKSyt977j4KNzSva3T144XG3Hnil04Xlz5+5sbSVO78Lc1GHrMrrg9gLmt45GQ2AbntmaqWiKqH0ePHsXatWtty//73/8QExNTo/cGBQWpGZLe3mX1fWW2o9SOtCcpWZ977jkMGjTItu7jjz/m10dEREREbinS3wdXdWqlWScByOe6VhaA/BqmPQxAnq6dx/Pw3c4UPLR7KwwoH4Rt1OmwZ9wotA9nulU09+Cj1AC55ppr8M4778BsloRqdffrr7+qUbKbNm2qt/YRERFR3Rn6dIP3VRcBeu1oM/OWXSh57ytYCosqfe+1nVtjUoLzG9qp+UW4d9UuFDmpDdkcfbQ1Gf/ZUV4rTWR7+2BOYkfc1zeh0jqbRES1tWfPHphOzV7v1KkTBgwYUKv3x8XFYejQoep5QUEBVqxYUWEbCUA+8sgjtmUZrJqZmckvi4iIiIjc0pROrdAhJMBpAPIPZwHI9xmAPB2lZjOeWZeEqQf2oV1Bvua1RZ274IKzep3W/smD0q5K51Vqg0jdj6ws7Yj9miguLsYTTzyBmTNn4uTJkw3SRiIiIqobrzP6wOeGywEvg2a9ee9BlHw2p9L3ScBM0oUOjQ1z+vq27Dz8d+1emKqoIdkcSA3M4r/WVrjg/iihI67vm4hof1+XtY2IPI99ELBbt2512kfXruU1gVNSUpxu06VLFxXctNqwYUOdPouIiIiIqKH5exkw6+xu6BRaMQD5bJUBSG26UKqZz3Ydhv+hw7gs9aBm/e6gEPS74nx46T2iuqDHcem3smbNGkycOBF///13jd+TlJSESy65BN98841mva8vb7QRERG5C0PPLvC5ZQrg61O+0tcH3uPPrfJ9XnodnjmjI7qFBTp9/a+043hl0wFYmmkAUkb7vblqJ6Ye3FfhgjutRxdcVMnMUSKiupLZilZ+fn512oe/f3k94Ozs7Eq3s59VmZycXKfPIiIiIiJqDOF+3nhnWHf0igh2GoBcFuXQPy81wrR5B7+cWtpzIh/fbE/Gg7u2aoJZJTod9o4bhY5Mt+q2Gj34OGHCBERERNiWjx07hptuugkvv/wyjEZjle+dO3cuLr74YpX6x0rqhzz00ENqv0REROQ+DB3bwXfmVCAoADAY4HPTldDHaesiVDaC8JUhXdE60PnAorn7M/DlniNojuS/+9ytWxBk0l4zvduxKx7q3wF6plslonrWokULTQmNujhypPycHRysvTljLzIy0vY8JyenTp9FRERERNRYgn28MGtoVwyOKb9mtgYgn+nSSxOANAzuB++Lz+eXUwtGSbe6Pgk3Ju1G6yJt7fh5XbvhoiE9eDzdWKMHH8844wzMmzcPZ555pm2dzF748MMPcfXVV+Pw4cMV3pOXl4d77rkHDz/8MAoLy39k8fHx+PbbbzF16tRGaz8RERHVnL5tK/jeeT18brwchk4JtRpB+PpZXRHq4+X09be2peDXlNqnbm/Kth7LxZ9rd+CCtFTN+iXRLXHm4J6IDy6fWUREVF/atGlje7527Vrk5ubW6v0ywHT58uW25ejo6Eq39fIqP+f7+NjNnCciIiIiclN+Xga8NKQzRrYun3AlTPqyAOSfkTGY37INvuvbDzqmB631AOzAAymYdOSQZv22kBYYdOkYePN4ujXnd/QaWFRUFD799FO89957eOutt1QNSLFx40ZMmjQJzz77LEaPHq3Wbdu2DXfffXeFtDsXXnihqvsYFBQEV5Kak8uWLcOhQ4eQkZGh0r/GxMSgR48eKtCqb+T/AY4fP46tW7di165dqj5Lfn4+AgMDERYWhu7du6NPnz6a0ctEREQNTR8dCcijluKC/fHKkC647a/tKDZXTLP61LokRPj5YEB0KDzZsaISvLMtBUv3p+OlPTs0I8cK9Qb81qs3XutU/YxSIqK6kD5EaGiomokoA0FfeuklPPXUUzV+/yeffKKZ+Thw4MAa1Ze0z5ZDREREROTOJAj29BkdEbTBgHkHMzUByKe79YIZOli2H0Ku0YxbureFjlmLqrX/ZAE+2pmKASYTcry8EWosVeuL9HrsPn8kro4IacivlJpq8FFIUO7WW2/FoEGD1KzG9PR0tV46tTNmzFCzINu2bavSsZaWlv2wrPVCHn30UVX30ZWkVsnzzz+PxYsXo6SkpNIg680334wpU6Y06AlFOvM///yzCoJu2bIFZrO50m0lTe2YMWMwffp0dOzYscHaREREVFOlS1dAHxUOQ+9uFV7rGRGMpwd1wgNrdsMx/Gi0WNT6D4b3QKJDkXdPUGwy49t9afhsVyrCT+bi7R2bkJifp9nm67gE3DK0O0f7EVGDMRgMGDt2LL777ju1LP9Kn0z6cFXNTpQ+iQw4ffXVV23rOnXqpPp4ldmwYYPteXh4eL39NxARERERNTSDToeH+rVHkLcBX+1N06Rgtfp892HklRpxb58EW9kUi9nMGZEOjGYLnlmXhFKzBasjo3FdyFm4e892DD2WhTlde+DaM7vzB90E6CyS89TFZLae1G2U4FlVOnfujNdeew2JiYlwJZmhefvttyMrq2bp3s466yw1wzMgoP5vjD722GP4/vvvVera2pAbBffeey+uvfZaNCSp6SmBTiEzXTmCmYiI7BlXrkPpdwsAnQ7eV1wIr8H9nB6g2UnpeHnTAaevRfv74ONzeiDa33mNyKZG/qYvP5KNN7Yk40hBMdrn5WLWpn8QeCpThFWanz8WXH4xZvbv4LK2ElHzILUezzvvPM2gS0nHeumll6pyGnFxcSojTUFBgRoYuW7dOsyePRt79uzR7OfNN99UAyGdSUlJUZ9h7dfMnz9fBSup+WEfkoiIiJoyuZ6VIOO727WpQu2NaRuJxwYkwpCTi+IPvoH3xWNh6NCuUdvprkrNZry5JRnfJ5VNVrOxWDA4+yhunDwCPSI567EpcNnMR3uSElQCU5999lmFmY5WV1xxhar5KGlNXengwYMqmHbixAlN+ydMmICEhASVimjz5s1YsmSJLZ3sypUrVerYd955p97TsO7du7dC4FFuBEjAU/6VEcOSenX79u2qTXJDQMiNg+eeew7FxcVqdiYREVFjM27YhtLvF5QtWCwo/eZnWPIL4D1qaIVtL02MRXpBscr37yizsAR3rdyF94d3R5C3W1za1NnenHy8tvkg1medtK1LDgzE7uBQ9DuRbVtXYDBg9hlnYGbvmtfRJCKqq5YtW+Lpp5/GAw88YFuXmpqqBobW1AUXXFBp4FF8+eWXtn6NpHlllhYiIiIiaookA+LULm3U/YmXKhlE/euhoyrweP/qv4Gjx1Hy3lfwueVqGBLj0Zzty8nHk//uw56cshiGhk6H9mf2YuCxCXGbO3TS0ZSAmLMZfDJjcNiwYS4PPErqoDvuuEMTeBw1apQKmErqIXu7d+9WQT1rOlmZ1fnxxx/jpptuapC2ybGROpiS4rVLly5Ot5F2P/744ypVrJWkQZLRyr169WqQdhEREVXGvD8FjrlUjT8vBfIL4DVhdIWU5bf1iENGQTGWpB6rsK99OQV4cPUevDa0S5NMQXq8uBTvbz+EeQcy4Jg8XVK0PNO1Fz5cvwoRJSU4EBiErReOwR1ndIOfweCiFhNRc3PRRRep/sSLL75oG2RZU+ecc44a+FiZQ4cO4ZtvvrEt9+vXz6V1cOS/7+jRo8jIyFDLMTExiIyMVCloXcld20VEREREFV2SGItAbwOeXrcPJod7H+ElxbhqyV9AUWHZipJSlLz7ZbMNQJosFny15wi+27gPPY8fw57olhW2iQvyw83dKy/hQO7HLdKuSlqV+++/HytWrKhyOwmsyXZV1RZpSFJX8b777rMtd+/eXaUTqqyzJ7MSJ02aZJvJKSN4ly5dipCQ+psWfNVVV6m6KVJzRWpMVke+7pkzZ+K3336zrZPg4+eff46GwJQ5RERU1d8k48I/YPzt7wqvGc7sC+/LL4DO4W9sicmMO1bsxIaj5TMD7Z0fF4nHB3RoMsXbJZ3InKR0VUQ9r7Tqm/m9TmTjhrxjSLh2MmJaBDVaG4mI7K1duxbPP/88duzYUe2BkQwxkjVGSj1UdV6WbCySrcXKz8+vQUpWVGfXrl34+uuv1WDNnJwczWstWrRQMzf/85//VDrY0xPbJd+z/eBbazmUhiynwT4kEREReZK/jmTjkX/2oMRcHobxMpvx+I7NOPtYpnZjH+9mF4A8lFeIp//dhzbbd2L6/j0IMJpwc/8zkRRUHkORnoRku+rNdKtNisuDj6tXr1YBPfv6idIxvf7667Fv3z4sX75cs70E/CS9T3x84/8POG7cOCQlJdmWJfBY3YxBGRksMx6t7rzzTtxyyy311iaZWRkbG1ur98ixPvfcc201WyR4KoFfSdFa39hxJCKi6pT+sQrGn8oHxVjpe3eFzzWTofP21qzPLTHi5uXbsP/kqRGCDjqEBGB020iMbhOB1kF+bvsFrEw7jte3HERKXpFa1lssavTjUd+Kbe4aFoi7eyegV0SwC1pKROS8Hyd9tY0bN6r+RW5ursoGIwHHbt264YwzzsDYsWNVINHdSYabd999V5XJMBqNVW7r7e2N2267DdOmTav3khru1i6p2Tl58uQKZVHeeOMN9d02FPYhiYiIyNOsy8zBfat3ocBo1gQgn9ixGUMdA5C+PmUByPZx8GQSlpq7PwM/r96GGbu2oefJ8gFvO4JDMaPvGTDrdPDS6fBAv/aY0C7ape2lJhR8lJQxb7/9tupMSafKSkZQvvDCCzj77LPVD/DTTz9VqUHtOzyBgYF48sknVZrRxiKB0PHjx9uWe/Togblz51b7PkkhNHr0aFs62a5du+Knn36Cq1133XVYtWqVbfmjjz5Sx7y+seNIREQ1YfxnE0q/mQfYjQQU+k4J8LnxCuj8tKnXpf7jjcu2IquoYp1oe93DgjC6bQRGtolAtL9r07dbHThZgDe2JGN1RvmFdWhJCR7dtQWtCgtwc//ByPcqC7hG+nnj1h5xOD8uCvomMpuTiKipkXqWUnPSnvQ527Vrp54fPHhQMzNTyGzOhx9+2GPbJcHOyy67DNu3b6/wGoOPRERERLW3PTsXd67chZMl5YPKmmsAUkrqvPDPbnRbtxGXpx6El5MQ1asdu2F7l054YmBHdGoR6JJ20ulxSVEkqVEhnSIJPtoHHmV0rATmrEEw6wxISTHTpk0b23bSwbr33ntVp6qw0Pmsh/r2+++/a5bPO++8Gr1PUqJKwNFq586dSE1NhatJuxyDhERERK7idUYf+NxwOeClTbNq3nMAxW99Dkuu9uZqbIAvXj2rKwIctne0/XgeXt+SjAmLNmD68m34YX+6qq/oCtLBeGXTAVy1dLMm8Ng95zg+Wr8KA48fQ+uiQjywext8dMDUzq0xe0xfjI+PZuCRiKiBfPvtt5oAn8wgfOSRR9RAzR9++EE95Pmjjz6qKf8hZSskE46ntuvDDz90GngkIiIiorrpHh6sUofKIGMro16PJ7r1xooIh3JqxSWqBqRpf4pHHW6ZoLUoOQsvf7sUt/3yC646dMBp4HFpdEvEDuqFz87txcBjE6Z3RXqeiRMn4t9//y1vhF6PGTNm4LPPPkN0dMXps5LaVIKSjqldZObhxRdfrNLBNDRJJ2Svf//+NX5vv379NMubNm2CqzmOkPXy8nJZW4iIiIShZxf43DJFjfCzZ0k5guI3PoH5uLbOlYx8+9+ZnWCowYxAuZTdeDQXL2w8gPEL1+GOFTuw4GCmSuHa0Ixmi6rreMmvG/F9Unp5oXmLBZekHsQbm/9FVEmxbfthRzPxU0ApbukRV21wlYiI6k5SxUpJDysZ/CqZea655hpNulh5PmXKFLz//vualKavvPIK8vLyPK5de/fuVQOFrSU6evbsWed9EREREVG59iEB+GB4D7QO9HUIQPbBSg8PQMpA8GeWbYbl/+biqU3/olVRxUllh/388b+BZyJu+pW44Ywu8DG4ZO4c1ZNG//YWLFiA48eP25Yl2ChBx9tvv73K2hTBwcEqvYukW/X1Lf+fU2owXnrppZpgZkOwr/UounTpUuP32s98dLYvV3AM2MbExLisLURERFaGju3gO3MqEBSgOSiWzGMoef1jmDPKa0SLM2Ja4MXBnRHmW/NBNBL8W5ORg6fXJ+H8hetw36pd+O3QURQaTfX+RazNOIFrft+MlzYdQI5doDPAaMSTOzZjRtLuiqP8QoLQomPzKS5PROQqUuLjxInymehXXnlllaUohgwZgquuusq2LP1amWnoSe2S8igPPvigreyJBDelhicRERER1Y/WQX4qANk+xL9CAHJVeCUByAOHmvThX374GD77ZB6mz1+Ac7PSK7xeqtPhi7j2mHfpJDxw5Wj0jgxxSTupfrk0dCwdqHnz5ql0qzV1xRVXqDQyiYmJtnVFRUWq3kVDKS4u1qRKDQkJQVBQUI3f37JlS7cKPu7atUsTfPT391ezS4mIiNyBvm0r+N55PXRhoZr1luMnUTzrc1iKSzTrh7YMw5wxffHYgEQMjmkBQy1KI5aaLfgr7Tj+u3Yvxi5Yh0f+2YPlh7NRbCpPC18Xh/IKce+qXbh9xU4kndSO5mufl4v3N6zG8KMZFd4nNS797p8OQyKDj0REDU2y69jOv3o9brzxxmrfc8MNN2gGzdrvwxPaJelWt23bpp63bt0ad9xxR532Q0RERESVi/T3wXvDuqN7WHmMoVSvx+PdKwlAvvNFkwxA5pUa8e5v/yLonf/D9J3bEGSqmH1qc2gY7j9rOHpNmYB7BnZiBigP4pLgo6T4vOeee1THJjw8vNbv79y5s0q5OnnyZDQGqYdoX5syNja2Vu933D4rSztro7G98847muURI0ZoZpMSERG5mj46siwAGROpWe89cTR0DmlZRZC3l6qN+PrQrlg4fgAe6Nse/SJDUIs4JIpMZixNPYb71+zG+QvW4al1+7A6/TiMdtcANbmwnrU1GVf8thl/p5VnerAam34Y725cg7aFBRVe8xozDD63ToEupOYDnIiIqG527tyJw4cP25YHDBiggm01GVhqP3g2JSUFu3fv9oh27du3z5ZuVTz11FMICNBmIiAiIiKi+hHq641ZZ3fDgKiQCgHI1ZUEIM2HjjSZw/9vZg5m/d8iTFmwCN1ytWV0RI6XN17o1B2/XHg+XrxoKM6MaeGSdlLDafRCf61atcKXX36Jvn37ntZ+ZLbe888/j8GDB+Pxxx9HQyoo0N4grG0HzHF7x3qLjWnp0qX49ddfbcsyOnb69Ol13l96esVp0vbsU+wSERHVhsx8lABk8XtfwZJ8GN4Xj4XXwN7Vvi/M1xuT28eoR2ZhMX5PzcaS1KPYnl3z+lf5RhMWJmepR6iPF85tHYHRbSPQJzLEaY1Jk8Wiaki+u/2QqmPgyMdkwh37dmJ8evkNZZsAf/hMmQRD9041bh8REZ2eFStWaJYHDRpU4/cOHDgQq1ev1uxLBsg25XZZ062WlJRlF5g4cSKGDh1a488mIiIiotoL9Dbg1bO64tF/9qisTNYA5GPd++Cp7ZswOLt8EpMuNgq6yNpP5GpsRUYT3tqWgtlJ6QgLCMENXgYEG7UzHhfHtMLXXbrh1jO74pzWES5rK3lY8PG2226r1/1NmDBBpQw9evQoGiv46ONTccZFVRxnFTrur7FI6tiHH35Ys05qeNSmfqWj4cOHV/m6t7d3hZqXRERENaULDIDvjGtg2rwTXoP61PrARfv74sqOLdXjSH4RlqQew5JDR7E3p+Z/i6VW448HMtQj0s8bI9tEYHSbSPQID4JOp8PGrJN4bctB7D7hfHBR64J8Vd+xQ35uxf++uFbwuf4y6MM5wo+IqDHJLD97vXtXP7jFqk+fPlXuqym266OPPsLWrVvVc8lO9NBDD9X4vURERERUd74GPZ4/szOeWb8Pv6QcdQhAbsTg7KPYERyK7SNHYoqfe2cv3HosF0+u24dDeUVq+biPL95r3wn37dmhllP8A/Bax24I7tYB7/Vrjwi/2sVZqGlp9OBjQ2jXrp16NBTr6E/7gFptOAYrHffXGGS25a233oqcnPIpzp06dcLdd9/d6G0hIiKqDZ2vb50Cj45aBfrh2s6t1ePAyQKVYlUCkcmnLopr4mhRKb7bl64eLQN8ERfkh38yK6YPsTo7KwMP7HZe18AwbBC8J54HnbdHXI4RETUpSUlJmuU2bdrU+L2O2zruq6m1S7Z76623bMsyYDUsLKzGn0tEREREp8dLr8NjAzqokjIyY9A+BevUg/vwVVx75CdlIFunxx294tVAaGGxWGzPXanUbMaHO1Lxxe7DcCxcsyi2Dc7NTMeW0DDMS+iA2/slYnx8lFu0mxoW73bVgJ+f32kFD4uLiyukjG1MpaWlmDlzpqbmh3QmZ82aVeG/rbaWL19ebdrVxx577LQ+g4iIqCqm7Xtg3p8CrwtG1vjiNSEkADd1C8CNXduoWZAShJRZkWkF2r/ZVZFtq9t+SHZWxcCjjze8r5wAr/49a/xZRERUvw4dOlShPEhNSX1F+XsjN3uc7asptcsx3apktrnwwgtr3W4iIiIiOj16nQ739G6HYG8DPtlVVrKlRG/AB+3L0+h/sy8NeUYjHuqXqErCGP9YBdOKddC1joG+VQz0rWPVc114C+j0+kb5Sg7u3I+sOYvxU4fuMDvJGGnR6XBvrwHoHx2Kzwd0QGyAe8/eJA8MPkoH6ciRI8jOzsbJkydVJ0hm5sXGxrq6aRVqNjoGE6tTVKSdUREYGIjGYjabcf/992tqh8jnf/DBB/UyW7S676e2s0SJiIhqw5SUjJJPvgdKjbDkFcD78gtqdYEtN2k7tQhUj1t7xGFbdp6qD/l76jE1y/F0nBEdij7DL4Puo69hOZJR9nmxUWVpVmMdiscTEVGjZ4axkgGZtRmUKZltZECptZxGXl5ek23Xxx9/jC1bttj6vU888USd2k1EREREp0/uUUzrHodgby+8sTXZ6TbzD2Yhr9SEpwZ2BFLTYTl2XD3MW3aVb+Tro4KRKijZOrbsuTx86y/NqbGwCNu/XoDEzdsQA+AWvRf+16Wn07SyM3rE4ZLEWBVgpebD5cHHtWvX4quvvlL/SuDR3jPPPINLL720wns2btyIzZs3q+f9+vVTNR8bkmOwUIKjtZGbm+uy4KN0HhctWqSpP/nuu+82+DEjIiJqaObD6Sj54GsVeBSm1RvUOn27NtBHhkMXFQ5dZBh04WE1Sm0qF/k9I4LV445e7bDp6EksOXQMfxw+puo+1lTbID/c2asdzoptofZpvv4yFL/8AQw9OsH78gvr9WKfiIjqlhlGHvZ9pNqS91iDfDJr0Gg0wsvLq0m1yzHd6l133VWrmZZ1lZ5elkqsquw5RERERM3Zfzq1QpCPF55fn1QhjalYdjgb0wu24+n9hxDubAfFJTAfOAQcOASTdZ0O0Mm9klMzJPXt42DolFCn9qX/sxnmHxajU2Ghbd3YjCNYHNsam1qUt6h7eBCeGNABccGNmwmSmnnwMSsrCw888ABWrlxZ6/fK6M/nn39ePe/evTt++OEHNKSoqCjVibPOeKyus+RIZnTaa4wOnXjppZfw3XffaWYhvvnmmzjjjDMa5fOJiIgakjktEyjSpkK3pByBSR72K+UCOyy07CL7VFBSHxkGfaf20Pk7n1Ei6Uv6R4Wqx7192uHfzByVlvXPw9nIN2r2bhPoZVBpXC/tEAtvu9mX+ugI+D4wvSztCUf5ERG5XKHdTRLrjMHacgwMyj6Dg4ObTLsk09BDDz1k6+P26dMHV199NRqDpHativRbu3bt2ihtISIiInJXE9pFq/sMj63dC+OptPr29h7LQejxnJrv0AJYsrLVw7x5Jwx9ulUafLSYzU6zSpmyTyDl/35C7P6DTt93SWqyCj566XS4qVsbXN2ptapnSc2TS4KPycnJuOqqq1QAsi6kIzJo0CA1W3L79u3YsGGDmgHZUPR6vUpRaq2ZKGlUpe0SlKyJw4fLcjRbJSYmoqG9/fbb+Oijj2zLBoMBL774IkaMGNHgn01ERNQYvAb0gs7HGyWfzQEqCQjaLrCzc9QDew7YVvs+MsNp8NFSXAxLxtGyQGWAP7z0egyODVOPB/qasSb9BH5LPYq/046j2GRGeGkpnj+ShPgrLkBYK+fXBvqIsPr5jyYiotMmfSPHQFxtOb7ndGc9Nna7PvnkE1s2IQn2Pf3006rfS0RERETuY2SbCBWAvH/NbnX/wZ5Rp8f0fmciMT8XiXm5tn9DjTUrIfN9oQX//L0DMQE+iPH3RYy/D6IDyv6N+Xw29CdzoZf6ka1i1b8nUzOgW7wcsaaKmaFKdHp8Ed8e37ZNQGKIP54Y2FGVt6HmrdGDj1J3Ytq0aZrAY3h4OM477zw1i3HhwoVYs2ZNtfsZN26cCj6K3377rUGDj6JDhw624KOQuhgjR46s0XutNTTs99WQPv/8czXD0UpmWUhnUo4ZERGRJzH06gqfW64uq/uYr50xUiWZDRnewulL5uQjKHnr87KFQH8VhLSmcTVEhmNoZBiGdWuFon7tkbX7ICK//xm6Eyeh+/pHWO64vkYpXomIyHWktqH0kSynRpBbZ//VhgxItZKgndRabCrt2r9/P2bNmmVbvummm9CpUyc0luXLl1ebdvWxxx5rtPYQERERubMzY1tg1tCuuHvVLlXr0cqs02FvcIh62FgsiCopLgtGWgOS+bloU5AP7TA3YJ23H9ZmOpk5abFg3oFUFcQ0ZRwFNmxXqyurRL6uRQRe69gVaQGBuLpTK9zUrS18DBzURi4IPsoIywMHymcdyAzIe++9V3W0hHX0ZXXOPfdcVc9QLFu2DA8++CAa0uDBg1Vg1GrdunU1Dj6uX7/e9lxGlQ4cOBANZfbs2baUtFb//e9/cfHFFzfYZxIREbmSoWMC/J64E6atu8tTiBzNhuVoNpBXVvfKkUrDWkmQUL3PKr8QlvzDMCVrsxgovj6IknqTZrMt5Wvpj7/C57Lx9fRfRkREDUECfBKUs9ZGlICdBPxqkxrbPshXH4HHxmqX2WzGww8/bAtstm/fHrfccgsaU2xsbJWvS5+ZiIiIiMr1jgzBu8O6Y+aKnTheXMXMRp0OWb5+6rEmojwzk6/JhHb5eeiQn4v2p2ZI7gt0XjJAgpc1mT2Z7e2DdxI7Y2l0S7QJ8sN7AzqodhK5JPgoBe9lVp7Vf/7znzqPaIyJiUFkZCSOHj2KgwcPIjc397RrbFQX7JSRo9JZExKIlKCpY2ocRxKktE+7KvUWG6qdixYtUsfTOlJW3HPPPSrAS0RE5Ml0vr4qDasjS2GRCiaaJSh59Lh6LsFJXYvK/xbL6zVSrK03KUxbdsIybgR0QUwvQkTkziIiImxBPklVKv3KmpbVOHbsGEpLSzX7airt+vTTT7Fx40ZNhpy61JYkIiIiosYlaUw/P7cnPtqZim3ZucgoKEF+VSVo7BQbDNgdEqoe1ZHAZHXmt2yDDxI6ItfbBxe3j8GMnvEI8Ko6TkLNT6MGH6U2o6RdFS1atFDBO0e1GdUp6UulMyYkJeqAAQPQUKTjNmTIEKxYsUItZ2RkqADkhAkTqp3pae/CCy9skPb9+eefuP/++23BUXHrrbfi5ptvbpDPIyIiagqkpqOubSvo27aq8XssRcVqtKCkGqkNfYd28Jl6MQOPRERNgMz4O3TokG05LS2txkE+2dZeYmJik2nX0qVLbc/79++vBgivWrWqyv1mZmZqlvfs2YOQkPJR7XFxcWjTpk2N2khEREREdRcT4ItH+pdf4+WVGpFZWIKMgmJk2P2bWVCCjMKy5461IquzoUU4bu17BtqfStvaIa9stmSgyYQ9QcF4s0NXbAsNQ5S/D57pn4gzY5yXtCFq1ODj1q1bNTMJAwNPb1ZAaGiopi5EQ7vjjjtswUch6U1lJqPMwnRm/vz5+P333zWdv+qCj5I29Z9//rEtjxo1CmPHjq3yPVL7cubMmZpRrlOnTlXtJSIiotrxufwCWC4eC0t2jm2mpDWNq0rreuyETEfRvMdr9FB4jTsHumoyIhARkXuQvpl97UEZzNqrV8UZ9M7Ito77aortkiw91113Xa3b+Pbbb2uWpd8pA1+JiIiIqHEFeXupR/uQspJ2jiRD4skSoyYwKUFJW3CyoEQFL412g69LDAbsCGmhHlY6iwUBJiPyDV5qsPbYuEjc0zsBIT6NXtWPmpBG/XVkZ5enMauPgvbWOpHCOqOyIUmnT4KHElS0/vdceeWVePHFFzWzLmX06Ndff42XXnpJM6PzgQceqDZN66ZNm2z7F61bt64y+CgB3enTp9tqdojLL78cDz30UJ3/O4mIiJo7nZcXdNERgDwcWMxmWE6cLAtE5uRC3yYW+lbOByIREZF7cgzoSZaeSy+9tEbvlW2r2pcntouIiIiImh6JSYT6equHpG11xmyxqDqSGXYBSRWgtAtYHi0sQb6XN9oE+uK2nvE4t3X9lR0gz9WowceioiLbc19fX6fb1Cbtak5OjtNAZEN65plnVI1J6yxOqecoNRVlVGlCQgIKCwuxc+dOTaBV3H333Rg+fHi9t+e2225Dfn6+bdnb21stO0tpW5Wzzz4bEydOrPf2EREReRqdXg9deAtAHkRE1CRJ/0dqHcrAUfHXX3+pTDLSn6qK0WjUzEyUfq3sq6m0q2fPnpX2xSuzf/9+VXbEfiCxfT1JplwlIiIiarr0Oh0i/HzUoxuCnG5jNFtQZDIh0MtQq/gNNW+NGnyUOo9WJ06cOO39JScn256HhYWhMfj5+eGDDz5QMwulzqJVUlKSejiSTuJdd92FG264oUHak5urLQArHdMFCxbUej9y/Bh8JCIiIiKi5iAoKAhnnXUWli1bppaPHj2q6iGef/75Vb7vjz/+QFZWlm156NCh9ToQtqHb9fDDD9e6TY899hi+++47zQDY6kqDEBEREZHn8NLrEKRnilWqHT0aUXR0tO35jh07Tmtf6enpmmBffHw8Gkt4eDjef/99lVZV0q06i/ZLR0+CeXPnzm2wwCMRERERERHVzY033qhZlv6dfVYZRwUFBarkhr2bbrqpys9IS0vDqlWrbI9du3a5RbuIiIiIiIgaUqOGqwcNGmR7vmLFCpU2NTQ0VLNNTaftfvrpp7bnku40Jqbxay1NmDBBPWQ06qFDh9RIU0lhI0FWaZPMkqwtqedhf5w6d+5c5fbPPvusmu14uiRtLBERERERUXMhA0lHjBhhy2gjJTVmzJiBWbNmqRmI9vLy8jBz5kzV77MaOXIk+vbtW+Vn/Prrr3j++ec173nnnXdc3i4iIiIiIiKPCT5KgKtt27aqYyS1EV9//XU8/vjjtd7PmjVr8OWXX9qWR40aBVeKjIxUj/rQp08f9aipcePG1cvnEhERERERNTdPP/00Lr/8chw5ckQty+zEMWPGqEGhXbp0UetktuKcOXM0aU1bt26Np556qtm1i4iIiIiIqCYaPVHvtGnT8Oijj6rnX3/9NVq1alWrlDCLFy9WdSqMRqNaltmFU6dObbD2EhERERERkWeSrDXvvfee6pNmZGSodZLZ5t133630PbGxsfjggw/qbQBqU2oXERERERGR29V8FJMnT0b37t1tyy+//DKmTJmC33//Xc2GdObkyZP45Zdf1HZ33HGHpt7F9OnT2bkiIiIiIiKiOpFSF/Pnz1clNby8Kh+fK69NnDhRbduhQ4dm2y4iIiIiIiK3m/loMBjUaE1JIZOWlqbWrV27Vj30ej18fHxs28pIz7fffluN9DSbzRX2JbUsJPhIREREREREVFehoaF46aWX8OCDD6psO/v27UNmZqZtFqIE9caOHYuIiIha7Vcy/QwePNi2bE2Z6up21bZ8iv1/A2dWEhERERFRdXQWi8UCF5C6j3fddRe2bt1a5xmUTz75pCZYSe7n2LFjtgCxBJMbslNMRERERERETRv7kERERERETV+jp121atu2Lb755hvcfffdtRo52a5dOzXy8/nnn2fgkYiIiIiIiIiIiIiIiKg5p1215+3tjWnTpuG6667DH3/8gX///RebNm1CVlYWcnJyVKrVFi1aICoqCv3798dZZ52Fs88+W6VuJSIiIiIiIiIiIiIiIiL34tLgo5WkTpU6FfIgIiIiIiIiIiIiIiIioqbJZWlXiYiIiIiIiIiIiIiIiMizMPhIRERERERERERERERERPWCwUciIiIiIiIiIiIiIiIiqhcMPhIRERERERERERERERFRvfCCi+3duxf//vsvdu/ejezsbOTl5cFoNNZ6PzfccANGjBjRIG2kujOZTLbnx48f56EkIiIiImokLVq0gMFg4PGmJoV9SCIiIiKipt+HdFnwcdWqVXj55Zexffv2etnfhAkT6mU/VL9Onjxpe/7QQw/x8BIRERERNZL33nsPERERPN7UpLAPSURERETU9PuQLgk+vv7663j33Xdd8dFERERERERERERERERE1EB0FovFgkY0Z84cPPLII5VO6QwKCoK3t3et93vnnXdi7Nix9dBCqk8lJSVISUlRz0NCQlyS9ikzMxOXXnqpej579mxER0c3ehuo6ePviPgbInfAcxHxN0S1wbSr1BSxD0megtdtxN8QuQOei4i/I2oWaVeLi4vx6quvatb16dMH1157LQYPHoywsLDGbA41Ah8fH3To0MGlx7q0tFQ9hPzGmHqK+DsinouoqeLfNOJviIg8HfuQ5Cl43Ub8DZE74LmI+DsiV2nU4OM///yDY8eO2ZanTJmiZkHqdLrGbAYRERERERERERERERERNQA9GtHWrVttz9u1a4cHH3yQgUciIiIiIiIiIiIiIiIiD9Gowcfs7Gzb8/POOw9eXo068ZKIiIiIiIiIiIiIiIiIPCX46Ovra3seGxvbmB9NRERERERERERERERERJ4UfGzZsqXteUFBQWN+NBERERERERERERERERF5UvBx4MCBtue7d+9uzI8mIiIiIiIiIiIiIiIiIk8KPnbp0gU9e/ZUz//880/k5OQ05scTERERERERERERERERUQPSWSwWCxrR2rVrce2118JsNmPixIl48cUXG/PjiYiIiIiIiIiIiIiIiMgTZj6KQYMG4fHHH4dOp8O8efPw4IMPIj8/v7GbQURERERERERERERERERNfeZjSUkJSktLsXTpUjz55JMq8BgeHo5JkyZhyJAhaN++PYKDg6HX1y4u6uvrCy8vrwZrNxERERERERERERERERG5WfDxkUcewZw5c+p9v8888wwuvfTSet8vEREREREREREREREREblp2lUiIiIiIiIiIiIiIiIi8kwMPhIRERERERERERERERFRvWj0IolnnXUWAgIC6n2/HTt2rPd9EhEREREREREREREREZEb13wkIiIiIiIiIiIiIiIiIs/EtKtEREREREREREREREREVC8YfCQiIiIiIiIiIiIiIiKiplnzkaixlJSUYPny5di8eTMyMjJQUFCA6OhotGnTBqNGjUJ8fDy/DCJyS1lZWfjtt99w4MABZGZmwsvLCzExMejcubM6fwUFBbm6iUTkIseOHcPGjRuxfft2HDlyBDk5OfD19UWLFi3QoUMH9OvXD927d6+Xz8rNzcWSJUuwZ88edS1lMpnUuSghIQFjxoxBREREvXwOEZG7MJvNWLt2LdasWaPOe3KOlXNdbGwshg8fjh49eri6iURETuXl5anrtt27d9uu2+QemPW6LTIykkeOqBmfHzZs2IAdO3YgOTlZXd/IfaaQkBB1f7xv377qYTAYTvuziouLsWzZMmzdulWdi4qKihAVFYW2bdti9OjR6l9qPljzkTyyw/jxxx/jgw8+wMmTJyvdbtCgQXjqqafUhRiR+Oqrr/DHH3/U6WAEBwfj9ddf54H0UNJxk5v8Bw8eVBdq8m9qaipKS0tt20yYMAETJ048rc85evSoOi8tXbpUfaYzfn5+uPzyy3HPPfeogAM1rQt++f3YP+Q7ty+//cYbb9QpuHznnXeqQFFdnHvuubjqqqvq9F5qHOnp6Zg3b566obRt2zbNb8aZTp064aabblLnpbqQAVsvvfQS5syZowZzOSOdVbmR9d///hdhYWF1+hwiIneycOFCvPDCC+pGWWVkkMeTTz6JAQMGNGrbyH3JgOf/+7//q/P7X331VYSGhtZrm8g9yPWanE+sfUh5pKSkoLCw0LaNnEtuueWW0/oc2d8rr7yC77//Xt30d0YCCueddx4ee+wxhIeHn9bnUeOSwI38bqz3IeS59A3k3qfVgw8+iI4dO9Z63/I3TwYZ1kW3bt3UPQlyXydOnFDXNr/++qsKPNrfv3KmVatWuOaaazBlyhTV16stuYcl9+LlnnxV9yYGDx6s7nvFxcXV+jOo6eHMR/IocrNs2rRparRqdWSbSZMmqT+2cvOMKCkpCStWrKjTgZAZJ+R5JAgoN+APHz5c7YVanz59TuuzZCbTrbfeiuzs7Go7H59//jlWrlyJTz75RM1CIvd29913459//lGBxupUFuipzurVq1Xnoi6YCcC9yf/v//vf/zQ3GKojNxHuu+8+/PLLL3j55ZcRGBhY4/ceOnQIN9xwg7rBURWj0ag6s3I99f7779fbbEsiIlcECB5++GH88MMP1W67b98+dVPurrvuws0339wo7SP3JgMU69qHPJ1rP3Jfu3btUtdhEiSSvltVanON5oz0U6+//noVlKouKCDXhf/++y/ee+899OzZ87Q+lxqeBJR//vlnFcCubuChzGKri02bNqmgVF1UNlia3MOff/6JGTNmVHsfy/HvmfQ7Fy1ahFmzZqmsD7UZaH3jjTeq+1o1uXchA/flNy4Docmz1XvwUf6wyqwMd+Fu7aGGI3/45AavfeDRx8cHF1xwgRpNJjPT5EbaTz/9pDqN1hFi9957r5r+LWnKiIjsyYjC6jpy9UE6pjLi9fjx47Z1ElS89NJLkZiYqC4YJcWi3BSzjiCT89j06dPVjN2AgAB+cW5MUpvUJPBIVFkaZsfAo4wSPfvss9UMHEmnJTcu9+/fr0a12o9eltn8Mqjhww8/VNdE1ZGMETKIyz7wKKl4Lr74YjW6WUbA7t27F7Nnz1btsrZPzkWyrjYdVCIid/Hiiy9qAo86nQ7nnHOOSrMqaQrT0tLUTfv169er1+WcLDfMpA8pg1mJiBwDQXWdTVYbcrNfrsHs+6ty38t63ebt7a36jJLJwjqjW/ok0u+UWZIyy4nce3C83I8gqgsZ1O4YeJR+49ChQ9WgUbmGsd6LkhSp1mscsWXLFkydOhVff/11jWZKy6DUO+64QxN4lCxdkoVH7rXLIAvpX8q1lpQWsk4eknv4X3zxBQdDeLh6Dz4+99xzakSGRNddORtDbvRJh2Ds2LHq5i15Pkl1IidMK7kB9tFHH1VIPSAjMWQmk0wDF3LDTtLVSX01BqrJ3ttvv12jm7VCLuzJc0mampYtW6Jdu3bqpr/MFJMb/d9991297F/SldgHHiUPvvwNs0+rKiPDZIS9PCQQaf1bJ+ezxx9/vF7aQQ1LLrrltyO/IetvSc4zMmK5PvXq1Qu33357jbdv3bp1vX4+NQz5OzNu3Dhce+21lc4yvO222/DNN9+o63FrZ1PqlknwUV6rzjPPPKNudNj/lmRWo2OnUwKUMuvHmqpcatM+8MADapYmEVFTIjPWJJOElQzoevPNN9UAD3sy21EGWcg1l3W2h6SdHjhwINq0adPo7Sb39dBDD6F9+/Y13p4pVz2XDGSQm/1y/W99yP0nOcfUh2effVYT5JTrQ7nmc6zJLf1H6W9KVh/rwLH7778fX375Zb20gxqW3KeUGnn2/cgff/yxRjPMakOCUdKHqCmWXWg656ERI0aodKpDhgxxuo2U65B+nfTnrOXLJEgo5xi5L1UdufdunwFA7i/IOse/hXI/Xn5jEnC0TgiSoOXixYtrfO+Vmp56Dz7KhbiMqpGp4ZMnT1bT/xuzkKicfCW/sAShJAgqwUfyfDLiS26OWcnI/HfeecdpznM58cqFltRrkxkCQkaByclPTrhE9nnITzcNCjVtEgSUCzT5O+YYYJYb/PVBBj7IyDKrzp0747XXXnMa0JbR9/I3bvz48bYUm3Ij7LrrrmO+fDcm36d05uT7c/Tpp5/W++dJoGjYsGH1vl9yDbluGTVqlKrlUt01tWz7n//8R50/Hn30Udt6uRElo1er+psmN6/mz5+v+R05Czxab4JIfVKZ8WPNJiFBTul0ymhaIqKmQO4XSL09e1KDyDHwaCWDmmUWyltvvaWWZZCHBBFk5iSRfSmG0y3HQE1b165d1T1RCRY5DnBft25dvXyGDBaTeuD2ZWCcBR6FtOH1119X92itwUpJvyr1SmWGN7knCRjLIBeZWCHX+PakBEt98/f3Zx/Sw8isw0ceeQQ9evSodltJfyr30SVIac26s2DBAjVTWrLtVDXTWwKNVtIPldTOzgbh6PV61UeV+/HWyUMyEPvbb79Vn0ueSV/vO9TrbelOZXqu1NKTdE/yR602tWpqIz8/X918veyyy3DFFVeoaL01H7bjCZo8k3z/9rOGJM1EdbWH5IRnf3NfZkIyZzkR2ZMZ/HLR1JAzWyWYaE8uDqv6PAlg2c9qkxtf9iP2yT1vQDgLPBLVhIwQlRmytRnMJzfI7TuZMqq0unpU0mm0v1afOXNmlWl2ZHSqnK+qOp8REbkzuXlrzSYh+vfvjwsvvLDK90iKQ/sU0zJoQ9KyEhHZp6yXAaUNmVlLrtvs719J/9BZ4NHKcWCa4HWbe5PyK5J9ife1qS5k8KoMmK9J4NFKsjlI6TJ7S5YsqfI98hnW0kDiyiuvRKdOnap8jwTVrfEjIQMnqqtrSk1XvQcfpX6e/NCsPyL5Y/j777+raf4ygvCxxx5TgUgJGJ4OSe8ktfskvavMSpE/ops3b7a9LiO7ZXabpKkjzyczh+zJqP/qSPoLqeVhJcFLGf1FRNRYZPT81q1bbcsS6DzjjDOqfZ/8bZORiVaSQqehBvgQkWvVNR2bzNy2J2maq6rT8eeff2rSDkqNjppkCJBR/faj+aW+CBFRU+xDykDm6sgNfBnoaiXXX9ZUhkREjUHus1pT3wvpF1500UXVvk/6mQkJCbblDRs2sC49kQcPgqiPPuTOnTvr/VpK0rLaZ5mQGM+mTZtq3VZqpsFHuUHyxBNPYO7cuRXSLklhY6mPJYHIQYMGqVHZEoyUPOMSkNy1a5e6EStBIEmjKTcvZPqtpKOTH7OMypFp5zKbUn6kkotYIvAyy9I+3abMgJR8wTfccAPrsDUD8juxP0nJSaxLly41eu/IkSM1y+w4ElFjksE5VZ2TKhMcHKxGpVlJ3Q77AThERK1atdIchGPHjlV6UGTwlaTMqW3acRmJbT+QS26G2dffJiJyVzLC3v7mvQyetj+fVYV9SCJypfXr19tKcFiDikFBQTV6r6RWtB88YX8eJCJy7ENKLKcyEsOxzyAhgxtkxm5N8Fqq+aj3mo9W3bp1U2ks5WbGu+++WyEftYywlqCifZ2r0yGpnyRFiqRBkQK81HzIrCH7GT99+/at8XsltY69+vo9EhHVhGPAUHLy1+b89ddff2n2VZvzHxF5NvtgovD19W2wc9Fnn32m2Zf9rCAiInckqVJl8JZVx44d1eCumpCBrjJAw5rNSfqQEsxkajwiagyne90m92rt9yUTOIiIatuHdLyHXttzUVX7Is/RYMFHK5mZIQ8paiwzHH/55RecPHmy3vYvgUZJPyfTellPqXmSQtv2JLd+TUn9JEktVlBQ4HRfRETuev5y3Hb//v311i4iavocU+TY1yhzxHMRETU3p3PeMxgM6NChgy0AIH1JGf0vtbmIiBqaY7/vdPqQvAdGRK7oQ0rJIUllX1paynORh2vw4KOVFBt96qmnVG1GSbEqKZlkNqRcpNeGjCaUfQ0bNkxN0eUsD3I82TlOEa+OnEitF2+S7jcjIwMxMTE8sIQVK1aoP7xHjhxRgyZkhHOLFi3UaGcZ0VPTdAJENek4yo2s2px7HG9wseNIVnK+krrY27ZtU7M65IJe0uLLIK3evXurUYZhYWE8YB5MShI41t8488wzG+RaSs5Fcn0us36c7YuIyFP7kPazj2R/DD6SkN/FmjVrkJycrEoKST0+qb0lfUfpQ0qWMEnzS1Rf56/anHukvym/P2v2MA5gJauSkhI1YUhmoEkNPpndL+eu8PBw9OjRQ00s4r1Szzdv3rxG6UPKeSg6OlqV27OWCJG/mbxP4XkaLfhonx5VipdaC5hKuhPJDyw/WPnBSS7hwsJCdaNMpvbKzX45uUkdP4mgd+/evc5FU8kzOdYwqu0fQ/vgo3V//INKYubMmVUeiF69euHGG29UdWiJaktGyVtnXQu5qJe6xTXleJ6qKhc/NS8bNmxQj8rICMNx48Zh2rRpHEThoT755BPk5ubaluPj49VNg4a4lpJrexmYI51Fay1uuaHFG6tE5M4cz3tyA6w2HGcC8DqMrJ577rkqD0a7du1wzTXXqOxdMviQqLYczze1uW6TfkBERIQt7bSkWJT7r7KemjeZHHTnnXdW+roMNhw+fLjqQ9YmvSY1HYsXL1aZK60kU+CIESMa9H68Nfho7Ucy+Oh5Gj346EhG6Mhj1KhRrm4KNVHWWhtWMrKwNhy3tw8GEFVFRoRJgPL888/HM888U+Mi70TOzjVyYVcbjtvz3EU1JTcYZESjdC4efPBB/Oc//+HB8yD79u3De++9p1l32223VVmLzPH8UdtrKTkfWYOP1v3xbyIRefJ1GPuQVFcHDx5UWcHmz5+PV199tdazboka4vwlWVKIqiJZTv7880/89ddfuOmmm3DHHXdwAIUHkcCf3Ne0JwNlqjo3nG4f0nF7x/v75BlcHnwkOl2OJzs/P79avd9xe57smjf549ezZ0+VWjUqKkqNupGH3KyXEYabNm3CH3/8ofndSWoKeU0Kt1dVjJmoqnNXbX87sr19qkOeu0hGGsr5S+pQWc9dwcHBKqV4SkoK/vnnH6xdu9Z2oIqLi/Hkk0+qf6+77joeQA8g5wG5ESDfqdXgwYMxYcKEKt8nWUesZOR7bWdiOJ6/pB0MPhKRJ1+HsQ9J9iR7iWTp6tq1qxpcL9dgktVErtPlhq6kwpc+pP0skY0bN2LKlCn47rvvVGp8orqcv6Q/KFkoTvf8xeBj8yUZTKQPKSXO5Fwk5y/5PUj/QEoQrVu3TpUkMhqNanvJcPL+++/jxIkTaiAFNX3ynd5///22GdHWzDnTp0+v8n28H081weAjNXnWP4BWtb1h5pjm0FrslpofSaEqs4CquniXDqLUU3vjjTfw5Zdf2tb/+++/eP755/HEE080UmupqXM819Qm5ar9e6z74bmrefv+++9VB6EqM2bMwK5du/Dwww+rlPdWL7zwgrphNmjQoEZoKTVkp/G+++5TMx/tbyb873//q3LWo9wYtT9/1CUFHK+liKi5XYfxvEdWUlLowgsvrHbQjdRT+7//+z+8/vrrtt9famqqSnNo368kqs09sLr2Ie2xH9l8vfjii6rMWVXlEmSWowQh5V7X8uXLbetl4IT0IS+//PJGai01lJdeegl///23ZjDqyy+/XO1MRsdzB+/HkzOsck1NnuPJ0H60f00UFRVVuT9qPiTlTU1GDUrd2f/+97/qBr7jzX/HgstEDXXuMplMmou92qbbIc9SXeDRSmZ1f/vtt5pAowSfJEBFTZukyfn99981nUa5welYl8yRBCbtR8DLzdHacjx/8XxERJ5+HcbzHlnJTKGazPaXfqYMdn3nnXc09fVkEOuSJUt4QKnG7K/bpD8oA9Bqg/fAyKpt27Y1qtMu98o++OADTJ48WbP+zTffZAamJu6LL77AJ598oln3+OOPo1evXtW+l9dS5FbBR7k4X7lypW2k11tvvYWvv/5a1Uyr7R9KoqpucNX2ppljxzEwMJAHmGrk2muv1RRflmDQjz/+yKNHdTp3ne7ACZ67qKbk5pfUGLL/DcpMyJ07d/IgNlHS8f/qq680o05ltKqkXK0J+9+CXJfX9lqK5yMiamp4HUauMmzYMEydOlWzbs6cOS5rDzW/8xfvgVFdSckOCVhaSfkhqQNJTdPPP/+MZ599VrPu7rvvxqWXXlqj9/Naitwi+Ch1huTmxxlnnIHrr79e/ajfffddzJo1S5205ActF19yw0Ru3BPVluMoQ8k7Xhs5OTmaZd7Ap9qQ85o9+1QFRFVxPNc4nouqI+l/q9ofUVWkpq2kCLPH81fT9Pnnn+Ptt9/WzGSU+itjx46t87VUbc9Hubm5mlRerH9MRO7udM97vA6j0yG1tu1Toktd7rpkHqDm6XTPX/bby6w3Zqyg2gxivfrqqzXr2IdsmpYtW4aHHnpIZUGyT7E7bdq0Gu+D11Lk8uBjRkYGrrjiCnz44YeqUG1lpKCp3CSZOXNmrUfsEEl+cntpaWm1Oij220sHwHF/RFXp37+/pmZCSkoKDxjViKRbsk+HKDfvZcBOTUndBXtt2rThkadakYFh9nj+anpkpoTUG7YnnchLLrmkVvtxPH/U5lrq+PHjKCgoqHRfRETu6HTOe4LXYXQ6IiIi0KFDB9uy3C+T+2JEdTl/OZ6PqiL9Tfs+p9z/qqo2OJEj9iGbvjVr1uCOO+7Q1I+98sorce+997rsWkruq1ZXLoSapgYLPsoP+NZbb8XevXtr/J6lS5fiueeea6gmkYdKTEzULEvR9pqS0YX2F/mSx5yjvqg25A9kWFiYbVluwHIQBTXG+ctxW8d9EdVk9qO97OxsHrQmZNGiRar+sP1o1TvvvFOlBK8tnouIqLk5nfOes+3bt29fL+2i5oPXYeSK89ehQ4eq3BdRdXjuato2bdqEW265RXPfcuLEiarOY205XvvU5lyUn5+vBrFaSTpfmVlLnqfBgo9Sz3Hbtm2adfIjGjhwoErzdf7556Nbt24VCtt+9913qg4kUU117NhRs7x58+Yav1d+a/bpfu1HHxLVpdaVnNNkRhtRTTiec2pz/nLclucvqi3HOn1Mldl0SG2V+++/X1M3XdLkSEfS1ecix+syIiJ3JDfM7O9F1Oa8JykLDx48qBnA6ph6jKg6vA6junK8bqvNPVQJPFS1L6Lq8NzVdO3atUv1Ge2z1owZM0Zl0qnLDOjTuR/PPmTz0WDBx2+++UazfNVVV2HFihX48ssvVQ3I119/HT/++CN++eUXDB482LadjN52fC9RVbp3744WLVpoLrxqOvNs3bp1muWzzjqLB5tqRQps29e6klmQjoMqiCrjeM5Zu3ZtjQ/Wv//+a3suF4pDhgzhgaZaOXDgQIUUYNQ00uRIqYLS0lLNdXZt0+RUdS6yP79Ux3FbXksRUVMgwcLevXtrZv8nJSXVuA9pP+uc5z2qLfn9JCcna9ZFRkbyQFKD9yEd74ENHTqUR51Oqw/Jc1fTsH//flx//fWamtXDhg1TMRqDwVCnffbp0weBgYG25Y0bN2r6qFVhH7L5aJA75HLRLj9qK4mqP/bYYwgNDa2wbbt27fDRRx9h0KBBtnW///57QzSLPDjt5fDhwzX1EhYvXlyj986bN0+zPGrUqHpvH3m2X3/9VbPcq1cvl7WFmh4ZfGN/sSZ//2pS93Hnzp3Ys2eP5qIvOjq6wdpJnmnJkiWaZZ6/3J+MEHVMkzN58mSVfvV0SJqbTp062Za3b9+uOcdUNftn2bJlmgE4UguZiKgpGDlypGZZBkfXxE8//aRZHj16dL22izyfBICOHTumqbsXHh7u0jZR0yGzrSWTnNXu3bvVjKbqyKBp+/utco9WstMR1cZvv/2mWe7ZsycPoJs7fPgwrrvuOs3fHYnDzJo167RSncp7zz777ErPMVUNwPn55581g+kdr8nIczRI8HHr1q2aUfQyOru64JH9TRO5kZGSktIQTSMPNWnSJM3yp59+qkmnWlmNUfsguZx45aKfqKZOnDiB9957T7NuxIgRPIBUq4u1cePGaQZPfPHFF9W+78MPP9QsS45+otqQv4H2ow3lWsy+40BNI02OlDF45pln6pQmp7prKcfzjDNyvrIPhEpphbqOnCUiamzjx4/XlEuYM2eOpv5QZQOt7QddyOAv+0xORNWRWSEy08TeOeecwwNHtXLRRRdplj/44INq3yOZ6KS/aXXBBReoPgBRbfojjgN1eP5yb1lZWZg6dSrS09M1g9flXqafn1+99yE/+eQTTWkQZyQLpn19SJnN7VhLlDxHgwQf7QsYy0moJlF0GW1tX+jYMQUFUVWkw2c/e1ZmBb399tuVbp+ZmYknn3xSs+7OO+/kQW7GvvrqK2RkZNR4exkxdPPNN6vfklXLli0rdAKIqnPrrbdq/k6+++672LFjR6XbL1q0CAsXLtTMWLrkkkt4oJtxZ0LqZZeUlNT4PatXr8Z9992nWXfFFVeoWWvknqS22A033KAG6NlfY7/00kv1Fuz7z3/+o5lBLaNRHUc225M09/YDcAICAjBt2rR6aQsRUWPNHrr88sttyxJ4fPzxxyu9aSY37R966CFNSrHbbrvttGYNUNM2f/587N27t1a10uQazL7untTclhkpRLUh1+6xsbG2ZekfSj+xMpLV4p133rEt+/v7Y/r06TzozZgErGuSdclKznVyrW//N1BKv9inMCf3mzAhqVbtJ3h17dpVDTK1z8B1OmQCRt++fTWZeqoaxJqWlqYGz1r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",
       "text/plain": [
        "<Figure size 900x320 with 2 Axes>"
       ]
@@ -1768,7 +1912,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21f8ad81",
+   "id": "439e9323",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
@@ -1782,7 +1926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcd6066e",
+   "id": "081d2d4f",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -1797,14 +1941,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "4d7d6b18",
+   "execution_count": 18,
+   "id": "43dffd6a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:50:59.192580Z",
-     "iopub.status.busy": "2026-08-17T04:50:59.192442Z",
-     "iopub.status.idle": "2026-08-17T04:51:00.813249Z",
-     "shell.execute_reply": "2026-08-17T04:51:00.812204Z"
+     "iopub.execute_input": "2026-08-17T06:42:37.116235Z",
+     "iopub.status.busy": "2026-08-17T06:42:37.116142Z",
+     "iopub.status.idle": "2026-08-17T06:42:37.860209Z",
+     "shell.execute_reply": "2026-08-17T06:42:37.859851Z"
     }
    },
    "outputs": [
@@ -1839,23 +1983,23 @@
        "    <tr>\n",
        "      <th>zero share</th>\n",
        "      <td>0.300</td>\n",
-       "      <td>0.321</td>\n",
-       "      <td>0.297</td>\n",
-       "      <td>0.345</td>\n",
+       "      <td>0.322</td>\n",
+       "      <td>0.299</td>\n",
+       "      <td>0.346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>median |move| (bp)</th>\n",
        "      <td>0.039</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>0.042</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q99 |move| (bp)</th>\n",
        "      <td>0.298</td>\n",
-       "      <td>0.329</td>\n",
-       "      <td>0.265</td>\n",
-       "      <td>0.407</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.268</td>\n",
+       "      <td>0.410</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1863,12 +2007,12 @@
       ],
       "text/plain": [
        "                    observed  pp mean  pp 5%  pp 95%\n",
-       "zero share             0.300    0.321  0.297   0.345\n",
-       "median |move| (bp)     0.039    0.039  0.036   0.042\n",
-       "q99 |move| (bp)        0.298    0.329  0.265   0.407"
+       "zero share             0.300    0.322  0.299   0.346\n",
+       "median |move| (bp)     0.039    0.040  0.037   0.043\n",
+       "q99 |move| (bp)        0.298    0.331  0.268   0.410"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1912,20 +2056,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "3209ddfe",
+   "execution_count": 19,
+   "id": "a133f0fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:00.815039Z",
-     "iopub.status.busy": "2026-08-17T04:51:00.814886Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.040810Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.040048Z"
+     "iopub.execute_input": "2026-08-17T06:42:37.861358Z",
+     "iopub.status.busy": "2026-08-17T06:42:37.861288Z",
+     "iopub.status.idle": "2026-08-17T06:42:37.967091Z",
+     "shell.execute_reply": "2026-08-17T06:42:37.966709Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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zw9LqLGJeaDbH3XffrdHMZsbbDKChDs7b/e3Lv3eq8WDZbHMrxzXSRvr9U8vbohAbuB3MgL9lPlgpnKEayjapxvvdDoZtmw0mYGcnoYXKYY0Ee46WETHYA7zRwkpO2IFoPjZ7sJSAop0MFMrIsQPmfCXyanF/XGuKBa3t+MBKo40mlsGyzz77FM2OHO0nyqOdHfMNJmhpn0fL+it2fGu9BmwSQiVOhm1fZWVKh3Pw2gb08012Gg1qcXvbjNPBZkbY93Sh8j21xraVZfOO5OSg0aYW36O2H/ve977neuwMpsSNrYP16qkEK5VjJV5r8T002vePw4Xv0draXoWMRC/Z4djP/Otf/3LZn4NhmXHVnAxYi/v8WkRZMKBExWZGWz1Bu/Rng62VGhjvZrNh7MDfZiwP1OyqUMaKlS2ymdDWf8NKlNiXhB1k2QCrpTVbCZB89TJHiqXmF6tdb6U9dtppJzfb2qL6NsBo9Zbz+fnPf+6esz33wbJllKKSB9PVeP/U6rYoZjAHOr0PeOx9t+eeew56GYPdJtV6v1tKss1gG6x8PQ1Ggg30WHmlpYXVgregxECBJHs/2We5WGNxu02+z6OVqbLHGE3746Gw2XODCQ5aCYB8ZbksSFuIlW8rpSRjrbFZ3MWyYC1oZw1La+W1WNrYSe9gWdkMK9+Ur9eEHSOWUmpz66231hZbbOGOKWyg0r5T85XLtUkeNrPbZj8OpUReviCyPZ987DNopZ9sX2Ulbex4yGabW/amHf/27rdVDbW6vYdyfGXfOzaBxb5LbJuPBGsIbPtcK/9i28j6VNm+xibg2Gttfczy9Zfr3p/bAPkee+wxIus7mtTqe9T6PdhnuZj+x0E2g9z6mQ2lMsBA7PnaLHV7XrVitO8fhxPfo7WzvWo5uDLY/Yx931gGymDZ8gp9Zy2N+/xaNHaeCTCMbADSdhDFGsGW2xSrm9X2tYEpa7Jts8ntoP7RRx8tOCN75syZrtb5QAdt+WaRWh1/G6i3Jlv52GNa3WIbGLP6/EMZfB0Me/7FBk3tINhm3PcvV2Jf1LY9BspesJMqywCxdO/Bfin3/jK1kil2AGrl4mw97IDVZvO/+OKLFQsoVOv9U4vbohy2DrY+Nnhr73k7KbA01GLBAJtNYk3yRvL9Ua33uw2UlBK0sxMs68Nhg5gWhLV9S7VnqdvgvT23Ut5b9tmwAXzbP9uJom0rm5log8GVPpEe7r5f9hm3g/qBlBJcKdTnZccddxywXFUt74+Hwh5/MLOwLQA10IC+7WvtRKKQwTYPrzbbj9rAZHt7e97b2L6tVl6LpZ31D7Na85Z1ZJ89q9ldbEDMjvXyDXLYvsVqpRcaxLZAff/jSitNa+VMrS73QJ5//nlXptDK8FRSoe8n+062mbb19fV5b2Mn/Hb8a2VULYg/0kbD9l5ppZXccYF9v9pkLFu27e8LfSfboIyVTD3kkEM0XNZcc02XgWm9zor1WrDjCNvvWKA/33rbMdLSHFzJ14uwVt+j/Qc811lnHRf0t2Mb2xfacbhlmPZ/va1Hl5UVLsSOU2zg0L7H7fjejh9tH5Gvp0O33/zmN650ba18V432/eNIWdq/R6u9vWo5uDLY/cyNN97oPj/FMtxtm2yyySZqaGhw35s2flHt89Wx/v6tJIIrQBE2A9dSe4uVnrGDkv5uuOGGopF9S7H7/ve/v0TjZuuFYINW+ZqyGqtjbDUfJ06c2PM7W898ke2NN964YGDF2ADZVltt5S42MPbSSy8tMdvW+i18/vOf77l+ySWX5K23ueqqqxZsZml/780aTBdKE7SeGpaOORDrF2AzYPM1q7VSWHYwONSGyvYlYE06e2/3bvZFWKmZedV4/9TqtiiVvZ9sln3/HkkWGLA0ZDuYy8dOkuykYbAHe4PZJtV6v9tM9GI1cq2/hjXm7D9objXJq1lP2rK5is2esaw8e72t6aBl6/VnB732WlvPlv5Bt+Hcvw2FDRrlC67Ye9cCd3ZAPhBLJbd9QqHshIGMhv1xLbBgZbESB6Mxa8XYjDKbAV6oWXS+mWlLCxtstjri3ey4yWbpF/qOsIas+Qy2wbU1oLbB4t6NQu3zZ5/RQgNh+UpN2Hu6UNkPO+6wOuAD9SS0v5199tkumG1ZAgO5+uqrXR+4gfbRg1VoEML2oYUGDrsD2Xvvvbe72Hdc/8DhcL7Wo2F72zHjD37wgyWOOW3CgmVEF5pZa8e0wxVcsbr/FgguddDN1t+y32wC2a233pp34Me++3u/Z2rls96fZej0Xq9ibKDMBkDzsW151llnLfH70fAetYFem7wx0LGFrX/v/Z1lrVx55ZUFl7fsssu6yVc2I7s3O863EqeFAnT2/rn22mvd8WgtGO7941DUymeL79Hqbq9S5CvDP5LK2c/YJKK//OUvRT9bti/qfx5nnwnbbxbr5zpcRsM+v5YQXMFSb6CSFHaQZJFpKwVWyoGDHcx/4QtfWOL3hQ5cjQ382cHZQGwnZQdkdgCTb2DPBowtQ8GW03vd87HBNRustwPFUllApr/+z9UOOvOto81ss5OxUhXqFWNl16ypeyG77767O8nIN5vIou9DGcyz3geFSpjYTP9Kqcb7p1a3RSnsS/yaa65xr39/NtPMDv7sC77QDDXLUBtKcKXcbVKt93uxGWc2W/OHP/xh3kCyPcd8g+rD7a677ioaWLGgwEDvg977bHud7WLlEEdq/zYUtt3t5NOyzvK91vmCK4Wa3ttzGGhywGjYH9eKTCZT9DZDCWJXm32mCimlRMJYZsdUvfcDNhO60KDQLrvsUvH3vc0YtX1V/wkNNlhmMwcffvjhvA1UrdzLQGxwq9AEDZuRPdAJde8BEBvAzndSbVmlNoC95ZZbqlIKHQNb34/egfNibB+32WabjdhrXevbe7fddss7gG8Dz5ZpbRN+8rHvWjsGs/dqpX3yk58c9MBgvuCKTVazAbLe26sWPuv5Zg6XeixigTArM5rPhhtu6DLbB/rOqvX3qE0GsoBHvveDPX7v4ySblJLvmKr7WNFKe/UPrPR+LjYb3bZXPlZerlaCK8O9fxyKWvhs8T1a/e01GoIr5e5nrKJIoUlItp+xzI+BzuFs21oQx747C2WPDJda3+fXGoIrWOoNtiRFb1ZKqP8Otrm5uWgNdpsVXuxLy6K5haLdlorfe3DcBpgtmm6P35/NKPu///s/N+BuGSw2q8MuxWaqjBRLp7SdeD42AGiD5MXYQXC+wTw7eLSDy8GklNqsjHJmhg1Ftd4/tbgtSrXXXnsVHFC3Gdg2s7JQKTT7gh+scrdJtd7vVg6j2HvLyjwVYu8ZO+Ec6bKBNohbaAa9sdeg0PugP0vpHg3sANVm6tl2zxd0smBr/9q19h1XKEhiyxxoxlCt749Hm1psbFuqgfrJ9VYss3ek2X5+oHJ0xkqcjUV2PJAvU9QGraxkUr7JQvb6WXmn/ve3yReFDDSpqL9iJ8x2DFLJk+pCGWJW29tO5K3Eos14tmOkWinXMxq2d74sxG7bbbedW7Zl/Bc6xhqO4Ep/dpxj3zs2IGWDWlbW0N7j9vve++JiWbDVLsdSabb9baArX5nH9ddf3wUK8pUEq/X36OGHH15WoK1Qb7jukqnF1sW+b2zfYu+tgcydO9cNJFsGaLWN5v3jSOB7tPrbqxTVPmcodz9jAe1i351WMabQ+Z/1PLF990ir9X1+rSG4AgyR1fIfKHvAsl4KlQmxAb1STjCsdnChwfGBBkltMOvBBx8c8PZW4sUyGnrvsK1Xh0Wd7WDKdu62gytl0KzSbJvZQGA+doJUSuNbKzeTjwWY7ITLvvAHM3g/Utulmu+fWtsWpbJZTMXYiZINJOd7n73xxhtuuw9mVky526Ra73fLYMt3Eti9Tysl4GA110c6uGLbLN8sqO4Z9vY6jFUWCLH07IH2DVbezA6C7cS4t8ceeyzvAFF3wGY07o9rSSknhwNNeBgtimWm1MoEjd79WJYmFvwqNoPXMtQKZWJbJmv/93Gxsh1WK7tYhm33QEi+4OJQJjQMZKAZ5r3ZPrL3YIFtO9v/2MCnlUKyvgp2LFyNmbG1vL3tPGG99dYr6bigUHDFvlds0s9wsO9F2z42290GzW3gbqhG8367P5uYcsQRR+QNrNjra6VSC2Uq1vJ71Bx88MFl3b7Y8yklM8ICUbbfKDQIaY9TC8GV0bx/HG58j9bG9ipFtd9/5e5nCp0HdX9vFmMBGOvDUkqmfCXV+j6/1hBcAYbASvzYgehAM0GsBnshheqElnO7gZp022BZvuDKQCcj1nTMLg899JArq2Qzn22AznoJ2M58pBRrOG7p23YZKkvhHsxgnjUsGynVfP/U2rYoVSnbxA5MbJAgX4qrDSbbjOdiZXAqsU2q9X4vVBPdlHoCWI0TxWI9iOzEcazUbc1X+sMyRizVPl+Zrf7BlUIlwawsmn0eRuP+eCjsO856qZUr3yzOZZZZxi2zUOCvWMP7Wlbs+2goJc8q/VosjWxQtH8PjHKzjwYKpBbbBxSrIV6KYg1ey2V9rj71qU+5hrKlsL4BNnhgl7///e897y3L8rb+W+WU0R2qWt7epR5zFjsuKHb8MVg2eGX7kUr2gDD5AhGjjX1XW2DFBjPzvb52Ptu7b8Joe4/aOXm55Ygrda5lE5IKBVeKPc5IGc37x+HG92htbK9aN5j9TCXOu+3c1ibVFgvUVFot7/NrEcEVYBAsYn7QQQe5ngT5SkxUajCi2CDvQI9jdZFtNoH1jxgMGyCy+9plzz331E9/+tOiX6CVMFIHn4N9nJEcTK7m+6cUtTADa7DbJF+5g26DDa6Uu02q9X4vNhOz2PYp93aVVGybjdam4eWwpqP5givWS8cGT7ozqOzEuFB/nXyN7EfD/nio3+GV7JVjJz3WD6dQXeJq1EquBOs31L8vUX/23GvltVgalRJoshrhtfjZHI7HsP4GNjko30ByMZbhfdVVV+nGG2/Uz372s5KyYiuhlrd3JY+vhiNwYL1eBvt6j9Vyjt1sYNwCK/kyEC0wcN1117lJAqP5PTqY85KROtca7ES24TBa94/Dje/R2thetW4w+5mxfN49Wh5jpIy9nD5gmNjgic1gsV4ENlh15plnFqzdXaweZKkH7IVKQxVKjbzkkktck6mh1qW09PpzzjlHI2GkamjagONgjGRD4mq/f0Zjc+ZKbZPBvg/L3SbVer8Xe82LbZ9qDjoUW/exMBBSjKWP5wsiWbq4NU/tdv/99+fd302dOrVgKnqt749rTbH6y1b+zDJERxurFV2ojKDZYIMNRmx9MLg+MoP5rh+JfcBwfP67+0YMtbeHDYhYTfnBlk8dS9u72sdX+di+6aSTThqWwMpYYO/db3zjG3mDWlYSutTASq2/RwdzXlLr51rDYbTuH4cb36O1sb1KUcp5aiXKQlZqPzOaz7treZ9fi8hcwVIvX0kKC6bYF8eECRPcIJQdjJRTF7JYanWpUdpis13yPY7VQLegiM1OueGGG1xAaLCNGW+55RY3s3S4sxWKbbNKGeyX00hk79TK+6eWtkWpbJuU8nyKpecOdmZIudukWu/3YgeGpZbtqMZMk2LbbDSXXirnO8tKMlgJx4FYGbDuPiqFSoLZMgrNKqv1/XGtsbKAd999d8Hb/OlPf9Ipp5yi0eT6668veptCjThRGye/gxnksH2ANQQfjZ//TTbZRHfccYfuuece3XrrrS5IaA10BzN4b5OVfvvb32q41fL2LvX7friOr/KxDPtivd/sXG777bd3gQQ7p+v9WbAedH/96181Fr322msusJJv1rSV47TASjmlnWr5PTqY85Jiz6fa51rDZTTuH4cb36O1sb1KUey9auXGhuscdTD7mUqddw9XWc3Rus+vRQRXsNQbrpIUFpApdtBbimK1FYvNNrKamGeffba7WGkPm2Hy9ttvux3lRx995MqYFKspbDs9O4EZ7uBKsW1mpc6sVmwlDiprXa28f0YTe67FejfY7MaZM2fm/bsNNtvJ90io1vu92Gv+xhtvlLTcUm83ktvMGunayV4tBv8qXRrs2muvHfCA9KmnnnJBJgvCPPHEE3lPimwZhbA/Lo+V47QJDYX6rlhtYispusYaa2g0sAGXf//73wVvs9xyy42K71SUr9A+wPYhljEw1FmNw7mvtmXvs88+7mIzI61pqvXksMH4999/3x0Dz5o1q2jd9yeffLJPucWlcXuXWue92HFBpY85C5W9NJ/97Gf185//PG9Q57777huTwRV7vezcNt9gnGUtWGCl3FKqtfweHa5zrS233HLIn49a7E0y2vaPo9VY+8yMhGJBmWLjVvY+tpK2taKU826bAFCIndtaBvxI4/1bHoIrwDDZcMMN3UBtvui6BTVeffVVd7tC/vnPfxb8+2abbVbyOlldXbv0Zzvr2267Le9M6GKD+ZVKGdxoo40KbjOrabu01GavxfdPrbOT5C9+8YsFb2NlkgrNkLD34EiVQ6rW+90CUNZkMF8artVTthrmhQZMLYW52KDrcG0zO4nIV6bIDrhvuukm12izUkbq/VBuU9JtttnGndQO9NpYxoptp3wnxVtvvbVWW221go/B/rj8k6fPf/7zuuuuuwqWKfj+97/vskEGOxBh+33rcTKYvlDlsADdd7/73aIzyipRfnSsGSvbY9NNN3XfmQOx98VnPvOZkhs+V5t952233Xbu0v8zaUF5m32dr7yNBUxtsGaLLbYY1te6lre3Zb5b36jNN9+84O2K9XosVj6xXDYInI8dX1100UUFs2VsAHmsfdZtoK5YYOWPf/yjC4yPpffoYBR6Pt3nUF/72tcKLmPu3LluIsJoPtcarv3jWPtsDcZY+8yM1PuxkBkzZhT8e6Gs/Wqw8ymrApOPvT+sL1YhDz744KCyy4aK9295aqcAJDDGWBp6sYOpCy+8sGCdRRvgtJTdQvofCA2GzaL9wQ9+4GbKDyYV0UqQ5bNgwYKyvkwLnbjZl89Q6irbgWGhrIVaMpreP7XCek288MILef9uJwlXXHFFzZwAVev9bhkNG2+8ccH72olUoUFVa2RZ7OB2OFhpxmIliGzdC70PerPnOFCAYjj2b5VWqBm9lXsodHJR6L7d2B+X7+ijj3YlRYsFR2ywxoKY5Q5unnHGGa7kW7HmmEP19NNPu8ymYqVEbdDSSo/WmiuvvFLnnXfegJfLLrts2B+/0D6j2vuNcuywww4F/26TcgbLAuQPP/ywqs2+U2zGaLH3Rb5j4Eq+1rW+vS+++OKCx5xWFtH2byN5jFWo9IuVMykWhC42AWm0fdZtkNsCK/m2i02qsMBKKc2oR+N7tFw2sF3su7BYwNACeIWyO1ZZZRV3GY2Gun8cS5+twRprn5mRUKx85GOPPZb3b5Z59ec//1m1pFjg0SYuFJqwaK/z5Zdfrmrg/VsegivAMNpzzz0L/t1Kttgs1oEOgh9//HEdddRRBQ/YLM3405/+9BK/tx20pfKWq9BM2kKzCArdz2aVlVM+aO+99877NxtQOvPMM0tu/NXNUp1t0Gu//fYbNcGVar5/Rit7X9jrPFApJJuFbdvDMn4K+cIXvqCRVK33+x577FFwGbYNf/SjH6m1tXWJYMTNN9+sn/70p6qWfffdt+DfLYh26KGHuky8fMEpO1C1DAMLMliQspBK7t8qXYYq38CRDbDkm2Vo99l9991Legz2x+Wx2YelZE1ZJqh9Bm3AolCQxd7Lti+3yQ8777yzKytW7v6gVPaZsM/9t7/9bfccbDZuMfb9U2u15LsHK2wAcaCL7b+GW7GsJMuyHA01pq2sbKEZtdZD6JlnnilrmZZdaAMftv/61a9+pUqz4yHbvuXO8Cz2muU7Bq7ka13r29sGmr/3ve8NOJBqgZVTTz216AxYy7qrpELb34LDhXph2P601IkYg3n8kf6sW8lnC6zke86W8TqUwMpoeI+Wa4MNNiha8vqHP/zhgBmplv39k5/8pOgs+WLnciNppPePY+WzNRRj7TMzEqw/VqHMJTvHsaBm/+NhC1JYn6laKgnW/R4oVg7YjvMHCuTa95gdl9tzrgbev+WhLBgwjGyGqQ3wFZrhbZkFDz30kOutYCcdNsBhg2KlDNjZYLHNKhnohOE///mPK/1i9YZtAN12joVm1Frd4kJZDp/4xCcK/i1fHUhLF7Ya89bsd8UVV1xiHay5b+/amtZk2XoJ5Ev1v/POO93B4YknnugOigdiAQXrL2Pb9fbbby/a7LJWVev9M5rZSaWdXK6//vquZJqVRvrwww9dH4piJxOWtjvSqfvVer9bjWWbhVooM8ZOGG2/YJ9dOxm321p5gGpkrPS211576Te/+U3BA007sLbnZzN9bEBn5ZVXdu/1lpYWVwbEPiPdpcXsdS+kkvu34aiXbXXTy2H3KbU+81jdH9s6WRbDYOy0004FZ7tawMEGIV9++eWCy7GBGXt/2sVOvK1UiwUqbLDABi9tgoTVca9kGYD+z9s+JxbEtQCPzTgv54TUSqDZex/lHS+ZRx991JWwtH2TDSD1HkSwzDzbtrXA1uv444/Xd77znQH/bu/Nb37zm+7vBx98cN7BMHuP2aCHDVDa4EF3Scrh6ENg+/jjjjvOfZYsG9uyc62EYqHHsu+2s846a1CvaSVf69GwvS3Tw84vbJuusMIKLgBsEzpKaXg7HFlulomRr9+F7Uutn0H/niv23f+HP/xBv/zlL8fMZ/2dd97R4YcfXnA2vx0Xl9N43AY4++/jR8N7tBz2fI499lidcMIJRct5/uIXv3DZ5vacbMDzv//9r9vfFGI9HP/f//t/qhUjvX8cC5+toRprn5mRYJ8bOyYu1GPEzgMtwGbjH7aNbdzjpZdeUi2y9TvkkEMKnnfY62nvAdvv2nvaztPsfNvGL6oZLOL9Wx6CK8Awsnq/P/7xj93OstDsCjtws5OVcliwxL6E87FovpW76S55YztpOwmx1OSJEye6WSa2w7TBaBtoLDaj32bN5mOD2HaQU+gLI1+6o5349B58tJJFts1s5n2+GbqWDmoXez52stB7QGr+/PluVnC+fhKjSTXfP6OdnWyX2oC1m508jbRqvd+t7Jw9pg06FJsdVaxh7Eiz/YVl9FgQrdjAsx2Q2oGpXQarkvu3SrPMm3KDK8Ua2fc2VvfHFmSwGbyDYc+vUHDFvmuvvvpqN5BYyoCjsUDhSMxKG8rz7s0mbtisQQzMZigW6mtlLMg4UKDRXqNaGRQyNgC344475i09Yscf9j1iWVjWT8P2A/bc7bvDAq8WmLXPwUjPMLbHtiyl7kwl64lkr4v1mbDvPwu22/eDBdstOFDotbKZ7fnK+lT6tR4N29ueq9V/L4cF34v1xBtssNtKwuZjx8a77rqrG2y1SSI2uGzHA/bdNJY+6/Y9Xew52UBkOawc1EAB9NHwHi2HZZFakMGyRAuxiVp2KYdlehUrcVQNI7V/HAufrUoYa5+ZkWDfF7/+9a8L3sbGroqNX9UKO/eyfovF1tcC5XapJbx/S0dwBRhmdlBvg7Y2i7pSpk6dqquuuqpoPdLebKaWzR62S7msP4PNzM7HSsxYhkWl2AHIaaedpnPOOafg7exgo1Azy7GgVt4/o4Ed3Jd74tN7kNpOJKuhWu93S5222U924jTS23uobKDESpNZxsRwn2xUev9WSXZSazMpbTZbKey266yzTlmPwf54cPtYK/VwzDHH1OxMuqEMRF1wwQVj7vujkiygat/d1vtoLLAZ2xYstEBpPjYQZ5mNdqlFNpGoUImoQo488sgRfa1rcXtb9qfNoh3M961NFPrZz37m/q00K7lk2X+FMmqtvGm+YJA9r6E0ta+Vz3p3Ju5IqcX36GDZREMrrXTggQdWNLPWJrDVYk+ykdw/DkWtfLYqZSx9ZkaCnZf/7ne/c4GncnWXTB6OXkCDZQFLO2+1stWDKe9r35+WpWRlzquB929p6LkCjAArv2T9C2wW8FBZuqDNPK103eJ8pk2b5no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voTXWXberT0lMScVcNo5l2mR8W8PyAyYuMBLrzCixK/az9TZpSsbVmIyrIcn8VAAA8DGODMrU1NSk4WSBlKEGU/qzrBUAAAAAwOgPoFjmidcVILHMlO5gQptngRBf3W1VLKxgFaJzYeDKS1l5sJY2T1m/q5m8TR60DBQvUEsu6CwFFlrJsEBpV/7L+q34CgJf7913h2Y8fK8yC7oyU6x02Lw5isIlS1BXimWkrL/BBkomEu66VWL48v7766BDvuqec3POd+ttbB6hlSuzbJ3eXNt4a/Iu61HSt6dJ78mHccXUlEpoUl1CDVWamAgAAEYfRt0BAAAAAKgxvSsmWLzEmtFnvM5eKVauq6Pr57Tny/JTAivR1av0l5X3smWkYnLlvlpzQecyfF8dXuDuk/UCtXYFaIKux7EARegHWvT265r7vyc057F/q/2j9yr63MZPnqy1N9ios7JDQ4M+tc022upTn3KBFAtsNDY2uQwVq/rg1qkrmGRlyN5tzd/H1EIidYm4C6LUx+OdZbus50mfQIrUkIzTxwQAAAwZwRUAAAAAAGqABUMsAGLBEcs06S1jAY+s5zJLrFSXayDv+y7gEHT1EYl1neSHLtBiWSyRFnXk1BZYlovf2R8lDN3jzHzuac174yWF1nE+JnltrVrw4v/UMfsjhb6vyP+4XPVQrbzW2lpp9TU1bsIE7bj757XfV/ZXfSrZFUDqDHzYuga9yni12yVbeB0sgOIayFtuSkxqSsQ1sT7Z9XuawAMAgOFFcAUAAAAAgCpzzeFdtsmSf7OgyPx0zpXCsowVKwGWtTJYYeTKXaUs2yMRc03oZ2V9F5Roz/lamOvMbrFsFAtehBaACQM9c8VFeu+e2yqy3sm6Oq255Tba8DM7av2tttG4xkY1peKakEq6y+Qpk7XscisqpkjxWNz1fTEWRDK2/t3lu6xvSiFxu01X0GRiKqHxdcme6y6wREAFAACMIIIrAAAAAABUgWWnuJJXLvvk46iK39X/xIIo1pR+ccZTm2clvawfSld+R2TBhki5SFpofVMyvtqDQF7gKx1Eas/6rjG9tXa3kmEWysi0t+qpC09z5b7KFYvHtd6WW2ultdZ2mSKN48dpvS230fpbf1oTxo93Jbisb4kFT6wsV2M87p6D3a+zNFdnb5POsl1S0prFW9kuu9LFyncN1O7EbptKJNxjAAAA1AqCKwAAAAAAjBALnHQ3pbdMks7f2c+dARXL6LCAyKKMpzkdGXV4Untg5b+sK0qkulhM7X7kgixZz1fGmtB7neW+cmGkjpwnL+hs7m6FwtKL5inX0qz22R/p1euuUOuH08ta3xXXWEub7bizPn/A17TRBhsolYwpijojINkwdIESawJfF48r0StQYo9dl7RsEmsnH1NTMq7xqWRPAMUCK/a3ZKwzYBLv13AeAACg1hFcAQAAAABgBFh5r+5yWL1/Zw3lm7OeMn6obBBoYTqrRVb6ywItFoCJYsrkfGWj0AVTYgpli2nN+uoIA5ehknPlv0Kl29vUNuNDLXr/PX1w39+16NUXCq5Toq7OleuyDJOpq66uDbb5jMZPmqAJy0zVJ7fdQWuttbrG19W54IitSnfPFouDWNZJKt6ZsWIlurobx1u4xH60oEoqEVODy15JqLErq6Xz7wRSAADA6EZwBQAAAACAYZYJ+gZWLGhiZb6asznNSec0oz3jSn/ZbdI532Wv+JGVxLLbhlpsvwukMC6ls57a/M5SXxbosKyWeW++rNevv0YLXn5OYYnN6Hc65Ova93unqK7emsvHXHCkLm6xnJgSimlcKukyTKKudbUm8Y3JhGsYb1kqdntXDiyZ6MxCsWCLBWpiH/dAsQCM/Wt/AwAAGEsIrgAAAAAAUGFW4suyUixI0vX/Hm05X3M7spqfyWlOJqNFaV8dQaDQynkplO9LuTBQ1g/kRVZGzNrBR/ICK/9lJcU6M1oshyUWRZr77H/1+NnfV2TRlxJYlspXfnSGdj74UDUkky6IYusXhlbiS2roCpZY8MSCKHFZUCWuukRnpor9a+W8LLBil3jX7aw3irHrdn/KfAEAgLGM4AoAAAAAABVkzenbfd+V0bKfLUjSan1R/ECLsp5ac9Z83tf89pwW5Tylc55rTG+JLUFkQZXOnileIPmKZDkqtgwLgFgAw0IoFrhoiEmtr7+spy44taTAipXiWmPTLbT3t7+vTT7zmc5MlURnOa+mpAVOrHRXwmWrWACloStw0s36qkyuT2lcyoIvNJcHAABLN4IrAAAAAABUgAukBJZpEioMO0tpdQSh2vxAi9M5zW1Pa3HOU3sQaWHGU0s2J9+PFMQjV4bLgjFpa0xvv3O5Klb2q7PRvVXVSrlm8FZ6K5I3f56e/eOVeuP+uwqu0yobbaKt9vqyVlx9Da263oaaMnWqmixwkrIm9AnVJ2KaWJdSkwVUrORXorP0l2WeNCY6e6V0syAM5b0AAAA6EVwBAAAAAGCI/DBUuxe4AIuV7GrJ+VqUyaol62tO2nM/L7DAijWhD3wFoWWEhLLuKFEoV/4rF1p/lUChKyMWdpbkiiIXAEkkkvKDUPOe+6+mP/RPvfvIA/LS6SXWI1XfoD1/+GOtuMbaWm7FFbXSqqtrfCrhSn91ZqgkNCGV6Mx+icc0wQIqybjLiKlLJNRg5b9crxT6pAAAABRCcAUAAAAAgEFmqXSW/uoMrLg+K16gFs/Th+0dmteWc5kobbmcWnOdGSxpz4p8RYpicsESy1hxy4tFqutqYh+PRUpauS7FlUxFXSW4Ynr8qp/q2dtuyrtOdY1N+s61f9J6m26u8cmk651ipb3s36ZUXJNTVvYr5XJiLAOlIdGZoWLZMFb9yxrVW+aKXQcAAEBhBFcAAAAAACgzsNLm+fKsBFgQKu2HSgeBC7Ckc75mdWQ0J2NZKp09VayBfS7qbD7fWeorUmANVqxVfMzyVyKlXEP5mOqSnVkjFoJJxeR6osQU07M3/KZgYGWdLbbS1888X6uuu4Er6zUukdDUxjot15RyfVIS8bhbVxdF6eJ6q3QFVyyrBQAAAKUjuAIAAAAAQAGWkeKyVFxoRMoFkdo9Tx1eqA7LRgkCdeQCzc1ktCDjqyVn/VR8LcpmFYVW0iuupoTU4VnZr9AFSyxDJJlMKgwC5bp6tFgAxGIcdj0Zs6bycTUkYvrfDb/TQ1f/asB1W2aFFfX1k8/QF/fdT76VBZM0oaFO0xpTmlCXckETC+PY8rsDK/Zfy2axdQAAAMDgEFwBAAAAACCPnMtMsfbynX1V2rxArblAbV5OuTByWSmLLKCSzak556s5l1O7H7rbpOJSGI/k+6FyiZilrKguZqW3rPZXQrEoUDoKlUgkFItLsXhM6VkzNfPpR7TgjVcUeZ5mvfma5n/4/hLrtezyK+jQ735fXzrwIE0eP8HyYVxflUl1VgLMQiwD6wzYWMYMmSoAAABDQXAFAAAAAIABZIJAGT90DerTnq8FWV9p31e7H6g167syYB1eoGbLVPF8LU5n1GpNUxSTFfbKBjHXNH5cMqFkIqb20FcmCJUJI6USUiyyvydcM/n03Bl66tpL9caD9xZ9LTbcYitddcvtmjRhnGtA35iwXirWP6Uz88WyXvrHTuwqTeoBAAAqh+AKAAAAAABdLJDihXYJlfEDF0ixAEsuCFypLwukLMxk1ZIN1G638321ep3ZK3Y766eSSMbkhzE1WmClLiEviFwmiy0vEU+4klwW7Jj31mv64ImH9NFTj2ruG6+W9Bq4wMqNf9XKy0xSYyqhunhnFootrz4Zd03paUgPAAAw/AiuAAAAAACWelbyyxrTW78TC6wEVvLLC5S1fipeoPmZnFpdSTBPOT9Uc9ZzPy/2AmW8QKHLFInkCogFCTXEpfpEzPVl8V3Xk7gak0mlknHFvawevPgnev2+O8va7nt++Sv61VXXaFxTk2tEbz1aUvGYu1jWCgAAAEYOwRUAAAAAwFLLMk3SVqrL7wyidHZXkZozVv7LV9YPNDudc2XBclHn7T5q6dBiL3RZLbGwu8295EWB6uIJNSWlMBYpE4Uug8U1lFeopmRC0dyZuuenp2n6C88WXK8pU6dptdVX17jGRm3z6U9rn3321jbbbOv+ZnGcZDxG3xQAAIAqIrgCAAAAAFhqs1WsB4oXhD2BlYT1P/EDzc1kXBmw1pyvbBgonfNdX5WZbTmlg8BltthtYwkpCi144mlcIqX6ZFJBFCq0EmHWpT7e2QNlUjKml/78Wz30h6vle7m86/SJ1dfQiWefq4O//H9uWZT6AgAAqE0EVwAAAAAASw0/jFyjeguOWH8VF1wJLbckpvpYTF4kvd/coY86MmrLdTatb7ZeK16us8RXYEGTmMtWaYxHSll0JSFlfFu69VoJVGdBlZSlr0gL3npF8599Us/+/S9qnjd3wHVaYeVVtNve+2jn3T6vz+28o5Yb16S4lf2KkZ0CAABQqwiuAAAAAACWCjnLUPGDnuvWiN71WQmCzgbz6azmZHKa19FZBsz1XPEDLc55yvqRfMtIiTqb3k9IxFWfiiuyHi1RXPFEXAnLUlFMDcm4mj96X49ccYneeuyhgut01Ikn63snn6plGuo0vs5KinGaDgAAMBpw1AYAAAAAGNMsGGJBlM4MlU6Ls54WZT0XWFnQkdO8bM6VA2vzA6W9UM2ZrCvvlQssKBPJc5kuUjwR09RUQslEQjk/kK+4KydmDeWbknElcxk9de1Veuym6xX4Xt51WmXNtXTyeRdorz2/qEl1SU2sS43Q1gAAAEAlEFwBAAAAAIzZnioWVAmi7pbzUhCGro/K4pzvyn7Nz+Q0uz3j+qhYECUbhGrLePKt8FcsplwYuPvHYtL4pAVQErI8l5as5/6eiMfUEI8pM+tDvfnfR/TIn36r1oULCq7XvoccqvN/8SstP3GcGpMJNVjKCwAAAEYVgisAAAAAgDGZrWL9UrrDKhYgsaBKhyv35WtuR04LsjnNz3g9wRf7r5X5SiRi8kKpLReoJee5rJT6eNw1qG+zniuWARMGmvfCM5r1zGN654lHtHjGh0XXaYVPrKxvfOdY/fB7J2hifZ3rqQIAAIDRieAKAAAAAGDMsUb1FiyxwIkFVKwpvf08uz2rWR1ZtXudfVQs/BKPSV4UKgoil+1iGS0tvq+MHygZi6tenZkrqZiUSMTV/M7buvPMH2rxjA+Krsdqa6+j7559nnb83G5aobFeKzTVKZFIjMg2AAAAwPAhuAIAAAAAGBOiyJrOd/ZHsQCJb/96vgu0WIBlXjqnWe0ZNec85ayHShip3sp8hVYOLHCZKgs7Mmqz656vZDymVDKmeDKlZeuSisVievPh+3X3T3+sXEdHwXWZMGmSjvjBSfrqN4/SlMYGTW2q0yT6qgAAAIwZBFcAAAAAAKOeZZk0e56iUC64YgEVy0KxHioLMjnNT2e0IO2p1WWkhEpEcZex0p7LqSWXU1sQqS0TqD30ZVXCEvGE6hNxTaxPaoXGRs159w3985c/03tPP1FwPZZdbnntus//6RvH/0BrfWJFLduQUlMq6ZYFAACAsYPgCgAAAABgVPOC0GWlWCEwC4yk/UBeGLpMlpmtGTV7vgu4WIeTDs8a3IdSzJrdR2rJeGr3fLWHkTzPVywec4GQcYmkWp5/Qv+57iotnPGR0i2L8z7+Zp/aRtt9bjd9aqddteHGm2pyQ0qT6pIuqGIN6y0DBgAAAGMLwRUAAAAAwKhkAZRcELp+KhZYsTJgHV6gMAxdhsrCjK/FVgLMD5T1A83PWomwQEEo5WKRcq65faCOoDPgUpeIK1w4X3Oe/o+mP3y/PnzpuYKPP2355fWz31+vDTfbSikrIZZIaFqjZaok1JhIuOUBAABgbCK4AgAAAAAYdSxIsjjrKRdElq+irB+q1QvUmIxpUcbXvIznbmNZKS25QFEYKp2x8l+hopiUjMXUnAvdbaRITamUWv73uO499yR56XTRx//cnnu7RvUrrbyyUvG46hMxLdNQp/GppBoScdefBQAAAGMXwRUAAAAAwKjSmvO1KJtT1HXdAiwtvq/GeFwLMznNTmddBkuH7ysbRGpQpJnprBZ4gQuspKKY2kPLVomUTMQVz+X03i3X6Yk/Xu2CMIWss+EndfL5P9WWn/ms69kSU0zj6xJarrHeBVXiBFUAAACWCgRXAAAAAACjLmOlO7Dims/HpHrFNKsjo0XdGSu+78p/RVHoAisLc4ELhoSRZa5YXxXJj2LKfPCe7j/9eLXMnpn3MeOJhL548Ne0w867aM+991YQi6suHldDMq4JqYQm19cRVAEAAFjKEFwBAAAAAIwK2SB0gZXuxvWWJWJlwNr8QPOynuuf0pbzXRaLH4Rq8yK15XJq9wPXzD6MrByYnQhHavMlb+Fc3ffjY9U6d86Aj9c4frx2+OLeOuEn52vlKZPVmEq6bJeJqYSakgnVJ+xfSoABAAAsjQiuAAAAAABqVhRF8sJIWZeNErgG9lYGzFjWiGWpzO/wlPF9zerIqjnnyQ9Ddbgm9zH5sUhW6Csej6lBUl0iocW5UGGmQ/8643sDBlZWW39DnXbNH7Xaqp/QuLo6jUsllIwnNKk+6Xq1WD8V67NivwcAAMDSieAKAAAAAKBmgyppP3CXXBjKj6SMH7gyYE3JpHJhoJasrwUdab3fntHijKcwitSe813ZMMtwCfxIE1JJxSLrwRJqsRfJD3z957yTNO+t15co/7Xfd76ng771bU2eMEFT6pNKJeJKxuJatjHlAiomGY8RWAEAAFjKEVwBAAAAANQMC46k/VBeGLqfrTF9YEGSUC7IUpeIucbxXhQqnQv1XmuHZrRnXeDECwK1WlZKzEqGRQrDSOOTMWWjQHPac7KQSyqR0AvX/kLvP/VYn8eNxeM68de/0ef2+KKSybgm1aXUmLCeKklNbkgpYQ1b7HaKqa7rZwAAACy9CK4AAAAAAKrOginWUyUIo86sE8tA8QKXfZJQzAVYlmlIqS4RV7vnqzXr6/2WDs1J55QOAuU8Xy2e3UoKLMtFkWKKND8XqdXzXaAlmUiq7c1X9NJtNy7x+MeefZ723HMvKS5NrktpUl3SBVaaUkn3mAAAAEBvBFcAAAAAAFVlQRXLSuktHYSu/JaV4rKASYNi7roFXRamc5remtbcdE4dfqD2rKfmXKCcHyobRYrCQMlYQpnQAjS+/DBSfTKhhpj0n6t+vsTjH3TUMTrsqKOtFpkmNdRpSl1SjUm7xBWPkaUCAACAJRFcAQAAAABUNWOld2DFYhlRJDUl4q5xfPf1bvPbs3qnJa35mawyXqDWrKdFOc9lvGQsYyUIXOmurDwFUWdwpj4h1ScS+ujW3+mjV17s8/gbb7W1TjznXAVRpCn1KZe1Mr4uoYYEzeoBAACQH8EVAAAAAEDVmtZb5km3+kRnpogFWyywYlLxmHJBpNacp7nprN5Z3KFZHRkt6MjJt/4sYeiiL+1BqJxnv7PKXknFrZRXFFgzFS2Tiul/V/xMj976lz6Pb49x+s8uVF0y5R57cr2VASOwAgAAgOIIrgAAAAAAqiIbhj1ZKVbyy+IpvbNYEjEp44damMnq9UUdmteR0cKMpzkdGVfyK+Ua10vNXmfPFQushLGEEjHLYIlUH4trSszXvaedqFefeGSJx9/vkK9p4y22UBBKE1IJ1SUSrok9AAAAUAzBFQAAAABAVbJWrNdKt0Qs5gIp3SzY4gehFqRzemNRWh+1pbUwm3PXc1Go8Ym4C6Y0p7PKBKHCSPJczkooL5KWbUgpsWi+/nLysZr51htLPP5mW31Kp5z3U8UV08T6hBqTCTUlEz0ZMwAAAEAhTMkBAAAAAFQ1a8U61vcOtDQkLeQhzUtn9XZzm2a0t6vV81xz+rpEXPWR1JLzNbcto2bPmtjH1BGGikWRy2RZvj6pBY8/oOu+dciAgZU99tpHv/nbHZo4abILqrjASirhAjoAAABAKchcAQAAAACMOOujYjJB4JrOJ1w4RS54Yj992NKhN5vbNb2lQ+1e0FkuLIoURqFa/MBlq7Tb9TBwmSp2P8s88ae/rtuvuFjvv/jsgI975HHf1XGnn6P6pJUBi6kxGde4VNKtAwAAAFAqgisAAAAAgBHlhVbGK5Ifhq7fSUPi48CK9VGZ3prW283teqe5Q+kgkucH7j65IFBz1pcXROoILbASyjq0NCVi+uDvf9aDd9+mxTM/GvAx4/G4zr7oYh34jSO7Hiumurg1sU8pSWAFAAAAZSK4AgAAAAAYUVYCzHqupP3QlQAz9RZYicc0tyOrdxa3692WtDp8X2EQuub1VkKsJeur3QVaYoqiQPXxhOoTMb17yx/0399cnvfxxk+YoEt/+wft/Pk9FMUiNSYSSsXimlSfJLACAACAQSHvGQAAAAAwYjJ+4Mp8tXq+u27luKzXSX08pnntGb28oEXvtnS4sl85P1RzzpcfBprb4anZC1zWShT5qovH1JiKK/HRO3r891flfbz9Dj5EDzzznD63xx6dzetTKTUkEqpP2uNySgwAAIDBIXMFAAAAADAiWnKeyz4JrIO9Na7v6q9il9cXtWt6a7s+aE27AMyCdNaV/coFoVpygVqDQApDV0oslYirLpHQlDCn639yssKgM1DT2xZbb6MzfnahttzqUy6QkvVD928iFlM8FnP9WQAAAIDBIrgCAAAAABhWVgJsYcZTm/9xEMSyR6yZvPVeeXdxhz5oS2tGW8bdrj3nu8b12cDX/HSgbBQqCgP5YWf5sMn1dUosmqsbfnyC5r//Xp/Hamhs0k133aNNt9xS9ZahEo8pF0VqTCaUiMdcIGdcKuECLAAAAMBgEVwBAAAAAAwrKwHWO7AyPpnUhLqEOvzQNa23oMqs9ozmZ7IuwyQIfLVkAy32rCRY6DJY/EhqSiUUzpiuf/3mUk1/6rElHidVV6e/PfBvbbzxJhqXSrrAihdGPfWwLaBigRXLXgEAAACGguAKAAAAAGBYs1bach8HVqbU12liXVKLs57ea2nXR60ZLcrmtDibk+eH6sh5mp/xlAsjBUEgu2cYScmOFr1y02/16p23KgrDAR/rnIsu1habbuaCMONTSbV5vsJegZXxZKwAAACgQgiuAAAAAACGjWWneFFnj5X6eMIFViwTZXZ7RjPbs2rxcpqbziobhGrzAy3yAnfbXOArHUkJRcq88aIeOfckZVub8z7OMd/7vr555FFKxmOu5FiHb2XEOh+XUmAAAACoNIIrAAAAAIBhYf1UrCRYtwl1naeg89KePmjLuCyVOe0Z5YJIOS9Uc9aTH4Tq8HPKBFIqLuU+el8Pn/1DeR1tAz7GhEmTdNwPf6QTvv99F1ixnix+FCoXfJzdQikwAAAAVBrBFQAAAABAxXlhqNasr2zQmYmSjMfVlIwr4/t6p6VdrTlfM9qzSudCdYShFuRyihS6pvVRFFc8FmjO4w/ruSsuGDCwsv7Gm+hHZ52jXXbZRZMa6jW+Lul6qViPFcta6daUTLjHBgAAACqJ4AoAAAAAoOJ9Vix4YmW+ustyTUolXXmw6S1pze/IaUEmp+ZsVq2BBWECxRUp7YfKBL7LYnnh8vP14b/vXmLZkyZP1vfOPFv7HXKYlmmq17hkQuPqkq6niitB1qsfS0MirroEgRUAAABUHsEVAAAAAEBFtVhgxQsUKVIyFnN9Vqxc14y2jAuuzE5nNa8j6zJMOnKR/DBwzeyzUSA/iun1P189YGClvqFBl990izbd6lOuYb0FVixjxcI3Vn6sq7WLY0GVhmSCVxYAAADDguAKAAAAAKBiMn6g5pyn7jhHUyrpGszPz+b0UWuHZrVnNKfD+q3k1JILlfYtsBLIl6+0F+mtW/6ot2+9fonlNjQ26cJrf+cCK7a8ZepTroeLPU5bzu95PMuSsaCKBXMAAACA4UJwBQAAAABQMc29Ah3jkklNrku6YMv7zR16tyWtmem0Ojxfi7OBK+OVDULrtKJsLtRLV12k9++9fYllbrPTLvrJry7TKiuvoqkNKU2or+sJnrRZxkrX7aznijWvtxJhAAAAwHAiuAIAAAAAqAgLmmS6GtgnFNOUhpTaPV9vLmzV64s7XObKwkxWuSBSRxC4f0MFCqOY3rnz5gEDK1tsv6OuuOGvWmFik5ZrrO/TnN4PraRYZ2jF4injUwnFCKwAAABgBBBcAQAAAAAMmWWgtHqdgRUzqT6pMAz1TnOH3mpOa3ZbWguznqzdvAVcMn6oIAoVhKHaP3pfr/3xyiWWuf7mW+rSP1yv1SaP1/i6VL/ASqhM0Lt5PYEVAAAAjByK0AIAAAAAhsQCHa53StgZ7LCSXRYIea+lQ68vbtXMjrTmpLMKo1BZL5QXSlFMikWhYrmMnr7wdIVers8yP737F3Ttbf/QBp9YXslEXHXxzlJfURS5UmBtXtAna6X77wAAAMBIIHMFAAAAADBoFuyw3im5wDJRItXFYooiqTnr6a3mNs1sy2pBu+cyW+pickEYKVQU+PL8UP89/xQtnv52n2WutPqa+tmV12idacu4/indWSlhFKndC9zj9NZI1goAAABGGMGVIWhra9N9992ne++9Vx988IHmzJmjhoYGLb/88tpoo4201157aeuttx7Rmr+Wdv/oo4/qwQcf1EsvvaTZs2e79UwkEpo0aZLWWGMNbbrpptpzzz215pprjth6AQAAABibLGjSXaIraec+FlwJQ33Q2qE3FqXVmvPVEvgal4hpcTaUH0leFMlTTG/8+WrNfva/fZYXTyR0/hXX6BPLTNG4uqRS8bjLhLEslXbfd4EbY2dZjcmEUvEYfVYAAAAw4mKRTTNC2f75z3/qzDPP1OLFiwvebostttCFF16oVVZZZdi38pNPPunWafr06SXdfvfdd9cZZ5yhadOmDds6LViwQEcffbT7+aqrrtKyyy47bI8FAAAAYORYBkouDBWGlrkSyI8iNSUTavcDtWZyemT2YrXmPC3syCkXdP59QcZXQqFa/ECLX3lB/zn1GEt96bPcH51/ob7+rWO0fFOdlmmoc7+z4I1lrHTfsrN5fVIJmtcDAACgSui5MgiXX365jj/++KKBFfPss89q33331auvvqrhdMMNN+jrX/96yYEVY1k3tm7vvvvusK4bAAAAgLHFskgsY8XiImnLXLFyYPG4vDBSxgv0+qJ2F1ixUmHtvufuMzedkyJfrRZoaW/T/35x9hKBla9+69v6xtHHaFJ9UpPrU+539jjWX6X7lhZQmUBgBQAAAFVGcKVMt99+uy677LKe61bya7/99tNf//pXPf300/rPf/7j/r7xxhv33MbKcln2hpUNGw5PPPGEzjnnHFfruJuV/Dr33HNdAOX555/XU089pZtvvlnf/OY3XemybvPnz9eRRx6pTCYzLOsGAAAAYOyxgIex4Ik1sbdyYNZPvj3n66O2tD5sS1uZBC1IZ1Sfimt+1pdvzewjKfAjvXfb9WqfO6vPMjfe8lM6/dzz9Ylx9Vq+qd71WrHsmM4eLZ2S8ZjGpxLubwAAAEA1URasDJap8rnPfU6tra2dGy8W0wUXXKB99tlnidvmcjmdeOKJrnxYN+tzcvHFF6vSrLfLm2++2XN9p5120qWXXqr6+voBb2+3PfTQQ/tk3nzve9/rKd9VSZQFAwAAAMYWayrfkrPeJ5HLKLFgRyYI1O75mpP29NqCFs3J5NSazaklFygXSPPSGSVioTxfCubP0r3fPkih15nRYhoam/TAk09py4026OqvEqrDD91jdbPfW48VAAAAoBaQuVKGa6+9tiewYg466KABAyumrq5O559/vj7xiU/0/O6uu+7S66+/rkp64403+gRWJkyY4Hq85AusmHXXXdf1WunN1g0AAAAAirFsle7sFcsksVJgzVlfizKepi/u0PyM9VnJqi0XKAgCLcxmFSl0QZZUMq5Xfn9Zn8CKOeHU07TJBuu5AIoFVKy/Su/ASkOSwAoAAABqC8GVEnme50p/dUulUvrud79b8D7jxo3TMccc03PdZnZZb5RKeuWVV/pc33XXXTVp0qSSmtnb+nWzAI1l2wAAAABAPhbwsKCK+zcMXf+ThVlPrZ6vWe1ZLcp2Zqxk/VCBa0IfyQ8DWcOUeFxqe/l/+uDxh/osc7W11tZ3jj22JyslY71cuv5my7fG9Q0JMlYAAABQWwiulOi///2vWlpa+gQxlllmmaL3+9KXvqSmpqae6w888IDCsHOmV6XKbvW2xhprlHQ/Cw6tuuqqBZcFAAAAAL2l/c7ARy4MXQP7Dj9wJcKas54W5XxXHsyuW+Cl3Y/kRb4Ukyw0Mk4x/e/qJcskn/WzC7Ts+CYXSPHDqCczxrqqjEslXHYMAAAAUGsIrpTooYf6zq7afvvtS7qfBVY233zzPg3kX3zxRVVK7+b03X1gStX/toVKiQEAAABYulnjertYRr4XRO5kcnHW08JM1gVUrOfK3I6M2j3rlxLI8wNlwkh1kZQIIz1/5QVaOP3dPsvcabfd9cUvfUkNXX1WbBnd6pNxGtcDAACgZhFcKVH/Xim9AybFbLHFFgWXNRRrr712n+vTp08v6X6+7+uDDz7oub7sssuWlIkDAAAAYOljGSUdXuB+th4rqXhMmTDUgoznginz0znNbs9qftpXu+8rF0ZK+76SsUh+Oq0nf/J9vX7P3/ssM5lM6rSf/kwTU0l3+zYv6FMOjFJgAAAAqGUEV0r0zjvvfLzR4nGtttpqJW/k1VdfPe+yhmrLLbd0gZFu//73v9XW1lb0flaerPftPv/5z1dsnQAAAACMLZaJ4sqBBaELtISRtNga12cymtGe1kdtaS2wDBbfV+AyW3xZmxRv4UI9fPK3NOPZ/y6xzP93zLf1yQ02dD+n/c7AjbEyYONT9FgBAABAbSO4UgLrtbJw4cKe61OnTnU9S0q14oor9rn+3nvvqVLq6up03HHH9VxfvHixTj31VHmel/c+lt3yk5/8pOf6hAkT9K1vfati6wQAAABg7Mh1NbC3f+0Si0LNy2Q1qz2tWW1ZzWvPamEmp0VZT4oiWVeWVCymVDyhJy84TYvfe3uJZX5hn331ozPPVmMi4RrYd6tPxF0D+3LKHQMAAADVQHClBM3NzX2uT5s2rayN3P/2/Zc3VAcffLC7dLv33nu1//776x//+IfmzJmjMAyVy+X05ptv6vLLL9eXv/xlzZs3r6dny6WXXqoVVlihousEAAAAYGyw5vUWXLEgSF1cmp32XdbKR60ZtfrWzD6n1qynSDHVWYAkFlMiHtOMp5/QvFeeX2J5R333BP36uus1panRBWK6NSTjakySsQIAAIDRIVntFRgN2tvbh9T4vf/tOzo6VGlnnXWWNttsM/3iF7/Q7Nmz9dprr+nEE08seJ9tttlGp59+utZZZ51BP649ViGLFi0a9LIBAAAAVJeVALNL2g9luSTNXqAF2Yw+bMlose+rNeurIwgUi8VVH5OyUUyxKJCvuF6/8dollnf2xb/U4Uce6RrYJ+LxPhkr9FgBAADAaEJwpQT9gyHlBlcsO6TQ8ipl3333dYGS8847T//73//y3i6RSOhrX/uajj766CE3sd9xxx0L/t3Kp22wwQZDegwAAAAAIy+KLKgSyAtD+VHoms7Pbs9pXjqn5pyvXM5Xm+fLC6UoJvlBIN/3lUoktODZpzT/jVf6LG+3PffSYd88UslYzAVT1Kt5PRkrAAAAGG0oCzYI5db/7X97O0mptA8//FDf/OY3td9++xUMrJggCHTddddpl1120SWXXOJKhgEAAABAb+1+ID8MlfFDeWGk1kxOad/Xwg5Pnu9pQc5XNggVRJ2BlUwQKZVKSvG43rj1+iU25nd+dIq1ZJF1WOl9SkRgBQAAAKMRmSslaGpq6nM9k8mUtZH7337cuHGqpBdffFFHHHGEWlpaen631VZb6aCDDnKlwqZOneoCKDNnztSjjz6q66+/3vViSafTuvrqq10w5pprrhnUej388MNFy4KdccYZg3peAAAAAKrDZauEkctWcaXBglCtOV9z01m1+Z7mZz1lfF+ZwAIroex/yXikhJJqnf6OZj7/VJ/l7bLHF7XxJpvKV6TGWLxnAlpdIq5knOb1AAAAGH0IrgwiuJLNZsvayP1v3395Q7Fw4UJ9+9vf7hNYOemkk/SNb3yjz+0aGxs1adIkV6LLgi4nnHCCC7SYZ555xvVsueiii8p+/BVWWKFoWTAAAAAAo4tlpHT+GyhSpAWZnBZ7nuZ3pN3PmVygTGhZKKHi6sxsaUwkVJeIafrdNy+xvCOPO165MFRT0m5jwRWpLm59ViimAAAAgNGJI9kSTJw4sc/1BQsWlLWR58+fX3B5Q3Httddq3rx5PdcPPPDAJQIr/U2YMEGXXXaZVlxxxZ7f/eMf/9Arr/StiQwAAABg6W1ibyXBskGk5qyvGW0ZzWjNaFaH5/7mRZHCUIqHgctusT4qqXhcqY42vXrfnX2Wt8HGm2iTbbZVYyKuccmEu93EVNKVAyu35DIAAABQKwiulGDy5Mnu0m3u3LmuUWOpZs2a1ef6GmusoUq5886+Jy5HHXVUSfez7Blrat/bHXfcUbH1AgAAADC6s1Y6vEAt2Zzeb+3QwkxOszuyslYp9ucwChWLurJXokgNiaTGN6T01t9ukNcvc//Qo45RUyqpplRn1sq4FEEVAAAAjH4EV0q01lpr9fwchqHef//9kjfy9OnT+1xfc801VQmWsWKBnt4lulZeeeWS7299WXp79dVXK7JeAAAAAEanIIo6+60EoWa1ZzQvk9OijKc57RmXxRKzhvRR4AIs6cCXFwVKJRJqrEuoqaNNj9/85z7Lm7rcctrvwANcObAJdUl3iZOtAgAAgDGA4EqJ1l9//T7Xn3vuuZI3sjWML7SswWptbe1zvXd2TSn6377/8gAAAAAsXdJ+4P6dn81pQcZ3TewXZrLKhqHisUgdYSAvCJVxpcFiikcxTaxLaFwyrkd/e5kyHe19lnf0Cd/TxKYmTapPqSGRqNKzAgAAACqP4EqJdtpppz7XH3/88ZLul8lk+gRilllmGW266aaqhP69W3o3tS9Fc3PzEr1YAAAAACydckHo+qk0Zz29tTCtdBBocdZz/yqWUGvWV9YP1OZLkWW3RHJBk7og0gPnnarHb72pz/KmTpumQ79xpCbVJVVP43oAAACMMQRXSrTttttq/PjxPdfvv/9+LV68uOj97r77brW3fzx7a5dddlGiQjO2pkyZooaGhp7rM2fOdJdSPfPMM32ur7TSShVZLwAAAACjiwVVLGvF+qe8tbhV2chXxvfVlvXkhVJ7LqeOIFDG891twpjUEIupPt2qf5x0lF64764llnnU8Sdo2qSJrnE9AAAAMNYQXClRXV2dvvKVr/Rcz+Vy+vWvf100a+Wqq67quR6LxXTQQQepUixIs8022/T53W9+85uS7ptOp/WnP/2pz++22267iq0bAAAAgNEhiiK1+75rVj+vI6PFucD1XWnJhbL2KGnfV7sFVcJIYRRT0t1SmpQIdNsPj9FHLzy7xDK333lnffvY77oeK3YeBAAAAIw1BFfK8K1vfUvjxo3ruW7BiXvuuWfA2/q+r9NOO61P4/vdd99dG2+8ccHHOOOMM7Thhhv2XC699NKCt+8d8DF//vOf3aUQy6Q5/vjj+2S5WLkyy6oBAAAAsHTJBqGCMFKHF2hWe1bZIFCHHygVi7QoF2px1neN7v3Qgi0xhbGYxiUSeuZ3v9a8t19fYnn7HXSw/nzb37Xs+EYl4wRWAAAAMDYRXCmDBSBOOeWUnuthGOr73/++zj77bL3++uvKZrOuVNjDDz+sQw89VHfccUfPbadOnaqTTz656GMEQbDEpRAL2FjJst7OOeccHXHEEa502fz58916WqbNu+++q+uuu0577bWXW8fe7Hn0LnsGAAAAYOnIWrFyYG2eNa/3tDDruWCL9bVv9zy15HIKIykXRgoUKYpFssSVxS//T8/e3DcT3nzvlB/rqt/+TsuMa6KBPQAAAMa0ZLVXYLTZf//99cEHH+iaa65x1y1wccMNN7hLPo2NjbryyiuHrafJZZddpsMOO0yvvfZaz+8effRRdynFMccc454XAAAAgKWLBVLsYgGWuemMmnOe4pGU9T3N6ciqww+V8TzXZyUei7kMl4mppB645pdLLOtHZ5ylk085xd2OPisAAAAY68hcGYQf/OAHuuCCCzRhwoSit/3kJz+p2267TZtssomGy8SJE/WXv/xF3/jGN1xvmFJZsMf6xpxwwgnDtm4AAAAAalcmCF0JMAuuzGjPKZlIaFHW0/yOjBZ5gctmsUb2TiyuVDwuf84Hmv3Gq32Ws9W2n9aPTjrJlQ1rSCaUoM8KAAAAxjgyVwZp33331Y477qi7775b9913n8tmmTdvngtuLLfcctpoo4209957a/vtt3eN54dbfX29TjrpJNcX5s4779QzzzzjMlkWLFjgeqzYOlgwyAIqFvCxdbfLSKwbAAAAgNpjPVQsayUXhprdnnOBlngUaGEmp/YgVHs2UNrz1ZCQEq53SlxT6hN68cF7l1jWTy+8yJ1bWPClPsEcPgAAAIx9sciK7KJmWJkxu3SLx+PuMhT2EtsMsmqw4M7RRx/tfr7qqqu07LLLVmU9AAAAAPTV7vlamPH0QVta7zR3KAwCfdjaoayVCMv4avF8hb7vMlGiWEKTG5KanEro6kP21LwPpvcsZ7U11tRzr77mbkc5MAAAACwtyFypMZUIpvRXrcAKAAAAgNpkE7AWZz0tymQ1qz3tAivzsjllwkgdYag2P1AsDJVKxhSLx1UXlyYkE2qe/lafwIrZ/8ADCawAAABgqUO+NgAAAAAsZZqzvualc5rRkVNbLpAXRWrNBvKDSIuyVh6ss8dKXSKlZDLuMlIaUgk9c/Ofl1jWAQceRMYKAAAAljoEVwAAAABgKeKFoWa1ZzQ/k9XiTE7Wr35xNqec72lB1lPO95WwjBUFSsVisi6N4+vr1DZrhv5759/6LGvjTTbRVptuXLXnAgAAAFQLZcEAAAAAYCkqB7Ywm9PcdFZZP1TGDxQqVHsuVLMXKO37isVjiqJQ9YmkEvG4EhZgiUl3XXGJKx/W2w9/+EPKEAMAAGCpROYKAAAAACwl2v1As9oySoehmnO+/EhKBxZw8eSFkaLIAikxVxYsFbd+KzE1xqX7fnG+Xrr/7j7LWmfddfXVQw6p2nMBAAAAqongCgAAAAAsBTxrWt+R1YK0p1Yr/xUELhNlTkdGHV7gslhMMvQVxeNKJZOql/Toby7V0zf/aYnlnX7aaUokrGgYAAAAsPQhuAIAAAAAS4FFWU8ftqW1OOcrG0QKg0jzszktzOQUhqGCWKSGhBTGkxqXiCsZk2a//D/994bfLbGsHXbYQQcffHBVngcAAABQCwiuAAAAAMAY15bzNb2lQ4uzvstgSUShMkGghemc0l6obOArFcVdKbCU9VlRTOn2dt1z/mmuT0tvX/jCF3TXXXcpmaSFJwAAAJZeHA0DAAAAwBiWC0LN7shoUc5TxvcV2iy7WMxlsKSjULkwUkJxja9LKReGqotHykUxvXf/P9Q8e2afZW277af1j3/8g8AKAAAAlnpkrgAAAADAGNaS8zUvnVNrLlCHH6opEVebH2heJqtF6UBh6Ku+zsIrgRRFCsKYoliktx75d5/lNDU16frr/0hgBQAAAKAsGAAAAACMXYuzOXeZ25FTqxeoLhF3mStz29NalPHlhYGSMSsHFpcXxRSPS7lIirc164Pnn+mzrK9+9atae+21q/ZcAAAAgFpC5goAAAAAjEEZP1C7F2p2W1qLcjklFVMyFlNrLqeZaU9p35cUqS6ZVMKyVmIxBaFcI/tZTz+mKLQwzMf+7//+r2rPBQAAAKg1BFcAAAAAYIzxw1DtfqBFmZw+aM+5BvUxRRqXjOuj1rRas54stJKKWa+VhMIo7kqCxWIxTWtK6d1+JcEmTJigXXbZpWrPBwAAAKg1BFcAAAAAYIxJ+6GaMzl91JaWH4Wu3FdDMqFZHWm93+4pEwRSGGl8KiGrExYqkmLSuFRc2Rkf6PXHH+mzvC996Uuqr6+v2vMBAAAAag3BFQAAAAAYY1krbTlP8zI5zc94Skiqj8VcGbC3WzLyvZw8xVSfiKkhGZcfs5JgccXjMY2LR7r5rJPcbXqjJBgAAADQF8EVAAAAABhDMn6ohVlPM9qyrnm9NauPJC3oyKk566s9DBWPQo1LJRVGkYLISoZJDfG4nr3xj/rw1Zf6LG+ttdbSPvvsU7XnAwAAANQigisAAAAAMEYEYajZHRnNaM+ozfdVl4gpGY9cUGVGJqu2bFZeEKohnrBe9tbGXolYTPFYTKlMWv/502/6LC8ej+uPf/wjJcEAAACAfpL9fwEAAAAAGJ0sY2VeOqfWXOCCJ8lIavNDzUpntTjjK+1HSiYSSiTiSsakRNxa3UtNybhe+usNSre29Fnej370I2233XZVez4AAABArSK4AgAAAABjQC4ItSCdU3POV4fvKxmXcmGk+dZ7JZ1Vey6nIBYpFcUVU6SJdSl5shhMTMlcRv+54Q99ljdlyhSdcsopVXs+AAAAQC2jLBgAAAAAjAEL0hnN7siqwwsURJFS8ZgWpXNalMkp7Vv3FakuFlNdMq4pDXWu5Jd1W0nEY3rln7ervaW5z/K+//3va+LEiVV6NgAAAEBtI7gCAAAAAKNcazan91uzygSh2j1fdfGYOnKBWjxP7UGkIAgUWoZKrDNrpSEWKYqkKB53AZdn/3Frn+VNmjRJxx13XNWeDwAAAFDrCK4AAAAAwCjmh6HebU2rw/fUlg0Uj0uNiYQW5jy1eoHSWU+ZUIpZA/swUkMyoYZUnULF1RiXWt57Sx++8VqfZR522GEuwAIAAABgYARXAAAAAGAUm9WWUWvOU7sfylOgCXVJtVlgJeup2fPV4oUKw1ChlQpLxjU5lVA2CBSPxaQo1O3n/XiJZR5xxBFVeS4AAADAaEFwBQAAAABGqSAMNaM9rcVZX14QaXwioawfaVEmcE3tLcASKlQUhVI8pon1CTWmUorFYvIWz9Pvvr6/Pnr91T7L3GKLLbTppptW7TkBAAAAo0Gy2isAAAAAAChfFEV6vzWtBRlPXhjJElGsQX1zNqd0FKg5G8j62FvWSiIeV2NdUlNS9QoVKBZL6N9X/VKz335jieWStQIAAAAUR3AFAAAAAEahdBBqRlvGBVbCSGpKxpX2ArX5nlqyObX5gaJYZHkraown1BSzTBdfuTAm5dr0yr/uXmKZG2ywgb7+9a9X5fkAAAAAowllwQAAAABglMkEgWa0dagl5ymIJAuhNCVjmp/JqiXja1Hat9QWZYPQNbKvr0somUwoVEypZEJv/etuedlsn2XuuOOOeuKJJ9TU1FS15wUAAACMFmSuAAAAAMAoKwc2qy2r91sySgeRckGgKfUpzUp7WpDOaX42p/YoVDaMXK+VhmTC9VhJxGIKJTXE43rhztv6LHPixIm6++67CawAAAAAJSJzBQAAAABGkcU5TzM7Mkr7gbww1LhUwkIu+rC5Q805Xx1+ID/nKxsGLmslrrjqYnaLSA3JpFpef0HvvfxCn2UecsghBFYAAACAMhBcAQAAAIBRIghDfdiaVodnvVMijUskNDGV1Ox2y2LxtTDnyfMCZaLQlQVLxqwMWFzjkik1JZNqCrK6/vQTl1guTewBAACA8hBcAQAAAIBRYm46p4UZT4HLVYm0TH1KLZmc5mZ8zcvklPMCBVEkLwg7M1aSSU1rqlN9QmpKpfSvyy/SnA8/6LPM7T7zGW255ZZVe04AAADAaETPFQAAAAAYBbwg0EetGeXCUEEgTU4lFcXlSoQtsDJhXqgoJtdrJRmPqz6R0LSGlMYnklI8ptzM9/XQzTf2WWZjY6N+99vfup4sAAAAAEpHcAUAAAAARoEP2jJq83z3cyoR07hU3DWwn2klwUJrbB/Jt8BLFCqeSKgxFdfKExsUi+JKJuK65fdXKQytpf3HzvvZBVpvvfWq9IwAAACA0YuyYAAAAABQ46zHigVSfAucxGKalIxbSxW9uahdrX6ojG89WALlfF9+KCUiaWpdnSYlO7NWWmd8qMfu+FufZa6/wQY69jvfrtpzAgAAAEYzgisAAAAAUOMyYeDKfUWSGuIxhbG4Zra1a0ZH2jW5b/UCKQyVi2JKxmOaUJ/U8uPqFEQxNSbi+svPzlBotcR6OenUHyuVSFTtOQEAAACjGWXBAAAAAKDGhVHkSnpZcKXD95X2PL2+yAIrUnsulO958tztYmpMpjS1LqnlGhvkh5Ee+uv1eunR//RZ3rrrraeDDzygas8HAAAAGO0IrgAAAABAjbOkEwuUtOU8tWZ9BVGkxTlPlsvSHniukb3dpiEZ08S6hFabOE4Nybhmzpylmy46b4nlXXDhRapPparyXAAAAICxgOAKAAAAANS4MIzkRZHmduSU9UMtyubc79v9QL4XyFfM9WKZkExp+cakJtel5EXSP397hXKZTJ9lff0bR2jfvfeq0jMBAAAAxgaCKwAAAABQ4wJZ1oqvRVnPNa1PB9anPqbWrCcl4gr8UONTSU1uSGqZ+nrX0b5t7lw98Ncb+ixn5VVW1S9+cUnVngcAAAAwVhBcAQAAAIAaF4SRFqSzavN85YJAScU0qyPnmtn7kQVapIl1STUkEmqsSyoZT+ju310lL9eZ4dLtpFNP1eSJE6v2PAAAAICxguAKAAAAANS4bOhrQcZT2g9cWTA/CpXxc65UmDW1X6a+zvVbmVxXp4l1KUXZrO6/+aYlslaO+H9fr9pzAAAAAMYSgisAAAAAUOMWZ3Jqzflqs/4qga9sKGUj+0tc41NxNaWSmlRXr+XHpRTFYnrt0YfU0dbWZxnfP/FENVrJMAAAAABDRnAFAAAAAGpch+erxfPUkvOVy3muDlgQRaqLxzSxPqnGVEzLNKaUTCQ1ORnXw//4e5/719XV6bCvfbVq6w8AAACMNQRXAAAAAKDGtXmha2jf5nkKAqkuIaViCU1MxZWKxzWlvl5N8ZgaEnElchk9/sB9fe6/++f30LJTplRt/QEAAICxJl7tFQAAAAAAFJbxQ7UHgTo8KWflwKJI41Jx1aeSGp9KalxdQk2pmFYcV6eH77lLuWy2z/0POeRgNjEAAABQQWSuAAAAAECNa7Vm9l4gr2uGXFMiqfqklErENC6V1ORUQtOampSMxXTT1b/uc99x48Zpn733rtq6AwAAAGMRmSsAAAAAUOM6Al9tvnqCK3WJmOoTKY1LplxwZZmGOk1rrNO/7viH3nn9tT73PeCAA9TU1FS1dQcAAADGIoIrAAAAAFDj5mXS6l3oa0J9wmWvNCYTWr4ppWlNDfK9nH53yYV97hePx3XyySeP+PoCAAAAYx1lwQAAAACgxrV7gazVSvcMuSl19fIiadn6lKY21mt8KqGTjz5a777xep/7HXzwwVp33XWrss4AAADAWEbmCgAAAADUuKzv9/xsQZbQeqmkklquqV7jEwn99le/0L233bJE1sppp51WhbUFAAAAxj6CKwAAAABQ4zK+hVM6xVyAJaZPNNUpEY9p7qyPdM3FfcuBmdNOP13rr7/+CK8pAAAAsHQguAIAAAAANS78OLbislaWrUsqlYgrFpN+/ZOzlctk+tz+4K9+TWedeebIrygAAACwlCC4AgAAAAA1zou6O650qk8mlFBMM178n/59x9/7/G211dfQb669RjGLvAAAAAAYFgRXAAAAAKDGeb0yV0xDMqZpTXX6wwDlwH520UVqamwcuZUDAAAAlkIEVwAAAACgxvlB35O4SamU3nvuGT372KN9brf9Z3fUl//v/0Z+BQEAAIClDMEVAAAAAKhxGb/vSdwK4+v1h1/8fInbnXb22a4XCwAAAIDhxVE3AAAAANS4qN9J3Aevvqwn//NQn9vsuPMu2mH77Ud83QAAAIClEcEVAAAAAKhxvVuuWJv6W3977RK3+dGPT1NdnCb2AAAAwEgguAIAAAAAoyhzxVu0QHfddkufv2+19db6zA7bKxnnFA8AAAAYCRx5AwAAAMAo8uF9f5PneX1+963vHKt6eq0AAAAAI4bgCgAAAACMkrJguUXz9d4dN/X524orraR9v/xl1ZO1AgAAAIwYgisAAAAAMEqCKx/dcJWCTEefvx3xraM1vqFesRj9VgAAAICRQnAFAAAAAEZBz5XFzz+p+Q/e3ef3K33iEzr62ONUR9YKAAAAMKIIrgAAAABAjWue/pbevvi0JX5/5rnnadKE8YqTtQIAAACMqOTIPtzYsnjxYt1zzz2699579cEHH2ju3Lmqr6/X8ssvr4022kh77bWXPvOZzyiRSFRl/V5//XXdddddevrppzV9+nQ1NzcrmUxqmWWW0bLLLqs111xTW221lbustdZalBEAAAAAalAYhnrlktMVpvuWA9t6209r/4MOVjJOOTAAAABgpMWiKLIMc5Tp9ttv109+8hO1trYWvN3GG2+siy66SGusscaIbeP3339f5557rv7zn/+UfJ/77rtPq622WsXXZcGCBTr66KPdz1dddZUL6gAAAAAo3YcffqhVV121z++WW3553f+fR7XqaqtpQl1SCTJXAAAAgBFFWbBBuPjii/WjH/2oaGDFvPTSS9pvv/304osvaiT861//0r777ltWYAUAAABA7cpkMkv87twLLnSBFQuqEFgBAAAARh5lwcp0yy236Jprrum5Ho/HdeCBB+qggw5y2SkdHR0ukGJZGs8++6y7jf3umGOO0c0336yVVlpJw+X+++/X8ccfryAIen5nJb8s2LL11ltr2rRpSqVSmj9/vp577jk98MADrqSZ53nDtk4AAAAAhqb38X23iZMmyYqBNSaZLwcAAABUA8GVMixcuFDnn39+n8DKJZdcoi984Qs9v7OeKzvuuKPrtXLyySfrjjvucL+3gMYFF1ygX/3qVxoO1vPlxBNP7Dnxqqur03nnnae99957iduuuOKK7vLFL35RJ510kn7xi1+45wIAAABgdARXkomExqWS9FsBAAAAqoQR9TJcffXVam9v77n+ta99rU9gpTdrHG99T3r3MbEskZdfflnD4fTTT1c6nXY/x2IxXX755QMGVvpbbrnl9NOf/lSrrLLKsKwXAAAAgMoHV8bV1RFYAQAAAKqI4EqJcrmcKwnWzTJDvv3tbxe8T0NDQ08zdxNFkW688UZV2j//+U89+eSTPdetTJllzwAAAAAYm8GVuhRFCAAAAIBqIrhSIgtetLW19VzfbbfdNGXKlKL3s8yWcePG9Vz/97//PeDJ0VDccMMNfTJmjj322IouHwAAAED1DHT+kEgkqrIuAAAAADoRXCnRww8/3Of6dtttV9L9Ghsbtfnmm/fp2/LCCy+oUt5//3099dRTPdctY8Ua1wMAAAAYG8IwXOJ3BFcAAACA6iK4UqLXX3+9z/XeAZNitthiiz7X33jjDVXKo48+6sqNddthhx0qtmwAAAAA1UfmCgAAAFB7KNRbonfeeafn53g8rlVXXbXkjbz66qvnXdZQvfjiiwMGfVpbW3XHHXfonnvu0fTp07VgwQI1NTVp6tSp2nLLLbXLLru4SywWq9i6AAAAAKg8gisAAADAUhJcOf/883X77be7n0899VTts88+Gs0WL16sRYsW9Vy3slupVKrk+6+44op9rr/33nsVW7eXX365z/U111xTDzzwgM4880zNmzevz98s4GIXe/xbbrlFG2ywgc455xxtsskmFVsfAAAAAJVFcAUAAABYSsqCtbe3u4CEXXK5XNHb33jjjfrud7/rLk888YRqTUtLS5/rlv1Rjv49UPovbyhmz57d8/OUKVN055136tvf/vYSgZWBvPbaazr00EP1r3/9q2LrAwAAAKCyCK4AAAAAtacmyoJZ9sW9995bsz1DOjo6+lyvr68v6/79b99/eUNpbGmBrG7ZbFann356n+b2FjzZbLPNXEmwuXPn6qGHHtKVV16pOXPmuNtkMhn98Ic/1M0336x11llnSMGdgfTO+AEAAABQPoIrAAAAQO2pieBKrRtqcKWhoaHg8garra2tTzP73sv9wQ9+oKOOOmqJ8mQHH3ywvvjFL+qII47QSy+95H6fTqd12mmn6S9/+UvZ62ABnEKsfJqVHwMAAAAwOARXAAAAgKWkLNhY0zuAYcptAt///pVimSsD2WOPPZYIrPQ2adIkXXbZZRo/fnzP755//nk99dRTw7KeAAAAACobXInHOZUDAAAAqonMlRKMGzeuz3UrpVUOK9fVm5XoGo716nbccccVva9lsRxwwAH63e9+1/O7++67T1tvvXVZ6/Dwww8XLQt2xhlnlLVMAAAAAB8jcwUAAACoPQRXStA/GNI/WFKt4IqV3LKL53k9v1tzzTW19tprl3T/3XffvU9w5Zlnnil7HVZYYYWi6wgAAABg8AiuAAAAALWHXPISWBmt3ubPn1/WRp43b16f6xMnTtRwrduGG25Y8n3733bGjBkVWy8AAAAAlUFwBQAAAKg9BFdKDGBMmTKlT7Ckd7ZIMbNmzepzfY011lClWKZKb5MnTy75vvX19X2yaFpbW+X7fsXWDQAAAMDQEVwBAAAAag/BlRKttdZafRrJv//++yVv5Pfeey/vsoaq1BJg+URR1Od6LBYb4hoBAAAAqCQ7/+gvkUiwkQEAAIAqIrhSog022KDP9eeee67kjfzss88WXNZQ9C/tZQ3kS5XJZJROp/tk6HCSBgAAANQWMlcAAACA2kNwpUQ77bRTn+uPPvpoSffr6OjoE4hZdtlltckmm6hSdt55Z8XjH7+Mr7zySsn37X/b1VZbrWLrBQAAAKAyCK4AAAAAtSc53A/Q3Nys2bNnF82gKOf2A7Gsi8bGRg2XbbbZRhMmTHB9ScwDDzyghQsXaplllil4v7vuussFWLrtuuuufYIhQzV16lRtscUWeuaZZ9z16dOn6/XXX9f6669f9L733HPPEs8RAAAAQG0huAIAAAAshcGViy66yF2G6/bdzj33XO2///4aLqlUSgcccIB++9vfuuvW0P7SSy/VWWedlfc+FlS56qqr+vQzOeSQQ4rWU+5dU9kCMcWCMYcffnhPcMXYel1xxRUF7/PRRx/p1ltv7bNue+65Z8H7AAAAABh5BFcAAACA2kNZsDIcddRRLnul20033aTbb799wNta8OWUU05xQYxuX/rSl4r2Wzn99NO10UYb9Vx+9atfFV2v3XffXZtvvnnPdcuqueyyy/Le3jJujj322D4ZNbaM9dZbr+hjAQAAABhZBFcAAACA2kNwpQyTJ0/Waaed1nM9iiKddNJJOvXUU/Xiiy+6kmFz5szRfffdp4MPPlj//Oc/e2673HLL6Uc/+pGGi2XuTJw4sef65Zdf7jJaLNAyf/5817jeSoZdd9112muvvfTaa6/13Hb55ZfXmWeeOWzrBgAAAGDwCK4AAAAAS0lZsGnTpmmNNdbQSOqdUTKc9t13X5eN0p0ZYgEWK6/Vu8RWf+PGjXPlwSyIMVzWXntt/frXv9aRRx7Z08PmySefdJdCbJ2uueYaLbvsssO2bgAAAAAqG1ypZB9HAAAAADUSXDnhhBPcZayyklprrbWW67eyePHigre1ZvMXXnihVllllWFfr6233lp///vfXTmy5557rujtd9ttN51xxhkuqwYAAABAbSJzBQAAAFgKG9qPVV/4whe0ww47uNJfVgbs/fff17x581RfX++CFdYvxcpvbbPNNmXNKkskEu7S+3o5LGPoxhtv1NNPP60777xTL730kluvtrY2VzZspZVWcuu0xx57FO3/AgAAAKD6yFwBAAAAak8ssrpWwDBZsGCBjj76aPezlUaj/BgAAABQngsuuEAnn3xynwlYvu+zGQEAAIAqolAvAAAAAIyizJVys9sBAAAAVB7BFQAAAACoYWEY9rlOcAUAAACoPoIrAAAAAFDDyFwBAAAAak+ymicIr732ml544QUtXLhQzc3NWrx4sZLJpCZPnqxJkyZphRVW0JZbbqlVV121WqsJAAAAAFVFcAUAAACoPSMeXHnmmWf0+9//Xk8++aTa2tpKuo8FWXbaaScdeeSRWnnllYd9HQEAAACgVhBcAQAAAJbismDvvPOOvv71r+urX/2q/vWvf5UcWDGzZ8/WTTfdpD322EOnnXZaWfcFAAAAgNGM4AoAAACwlAZXLEvloIMO0hNPPDGk5Xiep5tvvlkHH3ywZs2aVbH1AwAAAIBaRXAFAAAAWArLgj322GP61re+5QIj/aVSKa2//vpac801NXHiRE2YMEG+76ulpcX1YLGeLNOnT1/ifm+++aYOPPBAF2hZfvnlh/spAAAAAEDVEFwBAAAAlrLgijWq/8EPfrBEYOUzn/mMDjvsMH36059WfX19wWVYk/v7779f1113nd56662e38+ZM0c//OEPdf311w/b+gMAAABArQVX4vERq+4MAAAAII9hPSr/1a9+pUWLFvVcX2aZZfSHP/xBv/vd71yD+mKBFTN58mTtv//+uvPOO3Xqqae6bJduTz31lO66665hW38AAAAAqDYyVwAAAIClKLgyf/583XbbbT3Xp02bpltvvdVlqwzW4YcfrquuukqJRKLnd1dfffWQ1xUAAAAAapWVTu6t9/kQAAAAgDEWXLFMk1wu1/kg8bh+/vOfa6WVVhrycrfffnsdd9xxPdffeOMNvfTSS0NeLgAAAADUIjJXAAAAgKUouPKvf/2r5+dddtlF2267bcWW/c1vflPLLbfcgI8FAAAAAGMJmSsAAADAUhJcsYyVF154oef6oYceWtHlW9+VQw45pE/vFQAAAAAYi8hcAQAAAJaS4Mrbb7/dUxJs/Pjx2nrrrSv+GJYN0+21115TFEUVfwwAAAAAqDaCKwAAAMBSElx57733en7eeOONXc+VSltnnXXU1NTkfk6n05o1a1bFHwMAAAAAqs0Pgj7XaWgPAAAAjNHgyuzZs3t+Xm+99YbjIVzAxgIsAz0mAAAAAIwVnuf1uU5wZfg9/vjjuvHGG/XMM8+oFtTa+gCDkclk3PvYLi0tLWxEAMColxyOhS5cuLDn54kTJw7HQyyx7N6PCQAAAABjBWXBKufhhx/WzJkz3STALbbYIu/tbr75Zt1999068MADtdVWW6naRnp9St1OY9XS/vzL1dbWpjvuuMP9vNdee7ny8ANpbW3VWWed5X628vHDOV5UyIIFC/T8889r8eLFmjx5sjbffHMts8wyVVkXAMDoNizBFSvTNdLBld6PCQAAAABjhUdZsIq54YYb9NBDD+mwww5j0JztxPukgsGK7qDJDjvskDe4Um3Nzc06//zzXSCod9A6mUxq77331qmnnqoJEyZUdR0BAKNLcrjT1hsaGjRcGhsbe37O5XLD9jgAAAAAUC2+7/e5Tlmw4bfddtu5QdZPfepTI/BoAIZbR0eHvvGNb+jll1921y1bZfXVV9f06dP13HPP6bbbbnP9g6+77jrV19fzggAAqhdcCcNwOBZbc48JAAAAAMMt1y+4Yv0nMbz2339/dwEwNlxxxRUusGLB6V/96lfabbfdev5277336oQTTnBBlmuuuUbHHXdcVdcVALCUB1cAAAAAAEtP5sqiRYv0z3/+0/38hS98wfUxyOett95yjdmtFE++AIZVJnjxxRdd3wubSLfyyitr0003VSqVGvD2DzzwgObOnauNNtpIm2yyiSv588ILL2jGjBlqb2/XuuuuqzfeeMMtz7z55puuqXZve+yxh6ZMmdLTQP7999/XOuusU7DHiVVtsAFbexx7nVZaaSX3WIWev/WdePvttzV79mw3m37ZZZfVxhtv7P4dTh9++KFee+01VxrJ1m/55Zd369q72sQHH3ygxx57rOTtVMnXzO5vg9uzZs1SXV2dPvnJT2rVVVcd8vMuZ3sP5fkXU+r2sf4ld955p6Io6tk2A7FlvfLKK4rFYq7Pybhx45a4jb2HP/roI82ZM8dt01VWWUUbbrhh3tdkoHJf9jj2+bZMLltn60PTO8B7//33u+3UzUpu9X7/L7fcctp1110LPs5///tfvfvuuyXd1raNvaaFtk1/tg+4/vrr3c8HH3xwn8CK+fznP68DDjhAN910k37/+9/rqKOOInsFAFASgisAAAAAUMP8XmWXazW4YgOvl156qRYuXOgGkQ8//PC8t7Xb3Xfffdp5552XCK5YsOLqq692A5w2yNybDYb/8Ic/1H777bfEMv/whz/oqaee0tFHH+0GaS+88EI3MNztmGOO0ZVXXtlz/cknn3SX3jbbbLOeQfNiDeS719NKCLW0tPT5mw1c77LLLjr77LP7DMLbALLNird/e5fSNjZYbQO+1rei0o21bXucfvrpLqDVn/XGsGDBmWee6QbfX3rppZ7eGaVsp0q9ZjY4b6+ZBX56s4H2c889d1DbZDDbezDPv5hyt4+9JhYYtDJVFqT4xz/+4QJhvVmw5Mgjj3QN2e0zdNBBB/X5+8UXX+yCnRYs6m/q1Kk6/vjjXTAhHwtE2uthgSYL8vT2iU98wgUfuh/z2muvdevb7Ze//GWf21vj+mIBk+6eLRboe/TRR/P2PbHnY9vL1ql/0KuQRx55RJlMxv1sn+mBWNDFgisWiLF16L/O3cEsY9vcgsMAAAz7t4EdHNkMkeHQ/eUIAAAAAGOV368Eci0GV2yg8Utf+pKbHX777bfnDa7Y+aE1lDf77LNPn79ZUMYGbZ944gl33fohWIaBPV/LaLBZ+KeccoqbRX/EEUcMuHybRf/OO++oqalJO+20k5sJbzP7119/fTeoaoOsljlgGRvWc6G3UgfMs9mse/ynn37aXbfHsGXZY1oGiwUKrMzQscce22eZlg1jg7bTpk3TGmusoRVWWMENvFvPB8sosftYhsUtt9zillUJFvg57LD/z95dQLdxbV0A3mLbseMwlJu2KaTcptxXSpmZmZmZmV6Zuf37ysxMKTMzJGnThsEsGPjXvs6oI1myJTPsL0vLliyNhqRI99xzzj6YMWOGOUYrr7wyRo4cafY1M0S4rny+U0891QRXmClSzH5qj2PGQBsDQOxzwWPGYAezmxjoYHYL9ykHvf09VwvRmv1d7Pa3pLX7h8GwL774wqwrgwkM4nnZIsx64W0MrIwaNQpnnnlmk+f93//+Z85Tnvfcbu7TmTNnmn3Kn1w+gwj7779/zv12xBFHoKGhwTwns2u4/5j1w+ynn376yQRuvOAKg1TMiGFGCW211VYZWTR8bEu4DAaaGGRh5ssee+yR834MejKwwuPCc7lQXvCHwSo+Nhdm5FRWVpr3KAZSsoMrPBdvvfVW8/s222yj4IqIiHROcOXKK680FxERERERESleqgeUBfOCJQyucHY3AxyLLbZYk/u89NJLZsC5f//+TQYvr7nmGjMIzSDA5ZdfbgZpPRxQZs+EG264AVdffTXWW289LL744k2Wz+flYPhNN93UpOwTMzQOPfRQM2i+xhpr5ByULsRll11mAisceD755JNNIMl/TDiAz4HY7EF4Pue6666LVVZZxQR8/L766isTjOH6c2CcA/Lt4YUXXjCBFa4LZ/pnD3RzgJ3HhIEV4sA/L4Xup/Y4ZgyscL3uvPNOU3bKw+DHiSeeaAbzb7zxRrOvi9Ga/V3s9rektfuHwR4+ltklzO7hoD4DHsTH8DYeMz4uV9DplFNOwbhx40xgKXuCKh/PTBr2Hdl+++0zSngx8HL88cebwAoDN7xPdjCC+4XBBw8zaBgk8oIrfLz/OBaC2V7M3mEWDINduYIrLLv31FNPmd+by7rJhY3qqbkyczxH+HcGoHhOioiIFEKdEEVERERERLoxy7Z7RHCFg9JeQIXZK7k8/fTT6b4s3oA+sZzYAw88YH4/8sgjMwahiYEMDoazRBcHWb37ZuMyOSDcUf1LWI6Js+eJA/IHHHBAk+PBgWIGcrIHttdcc02z/tkD/V6pKZZq8oIK7YVZH7T66qvnzCBghsFOO+2U0XelUO11zIhBguwBefbB4P4lZq5wwL8YXbG/23P/sDcKM1SIwSVmsnz++ecmOEIMNi299NJ5S1xln3/E43zCCSeYgAn35/jx4zP+/n//938mI4YBGwY6cmV5sK8Qz+/2xowhHisGZ3/44Ycmf2fGGwOF3IbsrLeWeKX7Wso68gJN2aX+iP1duI68FNqzRkREej8ViRQREREREenG7B5QFszDQU8OlLO0D2ew+we2WVKIpZC8sjp+HORlGSMOOO+99955l8/G3ewd4pVZyrbWWms16U/RnriezEzhMchVUqkQzNxhTwvuD/bgsOcFz7z+GMymaC8sAeZlarB5PEuYtZf2OmYMyjGQkAsHspllwf3E0k7MJOnO+7u9989+++1nbmdgwes1wvVn+TSWe2sJM1FYAo3ltpi14vVP8YID2dv+5ptvpoOfxWaftBVLi6299tqmlBuzV84555yMv/M22mKLLUzmW2tKyvsDurl4QcZcgTxm2rXUO0ZERPqeDgmueDVNO1O+hmciIiIiIiI9mW31jMwVL2jChtYsHcTSRcyY8HjZLBxEzW4Sz8Fvb2Y5+zmQNxDs/+kNBnsZGdk6+nsoe4EQM3T85ZQKwaAMsw6YoZDduN2PA7u8b3vMjmeGwfXXX2/6oG6yySbYYIMNTICCfTSYleD18WiN9jpmY8aMyfscbJ7O/cxsCpZqKia40hX7uyP2z6WXXmpeV97fOd7C25rDXjq8DwMz2Q3p/fwZGixT5q1LMf1M2hP7uDC4wuAs+wCxD4+XMeZl2eRrSN8cL6jCDKGWAnHkPa+IiEiXBFeOO+44cxEREREREZG28Wba94TgCjMlVlttNXz00UcmmOIPrjz77LPmZ66SPhw8J86wP+uss1p8Hg6C8pI9E72jB0VramrMz2IDK8TBYvZAIZYtY0mnIUOGpNeZZaRee+219EB3e+DzsLwTswCYwfDiiy+aizfoz1JV7C+Sq4RUS9rrmLWUhcC/87mYddLd93dH7B82o2dQiQEHYhk33pYPg1DsWcJzlcEzNmpfeOGFUV5enm7CzqALM3f87y3sv+Pth5bKZ3UUBv+YXcUsKwakvPeKJ554wqwrA4Is6VYsbnu+cl9+3t9ZLk9ERKQQKgsmIiIiIiLSjfWk4Aptt912JrjCXhYc1GepHfaLYNPrfMEVrxwPS3qx5FEh2pJ10VpsNO4NRBeDDcC9gf4zzjjDlInKXn9m+niD/e2JvSLY64bBFR4Xlmbj8WD/ivvvv980tH/00UdNlkgx2uuYtdRLxfu7t++7+/5u7/3DdfQCK3TvvfeaUmJef6NsN910kwmsMKDCkmq5srnYA8Yri+ZhnxWW8WOmS7Hnd3th8IfBI2YcsQwY3yu4Pk8++WQ6s6U1uC/effddUxquOZMnTzY/F1lkkVY9j4iI9D0KroiIiIiIiHRjPaWhvYflp84//3yTacAeDuyR4JUEW2mllbDQQgvlHPwkZhVccMEF6K689WT5JPbTKDRTho3IafHFF8e+++6b8z5e8KmjMHODF/aK4YA1yy+xuTn7ctx+++3mmBWjvY6ZV2otlzlz5pggEBXTA6Q77O/22D8s5+ZlvTDo8NNPP+G7774zx+2xxx7L2UPE2/aDDjoob5m8XNvOwAYDbAwwsKl8sU3j28suu+xigkIMfnE9WQ6NQREGf7J7NRWK5z0xI4YlC+ebb74m9+FzeOead38REZGWdP5UHxERERERESlYdsmi7h5cYUmdcePGmd8ZVGGpI6/nBLNacll33XXNT86mZ3PvjuL11Wip90I+bLjtNchmqaJC1dfXm5/NBWOeeuopdBZmKHCfsyxYrsbmheyn9jpmfOyUKVNy/u35559Pr08x5aDaur/bep60x/7h655N7FlejEGis88+G1dffbXJ4GGQ5corr2w20ydf8/Zvv/02b0BrnXXWMT+ZKeOVwCuEv19NW/aZV1rwP//5j/md2SsMItHmm2/e6l67LDfmZQV5gd5s3u3clvXWW6+Vay8iIn2NgisiIiIiIiI9qCxYV5TDKpYXRGF2BEv6cICYg70cIM2FJY423XRT8zsHlDl4nA9nnzeX7dAc9tygCRMmtOrxXM+NNtrI/M7BbfauyGXixIkZ/R28bB02OWfJqmzXXnttOuOgPTHLIV8zdwa9vHVhn4ti91N7HTMOxp922mkmEyi7KfsNN9xgfud5U0yfm7bu77aeJ+2xf1ga69NPPzUBIgZVWGaM2TDnnnuu+fv//d//4e23326yrAUXXND85Osu+72DjeFPPvnkvOux3377meAC+9Gwj26uPjc8Xj/88EPGbewB4wV927LPPF7TegYwX3/99TaVBPN67my55Zbmd2Zp8bzw47G56667zO/M2Ml1rvE8euihh8ylrQEkERHpPVQWTEREREREpBtze1jmCq255prpxtSXXXZZevZ4ZWVl3sewdBIHmNmQe4cddjBZIsxW4GMYnOGyOAjKQU42YT/++OOLXq/VVlvNDI4yKHLiiSdiueWWS2c3bLbZZgU18r7wwgvNerBcEQejOdt/7NixZvCbGRgsqcRMhWeffTbdrH3DDTc0A7xsbs7+HxzAHTVqlBm8fuONN8xgNZ+bZbDaEweMX331Vay88spYYoklzDFheSUOsnPQmmWXGKxjA/TW7Kf2OGYcyGYvGA5+M5OGA/W//fabySRghhCvNxcQyKWt+7s9zpO27B8GfhhcoVNOOcU0pfcHLhm0ZHbJ6aefbs6zoUOHZpTVYnbKxx9/bLabAR6eh8xOYiYQ+6lwn/P5s7GM2JlnnonzzjvPPAcz0Li97EHCjBiWzho/frzJpGHvFw/Pfe6jr776yvRZ2nHHHc068dziOecFJAvFzBGW7mIJL1pqqaWwwgoroC14DvE8Y+kvBmq4H7ldDISyJxGznZg1k+99hefNrbfean5neTKWURMREemw/w34HxM/GPEDJ/GDzAMPPGA+GLVl1s0+++yTbq7GD+fef24iIiIiIiK9UU9raO+tI78P3n333ekyRS31cOCALxurX3zxxWbAmIO4vGTjYO3o0aNb3Q9mrbXWwgcffGAGmr2yU8RB70IGzTloz/VkL4y33nrLDELz4sdBW38giaXS2GicjcQ54P/II49k3J/rxO+6hx12GNrTsssua7aV/St4ybUtHEhfddVVW7Wf2uOYMUuB3/E5XnDLLbc02Y/XXXddk8yalrR1f7fHedLa/cOMJwYC+LrnmMdee+3V5P48ZgxkMNhx6qmnmiAaS715wRVm/dx///0msOPPiGFJMQY/GPx78cUXc67z7rvvbtb7kksuMcEfBpn8mNmyzDLLNHkc1/mQQw4xj/EfRwaqig2uMCjDHjPXX399RiZLWwwfPhz33Xef6VfDgNaDDz6Y8fcxY8bgqquuSmctiYiIdGlwhem7XmCFHyqZetmWwIr3wZD/2R1++OGmAR8/yHK2jVfPV0REREREpLdx7J6XueIN8noT4zjL2+uj0Bz2VGCmC2ePv//++2ZWOSfucbCXA9BsNM3vhd5Asj9TgbPul19++WaXz/XgQDQzEr788kuTuZBKpczf/APmHFjnujAjJRcOwHKiH7MBuJ5ezxDOfOcg7SqrrNLkMSuttJLJIuGFg9/sqcHZ/auvvrpZbw6Ue4PI2ce4pfXJ58ADDzTZNdxWlkJig3TuT24rB8iZSZGrL0mh+6ktx8zD29lPhNkzDDxwX3IAn4/hoHy+3iEtacv+Lmb7W1Ls/mEpMGZDMcBw7LHH5lxmeXm5KW3GwA3x2DK7w8PAH4MTLBvGTA0GVTjhlRkhHJfhPm3ufGIZNr6muP2c5MosF64vG97zdcygXDYG6Dg+wwwPZuowqMp9ztelh1lT3j5vLouNuE+I697aRva5SrWxXBqzer744ov0dvH1yvOiuZKLPGe8dff3mBERkb4t4DJK0c6YYsz/iL06lAyIeI3y2gPTsP/3v/+lZ7KwOWK+D2rStThLyJsNxC8fuT6EiYiIiIhIfv2HDkfNzOnp6xxw5cCqSE/Gcl3MpuH3xdaUeJPejT1fXnrpJRMkYuaPiIhId9QhnRAffvjhdGCFMyHaM7BC/ODlzXLgrA+m6oqIiIiIiPRGnP3dEzNXRERa48cff8Qrr7xift9zzz21E0VEpG8FV5566qn07wcccEC7L58psDvvvHP6OtM6RUREREREeqOe2NBeRKTYgAr7u7DE/MEHH2yCyuw5k6u/i4iISK/tucK6pV69WTaRY63QjrDpppvizjvvNL+zXqaIiIiIiEhvpMwVEent3n33XVNS3jNixAicc845XbpOIiIinR5cYVMwD5vjtbb5XEvYTIzN6NicjRcGdRZccMEOeS4REREREZGu4jp2xnVlrkhvwD6tbHbO7/YiSy+9tGkYX1JSYs4LNrHnhF0REZE+FVz5559/0r8vsMAC6EgMpjCwQn///beCKyIiIiIi0usoc0V6o/3337+rV0G6kXXXXddcRERE+nTPlaqqqvTvw4cPR0di5opn7ty5HfpcIiIiIiIiXUE9V0RERERE+kBwJR6Pd1q6eiQSyfm8IiIiIiIivYXKgomIiIiI9IHgyoABA9K/z5kzBx1p9uzZ6d8HDhzYoc8lIiIiIiLSHTJXgsF2/xonIiIiIiJFavdP5f4gx5QpU9CR/P1dFFwREREREZHeSGXBRERERET6QEP7kSNHpn//8MMP0VEYuJk4cWL6+ogRIzrsuURERERERLqC67q9MrjyxhtvYPr06RgzZgyWX375rl4dacVEx3feeQfhcBg777xzn95/v/zyCz7//HMss8wyWGGFFdBX2LaNJ554wmTS7bjjjggEAl29SiIiIj0/uDJ27Fjzn6vjOCYA8s0333TIh+XXXnst/fuoUaMwbNiwdn8OERERERGRrsTvVdm6c3CFA+4ceF9yySWx8sor573fvffei08++QSHHXaYgis90M8//4zzzjsP0Wi0TwdX+Po86aSTTIDlmWeeyXu/uro6/P7775g2bRqqqqpQXl6OxRZbDEsssUTBz5VKpfDFF1+YcRbu98UXXxyjR49u8XGJRAIff/wxZsyYYSbDrrrqqubxzfnoo48wYcIErLPOOlhwwQVz3ofvQ1zu888/b5a3zTbbFLwtIiIivUW7B1dYnoszNr777jtz/brrrsNdd93Vrs9RX1+P22+/PX197bXXbtfli4iIiIiIdJfZ4T0puPLggw/i7bffxj777NNscEWkN3j00UdNoGnLLbc0AcVsr7zyCh566CF89tlnJjiSjYGLo446Ctttt12zz8NlXHvttZg7d27G7Rx7ueiii0wGWC6c7Hr00Udj6tSp6dsWWmgh3HLLLSY4k8vff/+Nww8/3ExgZUZKc7jsl156CVdffTU22WQTlJSUNHt/ERGR3qZDOiFuvfXW6d/fe+89PPnkk+26/EsuucTMuvDwg4yIiIiIiEhv09OCKyJ9BTNCrr/+evM7gxG5vPzyy6ZcellZGVZaaSVsvvnmJsNjtdVWMyXV/vrrL5x66qm44YYb8j7PjTfeaLKEGFiZb775zOPHjRuH0tJS/PDDD9hrr73Sk1v9ampqcMQRR5jAyiKLLIKtttoK888/P/7880+TMcb1z+X8889HQ0MDLrzwwhYzXLhcbhOzae6///4W9piIiEjv0+6ZK7T77rubbBXW0KWzzz7bpL1yJkNb6w1fddVVeOyxx9K3rbfeeuZDioiIiIiISG/T08qCifQVL7zwAmbNmoXlllsub3mvLbbYAvvvvz+WXXZZUz7djyXCGJT5/vvvcfPNN2OnnXbK6GFLDJ4wuELMbrn44otNUIYY0Nh3330xadIknHbaaaYsmf+94dlnnzWTUtkHhoGPWCyG2tpa7Lrrrvjtt99Mxkl2xgy3iaX9uC4MABVi++23N6XBmF1z4IEHNtlOERGR3qxDgiv8T/uYY47BWWedZa5blmXSRfmf+CmnnGICLcXijI4zzjjD1OX1RCIRHH/88e267iIiIiIiIt1FT8lc4Wz4999/3/RbIfag4GCr32abbWbKSOfbzq+++sp872NjbJYsylfqKFsymTTlj/jcDEYtsMACZkCZ3xeLxSyDiRMnFnTfXXbZJeexKHZ93njjDTMxkdvLfqXcF19//bUpz8ReHRtssAGGDx+evj+/X3/77bdmX3H5I0aMMBMO+T28LZitwOf1SkjxORdeeGGTLVEIPo7bXV1djaFDh2KVVVZp8bs/J1D+8ccfZlsZCGA2xqKLLmpKbOUbpOd+ZQCAQQav3wufm9kbc+bMMeWsOAnTLx6P48svvzQBDZ5fzLhgUKQtgQDv/GZwIZ+NN94479+4fznGseeee5rjyH2fHVy57bbbzD7i7RdccEE6sEK8jSXB9t57b/z66694/fXXsemmm6b/zv4stMcee6TPDR4PToZlVgr3hz+4wl4wrBIyZMgQM25TqLXWWsvscx5DHheeryIiIn1FhwRXiB9y+OH48ccfT9/2yCOP4LnnnjPpqJzBwQ8zzX3Y4iwQfiBgWTH+J539xeLcc8/F0ksv3VGbICIiIiIi0qV6SnCFg/0sXeRviM2L34orrpgzuPLqq6+awV6v8oGHgQb28Mw3uM8eFhx8vueee8yMfL/BgwebRuM77LBDUdvxxBNPmO+sheCy/ceitetz7733mkmELNXEQMMVV1xhvgt7GAjwgivs8cF9MnPmzIxlsOwUswaYCVHs+cHB+5tuuslUn2B/02w8bscddxzWXHPNnI/nuvDYv/baa03WiYP0HMzPFYC69NJLTWCJAY9sLF/FbIxc1S/Y44TPx5JVbLh+zjnnYPz48em/M7DiBVe4PSzdxUAIAyx+PK/OPPNMU2KrWMwWYSCJNtxwQ7SWfzykf//+GX9j2S5vu7bddtucwTNml4waNcqcN3wd+YMrXn+W7NcP9y0xEOXH847H8pprrkFlZWXB28AAFQMqHO9htoyCKyIi0pd0WHCF+IGHM0jYd8XDDzf8QMgL/xPmTBjOauGHCs5S4cwc1gZliqs36ymXQw89ND1LRUREREREpDfqKcEVNslmpYJ3333XfI8bPXp0k/LNuQIrHFxnQIID8Ryk5gDz77//boI1HLzm976nnnoqY8a+Nzh/yCGHmEwT8jIRuG84I5+D36effroZQGbQoVAMIHBd8mF2zuTJk03jbn/WQ3usD4MT3HY+//rrr2+yAZhlwZ/EQe9bb701HazhwDoDDJ9//rlZJ/btYMYQgy98XKEYfGBZKm/gnSWs+vXrZ4JdzOLhpEkGynIFV5hxwcwJDu4z84YZJ9xGrhMzWDgmwAwLbo8fAx0PPvigyebh4xg84oA+s1e4v5gFweoX3GZOzMyFz+2VxeLzsrk79wfPPWKAa5999jFlt4i3c3ImH8cG8zxP2Uz+sssua7GhfDavogbHMrKzTYoJat19993md2YfMYjlx2wUL9g1duzYvMvhecD9z8wXPx5Dmj17dsbt3nXv7972MLDIoFS+/d0cZmYxuPLxxx8X/VgREZGerEODK/ygxA/K/EDEWTD88ODHDzUTJkwwl0IxAMMPaMV++BEREREREelpekpwhYEEXhgM4aD1GmusYbICWsIBZGYfXH311Rmz5TkZj707GSx46623mpRX4ndMBjIYdLn88stNdQT/90wGCxhs4HI5YMwyY4XYcccdzSUXVlNgZQYGLvic/jJf7bE+DKwwIMUsEgZP/D799FPz3Zo4+M3yTfxu7J0jDKjw76+88orpUcqSZYXgY71G5MycYYZKdmCGQa5cGS1eiTJOjLz99tszSnExMMPABr/rMyCUHVxhEIRlrlgqLjtLgpMtGfDgvmZ2CzNLcjVW53PzXLv22mtNU/VcjdkZWGGwivfxrx+zjLjsBx54wNzPK21VKB4PL6hQCAbfGLzwMlK4fxhYZPCKgRWeG9lBPf84CSel5sNAHjHAxufx9hWDTTwfmInF/Uwck2F2CXll9/gYvtZ4PrE6SGt4gSFmXPE1na8HjYiISG/T4Z3G+OHy5JNPxh133GE+bLcWP+BxJhM/YCmwIiIiIiIifUFPCa601oABA0xgIHuAncEBrwS0v++mN/Oeg+J05JFHZgQyiBklzEhYddVVzQC8d9+2YJCH/T55PPjTG6xuz/XhoDj3RXZghVhqjAPjzBBi8MYLrHjnwwknnGACBMSJjYVinw0GM4iZR7kyXliejcGyfBiQye5xwkDFEUccYX5nFhIDCn7M/OHz5So/VVFRYSZUDho0yAQhmDmTzwEHHJAzsMJMDq+8G/uaZK8fA2PMJFpsscVM4IgBqWJ4fXkKzVphpg63iRcGdXiMuAwGytgMnvs4GzN/PPl6FXmvIeL54S9Hx1Ji3M/sxcJycXfeeScOOuggk4XEfexlqDDwx3U59thj0yXDGLTiej399NNmX7bEvx+KmTwrIiLS03Vo5orfuuuuay78j5wBEs708BrlNYf1Q5l+zCZv/OAjIiIiIiLSl4MrbWnC3d1w0DtfH86llloKP/74Y5OeHOxDwcF67geWpMpn6623NuWfvFJdrcU+FMzqYAlr9kxhdk5HrA+DI/7G9f5zwHvMTjvtlDOLg/id+YMPPjAD5Wx2v+CCC7a4bRy0Z8YEAwzs2bHffvu1+Jhc25Xv+BEDStyH3sB9Nma+sI8Ky4lxP3oVL7heDFz99ttvpvRVLgwg5PLmm2+a5fDcytd3hwEWBhiYNcJxCgbGCuWV1vICGy3hMWMwiXgeMcuEGUEvvviiKYN24403mnJsfg0NDRmPz8ffi4XHkUEpf8N7BpG4P3ghBlyuvPJKcz/uWwZdmMXinbvMwuLkWP97D4OdDAzlC+zyWHEdmQXj7xckIiLS23VacMXDGS/erBd+4OMMIDZa44UfHvjhhx9Q+B89/4PPNWtHRERERESkL+jtmSssiZSPN1ufA7Z+HIj3/v7yyy+b370Bef9Pltki9u9oLWYccNY/l8EBfpayytZe67PooovmvJ3BJa8sV3PVIPx/Y/ZAIcEVZqqwl+l9991nMiqeeeYZ05CcZZ54yW6ynitAke87uz/bIjtzhb744guThdNcZgp5mTW51j3fPvvpp5/MzyFDhuDJJ5/Me0y8Y1HsOeJlleQLDGZjQCP73OH5wHJc7E/D3jEMbvn3pT9owgBVvgCL//WR3fSegS+WLnvppZdMPxsG75hZxcALt/+ss84yPy+++GLzvsKJsCzjxkAhJ8fyORk8ZJm++eabz7wW8uG5wiAas6FERET6ik4Prvjxw14hH/hERERERET6ot4eXCmk8Xp2705OzCPOkOfgcEs4+OzvRVEoPu+pp55qMgzY14LZBf4+K+29PtkD4x5/qafmgh3+LAr/Y1py0kknmb4wbEj+ww8/mAtxgH2VVVYxpbdYoru1xy/XMWRghQEF7geWEmcJuAUWWMA0WffOb2ZaMCCQ6zVAPBb5sri8AX5m8RRyTIrZX15Qhc+RrxdNIViZg/11NtpoI/P89957L0488cSM5/AHmLJ7svj/lusxHpaSy862ogcffNBkzRx44IHpEnxeSTkGghh0I/Y8YuYW1++QQw7J+/7DjBxiyTEREZG+okuDKyIiIiIiItJ3gyutwSwA4iz87Ebp+bSmlBrLIzEThUELNmzP1R+kM9bH31/FXyoq3+A25RuIz4VBHgYgjj76aLz//vsm8MFBdwZZWM6bF/bj8HqotIerr77aBFaYbcMAQ65yaGxGz+BKa3jHhEGx1Vdfvah9XAhm5TDbxQustRaXs/LKK+Pdd981JfCygyIeVv3ItY+8v3l9bgrdDmZD8RgwoMXjTgwWsb8KA3g77rhj+r7MZGK5dv6Nl1zN6nksvXOzuf4wIiIivY2CKyIiIiIiIt2UgitNLbzwwulMj1xlutoDS0nddtttJjuCg//ec3bF+nBQneuRSqVMWe2xY8fmvB//5snX36Q5DB6xB4nX6Jwlnrg9r7zyiml6vtdee7VYJqwQzJJhAIc4sJ8raMD7eEGD1vACEyw33hHHhPv3u+++a3XwJ1ewNLt02pJLLmmyelgSjMGuVVddNefj+TdaZpllCn7OCy+80GTLXHfddemAjBcoYqm+7OAfS4IxsMK+OLn4+yIxYCMiItJX9J5OiCIiIiIiIr1MTwuueGWzOCDcUdgLgv7880/TIL69ffLJJzjnnHPM72wInm9Qu7PWh5kl7H9C7ImSz9NPP21+sm/H4osv3ubnZb+Sk08+2fzOwA5LbLUHZjl453W+UmgsCdaW3h3rrbee+cl+Ll6vm/bknRMsGdcWLCvm9ZzJ7h/DEl/s80PPPvtsk9JqxG379ttvze/jxo0r6Dlff/11vPbaa9hmm22wzjrrpG9nSTaaPXt2k8d4QRXvPtm8deDfiwnyiIiI9HQdElzhBz5+GOWFzdk6CmegrLTSSuby1FNPddjziIiIiIiIdAXO4O9JwRUOyHsN1TsKe1Vsuumm6X4hXvPyXKZPn45ff/214GUzgMBsCgYTWAZru+2269L18eyxxx7m59dff41bbrmlyd9ZvswLruy+++4F90LhoHl2OSo/f7CIZafaA0t2DR061Pz+2GOP5QwYnHfeeW16DgYlWG6Lr59jjjnGBL7yYXmvSZMmFb18mjx5cs5ghLdvmzvWLON22mmnpTNGGOzIts8++6Szku64446Mv8XjcTPmwqALX3dexlFzmK3CcRSWujv99NMz/sZl8LgwY+mDDz5I3/7zzz+bc4SBsHxBO56XxB49zLYRERHpKzrkfz3O9Hn88cfN76yhyv9gm3PmmWem78+ZQV7jtJYwbdZrINeRM6MKxXXQBwkREREREemrmSscdH7ooYfw4Ycfmubc/D7oZSdsttlm7daPgQPEHLhmqaIddtgBa6+9tsnuYGkrDlYziMEgBzML2Mz7+OOPL2i5DKzw8QwkcKCZ25LPLrvskj4WHbU+Hg6cv/jiiybj4Nprr8V7771nsjOYKcQAyBtvvGEG2dmYnE3HC8XgwE477WTKPrHcGEtCcZCd37OZjfD222+nsyL4t/bCfcdya88//7wp/8VeNewTw0F8bifPe5Ygq66ubvVzsKfIrrvuit9++83sP+4vno/MCGFAhCW9uI18zrPPPrvZ0m/ZWLKL/VwYjGO/lG233TbvvmVAgucCzydmFXEcg+cJs3O8jJB99903Z4YU+51svvnmeOmll3DVVVeZ/jdrrrmmOT7PPfeceX4G0hhkyZdV4sdlsITXZZddZkqmZeP+uvHGG83rgAE9Zk09/PDDJkjFQGO+TCP26iEvyCgiItJXaEpBG3BGBz8MsgYtPxDOmjXLBFf4QXzMmDFm5on3gbc74MyW7C9n/ABb6KwmERERERHpXD0tuLLJJptgrbXWMjPf+V2JFw8HmNsruMKZ948++iguvvhiUzJp/Pjx5pKN381Gjx5d8HKnTJlifjIYcv755zd7XwZRvGPRUeuTHSy45JJLzPMwoJJdgmyjjTbCpZdemncAPBceD5ZxYvP6fCXHtt5663bvW3L44YebAAODBsx68DIfvH157rnn4v7770/3ZmmNkSNH4oknnjDHkaWwvEuu/inFBFaI36GZIcT9zf2WK7ji37cM8OTCYMtRRx2VzkzK5YorrjABIWb5ZJ9XFRUVJrBSSFCD5ccYKGFwZvvtt895Hwb+eD8G726//fb07SussEK6RFw29p5hYJHBsK222qrF9RAREelNFFxpJX6w4QdbL3PGXz+WM1R4YdBlqaWWwpVXXtnqD9DthR/omBGUneHDWUFMYxcRERERke6npwVXONnsrrvuMpkrbLTNmfkssUX+wMqGG25oekwsv/zyeZfFYAxn0jNLIBcOLHMGPrNAOHOes/j5/YyD8wxiMItj2WWXLWoy2Y477oiGhoaC7pt9HFq7PoXsC2IWActlHXTQQXjnnXfMd06eHwwiMFOmNd852XycJbanTp1qKlD8888/ZhIhJwiyKTyX6zWH92OmC49NcxMJ2Sid9yFm8PjxcczAYcYG9xXPEwYQeKz/85//mN8Z6FpiiSXMxMVin9vDbBFmYnAyJAN+/MlJh8za4N+4z/OdXy1hgILbwHOd+y87syd73/Ing3Zcb2YHMfDCbKGWql/wuLPCB4/7W2+9ZfYLb2NGDANqPO8KwSwdjglwOc0915133mmeh8E7ZqxwHzF4k+99xytHx2AjS76JiIj0JQE3V1e0Niq2zFdry4K19nFtxdkp9957b5Pb+WEj15cfzhy65557WiyP1lH4ZYZfElgrNVtHB1eYzXPYYYeZ32+99VYzM0dERERERArDAWEOcPtxMHeNNdbQLhTpYtdddx1uvvlmEyQ644wz0NewvBoDPAwYvvrqq+meSyIiIn1FhzS0781Yc9cfWGFAZb/99jPpzJwJwtlZ/Pvqq6+evg9rqh555JFmlkxX4Ie9XIEVERERERHp3npa5opIX3LwwQdj+PDhptwWe5n0NXfccYfJzuKESgVWRESkL1JwpcgsDNY79X+pueGGG3D66adj1KhR5jp7mLCGKQMsTIv1MM358ssvR2djcz5+4CGmGzP1WUREREREem5wJRjU1ziR7oDf/y+88ELT7P2nn35CX8KSYbzsvffeZsKpiIhIX6SeK0W45ZZbMnqsMPWXKbC58AsP6+Eyk2XChAnmttdee8006mMzuM4qB3baaaelaxwfeOCB+PTTTzFjxoxOeX4REREREWkbZa6IdG/rrbeeufQ1HPPgRFMREZG+TFOeCsRG9U8++WRGHxWvl0g+ue7DdOHODAZ5s2cWWWQRU5pMRERERER6DgVXRERERES6JwVXimgkWVdXl76+8cYbo7KyssXHbb755igvL09ff/PNN3N+QWpvDKrcfvvt5nc2l2OqMoM9IiIiIiLScyi4IiIiIiLSPSm4UqB33nkn4/raa69d0OMY0Fh55ZXT1+fOnWtKhXVmObCdd94Zq622Woc+p4iIiIiItD8FV0REREREuicFVwqU3ZxuxRVXLHgnr7TSShnXf/75Z3Sk2267zTSyJzawP+WUUzr0+UREREREpGMouCIiIiIi0j0puFKgP/74I/17KBTCQgstVPBOZr+TfMvqiCDQrbfemr5+zjnnoKKiosOeT0REREREOo7jOE1u4/cRERERERHpWgquFIClvHjxMBskHA4XvJNHjhyZcX3ixInoCJZl4fTTT0+XA2NfmE022aRDnktERERERDqeMldERERERLqnwiMErVRVVYWpU6c2e594PF7U/XM9riNVV1dnXB88eHBRjx8yZEizy2svzFj54YcfzO/MVmHWioiIiIiI9FwKroiIiIiI9NHgypVXXmkuHXX/zlBfX9+kSX0xsu+fvbz2wD4u/nJgJ598MoYNG4aO1lIgbM6cOR2+DiIiIiIivZWCKyIiIiIifTS40htkB0NKSkqKenz2/evq6tCR5cDGjh2LXXbZBZ1hvfXWa/bvkUgESy+9dKesi4iIiIhIb6PgioiIiIhI96SeK63gum6b7h8IBNCebrvtNnz//ffpLJkLL7yw3Z9DRERERES6R3AlGNTXOBERERGRXpm5wobviy66KDoTe4x0lLKysozriUSiqMdn94bJXl5b/PLLL7jlllvS14844ohO3ffvvPNOi2XB1PtFRERERKR1lLkiIiIiItKHgivHHXecufQW/fr1a1NwJfv+2ctrSzmw0047LV0ObMkll8RBBx2EzjRixIgWy4KJiIiIiEjrKLgiIiIiItI9KZ+8AJWVlRnXZ8yYUdROzr5///790R7uuOOOdDmwUCiEiy66COGw2uiIiIiIiPQWCq6IiIiIiHRPGokvAIMhgwYNwuzZs9PBkmQyiWg0WtBOnjJlSsb19irbde+996Z/33nnnbHYYouhrq6uqP4vLFnmf0xJSYkJ1IiIiIiISNdTcEVEREREpHtScKVAiy++OD755JN0gGLSpElYYoklCnrshAkTmiyrvb9oPfzww+ZSrB122CHj+k033YRx48a1y/qJiIiIiEjbKLgiIiIiItI9qSxYgZZaaqmM61988UXBOzn7vksvvXTBjxURERERkb5LwRURERERke5JwZUCbbjhhhnX33///YIeV1tbiy+//DJ9fejQoVhuueXQHsrKyoq+BIOZh7y0tDTj7yoJJiIiIiLSfTiOk3E9EAiYi4iIiIiIdC2VBSvQ2LFjTWP7qqoqc/3NN9/EzJkzMWTIkGYf98ILL6ChoSF9faONNmq3L0Pjx48v+jG77757RibNE088YXq1iIiIiIhI989cCao/ooiIiIhIt6DMlQKFw2Hstttu6eupVArXXntti1krt9xyy787OxjEnnvu2exjksmkaTDvXXhdRERERET6pibBlaxMdBERERER6Rr6ZF6Egw46yGSveB577DFzyYVBkVNOOQVTpkxJ37b11ltj9OjRzT7H+eefj5VXXjl9YYN5ERERERHpm7KDKyrjKyIiIiLSPSi4UoT+/fvjvPPOyyjrddZZZ+HEE0/Ep59+asqE/fXXX3juueew884744033kjfb7755sPJJ5/cvkdPRERERER6NQVXRERERES6J/VcKdIWW2yByZMn46qrrkrf9vzzz5tLc0GZW2+91TSzFxERERERKZTKgomIiIiIdE/KXGmFQw45xPRSaamZPa2++up49tlnseSSS7bmqUREREREpA9T5oqIiIiISPekzJVW2nDDDbHmmmvi9ddfx6uvvopJkyZhxowZiMViGDZsGMaMGWN6rLBvSjH4+LKysvT1aDSK9lRaWpqxfDXEFBERERHpvpS5IiIiIiLSPSm40sZABQMovLSXc845x1w6yt13391hyxYRERERkfalzBURERERke5JZcFERERERES6qWTKyrgeCoW6bF1ERERERORfCq6IiIiIiIh0Q47roiGVyrgtrOCKiIiIiEi3oOCKiIiIiIhIN1Rv2U0zV8Kq7CwiIiIi0h0ouCIiIiIiItLNJGwHDZaNuqzMFZUFExERERHpHhRcERERERER6WblwGqTKcxuSKIhqbJgIiIiIiLdkYIrIiIiIiIi3YTrupiTSGF2wkJNykJcDe1FRERERLolBVdERERERES6CQZUqpIW4raNGQ0pWLZ6roiIiIiIdEcKroiIiIiIiHSTPit1KRtJ20Hc4u8WYDsZ9wmHQl22fiIiIiIi8i8FV0RERERERLpBOTA2sI/bDpK2jYaUhWAwAMuxM+4XDOornIiIiIhId6BP5iIiIiIiIt0ga4XBFQZVEraL2pSFSABIqOeKiIiIiEi3FO7qFRAREREREenrWSssATYrkUJ9yoZtO0g4LhpYIkzBFRERERGRbkmZKyIiIiIiIl2ctcIm9tWJFAIBNrW3EQkGMTeeRCKVyrhvSD1XRERERES6BWWuiIiIiIiIdGHWSlUqhapkYxAlFABssKG9jenxJOLKXBERERER6ZaUuSIiIiIiItJF2MC+Kp5Cg+UgEgwgYblwAUyti2N2PIGUndnQXpkrIiIiIiLdgzJXREREREREusicRBJzE1Y6i2VWQwrVloW/axOoNf1XFFwREREREemOFFwRERERERHpAvWWjdnxFCzXRcpxMTtpocGxMLmuATWpJOK2C9dxMh6jzBURERERke5BwRUREREREZFO5rDXSjyJBsuG7bqoTaZQn7IxN5lCTTyFuOUiyawVR5krIiIiIiLdkYIrIiIiIiIinawuZWNO0jIZK4mUjeqkhaTroj6ZQm3SQq3lwHEd1grLeJwyV0REREREugc1tBcREREREelEKdvB3EQS9SkGVxzUWhYcuEhaNqoTFuYmbbiuDcZWgioLJiIiIiLSLSlzRUREREREpBPVWrbpt8KsFcthSTALCctGTdLGrGQSCceF7TgoCYcQVFkwEREREZFuScEVERERERGRTuKa/ioWGiwHCdvGrHgSdbaDpO1iZjxuslZsBlQCIURmTcHUn7/PeHxlZaWOlYiIiIhIN6CyYCIiIiIiIp2kzrKRcBwkLAdz4inMjidhuQ5mJRKYm7BgOYDt2igLBfDFg3fBzSoLtvnmm+tYiYiIiIh0AwquiIiIiIiIdFLWCkt/sdfKrEQSs+IpuG4A1fHGhvYJ24Vt2wgFw3BmTMEPr76Q8fhhiy6G7bffXsdKRERERKQbUHBFRERERESkEyRtB/WWZQIpDLLYcFGftDAnkTC9V+osCwHXMVkrn5usFTvj8VsfegxCoZCOlYiIiIhIN6CeKyIiIiIiIp2g3nJQl7RQnbJQlUiYpvZzEkmw8NechAWGTcLRMOqnTsHPrz6f8dihi4zCBltvq+MkIiIiItJNKHNFRERERESkE0qCsXl9tWVjdn3KBFYSKRv1toPqhIWUZSMaDIDJKt8/fHeTrJWNDzoSI8rLdJxERERERLoJBVdEREREREQ6GBvXz2pIoiqRQlWS2SoBTI+nkLAs1KdsxMJB2Agi1lCFH199LuOxQxZaBOO22QHDy0p0nEREREREugmVBRMREREREelAtuPgr7o4amwLMxuSSNkuahKW6bGStFwEXBdhBBCIBPH7my/BsayMx4876EiMGtgP/WP6+iYiIiIi0l3o07mIiIiIiEgHmlqfQF3Kwpw6Zq4kYbsO5qaScB0XjusiGg4gzKyVgItvX3g647HlAwdh0222w/zlpQgFAjpOIiIiIiLdhMqCiYiIiIiIdJDaZAozGpKY3ZDE9HgSkUAADSkHdZYNy2VwxYHrAuFwED+/9iJmTfgt4/Frbr09lh85BJFQEAqtiIiIiIh0H8pcERERERER6QAJ28GMeGPz+in1SSDgImG7qE6l4Fgukg5vchEJhvHlg3fhwztvaLKM3fbZB5WxiPk9GtLcOBERERGR7kKfzkVERERERNqZ67qYXh83GSsz6hKwmKECIGnZJuiSMhkrbGQfxqQP3soZWFlihZWwxkorIhwImKyVSFC5KyIiIiIi3YWCKyIiIiIiIu2M2SosB1aTSmFKQwJBF0ikbNRaFuoYXLFdEzBxrRQ+uvXqJo8PBIM46IRTUBIMgXdk1kpIwRURERERkW5DwRUREREREZF2ZDsO/q5NoN628XdNAwJwTdZKjWVjdiKFuqRlAiahRD3evPAkVE2ZnPH4/kOG4opHnsbWW20JN8AvbQHEgkGUqCyYiIiIiEi3oZ4rIiIiIiIi7Whu0jKN7P+pjWN20kL/SAhTahOoTaQwN26bBvapudPxwsmHofqfvzK/oMViuOSRZ7DK0qMRDgZMKbCySAgl4RDCQc2NExERERHpLvTpXEREREREpJ2kHAfVCQuzEglMq0sgGgygJplCVcpGVTxl/l4SDuLLW65qElihzfc/FKMXH4X+kYgpBVYaDiGqrBURERERkW5HwRUREREREZF2UpO0UG+lTFkwtkixHNeUB6u1UqhzgEggAGvyBPzx3ptNHrv4qqtjn6OOMUGVaCRkyoFFgo0BlkBAzexFRERERLoTBVdERERERETage26mJtkYCWOOYkULBeoT1moarBRn2Q5MAcV0RA+ue/WJo9dY6c9cMadD6FfWTkqoxGEAwETZGGWC8uDiYiIiIhI96KeKyIiIiIiIu2gJmFhRn0CU+oTsFwXIQAJ20aNbSHFrJVQAH+Nfw2/jn8943HzL70s9jzzAgzrX4IhpVGUhsOIhYIYFAsjEuJSRERERESku1FwRUREREREpI2SloUJ1fWoSlqY05AyfVIsy8KchI245cJybcR//h4vX3pWk8dufPBRGBiLYWBJFLEwy4AFMbgkosCKiIiIiEg3prJgIiIiIiIibeC6Ln6aU4eZ8QTmNiSRdF24joPpDcxgcRC3bIRdG29eeS7sVDLjsYusuCpWXH8chpZHTUBmQCyCkf1KEFXGioiIiIhIt6bgioiIiIiISBv8WduA6Q1J2C4wrSGJkmAANSkbDY6LuOMCAReTx7+OqsmTMh43YOT82O+KGzCkNIbSUND8HFQSNU3sRURERESke9OndhERERERkVaaVZ/A5Jo4EraDqXUNCIcCQACYm0gggAAcGwg7Lr584M7ML2KhMA649g6MGDHMlAPrH42iMhpGiTJWRERERER6BAVXREREREREWlEKrCaRxKTaBtRbFqqTKdhuANEgMDueQMJxYdkOXFiY9O5rmPvXxIzHr7Tldlh49JKojEYxpCSMAbEwYuEQwsGAjoWIiIiISA+g4IqIiIiIiEiR6i0bU+sT5ieb2FuuawIr9SkHdUkbcIEU01bsAL743x2ZX8JCIWx64OGIBgMYWhZBaTiE/tGIKQ0mIiIiIiI9gz69i4iIiIiIFCHlOKhNMahiozppIWk5JjCSdFxznX1WLBewnQAmMmvlzwkZj195i20xYIGFMTDGcmARlEcjqIiEEAgoa0VEREREpKcId/UKiIiIiIiI9KRyYMxWqUkmMSeRQp1lIRoKIG45qE4mMb2B/VdcpCwboQCzVm5vkrWy4YFHoDIURGVJY4+VISVRhNXEXkRERESkR1FwRUREREREpEBsXF+fsvFPbRx1lg3HASzHRlU8hekNSdSlXARcIBgMYsqn72HOpD8yHr/y5ltjscUWR/9IGINLIhhSFkN5JKT9LyIiIiLSwyi4IiIiIiIiUgDbdVGdsPBXbRyzE5bJYmGAJQgXM1MpUxIsiACcgI0IgK+feCDj8YFgENsderTJdOkfC2NoaQwV4bDKgYmIiIiI9EAKroiIiIiIiLQgzsb1iRQm1yVQnUgiGAigKpkywZS5yRTmxpNwHSAYAAIOMGfi7/j7i48zlrHc+uOwwOJLoDQQwILlMcRCIcTCaoMpIiIiItIT6ZO8iIiIiIhIC6XAalIWpjYkEbdt01+lLpUygRHHdTC1NoGUAzhw4Ti2ibB88sCdTZaz6d4HIBYIYLEBZegfi5rgSkS9VkREREREeiQFV0RERERERJopBVaXtDA7nkJdyoLluEg5DipjUViWi6m1DYjbFlvdA45rmth/dv+dmPjOqxnLmX/00lh2tTUxoixqAishBNRrRURERESkB1NZMBERERERkRyStoM6yzLlv2qSDKAA9SkL0XDQZLNUpZKIOyasYhrbs4n9D689jy/uu7nJsjbb/xCUx8IYWBpFJBhAaTiEaEhz3UREREREeioFV0RERERERLIweNJg2ahJ2qhO2bBcB5Zlw3FdJGwXc+NxzI7baLAtk90SgIuJX3yCd/97fpN9ucLGm2PdrXfAkJIoKiIRUwqsIqqvYiIiIiIiPZk+0YuIiIiIiPi4rmt6qzBzhRkrvM70lFrbge0CM+Jx09y+KmWZYAuDK7Mn/o7Xzz8ZjpXK2JcLL78yDrr4KgwujWJkvxjCwYAJrChrRURERESkZ1NwRURERERExCfhOHAcF9XJFIKBAAIBYG4yhbhlY2Z9HFUpG1VJy5T3SlouErNn4YUzj0GytjpjPw5aYGEcfM1tGFZZgZHlMdPAvl8kjLKwvoaJiIiIiPR0KvIrIiIiIiIyD7NUWBKs3mLJL8fMRpvdkEQ85aA6ZaHWckxD+4AbQCwIBAIu3rz6ItRN/SdjH5ZWDsDBN9yFReYfgZFlMQyMRVEeCZteK8xeERERERGRnk3BFRERERERkXkYUKlLWpiTSCEcCJqG9g2Wg6TjoCqeggMXdSkbJZEQUraLCR+9j0kfvJ2x/0LRKPa/6lYsteQSmL9fCeYvL0VlLIJQIIASNbEXEREREekVlI8uIiIiIiICmIyU+pSF2Qn2TQkgFHBRm7JhOw7mJJJI2rZpZh9EAAHXRW19A9656aom+27bsy7GCqutgRGlJRhSGkMsHDKBlVg4aMqMiYiIiIhIz6fMFRERERER6fNYDoylwJiV4gKIhYIm2OK4DuosGzVJG6mAizrbRiQENCRTePmaizH3zz8y9t2oNdbFuG22x5CSCAaVRtAvEjLN61kKrCQU6vP7WURERESkt1DmioiIiIiI9Hnss+K6MAGWcCAAy3FQk7BRlbAxsyGFBscyzesDDsCWKeMfuBs/vfhUxn4LhsLY8eQzMaJfKYaXxVAZjZim941N7BVYERERERHpTRRcERERERGRPs2Z18Q+btmwXNdkrUyvT6DBsjE3kUBVMoWUw7JhLkoiQcycMg3v33Vjk+WsvdeBWHHpMRhcEkFpJIxIKGga2Jeqz0qvkUwmMXHixPT1oUOHYuDAgV26TtI7TJs2DVVVVeb3YDCIxRdfvKtXSURERFqg4IqIiIiIiPRpbFjPAEttykIYAcyNpxC3LcxN2JiTtBBgoTDXMRkrkYCLD598EE6KfVn+teR/xmH7I0/EsH5RREONmSqxYAAVkTAC6rPSa/z111/Yeuut09dPOeUUHHjggV26TtI7XHPNNXjqqcZsuGg0im+//barV0lERERaoJ4r7aihoQGWZaG7sG0bNTU15qeIiIiIiDTF8l8J28bcpGWCLAk2r0+mkLRd1CWTSDou4q5rMldKggHU1Cbw7XOPZSyjYuhw7HrBlVh8UD+URcIm84XN6/tHw6bfioiIiIiI9D7KXGmDqVOn4tlnn8XLL79sZjBVV1eb9N3Bgwdj2WWXNTOaxo0bh1gsho7mOI6Z2TJ+/Hh89dVX+OmnnzBr1izTmJMz5SorK7HMMstg7Nix2HHHHTF8+PAOXycRERERke7OBFTmlQTzsldsxzGN7WvZyD5lwbEdhIMugoEAPn/1OcTnzslYxtgd98SCA/tjSL8SBNwAyiMhk7FSGtbXLRERERGR3kqf9luBAYsHHngAV155JeLxeJMgx4wZM/DWW2+Zy2KLLWbuN2bMGHSEX3/9FU888QSee+45zJw5M+/6zp07Fx988IG53Hzzzdh1111x0kknobS0tEPWS0RERESku0s5jrnUWbb56YK9V1w4LlCbmBdYcQDbcREKAXUpC589dE/GMsKxEqyz4y5YYkA5gi4QDQdRHmVgJYQw64iJiIiIiEivpBz1Vrjgggtw4YUXNgmsMEOFmSt+v//+uwlkfPzxx+gI55xzDu655568gZV+/fohxG+CPqlUCv/73/+w0047mewWEREREZG+iBkrtSkb9SkbSWapJGwTQInbNqoSSSQsF7ZrIwUXUZdZKy9h7p8TMpaxwmZbY4kF5kd5NNIYWIk0lgIrUTkwEREREZFeTcGVIt1333148MEH09fD4TAOPfRQvPnmm/jmm2/M5aGHHsK6666bEcw4+uijMWnSJHS01VdfHWeccQZefPFFUybsiy++wHfffYcnn3wSe+yxR0ag5bfffjPNF5PJZIevl4iIiIhId2K7LuKWg5pUCg2WhbjjYFY8gaTtoDqRAtvVuy7M9QgC+LuqFh/dfVPGMgLBILY56DAsVBFDv2gY4WAQ5WEGVkKmhJiIiIiIiPReKgtWhOnTp+Oaa675d+eFw7jlllvwn//8J31bJBLByiuvjDvuuAPnnXceHn74YXN7VVUVLr30Utx6661ob8yW2XbbbXHAAQdg9OjROf/OsmS8bLbZZiYY1NDQYP72448/moDRwQcf3O7rJSIiIiLSvbNWUmhI2iZ7haXBLBdIuTYakg4CroOkY8N1XMyurcFz5xyPmr//zFjGSptvgzWXHYNhpSUmmNI/HEbpvIb20v3Yto05c+agpqbG9KQcOHCg6U/ZEfj9j6WZBw0ahIqKijYvj+WnvXXnhLn+/fubbWgviUQCs2fPNhMDuc7l5eXoKKwAwe/W/MntGDFiBLqDjt4HXDaP35AhQ0yFCREREen5FFwpAnuVeEEJYtaHP7Dixw/pZ555Jj7//HPTF4XYg4WZJAy+tBcu66yzziq4pwszW84991ycdtpp6dvuv/9+HHTQQR32xUJEREREpLtlrdQmLVQnbMyMp5C0bSQdF5Yzr+dKIIC4CyTcxusvnHcCpnzxUZOslT2OOh6Dy2KmDFgoGEApfwYC6rXSjVRXV5sJb16lAQZY/BPjVl11VWy00UbYeeedUVJS0qbn+uSTT/DII4+Y56qvr0/fPnToUDPJbb/99sMCCyxQ8PLq6urw9NNP46WXXsL333+fsUwaPHgwll9+ebPsTTbZBGVlZUWt74cffmgqHrz77ruYMmVKxt8YXFhrrbWw4447mp+FYEWEiRMnZmw3A1jEagqPP/44Xn311YzS1FtvvTVOOeUUE4jyLLjggkX3Bs1+7kKDNu29D7LxnGNJ7tdff90cT/+x23zzzbH77rtj8cUXb9WyRUREpOsFXHY7l4JmsayxxhrpD7T8sMcPYC3NQmKjeTaO92y33Xa4/PLLu3SP85BvsMEGGR8en3nmGSy11FLt/lz84HzYYYeZ35m1ww+RIiIiIiJd9TnYMgETBzPrk5hcW4+pDSkE4YLzjFgCrI5BF8vGtIYEauMp/PDmK3jzolObLGvdPffDhZddgQXLSxBi38VAABWRMPrPKw8mXe+xxx7Df//734yB+3xGjhxpJqHxe1Jz2FNziy22SF9nYGDfffc1j2XwoDkMfpx++unYZZddWlyfjz76CKeeeiqmTp2KQvB71nHHHVfQslmumlUWPvjgg4KWvfbaa+PKK69s8btcrn3DgBIrODDAkGvoYauttjITFnlfD7ebVRmKwTLY3LcelsrmcensfeDPNOL97733XvN7PqyGcfzxx5vJjpwA+dRTT5nbo9GoKfMtIiIi3Zs+9Rfo/fffz5gpxJlBhaR3b7rpphn3Y/aKZVnoSsxQWWWVVTJu++uvv7psfUREREREOhqzUqpTFurmNbCvtWzMTdpgR0LHBYJuAI7jmt4rcxNJxHm/+gZ8fPu/ZYE9q++6D44+83zEQiEEAiGEgyGUhUPmugIr3cN1111nMvyzAysspbXwwgs3KfnEiWdHHnmkCcgUiwP5/sAKl83nyC7bxe+TZ599tgk0NIfVDg455JAmgRVm1jCrY9FFFzWD/Cz/7J/UdtVVV7W4rlz2rrvu2iSowMH8+eabz6x3dgYMvwszaPP333+jWOecc46plOAFVrjshRZaKGP/87u1v0yWF2AoBjN8/BlJzIjpqn3AbWUVi7vvvjsjsMLv4SwJxu33sqQ4NsAgDO8rIiIiPY/KghXonXfeybi+5pprFvQ4fkBjIOPtt99O19798ssvMXbsWHSl7BqvTJcXEREREemNLMcxQRVv3jx/r04mTcmvQMA10ZVIJIi6uI3quIWahGUyWN695SrUTc8c4F5mo82w9xnnoH9ZiclS4YUlwVhgtySsuWvdAQfaWdI5O0OCAYsll1wyfdvXX3+N66+/Hu+99565zpJhDAYsssgiBX9fe+WVV8xyiJUOmD2y0korpf/+008/mXXh/TwXXXQRll566SYT3jxcB1ZO8Oywww7Ye++9zWP8pZxra2vNd0uWtXr55ZdbXNc///zT9Nrk4zwsKcZlr7DCCiYo4e0HBiBuuOEGfPzxx+a2yZMn49hjj8WDDz5ovuMWgiXAvvrqK/M7M4JY0YDP420Dy2fz76wKwfV44oknzO2//PILfvjhByyzzDIFPc8///xjSrJ5mAnDkl5dtQ8YTGImjYc9cpjBw+dghhSxrwsrYTCw8scff+Dqq6/O2T9VREREujd9+i/Qzz//nHHd/4G5Jdn35YfFrjZt2rSM6wMGDOiydRERERER6Uj1lpMOrDiui5TT2MQecJFkgIWToJI2quIpzGFZsJSFTx+5D7++kFnmKVJSiu1OOANDS2MYGI1gcGkU4VDA9FwpZ5BFPQy73MyZM03wwo/lopjV4Q+sEAfT77rrLjPo7WGmActS+YMbzfECKzvttJMpAZX93Y+llxnAYVaMP7OBGSy5KhqwN4nXs5MYCGBZLQYasntkMvtj3XXXNX9nTw9mY+TDYAHLT3lBBZajuvbaa02GD/vOeEEFLxjA4NJ9992HvfbaK307y1Q98MADKJQXWDniiCNMiegVV1wxYxuWWGIJ0+uGtt9++7yZKC3hff0lx7KX1Zn7gN+zr7nmmozlMEDDsmdeYIX4XBtuuKEJwrCPKoMt7KsjIiIiPYuCKwXibBL/B6RiGhEy7Te7Fm1X4heFzz//POM2ppaLiIiIiPQ27K/CgAqFAkDKcVGTtMwlZdtIODbCQRfT6uKotm3MTCTw9y8/4qu7b2iyrI0OOAyLLLQghpeWYFBpBOXssRIJm5JgCqx0Dw899BBqamrS11keilkDzWEJJ39QhKWfXnjhhYKfk0GC888/v0nww++YY47JqH7A74RedQO/H3/8MeO6P/DTHJYJO+GEE5rNImHgxsMAEhuqN4fbw33DIJTnnnvuKarMNRvBM9ujJQxusOSZ5/nnny/4edg/1D9pcP311++yfcDzz19OnMGxjTbaKO/ymbXD4Et2mToRERHpGVQWrABz5swx5bw8w4YNM7NcCsW6rX4TJ05EV2LauP8LBz/Ejho1qkvXSURERESko4IrxJntCfZdSaTwZ00D6i3bBFrs6rn49I/fTAN7NrmvSabw7cP3MIUhYzkLr7Ayll99TUz9/muU9u+HmpIIpscizQ6o9xUMLrCXRHfw6KOPZkyKO+mkk1p8DI/hySefjD322CN928MPP2zKcRWCgZNCvh8y+OFlahD7tIwbNy7jPv5yVbnKObcWMzA8nCi45557FvQ49nVhgOCoo45KZ2YwQMEslEL3TSF4DLbbbjsTaPB6yIwfP95kdzSHZdH8368ZTPNnoHTmPuB7jL8cGI8dS9G1hK+dfffdFzfddFNB6yMiIiLdh4IrBfAHIihf/dbmZhF1l/4m/LDO1Gc/NuYTEREREenNWSsplgOzHPxZ24A62wFjLi/ecj2euuVaODZLhDVv0tdf4Ip99bk5FwYx2CeEl640YcIETJ8+PX199dVXx4gRIwp6LPufsNeKN1DPwXNmIGQ3Ns/Wv3//vJkS2ZZffnkzqc2risA+ISxD5m9Mn12umb0/t9xyS7QFJwp6Jbq8HiM8ZoXK7jf62WefFRRc4STDYsppM7hy4403pkt8sdxXS8GVp556KuN6vpJgnbEPeP75y28zcFZocGzbbbdVcEVERKQHUnClAP60XiopKSlqJ8disWaX15kuvPBCTJ36b1PO+eefH/vss0+rl+dfVr6sHxERERGRzsYB2vi8oAl/cvh6ZjyB2fEU/4i5s6bhyZuuzujVIK3DXhbnnXceDj30UAwfPrzLdqPX/8RTaFN6//294Aq3iQGW1VZbrdnHLLfccgU3eCf21/CCK3V1daY8GDN//H/3O/fcc00AZosttigqGODHktD+85yTBfOVqvbfz/87g0ze99gpU6YU9LzcN8VgNgmPgdec/s0338TcuXPz9gdNJpN4+eWX09fZEH7MmDFdtg/YjyU7YFeohRde2GSwsGeQiIiI9BwKrhQgOxiSHSxpSXYwpquCK6z/6m8MyNRrNnssNljkt9566zX7d6ZkL7300q1evoiIiIhIa8RthzEU2I5rKnylXAczGpKIWw5SjoOSgNpP9jbZE7842F6M7PsXEkRYbLHFinqOxRdfPOP6P//8kxFc4SD7BhtsgLfeeitdRYGlzS6//HLTu4MZFAzAsFR1oZhR4XfFFVeYS2sx4NGa3qOFYOaJF1xhk3f2vslXvuuNN97IKN+dL2uls/YBj2Vbzg3eX8EVERGRnkXfKFqh2Nlt2ffvirrMH374IS6++OKM21j/lQ0GRURERER6k6TtmJJg/BzeYNmIhgKY1ZDEP/Vx1FkpIAAMHDQI6x10JALB1mUDyL+YUcGG7l2ZtUL+gXavZFcxKisriy7nnP2Y9niOSy65BEsttVTGbTNmzDB9YNgcft111zWBltNPP91kdzCDo5j90lYNDQ0F3a81Tdo33XTTjFJs/smB2fx/4znIfitduQ+yj2Wx50a+DB0RERHpvpS5UoDsOrvxeLyonZx9//ZqSlgoprMfeeSRZuaPv8bscccd1+ZlswZwS2XBurr2soiIiIj0HZbTGFCxXRf1KRuRUMAEWyYzsJKyEXAAy7XxRzyBpXc7CAM33A5/ffUJvrj6PDipzEHq/kOG4qBLrsKIAQMwf0UphpTGGJdBv0hIjey7YUP77CBDsRUHsu9fyPe+YkqCFfocLFnFQMpdd92F+++/P2eWxOTJk82FDdSZxcLve7vttlvO52T5MT9mxxS73tm9VAqRr7F8c/hdeeONN8Yzzzxjrn/zzTemfFd2Fggb3r/33nvp6+ussw6GDh2ad7mdsQ/aev61ZX1ERESkayi4UoDsYEgikShqJ2ffv6WmiO2JH0QPPvjgjA+T/OB55ZVXZjRObK2WGkS25gO1iIiIiEhrMFOl3rJNgKXOshEOBBALBvFbdS1mNiRhm0b2LhwAsxMOZs6cha8fvB1/PPdok2WNXGwJHHPLvVh80UWwcEUJRvXvh1AwiEgwaIIr0v1kZ0pkD6i3JPv+FRUVRT+mvZ6jtLQURx11lKk2wCDCBx98gE8//RS//PKL6cHiN336dNObhX//73//2yTwl71fmBmz6qqrortieS8vuEL8/YQTTsi4z3PPPQfLsjIe05zO2AfZ4wZtPTdERESk+1NZsFak5zIluxj8sNuW9ODW+vvvv3HAAQdg9uzZGU31brzxRs2KEREREZFep8F2kLRtE1jhFx2WA/t1Tj0m1jSYrJXqpIO6lIWqRAoTfvsVLx67T87AyoBhw3HivY9g5PwLYkAkjJFlJSawwiHrWEhfobqr7DJgxX5vy75/IWXFiu2Rkf0cLX03ZDbDhhtuiLPOOssEGdiP5Pbbb8fee++NwYMHZ9z3+eefxyOPPNJkGQMHDsy4zoyX7myNNdbA/PPPn77O7c4OKD311FMZ+5Bl0prTGfsg+1gWe/6p34qIiEjPo28GBeBsIv8HV37oaamubXONEBdddFF0NH6Q23///TOaOo4ZMwa33XabmQUlIiIiItKbsPQXLw0WB2FdlISDmFafwIx4HHMSFmqSFmqtBGpSFr7++CM8d8y+qJv6d85l7X/hlaYnS/9IBCPLS9Av0pjwXxoOIRzs/P6J0rpm8T/++GNRu+6HH35odnm5/Pzzz0U9R/b9C3mO7O+m6623ngm2vP322ybI4vfggw82eUz2c3gN47srZt5ss8026ev8TvvRRx+lr//000/m4tliiy1anDzYGfsg+zmKOTcYPPr111/bfZ1ERESkYym4UiB/jVeWG5gwYULBO/mPP/5o0wfoYrHPCTNWJk2alFEHmTV7C0ltFxERERHpSSzHMeXAGFxhrxWGV+bEk5hen8DcRApxy4bj2Eg5wJevvYhnTzkUydrqnIO6Wx91ApZd+z+oiIYxvF8Uw9hnJRAwwZqosla6tRVWWCHjOoMPxZRyZuktf4mnQr63MSBTaMYBJ+h9+OGH6esjR45stk9ISxhQOOOMMzK+q3JAP3siIKsX+Ms1v/XWW0WXuu5s2WW+/Jkq2U3uWyoJ1ln7YLnllsu4Pn78+IIf+/HHH6OhoaFd10dEREQ6noIrBVpmmWUyrn/55ZcF7+Qvvvgi4/rSSy+NjlJbW4uDDjrI1OL1N+u7++67m6RCi4iIiIj0BiwDRvUsCZa0ML0ugTmJlLnUmr4MLmptF5N//B6vXXImnFQq4/HBSATr7nsoznr2TWx72DGojEYwsjSGEf1KEAk1BlVKQuqz0hPKOa+00krp65wQ5894aM6zzz6Lmpqa9PX111+/oB6Vtm3j8ccfL+g5XnzxRVRX/xvUa6mUVSG4jqxQ4BePxzOus3IBS4t5WDY6V4ZLd8LvsCuvvHL6+muvvWa+63J/s/yZZ9SoUU2Carl0xj4YNGhQxvn32WefmR6ohXj44YfbdV1ERESkcyi4UqANNtgg4/q7775b0OP4AfCrr75KXx82bBiWXXZZdAR+iD700EPx3XffpW+bb775cO+995rnFRERERHpjVkrrgs0pCw0JG3UWw4YaqlKWqhKpWBZLqoSFqqqa/DyxWfA8TXBplhFJXa+6nZsc9SJmH/hRdAvHMGQshgGlZagMtpYBqwsrMBKT7HnnntmXL/44otbzAhgSeVrrrkm47a99tqr4OdkDxT2u2zO3LlzcfXVV2fctssuuzS5X2uyKfz9Q0pKSnL2ijn88MMzrnN7v/nmm6KfqzMzXvwZKTyGr7zyCt57772MXiaFZK105j7wH1OW+rroooua9IvJxmyml19+uej1EBERka6n4EqBxo4dm9HYninm2Y3q882A8s8cGjdunCkr0N6Y+n3kkUea2TEeppjfc889JsAiIiIiItIbJR0XKcfB3KSFpOOgwbZRk0yZgErAcVGVTGJOIol3774JcydllustHzE/tr3hPiy7+poojYZRHg1jvvIoBsUiGBgLIxoKoZ8CKz3K5ptvnpHJwYz+o446ClVVVXn7YzLzf9asWenbmOHgz5poKXOkrq4OBx54IP7666+c9+GyOQlu2rRp6du22morLLnkkk3ue8EFF+CYY44xGTfM0mgJsy/8lRLWXXfdnPdj9QR/fxYGCPbdd18899xzpux1S5iBcckll2CPPfZAZ2EvFQaL/OXA/OXBuO+33XbbgpfXGftg6623xujRo9PXWWruzDPPzNuzld/feby97REREZGepbEzo7QoFAqZD1E333yzuW5Zlpl5dNlll+V9DFO+b7nlloxltDQDih/M/TNhmL7cUgN6fug+6aSTzCweDwNBDKwsssgiOroiIiIi0itxQDRp2ahOpkyQhb1VUraNRMqG5biYVp/ElNokZk6fiu+fyiy7EwyHsdF5V2GhUYuZoEooAMxXFkVFJIyKWBiloSDKIqEOmRglHSccDuPKK6/ETjvthPr6enMbvydxoH7nnXfGaquthsrKShPw4MD3Y489ZqoN+CeoMdugUDvuuKOZUMcSZGzCzuddZ511MGTIEJOtwsbpjzzyiOmL6X8ODrjn+27HDA1eWGaKwRIGi1j+iuvNviEMFLH5+UsvvYTPP/88/Vj+7Ygjjsi7rqeddpoJNrG/B3H/8HskM2823XRT0zOEz0ncJ8yI+fHHH02gxytvteiii6KzlJeXm8mJXhmwTz/91Bxfz1prrYXhw4cXtcyO3gc8Bjx/mEGVmld+8MknnzQBsF133dVUseD3ewb1Xn31Vbzwwgsms4X9d/g3lj8TERGRnkPBlSJwNhJnBvFDMnHWDD98Zaeee2nLJ554YkZ2C2fV+JsN5sKZMP6avYcddhiOP/74Zr9QnnXWWebDt4dN69ljhU3sRURERER6K8t1EXccJGwXKYtZKw7itou47WBGQxz/1MURdx18/ci9cFKZM8dX3OcwLLzkMhheGkE0GMLCFWWoiEVQGYuiPBxGSTiEkAIrPRK/c7E08iGHHJL+7sam85z45p/8lm2BBRYw36MGDx5c8HNxoP3SSy/FySefbAbq/+///s9c8uEkOD6HN4DfHPYFeeaZZ8ylJRzU53pk9wr1Y2DizjvvxLnnnmsG/D0MNvh7djbHn0nSGVj2ywuu8LuvF7Dw/lasztgH7AFz7bXX4thjjzWTMmnixIm4/PLL8waRbrjhBjzwwANFbYuIiIh0PeWdFoEfejgLxZ+uy7Tto48+2syGYp3d3377zQRHOINp/PjxGR/U+YG7vTFzxv+hkB+q+aGNM1/4YbzQizerS0RERESkJ3BcF/WWjfqUbfqtzE0kTQmwhO1gen0CUxoSJuhSNX0afnnx38/L1G/oCKy++74YFAtjeHkJFqssxfCyKPpHIxhUEkEkFEAspK9KPRkHuDkZjuW3Wso+4oA7J8zxexwbqRdryy23NEGbESNGNHu/Nddc0zyHv2xUruBLseWhVl11VZMdw5JULYlGoyYIw/XlRMFCsYTZ2Wef3e5N4FuSLzvFy2ppjc7YB1y3+++/32QcNYfBMC6vmPUQERGR7kOZK0XaeOONceqpp5qghleXlem8vOQzcOBAk2ZcyOykYj366KMZ1zmTp7lU8Hz22WefvKnpIiIiIiLdCT+H16VsJG0H1Ukb0+NJE2gJuAHMqW/An7UNJuhSl7Lwyc1XwE5mNqAeu9eBGFBaihHlMQwpLcX8/WKIhIIYXBI1fy8NqRxYb8Dek1dddRWOO+44vPXWW/jyyy9NBgvLPbHpO8tzsUzYeuutV3B5KQ7M+ysE8LsecRlvvPGG6c3Jyz///GOyZvh3ZtJsttlmBfVxYdkqTt77+eefTTmqP/74w0yGY8lpLo8lpPr162cm07GHCJ+3peoIubCvDC8//fSTKXnFxu58HpYcY7CJ+2ehhRYyz8Gg0IILLtjqfdMWDDSxtDZLr/ltsMEGbc6i6Yh94MfjzfXmRMzXX3/d9OThucfzjsviOcHAmIfnoLf/uC9FRESk+wu4hXRukyb4AemMM87IaEqYy3/+8x9cfPHFGDZsWEF7kQGOYsqCrbTSSu2SddJRwRXWMuY20K233lpUir2IiIiISC4Nlm0yVGY2xDGhqgFVqRQSFkuC2fitqgEpy8asRAq/vPocPvjvuRmP7Td0OA59+GWMqOyHBSpKsXhlPwyMRlASDqI8GjGlwCqimoMmIiIiIiLN07eGVmKTQs4+4awk9jv5888/MWPGDMRiMRNIYdNBpmXzZzE4C4mp4J6ysrJm78/ZQO0xq6Wl5xERERER6WoMqLAcGH8mbBt/1yVME/vqhA3LdTCrIYmUlcKchI2/f/wen9x4WZNlrHf4iRhUUYbKWATDS2PoF4kgFAqaHitUOu+niIiIiIhIc5S5Ih1KmSsiIiIi0p7ZKsTk+1/m1JreKlMbGkuCpRwHU2vjmJtMYdY/k/HK8QegYfbMjGWM3nAz7Hvp9aiIhTC6fxkGlMQwuDSKfpGQCarwol4rIiIiIiJSCGWuiIiIiIhIt2bPy1bxTK1PYnp9HLOTFupTFpKOjdkJC7WWjbrqKrx17vFNAivlQ4djh1PORXk0hFEVpSiLhVEaCZpgSkmo8acCKyIiIiIiUigFV0REREREpNtnrXgYTJlWV4+5KRu1CctkrSRSFhpSFub8MxlvXnw6qib+nvH4SGkZ9rzqFiwwfCiGl0XRPxZFaSiI0nAYpeGgaWavcmAiIiIiIlIMBVdERERERKRbYvmvesuB5bim10pN0sKf1XX4szaBWfEE6lIsFWbj85efxTcvP48pX30CJ5XKWEYgFMJOF1+DpZddHgNLIhhSVoJwMIjySARlYWarhNBPfVZERERERKRICq6IiIiIiEi3VGfZJrDCIEtV0sI/tfWYVBvH7DjLgqUQCgHv3HMr3rvjhrzL2Oyks7HyuutjSFkMQ0tjpvTXgFgYZZEgKqJhlEdCCAQCnbpdIiIiIiLS8ym4IiIiIiIi3U79vMAK1aYszKiPm4wV/pwVtxAJAhO//Qrv33Vz3mWssffBWH+n3TGsvASDS6LoHwmjLBpCWSSEfpEw+kcjnbdBIiIiIiLSqyi4IiIiIiIi3UrctpG0HaQcx2Ss1CYt/FXTgOl1CcxNOaasl91Qh2fPPw2u828/Fk/5yPmx3hEnYd3NtsR85aUYVhZFSTCIflFmrIRQolJgIiIiIiLSRgquiIiIiIhIt8GgStxyYDsOZtcnMCOewO9VcVQnU6izbUSDLOHl4pX/uwNVf//Z5PHrnHYRlt9wM8xfWYGFK0owsCSKIIKojEVMSTA2si8JBRENBbtk+0REREREpHdQcEVERERERLocG9Y3WI3ZKpbjmNJfs5MWJtQ0mMb1lusCroOw6+DXSZPx6YP3ZDw+FIliu5sfwIJLjMbg0hLMV16CASUxhIIBVEZCiIYCpr9KaTiESCiIoPqsiIiIiIhIGyi4IiIiIiIiXY4BFNt1TeZKTcpCbSKJP2sbUJO0TCAkbjuIBAKYXFWHFy8+A3YykfH41fY6GIssuST6xxob148oK0EsGDDBFGapDIhGzO+hQMBkr4iIiIiIiLSFgisiIiIiItKl4lZjYIU/2ch+TiKJf+obTL8VqncclLKB/bRZeOiEQzDjp+8yHt9/xHxYZbd9UR6JYFA0jMGlYfRntko4BASAgSURlEbCYLJKv0gIAWWtiIiIiIhIGym4IiIiIiIiXcZyXJOV0mDZpnH91IY4ZsdT5tKQbAy6BAI2psytxUMnH9EksEJrHXYCKvqVYUAsaprXz9evBOWRMFKOiwGxiPmd2S8sC6ZyYCIiIiIi0h4UXBERERERkS7hui7qUpa5sPzXP7UNqErZSDk2qpI2HNeGZbuYnUjisQtPx/Tvv2qyjGW33RXLbLAJBpfFsFBFCebvV4L+sQgStmuCKWXssRIMmt+VsSIiIiIiIu1FwRUREREREel0bFrPjBUGVmY2pDC9vgH1touUbWN6PAHXceEigFmJOF67+VpMePvVJstY7aBjsM7eB2C+8jIsWdkPC1aUYkAsjLjtIhIESkJB02dFpcBERERERKS9KbgiIiIiIiKdiiXAEraDpONgekMKcxqSqEmlMCduz+uz4iIYDOKfqlp8+szj+PKhu5ssY+x+h5GOR0gAAIjmSURBVGH9fQ7BfBWlGD2gDIv074dhpTHUpyxYro1YOISScEilwEREREREpEMouCIiIiIiIp1aCixpO3BcF9PrEphRX4/JdUnMiCfM7dEgm9C7+HtuLX7+8D28d90lTZYxepOtseGBR2G+8hKMrizF/P1KTWCFmTA1lo0gAoiZUmBhhINBHV0REREREWl3Cq6IiIiIiEinSTgOLNfF37Vx/Dy7FtMb4qhOObBdB7btoiqVxMxEEjN//RlvXnQKXMfOePwCK43FZqeeh4X7l2FEaQmGlJVicGkUtezbkrIQQAAlptcKS4IpsCIiIiIiIh1DwRUREREREekUzFaZWZ/E1PoEfplTg9nxpGlgz/4rDKwkXAfxlI3kzOl45cxjkKqvy3j8oIVHYesLr8OywwZhZFkMw/uVYnBJ2ARVUg5Mf5VwMIBYKIjKWBTBQEBHVkREREREOoSCKyIiIiIi0inlwKbVJTCxph6/VdVhSm0Dai0brhtANOAi6dhIMsBSX4fnTj8adTOnZzy+dOBg7Hn17Vhx4fmwaGUZhpZFTdmv2hQzWwIoC4cQCgL9wiEMKlFgRUREREREOpaCKyIiIiIi0qESlo1ZDSn8OKcGP82uMZkrcccBi3bFggHTK6XOdlEzZzZeOO0ozPr9l8wvLbES7HHlrVhpycUxvKwEoVAQ4UAQNSnblAFjYCUWDmJwLGKa2IuIiIiIiHQ0BVdERERERKTdsdQX+6DMiacwN5HCtPokfplbY34ySyUWCsNxLMRtC3XxFN7477n4+dXnmy4oEMD251+JNVcfi8ElEVP2K+ACKddFKBAwwZT+kTAGlURUBkxERERERDqNgisiIiIiItKufVXqUhaqkxZqkhbqLQcz6hOYWF2P6fUJJC0XkVAQsaCLuN3YcP7je67OHVgBsNmxp2PcFltiRL8oSoIhhENB9IuEURIKIRoKoDISxoCSqI6giIiIiIh0KgVXRERERESkzWzXheW4JlulPmWjKpnCzIYkapM2ptXHTSN723UQDYXQLxJCwrGQCriYNuF3fPnY/3Iuc43d9sGeBx+KhSvLEAwALlgOLIDySAgjy6KIhUOIBBsDNCIiIiIiIp1JwRUREREREWl16a+U45oLM1bilm36p9QmU5jRkDIlv+bEE5gTTyIFF9FgGLbjoMGyUJW0TDDmrZuvhmNZTZa9/oFH4uBjT8IiAyuQTNlwQ0BZmFkrIQwri6E8GtFRExERERGRLqPgioiIiIiIFM0LpHgStmOuVycszEmmUJNKYHJdEjUJy/RYqUulEHddVASDqEs6Jtjyw+sv4q+P3sn8ghIrwakvvIPFRo7AQv1LzeOCbFofCpv+KgNjEVRG9TVGRERERES6lr6ViIiIiIhIUZLzAimehpSFKfWJeT1WWBLMwuz6JKqtFCy78XrKdlEWCqLOtlFnO5j266/48PpLmix7j8uuxyLDh2GBfjFTQsyGY3q08PeBJWGTtRIIBHTERERERESkSym4IiIiIiIiBWOGSoNlp6+zzNc/9XHMiidRl7RRk7IwO2GhIZVCbco2GS4IAOFwEA1JC3988Ql+f/dN/Pry03CSiYxlj9lgY6y10cZYoLwUsWAQ1ZaNWChkmtcPKgljZGkMQQVWRERERESkG1BwRUREREREmuW6LpKmt4pj+qR4/VbqUjam1MUxK9FY/qsmmUKdZSPhMLvFRXJeYMV1AddK4cXzT8Uf41/L+RwVgwZj//MuxfCyGCLBAJxAAJXhEEojIfSPRjB/vxiCal4vIs28T/3666/p6wMHDsTQoUO1v6Qgs2bNMhfPqFGjEA7nHjL7448/YM3rFVZeXo755puvx+3l3rANXWHOnDmYMWNGu+232tpas0zHcTBo0CBUVFS005rCHN+ZM2fCtm3zXhiNRtGbXofSNebOnYvp06eb3/v164f555+/zx8KnaEiIiIiItJsCbAG2zYBEtt1kbBszEmkTAmw6mQKc5MWahKNP2OhoAmmwHFQlUjyB1g8zHIsfP7kI3kDKyzztfeFV2D40GEomVcCrDwaNr8PLY1iQCyCkAIrItKMZDKJrbfeOn19n332wZlnnql9JgV58MEHceONN6avv/fee3mDc3vvvbcZtKaNNtoIN998c4/by71hG7oigHvIIYfgm2++Mdcvu+wybL/99kUv55NPPsFTTz2Fjz76CP/880/G3yKRCBZYYAGsssoqWHXVVbH++uubQHGhpk2bhieeeAIvvPCCCaAxaON9zlpwwQWxxRZbYKeddjK/9/TXoXSNmpoa7LDDDkilUigrK8PLL7+M4cOH9+nDEezqFRARERERke6HgZTaVGMPFXte1kpVPIV/6hKY3pAwAZaZ8SRmNiQwM54CXJhyYXPiFmYkEmhgSTD2ZnFsTJswAZ/fdV3O5xk0YiSOvuEOrLHhxhgYDaNfhL1VIugfDWPBilIMKY0hrMCKSNtez7aNX375JX3xBlVFpGf6/fff06/nqVOndvXq9AmPP/54OrAyevRobLvttkU9ftKkSSaoxcuTTz7ZJLBCHLCeMGGCea7TTjsN48ePL3j5Tz/9NLbccktcd911+O2339KBFS8w9Oeff+LWW2/FVltthQceeKCodRfxMDC36667mt/r6+txxRVX9Pmdo8wVERERERHJwJJf1UkLccuB5TomuMIsFTait2zH9F3h9WqWA0vZCAdDqEtaqE0lYbtAbdKCjQBsx0ayqgqvn38i7ERmf5VgKISdjj8Vm+yxDyrLy1ERjaAkFMCw0hhKIiEMjkVRHtHXFZH2UFVVlZHVcfDBB+Okk07SzhXpoZh9wIFNYjbCNddc09Wr1KtxX/v38dFHH11UqdJ3330XxxxzTPqYeUKhkMlMYYkxlgfje3Vr3H333bj88sszbmO2ypAhQ8xzsIyTF2yJx+O44IILTJbLCSec0Krnk77tsMMOw6OPPmoyRp9//nmTKbrCCiugr9K3FRERERERMRg0YRmwpOmnYsFymJDioiqRMlkoSdtGVdJCfSKFWUkLdZaLEIImw2V2PJFeRsJxEXBdzJ78J97/77monjwpYw8PXWAhnP/kSxg2sBLhUAj9QkFEQkGMKCtBSThoyoCxLJiIiIhIV7vvvvvSvUAWXXRRjBs3ruDHsvzX4YcfbrJSPOussw723HNPrLXWWigpKUnf/vfff+Odd97Bww8/jJ9//rmg5b/11lu48sorM4Iq++67Lw466KB0SS32yfjf//6HW265Jd1r57bbbjM9TbbbbruCt0WEeF7xvGGAha699lrcc8896Kv0jUVEREREREz5LwZWWA6M/VTmJlMmS4XlvjjX0XVczKpPosaal9XC+zouau0U5tRbQMBFIuUgCRdOXS2+eOAO/PTMw3DmfYn3BMNhHHnl9Rg2cCAiQdYpDqAyxr4qYZSFQyiLhFAZjeiIiEhROKC4xBJLpK+rTr90FA5Ie30wemoj+N6wDZ2FTeeZGeLZf//9C85aYVDjxBNPTAdW2Jz94osvzhvQYHPwPfbYw1xefPHFFvutcLkXXXRRRgmw888/P122yTNgwAAcddRRWGaZZXDEEUeYMmHEbBcGipg5I1KMAw44IB1c+eCDD/DZZ5+ZPkF9kYIrIiIiIiJ9GL9g11k26lO2yTpp/N1C0nbNYGUsFEBtMoU/a+Kot1KosxxTCsxyXFi8L9NbHBtzUxYCwSDmfP8l3rjoNMTnNM7wzHbIhVdgzKqrIRZmHwhgZL8oKmJhk61SFmaARW0hRaR40WjUlCcR6Wj3339/j9/JvWEbOgv7n1RXV5vf2cCbPUsKdemll2b0uLrwwgsLzhRhubeWcHB78uTJ6esbbbRRk8CK34YbbmgCN17PldmzZ+Pee+81gReRYjCDa7XVVsMnn3xirjMAqeCKiIiIiIj0KZzpOCdhoZqN61M2HBYBc9m8vvFnIBhAVTyJKXVxTKtLoNpqvB9LeJUgAAsu4paFmcmUCcT8/cZL+PCaC+BY/5a+8Nv1+FOw1a57mACK4wZQWRpG/1gElbEwKqJhlIRCnb4PpG9iXXvOKB40aBAqKiratXE86+bX1NSgsrLSzDrma6NdXqvzlsv6+f379zfL7y4SiYQZpOMsau7TjpwFzX4B7B/An9wPI0aMaNfld9Qx7Ipt6cxzv63bxdr93O8NDQ1mn/Nx7bnfO2O7W3OucZ04cM/XDNeNr2/pvPOikPfe//u//0tfZ8P4fv36FfTYn376yTSZ96y77rrYYYcd2nX9nnjiiYzrzEppyaGHHmrKjvH8oyeffLLTgiud/Trk+cP3Iva6KS0tNc3Ym8PPvl7vG5ZrY8YPH9fb8P9rZmRxe/ma4na25nW18847p4MrLE/3559/YqGFFkJfo8wVEREREZE+KG7ZmJtImcwTlgTjFyzHdc3vkUDABFDqUklMb0ji77oE5iSYtWIhjACcoIN4IIhpdXHMsWzzuN8fvhvf3H9bzueab7HFcdCZF2D9jcchGgwiGAygNMSASgj9o2EMLoki2ImDJdJ7cSBl4sSJGaWhvLIqHAB45JFH8Oabb2Y0FeZ9NttsM+y3335YYIEFin5ODoxyoIrL/eabb9IDVhSJRMxMTs4m5iCEv7Z+S+rq6szA3EsvvYTvv/++SSPkwYMHY/nllzfrvskmm5gZ1dlYv5/L8WZdezh49Msvv+R8Xg7uLrbYYi2u34cffmjK1rBR85QpUzL+xoEz9hLYcccdzc+2HrvvvvvOzB5/9dVX030PaOutt8Z///tf8zvfw3799df03/jYQkuDtfcxbOu2dNdzv72369tvv8UzzzxjziH/cokDr2ussYbJEth0001bNfD3+++/m4HxV155xZzz/mWzFNJuu+2GFVdcsejl/vHHH+m+FQyKFFNWi300+LpmGR3+7pVnIr6GV1ppJbPNzFrIPtfYgNxreO4vA8VAYL7XMy2++OJNyli1tA3cXzNmzEhfHzlyZNGD4dmvyUIGtzvjvCgG+6XwfbSYbBIP31P82HelPfF9l/83eEaPHo1ll122xccNHz4ca665Jt577z1zndv3ww8/mJJhHaEjXofZ5xaDA8OGDUuf24899pj5v9P/f9PKK6+Mhx56qMmyuP1cN/a64f7k6ym7VNvqq69u3v+5jEL89ttv6dcog/Tc5y0FO/wZTrFYDAsvvHCzj+F7uT9ria/hfBMbOPHhhRdewHPPPWdeX977iIcBluWWW84cDwYQC528wUyoaDRq/m/g9jJQd9xxx6GvCbj+d3KRdsYPVYcddpj5/dZbbzVfAERERESk6wMrdSnbZKzwJ3unhAIuGmwHHKZosGzMqE9iRn3cBFWqUzYabBtBFwgHgLgLzGmIo9rcP4gf77kBPz/RtMRIWUV/7HbsSdh1/4MwrKIULpfOZYQC6BcJoyISwpCSaKfOQpXejYM4/sGvU045xTT2Pffcc83Ab3M4sHn66adjl112Kfj5OIDDgWPOxG0JBye5HhtssEFBA3qnnnoqpk6dWtB68HsWBzSy1/3AAw9MD6AVigNerJ2ez6RJk3DeeeeZweFCrL322qbZckvfBXMdOw76s6wOGzHnGrrgAOtVV12Vzp5hsMmzzz774Mwzz+ySY9jWbemu5357bRfHCVge6eWXX875mGxjxozBNddc0+Jgo99dd91lmixz0K853EennXYabrrpJtx4443p2/m6yRec4zntDYQy6HbzzTe3uD4cPGVvDAYkC9lmPjfXy1+C6pxzzjFBsmJ9+umnZvC0mG3gewAbrnv22msvnH322UU978cff2xeh/59fcYZZ+S9f2ecF8Xi+7CXfcIB/Pfff9/0TWkJ34+4j72B+kUWWcQM4LcnlgTzH5O9994bZ511VkGPvf322zNel/z/o72DPx35Osx+v+e5ytcHt+u6665LBw79GLj0B7y+/vpr3HLLLXj77bcLOt+818oVV1zRYnbmtttuazKXaIUVVkj3JsmHrwt/FhIDkQyKM3CRz4MPPmj663gYkFxqqaWa3I/rccIJJ5j370IwM+uQQw4xl0J6Cx122GEma8ULRL3xxht97nO9ChqLiIiIiPQR/PLIwAkvsxMpzKhPoCaZQl0qhZkNSdQmUvi7No7JdXFMqY+bjJVp8QRmx1NIWQzCOCbQMrchiTr2ZOHs1p+/yxlYGbHwIrjm6Zewx8GHobJfiQmsRIIB07CeAZWB0TAGKbAinYCDFv7BZQ6KcDAue2YmZ4FyoIqDxIXgAA4HsrIH5blcLj978IUzaI888kgzmN+cL774wgxqZAdWOIuds75Z55yBCv+gBwcl2zIwXyiuG+v5ZwdWOADEWbPc7uwMGg5GctDeP/u7UBwsY28Ib+CLy2bJkfYqO9ZRx7ArtqUzz/22bBdnlfN84Kxy/4AmM4RYPoyD0NmP5WxyPsY/S785N9xwgxkAzR7QZZZN9jl63333mQbjHYkz7HfaaSczczx7EJfHgtvMWff+QXtmjfgHmTsbs7X8QQv2M2ppgDybvyQWbb/99nnv2xnnRbGYwcZsLw+zFwoJrHiZXP4MCGbb5AoQ/PXXX+a9nr8XKztbqZjsj+wMDH8WSHvp7NchA4T8f9ALrPD/TGbkMSiWL/DDoID/fGPGCLNM+P/skCFDmjyGgYPdd9/dZIQ2h5lB+c6FfBMq/FgG76uvvir4MdynSy65ZM7JEAxcZQdW+LpiIITbyeCV/7zmtjFoyXUoxDrrrJP+nf/Pd9TrsTtTWTARERERkV6OzefZrD5l20g4DmY1JDHXBFVsVCVTqElYcIMBxFM26pMWqhMpzE2l0GC5YAQl7DoIBENIOK5peB93XdjMQHFdfH970wHdJVZaFWffdo/5whaLhBCZF1gpDYdQEgqan7FQEKE+NrNNOh9nCnN2qje4xdm5nL3qn9HJARn/jGLOLl966aWxyiqrNDtomD3TmzPMGRTxD3Dwua+//vp09ggH6zgYzYHCsWPH5lw2/+4faGONfs5I5jr5Z4OyXvqXX35pZsJzpncuHDxZYoklzPNy8NI/EJNr4IjyDYqzlvrBBx9sntfDklJcN87M5WCNt40MwnBgjTPXiaVLjj32WDPTtrmZuH4sL+UNLjFThLNj+TzePuBgYEuDT83pyGPY2dvSmed+W7aLQawDDjggo1QPZ/cz+4WNkb0yWBzs5AAdZ6F768fHHn300XjqqaeaLVnDwfDsoATL0x100EEYNWqUuc7yNczmuPrqq836MajUmvJghWDgk9vMvg8eDvbytcTXj78cmzeg+uyzz5rXdTYO+vL1TBws9coOMdusub42hcw+zzf7nue+t/9ZNmnjjTcu6LHcFv+5xRn1PLdy6YzzojX4/uovqch1KRRLL/l55xfLYrE0FY/xhAkTMu7Dsl4cqGa2D7PkWuJ/T6diel1kl2crNKuhUJ39OmQ5R29Qn+9zxxxzjDleXtCA/wcxMJKLV4qP988uoceMM55rzHBhWT4vqHXJJZc0GwxicOWee+5J/5/BLBRmveTCAFuuyQcsvZnvnONrwfv/1XuPz5UtwnX0Tx7g+zTfe7if/f2d2B+L5yy3laXDCsnk9KyWtY7jx48vqDxdb6KyYNKhVBZMREREpGsl7caeKrbrmiDKzHgC/9QlUJdiI/sUkpaDUDCImkQKVSkLKdvB3IQFy3XMTCz+DQ7M9XqHQRXXzEKs+eMn/HT/bfj7y8ZGlp5Ry6+ISx56EkP7l6MiHEY4FEJ0XsZKaThomtaHgwH0C4f6XNkA6XjZZYs8nDXOgeN85xwHEFmOxMOeIxz8yjVLmaV0OCjqn4nKkkocCMyHz83Z/f6gB2doc5asH2e4cgDKw0GQk046CYV87+LMX5b+yIUDRP6ZtIUu18PBIc4Q5/oR9wtLaW2++eZ5H8PBH263PxuCZV/233//oo4dGzQzMNOSYsqCdeQxbI9t6a7nflu3i02zX3vttYL3Od15552mrFwhpY84oM/j6s/6YiDMX94q+7w+/vjjc5Zraq+yYAwaMHvLw0HHO+64w/Qlag4Hcpkhla/ROAeEvf45PCacaV6MQraBA778mzezn/0Y/OdKc1iiiGXjCjnWHX1etBa31QsuEbPA2JeiECeffLJ5HXkYUOGANdfR35MoFwag2XSeWXLNfU7isfH33OB5li9ono2BDb5fshcHMYuEwaT20Bmvw+z3e392FAMfhQQU7733XhNkKCSgw2PH/7vYm4Z4XLi++UrSMfuDQQcvi6a589Nf3o3r7QVNs8uY+XE9/JlgLA/G3jV+PM8YrPOWx14qDGS1hBMoGMDk+3ohPcZc1zUBeS+bhxle7LHTl6gsmIiIiIhILxW37cbAiuNgdkMCv1XV4ttZNZhSn8D0+jjqUzbitoOpdQnMSqRMIGZOwkJiXjNnx3VMn5XaWf/gq8fux+e3XYWPLj8TL+2zBd444cAmgRU6/qLLMXrwAAwtjaEkwkyVAAbEwqiMhlEeCaNfJGR+KrAinYUzvTnw0Nw5x1mu/uADB5FZhz0XDpL5B+XZsLulgUAO8vuzBjhoyRJB2X788ceM6xyQKQTLhOULrLQHZih4gRWvD0FzgRXi/uZ2M5PBw5m8uWrh57PWWmt1SDCiI49hZ29LZ577bdkuzor2D6Bzf7e0z4kz3dm43D/A7W+K7cfSVf4BXR7XfAO6xJnbHKBnoKwjcLa6P7DC0l933313i4EVL0slX2Cls3C/+GelM3OFgdpCgyseBup4LLrqvGgtf/YJB72Z1VAof3Ny4vsnj6c/sOKVhMsuW8UJLMz8YwC8uV4g/ixCLyOxUNwefx8eBuoY5GgPXfU6ZGCYPXsKzdTieVZopgyPEQNtXoYmj0tzfVTYt8QfiPNnmTRX3sv/OuH5l6/8WHYZMf97uIcZNl5gxeubVAhmr/LzRCGBFQoEAibrysMMor7W3l3BFRERERGRXoJZJZbjmCAJy32xp8qMhgQmVdfjh1nV+GlOLaY38LY44raLlO2a+7qOqf5lmtbDdVHCsl1BIOba+OTuG/DQPtvh8zuvxS9PP4Q/33kVieqqnM+/6c67YeyqYxFllkooiKElUSxYUYrhZSUYGIuYoEqkleVJOnKfzUmkdGnDPuA+7M44eFxInfzs4ES+JuD+ARUOChWSAcLBB85k9ss1IzV7sIwDNN0Bs2I8LGPU3ECZHwe5mCXjn43vD9IUcuw6Qkcew87els489/M9RyE4S9zDWfIs5VQolhvzz4rP7vnj8TeE5vFiKbSWMPOIGQIdwb/NXkZGe5eu6mj+2fHMcigkoMjXOUsaedZdd10TAO6q86K1fv755/TvLNPFJuOF8pcTI/Ye8YIXzABiWUIG35j9wIF3BiRYmsqPtzHLKR8vc4n4OveXeSpE9uC5f3lt0VWvQ2ZaeMGPjsBSauutt176Oo9fc/x9dhjoyA645QqUsAwoA27EiQifffZZzsf4X188N3Nl0GT3ecnuh9aeRs0r9eZ9jmEZ0b5EPVdERERERHpBTxVmqfAnZ4sxW6U2xRJgKUytacDfdXFMr08i6bIEWABloRACIQf1KQdxx0FVorG/CnurIBQAXMCuqcJTZxyD6T8VNhC6xkYb46zL/otyZqiEQ+ZnWZilwIr7st+Z3pg8C1d+NcEECKT1GDg7ecVFsdECuQfPuhJn5q6//voF3ZclRjhA4NWx58AJZ336Z8GyRr6/dwLLXzTX68CPZTM4aDJx4kRznUEGDmb5BzyyZzBzpjhLeXSlqqqqjL4ZLPdSzCBe9oxaDhYVMlt4vvnmy8gUaS8dfQw7c1s689xvy3bx/6V33303fZ0DlPl6++SyzDLLmNeG1weA51D264KD6+y74D9W/n4mzWEGBDN8WtNUPB8OjPoHQFnaaJNNNkFPw31zwQUXpAfe2dukpYw6Zq34Z8xzwLirzovWYiDJn31R6HtEvkC5V36LGTfZQVovy4zN2Pk69JciYwbLdtttZ7Ke/Bio8TeKL7SXlV92SUMeY/bvaYuueh1yWxi06mh8r3z99dfTfat4XPMFdPh/H3u1+IMo2QE0BvC8bCYGu1ZeeWXzf5L3fwzfQ/wBHeJz+oMuubJWcn2eYC8U9j7qCCOyXh8sV5evZFpvpOCKiIiIiEgPxawTZpt4iQPMIJgbZ6P6FKbUJvBbdR1mNCRRZ1mMlyCIIPpFggg4LmbGbdSnUqhLstm9hUAwAASCCDouJr7/Jj665SrUzfh3YCGf0WOWxSEnnYoNt9zaNKtnCbAB0Qii4VC3b1h/yRe/ozbVPmUw+jIGp7gvu2NwhWU5ihl04sCGN8DMchwskeQ1kCavQbin0Ibm/vt7gyYcHOPgvL/sDp/f79xzzzWDlOypUOys5Pby+eefZ5T4YEmjfM2P/ffz/87ggzc4629a3ZxCexsUq6OPYWduS2ee+/meoxCctc0gnYcDxYWcQ/7r/kH0XOcQS9H4yxpxULdQHNDnoGP2udEW7IngzwTggGkhWUTdDV+7HPRmUMXbz7/++muz54a/JBiPW74gX2ecF63F7Bt/gChf/518cpXiY4nEE088sdnHMXuDwU0vm4EBFPZ6yn4cA598Dm8/tKYMk3/7qD3+j+mq1yFLthVaxqolzPhgcCxXqTR/4IfHhudevnODgWeuE5vFe4GS7OCKP2uF+4rv2QyWPPLII+nHZGPwKvu9JZcxY8aY5XlBOAbt+Hreeeedc/YKa4uhWfugPV+LPUHPe2cXEREREUFfL/01r7E8S3rxJzNXLBtzEynUpFKYVFOPKXUJVDVYqHdsJGwHtuOiNOKgLsF+J3ysjZqkjXrbwu8vP4O/33sTSMZR889kNMzJ32x12AILYsllxmDcJpth/Y3HYeD8C5rbo4EghpRG0D8W6Xalv6TvYg32YmTX1P/nn38yBhH9M5nJX2e8ENn3zx6A4EzPDTbYAG+99VZ6kIclqy6//HLTvJiDLhwEz57F3JGY6ZFd3oaX1vIGQluy0EILoSN09DHszG3pzHO/LduVfQ6xzJy/1Fx7nENc37ZsP+/fnsEVf6NxWnrppdFTsTSYF1wh/u5vVp898Pvbb7+lr3MwOV+QrzPOi9bKzjwptqRSrvuzRGIh/UBY7sw/6M4eSNnBFQZWOHDPTBFqTbaHN+jf3DoXq6teh23JkmAg7cUXXzT/7zLgV0zvHpZ/yxdc4XnP/6+9cnW5AiX+27wyYgyWeIEzZrawz5G/T1P2cvJlrrCs6K677mqCc17GC3vSMMjCzxnsl1VMZlFzyrLOnezXT2+n4IqIiIiISA/AgEpyXj8VD4MmtUkL1cmUycCYFY/jr5oEZsaTplk9G9Pze3w0GDDlufrFIpg5bSp+/uoLzKmrR8qy8fvTD2LObz+1+Px7HHMijjjpZCxYWY5QIIh620HKcUwgJRIMoCIaQmU0ijAzYHqIM1ZeTGXB2rEsWHdUbH+D7Ptn1833z7Imf0Pg9lg+XXLJJdh///1NyRHPjBkzTH8Pr8cHB0OYLbHxxhtjnXXWaVVJmEJlb3NbeYOBLSmmPFAxOuMYdta2dOa535bt6oxzKLu/QLHbn11Cp62yB/qLaTbe3fC9hs3G//77b3P92WefNYP9uTId2EvEjyWtutt7S2sCD8XO9M/ul8V9xffqQve3P9uPmUJ8PWa/V/E+3jYzC4UZCsX8X+DfRg7mF9NTJp+ueh225j2WWSk33XQT7rzzzlaXBGzpnGPAxAuu8PXz119/md4t3vN/+umnTYIkDKQwg+fHH380ARYG2pi96vEH3hZddFEMHz487/NzcgaDnf6ADF93fJ16r1UGh3jObbjhhmYSR2vOg5IO6t/TUyi4IiIiIiLSDeQq6ZByXFiua4IY/j/HLQszGlImU2VWQwq1dgpTa+OYEWfvlBRYxSDlOkjV1uKv8a/iz08+wMyJv8O2UqiemjmrsCWx0lKcdfWN2HHnndAvGjEBm7idQjQYND1VwvxCHgmZAfZgNy8Dlo1lrNaffxCqk1ZXr0qP1j8a7rYl4IoNOmQPoGUPsPlr3Oe6f1uX7w2sMIhy1113mRmnuWZjc1Y8L08++aTJYmEpmd122w0dgSWismcItyWYwz4dheioxsSdcQyzdWST5c4699uyXdnnEAfq2zJLPtcs9ezB0WKPa3sHKLPXp71KFnUFDrxvu+22uPnmm9PBXg4Ys1G9X3bDe2Y+NVc6rjPOi9bKzjDJLqHVkuxgGgfUCx20ZiCGvVdYctD7fMg+UdnBFT6H16/Dy8DwBu4LeR/0B7cY1MhVyqxYXfU6LPY9lvuUgQdmrOQL1jAwxPXxl/PjPvP37GqpHFt2VgmDHN4x4vH1Mjz4XCzj5Q/KMLhC/uAKgzn+Hmj5slb87zsMHj344IPmJ8+RbHw983XLC9fjgAMOML2BiiljaGeVT+uJJRDbom9trYiIiIhIN8IvZXG7MRvFbeF+7K0yJ55EXcpGQ9JGtWVhVjyJmQ1JVMVTqLJSsBzAsZmx4uK35x/Dp/fdgkRNy7OP81l/q21x5sUXY8EFFkQgEEJtyoLjuoiFguYSCbCHS2Pz+p4WWPEwKMDAkPRO2YN3xd4/u7lv9uzY9l6+h4NwRx11FA455BC89957ZiCTM1xZsiR7kI8DPezNwr//97//bZcBsua2mZk1q666KnqqzjqGXa07bVf2Pj/22GPNYH17ys4UaOv2t/c29/QyOSwN5gVXiLPes4MrLF/lDwY3l7XSWedFa2UHQgoJNmaX2XvllVfaLUMuV5CdARh/CTaW5Co0uML7+gMDXFZvfB3m88QTT2QEVhiE2GWXXUw26DLLLJM3E+bRRx/F2WefXfDzMGDC91Ivo4eBEj4P+bNJWArMH9Bj0OSee+5JP8bfA41BzOxSYs1hoGOfffbBnnvuafr58DMFPy+wL5R/WV7w6JprrsH7779vJngUGuyKZ70+2iMLqidRcEVEREREpJNZjmOCKuyD0lILUvZGmVWfwPR4CnHLwdxUAtPrU6aJ+JyGpMlsSVg2Ug5njQU4vRLvXHwqJrzX2LOhGMFQGMuP2xSrrLY6Ntx0M6y45Gg4CCDFlXRthANBk6XCxvXl/DmvaX1PDaxI7zdz5syi7s8ZnM0NcGUPkGXfv9jltzTgxoENlurghThA88UXX+Ddd981A0P+WcvPP/+8abbe3hks2TOwmTHTk4MrnX0Me8u5357nEEvjtLfs9S32uBa7v4rd5uxePz0N++uwPwMHd+n11183ASP/ILS/LwuzL7bZZpsuPy9ay9/jotAyec31MLKs4jJkswe9cw1yZ/czYaAlX3PzbP6gTK717S2vw3y8wIUXCHjggQcyMkfaq5QdXwf8f/nNN99MB0oY1OIkCH/QJDtIwv9jmY3D82DSpEkmGMasT/9jGIwp9Hh768KgjZftwiwY9rdhsIWfJ7yyf8QgzA033NCk108+1Vmvj+zXT2+nTpMiIiIiIp2AX6bYNyVu26Y/iuULrDA0wV4l/gtvm1mfwG9z6zGhJo5JtQ34paoWP8+px5TaOKqSKbgBNrK3zPKSThBwA/joxsuLDqwEwxFsfPBRuOXLn/Df2+7C8ccci9XHLINAMMhFmhJgFdEwyiJBVEbDGFwSRUW0sXG9AivSnbEZbFvunz3glH3dK9tRKM4UbW55LeEM2PXWWw9nnXWWmSW+9957Z/ydpT/aW/Y6ctClJ+vqY9hTz/22yF6Wv89ARz1Hsdvv73HUHtgzIbvRe0/H7BX/TPWXXnopfZ1NwMePH5++vvbaa5uShV19XrTW4MGDM0q55Sqn1JzscmhtDTLk6tmz7LLLZlxn4L1QXpDMU0hgoSe+DnPhsfQHl3bccceCt781AUB/6S5OiGAGKsuy+Y9XdnkvZgD5zyEvy8Wf7bL00ku3qVcUg0oM6rA8GoOl/On3yCOPFFwOb1rW66PQ8p+9hTJXREREREQ6EPulMOPEzqrLzKwVBFzTHJ4BCpYGS9kOGiwHNcnGkl+8zObPZAp1SQv1zFBpiGPSV5/ir68+Q0NNNZivEnRduI6Dad99iaq/JjZZh0i/ciy46pqoGDgIsYr+WGClsRi60KKIhUOIhUIYNnQwFhtciSUqK9A/FjHN6uvn1U8uCYXQLxwy5b94CWfVIRfpzjgQzkGqIUOGtHhfDnb4By5GjhxpGr36rbDCChnXGeA47bTTCq5F7zW29QZP2jKAzZnMZ5xxhlnm77//nh7IytXUuC2lwjhb3ZtBS2+99ZbZlmJr6XcX3ekY9qRzvy3YdHnEiBHp7A0O7LKcXUuD78VgWSNmUXjlt/wD/S35888/MWHCBLSnBRZYwAwwcsa5F5ScPXt2j57Rvfnmm+Piiy9ON/FmpsrOO++czpzzZ1v4AzFdeV60BXu4eMEB7zgWiuW5GGDzggUMrnA7ub0tYYbixIkTM95n+JrMxgAWA0BeSSa+NxXa1N7/+uD/D152ZG97HeaSnUXWXF+gbP7MkUJlZ6XwvZbvBV5/Gp4TucqyMeDiBWD4GJYs8wf3i8laaQmzYA4++GCzfcxk8bJ0eEwWWWSRFh//T9broz37H/UE+mYkIiIiItJBmKXCHin+wAp/Z/AkaWaDBcx9vv9jEt7/8mu89dmXePXTz/Hap583/vzkM7z9xZf48qtv8OPX3+DtO67HbTtuiGdPPQJfPnQ3fnr+cfz6/OP4+YUn8MtLT+UMrCy+/qY48JGXsNdl12GLk8/B+occh2XXWBeLL7IIVl18cWy+/DLYfPEFMHb4YJTHwqhOscQYi4HBNKyviIYwtDSCylhEgRXpcdhk9fHHHy/oviyL4S9tsdFGGzW5D2eJrrTSSunrHAgqdLDl2WefTdddp/XXX79J0+Ri8fHZM25z9QbIrn+e3dS9OXysf+CNg0IdkSHTWbrbMewp535beQ2ZiYPwt99+e7sun8eBx8PDEjfeIGFLHn74YXRUMML/mrv33nvbbdn+13Qxr+e24KD5uHHj0tcZDOHgq9eDxV8qr9BzqKPPi7bwv7ey54m/kXkhNt1004zrzz33XEGP4/38GQMsK5WrQTjPAQZY/O/NzEBoCY+bP3NjxRVXbLdgand8HWbLLtFWaMk2BuJZoqtYo0ePzghy8/8b//85+ZrS+4MyvP/HH3+ccV601My+NbIDTV4gtSW//PJL+ncGlXtyELk1uuenABERERGRHi5u2SZjxcPwCpvBN1g2gnBNAOPFN97ERmuvjTWXXhzbrLUadl9/LRy68X9w4pYb4tIdN8Nte22LB/fbAY8etBOeOHw3fHH/HUjW/juw15JRa62H7c+7HMOG8EtzEBWRMBbpX4olB5dh5WGVWG2+ARg1sMz0Tqm3LCRsF5FgAKVhBlRimK9fCUaUlSASCnXQXhLpeBys89cSz4UDZ1dffXXGbV7T2WxsCuvnn8mdD2cts0ms31577dXkft5M1mKwB4qHs5hz9QDh7f5Mk2L7Pxx++OEZ17ktrSlz1Jrt6wgdeQx787nfFgcccEDGOfi///2voIHgYs6h7PW+7LLLWjyuHGS+//770RH23Xdfk/XlYYPoYsrqNRc08fe26Mx+LtkZKQyqcB9+9913GUGlQjPbOuO8aK8st2JLVrH/FUs5eu68886MPlm5MFvglltuybjNyw7Kt//8+Fpu7pxn0JWvC78DDzwQ7am7vQ6zDR8+POO6PxuxuffJ888/v9XP6c8y8ZrKt9SUnkEvL4jK/3/42vDwfaWl3mdt/TxBhWSRxePxjKDT8ssvj75GwRURERERkXbsq8LyXiawYjvpTBXbdlCVSGFqfQJ/VTfg1S++w2677op9ttocP3xVeI3sYozZdGtsdd6ViEUjphZwZSyMBfuXYoHyUiw7sBJjBvfHkJIoQmCPl6ApEVYSDqI0HMSAaATDSqMmyCLSk3EWbV1dnRk8ylcrnYNdhx56aEbN8K222gpLLrlkzvtz4NA/o5kzNo866qi8jW6nTJmCgw46KGNQjZkgK6+8cpP7XnDBBTjmmGPMLFUOgrWEGST+uu3rrrtu3vv6y1d99tlnOTNc8mFtd39/Fw7acOCYM6z5vtcSli275JJLsMcee6A76Mhj2JvP/bbgzPjjjz8+fZ3nzXHHHYf77ruvoJnjDBJdf/312GSTTZodwFxnnXXS13/99VfzevJnG2UP6LIUDoMYHZGBxEHcI488Mn2d23nIIYeYElrN4evroYcewv7775/3PksssUT6d5au6qxG4Jwt7y9t9cwzz+DJJ5/MuM8OO+zQrc6L1vJnheTqU9ISzt7n8fYP0PN8y3es+He+Hv0ZMnyfai4LiAPs7MPl4Wud+zPX+zv/Tzn77LMzAuMcCGe5qfbU3V6HuUr2sWybP3OPl3yYncX//1j6qrXr588y4fuyPxiZLwOF5d1YltPjz3bhcSsrK2v2Oe+44w4TfHvzzTcLym7j/fx9lJZZZhnTe6glX375ZcbnFf+x7yvUc0VEREREpB2wvFfCchqzU2ynsWG966LecvD2++/j8XvvwsRff0F9XR2m/vFvOYZWCwbRb9hI03Te66bA3i1DRi2BVXbeG4utMhb9wiznFcLAkiiGxKIYUhrB8LIY+kXCpsZ2ynFNQCUUCJjr/MnMlVgo2KYeDSLdBRvVspQTSz9ts8022GmnncwXf5bo4EAWZ5CyaSubMfsH+84888y8y2R5liuvvNIsq76+3tzGWagsb8MZxquttpqZVc6Ba86Ifeyxx9L1573lX3TRRTmXzQGKV155xVw4MMdgCQfXWI+dy+RsVQYAOFjFQRD/YB//dsQRR+Rdb273999/b37nunHQfffdd8dCCy2U0bg5FAphscUWa/J49iVhEIKlSYjbzga4zI5g+RuWE/FKgXB7OQOWzeI5IOT1hGGPhe6gI49hbz7324rBAp4THJD3ykAx6PZ///d/Zt+zXBvXj+cgB2KZkcH7s9G5d+621E+Cs8u322679EAuez5w2bvuuqsZqGQmAQev33nnHRMU4KAje1rwHM4OErSHww47DF999ZUpKUScwX/iiSfi7rvvTgf5WKqO5yFfMwx8vvbaa+YYNfd64cA/3ye8/ch9y4Angy7ZZQAZWG2vQWsuZ9ttt8Vtt91mrnOd/TPquc6ccd/dzovW4AA833v/+OMPc9177yt22/ge+P7775vrXF8ed56PzFjgeyaPNbeFATX/65FZiNdee22Ln8d4zvN9i9kNXl+srbfe2mTWcYCc+43v3Q888EBG+SYuPzuLpb10t9dhtv322w8XXnih+Z2f1RmQevnll7HZZpuZ/xNZfovBFK4fJxHwnGSGFdef52Wx8mWn8PzKzqTJflyusmr5lufH7eJ5xwtL+vH9f9lllzXvEXzP4f/7/H+N/z+/8cYbZlv9GBArxCe+bDyeq81N8uitFFwREREREWmlxuBJY18Vh6W+7MZ+KvW2hbn1KUyvq8Et55yB9558tN32cTASxahtd8Po7XdH5cChCAaA0lAI5dEQyiNBDCktQSUbz0ciGBANIxYMYHBpBINjUVTGoiiJBM16Oy6/rDf2fTH9VSIhRLpp7wCR1uJA36WXXoqTTz7ZDF5yUKS5gREOOHDQs6V64Qw+sH8CZyVzYIw4G5nlXLJLumTPmOXyC5kNyvr5HGz0Bhybw8AKt5MDafkwkMLBNS9IwEFcXrJx0CvX7QxIsKzNueeemzH4xcE6/4Bdc/xBnK7WGcewN577bcXBXGY+eIPz3gB9ob02WjqHeHy8Gdte4IyDuDfccEPe1w5LKX377bfoCBxs5HMzY8Dfl4SD7F5gIJ/mAgZbbrmlOU+ZVUV8DeYLjHHgPle5wLaUBvMfP3/5oUIa2XfFedFa3M/eucOMD74vF/Ma4fnFzBoGsxlkI/Y34jnKSz4MdN50001moL8lbHZ/6623mufw3suYbcEAVT4MZHC7cgXS20N3ex3m+v/w3XffTQc9yZvYkG/9/vvf/+bNbiwkUDf//PM3KdPYUt+UfEGUYvut8P99Bo94KeQ9ixMnNthgg4KW/bZvHzJw5s9s6yv07UlEREREpAWNwYjGkl8MpLDE16x4En/XNuCf2jhm1icwpaYBv86twR9Vdfhq2hx8NH02Lj35xIICK0OWWwWrnnE51jz3aqx7/jVY57xrsca5V2P1c6/GaudejbH8/byrsfZFN2DLB1/CGgcejQWGj8SIiijGDC7HCsMqMXpAhSn1tfTgCsxfWYbR7K0yoB+WG9IfC5aXYUhZDLFw0GTUJO3Gni8elv9SYEV6K28QsqUv/BysYANwNp8ttB7/U089ZcootTSzmIEJ9vng8hdeeOFmB7iLnWHOsjDMQOBM5ZYG4G688caMxrrF4mAvB+y5P7Mb3zaHZaY4uMwyZt1JRxzDvnDutwXP7xNOOMEE+ooZIOQ+5+zyV199tcX7MtOB2UYtZVBw4Pqee+7JaMDdEfi6ufzyy00WQiHZWwwUcPCXwb98OBOdA+pdcS7my07xslq663nRGsz68mcWcoZ/sXisuF3HHntsi0Eg7gc+5xNPPNGk50tzmJHADItCzuWxY8eaoH0h2Q9t0d1eh37M5mGgh9k9/L05DECxTF1bS8/l2t9rrbVWs49hZpu/vxIxM62Qc4MTJfj/VjH4fzWPBctgFoJl6H744Yf09da+/ns6Za6IiIiIiORgOY7pm2LZDlLzAiuW6zb2U0lZmB1PYXYyZX7/5aef8M2HHyJeV4OE7aDBdlE3dya+e6n5GedlI+bDMgccg/nX+I8pyQXXNY3vWbnY5QCrwzJdQCgARIIhlIRC6B8NobIkivJQEINKIhhQEkFFNIz+0SgGl0QRZbmwSMj0TmH4hMEUYgkwmKVnioaC5iLSm7EmPQfFOMOSF5b74AzfgQMHmoETlgJpTf+M+eabD1dddZXpEcBSLKw9zuwHzhLlTHHOPmaJKT5/c6U//KW3jj76aNNDgWVvWI6GM6U505nry1IlnHHMQAn7oHC5xcw85qAl9wPLDrGk2MSJE03ZFpZk8XqncCCwJew3wgsbPLPkjTejm7N6OZjDbeeAGdeRz+mvb9/cALS/jwSPTTEYHPE/nvu+K45he2xLdz3323O7GBRk8IDnIEuvcVY/9zvXjceS+56z3znYx/1fbOCHpbAefvhhU8qJA+98Hm+7OYOcfSxYvsYLZjIbwb9tzQ1KspSPt+08fwrFclAcoOW2stQPy/vxdcNSYXzdLbLIIuZYjBs3LqMRej5LLbUUXnjhBVPOh9vJ9wu+V7Dfhr8XUq6AbWu3wcP+SewdkT0Q3NZZ6x19XhSL72MsweaV9WIAo7kG8/nwfGLZxn322Qevv/66OV7sdcQyYDz2DHpz1j9fr4W8X+bC5uPM/GEpQJ4XzGZitgj/3+Df+Hpnaa6O6KnU2a/D7Pf7Qhqv53o/Y9CfJcLYB+nrr7825xqXzfd9Hge+Xv19Txjo8D9vMRlT/D/T3++Gz8NzuDncLwySM/vMw4kNhZTB43YxSMvzgP9X8/2GpS75/zTfJ7xSbNxW/l/N85xBumL4+0dVVFSYde2LAm4h3edEWokvXNYYJc6q6O6p0yIiItK38aMxgxB1lo3qRApJ10UiZaEqyUsKNXELVakUfv3lJ7x487X47aP3kWyoN0GRQpQOGoKhy66IsuHzY75V18SgpZZDOBSBzYcHgEDQK9IFRAMBk2nSPxY2DeYrwmEMKI2gIhJBeSxsmtEzqBIOBFAWDpkAie26pjl9S91SGMdhpgr7qyhjRXoT1g7n4JHnlFNOMaVSRHo7nfsiHYPBK68xPQfEWVqJgTCRvv6diYHgyZMnm+ssAXfqqaeiL1LmioiIiIj0eizpNS+BI+dt7rzMlFpmpCRS+GPmTMyaVYOJf/6BL94dj9mzZ5kMkJRtY/afE/HXl5/Cta2i1mHYSqth3fOuQyAUMNkwbERvB4OIBgMoDYURCrgIh4KIBIKmofyAWBiDomH0K4mZ4AkzVgbG2EclYoIujesNc18GSPh7rkAJm9wziBIONoZtvMb1IiIiItK8//znP2ZmP7MJ+XmRmRjMMhTpy8aPH58OrESjUey7777oqxRcEREREZFeiwETluliRoeH11O2g7jDny7qUxamV1Xjp0l/4reff8ZrD9yL3z79EK7N4lztI9p/AFY5/lwkGfuwXRNQYcCjfyyKsnAATiAA5ptURqMYGAuZwArLfJWEgxhWGsPAkogJopjsFBMRCphG9gyYMHjix2uhecEUBlsUSBERERFpHU5KYb8UryrLo48+isMPP7xJLwyRvuSOO+5I/7777rv3yUb2HgVXRERERKTHYvaJFzhhZgl/50/ezlv50wuo1Cbi+PrHXzGnugqTJv+Nj995B7NmTEO8rg4Tv/yssbxXGwWCocboRvqGACoXHoVVjzoDFYOHNgY+wgGUhBqzVWLRIEqDIVTGwhgci6F/LIJ+kTDYAqU0FERFNIL+0bAJrDBgwseXhHM33uTfGHwpCQVbbMwsIiIiIoXZYIMNTIN29mRir5n//e9/OPLII7X7pE/i68DrA1NeXo5DDz0UfZmCKyIiIiLS4zDbZG7SQsJqzErhhQkd/FlvWaiLJzEzbiHhuvh74gQ8fcdN+O6dN1A7a2a7rkfZyAUwYo31UTZsBIaOWQGDRi2BMHunBALmg3Y0FDLN6UNgJgnLdwElEfZQiaEyGkQFgynhUPpnaSiAKPunsHQXs06CjcESlgHLzkBhZoq5BIImi8Ur+SUiIiIi7YvNz3faaSfTIP6ee+7BHnvsYRqzi/Q1V199dfr3o446qs/311ZwRURERES6Jda1tlwX1UnLNJefm7AQt2wkHRcJ224MpKQsJByrsSE8K245QFUihZm19fjp44/w47uv48eXnoaTSrVpXWKDhiAYCplsmFA0ZhrRz7f6Ohi11n9QGomawIiDAGy4pmyXyTJxGSgJojQcRjQAlMTC6BcIYlBZ1GSosJxXaSiEWDCEiih7qjBjJQjm3DBE4jWcZ1Amuwk9m9grkCIiIiLSOcaMGYODDjoIb731lrn+wgsvYK+99tLulz7l22+/xZw5c7DEEktg2LBh2HvvvdHXKbjSjubOnYuSkhJz6U6663qJiIhI7wyGpNjLhCW6nMbr5JXrarxfYwkvx4QRANdxkWT2CRvGm8fZjWW8UjbqUo1BFN6Xy7UdB5btosFyUJtkoMVBwnFMb5WZk//EHx++i0mfvIcpX38KO5Fo9bb0X2RxLLDepigZPh8Gj14G/YYNN6khYZOBEkAkFEYkFATDHswq4e/MTrFcBw6A0nDENKNnwCTCTJQgUBYOo38kjMqSqGlOH2NgpYUyXgymsO+K+qaIiIiIdK0TTzzRXET6quWWWw7PP/98V69Gt6LgShv89ddfePrpp/HKK6/gzz//RGLeF3g2tWJEe+utt8bmm2+O0tLS9jpePXq9REREpOcHT9KBEv4+rwwXAyVVCQvVqZQJnDAAwmbxjKU4DJywgbzXA8X87d8gC2/1ynqZZTNYMq/hPEt+1aRs89j6eByTvvsKs/6aZNJT+PyJmmr8/fnHqPrnL9ipFBpmTS+6P0rloosjxMyTEfNh5AqroWTAAESGDEe/hUaZgEcg+G95LwZIgoHGnirRIDNSWMYLiEaipoyXFyCJsp9KmEGTMEojQQyIRTCiNIqKaBRlkaB5bHYTerM+prSXV+qrseG9slNEihONRs1sSo9KtkhfoXNfRESk8wVc71uyFMyrr3jttdcimUw2e9+FF14YV1xxBVZcccU+uV6zZs3CYYcdZn6/9dZb+3wdPhERke6Mzd/5eYJlt5gR4jWGZ0ZJXcoyt3v384Iq/BuDIfF592MwJW5zGQyAMKPENUEZ/vOWPW8xSFm2WVbcdlE1eyZ+/eh9/PrBO5j83VdIxePz1mpes/p4HFbCu611ykcugFFb7Yz+Cy+GYCSMioVHoaS80jwDAxqheQEO/s7nDboBhEONc5HCJsOETeYjKJuXSRKbFyjx4iSRQAAl4bBpSD8gFsKAaBgDYzGURkLm/l6JL/70B1fMc6rMl4iIiIiISI+izJVWNrF6/PHHm9xeUVFhskT8gY1JkyZhzz33xO2334611167T66XiIiIdA2vPJc176eHwQ5n3k8GOyxmiTBAYtumFFdjc3gvkIL09Ym//Y6vP3wXc2fOnJd1gsbSXsxQMcEVZqu4+OeP3/HrZx+joaYqY32am9FjtzAxpNWCQQwcPQYL/GdjjNpse7jhEIIMiLhAKMA/B8AuJ0EEEJpX5ssNNN5WHgujMsKMkxBKwiFTvotBEl6YmcIgSigUMOW9osEASoNBVMYiGFLKDJVQk+wUPi5XxoqIiIiIiIj0PAquFOnOO+/MCGBEIhGTmbHbbrthyJAhZkbmjz/+iJtuuglvvPGGuY9lWTj22GPxyCOPYLHFFutT6yUiItKXMXjhNVovOHMk6/4MbDTGORozRbJzjvk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F+RyCmjRpYsLyvo9jvYczSZi+E8HjLiqr5O0Xk9dxV28Rr0wPOqXiGMbiLfWVjOtjfgWzYGL1oYp6TMKUjPP2RvGWEPzuu+/MbrvtlvZtOCUrvYLXEAAAgFgIrgAAAKDAggOCiQSb16vhd5AGZb23q7xQlPr5QXosDVLmVeJI5Z7UG2HkyJF22RtYqVixorn33ntD9a1IJyfYkZ++A7GsX78+5u0q09WjRw/bSDpeY/d4mS2axe8tK5au55Cs93Gs93AmUeAwDO9xl3iBlWQf96Ig7DEsjOtjfjnN4R3ly5cv0OPVr18/0vrBUophgt6FsY1Yx8J77QcAAIiH4AoAAAAKLGw/jLBUziXZlIkShoIr6tHiLUsmN910U66SPamyfPly88ADD4RaV30x9t13X3d53bp1Sd0Xb7ZJ0AknnOAGV0QzxmMFVzR73Fuaq3v37gmDVIX5HLyizJAvSipVqhRqvXQd9+J0DAvj+phf3r5RTn+iwjwmweC2MhIzYRuxglrBYwUAABALwRUAAABkHG/JIWnXrp055phjCvSYYWdpq7RRsN6+Bt0Ls4G9BrnHjx8fal0FM7zBFW8QSbPtL7vssgLtS5s2beL+bvfdd7d9Nr799lu7/M4779jybRUqVPCt5y3Tk1dJsMJ+DtkgXqm2RMdd7/mbb765QNtVr5FsO4aZrFy5crnKLxZGwDpesC1M0KkwtiHeXjTJyOoBAADZgeAKAAAAMk6wF4DKSClDozCogf2iRYtyDfBdfvnltrRVQfsUpJr2T5kvThmgVB83Za84wRUFpiZNmmR69erlK2v0xhtv+Pqa7LPPPhn1HPDvcfe+5zt37mxq1apVrA5P1MH64iSYBbJp06YCPV7UfjDBLJIwfboKYxuxjoXKQAIAAOSF4AoAAAAyjgZ0NePYmYX8888/F8p2n3/+efP222/7BticPgVLly411157bcLm6slSt25dM2zYsHxlZajJ/I8//ugOGC5cuNDssssuJlWUUTR8+HC3jI4CUN7gyocffmh+//330Fkr6XgO+Pe4e6mUm7LGMk2wnJUCeGFLXBWnPjD5ua4kanAf1fz58wu0/k477ZQR24jVb2jnnXeOtF0AAJCdimdRYQAAABRp6sehclOOefPm5SrVlWwazL/99tt9s7xVmqt169bube+//74ZN26cSTUnUyfMv1133dV337Zt2/qWP/roo5Tua40aNczhhx/uLs+YMcMsW7YsZiN7lZryBl7iKezngNjHfdq0aRl5aIJ9NcIGTJQNpeyqbKXrSpUqVdxl73ma32tmsJxWIl9//XXksnGFsQ1ZsWKFb7l+/fqhtwkAALIXwRUAAABkpEMOOSRXVkmqKDtl4MCBvkE89Zto2rSpueeee3wDkiNGjLAN2sMIzqYP9pJJhfbt2+c6bqne7nHHHecru/T666+7vWMmT57se02Ds+cz5TmkSjreA/mlLBVvbxH1ytm4cWPKj0nUUl3BUmW//PJLqPsRpMvpk+RQRlhByqTpvTFlypRQ66q8l/f4KxOtTp06GbEN+fXXX30Zi2TKAQCAMAiuAAAAICP17t3blClTxl1+5plnbAZLKtxyyy2+AdoTTzzRHHvssfZnDbJ5G3urCfSgQYNCzYAP9jiI2j8gP9Tcfu+99/aVdnrhhRdSus2OHTv6BrydBvbqteJtmu0NwmTac0iVdLwH8kuBryOPPNJdVjm3hx56KOXHJGo2ScuWLSMHTVS2buzYsSbbeTM5FLhYsGBBgR7v0UcfDRWgGT16tC94HfZaUBjbUMDTW3qyVatWNssOAAAgL/zFAAAAgIxUr149c/LJJ/uyS/r162dnW0ehnh+LFy+O+/sJEyaYV155xV1u1qyZbWrv1b17d9++/Pbbb+amm27Kc9vBLI25c+eawnDxxRf7lm+77TZb0iwKBZs+/fTT0JkITjDKua/K83hLgqmUU9euXTP2OaRKut4D+dW/f39fZsnjjz8eOWtM5aYSZRsE+1lEPSYKvHkDNAq8rV69OuF9VPJPQbpsd8ABB/iW58yZU6DH+/bbb83IkSMTrjNz5kwzZswYd7l8+fK2pGGmbEPXc2/Qc//99w+9bwAAILsRXAEAAEDGuvLKK32z1DUIpqySp556Km4GgGYhK8Nl1KhRdsD/ggsuyNWs2Pt4N954o29A7t577zUVKlTIte51111nmjdv7gvKeIMHseyxxx6+ger//ve/tlRWqimTpE+fPu6yskc0aD506NCEwSllKrz88svm/PPPtwGl6dOnh95msFH9fffdZ7766it3+eijj7bHN5OfQyqk6z2QXy1atDBXX32173zSOXLppZeaH374Ie791PfkzTffNJdcconp0qWLmThxYsKAU+3atd1lrbt06dLQ+6jzs0ePHu7y2rVrzXnnnRezPJjeKwMGDDDPPvus7TlStWpVk81Ums+bEfjZZ58VKAAuDz/8sH2PBN/XW7ZsMS+99JL5v//7P18GmwKnYcoDFtY2ggHYww47LNT9AAAA/MVuAQAAgAyiQVQNqilA4sxu10CqZqHfeeedtum9ZsGrRr5KC2lgXbPTN2zYkOdjO+W9lBETL4Di5QRe/vOf/5hNmzbZ25S9ss8++5jGjRvHvI9m16vZ+7vvvmuXVXqmU6dOtnG4BpdLlSrlmy0dDFAUxFVXXWVn8zv9TzRI/uKLL9p/jRo1Mk2aNLGDzXouGhhXQKogDa4VBFMgwRmA//jjj32/z89zK+znkArpfA/k11lnnWUbfKvMkuPtt9+2/9ToWwEYHXeV2tJxV5By0aJFkfrJHHPMMTZIKqtWrbLlyNq0aWPPZ+/gvzLJzjnnnFz3VxBH+6PrgagPkoKprVu3tqX8tG/z588333//vd0vHefhw4ebYcOG2X3OVsogU/aKE3T85JNP8v1YRx11lA1oTZ061WY3Kaip97X6nOgarOw1XZO99N6P9XqmcxszZsxwf65Zs6YtSwgAABAGwRUAAABkNA3mara/MhbUw8MZwNWMZWVGeLMjgtScu0OHDqZBgwa5fnfHHXf4GtMHS3/FooFeBWCGDBlilxWYUYBG+1e2bNm4AQLNjHYGdHWfadOmxVw3mQPrGkx2AlD333+/L4ikwXD9S6Rp06bm4IMPjrRN7b8CX0G77bab2W+//UxReA6pkK73QEFcccUVNlimYMSaNWvc25csWWL/5XXOdu7cOeE6F110kQ04OSX7FOyMVcKtXbt2MQfK1eNHgVdlLDjB1K1bt5rZs2fbf17lypWz70vtk55PtuvZs6cbXFHPFQVE9VpHpevrPffcY19LZcDoNfQGKoJ0/JXR5g0opnsbejyVjvQG/byZZgAAAIlQFgwAAAAZT7P/7777bluKS4PP3pJCQWpErCwKlYV56623zGOPPZZr/ffee88888wz7rJmuqupfRgKwCgQ49DMeM2Ij2fXXXe1PV00AzteACaVVFrrgw8+sP1qdt999zxL8PTu3dtmFCiQpYHtKFSqKdbAZJTm1el+DqmQ7vdAfikTRMddZcIU4ErU5Fsz/vU6K+Ch88t7jsRSvXp1M378eNsXQ5ln+aGA3auvvmoHxL3ZLt6gigIJzjrIoSwhb8+aSZMmFSgTRueagt8KPseibMARI0aYRx55xL4mmbSNjz76yGY9ZlpwEwAAFA0ltkfJ3QYAAAB2UHNxp4m0SgRpwC7sTGEFSRwqMaRSPlGpVIz6NKgskEoAaQBOpWI0+B6rZ4qXBhO9tftVZkZZDmGp34sCN8HAQl49RVS+SmXLtN/KXlD2jUOlxQqjkbJKMOnY6bjpeWhgu0aNGva4aYC8oDSw7s10cGaTK9Mg056DAmPeht4KCHgHnROZPHmy3Y+o7//CeA8U5HklonNGZc103JUtokFsHXeVZwvb3yLeNUGvp/qjaKBb57ND57T67+RFx/Kbb76xr4myHVReTO+HKlWq+NZTXxhnMF3rxOuvoYwObyaN9kH7kuprnI6BNzMjynajUAbPuHHj7M/K7NN5myh4FvZ56XVUNpJKdSmApqy1eGUTY1F2l8qWOc4991xfD6BkbMNLAVt9ljnBuueeey5fjwMAALITwRUAAAAAALKIgjjdunWzpdRE/XVUQjHdwgRXkkV9hdQPyQlwPvjgg6Zr164p2RYAACieKAsGAAAAAEAWUSnEXr16uctjx4412UalIZ3AirJwunTpku5dAgAARQzBFQAAAAAAskz//v3dXjXTpk2z5eSyhcoJPv/88+7ywIEDbTk5AACAKAiuAAAAAACQZRo2bGjOOOMMd1llsbKF+s2oBJkceOCBti8UAABAVARXAAAAAADIQgMGDDC1a9e2P6up/ezZs01xt3r1avP444/bn0uXLm2GDBmS7l0CAABFVOl07wAAAAAAACh8lStXNg8//LD58ccf7fK2bduK/cuwdu1ac80119if69SpY5o3b57uXQIAAEUUwRUAAAAAALJU69at7b9s0aRJE/sPAACgoAiuAAAAAACAtCtfvrwZNmyYu9yiRYu07g8AAEAiJbZv37494RoAAAAAAAAAAABw0dAeAAAAAAAAAAAgAoIrAAAAAAAAAAAAERBcAQAAAAAAAAAAiIDgCgAAAAAAAAAAQAQEVwAAAAAAAAAAACIguAIAAAAAAAAAABABwRUAAAAAAAAAAIAICK4AAAAAAAAAAABEQHAFAAAAAAAAAAAgAoIrAAAAAAAAAAAAERBcAQAAAAAAAAAAiIDgCgAAAAAAAAAAQAQEVwAAAAAAAAAAACIguAIAAAAAAAAAABABwRUAAAAAAAAAAIAICK4AAAAAAAAAAABEQHAFAAAAAAAAAAAgAoIrAAAAAAAAAAAAERBcAQAAAAAAAAAAiIDgCgAAAAAAAAAAgAnv/wFJLksq0lRPywAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x350 with 1 Axes>"
       ]
@@ -1967,10 +2111,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06db051f",
+   "id": "07c6f05e",
    "metadata": {},
    "source": [
-    "All three statistics sit inside their predictive 90% bands. Two limits on what\n",
+    "All three statistics sit inside their predictive 90% bands, though the zero\n",
+    "share sits close to the lower edge — 0.300 observed against a band starting at\n",
+    "0.299 — so it is a pass with almost no margin rather than a comfortable one.\n",
+    "Two limits on what\n",
     "that establishes. The heading is literal — the evaluation batch was seen during\n",
     "the fit, so this is adequacy, not generalisation, and a held-out split is the\n",
     "next thing to add before any of this is used to compare models. And the check\n",
@@ -1982,7 +2129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d688532",
+   "id": "adaddc9e",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -2002,21 +2149,24 @@
     "$$\n",
     "\n",
     "with $\\hat\\sigma$ estimated from successive differences. Positive $z_w$ means\n",
-    "the loss fell. Averaging divides the noise by $\\sqrt{w}$ while a steady drift\n",
-    "accumulates linearly in $w$, so the detectable drift shrinks like $w^{-3/2}$:\n",
-    "the horizon does not merely smooth the picture, it sets what is visible at all."
+    "the loss fell. For noise without long memory, averaging divides it by\n",
+    "$\\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable\n",
+    "drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,\n",
+    "it sets what is visible at all. A fixed replay order is not noise of that kind,\n",
+    "and the printout below shows where the generic scaling breaks — in the\n",
+    "notebook's favour."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "bf579ff3",
+   "execution_count": 20,
+   "id": "9026b386",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:01.042829Z",
-     "iopub.status.busy": "2026-08-17T04:51:01.042690Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.047609Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.047210Z"
+     "iopub.execute_input": "2026-08-17T06:42:37.968140Z",
+     "iopub.status.busy": "2026-08-17T06:42:37.968084Z",
+     "iopub.status.idle": "2026-08-17T06:42:37.971056Z",
+     "shell.execute_reply": "2026-08-17T06:42:37.970778Z"
     }
    },
    "outputs": [
@@ -2025,9 +2175,11 @@
      "output_type": "stream",
      "text": [
       "  per step (w=   1):  mean z -0.001   sd 0.99   |mean|/sd 0.00\n",
-      " one epoch (w= 300):  mean z +0.014   sd 0.02   |mean|/sd 0.57\n",
-      "two epochs (w= 600):  mean z +0.032   sd 0.02   |mean|/sd 1.80\n",
-      "1.5 epochs (w= 450):  mean z +0.044   sd 0.46   |mean|/sd 0.10\n"
+      " one epoch (w= 300):  mean z +0.014   sd 0.03   |mean|/sd 0.56\n",
+      "two epochs (w= 600):  mean z +0.034   sd 0.02   |mean|/sd 2.06\n",
+      "1.5 epochs (w= 450):  mean z +0.021   sd 0.45   |mean|/sd 0.05\n",
+      "\n",
+      "control spread is 18x the widest aligned spread\n"
      ]
     }
    ],
@@ -2051,30 +2203,34 @@
     "# control: a horizon that is NOT a whole number of passes, to test whether the\n",
     "# variance collapse below is really about epoch alignment\n",
     "control = [(steps_per_epoch + steps_per_epoch // 2, \"1.5 epochs\")]\n",
+    "spreads = {}\n",
     "for w, label in horizons + control:\n",
     "    _, z = signed_z(stage2, w)\n",
+    "    spreads[label] = z.std()\n",
     "    print(\n",
     "        f\"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}\"\n",
     "        f\"   |mean|/sd {abs(z.mean()) / z.std():.2f}\"\n",
-    "    )"
+    "    )\n",
+    "aligned = max(spreads[\"one epoch\"], spreads[\"two epochs\"])\n",
+    "print(f\"\\ncontrol spread is {spreads['1.5 epochs'] / aligned:.0f}x the widest aligned spread\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "515cad6d",
+   "execution_count": 21,
+   "id": "2b9566ba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:01.049191Z",
-     "iopub.status.busy": "2026-08-17T04:51:01.049067Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.303878Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.303222Z"
+     "iopub.execute_input": "2026-08-17T06:42:37.972046Z",
+     "iopub.status.busy": "2026-08-17T06:42:37.971991Z",
+     "iopub.status.idle": "2026-08-17T06:42:38.094284Z",
+     "shell.execute_reply": "2026-08-17T06:42:38.093942Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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hFtgDiFAAhB5sCckyeIdw2zrL0AxszgAAhDoQoYDZZMdvd8RLAwAEF7afbbCVAb+Ad2pqahwiFBMZ2eYSDgQXkpXL6aef7rDKcYfFA14U5wXxfXv30ZAz2kST5uZj5WPatGkej2Uxky2N2AqkoqLCo6s3Pr+cYMBCzOTJk4WwocYqT6tzc7644KRsHj9tohCi2LonP+OY+7Qjicnq8nTfPocQxaIUw+762JrHH/x5Fr6Q4sUxLNLIwdamfI3CwkIhNF100UXic8lCiu+b40u5C1HS95wP7nn2z3/+kx577DE3waKCOnaJ9hkbzVfMAE4Px1eS7pEtxTh+F8dl4phVHMfLXaCU4naFdQhTdG33OF/S33Jlw9fxznSPaROimPKqalsLUfmp+6kw/bAI8jto3CyzkwMAsFncrPoadf0aCCy/k7YtE24iOT5PRKR/4xUAgDbw2LiyKFu4RgMAgFAEIhQAwYtuQhQvunraId7Q0OAQRHhBmRdTlcK/5XMOGDCAJk2aJBZF2eUS8I37wrzkJsou9OrUkW6ZMDjoXPPdPH6IuLdCjc7J7tGYIUOGeF0QHzx4MCUkJFBpeaVqF5CSiOm+oC65KFu9erXDYkaK++H8P8c1UmuVp9W52apJjn59ejruKzcng/r1brMUKiktV5Wn0jNgJOsctW7zAn0WvnD+vS8BhV3rLV261CFElZeXU3x8vHDDN2bMGGH1xDi775Oekaf4ULGxsS5/s1hRlLmPuvUcQMOnzqFA4D5Fatv4/4kTJ9L8+fOF+MXuYp9//nnhTtC5HEj9VH19g9dz19XVuVg2uR/PVmb+HO9MePixZ6H2mVoNfq5MSU4KhCgAgCpSdq4UsWKAMZTmplBjQ1sfVpyZQP1GhvjcysemFwD0pqIoizIOtnlkAACAUCOUY8cCEAroJkQ9/fTT4uXOAw88QEuWLBHvOWbRxRdfrFcSgAeXbe4ut+wEx1JaMLI/ZVa1LejanSHRnSg2SlvLNGnx2purN+fvW1q06+Tr69ueC1vnKLHQYVFaKY2NjZqc21u+OAsyLc0tfucpu6STkN77OtZoevZsE90YFpaUClFSHDLOE0lkYrefLLSx9VF2drYQWlioko71Rd7RNiu+ypI2sVEP2J0ju0lk130cn8rZKkqKa1VcUub1HJKoyG4WnZGOZ9eF/hzvTHnFsYXXmGh5wQoAAIIZiFDG0uq08YGtogCwMw21VaIcs2tgu1KS0+aNwViw8AuCj8riHMpL3kt9hk+k2H7DKBSQ3KN37NLNp7cRAAAwA9u75gO+4bg1P/zwQ7v4MnaEhRutxZtggq1NlMTgkRbNu0V7j2ujlNrKEoru3JFYI7rp+mvp+pvax1JSG+/H033dfvvtdNVVV/l9bnYxJ4dk+eSeLzHdoqmiskpxnjpb/LDowOmWLLqsQnR0tLBaZWHGlxAlidYsMnFb4hwfSoIFJxaiWKxiV308AObYU+PGjWt3vieeeEKIQRKN9TWO95FRb3lNC6d51apVpBZOE6eHn39WVpbLd6NHjxb/19bVUWFJGfXp6WqxJcEuG51/7348uz/NyckRcczUHO9MhZMQ1T1GbyEKCw4ABBtVJXlUnJ1EfYdNpM4xxzYcAABCEx6P+XJrHEw0NdRRwta2Da9jZ82nqK72FKMQpwwAbUjb3+ayPfPw5pARokqyk0SMLy28jQAAQFAIUSeddJLDldGIESOMvnzIwZYLHBfF2UKEF42lGDZqycvL8/p9aWmpX+cF2hAXFycEAUks8ERSUpLDfdyoYfKu6tRQU15EY0YMppz8YoqPP6y560eOk8T3xa7vAjk3W/OcffbZxz5wmpsfONImFPTp2Z2iux5zhzdq5DDKyslTnKcTJkxwfD516lRKSUmhDRs20KOPPmroYkAd74Tq1FU28Dinc8+ePQ6BRI6hQ4cKcYVFFhaanONDSfB7tjiShChm1qxZHu/Xu1BaqZl46Qy7zePreoqJ5dwW7j2UQmedNqPd8fwMWYjzZOXlfPxvv/1GV1xxharjnUlNzxD/9+0VS926aiMSAxBSbjx40fV369amhnqqrSqh6Nh+js+CndR9ax1unSbPXWh2cgAAJsf+Orp7NUV0jKLhU+aERDtYXtA2jmKKsxNp4NiZpqbHXoTp0i/XVZZSx87RFB7p3xgeBDvYGKc1LELp7W0EAABsJUT94Q9/EC9gDG+++aZL7Ba2KHjyySf9Pt/s2bO9fs8xa8aPH+/3+UFgnHPOObRixQo6dOgQrV27lubOndvuN6+99ppjQX7K+NFErcpd5HljzolTaf3WfbRl+x7asmWLEJ214pJLLqHFixeLewrk3GwZeMsttxwTs34f+5ZXVtOKNZvE+xOmu5bf2aecQOt/26Y4T50FGo6p9P3331Nqaip98cUXx1zC6TzmLi/IpIxDv1Gnrt1pzCzP1o8soLAQtW9fm2s8b7B4wnm3fPlyIbqxMMUClfP3LDqxWCcJUXKCC8dreuyxxxx/x29e7Hgfd/IlXtPhS8hraW4WCy3uv/vf//7ncO/oLsJzvKjp06eLvFj26xZRjt155513HJZkHGNK7viPP/5YlFV3wczb8c4cOtzm0nDiWP9jiikndHZIG0V9TSUVpB2k7n2HUkxvbUR+oIyWlmZK2bGSmpsbafTx51JEZJSIM9RQXy2sg0I+5g0wzCVYeGQUhUe0We7nJu+h+poKGjz+RFEmATA6VmNddRlRNYvT2dS9r3ysU2AxgsSKrSw/jbKObKWOUV1p3MkXmZ0cAADwDmJTAWAIwb81KoRhF1avv/66y2cPP/wwjRo1yrQ0AX1hax9JCLz//vuFeCNZw7HFzl//+ldauXKl+Pu2226jqCjtdqdNjhtJJ8+YKHa/sQu9t99+28XypampSbio+/HHH+nuu++mxMRExedm4em8884L+NxVVVV00003CdFAIjk9m5565VOqqKqmLp070YVnuopcp598vKo87dy5s0u6582b54iJ9+9//9sl3XzNdZt3CmspLWERSrKKkuPkk08W/7PA5itelyQqbdq0yWOMOXZBOGbMGHFvkoWVXBw6dl3IQqD06tWju+Pl/LmnF8ejcmfZsmVC3Pptw1ra8csXlLp3jSgn/GJLJI5V+NJLLzmex4wZ7S2e7rvvPiFeZeUV0b/f/ZryC9pcOFZXV9N///tfUa6YRYsWiRhYcsfz9bj8SZajSo+XOHDoiPh/yviRsr8B1iV9/3qx6JJ+YIPZSQm5iR/vgq+rKReuPnnxlWERiilIP6RJEgHwRm1FiXAJlrT9ZxFzqb66gooy40V8ivzfYyECYCRNDbWO9y3NbZtxADASFqGc+2MA2hMcoisAAADlIEZUkLJr1y564IEH2lzV/M5ll112zCLDT9avb/Oz6801H8eAAebsgouIiBDWOTfccIOIhcPC4+OPPy4sMfjZSOWBywILMolbl2r6qO68/hIK79iFNm7ZIRbg+cUWMpwudo3mXB5vvdV3HClnnnvuOWpubqZffvnF73NfcMEFtHTpUhFnivNEEqeY8PAO9OBdNwpBxJnw8HBVeerOCy+8QHfddZew5HrvvffEKyamGzXU11NdfZsA5GxdZBQsLg0ZMkTEfeKYTQO8hF5zF5U8WTvxZ5IA2Lt3b8MEb34Gn3/+uXgxkRHh1L17LFXX1AiXfBLHHXccvfzyyx7PwW4Eb7nucnrv069p14FEuvjy64R1G5cNFjmZc889l26++WbZ47m9ffHFF4XVHOenmuOZ+OQMKigsougunWjWNFiV2pH6Wu+uJYF+tDY323bBFbFAgoPshB3ifxZD66rKqKX1WJmsq/IeizGY4DFRS3OTwyrMjgRNTKVA7yNY8gEAg11i5qXsoU5dY6nX4LHIewAAAMANCFFBSHx8vNh5X1dX5/jsjDPOoKeeeirgc/uKz8Ou+YC5sLjA7uDef/99YS3CsWlKSkqEoMIxi6677jo6/3zP7toCJapjJP3zsXvpcEaZiBm0c+dOqqysdJSNvn37ikV7Lo/jxo1Tde5OnToJQWj16tV+n5vFJLasYldpBw4cEAsmHcLCaOK44XTj1X+giePHit3LWuYpC1YffPCBcG337bff0v79+6miolLM72NjutJxUybQzbffS4HQ1FgvAtR37zNUxAJQQocOHejKK68UVlps5XX75afJ/nbAgAFCLMvIyJC1duLP2AWerzhIWsPiIrscXfr9/1F8chpVVFZTUXHxMfeTU6bQxRdfTPPnzxf3LMeVl5xHA3pE0fcrfqMjyRmOmFJclljA59hP3han2OUjx93issVlU+3x67e17Zife/J0UY8ACHaXFK2ICwBAUFqGVpcV0PCpc6lrbB+zkxNSNDXUibFgt14DqUtML7OTA0BIUpB6gEpyUsT7mN6DKbITYr4CECrw2lLmoU3U3NRAwyafLhurG4BQx9SawW6L2H0cW+8kJCSIhV3nHeRqYNdMei2u24n09HSxIFpRUeH4jN1RvfLKK8JyBIQGvADPbuT4VfO7ZQh/5q0MdIuOdli8sas1OTjWEVsm8fnkOPPMM8WLO2MWi1paWoRLNi0I9NzS8RwzKC8zlQpTtlFkRAR1iemteZ5KsGC1YMEC8eK8y8tOo/z4zRQZGUGdo3vS6JmuwZTZ0kvNs8g/vIFyEndSWV4ajZpxlstvOJ/kBJDLL79cCCcbNmygK86cSr16xMhei8U/duHH5/LkIo9jH0lplqzNjICfPd/HtGHRIhYC50efMSdR9179VKdjyvhR4jVw3CwK69RDHO8e78kb7O6QX5yG8vJyxcfX1NbR5l2HRHk4d84ssusCGMdI6tK9d/DsJgcAAKAqVpsUHJ1dlE44za4xge3Zh2Ue3kJVpXkiVuHkuQvNTg6wAxrvPWmoq6aKwiyK7TeMIjp2olCkqtTJdXxjPYQogwshW6Q11lVTp2ht1h0AUENFURaVF2aK90WZCdR3+ERkIAAeMEWZ4AXgDz/8UMR54QVdrc4Z6nCMnBtvvJEKC9vimzDTpk2jN954Q9ViKgguunTpIl6ecF4w5ve+LN6YPn2U73Dlc8bEyIsbgRDoudmKqmfPHlSWfqwZVLqA7i1PlYhSPWN7UEmkfPOr9lkUHmlzAVRTUaRaxLnjjjvo+eefp++Wb6A/XX2B7G979uzp9VwsyClJs95w/rJ41ykAMYzPEevjfn0d7yu/nFmyegtV19TRNVctoH69e5AdJ37s5rO5uZEGjTueeg4crem5AQDAboS6y0V2zweMhUUoOZzdV1sRbGAJDlJ2rhTiC8fLHD3zHLOTA0IMER9450rhKnvoxFOoe1/jXd8DexOot4bmhnrH+0bExgNAFnk/RTrBO+p54ZODx2slQgGi4uJi4XaMXYZJjB8/XsSj6dq1K7IIAOCRa6+9loYNG0ZrNu2mgqJS5JLBVFRW0bJft1L3bl3ppuuusuUCEsMilHOcFADUADd9IKixQRsOAAhBYU7jy7AIxdRWlthyLAu8Y4dnKMVrzTi0yeykBAWmPXMblDUAgI0sothFHLuBcmfgwIFiMZRdUvnjQm7QoEEUqrALKLaESktLc3w2atQoYXWmlzUKAMAm8EDOy4SWrSW/++472rXqc4ru2pnsS4ADVpM2r3eK6kj/ffIO6tgxUmwasKMUaIeJKQA+QTkGIDCCpSsIbWM2AIBGwNIPAAAAMFmI4hhQn376qeNvDhy/cOFCuv7664UIBfyLs3XrrbeKGFsSQ4YMoY8++kiVaygAgDGrD3VVZZQVv81S2c0bAHr1kI/5FRKYtIDGQqDRec/+02vK1LlxBKFBcFgmYRUZAACAnQkLuY1xwUMwjKNsCjYUAQCALTBUiGJLKHbNJ/HMM8/QpZdeamQSgor6+npatGgR7du3z/EZx2j5+OOPqV+/fqamDQAriSzr168X7zl2kNkk71xJra0tZicDhDBHd6+iuppys5MBfFiZYSdtCIJFlOAgzGZrkSGxOKycUI/vFTSgXANgaSqLc6kkJ8XsZABwDDuN3QCwMYYKUYmJiY73s2bNgggVAI2NjXT33XfT9u3bHZ/17t1bWEINHjw4sAcFQBBN/HgxlwVapb/VG6NFKLZwsNYTAWZTV62xCIXFc6CSluZm5JlUfTDrBQDoQCgKasHiKhgbUUAokLZ/ndlJAAAAYAIdjLxYWVmZ4/3pp59u5KWDipaWFnr44Ydp3bpjnXdsbKyICTVy5EhT0wYAAEBrgmNhBQCmtaWFkncs17SIQ8wBlsNpY0volM9QuU8AggQDq2zotIPBjLWfoXMZC0UhXhdMEveDo71AGQTAEkJUTEyM4z3iF/nP3//+d1q2bJnj7+joaHrvvfdo3LhxAT4hENoEQ4cP2oHHCgCwEBwnr7620uxkAKAx6GyDxorGYpb9oVc+7ZRWe1vH5yTuNDsZAABgGg21Vch9AILdNd/AgQMd7ysqKoy8dNCwZs0a+uqrr1w+Y1HvjTfeUH2uJ5980uWZAO0mtCU5yRQeEUmx/YaHRLYGiysMYA1rCV4EUu2WBEXQNIJj15rFMCuotwGPEuUFABDqtDQ3UWnuUbOTAUKYlF2/iHIIAAChCLd/CVuXmJ0MAEISQ4Uodsf39NNPi/d79+418tJBQ21tbbvPMjIyxEst999/v0apAs5UFGY5dph16hpLnaJjkUEAKKC+ppKO7l5NnbrF0vApc4z1kR+MG6CBapqbGqmiKIu6du9DHTtHIwcBAApBJ+K8KQkxbryTn3qAijLjUbuAaRguQmHPkjLQlaCMARNjJgfWUGGzHQAWdM03fPhwOvHEE8V7jm+Ul5dn5OUBMITK4mzH+9rKEuS6nYA7FlPJTthOTY11VFWSRw1w3QVMIDdpF2Ud2UpJ239G/usE/PZ7n/OW5aVR0vbloh0EdgKrrEA5EKG8w+0ge5eAxwVgpocGlD8Q6kBYAQDY3iKK+etf/0pXXHGFsOx55JFH6N1336WOHTsanQzbMnPmTHr77bc1ORfc8ulEUIoZwM7xWDrH9LLF7uTmhnpXF33g98yweEZYPX0qKM1LFf+3tDSbnZQQm9S26m7pxu5y7UDmkS3i/9R9a2ny3IVmJydoLQHS9q0zOxmWJCQXXm1wzzyWK85KpB4DR1GXmF4UzPAmPqkdjOjYmWJ6D6KQwwZjdrW0tvK4Ppzs5KGhc7ceNGzKbHVzKOs3J/4TzPcGrAXKGgBBjeFC1NixY+m1116jP//5z7Rlyxa6/vrr6dlnnxXWUsA3/fr1Ey9gD+w2oa8uKxALsN16DvD7HNjtbi1Sdq+igWNmUK/BY81OCghRijLiqefgMdShgz0WIKwi1gTfMpR5JG5dSmNPvMA2YhTQl8KMI1RdXmhYNjfW1YixlV3awIK0Q1RVmkeD404ISRelVty4k7xzpVjIL8lNUS5QW+82FFFTXuR4z+XQXyEK8xFrcXjjtzRs8um2EBYlDw2VJbnCQ0NUlxizkwQU0epzE0qHcMOXPwNaR7JifxQsqO0jeFNb+v71FB4ZRUMnnYpnA0AAGN4Sp6amUufOnenBBx+kf//737R7924677zz6JRTTqGTTjpJCFLdunWjDh3UeQ0cOXIk9ezZU7d0A+AP7FaCRZ3ITl1ssfsr/cAG8X7kcWeKGCmhRrBOWnOSdkGIAqaRm7JH/N97aByegk0n8VpOhuXaWV6o5+/C3MZ/xyZ+HWnopNP8SgcvKJUXZFDPgaP8TjcIng08TfXt461qjXM55/KXumcNjZpxFlkdFjvyU/eL9+ymlMeDwCrWJIGcgEKE4LhRK85HGmqrhEgY02ew34v5PM+0g6VvyHtoCFLxI37TYho182xbCIssgmYd2iI28vUbMdns5ITUGDQrfpvYODRgzAyXOQe71JU2MVUW59hCVAfAqhguRL3xxhu0ZMkSl89aWlpo48aN4uUvL7zwAl188cUapBAAbd1LpOz6heJOucTy2VpVkut4zwt2oShEAQA8W0ryAkRsv+HtRAKlCz9lBekQooAszU0NYsc/T/xGH3+uyyKXy8SvKFssggXXAqVV0wW0pKbimJWHpXESAlXHObWZF4BQIjtxB0V1jaGusX2VH4TnCdwsi9lau2f5aBo07njkTTBjYt1nIYCFdz0siJubGyk7YQeNnD6PrI7kPrgg7aC5QlRraFmJleamUGnuUfE+ukd/lzlHY121431z47FwAgAA9agzOwIAqN7F1tig/85b87HYKMVfgnMDWOgRpDv5zKCpoZ6O7vlV7A6T4ieFchMB9IHFpsb6GqqvrWxXzlwmfk0NeAQgcDx0EfU1FZS07WfKTdrt8RD+vqW5OaTcNQeGnfthO6ddHu7L7YYVLYMMwaTbLslJoZSdv1BNRbFsfEf29gFCE88xPrUXYBI2/Uj11RW69LHsng8AOXisJ9FQV4WMAkAnIEQBYDUssFARshM/EwmtBSoFExyVYpIRkyOzMPPe6muPDcilHWLuoOzqQPAWZ58LA60tgS32+5unwdyGAN9kHtpCdTXlVJSV0G6hqqIwixK3LaOje1YjK4MQ1H0AjsVGqqksFt48gAab27AxThW8GYnjwjU11YsNcO6wq2aOuQhCAYzJAQhmDHfNd9VVV9HJJ5+s+XmPO+44zc8JAABGkJ96gIqzEmnIhJMoqltv+vHHH8Xn8+bNE7HvrBasdNu2bZSRkSHi+g0e7K+bLNAOBc+4uLiY1qxZQ3379qXZs2drlokcC6S2ooSGTZ1NHTt1PfYF5gEAACMxaVOG8y5Yd3E9/eBG/1zVucPntVBfrjVBI+gE7yMCAOhM0LSDZuDU97pbwLe2tFDy9uXCen7UzHOoc7cefl4j0ESCoAblA4DgFKJmzpwpXgAA7eHFk2+//Va850VqXqwGymgTesxZfWD/z0za/vU0bMZ8+tvf/ib+jouLo4ayTCrNSaEhE0+h6B79yGyKioro9ttvp06dOtH8+fMpGGhoaKADhxMp/WgiRUZEUF3HfjSz7zBF4l91dTXt2bNH5EtMTAxNnDiR+vVT/pxqa+to7+FkKq+opqElRN07VFDPWPkgut27d6f33nuPcnJyRLzFESNGUKA01FU73KHlJOyg4VPnBHxOEGQE+QI6ABAfAHAFC+pmgv7WTEK+7FtwvMeWeg31ba6acxJ30qgZZ5mdpBChNbTmI2EKb92CdQQAO2G4EAUA0I/m5maHiPHpp59CiFKBVd2LFaYfFv+n7l1Dk+cuVOxaUa9J1EsvvSTEl7vvvpu6dOlCdqaqqopef/11+vrrr8U9OXjnKxo0aBD98Y9/pBtuuEFWvHrllVfos88+o7q6OsfnHTp0oDPPPJOefPJJ6t27t+y1peP/9+mnVN8g7fr7gTqEhdHx0+Lo1oXzKTYmut1xERERdOedd9JDDz1Ezz//PL399tsUKM6u0FiUCgS49bQ3Vm0Hg3niZ/k8B37A5bT9c+V+2V4lWGVqUZQBAABogfvYCP0LMBmM1wHQDsSIAsBi2H4Xlo0XCq3k/s6KJCcn0w8//ECxsbG0cKEyUcyq5Ofn04IFC+ijjz4SItTwIYPo5BkT6bRZk2nihDjKy8sT9+qJlpYWuvfee+n9998XItTYsWPpwgsvpFmzZolB6i+//ELXXnstlZWV+TyeRaihA/uK606fNlnU/217jtAT//mIKqtrPE7G+FpDhgyhtWvX0s6dO8lK9UO+/bJ5u2Y4IZxfrYEJNX634xCEgGaEcP0FflNRlE3xmxZTcXaSRrkYFtqLfTYd02MuAiwLxkkAAAA0ABZRAGiNPec9APiErX9YRLngggsoKirKtjnW2NhIixYtotTUVBowYAD95z//ob6d66ksP118z7G66qkzbdmyxePx33zzDf3666/i/SOPPEI33nij47vt27fTrbfeKs7NFkvPPvus1+MX3biQzjxhnHg/ePyJtPLH/6OnX/uMcvKL6dPvfqE7/3hJu+PZ6uriiy8W1lz8TPR1d4sFVRAci5i23+QRIgTNcwqzxmJ0ZXEOleWnUd/hkyiqi7zb15DGIkUu/cAGh9upXoPGmJ0ce2KRZxkoEKMACC6hzGrxnq1OMFgf4XkDIA+EKACChK1bt1J6ettCOrNu3TrKyMhw/N21a1c6//zzRSwbtqRgJg9qLyakZeXR1kPZFLs3hU4//fR28W6Ki4tpzZo14j1blHjqZI8ePSqsZ2pqaqhXr140depUET9HCyoqKmjfvn0iHZ07d6Zx48bR8OHDZd2f/fjjj+L9vHnzqGfPnlReXk579+6l0tJS6tKxA8WG1VPnTspElUDui93AHThwgHZv209RHSOpT69YGjZIWQyv2rp6SkjJpJTyxRQdHS2u26dPH6/HsGCUlpVPeQXF1NjUTJVRw6h///40cuRIRdf0lP6ffvpJvGcRxB3ncvWHP/xBiCXOxMfHi/tnPJWr8ooq2rP9MDU31tEZJ08nPfnkk0/o8OHDQkz74IMPaNSoUZR5eLPLbzh9l1zSXgRi3nnnHUccNmcRimGrqDvuuEO4MFy8eDHdc889It/ljr/84nMd8ZmYiWOH04L5s+mLxb/S+i376KoL59IoD2ngtLEQtXr1aiosLPRZHnTFabJgJdd8Lc1NVJKTQp279aSusSbmDwAAMCasq3DsSaamrIjGnXyRzlez/8IR0J7mpkZqaqijqC7dkL0gpLDSmNgWKBRqFC3wy/7G+H6qrqqM0vato+ie/cWmQzsRDIKQKrzerrr5bsjlHQB2E6KamppEsHdeHObFSnZnxIvNHAuDA7Pzgu+UKVNoxowZNHr0aLOTC4Al+eqrr+jnn392/P3hhx+6fM8xb1iI6tixIz3xxBNCqPjvP/5CQ/p1d/ndDyt+o007D3q09mBWrVol4t9wXbz88svbPvy9nz2clE6fvvAxJR9NczmG6zKLFw8//LCo0/7ArtJeeOEFWrFihYiF5cz06dPpH//4hxClnGG3aVLMrBEjRtCrr74qrFG4zZHoGBlBl18wh6694lLZa7P7s3/961905MgR1feVm5srLG6WL1/ucl2GYwBddM4cumieZ4sWvs+vFv9KS3/dQg2Nx44NDw+nq6++WjwfToM77Nrtw2+WU1FJ+bEPP25zM8ei3c0330xXXHEFqYHFR3Zhx4IHt8fuOJerCRMm0MSJE12+f/fdd2nZsmWy5Wrb3nh694ulNHhAH5p3ynEUCN4Gh5y+zz//3CGksgilhoMHD1J2drZ4f9VVV3n8Deftyy+/LK7Fbvo41pSa4886dQb9349rqKW1lbbuOUKz5rX/DbvmY5eAiYmJot5ff/31pAlBNGguSDtIhRltdXbS7CspzE0cBcoxN7aNuokfdiACfwj2BUMpyLth2Dk7g6cbNH080NrSQknbllFjQy2NnD6PusYq24AFgFZwGcxN2UMdOoRTv5FTMUawLfZtmDMObRJtIG88HDh2JnUIt8QSrC3RfT5i57ELADbC1FaQrQp4Z/qXX34p4nV449tvvxX/syUAB4/nBXUAwDFOPPFEYSH03XffOawtnK0kevToIf5nC57x48fToUOHaP/hJBrSz1UEOZSY5mJl1U4w2LZN/H/CCSe4fL5zfwL9+92vqampWViasGUIX5MtiNj6hNPFlkxc39VaR6WkpIiFdrb8YEsbFp6GDh1KlZWVwhUaC9kszLDAEBcX5/EcLJ5xWnr37i1cmfFi5Y4d26moqJg+/2E1NTZ3oNtuuqbdcVu276Ynn31FuHNTe18sEnB7xdZbDAsHfWI6CoGpoLiM0rPyadX6rbJCFKeZxfnePWJo7MghFN1rEO3fv1+Icv/73/+Eldtf/vIXl2M2bdpE/373KyFidOkUReNGDaFu0V2IomIpJydHpHvJkiWqhSjOZ2batGkev3cuV1xu3IUo6Xi5cnUwsc0qaNLY4f4tCipcMGExkfOBkfqRrKws2rhlJ5UW5lLvnjHUb/QM2eP5WUscf/zxHn/D5YOfNT87fl7eji/POtTueH5eQwf1FRZtyWltopUn+FlwGeP8DEiI8rKz0GU3l81cShRnHYuz0dLSTOE2EqKwi06XTCWjqa+poKLMBOrRfwR16d7b8OsDs+C2stWWC2mBtD1ot5TDY5ygcUPphfraSrEAy2Qn7KCxJ8w3O0kgxGDXpMVZieI9C6Hdeg00O0lAJ6zapjbW/R7zF/1kSJUnbIwDwIJCFC/OPfDAAy6uxJTAi3i88MoLqU8//bRwtQVCh9aqamopLCErE5FXRp2K2yZdEs2pme1+16FPTwqL7tr+BH4uAlx55ZXCJZokRLHVi7tYJMGfCyHqSDLNn3tMBMnMKaCyiioaOngg5RUU0Y4dO4QVj7PVjSQoOJ+7orKKXv9ksRChhg8bTB9+9KmwwJJgi42HHnpIiCAcM8dT3Bw5+Prs3oxFKLYCeeONN1wsn9jF3l133SWslh588EHhPs5Tx8/XvvDCC4VlU6dOndqOLcimvz7yAP26aTd9s2QVnXbqiTQg5thidVV1LT3337eFCDVmzBjhUk3pfbFbQE4Xi1AsfrFVFIuFB9Z+eSzt5ZV0KDlX9t5ZyLjqorl06bmniUX0yXMXCiuv++67T8QY+vTTT+m2225zOeajjz4SItTkuBH0yB1XCzeADB/LcJvrLo4ogcsC48kayr1csTBy0003UWnuUbEDsaiqSTw/dgvIoo+ncnX4dwF00rgRLufkOE18jC/yUvZRU2OdeH8gu5EiozoLy7HLLrvM5XfSvXMZGTx4MN19993Casl5AS36nW/ottsWiVhP7mWJYz8x3Pd06ybvZmbYsGHi+bE7R2/HO9msudC/by8hRGXnFckOhHljxtdffy3KPltfubtDtNJAHQuUwEGYDRYSNBJdj+7+VbRLJTnJjjYY+MACjx+EGsFZ6IJiESwY7gGYCrtFc94corsQFUSeBSw5PguwXcB8BBwrR4GUQeQjALYTotgFH1sJ1Na6LtardRPF5+BA7VrFngHWpnH5OmpauZ6oxSKDIBm4NLqXyIYNH7T/YYcwijhnNkWeN0eXdDQ11lNtZYmIj+IOCyLsuu9wYqqwzuEFex6YHfxdDDhhxjRKzy0SohO7EpOsYJKSkkQsIJ7cOluDLFuxWog2LJY89df7XMQayfKEj33zzTeFUHTvvfe2ixEkB7tz42NZtGB3Z+7u99j6hF328TXYOoSFi5NPPrndediC6plnnhEu5CT4/Z+uuYASUzMpM6eQvvtxBd113TFryzWb9wiRja/92muvqbovjk0lCe0cy4etuNzp0b0bnTV7GNVWeRZXzzrrLFpw/ikun7GI9thjjwkhiq1KOe5SF6fvJdFm7knTHSKUu0DCLzWwyCHFGxs4UH4CJ5UrFkbKCrIoO6FNtNyZUumIDcVWZO7lqk0ArRblasJY17SxpdnKlStVpZdoieP5ugtRBQUF4n+2JrvzzjtFOvi5jh05mGqrK+lIUjqVV1YL4TAtLU2UGWc4xhjjaxOEZIHIbmb9OT6GrdiIqLqmTVzzhFQe+Ros9CmtUwDYFlmX/9YcF0jiOLCbGtqqQcDx9r/BApivbLdmPQbAcCDAWYZgd9tqF9A/AGMKmp/fAQCsLUSVlJSIHfzOIhQvgp9yyil09tln06RJk8TiWnR0tFggZ9dbvKOcXW8tXbpU7DCXSEhIEJYB77//vtG3AQymtaKKmlasC64OoKVV3FPEKZ7dsgVK5qHNFNOYTcOnzG63+4td07G4UltXT0lp2RQ3aqj4/GBCm7XG9KkTqf/QOiEYsHWLJBjwe4bFIOeF9G0794j/p4wfSUMGeRYqrr32WnrrrbeEJQyfh2MrKYEFFynN3D54gtsM/v63336TFaJYkHAWoSRYPOOYRB9/s5J27TtErdee51hk2ns4WfzP5+MYU2ruS0o3u/LzJEIpgS24iNqLVGzJw9Y03D6yW9MRToY5AwYMEFY3uw8m0inHT9LEHRnH7WMxivEW40sqVyyQ7dy2ifr/nq4tWzY7hCpOt3u5OvB7uRs+ZADFuFkJnnTSSV4tjyTKCzKopblRvI/pM5TCIyI9xs+qqqpy/M8i1C233CL6kZyEbVSWn071DY308eJNtGrNOmFdyP3SnDnHxGK2SGM8lSVnJKs79w0X7sfLTaw4dhlT39Age43Y2FiXvhVCFNANvfteTPyAzQt08CySObtjpRAlWJ4lAOYSKi4odQNZh/wC9iJoxoIABJkQxVYFvKgpwW6e2FWWu5UDw26GeLGbX7zAyW6Sli9fTv/85z/FohuzceNGWr9+vYiHAwA4Rmtrm3CQm7ynnRDF1iAs6rB1IotPLETxIgq7R+vQIYymToqjYY2dRH1lwWDRokVe40NlZLXFsRk93NViyJlevXoJwYgtdtzdlfmK6SMt3H///fe/31tru//ZfR4j58bNPWaRMyOHtuVPZVW1cE3IlkpMTn6xT3d0cvfF1lnMjBny8YZ8waISFZfIWtywEFVfX0/kpNNcfvnltHnzZvptx0FKTM2iE6aNF8+3/6RCl5hhapCseBhvopBzudq97yCdf+pE8Wz27j8oNhywFR0f716uJAF0ctzodudcuFCZO6vErUtFLAJm7Kz5FNXVs6Wss4DE/c7999/v4tKOrcgeeeBu2r3vgHCr+NVXX7kIUdLxLDx6g10zivNFRXm8vq/jG5uaxf+RHsQ0T8/CuV81HIy5QRAQPEICsK0FAsogAIFVIdsOSFzTDSscYHgJRP8D7IBlmngLjBkBsDGGClG8S/7bb791/M1WBmxJIO0cV8J5551H48ePp2uuuUa4CGM++eQTCFFBTlhMNEWcO8cWrvkUw675zp0j7s0M2DpFEqIWnD+bUlIzqLK6lsaMGCQEhWHDJlGXLl2ENSIvqrN1iRQnyF2I4rrNRHfp7PWabE3Dgo1klaJGBNmwYYN4+ULu3N4seaK7HGuDamrrHUIUW4wxvtx/erovFoncrVaM8O/P7gIPbl1FXy1ZQwVFZbRk9RbxevGdr4TwwrHErrrqKiEMKYXLg/uz9lWu9uw/JISotKw8Kq+oFPGM2NKV/3cuV2xpdTipzYXhlPGjSG+cxZtzzjnHY1yl6K5d6dRTTxXuFdn1oct30dEeXe65I33vnHeejpd7xpJLvs6dXYUsl99UV7c7LwABYdZCRCjP57D4454hBmR6KBc4AKxOkMzzgH1BvyyTLwY/BwAs1A7Yd6MDACEuRPEufWmXOC/OcVwXNSKUxPDhw+nxxx+ne+65R/zNbp54cZQXN0HwwrGUIk47nloKPVuIWIWijHiqKMx0+WzkjLPa/a5Dn54U5uaGzEhYTHr77bcp8WgWNTQ20p79bRY8k8a1uaCLjIyk4447Tri7Y9GAF7rZ6oIX7p3jQ0lWH2xNVOfFjZizm7LOnb0LVu7nZti93ahRvoWKMWPGePzcm4BS19BmTcV0ijoWVykqMpKqqc5nPDtP98XtEYsNvoQbPTj/jBNp3qnT6UB8Kh1JThfPODk9V7gzZYtStiTlTQBKhS62SuXf8m45X5Y3Urk6FJ8kypVk7cQCladylZeVJ+KLdQgLo4nj+Pm6Wgqxq0U5Kzdn8lL2OeKxHMhupMiozkJsc48R5Rwfyz3ml7v7Q8b9fqXjOdYU92dyLvqys7Md/ZW34+XcmBQUl4q/B/TtJTvxc7ZUY8s8awc+x+AdaDHxg4AA9CekFxtaQ2/xFpYAAIBQx32Mb0+3ikrSa7d7AoaCaQYAwSdEOcd34rgb/rqJcj6eA7SzS67k5GSv7rNAcMDCTbiJ4o0Smhvzqa6lzVpPInzEELIaLAZERIRTQ2MTJaRk0p4Dh8Xnk38XoiRRgQUDdqMmWZKwRaK7hVD/fn2oqLiEMnMKZK/HsXEyMzN9CgDuDB06VCzasxD18MMPk7+kpKSIeEOeBqNZuYXi/44dI6m7k4Va717dqaS8kpKSklTfF6c7Ly/P4VrQaDpGRtKMyWPFixk56yL64IMP6I033qC1a9fSpk2bhNWPEtgarm/fviIeFT8LX+WKxaaGhkZRrg4mpLkIUe7lqiq/7fvhQ/pTdNfODvd6El9++SWtXLlS5d0vacuDjh3bCVHOccYkq1pPcN/iyaKJyz/Dllz79+8XbmPdYXeJhw+31ae4uDivx/f30JyxgJeWmSfeDx/c32ca+T579+5NuqBoYRGTOgBAiMBtor+ivE2FGn/wy7WYRfIHwhQwHSyGghBs95zFMCukB5jYL5t2XXMuC0CoYagQxfE2JCZMmBDQudgqg91MSQtxzucGIFRhwYDrBi9y88sbbI04btQwOpRwlPYfOUoHDiVQZEQ4jRs1lKpK8sQAUBIPODaU5PrL3S0fM33KJDp4OIF2HUikysoqGuDhekuXLnXEcZo1a5biezr99NNp586dIj4cW0H6Y0UpXf/aa6/1+N1v29vcr02MG0MRTi7rJo4dLqyJWLhh6xNP7v3k7uu0004T1prr1q2jnJwcGjjQNU6X0ZNNFlTuvvtu+uabb4SYxOK9UiGKYcFl2bJl7VzVucPPZ9q0acKNI5erI0npQhRjgUrCuVy11le4WOK5w+Kht7hUEuUFGdTS3PYcYvoMofCIjqI+uDNkyBAaO3asiOHF1la33HJLu980NTU73FBKwpFzPrAQy9ZuP/30k0chisuqiN1FRPPmzfN6/K1Xndfu+C27DguBmDl+avv4iRIsZDGc33KWWSENFnLsMzOzxMTPEokAViDEioKzaBTIDnj77Z4H3jBtIRjFCAQJaBOt6KEBAJRBAEJKiJIW5tS65pLDeae687kBCOXJHrvoYoE2O7/YZ8ydKeNHCyFq1W+7qLqmliaMGUZRHSOprrqMqkpyaeLEiUIE4AVvtnJxt2yRuOD8s+iLr3+g2roGeuGVd+it9050/J45evQo/ec//3EIWUpc7EksXLiQPvzwQ8rNzaUHHniAnnvuOdl4OGy5xJaSnuIysRu4jz76iG688UaXz1dt3En744+K9xee6yoanHnKcfTTqi0iFs9f//pX+u9//6v4vq644gp69913RayoP//5z/TOO+94tFpJScuigb3UuRVtaW6ilqZj7gSdYdEusqWFQ5C1Iy0tjUpK2lxbqrVIZZGNhah9+/b5/C3nBQs5XK5q6uqFUOksIDqXq/DfEzpprKsLO+fnr4TErUsd1lRjZp1PnbrKxwS7+eabhXUdW2X9/PPPIq6WM599+TWlpra5FLz00ktdvuPnz2ni5/ndd9+JY53rBFuNOZcJd4so9+OPnzichvc91h8Wl1XS5z+sbsuTccO9WkRJz8KTOOwv2IFoEbATNCQXH7BoFdxtEJ4vAMZhp/6ifbtnn7RbHhuVA8vlm5X6YwVpscr4ob66gkrzU6lH/5EU1cX3ZkqgBhOfsUXKFwDBgKFCVI8ePRzv2UIgUKQYHFIMEwCCgUAHURy/iRfXv1qylkpKK2jAgP60L6OGOneKovPnz6cOHcJdhKgvF/8iYvS4W6UUZyVSt14DhQUHWwSx1Q9bmMyYMaPdNQcN6E8LLz6DPvthNW3csoMuuugimj9/vqjzbHmzePFiESuJxYd//OMfqu6HrUdeeuklWrRoEa1atUoIHGeddRaNHj1aLOqzezVe/OfPMzIy6Ntvv/UoRLE1E4tYLD6wtRKzccM6+m3TFvF+5rQJNOfUE6gkJ9lxTL8+Penm666gdz/+UlxbzX1xGvh6bMV18OBBOuecc+i8886j6LBKam5uoYKiUtp3JIUorAO99o+7VOVJ/tH9jnhI7rDQFhUZQZPjRlDf3rHUo3s3amlppW/WHhGWOk1NTcI6a86cOaquecYZZ9BTTz0l3BByPrPrQTlYmHn99dcd5eq4aZOpqaGOCtIOUnSP/hTTZ/CxcsWuKzt0oPFjjsVu0puLL75YiGobNmyg+++/X7j+GzW4F1WVFdHew8m073CKwxrvwgsvbHf8n/70J1EeWIhki6pLLrlECE7sivH7778XFroslnIsQ084H//oP/9Dp58whYYP7kcNGw7TT0uWUXllNXXpFEU3X+kqkDnDsau4XEmuavVarFESn0dRm4Wxuy0wbaE8TP+Jn1UWKNyBOGFjV3IAmExDbRV1CI+kiI5t8VSDoV45t4k8dgQ2xgpihofrNzc1UofwCFuJldbAmuMoq5G8cwW1tDRTaU4KjT/V1UW8v+NXlFUAQDBhqBDl7Jpq9erVYoHWX9g6QorBwQwY4MkZGAChx1133SVcjpWWltIPK3/7/dNvqU+vWBrTm61F5lOH393PjRs9jDpGRjjcgDkLUdJEkEUFFgzE95MmyVojXXLOqRQZEUH/t3SdWGB/7bXXXL5nd2gvvvgijRjh2QWbN0455RT6/PPP6cknnxT1nt3LeYKv4UmEYp555hl68803hRDFL2dmThlLf73nFo+DvIULLqTeA0fQyy+/rPq+zjzzTHrvvfdEulm8cU83X+6U46eRWoqyEmS/Y4Fn29attHnXIY/f8zNkix332Ee+4BhR7GaORRsW4NjNnxxTp06lqKiOVF/fIP4+bvpkyorfRpXFOVScnUST5y50KVejhg0UQqkLgcwNfUx6+Tm/+uqrQjz84YcfaMWKFS7fs3tLtoR64oknxHt3uA6wdR2LWGyB5v5cBw8eTP/+979pzJgxHq/vfvyvm3a7fM8C4j03/YGGDOwr3VC7c7CQxuIwP28ug5bBxEk9LyzUlBdR19g+YoEB82V7EuzCTEtzs6MPBlYluMugVwJZNDZ7wTnEFujrqsooacdy6tAhgsaf6mq9bRa8YFpdmk8RHTtRp2jP43E1lOWnUd9hEymqq2tsWgD8pao0n9L3r6duvQfR0Imn+Px9+oENNOaE+RACLExzYwOFRyp0UW5AN8UiFNPU+LvHJrepUWN9LZUXpAtX8h07+Z6PZx7aREMnKXenryXBPib3H4jYANhGiDr55JMd7zk+B1suLFiwwK9zPfvss44YOLwzn+N+AABIuIdbsvg7+vKDVygjp4AamlqpQ0RHigpvpoa6aqoszqbufdusWVg4WjB/NuUXlghXZmNGDGqXhXPnzhUu76R4Pd6YP+9EuuSyP9DhtCJxTG1trbBW5MVyFh48LeorZcqUKUI0YHduHHuJraB4wsvu7vr16ycswVgAkIN/w4IBi1Dspo8tSrp0iqThvcJp/Ohh1MldCHHi+uuvF9ZQLJyovS9u99gSaevWreK6SQe2UZfOUdS7Z3eaNn4UDR8xmmqr2tzlMRzrR2oX+RolRZ7Pe9KMiVRRWd0mgFW2PR+GBY6tyz6m/UeSKa+olErKKsSge8KM00R6p0+f7vdk6pprrhFC1I8//ijcDcqdh+/hj1ddRkeTE8RvJsaNpaL0Nusd93JVmptCk+NGtjtHTuJOGj51rm4TP3YPyxZrbJ3EcbwSD+6i+ppK6te7B82/5AqaPNP75LR///702Wef0a5du8SzZSsotoybPHkyzZ4922fMJun45d99IoRjtoLqP3gk9esWRtMnsrWf9+6Zn4H0TEAbmYc3C7Gze58hmkzYSnOPUo8B7csm0IFWC0z8DJhrZyfsoLK8VBo66TTq1gsbqOwIL8qEOVvZ8U5lpWMbC6zn5CbvofqaChoy/iTlC3cGgQUvdeQdbXPP29LSJBbXNXkGAYpj7NY7bf968V7OEqC6vFDE8fTmQtmZ3OTdNHzqnJASQ+1i/dDUUE9FWfHC20F0j35kSdzyMm3vWtHWcGxZUiBEsdvv6rIC/+4vSMqjcTg/q1ZFHhoK0w+LtrD/yKkU21/9Zlcz4DJYV1NORRnxFHfKJT5/X16YSaGGMeOBME3SwMIih7WIjrVoGwhAqAtRvGDK7ovi4+PF33//+9+pS5cu7eJzeIN3gD/99NNiMVTi3HPP1SW9ANiVbt2iae7J08X7qC4x1Dm6B5UVpIu/W1vbBFyJy85tc1PHC6688OrOsGHDRJ1TSveYbnTZZaeTXrAgxS9/CA8PFyIBvxieuKfuXaPoWHbHd9ll/pnXs0vDU089VbwOrG0fJ8oZjqXknN8liZ5/d80lZ4r/J8+cSQfWHhOimOiunYVQ5QxbIQUKxyJiYW3z5s3CiohdDcpxzRWXUFFmW1vvHFfLvVwdWPulx+P52UjWLXoycuRI8co8HEdl+W11ZMjg9oKs3EIBi3v88gc+fvKEcTSkV5sIOnj8iZQdv71dHXWH3VByfCjuTw3t/yy+LsIilMuEzWU+q35Cw1Z8ZglRVluQtaJbO24jqkrzqPdg1zhs8uh/D9yHcjnsP3q6x12ukuvXtP3rFLXJRZkJ1HvIOF3SGsr4WuRVUt75N0d3r6bG+hoaNSMw96hGwRY0Ur+cn7qfBo71r+9ShE0W0oG2baZUvpi6ylKKjOrSToTiesPEnXwJRUZ1tl1/GIrItYnZCduooihbiAFazDOMwJ/yJBeXN9j7SQsO/WQFef7fFkJUa6sQoZjGhjYX9kBN9lmvUCZv59AH9dRv5BQKD2+/5gEAaI//5gl+ct9997mISn/5y1/ozjvvFO6JvDUsdXV1tHTpUhHb48svjy1cshsujtEBQPCgvoNtbWmhgvRDjoV0k5Jhq118wD8eeeQRIeixi0LJKlUv5OJgBTNK6s8rr7wi/n/00UcDsjJ0uqp8xW+VD8QrdtZXV2hwfRBaaNNH8CYCXvzKTthOVoHFSxZCMw9tVn6Ql7EvWwIAay5ksPuxmooiIUTl/74Q5nYVhZ8Zh8NNEM+rqtsWwrzR0tzkcDEEgNr23dOCf0nWsY1T1WUyVlwWXGgEnmERympwXDEen2plJchADLUJSjaRQNgGGsMilBTDGwBgQYsohi0RrrjiCvr6669d4kXxiy0OJk6cKGJJcQyNpqYmqqqqEnFZ2IqKxShneAHuqaeeou7dlZn2A2AIJggxvMta6vy6xPRqHxxY7yRh0ug/NtPtxo0bJwQQbpNTU1OFK0jdwFpEWzY41S92ScnWZGyZxm4hzQ7EW5KdRGNPvNDn7zHxswk2bMsrirIs5xKIBQoQ3DgLNM4CTxv2q0dyFKQepP6jplJItXs2bAc9YsbGMC/7Wtp9D+yDjepE1pGtVFmSK6zzzLDSsqLFRlDcm2fPfMDIfA9xXMo/yiAA9hKiJJd8HGNlyZIlLp+XlpaK+C1KYFdPHGj+7LPt4Q4DAKN2pLEv645RXdUPKIJ44Bxq6O1O4brrriOrETwTP+8jW451psZVpt6LsOJ/G+Q9C2E1FcVUlHGEeg0eS11j+5qdJGCxiR/E0tAhIMvtVuv2U3z+3KRdwiKAXb12CA98mleYcVixEBXw/Vm/KwE+cN0Ip5HAh3IBVMAiFNACrLTrAcaaAABgkhDFbp3+/e9/0ymnnELPP/+8EKDUwFZTvBA3fvx43dIIALD/zK9jx460YMEC8b5nz56+D/CxOFWalyrcivQfM526dtc3dlFQiStBchtWCQzdWF9NnbspKM8aP7x2lpY2I2XXL+J/dp1m+VgGqDMgVDGr37N38ybguIrF2W2uz6K6dqd+IyaH7rgDBI7ti4KNK7XF3JyH/OK57euCTfBlUen4mef6EfLlNJiwwljEAkkAIFgxRYiSuPTSS2n+/Pm0bNkyWrlyJe3du1dWlBo6dCjNmDGDLrvsMpo1a5bhaQXAtgM3kztyXtQwK25Up06dNLUeYXcPDAdatvxCtsE01tVQQfpB6tZrEMX0HqTRWTECdMmNlhZK2PqTiNsxbPLpGuYzABZCr2qvc3OCBfwgwq9xk/n9FVtCSdQriAEFggfLxGd1SgfaRJstugJgaVBHgHWw++ZMAEJaiJIsFliQ4pcU/4LFKI4NxTGgOP5T7969EQcKhE7HZJFxlhYTyKKMeCrMOEKD4mbZYtHccoMKG01MMw9vpuryQirJSWkv0oUZK07aevHDS7Y0NtQKEYrJSdxpmTplix2I/noEammhsA4dtE6NbeH8yE7YLt4PijvBz3rcGjxtNACeRACzmsQws/tBG/QFwdKnBQMy2dy+7LbafmOcFn2d82ccG7g4K5EGjp1pjKvhIK0SystEkGaAN8yoKkZks9lzRNsVJdslWEeQFwBoheVWVzj+RVxcHM2cOZOOO+44GjVqFEQoAALxyW7ipCs3ZQ81NdZR+oENZEnMHowGESxCaU3GoU1UVZKn+Xlti1N51W1B3luVCCiuij3r2pHfvqeq0nyzk2EZyvLThItSfpXnpxt0VYPKTsDxbexZxu24cFhbUeJi/WMNzF/g1qZfCL1yDOFJO1wW9U1oE+WeZWH6YdGfVxRmkV1xvrfshB1UV11OR/f8amqarE47UdPgImnrjXFWRm4+gvwGqkD9BMAsLCdEAQA0doWhcFDmflx1WYFYbAwpLLZT0v/FEWvdRyAThdR9a3VNCvBOqE+im5sbKXXvmoDOUV9dIV7qsGa+N9RWHXtfV01WxYxya9XF7OamRso7uk/ERrMDvvKRF5KTd62khC1LhIWe8WkI4DnrXS5dxjAmlMeAL2nNOuQOb5BJ3LrUEY/LNEKkf9aiPec2sLmpgdIPbtQkTaGHTcuar7KjSx3yfE7hNSI7mVpamskO+NrYwGNA3qhl1Hgr1Ocjgd5/S3MzpexaRSk7fxHvg4GyvDTxMgWLrRkBYDcgRAEQEoQFFBPJiGuBIKbdhkTjJhM82Ob4VXZB8Y72MAtN5GwxOWw1TbhJ3L5MvJxFHNvx+zPWpO7aobgE0X0VpB4Q1gAZB38TopTdYbekTEtLEzU11mt45rCgEiU9tfWKXFC1at+X1VaWUtr+9ba2RnHfIFNfW+koi5Z1ORZgmfavHDttjPO7MLXfGFdZkkshBRY5LdUWcx8ayBiO5yIcXzg7cYdwW293WExL2PKT2KhVXuCfdXzoCkv++goPLL9Kc1OopqKIaiqLxXu7wyJo5pEt4lVdpr1XljZc8zx0yywAQSJEcfynkpIS8WpoaFB9fHV1dUDHA2DlyYMVFjCMxi4+2w3HpkXB/IHa74vmra2UsusXMVnSb5AKgDzOVqVBYWHqXLV1a7b1aT/071v1OX+gAmZ5QYbjvRRjzt606j92UNKH+Yhj46m82XV8F6iAeXT3KqoszrGPNYo9H1NQk7ZvHQUzVm8b/B3Xmz6/85FupfleW1Uq5hP+wtZ4EiVmW1JqgLNr3LyUfSFZJ4zmyKYfhCjvL84bd9Rs4jF/Tu/G7+mpdnKb7ne+WOzWAAglDBeiKisr6ayzzqKTTjqJzj77bPG3WrZs2SKO59e9996rSzoBsBN6+mS33AAEBIErDD3KWqvHiVJddZmYzGQe2kS2wMuk3TV/IN6CEMTsRS0v+Nt++TouYesSqq0s8TNVIJTQayyYtG2ZIlc+cguHlndFhWGuTmVQu/MC32C+pm+e+GeB+/v17TiX9jTeku7DiJi1oJ2YiZhwTkURHQwAtsZwIeq9994TlkzMLbfcQr169VJ9jjPPPJNmzpwp3v/666+0a9cuzdMJgL3QMUC1gsGzXQcD1k+31dPnmdZWfeJ2qMaOEz+V98HWEgVphzRz+2b9OhEamLWgpOa6/i4+qC1jfuVFsNT938lL2UshQ6ua78McZcQWi7C6pzFwt2iejmtsqKXK4uyAUqbo2hrljy3KQkjg6TkY5x7Sny7KdAueANxUxf/2A+Um7zE7Kfaaj5jRVNi0jBlD8LfdVuif7NrOyRJ2zHqbXQFbIY8BABYSotiN3jfffCPeR0ZG0hVXXOH3ua655hrH+y+//FKT9AEQDPhafDBkERCENL7LDMqUf4v77V1ScdDZ/NT9wk+7wocTks/DtHbMeddoUEz8Wj1aO/gSQiuKskWMmLqqMgpadCxjgZTfoO7Dw4haW1oodc+vlLh1KTU1aBkzKoD8NCvPnZuYVhM2mFigrBVlxlPC5h+FK0CjYJeXWfHbqCD9EFkGLZ6FP+dw6ue0bnuMaMus3F56Gx/yGLCpqV6Uf6BFOB6Ny67B42welwVqiepPzFpV19ShrhlVf1nwsLylrzsGtm0NddVifqqrMO7hdo7uWkXJO1dQWTC4QgcgyDFUiGLLJcka6vjjj6eePXv6fa65c+cKMYtZs2YNNStwGQGAnSnLTxMm2Z5c9Oi5wGkZ6xYDUJuP1eWFFL/5R8o/ul/2N82NDZSdsJ1Kc48G1YTY0mVG8hzhPEoNChGgPbzwIA369SY4hBSg2SLK7+WBY3ew+7jywkzZ49MPbBALwym7VmnyAKxoJaVne81ii13gPs/IslhZkiv64oY6tg49YJ32LeDyEBbgwqEvF7nBuSmJF77YgouFb/9Rd68sQPEYj8eC9dUVsr8ryUkRL30I/v7Z9LGlgXhqj0y1WLdJ/VdfZoy/L71d2bFIkrhliXgFGt9PGW33U5SZQEc2fkcl2clBPR/h+Vb85sWUvH25rcQo1/ZD3/zLjt9ONZXFQhg3YoOQRF1NufifN4ZosnlbiVcgm7aNAISUELV37zHXItOnTw/oXJ07d6a4uDjxvrq6mpKS7B/4EQCBTIeWeXiLCMbocyHP3/5Q7rgQ34HojdTdv1JjfY3XXbC5ybvFwgMPipROCHiQqzQmiFF5l596gLKObFUW7N5XcGCbPm/TMSPbNBqEa/HM2T+/ovJnAaT7NXLiZwSenqMUJDjj4G8+j29pabLsglbgZVTH+9Iofp7eizeleal05LfvKSdpl2ELei3NjR4DwmtRFoxZxNMQHa1RrFpvrUBNeZHjPYtgnqgqyRObkvjFbtTsCi+85iTupITNP4l7CkgMVbs4qMgakUyHLYQ5b1TVQQuk2y7wppbkHSvEnMTnxgez261W49NRkp0k2iF+FWcn6nINT2Wb57vcPmQn7tDqIt7/9vu0rQG7Sua5SH1tpcc20GqYMeeury4PeAOBXLqP9Q/mxilTNJ7GBk4ArCFE5ecfG3j37ds34PM5n8P53AAEM0bv7rKLaMCun1gsCWzhSN1ARsnuxMriXMd7JYvonP6ELT9R8s6VjgVe1Wg8HmN3WgVpB8UiY2H6YfuUGTsG0/UUG1jDOq3kXByUnif4eSn7yCrwwg67W0ra9rOtdiAGM3rVKS1cn+m1c7yxvpbSD2wUO3/bJ0nrXSBanNtYuN3gvC/OCmTxS50lj2veaFcmpd3dytythQW9O7bgtkZp1cxiMSzM89TaeTxXU15IdoWtW4uzk6ihvppS961t/wM1RnlB6CqcxydsIcx5w54s/Ef7NoU38/CmuJKcZFVlm+cuVokfyhv12NqxtqpUzEnyju7V3eJBNSZnlfMYOSBragvMR/w5q960Os9BbNAmOQiufXGuhIUZ0z94uoa369qpfAAQ7DGiJMLDwwM+n/M52CoKgGDFm+s3vQeEqhcfAh3c+NFps3jDk2MWS/il/FqkO2p3oDtbQhWkqrgXHWGrLwlelPMVDya4F6zMwxBXg628iJzQJjpmHBYur5wuKpsyvck7uq8tFlF9tXD5YEt0nvjxJIyF7/qaykDOovqaesDPuiw/3e97adTBVSXf69Hdq6iiKKtt56+B1nkBtakuj8j+qw/txioK47Cxyza2KG+sO9afeYOfMV/Ln/GX/HhKZ/eQOrbFqtNu/6LmF7Jl0I8+WwReryjxq9yIsqDDOMG3BYCTVZ7mG+OsP7Z0ttDJt9BmHiYveY9wIZmdsMPzpj234sIiBm+Ki9+0mBprffepRiwEO1taMFUlPjYiW3EhOCjaRn0sre0gNjtTmHHE7CRYEjPi+SntHyoKs9rE7MpSa7YPAIQIhgpR3bt3d7wvKjrmxsBfCgsLPZ4bgGDD545cH+5YAhok2qCTdl4QLM9PD6LJQeCWAXrAk1grxzPxHM9G/+vy4NZbnByz4IWjxK1LxS5mT7i3GXXVZQEt6HuLkeEvzmWKdxnXVBSTLTCwenJw3rT96yhx21LtrcY83UeYfguDhWmHKPPwZnEvnvoybwuclUU5lLhtmS756xyLrV0e69kW26AfNgMuB85lUM4ahWMUcKyCmooi4RpNyZm1SJsefUzS9p/l+xnnS5rgms9ui4ha3YdLO6hgvK30/Kl711DyrpXC1ZbH85hteqEXcvmj8rn4stplMYaFZnb1phk61oFArZDVemjgcRaPB5ubG/1yt8obmcy2YLeDeGldtJ84cZ3jTUZsXa4EbivLCzKFO1PruLo+dm0eU3CcSntgQy8hChH3o7DtTT+4UbT5vLFMCbwhjvvi9p4QfM9H4JoPAIsIUX369HG837lzZ0Dnqquro8OHj7mI6tWrV0DnA8DOuAwoNJ4EKRrEmz0XDpLFD7ss6viyiPL5PILQFQbHiODBLcfJcY4XoZ4wzV0NsosY9mXOcR30ziduLxK3ay8CuKPabaXRdclxPeMmfs6WG816BQfWwGe/ksVTl80XKq/BVnP+Xdf79+2CH7caaY3i/2Ja0C5WS7isTYX5dKVT57aj3lgCexbcx3D65eOxteo2fsCCrtfMUb3xS8nvJAt5o2KuaYHLfWleBrU9X17KHtHX8O54szdQtWsbdHCLpnZTonOdd47Fp5Sju1eT/pjges8P9HIhK3OxY1fSzXuCf4flJO4Qm4yECKDgHCwYZBz6TQgBijbGKUqXtmWiji1rbDEfMel4E+qYkvmWUpGcx1sshEpW8gAAGwpR06ZNc7zftm0bZWdn+32upUuXUn1920JLly5daNSoUZqkEYBA0TsYuOeLko6uMKzf6RrissyU8qNgB7IpgyJfEz+zJ/TGT/ycd8TZZ3ecHCrzyoSJn1XKWCBl09m6xsMPAruATu2g+45UtRM/DRNiPfx9ZooMX614w/rg817baaHOFlEKyqCiGGQUEA01lZSX7D12SaDwLnH15cT/OhpKZVAtSqzyjKa+qpyKPcSyMxJzxFDl1yzNTdU8rbaej3jLAiXZY0Yb4XN/ib8b43QoDwZhuOWQinLO1lCM1/GvE+UFx7ycqN3kZ8p6jF3QPW+0EEPlLGMVeGhQeE1P9dP9M2cvIWqSCQDwjqGj5SlTplC3bt3E+8bGRnrqqaeoxY8dSOyS75VXXnH8feKJJ1JkZKSmaQXAtvi9GKaNK4yAMXTg6Hpvui+kqs1Lqw5ufM7rfAhVsicw5oZ5Z1PyjpWOCZGV8Dhglxl0G4GL0GDipK7dtQMsg0bhkg6ne8g6soUStvwk4jXYGX8mfh7Po7juW2dhwVAxNJDy7HLssffsH59dvFmlrijHKb2t8nVM/1S0erWeY5c9Wp7THd4lrgey5UFROVHr0q7VEuUv0DSYVQa9wS79TN+9zc/XfZwdiMWUBcpKKOH8rEwvSw5c65evdPk/H/Efz+e0xlhaq/mI2eXBSnnos4xZpN2ySjqM8NBgZvkIunwGIBiEqIiICFq4cKHj77Vr19Jf//pXh2WTEtiK6uabb6aCgmNuef74xz9qnlYA7EWY8sWxQHzfWwju6IuzEtvFvbG/32P7T/xMXyzw5BbNaVDKLh5qq0qEewjtr2mtCZIRVnnmDOzVWeVZpy61IYmg7dy9mT35aVVXTpzdGSku963aLurogT/9iJ6TT637YY7TkLxzhXA5wrGvbI1L26twWmNWG613M+RcBi3uFo3LYNK2ZZS8Y8UxFzlmjx38JYj6/4ALqUvMWhXtlqLYWjrOR7R6bBpZCVtuLqMkRpwZ4ywzXIFbHQ0MotSUP//LqpJn4z0GdvsztqpzJa8J6sqgFeYjzs+M42dyHGHVLs+94TIUaTXcQ0MgYzyv5dlj3TL/eQJgRwz3H8AiUmxsrOPv77//ni655BJavHixV0GKraDeeOMNuuiiiygh4ZirgVNPPZVOOukk3dMNgL4E1om57jCkkNjZUVWSK/zmc9yb6tJ8siyB7Cg0wIWQX3hIV5gqMVSPRNkfX5M5bVwNqt+x7vuMxpdTJe67zFzI8ZQn+qdHz8XQVp87EHWb+BlFwGVUTyGqVdOy6BxrgRchAsXI+CruedGqpNy3s6gMYJe8RcdE7ulWNd5wyZ5WQ0SAoowjInYhu70pt6B1cjCMk824L7/bciVjDT3zOTgfoTyK8tIC/XKA+N44p/Jz/xMSXJjS5qm7Jm9wSNi6hMzGbOHJ1/Vzk/eIvvjonl/9Ot4qOMcCVbwpyR63BkBQEmH0BVmEevnll+mWW26hpqYm8dnRo0fp4Ycfpscff5wmTJhAgwcPppiYGPF9eXk5JSUlUWpqarsB6KBBg+iFF14w+hYA0HXgHriLkBZtBw0q0+NrEtpYV0NZCduoW48B1HtonN/Xqy4rdLz3xwWOR6ywk1WDJJiyqMt59/uzM8OKzuyBshY+2dXcg/13XRsXZNuOmF2eLRUbpVWjflLzhSV3Uc7XzwNIgK6LtIG1JfmpB0QsmsETzNkU5lrXA7gXA9pU3et1q47lUeONMc1NDY73Lc1t80GzKM5K0NASzfNPAnJJpwQLDgk074eNWPw2SVRsVx/1aI98nTPMfmKrb7doLZpfT9V41Ef6KoqyKTdpF/UZNpF6DhwVULp4LtyxU7TrpiRDYkTpeGoXC0uNyqDGfaPauJam4bJxTPvTs/DHcbw6d+vp11hHeb63evbQYIhlcqtt2kYArI7hQhTDFkwvvviicMtXU1Pj+LyhoYH27t0rXr4YMWIEvfXWW9SrVy+dUwuAvTAnOLByMo9sEebfVSV51HPwGOrQIVzbiZ8FJ+P6PstWG/rDblX3sdqJn4/zWXUHl4JfkyHovWBmBla4Dwu2TfU1lQEd79/ET+2z0Hfy7C/uE21/RQYlxwXUD+vsIqUg7aD4P/3ABr+O54U4/xFBotSXQV+/s4LQqbcSpfbMqvPEQpXVB4UZRwI63u4bHzTF3bOvH3VJrqypzmcDN+zUVbUFtO8Q7rS0ouLySsRYLUUFRf2VAktJt5NaDz/nI3L5w2UwLCxcfTJcxjHHMlbqN7MTtgckRJUXpFPm4S1iQ1CvwWMpUMyO5Wvrsb6vdsuCadSCvJS9ImRC19i+fh3fzjrfZ9WV99AQyMY4b20jRCcAbOyaT+L888+nb775ho477jhVx0VGRtKVV15J3333nRCjAACkyQBHfuKn7YCJd8scO7lGJ/XzPEYMKFwmjgqu5zrRVJ++vJR91NzcSPrieTeSfjsQNTif3gsSfhhEse/y8sJMp8PCZNKpXTnVpcxrcE6td+drbhnqL0ZOOP0QHNL3r9fECtLsiZ8pk0NvbgqV/F7VpfS8P3Prib8CloTauACmLiTofG3nezNjU1Kgba6/x9dWllBZfpp2LiK13hFvM2EuMPQcC1qzHayvrqCkHcvFyzUmTZhiEUpJzFJTxzQWGE55JtCNcdZ1Q6uG3MTdjjrnbG2qlMb6WtGG+jdvCjN+3G8lrxCq5/b29dzhDRahGBFryo94lSxkBYJLf6NV+QhS0RCAkLWIkhg9ejR9+eWXtHPnThEjaseOHZSWluZRfJo4caKwpLrqqquof//+pqQXANvEIdKw41Q2kbRSR23+YE8z/MjWwozDZE66lAtu6icfan/edoAlhAgvcIwzVbTqVM69PA+r5qFvVxgmp9tXHdHlkurvmf3CBxSfR4+Jn274aJesUNZbA+iHZcu8Be5LS7yUQd3cQzpfz8dGDEtbRIUF0Laa3aZ6WcRP3rlSvG8eo34B1hThSo86qcvjaTXJlZZGFlEGUZqX6nhfVqA+3pnzhiSBTu2J2sVwl9ivisqCGRtCfH2v9XzE752P5ogSCstSbvLuAGLWaoiOmyn0wlfdML3dCjQb7fIcNPLQ4LXs2yQvALADpgpREjNnzhQvpqqqikpKSqisrIwiIiJETKnevXtTx44dzU4mAMoIM2Hi5+8ORCUdtQ06XUXByj1QnN22e8fSoqIPzBio+1qI8zdN3lxhEIVbbuLX1FBPeSl7qEv33i71Vum1mhuVL5p5ylM9nr1wg+giKmqzOKL9wpvvxQXr7UBUc6Cy46pK8yk3cZfneHua1kHPkzWXiZ/C/FYtInr6vdtnZggB7n2trm2xDfphM+5V5LkhcQFsgg8dKqBTm72YJkNTQ528ax+DcM4bSwjalkHzQqjgJ36OPxX0M2pR3B5ZwaLXpm6afYsAWluGej9fS3MzNdbXmNse+HHPHL/ZHaVjOl37XZVzYiX4PI/WmyWtUnUMHCs557EhdcDNQ0Mgi3GqPTRYtG0EwOpYQohyJjo6WryGDh1qdlIAsCea70DUv4MNeJDiRxprK0pEnCqlv+3YpZvxwqRFBzceF6R0dM3nKx+amxpdJn5GwQGGefcr74btNWjMsS8CGOB7mviV5aVR1pGtFNNniPMP9SsfauMCmIDlLaIMmvil7l0j/s+K30bh4ZGkP15EIo3L/bFLWFOQb1819LO4UrQpwe/z20O8KUg7RA21le1jcylwD+la9yzSNths8UdZPbSmWKU3lhCfLFiNVbXLFnGracZ8RNfzaIRlrVPkNslInhH8bZP8iFPGeXR0z2rf5zbSIEq3i8n3w1oiuzHOUvhWoizRRwQJUlnz5qEh5DclAWADLCdEAQD8wGXTmtaLD8G5sFBXU97+Qw95V5pzVPg9jurspxClEitYcfjEo8efMFkf6u0G4BrGX+DvUn53y+MVHbLVWchUEmjaXyRXLyU5ycYvyhi1+KB1uxXqkz6DnptWEz/Fz6vVOs+3fbvm74laQ3MBUSX5qfs9f+Eihvp/flfLcpvmmR9xGfw6twcqirIDjvdlBh6tjVX7A1ZiXeLzg6DD3/mDfMzaFp9jsoK0g2R7ZO5faqOMap/Uxqy1ioeG9tYRGl7PmxDV0izi1fnG+nVfjXU8348xawX6ernwF1/l3rbjCa3Q2EtKZXEO1VYUU2NDrbyHBoVumgNyzWmh+QgAdgRCFABB545FRYeo5LcWDQ7schYt0iiziNrS0uRXLBUtsMzgVcl432W+2mrcDsSWlvbPxq/LazepCWiCpPDQMF0nfp4TYaWJX/s4Md7FTr3rElvluSWAjEbPyRCLoByA2EVkFms96id+qrGqGOpexny6eyHd4EDjGQd/I6vCLiTDOnSgrt37aH5uo2NEHbuwjft0P/DV37iLUH4J0zbNHr3jXto2L7S+Tx/5nLpvLZmNpTfvBeShgax5D60BPg+DYkSZ1vaHaXtsS1MjleWnUXhklOsXGt2fardofvwmUHz1bZ7mI+ZudLGCxwDtyE7Y7vmSOsSsbWeBb5mGEAD7AyEKAI0xY7HWxR2LxoNk1eczJUaDlQcGKgefVnSLpsgFlbxrPvc6Yc5Azle5VJ+m5uZG/y4lmwJjJsQBX7ddMixSTk1aDGIRKnHrUjIad8tDPeuY513mbmKoTm2/Z5/sGp0nAHyJn0bCgcZrKop8/1DGpYme1JQXOVxIjj3hAory4eZWbdl1uReZMqjp4rj1mjsVLksVncS/c9sVhffV0tJM6Qc2UmRUZw+ncGoHgymf/LoV5/lIi6blxvAyqLGYKmJXKjmnzG/qqsspeccK6tZ7EBmCi1c0i5Rrtckw20uIGRqAjvUk88iW9vM6t01JgbpVdPqBKcZjWrvnNFqcdveeoWasZASK20E1uLmr1801n6eiYZGmEQC7ASEKAKvRGuigR8ZNgb/J8TGAKi/IpPzUA36duzD9MHXsHE0RHib2PpHzGOF0r+wujXdi8y7s8MiOrsfbYbEggEUjPfE04XAe9FnFPaSek7HKklzF6VI36FZqEuXqhkAxPo9xS6tF4x20y/t2ogAZRlleKjU11vn+ocYTo9yUPdpYxrqcw38xVCtrlPaLAFZtq62zU7K+yoOrWYU01lWLHc4xvZ1i0GkIn1uiqiTXpxClHvWLD743DOn1LPUuI4FvSpI/tVXroXFUFGVZa5OaFR+JjmkyZCyoZfqd2qOy/HTKSdxJfYdPpN5D4hQko31Cqkrb3EHXVpX6m6AgrP+e5yNScn2VmYA2Pqj4zp9+Sht02iDkPv7xsjEqkPPKpt9K8ZfVWsfrSHV5IWUn7LCUSORcL9hyn91Xjph2hlj/0es6+glt6p6tpTyJAGAxDPRhAQAwAu6INV2A8HGujEOe3QEpSUNB+iHKit9G9dUVfqTL+a3na/FgjN3FpO1fr+B8Vgkm6iToWCI9fgz4fe7K1tLyJ7CJH8f/UubT3ZW0fevckuiihjre5R/dT0d++174tNYaI3bZuQ7sdbxOoFZZ7QInu+eNfnXJ/4WJwOCyayZtiz36lw/PeWh+29jeIsrnEfqlxfs2fJ+WnZmHt8jHYLL44oBc2+v1GAuUHz1wzQtt642lXY4poL6mUnksR92LR3CWv/YeGjQuM3qKIX6emy2iCzMO+7znzMObqbmpgXKT3TaQmISysYn585F26QzTwQpUyXUDFVz0zj4F3jR4zJi07WeqLiv056RefmVQjChFZbbVcvFCjZyP1FaUKJsTyzzbwxu/pZJszxZV6mh/j/U1FVRemEkNddVClNeWMEPcNHsu58HbnwOgJxCiAAgCnAfMqgaDChaFjBhc8uBELTXlhZ535TsNrqSd2IpcFumEqwCipQAjnVH7AZDviZ13qzv3MhO46ze1ZVDaCun7lzlJuyh550q/0uXxmh7EVl58UCSGqt0paZkdqhawyrNKVnizGtR9Z5znTCjNPSoWvxQvwqq5oj8TPwXlVj5IuklijscD7GK5pYyizHjdr6GPVYhGO80D6Kp1wZ9b0TE+jzFu0fS5RmVxLiVuW0pHd//a7j6CVZQ0DZcyyO+1y1+/5yM6ll1nESqod8Mr0gCsUpdUzEeMihHl6rrD42/qqsp+H6eFqY4R2dRY73wxj30iz3fqasrp6J7VChJsTL74wpu3Dc4vl/s2C53EUF37bpkixuUvO3GHxn1/2/Etzc0uZVbPMqilVZdLO+6SJKu0dwDYE7jmAyAocJ/4efmlyo7d7wGIquPUXYPd7bH5+bFLaTAYMNFfsuygWyfXB4HiaeHGNRBriwUtTnR+vjITP10vqSCfS/NSLRGHSnu8iwDG7t7312JPf3iyx1anklDUf9RUbS+gYOLX1FAX4DU8Xdb8ctneCk/bvlcXCwWNyyNbAyhJgSpUudFqVVTXC9IOuOSBaQvEOohDmrvRkU2k+XXO0/1z3CZfsGU8U1tVIsZRYeHhZDgWGWPaIn6k3HH+nk7BgSm7V1Gn6Fjq0X+EqnM31FSaYkXuD2o3o3jbSGeFPljgY5HdfFfhyq+ftGM5dY7uSVFdY1RdIevIVtIDxX2LsI73nS/uopHHeqnweVWV5At3l+ER7m73KXACPof3zQ7Ogoz2eEm8gVXWlycDrcct7T00eD5/UWaCehGRzyXFV/WQiZZpCwGwGRCiADCA5sYGYYrcuVsPfSZ+LjqU5isd/h2m5j5UXqIkO0n+BBab+LnQGtjvk7b/bBkxqh1edq0pXfSTK7u86y3z4Cbq1mcQ9R+pfCHddjudVdS1doNuLSeoKicIZXnHYsAYRrtNrT4WSaxQFGSyNWXXKhE3whl/2nFXy9jWdiIQu4fUWohSMvHj4Oqq8THx8xd2w5l5aDN17zeM+o2YHNjJVApPzRrs3G1paaYOHUxYRJfh6K5Vvn/keyN4QPiqK3XV5VSSk6LlFckq8OJfWFi45zZA4wUTPRdbCtIOUk1Fseo6ydbuddVlxz5QksQw8x9nMC9cudyb6SKAOs8M/OrYKTrA9Gt0zxoXEd7A5xrLMrBNick7lrebj5gx5va1Mc79PpVvQmjVT1z1kgQWy9UKUVLsMDNRki8ZCjYN+LiI461UltnjhNn4nn+41Z2dfoyJ3WIUs9v3PsMmUPc++sT21PT5S1+5/CRMZw8Nns+fm7w70IsEdjwAwAFc8wGgNR46PxYQeOBRXpCp/0DcX1cYMp0rL3yxW6fC9MNqE2W/SX2r9r6a62tdd0sGkiA+V0N9tbefWAO358ETK9ev1SWafeuzOwn1ZdA4gdI/ATnAh6dZuXduP9y+UrCKXJSVoEEaAj+F6/m0PSEvknKQ3eqyApXpUHLuIsWuG9Wj3w7Etl2wzT7P39hQq66utOr3LFP3rhXtKC98B4qvxQb3v1N2/hLQ9din/pGNHG8u11NiyAy4XfaFkjYkIHzce5O7CxiTXN7qct52p9NABJAzRmltoYbaKqopL9JcSOGd8uxK2ZubMyUWHrL5qzq92lsO5qXsJbuj2qOCp3wMoB/yXwRQ/tNAF7b9illnQPuduneNpudjgT+w+Y0xaO2K06sY6s17g4GTNVM24Skow86eTJSd0/1PK054GXWbkgKFYxTzpiqek6hBz7GYkv7N5fp6zMl1jkfqDo+FknesFO7PAQDqgRAFgAFIC3FZ8eqtExQNYLzEiOKJQiA7sdn6iGNH5B3dJzpd62Mdk6i0/etUD6J5t77j92btvAlwF6j7fbL7BCPTKuWbaZMWI1zwKHSF0Y7WQPxhWwdffv7d605ZQWBlMGXXLyLI7tE9v3pKjAXbpt8T5TLv1GMHorETv3bXlD5raQ58kTFQXdiHO5ampsAsooqzk6ilpaldv6IKM/oUnfz2i1PzP4u6RDSCdn2Ajrfa0thICVuXCBdmeu3Ar60oDewEMs/adbNW+2/1Rq3A5ovqskIqzkpU3O4ZWx/cxFB/9uhoJih6SJOmv7UxqrVZ3wfUV6uP96vFddvh3M+4He++YK7eXb38uNvrnMNlDV7vnXFmjMv0mY+4PkslpzRjjOP+t49NSkamxaCTeosx6ngmzmNB3S2i9Fnidm4vOAY0b7TNT93v/itdrg1AsAHXfACEABVF2X4f67zjjd936d5b4ZHKO+KAB44WXWjyJ4iqkh3mLr93dktjJq1a/Fi/3eK+B71h1iiDKiaNdg4eHzA+d7m6/q2FBYw/mL2DU/fru4gM2k382lxPSpdodXEvmHl4C3Xv6+qOhDdKqEmrISKAwusFs1BiBM7PwVdcAu/IL146U1tZKoSYyI6d1STT53k1jwcm4me5Xq+uqtTvtNRUFjveF6Ydom49BwSS1ACflX8/NwJP9+UeRyjQIcfRPas9XJisgYsOpXW8UP/OZ2gba5P2XO3YQMnvPZZLGXhMxi7rB46ZSR00jtnmGrNWWys6FtsKUg9STJ/B1GfoeN+ncVzfHuXCH5S6CrcPgdUNX3+bho6bghRd3mVSroOHBidXoUrvT8mzcZ6PBHM9BsBoIEQBYCQ69V+usUFatN2BqEGa9MY57WYMrhSjNk8U/F5rdxuaYOjmL+sNCnXf7SihVx2TiffFu6/ZL3mnaN+x7tzRPmC0+45Dfa9nPP404h52/eu4A5HP16LANV/gt9bqYhXEaBvzx098WFbYvwxqD8cpiekzhMIjIrU7qUqLqED7DE8WadWlBQFbvGkjhnq/N3azrOCsZFcCja1n53TlJu1SemLli4B+7Oz3bn2mEFmDqFbh5jCsQwdFsfL8mg+oHqa36jQfUbGZz2Z9DYv5+akHxPuOnbpS3+GT9LuYz7xRWc9+jzHDLpXbCVGK9zzoO0doVTDuYwt/l/hCnvJJ1cY4feK3eYv35S/SJo2yvFSK6NiZuvUKbFNF+wu4X8+isZ31xFOsQL2bKZ02xrleQ5/TAhCKwDUfAFbDL5Nu112wlsZT+lS7RvC+CB0sEz+tRZaS7GQhJHDcL1PTGoBBlDSBqK+pbHsGXo/RcfeVBTBmctPqMnFlVwQZh9T5JXeGF5E8lz+1Zd27BZSREz/v7liME8ldrCF+f6/vDkS3BQ8PEz//20kNdjTrjg/hSW/rl5YWamn2ty03cKOIUz5wHKD0Axt0O7/MD0hvzBChBK023ZQjg/q67r0fUHaMFunwdT79+yM18XqaGxt0ifUlcNsY51wmOY1NDXVO36u7fmN9DSVs/pGSti0LoO3znFZbe2jw55om9qv8HJ3jb2pOmIq6p2k2yJ9MXVkPNFG+j+f4QhxnyFRX4Yp8hasbC7IbbSWUF6RTVvw2sbGELfMCol269FqjUJAUSysl+o1PWLD0VQY1mY8oyF9JZAcAWEyIamgILAioJ8rLy+ngQXPc7gBgBVw6V8su2umJ8z1bwx82T/StBAcWzk7cIYQEFqTUoMiSwsByl390HyVuW+rT3ZqhVnkuPtn9P49SqxX/XWG0mjaJ4UD38ZsWU/L25YGLoSY3c0UZ8ZSya5U+MRG0JMDd3QoOOPbe08RSizqoUT32fW8Bbohodzb9CinXn+QdK8SibGN9WwxK6+KaD9VlBRqems+t/XPQ69lJMa0a62o06Z+MjBHlet1QHGcGgD8itY55zIu1HOuLY8AaDbtX9ReOQ9Lc3CgWjiuKMpUfaNZYUGY8xxty9LumvTByg05I4iV7ffXFqqzodSiDvlxuB4Jz7OK6qsBc3LezivQlTFkBE+qdc760NDfp6inDo0WUx43QPs/q+xxu8L0BACwoRP33v/+lzEwVg0cfVFVV0c0330wpKRZw0wKAWWjsekbpdTT7rR0nYV6uWZKTTEd++97DIeYNRlkEkPAVaDxw/9Y6LtC2tlJhxhHxtk2I0ipP9d+BqDV67bJ2Oa+Gt8VBoltamsSuaHZlFQjtyqjBdSs3ZY9wzZJ+cKPnH5i9MOQhLgG7M9IW1x2I7osW3A5mJ2z399SaU5hxWNsTtlt88FcUUF9WqopzRTxBtsRpHyg5tMRQ1997WmjwsDvZxyJMRWEW6QX3W/FbfvS8kSLgZiNwqzyj2lJdFqAVJJ0tMPS9R+8LcGbDfadkPZWj1J2f07E+f6PJvSq7Tn1NBaXtX0+luUd9nM3Y5+2L+M2LA178NmIsqLZ86IH7s1O2Mc7/8/v63B3ejMQxKh1eGmQvpKacBNY2mtHe+NyYo8mmpMBPYQlX4hrD45XshB3ym5J+v77W8dIUH+7hurVVpZS2by1pRpjvjXF+9wNh1uzLAbA7hgtRhYWFdMstt1BpqVPAXD+prq4W5zpwACaQwB6IRdiaCu0nflqMzjTuW4N9F5q3wQgPCD3fv3l5UlWSS1bHr53rFhkUetoF6562vJR9vtMbpsovmleLCX8WOpqbGqg4K1GXeuxqBaXzczOoWKhqz9UsvGqUfufyxrtfC9IOaXNiD+d3vj92v8TtYGleqqejfJ1V/hpGoWity4PAYeBYwnFZLVxU6YjeYwHnANUun7e2Um7SbspL3uvpS6/CCFte6EKrtIHi2P9aLkDL1Uc9ngvv+s2O306F6f4JvJ7qdV11gDvTSVlcTZc0G9K+WGOcohb/yohhq8WUvn+DsLBiF1uaJSnQ8qDAOp7rjq80q+r3dBKcNbVedb24/4caNSdWkpbWVkreuUK0J0d3r9ZQhNfBukilNYj4fZg2YqjYfPHbD4rS5K1c6vVcA98U4SNdOvcxvCFO7cYvw2IZe8kHjhnqzTpU/aakFtlnyuNEbTzV2LMvB8CKmBIjKi0tjW6//XaqqzvmJ1otfOyiRYtozx4lgXcBMA5fA5r0AzI76AMZaLXauxMNNC6Ay0JUmDHpsFvw0WInFyxqB91KBqx6T5odh6ra6tiq/J41rBaOa7ndJ1tkeBMEeUFFjUm/tzzPPLSZknYsV3SMdrs4vcHxvFoDcG3hI10eXGHoEQut7VLKrQ6sIpRKsPWMJrE1HO4hPU/8mhrrLRegWle3hG0f+PhbQ9x0b6Vpt5r1sCfY7aoW5+d4VEVZCdRQH2D8Bw1pDDQWhTsmti+8+FqSm9JmDWCSi1J/47L5tiLUJl+5rWVXyByPSS2+LNf9Ru89IC4xorz3+0bGxTIKpW2slu6bvF2T62bWka0Usvjc9yLzA6XP8fdxZlNjnXU2Z3pyD6ljPfx/9t4DTK6rPPh/7/TZ3qtWK+2udtWLJVmWjQuuuGFwqHES8oVASMCUj/ZP6CFOgIT2JSH0EkJPaAZjG2NiMC5ykS3bsnpfaaWVttep/+c9uzM7fW45995z7n1/zzP2zGrm9Hva29jer0T6GDNHbxzFrLObRecRPcllf07a0vfM7WnJetm1X1i0yDI1/xx39TmxxdAVLVqiFitb6ZQzx6DhghIEYacgCkEB0rvf/W5IFNFkLBdn6q1vfSvs2rVL6qC8hDvRahGljpyDn8E0SoHuB1CYhpc8HJLjTjGhCfc4Gnra2dEbGB5jUFM2Jb5jbNOrTxicVJVO5vOf++/jwyfVB81l577i6+fEefNcS1kBurbMtMwqK7jKaUt012OasFjn+BZBA3HhT8XbZXJEY/yOYj7ZzTzkC4Jm4SnXvDMFgLZt5YWg2Hw9N1nM8wJqettzZjj54qOWCmLMvPiZHh9Ov49GCuyvbGhj0SzxUfkEY3PmKkWoKSeuYcKRtCZB/nOpeeOiVFnVr/nJPEukmIYzS6k1HS9g9VkmWwV/xTjLBPRaBOFZsiFzFeOy5xd156Hcf58ZHy5qbVzw95l7QUN7XVHGpXqMu7M3kLdgym72KkEUVoyLzE7C7OSI/oQzhVoSjk+CEBXLT6/r169Pv3/ggQfg4x//uKbfR6NRePvb3w4PP/xw+m8XXXQRXHvttVzLSRDCkDRHKxTNofUIxY4/9zt2yX1yb7kLFS2LtVbza+31xaDuGBugGHjw0+KOR6rNn1ZsU5riIwBa+jewzE1R7u/ZxZPZ7WjBwc+ucY4uAkvHJ9CnCc8DNW2yNB7sab9UGbX239kj2uINZWsgZv69+AWGqjLZffBLggo3HhZaQEmM6XNIZvqZbzUKouVc00tcgFnY7sIoA+rSD+LcThnJnT9hkotHAwwf5+uetTR2P1OF3SSXwnDsJrV5lfne2LnjXPLEvZRoaPNsYDw3W+uR7hubnwWN6wEKL9VbHGZbx3NTQkgmITo3k/GRTxuigG0yyzuFwfKWu5Mws+u1nEcKGMqpysK4ZDT3jSpOvvAHLlZ5WgSqeb9NJrPnUCn3iQQhJpYLov78z/8cXvOa16Q/f/e734Uvf/nLqn4bj8eZFdVvf7sU3G7Dhg3wla98BSoqKkwpL0HIh7pFcuiwPreWaoVXWjaMhQKDT48Ns/gDmoVlRTbAuJEafPFxbhtaPdYWhTZztlhJmJGnlnOlobY3ScBpOC7A0lv0/Y+CT9TCMlKkUuC40RIPRD/8XBRlH/yMYeelcV5cFjP8+RtO05rLj2LzINf+saGv0Z0bWuWNnzupQSGizL8XTahw7CdtFlHWt5H6PnbAGBSUcmPQurwFEURxagA9rvRkceF8LkcQNXnhDDd3rXa6peLF7JSxGNZZ9bVIQFsyVlzRH4FtRGens+JJYqwYnvN1Zh+MnzuhK+afrpi1nJS6MoWQht31p2QQKr026ELhqJiQUaR4PKpNIKsSTNcp6H9uFOHLqtX1arH0S8YdK1Om0dOHpV7PCEJkbPHn8dGPfhSuuuqq9OfPfOYz8LOf/azkb9CF3/ve9z6477770n9bs2YNfO1rX4OqqipTy0sQpi/uJdc1LRpX6n2Pj+QurpwOAXo2oIViuRzZ/QCLP3B094Nlf686PgbPDYQet0xmHgRsJrceasaBnjgy5Vyy6QXd4hkjmVe3wf1PGExTf2BWfpnwSSb/gp1vefXOX7riSGmKh2bPuQ8vd3AMmnkZnxf/w4A1SikNRLvmSMz3xAsPl/hC6blo9Ez2Gss1ILzucWVdW8YiC+PP7DFY/PJBbgHV9Og5FoC8pDZviThlWP9CSj7cxqOAFlG6+jZZOqamsfIU6TuBxuCxPf8Lpw8U2avkFVOD9r0BdM2JOjBlbUnKMe7tPHucyVCEnJk4D0efeTDvwtcQGVWbmx4v/VUjz2JS1Oe6QDlMLlt2rGaxFeOsdtN8/tQ+VcIvXc+xBsU4O/fSluST2RZZwlj9+ef1m5hDkCCkxBZBlNfrhc9+9rPMmik18X7gAx+ARx55pOD3U//+i18sBTBetWoVfP3rX4fa2lrLyk0QlqDnHJ2xyE6cH4Sjz/6Wa/p2UjD2QB52aIOLp+lqKxljcGzoGAyfeLHUl9l/Tzz/sKmxmayN2ZIsKAwwjVw3BJw0EPP/yaw2TBpyl1AoPT3kjkF1OYk/iWJ8kQOP/QISJmt+Fn3GDDx7I4OHctICUzj81K/h9IEn+SWYU2cM0K1GWJN5Kac6q4xGsS1GVJk+3veHn8CxZ//XvnKUEFAV0pAXRZiSYmp0iCkzoEKOngswFESf3Fv4XJWXjo65OCsmiIljcHZihF1Uj53lrxlvBjKsD7kUjiGkk2S2ZdmxZ0qcR6zCUgGBLZIokJ1irpj3PfJTGNy/FBNcLPS1u9lrTebcnD4PWWiZrHo90DpuTarChcEDzFK02H4W99Kl58jiCiHI1MiQqnIM7lvy2uKkZ39+ehwis1OFy8rTErLIvUwiydHiV+sgFGxfSRAiYVuEY3Sl96UvfQm6urrSsZ/uvPNO2Lcv/3D4sY99DH784x+nP69YsQK+8Y1vQENDg6VlJghenHjhDzBbNJC2PXDXQCx2CWSGGytOPtlNyVPyCwo9dYvMTekac3wFEwbIiK+jxrqwRELciqTl4FdybGo++CXNE5Q8/gsVcXiKbOY5uaSavHBa+4/UXMAmiz/zVglI0aqo4OWtBQc/zXXM+H6+9Zw57YWa2Gj5MD+jzf0HT2UEtXNlgcyNZcyhSdUEP0dhiuma2Bn9kFkmGZRFzh17vmwQ7dEzR0r8q/G2RWHo0JFntf2I9WmmIgSYxuGn7meuuwoK1crE5kDFLIyHWhrr92U47xjbW1jD2NmjOqzqsoWhaqyOcb3QE7NWfYnsUUrCNWbfIz8z3cJLz1xX0HW0gOA+JteDR3mSmr9rlqB7KT6PhfNMpiAqEYfo/Kyp01yuq3AjgrbS7g3NWdPxHIDxUdGN42JGeS7iTr34WIly8TmP6FEIyGr3Ygtx6itZ5dRQSINjl7mrf+zugsqZvObmPA8N2f9Y9HfRuWnt+RAEIbcgCmlsbISvfvWrUF9fzz5PTU3BX/7lX8Lp00sXQ//4j/8I3/ve99KfUXD1n//5n9Dc3GxLmQmCB7jZOfTkvaq+q9flmxloWYCtFbqQBqIWJs6fguPP/X7hcMKrPbWf+/QdMjS5RTN2MEKXNS/+4SdlLwqLlavs82L0LrnIAaRUOxYUjNgkII3MTZexnLPeRaOevE3JmtecbrY7UI1u0ZAJjW4wzT746b4QzrsDt2kMMutItT/Ung8KAZg2q05MfzaLridJ4S8Y0Gru0JNLLseNz0Xa6zCcEzNIT96FBPK84l9qGT+53z3+3O/g+J6HuJTDjRdTKAA4/vzvYW5qTHVd9bTHuNnWbkXKpHaM4vx34dQBle6ks/OKzs/Akd2/0fITS+aBk3sfBZkw+znLE3SrOnoXt0jN+65ef7qLaZ45+DQceuJezWvxmUO7Yf+jP4fZKRXnGCMUWQ9KKRiaEf/JCDPjw1zSsfY8YswlourQBskkDB1+ho0nvc8i3j3kJwy2KsYVs8QskYnWYhEEIaIgKmXd9MUvfhFCoRD7PDw8zIRR4+PjLHbUt771rfR329vb4Zvf/Ca0trbaWGKCEA8nW9qoIutOzp7gwKo2JwJ1E24IS2l4aSfpqMMpC6A8MsQuqU/tfUxfOUwUAiglXPNpiylitxBgUVtdcz7GL2Atic1lsisKfeOSW+pFrVFK1VGz1qfp7aVPYzn/WbZH41mPWzS189D4uePMLRpqs+ZZNqgWfpnZLtlj0Lp8BYGDQF6/INZo+1rTP9NlLheNrnEyWN5pokBzoGVP0a9zeM5OvmiPUETt83LwiV+xC8uCe2YrFFMsU1zhk4y+vMspbmkQSGtSWCv853zLbPtAAej5U/thdmpUlZvCfAFZAk7ve8K8caMoRQUipfoCBRtayiSN0N8m67eFc6F5dwaoOIhujbUqk5Xuv6SpLil5r9Guv28jCCcJopDNmzczoRPGjkIOHz4ML3/5y5nrvhQtLS1MKLVs2TIbS0oQVqBD4zDpso1YicsXFCCgxVkiXs4diNHLB+2/P/Hc70EkpkfPFvw7at1pddVjlbWcqt8mDWog5lzO6b2oM/uCqphARHO+JfvO3MlF78HS1gNpUq9mq9ZskjC4bxe7/NLvttLk/itSLs39U0KgJ+zBz8Z12Ko2GTq0dFEUj+RaA6jXpDWTYuk7RUBQymrDkEWUYSsM+9tXiLmBg1Wa1Lig/qk9oBpXvnaMSRGeRQIp0ff6jyNZZ5D5aX0uLNW4yDRCMdd8fMemmOcRO+e87PbVcR5RWfaUVSx7Pz2uPp+CmZpBcdd8wrj9JwhCPEEUcs0118AHP/jB9OehoaEsF35oCdXd3W1T6QhCA1YY5OQtttYIAXTHs7CwPdCXM8bgGtIRAF5blto3NrGYGrce1lFsLGAbDh/fK6T2Wf6G0pyDX2Y+yqKSROkfJG29gFXrCgO12bRlAlJgrVu0XAGgNi0/1dYoZ4/DyJnDzIJIrwsTKzUQs//OUxgqJva6h9QRGDw7Ae35eDz6XLqYfQmQWY5MjViNY8oqa2q+yPfcOA0R90lWkjvv8bBGUfVTDWuMpX2kwdUVvyyT7hlnauZpDe2BVkboIlVfodQrxhkc7EXXYdXlMtlVeDEFLa6CKIuGedm9XM4YNLr3M/L8qovNlTSeT+aZWM0YLJSGyedkbtbxLphPCUIEhBFEIX/8x38Mb37zm7P+VldXB1//+teht7fXtnIRhJWgL3H0a6/e17R1hxAUTsgCBoM2FSe4wuCJpssH/ZU+8tSvNXxbfz6Z2oMeT3lB1MzkBXWbYm7Paq5rvjL56nwuhNA45yyMN551Zrubd3mdGbxda0DdNGYe/DCZYmm54iDHwRJAZzslYosuLRHmjUXl5afG5yR7rOgc62YL5IvUSesFmIyXuVpilPDPOyF12/HC9dYoNnX94P4CrsZsKGTu/IOKXHaPQf37NoGfY5VFw7lIS/3R1d25Y8/rLJJ6q+hiMV01CwFUKJ4UjAVrotUcCkCKuqzmKWgQdXwmBRF8F1WMK/Fz1ZbtS/3rUVQoZxZORN/vVCdfbC8o6LghCJcjlCAK+b//9/8yt3xIdXU1fO1rX4PVq1fbXSyCUA+H9W7i/GDhoI48LmEtWpB5afiIJIgrlaewG2SLNDutqn9kftqig9+SIEpRIYgqnIi+nxm1iuBqgWDVfGHYTxRYRqablHLjykiMpgSHMWi+VV6iiDWKRK4wkjrHoI1TPvrq1zu3abp8yJpLkuKNQeaNhWJEZTWIBaDiRTxTGCozRmNEucLtj2KKQN6R+2Y7cMBFa/mxIF4do3MzGupg5EzMwRqlkHCKq3W8E84jvAS42tKZGT8PI4OHdOV89ugeDVbdRiyi4qZYRHHt06LW8RrHoJTW8QQhHz7eCX7gAx+Ae+65x1AaicVFa3Z2Fv70T/9U1W/uuusuuOmmmwzlSxAiEY/Oq9doF29/bnDDo3Xjql5gwgupLlp1gocUxYRNGdfDj0a3aGpJcNl0m62BWMQyRyoNRHsut/H3el1xHX/2IZ0u2TVqKmq0yiuciLkHPzdrIOZpoWuscywyD/FYxHhBTDw0l5zDdLj3MwVXj0H79oLZY9emthahi92glGSjC2IuCF5EO117yYLqOlrYFoeevNeSNTvbIqr8XnB2csRea5TF6mGsZq0u/bV4g3HKOnxk9wO68x4fPpnxSf24SrelBktDQ+6gFxLR9ztVaRffa7rBVThByAh3QdT8/DzMzGRriOglFouxlxqiUYdo5hGEkQ2FCmLReTh/QmOcGD0YWMe1bxrAetywUUmq9S2u3ZIgV5NQNCGHaUIAjhRyhTE2dEy7+71S5RR1mNt4+ZcZ6w0PZKX9nusXlGp1x2K7RVT2P4DTyR9z2uq8/7GfZ1nX6UWTQDWp9fKhuPs11c+cXe5YXGGpYnwe5DJ3Ov9x1zQH4j4b12JXYGQtNl0pIml4jJddQ3UnTYpx+pqtdLvZKqwoFZPTQLESyQzFOJ2KJ+buBbNd86XyQwFLQaGYXqzqWsMOGuwZg8XGRqrv9Y6B3DOxuFZ5SdeeRwhCRrgLogiCsIA87Rv1i6xeX9hWUlYQlSzjrk1VJtp/kvXzAm0+dva4NUI+y1B5Ya6xMU/vfwLmZsZ1lik3c5VCFCMaiHrdopnvmy/7YyIBJ198lHcmICL5vvm1lXNq5AwLUi0q7ODHwR1Lof7jNS5ZOkWtUVwgBDAmh+IihCoYL04wzJwHC41BtGYdGTwIMxPnwenka2Krb2u8WDp/cj9Mj54DU5DEu40Z1iiD+3aVdq8tKaouEkWcioy4CrdAoK3HvWoWUnjJMIhqiyhw1DzD5pdMaxRBPTQUOo/oE0IlJexcY+cRbuR6ylCFDst2vVb4pnto4GQRRRCEnIKoLVu2QDy+JDW3is7OTsvzJIjC8N2AmK1BYoRSlgCGNBAl0GTO29gkAU7ufURPSuA2VxjchFBlMDIGE4mYcYuosmXiewFmRoBq012+lDIcUvO79EdtCRzbk+FezwhlDmSlraWsiBGVsFQLHT+fPvAEjJw+DNKRtCtAvDH0aUhbaAVjtkVUTkEunNwPQ0eeBVdgoG1xHT5z6Gk+xbBtH5MUsg+cKIQqSlJ/n8xMXIDxp/W7pcrO1pyxYMUlplmu+Zzksk99GwlaZwN9wcNVeEGyimTOGOSJZecRrT8TxD2rJoG2xrbk4ZrPzP4rrRgn6JxAEC6HuyDqjjvuYC+CIEwk7/JPQMGNERNwEeuTi4s2NuU21aJt8tLlMXLwi5t08OOq+ZYvDHUPYmgA63KLpvbrHNxDWu2ab/L8oJxCKB7YqAVrT/10aNJagGtcohURBBOaG9FgH7h5HS4gkNfQnmNn+T2rZl3+Zq7DxXLWl7C+n5lpCSD0/JEU/zyiNm+tyiM83DSbDbd1XuAhKHwddOwF1Y7ZbC8h/GJE8Y3dneSj3GzVnpogXA655iMICbEzQHVOQcxJtuymgb/lh+aUZBCWGUXkQ6mpB78kF9d8ppNZ96R7x4DNJz/TUs6aBzm6wuB6UZOTVnTeithvfNF9gSmKFqyJlw88sDwvV90h6BMO8i+GjnxlWVpkvrznjaqqitMeRmOjyOKhodDzNz89AaNDR8A5iDOurJ4f+Lhptvo8YkJ/WTbXajaPBxEouxfUXc4cV+E6N1lm75Fz52r8fOy5h2BqZEjK/iQIp0OCKIIQHRXroSiu+rghwSZAisOpQUQJRl8mc1NSzXSFwdM1Hy/wIJAvDOWvmSvK3JJ78BHFEkBv4GitY9CIhWmBv+ovVHbi7rqEFVUhxCLJS+5cYJbrVk0UTNs9kihRrHHsWieEmH9EKIOdiDIPlsyXj1s0Ucl//pJwYNcvwZUIeh7Jnqu0WkTFTbKISprmocGMudl0QYbK9PPOI6IohJh4Hsm88+ClvJVKDZmfmdBbtKLpjw+f0C6EIgjCMgRVqyAIeeG1+Sp5wSlIYMxSmxFDGogmWxtFZqfg/Ml9htLIr58DLyOS6qomirCC5xhMxGPmWETxaipFHGGMEZbGThl3K3mu+CSpqxENRB7Cbgtd88k4/rLRWn4XrAGZ6BaGFh/H8o8ZCyixFxQlNoWbcYV1vBYBtYhjMOnw/nXBPLrkcrvM9+wcf6YIQ5NyuObjpJwp5PyRg6juIctZKhmK3Z05D+qWQxX4YRI9KczCgcd/yVUxDt/HoxFjaRIEYSpkEUUQEiLMRs0kDUTTrI0Wi3T4yfshFps3mJYgfWAqajXezS6H3rz5aCAKC6cxKMx8YgTbLKJ0XAqo9skeN9xHBS/ReDZVbl1c5FtdHEGw+nzNdMuoNc/o3AwcfeZBg0VIWmqlaAta+syuMZiU1/LcqMWLK5SS0hS6SBRFScScfMvtBY3OqdPjw3D6wJOG0pBCWGYVSfHLZShGlICu+RY8NIja8BYgStUVO0MmqEkkv2x4tpm8cNp42jQPEoR02CKI2r9/P4yNjbH3AwMDUFdXp+n3x48fh6GhBVPLFStWQGtrqynlJAhpDn4iYqIGotENr2EhFMcNz+zUKIiK+na2bzyaJUTJvpzin8fQkWcN932WNYowJyF7sK3+xQ5+qdgUvFwCcdRA5NlWufOgXt/xtqJ3PRFoHRb5EqjYWnnm0NMwPzvJOW397RCZmwLpEEYYag7l62OsvngBdmzPQ4bScLsQQJQxV6ochiwBTBKGpsp05OkHBOoDMfqyIC4+j2TF5zHBImp06Kjxs2imNQq2g4z7KpUx5fL3uWII48uOjaQRxbjMeZCnYlySn/KQIGsRQRCCCqLOnDkDr33ta2F2dhba2trg3nvv1ZwGCrHe8IY3sIVi06ZN8IMf/MB5GpAE4TBNbKcd7EU5fFuDmG4ITNNAxN+ZXKfh43sNplBAA1FnmcfPnQDbUFnmsgIOu+RQOi4F1F5WcLHKKyEk40HeGHTRVkwY4a+eYugYA7rdwBX5GlpEmbNXcNEgzEOkvaAitJtmLJdRIZQs+1VzEWQeLIWgY5AbnM6Ec9PjIL0bZxvPI6UsO3gpJZlhEXXqxceMuwrPEdgKsz+yAFFc5KIw0RcI5f+D0WeCyRUzPDRwjVnLj7y5mu6GCUJoLLfv/fd//3cmhELe+ta3Qjgc1pwGCp+uvfZa9v7ZZ5+F3/zmN9zLSRCiYGqweZdpIEbmp/n5DBZGGGgiMtTJpDGYnQW/djh7dA8k4pzc/gly+BGjje2qu3oBmaY6JjlpIJb4HfplN0xWneQdf3rH4Mz4efZaTACcRNn2UFnfRGIp3l6Kk3sfhZnJC3qLVroMTpNDabhMsWsfUjJfI0IAE13kGlcGAUc+94VIKfIUi/GR9dE295BJ84UAosbnkUFY5oLn7Nyx54v/o24LoRzXfMKOwdyJAFyH2jhmZjI5cqbEv2ZYrWn02iBHzNrs84heDw3jwye5lYkgiOJYuppNTU3BL3+5EIyuqqoKbrnlFt1pvf71r0+//+EPf8ilfAQhDUmAWGReTOFHeiOW1H3gzXJDwPlWCd0B8cANBz/1Gu+CucIwOAbN5sLgAcNp5D4nrgM1MJPJ9CWRbXMhlkPrqVPtY5Vx8NNbv3issOA9Fp2HfY/8FIySfziV7+Cn95JyfnocDj/9a/aan54AuzhzeHfRfs5H2+VDod/yYOzsMS7pCLkHcmE9pkfPShcjCpVCnOWhwB54use0M9aX5fF5zLRMdjMytIXmGFFxU6w8cNzwGTsuP48AwIkX/gD7H/nZ4l5QgjGokez+5eeab3rsHD9LTBmefYIg7HHNt2vXLpiZWXDFsWPHDqioqNCdVur3mN6jjz4KkUgEAoEAx9IShLiMnDkM50/ug8bOVdDRv82+TY8Ji37uxpi3G4Kxs8e5pOOKg59Kf9mENiaGT3Fpsqx+wfdu6qYkwPE9D8HMxAXo2XKNbcWYnRyBiprGvL/zsE4zy8KNCS44WIYuzNXuvXyYyrh8Hz9vrwalJf0g4PySJ6hIShynjF0wamvkRDwGZ4/sgXB1A9Q0LwO7OHf8BXOs48uNawHGpJvnwNQ8ePy530PT8tXsTGJXnxzdXcA7CgcLBbOsUaKRWRjc/wSXtNwwBpNlziPxWBS8Pj8IB4fzk2n9i8pc8Ri3tLLf66s37ullIxaZhbnpMfZ+cP8uaFq+BkREjUWrqr2WziEdj0fz/nb64FNgnms+bkkTBCG7RdRzzz2Xfr9hwwZDafl8PlizZmGiRyHUvn37DJePIGQBhVDIhcGDtpZj+OSL5rvm4+zjl9eG3kkHPz1ax2NDx+DwU79ecktlE7NqDy1a3BuZHHh2ZuK8ozSR7SA6P8NcUKAlyOC+XbaWpfQ8XMh1nbp+yr5Q59e3UyNDTIBmxsHWrfE6PYpXLteYHIoqgoKCk9ZhPQwdeZYJgY4//3t+F4q84SQEEBYBngM7QS8DkbkpOH3gSfbZrnlwfnayxL8aEYaWcc1noP9HTh/S/VteZZCH4nU8c2g3vPjwj9m5RNR1OEvBUuMNeZa1H8e+xrVj7+//m5OHBj6Wobi3l43MtTcWmQPRKPxMJDX9XoT9nnbrPneeRwhCFiwVRF24sHRh2NTUZDi9zDTOn7f3MpQg0gi+WFvmY9/I5YOJBz9uiFAGDqAW4YHHF1ym5lJq43nyxUeZQOXI0w/YevDDA2gxjAVJT2Zp6HGJp8MTvPB3yBjUQ+bBnLklc1hb5AtDQUyytGDBtZgRQNwUkjxj0AnQ4QVjRMl3+aB3Dc28AGNzopDzoHkWUSJcOrtdGJqHgGPQ0F4wyyJKzLlF9EtiK5Qz8TnEc4m46HRnxoJEJYWa8wrh5nlQhhhexdE+nkQdg3QeIQi5sHS2TGRM1LGYcc29aHTJxHN+ft5wegQhLQLtCZY2KHyEAHTwMxfUyERt1iI9IeWGtNAYNOKuCQVu+x/9uWkxCNwcm0JvmbPiyAksBChUPxn7qTAFXPMJelFXEg7dIfIY5FZpAcetY54lHmNQMO1fUfduvHHMGCSkxRVCgKSDym9kahR0vpHCetWkNs48B7O9oGB9FJmZZEqn3BCsfsXmQbfsQQhCViyNEVVfX59+f/as8cC2mWlkpk0QjiC1gAq64JtKRp1j0XlmjeIPhkEknHLwK+nOR/ahl+Tb39G5GQhWVIMoOGUMGkXxeEE0zh5+Fmpauowloogt8HXOJWzSkWNwfmbS3PlKgK7PtJ5mn0UolC7EjSlnFF6WycIiQxl54aa6yjRGRSiDyaie3wRri1S5RZ2fuZHb7oL1g1WIaBF19NnfQjBcDb5AiMv8JaYbYPHdBxIEkY2ls2Vra2v6/WOPPWYorfHx8ay4UM3NzYbSIwiCLzw3BGiNIpxrNBdseGQ9+BVEi2KUgUC7VsG03+ngt9AWike4Q/7YueNw4vmHsw9sBoo4+OLjLLaT8BqIglllWHr5INYQhAOP/wKmx4dNW59FeObo4iGzMcToEzP7TDSrZIQUQnLbQ5wxmH4eHC4MzSujBGXWjKPqpBiKSTp54QwIRaEYURKsRVqfdVx/UCGxVPeJah2PMfRQsVevm+ZM66LTB59KxwQUCVqLCUIuLJ0tt23bln7/zDPPwAsvvKA7re9///sQjy8cSBoaGqCnp4dLGQnCKHZsvmTc8GndXFw4tT/jc1K8DY8AZeJOqk6S1s35z4UT6md8jHm84lmjpEgu7lO0Esk5NOJYRq1G4ciNXaBI/BwVGYOnXnwMTr7wSMk0RL18wBh+vOZEFK5muXcRYf4pUAYZ3bHwmcsF6I+cPdLc1FjRcaJnLO77w09hdnI0MxOwG2esw87G+XtB51vHYyzQifODglpjFAcVktgcoXeeKPC7Y3v+l6+rNQ44aQwWG2O4n0LF2OnRcyWt450+36QEouKfR+TbCxKEm7D05DwwMJC2isJF+cMf/jDMzMxoTmf//v3wpS99Kf35JS95iZQHT4JQg2wbmsnzgzB84kVIGtBcleFgL0MZ3QrTWEOy+kj9GhGZnYKR04dBaJT8g59scwWv50xEt2glh52KOp3cK3LQ7aW+yR9zcu7F8FJn/NyJgv82OnSUCWFkc8dilNy+HT6+F07vfwKEj0vhsPMA7qXOHXsexodPSrUnicxNw8EnfgXnM5SItJPMu4w++cIfQCjcYI2yeDkr2x5jdPAw1+ciGpmFmYkLIBwOGnPTY4Uv+Y/teQiOP/c7IS0xyoHW7FlxhDSuUYXGcCwyB6LgJA8Ns5MjcGDXL4v+G/YjzgOyKSUZYXLkTMk6i3oecauHBoKQBUtnS1x4/+Iv/iL9+fnnn4c3velNMDSk3t3Mrl274I1vfCNMT0+n08TPBCErqN0Zi2WYS0u+iZubHoehw8+wSys94OZ6cP8uEJmCm24HItrFllrOHHoaxs4e031pYuzizCKSOdpfRjQuBWZs6Cic2vd44TlSYLdohZDtEq8sThlvySS73ConbCqXhhSD0CCG2sgEHGOZXKLcs1OjcPboc0yzvnw6IBwoROPZR1nuhQTASZYAxUAh/d7f/w8MHdoNMg1C3DccfvL+wgJrneeRw0/dv2DpJ9B5xCljENv1yO7flLRSQWG0bJw+8ITBx0KcZ6oYmWNQ1rMjtrNRJTBZlJK0uC3NW8NFRNoxRxDuxfLZ8vWvfz10d3enPz/55JNwyy23wKc+9Slm6ZQosFmcn5+HRx99FN75znfCG97wBhgeXvJ5f/vtt8Pq1astKz9B8CQRj8OhJ+81lAZzUyLgAlwsNkU5InNTIANOOfiV1syTN8guHiYyLwv4WM2K1Q5OGYOl6oJCqNEzR0rGR/KIbBFVABmfJ7V9Jqt1OgqujZBIxFznblWEy6aZ8fPgBHBOmLxwmlmT60/E/v6wBNHqKVhxzNKIx/m+nFWeiMxOjcD5k0sxpbXGVTFjveCKQ+LzIMWskmUHrUMnzp/S9Vv8nQweGkTcH+ghYdDlIbOIkrPqzjtDynkcIQjX4LM6w2AwCP/xH/8Br33ta2FycmGDh///2te+xl5VVVXQ2dkJNTU1EIvFYHx8HE6ePAnRaP7CsHnzZvjIRz5idRUIojQaNiCx6JzuuBUpUJDl9fqpV6zEQQe/kiTl1v6aPH+ab4KCdbGsB71cUGCILpwMWWmIKgBxRhepq5uk45HH3C2DK0UnIpqFlhHQ9ZTRtpAtfooTcJJCCHo0KGV9XI7DT/1aOIu1Uu7eHIOka28eHPZxKPQRkfnpCV2/mxo9CzLglPMID2FqVf1CGBLhKNBHTrm7yBt/7LMA50J6LghCHEEU0tvbC1//+tfhHe94B5w+nX1RODU1xSyjynH55ZfDv/zLvzDBFkHISmn/teo3B07ZSMi1yU3mxRVyGmrGFV7CoOsgNyDchVOuMFS4Z0QFST6X+EJpKJd7hmTsJxmeB704pD94z+2RmcmyqYiIlFZ5HJoSXSI7jbNH9pT8dxHWvNxnSdb9OHpoOLjrHkNpzEw4w0KxHFnjToQxKEAZROHoMw+C8Mi4RpUiWVAzCaSEQ98MChZL0xXQHEgQ0mGbKvvGjRvhJz/5CbOMCgQCqn/X0tICH/zgB+ErX/kK1NXVmVpGgrCT6bFhOPD4L+HC4MGy33WdFqwIG46cIqDfeDkpvukePX0Y9j96N3MZVApVsSscgEiH/fzLriQLai8jcQE1qLkh2IUVTwpduIr0jFhlieJUylopCtjV8XhUt+a5vQjYmCahRVCDLuHKpWY7AhSBB45eh52OhOuu+yzXks7u86ziYMxakA8Zy2xUgC7aOOK4r5BSKYkgXIQtFlEpUJD093//93DnnXfCPffcA0888QQ8//zzMDIywuJCIZWVldDW1gYXXXQRXHrppXDttddqElwRhKwH8pSG//yMjJcqzgYtAaZGi8eskYlS42vkzIJf8sgZ51l76UOcDTvGTfL5Q1nuSIy4t7MLdCflD4TtLoZ0CCHwEaAIhDZ4WmuIaPkxfHwvyIgQz7MkZI07AZrNKYpgM5MX7C4CwWkujgrqns7dgii+iLb+OsEyFM8jPp+TPS3J1yeG3A7SvooghMZWQVSK5uZmeMMb3sBeKebm5sDr9YLfT7FvCAcv9LRISouIfvCd5NJMRETb1JaKMScT0cgsOBXRxoz5rvmcW19HQd1EEFwYPpEt/JydGJGyZd1i2c4buy/ccR3GuDSZHHryPtvKQ6iAx75QoL0lewYEKo8RjMTIE56kc885hcsgQLkEKANBiIoQgqhChEJLmt4E4VRk1d61G1rYCVvGnVNi4hAWwldLD3/L3E0IcPAriKDFIkxA1DEoIePnjoNrSBocP1kGUeKNwbNHS8e1IgiCcNJ5BD005EH7A+HgvV6mzyMCcO7Y83l/i0cjtpSFIAjJBVEE4QZSrs8IgpDBJzvf5AgXwD0sQAIUxSvEBWzuRQhaiY4PZ2tmE4KRHjbGx48IY9ApnDm02+4iyAlddhIEIQG8rUZEsEIphqyuwh0PZ/d1qfOIiOvw+PBJ9iIIQlxIEEUQBEEQkmkgEnLA/bJepANfTlEovoPLEGgoEgRB2ALNg4Qdw47OI4Qgcx8pJZVqI1ogCEJ4QVQkEoF9+/bBiy++COPj4+zl8/mgtrYWGhsbYePGjbBy5Uq7i0kQUmspOQZqYsKWcUcDj+AxZoy55jOYBEeEKAShA9qnEAThRhQQw5UUQRiCziOE7VZ5qJzppX4gCEJOQdRTTz0F//Vf/wUPPPAAE0aVAgVSt99+O/zxH/8xdHR0WFZGgiAIgiCtL8L2Q2A6DfuFQMMnXrS7CIQO5qbGYG5qlEPb2T8GCRlJcvs9CVQJgnAjNPcROkZNgfFjYD0WSjGOIAjZ8NiV8cTEBLz73e9mQqV77rmnrBAKuXDhAnzlK1+BG264Ab75zW/SIkwQBEHYIAQgCAPjztC5j8YgoZ/I3BSL3cBDiEhjkSAI6VDIIopwALQXJDSPGd5DUBzFOIIg5MMWQRQKlF71qlfBL37xC12/R6HVP/3TP8F73/teOggThCuhTQ+hYpS4KDgwISpmuMKgsUjoY+zsMWo6wkHQmkzYDY1BwvqzBJ1HCM1jBngrxtF5hCAIiVzzoRDpL//yL+H48eNZf6+pqYGrrroK1q1bB8uWLYOqqiqIxWIwOTkJR44cgWeeeQYeeeQR9rcUd999N7S2tjKBFEEQBEGYCQUHJrQPmvwPhlw8Ll5gDB1+hjqD0EwiYw9NEHZg9AI16/ckAyDsgJSSCJuh8wihY9DwHoTUCQRByCOI+s53vgN79+5Nf66trYV3vOMd8OpXvxoCgUBZS6p/+7d/g+9///uQSCxI4b/xjW/AK1/5Sujr6zO97ARB2E90bgZmucS3IAiN0J6b0DxkzLGIGjl9iPqC0D5+EnFurTY/PU49QNgKxW0kCMKVkBCAMHQeMa4YR+svQRDSuOaLx+Pw5S9/Of25s7MTfvSjH8Edd9xRVgiFNDY2wkc+8hH4/Oc/D36/P53mf/zHf5haboIgjKEAP5/spw8+yS0twk0kITo/C2NnjxsWAhCEhkGT9XH0zBEY3LfLwBgkaSihn0Scj0UUjsPTB5+iriD0DB4YOX0YZsbPGx6DZBlKaEXhHCOK1mRCx6gxPu5IM47QPGiyx93w8b1w/NmHjKdH55ISTURnNoIQQhC1e/duGBkZYe+9Xi987nOfg+7ubs3pXH/99fC2t70t/fn3v/89E0gRBOFsEok4s4giCD0c3f0bOLn3Ef2NRxtKQiPxeDRLCHBq3+MwM2HgApbGICGAIIrGIaGXC4MHYHD/LpifnTTUiFMjZ6gTCF3niPHhk3DgMX1xqnOJRWapFwjrFdkW94LJRQ9BBKFm7ksRj0Vh6MizEJmfNjAEKUYUQRCSCKKefvrp9PvLLrsMNm7cqDutP/uzP4OKigr2fnx8HA4ePMiljARhGLooLMjUyBDse+Rnupv1/Ml9sP+Rnxm+vCDcefBDSyijYyelgZggxQdCB2ePPme43cgqj+B1EWFsHJKWJ2HfPIhE5vRfoBHu5sTzDxvaD6b2grOTIxCLznMsGUFoO98Mn1gKd0EQVgrQU/vA0weeoIYnCEJsQdT580tawDt27DCUFgqhNmzYUDBtgiAEQ1Hg6LO/hei8MWsmPPBx0+gmCK0sbrrPHt1DbUfYAgkACCPEYxHDDTg3NUYCUYIgwO17wcF9dAFLaGf87AlmyWREqS21F+Ql2CfcBbrHNcziGEQLU4IgCK34wEImJ5e0j+rq6gynl5nGxMSE4fQIgiAIohgT50+BLxhm1nkEYQeTF06D17cQI5Mg7ODgE7+CuhbtbrUJgguLl188Y48ShD7IMpTQM2qS8PxDPwBF0a8PPjF8Cnz+EDU/YRukGKeqlUzvB4KQFUsFUTU1Nen3qVhRRhgdHU2/r62tNZweQRAEQRRj4vwgexGEXaA13rljz1MHELYydu449QBh2yXu3PQ4tT5hGzMTF2D83Am6iCVsc7WMinH4IgjbIBfNBEHI4pqvqakp/f6xxx4zlNbU1BTs2bPkHqm5udlQegTBi5TvcMLEIK0EQRAuheZTgiDczMFd98DI6UN2F4NwKRgb6sQLf4C56TG7i0IQBGELZw49zSzzCIIghBdEbdu2Lf3+0Ucfhd27d+tO6xvf+AbMzc2lXfT19fVxKSNBEARBEARBEAQhJrNTS14xCIIgCIKwjunxYTj+/O+pyQmCEF8QtWnTprRVVCKRgHe9611w5MgRzen88pe/hC9+8Yvpz1dddRV4PJZWhSAIgiAIgiAIgiAIgiAIgiAWICdJBFEUS6U3KCx6y1vekv585swZeM1rXgPf+ta3YHZ2tuzvh4aG4AMf+AC8+93vhlgsxv7m8/ngr/7qr0wtN0EQBEEQBEEQBEEQBEEQBEEQBKEdH1jM6173Ovj5z3+eju80OTkJ//iP/wif+9zn4PLLL4d169ZBR0cHVFVVQTweZ/+OVlPPPPMMPPnkk8ySKpM3velN0NPTY3U1CKI4FLyRIAiCIAiCIAiCIAiCIAiCIOwRRPn9fvjSl74Ef/InfwKHDx9O/31mZgbuu+8+9lLL7bffDu985ztNKqmcDA4OwtmzZ2F8fBwaGxuhtbWVvQiCIAiCIAiCIAiCIAiCIAhzSJJvPoIQRxCFNDQ0wA9/+EO466674Mc//rHm34fDYXjve98Ld9xxhynlkw20GvvmN7/JLM1OnDiR9++rV6+G2267jQn/AoGALWUkCIIgCIIgCIIgCIIgCIIgCMJ92CKIQtD13j/90z8xYdJ3vvMduP/++2Fqaqrkbzo7O5kV1Gtf+1pobm62rKwis2vXLnj/+98Pp0+fLvqdffv2sddPfvIT+PSnPw39/f2WlpEgCIIgCIIgCIIgCIIgCIIgCHeiJJNiBLTB2E+HDh1iApOxsTFm5eP1eqGmpoYJnTZs2ABtbW12F1Modu/eDW94wxtgfn4+y/XhwMAANDU1wdDQEOzfvx8yuzhljdbV1WVKmS5cuABvectb2PsvfvGLzD2g2zh94Em4MHjQ7mIQBEEQBEEQBEEQBEEQBGER9e09sGz1DmpvghDJIioXj8fDLHXIWkcdw8PD8Dd/8zdZQqiXvexl8Ld/+7dZArujR4/CRz7yEXj88cfZ55GREXjzm98MP/vZz8hNH0EQBEEQBEFwRoknIDg2B5GqACSCwhy3CIIgCIIgCLMRw96DIITEY3cBCH184QtfYEKlFLfccgt87nOfy7MaW7lyJXz1q1+FHTuWpPFHjhyB7373u9T0BEEQBFeOxALw5akm+OJUM/xhvgqmEh5IJN19BjkZ88OzkTAMxvzsbyNxL+yar2Cv4bgPIknF7mISBMGLRBKa9pyD3p8fhGW/Owkrf3UYmp8eAv/kkuIYQRAEQRAEQRCEGyEVPQnBeFA/+tGPstztffjDHwZFKXyZFQgE4K677oKbb745bUGFbvMw1lY4HLas3ARBEIRzQaHT/5tshamkl31+MlKZ9e+b/TPQ75uD1f45WO6LsL+dj/vguzMNMJrwQo8vAi8Pj0GtJw4yczbug6cjFXAh4YODsRAMxgNlf1PviUGzJwYzSQ/0+OZhrX8WfJAEv5KE1b458JKsiiCkoPbIKNQdHk1/VpIAtcfH2WuysxourGuGWOWCUJogCIIgCIIgCMJNkCBKQu655x6IRqPpz6961augtra25G8wJtT1118Pd999N/s8OjoKDz/8MFx33XWml9dtCBJ2jSAIwlJQ8JISQhXimWgFe8EswHLvPNuAHIkH0/9+Mh6ER+Yrodc3D8u8EejwRmGNfw6avTHLvSjk6nVMJjxwKBaCg7EgBCAJDZ4Y1HniMJbwwgvRMKv7bNIDc0kFxpPat1ajCR97IafiAfjdfHX63zq9EahQEoBFavLEoNoTZ22Egj0PCagIQij800v781yqByfZa6a5AsZ76mC2sQISweJzpmqSSVDiC5NX0lfA2UUyCb6ZGCQ9AEmPAsHxefDOxyEe8kK0IgCxCl/+pCcQ3tko+GZjwCa8RJIJ90Ijs+Cdi0EUhXqKAt75GPt73O/FAMjgm4myv881hGC+NsTqmPRiAySFritBEARBEARBOBkSREnIAw88kPX5tttuU/U7/F5KEIX8+te/JkEUQRAEwYV2bxRaPFE4lyiv7X8iQwCVSRQ8sC8WZq8UPd45WOWfh25vBPZGQ5AAgGXeKLOc6vbNQ6snxu4Vz8T9sC8agrNxP8wkFSYoQoFNDBRmefVIpArGE14mLGv2RJlQp8YTh4CShPmkAsdjQTif8DGBD/5mi3+a+S9GwdBZFXUyi0yLqgM5/1alxKHNG2XWVOv8s3BxYJrd1dJdK0HYw1RnNdQeHWNCkWJUDM+wV4p4wAvztUGIB70QC/kgOLHgvQDjS0VqguCJJQA9ePpQeOT3QsKngDeaYA96xdlpCI3MsXkrlVYs5AUFBTbxJBM8eSPxhe+XAPONVvgg4fey7ye8CkSrAyw/nFQwHfa9sI+li4IszB8/42+wAJ5InH0PBT6YP5YbBUIonEPhkCeaYPl4o/GFv09FmeAM841WBiDpVdhv8BVebJ9S7aiKwwv/w2QSvoVyxSr8C/WtDiy2z4KyQzzgW/xOgv0f2x77ABcYbNdEwJMlxML6BsfnWOL479g//qkI+y3WIeHHuvlZnlhfrAvmO18fgnjIt5TG2BzEwn5WHoIgCIIgCIJwMj4zrHX27t0LVoNu59asWQNOZ2ZmBp599tn05/r6eujr61P12y1btoDX64V4fMHt0SOPPGJaOQmCIAh3gQKdt1adY/GhziT4XagdiYfYqxheSEI8fQ2rjuGEn71KsTua7VpQRFCodijmhUMA8GikCr493QghJQGTSS/UKnFY5Z9jQruAkoD5pAc8kIQaTwLq0LJKSTCXgNWeRNq1oldJQgiSzFViFBSIJhUmlKtSEswSC3//bLQCzsX9aSHYSl+EuRAkCAJgrqkCTl7VzYRRtcfGmQCk3OyEgp9MwVSKinP5fysHEzpFtLs39c3F2CuL87NgOovlDY2ZG0ML+8AbW5jrAlMR9oLz2to37vMwARIKqtBCy8i0h4I+z6IVW4po2AeTXTUw1xiGufoQJIKkLyoDSjzBBLMxtG70ephwEYWPKDxOBFBIq4BvOgL+mSh4IgnwRONMeMuEuAHvglVfChwSxUyd0fIRBbzRBHtmAhPzzNIRB3dSUdi4RCExfgfzRIE1CjhRIIrpojAY/596zqfbKiFWaY3wE4uE7pqPxQPQ5olCty/C9hP1njhULe5BCIIgCIJwB9x3uA8++GCW1Y1VrFq1yhWCqCNHjkAisbRh27Bhg+rfVlVVQW9vLxw4sKBTPTw8DOPj42Xd+hEEQRCEGjp9Ufj72tOwO1oBB6NBJiBCSyW0KkLXdSjU4I1WIZSV9PrmmFUWXrZEUKiTVJigqGnR3SBaaqHQ6FTcD0diQZgp4dpQDfPgYQInZDTpg12RKthV5jfoZnA64WG/1QPGskLXga3eKHOpiOlhj0wnPXAmHgCsKca92uKfYUIw2WOAEUQ5InUhGN7Sxl54eRw5NQs9z5yGUIzGvsygIMuLAiwO5AqhEP9sDBoOjGT9Dd04oiABLdMwthhaXjFrt0SSuSZMCRXwO0osyYQdKBjBf0eLrngQXRIuuDREwQf+u286BrFKH/s3tNiKVvkXLNIUtEhDl41lXDUu+rBV0FIPXUE6wNWhEk1Axblp1j5x/4I1HAqI8DPWD4U+KABKCfgFcTEAAN0MSURBVH1QmIQuLsPnZ5f6AAVIngWrvhQojGLplxFaZgqsUeCJ/YlCKmaxNxdLWwry1Plo3gMQqfTD5PIaJrBieaMwTVGYUA0tG+MBtO5DKRa6vfRAcCICvtko+x4K1Vg5/R72XRxLzApyNgbJeAIOeivhKaiCZ5NVMBrzwIzPD1WxKCyfmYbfe4JwLhSCGZ8P1s9NQP/cFOwLV0P73CxUJWIw7l9wuRkOAMx4vNCcjEJLIsIs4oPVXlgzPwl1k3OsfXBc45g9EaqAwagfls3NQMPcPDREIwtC4/k4a0P2zKDV4nRkobxVfohULfQz1gf/LTQ6t1g/HxMGY71Twj98hvC59U1FYN7jgfmKAMSrAzDnWRD0eZJJ8CaTMBrxQDSahJUTk1ARjwEG+cT8FtommrYYxbIv5O1jbv1xSkjOJcA3HYXZhAKj/gCgU+aY3wtDdVVwurYSQjU+WAMzUBMGUAoED40mAfyLf8b0cN+NLqQrPAlo4L33QsFoLLkwv1jpJzo3BEJq/sH2Z88KNmSSPZ++1LMTXbAqZooa83GIVgWYJS72RaQmAHMN2KA57gSK+erWWtbFdFN5+ycjaUtlLBuO3/maIJvfmRXzYr+y50hBa+SF9YJZ68YT6TolfF42brFe7N+9KIxeGKNsvOJ4wPG1aH1LEAQhEjQzScahQ6j3vMSyZcs0/b6zszMtiEqlt3XrVm7lIxDSDCcIwr3gmeuiwAx75WrE7o2G4clIBZyMB5jYY3zRcufy4CSzqvnf+Wo4HCtu/SQqjZ4YtHqizGqok1kJzUO/bw7vHzQzFPcxAc5w3AfPR9FNIbojNO+QP7IYm0ovKHaaSHphIuaFg0X67vloBfx8tj7dVgokIQkKDPhmYUdwGtb559iliSfjzM+UuplrQj9zmYjjA4V5OIb2x0LsfI+XKyjoDCpJ5prw5tA4s5DDv6eEfj4L7kewLBFQWPwwWKwDlv9oLAiH40E4EA3BTNLDXqgFjsI4dKe4yjcHq/1zaas0wjqwz347Xw33z9Uw4e32wDQTGq/wzTNhauoyTy3Y38OLsd6OxwLsmZhLeuBkzA9HKkPQftEKuPXMKVg7MQabx0fNqhaxyIzXC0G0VJF8T17IUk4tKDTQA1rS4OU4Xnwyax2cmxcFIXhJjheoqceDWf34PMzdIV5+4kUpXnzO1YWYkAovSicrgzDu8cHz3ip4PFgPh5Ihtg70K7Pw0snzsGJuCqrH55iVGhPioKvD1AUr5rt4CYuCDhTORKoDzMUhvmfuLPG7KgViofMzUHNigpUThW8oqMAL69D5WfDgQ2wA9vucNNQKjpRcgSe2A8ZFM5nAdBQaX7xgStrLAeCajM8oWKmORcGXIUhA0QiHKHmMbo3fR6ETwGTRf689CqYTvlDc6rQTppc+DA1n/RvuGMYCARgJBmHK54eG+XkmHDtRWQ2jgQDMBHywu7oe9lXXgT+RAG8yATNeH6yCGYj6vTAc98OO6BhsmBpn8fUGg2GYqAzBTEUAJhUfTMQ90BefgcpEDJbF56FnahLap2cgMB9jQtjkfAICszEIRGJMAILPI8KEKMnF/ysKRD0eiCsK+D0oYPHAucowTIKX7ZVQ4BicjQLEEjDiD8Kk1wfg90D9/Dx4w17whzwwk1CgGuIQSCaYK1eMDbgQL3DhumUhLqCHzQ/4d9XP8NmMtl0Ehfw4t6FlKgqEfIuWxVi/lMtZFBrhe5x3UFjN5iHfkpARBdP+jHlMSaW7GMNQDUyghL81OB9lpodzMs7l6Go2JZyaqw+zcmJ50VoS59dU/byRBITPz7D2xnrgXItzJdYFXdji91CAh3Uvq7jABLkLVqDFBJYoQMRyYN5sPQn7wRNPMLe+KbfAuB6kxhezcF4UyOHag//GXAaje2CmULGgjJFyJcysQRcVCJjFeCLJ1jj8HrrX9c7FmdJBKv/UmoYbRfwOthnGmkSBZa6L3lJQ3HiCKA4JoiRjcHAw63N7e7um33d0dGR9PnXqFAmiCIIgCNPBs8T6wCx7FWNncBpG4l5m5XQiHoD/nauGFzPiRRnKf9ENDMaxgkWLndPMYmfhQIFxo9CiB+NToRZpio3+GfYbjFOFLmVQuHEh4WNlrFQS7Dc8lUHbvDH2Qq4PT7AL86GEn5W/xRODQ7EgPDxfBX+IVIOMYNuleCRSzV6ZMCEAJNmlflkB3KL8ZiYJ8PB8NXtlgpfQLw1OwqbADBP66BVK4WUICr8wrhi6OsQz6Om4H341W8vilo0nl+qEF6xBJcFilRW11ltUTH5wvob9v0KJsxhrF/lnmCDzXMLHBIRoabYtMM3GKApsKwURWKUEbygARPC+BAWCKGBG60cUAqKFXIs3miGoXYjlhqDCMl4+TiY9cDgWhHFW1zj0+eZhbyzMNLjX+mehx6f+In0w5offz1cxgSUKlrBk+HxiX2D7YT74/OP4QmFmrtAU++LBHO9wfb45CCsJSCQVaPZGmaAqvuiyMuW2cgrzSgI8E60o6e7zTLgCvtzTz97XRxYu9jaNj7JX18w0HKusgta5WaiIx/Mua0txOhSGow21cDochrGkD9ZHJ2Hl1BT4IQG+oAc8fgUSi9YKLE5TPMniUeGlCl6c4SV84PwsJGdi7DLGk0jAcGUFuzxsmJmDmgi60MoHR6IaG8qEosBEZRDmvF6WfnU0AqFEAsZ9fniouRXmPV5YOT3FLAeqYjGojUYg4vHAhWCQXZwOB0Mw4fPD4apqmPb6ILY42V4IBOFIZTWsmpqA2mgUDldWwUggyN7HPQqM+wMQSMRh9cQ4NEXmoX12FirjUQjFEyyv7pkpWDM5wdLCC9zBcAU0RObBh5dXHgWqo1FuF+SygRYFntjCs4vjoxQ4BWD8MW/G99ACJfN3jYv/3wQArwMFhoNBmPX6YOXMFLdLexxn86EFC4eY3wPzCQUC0Tjr25jigYpoDKrxgltuuaTU1Efz53O3PmNGwbm3IRJhr0xWTRUXrPEU+mWVJZ5kAs1C5KomVUHhZ76lyN+LkrEVwvkG8GWQlEtbtEzNs17FV3TBGinNaXVlznN5q8Na1gjMQmp6IVajHne/5UBLUOb+c5FU/Mo0i4IjFBah4A6FQ+x7EXRpGmOCIqNKAFbDrGbDfpgNB+B8YyVEAj7mzhzXmYrYwqkS947BC16Ie1vB07McFD9duxNEJtyfiI9//OPw4Q9/GKwmHOZzUSU609PZGhxa3erV1NSUTK8cQ0NDJf99dHRJy/PMmTMwO2uBf3nBODN0FsaGzdEuIwiCcAudAHAHAMwmPOySNwwJFvMIL5fnkgqcT6DLPz9zZzefVNgLXb9dGpiCkCcBY3EvjCV84FOS7HK61hODUI5KIp73TsWDTLiEFk14esBLdHSZh9ZN7OI/53yLx+3MlfScBW3BDnGoxAkAKGq5cfGFFkKoWXou4YfH5ithfzQIU+AFtKFCl3l4hYxWZ+Wj1IhD6V2Gdu5hLw9UKkEm1LkqOMn6G4UffmVB1IXjIyVUwfhXe6Jh9nccU8djQTgaDy6m5odKxcOGxBxrVzzUZx/sz+goI14ljQHAi+wT06FNS6u+D6kYHn5o80QgrCRhOqHALHiYxQ2Wu0GJsRhtGOMLx28k6YGNgRk2ZrCuDYsxwfBgXOdVf2GDdwP7oyEmDJ5MeFgsD2wTFNZi3gulSjDLv4XPmGPmxXXqciIACvihRokzF5Cp3y6RKlNw8YYpsXhE8bHyV8HCs4h5owC5wxtlgiAUCqFgE2OlLVDq0jya835BCFGKwhFvi12YqI9xdHbx9WhNLQC+8rJYyMOfTIAvkWDCGhSeoBurmkgMWudnmRsoFM5EvLlXi1UA6SST0Oedh17/PKzwzkO3bx58TBoYhfhsFJ6LVMB+fx0829QBsSJiJcw/yPL2M0vGukgU5rweJiRCTfvKeBxCiRgrDwqMwvE4c6+FnAhXwVhgwcVWZt1C8ThEvB5IKIt51jeAZiLz7JWOmjs9yV6zOc/Vk/gG26iqKieBVqiMRlj5UdiFWtSZBOJxJiBEwZhnUXjYOjcHddEIxDweqIhFwZ9MwvM1dXCyopLVH9M4GwwxocicxwvBRALa5mYhmIjD8YoqZh3QPzkO7XMz0Dk7y/4tgHF9IMnaGT+XjlzoEKZw3gA4xTvdyeJPJF4Za7zqlpI4uh+T606XIAi3UFxOKh+LV54VhwEqCvwz7kVm4SiM3fMYQFsLhN70WlDQ5ajL0Oq9i3APPjMEQm4RCtnBzEz2hUcwmLocUUfu93PTK8eVV15Z8t/9fn86VtfFF1/sSkEUQRAEQRDi8QcA+Dy4A+ujtRIi8ozdBZCYcvH19PC/JqRJEARBEITA/NPfghsh94REMfRFpiZsY24O/RkvEQikNGXVEQplG0mToIggCIIgCIIgCIIgCIIgCIIgCLMgZ5WS4fNld1k8rs0nbjQaNSTIeuihh8q65ku5Zty1axfU1dWB2zhzaDeMnTtudzEIgiAIQlySAC9GQ7AvFmJaUQuxfRQWGwndrmGMqC5vlLlh6/fNMRdtGPunwhOHZk8sy9vgXEKBF6NhOJPwM1dyE0kvTKfdtZlLsxJlrs/QTeLR2MKeCt3PoXvE1f452B6YgQ4vRlQCVieM+4Ru5qKL3z8cC8Ex5v4vIJULxWJgXC58FY4sJAJJqFcwjleSuercGZhibgzR1SfGmXo2WgHjCS+LX1WvxGEm6YGRZPF4ZehWLcnixcXZOMXxinGp+n3z0LAY681gcVnMOozBhc9HkzfGXC/ieMPx/mykAkYTXhjj6IIT+w9j5qFrRgzJhHnh84QuHrHNcOxiWS6UaJdioHvJzYFZ6PREoNqTgHZ8NuQf9tpJAnP7+Uw0zOLMjSb9UKvEmOtZjKmGLmUrFt3QTiZ9MBz3svguNZ44TCa8MA0e1vJWzXP6SMIKmINV81PQl5yFhlgEOmZnITAfA28szlxNDlVVwPPVdfAbbz1MqnFMmExC4zy6SZyF1vk5FksNn0GML9YYmWP/RzeJGBcK3R5ivC90mRjxeGHc64Npvw9erK6D/dU17LfoZpHQRocnAm3eKHT5IlCtxFlMx05flI1N0cB5CgkuBqpBl82jCR97rtBF7GTSu+gMVmHrQqUnzvYZWL9mHvN3JkncGyisLDinRxbXDb2xK4vnkwT/TAwCE/MsDqCSir+jKJD0KOCdizH3ofEKP4sXGAv7ITA1D55IAvz4/VgCohhrLeiDCcUHEZ8XznsCMOgPwYg/wFzioovgOk+MrUtHYkHWZit882w8YIQcdIuNrrPrlASLFYltjOsGjpMB3xzrF4wViXM/PvVNniiMxLwsNmH17Dyc8Ibhgmchhiu2z+EsF8li4EkmoDoaY65X0S0txkGUYTELxWIsRmXn3CxM+3ws3uKkzweNkQhzPYuuc6d8PjgbCrHYlWkXuuiGDl3SJpJQH51nrmQx5iL+H13YYs1nvF6Y8vmZi9BUS+CzhX/D7zRH5lj8RXSJi985GwzDqD8Asz4vDIXC4E0mIbAYxxFTwBiYOFYxviWudBjnMRhPsPLh7xBME92Rznq9rC4YIRRdB+M60Dw/z76bUABqojEYCQSYK96qaBTa5ueY6+AzoYo817x5bRaPQf/kRNrVbtfMDDRG5qFrdhrqcu5WcRZEN7zeRJLNjXMeD9zTtgwueu3LYE19rotggnAvJIiSjIqKipIWUuWYn58vmV452trayrrmS9He3g6NjakQte5BmToNoaQbPJETBEEQhH5wR/HSvL8mF1+RjK1q+cPbivQ7PMAuXSAdiwXghzMNcCCWGzZbHUFIQJ9/fuFyGJLs0h9jIi3zRqDXNw8Dvih40gf13IDd3sWoXgvk7qC6AOAK9i4OM4k5GIwHWJwnFLThBdlZFgPNw2KZnU/44GQcYy0BVClxdmGNjCYXLtWmEl44ZtJFDV704uVZKuYUCg3xEq8dL+s8UXYptcy30B4Yaw1bA2OTTaCAJOGFwbgfhhMLdcE4URh/DYU9eIm+zj8L6/2zrJ8wPlcdu8xciHuGv0ehIl4soIASL++G4n52EYZgf+wMTrHLL8wT42RdEZxksayQC3EvHIyF2EXYikVhIKtP1p0DuhMPs5h0mwDgJlbLzAvIOCSTUfbXVN9PJzysXMu9ESZUTAlsFq5eUgpe+sZbMVYW+fvti/HEsAwoPMNYYCjw3BcNwe5oBeyNYnS90pcsXd55uDI4Bf3+OWjD/iv49fxg9PFklAXoXhCIeNIjfirhYf2IfY+XvvWeGNR64hmx2PCs4L5YCbngfLAj3ba57YutmhIypeKmpVh6j218Lu5jz8epeIBd/r4QDTPhqJlgbLjlvgh0evF5TzKh2rmEjz3TKJC9LDgJm/xzUOXB/q5cfGVTs/jqB4BXJqMQgyj4FWDx6HAuGE162eU1jh0cNzjGMbWJZAgmEpVszGEtUWCHLYJCBJxjTsUWxtYVoSlo8sTYuEu3XBJgB5uPAI7EQnA0FoTj8QCLNelWUMCM7TKWXLgWQoFoqzfG2hfn2FZvFFb55qHPNwfLvNHF+RP/I5ZgQA1lbjHAbeQ/ldmrV/3i/1eVvEZMrZdKxt8y93CZc5Gaub8aNuT9DfdIs2yPhPsg3C/g2o77CjtZiAO0gDbVbvvAufJQK8ChnL+fV3FRnNpdY51PgzXgHrMF5yPcV3ijbA8aTXqhIrEwZ/s8cdjsn4GAkmD7jdSagfvE++c6WMzfCk8C1vmn4VJflAlPcR+JwtB6UGA+tc/OGKe478c0cC+FeyLc9020N8E87oWVBJxTMGbxPAxBHKLzSbYHbUvMw4zihbOBEMyCF87EfTAYCcF5xQ+vGlgG161bblGLEYQckCBKMnIFR1pd6+XGhNIqiCIIgiAIgpCFFb4IvK9mCI7EAnAgGoILCR+7mERBEgpT/numPm29g5duFwWm4ZLANBM84eUn/t8KJdcKTxJWeZaUhaohwaxrtDKeQMGVwoQyeGBG4Q0KsfACBy3QhuN+JghCqxe8yMEDeK6gAg/VKBxq9kahwROHtf5Z9n8t1ClxJozAo/fG9FVNcdBKZulKpzQHo0F2mbAxMLsouCpMozcOjd5pMAr2v7KYHoJ5dmYIDgoLbqwFy1C7KJzES+RW7xRcGZpilzfH4gHYE6lgAkG0pkLrqWQSYGtgGq4PT2ju2xR4OYPjCAkqS2ngBQ6CQrrMdiL4g2Nx4RnAuWNprKOg91AsyKwVTsQC8GKsfPxmFHCjVQsKuVE4Ue+Js7kALwFxHsT5AC0fcJ5E4Q/PcZ+yjEDQUq46Yy7UTAn5CJYZrTJwXC7MOQvCKZxP0OoC50q0+ktZ6IaVBLMIRMtVvIDt8c1DrRJnigI4z+J6cjLuZ2sJtk1KOIaWHGpYEKInmQCv0G/qlBgT+GG+qKCAzxY+c1g2XJ9QcSG6+B7LiZep2HctnhgrLz7v2FdHFq1uUVgZgiR0L6aHCgRY3xQ4L0hg1EG4kNQeCQUAKWYSCrNmHor72DOJ+x58PhaE0wob97sjFUxIbrZwvhQpy+lMUMCBShr4V7QuxD0pClJQSIKCPNyXokAEv4FKSaj0gQoHqHhkZ13UgqVEiyCck/CF81wfU96aY/MO1h3np5GEl82duMbg33DfkFpjcF5NKRbp4bKgOsVwVPZasNrD/TBoW9+ydI7iMJCxDte1dkPHmp3gpUmVIMQURA0ODsJ9990Hzz77LOzbtw/GxsZgcnISPB4P1NTUQEtLC6xfvx62bt0K119/PVRWFtPfcD7Nzc1Zn4eGhjT9Pvf7TU1NXMpFLLGgM0wQBEEQhCj0sMu8lIbuEleHJplLtqmkB7q8EXbZITO1OYd2v5KAGs9C3S8vcCiPJoFpGKP1EV7k4MXHlsBslhWBaOBFVOZlFFEcvNRZ55mDdX5tHhQIuUGBxdbADHshaEHwfDTMBB94uYcCFhRArESBhAYXZOEMgaNTSAunfKWEphOq00NhDlqm4SU4XhjjpSa6ZDseCzChEQr/0Xp0R3CaCftSDMd9zMINBUkoHDZ6d8lsTxaF0+g+Fl/loPtSQiZwv7bcE2HC2kJcFJiBV1WMMjeAqG6EgmYUHuN+Bx8vtJZGi7+zi5akuA9EZR2cEauUBJsncb7E36CQJLQo6EKBFwqOUNmHKXckvEzph1ltJ/xwMhaANf5ZZjW9ITDLhL6oEICW4jgH4POPbnb1gEJoVCo5FVtwXYhPOFpCp4TnqHSEgvXUd1PrAZYZP2N90LobBXU456BCCZYb64ftkmkZlAnWG63gU4JudMXIhEyLlqpTSS8TIKHFEu7P2j3R9Jwym1SYUEqrG0rsAytYUKpZmi+5kcS0xRcaEoTrBFGHDx+GT33qU/C73/0OEon8Bx/jH124cIG9XnzxRfjRj34EH//4x+GP/uiP4O1vfztUVy+5O3ELPT09WZ/PnDmj6fe53+/t7eVSLoIgCIIgCBkpfQHpbNAVViEBHUEQzrIMxRdhPnjv2OXDGErZ68rlZTzZoUCQe1wigiDSAhC07sSXHfOjGkGwGtD6sZhi1QL6rcDROhSdGGOsVRS+pQRQKDBH172Z1pNaQPfNBEEQQgiivv71r8NnPvMZiOYEeCvH9PQ0/Od//ifce++97Pfbt28HN5ErOEILMrWgsO/gwYPpz+FwGDo70Ss+QRAEQRAEQRAEQRAEQRBuAq1D0XXnpkW3pQRBEGaRiq5sKZ/97Gfhk5/8ZEEhVGNjI6xbtw4uueQS2LZtGwwMDBSMY3Tu3Dn4y7/8S/jDH/4AbgLdFGYKj44cOQKjo6Oqfrt//36Ymlpyy7Jx40ZQyFyUIAiCIAiCIAiCIAiCIAjCEBSugyAEsoj67W9/C1/84hfzrHzuuOMOuPbaa6G1tbWoJc8999wD3/3ud2FiYsFP89zcHPzf//t/2d9RgOUWrr76avj2t7+d/vzAAw/Aq1/96rK/+/Wvf531+brrrjOlfK4HnYMTBEEQBEEQBEEQBEEQBEEQBGGtRRQKlDDGUyZ33nkn/PznP2eCqEJCKMTj8TDLqHe9611w3333wRVXXJH+t7GxMfjc5z4HbuKGG27I+vy9730PkmWEH5FIBP7nf/4nq01R8EcQZuLzl3GIThAEQRAEQRAEQRAEQRAOIDo3Y3cRCEJYLBVE7dq1CwYHB9Of0ZrpbW97G/h86g2zGhoa4D/+4z9gx44d6b/96le/YoIWt4BxsbZu3Zr+/MILL8APf/jDkr/5whe+AENDQ+nPt912G7S3t5taToIAWIwOSgiNotjipZUgCIIgCIfj84fsLgJBEARBEIRlzEych7mpMWpxgiiApbePTz31VPr9ihUr4E1vepOudFBw9bGPfSz9eXJyksU/chMoxMuM73TXXXfBvffeW/C73/nOd+BLX/pS+nMwGGSWaARhNhSDjCAIgiAIwr3QXpAgCFVzBSkwEgThIE4feNLuIhCEkFgaI+rs2bPp99dffz1zD6eXlStXwurVq2Hfvn3ptDds2ABuYdu2bUyY9P/+3/9jn+fn5+Ed73gHsxS76qqrWMwsbBMUTqHFVCYotOrs7LSp5ARBiGgRlUwmwKmHWgoWShAEQRAE4U78gTBEI7N2F4MoByrZOjTWMp1HCMJ9JOJxu4tAEEJiqSDK6/Wm33d1dRlOb/ny5WlBVGbabuGtb30ri5H1n//5n+m/Pf744+xVzJLsve99L9x6660WlpJwNRlWe0aobmiHYGUtnD+58LwTnCEPigRBEI7E4/FCIkEHYUL+vSBB0Bh0vvWkQ+VQBEG4kEQiZncRCEJILHXN19bWln6PFjxGmZ2dLZi2m/jABz4AX/7yl5mrw1KsX78evve978Gf//mfW1Y2guDlYiFQUQ2Vtc3UoCZBrjAIgiAIgqA9BuFEaJ8rCw4WWpNAniBcR5IUwQjCfouoTNd5e/fuNZRWIpFIx4WqrKxkrvrcypVXXslee/bsgcceewyGhoZgfHwcGhoaoL29nf1bb2+v3cUk3IjC7wCpuNDq0TKcfDhysJsPgiAIK1m1/UY4+sxvIRado4Yn1OPgLQYhCTQG5YD6iSAIB0Gu+QhCAEHUzp07WWyiwcFBuP/+++E973kPi2Wkh9/85jdw7tw59v6WW26BUCgEbmfjxo3sRdhLki69TZBEKcy9EGEOFEicIAiCKEeoqg48qBQSpbYiNO0yqLkIm6ExKANkuVYej8dH7r4IQhLINR9BCOCaDy8777zzTvZ+amoK3ve+9+ly0Xf8+HH42Mc+xt5XVFTAm970Ju5lJQhCvI03YRZ0QCcIgnAmfOd3hZRCCK1jhqPVNblpNg8nCwF4jcFguBrq29zrhcVsFMXSqykpWbn5pbBs9Q67i0G4mM6B7VDb3GV3MaQgGacYrQRRCMtX+1e+8pXwxje+kb1/+OGH4U/+5E80uen75S9/Ca973etgeHgYgsEgfO5zn4OuLpoICcLJBz9Mh1zzmQdZRBGE+PRtuwHqWkvHgySIPDjfLZN1snjUNC0DN+D1+qHnomtpDJqFk900c6KmpQuaulbbXQzn4uAhyO9M7AGPl5QzCfuob+uBYGUtdYEKkkDhAQiiENxXsdnZWYhGS/vseMtb3gLNzc3w+c9/nsU1uv3222H79u1wzTXXwJo1a6Cjo4PFfYrH4zA5OQnHjh2DZ599Fu699172HkEXf//wD/8A69evh4mJCQiHw+D3+3lXhyAIISDXfITOkaMoFCKKcAR46PP6aJ9D2AtZRImHa5RJFuu5YDVBWsaE9rFjeAjieYSEACair59QSWfs7MIdkeNRaB0WkaqGNpgaGQJXQCETCIIQTRD1oQ99CO6++27NMXV27drFXmrBOFP/5//8n/TnT33qU3DbbbdpypcgzIE0H9LwuhyhTbeQrjCq6lthavQs9/IQBFH4AoyEAITdKB5ynSScOzXRBVEchQBp95BxClQmChU1TTAzcR5c4XYQPTSQe1LhhOquEcanLKJoDAqHPxAGt4DPGwnkCYIwAp0mCcJmHO2TnVPdSAPRZHR2kz9YAeLDZwyiOyB0jUaIhatc1bELMNq2iYbo8UJ47zHoAkwwJLiA5ScEWPwfzYNiCQFk6A9ucii6gBUROWJLcRSGer180iJcNgb5IcW8TxCEsNAMQhB2o/PgF6yoAeHhdT9CJuAmQ5cP5fB4fOxFiIWbDkIsVp7LDroy4LY+IWsAQvug4Tb6XPnMWYeTrVG4HUhcte+wHBKGqmgidFdP5xHRcNu8QHtBgiCMwH0Vu+qqq1j8J6vp6+uzPE+CsBN/qALmZyYE7wRegVkXDn54+ZBMJrikSWS3r75+cc+mm41B0kAUDjkuwPhB1iji4bbLBxqDYoHrsPiW9fz2gm585oTvJgnWYW57BWVhDqTziDnonsskGIP8QKs8sogSDleNwQUFTYIgCL1wn0FuueUW9iII15LUfjhKJp0pBOB3SbyQDh7+4nESRImCFGOQoysMuoAVDxnGIE/oAlZARL984Fw+GoNiIYMwnncZaS0WCzetw6k9JZ1HzGpg5yrGcXNXj20kQX3dhgxjkCe0FyQIwgjumjEJQkjIFYbaJqLAmIJtniW4AOPFglUeaSCKhtv6xG31lQG3XT7QOizgZZDwS7HC9VmjeVCsi3IZhKE84/O4cd63CvLQoKaNPGSNIiBumxNoL0gQhBHcNWMShIOQYsPDzRNGyh0LXcKagl5PGDK4x+F0QbJw8BNv/EkxD5iI2+pPBz/xkGIe5IjH67e7CIRscyBf43jw+MglkCk42RqF215wIZ2kVvcXhKnIIQwFfh4ayDWfcLhtL+i2+hohmSBvPgSRC80gBGE3TvbJzlkDUURBgDNw7uWD0zfdocpacDMi9onbfLKHqxrAzYg+D/KOH0TCULEQffzxHYML6XhJGCoWEpxH+CFmXf3BCnC1MNRlinGkmCkerhKGCnoeERWKb04Q+UiwahOEXGjVknO0Kwxem+7FNiINRHMgVxhq2kjU5VKCecCV/WIOImrBSrEWmYgUF2AOH4Ouhj1/gj+DnK1RSBhqDnQeUT8GdQX3lUjhQL4x6KJ1WBFz30VKSS4ag6QcrIlEIm5WNxCEtLhrxiQIEXGwKwxupJpIsIOfY3CwBiJvqzzhELRYViHDGOSJkBewoj4bFuFRBBfMKC4Ygy7GbXMgQmNQMDfNbjqPEObi4DMxr/OIqHUVUThmJaL2i1koAiolhavF9NBArvkIIh93zZgEISBO1v7ityldtIhKko9dM3C0FiwnRK0racGKPw/yhFxhCIigc4NZkBBAvLVJ1PUpBbfyLaZDbqnEwlXnEVGfNUGLZVk/idovJiDsfC9quSzCbUohIoZLEPVMnCSLKILIw10zJkGIiINjRPEiHRxYMIuoYLgaHIGDNRD5haYQ9HkTtVxacbBVntOFAMJeilgAi9UgfP15x4jyc02PMIYiukWeCy6ZnIODhQCcXYUTZuHgvaDTzyMOQfcz7rJ+EVIhRNA+IIsogshHglWbICTDImGJ+JdfJmwIBBNEuR0pBFHkCsPRSDEPOtwVhqgHPytw2/gTVRiaSdeanZKsTS4ag9ysUUAKQpV14K54oZJ0DAeqmzoFjVnrkD7QLQNwSP1VIOr65pg+cLJypsMtojKpWZyrRYBiRBFEPu6aMQlCSGjDo3ZzJ5pFVC6rtt8IMqJbk9Aphw6ZcUgf6L8Ac9c2RkQhgGMuH3TgtvEnw+WDx+cXNk6AGUhhCcANOeaaFRuvBDchwzzIY53q33Ez+INhkIGKmiZw01hyl2KcoPNgRrkq61qgpmkZyAgJ5OXde2SWKVRVDy3d60AEyDUfQeQj3gxCEC7Dyf6w+flkX/y/aIKonPp5fQFwEyJuQs0ag3IccmXGwe5YHB8jSvy1yCxkmBd4X1qJKAx1NS7aC6bTEW0vKGGfuK++xssYrKhZ+pAU+xlbvu4ycBNuWYu71u4EGeqH+4T6tpUgJ3QecQyCrE0U45wg8hF/1SYIQtpNN4+Dn88fhNqWbvaeFnKxkGMMgqM1EJ3SB/o1EMW2zuCNkIK3jK6rbmgH1/WHoHODabitvoIjxxjkpZS0mI7w9ZUUJ1smu23ISPqMOFk50yiVtc1Q17oCxEVxRp/oHoISzIMcWLn5aruLIB3kmo8g8nHHjEkQIiOhT/ZQRa26L3IoYv8lt4LXtxAc3RcIgdABTWXddOvELZvuzL5NafgJU/eMIbfgCkMcn9iuFcy4TCCaOQ+GaxqhZcV6cA0C9ofZiLYOux1h1qJScHpM2no2sf83LRtg9SbrPJP3tGp/J8E8qLduxfCIGK/RzWdil+0FkdReS5iYdOJPA6pwtntIYzQvXwtV9a3pz17vwv2MKGSuRSKtS8lEwu4iEIRwOH/GJAinYuMCu3LL1ariRPA4+KWEUMiytTvZxUN1Y4fhdIklyB92aerbe9Jt1L5qK3N5smLTVUIMocxnjLnCaO8FKXGyJjYH+rbdALL0nUiHP7PBuvK+4BQdtFLGGDhtvZvVK6VYiJvGnyxzII9npHv95VDV0Mbe+0MVMLDz5TBwya0cSkcYRoZnjnMZu9ZdxsZ1RXUjCIEMfeDyedAMQVTf1htg2ZodIAJu2wvl4cJnsG/7y6BzYHtaScR+xDyPkCCKIPJx36pNEI7RQJTg8eW8AaioaYQ1l70SujdcwTVdQh9u0EDEgM/LVu/IEozWtiwXxypAkE22XbhhDKIGYri6If25trmL/T9UKZ4QwG1IsQ5zoqN/W/o9KoM0L1/jiudPdKQQhnJYp0JV2Vr//mBYnHXYKZBLKtWgVcDApbfByouuMbNHCJW4aS3OnPvDNQ3i1N0h5xHd9zIu3A8FwlXQ0NEHngylYVvJ6jpxxmMyEbe7CAQhHO6bMQnCMQc/OxdY+/JGqw9RNFwE2uMYRD6f7Fa5ohBmrKksn+jldaVA3ig5TYOWoau23wiNnf0gpisMOcegHlwx/gCgdeUGdtlAiIcbL79ER9Z1WO/cLcMYNKNPUBiqxjuEHXsoWccgeWjQ1WggAk45j+jFLftBuc6SYoxBihFFEPnQjEkQnEkmkxp/4dwLWPdtQt1WX4+t7gAI1guubgYZLsB4gxdfudYBhI3jT/RHkMO6FKyslWh9k6WcHNdhG6usyk2zNGPH3cgoBOjb9jKVwiAag07GjXtB8XD3M2Zr7G6r4oSJ3sWC7jXIIoog8qFVmyBk3bgYWGwNu3FR/XMxNwT8cEj9FPkOfmqfG+2CYZX5C9L3br/gs6sfguFqseM22YbimGlRFRI8f6LMVYQ52K2UVNXQruJbiiueNbdiZAwanZ8CoUp1+Th9/ORVz+H1zUWC/nXfeUSMcjndRSmeRzpXX2xL3kIjkIMGRRDLWYIQCRJEEYSkGDpUGdywL2x6y6fh+INfHu6qr90XYEQ2UrtF01tsSatLOGkOtGcQ+gNhW/IVHrfNCTbus8JVDRSnqSAuG4Q2nkeIdEM6pCnk8xKCMRMJMc//Vgrp7ByDoggjiXwq61rSsX0JgljCB5y5//77Yf/+/WA11113HaxevdryfAnCLmQVAmCMp0Q8Bk5AlI22Xbi6/oJUXcR5AA9ESUi6oO7WDALZDpiyldcIdlqFNnWthkQyDmeP7OGSns8fhFh03hF96qa1CcegXW5f2vo2w/i5E2W/56b+kBobXPPhbw0ZiqjN221jUNbqShg3ecXGK+G5334P3E7enlyEMYjjwiRLtPy8rMnGzrxl2guKUtbO/u3g9QfsLgZBuEMQdffdd4PVLF++nARRhCBo3PDY4ZrPqoNfAVZdfBPsf/Tn4EjE2PNYtlmzXxBACBmX1aKDn93jT+/FR6iiFuZmxsGRiDD+LMTuMajKKkplnwQraiA2Pmy4TIT+sZRMJvT9DuwRRC3kr++hRw3h8eGT4EhoHrS4sVzW4AVwu7DX7rWYF/5gBUTnZ7T9SJSuF6UchnFMRQgRoOFEEAVxxqpNEBITrqq34dBh36ro9TlJK8TluwuXH3zFQHFtPWx1RWhEEcDjhbqWbnAsLpoXcAw65gLQIfUQRQtW6yUqatXr+6199TWSd0f/Nsf3q2zobmNDrvn0/1RbNu4aP26rr6zrV+5eUO5+U4Sri1Xro51umhdLoN+9rj8EjgT7Xoh5QYQyEIR4kCCKIGyioqYJGjp6ob6j13rtL6vWRCE2ACaSUz0RNt2yuGOxEw+XoKFi1D23D9x38HMBgj9nmQG4RRh/vFBTFztd8xmhvW+L3UUgFqlr7YY1l70SKuqa9c+DOg1QbX1eBZ/XjLSjtPOgDW7RjLaVrPtQ/jijHfR7aJCz/nrvAEREyD7QUSYRz1Wmtb0CUNXQmvWnYLiaT6EIcZ8LgnCia76PfOQj8P73v7/s9+655x747Gc/C7Ozs+zz+vXrWZynNWvWQHt7O1RXV0MsFoPJyUk4duwY7NmzB371q1/B0NAQ+/6yZcvg4x//OKxatYp9rqmp4V0VgjCV1p6NUFXfCtN6XeEYcs3nMb6oqshehs0b4T5BQPfGK+HoMw/aXQwHY5WKs83WKJIfLtCtWzSysAfjiWPmfRUuJm2dA5lBoJq2VgrGatSWl0R9KlNZkSSw+AHJhHa3fPbXV6XGcYHvOGaecBTWu2m2bA4tULXOge0wuP8JcCSyzYMuPY/koi++qiLmmBNgDHJZZ0TfCxolp2qhqjqYn50s/F0B+lQLtM8gCBcJolCAhK9S/Nu//Rv867/+K3vf398PH/3oR2Hr1q1Fv7927Vq46aab4H3vex/893//N/zzP/8znDp1Ct761rfCF77wBdi5cyfvahAEF+1wUePz2Lkwi6AZsmz1Dji173HD6eS1owB1wzLpO8joycz++panfFsEgpUQmZ8GK2nr3QxDh5/h3AeCuCFQrJkXRJhLeFHTtAwmzp+yNM+VW66GA4//km+iDuoTVTF7HFRfQlLLZCNWeVYGci+Ut5nfNzEul1DtKAD2emjQn0Bd6wrbBVE9W66FI7sfMJ6QgEuRrvOIDXGTrSMp5Pmc1xlIyEt/DuNCzTrhKDfNNlmGj509zjVNYcYjjQuCKIjl4nu0hEoJobZt2wbf//73SwqhMvF4PPCa17wGvvOd70B9fT3MzMzAnXfeCWfOnDG51AQhHoY2PFYd/ATZA+QSrKwFx6JjXPj8QZ1ZSawBZhCjB46mrtVClMMMLNv821x3/fE0QAiCFTVQ29zFP2FB6mfFs2X3HMjzWRNxLnEDRhVH2BhUrHZNm8pcoMsenZ4JTEHSZ0m/eycj9eVhsWBRPibA9XkUDQufA7vXYjsxunb377yVU0GKfrANHuuTDHtBWdecFLW84ubmKoQI0Cwy75EIwkw8VluKoDs+pKKiAj7zmc9AZWWl5nTQiupDH/oQe4+u+77yla9wLytBmE5qsZTx4Cf5moqbgvq2lRwSUqQ+kKA2KFrm+EOVpudFmIVzNRDLfsfuuhvI3jKrxTI0LV9jdxGEJXMMFhtrC25q7XQPKfc87Q9W2F0EYSjXT8UurLX3r8JtbLDnQs0lXYGBKvK4dCvoksl613x2nkecMwZt3w/ZjJ1xymSH11xsuzCmEIpF9ZJ4CIlwHjFl7IjSJ7TXIYiCWLpi7Nq1C06cOMHeYzyo1tbs4HhaeNnLXgZNTU3s/d133w3z8/PcykkQVmKLaz4eF8Wqyq0IuzPo6N8OzkTR5KKw2chFtKwHPwFc6OAzWFXfxiUd0dBapkJzmao0xKu6o/pR/3MtX10aOvry/5jRJ4bcn0mD9f3Wv+MWy/MUDpXrkVJUEOVR4/Ep4/vyPZ+EuYSrGqChvbfwPGi6hwZrxqOw414BWLn5akfW19IySDAGRT2PmIUIY5CTJErFV+yMF4o7bxHa2gCmFV/ydiEIB2PprPnss8+m369bt85QWl6vF1avXnBtNDExAUePHjVcPoKQyx2LfZtuvRuegE7LG+4oGKzd68xNt5YiLJZXb7mNxQUQoK1spnvjFc4cg1qEoWsugf4dNxdIQq37T3vqK6vAJRfpD68c6NlyDXSs2qpLCGB/G8rbf8FwNVuHebkpFWsOFNeFV/a6bdQaxWaXbBzw+UPg9jHX2rsROldfrH9Pp7H+lXUtSz81OA6csIZV1etXzE0j4hgk13zZJK2NQU1om5tROXPgklvzxq06xTh5PTSIgBPmcYIgBBZEnT17Nv0eXfMZJTONzLQJQir0rr0aNz2Z1heGF3wdGy4Mhtpz0XXSb5ZER0vfGnbLY/fGW3KcGhtAy7hAF5mBcFX+wU+CicKq8S/Vc6ZIVt7FS9GCFk8Zlz5FL2htrSvn4NglkpKtT22/FDKBUsJQbQkVeW8m4hrHw4pNV3JPU7rnJYVilRV0xvcVG60BhOgmswohROWk2BdL+7xyZPm6y4wnkteOcrVrfXvPwnlEMsW4xcxBakxpO857ZL2lEKAMBAFuF0RFo9H0+8HBQcPpZaYRiUQMp0cQlpK+41LkO/jpyLO+oxf8wTCIgYMPfro2PLqlofpjrsi+MROl/Jnuw9h7QcplEHWa2RLXlTRbhSeZTJSPzyPDGBRlrrIMZ9a3qBU3B1eoejEy/kV5dsLVDeDx+sDVGDyPaP5d1pgVYxwQ/NHyjKOyYnVjBwQra/XlpXFe8/oCmb8GYbBJIaS2ZTk4EotCEYiynpl9HjFrDFbULIRbcSYSjw2CcIogqrm5Of3+gQceMGRijEKovXv3pj8biTdFEPZg0MRe62Yg68La2KMvvRCBEyK2g6WbYY1Zeb1+EOExy3OLKV43yg2P50JlGlIf/kSAe/M5pz8y96hFY0TZLvvllzmvZ0nIgOUCkzQaI8pOYZAhz3yKpvKYud8yPvadM+/pQvNxhJ9SkurfaxxvVsFrXOelY3/VNK0F/TtvhRUbr7TnXCVAWxlCkLNo5vPE+lGAYukZT3nzgtopxqYKM5tQQcaAEUUbHrHyMs/3wrSJIMUgCNGw9LR40UUXpd8fPHgQvvvd7+o+tH384x9PH97QRV9/fz+3chKEITQKWN3m3slJB7/8hM1JVroyOKugwo5jIwcm0dom9zeZz2ix9Gyf03TmH9Ks8StAp7rcIqqoIMpuBBwawraV5HBzWcV53lQ3Dyvizd+mIlvdksbihdpdX0ePJYnbQEORDM8HAlbfinjRhBUDQ3Hs+GvvW7qbtRuvzwZlVQuwfX0kCEGx9LR4ySWXQEvLUoDSu+66C77zne9oSmNmZgbe8573wG9/+9v032644QYIhYwHmyUIKzEadFTrwpalmWaVxYKIByOeCFk/RY7NlYhNRwh07lMcebGNbqDaereAoxFyXtRBlkVUMSGA7SZRFvWb+jp6FJtj3zmkO3JRirrm05hO5g8MP6v6fr96523gKBw65lRjwFW4VgvK1p6Nmr7vVsS4/NRTBuuFoWK0lf57g2Llb125kT1fNU2dYAcitCsPhQenKk30bb0BKuuWvFWpQ7K2EKHvRCgDQQiIpbc7Xq8X/vZv/zb9OR6Pw9///d/D6173OvjlL38J09PTRX87NDQE3/jGN5jQ6Re/+EX671VVVfDOd77T9LIThHAYWNhs3Rw6eEGWdtOtuGcMtqxYD44ipw9EGIM8DirZLj5KbFXsqi7zOqI9c3Q94QsEQRwUvgIbB83vmZrKRWNE2VxfEWMXFBWYOBA+bZs0aBGl2S9axi+tt8gPhCrBH6rQk4rqb9Y2d4GtWPjI8ehDexXjtOVVWbek0Kopz5yMei661tTziM8n0jrvAoz0pU3reM+Wa01Nv661G9a85HbrlJ+csv9TiterpGWyndXX0faBimoQC4eMH4IgVGF5dNabbroJnnrqKfiv//qv9N92797NXh6PB7q7u6Gjo4MJmGKxGExOTsKxY8fg3LlzeWn5/X745Cc/CW1tbRbXgiA4YpErDCMHP1noWLUVRk4fhoraJhg5fcju4hCCHVhQO7B5+RqYGj1rPDGnHLgEFYZm+flGi6g4h4IRliCGMJQvIljleTw+qG9fCRcGD2bEYBCvrX3+IFQ3tLN5Fss4PztZ8vu8hXnWxklUNLtj5h4jyoA1iuEQUbr6zvz+CVXVQeOyAeZe8+gzD1pi6To9ln9OlWUMZqVlORrHr97xo2TvBStrmw0L4ErR1rcZZidH2Ng4te9x09s8z62VAGsDr7kdFchGBg9C19pL4eizSx5xZFszMve1PVuuYdYokyNnzGtPRWHjIhaZA1sQYAxyKUOudXyi0IFEfKUkNyJCu9itsEYQomK5IAr50Ic+BNXV1fClL30JEoklH/z4/ujRo+xVjrq6OvjUpz4FV155pcmlJQiT/D0vbmx4HKq0ft+qYOJWbwD8oUpYdfGN7L0VgiivL5D9BxE2GxaWwcjmyo6DH2rSinChzJP8ANUCjEEeZFnZlOoz6+qLlyHnjj2fkbUipMUijnO8FA0EKyEyX9zSnAdo4XX+xIvgdIq75tPWp1X1bTA1OqSrDEwTl9P4CVZojFOmKVsFOldfzC55jz/3u7KCKKK0cs3omSNQ1dgO0bkZaioNY1C7yyH9dA5cDAce/4VQl1+Wovk8kmnxrN+iTzcWdA+eDzr6t7H3JQVRnGjoWAUT5wdBKDitV60rN7D9V6mxYuiyV+NPa5qWwcT5U8CFvCsDPW1W+Depeciqi3BucQw5om8uzv5NMuOuEutYXC/Ounl/2ZpL4NSLjxnKtui4KDVeNObVveEKdh8TqqyD4RN7wVKcch4mCIdi240cutP7wQ9+AFu3btX0O7SCeuUrXwn33HMPCaEIl6NtgfV4/aa5zlL5g4X/mbgxUJ22zjIEw9lm7A2dfeCETXeh31Q1LFiatnNy6bDq4ptAXOTYrOb6eceLDl8gDMLB4RnP1FQWRXhYVd8KMoAHPxQQtfZuKvtdo/MxapXnJwqOo+gFi8b2wws1u2nr3Vzwkp7b2mzBWp9JU9dqKZUx1BCsqIG+7S+Dtp7yz7JasgX72srf0N5rmSKTISycg0IVteAP2rcOcxmDRhXjNHtoyNKM05lnZu5q0lCsfX5Vps1DaNnetwU8OS5RRdDC5ymQ5VkftKzKSV3T75et3sGhFBb0j4ljoL69J+tzdWMHeP3Z7iiFEMhzLoLXHyicjcXPG7q4zSkAiHpuXbHxSghX11u0luUmCgIgRCEIQjhsPU1s3LgRvvvd77L4UG9729vgJS95CTQ3NzNhU4pQKARdXV1w4403wgc/+EH43e9+B5/4xCegsbHRzqITBD9LDQvWp4FLbuXqjkXctde8DNGNAm60sw5+Hp9wBz9eTdC9/gro23YDNHYNcEkvVJmjgS++BxhdVFQ3mipgyLwQX7HpqrzviHDw46GBmFnPon7MNT5veQc3WzC/f9AVCwrNrLowzrICFmEO5ERdS3f6fXWRYN8LY11dnVG47/HydUSgbs3J/g66JzUTs+egcFX2hQZPIY1mRBjvGssQqq7L+Km23+ZeNC7kbVEbKGKMQbR+4nMRLc4YRPdxxoqgVTHOp9/NuAB7HDWYfR5A69qM3EBIBC0Wxk7KxBarPB1odSVp5hjEOTBzny5u/F3jbRCsrGFnO6/XD8tWXwJioHBLg4eL0vLzsvnPDIv7l5WjIBOQIMUgCNGwxTVfLn19fXDnnXdm/W12dha8Xi8EAoU1DwhCdjCGQrEVqqV7HQyfeBFaVq6Hs0f2GN5gBsJVtgSothq1baKn/j5/KMftoqhtyMetA2pXGr2cKJ2lBFrVOli29hKYHj3HDi5Hdv+Ge/pZlkKijkGVxUq5rSkECp8q61tgdnIUOlZty3J7pHceXLHppUXTUYfC7ZCv5eBnmftWFTR2rkrHKFrIo1Amgo7LAqzc9NKScWZ6uwYWXLHEIoIKInjmX8LtUdF/Uyxtk3BVA3RvvAL2PfJTISwmeczB/lJWrSpcmGkpA7ZfVUM7jJ09rq2QS5nJEe/IxItlFKLka8Ur9lnlGewUdP/oD1WkEis4R46cOQwNHX1c4m31bb0BRoeOWN5W1iuKqfXQoD3l/h03s3U4282r3WtRIfh4aHAk6WqaF6fM7DFR9jwiQFeqPWtmejwp5PK8Z+t1kEzES+7dtcwxqFiIrotdF5aCI2svfxXs/f1/l/bQIMAgdM2cRhAaEfYmMBwOkxCKkBMVazG6TSqlGY0m72uveBUTSJmCxgNZ70XX6c1I5+/0Zmemmw2QAiGsskyySONH0rR29vqCzGUjxiuzBYkOfijUWPqNUtAHOsZ88wcrCuejsbLBYpZVutBpCVD0Pl+AjjMoPJSRlAvSgigKVNQ0MoGUEOAQ0XFJb+6aYO3lQ6iqtuz8Yumhn0Pbthq16NJQhOYVa7mWX3VbWzy/OXUfVBCDda3IurzLTytUXQ/L113GzTVtuKbBWJk5dK0Vc4SrxqDBNkCvHTIhS9+my+lYYa8KVBZpZZkzJtaNp0V7rqt1zeTJyjieRyRRjEPvD1KMSRHKQBACIqwgiiCcjC/DvUmxRbJs0E+NC1vm5kHrwlwqULue/G1HoKJwR2XfrtiY79ZNZHhYZxUbg1w3qqlznyX+3wXZZJtBZrUcWkUnguNeljHJ7RnVkgzvtuE5dUnSb04aQyiM9wWClvaJUHsx08ptpTCywJ8ksogqV1ZV6RuorygxILmjuk3kfB55wrx2pLBjHdKcp1h9VvQZtn1Ntzt/9fOj2jh/1NbG2tlSBCwSQRBLOHT3RxCSo2LzaGjRt31z6hB3bwI2o9pxUd3Ybu8GUvKDn5AuB62M11G6IOBIFBu1CXXgZq1vHnXPTsMCIbZDDv+mXyyXq7KVTSJE8xsQAhitgI4lx4pnxsw8MG2hnjuzhdEmnEeMtZ+YLsCsRZIKC7o/MDwe9VZLhWvtUnOX3nnNKsU43nCJxcdhDGa3uxhjOq9PFX6W7WaMF6HWTIux+5xAEKIiRIyomZkZ2L17N7zwwgswOjoK4+PjkEgk4LWvfS1s2bLF7uIRhOWYsmAb0VgseDjV+TszUZ2dnnJJspGQppgWFTTTlRXX8ShJQ9sBZ9c5RceK4pIxbKrLUfEsdDB+4uTIGTCdcnVX+I8DaQ/jRdtK0vpww+z6qxACaI6HpCl5DYmV+pbDx4mt1ROgbQ3pxTlgHbVZKYm1oQDDQNrn3sZiaokXquW7IijGGX22K2qbQCpsEzhYKKAU/jwiQyxvgiBsF0SdOHECvvzlL8PPfvYziETyA0Hv3LmzoCAKv/+d73yHvb/tttvgjjvusKS8BKGO8htFXyC09EGnBqIUBz+LsfzgJySilisHGw2itB7mdI1BS8aHIuS4VHO4KfusZtajVJ0kcoG0kEgqDesD+5oG56o0dg1YI4gqi5r+tlsayrGMkl2ilExRlpgYXIShnK1LSn5b0RWnzOrHxNYLWIsxOgZ9gbD1z5IBCwMR9jiqUBxWHz0IbjWemav1WVqgGGdptczIjLc1k+ofFS9H0aa29zm2O39hcfL8ShCSY5sg6qc//Sl87GMfY9ZQWrnsssvggx/8IBNenT59Gl7zmteA318+YB1B2E3z8jUQqqzLEkTp3zxYd/kgC2rbUvfBz0QBBi+sClhqHOszldU9hXSUGYN1rSvYXCg9dMAp1CiOaZesuZSXQZAzmiaDpOUXq+XncXkaOVxVb+maheklOaZp6SW6lrxMLZYz5rjWno0QqqjNjlGmu16cPS444KLWzPxYm0lwHpFlLtYcN1mWeqXK6YD5Sjec6y5M3+fWi4vAjSAIwjpsURn70Y9+BO9///sLCqHUbAaamprguuuuY++Hh4fhf//3f00pJ0Hwpq13M9S1rSj7PTXPgeaNc8b3+Rz8BNzAmGoxXsD6RMg2ELBMTtQCLaYVR1Z5Zfu2a+1OCFXV5TZc8aYu4RbMrmdQpmD0dqXtpDKVLKumsWCDAonlzSlP/xlG5zxQVd8KTV2roa5tpfG8NBQhiWIo2ddeVchdRxwfqtHZny3d66CmeZmaDFQUwZCLBnAislvlVTd2GE7DHXONtvnXlH1O0S2yhftUE/KyYvxU1DTBys1XcymHTHtXEeGueMMhnmPHqq3cykMQRDaW75JefPFFZgmVAi2ZMBYUutp7+umn4ZZbblGVzvXXX59+/8ADD5hSVoKwhIIbHJM3M5pjCui9ALNYA1Hyg58VeLw+aF6+Nuev4m+e9W4mMw9+lhhE0cHbhODAzq+Xkw6wstRFyxgTsk4cYjBkJccrdqLNTWW0TfyBMFTWNqvLS2dlW1ash/a+LeXLqkYpiVwCFWg2ua1fKmqbVe9neeZdMC3FIWcmte5+eSFau2mkadkAh1R0zF8ytJuI+1MR99F256+iDXq3XsdH8G9xVXnM+6m20eKu3u79htUEK2rsLgJBOBbLXfN95jOfgWg0yt7X1NTAF7/4Rdi6Vbu0+dJLL02//8Mf/sC1jARhe/wZxYwNHm1I3UC5TeLal/wRKB4S2JndznYcALVbZ+TTunIjnD26B2TA7kO2kfzNjFPm5j5Jo8J1UUPnqvJpqMnH1vYRpL0ddlERrmmE6fHh8l9UzH3+VbWlRoGqPTOP2GNC05i1qCq4T0vGEyq+aG6BeI9Bczw0iIdqxThO9Rdy3i1TpM6B7VBV3wa2Y8cYFLC7DOOQZ7nUs1TKQ4Od9ec6jzqjG51XF4JwGJbeRp49exZ+97vfpT9/4hOf0CWESgmxOjs70+75zp8/z62cBGE3Zh8orPKHbfnBSG29HLJZ1lM3YYRQFlnlCXk4F5i61m5bDkT5/STes6y7jCJjRhUEb5ZAsBLae7dA68oNlsfnsQPV+Sqcnmmzn0mh1m+z65r51rp6e71+8+JSWICbrOO5WtkWSsv0PrbmPEJYT7m+aujog0C4Ku9XVmPnmKqoaXSsYpwYz6oIZdAHxhRXg2OE+SJWwyltSxACYulO/eGHH06/X79+PVxzzTWGJtOurq70+6NHj3IoIUFYT8Exb7I7Fs2HdEnWYcs3Y5zzayynoV8Ef7DCUL62We9wrkdZ13yLeWpyw6Abq9Sms/MxfPBz+aYb3XLZQqlmF75LONtYmDwGK2qboGn5auamtHQ5ypfJ9gsA4ceGs8ns/7LjSRQU6+OcWL83c2xm6pEkjqHtc6hZyB5Xhke/CNy3We1eoJilziM8x6wvEIKuNTu5pecouIxBDr+xaRznC2kzUOw6c4JkSFdggnANlgqijh07ln5/xRVXGE6vuro6/X58fNxwegQhFSo3Rg3tvWAblm/eTDz4WVCXmuYl4boWMgUrwlg8Cbw3xAvDcsFppThgi1CGghgvl7qDvmKKSy7V2fHM3lGe+kQdl4WE08ax8iI116WbEbdZ+RdtnFxECTsvmUtN44KXBlWods2pcG1vX9AmQbvl48q8MVg4jpKNY95s13wcXZRW1DTp/u3S97V9vXAS5veXqfOgwHNsZV2L3UWQhKU+DFXVmTweLBgvBZVqDSbJ4yxh0X7U9n2PuFOCpWTtke3uE4IgSmLpjeXo6Gj6fUdHh+H0PBkXrpFIxHB6BGEPiqkbms7VFxfIUmv6cizmtm8EbUKaeDNKmXGvyy+3huwz0vD5g+BIBDj4cdFAlABhNZk1wbkOjpqDTRCGqmifYu4xea9vKzZeCeaguMcSJMO6nLeAcyF9fmOwpqmTuYHiOm8ZbOvuDcaVEu3YC4q01yzWn7pc5Jpcr0IKQNrHo7lnJn6oLBOvsgvSBpkKDmL2SyG0nUeIYq0oYJvxODuKWC+OZdSkRGMq/NtZnjmIINyHpYKoTMFRIpEwPGFkCrbq6gxqkxCEXSjma8Hmp26VxULhPMvF5tANxYgSctMVDC9Zr4pcTq7YUT0bmzQQqhL60MbfMtRYHdt6N2tITrz2LIXwz7ZKwb06S4D0f+zBQFujFnamIKV0fRXz+t+U5hPbTY0uoZXBKrX15Mw5AoxBFI6ZgpusUTKK07RsoPA/6ElMy69UtonH6+WVpfAIvw5agYDCUFvzTLptfCjOKLdqmTLf+lo1Rurbe8CJiHgWJQjCJkFUQ0ND+v3Zs2cNpRWPx+HFF18smDZBSI+qtdPAAqtViMR5M9SyYj3ItukolLY4m5z8OEgisXJLfjxAy5DEWIxfMGCjJlF6f8/ZHYJKIYCaucnj8UFb3xaV2SrWCAEqa6FWpytOQiCrAw3PW3P3Oq5rBo9g4E68DLN2Xc7IS4tlMs91SdH4Pbv73IL8xdmbWVGHpe/5Q86wRin5bc71WrX9RjAFE9tflvEti2JcwTEoXtH1Y0FdTBmTOsePPVZ56vOprG02IXsDyskeDzQu61f7bZAL2cpLEO7BUkFUd/eSu5EnnnjCUFqPPfYYTE5OsvfhcBh6epwpzSecjx0CDhEPB1xwar20oIjVbj5fEPwF41IIdPAzof6mPcOiumFUWayGjmKWSeZ1fkNnH3h9fuHmURHitXDX4GT96JB5mGPbNC9fC5V1ai8ftAhDQTxctA7rfX5UxwRRkb6dl9LW7iWtURKQbU+dadWY1UZ6yqS7GgYuQW2eLwzH55FcWMQfCRXjxCumHJQ6j6hUFJMDvvXoWncZ1/ScMoC5jxdnNAtBOBaflZlddtllbJLBeCYoiDp06BD09fVpnoTQGupf//Vf05+3bt0KgUDAlDIThOkXxQUDfCpZh5mCblxogS3QbM5qlL6tN8Chp+6T+vCbSMYL/4PJxfQHwtDasxFOH3gqI0/FWWNNEbRcBejZci3MTY1CXdtKbuNYrdCjWJsUnVsJW2PUqR3DlvZfsSJhWVUUN1xdv/R9GdEkG7NZkcZS2YgGLei6Fmjo6GOKGYWVMwokn1WZInnJOqZEQnMTCtTmxYZFgX9AF7XzMxNQ27JcZ1YmnEcsGL/47FmO2nVMl8BQZA8NGRQoUlPXapgePQctK83xjFHuXFBo7hWq7WRSjMvORJxpMWtPWrxQjZ2r+OXJsinfAM3L16he/0uSNPPsJ0pH2o+Q5xGCcAiWWkS1tLTAtm3b0hcXH/jAByASiWhKA3/3T//0T7B79+703175yldyLytBiI6hjaXmGA6ybEpMLKcNbRCuaYC6lsKB6zUdUBc1Zqsb2sFyilxSm30wWn3ZK+z1ey3NM1Manz+o6nuZG/BCG3fUOkbXD2ZYJsnaR2qfAVGFiwVR+I+/opeIJdqlVJupFZypF4ZaSW7Zy+dfUdukLmkZxplgZczsfzXjqq61W9uluFjV1V9AB1so2T0/a5mDgpU10HPRtUXdMOmdz4zMg2bPoQOX3AorNl0FVmP3uBCBQn1b3dgBfdtvMC8+XAmSyYSG+Ylv/6EArmieBseKU8caj3oVSqP3ouugc2B7fqzWIr+xon2FEoa6cKwZhtqFIMQWRCHvfOc70++feeYZeOMb3winT59W9dtTp07B3/zN38C3v/3t9N9WrlwJN910kyllJQghFvpiixsteqXbTW37GkGS/djy9S9hr2LuAMzcABe9nLPg4JefuiQdpgXengxy+qX/klv1pGI4XyG7ylRlNxErbD9eXwB6OMWYSwnkW1duUPkDdd8R1TUa1re+baVpMRmLZ2xi2qK5JhVkH6ZOE1ex7SJJrUJDSRT5+sUSip4ROKZlJoq5ZQyEq8Dj8RrLUxdqM3HRWLV5DPK23lZL/45bFoUeNq1fljzX8ozjYGUts072ePk5hmL7QIEtQ3EOXLHxyuLZS9R/6nFinQjCOVjqmg9Bi6jXv/718L3vfY993rVrF9x4441w/fXXM9d958+fT393dHQUnnrqKThw4AD8/ve/h4ceeghisVj639Ed3yc/+UnweCyXpxEEN+y4FNC64eCyQbFkH6zI7T6hQJpaTb0LpYFWKLa4JimlgSgQ+sa3CBtcxfQyqbZgyjjg85nTVKSh0i2aKZcPKquIQo+zR58D92DwMJ5KQ823FI+m+WX1pbdBPBaFYEW1jnLIZ2HR3L2Wjb9kImF4HtQ2R2Z815aLP759VdvcBePDJ1V8M2lqXYq6GLVT6FIm60CwEirqmqCqvi09D1px4WVmHrJohGfHjuKWaOFn2lCbyNGeQo8Ti84jOhIxJ12dqF0LeVNoz8F3jhL0GTLc1/p+n7Xn11UGM9tTQ9qcnhWfPwQDO2/lKnhzD+oVnEXTkyII0bFlRvrQhz4E586dg9/85jfs89zcHPz85z9nr0zQBV8xvF4vfPzjH4dNmzaZXl6CkEHjSqQ9K/pdxouH6fFhEBNBN+16yRpy1lsaOfkyRzpsOvgJ3d/F3EMWOjhwKideAqL2IQqAiwmivBlWAkK5LJQFjV3lC4TYy55CmGCNIugUauqzztK2tuLhmkYWb/DA478sUByTrXhVpa/ZpASsYsXml7JL2JmJC2ApZq83Fj97oYpamJsZL1yUrLpKcP4w+AwVvMBXk4bVZzO19VI4nTUFXA9E2+cXU1yxu5z+YEXW+fn0wYzYtlKhvh3RNePE+UFNqaNL0QunDmgskTxj0KwpCtN2oxCq3HPd3ncRnDn0NK/M+KRDEC7CFlMiFCL927/9G9x5553svVbq6urga1/7GrziFa8wpXwEYS36Fi9jG2eNv9WYV0f/NqhpXqbq4JfnMkM0DVDJ0Bf42IySiKIpmakZVzp/GSgXi6kU4aoGEBdFd4DYchS3mlFMdUVV1dAGSgmLbTxUh6vqoaKmCepaV4ATsDQ2i9muPFXUhX3HVosUlZaDxf8x/c4pgZbtskQoq4Rk2i2TynhmJsTbKJevmjwz3fbZfSEsKsvWXAJNywYK/6Mm990OUDShMSK5YlxpzHUVrj5GlJVjGhWRMHbbstU7mMs4Z5xHSrdfpiJWUXKS6Fi1VXXJShVDc9+q3GdZeR7J2q9xGKoVdc3p9+Fqkc+K9hEIVbG1WAaBJ0HIgG23tuhO721vexvcfffd8OpXvxpCoZAqARTGiLr//vth586dlpSTIIzSunIj2zyt2n5j4S/oXrtsXvQMbtJxMUdrAdx826ScbmGG5lLbsjz9ngkACU3ouoAt0/XWHWLVXwyGq+u1JmehKwyw4fKh0J+svXzo3XYD9G69rqTAykmgmzNumD7OyrtFM5Km9p+K9VwVhYeFA2EeXD1CGU8M94BoBYAB5EtmpaHg5iol8RDoaSyfojDLvML/pHB7rvTPc854hlEphBdq+9jaftJP19pLNf+GBMulGif7Y2VtM9S39+jbCwr6+PFz96uNQLja2FpgZntqUEixcm9U07QM2nu3sPuqygyhlPQoBucnJfv7GHe1SGI6CkcQ7sZ2O83e3l74h3/4B/jwhz8Me/bsgWeffZbFiRobG0sLn5qbm1lsqXXr1umyoCIIO0FzaNR810tRc21D91n2L5i4mBdd0HVi5uXDQj+IqTEeqqqDni3XsjLqu9C2fzxYg1vqaS+6Dk8mdk2x51aEC3AR5mIrQeWDsvF2VHs0UmfpI8aFvZbvlvhynjcmY4UMhKtgbnqsfFoaxqkIz1WpIuAYnB47xyVB059fNVZ5Nre30TYIVtQwK3qumO6ZT7E0dmbpNrZropKZ7Hr2bb0Bxs+fhMbOftj3yE/NyEJ66lq7YeL8KRg/d8LcjGxoN6fvw2Stn955FhVdTzz3e6isb+EiIFBdDhV5FY1TZrtusQJNy1ez97OToyAtWec953hoIAgnYrsgKkUgEGDCJnwRhJs2gHoXL/kWPXHKK27b6S9XSoNJlg2k3Qcjry+QdTE5PzPBeQzZVD+D7ar/2eDrJgI4ukXjFaDaXNdlfIQA5dygJhJxU9LOSNC6YOEiXDZrdvGiuzimpLd83WVw/LnfMTeS3MZCZjJmzPMGkwyEKmGaV1nMdg+pcgxqmZvs2P9YnWex/DoHLoa5qVG4MHgQHINS+IOlMUE0d6/1Y7DUMxKuaWAvnlj/nImxuNR39MLYuePsfV3byvKKJ0KgSJ2nKGfavHOdTec8jEu4asdN7L1o8baKxogq7KJBPEyPAWodWhVCSiJiXxGE4LjDFwxBiIzehVcCDSerY06YK9wQuL3L1VvIottwGZbRTv5gmLkgQFcYbT2bLC+Ls1GEGoRa4gLwscaxj2Llwr/377gF7IbrwU+APhB1HKhdF4OVNdB/yS2cLVIy8hPQirjcGNRk+ZzZtGbU1QyLqKw0xesfrW2BiiRqv1vXtgKUgnFJNbShjkd+4JJbwSwy+98fqmDu5VDpYNnaS/QkVvqfOboB5Ind+RfEpecRFPT3X3wz9O+4GfzBCqnKbjb2edWQtM2ttGYq9RtV+iDq8uGqjCUQQsaVUqx7HnM98lDMcoIoDwmiCEJ47N9A2m29YpS+bS8Dr9fPPV3Z28XWehSUVfAvR6mNJrrMxODAXv+SdRQ3lOJxvPjlofDvO53p1WTE/vFwcKGbexBMufFklhtaEdg1H3eK9F91Uye7pDSFrPhg+ZfE4aoGTQc/tf0igls0reXgO+YwLWvGsLb4PLb7mLHu8qFIuxSKNWM4X7vbdaEQ2Z9YmTRacyr8Y9c0d68tUC6rKJ+Xx+c3Mfvs/Hu2XAOrL31FwYtB09pFiLEpFlaOQTXzs5X7HVRwQJeb+nDueSQ7eYc9M1LVRxLFOJWIcJZBi1K0OG7qWnDvJ0q5jCrG6a1D79brWdytFRuv1PV7gnAD0giipqam4ODBg3Dy5EmIRCJ2F4cgLNuMpnz2av2d2di9wUDhUtOyAVXfDVfXg5PJ7Au7x4Us48cwGtsZXWCZjZ1939a7mR1Alq97CXg8Pu7joXP1Dna5Vt/Wk/EjdfXNc0e3lKl5aOyLUn3H51lJ2ucCUzHLFUbuBey1TADQtXYnvzzKlkHr9/leGNo+32vMvnXlBg5ZGqyzDoFQ0XbO+DtagOO+pGXFeli+/iVGSqixcNZlpTtzE8uI1s1o1RyqrCubIY+5lKVhw3On9lnHOKF6lWtk2JfZPucZJBiu5pSStnlsYOfLOWSpIk8J+0eGcc/TKwaPddjRz2le2RWbYtba04Z6zyMNHb1ZgqhS47I2Q3nRruear6twJe/eqXvD5VDd2MEvD4JwGLYIoqLRKMzPz7NXoswk8Pzzz8Mb3vAG2L59O9xyyy1w7bXXws6dO+GjH/0oTExoi+lBEJbA2fS+efla6BzYzrQriOxFvn3VRdY1SZ6lvgkbxEJJCujaiAsyH1IEodAYzD1A5LoHwE2xGWPX6/NDe98WqG3pMud5UxR24aiLos+QOAc/PYRrGoU7/OPYKjW+eAqicvPBOHm9W6+DutYVfNLPbMui7arSKonDPC7iZRnuTfLd2BQuJ2qGmmIZqnkMJk3dl+Aln+65StfzrFYYpFjuVln7mFX4WB4Ktr/Q3A4axmjWGm+028q2m8K/vQXrKz34fEHV312142bjHhoKGRqWFUaIKzy1fd0rUE4R11teoLKEHecR3C9YjlXjXqWHiuIWURrzk/huAD0ldFmgnFluvTTzPEIQhICCqJmZGbj66qth48aNcMkll8Dw8HDR7z722GPwx3/8x+z/mQIrtI763ve+B7fffnvJ3xOEE0A3Vw0dfVBR4NJRE7RGckDejZ8MGyqZNnLlDhyi1KWjf2vW5+4NV8Dqy14JTtFANHLwE+2yUs+lN7qlytZuLSUwkWQKVdkvwaraEmmoL5KaRDDmimVxARRx4i6WKxO6/0JN+9aVGzO+KshYKwLXyweeQoDCOWS84yME4DrvCdXVzrTW0Ia580NNc6fq76JiCloIuoHebdez+qpBmn2HzmIanf95tg8qclpqnWojHm9xTwSir8miPys8y1F8LyhGXa1oN/SUYFbfllI0Qld5oao6rhaeqTh4HXYIWQlCciwXRH3rW9+Cc+fOsfd/9md/Bq2trQW/Nzs7C+95z3uY1VQx0E3fO9/5TtPKShCygW6JrDC5J9SBm55MGjtXmRPAkrdQRJDNP3ecWq8syowFRQFfoLj2rnkxJDglo7J8Pn+IvwWEgBqIda3dTFEhjeBDvLqxHfwBY9Yi6JIVNSrb+6yziA2Eq6C+vQdClbUF4/+Ug6tLwkw4Pq96L6sw9lhWXDjB5tk8H/1cA1Rn1tXc+cGn0crK51dvpWEaVo4Fq6Zn1Hy3eqIt5SqJ5xxQJq32VVvZeqPG/Sla7FrmFlvzFlfhvj5kuaQiSjS+xsYx0FctK9erco1VKJ6a6XB+bgcuuZXFh7ONgvURay9gPkrB/QcqAOpRjOM2T9m2J7O5/8vU28thj7Tq4pvYs1dRq/1cQBBux1JBFF4G/fd///dCxh4PvPa1ry363R/84AdZ1k4NDQ3wqle9Ct70pjfBypULQcuRJ598Eu69916TS04Q5tK39QYu6bT1bVZnci+piwY70XPx0NG/Le/z2sv/CJwMuhypb1upLnaMBJQSYuAGdNmaSwr/oxXj3cZnqmT/8XDHoiaWDvuOUnA+7Vp3qS5rFNXzlJUWcRqSsl3ztVy7eLzMNVHfNv1rHrpkxQsXX2BJ2GgKOVVZtnoHe+ZR8FLqe0hn//YsN3SpywdLA9jLIgTQWk7NLqgUy1zzcSej6I2d/VBZ2wxVDW1lf4b7wIFLbyufqOHiqU3L/LGYZS0o2n4zpzx4MWlEKSlc1cAUm1jsycw4ZSaPbRRuonstNe5P1Y4Ny9YsgZ772tblWWuiVmxf5yWi6HOW0YQorMqL3SVZE+N+CBV0ZKK8UlLpTsCYRKGKWui56FptGZfp22BFtSrrMzXxcHFsZZ6Xip1HSlm0lcXK9a7s9ksR6hnKniuz1wB8XvC+IlhRY8gtPSoiEAQhuCBq7969cOrUKfYeXfN1dBTXUvnxj3+cfo/f+8lPfgJ33XUXs5L62c9+BhdffHH631PCLYIQAT2uc8I1NmhjGUWR6+CHAZxFwNBm06wNKcdNLLp9KyqcyTlA5JcDTIX34R2tI9QI3SxFMTeB5u61UFXfaq/mpYr5NPM5K+6Tne944BWbSDuZBz/rT36lLz+TBQ9uPDQR9QZUVp2+mjQKlAEvVxo6+7IsUrQFRVaku5DM3PcU7xe7ypq0xDrNDCFAZv+i1RleuK3c9NKcLxVu11x3kqaNFc3JmlSOjPbn5cbQLGqaOmHNZa/UFFsos928/gBTbLIi5pp4iNGHRmjr3cLcxnWvvzzbmtRCMH9jv1/DLLLz9oKqYivqp7a5q6S7XL3PPO5tHYtd816ZbPu2vwxWbr5ad/KNywZg1Y6bmIIG7+ezFCklxEzX5yX3oxn3D8X2H8tUWJlqQY/lPm8y1+FSd2Ohygx3eRaD/ZhnNa9HoUKQvQVByISlN7N79uxJv9+8eXPR7w0ODsL+/fvTn9EKqq1tSQMwGAzC3/3d36U/YwypaDRqSpkJQibMu5QqnS5qZooO+gnH9qmobgSPz2CgYJv6D4U3uLnk7fMcBSqlNlSp+B9qtO3UbsZQkwwtDJxKoXaoa+lm/8/TvJSI+taV7OCY5WebA6FS8X6yUHso8JQVAmB8JZ4YcovK6RBTbg7BZw7nwN6t16v6vlmUytcuK4vUBddS8oqqC++i5S1oqWCgzEqBOcayw2+ZfLKEMMW/6w9VMqvZlJsvO+CxX8FLUNRyruOsiFDyUpeb693sjys2XqXt91lpCXr5ooifFwqTeJSzlMY3ISaojNHWuwlqmrPdd5tJ3r6+ZyOs3lnMarI8eHmMeyjeVjihqvqS5e5cvYPl27PlWvms8mzOs9S5zy4rLlRs0219Y1IbovvyLNfPOWMwEKpkSoiexb1MRoF0n0fCVfWGLHNyyfQKYe26rl4QjYo16NK0e+OS60Jb4LKPEXQvRBACY7JqfjYnTpxIv+/sLB7w9Iknnki/93q9cOONN+Z9Z82aNSwNFFqhEOrw4cOwejX5ZyYERMCDOu/NG24kLgweBJHBwxK6qsHDvy7NlUK/KeeGinPfYx06By4umY+evkX/6Hg5V6wPUVMQNXjRymT/Y3eDtNj8LGIwU7x4MNN9huFn26Ymau3ZBCOnD2eUQ01B8CJehQVkEUuFyrpmZr0SjcyC2JRri0yXVKW/iXGO8OUIFP7P5/jwSW7pZV0+FNGCRYuG8XNLe2MjoHUFWmKdO/4CiGoJjgKc/ktuhVhkVrcw2+i6WtPcBcvXvQRGTh+EqdGzutIY2PlyNkfNTY0BT3ivDTgGy1mAYdy2vN8VnVhz08/+A1oBxqLFY/tah2LqmNLj+cC0dZ3zPNi4rB8unDpgPCGVbcrDOtZ1cHJ/nOdmNvPfbdoMYlwxjAN5/tSSQnKuEA8tsSKzUyaXRLyzu14KzW+ogDBy5nC+Ag7PfB3UhgVRdLR/MctpRdHkst7qNcgMi320ZONtzWbbOHX4UCcI6S2iJiYm0u+rq4trhD/99NPp9+vWrYP6+sLBTnt6li5Tzp7Vd5gkCCeCmjU8wUtddAeDmvS8rXGsxB8Mq3bn4KhNh4pDKwagzvhBzs+VhUtDnoKcgknpTz/PXREneAoT8QCNF8+mx7YRkHIbfbzAzNRANHowyOy3UhexaKEhO9kuwdS5wjALXjG2VCXBeRLGMYjxBjQWQpsgKuf75RQLsv6Oa5dA7m6zUGcQldY21i2E4uFyUVGgtqXLkOsanMNxvAjhjqXkGFQsvRysb+/NcklpZftkzoOl8sVLbvYdUKC+zW6hvML3Zxymhw6rLRUVgP6LbzaeiJtRxMyz0FyjZv7JtjQtb3EsZ/vZO2ZRCIlW/Ly8GwixFnKlvKvXbIpbx6txzadHgdTnD6XjpgqJZmNtvmPI0pisbl+DCEJ0i6jMQ0IkElEliNq6tfiGuKZmyYR1enqaSxkJwjB23hMtLrrLN1wO50/uK6rphBqIai0B0NoJL9AxQLaaINmEHWSawpuZi5qTpz2bMbPGpsiB7QtusvP+plEzvLwvDBNR0da5GvlFY4HwP/jJgCMuA1R75lNsinWUVYji/+Ip744F11Y14HjuWrMTJs6fyvitTuteHZTPR+uljQOfgXJrhY61xOgY5B2vRVtMI6tJqr6AXX3pK9JCUSvGl7ljUoPygaDrg1p32Xj5ylwaljmz4PnH8SS19q0oa4WFWFAWTRZFAp8nRGx/e88jmfkYVYwrvxdUd57LsS7fcTPEYxEIhKtK52/pnqywVpJt+0I1fVfqOzaeRwjC6VhqEZVpBXX69OmC3zl37hwcPLjkHmrbtm1F05ubm0u/DwTUm7MShKXYsPFE/8Wo1VjUzYsC6RghpQhXNUB7X+mgnUQpeG9MLNroWJKNczZtGO8KqW7sAPGQ9OCr6vBQ6ueZgqgkl3zxEgznVjxQYaB4vWXjjmiXG445kJWvR8kA1RljMNVHeg/jqy97BYRrGqBp+RpmmYxzTXVTZ5lLaHusUaTp/6LlTGr4bqblZVLsMbj0x7JpFhJooAVZXfvKdKzDpRKWSq+ARYRZ4yOpzToeX3ajqS0keazMunhE5TmMKVKuzbKt+wkpDf6yBOhWjC/t89Sq7Tey83Ehq2ZunjcIU1A1dlTpxakbg7wV41KKlyiULyqEsulcoNYy2RostIiyva4EIR+WWkR1dy8dXp566qmC37nvvvvS7z0eT0mLqLGxJd/stbX2B10kCJnIiqGiLwUprEa4klNN7ho+btvIqKgvXvwbCqaelZ057bvq4hthcuQMc7tnLQ4bLzynkUzXfEU0EDUn6fFA3/YbIRGPcb/ELDWXlBu3mXN5oKK422NrUFR1JI9nsWQaetPPEqjoS2KpCJlrpLExmHJ3hhZUvduuFzBugEhg7DiHzY0CrHm9W69j/2/v3wq+YLiIolOx8adyXBoobtbYt8gCydzfqPu5qvgjDhin6A6TLvgtRBALK1FBl3bF3NqtvuyVsPf3/w3uwt3jgVW/qFtlfYKoQucCtPhsXr4W7IIELgRBSGkRtXHjxvT7J598Ep555pmsf0d3fd/+9rfTn9euXVs0PhRy+PDhgm76CMKtaBOMqPmu+Ida1JBETV10W+TazVdW0Thc8Brwya62ndR+Dy0AgpU1aY1YxM5NeCFQI62xc1VWbAxbsGSMmpeHmkv0vLGp4uDH43IudemKggARNOlzL+hQCzxUWQvLVu8A+RFjri02DxaLyVX68iFpzjMqRlMxa8EUFTWN4BrMbn/DwtD8Y55RBZqFeH5boIZZ5ImJ3e4huayFIu85bQZjguKck4pZK/T+3ATErq9izXlEB6HKOkvaU43LXbH7kAMuOI/wdM2nFhRE6XEpa9kzJ6kyRHEc/pwShFssotavXw8rVqyAY8eOsUP5X//1X8N73vMe5n5vaGgI/vVf/xWOHz+e/v7LX/7yommdPHkyyyKqoWHpEEwQ9uK0RVhsKmub05q6wsHdYEqxZlPJ4QDRsmI9DB/fC50D2zVkWzrfvu0vg9nJEaisb3XxpjibXGtEEQ63Wn2dm2NFtHTwSyTjhtJEISOOZ5Ep9JzZchmrziDKOYe7EtWob+uBc8degGQizi4P1Kepvm2y+tjGuQ5dBbb2bGTlqWpoB1tR2XwizJWmx2XkaJWnGruGoYzuIZ2MiV2Arqn6BIxZi9b7g/t3QfPyNZbnLUw8nXLwejY1KMb1X3wzjA+fYGsyYRKKmvOI9YPQVOv7jL/j81fse5lKSRij0Ml4M4Rkrb2b7LXWzzmPFPPeI8NekCCciKWCKOStb30rvPe972XvR0ZG4O/+7u8Kfq+xsRH+6I/+qGg6Dz74YPo9Wk01NzebUFqCIIpBC7c4ZB5ARemX1pUb2GHc4/VxtfwwFodJjLbhhqrqWC+E1I0O3+LFvpfpwqdp2YChYjV3r2Njz5FjzeT+Luq2VdDm4RnrCP33D1xyKxMApMaPKPNzQQyUDevV0r0O3IbpgkA1l61q3XqmrfK4lMy98PTMx2M+yBI2lh6DdignoPXSyb2PQuOyVRblqCa4izntUN3YDqsvvQ3cQPHLee4ZGU4CvSq0VGpTJqprWwGzh0bz/l7b3AWiK2dOjw/bIgwtT1LK84hR8ByMXgumLpyBrrWXqv+hhGs1nr1w3xuZndKmNMoLhyufEoSTsFwQhVZOu3btgh/96EdFv+P1euGuu+6CqqoiAfgA4N57702/X71ag6YpQRD2bHBEvoBTRSErD945iNFGvMrBUwhFqCW37/huym2/SFcroPJ4oPei62B2ahTq21aCFYgWK88yDcRMASIoqnL1+gLg9fohHo+KN8bKzoPZ9S0njCLspfhzqdPyTDRKPC+8g6Rrzd9K6jt64dyx51W7xuKFaWMjxwVosXyy/y7WGpSyXlp92SvKzOuK6euraOuzMey3NLEKu+behs5VcObQ7uy/dfRB+6qLOOVgTr26N14Jc1OjzGW9/f1kbt/p3itq/p1By+RFrwU4B2kps8+vVQGuCIr17urxZfdeQu15pKSLbUH2NwThRCyNEZXi4x//OHzwgx9kVk+5oOu+L3/5y/DSl7606O/37t0LTz/9dPrzmjUian0QhA3Ytl4mHXXwwxgIwYoaAQ9Q5d0D6E86Iw1LXHvb5DbMQQh9OWpYcGL84FdR28Tc6mkRiGbFliJMAw93/ZfcKmALZ1pEqfi6aIdUjY+QakSrpyjzYqY1SpkrDz2CYaOXIB5vhkAmnX2mlRSYi4VdgbEjMXbVys1Xm68Eo3Fvq06I5DTEs9SXkYaOXi7zidaxpndsSjmmc8YnKi9lWtWnCFXVFfy7SMpBKITHmKZi7mUVafI0o6+0zIMYE7mmeRn3MshKx6qt5iUu4ZRFEE7AFnV1nIj/9E//FO644w54/vnnWXwo/Nvy5cuhv7+/7EQ9OjoKb3nLW9Kfr7nmGgtKTRDqkFX4QixR17aSXWg899vvlWgW2rnwpqqxnVmwZEHPkzZkHpZmXaJroK51BVwYPGhP5i7DzIDLfC7DFFvWeE1lF/yC1+hlTmf/dhg88ASYQsnQSmK3q1qWrdkBh5+6H4IVtRCoqDa9bvhslO9zc/L3eL3aYrHphOtFO+9pio4fjmuDYFilUpzlU5b6GE0mZ2MdPNd+kepVBrRex71GPBZxbB1FO490rb9M8gbkS6i6XvuPVM4/bHwTBGE5tvpN8ng8sHHjRvbSwmWXXcZeBEGYe5h2q1CtoDYtBmLNO+Gav0ksGlyTmZyr7B/NxSyivcvxAqvQ+MMYI8PH9wJPMtvILk1NFG6YQk5/8KifWZeUMlzs9my5BhQD2q6y1dcUZK+3O5c8W/EHiwfvrm/v0S6IsnAMZseIsizb7DKUqC9q8K++9BVsT6N2TmrvuwjOHt3DsYTyPlfoGikWnbO7GHl9J6XVSSkcVh2eFLRskbi9RBy7IpZJJDpX74Capk54/qEfOOI8IgXFwqu6tE14CYsKjUucYwd2vhz2P/pzLnkQBKEOEe12CYLQjf4NSteanY5q986Bi8HnC8Ky1TuKfgfdFxSCm1sX3htGB+8/UVjjtJhSGKOotWejLpcCocpaCIYXNNi1nFbyN9mKprFv70C1W2jo4AdMB90broCWFdoCfMtOluDahgM/i+ugM99cxQSez5CZzyO6QcqMZZSVr8fDYiuYgXMuH8vHKdPipqmpa4BDmZyBLxCCvm03GE+o6DNd0ixPbeLpd2WVkyz1WlmqbuX3Tlpo69uSfu8PVTrq2UflGHV7wXIJFa5ze+9S23FRTuTtKtwCxTg7sHoM1jZ3sXV2xcardFmbqltDyu1BZNJIsPs8QmSC4RJClXWmWeEHVKwbBEHwhQRRBEEw6tpMstrQuJHjdfGJftVXv+SVTKO6GMvXFbasNOID3FBbKPYG9VxK2oJNN4csei66lm1M0X2TujytPUxgjCK08sKLQK209W6G/ktuATOFDEKjoq9kv4gwm0zBrh43eKgB29BeOD4FYQ4rt1yt6fuZApzqxg6QEbzg6t5wueX5amqvYnONIuslG6F2bIarG7IvwDiuO5pigDh1vUsW3jup+mLGs9y79fpF6z9z9+92jMEVm4vHzTZKo17Bc1ZYWYeOTQfRte4yWHPZK6G6sV3zb3l5CigGxvWzEjPODmKnKcfzWUrYyfaJGwU/txIEoQkSRBEEdyS5jLDkUKu9LRo6+izbxKG2ayEKWebwPGjhgZkn1l3Ii7WZraxthlUX3wgNnfzGjNUUs5bSffBT6xPbp9PNgcnxRVR8Ccyk0LOk/vkSa+6vqm+DqvpWpk3dunJT0TkQL1qNYEUAbuuM8pK2XvxqVYLAS1e0+kWroo5V20A0MjX5dc85JoFu66yIKZTGrMdE6zA1UYlFRJiVYRnCNY0lBb1aLMq0wEPxKmt9ktSdttExhG1QUdMI/mAYnAauCbna+rnttTAG9LVh0f2NII+1LIpx2vNULG/HckpxxRTUdHuryKtj4TrjPpVP+iZRLB/B5lufP5huT0OhFUxo1kwvHM3L1xQtf9OyAVP3GkKeVwjCpZAgiiAchG0WAg5b19VuuvW2d7jG2MWvvJR3t+E2Gpf1F/y71G4KXdq1vsDSJVhD5ypDbabm8rRskorCNE1X7bi5qEUUfgdjY5VIRHOeeX/TOiAE0fwvWu6MA74IVnlo9btszSWLfWzSYqyzmq0rNzKFARSUFbvst6IcxSh4AaapCTPcotl0KSWyIKhg2RRr64KW7/iMdK8vbnXX0V9YiKtKAGXAeheFs2h5ahkO26s7qfLFXKYVc1sqVDxfW9ZBcec9WSk2F5ntJUTEYaL9mbJfIbR32w3MQwhav4kGCpkGLrkV+ra9rIjF6wKtPYWV5vTAZX+ekYbt8yxBOAzuN10//elPYc+e7CC3r3jFK2Djxo1F/50HmXkQhEjIvXAVL3vHwHY4+syDbHMRCFeBk1BjjSLCBeQS5coi50Wwm1Fz8DNmveME7K/r8nUvgQuDB9iFO14qYmyL+ZkJFvPMiBVJsKIaZibOcylj2THhqjHDH9KwLA5qYKMLVW77JFUhSqwbz9l52bTX0yos5jxvonu5M4d3s/cocBw9fRhEwh+qKBkrlH0nGGaXsBPnB8sKAbD9EpDgXk5CPHBdP3uU/51FIYq5TDPbLRpvyu83QECMFwqVfgb37YLGZRqVkBx8Jnb6eUS0vR9aTqY8hMSjEW7pomvauekxw+mIcFcksuIOQbgN7oKohx9+GO6+++6sv23atCktJCr07zzIzIMgiDIk+WgS91+MmvYhrm5LRNi4FrRGEaBcuXGclu7uLNoMc2wCLH/2hlCsDb3d6LKIssSDiYmZqLA0Ee3gV9vSxV4pUNOvlLYfIRGqhrr964IZtPZshLNHrLmAFR6plYnMBy+XVm2/EZLJBHN3iIKp0aGjhi/RcQ1MxGNgJ+m9rdVDIOkAjW0te2Y137WhahhrlocgCmN+nj38LLT1bdb824Lnq5z2EuHcZNo+0zTXgXzbDM/EAztvBd7g2Bk6/Aw47TzCYy9PQgWV7WRgfkDFuHB1PRdBlCpKFdXmec5p8yxB2A255iMI3gh0DjSbYGVNWZ/TMmKfWzQtm5xihzPFtYGHMb4Ylr1rzU5Dm8hicZusRPUFnoPmm+yDqTVjUN2YkPN5sAfF8IWDnvkHNTZ5kH2Rq04YKux8aeDQrMYdlC2ILByUZS42oXlQAJWKNdfWtwUaO1dB19qdLIaZ3kvHVRffBHZDF09uh8/DgjFR1lz+R7pi0hWdi3OEjuXGqvaxrGhSjJMZVn7OdUDrUKRz4GJD6fDa2xhBnWs+xdY10J/hGpsLGl0wy/4MOIasMWi8T7Ts71OeMMpZYBMEsYDEQSgIgtCC2ZdlKzZdBWcOPl38oKXILYjSs8nkszEtvLNfvv4yOLbnIQhV1PJ33SFRX+XGeUBN/lTAVr0EwtUgzcHPRnz+EMSic9ZmmvE40MGPwPhW4+dOQFNe8GO9z4bG+E+SzpXasbGisgh4bEK0eRDX32Ixl7Tg8dh/RBVGIJvbxxmfMy9gnagYZgo2PDLoulcPPD1OWA6HuUkkRQ8tFoedq3cw147oGlSGebsUyqJCAdc0Offryi3XMDfZRtxiS7E3kWfYuA6M1drWs6noM08QRDbcd/mf+tSn4BOf+ETW37wZC1ihf+dBZh4EQVhPdUM7VO+4WfpdltcXKO7HWCC3J9WNHdC/A10jhi070Ih0ICwEtkNpIZTY5Tf74MfFrV3GWOu56Bo4f3J/WvPTMNYbRJUoAMGr38ycNyrrWtjLUmiYCI/aNdGoK7NMJRBvwJgChIFSmPx992KFEMDoGGxc1s9iW6H1WV1Ld+m8LJy8hNsvSjpvF1JKsk+IUawRrS+PmeOLh4tL7CMuF9ICPEbCCORzyXgOMK4qT28Wdnho4IukEx5PFGvnBW7PPEG4BO6CKI/Hw156/50gCANoOJxYcpCRbB/Ut+0G8AcrVFqj2E+wokbV93i75OCCRReFsqLq4KfYP/46B7aLfwFlFIdVx/H9ZQQd041IccucNF+mx6WAy1fm5VdNUyfMToyocMFkYt84qN/TKPY/O8JewOZY8Pduvc7uYghFz5ZrWVydpq4BkB0ZxmBlfQtMXjit8sHVaGns2O2JPBXTZx2uuGB/mzSxnY2l7fUFsxRXCb2dQi1HEGZhv98DgnAYIl1KGcZBVVFDqKqes4BOW1r2umpwwW5LkacpzBgL4gsckirK6rJJSbJxiP1meOwqgghW1MQFEOyZwqDSKUKVtdzStWNtSlsbCfDIl6p/94Yr2Di1bf3WmK3dLqFEe2Yc6xbNxVTWNfMRztk8VAPByuIeGgSqDArhzxx6mlknp+LCSYVEbvKcgvB3JVoNoswYQwXSVLt++gJB5ipubnIUWlZuAFngvT+we79DEERxSBBFEAShF0dtcFTERuFYX5kuo+RBkfvg59hnSz4NbLcG201KLgytbemGuakx1odVDe0gMw2dfbp+l9dvFggz7bzskGItldRiSxRrFCn6OBcJiywS/RffDL5gWJcwlMd40TKn+YNhWL7uMsN5FilJkT/bMcA4zmOKs559WcrpJgt05jZdl+t0sfoS4xHrwY1jkiBkggRRBOEgtC26Vi/QqnfdtmHkMkmUDY/ucoi9n+aCKH3Esz5SCZd4H+os0kC0BkWYw+3ay18lp1YzbyQUhuIa1ta7GaQjY8x7PD5oXrEWmpbJ71bLsnGaO1YFvyCTBVEEUU5Hzd7bSXsdNQQqqkmb3yKcdjbgRUVNkyaFEC3PqGVtrnsfp0YpiTCbho5eaOpavdAPqvpSwzpB+ySCsBUSRBGE6bjr8OTmg2MetHctc3mmphGtH0N2HjrqWruhrnUFuP0Cv1gf2CGkcdshNFsIpdnvV/bn3P5S1X/WanNrLV/WGFSYM8Li/y4RorgwqWpohZbudfoTEKQehPzzbrimsfA/yPmI08VbGkWaMcgNG6pitWWW+bjLK4Qm95Ya+0mq+wEJ3TQ7hfKxN0vARUGH+pUgzIJUvQiCM2JtmgnZyb30NC2fzEMBjWHb6Fp7qfrAsnbEbbEqT5pGrcNwn0p0oWACUl2ouARfIJx+7/GKoXNnloCSzcmSCj9LzUV2PlcV1Y3Q3L0WaluWgxBIuSeTsczioHmvZfUY0ZmfkGdk1WVyyDzrEMwcS+rWa3PHQ0HhlsXPj6yKVSlY3LrFeKOtPRvtLg5BEBlwP5396Ec/gmeeeQas5tWvfjVs3iyhOxLCcTR09MHpg0/Zk7nInvlUIpNWkUxlJWS9zHERGg889PyJR9k+EfwZzIoR5aSLNq1YfPmQLXQw3n544TAzNgz+UAVU1rcaTk/YDRNhCi0r1xdXCKGhYALmN6obFQY6+7fDmUO7oa1PsvsRN3SV2n2CpPsJR+yDFqGzhpWNXWrcKJr7DRWRBna+HOKxKAQrqg0XjyAIgQVRjz/+ONx9991gNRdffDEJogghaFzWD5HZKTh/ar/dRSFsoGXFejh37Hn2f6k2rxkXj8XKberBwg0HT0IDijgaiITuwMLBylomEEgjunZlVvEUV2qQyg6uUz5/EPouvtFRl2GEIFjyeNMcIgSSzx8Y36e+vQcUjweSiQRIiUV9QHs/g+0n+bOiC776M4RJ+AIh9srDjWOWIARCDH8VBEEIiRs1CI3SunIDNLT3Mk1sWw5+PDZWijMPLarzlGRzmlsfa9rUIveQarCqmyQZD6agteoKQLi6ARo7V0FkbpoJ5I8981uQEhd3uxOeAb3zoeP3PXb3pd35F4OzABkF8rHoHLgV7vsREvDr6wcPvygMmpUsTJyDXSn4IHIgV+GEAWhNIQhnCaK8Xi97qSGRSOjWHPV4PFmbEPxMEIQDtL9kPVxklBuFUAQnHH4naBgnbaQzrfKKzQNJE2OSOKktLaRQu3X0bwM50egeUtb1SlrkudxGlzCJeMy09GXCUcI9lUNwxaar4OTeR8rHmaJ1x3Khv+PmbQVPdA6r0yJW1Ev1eOA4jTluDBaZy1LrIColSYXmedmB/Sl9XG07S0IQhOWCqE9+8pPsVY6vf/3r8PnPfx7m5ubA7/fDlVdeCddddx2sWbMG2tvboaqqCuLxOExOTsLRo0fhueeeg1/+8pewZ88e9nv8zj/+4z/CJZdcwrsKBGGczA2miefv7vWXw/Hnf5+ZMYiKIzfd5eokSp1VlCP7MrnY9wWpjwZClXUwNz0GbqKhoxfGho5D98YrmJtQgnDd3GzmBZiEF8d1rStg/Oxx6Fp3GciNPG3fe9F1cHT3gxCLzQtbdqsERP7gknJOVUMbyEdS8/wZrq6H/h03m1gmebFFaCLhvF2KkoozhcakTOu8qr4ysz7OGit20r/jFpidHGHz/tjQMZAFNXErua+fMj2jdqAU3ltE52dAUcgggSBkwxbXfB/60Ifghz/8IXuPgqSPfexjsGLFirzvoZVTQ0MDe23duhX+/M//HB566CH4yEc+AoODg/DGN74R/vmf/xluuukmG2pBEPZT07wMhMZhBz/TEWQT6qSL6eXrL4NTex+D6qZOcAudAxdD+6qt4PF4YYS3IErF0AhX1YOdh0PCzgtDMfqksWsAhg4/Y1CbUoy68GDZmkugfdVFLHaSkBfHWc0uYLvrKFKoqg6Wb3gJHNn9G3A7gVAlW5fmZyageflaEJ9yHU57W8I5BIKVEJmfzv4jh3kY1xtcOwxd2Au4HBDq8QfD4A/ad/6yaj8h5L7FBXXp2XINjA4dhbrW7gL/Kk89CMKNWC4+/u53v5sWQl177bXw1a9+taAQqhhoOfX9738fOjo6IBaLwf/3//1/cPjwYRNLTBBik6lpKpogCS9iUvhDlRwKRJTD6wvobKSk7k2grtwsElIGK2qgd9v10LJiHbgJFELZBQr9Wrq1t7dThQArN18NFTVNzIJV3cGPLjmN0risHzpWbQUzyLUeDYZrQHRwfJUXQsnwzMlQRqKYpW573xbwqHTfbjWlL8vlHHftvVtABpzqVk6Wy+LuTVdCZW0zmBGfauDS27inKyu4D3SyUMByVDRNQ8cq5h7QHFfh5u3V3eoqXO14T60ZgXAVi82NZ32CIOTCUkEUutr74he/yN7X1tbCXXfdxdzyaaWtrQ0++tGPsvfz8/NMmEUQhMYNqwWb24bOVVDfthKaulZDZV0LOApBDwd4ydO79XqDqSiq/+y4fiW4zEOtPRtNb0lZDuhV9a3Qu/U68S1YVSKDCwwUxNa391gyvnyBIHSt2WkoL4IwQri6wRENKK5Awt5LQb1rXdPy1dDYuYp7eQrm1bUaRKZl5Qa2dvl8Qahp7gLZybuoLjNGSo2hUGUt9Fx0LQRCVWCGRYzWPYMqV+EKP8U4q+hadylUN3ZIHEMzG6yL6OD+bGDnyy3Lr6GjD5y1fxdHICbu/oAgCD1Yepvw8MMPw9mzZ9n7G264AerqlqwltHLFFVewOFHIr371K5iezjEpJwhCiMtA5hKob4s0l8Y8NZTsqnNFTaN2ixhx9ppECWptvkAx9yCQdGR8HqkpMIf1bX8ZO2z3bbuhwNcln+e1jq/F6lbU8dcmFwrZ+9W2GEnWtJvol1/SUabbAuHq9PvaluVSxOkyG9RKN3POQOsao+4h8UK6/5JbwOtTpwQr8nqGlgAp9LpcdRp6FePUnu2MPqs4BldsvNIy4bDZLFu9g609y9e/BETGyuejc2C7uRkYPAPhvr2hvZeDwqr4iDx/EwRhsSDqxRdfTL8fGBgwPLn09/ez97Ozs3D8+HHD5SMIgnDiJb+adLMOWJbt3TSWyyJE3rx29G9nmsd4mDWKHLE6ytO19lK7i+AqUHsaD9tLVhj6n9GeLddCy4r1YA5Gn2Nx5wGzsX7eTdpSFjtdmPK+pM8TBBCmgVYe3RuuYO4/jQoBzbBCycKi/Qy6v8JYQ9pJqlZq49FvXr9e99VigftAtLaua+mGqoYFxVwpUDUeVbhFswF/IGxDruLUPxdfIMT2gjwU5Np6N2s+A5OFjL6QCZ2rL2YKq87EGYodBOEGLBVEDQ0Npd/rccmXS2YamWkTBFEekTb3MpSLsGfTX9NkX5BbUd1MoIUhD5cY6D5v4JJbQRhUeGMpROEgtYQMVNY1C3WBrlcgb+bcGKrU7z2AKE1VfRuEqxqs6V+J70fQnZnliCMLLbo3wVh0Ri11cB0uFDvGjDlF2LgjZfb9qy99hWPcT/ICrbow/iS6ezPj3CT6Jb/V5UNhi1HrR7eD7YcCa+y73DiyzcvXwJrL/wiERM3z5aC7CxQwpmhcZsxwQCaau9earxhCEIQ9gqjMDfCJEycMp5eZhrCba4JQSUV1o1TBhd0abDtFVV2rBJp0OgOzCnIARdcBFIDUGMHKpQCuuRpweHmR6d5FFZYdtsQYg1bhyXAVVNXQBk4iXLs07rwSuRBSMw+aPVeiq6GVW642NQ8ZSVleqLWmq2kqHJ8NhQi9266H/h23cC3fUgYgPSgkoTFortsojCGIFgHFUbL3aA669FSDfVZMzmznYsJ3YcZV1pWOGGVC93NGhc5uB4Wn6B5z4NLbCp491LrMtAI194pW3z2i4owVcyEKC/H8jVaoQrmRNHl+auvZBAM7BVLOJAiH47Mys9bWpYvb++67D971rneBz6evCIcOHYIDBw4UTJsgZAQP+vPTExCqri/pEmluelyKAKFOhF0Kbr4avF4/+EMVEIvMqRJeTQwPMjcuIpNtCWDOZi9UVQtTowttpuZAZ4frgAXBlxgHXx5U1jYz4XYsOg/1bT0gMk6JlaH3MhJdnMxOjkBbz2Y4c+hpcAqtKzdCPBphQtFgxVJsFdMx7JnP/jhlNY2dlsU3yJyTrXZZl3Who2L96d12A8xOjahWCEFXNJVnmgsKefHyVZgLWAHp3nB5loY0oV/ZbGbyAll5E7aBws7JC6ehc+BioXvBHlfhYvXThZP7oXP1DpCWImsq1/2MReu2CEpJ/mAFrNh0FVgFnr+d6L4P94C4DiO+oOAKwgThcCwVRF188dLG5+TJk/CFL3wB3v72t2tOJxKJwIc//OH057q6OsMxpwjC7stV1EAJ15R2PbFy8zUwNXoGqhuLuCujyxTTQZ/shZu+8CYUD3wd/UkbLrq05ReuWhKAFnWVZbAO2BYnXvgDE+jlHkbsFkL0XHQtjJ87AU3LBiA6PwtOomn5apCBzMtvVRfhOsZjSpivCYsED0ZjjYgKarl2rd2p+vtSCQVSZZWpzEXAuW/09GFWl/r2XjhzaDeI7KK0WkNcFFxvSs6DJvWfKNbFmXh9SxrV9W0rbS2LqJjRb3iJODV2lmm1Eznt7YD5UwbQ/Rm+rAb7l/s2yuCYQYucWHRRMU4Ry9IJ+whjgGG7TY8Pgyw4yzuRmrpYV1/m0pDmSS7u9xLxGFOMC4TKxzSkNicIhwiitm7dCt3d3XD8+HH2+d///d8hFovB2972NggE1Jmanjt3Dt73vvfBU089lf7bLbfcwiXmFEGIDl6+1LWu0Hdg1rGBCVipvZ7GeQfSQhsZFGhNjZ7N+aJZBVC3yV298zZ2MDPLFz8e/Pq23QCiWg7hC5FWECX5IQXdbKEwEC9Ka4oEPzZ60F2x8Sq4MHgQalvUB1dWvNZahxBiUOwAarfQ3CzQ5Us/xoxTrLeIEg9FwrlVUS0YxnlwbmqUxTgSia71l8HRZx6EUEWtDe7YzH2usT4YZ8bqC10jQrXUnshNoPeC0/ufgCYbBDZuQNV4tMBVOLodO77noQXFuEAwe/oUYImnC3A5Y9YSYoN3He2rLrK7GARBWC2IwkX1Ix/5CPzFX/xF+m9f+tKX4Fe/+hW87nWvg2uuuYYJqnIXX7SA2rt3L/veD3/4Q5iZmUn/W1NTE9x5551WVoMghELtpVhjZz8z9UcaOvtUuRHp7N8OVoDu7s4cfIppgLmFZWt2wvDxF9iluCigu0F8EYQdoPungZ23LbjKUuG6Uc8FBY7vtt5Nqr/fs+UaV1/Kq7oMEeDSRhZrFKRj1VY4j253BlSsrxZXwVNE6EqXYsbhKcBEF7LzMxO6f1/d2M5e6rBuEKKCzsAlt7K1QNwxJ2q5+IHr3sT5wZJ78pQyVVGrOkmtI2qaOpn7cxx/iUQcZIH78+LwYY5ugvsvMSk2IOEOSFglHnIuOwThSiwVRCGXXXYZvPvd74ZPf/rT6b+dOHECPvWpT7FXZWUltLW1QVVVFbOWmpqagtOnT0M0Gs1LC7/7+c9/nrnmIwiiNKjxhUFCETWaphjA2yrwQLvq4pvY+0RcnoOfkZOaPxiGjv5tMDUyBPOzk+pys/liJjP/QrE2CMKsi3A7Thl42YvasoTYgh2eoKUIaklX1JWwBEgaaxO0QhHNEoUw6QLdpDV7xcYr4dyx54tajspMoSD2YuH8my5c98qtfcvXXw4zE+ehslb7GpkZc8znFy/+mN17bRnKb2Yema7yPEU83mTmr8VNK0FoVxwpPNYz94k1TdrXYhTijw4dhWDYDu8zBEEQLhNEIW9+85uhpqYGPvGJT8DsbLYLpOnpaTh8+HDZNLq6uuAzn/kMbNy40cSSEoT4hxgtF2HWuzrRgdznP8ezbPUOJjxr69tid1EIG3yyy35BQ+hHOpd0OsYqWokMXHpbyYDaqEk9OzWSdvNB8MB8V0xOErKisAZdSxEuIW946h2v/OZwdPFYUgBQYv6t7+iFifOnWD3q2ilOGZFNqLqeeeVAJb2OVdtKn0dGz0Jb72ZqQsJyMOYsKoXEY1ENFsZLoDIqxvyuLKX4RKSh8ydBOAvbTtDoig+toz772c/C/fffX9DiqRD19fXw2te+Ft7ylrdAOBw2vZwEITrSXQ4KBmreJZMJEBa9ggaT7tXq23vYiyBsR667Y0lxXyOjtWop0L98ZG4aQlW1LOCxOzB3HIQqlzwbVNa3gpvBWHlo7dTQUd6FMmGHENt9cyLvvSu6u0WX3KIjm4BaXBTNF849W6+DZCJR0kqeziPufnbMFEx4vUuWeKXGILrx1AsqMmmJV0vYDd23EQRPbFXlTFk1jYyMwG9+8xt45plnYN++fTA6Ospc8nm9XmY51dzcDOvXr4etW7fClVdeCYGABFYdBCG5n3S3gL7wT+59FOoNa2WKfGAVuWwCQs0lLF7f0vrvC5AyipXQpVxq3IWgd+t12W1DloKGCFXVQefAxRCLzAmn6GBE2UfPM4OCqNqW5cw1qBAKSi5ZD622LiYIvljwoPJ4RlQWk8UKLeuqmRAL58yhzd3rYHz4JDtzONEFLkEQhN0I4VOkoaEBXv3qV7MXQRDuvhy0uj4VtU0wsPNWfT92VtNbDl3ecm5PSwakvYO+pmkZi08Wj0ZKBlInbEIggQzNL3LR0NFrX58q4lziYh3R5Q9BSI1AawFBEOpoXbkBzh59Dtpd7n6dxdW+5FY2j9FeUtL1hdYgghAaSwVRn/rUp+DBBx9kWmehUAi+9rWvQVNTk5VFIAiCIAhCUhSPB1ZueqndxXAsetyMkHtYgiiCTXfxTlNQIgzMs3ZdxjnBwszitkNXXYl4zNI8CSLXIrexs1+OmNImr3N43iAE7e8i6wudRwhCHiydYc+dOwdHjx6FY8eOwdq1a0kIRRAEQYh3aSFQUQhzIU3HBbo3XAGNnatY8O/sBqIRSNA4kA3DlzG0BmY3hwX7k5LCQ17ZU78KTc/ma5g1ZFvvZoMp0cJNbaYfOYRQBGE1NK8ShLQWUZWVlen3AwMDVmZNEAThAGgT5ESWrbkEzh19Dtp6t4h/s+TwIehWbbqapk72Eg939gdBiIJbrKsUZUk3kxQUCDsI1zTAqotv0vdjkR9TkctGEILi3vPIMpi8cBpWbLpKMEtfd/YHQThCENXS0pJ+Hw5TkHGjJBIJ2LNnDzzyyCPwwgsvwKFDh2BsbAzi8TjU1tZCe3s7bNq0CV760pfCtm3bDOdHaEEOVxi1zV0wcX4QVmy8EoRBIp++slzQ0KWK2NS3rWQvgpAdWeZEQiIEsJbVv4bS8yALHau2wqEn7wV/sAIqashtPEF7Z4IQ+ixPy6sjWb7+JcxFqdfnz/4H6m+CcBSWCqJ6enrS70+fPm1l1o4ChU8//vGP4d5774XR0dGC35mcnIRTp07BE088AV/96ldh3bp18JGPfIQJpggiRde6ywov9oR2eGyQkvw0omS4EFY83izf+ARhNzI8N1ZC7UHQOCDcQLCyBgYuvQ08Hh/FBiEI0+T/tMdyLPbrjEgNKY0utQPdSxGE87E0RhRa5qTc8z344INWZu0Yjhw5Aq9+9avhe9/7XlEhVCHQYur1r389fPvb3za1fITNptUaNZBosSfspLqhnfnD9/mD0NojkJBc1nOyBRqIjhZMCGB5QTgBGkdutZLOhS6W5AL3Ih7vkoKMqDh5XAWCS278CcIO3OoSjSCchKPPqwThACxVQQ+FQnD77bczYciBAwfgZz/7Gdx2221WFsFxAXO9Xi9ceumlcPnll8PWrVuZ+0N0e3j27Fnmsu9b3/oWs4xC0GXfXXfdBTU1NdTuTsUBF6lOPmAT2SgeD/RtvxGSiYQUlz8EQThz3SHcPb6csO9AIcrS+5Cm37asWA/njj0PbYUUQhzQNoT4F+ytKzfA+LmT0LV2p91FkRIrLl2zxw/NC4S9OFrQQPtqajeCcDiW+0J65zvfCY8//jgTRH3oQx+C6upquPrqq60uhvSgsAktnP7kT/4EOjvzA4xju/b19THrqfe9731w//33pwVZKIy64ooroL6+3oaSE4SDoAsaDk2ogEJCKIIgCHFx0Von8mV9KeraV8LYueNMsaOho0+zEKCxsx98gUVhlnu6WxVV9a0wNz2WJ/CzFDmHpSZhKL4IDtDzSxAEQRCEwFjqmg+pqqqCb37zm3DRRRfB/Pw8/PVf/zW8/e1vh8cee4xZ7BDlL21vvvlmFh/q/e9/f0EhVK7A6tOf/jSsWbMm/bfx8XH40Y9+RE1NEAQhO1ZfTrnoQprI7u/a1m5qEhPwB8LZfyBNWAERf97zeLzQs+Ua6N16nS4L47QQisijZeUGaOjohY5VW8EfqqAWKkJlfcvSePRS7FlCHsuVcNWScm51Y4etZSEIgiAIp2O5RdR3vvMdePLJJ9Mu5GZnZ+G+++5jL3Tdh1Y8DQ0NLJaUFlcZd9xxB2zbtg2cTk9PD3zmM5/R9JtAIABvfetb4W1ve1v6bxij681vfrMJJSQIQghIYOCYS+poZBYq65YueAh+NHWtganRs+x9uLqBtTWxBO7DujdcAdNj56Cle50lTeMLLLkVa+pa7fjuWLHpKhjc/wTUkaCvIJlzX3VTaeUr/Yh/UUrYBwZO7xy4mLqgDG19WyCZTEC4upEEm4RUblCDlTXQtfZSiEXmoK51hd3FIXKoqm/LbxNBxxJhvkC7qqHAeDABj29JoaKqod2SPAnCLVguiNq9ezfcc889Bf9tbm4Onn/+eV3pXnXVVa4QROll585sn9vHjx+3rSwEIQvN3Wth+PheCIarxdD4oz236+jdej1MXjgNNc1ddhdFaLrW7ITzp/ZDe98WTb/DwwxaEWBMFa8/QIKoAtQ0dbKXVXOUx+uDvm03wPzMBNQ2LwenE6qqY1YsRPH2QWFoMhGHaroIsAaHu4ETkpy5s61vM5za93haEKY7WQsva9FtIV7mEzZgQj+jlWUi4R5vNaQMUpoVG6+CC4MHiiolmeHWtv/im9kZqK5tJchCZ/92OH9yH7T3b9X0u6blpBhXLq70stU7YGp0CNp6tZ31jKxpmOfs5Ai0FoqhSRCEPIIowh7QJaLf74doNMo+j40t+DonTIIEBo6gdcUGqKprhVB1kXhq5EKJsWzNJTD44uPQ0KktLgVRHnQDJFK7iqrNWte2gr301IeszcQDrdPwRRCIakGoaGTMlxijlSDUghev/lAlU4TCCzjC3aBFCF7AWq0Ide7Y81Df3mtpvoSYVDe2s5fVlmr4kgk8s+k5t+Ez3rPlWmZNSopxhalv72EvVSg25EkQhLiCqGAwCBUV/P1ro5CFKM7IyEhaCIXU1Mi1qBOEHeDh33Tz75yL/UCoEiJz0yAT9W0rmU91ry9gd1EIgiAIwhCZ8ZJ8PoqdRFgPKklU1bfyTZSEodLStXYnjJw+ZGn8IrRGXb7+JYX/UUydJGhevhaGT+yFipomu4tCuFQxTi8LinHNdheDIAjCmYKou+66i70Ia9m1a1fW5+XLne/uRhhICZbQwMrNVzMXY2kf5TzGjwWbdTRfJ5xBY+cqGBk8BJ1rdthdFIKQg4w5Fq0YCMnIWSIbOlbB+LmT7GKorl2/SyBLXPYSBOF4MHZiy4r1cs47FgoMWlduYMI6FKIRBEEQBCEm5JrPJfzP//xP1ufLL7/ctrIQhCPhdNAKhKugY9VW48UR7SBKmIIZPtnbV22FlpUbSLhIEBoE8ei6Y3r0HNNcdxpO0zwuh8frzYrb5aY4KYQAkOUSYceYccA8j54syKqEIAiCIMSGBFEu4NFHH4Xf/e536c9erxduu+02W8tE8AOtFo4+8yAEQlWkiU0QBJdLZ7JwIwhtYEBjjAPkNqENQRBWQO4VCJuhIUiohPZBBEEQRClIEOVwJiYm4AMf+EDW31796ldDd3e3rvSGhkoHSh0dHdWVLqEf9CE/cMmt4PUHaeMnOWZYtxAEQViHu4UwdPkiJ6ZZEGclS+s7QRAE4VDIkpMgCIJQCQmiHEwikYD3ve99MDg4mP5bW1sbvOc979Gd5pVXXlny3/1+P6xZs0Z3+oR+d26E9ZDgiCAIt4NWQITTcbmA0eX1J2RmaezSnpUgCIJwB7RvIwiR8dhdAMI8/uVf/gV++9vfpj/7fD749Kc/DdXV1dTsJkOXFu7A41mS5de1rQBRofFIEISZ8XTScw25pSMIgiAIqSGhJUEQskGKcQQhD2QRpYGZmRnYsWMHmM1zzz1nOI1vfvOb8LWvfS3rbx/96Edh27ZthtJ96KGHyrrm+/CHP2woD4KQ6QJ2xcYrYWbiAjR1rRZLoESKQK6DhACEHSxbsxOOPP1rCFXWUZxCgsiCFmK3gHvACyf3Q8fAdruLQrgdkRVCRC4bYYja1m4YOvIse1/f3kOtSRAEQYgriIpGo/DEE0/Anj174ODBgzA+Pg5TU1MQiUQ0pfOOd7yjrNs4HlJ2reWyg5/+9KfwiU98Iutv6I4PY0MZBV37lXPNR+RCboucTHVjB3sRhC2QWzTCZsLV9bD60leAx+sjYShhC50DF8Pg/l1Q37bS+OUobdkIHbT3bYGW7nXg9Qeo/QjLEdmCibwyuINAqBL6d9zM7suCFTV2F4cgCIIQGNsEUSjQ+fKXvwzf//73YXh42HB6Y2NjXMolOw888AD83d/9XZZp6lve8hZ405veZGu5CILQi7iHS8J+fIFQwfcEYSV0+epwBFdib+joheqGdvAFwyASZKXqLmgeJNwi+CHhElEIEkARdtK4rB/OHHqavaczMUGIjS2CqNOnTzPhyP79+0EmKisrmeWWqPzhD3+Ad73rXRCPx9N/+9M//VP2N4IgCMJ51LWugLGzxyGZTEBDxyq7i0MQBGEL/lCFeYmTOynCdBTzkyW9Jsfi9QUgXNUAc1Oj0LX+MruLQxAEYTkNnX3g9fmZQBT/TxCEuFguiJqenoa/+qu/ggMHDmT9vaKiAtrb2+HChQtp66Zly5axv4+MjMD58+ezvt/c3AwtLS3pz3V1dZaUPxgMgog89dRT8Na3vjXLdeDtt98OH/jAB2wtF0EQAkCXaI5F8Xhg5eaX2l0MgiAIohQkBCB4QXs6Im9IKNBz0bUQi84xF2nmQJMYQViFx+NNv/cLZu0tcptRfDKCkAPLBVFf+9rXsoRQ/f398N73vhcuvfRS8Pl8LJbR3Xffzf7t7W9/O9x2223s/ZkzZ+BXv/oVfP3r/3979wEeRbk9fvykEALpIYEEAkQgEHoXURQVsFAEVCwgFkBEvYoFvepVuV69oui1YVcsiApiwT/qhWtFRUQRpClVqvQaQDr5P+f1t+vsZjfZTXZ2N7vfz/Psw8xmdubNbuZl9j1zzvuKKeWn80jdeuut0r9/f4l2S5YsMcG9AwcOOJ/r2bOn/Pvf/6YsSKjwJREhF+a1lAAAlQJlmAAgvMXGxUlCnF1BqMDg/xLANwnVkiUtu64cKNp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GjRo1XNYTEhI8bpeUlFSu+VrUaaed5lJmSjN7/AkSldfy5cvls88+K3O7nTt3yttvv+3yu/bo0SPg7bEGmnQ+oMmTJzvny6lWrZq0bt26xGs0GKBzQDl8++238uOPP3rcZ6BU5LNG5T1PNRBiLWmnGTtl0YDYu+++6/Mxzj77bKlatapzXeek8qWsnwZffc1wDFV/448xY8bIlClTnOv6njz77LO2BMABAAAQnQhEAQAAwBZatk3njzl8+HC5swwaNGjgtbSc1cqVK30+hs4xc/3117s8d9ddd8mcOXPEH0uXLpX58+f79Zp//etfsmnTJq8/1zlxtC3WeWTOP//8EgP6gaABJWuWiWY/OOjcQN6CC9ZMKs3usAYX7CjLV5HPGpX3PLVm1ml2lHuWoDvd5sYbb/R5fiiVmpoqvXv3dq5v3brVnKOl0fnQNHuoMvQ3vnj66afltddec5kb7KmnnrIlqAwAAIDoRSAKAAAAttCgyh133GHmdtE77ufOnWsGvT3RQXANatx3333O5zQQ0qtXL4/bu8+Z9Oabb5pScb7q27ev9OvXz7murx0yZIg89NBDJvvCG/3ZpEmT5PLLLzf70JJ2vsrNzTWvHzhwoMkk8pTNMXz4cJc5WrQk2ciRI8UOGRkZUlhY6JKJ5VDaILT1Z9bXlPW68qroZ43KeZ6ec845Lut33323fPjhhyUyo7Tc38yZM+Xiiy828zZp6UhrNlVZbrnlFklLS3Oua1BOA1qe+gHN/tPzV+eSql27dlj3N754/fXXZdy4cS5Bs0ceeUROP/30gB4HAAAAcC3aDQAAAASYlnvTO+71oYPWjRs3NpkSmo2gA9462Lp48eISmQw6QOxtsDcnJ0c6dOhgBs2VZgro4KnO+aJlw2Jj/7rfqmvXrnLWWWeV2IdmPuzZs8cZ+NG2vPrqq+ZRUFBgymmlpKTIgQMHzHaazeEoXVceerzrrrtONm7cKEOHDpV69eqZQJC+JxqE0kFm6xw6Wh7r0UcfNW2wi2Yw/frrryWeP/nkk72+Rt8X/Vz097DS38efwXlfBeKzRuU7TzUrr1u3bvL555+bdT0Pb7/9dnNOtGrVypwXmgWlbXKclxpI0eCObudrZlRWVpY8+OCDJuDrCMBpxpeW0dTylPq7aenKZcuWyZo1a5xzX91///3mPHYoaw6rYPc3ZdH3TQOPVvq7apDcU6C8NPo+eMuKAwAAABSBKAAAAASNZlToAKg+vNFB8FGjRskVV1xR6r7+8Y9/yKWXXioHDx4061rOTjMjPGX+eApOaKDnueeek2eeecaUpTt06JDzZytWrDCP0jRr1syvOVR0YF0H0W+77TbzPqxbt848PNEB6f/85z+2lLpzz2DSgXCr9PR0l0wpT7Rd77//fol92aWinzUq53k6duxYGTZsmEtJOi2f52mutcTERBNY0YCWv7p3724ygbStjgCWZorNmzfPPNx/74cfftgE06zKysIKdn9TFv0crIFvtX79evPw13nnnUcgCgAAAKWiNB8AAABsoVkDN9xwg/To0UPq1q1bZsaAZiZcdtllZi6Ysga3HQOz7777rpxxxhnmWOWhbfrb3/5mBrb1rv78/PxSt8/Ly5PBgweb8mRawkszM/yh5cb0dTpwqwPn7nQwu3///vLxxx+Xa0DdX5qtonPCuM8dZc1U8cRTgMzOoFkgPmtUvvM0OTlZJk6caIK3NWvW9LiNltW76KKLZNq0adKzZ08pL32tYx+ezk0NJPXp08cEYHWbvXv3uvxcM8fCrb8BAAAAwkVMsfttUAAAAIAN9u3bJ7///rspO6V34+tArgZBdABXyzrpoGt5aRbD8uXLZfPmzaa0lXWOmyZNmvg1gKslyFavXm3Kfum+kpKSzLwzWj7LOpdMWf7973/LhAkTnOuaWaH7sr4fmgWhbdZLch1ob968uVSrVk2Cafr06S6D6jqvT9OmTUt9jX6G//vf/0oE2XwtI6i/988//1yu1/r7WetxrNkmGujzNSBifW90vi5vc+f48/sEoz2ReJ7qOaLl6vTc1DmWNHNPzxk9LzVLyUrnktKsLqXt9TdbT/f/yy+/mOwrDcpqiUE9jgbGHGbPni1XXnmlc/3xxx8vVyAsUP1NeY779ddfB2RfGjT3FigEAAAAFIEoAAAAwAZlBaIAVF6PPfaYvPDCC851zXLSjDIAAAAAJVGaDwAAAAAAP7LG3nnnHed6bm4uQSgAAACgFASiAAAAAABRa+nSpbJmzRqfttWSfzpn1a5du5zPXXzxxTa2DgAAAKj8CEQBAAAAAKLWsmXLpHfv3nL33XfLokWLvM5RNWvWLLn00kvliy++cD6vc3VdcsklQWwtAAAAUPn4NisuAAAAAAAR6siRIzJlyhTzyMjIkObNm0tmZqYcP37cZD8tWbJEdu/e7fKaKlWqyCOPPGK2BwAAAOAdgSgAAAAAAP6PBp6+/fbbUt8PDVI9+eSTcuKJJ/K+AQAAAGUgEAUAAAAAiFotWrSQvn37ypw5c2Tz5s1et4uJiZGGDRuabQcOHCjJyclBbScAAABQWcUUa7FrAAAAAAH1888/y4oVK5zr/fv3l/h47gMDwtnOnTtl06ZNUlRUJHv27JEDBw5IUlKSpKWlSUFBgcmEAgAAAOAfAlEAAAAAAAAAAACwRaw9uwUAAAAAAAAAAEC0IxAFAAAAAAAAAAAAWxCIAgAAAAAAAAAAgC0IRAEAAAAAAAAAAMAWBKIAAAAAAAAAAABgCwJRAAAAAAAAAAAAsAWBKAAAAAAAAAAAANiCQBQAAAAAAAAAAABsQSAKAAAAAAAAAAAAtiAQBQAAAAAAAAAAAFsQiAIAAAAAAAAAAIAtCEQBAAAAAAAAAADAFgSiAAAAAAAAAAAAYAsCUQAAAAAAAAAAALAFgSgAAAAAAAAAAADYgkAUAAAAAAAAAAAAbEEgCgAAAAAAAAAAALYgEAUAAAAAAAAAAABbEIgCAAAAAAAAAACALQhEAQAAAAAAAAAAQOzw/wHWnQbjzCgUpwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x320 with 1 Axes>"
       ]
@@ -2082,7 +2238,7 @@
      "metadata": {
       "image/png": {
        "height": 344,
-       "width": 859
+       "width": 849
       }
      },
      "output_type": "display_data"
@@ -2109,7 +2265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e97e4e3",
+   "id": "62b3f190",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
@@ -2122,10 +2278,10 @@
     "shuffle is done once on disk and replayed in the same order every epoch, so a\n",
     "window of exactly one or two passes averages over the *same rows* every time\n",
     "and the batch-composition noise, which dominates the per-step view, cancels.\n",
-    "Widen the window to one and a half passes and it does not: the control sits\n",
-    "at more than twenty times the spread of its neighbours on either side, even\n",
-    "though it is *wider* than the one-epoch window. Nothing but the alignment\n",
-    "changed.\n",
+    "Widen the window to one and a half passes and it does not: the printed ratio\n",
+    "puts the control an order of magnitude above the wider of its two neighbours,\n",
+    "even though it is itself *wider* than the one-epoch window. Nothing but the\n",
+    "alignment changed.\n",
     "\n",
     "What survives the cancellation is Monte-Carlo noise and the parameter drift\n",
     "itself, and against that much smaller yardstick the drift becomes visible —\n",
@@ -2163,14 +2319,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "8d0899b1",
+   "execution_count": 22,
+   "id": "0e261f58",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:01.305788Z",
-     "iopub.status.busy": "2026-08-17T04:51:01.305644Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.308927Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.308378Z"
+     "iopub.execute_input": "2026-08-17T06:42:38.095304Z",
+     "iopub.status.busy": "2026-08-17T06:42:38.095237Z",
+     "iopub.status.idle": "2026-08-17T06:42:38.097412Z",
+     "shell.execute_reply": "2026-08-17T06:42:38.097069Z"
     }
    },
    "outputs": [
@@ -2178,7 +2334,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "stage 2 smoothed reduction: 188.11 nats; 99% of it reached by step 1,946 of 2,500 (78% of the stage-2 budget)\n"
+      "stage 2 smoothed reduction: 188.11 nats; 99% of it reached by step 1,947 of 2,400 (81% of the stage-2 budget)\n"
      ]
     }
    ],
@@ -2189,8 +2345,9 @@
     "smoothed = np.convolve(stage2, kernel, mode=\"valid\")\n",
     "total_drop = smoothed[0] - smoothed.min()\n",
     "# \"valid\" convolution starts at the first full window, so index j of `smoothed`\n",
-    "# is the trailing average ending at stage-2 step j + steps_per_epoch - 1.\n",
-    "t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch - 1\n",
+    "# is the trailing average ending at stage-2 step j + steps_per_epoch, counting\n",
+    "# steps from 1.\n",
+    "t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch\n",
     "print(\n",
     "    f\"stage 2 smoothed reduction: {total_drop:,.2f} nats; \"\n",
     "    f\"99% of it reached by step {t99:,} of {len(stage2):,} \"\n",
@@ -2200,7 +2357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "503c7809",
+   "id": "95b6835c",
    "metadata": {},
    "source": [
     "Two honest qualifications about any stop, this one included. What such a rule\n",
@@ -2226,7 +2383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1068af23",
+   "id": "da4702e8",
    "metadata": {},
    "source": [
     "### Choosing a path: why the fast one is not taken here\n",
@@ -2244,14 +2401,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "6fb1c55d",
+   "execution_count": 23,
+   "id": "9bcbaadc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:01.310400Z",
-     "iopub.status.busy": "2026-08-17T04:51:01.310292Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.340763Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.340230Z"
+     "iopub.execute_input": "2026-08-17T06:42:38.098344Z",
+     "iopub.status.busy": "2026-08-17T06:42:38.098291Z",
+     "iopub.status.idle": "2026-08-17T06:42:38.113847Z",
+     "shell.execute_reply": "2026-08-17T06:42:38.113474Z"
     }
    },
    "outputs": [
@@ -2272,7 +2429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dda7204",
+   "id": "c95425de",
    "metadata": {},
    "source": [
     "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
@@ -2286,7 +2443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ee74a58",
+   "id": "b1491e9e",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2298,7 +2455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76ddfbb7",
+   "id": "77e1eea5",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2310,7 +2467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd3e44a9",
+   "id": "d0818366",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2318,14 +2475,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "e86e26dd",
+   "execution_count": 24,
+   "id": "340902f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T04:51:01.342328Z",
-     "iopub.status.busy": "2026-08-17T04:51:01.342216Z",
-     "iopub.status.idle": "2026-08-17T04:51:01.366379Z",
-     "shell.execute_reply": "2026-08-17T04:51:01.365773Z"
+     "iopub.execute_input": "2026-08-17T06:42:38.114884Z",
+     "iopub.status.busy": "2026-08-17T06:42:38.114828Z",
+     "iopub.status.idle": "2026-08-17T06:42:38.127903Z",
+     "shell.execute_reply": "2026-08-17T06:42:38.127598Z"
     }
    },
    "outputs": [
@@ -2333,7 +2490,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun, 16 Aug 2026\n",
+      "Last updated: Mon, 17 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
@@ -2365,7 +2522,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "668c6eda",
+   "id": "b2258e9f",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index ca4f1483f..b3a1a1d05 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -31,7 +31,7 @@ aggregate trades as daily and monthly files at
 [data.binance.vision](https://data.binance.vision). Widen a pull across
 symbols and months and the feature matrix can outgrow the RAM of an ordinary
 workstation; at that point in-memory minibatching stops being an option. PyMC's
-{class}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a
+{func}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a
 disk-backed reader, so it cannot help once the array no longer fits. This
 notebook fits a hierarchical hurdle–Student-t model of *next-event price
 moves* by streaming minibatches from disk with pymc-extras'
@@ -43,15 +43,15 @@ rows that are generated inside the notebook. It tries to teach three things:
 2. **The mechanics, end to end** on an archive small enough to rebuild here:
    an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
    what a stopping rule has to be able to see before it can fire.
-3. **Honest reporting.** With the truth known we can watch the posterior means
-   land close to it while the mean-field standard deviations stay far too
-   narrow to cover the distance that remains — standardized gaps of 5 to 7 on
-   rows that are cleanly identified. Which widths could be trusted in general
-   is a calibration question that needs many simulated datasets; this notebook
-   fits one, so it reports that gap and withholds width claims rather than
-   certifying them.
-
-The notebook runs in about a minute. Everything below executes on synthetic
+3. **Honest reporting.** With the truth known, recovery is checkable — but only
+   after a correction the fit forces on us: the cyclic replay puts the optimizer
+   on an orbit, and the last iterate misses the truth by several of its own
+   posterior standard deviations purely because of where in that orbit the step
+   budget ended. Averaged over one full pass, every identified quantity lands
+   inside 1.2. The widths themselves are a separate question this notebook
+   cannot settle on one dataset, and it says so rather than certifying them.
+
+The notebook runs in well under a minute on a laptop. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
 possible: nothing here is a claim about any real market.
 
@@ -67,10 +67,17 @@ first is about *validity*, the second about whether streaming is doing any
 real work — and they are worth internalizing before reaching for `total_size`
 on your own data:
 
-**Gate 1 — validity: the likelihood must factor over rows, sampled uniformly.**
-No latent path coupling observations, no label shared across rows, and every
-row given the same inclusion probability by the batching scheme (unequal
-inclusion breaks the plain $N/B$ rescaling). This is exactly why the classic
+**Gate 1 — validity: the likelihood must factor over rows, and the batching
+must weight every row equally.** No latent path coupling observations, no
+label shared across rows, and every row given the same inclusion frequency by
+the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How
+the batches are *drawn* is a separate matter that this gate does not settle:
+fresh uniform batches make each step's scaled gradient conditionally unbiased
+for the full-data objective — the identity above — while a fixed shuffled
+order replayed every epoch visits each row once per pass, targets the same
+finite sum, and yields cyclic rather than unbiased updates. This notebook does
+the second, and returns to the distinction where the fit is described. The
+factorization requirement is exactly why the classic
 stochastic volatility model of the {ref}`stochastic_volatility` notebook
 *cannot* be minibatched: its latent volatility path ties every observation to
 its neighbours, so a random subset of rows does not carry $B/N$ of the
@@ -194,8 +201,8 @@ $q$ and $a$ are standardized once over the whole sample. On a real archive
 both would instead be trailing statistics built causally from the tape and
 standardized with constants frozen on a burn-in window — a construction that
 is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
-the standard way to encode intraday periodicity smoothly
-{cite:p}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
+a low-order version of the Fourier encoding of intraday periodicity in
+{cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
 back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
 $b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
 so a thin symbol is pulled toward the global intraday shape where its own data
@@ -372,10 +379,12 @@ steps would only ever see early dates and the first symbols, and an early
 stopping decision would be biased by construction. The fix is to shuffle
 **once, globally, on disk at ETL time**: every row gets a deterministic hash
 key, rows are scattered across shards by that key, and each shard is sorted by
-it. After that, sequential reads follow one fixed, data-independent uniform
-permutation — replayed identically each epoch, which is single-shuffle SGD
-semantics rather than fresh per-step subsampling — and the loader can run
-with `shuffle=False` at full speed.
+it. After that, sequential reads follow one fixed, data-independent
+permutation — pseudo-random, because it comes from a hash of the row index
+rather than from anything in the row, and replayed identically each epoch,
+which is single-shuffle SGD semantics rather than fresh per-step subsampling —
+and the loader can run with `shuffle=False`, without a copy through the
+shuffle buffer.
 
 ```{code-cell} ipython3
 data_dir = tempfile.mkdtemp(prefix="ticks_")
@@ -410,15 +419,16 @@ for i in range(n_shards):
     part = table.take(order[i::n_shards])
     pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"), row_group_size=BATCH)
 
-head = pq.read_table(os.path.join(data_dir, "shard_000.parquet")).slice(0, 10_000)
-print(
-    f"first 10k rows of one shard cover {len(np.unique(head['hour']))} hours "
-    f"and {len(np.unique(head['sym']))} symbols"
-)
+# the head of every shard has to mix hours and symbols already; check all of them
+for i in range(n_shards):
+    head = pq.read_table(os.path.join(data_dir, f"shard_{i:03d}.parquet")).slice(0, 10_000)
+    assert len(np.unique(head["hour"])) == 24
+    assert len(np.unique(head["sym"])) == n_symbols
+print(f"first 10k rows of every shard cover all 24 hours and all {n_symbols} symbols")
 ```
 
-The printed check is the one to keep: the head of any shard must already mix
-every hour and nearly every symbol, asserted rather than assumed.
+The assertion is the one to keep: the head of every shard must already mix
+every hour and every symbol, checked rather than assumed.
 
 Be clear about what this cell is and is not. It is the pedagogical miniature:
 it builds the whole table, the whole key array and the whole permutation in
@@ -432,7 +442,8 @@ So that the fit really does run against disk rather than against arrays still
 resident from the generator, the row-scale objects are released first:
 
 ```{code-cell} ipython3
-del table, key, order, y, m, d, q, a, ylag, sym, hour, logit_pi, log_sigma, t_draw, sigma
+del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves
+del logit_pi, log_sigma, t_draw, sigma
 gc.collect()
 print("row-scale generator arrays released; the fit reads from", data_dir)
 ```
@@ -446,9 +457,9 @@ why the row counts were chosen to divide exactly. That is worth doing
 deliberately: on this path the loader hands you the row groups it finds, so a
 ragged geometry gives ragged batches, `len(loader)` (which is
 `total_size // batch_size`) stops matching the number of blocks an epoch
-yields, and — as the fitting section shows — the recorded loss picks up a
-deterministic sawtooth that has nothing to do with convergence. The model
-reads
+yields, and the recorded loss picks up a deterministic sawtooth that has
+nothing to do with convergence (the fitting section explains why the recorded
+value scales with the block size). The model reads
 one `pm.Data` placeholder; everything derived — the Fourier basis, the
 integer symbol index — is computed inside the graph, so advancing the stream
 is a single `set_value` per step.
@@ -484,7 +495,7 @@ diagnostics below, which is why the shards divide.) Only the shuffle-buffer
 path drops a trailing partial batch, and this notebook never uses it.
 
 Second, a precision about Gate 1 that matters for how the fit should be
-described. The permutation is drawn once, on disk, and then replayed in the
+described. The permutation is fixed once, on disk, and then replayed in the
 same order every epoch. Each row is included exactly once per pass, but the
 batch at step $t$ is a deterministic function of $t$, not a fresh uniform
 draw — so the individual updates are not the conditionally unbiased stochastic
@@ -587,7 +598,7 @@ def build_model(symbols, batch_init, total_size):
 Two implementation notes worth pausing on. The likelihood is a
 {class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`
 gives the term *observed-RV semantics*, which is what makes PyMC's own
-`total_size` minibatch rescaling apply {cite:p}`kucukelbir2015automatic`. And
+`total_size` minibatch rescaling apply. And
 the hurdle's two logs are written with `softplus` —
 $\log \pi = -\operatorname{softplus}(-x)$,
 $\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at both
@@ -605,10 +616,12 @@ model = build_model(
 pm.model_to_graphviz(model)
 ```
 
-The plate diagram makes the two-link structure visible at a glance: five
-partially pooled effect families on the symbol plate feeding two linear
-predictors, one shared tail parameter, and a single observed node whose batch
-dimension is whatever the placeholder currently holds.
+The plate diagram is an inventory rather than a picture of the two links: five
+partially pooled effect families on the symbol plate, one shared tail
+parameter, and a single observed node whose batch dimension is whatever the
+placeholder currently holds. The two linear predictors live inside the
+likelihood's `logp` and are not separate nodes; the equations above are where
+that structure is visible.
 
 The loop below is the whole streaming adapter: a `pm.fit` callback that pushes
 the next block into the placeholder after each step. pymc-extras#710 proposes
@@ -636,17 +649,34 @@ class StreamAdvance:
         self._shared.set_value(next(self._stream), borrow=True)
 
 
+class ParamTrace:
+    """pm.fit callback: record the variational parameters after every step."""
+
+    def __init__(self):
+        self.mu, self.rho = [], []
+
+    def __call__(self, approx, losses, i):
+        mu, rho = approx.params
+        self.mu.append(mu.get_value())
+        self.rho.append(rho.get_value())
+
+
 stream = StreamAdvance(model, loader)
 stream.prime()
+tail = ParamTrace()
 
 with model:
     advi = pm.ADVI(random_seed=RANDOM_SEED)
 advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)
 approx = advi.fit(
-    2_500, obj_optimizer=pm.adam(learning_rate=0.005), callbacks=[stream], progressbar=False
+    2_400,
+    obj_optimizer=pm.adam(learning_rate=0.005),
+    callbacks=[stream, tail],
+    progressbar=False,
 )
 ```
 
+The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.
 The two-stage learning rate is deliberate. A constant step size that is
 comfortable early is too large near the optimum, where it keeps the
 variational mean bouncing instead of settling; dropping it once the descent
@@ -688,6 +718,54 @@ ax.set_title(
 ax.legend();
 ```
 
+## The last iterate is not the answer
+
+Before reading a single parameter off this fit, one property of the replay has
+to be dealt with. The batch at step $t$ is a deterministic function of $t$ with
+period one epoch, so the gradient sequence is periodic and the optimizer's
+iterate inherits a periodic component. Two measurements separate that component
+from genuine progress: compare the same phase in consecutive epochs, and look at
+the spread within a single epoch.
+
+```{code-cell} ipython3
+mu_t = np.asarray(tail.mu)
+sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging
+ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart
+drift = np.abs(ends[-1] - ends[-2]) / sd_unc
+last = mu_t[-steps_per_epoch:]
+swing = (last.max(0) - last.min(0)) / sd_unc
+print(
+    f"epoch over epoch, same phase: median {np.median(drift):.2f}, max {drift.max():.2f} sd\n"
+    f"within the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd"
+)
+```
+
+Read those two lines together. Between the same points of consecutive epochs the
+mean barely moves, so the systematic descent really has flattened — the fit is
+not being stopped in mid-descent. Within one epoch it swings by multiples of its
+own posterior width. The optimizer is not converging to a point; it is orbiting
+one, on a cycle locked to the order the rows are replayed in, and the last
+iterate is whatever phase of that orbit the step budget happened to end on.
+
+That is not a subtlety to note and move past. It means the number you would
+report depends on where you stop, so the fit is summarized by averaging the
+variational parameters over the final whole pass — a full period of the cycle,
+which is what makes the average cancel it. This is Polyak–Ruppert averaging, and
+it is why the step budget above is a whole number of epochs. The same tail
+average is what [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)
+applies to its own iterates for the same reason.
+
+```{code-cell} ipython3
+rho_t = np.asarray(tail.rho)
+approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))
+approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))
+print(f"variational parameters averaged over the final {steps_per_epoch} steps (one epoch)")
+```
+
+Everything below reads from that averaged approximation.
+
++++
+
 ## Did it recover the truth?
 
 ```{code-cell} ipython3
@@ -721,7 +799,10 @@ recovery["z"] = (recovery["mean"] - recovery["truth"]) / recovery["sd"]
 recovery.round(4)
 ```
 
-Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
+The table is a selection — the scalar globals and the three identified sums —
+and the figure after it checks one of the five symbol-effect families; that,
+plus the two curves for one symbol further down, is the extent of the recovery
+evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
 global coefficient and the mean of its group effects are only *jointly*
 identified: the likelihood constrains their sum, and the prior only weakly
 splits it. The identified sums in the last three rows land within a couple of hundredths
@@ -734,24 +815,28 @@ estimates of their generating values; a sum-to-zero constraint on the symbol
 effects is the standard reparameterization when the split itself matters.
 The last column standardizes the error by the posterior width, and it has to be
 read against identification rather than straight across the table. On the three
-raw split rows it is not a recovery statistic at all: `alpha0` reads 404 because
+raw split rows it is not a recovery statistic at all: `alpha0` reads 379 because
 a prior-dependent decomposition can have an arbitrary location and still a
-narrow width, which says something about the split and nothing about the fit.
-On the identified sums every $|z|$ is below 2. Between those two extremes sit
-the rows that are identified on their own and still miss by more than their own
-width claims they can: $\theta_d$ at $-7.1$ and $\lambda_q$ at $+4.9$.
-
-That gap is the result of this section, and it is worth being exact about what
-it licenses. A z-score against a point truth confounds three things — the
-sampling noise of this one synthetic dataset, bias in the variational mean, and
-overconfidence in the variational width — and a single fit cannot separate
-them. What it does establish is a direction: these widths are too narrow to be
-read as uncertainty statements at face value. The stance behind reporting the
-gap and stopping there is a risk-management one, and it is worth stating
-plainly: material disagreement between independent-seed fits is enough to
-withhold a claim about a width, while agreement only removes the instability
-warning — it does not validate calibration. Replication can veto a width; it can
-never certify one.
+narrow width, which says something about the split and nothing about the fit
+(`beta_a`, at $-4.9$, is the same story in miniature). Everything that *is*
+identified — the three sums and the six standalone rows — lands inside $1.2$
+posterior standard deviations of its generating value, and four of those nine
+are inside $0.5$. Without the tail average the same table showed misses of
+three to seven; that difference was the orbit, not the estimate.
+
+Two things this table does not license. It does not say the widths are
+trustworthy: they are the widths of a mean-field approximation, which has no
+correlations to spend, and z-scores this small on one dataset are consistent
+with widths that are somewhat too narrow, somewhat too wide, or right — sizing
+that needs many simulated datasets, and this notebook fits one. Nor does it
+transfer to the raw splits: no amount of data fixes a ridge, only a
+reparameterization does.
+
+The stance behind stopping there is a risk-management one, and it is worth
+stating plainly: material disagreement between independent-seed fits would be
+enough to withhold a claim about a width, while agreement only removes the
+instability warning — it does not validate calibration. Replication can veto a
+width; it can never certify one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?
@@ -940,7 +1025,10 @@ ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
 ax.legend(loc="lower right");
 ```
 
-All three statistics sit inside their predictive 90% bands. Two limits on what
+All three statistics sit inside their predictive 90% bands, though the zero
+share sits close to the lower edge — 0.300 observed against a band starting at
+0.299 — so it is a pass with almost no margin rather than a comfortable one.
+Two limits on what
 that establishes. The heading is literal — the evaluation batch was seen during
 the fit, so this is adequacy, not generalisation, and a held-out split is the
 next thing to add before any of this is used to compare models. And the check
@@ -968,9 +1056,12 @@ z_w(t) = \frac{\bar L_{t-2w:t-w} - \bar L_{t-w:t}}
 $$
 
 with $\hat\sigma$ estimated from successive differences. Positive $z_w$ means
-the loss fell. Averaging divides the noise by $\sqrt{w}$ while a steady drift
-accumulates linearly in $w$, so the detectable drift shrinks like $w^{-3/2}$:
-the horizon does not merely smooth the picture, it sets what is visible at all.
+the loss fell. For noise without long memory, averaging divides it by
+$\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable
+drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,
+it sets what is visible at all. A fixed replay order is not noise of that kind,
+and the printout below shows where the generic scaling breaks — in the
+notebook's favour.
 
 ```{code-cell} ipython3
 # Stage 2 only, on the full-data scale: after the last planned optimizer change,
@@ -992,12 +1083,16 @@ horizons = [(1, "per step"), (steps_per_epoch, "one epoch"), (2 * steps_per_epoc
 # control: a horizon that is NOT a whole number of passes, to test whether the
 # variance collapse below is really about epoch alignment
 control = [(steps_per_epoch + steps_per_epoch // 2, "1.5 epochs")]
+spreads = {}
 for w, label in horizons + control:
     _, z = signed_z(stage2, w)
+    spreads[label] = z.std()
     print(
         f"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}"
         f"   |mean|/sd {abs(z.mean()) / z.std():.2f}"
     )
+aligned = max(spreads["one epoch"], spreads["two epochs"])
+print(f"\ncontrol spread is {spreads['1.5 epochs'] / aligned:.0f}x the widest aligned spread")
 ```
 
 ```{code-cell} ipython3
@@ -1029,10 +1124,10 @@ independent noise would predict — and the control row explains why. The
 shuffle is done once on disk and replayed in the same order every epoch, so a
 window of exactly one or two passes averages over the *same rows* every time
 and the batch-composition noise, which dominates the per-step view, cancels.
-Widen the window to one and a half passes and it does not: the control sits
-at more than twenty times the spread of its neighbours on either side, even
-though it is *wider* than the one-epoch window. Nothing but the alignment
-changed.
+Widen the window to one and a half passes and it does not: the printed ratio
+puts the control an order of magnitude above the wider of its two neighbours,
+even though it is itself *wider* than the one-epoch window. Nothing but the
+alignment changed.
 
 What survives the cancellation is Monte-Carlo noise and the parameter drift
 itself, and against that much smaller yardstick the drift becomes visible —
@@ -1074,8 +1169,9 @@ kernel = np.ones(steps_per_epoch) / steps_per_epoch
 smoothed = np.convolve(stage2, kernel, mode="valid")
 total_drop = smoothed[0] - smoothed.min()
 # "valid" convolution starts at the first full window, so index j of `smoothed`
-# is the trailing average ending at stage-2 step j + steps_per_epoch - 1.
-t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch - 1
+# is the trailing average ending at stage-2 step j + steps_per_epoch, counting
+# steps from 1.
+t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch
 print(
     f"stage 2 smoothed reduction: {total_drop:,.2f} nats; "
     f"99% of it reached by step {t99:,} of {len(stage2):,} "

From 0e3dba8d707fe30bbf2e5aa49962006f6bdfdfdb Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 17 Aug 2026 10:37:05 -0500
Subject: [PATCH 38/42] The ridge rows are a fit still moving, not an arbitrary
 split

A second audit round, this one aimed at the tail-averaging section, found
six things; five held.

The largest changes what the recovery section says about kappa0, alpha0
and beta_a. The notebook called them "prior-dependent decompositions, not
estimates of their generating values" and said "no amount of data fixes a
ridge". The likelihood is exactly flat along (alpha0 + d, b - d), but the
hierarchical prior tilts that direction toward effects that average to
zero -- and the generator standardized them to average exactly zero -- so
the prior points the split at the generating value with a posterior width
near tau/sqrt(12). Along a direction that flat a gradient optimizer
crawls. A new cell warm-starts a fresh optimizer at the last iterate and
runs sixty more passes: kappa0 goes 0.532 -> 0.579 -> 0.628 toward 0.847,
alpha0 -1.702 -> -1.722 toward -2.996, while the loss falls 0.37 nats per
pass. The raw split rows are a fit still travelling along a nearly flat
direction, and their reported widths (a few thousandths) are the
mean-field conditional width across the ridge, not the tenth the ridge
posterior has along it. Both are now said; both are reasons for the
sum-to-zero reparameterization the notebook already recommends.

Second, "the mean barely moves, so the descent really has flattened"
rested on the last of seven same-phase increments, which happened to be
the smallest. The cell now prints all seven maxima (6.37 2.93 2.59 0.75
1.39 1.45 0.58) and the last-pair median (0.11), and the prose says what
that supports: the phase dominates the last iterate, the average removes
the phase, and a slow drift remains that the average does not remove.

Third, two counts were wrong: "four of those nine are inside 0.5" is
seven; "misses of three to seven" without the tail average is a range no
row occupies -- the last iterate runs to 3.3. The replicate cell now
computes the last-iterate summary so that number is stored, and adds two
independent-seed refits: max |z| on identified rows 1.16 / 1.15 / 1.14,
across-seed spread of the means about a tenth of the reported width. The
"replication can veto a width" paragraph now describes a check that was
run.

Fourth, "the visible band narrows after the change" is false (sd 13192
before the anneal, 13141 after); the band is batch-composition noise and
the learning rate does not touch it. The sentence now says where to look
instead. The loss figure's two-line title, which overran the axes, is one
line.

Fifth, the warning filter's comment described a RuntimeWarning that never
fires; only the two numba object-mode warnings do.

One finding was refuted: the pushed head of pymc-extras#722 (f98a4ff)
does Polyak-Ruppert tail averaging over 75% of the trajectory, so the
notebook's statement about it stands; the reader had a stale local branch.

Also: the alignment claim now says averaging over a whole number of
periods removes the phase dependence exactly and a misaligned window
leaves a residual of order one swing over the window length; the
duplicated Gate 1 paragraph is cut to what it adds. Cold runs 29.1 / 31.2 s
(three fits plus the continuation), peak RSS 587 MB.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1034 ++++++++++-------
 .../streaming_tick_data.myst.md               |  248 ++--
 2 files changed, 778 insertions(+), 504 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index bed307444..b6e44ca12 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8ab3ab09",
+   "id": "b8aaffef",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa221894",
+   "id": "9514daf0",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
@@ -43,8 +43,9 @@
     "   on an orbit, and the last iterate misses the truth by several of its own\n",
     "   posterior standard deviations purely because of where in that orbit the step\n",
     "   budget ended. Averaged over one full pass, every identified quantity lands\n",
-    "   inside 1.2. The widths themselves are a separate question this notebook\n",
-    "   cannot settle on one dataset, and it says so rather than certifying them.\n",
+    "   inside 1.2 — on three optimizer seeds. The widths themselves are a separate\n",
+    "   question this notebook cannot settle on one dataset, and it says so rather\n",
+    "   than certifying them.\n",
     "\n",
     "The notebook runs in well under a minute on a laptop. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
@@ -53,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42854ead",
+   "id": "ad0e631a",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -104,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b9fb0f3",
+   "id": "c3d9a10e",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -130,13 +131,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e758565b",
+   "id": "fd287cf4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:29.154802Z",
-     "iopub.status.busy": "2026-08-17T06:42:29.154626Z",
-     "iopub.status.idle": "2026-08-17T06:42:30.576941Z",
-     "shell.execute_reply": "2026-08-17T06:42:30.576439Z"
+     "iopub.execute_input": "2026-08-17T15:34:45.125533Z",
+     "iopub.status.busy": "2026-08-17T15:34:45.125403Z",
+     "iopub.status.idle": "2026-08-17T15:34:46.714399Z",
+     "shell.execute_reply": "2026-08-17T15:34:46.713892Z"
     }
    },
    "outputs": [],
@@ -163,13 +164,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9a25b4a9",
+   "id": "02ff3159",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:30.578238Z",
-     "iopub.status.busy": "2026-08-17T06:42:30.578099Z",
-     "iopub.status.idle": "2026-08-17T06:42:30.580961Z",
-     "shell.execute_reply": "2026-08-17T06:42:30.580645Z"
+     "iopub.execute_input": "2026-08-17T15:34:46.715574Z",
+     "iopub.status.busy": "2026-08-17T15:34:46.715410Z",
+     "iopub.status.idle": "2026-08-17T15:34:46.718503Z",
+     "shell.execute_reply": "2026-08-17T15:34:46.718091Z"
     }
    },
    "outputs": [],
@@ -180,16 +181,14 @@
     "az.style.use(\"arviz-variat\")\n",
     "# the loss figures carry the fit story; keep the fit logger's lines out of the output\n",
     "logging.getLogger(\"pymc\").setLevel(logging.ERROR)\n",
-    "# pytensor's predictive path emits two benign internals warnings (a 0/0 inside its\n",
-    "# own allclose diagnostics; numba falling back to object mode for the python\n",
-    "# `random`); neither is actionable here, so they are filtered by origin\n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning, module=r\"pytensor\\.tensor\\.type\")\n",
+    "# the predictive path compiles the custom `random` and the minibatch wrapper through\n",
+    "# numba, which falls back to object mode for both and says so; not actionable here\n",
     "warnings.filterwarnings(\"ignore\", message=r\"Numba will use object mode\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "449cee9f",
+   "id": "13681964",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -264,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40d16f8e",
+   "id": "1e9a22a0",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -282,13 +281,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c9879503",
+   "id": "d6009520",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:30.582011Z",
-     "iopub.status.busy": "2026-08-17T06:42:30.581948Z",
-     "iopub.status.idle": "2026-08-17T06:42:30.584284Z",
-     "shell.execute_reply": "2026-08-17T06:42:30.584033Z"
+     "iopub.execute_input": "2026-08-17T15:34:46.719672Z",
+     "iopub.status.busy": "2026-08-17T15:34:46.719602Z",
+     "iopub.status.idle": "2026-08-17T15:34:46.722539Z",
+     "shell.execute_reply": "2026-08-17T15:34:46.722203Z"
     }
    },
    "outputs": [],
@@ -330,13 +329,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "432bf099",
+   "id": "17f5f07e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:30.585272Z",
-     "iopub.status.busy": "2026-08-17T06:42:30.585222Z",
-     "iopub.status.idle": "2026-08-17T06:42:30.737603Z",
-     "shell.execute_reply": "2026-08-17T06:42:30.737281Z"
+     "iopub.execute_input": "2026-08-17T15:34:46.723421Z",
+     "iopub.status.busy": "2026-08-17T15:34:46.723368Z",
+     "iopub.status.idle": "2026-08-17T15:34:46.881409Z",
+     "shell.execute_reply": "2026-08-17T15:34:46.880998Z"
     }
    },
    "outputs": [
@@ -404,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae083a28",
+   "id": "71667c76",
    "metadata": {},
    "source": [
     "Each constant above is a setting, not a measurement, and it is worth reading\n",
@@ -436,13 +435,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "22556b3d",
+   "id": "ca8b85ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:30.738711Z",
-     "iopub.status.busy": "2026-08-17T06:42:30.738646Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.127100Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.126730Z"
+     "iopub.execute_input": "2026-08-17T15:34:46.882449Z",
+     "iopub.status.busy": "2026-08-17T15:34:46.882395Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.286750Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.286370Z"
     }
    },
    "outputs": [
@@ -481,7 +480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2362735",
+   "id": "b3a844f2",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -504,13 +503,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e04b7efe",
+   "id": "bf1d0322",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:31.128107Z",
-     "iopub.status.busy": "2026-08-17T06:42:31.128022Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.335821Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.335407Z"
+     "iopub.execute_input": "2026-08-17T15:34:47.287654Z",
+     "iopub.status.busy": "2026-08-17T15:34:47.287590Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.511674Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.511310Z"
     }
    },
    "outputs": [
@@ -565,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ea61d1a",
+   "id": "066077bb",
    "metadata": {},
    "source": [
     "The assertion is the one to keep: the head of every shard must already mix\n",
@@ -586,13 +585,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "613dd96a",
+   "id": "6e0dced3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:31.336900Z",
-     "iopub.status.busy": "2026-08-17T06:42:31.336843Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.373208Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.372821Z"
+     "iopub.execute_input": "2026-08-17T15:34:47.512746Z",
+     "iopub.status.busy": "2026-08-17T15:34:47.512685Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.550430Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.550025Z"
     }
    },
    "outputs": [
@@ -600,7 +599,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_0c_z9zdw\n"
+      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_02wuqs0r\n"
      ]
     }
    ],
@@ -613,7 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4333815",
+   "id": "de71e750",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -636,13 +635,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "72db2140",
+   "id": "a29154c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:31.374203Z",
-     "iopub.status.busy": "2026-08-17T06:42:31.374142Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.421480Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.421082Z"
+     "iopub.execute_input": "2026-08-17T15:34:47.551630Z",
+     "iopub.status.busy": "2026-08-17T15:34:47.551561Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.599144Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.598777Z"
     }
    },
    "outputs": [
@@ -679,7 +678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c579f99",
+   "id": "234ee8a4",
    "metadata": {},
    "source": [
     "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
@@ -689,26 +688,23 @@
     "diagnostics below, which is why the shards divide.) Only the shuffle-buffer\n",
     "path drops a trailing partial batch, and this notebook never uses it.\n",
     "\n",
-    "Second, a precision about Gate 1 that matters for how the fit should be\n",
-    "described. The permutation is fixed once, on disk, and then replayed in the\n",
-    "same order every epoch. Each row is included exactly once per pass, but the\n",
-    "batch at step $t$ is a deterministic function of $t$, not a fresh uniform\n",
-    "draw — so the individual updates are not the conditionally unbiased stochastic\n",
-    "gradients of the $N/B$ identity. This is single-shuffle, cyclic finite-sum\n",
-    "optimization, and it is the arrangement the epoch-scale diagnostics later in\n",
-    "the notebook exploit."
+    "Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a\n",
+    "deterministic function of $t$, so what follows is single-shuffle, cyclic\n",
+    "finite-sum optimization — the arrangement the epoch-scale diagnostics later in\n",
+    "the notebook exploit, and the reason the fit has to be summarized with some\n",
+    "care before anything is read off it."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "044702c7",
+   "id": "77e3fef3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:31.422539Z",
-     "iopub.status.busy": "2026-08-17T06:42:31.422472Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.426456Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.426107Z"
+     "iopub.execute_input": "2026-08-17T15:34:47.600268Z",
+     "iopub.status.busy": "2026-08-17T15:34:47.600195Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.604742Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.604353Z"
     }
    },
    "outputs": [],
@@ -806,7 +802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cad1f553",
+   "id": "26245f2d",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -827,13 +823,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "82de0750",
+   "id": "45726fcf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:31.427381Z",
-     "iopub.status.busy": "2026-08-17T06:42:31.427329Z",
-     "iopub.status.idle": "2026-08-17T06:42:31.575875Z",
-     "shell.execute_reply": "2026-08-17T06:42:31.575392Z"
+     "iopub.execute_input": "2026-08-17T15:34:47.605658Z",
+     "iopub.status.busy": "2026-08-17T15:34:47.605603Z",
+     "iopub.status.idle": "2026-08-17T15:34:47.764908Z",
+     "shell.execute_reply": "2026-08-17T15:34:47.764522Z"
     }
    },
    "outputs": [
@@ -852,8 +848,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M805.01,-8C805.01,-8 851.01,-8 851.01,-8 857.01,-8 863.01,-14 863.01,-20 863.01,-20 863.01,-94 863.01,-94 863.01,-100 857.01,-106 851.01,-106 851.01,-106 805.01,-106 805.01,-106 799.01,-106 793.01,-100 793.01,-94 793.01,-94 793.01,-20 793.01,-20 793.01,-14 799.01,-8 805.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"831.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M810.01,-8C810.01,-8 856.01,-8 856.01,-8 862.01,-8 868.01,-14 868.01,-20 868.01,-20 868.01,-94 868.01,-94 868.01,-100 862.01,-106 856.01,-106 856.01,-106 810.01,-106 810.01,-106 804.01,-106 798.01,-100 798.01,-94 798.01,-94 798.01,-20 798.01,-20 798.01,-14 804.01,-8 810.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -872,16 +868,16 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M716.01,-114C716.01,-114 940.01,-114 940.01,-114 946.01,-114 952.01,-120 952.01,-126 952.01,-126 952.01,-223.82 952.01,-223.82 952.01,-229.82 946.01,-235.82 940.01,-235.82 940.01,-235.82 716.01,-235.82 716.01,-235.82 710.01,-235.82 704.01,-229.82 704.01,-223.82 704.01,-223.82 704.01,-126 704.01,-126 704.01,-120 710.01,-114 716.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"930.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M843.01,-98C843.01,-98 813.01,-98 813.01,-98 807.01,-98 801.01,-92 801.01,-86 801.01,-86 801.01,-52.5 801.01,-52.5 801.01,-46.5 807.01,-40.5 813.01,-40.5 813.01,-40.5 843.01,-40.5 843.01,-40.5 849.01,-40.5 855.01,-46.5 855.01,-52.5 855.01,-52.5 855.01,-86 855.01,-86 855.01,-92 849.01,-98 843.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
        "<!-- tau_ph -->\n",
        "<g id=\"node2\" class=\"node\">\n",
@@ -905,58 +901,116 @@
        "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
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+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
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+       "</g>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"828.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"828.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"833.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
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+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.25,-195.25 716.13,-191.43 706.02,-188.26 706.25,-195.25\"/>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"701.54,-196.97 711.36,-193.01 701.21,-189.97 701.54,-196.97\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
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+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M257.84,-279.53C265.93,-266.08 276.97,-252.11 291.01,-243.82 358.84,-203.75 567.86,-192.26 705.62,-189.12\"/>\n",
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        "</g>\n",
        "<!-- lambda_a -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"249.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"249.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"349.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>lambda_a&#45;&gt;y_obs</title>\n",
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        "</g>\n",
        "<!-- eta -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>eta</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"461.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
@@ -964,7 +1018,7 @@
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        "</g>\n",
        "<!-- nu -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node11\" class=\"node\">\n",
        "<title>nu</title>\n",
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        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
@@ -977,8 +1031,36 @@
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        "<polygon fill=\"black\" stroke=\"black\" points=\"464.51,-357.45 461.01,-347.45 457.51,-357.45 464.51,-357.45\"/>\n",
        "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M475.59,-287.76C485.04,-272.34 498.79,-254.3 516.01,-243.82 548.42,-224.1 635.62,-209.64 710.38,-200.41\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"710.78,-203.88 720.29,-199.21 709.94,-196.94 710.78,-203.88\"/>\n",
+       "</g>\n",
        "<!-- tau_sh -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>tau_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
@@ -999,217 +1081,131 @@
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-       "<!-- alpha0 -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M373.96,-279.73C382.07,-266.32 393.09,-252.32 407.01,-243.82 455.13,-214.44 595.99,-200.3 701.63,-193.67\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"701.75,-197.17 711.52,-193.07 701.32,-190.19 701.75,-197.17\"/>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"574.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"574.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
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+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M596.25,-280.34C606.07,-267.18 618.65,-253.19 633.01,-243.82 658.1,-227.44 687.98,-215.83 716.9,-207.61\"/>\n",
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        "<!-- beta_q -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node14\" class=\"node\">\n",
        "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"565.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"565.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"683.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"683.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"683.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"683.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
        "<title>beta_q&#45;&gt;y_obs</title>\n",
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-       "<!-- tau_k -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- b_k -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
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+     "iopub.status.busy": "2026-08-17T15:34:47.766018Z",
+     "iopub.status.idle": "2026-08-17T15:34:53.221511Z",
+     "shell.execute_reply": "2026-08-17T15:34:53.220988Z"
     }
    },
    "outputs": [],
@@ -1305,15 +1301,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb0ea6e5",
+   "id": "865f598f",
    "metadata": {},
    "source": [
     "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
     "The two-stage learning rate is deliberate. A constant step size that is\n",
     "comfortable early is too large near the optimum, where it keeps the\n",
     "variational mean bouncing instead of settling; dropping it once the descent\n",
-    "has flattened is the cheapest fix. The loss curve below is the evidence\n",
-    "available here — the visible band narrows after the change.\n",
+    "has flattened is the cheapest fix. Do not look for the effect in the width of\n",
+    "the loss band below — that band is batch-composition noise and the learning\n",
+    "rate does not touch it; the effect is in the level, and in the parameter\n",
+    "trace examined after the fit.\n",
     "\n",
     "Before plotting it, one property of `approx.hist` that is easy to get wrong and\n",
     "that changes what the numbers mean. PyMC normalizes the minibatch objective\n",
@@ -1336,27 +1334,27 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "effafd18",
+   "id": "fb7860f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:36.674531Z",
-     "iopub.status.busy": "2026-08-17T06:42:36.674461Z",
-     "iopub.status.idle": "2026-08-17T06:42:36.783578Z",
-     "shell.execute_reply": "2026-08-17T06:42:36.783271Z"
+     "iopub.execute_input": "2026-08-17T15:34:53.222751Z",
+     "iopub.status.busy": "2026-08-17T15:34:53.222674Z",
+     "iopub.status.idle": "2026-08-17T15:34:53.324523Z",
+     "shell.execute_reply": "2026-08-17T15:34:53.324157Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 330,
-       "width": 1048
+       "height": 336,
+       "width": 855
       }
      },
      "output_type": "display_data"
@@ -1372,17 +1370,13 @@
     "ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))\n",
     "ax.set_xlabel(\"step\")\n",
     "ax.set_ylabel(\"negative ELBO (full-data scale)\")\n",
-    "ax.set_title(\n",
-    "    \"Streaming ADVI loss, y clipped to the plateau (early losses are \"\n",
-    "    \"off scale).\\nThe visible band is batch-resampling noise — \"\n",
-    "    \"remember it for the stopping section\"\n",
-    ")\n",
+    "ax.set_title(\"Streaming ADVI loss (full-data scale, y clipped to the plateau)\")\n",
     "ax.legend();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c1c9210d",
+   "id": "b222fca0",
    "metadata": {},
    "source": [
     "## The last iterate is not the answer\n",
@@ -1398,13 +1392,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "57c93a4a",
+   "id": "7acd60f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:36.784722Z",
-     "iopub.status.busy": "2026-08-17T06:42:36.784655Z",
-     "iopub.status.idle": "2026-08-17T06:42:36.791768Z",
-     "shell.execute_reply": "2026-08-17T06:42:36.791384Z"
+     "iopub.execute_input": "2026-08-17T15:34:53.325788Z",
+     "iopub.status.busy": "2026-08-17T15:34:53.325700Z",
+     "iopub.status.idle": "2026-08-17T15:34:53.333293Z",
+     "shell.execute_reply": "2026-08-17T15:34:53.332825Z"
     }
    },
    "outputs": [
@@ -1412,7 +1406,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch over epoch, same phase: median 0.11, max 0.58 sd\n",
+      "same phase, epoch to epoch — max over coordinates, per pair: 6.37 2.93 2.59 0.75 1.39 1.45 0.58\n",
+      "                              median over coordinates, last pair: 0.11 sd\n",
       "within the last epoch:        median 4.31, max 41.2 sd\n"
      ]
     }
@@ -1421,46 +1416,55 @@
     "mu_t = np.asarray(tail.mu)\n",
     "sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging\n",
     "ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart\n",
-    "drift = np.abs(ends[-1] - ends[-2]) / sd_unc\n",
+    "step = np.abs(np.diff(ends, axis=0)) / sd_unc  # (7 epoch pairs, 154 coordinates)\n",
     "last = mu_t[-steps_per_epoch:]\n",
     "swing = (last.max(0) - last.min(0)) / sd_unc\n",
     "print(\n",
-    "    f\"epoch over epoch, same phase: median {np.median(drift):.2f}, max {drift.max():.2f} sd\\n\"\n",
-    "    f\"within the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
+    "    \"same phase, epoch to epoch — max over coordinates, per pair: \"\n",
+    "    + \" \".join(f\"{v:.2f}\" for v in step.max(1))\n",
+    "    + f\"\\n                              median over coordinates, last pair: {np.median(step[-1]):.2f} sd\"\n",
+    "    f\"\\nwithin the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bd086d04",
+   "id": "af6392a4",
    "metadata": {},
    "source": [
-    "Read those two lines together. Between the same points of consecutive epochs the\n",
-    "mean barely moves, so the systematic descent really has flattened — the fit is\n",
-    "not being stopped in mid-descent. Within one epoch it swings by multiples of its\n",
-    "own posterior width. The optimizer is not converging to a point; it is orbiting\n",
-    "one, on a cycle locked to the order the rows are replayed in, and the last\n",
-    "iterate is whatever phase of that orbit the step budget happened to end on.\n",
+    "Read the two measurements against each other. Between the same phase of\n",
+    "consecutive epochs the largest movement decays from about six posterior widths\n",
+    "in the first pair to about half a width in the last, and the median coordinate\n",
+    "moves a tenth of a width per pass; within a single epoch the same coordinates\n",
+    "swing by multiples of their width. So the optimizer is not converging to a\n",
+    "point. It is orbiting one, on a cycle locked to the order the rows are replayed\n",
+    "in, and most of where the last iterate sits is the phase of that orbit at the\n",
+    "step the budget ran out.\n",
     "\n",
     "That is not a subtlety to note and move past. It means the number you would\n",
     "report depends on where you stop, so the fit is summarized by averaging the\n",
-    "variational parameters over the final whole pass — a full period of the cycle,\n",
-    "which is what makes the average cancel it. This is Polyak–Ruppert averaging, and\n",
-    "it is why the step budget above is a whole number of epochs. The same tail\n",
-    "average is what [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)\n",
-    "applies to its own iterates for the same reason."
+    "variational parameters over the final whole pass. Averaging over a whole number\n",
+    "of periods removes the phase dependence exactly, which is why the step budget\n",
+    "above is a whole number of epochs; a window that is not a whole number of\n",
+    "periods leaves a residual of the order of one swing divided by the window\n",
+    "length. This is Polyak–Ruppert averaging, and the same tail average is what\n",
+    "[pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722) applies to\n",
+    "its own iterates for the same reason. What the average does *not* remove is the\n",
+    "slow drift the first line still shows: a tenth of a width per pass in the\n",
+    "median, and much more along the directions the recovery table is about to\n",
+    "single out."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "7a5882d7",
+   "id": "b93f5d10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:36.792825Z",
-     "iopub.status.busy": "2026-08-17T06:42:36.792766Z",
-     "iopub.status.idle": "2026-08-17T06:42:36.795563Z",
-     "shell.execute_reply": "2026-08-17T06:42:36.795254Z"
+     "iopub.execute_input": "2026-08-17T15:34:53.334266Z",
+     "iopub.status.busy": "2026-08-17T15:34:53.334195Z",
+     "iopub.status.idle": "2026-08-17T15:34:53.337050Z",
+     "shell.execute_reply": "2026-08-17T15:34:53.336683Z"
     }
    },
    "outputs": [
@@ -1481,7 +1485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "150fb417",
+   "id": "2992cd6a",
    "metadata": {},
    "source": [
     "Everything below reads from that averaged approximation."
@@ -1489,7 +1493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "385a4492",
+   "id": "7903d0e3",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1498,13 +1502,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f507ce2a",
+   "id": "0ae57190",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:36.796446Z",
-     "iopub.status.busy": "2026-08-17T06:42:36.796389Z",
-     "iopub.status.idle": "2026-08-17T06:42:36.916634Z",
-     "shell.execute_reply": "2026-08-17T06:42:36.916251Z"
+     "iopub.execute_input": "2026-08-17T15:34:53.337959Z",
+     "iopub.status.busy": "2026-08-17T15:34:53.337898Z",
+     "iopub.status.idle": "2026-08-17T15:34:53.464252Z",
+     "shell.execute_reply": "2026-08-17T15:34:53.463933Z"
     }
    },
    "outputs": [
@@ -1689,9 +1693,91 @@
     "recovery.round(4)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "811f8f90",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T15:34:53.465300Z",
+     "iopub.status.busy": "2026-08-17T15:34:53.465228Z",
+     "iopub.status.idle": "2026-08-17T15:35:03.674983Z",
+     "shell.execute_reply": "2026-08-17T15:35:03.674559Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "identified rows, max |z| against the truth — last iterate: 3.35; tail-averaged, seeds 0/1/2: 1.16 / 1.15 / 1.14\n",
+      "across-seed sd of the mean, in reported sd: median 0.08, max 0.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Is the table a property of the problem or of one optimizer trajectory? Two\n",
+    "# independent-seed refits (same data, budget and tail average), plus the\n",
+    "# notebook's own last iterate before averaging, for the record.\n",
+    "raw_splits = [\"kappa0\", \"alpha0\", \"beta_a\"]\n",
+    "identified = [name for name in recovery.index if name not in raw_splits]\n",
+    "\n",
+    "\n",
+    "def summarize(post_):\n",
+    "    out = {name: float(post_[name].mean()) for name in scalar_params}\n",
+    "    for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
+    "        out[f\"{icpt} + mean({b})\"] = float((post_[icpt] + post_[b].mean(\"symbol\")).mean())\n",
+    "    return pd.Series(out)\n",
+    "\n",
+    "\n",
+    "def refit(seed):\n",
+    "    stream_s, tail_s = StreamAdvance(model, loader), ParamTrace()\n",
+    "    stream_s.prime()\n",
+    "    with model:\n",
+    "        advi_s = pm.ADVI(random_seed=seed)\n",
+    "    advi_s.fit(\n",
+    "        6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream_s], progressbar=False\n",
+    "    )\n",
+    "    approx_s = advi_s.fit(\n",
+    "        2_400,\n",
+    "        obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "        callbacks=[stream_s, tail_s],\n",
+    "        progressbar=False,\n",
+    "    )\n",
+    "    approx_s.params[0].set_value(np.asarray(tail_s.mu)[-steps_per_epoch:].mean(0))\n",
+    "    approx_s.params[1].set_value(np.asarray(tail_s.rho)[-steps_per_epoch:].mean(0))\n",
+    "    return summarize(approx_s.sample(2_000, random_seed=seed).posterior)\n",
+    "\n",
+    "\n",
+    "approx.params[0].set_value(mu_t[-1])\n",
+    "approx.params[1].set_value(rho_t[-1])\n",
+    "last_iterate = summarize(approx.sample(2_000, random_seed=RANDOM_SEED).posterior)\n",
+    "approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))\n",
+    "approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))\n",
+    "\n",
+    "replicates = pd.DataFrame(\n",
+    "    {\n",
+    "        \"last iterate\": last_iterate,\n",
+    "        \"seed 0\": recovery[\"mean\"],\n",
+    "        \"seed 1\": refit(RANDOM_SEED + 1),\n",
+    "        \"seed 2\": refit(RANDOM_SEED + 2),\n",
+    "    }\n",
+    ").loc[identified]\n",
+    "z_rep = replicates.sub(recovery.loc[identified, \"truth\"], axis=0).div(\n",
+    "    recovery.loc[identified, \"sd\"], axis=0\n",
+    ")\n",
+    "seed_spread = replicates.iloc[:, 1:].std(axis=1) / recovery.loc[identified, \"sd\"]\n",
+    "print(\n",
+    "    \"identified rows, max |z| against the truth — last iterate: \"\n",
+    "    f\"{z_rep['last iterate'].abs().max():.2f}; tail-averaged, seeds 0/1/2: \"\n",
+    "    + \" / \".join(f\"{z_rep[c].abs().max():.2f}\" for c in [\"seed 0\", \"seed 1\", \"seed 2\"])\n",
+    "    + f\"\\nacross-seed sd of the mean, in reported sd: median {seed_spread.median():.2f}, max {seed_spread.max():.2f}\"\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "a8abeaf7",
+   "id": "e1303f43",
    "metadata": {},
    "source": [
     "The table is a selection — the scalar globals and the three identified sums —\n",
@@ -1699,39 +1785,115 @@
     "plus the two curves for one symbol further down, is the extent of the recovery\n",
     "evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
     "global coefficient and the mean of its group effects are only *jointly*\n",
-    "identified: the likelihood constrains their sum, and the prior only weakly\n",
-    "splits it. The identified sums in the last three rows land within a couple of hundredths\n",
-    "of the truth, the same range as the other likelihood-identified rows.\n",
-    "The same translation ridge exists for every global-coefficient/group-effect\n",
-    "pair in this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
-    "$g$/$b^{(\\sigma h)}$, whose sums we spare you) — so raw split rows like\n",
-    "`kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not\n",
-    "estimates of their generating values; a sum-to-zero constraint on the symbol\n",
-    "effects is the standard reparameterization when the split itself matters.\n",
-    "The last column standardizes the error by the posterior width, and it has to be\n",
-    "read against identification rather than straight across the table. On the three\n",
-    "raw split rows it is not a recovery statistic at all: `alpha0` reads 379 because\n",
-    "a prior-dependent decomposition can have an arbitrary location and still a\n",
-    "narrow width, which says something about the split and nothing about the fit\n",
-    "(`beta_a`, at $-4.9$, is the same story in miniature). Everything that *is*\n",
-    "identified — the three sums and the six standalone rows — lands inside $1.2$\n",
-    "posterior standard deviations of its generating value, and four of those nine\n",
-    "are inside $0.5$. Without the tail average the same table showed misses of\n",
-    "three to seven; that difference was the orbit, not the estimate.\n",
-    "\n",
-    "Two things this table does not license. It does not say the widths are\n",
-    "trustworthy: they are the widths of a mean-field approximation, which has no\n",
-    "correlations to spend, and z-scores this small on one dataset are consistent\n",
-    "with widths that are somewhat too narrow, somewhat too wide, or right — sizing\n",
-    "that needs many simulated datasets, and this notebook fits one. Nor does it\n",
-    "transfer to the raw splits: no amount of data fixes a ridge, only a\n",
-    "reparameterization does.\n",
-    "\n",
-    "The stance behind stopping there is a risk-management one, and it is worth\n",
-    "stating plainly: material disagreement between independent-seed fits would be\n",
-    "enough to withhold a claim about a width, while agreement only removes the\n",
-    "instability warning — it does not validate calibration. Replication can veto a\n",
-    "width; it can never certify one.\n",
+    "pinned by the likelihood, which is exactly flat along\n",
+    "$(\\alpha_0 + d,\\ b^{(\\alpha)} - d)$. What tilts that direction at all is the\n",
+    "hierarchical prior: shifting every symbol effect by $d$ costs\n",
+    "$\\sum_s (b_s - d)^2 / 2\\tau^2$, which is minimized when the effects average\n",
+    "to zero — and the generator standardized them to average exactly zero, so the\n",
+    "prior points the split at the generating value, with a curvature that makes\n",
+    "its posterior width roughly $\\tau/\\sqrt{12}$, around a tenth. The same\n",
+    "translation ridge exists for every global-coefficient/group-effect pair in\n",
+    "this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
+    "$g$/$b^{(\\sigma h)}$, whose sums we spare you).\n",
+    "\n",
+    "Two things follow, and the table shows both. Along a direction that flat, a\n",
+    "gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its\n",
+    "way to $-3.00$. To see that it is still travelling, warm-start a fresh optimizer\n",
+    "at the last iterate and give it sixty more passes at the same learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9abcbd7f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T15:35:03.676124Z",
+     "iopub.status.busy": "2026-08-17T15:35:03.676059Z",
+     "iopub.status.idle": "2026-08-17T15:35:11.082814Z",
+     "shell.execute_reply": "2026-08-17T15:35:11.082391Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kappa0 after +0 / +30 / +60 passes: 0.532 / 0.579 / 0.628 (generating 0.847); alpha0: -1.702 / -1.711 / -1.722 (generating -2.996); loss per pass over those 60: -0.37 nats\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Continue from the last iterate on a fresh optimizer, so `approx` and its loss\n",
+    "# history above are left exactly as they were.\n",
+    "with model:\n",
+    "    advi_more = pm.ADVI(random_seed=RANDOM_SEED)\n",
+    "advi_more.approx.params[0].set_value(mu_t[-1])\n",
+    "advi_more.approx.params[1].set_value(rho_t[-1])\n",
+    "stream_more = StreamAdvance(model, loader)\n",
+    "stream_more.prime()\n",
+    "where = {name: approx.ordering[name][1].start for name in [\"kappa0\", \"alpha0\"]}\n",
+    "path = []\n",
+    "\n",
+    "\n",
+    "class EpochMeans:\n",
+    "    def __call__(self, approx_, losses, i):\n",
+    "        if i % steps_per_epoch == 0:\n",
+    "            mu_now = approx_.params[0].get_value()\n",
+    "            path.append([mu_now[where[\"kappa0\"]], mu_now[where[\"alpha0\"]]])\n",
+    "\n",
+    "\n",
+    "advi_more.fit(\n",
+    "    60 * steps_per_epoch,\n",
+    "    obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "    callbacks=[stream_more, EpochMeans()],\n",
+    "    progressbar=False,\n",
+    ")\n",
+    "path = np.asarray(path)\n",
+    "more_loss = np.asarray(advi_more.hist) * ELBO_SCALE\n",
+    "print(\n",
+    "    \"kappa0 after +0 / +30 / +60 passes: \"\n",
+    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 29, 59], 0])\n",
+    "    + f\" (generating {truth['kappa0']:.3f}); alpha0: \"\n",
+    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 29, 59], 1])\n",
+    "    + f\" (generating {truth['alpha0']:.3f}); loss per pass over those 60: \"\n",
+    "    f\"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5ae9482",
+   "metadata": {},
+   "source": [
+    "The ridge coordinates keep moving toward their generating values, at a rate the\n",
+    "loss barely registers. The raw split rows are a fit still travelling along a\n",
+    "nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —\n",
+    "are the mean-field *conditional* width across the ridge, not the tenth the\n",
+    "ridge posterior actually has along it: mean-field has no correlation to spend,\n",
+    "so it reports the narrow direction as if it were the wide one, which is why\n",
+    "`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the\n",
+    "symbol effects is the standard reparameterization when the split itself\n",
+    "matters: it removes the direction instead of asking the optimizer to find its\n",
+    "way along it.\n",
+    "\n",
+    "The identified rows are a different story. Everything that *is* pinned by the\n",
+    "likelihood — the three sums and the six standalone rows — lands inside $1.2$\n",
+    "posterior standard deviations of its generating value, and seven of those nine\n",
+    "are inside $0.5$. The replicate cell puts that in context from two directions.\n",
+    "Read from the last iterate instead of the tail average, the same rows ran to\n",
+    "$3.3$; that difference was the orbit, not the estimate. And on two further\n",
+    "seeds, everything else held fixed, the identified rows land inside the same\n",
+    "$1.2$, with the across-seed spread of the means about a tenth of the reported\n",
+    "width. That is the replication check the risk-management stance calls for, and\n",
+    "it is worth stating what it does and does not buy: material disagreement\n",
+    "between seeds would have been enough to withhold any claim about a width;\n",
+    "agreement only removes the instability warning — it does not validate\n",
+    "calibration. Replication can veto a width; it can never certify one. The widths\n",
+    "here are those of a mean-field approximation, and z-scores this small on one\n",
+    "dataset are consistent with widths that are somewhat too narrow, somewhat too\n",
+    "wide, or right — sizing that needs many simulated datasets, and this notebook\n",
+    "fits one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1739,14 +1901,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "ede5a7d2",
+   "execution_count": 18,
+   "id": "3cdfc9f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:36.917635Z",
-     "iopub.status.busy": "2026-08-17T06:42:36.917574Z",
-     "iopub.status.idle": "2026-08-17T06:42:37.011959Z",
-     "shell.execute_reply": "2026-08-17T06:42:37.011565Z"
+     "iopub.execute_input": "2026-08-17T15:35:11.084306Z",
+     "iopub.status.busy": "2026-08-17T15:35:11.084211Z",
+     "iopub.status.idle": "2026-08-17T15:35:11.181545Z",
+     "shell.execute_reply": "2026-08-17T15:35:11.181198Z"
     }
    },
    "outputs": [
@@ -1809,7 +1971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10c1a2b2",
+   "id": "bf2fd3ee",
    "metadata": {},
    "source": [
     "What the figure supports is narrower than the usual slogan, so it is worth\n",
@@ -1834,14 +1996,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "60591480",
+   "execution_count": 19,
+   "id": "42a45d61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:37.012998Z",
-     "iopub.status.busy": "2026-08-17T06:42:37.012926Z",
-     "iopub.status.idle": "2026-08-17T06:42:37.115067Z",
-     "shell.execute_reply": "2026-08-17T06:42:37.114696Z"
+     "iopub.execute_input": "2026-08-17T15:35:11.182668Z",
+     "iopub.status.busy": "2026-08-17T15:35:11.182603Z",
+     "iopub.status.idle": "2026-08-17T15:35:11.289562Z",
+     "shell.execute_reply": "2026-08-17T15:35:11.289240Z"
     }
    },
    "outputs": [
@@ -1912,7 +2074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "439e9323",
+   "id": "d0611fb8",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
@@ -1926,7 +2088,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "081d2d4f",
+   "id": "d14126af",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -1941,14 +2103,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "43dffd6a",
+   "execution_count": 20,
+   "id": "19d49161",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:37.116235Z",
-     "iopub.status.busy": "2026-08-17T06:42:37.116142Z",
-     "iopub.status.idle": "2026-08-17T06:42:37.860209Z",
-     "shell.execute_reply": "2026-08-17T06:42:37.859851Z"
+     "iopub.execute_input": "2026-08-17T15:35:11.290628Z",
+     "iopub.status.busy": "2026-08-17T15:35:11.290566Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.121948Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.121262Z"
     }
    },
    "outputs": [
@@ -2012,7 +2174,7 @@
        "q99 |move| (bp)        0.298    0.331  0.268   0.410"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2056,14 +2218,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "a133f0fe",
+   "execution_count": 21,
+   "id": "5afdb134",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:37.861358Z",
-     "iopub.status.busy": "2026-08-17T06:42:37.861288Z",
-     "iopub.status.idle": "2026-08-17T06:42:37.967091Z",
-     "shell.execute_reply": "2026-08-17T06:42:37.966709Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.123125Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.123044Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.233957Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.233517Z"
     }
    },
    "outputs": [
@@ -2111,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07c6f05e",
+   "id": "be8cc571",
    "metadata": {},
    "source": [
     "All three statistics sit inside their predictive 90% bands, though the zero\n",
@@ -2129,7 +2291,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adaddc9e",
+   "id": "6e1c5004",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -2159,14 +2321,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "9026b386",
+   "execution_count": 22,
+   "id": "ffc90186",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:37.968140Z",
-     "iopub.status.busy": "2026-08-17T06:42:37.968084Z",
-     "iopub.status.idle": "2026-08-17T06:42:37.971056Z",
-     "shell.execute_reply": "2026-08-17T06:42:37.970778Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.235405Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.235320Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.238639Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.238272Z"
     }
    },
    "outputs": [
@@ -2217,14 +2379,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "2b9566ba",
+   "execution_count": 23,
+   "id": "8cb6352e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:37.972046Z",
-     "iopub.status.busy": "2026-08-17T06:42:37.971991Z",
-     "iopub.status.idle": "2026-08-17T06:42:38.094284Z",
-     "shell.execute_reply": "2026-08-17T06:42:38.093942Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.239534Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.239478Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.360764Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.360415Z"
     }
    },
    "outputs": [
@@ -2265,7 +2427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62b3f190",
+   "id": "1da9d89c",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
@@ -2319,14 +2481,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "0e261f58",
+   "execution_count": 24,
+   "id": "21d9ff12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:38.095304Z",
-     "iopub.status.busy": "2026-08-17T06:42:38.095237Z",
-     "iopub.status.idle": "2026-08-17T06:42:38.097412Z",
-     "shell.execute_reply": "2026-08-17T06:42:38.097069Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.361903Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.361822Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.364024Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.363671Z"
     }
    },
    "outputs": [
@@ -2357,7 +2519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95b6835c",
+   "id": "2760720c",
    "metadata": {},
    "source": [
     "Two honest qualifications about any stop, this one included. What such a rule\n",
@@ -2383,7 +2545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da4702e8",
+   "id": "1558367e",
    "metadata": {},
    "source": [
     "### Choosing a path: why the fast one is not taken here\n",
@@ -2401,14 +2563,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "9bcbaadc",
+   "execution_count": 25,
+   "id": "4041d072",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:38.098344Z",
-     "iopub.status.busy": "2026-08-17T06:42:38.098291Z",
-     "iopub.status.idle": "2026-08-17T06:42:38.113847Z",
-     "shell.execute_reply": "2026-08-17T06:42:38.113474Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.364955Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.364889Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.381550Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.381188Z"
     }
    },
    "outputs": [
@@ -2429,7 +2591,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c95425de",
+   "id": "9ebcb8bc",
    "metadata": {},
    "source": [
     "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
@@ -2443,7 +2605,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1491e9e",
+   "id": "a621131e",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2455,7 +2617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77e1eea5",
+   "id": "24b6b103",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2467,7 +2629,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0818366",
+   "id": "6c67f4a0",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2475,14 +2637,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "340902f2",
+   "execution_count": 26,
+   "id": "241f4e82",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T06:42:38.114884Z",
-     "iopub.status.busy": "2026-08-17T06:42:38.114828Z",
-     "iopub.status.idle": "2026-08-17T06:42:38.127903Z",
-     "shell.execute_reply": "2026-08-17T06:42:38.127598Z"
+     "iopub.execute_input": "2026-08-17T15:35:12.382612Z",
+     "iopub.status.busy": "2026-08-17T15:35:12.382555Z",
+     "iopub.status.idle": "2026-08-17T15:35:12.396040Z",
+     "shell.execute_reply": "2026-08-17T15:35:12.395662Z"
     }
    },
    "outputs": [
@@ -2522,7 +2684,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2258e9f",
+   "id": "fee0465c",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index b3a1a1d05..40e22638d 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -48,8 +48,9 @@ rows that are generated inside the notebook. It tries to teach three things:
    on an orbit, and the last iterate misses the truth by several of its own
    posterior standard deviations purely because of where in that orbit the step
    budget ended. Averaged over one full pass, every identified quantity lands
-   inside 1.2. The widths themselves are a separate question this notebook
-   cannot settle on one dataset, and it says so rather than certifying them.
+   inside 1.2 — on three optimizer seeds. The widths themselves are a separate
+   question this notebook cannot settle on one dataset, and it says so rather
+   than certifying them.
 
 The notebook runs in well under a minute on a laptop. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
@@ -150,10 +151,8 @@ rng = np.random.default_rng(RANDOM_SEED)
 az.style.use("arviz-variat")
 # the loss figures carry the fit story; keep the fit logger's lines out of the output
 logging.getLogger("pymc").setLevel(logging.ERROR)
-# pytensor's predictive path emits two benign internals warnings (a 0/0 inside its
-# own allclose diagnostics; numba falling back to object mode for the python
-# `random`); neither is actionable here, so they are filtered by origin
-warnings.filterwarnings("ignore", category=RuntimeWarning, module=r"pytensor\.tensor\.type")
+# the predictive path compiles the custom `random` and the minibatch wrapper through
+# numba, which falls back to object mode for both and says so; not actionable here
 warnings.filterwarnings("ignore", message=r"Numba will use object mode")
 ```
 
@@ -494,14 +493,11 @@ short block is weighted up by its own size — but it would cost the clean
 diagnostics below, which is why the shards divide.) Only the shuffle-buffer
 path drops a trailing partial batch, and this notebook never uses it.
 
-Second, a precision about Gate 1 that matters for how the fit should be
-described. The permutation is fixed once, on disk, and then replayed in the
-same order every epoch. Each row is included exactly once per pass, but the
-batch at step $t$ is a deterministic function of $t$, not a fresh uniform
-draw — so the individual updates are not the conditionally unbiased stochastic
-gradients of the $N/B$ identity. This is single-shuffle, cyclic finite-sum
-optimization, and it is the arrangement the epoch-scale diagnostics later in
-the notebook exploit.
+Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a
+deterministic function of $t$, so what follows is single-shuffle, cyclic
+finite-sum optimization — the arrangement the epoch-scale diagnostics later in
+the notebook exploit, and the reason the fit has to be summarized with some
+care before anything is read off it.
 
 ```{code-cell} ipython3
 def build_model(symbols, batch_init, total_size):
@@ -680,8 +676,10 @@ The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.
 The two-stage learning rate is deliberate. A constant step size that is
 comfortable early is too large near the optimum, where it keeps the
 variational mean bouncing instead of settling; dropping it once the descent
-has flattened is the cheapest fix. The loss curve below is the evidence
-available here — the visible band narrows after the change.
+has flattened is the cheapest fix. Do not look for the effect in the width of
+the loss band below — that band is batch-composition noise and the learning
+rate does not touch it; the effect is in the level, and in the parameter
+trace examined after the fit.
 
 Before plotting it, one property of `approx.hist` that is easy to get wrong and
 that changes what the numbers mean. PyMC normalizes the minibatch objective
@@ -710,11 +708,7 @@ ax.axvline(6_000, color="C1", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
 ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))
 ax.set_xlabel("step")
 ax.set_ylabel("negative ELBO (full-data scale)")
-ax.set_title(
-    "Streaming ADVI loss, y clipped to the plateau (early losses are "
-    "off scale).\nThe visible band is batch-resampling noise — "
-    "remember it for the stopping section"
-)
+ax.set_title("Streaming ADVI loss (full-data scale, y clipped to the plateau)")
 ax.legend();
 ```
 
@@ -731,29 +725,38 @@ the spread within a single epoch.
 mu_t = np.asarray(tail.mu)
 sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging
 ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart
-drift = np.abs(ends[-1] - ends[-2]) / sd_unc
+step = np.abs(np.diff(ends, axis=0)) / sd_unc  # (7 epoch pairs, 154 coordinates)
 last = mu_t[-steps_per_epoch:]
 swing = (last.max(0) - last.min(0)) / sd_unc
 print(
-    f"epoch over epoch, same phase: median {np.median(drift):.2f}, max {drift.max():.2f} sd\n"
-    f"within the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd"
+    "same phase, epoch to epoch — max over coordinates, per pair: "
+    + " ".join(f"{v:.2f}" for v in step.max(1))
+    + f"\n                              median over coordinates, last pair: {np.median(step[-1]):.2f} sd"
+    f"\nwithin the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd"
 )
 ```
 
-Read those two lines together. Between the same points of consecutive epochs the
-mean barely moves, so the systematic descent really has flattened — the fit is
-not being stopped in mid-descent. Within one epoch it swings by multiples of its
-own posterior width. The optimizer is not converging to a point; it is orbiting
-one, on a cycle locked to the order the rows are replayed in, and the last
-iterate is whatever phase of that orbit the step budget happened to end on.
+Read the two measurements against each other. Between the same phase of
+consecutive epochs the largest movement decays from about six posterior widths
+in the first pair to about half a width in the last, and the median coordinate
+moves a tenth of a width per pass; within a single epoch the same coordinates
+swing by multiples of their width. So the optimizer is not converging to a
+point. It is orbiting one, on a cycle locked to the order the rows are replayed
+in, and most of where the last iterate sits is the phase of that orbit at the
+step the budget ran out.
 
 That is not a subtlety to note and move past. It means the number you would
 report depends on where you stop, so the fit is summarized by averaging the
-variational parameters over the final whole pass — a full period of the cycle,
-which is what makes the average cancel it. This is Polyak–Ruppert averaging, and
-it is why the step budget above is a whole number of epochs. The same tail
-average is what [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)
-applies to its own iterates for the same reason.
+variational parameters over the final whole pass. Averaging over a whole number
+of periods removes the phase dependence exactly, which is why the step budget
+above is a whole number of epochs; a window that is not a whole number of
+periods leaves a residual of the order of one swing divided by the window
+length. This is Polyak–Ruppert averaging, and the same tail average is what
+[pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722) applies to
+its own iterates for the same reason. What the average does *not* remove is the
+slow drift the first line still shows: a tenth of a width per pass in the
+median, and much more along the directions the recovery table is about to
+single out.
 
 ```{code-cell} ipython3
 rho_t = np.asarray(tail.rho)
@@ -799,44 +802,153 @@ recovery["z"] = (recovery["mean"] - recovery["truth"]) / recovery["sd"]
 recovery.round(4)
 ```
 
+```{code-cell} ipython3
+# Is the table a property of the problem or of one optimizer trajectory? Two
+# independent-seed refits (same data, budget and tail average), plus the
+# notebook's own last iterate before averaging, for the record.
+raw_splits = ["kappa0", "alpha0", "beta_a"]
+identified = [name for name in recovery.index if name not in raw_splits]
+
+
+def summarize(post_):
+    out = {name: float(post_[name].mean()) for name in scalar_params}
+    for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
+        out[f"{icpt} + mean({b})"] = float((post_[icpt] + post_[b].mean("symbol")).mean())
+    return pd.Series(out)
+
+
+def refit(seed):
+    stream_s, tail_s = StreamAdvance(model, loader), ParamTrace()
+    stream_s.prime()
+    with model:
+        advi_s = pm.ADVI(random_seed=seed)
+    advi_s.fit(
+        6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream_s], progressbar=False
+    )
+    approx_s = advi_s.fit(
+        2_400,
+        obj_optimizer=pm.adam(learning_rate=0.005),
+        callbacks=[stream_s, tail_s],
+        progressbar=False,
+    )
+    approx_s.params[0].set_value(np.asarray(tail_s.mu)[-steps_per_epoch:].mean(0))
+    approx_s.params[1].set_value(np.asarray(tail_s.rho)[-steps_per_epoch:].mean(0))
+    return summarize(approx_s.sample(2_000, random_seed=seed).posterior)
+
+
+approx.params[0].set_value(mu_t[-1])
+approx.params[1].set_value(rho_t[-1])
+last_iterate = summarize(approx.sample(2_000, random_seed=RANDOM_SEED).posterior)
+approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))
+approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))
+
+replicates = pd.DataFrame(
+    {
+        "last iterate": last_iterate,
+        "seed 0": recovery["mean"],
+        "seed 1": refit(RANDOM_SEED + 1),
+        "seed 2": refit(RANDOM_SEED + 2),
+    }
+).loc[identified]
+z_rep = replicates.sub(recovery.loc[identified, "truth"], axis=0).div(
+    recovery.loc[identified, "sd"], axis=0
+)
+seed_spread = replicates.iloc[:, 1:].std(axis=1) / recovery.loc[identified, "sd"]
+print(
+    "identified rows, max |z| against the truth — last iterate: "
+    f"{z_rep['last iterate'].abs().max():.2f}; tail-averaged, seeds 0/1/2: "
+    + " / ".join(f"{z_rep[c].abs().max():.2f}" for c in ["seed 0", "seed 1", "seed 2"])
+    + f"\nacross-seed sd of the mean, in reported sd: median {seed_spread.median():.2f}, max {seed_spread.max():.2f}"
+)
+```
+
 The table is a selection — the scalar globals and the three identified sums —
 and the figure after it checks one of the five symbol-effect families; that,
 plus the two curves for one symbol further down, is the extent of the recovery
 evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
 global coefficient and the mean of its group effects are only *jointly*
-identified: the likelihood constrains their sum, and the prior only weakly
-splits it. The identified sums in the last three rows land within a couple of hundredths
-of the truth, the same range as the other likelihood-identified rows.
-The same translation ridge exists for every global-coefficient/group-effect
-pair in this model (including the vector pairs $c$/$b^{(\pi h)}$ and
-$g$/$b^{(\sigma h)}$, whose sums we spare you) — so raw split rows like
-`kappa0`, `alpha0`, and `beta_a` are prior-dependent decompositions, not
-estimates of their generating values; a sum-to-zero constraint on the symbol
-effects is the standard reparameterization when the split itself matters.
-The last column standardizes the error by the posterior width, and it has to be
-read against identification rather than straight across the table. On the three
-raw split rows it is not a recovery statistic at all: `alpha0` reads 379 because
-a prior-dependent decomposition can have an arbitrary location and still a
-narrow width, which says something about the split and nothing about the fit
-(`beta_a`, at $-4.9$, is the same story in miniature). Everything that *is*
-identified — the three sums and the six standalone rows — lands inside $1.2$
-posterior standard deviations of its generating value, and four of those nine
-are inside $0.5$. Without the tail average the same table showed misses of
-three to seven; that difference was the orbit, not the estimate.
-
-Two things this table does not license. It does not say the widths are
-trustworthy: they are the widths of a mean-field approximation, which has no
-correlations to spend, and z-scores this small on one dataset are consistent
-with widths that are somewhat too narrow, somewhat too wide, or right — sizing
-that needs many simulated datasets, and this notebook fits one. Nor does it
-transfer to the raw splits: no amount of data fixes a ridge, only a
-reparameterization does.
-
-The stance behind stopping there is a risk-management one, and it is worth
-stating plainly: material disagreement between independent-seed fits would be
-enough to withhold a claim about a width, while agreement only removes the
-instability warning — it does not validate calibration. Replication can veto a
-width; it can never certify one.
+pinned by the likelihood, which is exactly flat along
+$(\alpha_0 + d,\ b^{(\alpha)} - d)$. What tilts that direction at all is the
+hierarchical prior: shifting every symbol effect by $d$ costs
+$\sum_s (b_s - d)^2 / 2\tau^2$, which is minimized when the effects average
+to zero — and the generator standardized them to average exactly zero, so the
+prior points the split at the generating value, with a curvature that makes
+its posterior width roughly $\tau/\sqrt{12}$, around a tenth. The same
+translation ridge exists for every global-coefficient/group-effect pair in
+this model (including the vector pairs $c$/$b^{(\pi h)}$ and
+$g$/$b^{(\sigma h)}$, whose sums we spare you).
+
+Two things follow, and the table shows both. Along a direction that flat, a
+gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its
+way to $-3.00$. To see that it is still travelling, warm-start a fresh optimizer
+at the last iterate and give it sixty more passes at the same learning rate:
+
+```{code-cell} ipython3
+# Continue from the last iterate on a fresh optimizer, so `approx` and its loss
+# history above are left exactly as they were.
+with model:
+    advi_more = pm.ADVI(random_seed=RANDOM_SEED)
+advi_more.approx.params[0].set_value(mu_t[-1])
+advi_more.approx.params[1].set_value(rho_t[-1])
+stream_more = StreamAdvance(model, loader)
+stream_more.prime()
+where = {name: approx.ordering[name][1].start for name in ["kappa0", "alpha0"]}
+path = []
+
+
+class EpochMeans:
+    def __call__(self, approx_, losses, i):
+        if i % steps_per_epoch == 0:
+            mu_now = approx_.params[0].get_value()
+            path.append([mu_now[where["kappa0"]], mu_now[where["alpha0"]]])
+
+
+advi_more.fit(
+    60 * steps_per_epoch,
+    obj_optimizer=pm.adam(learning_rate=0.005),
+    callbacks=[stream_more, EpochMeans()],
+    progressbar=False,
+)
+path = np.asarray(path)
+more_loss = np.asarray(advi_more.hist) * ELBO_SCALE
+print(
+    "kappa0 after +0 / +30 / +60 passes: "
+    + " / ".join(f"{v:.3f}" for v in path[[0, 29, 59], 0])
+    + f" (generating {truth['kappa0']:.3f}); alpha0: "
+    + " / ".join(f"{v:.3f}" for v in path[[0, 29, 59], 1])
+    + f" (generating {truth['alpha0']:.3f}); loss per pass over those 60: "
+    f"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats"
+)
+```
+
+The ridge coordinates keep moving toward their generating values, at a rate the
+loss barely registers. The raw split rows are a fit still travelling along a
+nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —
+are the mean-field *conditional* width across the ridge, not the tenth the
+ridge posterior actually has along it: mean-field has no correlation to spend,
+so it reports the narrow direction as if it were the wide one, which is why
+`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the
+symbol effects is the standard reparameterization when the split itself
+matters: it removes the direction instead of asking the optimizer to find its
+way along it.
+
+The identified rows are a different story. Everything that *is* pinned by the
+likelihood — the three sums and the six standalone rows — lands inside $1.2$
+posterior standard deviations of its generating value, and seven of those nine
+are inside $0.5$. The replicate cell puts that in context from two directions.
+Read from the last iterate instead of the tail average, the same rows ran to
+$3.3$; that difference was the orbit, not the estimate. And on two further
+seeds, everything else held fixed, the identified rows land inside the same
+$1.2$, with the across-seed spread of the means about a tenth of the reported
+width. That is the replication check the risk-management stance calls for, and
+it is worth stating what it does and does not buy: material disagreement
+between seeds would have been enough to withhold any claim about a width;
+agreement only removes the instability warning — it does not validate
+calibration. Replication can veto a width; it can never certify one. The widths
+here are those of a mean-field approximation, and z-scores this small on one
+dataset are consistent with widths that are somewhat too narrow, somewhat too
+wide, or right — sizing that needs many simulated datasets, and this notebook
+fits one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?

From 299abb2decb751e9f2f26885c7f47eb2a948408e Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 17 Aug 2026 22:06:58 -0500
Subject: [PATCH 39/42] Retrigger CI: the pre-commit runner failed downloading
 actions/setup-node (429)


From d70d11aec1e539e6f17e658349e325fd9c32d764 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 17 Aug 2026 22:56:00 -0500
Subject: [PATCH 40/42] Judge each refit in its own width, vary the replay
 order, and stop freezing review state into the page

Third audit round; eleven of fourteen findings held.

The replicate cell divided every fit's error by seed 0's posterior sd,
while the intro said "its own posterior standard deviations". summarize()
now returns mean and sd for every reported row and each fit is judged in
its own width: last iterate 3.42, seed 0 1.16, seed 1 (same order) 1.14,
seed 2 (other order) 1.13. The second refit runs on a different on-disk
order -- the rows re-keyed with a different hash salt and re-sharded --
because the replay order is the one ingredient the orbit account is
about, so it is the one worth varying rather than holding fixed. Spread
of the mean across the three tail-averaged fits: median 0.08, max 0.10
of the reported width.

"Every identified quantity" now says what it means: every identified row
the recovery table reports, nine of them. The claim about the ridge width
is stated as the conditional calculation it is (Gaussian in the shift
with sd tau/sqrt(12), a tenth for tau near the generator's 0.35), not as
a measured marginal. The orbit account no longer says the gradient
sequence is periodic -- the data term is; the parameters and the
Monte-Carlo draw are not -- and no longer says one pass removes the
phase "exactly". The #722 sentence says "the same device for a
neighbouring reason" instead of "for the same reason". "Cancels" at
aligned horizons becomes "all but drops out", which is what sd 0.02-0.03
means. The loss cell prints the recorded sd on both sides of the anneal
(13,192 vs 13,141) so the sentence about the band rests on an output.

The continuation labelled its first recorded point +0 when the callback
fires after the first epoch; the pre-continuation parameters are now
recorded as +0. The post date carries a day, as the siblings' do, so
ABlog stops filling it from the build clock. Two DataLoader roles that
cannot resolve (the pmx inventory does not have the class and conf.py's
mapping points at a moved URL) are plain links to the pinned source. The
prose no longer says #710 "proposes", #635 "develops", or #733 is "under
review" -- state verbs that go stale; it says what each PR contains. The
out-of-core paragraph says a hash-assigned two-pass design yields an
equivalent shuffle, not the same permutation. The notebook times itself
and the watermark names the machine (a second %watermark call, so the
repo hook's regex still matches).

Not changed: the pixi environment (a repo decision, raised in the PR),
and the framing (author's call).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1036 +++++++++--------
 .../streaming_tick_data.myst.md               |  213 ++--
 2 files changed, 687 insertions(+), 562 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index b6e44ca12..fb2caf655 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b8aaffef",
+   "id": "343b2a4a",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
     "\n",
     "# Streaming variational inference on high-frequency tick data\n",
     "\n",
-    ":::{post} August 2026\n",
+    ":::{post} August 17, 2026\n",
     ":tags: variational inference, minibatch, out-of-core, hierarchical model, time series\n",
     ":category: advanced, tutorial\n",
     ":author: Yicheng Yang\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9514daf0",
+   "id": "0d1a4e70",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
@@ -30,7 +30,8 @@
     "disk-backed reader, so it cannot help once the array no longer fits. This\n",
     "notebook fits a hierarchical hurdle–Student-t model of *next-event price\n",
     "moves* by streaming minibatches from disk with pymc-extras'\n",
-    "{class}`~pymc_extras.variational.dataloader.DataLoader`, on 300,000 synthetic\n",
+    "[`DataLoader`](https://github.com/pymc-devs/pymc-extras/blob/8db1880d410e509be02abf9b085f08c3d4514fd1/pymc_extras/variational/dataloader.py),\n",
+    "on 300,000 synthetic\n",
     "rows that are generated inside the notebook. It tries to teach three things:\n",
     "\n",
     "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
@@ -42,19 +43,20 @@
     "   after a correction the fit forces on us: the cyclic replay puts the optimizer\n",
     "   on an orbit, and the last iterate misses the truth by several of its own\n",
     "   posterior standard deviations purely because of where in that orbit the step\n",
-    "   budget ended. Averaged over one full pass, every identified quantity lands\n",
-    "   inside 1.2 — on three optimizer seeds. The widths themselves are a separate\n",
-    "   question this notebook cannot settle on one dataset, and it says so rather\n",
-    "   than certifying them.\n",
+    "   budget ended. Averaged over one full pass, every identified row the\n",
+    "   recovery table reports lands inside 1.2 — across three fits, one of them on\n",
+    "   a different replay order. The widths themselves are a separate question this\n",
+    "   notebook cannot settle on one dataset, and it says so rather than certifying\n",
+    "   them.\n",
     "\n",
-    "The notebook runs in well under a minute on a laptop. Everything below executes on synthetic\n",
+    "The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
     "possible: nothing here is a claim about any real market."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad0e631a",
+   "id": "3aa8f737",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -105,14 +107,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3d9a10e",
+   "id": "654e4e52",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
     ":::\n",
     "\n",
     ":::{note}\n",
-    "The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into\n",
+    "The `DataLoader` merged into\n",
     "pymc-extras after the v0.14.0 release, so it is not in a published version yet\n",
     "and `pip install pymc-extras` will not provide it. The outputs stored in this\n",
     "notebook were produced against the merge commit itself, which is also the\n",
@@ -131,13 +133,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fd287cf4",
+   "id": "19fa0246",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:45.125533Z",
-     "iopub.status.busy": "2026-08-17T15:34:45.125403Z",
-     "iopub.status.idle": "2026-08-17T15:34:46.714399Z",
-     "shell.execute_reply": "2026-08-17T15:34:46.713892Z"
+     "iopub.execute_input": "2026-08-18T03:55:02.042215Z",
+     "iopub.status.busy": "2026-08-18T03:55:02.042148Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.044039Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.043601Z"
     }
    },
    "outputs": [],
@@ -146,6 +148,7 @@
     "import logging\n",
     "import os\n",
     "import tempfile\n",
+    "import time\n",
     "import warnings\n",
     "\n",
     "import arviz as az\n",
@@ -158,19 +161,21 @@
     "import pytensor.tensor as pt\n",
     "\n",
     "from pymc_extras.variational.dataloader import DataLoader, parquet_source\n",
-    "from scipy import stats"
+    "from scipy import stats\n",
+    "\n",
+    "NOTEBOOK_T0 = time.perf_counter()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "02ff3159",
+   "id": "87a9eaed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:46.715574Z",
-     "iopub.status.busy": "2026-08-17T15:34:46.715410Z",
-     "iopub.status.idle": "2026-08-17T15:34:46.718503Z",
-     "shell.execute_reply": "2026-08-17T15:34:46.718091Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.045242Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.045097Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.047936Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.047616Z"
     }
    },
    "outputs": [],
@@ -188,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13681964",
+   "id": "8ab3430d",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -263,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e9a22a0",
+   "id": "1b424a94",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -281,13 +286,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d6009520",
+   "id": "ae300b6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:46.719672Z",
-     "iopub.status.busy": "2026-08-17T15:34:46.719602Z",
-     "iopub.status.idle": "2026-08-17T15:34:46.722539Z",
-     "shell.execute_reply": "2026-08-17T15:34:46.722203Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.048898Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.048829Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.052032Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.051622Z"
     }
    },
    "outputs": [],
@@ -329,13 +334,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "17f5f07e",
+   "id": "38697c5e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:46.723421Z",
-     "iopub.status.busy": "2026-08-17T15:34:46.723368Z",
-     "iopub.status.idle": "2026-08-17T15:34:46.881409Z",
-     "shell.execute_reply": "2026-08-17T15:34:46.880998Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.052914Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.052846Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.238459Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.238035Z"
     }
    },
    "outputs": [
@@ -403,7 +408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71667c76",
+   "id": "7a14a7ed",
    "metadata": {},
    "source": [
     "Each constant above is a setting, not a measurement, and it is worth reading\n",
@@ -435,13 +440,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ca8b85ce",
+   "id": "d60c3c20",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:46.882449Z",
-     "iopub.status.busy": "2026-08-17T15:34:46.882395Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.286750Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.286370Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.239576Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.239498Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.675479Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.675108Z"
     }
    },
    "outputs": [
@@ -480,7 +485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3a844f2",
+   "id": "5d6c5c2c",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -503,13 +508,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "bf1d0322",
+   "id": "90835363",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.287654Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.287590Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.511674Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.511310Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.676474Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.676405Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.911330Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.910890Z"
     }
    },
    "outputs": [
@@ -564,7 +569,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "066077bb",
+   "id": "b29c321d",
    "metadata": {},
    "source": [
     "The assertion is the one to keep: the head of every shard must already mix\n",
@@ -574,9 +579,12 @@
     "it builds the whole table, the whole key array and the whole permutation in\n",
     "memory, which is exactly what one cannot do once the data stops fitting. What\n",
     "follows it — the inference — is genuinely disk-backed, but the preprocessing\n",
-    "above is not. The out-of-core version of the same permutation is two passes:\n",
-    "hash-scatter rows into shards with bounded row-group appends, then sort each\n",
-    "shard independently, so no step holds more than one shard.\n",
+    "above is not. An out-of-core design with the same properties is two passes:\n",
+    "assign each row to a shard from its hash key, appending row groups of bounded\n",
+    "size, then sort each shard by key independently, so no step holds more than\n",
+    "one shard. That yields a different permutation from the rank-based split used\n",
+    "here — shard sizes come out approximately rather than exactly equal — with the\n",
+    "same guarantee that no shard's order depends on anything in the rows.\n",
     "\n",
     "So that the fit really does run against disk rather than against arrays still\n",
     "resident from the generator, the row-scale objects are released first:"
@@ -585,13 +593,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "6e0dced3",
+   "id": "6bbf642e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.512746Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.512685Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.550430Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.550025Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.912380Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.912303Z",
+     "iopub.status.idle": "2026-08-18T03:55:04.949624Z",
+     "shell.execute_reply": "2026-08-18T03:55:04.949262Z"
     }
    },
    "outputs": [
@@ -599,7 +607,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_02wuqs0r\n"
+      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_d8c2tayb\n"
      ]
     }
    ],
@@ -612,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de71e750",
+   "id": "64decfd8",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -635,13 +643,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "a29154c6",
+   "id": "924104ae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.551630Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.551561Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.599144Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.598777Z"
+     "iopub.execute_input": "2026-08-18T03:55:04.950824Z",
+     "iopub.status.busy": "2026-08-18T03:55:04.950744Z",
+     "iopub.status.idle": "2026-08-18T03:55:05.000971Z",
+     "shell.execute_reply": "2026-08-18T03:55:05.000569Z"
     }
    },
    "outputs": [
@@ -678,7 +686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "234ee8a4",
+   "id": "e2d4d7d5",
    "metadata": {},
    "source": [
     "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
@@ -698,13 +706,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "77e3fef3",
+   "id": "09eefc4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.600268Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.600195Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.604742Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.604353Z"
+     "iopub.execute_input": "2026-08-18T03:55:05.001978Z",
+     "iopub.status.busy": "2026-08-18T03:55:05.001910Z",
+     "iopub.status.idle": "2026-08-18T03:55:05.006024Z",
+     "shell.execute_reply": "2026-08-18T03:55:05.005718Z"
     }
    },
    "outputs": [],
@@ -802,7 +810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26245f2d",
+   "id": "1d5cbc7a",
    "metadata": {},
    "source": [
     "Two implementation notes worth pausing on. The likelihood is a\n",
@@ -823,13 +831,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "45726fcf",
+   "id": "f2af23ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.605658Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.605603Z",
-     "iopub.status.idle": "2026-08-17T15:34:47.764908Z",
-     "shell.execute_reply": "2026-08-17T15:34:47.764522Z"
+     "iopub.execute_input": "2026-08-18T03:55:05.006976Z",
+     "iopub.status.busy": "2026-08-18T03:55:05.006918Z",
+     "iopub.status.idle": "2026-08-18T03:55:05.188197Z",
+     "shell.execute_reply": "2026-08-18T03:55:05.187721Z"
     }
    },
    "outputs": [
@@ -848,8 +856,8 @@
        "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M810.01,-8C810.01,-8 856.01,-8 856.01,-8 862.01,-8 868.01,-14 868.01,-20 868.01,-20 868.01,-94 868.01,-94 868.01,-100 862.01,-106 856.01,-106 856.01,-106 810.01,-106 810.01,-106 804.01,-106 798.01,-100 798.01,-94 798.01,-94 798.01,-20 798.01,-20 798.01,-14 804.01,-8 810.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M796.01,-8C796.01,-8 842.01,-8 842.01,-8 848.01,-8 854.01,-14 854.01,-20 854.01,-20 854.01,-94 854.01,-94 854.01,-100 848.01,-106 842.01,-106 842.01,-106 796.01,-106 796.01,-106 790.01,-106 784.01,-100 784.01,-94 784.01,-94 784.01,-20 784.01,-20 784.01,-14 790.01,-8 796.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
@@ -868,63 +876,149 @@
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M834.01,-98C834.01,-98 804.01,-98 804.01,-98 798.01,-98 792.01,-92 792.01,-86 792.01,-86 792.01,-52.5 792.01,-52.5 792.01,-46.5 798.01,-40.5 804.01,-40.5 804.01,-40.5 834.01,-40.5 834.01,-40.5 840.01,-40.5 846.01,-46.5 846.01,-52.5 846.01,-52.5 846.01,-86 846.01,-86 846.01,-92 840.01,-98 834.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- tau_ph -->\n",
+       "<!-- kappa0 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"819.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.87 76.86,-251.93 91.01,-243.82 141.72,-214.74 497.29,-198.67 692.08,-191.96\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"692.14,-195.46 702.01,-191.62 691.9,-188.46 692.14,-195.46\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
+       "<!-- b_sh -->\n",
        "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
+       "<title>b_sh</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
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        "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"149.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M169.41,-279.73C179,-266.15 191.71,-252.02 207.01,-243.82 288.02,-200.38 537.25,-190.2 691.54,-188.19\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.47,-191.69 701.43,-188.07 691.38,-184.69 691.47,-191.69\"/>\n",
+       "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"661.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"706.14,-345.73 615.89,-345.73 615.89,-288.23 706.14,-288.23 706.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M661.01,-393.28C661.01,-381.58 661.01,-368.79 661.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"664.51,-357.45 661.01,-347.45 657.51,-357.45 664.51,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"257.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M273.86,-279.57C281.96,-266.13 293,-252.15 307.01,-243.82 369.92,-206.4 561.79,-194.08 691.93,-190.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"691.74,-193.56 701.64,-189.77 691.54,-186.57 691.74,-193.56\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M373.98,-279.75C382.09,-266.34 393.11,-252.34 407.01,-243.82 453.73,-215.2 589.92,-200.95 693.04,-194.09\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"693.23,-197.59 702.98,-193.45 692.78,-190.6 693.23,-197.59\"/>\n",
+       "</g>\n",
        "<!-- tau_k -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>tau_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
@@ -932,7 +1026,7 @@
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        "</g>\n",
        "<!-- b_k -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
+       "<g id=\"node21\" class=\"node\">\n",
        "<title>b_k</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
@@ -945,267 +1039,181 @@
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        "</g>\n",
-       "<!-- theta_r -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- y_obs -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"833.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
-       "</g>\n",
-       "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
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-       "</g>\n",
-       "<!-- kappa0 -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
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-       "</g>\n",
-       "<!-- theta_d -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"241.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"241.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M257.84,-279.53C265.93,-266.08 276.97,-252.11 291.01,-243.82 358.84,-203.75 567.86,-192.26 705.62,-189.12\"/>\n",
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-       "</g>\n",
-       "<!-- lambda_a -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"349.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M369.85,-279.66C379.43,-266.23 392.02,-252.24 407.01,-243.82 456.94,-215.77 600.26,-201.34 706.96,-194.29\"/>\n",
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-       "</g>\n",
-       "<!-- eta -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"461.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.39,-279.68C482.51,-266.42 493.42,-252.54 507.01,-243.82 538.23,-223.78 623.11,-209.41 696.46,-200.3\"/>\n",
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        "</g>\n",
-       "<!-- nu -->\n",
+       "<!-- beta_q -->\n",
        "<g id=\"node11\" class=\"node\">\n",
-       "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"506.14,-345.73 415.89,-345.73 415.89,-288.23 506.14,-288.23 506.14,-345.73\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"461.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
-       "</g>\n",
-       "<!-- eta&#45;&gt;nu -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M461.01,-393.28C461.01,-381.58 461.01,-368.79 461.01,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"464.51,-357.45 461.01,-347.45 457.51,-357.45 464.51,-357.45\"/>\n",
-       "</g>\n",
-       "<!-- tau_al -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "</g>\n",
-       "<!-- b_al -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
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+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M574.76,-280.23C582.93,-267.05 593.78,-253.08 607.01,-243.82 634.11,-224.85 667.25,-212.34 699.2,-204.1\"/>\n",
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        "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
        "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M475.59,-287.76C485.04,-272.34 498.79,-254.3 516.01,-243.82 548.42,-224.1 635.62,-209.64 710.38,-200.41\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"710.78,-203.88 720.29,-199.21 709.94,-196.94 710.78,-203.88\"/>\n",
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-       "<!-- tau_sh -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
-       "<!-- b_sh -->\n",
+       "<!-- b_ph -->\n",
        "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
+       "<title>b_ph</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
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        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
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-       "<!-- lambda_q -->\n",
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-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
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-       "</g>\n",
-       "<!-- beta_q -->\n",
+       "<!-- theta_r -->\n",
        "<g id=\"node14\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"683.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"683.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"683.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"765.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
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+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M780.63,-279.01C786.04,-266.2 792.2,-251.62 797.97,-237.95\"/>\n",
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-       "<!-- beta_a -->\n",
+       "<!-- lambda_q -->\n",
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-       "<title>beta_a</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"874.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"874.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"874.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
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+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
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-       "<!-- alpha0 -->\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node16\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- alpha0&#45;&gt;y_obs -->\n",
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-       "<title>alpha0&#45;&gt;y_obs</title>\n",
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+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- g -->\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
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+       "</g>\n",
+       "<!-- c -->\n",
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-       "<title>g</title>\n",
+       "<title>c</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<title>g&#45;&gt;y_obs</title>\n",
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+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M963.26,-280.82C954.84,-268.03 944.15,-254.18 932.01,-243.82 923.53,-236.58 913.94,-230.01 904.03,-224.14\"/>\n",
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-       "<!-- c -->\n",
+       "<!-- g -->\n",
        "<g id=\"node18\" class=\"node\">\n",
-       "<title>c</title>\n",
+       "<title>g</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
        "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<title>c&#45;&gt;y_obs</title>\n",
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+       "<!-- g&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>g&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1065.27,-280.21C1057.11,-267.03 1046.26,-253.07 1033.01,-243.82 1005.44,-224.56 971.68,-211.97 939.2,-203.73\"/>\n",
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        "<!-- b_al&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
        "<title>b_al&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1372.15,-279.58C1364.06,-266.14 1353.02,-252.16 1339.01,-243.82 1277.15,-206.97 1089.01,-194.48 960.43,-190.27\"/>\n",
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-       "<!-- b_k&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>b_k&#45;&gt;y_obs</title>\n",
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        "<!-- b_ba&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
        "<title>b_ba&#45;&gt;y_obs</title>\n",
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       ],
       "text/plain": [
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      },
      "execution_count": 10,
@@ -1222,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "790f187c",
+   "id": "db6ed11b",
    "metadata": {},
    "source": [
     "The plate diagram is an inventory rather than a picture of the two links: five\n",
@@ -1233,22 +1241,22 @@
     "that structure is visible.\n",
     "\n",
     "The loop below is the whole streaming adapter: a `pm.fit` callback that pushes\n",
-    "the next block into the placeholder after each step. pymc-extras#710 proposes\n",
-    "an interim wrapper around this same data-advance lifecycle while\n",
-    "pymc-extras#635 develops the longer-term ADVI API, so the twenty lines here are\n",
-    "deliberately the minimal version a notebook can own."
+    "the next block into the placeholder after each step. pymc-extras#710 contains\n",
+    "a wrapper around this same data-advance lifecycle and pymc-extras#635 a\n",
+    "longer-term ADVI training API; the twenty lines here are deliberately the\n",
+    "minimal version a notebook can own."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b20eaea8",
+   "id": "18b8dd72",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:47.766125Z",
-     "iopub.status.busy": "2026-08-17T15:34:47.766018Z",
-     "iopub.status.idle": "2026-08-17T15:34:53.221511Z",
-     "shell.execute_reply": "2026-08-17T15:34:53.220988Z"
+     "iopub.execute_input": "2026-08-18T03:55:05.189263Z",
+     "iopub.status.busy": "2026-08-18T03:55:05.189180Z",
+     "iopub.status.idle": "2026-08-18T03:55:11.501552Z",
+     "shell.execute_reply": "2026-08-18T03:55:11.501057Z"
     }
    },
    "outputs": [],
@@ -1301,7 +1309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "865f598f",
+   "id": "c743d883",
    "metadata": {},
    "source": [
     "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
@@ -1309,9 +1317,10 @@
     "comfortable early is too large near the optimum, where it keeps the\n",
     "variational mean bouncing instead of settling; dropping it once the descent\n",
     "has flattened is the cheapest fix. Do not look for the effect in the width of\n",
-    "the loss band below — that band is batch-composition noise and the learning\n",
-    "rate does not touch it; the effect is in the level, and in the parameter\n",
-    "trace examined after the fit.\n",
+    "the loss band below: the band is dominated by batch-composition noise (the\n",
+    "stopping section shows it all but vanishing at epoch-aligned horizons), the\n",
+    "printed spread is the same on both sides of the change, and the effect is in\n",
+    "the level and in the parameter trace examined after the fit.\n",
     "\n",
     "Before plotting it, one property of `approx.hist` that is easy to get wrong and\n",
     "that changes what the numbers mean. PyMC normalizes the minibatch objective\n",
@@ -1334,16 +1343,23 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "fb7860f5",
+   "id": "636ebe40",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:53.222751Z",
-     "iopub.status.busy": "2026-08-17T15:34:53.222674Z",
-     "iopub.status.idle": "2026-08-17T15:34:53.324523Z",
-     "shell.execute_reply": "2026-08-17T15:34:53.324157Z"
+     "iopub.execute_input": "2026-08-18T03:55:11.503082Z",
+     "iopub.status.busy": "2026-08-18T03:55:11.502993Z",
+     "iopub.status.idle": "2026-08-18T03:55:11.624799Z",
+     "shell.execute_reply": "2026-08-18T03:55:11.624482Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sd of the recorded loss over the last two passes at each learning rate: 13,192 (lr 0.02) vs 13,141 (lr 0.005)\n"
+     ]
+    },
     {
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",
@@ -1371,34 +1387,40 @@
     "ax.set_xlabel(\"step\")\n",
     "ax.set_ylabel(\"negative ELBO (full-data scale)\")\n",
     "ax.set_title(\"Streaming ADVI loss (full-data scale, y clipped to the plateau)\")\n",
-    "ax.legend();"
+    "ax.legend()\n",
+    "print(\n",
+    "    \"sd of the recorded loss over the last two passes at each learning rate: \"\n",
+    "    f\"{loss[6_000 - 600 : 6_000].std():,.0f} (lr 0.02) vs {loss[-600:].std():,.0f} (lr 0.005)\"\n",
+    ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b222fca0",
+   "id": "d4472238",
    "metadata": {},
    "source": [
     "## The last iterate is not the answer\n",
     "\n",
     "Before reading a single parameter off this fit, one property of the replay has\n",
     "to be dealt with. The batch at step $t$ is a deterministic function of $t$ with\n",
-    "period one epoch, so the gradient sequence is periodic and the optimizer's\n",
-    "iterate inherits a periodic component. Two measurements separate that component\n",
-    "from genuine progress: compare the same phase in consecutive epochs, and look at\n",
-    "the spread within a single epoch."
+    "period one epoch, so the data term of every stochastic gradient repeats with\n",
+    "that period — the parameters and the Monte-Carlo draw do not, so the realized\n",
+    "gradients are not literally periodic, but the iterate inherits a component\n",
+    "locked to the replay order. Two measurements separate that component from\n",
+    "genuine progress: compare the same phase in consecutive epochs, and look at the\n",
+    "spread within a single epoch."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "7acd60f5",
+   "id": "57d2464c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:53.325788Z",
-     "iopub.status.busy": "2026-08-17T15:34:53.325700Z",
-     "iopub.status.idle": "2026-08-17T15:34:53.333293Z",
-     "shell.execute_reply": "2026-08-17T15:34:53.332825Z"
+     "iopub.execute_input": "2026-08-18T03:55:11.625933Z",
+     "iopub.status.busy": "2026-08-18T03:55:11.625864Z",
+     "iopub.status.idle": "2026-08-18T03:55:11.634700Z",
+     "shell.execute_reply": "2026-08-18T03:55:11.634274Z"
     }
    },
    "outputs": [
@@ -1429,7 +1451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af6392a4",
+   "id": "9701684f",
    "metadata": {},
    "source": [
     "Read the two measurements against each other. Between the same phase of\n",
@@ -1443,28 +1465,29 @@
     "\n",
     "That is not a subtlety to note and move past. It means the number you would\n",
     "report depends on where you stop, so the fit is summarized by averaging the\n",
-    "variational parameters over the final whole pass. Averaging over a whole number\n",
-    "of periods removes the phase dependence exactly, which is why the step budget\n",
-    "above is a whole number of epochs; a window that is not a whole number of\n",
-    "periods leaves a residual of the order of one swing divided by the window\n",
-    "length. This is Polyak–Ruppert averaging, and the same tail average is what\n",
-    "[pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722) applies to\n",
-    "its own iterates for the same reason. What the average does *not* remove is the\n",
-    "slow drift the first line still shows: a tenth of a width per pass in the\n",
-    "median, and much more along the directions the recovery table is about to\n",
+    "variational parameters over the final whole pass. To the extent the order-locked\n",
+    "component repeats from one pass to the next, a window of exactly one pass\n",
+    "averages it out — which is why the step budget above is a whole number of\n",
+    "epochs — and a window that is not a whole number of passes leaves a residual of\n",
+    "the order of one swing divided by the window length. This is Polyak–Ruppert\n",
+    "averaging; [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)\n",
+    "applies the same device to its own iterates for a neighbouring reason,\n",
+    "minibatch noise in where the iterate sits. What the average does *not* remove\n",
+    "is the slow drift the first line still shows: a tenth of a width per pass in\n",
+    "the median, and much more along the directions the recovery table is about to\n",
     "single out."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "b93f5d10",
+   "id": "e3a66cbe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:53.334266Z",
-     "iopub.status.busy": "2026-08-17T15:34:53.334195Z",
-     "iopub.status.idle": "2026-08-17T15:34:53.337050Z",
-     "shell.execute_reply": "2026-08-17T15:34:53.336683Z"
+     "iopub.execute_input": "2026-08-18T03:55:11.635821Z",
+     "iopub.status.busy": "2026-08-18T03:55:11.635741Z",
+     "iopub.status.idle": "2026-08-18T03:55:11.638678Z",
+     "shell.execute_reply": "2026-08-18T03:55:11.638374Z"
     }
    },
    "outputs": [
@@ -1485,7 +1508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2992cd6a",
+   "id": "1bdf9332",
    "metadata": {},
    "source": [
     "Everything below reads from that averaged approximation."
@@ -1493,7 +1516,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7903d0e3",
+   "id": "7cad2596",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1502,13 +1525,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "0ae57190",
+   "id": "1f379389",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:53.337959Z",
-     "iopub.status.busy": "2026-08-17T15:34:53.337898Z",
-     "iopub.status.idle": "2026-08-17T15:34:53.464252Z",
-     "shell.execute_reply": "2026-08-17T15:34:53.463933Z"
+     "iopub.execute_input": "2026-08-18T03:55:11.639685Z",
+     "iopub.status.busy": "2026-08-18T03:55:11.639626Z",
+     "iopub.status.idle": "2026-08-18T03:55:11.786473Z",
+     "shell.execute_reply": "2026-08-18T03:55:11.786101Z"
     }
    },
    "outputs": [
@@ -1696,13 +1719,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "811f8f90",
+   "id": "35bb8334",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:34:53.465300Z",
-     "iopub.status.busy": "2026-08-17T15:34:53.465228Z",
-     "iopub.status.idle": "2026-08-17T15:35:03.674983Z",
-     "shell.execute_reply": "2026-08-17T15:35:03.674559Z"
+     "iopub.execute_input": "2026-08-18T03:55:11.787480Z",
+     "iopub.status.busy": "2026-08-18T03:55:11.787416Z",
+     "iopub.status.idle": "2026-08-18T03:55:24.896360Z",
+     "shell.execute_reply": "2026-08-18T03:55:24.895919Z"
     }
    },
    "outputs": [
@@ -1710,28 +1733,51 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "identified rows, max |z| against the truth — last iterate: 3.35; tail-averaged, seeds 0/1/2: 1.16 / 1.15 / 1.14\n",
-      "across-seed sd of the mean, in reported sd: median 0.08, max 0.11\n"
+      "identified rows, max |z| against the truth, each fit in its own posterior sd:\n",
+      "  last iterate 3.42; seed 0 1.16; seed 1, same order 1.14; seed 2, other order 1.13\n",
+      "spread of the mean across the three tail-averaged fits, in seed-0 sd: median 0.08, max 0.10\n"
      ]
     }
    ],
    "source": [
-    "# Is the table a property of the problem or of one optimizer trajectory? Two\n",
-    "# independent-seed refits (same data, budget and tail average), plus the\n",
-    "# notebook's own last iterate before averaging, for the record.\n",
+    "# Is the table a property of the problem or of one optimizer trajectory? Three\n",
+    "# more readings of the same data and budget: the notebook's own last iterate\n",
+    "# before averaging; a refit with a different optimizer seed on the same replay\n",
+    "# order; and a refit on a different on-disk order — the replay order is the one\n",
+    "# ingredient the orbit story is about, so it is the one worth varying.\n",
     "raw_splits = [\"kappa0\", \"alpha0\", \"beta_a\"]\n",
     "identified = [name for name in recovery.index if name not in raw_splits]\n",
     "\n",
     "\n",
     "def summarize(post_):\n",
-    "    out = {name: float(post_[name].mean()) for name in scalar_params}\n",
+    "    \"\"\"Posterior mean and sd of every row the recovery table reports.\"\"\"\n",
+    "    out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}\n",
     "    for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
-    "        out[f\"{icpt} + mean({b})\"] = float((post_[icpt] + post_[b].mean(\"symbol\")).mean())\n",
-    "    return pd.Series(out)\n",
+    "        total = post_[icpt] + post_[b].mean(\"symbol\")\n",
+    "        out[f\"{icpt} + mean({b})\"] = (float(total.mean()), float(total.std()))\n",
+    "    return pd.DataFrame(out, index=[\"mean\", \"sd\"]).T\n",
+    "\n",
     "\n",
+    "def reshuffle(src_dir, salt):\n",
+    "    \"\"\"A second on-disk order: re-key the already-shuffled rows with a different salt.\"\"\"\n",
+    "    dst_dir = tempfile.mkdtemp(prefix=\"ticks_reorder_\")\n",
+    "    full = pa.concat_tables(\n",
+    "        [pq.read_table(os.path.join(src_dir, f)) for f in sorted(os.listdir(src_dir))]\n",
+    "    )\n",
+    "    order2 = np.argsort(\n",
+    "        splitmix64(np.arange(full.num_rows, dtype=np.uint64) + np.uint64(salt)), kind=\"stable\"\n",
+    "    )\n",
+    "    for i in range(n_shards):\n",
+    "        pq.write_table(\n",
+    "            full.take(order2[i::n_shards]),\n",
+    "            os.path.join(dst_dir, f\"shard_{i:03d}.parquet\"),\n",
+    "            row_group_size=BATCH,\n",
+    "        )\n",
+    "    return dst_dir\n",
     "\n",
-    "def refit(seed):\n",
-    "    stream_s, tail_s = StreamAdvance(model, loader), ParamTrace()\n",
+    "\n",
+    "def refit(seed, data_loader):\n",
+    "    stream_s, tail_s = StreamAdvance(model, data_loader), ParamTrace()\n",
     "    stream_s.prime()\n",
     "    with model:\n",
     "        advi_s = pm.ADVI(random_seed=seed)\n",
@@ -1755,29 +1801,35 @@
     "approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))\n",
     "approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))\n",
     "\n",
-    "replicates = pd.DataFrame(\n",
-    "    {\n",
-    "        \"last iterate\": last_iterate,\n",
-    "        \"seed 0\": recovery[\"mean\"],\n",
-    "        \"seed 1\": refit(RANDOM_SEED + 1),\n",
-    "        \"seed 2\": refit(RANDOM_SEED + 2),\n",
-    "    }\n",
-    ").loc[identified]\n",
-    "z_rep = replicates.sub(recovery.loc[identified, \"truth\"], axis=0).div(\n",
-    "    recovery.loc[identified, \"sd\"], axis=0\n",
+    "other_dir = reshuffle(data_dir, salt=1)\n",
+    "other_loader = DataLoader(\n",
+    "    parquet_source(other_dir, columns=columns), batch_size=BATCH, shuffle=False, total_size=\"auto\"\n",
+    ")\n",
+    "readings = {\n",
+    "    \"last iterate\": last_iterate,\n",
+    "    \"seed 0\": recovery[[\"mean\", \"sd\"]],\n",
+    "    \"seed 1, same order\": refit(RANDOM_SEED + 1, loader),\n",
+    "    \"seed 2, other order\": refit(RANDOM_SEED + 2, other_loader),\n",
+    "}\n",
+    "tru = recovery.loc[identified, \"truth\"]\n",
+    "z_own = {\n",
+    "    k: ((v.loc[identified, \"mean\"] - tru) / v.loc[identified, \"sd\"]).abs().max()\n",
+    "    for k, v in readings.items()\n",
+    "}\n",
+    "means = pd.DataFrame(\n",
+    "    {k: v.loc[identified, \"mean\"] for k, v in readings.items() if k != \"last iterate\"}\n",
     ")\n",
-    "seed_spread = replicates.iloc[:, 1:].std(axis=1) / recovery.loc[identified, \"sd\"]\n",
+    "spread = means.std(axis=1) / recovery.loc[identified, \"sd\"]\n",
     "print(\n",
-    "    \"identified rows, max |z| against the truth — last iterate: \"\n",
-    "    f\"{z_rep['last iterate'].abs().max():.2f}; tail-averaged, seeds 0/1/2: \"\n",
-    "    + \" / \".join(f\"{z_rep[c].abs().max():.2f}\" for c in [\"seed 0\", \"seed 1\", \"seed 2\"])\n",
-    "    + f\"\\nacross-seed sd of the mean, in reported sd: median {seed_spread.median():.2f}, max {seed_spread.max():.2f}\"\n",
+    "    \"identified rows, max |z| against the truth, each fit in its own posterior sd:\\n  \"\n",
+    "    + \"; \".join(f\"{k} {v:.2f}\" for k, v in z_own.items())\n",
+    "    + f\"\\nspread of the mean across the three tail-averaged fits, in seed-0 sd: median {spread.median():.2f}, max {spread.max():.2f}\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e1303f43",
+   "id": "10d71bbc",
    "metadata": {},
    "source": [
     "The table is a selection — the scalar globals and the three identified sums —\n",
@@ -1790,8 +1842,10 @@
     "hierarchical prior: shifting every symbol effect by $d$ costs\n",
     "$\\sum_s (b_s - d)^2 / 2\\tau^2$, which is minimized when the effects average\n",
     "to zero — and the generator standardized them to average exactly zero, so the\n",
-    "prior points the split at the generating value, with a curvature that makes\n",
-    "its posterior width roughly $\\tau/\\sqrt{12}$, around a tenth. The same\n",
+    "prior points the split at the generating value. Conditional on everything\n",
+    "else, that cost is a Gaussian in $d$ with standard deviation $\\tau/\\sqrt{12}$\n",
+    "— a tenth for $\\tau$ near the generator's $0.35$ — which is the scale of\n",
+    "uncertainty the split actually carries. The same\n",
     "translation ridge exists for every global-coefficient/group-effect pair in\n",
     "this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
     "$g$/$b^{(\\sigma h)}$, whose sums we spare you).\n",
@@ -1805,13 +1859,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "9abcbd7f",
+   "id": "2c788a8a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:03.676124Z",
-     "iopub.status.busy": "2026-08-17T15:35:03.676059Z",
-     "iopub.status.idle": "2026-08-17T15:35:11.082814Z",
-     "shell.execute_reply": "2026-08-17T15:35:11.082391Z"
+     "iopub.execute_input": "2026-08-18T03:55:24.897491Z",
+     "iopub.status.busy": "2026-08-18T03:55:24.897405Z",
+     "iopub.status.idle": "2026-08-18T03:55:35.156112Z",
+     "shell.execute_reply": "2026-08-18T03:55:35.155711Z"
     }
    },
    "outputs": [
@@ -1819,7 +1873,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "kappa0 after +0 / +30 / +60 passes: 0.532 / 0.579 / 0.628 (generating 0.847); alpha0: -1.702 / -1.711 / -1.722 (generating -2.996); loss per pass over those 60: -0.37 nats\n"
+      "kappa0 after +0 / +30 / +60 passes: 0.533 / 0.579 / 0.628 (generating 0.847); alpha0: -1.702 / -1.711 / -1.722 (generating -2.996); loss per pass over those 60: -0.37 nats\n"
      ]
     }
    ],
@@ -1833,7 +1887,7 @@
     "stream_more = StreamAdvance(model, loader)\n",
     "stream_more.prime()\n",
     "where = {name: approx.ordering[name][1].start for name in [\"kappa0\", \"alpha0\"]}\n",
-    "path = []\n",
+    "path = [[mu_t[-1][where[\"kappa0\"]], mu_t[-1][where[\"alpha0\"]]]]  # +0 passes\n",
     "\n",
     "\n",
     "class EpochMeans:\n",
@@ -1853,9 +1907,9 @@
     "more_loss = np.asarray(advi_more.hist) * ELBO_SCALE\n",
     "print(\n",
     "    \"kappa0 after +0 / +30 / +60 passes: \"\n",
-    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 29, 59], 0])\n",
+    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 30, 60], 0])\n",
     "    + f\" (generating {truth['kappa0']:.3f}); alpha0: \"\n",
-    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 29, 59], 1])\n",
+    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 30, 60], 1])\n",
     "    + f\" (generating {truth['alpha0']:.3f}); loss per pass over those 60: \"\n",
     "    f\"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats\"\n",
     ")"
@@ -1863,33 +1917,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5ae9482",
+   "id": "f73ad7ef",
    "metadata": {},
    "source": [
     "The ridge coordinates keep moving toward their generating values, at a rate the\n",
     "loss barely registers. The raw split rows are a fit still travelling along a\n",
     "nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —\n",
-    "are the mean-field *conditional* width across the ridge, not the tenth the\n",
-    "ridge posterior actually has along it: mean-field has no correlation to spend,\n",
+    "are the mean-field *conditional* width across the ridge, not the tenth just\n",
+    "computed along it: mean-field has no correlation to spend,\n",
     "so it reports the narrow direction as if it were the wide one, which is why\n",
     "`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the\n",
     "symbol effects is the standard reparameterization when the split itself\n",
     "matters: it removes the direction instead of asking the optimizer to find its\n",
     "way along it.\n",
     "\n",
-    "The identified rows are a different story. Everything that *is* pinned by the\n",
-    "likelihood — the three sums and the six standalone rows — lands inside $1.2$\n",
-    "posterior standard deviations of its generating value, and seven of those nine\n",
-    "are inside $0.5$. The replicate cell puts that in context from two directions.\n",
+    "The identified rows are a different story. Every row the table reports that\n",
+    "*is* pinned by the likelihood — the three sums and the six standalone\n",
+    "coefficients — lands inside $1.2$ posterior standard deviations of its\n",
+    "generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.\n",
     "Read from the last iterate instead of the tail average, the same rows ran to\n",
-    "$3.3$; that difference was the orbit, not the estimate. And on two further\n",
-    "seeds, everything else held fixed, the identified rows land inside the same\n",
-    "$1.2$, with the across-seed spread of the means about a tenth of the reported\n",
-    "width. That is the replication check the risk-management stance calls for, and\n",
-    "it is worth stating what it does and does not buy: material disagreement\n",
-    "between seeds would have been enough to withhold any claim about a width;\n",
-    "agreement only removes the instability warning — it does not validate\n",
-    "calibration. Replication can veto a width; it can never certify one. The widths\n",
+    "$3.4$; that difference was the orbit, not the estimate. And on two refits —\n",
+    "one with a different optimizer seed on the same replay order, one on a\n",
+    "different on-disk order altogether — the identified rows land in the same\n",
+    "range, each fit judged in its own posterior width, with the spread of the means\n",
+    "across the three tail-averaged fits about a tenth of the reported width. That\n",
+    "is the replication check the risk-management stance calls for, and it is worth\n",
+    "stating what it does and does not buy: material disagreement between fits\n",
+    "would have been enough to withhold any claim about a width; agreement only\n",
+    "removes the instability warning — it does not validate calibration. Replication can veto a width; it can never certify one. The widths\n",
     "here are those of a mean-field approximation, and z-scores this small on one\n",
     "dataset are consistent with widths that are somewhat too narrow, somewhat too\n",
     "wide, or right — sizing that needs many simulated datasets, and this notebook\n",
@@ -1902,13 +1957,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "3cdfc9f5",
+   "id": "7704a8e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:11.084306Z",
-     "iopub.status.busy": "2026-08-17T15:35:11.084211Z",
-     "iopub.status.idle": "2026-08-17T15:35:11.181545Z",
-     "shell.execute_reply": "2026-08-17T15:35:11.181198Z"
+     "iopub.execute_input": "2026-08-18T03:55:35.157768Z",
+     "iopub.status.busy": "2026-08-18T03:55:35.157636Z",
+     "iopub.status.idle": "2026-08-18T03:55:35.326837Z",
+     "shell.execute_reply": "2026-08-18T03:55:35.326371Z"
     }
    },
    "outputs": [
@@ -1971,7 +2026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf2fd3ee",
+   "id": "2b2526a2",
    "metadata": {},
    "source": [
     "What the figure supports is narrower than the usual slogan, so it is worth\n",
@@ -1997,13 +2052,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "42a45d61",
+   "id": "76ea4b99",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:11.182668Z",
-     "iopub.status.busy": "2026-08-17T15:35:11.182603Z",
-     "iopub.status.idle": "2026-08-17T15:35:11.289562Z",
-     "shell.execute_reply": "2026-08-17T15:35:11.289240Z"
+     "iopub.execute_input": "2026-08-18T03:55:35.328151Z",
+     "iopub.status.busy": "2026-08-18T03:55:35.328046Z",
+     "iopub.status.idle": "2026-08-18T03:55:35.486660Z",
+     "shell.execute_reply": "2026-08-18T03:55:35.486030Z"
     }
    },
    "outputs": [
@@ -2074,7 +2129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0611fb8",
+   "id": "082bd7be",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
@@ -2088,7 +2143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d14126af",
+   "id": "13a07216",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -2104,13 +2159,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "19d49161",
+   "id": "a075e60a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:11.290628Z",
-     "iopub.status.busy": "2026-08-17T15:35:11.290566Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.121948Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.121262Z"
+     "iopub.execute_input": "2026-08-18T03:55:35.488204Z",
+     "iopub.status.busy": "2026-08-18T03:55:35.488103Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.561372Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.560924Z"
     }
    },
    "outputs": [
@@ -2219,13 +2274,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "5afdb134",
+   "id": "c05822a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.123125Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.123044Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.233957Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.233517Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.562619Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.562538Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.710516Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.710058Z"
     }
    },
    "outputs": [
@@ -2273,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be8cc571",
+   "id": "81899fba",
    "metadata": {},
    "source": [
     "All three statistics sit inside their predictive 90% bands, though the zero\n",
@@ -2291,7 +2346,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e1c5004",
+   "id": "2c46b7f0",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -2322,13 +2377,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "ffc90186",
+   "id": "975568ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.235405Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.235320Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.238639Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.238272Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.711693Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.711598Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.715435Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.715043Z"
     }
    },
    "outputs": [
@@ -2380,13 +2435,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "8cb6352e",
+   "id": "706767de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.239534Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.239478Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.360764Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.360415Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.716451Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.716380Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.878080Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.877539Z"
     }
    },
    "outputs": [
@@ -2427,7 +2482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1da9d89c",
+   "id": "a6aa147a",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
@@ -2439,13 +2494,14 @@
     "independent noise would predict — and the control row explains why. The\n",
     "shuffle is done once on disk and replayed in the same order every epoch, so a\n",
     "window of exactly one or two passes averages over the *same rows* every time\n",
-    "and the batch-composition noise, which dominates the per-step view, cancels.\n",
+    "and the batch-composition noise, which dominates the per-step view, all but\n",
+    "drops out.\n",
     "Widen the window to one and a half passes and it does not: the printed ratio\n",
     "puts the control an order of magnitude above the wider of its two neighbours,\n",
     "even though it is itself *wider* than the one-epoch window. Nothing but the\n",
     "alignment changed.\n",
     "\n",
-    "What survives the cancellation is Monte-Carlo noise and the parameter drift\n",
+    "What survives is Monte-Carlo noise and the parameter drift\n",
     "itself, and against that much smaller yardstick the drift becomes visible —\n",
     "the ratio in the last column rises with the horizon. Two lessons sit on top of\n",
     "each other here. The horizon is not a smoothing preference, it decides whether\n",
@@ -2456,7 +2512,8 @@
     ":::{admonition} Why a naive rule does not merely stay silent — it fires\n",
     ":class: warning\n",
     "It is tempting to accumulate per-step evidence with a one-sided cumulative-sum\n",
-    "statistic {cite:p}`page1954continuous`, $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
+    "statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to\n",
+    "the standardized improvement: $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
     "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that\n",
     "recursion behaves sensibly while there is signal: if the standardised\n",
     "improvement stays above $\\kappa$, the increment is negative and $S$ sits at\n",
@@ -2468,7 +2525,7 @@
     "for this horizon to see\" — it announces the same thing at the same pace in\n",
     "both cases. Which is why the fix is not a better threshold but a wider horizon,\n",
     "plus a second yardstick asking whether the remaining improvement is negligible\n",
-    "relative to the reduction already achieved. That design is under review as\n",
+    "relative to the reduction already achieved. That design is implemented in\n",
     "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this\n",
     "notebook shows the observation problem it exists to solve rather than shipping\n",
     "a second copy of it.\n",
@@ -2482,13 +2539,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "21d9ff12",
+   "id": "a54698c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.361903Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.361822Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.364024Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.363671Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.879250Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.879158Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.881551Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.881220Z"
     }
    },
    "outputs": [
@@ -2519,7 +2576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2760720c",
+   "id": "aa3d34bb",
    "metadata": {},
    "source": [
     "Two honest qualifications about any stop, this one included. What such a rule\n",
@@ -2531,9 +2588,9 @@
     "\n",
     "## Where the pieces live\n",
     "\n",
-    "The streaming stack this notebook exercises is being contributed to\n",
-    "pymc-extras. The map is by role; each pull request's current state is on\n",
-    "GitHub rather than frozen into this page.\n",
+    "The streaming stack this notebook exercises was contributed to pymc-extras\n",
+    "as four pull requests. The map is by role; each one's state — merged, open,\n",
+    "or closed — is on GitHub, and nothing on this page depends on it.\n",
     "\n",
     "| Component | Role | Form in this notebook |\n",
     "| --- | --- | --- |\n",
@@ -2545,7 +2602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1558367e",
+   "id": "92c747e8",
    "metadata": {},
    "source": [
     "### Choosing a path: why the fast one is not taken here\n",
@@ -2564,13 +2621,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "4041d072",
+   "id": "060693c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.364955Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.364889Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.381550Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.381188Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.882612Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.882531Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.904706Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.904333Z"
     }
    },
    "outputs": [
@@ -2591,7 +2648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ebcb8bc",
+   "id": "245b9e0c",
    "metadata": {},
    "source": [
     "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
@@ -2605,7 +2662,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a621131e",
+   "id": "97cc592a",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2617,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24b6b103",
+   "id": "5fafcd7d",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2629,7 +2686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c67f4a0",
+   "id": "c6505d9f",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2638,16 +2695,23 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "241f4e82",
+   "id": "840602e2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-17T15:35:12.382612Z",
-     "iopub.status.busy": "2026-08-17T15:35:12.382555Z",
-     "iopub.status.idle": "2026-08-17T15:35:12.396040Z",
-     "shell.execute_reply": "2026-08-17T15:35:12.395662Z"
+     "iopub.execute_input": "2026-08-18T03:55:36.905957Z",
+     "iopub.status.busy": "2026-08-18T03:55:36.905876Z",
+     "iopub.status.idle": "2026-08-18T03:55:36.940293Z",
+     "shell.execute_reply": "2026-08-18T03:55:36.939827Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wall time for every cell above, on this machine: 33 s\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -2673,18 +2737,28 @@
       "scipy      : 1.18.0\n",
       "\n",
       "Watermark: 2.6.0\n",
+      "\n",
+      "Compiler    : Clang 21.1.4 \n",
+      "OS          : Darwin\n",
+      "Release     : 24.6.0\n",
+      "Machine     : arm64\n",
+      "Processor   : arm\n",
+      "CPU cores   : 10\n",
+      "Architecture: 64bit\n",
       "\n"
      ]
     }
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor,pymc_extras"
+    "print(f\"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s\")\n",
+    "%watermark -n -u -v -iv -w -p pytensor,pymc_extras\n",
+    "%watermark -m"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fee0465c",
+   "id": "4654151d",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 40e22638d..0b720c77a 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -18,7 +18,7 @@ myst:
 
 # Streaming variational inference on high-frequency tick data
 
-:::{post} August 2026
+:::{post} August 17, 2026
 :tags: variational inference, minibatch, out-of-core, hierarchical model, time series
 :category: advanced, tutorial
 :author: Yicheng Yang
@@ -35,7 +35,8 @@ workstation; at that point in-memory minibatching stops being an option. PyMC's
 disk-backed reader, so it cannot help once the array no longer fits. This
 notebook fits a hierarchical hurdle–Student-t model of *next-event price
 moves* by streaming minibatches from disk with pymc-extras'
-{class}`~pymc_extras.variational.dataloader.DataLoader`, on 300,000 synthetic
+[`DataLoader`](https://github.com/pymc-devs/pymc-extras/blob/8db1880d410e509be02abf9b085f08c3d4514fd1/pymc_extras/variational/dataloader.py),
+on 300,000 synthetic
 rows that are generated inside the notebook. It tries to teach three things:
 
 1. **When minibatch VI is even valid.** Two acceptance gates that most
@@ -47,12 +48,13 @@ rows that are generated inside the notebook. It tries to teach three things:
    after a correction the fit forces on us: the cyclic replay puts the optimizer
    on an orbit, and the last iterate misses the truth by several of its own
    posterior standard deviations purely because of where in that orbit the step
-   budget ended. Averaged over one full pass, every identified quantity lands
-   inside 1.2 — on three optimizer seeds. The widths themselves are a separate
-   question this notebook cannot settle on one dataset, and it says so rather
-   than certifying them.
+   budget ended. Averaged over one full pass, every identified row the
+   recovery table reports lands inside 1.2 — across three fits, one of them on
+   a different replay order. The widths themselves are a separate question this
+   notebook cannot settle on one dataset, and it says so rather than certifying
+   them.
 
-The notebook runs in well under a minute on a laptop. Everything below executes on synthetic
+The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
 possible: nothing here is a claim about any real market.
 
@@ -109,7 +111,7 @@ the first place.
 :::
 
 :::{note}
-The {class}`~pymc_extras.variational.dataloader.DataLoader` merged into
+The `DataLoader` merged into
 pymc-extras after the v0.14.0 release, so it is not in a published version yet
 and `pip install pymc-extras` will not provide it. The outputs stored in this
 notebook were produced against the merge commit itself, which is also the
@@ -129,6 +131,7 @@ import gc
 import logging
 import os
 import tempfile
+import time
 import warnings
 
 import arviz as az
@@ -142,6 +145,8 @@ import pytensor.tensor as pt
 
 from pymc_extras.variational.dataloader import DataLoader, parquet_source
 from scipy import stats
+
+NOTEBOOK_T0 = time.perf_counter()
 ```
 
 ```{code-cell} ipython3
@@ -433,9 +438,12 @@ Be clear about what this cell is and is not. It is the pedagogical miniature:
 it builds the whole table, the whole key array and the whole permutation in
 memory, which is exactly what one cannot do once the data stops fitting. What
 follows it — the inference — is genuinely disk-backed, but the preprocessing
-above is not. The out-of-core version of the same permutation is two passes:
-hash-scatter rows into shards with bounded row-group appends, then sort each
-shard independently, so no step holds more than one shard.
+above is not. An out-of-core design with the same properties is two passes:
+assign each row to a shard from its hash key, appending row groups of bounded
+size, then sort each shard by key independently, so no step holds more than
+one shard. That yields a different permutation from the rank-based split used
+here — shard sizes come out approximately rather than exactly equal — with the
+same guarantee that no shard's order depends on anything in the rows.
 
 So that the fit really does run against disk rather than against arrays still
 resident from the generator, the row-scale objects are released first:
@@ -620,10 +628,10 @@ likelihood's `logp` and are not separate nodes; the equations above are where
 that structure is visible.
 
 The loop below is the whole streaming adapter: a `pm.fit` callback that pushes
-the next block into the placeholder after each step. pymc-extras#710 proposes
-an interim wrapper around this same data-advance lifecycle while
-pymc-extras#635 develops the longer-term ADVI API, so the twenty lines here are
-deliberately the minimal version a notebook can own.
+the next block into the placeholder after each step. pymc-extras#710 contains
+a wrapper around this same data-advance lifecycle and pymc-extras#635 a
+longer-term ADVI training API; the twenty lines here are deliberately the
+minimal version a notebook can own.
 
 ```{code-cell} ipython3
 class StreamAdvance:
@@ -677,9 +685,10 @@ The two-stage learning rate is deliberate. A constant step size that is
 comfortable early is too large near the optimum, where it keeps the
 variational mean bouncing instead of settling; dropping it once the descent
 has flattened is the cheapest fix. Do not look for the effect in the width of
-the loss band below — that band is batch-composition noise and the learning
-rate does not touch it; the effect is in the level, and in the parameter
-trace examined after the fit.
+the loss band below: the band is dominated by batch-composition noise (the
+stopping section shows it all but vanishing at epoch-aligned horizons), the
+printed spread is the same on both sides of the change, and the effect is in
+the level and in the parameter trace examined after the fit.
 
 Before plotting it, one property of `approx.hist` that is easy to get wrong and
 that changes what the numbers mean. PyMC normalizes the minibatch objective
@@ -709,17 +718,23 @@ ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))
 ax.set_xlabel("step")
 ax.set_ylabel("negative ELBO (full-data scale)")
 ax.set_title("Streaming ADVI loss (full-data scale, y clipped to the plateau)")
-ax.legend();
+ax.legend()
+print(
+    "sd of the recorded loss over the last two passes at each learning rate: "
+    f"{loss[6_000 - 600 : 6_000].std():,.0f} (lr 0.02) vs {loss[-600:].std():,.0f} (lr 0.005)"
+);
 ```
 
 ## The last iterate is not the answer
 
 Before reading a single parameter off this fit, one property of the replay has
 to be dealt with. The batch at step $t$ is a deterministic function of $t$ with
-period one epoch, so the gradient sequence is periodic and the optimizer's
-iterate inherits a periodic component. Two measurements separate that component
-from genuine progress: compare the same phase in consecutive epochs, and look at
-the spread within a single epoch.
+period one epoch, so the data term of every stochastic gradient repeats with
+that period — the parameters and the Monte-Carlo draw do not, so the realized
+gradients are not literally periodic, but the iterate inherits a component
+locked to the replay order. Two measurements separate that component from
+genuine progress: compare the same phase in consecutive epochs, and look at the
+spread within a single epoch.
 
 ```{code-cell} ipython3
 mu_t = np.asarray(tail.mu)
@@ -747,15 +762,16 @@ step the budget ran out.
 
 That is not a subtlety to note and move past. It means the number you would
 report depends on where you stop, so the fit is summarized by averaging the
-variational parameters over the final whole pass. Averaging over a whole number
-of periods removes the phase dependence exactly, which is why the step budget
-above is a whole number of epochs; a window that is not a whole number of
-periods leaves a residual of the order of one swing divided by the window
-length. This is Polyak–Ruppert averaging, and the same tail average is what
-[pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722) applies to
-its own iterates for the same reason. What the average does *not* remove is the
-slow drift the first line still shows: a tenth of a width per pass in the
-median, and much more along the directions the recovery table is about to
+variational parameters over the final whole pass. To the extent the order-locked
+component repeats from one pass to the next, a window of exactly one pass
+averages it out — which is why the step budget above is a whole number of
+epochs — and a window that is not a whole number of passes leaves a residual of
+the order of one swing divided by the window length. This is Polyak–Ruppert
+averaging; [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)
+applies the same device to its own iterates for a neighbouring reason,
+minibatch noise in where the iterate sits. What the average does *not* remove
+is the slow drift the first line still shows: a tenth of a width per pass in
+the median, and much more along the directions the recovery table is about to
 single out.
 
 ```{code-cell} ipython3
@@ -803,22 +819,44 @@ recovery.round(4)
 ```
 
 ```{code-cell} ipython3
-# Is the table a property of the problem or of one optimizer trajectory? Two
-# independent-seed refits (same data, budget and tail average), plus the
-# notebook's own last iterate before averaging, for the record.
+# Is the table a property of the problem or of one optimizer trajectory? Three
+# more readings of the same data and budget: the notebook's own last iterate
+# before averaging; a refit with a different optimizer seed on the same replay
+# order; and a refit on a different on-disk order — the replay order is the one
+# ingredient the orbit story is about, so it is the one worth varying.
 raw_splits = ["kappa0", "alpha0", "beta_a"]
 identified = [name for name in recovery.index if name not in raw_splits]
 
 
 def summarize(post_):
-    out = {name: float(post_[name].mean()) for name in scalar_params}
+    """Posterior mean and sd of every row the recovery table reports."""
+    out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}
     for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
-        out[f"{icpt} + mean({b})"] = float((post_[icpt] + post_[b].mean("symbol")).mean())
-    return pd.Series(out)
+        total = post_[icpt] + post_[b].mean("symbol")
+        out[f"{icpt} + mean({b})"] = (float(total.mean()), float(total.std()))
+    return pd.DataFrame(out, index=["mean", "sd"]).T
+
+
+def reshuffle(src_dir, salt):
+    """A second on-disk order: re-key the already-shuffled rows with a different salt."""
+    dst_dir = tempfile.mkdtemp(prefix="ticks_reorder_")
+    full = pa.concat_tables(
+        [pq.read_table(os.path.join(src_dir, f)) for f in sorted(os.listdir(src_dir))]
+    )
+    order2 = np.argsort(
+        splitmix64(np.arange(full.num_rows, dtype=np.uint64) + np.uint64(salt)), kind="stable"
+    )
+    for i in range(n_shards):
+        pq.write_table(
+            full.take(order2[i::n_shards]),
+            os.path.join(dst_dir, f"shard_{i:03d}.parquet"),
+            row_group_size=BATCH,
+        )
+    return dst_dir
 
 
-def refit(seed):
-    stream_s, tail_s = StreamAdvance(model, loader), ParamTrace()
+def refit(seed, data_loader):
+    stream_s, tail_s = StreamAdvance(model, data_loader), ParamTrace()
     stream_s.prime()
     with model:
         advi_s = pm.ADVI(random_seed=seed)
@@ -842,23 +880,29 @@ last_iterate = summarize(approx.sample(2_000, random_seed=RANDOM_SEED).posterior
 approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))
 approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))
 
-replicates = pd.DataFrame(
-    {
-        "last iterate": last_iterate,
-        "seed 0": recovery["mean"],
-        "seed 1": refit(RANDOM_SEED + 1),
-        "seed 2": refit(RANDOM_SEED + 2),
-    }
-).loc[identified]
-z_rep = replicates.sub(recovery.loc[identified, "truth"], axis=0).div(
-    recovery.loc[identified, "sd"], axis=0
+other_dir = reshuffle(data_dir, salt=1)
+other_loader = DataLoader(
+    parquet_source(other_dir, columns=columns), batch_size=BATCH, shuffle=False, total_size="auto"
+)
+readings = {
+    "last iterate": last_iterate,
+    "seed 0": recovery[["mean", "sd"]],
+    "seed 1, same order": refit(RANDOM_SEED + 1, loader),
+    "seed 2, other order": refit(RANDOM_SEED + 2, other_loader),
+}
+tru = recovery.loc[identified, "truth"]
+z_own = {
+    k: ((v.loc[identified, "mean"] - tru) / v.loc[identified, "sd"]).abs().max()
+    for k, v in readings.items()
+}
+means = pd.DataFrame(
+    {k: v.loc[identified, "mean"] for k, v in readings.items() if k != "last iterate"}
 )
-seed_spread = replicates.iloc[:, 1:].std(axis=1) / recovery.loc[identified, "sd"]
+spread = means.std(axis=1) / recovery.loc[identified, "sd"]
 print(
-    "identified rows, max |z| against the truth — last iterate: "
-    f"{z_rep['last iterate'].abs().max():.2f}; tail-averaged, seeds 0/1/2: "
-    + " / ".join(f"{z_rep[c].abs().max():.2f}" for c in ["seed 0", "seed 1", "seed 2"])
-    + f"\nacross-seed sd of the mean, in reported sd: median {seed_spread.median():.2f}, max {seed_spread.max():.2f}"
+    "identified rows, max |z| against the truth, each fit in its own posterior sd:\n  "
+    + "; ".join(f"{k} {v:.2f}" for k, v in z_own.items())
+    + f"\nspread of the mean across the three tail-averaged fits, in seed-0 sd: median {spread.median():.2f}, max {spread.max():.2f}"
 )
 ```
 
@@ -872,8 +916,10 @@ $(\alpha_0 + d,\ b^{(\alpha)} - d)$. What tilts that direction at all is the
 hierarchical prior: shifting every symbol effect by $d$ costs
 $\sum_s (b_s - d)^2 / 2\tau^2$, which is minimized when the effects average
 to zero — and the generator standardized them to average exactly zero, so the
-prior points the split at the generating value, with a curvature that makes
-its posterior width roughly $\tau/\sqrt{12}$, around a tenth. The same
+prior points the split at the generating value. Conditional on everything
+else, that cost is a Gaussian in $d$ with standard deviation $\tau/\sqrt{12}$
+— a tenth for $\tau$ near the generator's $0.35$ — which is the scale of
+uncertainty the split actually carries. The same
 translation ridge exists for every global-coefficient/group-effect pair in
 this model (including the vector pairs $c$/$b^{(\pi h)}$ and
 $g$/$b^{(\sigma h)}$, whose sums we spare you).
@@ -893,7 +939,7 @@ advi_more.approx.params[1].set_value(rho_t[-1])
 stream_more = StreamAdvance(model, loader)
 stream_more.prime()
 where = {name: approx.ordering[name][1].start for name in ["kappa0", "alpha0"]}
-path = []
+path = [[mu_t[-1][where["kappa0"]], mu_t[-1][where["alpha0"]]]]  # +0 passes
 
 
 class EpochMeans:
@@ -913,9 +959,9 @@ path = np.asarray(path)
 more_loss = np.asarray(advi_more.hist) * ELBO_SCALE
 print(
     "kappa0 after +0 / +30 / +60 passes: "
-    + " / ".join(f"{v:.3f}" for v in path[[0, 29, 59], 0])
+    + " / ".join(f"{v:.3f}" for v in path[[0, 30, 60], 0])
     + f" (generating {truth['kappa0']:.3f}); alpha0: "
-    + " / ".join(f"{v:.3f}" for v in path[[0, 29, 59], 1])
+    + " / ".join(f"{v:.3f}" for v in path[[0, 30, 60], 1])
     + f" (generating {truth['alpha0']:.3f}); loss per pass over those 60: "
     f"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats"
 )
@@ -924,27 +970,28 @@ print(
 The ridge coordinates keep moving toward their generating values, at a rate the
 loss barely registers. The raw split rows are a fit still travelling along a
 nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —
-are the mean-field *conditional* width across the ridge, not the tenth the
-ridge posterior actually has along it: mean-field has no correlation to spend,
+are the mean-field *conditional* width across the ridge, not the tenth just
+computed along it: mean-field has no correlation to spend,
 so it reports the narrow direction as if it were the wide one, which is why
 `alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the
 symbol effects is the standard reparameterization when the split itself
 matters: it removes the direction instead of asking the optimizer to find its
 way along it.
 
-The identified rows are a different story. Everything that *is* pinned by the
-likelihood — the three sums and the six standalone rows — lands inside $1.2$
-posterior standard deviations of its generating value, and seven of those nine
-are inside $0.5$. The replicate cell puts that in context from two directions.
+The identified rows are a different story. Every row the table reports that
+*is* pinned by the likelihood — the three sums and the six standalone
+coefficients — lands inside $1.2$ posterior standard deviations of its
+generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.
 Read from the last iterate instead of the tail average, the same rows ran to
-$3.3$; that difference was the orbit, not the estimate. And on two further
-seeds, everything else held fixed, the identified rows land inside the same
-$1.2$, with the across-seed spread of the means about a tenth of the reported
-width. That is the replication check the risk-management stance calls for, and
-it is worth stating what it does and does not buy: material disagreement
-between seeds would have been enough to withhold any claim about a width;
-agreement only removes the instability warning — it does not validate
-calibration. Replication can veto a width; it can never certify one. The widths
+$3.4$; that difference was the orbit, not the estimate. And on two refits —
+one with a different optimizer seed on the same replay order, one on a
+different on-disk order altogether — the identified rows land in the same
+range, each fit judged in its own posterior width, with the spread of the means
+across the three tail-averaged fits about a tenth of the reported width. That
+is the replication check the risk-management stance calls for, and it is worth
+stating what it does and does not buy: material disagreement between fits
+would have been enough to withhold any claim about a width; agreement only
+removes the instability warning — it does not validate calibration. Replication can veto a width; it can never certify one. The widths
 here are those of a mean-field approximation, and z-scores this small on one
 dataset are consistent with widths that are somewhat too narrow, somewhat too
 wide, or right — sizing that needs many simulated datasets, and this notebook
@@ -1235,13 +1282,14 @@ At the two epoch-aligned horizons the spread collapses far below what
 independent noise would predict — and the control row explains why. The
 shuffle is done once on disk and replayed in the same order every epoch, so a
 window of exactly one or two passes averages over the *same rows* every time
-and the batch-composition noise, which dominates the per-step view, cancels.
+and the batch-composition noise, which dominates the per-step view, all but
+drops out.
 Widen the window to one and a half passes and it does not: the printed ratio
 puts the control an order of magnitude above the wider of its two neighbours,
 even though it is itself *wider* than the one-epoch window. Nothing but the
 alignment changed.
 
-What survives the cancellation is Monte-Carlo noise and the parameter drift
+What survives is Monte-Carlo noise and the parameter drift
 itself, and against that much smaller yardstick the drift becomes visible —
 the ratio in the last column rises with the horizon. Two lessons sit on top of
 each other here. The horizon is not a smoothing preference, it decides whether
@@ -1252,7 +1300,8 @@ the data was shuffled, not of the optimizer.
 :::{admonition} Why a naive rule does not merely stay silent — it fires
 :class: warning
 It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
-statistic {cite:p}`page1954continuous`, $S \leftarrow \max(0,\ S + (\kappa -
+statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to
+the standardized improvement: $S \leftarrow \max(0,\ S + (\kappa -
 \max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that
 recursion behaves sensibly while there is signal: if the standardised
 improvement stays above $\kappa$, the increment is negative and $S$ sits at
@@ -1264,7 +1313,7 @@ rule therefore cannot tell "converged" from "still improving, but too slowly
 for this horizon to see" — it announces the same thing at the same pace in
 both cases. Which is why the fix is not a better threshold but a wider horizon,
 plus a second yardstick asking whether the remaining improvement is negligible
-relative to the reduction already achieved. That design is under review as
+relative to the reduction already achieved. That design is implemented in
 [pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this
 notebook shows the observation problem it exists to solve rather than shipping
 a second copy of it.
@@ -1300,9 +1349,9 @@ improvement small enough is indistinguishable from none.
 
 ## Where the pieces live
 
-The streaming stack this notebook exercises is being contributed to
-pymc-extras. The map is by role; each pull request's current state is on
-GitHub rather than frozen into this page.
+The streaming stack this notebook exercises was contributed to pymc-extras
+as four pull requests. The map is by role; each one's state — merged, open,
+or closed — is on GitHub, and nothing on this page depends on it.
 
 | Component | Role | Form in this notebook |
 | --- | --- | --- |
@@ -1362,7 +1411,9 @@ of this choice.
 
 ```{code-cell} ipython3
 %load_ext watermark
+print(f"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s")
 %watermark -n -u -v -iv -w -p pytensor,pymc_extras
+%watermark -m
 ```
 
 :::{include} ../page_footer.md

From 3bfc8e191adac697d4190f99d9aea0baa25ebf14 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Mon, 17 Aug 2026 23:25:09 -0500
Subject: [PATCH 41/42] Final pass: prose register, two figures, page mechanics

A last review before this goes to the maintainer, aimed at two things:
whether the prose reads as written by a person, and whether the figures
show what the text says they show.

Prose. Twenty-four passages that announced their own importance ("worth
stating plainly", "That is not a subtlety to note and move past", "Read
the two measurements against each other", "Two lessons sit on top of each
other here") or labelled their own virtue ("Honest reporting", "honesty of
the showcase", "genuinely disk-backed") are rewritten to say the thing
without the frame. Em-dashes go from 73 to 2 (the two Gate title
separators); asides become parentheses, commas or their own sentence. The
paragraph-closing aphorism about replication is cut; the sentence before
it already says what it says. No number, citation or caveat changes.

Figures. The loss plot clipped y to the 0.2-99.5% quantiles of the whole
trace, which the first few hundred steps stretch to +645k, so the plateau
was an unreadable ribbon and the title's "clipped to the plateau" was
false. It now clips on the loss after step 1,000, overlays a one-epoch
moving mean, and prints the spread and level over the two passes either
side of the learning-rate cut (sd 13,192 vs 13,093; mean -96,528 vs
-97,746) so the prose about the cut rests on an output. The block-contrast
plot's aligned horizons were invisible at the per-step scale, its title
overran the y-label, and its frameless legend sat on the spikes; it is now
two panels, the second zoomed to +/-0.12 where the drift the prose
describes can be seen. The predictive ECDF's legend moves off the curve.

Page. The component map is retitled "Related tooling in pymc-extras",
loses its self-review column and status framing, and absorbs the
Pathfinder applicability note; two inline PR mentions go, since the map is
their home. Long print statements are split so no number is cut off at
the output cell's width; the temp path is reduced to its basename; the
install line uses the shorter git+ form so the hash is not clipped; the
watermark no longer lists pytensor and pymc_extras twice.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../streaming_tick_data.ipynb                 | 1309 +++++++++--------
 .../streaming_tick_data.myst.md               |  404 ++---
 2 files changed, 859 insertions(+), 854 deletions(-)

diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index fb2caf655..b7b8509b0 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "343b2a4a",
+   "id": "9fa79e63",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,10 +18,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d1a4e70",
+   "id": "34e92bce",
    "metadata": {},
    "source": [
-    "Exchanges publish per-trade archives — Binance, for instance, distributes\n",
+    "Exchanges publish per-trade archives; Binance, for instance, distributes\n",
     "aggregate trades as daily and monthly files at\n",
     "[data.binance.vision](https://data.binance.vision). Widen a pull across\n",
     "symbols and months and the feature matrix can outgrow the RAM of an ordinary\n",
@@ -35,19 +35,19 @@
     "rows that are generated inside the notebook. It tries to teach three things:\n",
     "\n",
     "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
-    "   time-series models fail — and a model class that passes both.\n",
+    "   time-series models fail, and a model class that passes both.\n",
     "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
     "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
     "   what a stopping rule has to be able to see before it can fire.\n",
-    "3. **Honest reporting.** With the truth known, recovery is checkable — but only\n",
-    "   after a correction the fit forces on us: the cyclic replay puts the optimizer\n",
-    "   on an orbit, and the last iterate misses the truth by several of its own\n",
-    "   posterior standard deviations purely because of where in that orbit the step\n",
-    "   budget ended. Averaged over one full pass, every identified row the\n",
-    "   recovery table reports lands inside 1.2 — across three fits, one of them on\n",
-    "   a different replay order. The widths themselves are a separate question this\n",
-    "   notebook cannot settle on one dataset, and it says so rather than certifying\n",
-    "   them.\n",
+    "3. **Checking the recovery.** With the truth known, recovery is checkable once\n",
+    "   one correction is made: the cyclic replay puts the optimizer on an orbit, and\n",
+    "   the last iterate misses the truth by several of its own posterior standard\n",
+    "   deviations purely because of where in that orbit the step budget ended.\n",
+    "   Averaged over one full pass, every identified row the recovery table reports\n",
+    "   lands inside 1.2 posterior standard deviations of its generating value,\n",
+    "   across three fits, one of them on a different replay order. Whether the\n",
+    "   widths themselves are calibrated is a separate question one dataset cannot\n",
+    "   settle; no claim about them is made here.\n",
     "\n",
     "The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic\n",
     "data with known ground truth, which is what makes the recovery checks\n",
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aa8f737",
+   "id": "19825fb6",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
@@ -64,10 +64,9 @@
     "Minibatch variational inference rests on one identity: if the likelihood\n",
     "factors over rows given the parameters, then the batch log-likelihood scaled by\n",
     "$N/B$ is an unbiased estimator of the full-data log-likelihood\n",
-    "{cite:p}`hoffman2013stochastic`. Two gates shaped this notebook's model — the\n",
-    "first is about *validity*, the second about whether streaming is doing any\n",
-    "real work — and they are worth internalizing before reaching for `total_size`\n",
-    "on your own data:\n",
+    "{cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;\n",
+    "the second is about whether streaming is doing any real work. Check both\n",
+    "before reaching for `total_size` on your own data:\n",
     "\n",
     "**Gate 1 — validity: the likelihood must factor over rows, and the batching\n",
     "must weight every row equally.** No latent path coupling observations, no\n",
@@ -75,7 +74,7 @@
     "the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How\n",
     "the batches are *drawn* is a separate matter that this gate does not settle:\n",
     "fresh uniform batches make each step's scaled gradient conditionally unbiased\n",
-    "for the full-data objective — the identity above — while a fixed shuffled\n",
+    "for the full-data objective (the identity above), while a fixed shuffled\n",
     "order replayed every epoch visits each row once per pass, targets the same\n",
     "finite sum, and yields cyclic rather than unbiased updates. This notebook does\n",
     "the second, and returns to the distinction where the fit is described. The\n",
@@ -85,29 +84,30 @@
     "its neighbours, so a random subset of rows does not carry $B/N$ of the\n",
     "log-likelihood. Any model whose rows share one outcome (for example, every tick\n",
     "of a match sharing the final result) fails the same gate through\n",
-    "pseudo-replication — the effective sample size is the number of outcomes, not\n",
+    "pseudo-replication: the effective sample size is the number of outcomes, not\n",
     "the number of rows.\n",
     "\n",
     "**Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This\n",
-    "one is not about validity but about honesty of the *showcase*: a Normal\n",
+    "one is about whether the streaming machinery is needed at all, not about\n",
+    "validity: a Normal\n",
     "likelihood with per-cell means and variances collapses to per-cell\n",
     "$(\\sum y, \\sum y^2, n)$, so one linear scan computes the exact posterior\n",
-    "inputs and \"streaming inference\" degenerates into a glorified `groupby` —\n",
-    "valid, but theater. A hurdle alone does not rescue it — an observed Bernoulli\n",
+    "inputs and \"streaming inference\" degenerates into a glorified `groupby`:\n",
+    "valid, but theater. A hurdle alone does not rescue it; an observed Bernoulli\n",
     "plus a Normal component still reduces to $(n_0, n_1, \\sum y, \\sum y^2)$. What\n",
     "does break the collapse is the combination used below: a Student-t component\n",
     "with an unknown $\\nu$, and row-level continuous covariates entering through\n",
     "nonlinear links, so no fixed-dimensional summary of the rows suffices.\n",
     "\n",
-    "The model below passes both gates. Each ingredient that gets it there is also\n",
-    "the standard choice for the feature it addresses, which is the argument for\n",
-    "it — not the synthetic data, which was written to contain those features in\n",
-    "the first place."
+    "The model below passes both gates. The case for each ingredient is that it is\n",
+    "the standard choice for the data feature it handles, not that it fits the\n",
+    "synthetic data, which was written to contain those features in the first\n",
+    "place."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "654e4e52",
+   "id": "3306be8a",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -121,11 +121,11 @@
     "install line to use until the next release:\n",
     "\n",
     "```\n",
-    "pip install \"pymc-extras @ git+https://github.com/pymc-devs/pymc-extras@8db1880\"\n",
+    "pip install git+https://github.com/pymc-devs/pymc-extras@8db1880\n",
     "```\n",
     "\n",
     "That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull\n",
-    "PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the\n",
+    "PyMC 6.2 and PyTensor 3.2, the versions reported by the watermark at the\n",
     "bottom of this page. Once the module ships, `pip install pymc-extras` will do.\n",
     ":::"
    ]
@@ -133,13 +133,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "19fa0246",
+   "id": "8c4c9bb8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:02.042215Z",
-     "iopub.status.busy": "2026-08-18T03:55:02.042148Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.044039Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.043601Z"
+     "iopub.execute_input": "2026-08-18T04:23:59.666230Z",
+     "iopub.status.busy": "2026-08-18T04:23:59.666160Z",
+     "iopub.status.idle": "2026-08-18T04:24:01.389770Z",
+     "shell.execute_reply": "2026-08-18T04:24:01.389320Z"
     }
    },
    "outputs": [],
@@ -169,13 +169,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "87a9eaed",
+   "id": "27ef0634",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.045242Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.045097Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.047936Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.047616Z"
+     "iopub.execute_input": "2026-08-18T04:24:01.390970Z",
+     "iopub.status.busy": "2026-08-18T04:24:01.390821Z",
+     "iopub.status.idle": "2026-08-18T04:24:01.393767Z",
+     "shell.execute_reply": "2026-08-18T04:24:01.393492Z"
     }
    },
    "outputs": [],
@@ -193,15 +193,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ab3430d",
+   "id": "7b28210e",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
     "\n",
     "One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:\n",
     "the return $y_i = 10^4 \\, (\\log p_{i+1} - \\log p_i)$ in basis points, and the\n",
-    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. Two consequences of indexing by\n",
-    "*events* rather than by clock time are worth stating before the algebra.\n",
+    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. Indexing by *events* rather than\n",
+    "by clock time has two consequences that matter before the algebra.\n",
     "\n",
     "First, prices move on a discrete grid, so an exact-zero return is not a\n",
     "measure-zero event the way it is for a continuous variable: consecutive trades\n",
@@ -238,8 +238,8 @@
     "sequentially, so it is the one covariate that could not see the future, and\n",
     "$q$ and $a$ are standardized once over the whole sample. On a real archive\n",
     "both would instead be trailing statistics built causally from the tape and\n",
-    "standardized with constants frozen on a burn-in window — a construction that\n",
-    "is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
+    "standardized with constants frozen on a burn-in window, a construction that\n",
+    "is out of scope here and easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
     "a low-order version of the Fourier encoding of intraday periodicity in\n",
     "{cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
     "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
@@ -252,8 +252,8 @@
     "The degrees of freedom are\n",
     "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
     "the floor of 1 rather than 2 leaves the support open to tails too heavy to\n",
-    "carry a finite variance. Be precise about what that buys: under\n",
-    "$\\eta \\sim \\mathcal{N}(5, 1)$ the prior puts only about $4 \\times 10^{-6}$ of\n",
+    "carry a finite variance. Under $\\eta \\sim \\mathcal{N}(5, 1)$ the prior puts\n",
+    "only about $4 \\times 10^{-6}$ of\n",
     "its mass on $\\nu \\le 2$, so this is a statement about support, not a serious\n",
     "prior belief in infinite variance. The estimand below is chosen so that the\n",
     "question does not arise at all.\n",
@@ -262,13 +262,13 @@
     "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
     "$\\sigma_{s,h} \\cdot t^{-1}_{0.95}(\\nu)$. A quantile is the safe choice here: it\n",
     "stays finite for any $\\nu > 0$, whereas a moment-based dispersion requires\n",
-    "$\\nu > 2$ — a constraint the model does not impose, even though the generator\n",
+    "$\\nu > 2$, a constraint the model does not impose, even though the generator\n",
     "below happens to use $\\nu = 3.5$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1b424a94",
+   "id": "aa18bf86",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
@@ -286,13 +286,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ae300b6d",
+   "id": "6f581a9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.048898Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.048829Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.052032Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.051622Z"
+     "iopub.execute_input": "2026-08-18T04:24:01.394690Z",
+     "iopub.status.busy": "2026-08-18T04:24:01.394636Z",
+     "iopub.status.idle": "2026-08-18T04:24:01.397725Z",
+     "shell.execute_reply": "2026-08-18T04:24:01.397336Z"
     }
    },
    "outputs": [],
@@ -334,13 +334,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "38697c5e",
+   "id": "03090818",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.052914Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.052846Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.238459Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.238035Z"
+     "iopub.execute_input": "2026-08-18T04:24:01.398871Z",
+     "iopub.status.busy": "2026-08-18T04:24:01.398814Z",
+     "iopub.status.idle": "2026-08-18T04:24:01.558916Z",
+     "shell.execute_reply": "2026-08-18T04:24:01.558359Z"
     }
    },
    "outputs": [
@@ -408,11 +408,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a14a7ed",
+   "id": "4ed882f7",
    "metadata": {},
    "source": [
-    "Each constant above is a setting, not a measurement, and it is worth reading\n",
-    "them the way one would read a specification for a simulator:\n",
+    "The constants above are settings, not measurements. Read them as the\n",
+    "specification of the simulator:\n",
     "\n",
     "* `kappa0` = $\\operatorname{logit}(0.7)$. If every other term in the logit were\n",
     "  zero, the move probability would be 0.7. The harmonics, the covariates and\n",
@@ -420,7 +420,7 @@
     "  printed above, not $30\\%$ by construction.\n",
     "* `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional\n",
     "  location: the next move is generated partly from the previous nonzero one. It\n",
-    "  is a synthetic dependence parameter — note that the classical bid–ask bounce\n",
+    "  is a synthetic dependence parameter; note that the classical bid–ask bounce\n",
     "  of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial\n",
     "  dependence, which this generator does not encode.\n",
     "* `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding\n",
@@ -440,13 +440,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d60c3c20",
+   "id": "02cc9bf7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.239576Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.239498Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.675479Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.675108Z"
+     "iopub.execute_input": "2026-08-18T04:24:01.560008Z",
+     "iopub.status.busy": "2026-08-18T04:24:01.559938Z",
+     "iopub.status.idle": "2026-08-18T04:24:01.986645Z",
+     "shell.execute_reply": "2026-08-18T04:24:01.986344Z"
     }
    },
    "outputs": [
@@ -485,36 +485,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d6c5c2c",
+   "id": "9b99a253",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
     "\n",
-    "The trap in streaming ordered data is subtle enough to deserve its own\n",
-    "section. A bounded runtime shuffle buffer only *block*-shuffles a strongly\n",
+    "Streaming ordered data has one trap that is easy to miss. A bounded runtime shuffle buffer only *block*-shuffles a strongly\n",
     "ordered stream: with tick data sorted by symbol and time, early optimization\n",
     "steps would only ever see early dates and the first symbols, and an early\n",
     "stopping decision would be biased by construction. The fix is to shuffle\n",
     "**once, globally, on disk at ETL time**: every row gets a deterministic hash\n",
     "key, rows are scattered across shards by that key, and each shard is sorted by\n",
     "it. After that, sequential reads follow one fixed, data-independent\n",
-    "permutation — pseudo-random, because it comes from a hash of the row index\n",
-    "rather than from anything in the row, and replayed identically each epoch,\n",
-    "which is single-shuffle SGD semantics rather than fresh per-step subsampling —\n",
-    "and the loader can run with `shuffle=False`, without a copy through the\n",
-    "shuffle buffer."
+    "permutation. It is pseudo-random (a hash of the row index, nothing from the\n",
+    "row itself) and replayed identically each epoch, so this is single-shuffle SGD\n",
+    "rather than fresh per-step subsampling. The loader can then run with\n",
+    "`shuffle=False`, without a copy through the shuffle buffer."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "90835363",
+   "id": "025ee8b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.676474Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.676405Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.911330Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.910890Z"
+     "iopub.execute_input": "2026-08-18T04:24:01.987706Z",
+     "iopub.status.busy": "2026-08-18T04:24:01.987643Z",
+     "iopub.status.idle": "2026-08-18T04:24:02.201253Z",
+     "shell.execute_reply": "2026-08-18T04:24:02.200893Z"
     }
    },
    "outputs": [
@@ -569,21 +567,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b29c321d",
+   "id": "5e9bbfa7",
    "metadata": {},
    "source": [
     "The assertion is the one to keep: the head of every shard must already mix\n",
     "every hour and every symbol, checked rather than assumed.\n",
     "\n",
-    "Be clear about what this cell is and is not. It is the pedagogical miniature:\n",
-    "it builds the whole table, the whole key array and the whole permutation in\n",
-    "memory, which is exactly what one cannot do once the data stops fitting. What\n",
-    "follows it — the inference — is genuinely disk-backed, but the preprocessing\n",
-    "above is not. An out-of-core design with the same properties is two passes:\n",
+    "This cell is a small-scale stand-in for the real ETL step: it builds the whole\n",
+    "table, the whole key array and the whole permutation in memory, which is\n",
+    "exactly what one cannot do once the data stops fitting. The inference that\n",
+    "follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:\n",
     "assign each row to a shard from its hash key, appending row groups of bounded\n",
     "size, then sort each shard by key independently, so no step holds more than\n",
     "one shard. That yields a different permutation from the rank-based split used\n",
-    "here — shard sizes come out approximately rather than exactly equal — with the\n",
+    "here (shard sizes come out approximately rather than exactly equal), with the\n",
     "same guarantee that no shard's order depends on anything in the rows.\n",
     "\n",
     "So that the fit really does run against disk rather than against arrays still\n",
@@ -593,13 +590,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "6bbf642e",
+   "id": "c443ae62",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.912380Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.912303Z",
-     "iopub.status.idle": "2026-08-18T03:55:04.949624Z",
-     "shell.execute_reply": "2026-08-18T03:55:04.949262Z"
+     "iopub.execute_input": "2026-08-18T04:24:02.202378Z",
+     "iopub.status.busy": "2026-08-18T04:24:02.202310Z",
+     "iopub.status.idle": "2026-08-18T04:24:02.240544Z",
+     "shell.execute_reply": "2026-08-18T04:24:02.240261Z"
     }
    },
    "outputs": [
@@ -607,7 +604,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from /var/folders/cy/37hb0cnx22lbt9_h8y4wtn2w0000gn/T/ticks_d8c2tayb\n"
+      "row-scale generator arrays released; the fit reads from ticks_gki1p36z\n"
      ]
     }
    ],
@@ -615,41 +612,41 @@
     "del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves\n",
     "del logit_pi, log_sigma, t_draw, sigma\n",
     "gc.collect()\n",
-    "print(\"row-scale generator arrays released; the fit reads from\", data_dir)"
+    "print(\"row-scale generator arrays released; the fit reads from\", os.path.basename(data_dir))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "64decfd8",
+   "id": "fac6fdf6",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
     "\n",
-    "With `shuffle=False` the `DataLoader` passes source blocks through verbatim —\n",
-    "one block per Parquet row group, in a frozen column order — which is why the\n",
+    "With `shuffle=False` the `DataLoader` passes source blocks through verbatim,\n",
+    "one block per Parquet row group, in a frozen column order. That is why the\n",
     "shards above were written with `row_group_size` equal to the batch size, and\n",
-    "why the row counts were chosen to divide exactly. That is worth doing\n",
-    "deliberately: on this path the loader hands you the row groups it finds, so a\n",
+    "why the row counts were chosen to divide exactly. On this path the loader\n",
+    "hands you the row groups it finds, so a\n",
     "ragged geometry gives ragged batches, `len(loader)` (which is\n",
     "`total_size // batch_size`) stops matching the number of blocks an epoch\n",
     "yields, and the recorded loss picks up a deterministic sawtooth that has\n",
     "nothing to do with convergence (the fitting section explains why the recorded\n",
     "value scales with the block size). The model reads\n",
-    "one `pm.Data` placeholder; everything derived — the Fourier basis, the\n",
-    "integer symbol index — is computed inside the graph, so advancing the stream\n",
+    "one `pm.Data` placeholder; everything derived (the Fourier basis, the\n",
+    "integer symbol index) is computed inside the graph, so advancing the stream\n",
     "is a single `set_value` per step."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "924104ae",
+   "id": "6a6e619d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:04.950824Z",
-     "iopub.status.busy": "2026-08-18T03:55:04.950744Z",
-     "iopub.status.idle": "2026-08-18T03:55:05.000971Z",
-     "shell.execute_reply": "2026-08-18T03:55:05.000569Z"
+     "iopub.execute_input": "2026-08-18T04:24:02.241614Z",
+     "iopub.status.busy": "2026-08-18T04:24:02.241536Z",
+     "iopub.status.idle": "2026-08-18T04:24:02.291898Z",
+     "shell.execute_reply": "2026-08-18T04:24:02.291539Z"
     }
    },
    "outputs": [
@@ -657,7 +654,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N = 300,000 rows -> 300 blocks of 1000 per epoch, conserving 300,000 rows; len(loader) = 300\n"
+      "N = 300,000 rows -> 300 blocks of 1000 per epoch,\n",
+      "conserving 300,000 rows; len(loader) = 300\n"
      ]
     }
    ],
@@ -679,40 +677,40 @@
     "assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH\n",
     "assert len(loader) == steps_per_epoch\n",
     "print(\n",
-    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch, \"\n",
+    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch,\\n\"\n",
     "    f\"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e2d4d7d5",
+   "id": "795ac2d7",
    "metadata": {},
    "source": [
-    "Two things that assertion pins down. First, **nothing is dropped**: verbatim\n",
+    "That assertion pins down two things. First, **nothing is dropped**: verbatim\n",
     "pass-through streams every row exactly once per epoch. (Ragged geometry would\n",
-    "still lose nothing — PyMC reads `b` from the batch actually installed, so a\n",
-    "short block is weighted up by its own size — but it would cost the clean\n",
+    "still lose nothing, since PyMC reads `b` from the batch actually installed and\n",
+    "a short block is weighted up by its own size, but it would cost the clean\n",
     "diagnostics below, which is why the shards divide.) Only the shuffle-buffer\n",
     "path drops a trailing partial batch, and this notebook never uses it.\n",
     "\n",
     "Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a\n",
     "deterministic function of $t$, so what follows is single-shuffle, cyclic\n",
-    "finite-sum optimization — the arrangement the epoch-scale diagnostics later in\n",
-    "the notebook exploit, and the reason the fit has to be summarized with some\n",
+    "finite-sum optimization, the arrangement the epoch-scale diagnostics later in\n",
+    "the notebook exploit and the reason the fit has to be summarized with some\n",
     "care before anything is read off it."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "09eefc4b",
+   "id": "91814adb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:05.001978Z",
-     "iopub.status.busy": "2026-08-18T03:55:05.001910Z",
-     "iopub.status.idle": "2026-08-18T03:55:05.006024Z",
-     "shell.execute_reply": "2026-08-18T03:55:05.005718Z"
+     "iopub.execute_input": "2026-08-18T04:24:02.292962Z",
+     "iopub.status.busy": "2026-08-18T04:24:02.292888Z",
+     "iopub.status.idle": "2026-08-18T04:24:02.297445Z",
+     "shell.execute_reply": "2026-08-18T04:24:02.297045Z"
     }
    },
    "outputs": [],
@@ -810,34 +808,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d5cbc7a",
+   "id": "5600c9e5",
    "metadata": {},
    "source": [
-    "Two implementation notes worth pausing on. The likelihood is a\n",
+    "Two implementation notes. The likelihood is a\n",
     "{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`\n",
     "gives the term *observed-RV semantics*, which is what makes PyMC's own\n",
     "`total_size` minibatch rescaling apply. And\n",
-    "the hurdle's two logs are written with `softplus` —\n",
-    "$\\log \\pi = -\\operatorname{softplus}(-x)$,\n",
-    "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$ — which is exact and stable at both\n",
+    "the hurdle's two logs are written with `softplus`,\n",
+    "$\\log \\pi = -\\operatorname{softplus}(-x)$ and\n",
+    "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$, which is exact and stable at both\n",
     "tails. The exact float comparison `value != 0` is safe here because the zeros are\n",
     "*structural*: the generator writes an exact 0.0 when the hurdle says the price\n",
     "did not move, and every nonzero draw is many orders of magnitude above the\n",
     "float32 subnormal range. On real data the same comparison is safe as long as\n",
     "returns are computed so that \"no move\" produces an exact zero rather than a\n",
-    "rounding artefact — worth checking once, in the feature code."
+    "rounding artefact; check that once, in the feature code."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f2af23ad",
+   "id": "6586a912",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:05.006976Z",
-     "iopub.status.busy": "2026-08-18T03:55:05.006918Z",
-     "iopub.status.idle": "2026-08-18T03:55:05.188197Z",
-     "shell.execute_reply": "2026-08-18T03:55:05.187721Z"
+     "iopub.execute_input": "2026-08-18T04:24:02.298353Z",
+     "iopub.status.busy": "2026-08-18T04:24:02.298294Z",
+     "iopub.status.idle": "2026-08-18T04:24:02.471869Z",
+     "shell.execute_reply": "2026-08-18T04:24:02.471397Z"
     }
    },
    "outputs": [
@@ -853,367 +851,367 @@
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        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1700.77,-478.95 1700.77,4 -4,4\"/>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M796.01,-8C796.01,-8 842.01,-8 842.01,-8 848.01,-8 854.01,-14 854.01,-20 854.01,-20 854.01,-94 854.01,-94 854.01,-100 848.01,-106 842.01,-106 842.01,-106 796.01,-106 796.01,-106 790.01,-106 784.01,-100 784.01,-94 784.01,-94 784.01,-20 784.01,-20 784.01,-14 790.01,-8 796.01,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"822.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M809.5,-8C809.5,-8 855.5,-8 855.5,-8 861.5,-8 867.5,-14 867.5,-20 867.5,-20 867.5,-94 867.5,-94 867.5,-100 861.5,-106 855.5,-106 855.5,-106 809.5,-106 809.5,-106 803.5,-106 797.5,-100 797.5,-94 797.5,-94 797.5,-20 797.5,-20 797.5,-14 803.5,-8 809.5,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"835.5\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M945.5,-243.82C945.5,-243.82 1119.5,-243.82 1119.5,-243.82 1125.5,-243.82 1131.5,-249.82 1131.5,-255.82 1131.5,-255.82 1131.5,-353.63 1131.5,-353.63 1131.5,-359.63 1125.5,-365.63 1119.5,-365.63 1119.5,-365.63 945.5,-365.63 945.5,-365.63 939.5,-365.63 933.5,-359.63 933.5,-353.63 933.5,-353.63 933.5,-255.82 933.5,-255.82 933.5,-249.82 939.5,-243.82 945.5,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1087.87\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1151.5,-243.82C1151.5,-243.82 1425.5,-243.82 1425.5,-243.82 1431.5,-243.82 1437.5,-249.82 1437.5,-255.82 1437.5,-255.82 1437.5,-353.63 1437.5,-353.63 1437.5,-359.63 1431.5,-365.63 1425.5,-365.63 1425.5,-365.63 1151.5,-365.63 1151.5,-365.63 1145.5,-365.63 1139.5,-359.63 1139.5,-353.63 1139.5,-353.63 1139.5,-255.82 1139.5,-255.82 1139.5,-249.82 1145.5,-243.82 1151.5,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.12\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1469.01,-243.82C1469.01,-243.82 1643.01,-243.82 1643.01,-243.82 1649.01,-243.82 1655.01,-249.82 1655.01,-255.82 1655.01,-255.82 1655.01,-353.63 1655.01,-353.63 1655.01,-359.63 1649.01,-365.63 1643.01,-365.63 1643.01,-365.63 1469.01,-365.63 1469.01,-365.63 1463.01,-365.63 1457.01,-359.63 1457.01,-353.63 1457.01,-353.63 1457.01,-255.82 1457.01,-255.82 1457.01,-249.82 1463.01,-243.82 1469.01,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.37\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M707.01,-114C707.01,-114 931.01,-114 931.01,-114 937.01,-114 943.01,-120 943.01,-126 943.01,-126 943.01,-223.82 943.01,-223.82 943.01,-229.82 937.01,-235.82 931.01,-235.82 931.01,-235.82 707.01,-235.82 707.01,-235.82 701.01,-235.82 695.01,-229.82 695.01,-223.82 695.01,-223.82 695.01,-126 695.01,-126 695.01,-120 701.01,-114 707.01,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"921.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M720.5,-114C720.5,-114 944.5,-114 944.5,-114 950.5,-114 956.5,-120 956.5,-126 956.5,-126 956.5,-223.82 956.5,-223.82 956.5,-229.82 950.5,-235.82 944.5,-235.82 944.5,-235.82 720.5,-235.82 720.5,-235.82 714.5,-235.82 708.5,-229.82 708.5,-223.82 708.5,-223.82 708.5,-126 708.5,-126 708.5,-120 714.5,-114 720.5,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M834.01,-98C834.01,-98 804.01,-98 804.01,-98 798.01,-98 792.01,-92 792.01,-86 792.01,-86 792.01,-52.5 792.01,-52.5 792.01,-46.5 798.01,-40.5 804.01,-40.5 804.01,-40.5 834.01,-40.5 834.01,-40.5 840.01,-40.5 846.01,-46.5 846.01,-52.5 846.01,-52.5 846.01,-86 846.01,-86 846.01,-92 840.01,-98 834.01,-98\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M847.5,-98C847.5,-98 817.5,-98 817.5,-98 811.5,-98 805.5,-92 805.5,-86 805.5,-86 805.5,-52.5 805.5,-52.5 805.5,-46.5 811.5,-40.5 817.5,-40.5 817.5,-40.5 847.5,-40.5 847.5,-40.5 853.5,-40.5 859.5,-46.5 859.5,-52.5 859.5,-52.5 859.5,-86 859.5,-86 859.5,-92 853.5,-98 847.5,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
+       "<!-- lambda_q -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"819.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"819.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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-       "<!-- kappa0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.34C65.81,-265.87 76.86,-251.93 91.01,-243.82 141.72,-214.74 497.29,-198.67 692.08,-191.96\"/>\n",
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-       "<!-- tau_sh -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_sh -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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-       "<!-- lambda_a -->\n",
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-       "<title>lambda_a</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"149.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
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-       "<title>lambda_a&#45;&gt;y_obs</title>\n",
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-       "<!-- tau_al -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- b_al -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_al&#45;&gt;b_al -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>tau_al&#45;&gt;b_al</title>\n",
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+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M70.32,-279.62C80.08,-266.01 93,-251.9 108.5,-243.82 159.96,-216.95 512.22,-199.93 705.75,-192.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"705.75,-196 715.61,-192.12 705.49,-189.01 705.75,-196\"/>\n",
        "</g>\n",
        "<!-- eta -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node3\" class=\"node\">\n",
        "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"661.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"478.5\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- nu -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
+       "<g id=\"node11\" class=\"node\">\n",
        "<title>nu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"706.14,-345.73 615.89,-345.73 615.89,-288.23 706.14,-288.23 706.14,-345.73\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"661.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- eta&#45;&gt;nu -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>eta&#45;&gt;nu</title>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"257.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
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+       "<!-- b_ba -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"158.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M175.27,-279.43C183.35,-265.97 194.4,-252.02 208.5,-243.82 250.39,-219.45 536.46,-202.03 706.37,-193.7\"/>\n",
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+       "</g>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"266.5\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M286.98,-279.88C296.57,-266.31 309.28,-252.16 324.5,-243.82 387.93,-209.04 576.7,-195.99 705.37,-191.09\"/>\n",
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        "<!-- beta_a -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"357.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"357.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"374.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
        "<title>beta_a&#45;&gt;y_obs</title>\n",
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        "<!-- tau_k -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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        "<!-- b_k -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
+       "<g id=\"node19\" class=\"node\">\n",
        "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_k&#45;&gt;b_k -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>tau_k&#45;&gt;b_k</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1270.71,-393.93C1272.69,-385.49 1274.8,-376.46 1276.86,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.2,-368.78 1279.07,-358.24 1273.38,-367.18 1280.2,-368.78\"/>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1388.5,-393.28C1388.5,-385.5 1388.5,-377.25 1388.5,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392,-369.43 1388.5,-359.43 1385,-369.43 1392,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
        "<g id=\"node10\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"457.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"457.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M474.39,-279.68C482.51,-266.42 493.42,-252.54 507.01,-243.82 538.23,-223.78 623.11,-209.41 696.46,-200.3\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"696.65,-203.8 706.16,-199.12 695.81,-196.85 696.65,-203.8\"/>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.03,-393.28C1511.35,-385.5 1510.64,-377.25 1509.94,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.44,-369.08 1509.09,-359.42 1506.47,-369.69 1513.44,-369.08\"/>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M492.74,-287.82C501.98,-272.43 515.48,-254.39 532.5,-243.82 562.29,-225.31 641.52,-211.06 711.09,-201.61\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"711.21,-205.12 720.66,-200.33 710.28,-198.19 711.21,-205.12\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"582.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M600.55,-279.96C608.7,-266.9 619.46,-253.09 632.5,-243.82 656.78,-226.55 686.25,-214.63 715.01,-206.4\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"715.79,-209.82 724.52,-203.82 713.96,-203.06 715.79,-209.82\"/>\n",
        "</g>\n",
        "<!-- beta_q -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"557.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"682.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- beta_q&#45;&gt;y_obs -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
        "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M574.76,-280.23C582.93,-267.05 593.78,-253.08 607.01,-243.82 634.11,-224.85 667.25,-212.34 699.2,-204.1\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"699.62,-207.6 708.51,-201.82 697.96,-200.8 699.62,-207.6\"/>\n",
-       "</g>\n",
-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M677.77,-287.94C687.38,-273.51 700.42,-256.36 715.01,-243.82 722.6,-237.3 731.07,-231.24 739.82,-225.71\"/>\n",
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-       "</g>\n",
-       "<!-- tau_ph -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M702.03,-281.13C710.3,-268.37 720.75,-254.46 732.5,-243.82 739.38,-237.58 747.07,-231.77 755.06,-226.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"756.72,-229.53 763.27,-221.2 752.95,-223.63 756.72,-229.53\"/>\n",
        "</g>\n",
-       "<!-- b_ph -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"782.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ph&#45;&gt;b_ph -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>tau_ph&#45;&gt;b_ph</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M797.09,-278.66C802.05,-265.99 807.68,-251.61 812.96,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"816.16,-239.52 816.55,-228.93 809.65,-236.97 816.16,-239.52\"/>\n",
        "</g>\n",
        "<!-- theta_r -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"765.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"765.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"882.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- theta_r&#45;&gt;y_obs -->\n",
        "<g id=\"edge16\" class=\"edge\">\n",
        "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M780.63,-279.01C786.04,-266.2 792.2,-251.62 797.97,-237.95\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"801.04,-239.69 801.71,-229.12 794.59,-236.97 801.04,-239.69\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M867.9,-278.66C862.94,-265.99 857.32,-251.61 852.04,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"855.35,-236.97 848.45,-228.93 848.83,-239.52 855.35,-236.97\"/>\n",
        "</g>\n",
-       "<!-- lambda_q -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"874.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"874.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"874.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"874.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1642.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M857.81,-278.32C852.41,-265.77 846.3,-251.58 840.56,-238.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"843.82,-236.96 836.65,-229.16 837.39,-239.73 843.82,-236.96\"/>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1629.97,-394.25C1627.17,-385.52 1624.16,-376.15 1621.25,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.61,-366.09 1618.22,-357.64 1617.95,-368.23 1624.61,-366.09\"/>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- g -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>g</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"982.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
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       "text/plain": [
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+       "<graphviz.graphs.Digraph at 0x1323b5bb0>"
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      "execution_count": 10,
@@ -1230,7 +1228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db6ed11b",
+   "id": "2493d7f6",
    "metadata": {},
    "source": [
     "The plate diagram is an inventory rather than a picture of the two links: five\n",
@@ -1241,22 +1239,21 @@
     "that structure is visible.\n",
     "\n",
     "The loop below is the whole streaming adapter: a `pm.fit` callback that pushes\n",
-    "the next block into the placeholder after each step. pymc-extras#710 contains\n",
-    "a wrapper around this same data-advance lifecycle and pymc-extras#635 a\n",
-    "longer-term ADVI training API; the twenty lines here are deliberately the\n",
-    "minimal version a notebook can own."
+    "the next block into the placeholder after each step; the twenty lines here are\n",
+    "the minimal version a notebook can own (a library wrapper for the same\n",
+    "lifecycle is listed at the end)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "18b8dd72",
+   "id": "da8e5940",
    "metadata": {
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-     "iopub.status.idle": "2026-08-18T03:55:11.501552Z",
-     "shell.execute_reply": "2026-08-18T03:55:11.501057Z"
+     "iopub.execute_input": "2026-08-18T04:24:02.473122Z",
+     "iopub.status.busy": "2026-08-18T04:24:02.473020Z",
+     "iopub.status.idle": "2026-08-18T04:24:08.293499Z",
+     "shell.execute_reply": "2026-08-18T04:24:08.292930Z"
     }
    },
    "outputs": [],
@@ -1309,18 +1306,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c743d883",
+   "id": "760139ed",
    "metadata": {},
    "source": [
     "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
-    "The two-stage learning rate is deliberate. A constant step size that is\n",
+    "The learning rate is cut once, at step 6,000. A constant step size that is\n",
     "comfortable early is too large near the optimum, where it keeps the\n",
     "variational mean bouncing instead of settling; dropping it once the descent\n",
-    "has flattened is the cheapest fix. Do not look for the effect in the width of\n",
+    "has flattened is the cheapest fix. The effect does not show up in the width of\n",
     "the loss band below: the band is dominated by batch-composition noise (the\n",
-    "stopping section shows it all but vanishing at epoch-aligned horizons), the\n",
-    "printed spread is the same on both sides of the change, and the effect is in\n",
-    "the level and in the parameter trace examined after the fit.\n",
+    "stopping section shows it all but vanishing at epoch-aligned horizons), and\n",
+    "the printed spread is the same on both sides of the change. It shows up in the\n",
+    "level, as the printed one-epoch means either side of the cut, and in the\n",
+    "parameter trace examined after the fit.\n",
     "\n",
     "Before plotting it, one property of `approx.hist` that is easy to get wrong and\n",
     "that changes what the numbers mean. PyMC normalizes the minibatch objective\n",
@@ -1334,7 +1332,7 @@
     "      + \\frac{b}{N}\\,\\mathrm{KL}(q \\Vert p),\n",
     "$$\n",
     "\n",
-    "which lives on the scale of *one batch*, not of the full dataset — and which\n",
+    "which lives on the scale of *one batch*, not of the full dataset, and which\n",
     "would move mechanically with $b$ if the blocks were ragged. Multiplying by\n",
     "$N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks\n",
     "that is one constant:"
@@ -1343,13 +1341,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "636ebe40",
+   "id": "65e7757b",
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-     "iopub.status.idle": "2026-08-18T03:55:11.624799Z",
-     "shell.execute_reply": "2026-08-18T03:55:11.624482Z"
+     "iopub.execute_input": "2026-08-18T04:24:08.294680Z",
+     "iopub.status.busy": "2026-08-18T04:24:08.294605Z",
+     "iopub.status.idle": "2026-08-18T04:24:08.458073Z",
+     "shell.execute_reply": "2026-08-18T04:24:08.457789Z"
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    },
    "outputs": [
@@ -1357,20 +1355,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sd of the recorded loss over the last two passes at each learning rate: 13,192 (lr 0.02) vs 13,141 (lr 0.005)\n"
+      "recorded loss over the two passes either side of the cut:\n",
+      "  spread (sd): 13,192 before, 13,093 after\n",
+      "  level (mean): -96,528 before, -97,746 after\n"
      ]
     },
     {
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",
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ZWdno7QFApAIvGFu0aBHR+wM7Z3CTCtjbypUr/Tph6Y3hoYceGnJ932Nes0BE0mErMNgZqmHdKWUAbk7b2qNHDznvvPMatT1tlNq+fbt3OdIOooHHarBrFqeUATjdzp07ZfHixX6BNg1Mzp8/36SIHjVqlPzjH/+Q119/3QQEXn31VbM8ZswYM6LypZdeqnf0TTzuk5xSBuAGOt9hcXGxd/nJJ5809051mTVrlkydOtW7rNdD4QYl7Xo+4JwDwA3iGpT0DRyGGnoebvpW5XszGs4k4wAQbZs2bfJbbt68eaMuSgO3B8BeHnnkEb9GuoEDB0pRUVHQdaurq/2ujXS9SK5nAs8fweaudEoZgNtMmzbNOw+QjsjREZO+85I1ROCxFek1S+D6wY5Vp5QBOJ0GAjR9se93saaLPv300820O3XRkYM333yzXHXVVbJ79+6E3ic5pQzADfLz8+W+++6TtLQ0s6wdr84880x5++23zbRcgR0nnn32Wfnd737nHZ2t9xj6/lDtxoHHVrNmzSKqXzj3JE4pAwASLfycE1H6AvKoqqpq9PZ8h6CHavADgFjyTeUTySTsodb37WwBwF4++eQTee2117zLesN82WWXhVw/8Hhv7Pkj8HzkpDIAN9HGo7vuusu7fP7554c9f1ldAo+t7OzsiN4fuH6wY9UpZQBOt23bNr/lFStWyMSJE72BAU2JfMIJJ0j79u1Nx4iffvpJ/vOf//gFLDWQkJycLH/7298Sdp/klDIAtxgyZIg888wzJiX08uXLTUa9CRMmmO9mnTtRf1ZUVMjChQv9UkX37t1b7rnnHmnXrl3IbTf2+iCcexKnlAEArgpK+s4B6ZsqpCE0qOl7QdzY+SkBoCECbyozMjIier/Og1TX9gDYg3aU0nmXfOlog759+4Z8T+ANYuD5oD7aSKgPTwNiOA1pdi0DcJPbb7/dm7a1TZs2cvnll0dlu4HHaqTXLIHrh3M+sGsZgNMFTn/j2z5zwQUXmCCBZzSTx4UXXmiCCRoY8GSFePPNN00qxZNPPjkh90lOKQNwk0MOOcR0ctA00A888IDpJKHf7d99913QlOvaYULTRtensdcH4RyrTikDAFyVvlV7tnh8+umnJt1XQ7344ovexquSkpI6e8sAQKwEphnRhvVIBK7v2xsQgD1oeqFLLrnEL9WOXpcEBinrO380JDWj7zkk2PnDKWUAbqEN/O+99553+Y477oi4MSpW1yyBAYpwzgd2LQNwulBpV0899VS57rrrah0nngwQOnL74osv9vv9Y489JjU1NQm5T3JKGYCb6AhJPc/ceeedtUZtB1qzZo1ce+21psPW5s2b61w38NiKxbHqlDIAwHVBycLCQm9aohdeeKFBc0pqapEnnnjCuxxqkmMAiLXA1Bh1zasSTOD6kabmAJBY2lB15ZVXyvz5872/KygokH/84x+Sk5MTUS/WhnTW8j2HBEsn5pQyADfQjg2+aVtHjRolAwYMiNr2A4/VSK9Zdu3a5bcczvnArmUAThfq2KqvQ5XSjljaMdxDU7vOmjUrIfdJTikDcAudL/ukk06St956yzuvraZt1c4O999/vzz++ONy7733yrhx46RFixbe7+1///vfJqX0vHnzQm7bKecDzjkA3CCuQUmdb+Dss8/2LusXzueffx7RNpYsWWImOvakNNIgpn5ZAUAiBF5ENrZhrL4gBgDr0NRlOh/KRx995HdO0I5THTp0qPf9gcd7Q25qPenTgm3PSWUAbvDHP/5RtmzZYp5rg384wQGrXbM4pQzA6YJ97o844ghvJ/K66ChKDQ74mjlzpmPPB5xzgOj49ttvTWdOT3rS9PR00xlLs0To70eOHCnDhw83QUu9BtLMEb4p7HVwi6aX1mkzgnHK+YBzDgA3iGtQUukXSNOmTb0n74suukjuu+8+Wb9+fZ3vW716tZlAXXsMa088jxNPPNH0qgGARMjNzfVbri/9SCCdxN0XDWOAfWjKoTfeeMO7rDfWOkKyrnkkA284fTNERHr+CFw/1E2tE8oAnO7tt9+Wd955x7t88803S35+vu2uWZxSBuB0gceR6tOnT9jvD1x34cKFjj0fcM4BouOWW27xSyWqKepHjx5dZxrSyy67TMaPH+/9nQ5Q+ctf/hJ0faecDzjnAHCDuAcl9eT66KOPetPu6BfSU089JcOGDTO9YXSuSQ8dzv+HP/xBjjvuONNbZtKkSX4T/nbu3NnkFQeARGnZsmWtOQ8ioR0u6toeAGvSbA+TJ0/2u2l+4IEHZNCgQRFlkPCkJVJ6jePJBBGOwPNNq1atHFsG4GQ6Ulgb5jxGjBghxx57bNTLKS8v9+tAEHgNUp/A9YMdq04pA3C6YPcczZs3D/v9zZo181sO9r0fj/skp5QBON0333zj13mhS5cucsopp4T1Xk3t6juK+9133w06v6RTzgeccwC4QWSz5UZxbkkNTF511VXei1edGP3HH3/0W++LL74IuY0ePXqYbZCPH0AiBaZoXLlyZUTvD1w/nJSPABJLO0n5zm2tQbm7775bfvOb30S8LT3mV61a5XdOCCd1mmfdwG05uQzAqXROJU/aVrVu3TqTXaY+up6vCRMmmA4SHjfccINfRhntjV9aWuptQNM0aJpGLHCOxsYcq04pA3A67SxeVlbm16CuGR/ClZGRUW8Kw3jcJzmlDMDpAued1XTR4dLv98MOO8ybUULbjzUV7JFHHhnTY7V9+/a11nFKGQDgupGSHoMHDzYpz/RLxLenazjzF/zf//2fvPDCC+ZmFAASfUPvey76+eef/RoW6zNnzhy/wAYXjIC16ehITScfOA9c4NxK4erYsaPf8nfffRf2e/VmvK5tOa0MwC3mzZtnssfU9/DNIKP+97//+b0eLKWY7/GlIzTnzp0bdr0Cj+twzgd2LgNwOs085auysjLs9wauG6wjUjzuk5xSBuB02oGooSOzVWD7b+D2lB5beow15H5Er5l8pwrTLDDB0lw7pQwAcG1Q0vMl9Nhjj5k0rePGjZOePXv69e71SElJMaMrdcj++++/b/KQh9sTFgBiLTBd49dffx3W+5YtWyYbNmzwLus8dMxrBFjX66+/buaR9HXjjTfK6aef3qhOWr6++uqrsN/re67RDl6B23JaGQAaL/D4mjlzZljvq66u9msU0wCEZq5xchmA0x1++OF+y4sXLw77vYHrBqZzjed9klPKAJwssA1Xv48jETgaO1ibsAbftO3YQ1O8LlmyJKzt6zGtnZw8Qt2POKUMAHB1UNJ3aPr1118vr7zyisyePVv++9//ytSpU03j30cffWR60L/44oty5ZVXMjoSgOUEpmx87bXXwnqfnuN8HXXUUVGtF4Do0blLJk6c6HeTp/Nejx07tlHb1VREeXl53uUZM2YEHd0UaMGCBfL99997l7VjV6gMEk4pA3Aq7YD55JNPRvwIPFZ0Xlvf1wNHQQW7ZtHMNb7ntbrOgb4jMzXbjdY7GKeUATjd0Ucf7Ze1SkdYh+uTTz7xWz700EMTdp/klDIAJ2vatKnfcuD0XfUJXD9we77zcsf6WHVKGQAgbg9K+tJ5DPQGu2vXrtKtWzczz0EkcxsAQLwNGTJESkpKvMvasWLRokV1vkcb61966SXvsp7njjvuuJjWE0DDaMObzoOt8755XHjhhXLJJZc0epdqWvqRI0d6l3VetOeee67e9z311FN+yyeffLLjywCcSoMCQ4cOjfiRlZXlt50BAwb4vZ6fn1+rrFatWkm/fv28y5riSwN1ddFz3z//+c+wj1WnlAE4nWau8h1Fo43+X3zxRb3v0xE7H3/8sd/8kqFG48TjPskpZQBO5vudrTQLXrhpkPXc5JsGWe87evXqFXRdvR/R1z1efvnlesvRc5p2qPQd+T1w4MCQ6zulDABIJMsFJQHAbvRGXNNLe+zdu9ekdAyc68lDJ2a//fbbZdOmTd7fnXXWWRHPqwAg9mbNmiWXX3657Nmzx/s7ndv6mmuuiVoZGtzU84jH448/Xuf8aO+8844ZFeRRXl5ebwpZp5QBoPE0+4wvTUu9bt26kOvrdBvz58/3S2Ooo6PdUAbgdFdffbXfaMmbb75Z1q9fH3L9qqoqufbaa/06ao0ePVqKi4sTdp/klDIAJ9NBJ23btvU7l2jGvPrSuGpwX885ekz5pp4ONU+izqF4xhlneJc1kHfbbbf5vd/Xzp07zbHse6936aWX1jk4xillAEAiEZQEgCjQhnQd4e2hDfHnnnuuzJs3z2+95cuXy2WXXSZvv/22Xw+28ePH838ALOaHH36Q3//+9+Ymz+O0004zDXbRpA1UF110kXdZbybPO+88k35HG708tB46slBHbfrSG/r6bjidUgaAxuvfv78ce+yx3mWd7+zMM8+slY5R5zC666675OGHH/ZrmNdj1S1lAE7XvXt3EyzzWLlypTmOPvjgA7/Ao2fuVl3XN7ivDefa8J3o+ySnlAE4lXZ+CLz21xHHer75/PPPawXb9D5Cj6FTTjlFFi5c6P19amqqTJgwod6Okr4jm7UjpP5Oj01fei7TY9h3rmmdZ3rUqFH1/j1OKQMAEiVpfziTbwAA6rVmzRrTU1gbxXy1bt3apKXWnrLLli3zu+DOzs6W559/3lw0ArCWE044we8mODk52aTA8R1REA4dieMbrAtGL8f0Bvs///mP3+8LCwulXbt25sZczx/bt2/3e11HcWrDVzicUgaAAzQgp8eTh6ZdDDVaKZB2DjjnnHP8gguehnO9btERDEuXLvUbwaDnPp238re//a2rygCcTjsOacAsMKDv+e7WeVd//vnnWvc4BQUFJiWyzgdthfskp5QBONl9991Xa/oGpXPTd+jQwaSmr6yslMWLF5vpIHzp9/fdd98dVup1Tfc6ZswYv23ovZye05o0aSJr166tFdzTDpaacjncEc1OKQMAXBmU1MYpzcW/YMECcwGnN46+vekjmaQ9VE5xAIgXndNI04v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       "text/plain": [
-       "<Figure size 800x300 with 1 Axes>"
+       "<Figure size 800x320 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 336,
-       "width": 855
+       "height": 341,
+       "width": 914
       }
      },
      "output_type": "display_data"
@@ -1380,23 +1380,37 @@
     "ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch\n",
     "loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 3), layout=\"constrained\")\n",
-    "ax.plot(loss, lw=0.5)\n",
-    "ax.axvline(6_000, color=\"C1\", ls=\"--\", lw=1, label=\"anneal: lr 0.02 to 0.005\")\n",
-    "ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))\n",
+    "epoch_mean = np.convolve(loss, np.ones(steps_per_epoch) / steps_per_epoch, mode=\"valid\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
+    "ax.plot(loss, lw=0.5, alpha=0.6, label=\"per step\")\n",
+    "ax.plot(\n",
+    "    np.arange(steps_per_epoch - 1, len(loss)),\n",
+    "    epoch_mean,\n",
+    "    color=\"C1\",\n",
+    "    lw=1.5,\n",
+    "    label=\"one-epoch moving mean\",\n",
+    ")\n",
+    "ax.axvline(6_000, color=\"k\", ls=\"--\", lw=1, label=\"anneal: lr 0.02 to 0.005\")\n",
+    "# clip to the plateau: the first few hundred steps are orders of magnitude higher\n",
+    "plateau = loss[1_000:]\n",
+    "ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))\n",
+    "ax.yaxis.set_major_formatter(plt.matplotlib.ticker.StrMethodFormatter(\"{x:,.0f}\"))\n",
     "ax.set_xlabel(\"step\")\n",
-    "ax.set_ylabel(\"negative ELBO (full-data scale)\")\n",
-    "ax.set_title(\"Streaming ADVI loss (full-data scale, y clipped to the plateau)\")\n",
-    "ax.legend()\n",
+    "ax.set_ylabel(\"negative ELBO, full-data scale\")\n",
+    "ax.set_title(\"Streaming ADVI loss on the plateau (steps before 1,000 are off scale)\")\n",
+    "ax.legend(loc=\"upper right\", fontsize=9)\n",
+    "before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]\n",
     "print(\n",
-    "    \"sd of the recorded loss over the last two passes at each learning rate: \"\n",
-    "    f\"{loss[6_000 - 600 : 6_000].std():,.0f} (lr 0.02) vs {loss[-600:].std():,.0f} (lr 0.005)\"\n",
+    "    \"recorded loss over the two passes either side of the cut:\\n\"\n",
+    "    f\"  spread (sd): {before.std():,.0f} before, {after.std():,.0f} after\\n\"\n",
+    "    f\"  level (mean): {before.mean():,.0f} before, {after.mean():,.0f} after\"\n",
     ");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d4472238",
+   "id": "069add66",
    "metadata": {},
    "source": [
     "## The last iterate is not the answer\n",
@@ -1404,7 +1418,7 @@
     "Before reading a single parameter off this fit, one property of the replay has\n",
     "to be dealt with. The batch at step $t$ is a deterministic function of $t$ with\n",
     "period one epoch, so the data term of every stochastic gradient repeats with\n",
-    "that period — the parameters and the Monte-Carlo draw do not, so the realized\n",
+    "that period. The parameters and the Monte-Carlo draw do not, so the realized\n",
     "gradients are not literally periodic, but the iterate inherits a component\n",
     "locked to the replay order. Two measurements separate that component from\n",
     "genuine progress: compare the same phase in consecutive epochs, and look at the\n",
@@ -1414,13 +1428,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "57d2464c",
+   "id": "61900a5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:11.625933Z",
-     "iopub.status.busy": "2026-08-18T03:55:11.625864Z",
-     "iopub.status.idle": "2026-08-18T03:55:11.634700Z",
-     "shell.execute_reply": "2026-08-18T03:55:11.634274Z"
+     "iopub.execute_input": "2026-08-18T04:24:08.459240Z",
+     "iopub.status.busy": "2026-08-18T04:24:08.459160Z",
+     "iopub.status.idle": "2026-08-18T04:24:08.468200Z",
+     "shell.execute_reply": "2026-08-18T04:24:08.467838Z"
     }
    },
    "outputs": [
@@ -1428,9 +1442,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "same phase, epoch to epoch — max over coordinates, per pair: 6.37 2.93 2.59 0.75 1.39 1.45 0.58\n",
-      "                              median over coordinates, last pair: 0.11 sd\n",
-      "within the last epoch:        median 4.31, max 41.2 sd\n"
+      "same phase, epoch to epoch, max over coordinates per pair:\n",
+      "  6.37 2.93 2.59 0.75 1.39 1.45 0.58\n",
+      "  median over coordinates, last pair: 0.11 sd\n",
+      "within the last epoch: median 4.31, max 41.2 sd\n"
      ]
     }
    ],
@@ -1442,20 +1457,19 @@
     "last = mu_t[-steps_per_epoch:]\n",
     "swing = (last.max(0) - last.min(0)) / sd_unc\n",
     "print(\n",
-    "    \"same phase, epoch to epoch — max over coordinates, per pair: \"\n",
+    "    \"same phase, epoch to epoch, max over coordinates per pair:\\n  \"\n",
     "    + \" \".join(f\"{v:.2f}\" for v in step.max(1))\n",
-    "    + f\"\\n                              median over coordinates, last pair: {np.median(step[-1]):.2f} sd\"\n",
-    "    f\"\\nwithin the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
+    "    + f\"\\n  median over coordinates, last pair: {np.median(step[-1]):.2f} sd\"\n",
+    "    f\"\\nwithin the last epoch: median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9701684f",
+   "id": "79e09ff7",
    "metadata": {},
    "source": [
-    "Read the two measurements against each other. Between the same phase of\n",
-    "consecutive epochs the largest movement decays from about six posterior widths\n",
+    "Between the same phase of consecutive epochs the largest movement decays from about six posterior widths\n",
     "in the first pair to about half a width in the last, and the median coordinate\n",
     "moves a tenth of a width per pass; within a single epoch the same coordinates\n",
     "swing by multiples of their width. So the optimizer is not converging to a\n",
@@ -1463,16 +1477,13 @@
     "in, and most of where the last iterate sits is the phase of that orbit at the\n",
     "step the budget ran out.\n",
     "\n",
-    "That is not a subtlety to note and move past. It means the number you would\n",
-    "report depends on where you stop, so the fit is summarized by averaging the\n",
-    "variational parameters over the final whole pass. To the extent the order-locked\n",
+    "The number you would report therefore depends on where you stop, so the fit\n",
+    "is summarized by averaging the variational parameters over the final whole\n",
+    "pass. To the extent the order-locked\n",
     "component repeats from one pass to the next, a window of exactly one pass\n",
-    "averages it out — which is why the step budget above is a whole number of\n",
-    "epochs — and a window that is not a whole number of passes leaves a residual of\n",
-    "the order of one swing divided by the window length. This is Polyak–Ruppert\n",
-    "averaging; [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)\n",
-    "applies the same device to its own iterates for a neighbouring reason,\n",
-    "minibatch noise in where the iterate sits. What the average does *not* remove\n",
+    "averages it out (which is why the step budget above is a whole number of\n",
+    "epochs), and a window that is not a whole number of passes leaves a residual of\n",
+    "the order of one swing divided by the window length. This is Polyak–Ruppert averaging. What the average does *not* remove\n",
     "is the slow drift the first line still shows: a tenth of a width per pass in\n",
     "the median, and much more along the directions the recovery table is about to\n",
     "single out."
@@ -1481,13 +1492,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e3a66cbe",
+   "id": "ed2fc78e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:11.635821Z",
-     "iopub.status.busy": "2026-08-18T03:55:11.635741Z",
-     "iopub.status.idle": "2026-08-18T03:55:11.638678Z",
-     "shell.execute_reply": "2026-08-18T03:55:11.638374Z"
+     "iopub.execute_input": "2026-08-18T04:24:08.469088Z",
+     "iopub.status.busy": "2026-08-18T04:24:08.469032Z",
+     "iopub.status.idle": "2026-08-18T04:24:08.471526Z",
+     "shell.execute_reply": "2026-08-18T04:24:08.471291Z"
     }
    },
    "outputs": [
@@ -1508,7 +1519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bdf9332",
+   "id": "8ecc8ccf",
    "metadata": {},
    "source": [
     "Everything below reads from that averaged approximation."
@@ -1516,7 +1527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cad2596",
+   "id": "79e5ff2d",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1525,13 +1536,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1f379389",
+   "id": "1e854f70",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:11.639685Z",
-     "iopub.status.busy": "2026-08-18T03:55:11.639626Z",
-     "iopub.status.idle": "2026-08-18T03:55:11.786473Z",
-     "shell.execute_reply": "2026-08-18T03:55:11.786101Z"
+     "iopub.execute_input": "2026-08-18T04:24:08.472467Z",
+     "iopub.status.busy": "2026-08-18T04:24:08.472406Z",
+     "iopub.status.idle": "2026-08-18T04:24:08.607117Z",
+     "shell.execute_reply": "2026-08-18T04:24:08.606808Z"
     }
    },
    "outputs": [
@@ -1719,13 +1730,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "35bb8334",
+   "id": "e8b5c59b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:11.787480Z",
-     "iopub.status.busy": "2026-08-18T03:55:11.787416Z",
-     "iopub.status.idle": "2026-08-18T03:55:24.896360Z",
-     "shell.execute_reply": "2026-08-18T03:55:24.895919Z"
+     "iopub.execute_input": "2026-08-18T04:24:08.608686Z",
+     "iopub.status.busy": "2026-08-18T04:24:08.608576Z",
+     "iopub.status.idle": "2026-08-18T04:24:19.861421Z",
+     "shell.execute_reply": "2026-08-18T04:24:19.861068Z"
     }
    },
    "outputs": [
@@ -1734,8 +1745,12 @@
      "output_type": "stream",
      "text": [
       "identified rows, max |z| against the truth, each fit in its own posterior sd:\n",
-      "  last iterate 3.42; seed 0 1.16; seed 1, same order 1.14; seed 2, other order 1.13\n",
-      "spread of the mean across the three tail-averaged fits, in seed-0 sd: median 0.08, max 0.10\n"
+      "  last iterate: 3.42\n",
+      "  seed 0: 1.16\n",
+      "  seed 1, same order: 1.14\n",
+      "  seed 2, other order: 1.13\n",
+      "spread of the mean across the three tail-averaged fits, in seed-0 sd:\n",
+      "  median 0.08, max 0.10\n"
      ]
     }
    ],
@@ -1822,33 +1837,34 @@
     "spread = means.std(axis=1) / recovery.loc[identified, \"sd\"]\n",
     "print(\n",
     "    \"identified rows, max |z| against the truth, each fit in its own posterior sd:\\n  \"\n",
-    "    + \"; \".join(f\"{k} {v:.2f}\" for k, v in z_own.items())\n",
-    "    + f\"\\nspread of the mean across the three tail-averaged fits, in seed-0 sd: median {spread.median():.2f}, max {spread.max():.2f}\"\n",
+    "    + \"\\n  \".join(f\"{k}: {v:.2f}\" for k, v in z_own.items())\n",
+    "    + \"\\nspread of the mean across the three tail-averaged fits, in seed-0 sd:\"\n",
+    "    f\"\\n  median {spread.median():.2f}, max {spread.max():.2f}\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "10d71bbc",
+   "id": "25d61f56",
    "metadata": {},
    "source": [
-    "The table is a selection — the scalar globals and the three identified sums —\n",
+    "The table is a selection, the scalar globals and the three identified sums,\n",
     "and the figure after it checks one of the five symbol-effect families; that,\n",
     "plus the two curves for one symbol further down, is the extent of the recovery\n",
-    "evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a\n",
-    "global coefficient and the mean of its group effects are only *jointly*\n",
+    "evidence here. Read the table bottom-up. The raw intercepts look off by 0.3\n",
+    "and 1.3, but a global coefficient and the mean of its group effects are only *jointly*\n",
     "pinned by the likelihood, which is exactly flat along\n",
     "$(\\alpha_0 + d,\\ b^{(\\alpha)} - d)$. What tilts that direction at all is the\n",
     "hierarchical prior: shifting every symbol effect by $d$ costs\n",
     "$\\sum_s (b_s - d)^2 / 2\\tau^2$, which is minimized when the effects average\n",
-    "to zero — and the generator standardized them to average exactly zero, so the\n",
+    "to zero, and the generator standardized them to average exactly zero, so the\n",
     "prior points the split at the generating value. Conditional on everything\n",
-    "else, that cost is a Gaussian in $d$ with standard deviation $\\tau/\\sqrt{12}$\n",
-    "— a tenth for $\\tau$ near the generator's $0.35$ — which is the scale of\n",
+    "else, that cost is a Gaussian in $d$ with standard deviation $\\tau/\\sqrt{12}$,\n",
+    "a tenth for $\\tau$ near the generator's $0.35$, which is the scale of\n",
     "uncertainty the split actually carries. The same\n",
     "translation ridge exists for every global-coefficient/group-effect pair in\n",
     "this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
-    "$g$/$b^{(\\sigma h)}$, whose sums we spare you).\n",
+    "$g$/$b^{(\\sigma h)}$, whose sums the table omits).\n",
     "\n",
     "Two things follow, and the table shows both. Along a direction that flat, a\n",
     "gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its\n",
@@ -1859,13 +1875,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "2c788a8a",
+   "id": "95f47703",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:24.897491Z",
-     "iopub.status.busy": "2026-08-18T03:55:24.897405Z",
-     "iopub.status.idle": "2026-08-18T03:55:35.156112Z",
-     "shell.execute_reply": "2026-08-18T03:55:35.155711Z"
+     "iopub.execute_input": "2026-08-18T04:24:19.862649Z",
+     "iopub.status.busy": "2026-08-18T04:24:19.862560Z",
+     "iopub.status.idle": "2026-08-18T04:24:28.160181Z",
+     "shell.execute_reply": "2026-08-18T04:24:28.159900Z"
     }
    },
    "outputs": [
@@ -1873,7 +1889,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "kappa0 after +0 / +30 / +60 passes: 0.533 / 0.579 / 0.628 (generating 0.847); alpha0: -1.702 / -1.711 / -1.722 (generating -2.996); loss per pass over those 60: -0.37 nats\n"
+      "after +0 / +30 / +60 passes\n",
+      "  kappa0: 0.533 / 0.579 / 0.628 (generating 0.847)\n",
+      "  alpha0: -1.702 / -1.711 / -1.722 (generating -2.996)\n",
+      "  loss per pass over those 60: -0.37 nats\n"
      ]
     }
    ],
@@ -1906,24 +1925,23 @@
     "path = np.asarray(path)\n",
     "more_loss = np.asarray(advi_more.hist) * ELBO_SCALE\n",
     "print(\n",
-    "    \"kappa0 after +0 / +30 / +60 passes: \"\n",
-    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 30, 60], 0])\n",
-    "    + f\" (generating {truth['kappa0']:.3f}); alpha0: \"\n",
-    "    + \" / \".join(f\"{v:.3f}\" for v in path[[0, 30, 60], 1])\n",
-    "    + f\" (generating {truth['alpha0']:.3f}); loss per pass over those 60: \"\n",
+    "    \"after +0 / +30 / +60 passes\\n\"\n",
+    "    f\"  kappa0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 0])} (generating {truth['kappa0']:.3f})\\n\"\n",
+    "    f\"  alpha0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 1])} (generating {truth['alpha0']:.3f})\\n\"\n",
+    "    \"  loss per pass over those 60: \"\n",
     "    f\"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f73ad7ef",
+   "id": "bc1211fe",
    "metadata": {},
    "source": [
     "The ridge coordinates keep moving toward their generating values, at a rate the\n",
     "loss barely registers. The raw split rows are a fit still travelling along a\n",
-    "nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —\n",
-    "are the mean-field *conditional* width across the ridge, not the tenth just\n",
+    "nearly flat direction, not an ambiguity in the model. And their reported\n",
+    "widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just\n",
     "computed along it: mean-field has no correlation to spend,\n",
     "so it reports the narrow direction as if it were the wide one, which is why\n",
     "`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the\n",
@@ -1932,23 +1950,22 @@
     "way along it.\n",
     "\n",
     "The identified rows are a different story. Every row the table reports that\n",
-    "*is* pinned by the likelihood — the three sums and the six standalone\n",
-    "coefficients — lands inside $1.2$ posterior standard deviations of its\n",
+    "*is* pinned by the likelihood, the three sums and the six standalone\n",
+    "coefficients, lands inside $1.2$ posterior standard deviations of its\n",
     "generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.\n",
     "Read from the last iterate instead of the tail average, the same rows ran to\n",
-    "$3.4$; that difference was the orbit, not the estimate. And on two refits —\n",
-    "one with a different optimizer seed on the same replay order, one on a\n",
-    "different on-disk order altogether — the identified rows land in the same\n",
+    "$3.4$; that difference was the orbit, not the estimate. And on two refits,\n",
+    "one with a different optimizer seed on the same replay order and one on a\n",
+    "different on-disk order altogether, the identified rows land in the same\n",
     "range, each fit judged in its own posterior width, with the spread of the means\n",
     "across the three tail-averaged fits about a tenth of the reported width. That\n",
-    "is the replication check the risk-management stance calls for, and it is worth\n",
-    "stating what it does and does not buy: material disagreement between fits\n",
-    "would have been enough to withhold any claim about a width; agreement only\n",
-    "removes the instability warning — it does not validate calibration. Replication can veto a width; it can never certify one. The widths\n",
-    "here are those of a mean-field approximation, and z-scores this small on one\n",
-    "dataset are consistent with widths that are somewhat too narrow, somewhat too\n",
-    "wide, or right — sizing that needs many simulated datasets, and this notebook\n",
-    "fits one.\n",
+    "is the replication check, and what it buys is limited. Material disagreement\n",
+    "between fits would have been enough to withhold any claim about a width;\n",
+    "agreement only removes the instability warning. It does not validate\n",
+    "calibration. The widths here are those of a mean-field approximation, and\n",
+    "z-scores this small on one dataset are consistent with widths that are\n",
+    "somewhat too narrow, somewhat too wide, or right; sizing that needs many\n",
+    "simulated datasets, and this notebook fits one.\n",
     "\n",
     "The more interesting check is hierarchical: what happened to the per-symbol\n",
     "effects of the two symbols with 1,800 and 900 rows?"
@@ -1957,13 +1974,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "7704a8e5",
+   "id": "e5df7a49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:35.157768Z",
-     "iopub.status.busy": "2026-08-18T03:55:35.157636Z",
-     "iopub.status.idle": "2026-08-18T03:55:35.326837Z",
-     "shell.execute_reply": "2026-08-18T03:55:35.326371Z"
+     "iopub.execute_input": "2026-08-18T04:24:28.161367Z",
+     "iopub.status.busy": "2026-08-18T04:24:28.161288Z",
+     "iopub.status.idle": "2026-08-18T04:24:28.272311Z",
+     "shell.execute_reply": "2026-08-18T04:24:28.271883Z"
     }
    },
    "outputs": [
@@ -1971,7 +1988,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "posterior sd of the contrast, widest first: S11 0.044 (900 rows), S10 0.023 (1,800 rows), S09 0.019 (4,600 rows) ... narrowest S00 0.007 (90,700 rows)\n"
+      "posterior sd of the contrast, widest first:\n",
+      "  S11 0.044 (900 rows), S10 0.023 (1,800 rows), S09 0.019 (4,600 rows)\n",
+      "  ... narrowest S00 0.007 (90,700 rows)\n"
      ]
     },
     {
@@ -2018,26 +2037,25 @@
     "\n",
     "order = np.argsort(-b_sd)\n",
     "print(\n",
-    "    \"posterior sd of the contrast, widest first: \"\n",
+    "    \"posterior sd of the contrast, widest first:\\n  \"\n",
     "    + \", \".join(f\"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)\" for i in order[:3])\n",
-    "    + f\" ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)\"\n",
+    "    + f\"\\n  ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)\"\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b2526a2",
+   "id": "cf677a51",
    "metadata": {},
    "source": [
-    "What the figure supports is narrower than the usual slogan, so it is worth\n",
-    "being exact. The posterior spread is widest for the symbols with the least\n",
-    "data — the printout orders them — which is the hierarchy expressing that it\n",
+    "The posterior spread is widest for the symbols with the least data (the\n",
+    "printout orders them), which is the hierarchy expressing that it\n",
     "knows less about those groups {cite:p}`gelman2006data`. The point estimates\n",
     "track their generating values across the board, including the thin ones.\n",
     "\n",
     "What the figure does *not* show is shrinkage in the strict sense: that would\n",
     "need an unpooled per-symbol fit to compare against, and this notebook does not\n",
-    "run one. Nor are the bars calibrated intervals — the paragraph above declined\n",
+    "run one. Nor are the bars calibrated intervals; the paragraph above declined\n",
     "to treat mean-field widths that way, and that applies to their ordering too.\n",
     "And whether pooling improves held-out prediction on real data is a separate\n",
     "question again, which nothing here tests.\n",
@@ -2052,13 +2070,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "76ea4b99",
+   "id": "9411e0a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:35.328151Z",
-     "iopub.status.busy": "2026-08-18T03:55:35.328046Z",
-     "iopub.status.idle": "2026-08-18T03:55:35.486660Z",
-     "shell.execute_reply": "2026-08-18T03:55:35.486030Z"
+     "iopub.execute_input": "2026-08-18T04:24:28.273666Z",
+     "iopub.status.busy": "2026-08-18T04:24:28.273555Z",
+     "iopub.status.idle": "2026-08-18T04:24:28.390958Z",
+     "shell.execute_reply": "2026-08-18T04:24:28.390637Z"
     }
    },
    "outputs": [
@@ -2129,13 +2147,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "082bd7be",
+   "id": "9a5a1487",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
     "for the curve: it answers \"how far does a move go\", and it stays finite for any\n",
     "$\\nu > 0$, which is why the notebook reports it instead of a conditional\n",
-    "standard deviation — the Student-t variance exists only for $\\nu > 2$, and\n",
+    "standard deviation: the Student-t variance exists only for $\\nu > 2$, and\n",
     "nothing in this model guarantees that. Under this generator, the fit recovers\n",
     "both shapes; on real data the same two curves would be estimates, and would\n",
     "need out-of-sample evaluation before being used for anything."
@@ -2143,7 +2161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13a07216",
+   "id": "d10f7955",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -2159,13 +2177,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "a075e60a",
+   "id": "b4bd5ced",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:35.488204Z",
-     "iopub.status.busy": "2026-08-18T03:55:35.488103Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.561372Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.560924Z"
+     "iopub.execute_input": "2026-08-18T04:24:28.392158Z",
+     "iopub.status.busy": "2026-08-18T04:24:28.392076Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.282774Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.282454Z"
     }
    },
    "outputs": [
@@ -2274,19 +2292,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "c05822a6",
+   "id": "f984820c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.562619Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.562538Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.710516Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.710058Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.283785Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.283724Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.406850Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.406523Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 800x350 with 1 Axes>"
       ]
@@ -2323,21 +2341,21 @@
     "ax.set_xlabel(\"next-event return (bp)\")\n",
     "ax.set_ylabel(\"ECDF\")\n",
     "ax.set_title(\"Posterior predictive ECDF: the step at zero is the hurdle\")\n",
-    "ax.legend(loc=\"lower right\");"
+    "ax.legend(loc=\"upper left\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "81899fba",
+   "id": "d2e54836",
    "metadata": {},
    "source": [
     "All three statistics sit inside their predictive 90% bands, though the zero\n",
-    "share sits close to the lower edge — 0.300 observed against a band starting at\n",
-    "0.299 — so it is a pass with almost no margin rather than a comfortable one.\n",
-    "Two limits on what\n",
-    "that establishes. The heading is literal — the evaluation batch was seen during\n",
-    "the fit, so this is adequacy, not generalisation, and a held-out split is the\n",
-    "next thing to add before any of this is used to compare models. And the check\n",
+    "share sits close to the lower edge, 0.300 observed against a band starting at\n",
+    "0.299, so it is a pass with almost no margin rather than a comfortable one.\n",
+    "What that establishes is limited in two ways. The heading is literal: the\n",
+    "evaluation batch was seen during the fit, so this is adequacy, not\n",
+    "generalisation, and a held-out split is the next thing to add before any of\n",
+    "this is used to compare models. And the check\n",
     "is one batch, one step ahead, with the observed `ylag` held fixed: it does not\n",
     "simulate a price path forward, and it says nothing about the hierarchy, the\n",
     "hourly curves, or the covariate responses, each of which would need its own\n",
@@ -2346,7 +2364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c46b7f0",
+   "id": "0927d253",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
@@ -2370,20 +2388,20 @@
     "$\\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable\n",
     "drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,\n",
     "it sets what is visible at all. A fixed replay order is not noise of that kind,\n",
-    "and the printout below shows where the generic scaling breaks — in the\n",
+    "and the printout below shows where the generic scaling breaks, in the\n",
     "notebook's favour."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "975568ff",
+   "id": "04e528be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.711693Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.711598Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.715435Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.715043Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.408096Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.408024Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.411261Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.410957Z"
     }
    },
    "outputs": [
@@ -2435,34 +2453,36 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "706767de",
+   "id": "8ec971ec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.716451Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.716380Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.878080Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.877539Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.412482Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.412395Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.591338Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.591043Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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hFtgDiFAAhB5sCckyeIdw2zrL0AxszgAAhDoQoYDZZMdvd8RLAwAEF7afbbCVAb+Ad2pqahwiFBMZ2eYSDgQXkpXL6aef7rDKcYfFA14U5wXxfXv30ZAz2kST5uZj5WPatGkej2Uxky2N2AqkoqLCo6s3Pr+cYMBCzOTJk4WwocYqT6tzc7644KRsHj9tohCi2LonP+OY+7Qjicnq8nTfPocQxaIUw+762JrHH/x5Fr6Q4sUxLNLIwdamfI3CwkIhNF100UXic8lCiu+b40u5C1HS95wP7nn2z3/+kx577DE3waKCOnaJ9hkbzVfMAE4Px1eS7pEtxTh+F8dl4phVHMfLXaCU4naFdQhTdG33OF/S33Jlw9fxznSPaROimPKqalsLUfmp+6kw/bAI8jto3CyzkwMAsFncrPoadf0aCCy/k7YtE24iOT5PRKR/4xUAgDbw2LiyKFu4RgMAgFAEIhQAwYtuQhQvunraId7Q0OAQRHhBmRdTlcK/5XMOGDCAJk2aJBZF2eUS8I37wrzkJsou9OrUkW6ZMDjoXPPdPH6IuLdCjc7J7tGYIUOGeF0QHzx4MCUkJFBpeaVqF5CSiOm+oC65KFu9erXDYkaK++H8P8c1UmuVp9W52apJjn59ejruKzcng/r1brMUKiktV5Wn0jNgJOsctW7zAn0WvnD+vS8BhV3rLV261CFElZeXU3x8vHDDN2bMGGH1xDi775Oekaf4ULGxsS5/s1hRlLmPuvUcQMOnzqFA4D5Fatv4/4kTJ9L8+fOF+MXuYp9//nnhTtC5HEj9VH19g9dz19XVuVg2uR/PVmb+HO9MePixZ6H2mVoNfq5MSU4KhCgAgCpSdq4UsWKAMZTmplBjQ1sfVpyZQP1GhvjcysemFwD0pqIoizIOtnlkAACAUCOUY8cCEAroJkQ9/fTT4uXOAw88QEuWLBHvOWbRxRdfrFcSgAeXbe4ut+wEx1JaMLI/ZVa1LejanSHRnSg2SlvLNGnx2purN+fvW1q06+Tr69ueC1vnKLHQYVFaKY2NjZqc21u+OAsyLc0tfucpu6STkN77OtZoevZsE90YFpaUClFSHDLOE0lkYrefLLSx9VF2drYQWlioko71Rd7RNiu+ypI2sVEP2J0ju0lk130cn8rZKkqKa1VcUub1HJKoyG4WnZGOZ9eF/hzvTHnFsYXXmGh5wQoAAIIZiFDG0uq08YGtogCwMw21VaIcs2tgu1KS0+aNwViw8AuCj8riHMpL3kt9hk+k2H7DKBSQ3KN37NLNp7cRAAAwA9u75gO+4bg1P/zwQ7v4MnaEhRutxZtggq1NlMTgkRbNu0V7j2ujlNrKEoru3JFYI7rp+mvp+pvax1JSG+/H033dfvvtdNVVV/l9bnYxJ4dk+eSeLzHdoqmiskpxnjpb/LDowOmWLLqsQnR0tLBaZWHGlxAlidYsMnFb4hwfSoIFJxaiWKxiV308AObYU+PGjWt3vieeeEKIQRKN9TWO95FRb3lNC6d51apVpBZOE6eHn39WVpbLd6NHjxb/19bVUWFJGfXp6WqxJcEuG51/7348uz/NyckRcczUHO9MhZMQ1T1GbyEKCw4ABBtVJXlUnJ1EfYdNpM4xxzYcAABCEx6P+XJrHEw0NdRRwta2Da9jZ82nqK72FKMQpwwAbUjb3+ayPfPw5pARokqyk0SMLy28jQAAQFAIUSeddJLDldGIESOMvnzIwZYLHBfF2UKEF42lGDZqycvL8/p9aWmpX+cF2hAXFycEAUks8ERSUpLDfdyoYfKu6tRQU15EY0YMppz8YoqPP6y560eOk8T3xa7vAjk3W/OcffbZxz5wmpsfONImFPTp2Z2iux5zhzdq5DDKyslTnKcTJkxwfD516lRKSUmhDRs20KOPPmroYkAd74Tq1FU28Dinc8+ePQ6BRI6hQ4cKcYVFFhaanONDSfB7tjiShChm1qxZHu/Xu1BaqZl46Qy7zePreoqJ5dwW7j2UQmedNqPd8fwMWYjzZOXlfPxvv/1GV1xxharjnUlNzxD/9+0VS926aiMSAxBSbjx40fV369amhnqqrSqh6Nh+js+CndR9ax1unSbPXWh2cgAAJsf+Orp7NUV0jKLhU+aERDtYXtA2jmKKsxNp4NiZpqbHXoTp0i/XVZZSx87RFB7p3xgeBDvYGKc1LELp7W0EAABsJUT94Q9/EC9gDG+++aZL7Ba2KHjyySf9Pt/s2bO9fs8xa8aPH+/3+UFgnHPOObRixQo6dOgQrV27lubOndvuN6+99ppjQX7K+NFErcpd5HljzolTaf3WfbRl+x7asmWLEJ214pJLLqHFixeLewrk3GwZeMsttxwTs34f+5ZXVtOKNZvE+xOmu5bf2aecQOt/26Y4T50FGo6p9P3331Nqaip98cUXx1zC6TzmLi/IpIxDv1Gnrt1pzCzP1o8soLAQtW9fm2s8b7B4wnm3fPlyIbqxMMUClfP3LDqxWCcJUXKCC8dreuyxxxx/x29e7Hgfd/IlXtPhS8hraW4WCy3uv/vf//7ncO/oLsJzvKjp06eLvFj26xZRjt155513HJZkHGNK7viPP/5YlFV3wczb8c4cOtzm0nDiWP9jiikndHZIG0V9TSUVpB2k7n2HUkxvbUR+oIyWlmZK2bGSmpsbafTx51JEZJSIM9RQXy2sg0I+5g0wzCVYeGQUhUe0We7nJu+h+poKGjz+RFEmATA6VmNddRlRNYvT2dS9r3ysU2AxgsSKrSw/jbKObKWOUV1p3MkXmZ0cAADwDmJTAWAIwb81KoRhF1avv/66y2cPP/wwjRo1yrQ0AX1hax9JCLz//vuFeCNZw7HFzl//+ldauXKl+Pu2226jqCjtdqdNjhtJJ8+YKHa/sQu9t99+28XypampSbio+/HHH+nuu++mxMRExedm4em8884L+NxVVVV00003CdFAIjk9m5565VOqqKqmLp070YVnuopcp598vKo87dy5s0u6582b54iJ9+9//9sl3XzNdZt3CmspLWERSrKKkuPkk08W/7PA5itelyQqbdq0yWOMOXZBOGbMGHFvkoWVXBw6dl3IQqD06tWju+Pl/LmnF8ejcmfZsmVC3Pptw1ra8csXlLp3jSgn/GJLJI5V+NJLLzmex4wZ7S2e7rvvPiFeZeUV0b/f/ZryC9pcOFZXV9N///tfUa6YRYsWiRhYcsfz9bj8SZajSo+XOHDoiPh/yviRsr8B1iV9/3qx6JJ+YIPZSQm5iR/vgq+rKReuPnnxlWERiilIP6RJEgHwRm1FiXAJlrT9ZxFzqb66gooy40V8ivzfYyECYCRNDbWO9y3NbZtxADASFqGc+2MA2hMcoisAAADlIEZUkLJr1y564IEH2lzV/M5ll112zCLDT9avb/Oz6801H8eAAebsgouIiBDWOTfccIOIhcPC4+OPPy4sMfjZSOWBywILMolbl2r6qO68/hIK79iFNm7ZIRbg+cUWMpwudo3mXB5vvdV3HClnnnvuOWpubqZffvnF73NfcMEFtHTpUhFnivNEEqeY8PAO9OBdNwpBxJnw8HBVeerOCy+8QHfddZew5HrvvffEKyamGzXU11NdfZsA5GxdZBQsLg0ZMkTEfeKYTQO8hF5zF5U8WTvxZ5IA2Lt3b8MEb34Gn3/+uXgxkRHh1L17LFXX1AiXfBLHHXccvfzyyx7PwW4Eb7nucnrv069p14FEuvjy64R1G5cNFjmZc889l26++WbZ47m9ffHFF4XVHOenmuOZ+OQMKigsougunWjWNFiV2pH6Wu+uJYF+tDY323bBFbFAgoPshB3ifxZD66rKqKX1WJmsq/IeizGY4DFRS3OTwyrMjgRNTKVA7yNY8gEAg11i5qXsoU5dY6nX4LHIewAAAMANCFFBSHx8vNh5X1dX5/jsjDPOoKeeeirgc/uKz8Ou+YC5sLjA7uDef/99YS3CsWlKSkqEoMIxi6677jo6/3zP7toCJapjJP3zsXvpcEaZiBm0c+dOqqysdJSNvn37ikV7Lo/jxo1Tde5OnToJQWj16tV+n5vFJLasYldpBw4cEAsmHcLCaOK44XTj1X+giePHit3LWuYpC1YffPCBcG337bff0v79+6miolLM72NjutJxUybQzbffS4HQ1FgvAtR37zNUxAJQQocOHejKK68UVlps5XX75afJ/nbAgAFCLMvIyJC1duLP2AWerzhIWsPiIrscXfr9/1F8chpVVFZTUXHxMfeTU6bQxRdfTPPnzxf3LMeVl5xHA3pE0fcrfqMjyRmOmFJclljA59hP3han2OUjx93issVlU+3x67e17Zife/J0UY8ACHaXFK2ICwBAUFqGVpcV0PCpc6lrbB+zkxNSNDXUibFgt14DqUtML7OTA0BIUpB6gEpyUsT7mN6DKbITYr4CECrw2lLmoU3U3NRAwyafLhurG4BQx9SawW6L2H0cW+8kJCSIhV3nHeRqYNdMei2u24n09HSxIFpRUeH4jN1RvfLKK8JyBIQGvADPbuT4VfO7ZQh/5q0MdIuOdli8sas1OTjWEVsm8fnkOPPMM8WLO2MWi1paWoRLNi0I9NzS8RwzKC8zlQpTtlFkRAR1iemteZ5KsGC1YMEC8eK8y8tOo/z4zRQZGUGdo3vS6JmuwZTZ0kvNs8g/vIFyEndSWV4ajZpxlstvOJ/kBJDLL79cCCcbNmygK86cSr16xMhei8U/duHH5/LkIo9jH0lplqzNjICfPd/HtGHRIhYC50efMSdR9179VKdjyvhR4jVw3CwK69RDHO8e78kb7O6QX5yG8vJyxcfX1NbR5l2HRHk4d84ssusCGMdI6tK9d/DsJgcAAKAqVpsUHJ1dlE44za4xge3Zh2Ue3kJVpXkiVuHkuQvNTg6wAxrvPWmoq6aKwiyK7TeMIjp2olCkqtTJdXxjPYQogwshW6Q11lVTp2ht1h0AUENFURaVF2aK90WZCdR3+ERkIAAeMEWZ4AXgDz/8UMR54QVdrc4Z6nCMnBtvvJEKC9vimzDTpk2jN954Q9ViKgguunTpIl6ecF4w5ve+LN6YPn2U73Dlc8bEyIsbgRDoudmKqmfPHlSWfqwZVLqA7i1PlYhSPWN7UEmkfPOr9lkUHmlzAVRTUaRaxLnjjjvo+eefp++Wb6A/XX2B7G979uzp9VwsyClJs95w/rJ41ykAMYzPEevjfn0d7yu/nFmyegtV19TRNVctoH69e5AdJ37s5rO5uZEGjTueeg4crem5AQDAboS6y0V2zweMhUUoOZzdV1sRbGAJDlJ2rhTiC8fLHD3zHLOTA0IMER9450rhKnvoxFOoe1/jXd8DexOot4bmhnrH+0bExgNAFnk/RTrBO+p54ZODx2slQgGi4uJi4XaMXYZJjB8/XsSj6dq1K7IIAOCRa6+9loYNG0ZrNu2mgqJS5JLBVFRW0bJft1L3bl3ppuuusuUCEsMilHOcFADUADd9IKixQRsOAAhBYU7jy7AIxdRWlthyLAu8Y4dnKMVrzTi0yeykBAWmPXMblDUAgI0sothFHLuBcmfgwIFiMZRdUvnjQm7QoEEUqrALKLaESktLc3w2atQoYXWmlzUKAMAm8EDOy4SWrSW/++472rXqc4ru2pnsS4ADVpM2r3eK6kj/ffIO6tgxUmwasKMUaIeJKQA+QTkGIDCCpSsIbWM2AIBGwNIPAAAAMFmI4hhQn376qeNvDhy/cOFCuv7664UIBfyLs3XrrbeKGFsSQ4YMoY8++kiVaygAgDGrD3VVZZQVv81S2c0bAHr1kI/5FRKYtIDGQqDRec/+02vK1LlxBKFBcFgmYRUZAACAnQkLuY1xwUMwjKNsCjYUAQCALTBUiGJLKHbNJ/HMM8/QpZdeamQSgor6+npatGgR7du3z/EZx2j5+OOPqV+/fqamDQAriSzr168X7zl2kNkk71xJra0tZicDhDBHd6+iuppys5MBfFiZYSdtCIJFlOAgzGZrkSGxOKycUI/vFTSgXANgaSqLc6kkJ8XsZABwDDuN3QCwMYYKUYmJiY73s2bNgggVAI2NjXT33XfT9u3bHZ/17t1bWEINHjw4sAcFQBBN/HgxlwVapb/VG6NFKLZwsNYTAWZTV62xCIXFc6CSluZm5JlUfTDrBQDoQCgKasHiKhgbUUAokLZ/ndlJAAAAYAIdjLxYWVmZ4/3pp59u5KWDipaWFnr44Ydp3bpjnXdsbKyICTVy5EhT0wYAAEBrgmNhBQCmtaWFkncs17SIQ8wBlsNpY0volM9QuU8AggQDq2zotIPBjLWfoXMZC0UhXhdMEveDo71AGQTAEkJUTEyM4z3iF/nP3//+d1q2bJnj7+joaHrvvfdo3LhxAT4hENoEQ4cP2oHHCgCwEBwnr7620uxkAKAx6GyDxorGYpb9oVc+7ZRWe1vH5yTuNDsZAABgGg21Vch9AILdNd/AgQMd7ysqKoy8dNCwZs0a+uqrr1w+Y1HvjTfeUH2uJ5980uWZAO0mtCU5yRQeEUmx/YaHRLYGiysMYA1rCV4EUu2WBEXQNIJj15rFMCuotwGPEuUFABDqtDQ3UWnuUbOTAUKYlF2/iHIIAAChCLd/CVuXmJ0MAEISQ4Uodsf39NNPi/d79+418tJBQ21tbbvPMjIyxEst999/v0apAs5UFGY5dph16hpLnaJjkUEAKKC+ppKO7l5NnbrF0vApc4z1kR+MG6CBapqbGqmiKIu6du9DHTtHIwcBAApBJ+K8KQkxbryTn3qAijLjUbuAaRguQmHPkjLQlaCMARNjJgfWUGGzHQAWdM03fPhwOvHEE8V7jm+Ul5dn5OUBMITK4mzH+9rKEuS6nYA7FlPJTthOTY11VFWSRw1w3QVMIDdpF2Ud2UpJ239G/usE/PZ7n/OW5aVR0vbloh0EdgKrrEA5EKG8w+0ge5eAxwVgpocGlD8Q6kBYAQDY3iKK+etf/0pXXHGFsOx55JFH6N1336WOHTsanQzbMnPmTHr77bc1ORfc8ulEUIoZwM7xWDrH9LLF7uTmhnpXF33g98yweEZYPX0qKM1LFf+3tDSbnZQQm9S26m7pxu5y7UDmkS3i/9R9a2ny3IVmJydoLQHS9q0zOxmWJCQXXm1wzzyWK85KpB4DR1GXmF4UzPAmPqkdjOjYmWJ6D6KQwwZjdrW0tvK4Ppzs5KGhc7ceNGzKbHVzKOs3J/4TzPcGrAXKGgBBjeFC1NixY+m1116jP//5z7Rlyxa6/vrr6dlnnxXWUsA3/fr1Ey9gD+w2oa8uKxALsN16DvD7HNjtbi1Sdq+igWNmUK/BY81OCghRijLiqefgMdShgz0WIKwi1gTfMpR5JG5dSmNPvMA2YhTQl8KMI1RdXmhYNjfW1YixlV3awIK0Q1RVmkeD404ISRelVty4k7xzpVjIL8lNUS5QW+82FFFTXuR4z+XQXyEK8xFrcXjjtzRs8um2EBYlDw2VJbnCQ0NUlxizkwQU0epzE0qHcMOXPwNaR7JifxQsqO0jeFNb+v71FB4ZRUMnnYpnA0AAGN4Sp6amUufOnenBBx+kf//737R7924677zz6JRTTqGTTjpJCFLdunWjDh3UeQ0cOXIk9ezZU7d0A+AP7FaCRZ3ITl1ssfsr/cAG8X7kcWeKGCmhRrBOWnOSdkGIAqaRm7JH/N97aByegk0n8VpOhuXaWV6o5+/C3MZ/xyZ+HWnopNP8SgcvKJUXZFDPgaP8TjcIng08TfXt461qjXM55/KXumcNjZpxFlkdFjvyU/eL9+ymlMeDwCrWJIGcgEKE4LhRK85HGmqrhEgY02ew34v5PM+0g6VvyHtoCFLxI37TYho182xbCIssgmYd2iI28vUbMdns5ITUGDQrfpvYODRgzAyXOQe71JU2MVUW59hCVAfAqhguRL3xxhu0ZMkSl89aWlpo48aN4uUvL7zwAl188cUapBAAbd1LpOz6heJOucTy2VpVkut4zwt2oShEAQA8W0ryAkRsv+HtRAKlCz9lBekQooAszU0NYsc/T/xGH3+uyyKXy8SvKFssggXXAqVV0wW0pKbimJWHpXESAlXHObWZF4BQIjtxB0V1jaGusX2VH4TnCdwsi9lau2f5aBo07njkTTBjYt1nIYCFdz0siJubGyk7YQeNnD6PrI7kPrgg7aC5QlRraFmJleamUGnuUfE+ukd/lzlHY121431z47FwAgAA9agzOwIAqN7F1tig/85b87HYKMVfgnMDWOgRpDv5zKCpoZ6O7vlV7A6T4ieFchMB9IHFpsb6GqqvrWxXzlwmfk0NeAQgcDx0EfU1FZS07WfKTdrt8RD+vqW5OaTcNQeGnfthO6ddHu7L7YYVLYMMwaTbLslJoZSdv1BNRbFsfEf29gFCE88xPrUXYBI2/Uj11RW69LHsng8AOXisJ9FQV4WMAkAnIEQBYDUssFARshM/EwmtBSoFExyVYpIRkyOzMPPe6muPDcilHWLuoOzqQPAWZ58LA60tgS32+5unwdyGAN9kHtpCdTXlVJSV0G6hqqIwixK3LaOje1YjK4MQ1H0AjsVGqqksFt48gAab27AxThW8GYnjwjU11YsNcO6wq2aOuQhCAYzJAQhmDHfNd9VVV9HJJ5+s+XmPO+44zc8JAABGkJ96gIqzEmnIhJMoqltv+vHHH8Xn8+bNE7HvrBasdNu2bZSRkSHi+g0e7K+bLNAOBc+4uLiY1qxZQ3379qXZs2drlokcC6S2ooSGTZ1NHTt1PfYF5gEAACMxaVOG8y5Yd3E9/eBG/1zVucPntVBfrjVBI+gE7yMCAOhM0LSDZuDU97pbwLe2tFDy9uXCen7UzHOoc7cefl4j0ESCoAblA4DgFKJmzpwpXgAA7eHFk2+//Va850VqXqwGymgTesxZfWD/z0za/vU0bMZ8+tvf/ib+jouLo4ayTCrNSaEhE0+h6B79yGyKioro9ttvp06dOtH8+fMpGGhoaKADhxMp/WgiRUZEUF3HfjSz7zBF4l91dTXt2bNH5EtMTAxNnDiR+vVT/pxqa+to7+FkKq+opqElRN07VFDPWPkgut27d6f33nuPcnJyRLzFESNGUKA01FU73KHlJOyg4VPnBHxOEGQE+QI6ABAfAHAFC+pmgv7WTEK+7FtwvMeWeg31ba6acxJ30qgZZ5mdpBChNbTmI2EKb92CdQQAO2G4EAUA0I/m5maHiPHpp59CiFKBVd2LFaYfFv+n7l1Dk+cuVOxaUa9J1EsvvSTEl7vvvpu6dOlCdqaqqopef/11+vrrr8U9OXjnKxo0aBD98Y9/pBtuuEFWvHrllVfos88+o7q6OsfnHTp0oDPPPJOefPJJ6t27t+y1peP/9+mnVN8g7fr7gTqEhdHx0+Lo1oXzKTYmut1xERERdOedd9JDDz1Ezz//PL399tsUKM6u0FiUCgS49bQ3Vm0Hg3niZ/k8B37A5bT9c+V+2V4lWGVqUZQBAABogfvYCP0LMBmM1wHQDsSIAsBi2H4Xlo0XCq3k/s6KJCcn0w8//ECxsbG0cKEyUcyq5Ofn04IFC+ijjz4SItTwIYPo5BkT6bRZk2nihDjKy8sT9+qJlpYWuvfee+n9998XItTYsWPpwgsvpFmzZolB6i+//ELXXnstlZWV+TyeRaihA/uK606fNlnU/217jtAT//mIKqtrPE7G+FpDhgyhtWvX0s6dO8lK9UO+/bJ5u2Y4IZxfrYEJNX634xCEgGaEcP0FflNRlE3xmxZTcXaSRrkYFtqLfTYd02MuAiwLxkkAAAA0ABZRAGiNPec9APiErX9YRLngggsoKirKtjnW2NhIixYtotTUVBowYAD95z//ob6d66ksP118z7G66qkzbdmyxePx33zzDf3666/i/SOPPEI33nij47vt27fTrbfeKs7NFkvPPvus1+MX3biQzjxhnHg/ePyJtPLH/6OnX/uMcvKL6dPvfqE7/3hJu+PZ6uriiy8W1lz8TPR1d4sFVRAci5i23+QRIgTNcwqzxmJ0ZXEOleWnUd/hkyiqi7zb15DGIkUu/cAGh9upXoPGmJ0ce2KRZxkoEKMACC6hzGrxnq1OMFgf4XkDIA+EKACChK1bt1J6ettCOrNu3TrKyMhw/N21a1c6//zzRSwbtqRgJg9qLyakZeXR1kPZFLs3hU4//fR28W6Ki4tpzZo14j1blHjqZI8ePSqsZ2pqaqhXr140depUET9HCyoqKmjfvn0iHZ07d6Zx48bR8OHDZd2f/fjjj+L9vHnzqGfPnlReXk579+6l0tJS6tKxA8WG1VPnTspElUDui93AHThwgHZv209RHSOpT69YGjZIWQyv2rp6SkjJpJTyxRQdHS2u26dPH6/HsGCUlpVPeQXF1NjUTJVRw6h///40cuRIRdf0lP6ffvpJvGcRxB3ncvWHP/xBiCXOxMfHi/tnPJWr8ooq2rP9MDU31tEZJ08nPfnkk0/o8OHDQkz74IMPaNSoUZR5eLPLbzh9l1zSXgRi3nnnHUccNmcRimGrqDvuuEO4MFy8eDHdc889It/ljr/84nMd8ZmYiWOH04L5s+mLxb/S+i376KoL59IoD2ngtLEQtXr1aiosLPRZHnTFabJgJdd8Lc1NVJKTQp279aSusSbmDwAAMCasq3DsSaamrIjGnXyRzlez/8IR0J7mpkZqaqijqC7dkL0gpLDSmNgWKBRqFC3wy/7G+H6qrqqM0vato+ie/cWmQzsRDIKQKrzerrr5bsjlHQB2E6KamppEsHdeHObFSnZnxIvNHAuDA7Pzgu+UKVNoxowZNHr0aLOTC4Al+eqrr+jnn392/P3hhx+6fM8xb1iI6tixIz3xxBNCqPjvP/5CQ/p1d/ndDyt+o007D3q09mBWrVol4t9wXbz88svbPvy9nz2clE6fvvAxJR9NczmG6zKLFw8//LCo0/7ArtJeeOEFWrFihYiF5cz06dPpH//4hxClnGG3aVLMrBEjRtCrr74qrFG4zZHoGBlBl18wh6694lLZa7P7s3/961905MgR1feVm5srLG6WL1/ucl2GYwBddM4cumieZ4sWvs+vFv9KS3/dQg2Nx44NDw+nq6++WjwfToM77Nrtw2+WU1FJ+bEPP25zM8ei3c0330xXXHEFqYHFR3Zhx4IHt8fuOJerCRMm0MSJE12+f/fdd2nZsmWy5Wrb3nh694ulNHhAH5p3ynEUCN4Gh5y+zz//3CGksgilhoMHD1J2drZ4f9VVV3n8Deftyy+/LK7Fbvo41pSa4886dQb9349rqKW1lbbuOUKz5rX/DbvmY5eAiYmJot5ff/31pAlBNGguSDtIhRltdXbS7CspzE0cBcoxN7aNuokfdiACfwj2BUMpyLth2Dk7g6cbNH080NrSQknbllFjQy2NnD6PusYq24AFgFZwGcxN2UMdOoRTv5FTMUawLfZtmDMObRJtIG88HDh2JnUIt8QSrC3RfT5i57ELADbC1FaQrQp4Z/qXX34p4nV449tvvxX/syUAB4/nBXUAwDFOPPFEYSH03XffOawtnK0kevToIf5nC57x48fToUOHaP/hJBrSz1UEOZSY5mJl1U4w2LZN/H/CCSe4fL5zfwL9+92vqampWViasGUIX5MtiNj6hNPFlkxc39VaR6WkpIiFdrb8YEsbFp6GDh1KlZWVwhUaC9kszLDAEBcX5/EcLJ5xWnr37i1cmfFi5Y4d26moqJg+/2E1NTZ3oNtuuqbdcVu276Ynn31FuHNTe18sEnB7xdZbDAsHfWI6CoGpoLiM0rPyadX6rbJCFKeZxfnePWJo7MghFN1rEO3fv1+Icv/73/+Eldtf/vIXl2M2bdpE/373KyFidOkUReNGDaFu0V2IomIpJydHpHvJkiWqhSjOZ2batGkev3cuV1xu3IUo6Xi5cnUwsc0qaNLY4f4tCipcMGExkfOBkfqRrKws2rhlJ5UW5lLvnjHUb/QM2eP5WUscf/zxHn/D5YOfNT87fl7eji/POtTueH5eQwf1FRZtyWltopUn+FlwGeP8DEiI8rKz0GU3l81cShRnHYuz0dLSTOE2EqKwi06XTCWjqa+poKLMBOrRfwR16d7b8OsDs+C2stWWC2mBtD1ot5TDY5ygcUPphfraSrEAy2Qn7KCxJ8w3O0kgxGDXpMVZieI9C6Hdeg00O0lAJ6zapjbW/R7zF/1kSJUnbIwDwIJCFC/OPfDAAy6uxJTAi3i88MoLqU8//bRwtQVCh9aqamopLCErE5FXRp2K2yZdEs2pme1+16FPTwqL7tr+BH4uAlx55ZXCJZokRLHVi7tYJMGfCyHqSDLNn3tMBMnMKaCyiioaOngg5RUU0Y4dO4QVj7PVjSQoOJ+7orKKXv9ksRChhg8bTB9+9KmwwJJgi42HHnpIiCAcM8dT3Bw5+Prs3oxFKLYCeeONN1wsn9jF3l133SWslh588EHhPs5Tx8/XvvDCC4VlU6dOndqOLcimvz7yAP26aTd9s2QVnXbqiTQg5thidVV1LT3337eFCDVmzBjhUk3pfbFbQE4Xi1AsfrFVFIuFB9Z+eSzt5ZV0KDlX9t5ZyLjqorl06bmniUX0yXMXCiuv++67T8QY+vTTT+m2225zOeajjz4SItTkuBH0yB1XCzeADB/LcJvrLo4ogcsC48kayr1csTBy0003UWnuUbEDsaiqSTw/dgvIoo+ncnX4dwF00rgRLufkOE18jC/yUvZRU2OdeH8gu5EiozoLy7HLLrvM5XfSvXMZGTx4MN19993Casl5AS36nW/ottsWiVhP7mWJYz8x3Pd06ybvZmbYsGHi+bE7R2/HO9msudC/by8hRGXnFckOhHljxtdffy3KPltfubtDtNJAHQuUwEGYDRYSNBJdj+7+VbRLJTnJjjYY+MACjx+EGsFZ6IJiESwY7gGYCrtFc94corsQFUSeBSw5PguwXcB8BBwrR4GUQeQjALYTotgFH1sJ1Na6LtardRPF5+BA7VrFngHWpnH5OmpauZ6oxSKDIBm4NLqXyIYNH7T/YYcwijhnNkWeN0eXdDQ11lNtZYmIj+IOCyLsuu9wYqqwzuEFex6YHfxdDDhhxjRKzy0SohO7EpOsYJKSkkQsIJ7cOluDLFuxWog2LJY89df7XMQayfKEj33zzTeFUHTvvfe2ixEkB7tz42NZtGB3Z+7u99j6hF328TXYOoSFi5NPPrndediC6plnnhEu5CT4/Z+uuYASUzMpM6eQvvtxBd113TFryzWb9wiRja/92muvqbovjk0lCe0cy4etuNzp0b0bnTV7GNVWeRZXzzrrLFpw/ikun7GI9thjjwkhiq1KOe5SF6fvJdFm7knTHSKUu0DCLzWwyCHFGxs4UH4CJ5UrFkbKCrIoO6FNtNyZUumIDcVWZO7lqk0ArRblasJY17SxpdnKlStVpZdoieP5ugtRBQUF4n+2JrvzzjtFOvi5jh05mGqrK+lIUjqVV1YL4TAtLU2UGWc4xhjjaxOEZIHIbmb9OT6GrdiIqLqmTVzzhFQe+Ros9CmtUwDYFlmX/9YcF0jiOLCbGtqqQcDx9r/BApivbLdmPQbAcCDAWYZgd9tqF9A/AGMKmp/fAQCsLUSVlJSIHfzOIhQvgp9yyil09tln06RJk8TiWnR0tFggZ9dbvKOcXW8tXbpU7DCXSEhIEJYB77//vtG3AQymtaKKmlasC64OoKVV3FPEKZ7dsgVK5qHNFNOYTcOnzG63+4td07G4UltXT0lp2RQ3aqj4/GBCm7XG9KkTqf/QOiEYsHWLJBjwe4bFIOeF9G0794j/p4wfSUMGeRYqrr32WnrrrbeEJQyfh2MrKYEFFynN3D54gtsM/v63336TFaJYkHAWoSRYPOOYRB9/s5J27TtErdee51hk2ns4WfzP5+MYU2ruS0o3u/LzJEIpgS24iNqLVGzJw9Y03D6yW9MRToY5AwYMEFY3uw8m0inHT9LEHRnH7WMxivEW40sqVyyQ7dy2ifr/nq4tWzY7hCpOt3u5OvB7uRs+ZADFuFkJnnTSSV4tjyTKCzKopblRvI/pM5TCIyI9xs+qqqpy/M8i1C233CL6kZyEbVSWn071DY308eJNtGrNOmFdyP3SnDnHxGK2SGM8lSVnJKs79w0X7sfLTaw4dhlT39Age43Y2FiXvhVCFNANvfteTPyAzQt08CySObtjpRAlWJ4lAOYSKi4odQNZh/wC9iJoxoIABJkQxVYFvKgpwW6e2FWWu5UDw26GeLGbX7zAyW6Sli9fTv/85z/FohuzceNGWr9+vYiHAwA4Rmtrm3CQm7ynnRDF1iAs6rB1IotPLETxIgq7R+vQIYymToqjYY2dRH1lwWDRokVe40NlZLXFsRk93NViyJlevXoJwYgtdtzdlfmK6SMt3H///fe/31tru//ZfR4j58bNPWaRMyOHtuVPZVW1cE3IlkpMTn6xT3d0cvfF1lnMjBny8YZ8waISFZfIWtywEFVfX0/kpNNcfvnltHnzZvptx0FKTM2iE6aNF8+3/6RCl5hhapCseBhvopBzudq97yCdf+pE8Wz27j8oNhywFR0f716uJAF0ctzodudcuFCZO6vErUtFLAJm7Kz5FNXVs6Wss4DE/c7999/v4tKOrcgeeeBu2r3vgHCr+NVXX7kIUdLxLDx6g10zivNFRXm8vq/jG5uaxf+RHsQ0T8/CuV81HIy5QRAQPEICsK0FAsogAIFVIdsOSFzTDSscYHgJRP8D7IBlmngLjBkBsDGGClG8S/7bb791/M1WBmxJIO0cV8J5551H48ePp2uuuUa4CGM++eQTCFFBTlhMNEWcO8cWrvkUw675zp0j7s0M2DpFEqIWnD+bUlIzqLK6lsaMGCQEhWHDJlGXLl2ENSIvqrN1iRQnyF2I4rrNRHfp7PWabE3Dgo1klaJGBNmwYYN4+ULu3N4seaK7HGuDamrrHUIUW4wxvtx/erovFoncrVaM8O/P7gIPbl1FXy1ZQwVFZbRk9RbxevGdr4TwwrHErrrqKiEMKYXLg/uz9lWu9uw/JISotKw8Kq+oFPGM2NKV/3cuV2xpdTipzYXhlPGjSG+cxZtzzjnHY1yl6K5d6dRTTxXuFdn1oct30dEeXe65I33vnHeejpd7xpJLvs6dXYUsl99UV7c7LwABYdZCRCjP57D4454hBmR6KBc4AKxOkMzzgH1BvyyTLwY/BwAs1A7Yd6MDACEuRPEufWmXOC/OcVwXNSKUxPDhw+nxxx+ne+65R/zNbp54cZQXN0HwwrGUIk47nloKPVuIWIWijHiqKMx0+WzkjLPa/a5Dn54U5uaGzEhYTHr77bcp8WgWNTQ20p79bRY8k8a1uaCLjIyk4447Tri7Y9GAF7rZ6oIX7p3jQ0lWH2xNVOfFjZizm7LOnb0LVu7nZti93ahRvoWKMWPGePzcm4BS19BmTcV0ijoWVykqMpKqqc5nPDtP98XtEYsNvoQbPTj/jBNp3qnT6UB8Kh1JThfPODk9V7gzZYtStiTlTQBKhS62SuXf8m45X5Y3Urk6FJ8kypVk7cQCladylZeVJ+KLdQgLo4nj+Pm6Wgqxq0U5Kzdn8lL2OeKxHMhupMiozkJsc48R5Rwfyz3ml7v7Q8b9fqXjOdYU92dyLvqys7Md/ZW34+XcmBQUl4q/B/TtJTvxc7ZUY8s8awc+x+AdaDHxg4AA9CekFxtaQ2/xFpYAAIBQx32Mb0+3ikrSa7d7AoaCaQYAwSdEOcd34rgb/rqJcj6eA7SzS67k5GSv7rNAcMDCTbiJ4o0Smhvzqa6lzVpPInzEELIaLAZERIRTQ2MTJaRk0p4Dh8Xnk38XoiRRgQUDdqMmWZKwRaK7hVD/fn2oqLiEMnMKZK/HsXEyMzN9CgDuDB06VCzasxD18MMPk7+kpKSIeEOeBqNZuYXi/44dI6m7k4Va717dqaS8kpKSklTfF6c7Ly/P4VrQaDpGRtKMyWPFixk56yL64IMP6I033qC1a9fSpk2bhNWPEtgarm/fviIeFT8LX+WKxaaGhkZRrg4mpLkIUe7lqiq/7fvhQ/pTdNfODvd6El9++SWtXLlS5d0vacuDjh3bCVHOccYkq1pPcN/iyaKJyz/Dllz79+8XbmPdYXeJhw+31ae4uDivx/f30JyxgJeWmSfeDx/c32ca+T579+5NuqBoYRGTOgBAiMBtor+ivE2FGn/wy7WYRfIHwhQwHSyGghBs95zFMCukB5jYL5t2XXMuC0CoYagQxfE2JCZMmBDQudgqg91MSQtxzucGIFRhwYDrBi9y88sbbI04btQwOpRwlPYfOUoHDiVQZEQ4jRs1lKpK8sQAUBIPODaU5PrL3S0fM33KJDp4OIF2HUikysoqGuDhekuXLnXEcZo1a5biezr99NNp586dIj4cW0H6Y0UpXf/aa6/1+N1v29vcr02MG0MRTi7rJo4dLqyJWLhh6xNP7v3k7uu0004T1prr1q2jnJwcGjjQNU6X0ZNNFlTuvvtu+uabb4SYxOK9UiGKYcFl2bJl7VzVucPPZ9q0acKNI5erI0npQhRjgUrCuVy11le4WOK5w+Kht7hUEuUFGdTS3PYcYvoMofCIjqI+uDNkyBAaO3asiOHF1la33HJLu980NTU73FBKwpFzPrAQy9ZuP/30k0chisuqiN1FRPPmzfN6/K1Xndfu+C27DguBmDl+avv4iRIsZDGc33KWWSENFnLsMzOzxMTPEokAViDEioKzaBTIDnj77Z4H3jBtIRjFCAQJaBOt6KEBAJRBAEJKiJIW5tS65pLDeae687kBCOXJHrvoYoE2O7/YZ8ydKeNHCyFq1W+7qLqmliaMGUZRHSOprrqMqkpyaeLEiUIE4AVvtnJxt2yRuOD8s+iLr3+g2roGeuGVd+it9050/J45evQo/ec//3EIWUpc7EksXLiQPvzwQ8rNzaUHHniAnnvuOdl4OGy5xJaSnuIysRu4jz76iG688UaXz1dt3En744+K9xee6yoanHnKcfTTqi0iFs9f//pX+u9//6v4vq644gp69913RayoP//5z/TOO+94tFpJScuigb3UuRVtaW6ilqZj7gSdYdEusqWFQ5C1Iy0tjUpK2lxbqrVIZZGNhah9+/b5/C3nBQs5XK5q6uqFUOksIDqXq/DfEzpprKsLO+fnr4TErUsd1lRjZp1PnbrKxwS7+eabhXUdW2X9/PPPIq6WM599+TWlpra5FLz00ktdvuPnz2ni5/ndd9+JY53rBFuNOZcJd4so9+OPnzichvc91h8Wl1XS5z+sbsuTccO9WkRJz8KTOOwv2IFoEbATNCQXH7BoFdxtEJ4vAMZhp/6ifbtnn7RbHhuVA8vlm5X6YwVpscr4ob66gkrzU6lH/5EU1cX3ZkqgBhOfsUXKFwDBgKFCVI8ePRzv2UIgUKQYHFIMEwCCgUAHURy/iRfXv1qylkpKK2jAgP60L6OGOneKovPnz6cOHcJdhKgvF/8iYvS4W6UUZyVSt14DhQUHWwSx1Q9bmMyYMaPdNQcN6E8LLz6DPvthNW3csoMuuugimj9/vqjzbHmzePFiESuJxYd//OMfqu6HrUdeeuklWrRoEa1atUoIHGeddRaNHj1aLOqzezVe/OfPMzIy6Ntvv/UoRLE1E4tYLD6wtRKzccM6+m3TFvF+5rQJNOfUE6gkJ9lxTL8+Penm666gdz/+UlxbzX1xGvh6bMV18OBBOuecc+i8886j6LBKam5uoYKiUtp3JIUorAO99o+7VOVJ/tH9jnhI7rDQFhUZQZPjRlDf3rHUo3s3amlppW/WHhGWOk1NTcI6a86cOaquecYZZ9BTTz0l3BByPrPrQTlYmHn99dcd5eq4aZOpqaGOCtIOUnSP/hTTZ/CxcsWuKzt0oPFjjsVu0puLL75YiGobNmyg+++/X7j+GzW4F1WVFdHew8m073CKwxrvwgsvbHf8n/70J1EeWIhki6pLLrlECE7sivH7778XFroslnIsQ084H//oP/9Dp58whYYP7kcNGw7TT0uWUXllNXXpFEU3X+kqkDnDsau4XEmuavVarFESn0dRm4Wxuy0wbaE8TP+Jn1UWKNyBOGFjV3IAmExDbRV1CI+kiI5t8VSDoV45t4k8dgQ2xgpihofrNzc1UofwCFuJldbAmuMoq5G8cwW1tDRTaU4KjT/V1UW8v+NXlFUAQDBhqBDl7Jpq9erVYoHWX9g6QorBwQwY4MkZGAChx1133SVcjpWWltIPK3/7/dNvqU+vWBrTm61F5lOH393PjRs9jDpGRjjcgDkLUdJEkEUFFgzE95MmyVojXXLOqRQZEUH/t3SdWGB/7bXXXL5nd2gvvvgijRjh2QWbN0455RT6/PPP6cknnxT1nt3LeYKv4UmEYp555hl68803hRDFL2dmThlLf73nFo+DvIULLqTeA0fQyy+/rPq+zjzzTHrvvfdEulm8cU83X+6U46eRWoqyEmS/Y4Fn29attHnXIY/f8zNkix332Ee+4BhR7GaORRsW4NjNnxxTp06lqKiOVF/fIP4+bvpkyorfRpXFOVScnUST5y50KVejhg0UQqkLgcwNfUx6+Tm/+uqrQjz84YcfaMWKFS7fs3tLtoR64oknxHt3uA6wdR2LWGyB5v5cBw8eTP/+979pzJgxHq/vfvyvm3a7fM8C4j03/YGGDOwr3VC7c7CQxuIwP28ug5bBxEk9LyzUlBdR19g+YoEB82V7EuzCTEtzs6MPBlYluMugVwJZNDZ7wTnEFujrqsooacdy6tAhgsaf6mq9bRa8YFpdmk8RHTtRp2jP43E1lOWnUd9hEymqq2tsWgD8pao0n9L3r6duvQfR0Imn+Px9+oENNOaE+RACLExzYwOFRyp0UW5AN8UiFNPU+LvHJrepUWN9LZUXpAtX8h07+Z6PZx7aREMnKXenryXBPib3H4jYANhGiDr55JMd7zk+B1suLFiwwK9zPfvss44YOLwzn+N+AABIuIdbsvg7+vKDVygjp4AamlqpQ0RHigpvpoa6aqoszqbufdusWVg4WjB/NuUXlghXZmNGDGqXhXPnzhUu76R4Pd6YP+9EuuSyP9DhtCJxTG1trbBW5MVyFh48LeorZcqUKUI0YHduHHuJraB4wsvu7vr16ycswVgAkIN/w4IBi1Dspo8tSrp0iqThvcJp/Ohh1MldCHHi+uuvF9ZQLJyovS9u99gSaevWreK6SQe2UZfOUdS7Z3eaNn4UDR8xmmqr2tzlMRzrR2oX+RolRZ7Pe9KMiVRRWd0mgFW2PR+GBY6tyz6m/UeSKa+olErKKsSge8KM00R6p0+f7vdk6pprrhFC1I8//ijcDcqdh+/hj1ddRkeTE8RvJsaNpaL0Nusd93JVmptCk+NGtjtHTuJOGj51rm4TP3YPyxZrbJ3EcbwSD+6i+ppK6te7B82/5AqaPNP75LR///702Wef0a5du8SzZSsotoybPHkyzZ4922fMJun45d99IoRjtoLqP3gk9esWRtMnsrWf9+6Zn4H0TEAbmYc3C7Gze58hmkzYSnOPUo8B7csm0IFWC0z8DJhrZyfsoLK8VBo66TTq1gsbqOwIL8qEOVvZ8U5lpWMbC6zn5CbvofqaChoy/iTlC3cGgQUvdeQdbXPP29LSJBbXNXkGAYpj7NY7bf968V7OEqC6vFDE8fTmQtmZ3OTdNHzqnJASQ+1i/dDUUE9FWfHC20F0j35kSdzyMm3vWtHWcGxZUiBEsdvv6rIC/+4vSMqjcTg/q1ZFHhoK0w+LtrD/yKkU21/9Zlcz4DJYV1NORRnxFHfKJT5/X16YSaGGMeOBME3SwMIih7WIjrVoGwhAqAtRvGDK7ovi4+PF33//+9+pS5cu7eJzeIN3gD/99NNiMVTi3HPP1SW9ANiVbt2iae7J08X7qC4x1Dm6B5UVpIu/W1vbBFyJy85tc1PHC6688OrOsGHDRJ1TSveYbnTZZaeTXrAgxS9/CA8PFyIBvxieuKfuXaPoWHbHd9ll/pnXs0vDU089VbwOrG0fJ8oZjqXknN8liZ5/d80lZ4r/J8+cSQfWHhOimOiunYVQ5QxbIQUKxyJiYW3z5s3CiohdDcpxzRWXUFFmW1vvHFfLvVwdWPulx+P52UjWLXoycuRI8co8HEdl+W11ZMjg9oKs3EIBi3v88gc+fvKEcTSkV5sIOnj8iZQdv71dHXWH3VByfCjuTw3t/yy+LsIilMuEzWU+q35Cw1Z8ZglRVluQtaJbO24jqkrzqPdg1zhs8uh/D9yHcjnsP3q6x12ukuvXtP3rFLXJRZkJ1HvIOF3SGsr4WuRVUt75N0d3r6bG+hoaNSMw96hGwRY0Ur+cn7qfBo71r+9ShE0W0oG2baZUvpi6ylKKjOrSToTiesPEnXwJRUZ1tl1/GIrItYnZCduooihbiAFazDOMwJ/yJBeXN9j7SQsO/WQFef7fFkJUa6sQoZjGhjYX9kBN9lmvUCZv59AH9dRv5BQKD2+/5gEAaI//5gl+ct9997mISn/5y1/ozjvvFO6JvDUsdXV1tHTpUhHb48svjy1cshsujtEBQPCgvoNtbWmhgvRDjoV0k5Jhq118wD8eeeQRIeixi0LJKlUv5OJgBTNK6s8rr7wi/n/00UcDsjJ0uqp8xW+VD8QrdtZXV2hwfRBaaNNH8CYCXvzKTthOVoHFSxZCMw9tVn6Ql7EvWwIAay5ksPuxmooiIUTl/74Q5nYVhZ8Zh8NNEM+rqtsWwrzR0tzkcDEEgNr23dOCf0nWsY1T1WUyVlwWXGgEnmERympwXDEen2plJchADLUJSjaRQNgGGsMilBTDGwBgQYsohi0RrrjiCvr6669d4kXxiy0OJk6cKGJJcQyNpqYmqqqqEnFZ2IqKxShneAHuqaeeou7dlZn2A2AIJggxvMta6vy6xPRqHxxY7yRh0ug/NtPtxo0bJwQQbpNTU1OFK0jdwFpEWzY41S92ScnWZGyZxm4hzQ7EW5KdRGNPvNDn7zHxswk2bMsrirIs5xKIBQoQ3DgLNM4CTxv2q0dyFKQepP6jplJItXs2bAc9YsbGMC/7Wtp9D+yDjepE1pGtVFmSK6zzzLDSsqLFRlDcm2fPfMDIfA9xXMo/yiAA9hKiJJd8HGNlyZIlLp+XlpaK+C1KYFdPHGj+7LPt4Q4DAKN2pLEv645RXdUPKIJ44Bxq6O1O4brrriOrETwTP+8jW451psZVpt6LsOJ/G+Q9C2E1FcVUlHGEeg0eS11j+5qdJGCxiR/E0tAhIMvtVuv2U3z+3KRdwiKAXb12CA98mleYcVixEBXw/Vm/KwE+cN0Ip5HAh3IBVMAiFNACrLTrAcaaAABgkhDFbp3+/e9/0ymnnELPP/+8EKDUwFZTvBA3fvx43dIIALD/zK9jx460YMEC8b5nz56+D/CxOFWalyrcivQfM526dtc3dlFQiStBchtWCQzdWF9NnbspKM8aP7x2lpY2I2XXL+J/dp1m+VgGqDMgVDGr37N38ybguIrF2W2uz6K6dqd+IyaH7rgDBI7ti4KNK7XF3JyH/OK57euCTfBlUen4mef6EfLlNJiwwljEAkkAIFgxRYiSuPTSS2n+/Pm0bNkyWrlyJe3du1dWlBo6dCjNmDGDLrvsMpo1a5bhaQXAtgM3kztyXtQwK25Up06dNLUeYXcPDAdatvxCtsE01tVQQfpB6tZrEMX0HqTRWTECdMmNlhZK2PqTiNsxbPLpGuYzABZCr2qvc3OCBfwgwq9xk/n9FVtCSdQriAEFggfLxGd1SgfaRJstugJgaVBHgHWw++ZMAEJaiJIsFliQ4pcU/4LFKI4NxTGgOP5T7969EQcKhE7HZJFxlhYTyKKMeCrMOEKD4mbZYtHccoMKG01MMw9vpuryQirJSWkv0oUZK07aevHDS7Y0NtQKEYrJSdxpmTplix2I/noEammhsA4dtE6NbeH8yE7YLt4PijvBz3rcGjxtNACeRACzmsQws/tBG/QFwdKnBQMy2dy+7LbafmOcFn2d82ccG7g4K5EGjp1pjKvhIK0SystEkGaAN8yoKkZks9lzRNsVJdslWEeQFwBoheVWVzj+RVxcHM2cOZOOO+44GjVqFEQoAALxyW7ipCs3ZQ81NdZR+oENZEnMHowGESxCaU3GoU1UVZKn+Xlti1N51W1B3luVCCiuij3r2pHfvqeq0nyzk2EZyvLThItSfpXnpxt0VYPKTsDxbexZxu24cFhbUeJi/WMNzF/g1qZfCL1yDOFJO1wW9U1oE+WeZWH6YdGfVxRmkV1xvrfshB1UV11OR/f8amqarE47UdPgImnrjXFWRm4+gvwGqkD9BMAsLCdEAQA0doWhcFDmflx1WYFYbAwpLLZT0v/FEWvdRyAThdR9a3VNCvBOqE+im5sbKXXvmoDOUV9dIV7qsGa+N9RWHXtfV01WxYxya9XF7OamRso7uk/ERrMDvvKRF5KTd62khC1LhIWe8WkI4DnrXS5dxjAmlMeAL2nNOuQOb5BJ3LrUEY/LNEKkf9aiPec2sLmpgdIPbtQkTaGHTcuar7KjSx3yfE7hNSI7mVpamskO+NrYwGNA3qhl1Hgr1Ocjgd5/S3MzpexaRSk7fxHvg4GyvDTxMgWLrRkBYDcgRAEQEoQFFBPJiGuBIKbdhkTjJhM82Ob4VXZB8Y72MAtN5GwxOWw1TbhJ3L5MvJxFHNvx+zPWpO7aobgE0X0VpB4Q1gAZB38TopTdYbekTEtLEzU11mt45rCgEiU9tfWKXFC1at+X1VaWUtr+9ba2RnHfIFNfW+koi5Z1ORZgmfavHDttjPO7MLXfGFdZkkshBRY5LdUWcx8ayBiO5yIcXzg7cYdwW293WExL2PKT2KhVXuCfdXzoCkv++goPLL9Kc1OopqKIaiqLxXu7wyJo5pEt4lVdpr1XljZc8zx0yywAQSJEcfynkpIS8WpoaFB9fHV1dUDHA2DlyYMVFjCMxi4+2w3HpkXB/IHa74vmra2UsusXMVnSb5AKgDzOVqVBYWHqXLV1a7b1aT/071v1OX+gAmZ5QYbjvRRjzt606j92UNKH+Yhj46m82XV8F6iAeXT3KqoszrGPNYo9H1NQk7ZvHQUzVm8b/B3Xmz6/85FupfleW1Uq5hP+wtZ4EiVmW1JqgLNr3LyUfSFZJ4zmyKYfhCjvL84bd9Rs4jF/Tu/G7+mpdnKb7ne+WOzWAAglDBeiKisr6ayzzqKTTjqJzj77bPG3WrZs2SKO59e9996rSzoBsBN6+mS33AAEBIErDD3KWqvHiVJddZmYzGQe2kS2wMuk3TV/IN6CEMTsRS0v+Nt++TouYesSqq0s8TNVIJTQayyYtG2ZIlc+cguHlndFhWGuTmVQu/MC32C+pm+e+GeB+/v17TiX9jTeku7DiJi1oJ2YiZhwTkURHQwAtsZwIeq9994TlkzMLbfcQr169VJ9jjPPPJNmzpwp3v/666+0a9cuzdMJgL3QMUC1gsGzXQcD1k+31dPnmdZWfeJ2qMaOEz+V98HWEgVphzRz+2b9OhEamLWgpOa6/i4+qC1jfuVFsNT938lL2UshQ6ua78McZcQWi7C6pzFwt2iejmtsqKXK4uyAUqbo2hrljy3KQkjg6TkY5x7Sny7KdAueANxUxf/2A+Um7zE7Kfaaj5jRVNi0jBlD8LfdVuif7NrOyRJ2zHqbXQFbIY8BABYSotiN3jfffCPeR0ZG0hVXXOH3ua655hrH+y+//FKT9AEQDPhafDBkERCENL7LDMqUf4v77V1ScdDZ/NT9wk+7wocTks/DtHbMeddoUEz8Wj1aO/gSQiuKskWMmLqqMgpadCxjgZTfoO7Dw4haW1oodc+vlLh1KTU1aBkzKoD8NCvPnZuYVhM2mFigrBVlxlPC5h+FK0CjYJeXWfHbqCD9EFkGLZ6FP+dw6ue0bnuMaMus3F56Gx/yGLCpqV6Uf6BFOB6Ny67B42welwVqiepPzFpV19ShrhlVf1nwsLylrzsGtm0NddVifqqrMO7hdo7uWkXJO1dQWTC4QgcgyDFUiGLLJcka6vjjj6eePXv6fa65c+cKMYtZs2YNNStwGQGAnSnLTxMm2Z5c9Oi5wGkZ6xYDUJuP1eWFFL/5R8o/ul/2N82NDZSdsJ1Kc48G1YTY0mVG8hzhPEoNChGgPbzwIA369SY4hBSg2SLK7+WBY3ew+7jywkzZ49MPbBALwym7VmnyAKxoJaVne81ii13gPs/IslhZkiv64oY6tg49YJ32LeDyEBbgwqEvF7nBuSmJF77YgouFb/9Rd68sQPEYj8eC9dUVsr8ryUkRL30I/v7Z9LGlgXhqj0y1WLdJ/VdfZoy/L71d2bFIkrhliXgFGt9PGW33U5SZQEc2fkcl2clBPR/h+Vb85sWUvH25rcQo1/ZD3/zLjt9ONZXFQhg3YoOQRF1NufifN4ZosnlbiVcgm7aNAISUELV37zHXItOnTw/oXJ07d6a4uDjxvrq6mpKS7B/4EQCBTIeWeXiLCMbocyHP3/5Q7rgQ34HojdTdv1JjfY3XXbC5ybvFwgMPipROCHiQqzQmiFF5l596gLKObFUW7N5XcGCbPm/TMSPbNBqEa/HM2T+/ovJnAaT7NXLiZwSenqMUJDjj4G8+j29pabLsglbgZVTH+9Iofp7eizeleal05LfvKSdpl2ELei3NjR4DwmtRFoxZxNMQHa1RrFpvrUBNeZHjPYtgnqgqyRObkvjFbtTsCi+85iTupITNP4l7CkgMVbs4qMgakUyHLYQ5b1TVQQuk2y7wppbkHSvEnMTnxgez261W49NRkp0k2iF+FWcn6nINT2Wb57vcPmQn7tDqIt7/9vu0rQG7Sua5SH1tpcc20GqYMeeury4PeAOBXLqP9Q/mxilTNJ7GBk4ArCFE5ecfG3j37ds34PM5n8P53AAEM0bv7rKLaMCun1gsCWzhSN1ARsnuxMriXMd7JYvonP6ELT9R8s6VjgVe1Wg8HmN3WgVpB8UiY2H6YfuUGTsG0/UUG1jDOq3kXByUnif4eSn7yCrwwg67W0ra9rOtdiAGM3rVKS1cn+m1c7yxvpbSD2wUO3/bJ0nrXSBanNtYuN3gvC/OCmTxS50lj2veaFcmpd3dytythQW9O7bgtkZp1cxiMSzM89TaeTxXU15IdoWtW4uzk6ihvppS961t/wM1RnlB6CqcxydsIcx5w54s/Ef7NoU38/CmuJKcZFVlm+cuVokfyhv12NqxtqpUzEnyju7V3eJBNSZnlfMYOSBragvMR/w5q960Os9BbNAmOQiufXGuhIUZ0z94uoa369qpfAAQ7DGiJMLDwwM+n/M52CoKgGDFm+s3vQeEqhcfAh3c+NFps3jDk2MWS/il/FqkO2p3oDtbQhWkqrgXHWGrLwlelPMVDya4F6zMwxBXg628iJzQJjpmHBYur5wuKpsyvck7uq8tFlF9tXD5YEt0nvjxJIyF7/qaykDOovqaesDPuiw/3e97adTBVSXf69Hdq6iiKKtt56+B1nkBtakuj8j+qw/txioK47Cxyza2KG+sO9afeYOfMV/Ln/GX/HhKZ/eQOrbFqtNu/6LmF7Jl0I8+WwReryjxq9yIsqDDOMG3BYCTVZ7mG+OsP7Z0ttDJt9BmHiYveY9wIZmdsMPzpj234sIiBm+Ki9+0mBprffepRiwEO1taMFUlPjYiW3EhOCjaRn0sre0gNjtTmHHE7CRYEjPi+SntHyoKs9rE7MpSa7YPAIQIhgpR3bt3d7wvKjrmxsBfCgsLPZ4bgGDD545cH+5YAhok2qCTdl4QLM9PD6LJQeCWAXrAk1grxzPxHM9G/+vy4NZbnByz4IWjxK1LxS5mT7i3GXXVZQEt6HuLkeEvzmWKdxnXVBSTLTCwenJw3rT96yhx21LtrcY83UeYfguDhWmHKPPwZnEvnvoybwuclUU5lLhtmS756xyLrV0e69kW26AfNgMuB85lUM4ahWMUcKyCmooi4RpNyZm1SJsefUzS9p/l+xnnS5rgms9ui4ha3YdLO6hgvK30/Kl711DyrpXC1ZbH85hteqEXcvmj8rn4stplMYaFZnb1phk61oFArZDVemjgcRaPB5ubG/1yt8obmcy2YLeDeGldtJ84cZ3jTUZsXa4EbivLCzKFO1PruLo+dm0eU3CcSntgQy8hChH3o7DtTT+4UbT5vLFMCbwhjvvi9p4QfM9H4JoPAIsIUX369HG837lzZ0Dnqquro8OHj7mI6tWrV0DnA8DOuAwoNJ4EKRrEmz0XDpLFD7ss6viyiPL5PILQFQbHiODBLcfJcY4XoZ4wzV0NsosY9mXOcR30ziduLxK3ay8CuKPabaXRdclxPeMmfs6WG816BQfWwGe/ksVTl80XKq/BVnP+Xdf79+2CH7caaY3i/2Ja0C5WS7isTYX5dKVT57aj3lgCexbcx3D65eOxteo2fsCCrtfMUb3xS8nvJAt5o2KuaYHLfWleBrU9X17KHtHX8O54szdQtWsbdHCLpnZTonOdd47Fp5Sju1eT/pjges8P9HIhK3OxY1fSzXuCf4flJO4Qm4yECKDgHCwYZBz6TQgBijbGKUqXtmWiji1rbDEfMel4E+qYkvmWUpGcx1sshEpW8gAAGwpR06ZNc7zftm0bZWdn+32upUuXUn1920JLly5daNSoUZqkEYBA0TsYuOeLko6uMKzf6RrissyU8qNgB7IpgyJfEz+zJ/TGT/ycd8TZZ3ecHCrzyoSJn1XKWCBl09m6xsMPAruATu2g+45UtRM/DRNiPfx9ZooMX614w/rg817baaHOFlEKyqCiGGQUEA01lZSX7D12SaDwLnH15cT/OhpKZVAtSqzyjKa+qpyKPcSyMxJzxFDl1yzNTdU8rbaej3jLAiXZY0Yb4XN/ib8b43QoDwZhuOWQinLO1lCM1/GvE+UFx7ycqN3kZ8p6jF3QPW+0EEPlLGMVeGhQeE1P9dP9M2cvIWqSCQDwjqGj5SlTplC3bt3E+8bGRnrqqaeoxY8dSOyS75VXXnH8feKJJ1JkZKSmaQXAtvi9GKaNK4yAMXTg6Hpvui+kqs1Lqw5ufM7rfAhVsicw5oZ5Z1PyjpWOCZGV8Dhglxl0G4GL0GDipK7dtQMsg0bhkg6ne8g6soUStvwk4jXYGX8mfh7Po7juW2dhwVAxNJDy7HLssffsH59dvFmlrijHKb2t8nVM/1S0erWeY5c9Wp7THd4lrgey5UFROVHr0q7VEuUv0DSYVQa9wS79TN+9zc/XfZwdiMWUBcpKKOH8rEwvSw5c65evdPk/H/Efz+e0xlhaq/mI2eXBSnnos4xZpN2ySjqM8NBgZvkIunwGIBiEqIiICFq4cKHj77Vr19Jf//pXh2WTEtiK6uabb6aCgmNuef74xz9qnlYA7EWY8sWxQHzfWwju6IuzEtvFvbG/32P7T/xMXyzw5BbNaVDKLh5qq0qEewjtr2mtCZIRVnnmDOzVWeVZpy61IYmg7dy9mT35aVVXTpzdGSku963aLurogT/9iJ6TT637YY7TkLxzhXA5wrGvbI1L26twWmNWG613M+RcBi3uFo3LYNK2ZZS8Y8UxFzlmjx38JYj6/4ALqUvMWhXtlqLYWjrOR7R6bBpZCVtuLqMkRpwZ4ywzXIFbHQ0MotSUP//LqpJn4z0GdvsztqpzJa8J6sqgFeYjzs+M42dyHGHVLs+94TIUaTXcQ0MgYzyv5dlj3TL/eQJgRwz3H8AiUmxsrOPv77//ni655BJavHixV0GKraDeeOMNuuiiiygh4ZirgVNPPZVOOukk3dMNgL4E1om57jCkkNjZUVWSK/zmc9yb6tJ8siyB7Cg0wIWQX3hIV5gqMVSPRNkfX5M5bVwNqt+x7vuMxpdTJe67zFzI8ZQn+qdHz8XQVp87EHWb+BlFwGVUTyGqVdOy6BxrgRchAsXI+CruedGqpNy3s6gMYJe8RcdE7ulWNd5wyZ5WQ0SAoowjInYhu70pt6B1cjCMk824L7/bciVjDT3zOTgfoTyK8tIC/XKA+N44p/Jz/xMSXJjS5qm7Jm9wSNi6hMzGbOHJ1/Vzk/eIvvjonl/9Ot4qOMcCVbwpyR63BkBQEmH0BVmEevnll+mWW26hpqYm8dnRo0fp4Ycfpscff5wmTJhAgwcPppiYGPF9eXk5JSUlUWpqarsB6KBBg+iFF14w+hYA0HXgHriLkBZtBw0q0+NrEtpYV0NZCduoW48B1HtonN/Xqy4rdLz3xwWOR6ywk1WDJJiyqMt59/uzM8OKzuyBshY+2dXcg/13XRsXZNuOmF2eLRUbpVWjflLzhSV3Uc7XzwNIgK6LtIG1JfmpB0QsmsETzNkU5lrXA7gXA9pU3et1q47lUeONMc1NDY73Lc1t80GzKM5K0NASzfNPAnJJpwQLDgk074eNWPw2SVRsVx/1aI98nTPMfmKrb7doLZpfT9V41Ef6KoqyKTdpF/UZNpF6DhwVULp4LtyxU7TrpiRDYkTpeGoXC0uNyqDGfaPauJam4bJxTPvTs/DHcbw6d+vp11hHeb63evbQYIhlcqtt2kYArI7hQhTDFkwvvviicMtXU1Pj+LyhoYH27t0rXr4YMWIEvfXWW9SrVy+dUwuAvTAnOLByMo9sEebfVSV51HPwGOrQIVzbiZ8FJ+P6PstWG/rDblX3sdqJn4/zWXUHl4JfkyHovWBmBla4Dwu2TfU1lQEd79/ET+2z0Hfy7C/uE21/RQYlxwXUD+vsIqUg7aD4P/3ABr+O54U4/xFBotSXQV+/s4LQqbcSpfbMqvPEQpXVB4UZRwI63u4bHzTF3bOvH3VJrqypzmcDN+zUVbUFtO8Q7rS0ouLySsRYLUUFRf2VAktJt5NaDz/nI3L5w2UwLCxcfTJcxjHHMlbqN7MTtgckRJUXpFPm4S1iQ1CvwWMpUMyO5Wvrsb6vdsuCadSCvJS9ImRC19i+fh3fzjrfZ9WV99AQyMY4b20jRCcAbOyaT+L888+nb775ho477jhVx0VGRtKVV15J3333nRCjAACkyQBHfuKn7YCJd8scO7lGJ/XzPEYMKFwmjgqu5zrRVJ++vJR91NzcSPrieTeSfjsQNTif3gsSfhhEse/y8sJMp8PCZNKpXTnVpcxrcE6td+drbhnqL0ZOOP0QHNL3r9fECtLsiZ8pk0NvbgqV/F7VpfS8P3Prib8CloTauACmLiTofG3nezNjU1Kgba6/x9dWllBZfpp2LiK13hFvM2EuMPQcC1qzHayvrqCkHcvFyzUmTZhiEUpJzFJTxzQWGE55JtCNcdZ1Q6uG3MTdjjrnbG2qlMb6WtGG+jdvCjN+3G8lrxCq5/b29dzhDRahGBFryo94lSxkBYJLf6NV+QhS0RCAkLWIkhg9ejR9+eWXtHPnThEjaseOHZSWluZRfJo4caKwpLrqqquof//+pqQXANvEIdKw41Q2kbRSR23+YE8z/MjWwozDZE66lAtu6icfan/edoAlhAgvcIwzVbTqVM69PA+r5qFvVxgmp9tXHdHlkurvmf3CBxSfR4+Jn274aJesUNZbA+iHZcu8Be5LS7yUQd3cQzpfz8dGDEtbRIUF0Laa3aZ6WcRP3rlSvG8eo34B1hThSo86qcvjaTXJlZZGFlEGUZqX6nhfVqA+3pnzhiSBTu2J2sVwl9ivisqCGRtCfH2v9XzE752P5ogSCstSbvLuAGLWaoiOmyn0wlfdML3dCjQb7fIcNPLQ4LXs2yQvALADpgpREjNnzhQvpqqqikpKSqisrIwiIiJETKnevXtTx44dzU4mAMoIM2Hi5+8ORCUdtQ06XUXByj1QnN22e8fSoqIPzBio+1qI8zdN3lxhEIVbbuLX1FBPeSl7qEv33i71Vum1mhuVL5p5ylM9nr1wg+giKmqzOKL9wpvvxQXr7UBUc6Cy46pK8yk3cZfneHua1kHPkzWXiZ/C/FYtInr6vdtnZggB7n2trm2xDfphM+5V5LkhcQFsgg8dKqBTm72YJkNTQ528ax+DcM4bSwjalkHzQqjgJ36OPxX0M2pR3B5ZwaLXpm6afYsAWluGej9fS3MzNdbXmNse+HHPHL/ZHaVjOl37XZVzYiX4PI/WmyWtUnUMHCs557EhdcDNQ0Mgi3GqPTRYtG0EwOpYQohyJjo6WryGDh1qdlIAsCea70DUv4MNeJDiRxprK0pEnCqlv+3YpZvxwqRFBzceF6R0dM3nKx+amxpdJn5GwQGGefcr74btNWjMsS8CGOB7mviV5aVR1pGtFNNniPMP9SsfauMCmIDlLaIMmvil7l0j/s+K30bh4ZGkP15EIo3L/bFLWFOQb1819LO4UrQpwe/z20O8KUg7RA21le1jcylwD+la9yzSNths8UdZPbSmWKU3lhCfLFiNVbXLFnGracZ8RNfzaIRlrVPkNslInhH8bZP8iFPGeXR0z2rf5zbSIEq3i8n3w1oiuzHOUvhWoizRRwQJUlnz5qEh5DclAWADLCdEAQD8wGXTmtaLD8G5sFBXU97+Qw95V5pzVPg9jurspxClEitYcfjEo8efMFkf6u0G4BrGX+DvUn53y+MVHbLVWchUEmjaXyRXLyU5ycYvyhi1+KB1uxXqkz6DnptWEz/Fz6vVOs+3fbvm74laQ3MBUSX5qfs9f+Eihvp/flfLcpvmmR9xGfw6twcqirIDjvdlBh6tjVX7A1ZiXeLzg6DD3/mDfMzaFp9jsoK0g2R7ZO5faqOMap/Uxqy1ioeG9tYRGl7PmxDV0izi1fnG+nVfjXU8348xawX6ernwF1/l3rbjCa3Q2EtKZXEO1VYUU2NDrbyHBoVumgNyzWmh+QgAdgRCFABB545FRYeo5LcWDQ7schYt0iiziNrS0uRXLBUtsMzgVcl432W+2mrcDsSWlvbPxq/LazepCWiCpPDQMF0nfp4TYaWJX/s4Md7FTr3rElvluSWAjEbPyRCLoByA2EVkFms96id+qrGqGOpexny6eyHd4EDjGQd/I6vCLiTDOnSgrt37aH5uo2NEHbuwjft0P/DV37iLUH4J0zbNHr3jXto2L7S+Tx/5nLpvLZmNpTfvBeShgax5D60BPg+DYkSZ1vaHaXtsS1MjleWnUXhklOsXGt2fardofvwmUHz1bZ7mI+ZudLGCxwDtyE7Y7vmSOsSsbWeBb5mGEAD7AyEKAI0xY7HWxR2LxoNk1eczJUaDlQcGKgefVnSLpsgFlbxrPvc6Yc5Azle5VJ+m5uZG/y4lmwJjJsQBX7ddMixSTk1aDGIRKnHrUjIad8tDPeuY513mbmKoTm2/Z5/sGp0nAHyJn0bCgcZrKop8/1DGpYme1JQXOVxIjj3hAory4eZWbdl1uReZMqjp4rj1mjsVLksVncS/c9sVhffV0tJM6Qc2UmRUZw+ncGoHgymf/LoV5/lIi6blxvAyqLGYKmJXKjmnzG/qqsspeccK6tZ7EBmCi1c0i5Rrtckw20uIGRqAjvUk88iW9vM6t01JgbpVdPqBKcZjWrvnNFqcdveeoWasZASK20E1uLmr1801n6eiYZGmEQC7ASEKAKvRGuigR8ZNgb/J8TGAKi/IpPzUA36duzD9MHXsHE0RHib2PpHzGOF0r+wujXdi8y7s8MiOrsfbYbEggEUjPfE04XAe9FnFPaSek7HKklzF6VI36FZqEuXqhkAxPo9xS6tF4x20y/t2ogAZRlleKjU11vn+ocYTo9yUPdpYxrqcw38xVCtrlPaLAFZtq62zU7K+yoOrWYU01lWLHc4xvZ1i0GkIn1uiqiTXpxClHvWLD743DOn1LPUuI4FvSpI/tVXroXFUFGVZa5OaFR+JjmkyZCyoZfqd2qOy/HTKSdxJfYdPpN5D4hQko31Cqkrb3EHXVpX6m6AgrP+e5yNScn2VmYA2Pqj4zp9+Sht02iDkPv7xsjEqkPPKpt9K8ZfVWsfrSHV5IWUn7LCUSORcL9hyn91Xjph2hlj/0es6+glt6p6tpTyJAGAxDPRhAQAwAu6INV2A8HGujEOe3QEpSUNB+iHKit9G9dUVfqTL+a3na/FgjN3FpO1fr+B8Vgkm6iToWCI9fgz4fe7K1tLyJ7CJH8f/UubT3ZW0fevckuiihjre5R/dT0d++174tNYaI3bZuQ7sdbxOoFZZ7QInu+eNfnXJ/4WJwOCyayZtiz36lw/PeWh+29jeIsrnEfqlxfs2fJ+WnZmHt8jHYLL44oBc2+v1GAuUHz1wzQtt642lXY4poL6mUnksR92LR3CWv/YeGjQuM3qKIX6emy2iCzMO+7znzMObqbmpgXKT3TaQmISysYn585F26QzTwQpUyXUDFVz0zj4F3jR4zJi07WeqLiv056RefmVQjChFZbbVcvFCjZyP1FaUKJsTyzzbwxu/pZJszxZV6mh/j/U1FVRemEkNddVClNeWMEPcNHsu58HbnwOgJxCiAAgCnAfMqgaDChaFjBhc8uBELTXlhZ535TsNrqSd2IpcFumEqwCipQAjnVH7AZDviZ13qzv3MhO46ze1ZVDaCun7lzlJuyh550q/0uXxmh7EVl58UCSGqt0paZkdqhawyrNKVnizGtR9Z5znTCjNPSoWvxQvwqq5oj8TPwXlVj5IuklijscD7GK5pYyizHjdr6GPVYhGO80D6Kp1wZ9b0TE+jzFu0fS5RmVxLiVuW0pHd//a7j6CVZQ0DZcyyO+1y1+/5yM6ll1nESqod8Mr0gCsUpdUzEeMihHl6rrD42/qqsp+H6eFqY4R2dRY73wxj30iz3fqasrp6J7VChJsTL74wpu3Dc4vl/s2C53EUF37bpkixuUvO3GHxn1/2/Etzc0uZVbPMqilVZdLO+6SJKu0dwDYE7jmAyAocJ/4efmlyo7d7wGIquPUXYPd7bH5+bFLaTAYMNFfsuygWyfXB4HiaeHGNRBriwUtTnR+vjITP10vqSCfS/NSLRGHSnu8iwDG7t7312JPf3iyx1anklDUf9RUbS+gYOLX1FAX4DU8Xdb8ctneCk/bvlcXCwWNyyNbAyhJgSpUudFqVVTXC9IOuOSBaQvEOohDmrvRkU2k+XXO0/1z3CZfsGU8U1tVIsZRYeHhZDgWGWPaIn6k3HH+nk7BgSm7V1Gn6Fjq0X+EqnM31FSaYkXuD2o3o3jbSGeFPljgY5HdfFfhyq+ftGM5dY7uSVFdY1RdIevIVtIDxX2LsI73nS/uopHHeqnweVWV5At3l+ER7m73KXACPof3zQ7Ogoz2eEm8gVXWlycDrcct7T00eD5/UWaCehGRzyXFV/WQiZZpCwGwGRCiADCA5sYGYYrcuVsPfSZ+LjqU5isd/h2m5j5UXqIkO0n+BBab+LnQGtjvk7b/bBkxqh1edq0pXfSTK7u86y3z4Cbq1mcQ9R+pfCHddjudVdS1doNuLSeoKicIZXnHYsAYRrtNrT4WSaxQFGSyNWXXKhE3whl/2nFXy9jWdiIQu4fUWohSMvHj4Oqq8THx8xd2w5l5aDN17zeM+o2YHNjJVApPzRrs3G1paaYOHUxYRJfh6K5Vvn/keyN4QPiqK3XV5VSSk6LlFckq8OJfWFi45zZA4wUTPRdbCtIOUk1Fseo6ydbuddVlxz5QksQw8x9nMC9cudyb6SKAOs8M/OrYKTrA9Gt0zxoXEd7A5xrLMrBNick7lrebj5gx5va1Mc79PpVvQmjVT1z1kgQWy9UKUVLsMDNRki8ZCjYN+LiI461UltnjhNn4nn+41Z2dfoyJ3WIUs9v3PsMmUPc++sT21PT5S1+5/CRMZw8Nns+fm7w70IsEdjwAwAFc8wGgNR46PxYQeOBRXpCp/0DcX1cYMp0rL3yxW6fC9MNqE2W/SX2r9r6a62tdd0sGkiA+V0N9tbefWAO358ETK9ev1SWafeuzOwn1ZdA4gdI/ATnAh6dZuXduP9y+UrCKXJSVoEEaAj+F6/m0PSEvknKQ3eqyApXpUHLuIsWuG9Wj3w7Etl2wzT7P39hQq66utOr3LFP3rhXtKC98B4qvxQb3v1N2/hLQ9din/pGNHG8u11NiyAy4XfaFkjYkIHzce5O7CxiTXN7qct52p9NABJAzRmltoYbaKqopL9JcSOGd8uxK2ZubMyUWHrL5qzq92lsO5qXsJbuj2qOCp3wMoB/yXwRQ/tNAF7b9illnQPuduneNpudjgT+w+Y0xaO2K06sY6s17g4GTNVM24Skow86eTJSd0/1PK054GXWbkgKFYxTzpiqek6hBz7GYkv7N5fp6zMl1jkfqDo+FknesFO7PAQDqgRAFgAFIC3FZ8eqtExQNYLzEiOKJQiA7sdn6iGNH5B3dJzpd62Mdk6i0/etUD6J5t77j92btvAlwF6j7fbL7BCPTKuWbaZMWI1zwKHSF0Y7WQPxhWwdffv7d605ZQWBlMGXXLyLI7tE9v3pKjAXbpt8T5TLv1GMHorETv3bXlD5raQ58kTFQXdiHO5ampsAsooqzk6ilpaldv6IKM/oUnfz2i1PzP4u6RDSCdn2Ajrfa0thICVuXCBdmeu3Ar60oDewEMs/adbNW+2/1Rq3A5ovqskIqzkpU3O4ZWx/cxFB/9uhoJih6SJOmv7UxqrVZ3wfUV6uP96vFddvh3M+4He++YK7eXb38uNvrnMNlDV7vnXFmjMv0mY+4PkslpzRjjOP+t49NSkamxaCTeosx6ngmzmNB3S2i9Fnidm4vOAY0b7TNT93v/itdrg1AsAHXfACEABVF2X4f67zjjd936d5b4ZHKO+KAB44WXWjyJ4iqkh3mLr93dktjJq1a/Fi/3eK+B71h1iiDKiaNdg4eHzA+d7m6/q2FBYw/mL2DU/fru4gM2k382lxPSpdodXEvmHl4C3Xv6+qOhDdKqEmrISKAwusFs1BiBM7PwVdcAu/IL146U1tZKoSYyI6d1STT53k1jwcm4me5Xq+uqtTvtNRUFjveF6Ydom49BwSS1ACflX8/NwJP9+UeRyjQIcfRPas9XJisgYsOpXW8UP/OZ2gba5P2XO3YQMnvPZZLGXhMxi7rB46ZSR00jtnmGrNWWys6FtsKUg9STJ/B1GfoeN+ncVzfHuXCH5S6CrcPgdUNX3+bho6bghRd3mVSroOHBidXoUrvT8mzcZ6PBHM9BsBoIEQBYCQ69V+usUFatN2BqEGa9MY57WYMrhSjNk8U/F5rdxuaYOjmL+sNCnXf7SihVx2TiffFu6/ZL3mnaN+x7tzRPmC0+45Dfa9nPP404h52/eu4A5HP16LANV/gt9bqYhXEaBvzx098WFbYvwxqD8cpiekzhMIjIrU7qUqLqED7DE8WadWlBQFbvGkjhnq/N3azrOCsZFcCja1n53TlJu1SemLli4B+7Oz3bn2mEFmDqFbh5jCsQwdFsfL8mg+oHqa36jQfUbGZz2Z9DYv5+akHxPuOnbpS3+GT9LuYz7xRWc9+jzHDLpXbCVGK9zzoO0doVTDuYwt/l/hCnvJJ1cY4feK3eYv35S/SJo2yvFSK6NiZuvUKbFNF+wu4X8+isZ31xFOsQL2bKZ02xrleQ5/TAhCKwDUfAFbDL5Nu112wlsZT+lS7RvC+CB0sEz+tRZaS7GQhJHDcL1PTGoBBlDSBqK+pbHsGXo/RcfeVBTBmctPqMnFlVwQZh9T5JXeGF5E8lz+1Zd27BZSREz/v7liME8ldrCF+f6/vDkS3BQ8PEz//20kNdjTrjg/hSW/rl5YWamn2ty03cKOIUz5wHKD0Axt0O7/MD0hvzBChBK023ZQjg/q67r0fUHaMFunwdT79+yM18XqaGxt0ifUlcNsY51wmOY1NDXVO36u7fmN9DSVs/pGSti0LoO3znFZbe2jw55om9qv8HJ3jb2pOmIq6p2k2yJ9MXVkPNFG+j+f4QhxnyFRX4Yp8hasbC7IbbSWUF6RTVvw2sbGELfMCol269FqjUJAUSysl+o1PWLD0VQY1mY8oyF9JZAcAWEyIamgILAioJ8rLy+ngQXPc7gBgBVw6V8su2umJ8z1bwx82T/StBAcWzk7cIYQEFqTUoMiSwsByl390HyVuW+rT3ZqhVnkuPtn9P49SqxX/XWG0mjaJ4UD38ZsWU/L25YGLoSY3c0UZ8ZSya5U+MRG0JMDd3QoOOPbe08RSizqoUT32fW8Bbohodzb9CinXn+QdK8SibGN9WwxK6+KaD9VlBRqems+t/XPQ69lJMa0a62o06Z+MjBHlet1QHGcGgD8itY55zIu1HOuLY8AaDbtX9ReOQ9Lc3CgWjiuKMpUfaNZYUGY8xxty9LumvTByg05I4iV7ffXFqqzodSiDvlxuB4Jz7OK6qsBc3LezivQlTFkBE+qdc760NDfp6inDo0WUx43QPs/q+xxu8L0BACwoRP33v/+lzEwVg0cfVFVV0c0330wpKRZw0wKAWWjsekbpdTT7rR0nYV6uWZKTTEd++97DIeYNRlkEkPAVaDxw/9Y6LtC2tlJhxhHxtk2I0ipP9d+BqDV67bJ2Oa+Gt8VBoltamsSuaHZlFQjtyqjBdSs3ZY9wzZJ+cKPnH5i9MOQhLgG7M9IW1x2I7osW3A5mJ2z399SaU5hxWNsTtlt88FcUUF9WqopzRTxBtsRpHyg5tMRQ1997WmjwsDvZxyJMRWEW6QX3W/FbfvS8kSLgZiNwqzyj2lJdFqAVJJ0tMPS9R+8LcGbDfadkPZWj1J2f07E+f6PJvSq7Tn1NBaXtX0+luUd9nM3Y5+2L+M2LA178NmIsqLZ86IH7s1O2Mc7/8/v63B3ejMQxKh1eGmQvpKacBNY2mtHe+NyYo8mmpMBPYQlX4hrD45XshB3ym5J+v77W8dIUH+7hurVVpZS2by1pRpjvjXF+9wNh1uzLAbA7hgtRhYWFdMstt1BpqVPAXD+prq4W5zpwACaQwB6IRdiaCu0nflqMzjTuW4N9F5q3wQgPCD3fv3l5UlWSS1bHr53rFhkUetoF6562vJR9vtMbpsovmleLCX8WOpqbGqg4K1GXeuxqBaXzczOoWKhqz9UsvGqUfufyxrtfC9IOaXNiD+d3vj92v8TtYGleqqejfJ1V/hpGoWity4PAYeBYwnFZLVxU6YjeYwHnANUun7e2Um7SbspL3uvpS6/CCFte6EKrtIHi2P9aLkDL1Uc9ngvv+s2O306F6f4JvJ7qdV11gDvTSVlcTZc0G9K+WGOcohb/yohhq8WUvn+DsLBiF1uaJSnQ8qDAOp7rjq80q+r3dBKcNbVedb24/4caNSdWkpbWVkreuUK0J0d3r9ZQhNfBukilNYj4fZg2YqjYfPHbD4rS5K1c6vVcA98U4SNdOvcxvCFO7cYvw2IZe8kHjhnqzTpU/aakFtlnyuNEbTzV2LMvB8CKmBIjKi0tjW6//XaqqzvmJ1otfOyiRYtozx4lgXcBMA5fA5r0AzI76AMZaLXauxMNNC6Ay0JUmDHpsFvw0WInFyxqB91KBqx6T5odh6ra6tiq/J41rBaOa7ndJ1tkeBMEeUFFjUm/tzzPPLSZknYsV3SMdrs4vcHxvFoDcG3hI10eXGHoEQut7VLKrQ6sIpRKsPWMJrE1HO4hPU/8mhrrLRegWle3hG0f+PhbQ9x0b6Vpt5r1sCfY7aoW5+d4VEVZCdRQH2D8Bw1pDDQWhTsmti+8+FqSm9JmDWCSi1J/47L5tiLUJl+5rWVXyByPSS2+LNf9Ru89IC4xorz3+0bGxTIKpW2slu6bvF2T62bWka0Usvjc9yLzA6XP8fdxZlNjnXU2Z3pyD6ljPfx/9t4DTK6rPPh/7/TZ3qtWK+2udtWLJVmWjQuuuGFwqHES8oVASMCUj/ZP6CFOgIT2JSH0EkJPaAZjG2NiMC5ykS3bsnpfaaWVttep/+c9uzM7fW45995z7n1/zzP2zGrm9Hva29jer0T6GDNHbxzFrLObRecRPcllf07a0vfM7WnJetm1X1i0yDI1/xx39TmxxdAVLVqiFitb6ZQzx6DhghIEYacgCkEB0rvf/W5IFNFkLBdn6q1vfSvs2rVL6qC8hDvRahGljpyDn8E0SoHuB1CYhpc8HJLjTjGhCfc4Gnra2dEbGB5jUFM2Jb5jbNOrTxicVJVO5vOf++/jwyfVB81l577i6+fEefNcS1kBurbMtMwqK7jKaUt012OasFjn+BZBA3HhT8XbZXJEY/yOYj7ZzTzkC4Jm4SnXvDMFgLZt5YWg2Hw9N1nM8wJqettzZjj54qOWCmLMvPiZHh9Ov49GCuyvbGhj0SzxUfkEY3PmKkWoKSeuYcKRtCZB/nOpeeOiVFnVr/nJPEukmIYzS6k1HS9g9VkmWwV/xTjLBPRaBOFZsiFzFeOy5xd156Hcf58ZHy5qbVzw95l7QUN7XVHGpXqMu7M3kLdgym72KkEUVoyLzE7C7OSI/oQzhVoSjk+CEBXLT6/r169Pv3/ggQfg4x//uKbfR6NRePvb3w4PP/xw+m8XXXQRXHvttVzLSRDCkDRHKxTNofUIxY4/9zt2yX1yb7kLFS2LtVbza+31xaDuGBugGHjw0+KOR6rNn1ZsU5riIwBa+jewzE1R7u/ZxZPZ7WjBwc+ucY4uAkvHJ9CnCc8DNW2yNB7sab9UGbX239kj2uINZWsgZv69+AWGqjLZffBLggo3HhZaQEmM6XNIZvqZbzUKouVc00tcgFnY7sIoA+rSD+LcThnJnT9hkotHAwwf5+uetTR2P1OF3SSXwnDsJrV5lfne2LnjXPLEvZRoaPNsYDw3W+uR7hubnwWN6wEKL9VbHGZbx3NTQkgmITo3k/GRTxuigG0yyzuFwfKWu5Mws+u1nEcKGMqpysK4ZDT3jSpOvvAHLlZ5WgSqeb9NJrPnUCn3iQQhJpYLov78z/8cXvOa16Q/f/e734Uvf/nLqn4bj8eZFdVvf7sU3G7Dhg3wla98BSoqKkwpL0HIh7pFcuiwPreWaoVXWjaMhQKDT48Ns/gDmoVlRTbAuJEafPFxbhtaPdYWhTZztlhJmJGnlnOlobY3ScBpOC7A0lv0/Y+CT9TCMlKkUuC40RIPRD/8XBRlH/yMYeelcV5cFjP8+RtO05rLj2LzINf+saGv0Z0bWuWNnzupQSGizL8XTahw7CdtFlHWt5H6PnbAGBSUcmPQurwFEURxagA9rvRkceF8LkcQNXnhDDd3rXa6peLF7JSxGNZZ9bVIQFsyVlzRH4FtRGens+JJYqwYnvN1Zh+MnzuhK+afrpi1nJS6MoWQht31p2QQKr026ELhqJiQUaR4PKpNIKsSTNcp6H9uFOHLqtX1arH0S8YdK1Om0dOHpV7PCEJkbPHn8dGPfhSuuuqq9OfPfOYz8LOf/azkb9CF3/ve9z6477770n9bs2YNfO1rX4OqqipTy0sQpi/uJdc1LRpX6n2Pj+QurpwOAXo2oIViuRzZ/QCLP3B094Nlf686PgbPDYQet0xmHgRsJrceasaBnjgy5Vyy6QXd4hkjmVe3wf1PGExTf2BWfpnwSSb/gp1vefXOX7riSGmKh2bPuQ8vd3AMmnkZnxf/w4A1SikNRLvmSMz3xAsPl/hC6blo9Ez2Gss1ILzucWVdW8YiC+PP7DFY/PJBbgHV9Og5FoC8pDZviThlWP9CSj7cxqOAFlG6+jZZOqamsfIU6TuBxuCxPf8Lpw8U2avkFVOD9r0BdM2JOjBlbUnKMe7tPHucyVCEnJk4D0efeTDvwtcQGVWbmx4v/VUjz2JS1Oe6QDlMLlt2rGaxFeOsdtN8/tQ+VcIvXc+xBsU4O/fSluST2RZZwlj9+ef1m5hDkCCkxBZBlNfrhc9+9rPMmik18X7gAx+ARx55pOD3U//+i18sBTBetWoVfP3rX4fa2lrLyk0QlqDnHJ2xyE6cH4Sjz/6Wa/p2UjD2QB52aIOLp+lqKxljcGzoGAyfeLHUl9l/Tzz/sKmxmayN2ZIsKAwwjVw3BJw0EPP/yaw2TBpyl1AoPT3kjkF1OYk/iWJ8kQOP/QISJmt+Fn3GDDx7I4OHctICUzj81K/h9IEn+SWYU2cM0K1GWJN5Kac6q4xGsS1GVJk+3veHn8CxZ//XvnKUEFAV0pAXRZiSYmp0iCkzoEKOngswFESf3Fv4XJWXjo65OCsmiIljcHZihF1Uj53lrxlvBjKsD7kUjiGkk2S2ZdmxZ0qcR6zCUgGBLZIokJ1irpj3PfJTGNy/FBNcLPS1u9lrTebcnD4PWWiZrHo90DpuTarChcEDzFK02H4W99Kl58jiCiHI1MiQqnIM7lvy2uKkZ39+ehwis1OFy8rTErLIvUwiydHiV+sgFGxfSRAiYVuEY3Sl96UvfQm6urrSsZ/uvPNO2Lcv/3D4sY99DH784x+nP69YsQK+8Y1vQENDg6VlJghenHjhDzBbNJC2PXDXQCx2CWSGGytOPtlNyVPyCwo9dYvMTekac3wFEwbIiK+jxrqwRELciqTl4FdybGo++CXNE5Q8/gsVcXiKbOY5uaSavHBa+4/UXMAmiz/zVglI0aqo4OWtBQc/zXXM+H6+9Zw57YWa2Gj5MD+jzf0HT2UEtXNlgcyNZcyhSdUEP0dhiuma2Bn9kFkmGZRFzh17vmwQ7dEzR0r8q/G2RWHo0JFntf2I9WmmIgSYxuGn7meuuwoK1crE5kDFLIyHWhrr92U47xjbW1jD2NmjOqzqsoWhaqyOcb3QE7NWfYnsUUrCNWbfIz8z3cJLz1xX0HW0gOA+JteDR3mSmr9rlqB7KT6PhfNMpiAqEYfo/Kyp01yuq3AjgrbS7g3NWdPxHIDxUdGN42JGeS7iTr34WIly8TmP6FEIyGr3Ygtx6itZ5dRQSINjl7mrf+zugsqZvObmPA8N2f9Y9HfRuWnt+RAEIbcgCmlsbISvfvWrUF9fzz5PTU3BX/7lX8Lp00sXQ//4j/8I3/ve99KfUXD1n//5n9Dc3GxLmQmCB7jZOfTkvaq+q9flmxloWYCtFbqQBqIWJs6fguPP/X7hcMKrPbWf+/QdMjS5RTN2MEKXNS/+4SdlLwqLlavs82L0LrnIAaRUOxYUjNgkII3MTZexnLPeRaOevE3JmtecbrY7UI1u0ZAJjW4wzT746b4QzrsDt2kMMutItT/Ung8KAZg2q05MfzaLridJ4S8Y0Gru0JNLLseNz0Xa6zCcEzNIT96FBPK84l9qGT+53z3+3O/g+J6HuJTDjRdTKAA4/vzvYW5qTHVd9bTHuNnWbkXKpHaM4vx34dQBle6ks/OKzs/Akd2/0fITS+aBk3sfBZkw+znLE3SrOnoXt0jN+65ef7qLaZ45+DQceuJezWvxmUO7Yf+jP4fZKRXnGCMUWQ9KKRiaEf/JCDPjw1zSsfY8YswlourQBskkDB1+ho0nvc8i3j3kJwy2KsYVs8QskYnWYhEEIaIgKmXd9MUvfhFCoRD7PDw8zIRR4+PjLHbUt771rfR329vb4Zvf/Ca0trbaWGKCEA8nW9qoIutOzp7gwKo2JwJ1E24IS2l4aSfpqMMpC6A8MsQuqU/tfUxfOUwUAiglXPNpiylitxBgUVtdcz7GL2Atic1lsisKfeOSW+pFrVFK1VGz1qfp7aVPYzn/WbZH41mPWzS189D4uePMLRpqs+ZZNqgWfpnZLtlj0Lp8BYGDQF6/INZo+1rTP9NlLheNrnEyWN5pokBzoGVP0a9zeM5OvmiPUETt83LwiV+xC8uCe2YrFFMsU1zhk4y+vMspbmkQSGtSWCv853zLbPtAAej5U/thdmpUlZvCfAFZAk7ve8K8caMoRQUipfoCBRtayiSN0N8m67eFc6F5dwaoOIhujbUqk5Xuv6SpLil5r9Guv28jCCcJopDNmzczoRPGjkIOHz4ML3/5y5nrvhQtLS1MKLVs2TIbS0oQVqBD4zDpso1YicsXFCCgxVkiXs4diNHLB+2/P/Hc70EkpkfPFvw7at1pddVjlbWcqt8mDWog5lzO6b2oM/uCqphARHO+JfvO3MlF78HS1gNpUq9mq9ZskjC4bxe7/NLvttLk/itSLs39U0KgJ+zBz8Z12Ko2GTq0dFEUj+RaA6jXpDWTYuk7RUBQymrDkEWUYSsM+9tXiLmBg1Wa1Lig/qk9oBpXvnaMSRGeRQIp0ff6jyNZZ5D5aX0uLNW4yDRCMdd8fMemmOcRO+e87PbVcR5RWfaUVSx7Pz2uPp+CmZpBcdd8wrj9JwhCPEEUcs0118AHP/jB9OehoaEsF35oCdXd3W1T6QhCA1YY5OQtttYIAXTHs7CwPdCXM8bgGtIRAF5blto3NrGYGrce1lFsLGAbDh/fK6T2Wf6G0pyDX2Y+yqKSROkfJG29gFXrCgO12bRlAlJgrVu0XAGgNi0/1dYoZ4/DyJnDzIJIrwsTKzUQs//OUxgqJva6h9QRGDw7Ae35eDz6XLqYfQmQWY5MjViNY8oqa2q+yPfcOA0R90lWkjvv8bBGUfVTDWuMpX2kwdUVvyyT7hlnauZpDe2BVkboIlVfodQrxhkc7EXXYdXlMtlVeDEFLa6CKIuGedm9XM4YNLr3M/L8qovNlTSeT+aZWM0YLJSGyedkbtbxLphPCUIEhBFEIX/8x38Mb37zm7P+VldXB1//+teht7fXtnIRhJWgL3H0a6/e17R1hxAUTsgCBoM2FSe4wuCJpssH/ZU+8tSvNXxbfz6Z2oMeT3lB1MzkBXWbYm7Paq5rvjL56nwuhNA45yyMN551Zrubd3mdGbxda0DdNGYe/DCZYmm54iDHwRJAZzslYosuLRHmjUXl5afG5yR7rOgc62YL5IvUSesFmIyXuVpilPDPOyF12/HC9dYoNnX94P4CrsZsKGTu/IOKXHaPQf37NoGfY5VFw7lIS/3R1d25Y8/rLJJ6q+hiMV01CwFUKJ4UjAVrotUcCkCKuqzmKWgQdXwmBRF8F1WMK/Fz1ZbtS/3rUVQoZxZORN/vVCdfbC8o6LghCJcjlCAK+b//9/8yt3xIdXU1fO1rX4PVq1fbXSyCUA+H9W7i/GDhoI48LmEtWpB5afiIJIgrlaewG2SLNDutqn9kftqig9+SIEpRIYgqnIi+nxm1iuBqgWDVfGHYTxRYRqablHLjykiMpgSHMWi+VV6iiDWKRK4wkjrHoI1TPvrq1zu3abp8yJpLkuKNQeaNhWJEZTWIBaDiRTxTGCozRmNEucLtj2KKQN6R+2Y7cMBFa/mxIF4do3MzGupg5EzMwRqlkHCKq3W8E84jvAS42tKZGT8PI4OHdOV89ugeDVbdRiyi4qZYRHHt06LW8RrHoJTW8QQhHz7eCX7gAx+Ae+65x1AaicVFa3Z2Fv70T/9U1W/uuusuuOmmmwzlSxAiEY/Oq9doF29/bnDDo3Xjql5gwgupLlp1gocUxYRNGdfDj0a3aGpJcNl0m62BWMQyRyoNRHsut/H3el1xHX/2IZ0u2TVqKmq0yiuciLkHPzdrIOZpoWuscywyD/FYxHhBTDw0l5zDdLj3MwVXj0H79oLZY9emthahi92glGSjC2IuCF5EO117yYLqOlrYFoeevNeSNTvbIqr8XnB2csRea5TF6mGsZq0u/bV4g3HKOnxk9wO68x4fPpnxSf24SrelBktDQ+6gFxLR9ztVaRffa7rBVThByAh3QdT8/DzMzGRriOglFouxlxqiUYdo5hGEkQ2FCmLReTh/QmOcGD0YWMe1bxrAetywUUmq9S2u3ZIgV5NQNCGHaUIAjhRyhTE2dEy7+71S5RR1mNt4+ZcZ6w0PZKX9nusXlGp1x2K7RVT2P4DTyR9z2uq8/7GfZ1nX6UWTQDWp9fKhuPs11c+cXe5YXGGpYnwe5DJ3Ov9x1zQH4j4b12JXYGQtNl0pIml4jJddQ3UnTYpx+pqtdLvZKqwoFZPTQLESyQzFOJ2KJ+buBbNd86XyQwFLQaGYXqzqWsMOGuwZg8XGRqrv9Y6B3DOxuFZ5SdeeRwhCRrgLogiCsIA87Rv1i6xeX9hWUlYQlSzjrk1VJtp/kvXzAm0+dva4NUI+y1B5Ya6xMU/vfwLmZsZ1lik3c5VCFCMaiHrdopnvmy/7YyIBJ198lHcmICL5vvm1lXNq5AwLUi0q7ODHwR1Lof7jNS5ZOkWtUVwgBDAmh+IihCoYL04wzJwHC41BtGYdGTwIMxPnwenka2Krb2u8WDp/cj9Mj54DU5DEu40Z1iiD+3aVdq8tKaouEkWcioy4CrdAoK3HvWoWUnjJMIhqiyhw1DzD5pdMaxRBPTQUOo/oE0IlJexcY+cRbuR6ylCFDst2vVb4pnto4GQRRRCEnIKoLVu2QDy+JDW3is7OTsvzJIjC8N2AmK1BYoRSlgCGNBAl0GTO29gkAU7ufURPSuA2VxjchFBlMDIGE4mYcYuosmXiewFmRoBq012+lDIcUvO79EdtCRzbk+FezwhlDmSlraWsiBGVsFQLHT+fPvAEjJw+DNKRtCtAvDH0aUhbaAVjtkVUTkEunNwPQ0eeBVdgoG1xHT5z6Gk+xbBtH5MUsg+cKIQqSlJ/n8xMXIDxp/W7pcrO1pyxYMUlplmu+Zzksk99GwlaZwN9wcNVeEGyimTOGOSJZecRrT8TxD2rJoG2xrbk4ZrPzP4rrRgn6JxAEC6HuyDqjjvuYC+CIEwk7/JPQMGNERNwEeuTi4s2NuU21aJt8tLlMXLwi5t08OOq+ZYvDHUPYmgA63KLpvbrHNxDWu2ab/L8oJxCKB7YqAVrT/10aNJagGtcohURBBOaG9FgH7h5HS4gkNfQnmNn+T2rZl3+Zq7DxXLWl7C+n5lpCSD0/JEU/zyiNm+tyiM83DSbDbd1XuAhKHwddOwF1Y7ZbC8h/GJE8Y3dneSj3GzVnpogXA655iMICbEzQHVOQcxJtuymgb/lh+aUZBCWGUXkQ6mpB78kF9d8ppNZ96R7x4DNJz/TUs6aBzm6wuB6UZOTVnTeithvfNF9gSmKFqyJlw88sDwvV90h6BMO8i+GjnxlWVpkvrznjaqqitMeRmOjyOKhodDzNz89AaNDR8A5iDOurJ4f+Lhptvo8YkJ/WTbXajaPBxEouxfUXc4cV+E6N1lm75Fz52r8fOy5h2BqZEjK/iQIp0OCKIIQHRXroSiu+rghwSZAisOpQUQJRl8mc1NSzXSFwdM1Hy/wIJAvDOWvmSvK3JJ78BHFEkBv4GitY9CIhWmBv+ovVHbi7rqEFVUhxCLJS+5cYJbrVk0UTNs9kihRrHHsWieEmH9EKIOdiDIPlsyXj1s0Ucl//pJwYNcvwZUIeh7Jnqu0WkTFTbKISprmocGMudl0QYbK9PPOI6IohJh4Hsm88+ClvJVKDZmfmdBbtKLpjw+f0C6EIgjCMgRVqyAIeeG1+Sp5wSlIYMxSmxFDGogmWxtFZqfg/Ml9htLIr58DLyOS6qomirCC5xhMxGPmWETxaipFHGGMEZbGThl3K3mu+CSpqxENRB7Cbgtd88k4/rLRWn4XrAGZ6BaGFh/H8o8ZCyixFxQlNoWbcYV1vBYBtYhjMOnw/nXBPLrkcrvM9+wcf6YIQ5NyuObjpJwp5PyRg6juIctZKhmK3Z05D+qWQxX4YRI9KczCgcd/yVUxDt/HoxFjaRIEYSpkEUUQEiLMRs0kDUTTrI0Wi3T4yfshFps3mJYgfWAqajXezS6H3rz5aCAKC6cxKMx8YgTbLKJ0XAqo9skeN9xHBS/ReDZVbl1c5FtdHEGw+nzNdMuoNc/o3AwcfeZBg0VIWmqlaAta+syuMZiU1/LcqMWLK5SS0hS6SBRFScScfMvtBY3OqdPjw3D6wJOG0pBCWGYVSfHLZShGlICu+RY8NIja8BYgStUVO0MmqEkkv2x4tpm8cNp42jQPEoR02CKI2r9/P4yNjbH3AwMDUFdXp+n3x48fh6GhBVPLFStWQGtrqynlJAhpDn4iYqIGotENr2EhFMcNz+zUKIiK+na2bzyaJUTJvpzin8fQkWcN932WNYowJyF7sK3+xQ5+qdgUvFwCcdRA5NlWufOgXt/xtqJ3PRFoHRb5EqjYWnnm0NMwPzvJOW397RCZmwLpEEYYag7l62OsvngBdmzPQ4bScLsQQJQxV6ochiwBTBKGpsp05OkHBOoDMfqyIC4+j2TF5zHBImp06Kjxs2imNQq2g4z7KpUx5fL3uWII48uOjaQRxbjMeZCnYlySn/KQIGsRQRCCCqLOnDkDr33ta2F2dhba2trg3nvv1ZwGCrHe8IY3sIVi06ZN8IMf/MB5GpAE4TBNbKcd7EU5fFuDmG4ITNNAxN+ZXKfh43sNplBAA1FnmcfPnQDbUFnmsgIOu+RQOi4F1F5WcLHKKyEk40HeGHTRVkwY4a+eYugYA7rdwBX5GlpEmbNXcNEgzEOkvaAitJtmLJdRIZQs+1VzEWQeLIWgY5AbnM6Ec9PjIL0bZxvPI6UsO3gpJZlhEXXqxceMuwrPEdgKsz+yAFFc5KIw0RcI5f+D0WeCyRUzPDRwjVnLj7y5mu6GCUJoLLfv/fd//3cmhELe+ta3Qjgc1pwGCp+uvfZa9v7ZZ5+F3/zmN9zLSRCiYGqweZdpIEbmp/n5DBZGGGgiMtTJpDGYnQW/djh7dA8k4pzc/gly+BGjje2qu3oBmaY6JjlpIJb4HfplN0xWneQdf3rH4Mz4efZaTACcRNn2UFnfRGIp3l6Kk3sfhZnJC3qLVroMTpNDabhMsWsfUjJfI0IAE13kGlcGAUc+94VIKfIUi/GR9dE295BJ84UAosbnkUFY5oLn7Nyx54v/o24LoRzXfMKOwdyJAFyH2jhmZjI5cqbEv2ZYrWn02iBHzNrs84heDw3jwye5lYkgiOJYuppNTU3BL3+5EIyuqqoKbrnlFt1pvf71r0+//+EPf8ilfAQhDUmAWGReTOFHeiOW1H3gzXJDwPlWCd0B8cANBz/1Gu+CucIwOAbN5sLgAcNp5D4nrgM1MJPJ9CWRbXMhlkPrqVPtY5Vx8NNbv3issOA9Fp2HfY/8FIySfziV7+Cn95JyfnocDj/9a/aan54AuzhzeHfRfs5H2+VDod/yYOzsMS7pCLkHcmE9pkfPShcjCpVCnOWhwB54use0M9aX5fF5zLRMdjMytIXmGFFxU6w8cNzwGTsuP48AwIkX/gD7H/nZ4l5QgjGokez+5eeab3rsHD9LTBmefYIg7HHNt2vXLpiZWXDFsWPHDqioqNCdVur3mN6jjz4KkUgEAoEAx9IShLiMnDkM50/ug8bOVdDRv82+TY8Ji37uxpi3G4Kxs8e5pOOKg59Kf9mENiaGT3Fpsqx+wfdu6qYkwPE9D8HMxAXo2XKNbcWYnRyBiprGvL/zsE4zy8KNCS44WIYuzNXuvXyYyrh8Hz9vrwalJf0g4PySJ6hIShynjF0wamvkRDwGZ4/sgXB1A9Q0LwO7OHf8BXOs48uNawHGpJvnwNQ8ePy530PT8tXsTGJXnxzdXcA7CgcLBbOsUaKRWRjc/wSXtNwwBpNlziPxWBS8Pj8IB4fzk2n9i8pc8Ri3tLLf66s37ullIxaZhbnpMfZ+cP8uaFq+BkREjUWrqr2WziEdj0fz/nb64FNgnms+bkkTBCG7RdRzzz2Xfr9hwwZDafl8PlizZmGiRyHUvn37DJePIGQBhVDIhcGDtpZj+OSL5rvm4+zjl9eG3kkHPz1ax2NDx+DwU79ecktlE7NqDy1a3BuZHHh2ZuK8ozSR7SA6P8NcUKAlyOC+XbaWpfQ8XMh1nbp+yr5Q59e3UyNDTIBmxsHWrfE6PYpXLteYHIoqgoKCk9ZhPQwdeZYJgY4//3t+F4q84SQEEBYBngM7QS8DkbkpOH3gSfbZrnlwfnayxL8aEYaWcc1noP9HTh/S/VteZZCH4nU8c2g3vPjwj9m5RNR1OEvBUuMNeZa1H8e+xrVj7+//m5OHBj6Wobi3l43MtTcWmQPRKPxMJDX9XoT9nnbrPneeRwhCFiwVRF24sHRh2NTUZDi9zDTOn7f3MpQg0gi+WFvmY9/I5YOJBz9uiFAGDqAW4YHHF1ym5lJq43nyxUeZQOXI0w/YevDDA2gxjAVJT2Zp6HGJp8MTvPB3yBjUQ+bBnLklc1hb5AtDQUyytGDBtZgRQNwUkjxj0AnQ4QVjRMl3+aB3Dc28AGNzopDzoHkWUSJcOrtdGJqHgGPQ0F4wyyJKzLlF9EtiK5Qz8TnEc4m46HRnxoJEJYWa8wrh5nlQhhhexdE+nkQdg3QeIQi5sHS2TGRM1LGYcc29aHTJxHN+ft5wegQhLQLtCZY2KHyEAHTwMxfUyERt1iI9IeWGtNAYNOKuCQVu+x/9uWkxCNwcm0JvmbPiyAksBChUPxn7qTAFXPMJelFXEg7dIfIY5FZpAcetY54lHmNQMO1fUfduvHHMGCSkxRVCgKSDym9kahR0vpHCetWkNs48B7O9oGB9FJmZZEqn3BCsfsXmQbfsQQhCViyNEVVfX59+f/as8cC2mWlkpk0QjiC1gAq64JtKRp1j0XlmjeIPhkEknHLwK+nOR/ahl+Tb39G5GQhWVIMoOGUMGkXxeEE0zh5+Fmpauowloogt8HXOJWzSkWNwfmbS3PlKgK7PtJ5mn0UolC7EjSlnFF6WycIiQxl54aa6yjRGRSiDyaie3wRri1S5RZ2fuZHb7oL1g1WIaBF19NnfQjBcDb5AiMv8JaYbYPHdBxIEkY2ls2Vra2v6/WOPPWYorfHx8ay4UM3NzYbSIwiCLzw3BGiNIpxrNBdseGQ9+BVEi2KUgUC7VsG03+ngt9AWike4Q/7YueNw4vmHsw9sBoo4+OLjLLaT8BqIglllWHr5INYQhAOP/wKmx4dNW59FeObo4iGzMcToEzP7TDSrZIQUQnLbQ5wxmH4eHC4MzSujBGXWjKPqpBiKSTp54QwIRaEYURKsRVqfdVx/UCGxVPeJah2PMfRQsVevm+ZM66LTB59KxwQUCVqLCUIuLJ0tt23bln7/zDPPwAsvvKA7re9///sQjy8cSBoaGqCnp4dLGQnCKHZsvmTc8GndXFw4tT/jc1K8DY8AZeJOqk6S1s35z4UT6md8jHm84lmjpEgu7lO0Esk5NOJYRq1G4ciNXaBI/BwVGYOnXnwMTr7wSMk0RL18wBh+vOZEFK5muXcRYf4pUAYZ3bHwmcsF6I+cPdLc1FjRcaJnLO77w09hdnI0MxOwG2esw87G+XtB51vHYyzQifODglpjFAcVktgcoXeeKPC7Y3v+l6+rNQ44aQwWG2O4n0LF2OnRcyWt450+36QEouKfR+TbCxKEm7D05DwwMJC2isJF+cMf/jDMzMxoTmf//v3wpS99Kf35JS95iZQHT4JQg2wbmsnzgzB84kVIGtBcleFgL0MZ3QrTWEOy+kj9GhGZnYKR04dBaJT8g59scwWv50xEt2glh52KOp3cK3LQ7aW+yR9zcu7F8FJn/NyJgv82OnSUCWFkc8dilNy+HT6+F07vfwKEj0vhsPMA7qXOHXsexodPSrUnicxNw8EnfgXnM5SItJPMu4w++cIfQCjcYI2yeDkr2x5jdPAw1+ciGpmFmYkLIBwOGnPTY4Uv+Y/teQiOP/c7IS0xyoHW7FlxhDSuUYXGcCwyB6LgJA8Ns5MjcGDXL4v+G/YjzgOyKSUZYXLkTMk6i3oecauHBoKQBUtnS1x4/+Iv/iL9+fnnn4c3velNMDSk3t3Mrl274I1vfCNMT0+n08TPBCErqN0Zi2WYS0u+iZubHoehw8+wSys94OZ6cP8uEJmCm24HItrFllrOHHoaxs4e031pYuzizCKSOdpfRjQuBWZs6Cic2vd44TlSYLdohZDtEq8sThlvySS73ConbCqXhhSD0CCG2sgEHGOZXKLcs1OjcPboc0yzvnw6IBwoROPZR1nuhQTASZYAxUAh/d7f/w8MHdoNMg1C3DccfvL+wgJrneeRw0/dv2DpJ9B5xCljENv1yO7flLRSQWG0bJw+8ITBx0KcZ6oYmWNQ1rMjtrNRJTBZlJK0uC3NW8NFRNoxRxDuxfLZ8vWvfz10d3enPz/55JNwyy23wKc+9Slm6ZQosFmcn5+HRx99FN75znfCG97wBhgeXvJ5f/vtt8Pq1astKz9B8CQRj8OhJ+81lAZzUyLgAlwsNkU5InNTIANOOfiV1syTN8guHiYyLwv4WM2K1Q5OGYOl6oJCqNEzR0rGR/KIbBFVABmfJ7V9Jqt1OgqujZBIxFznblWEy6aZ8fPgBHBOmLxwmlmT60/E/v6wBNHqKVhxzNKIx/m+nFWeiMxOjcD5k0sxpbXGVTFjveCKQ+LzIMWskmUHrUMnzp/S9Vv8nQweGkTcH+ghYdDlIbOIkrPqzjtDynkcIQjX4LM6w2AwCP/xH/8Br33ta2FycmGDh///2te+xl5VVVXQ2dkJNTU1EIvFYHx8HE6ePAnRaP7CsHnzZvjIRz5idRUIojQaNiCx6JzuuBUpUJDl9fqpV6zEQQe/kiTl1v6aPH+ab4KCdbGsB71cUGCILpwMWWmIKgBxRhepq5uk45HH3C2DK0UnIpqFlhHQ9ZTRtpAtfooTcJJCCHo0KGV9XI7DT/1aOIu1Uu7eHIOka28eHPZxKPQRkfnpCV2/mxo9CzLglPMID2FqVf1CGBLhKNBHTrm7yBt/7LMA50J6LghCHEEU0tvbC1//+tfhHe94B5w+nX1RODU1xSyjynH55ZfDv/zLvzDBFkHISmn/teo3B07ZSMi1yU3mxRVyGmrGFV7CoOsgNyDchVOuMFS4Z0QFST6X+EJpKJd7hmTsJxmeB704pD94z+2RmcmyqYiIlFZ5HJoSXSI7jbNH9pT8dxHWvNxnSdb9OHpoOLjrHkNpzEw4w0KxHFnjToQxKEAZROHoMw+C8Mi4RpUiWVAzCaSEQ98MChZL0xXQHEgQ0mGbKvvGjRvhJz/5CbOMCgQCqn/X0tICH/zgB+ErX/kK1NXVmVpGgrCT6bFhOPD4L+HC4MGy33WdFqwIG46cIqDfeDkpvukePX0Y9j96N3MZVApVsSscgEiH/fzLriQLai8jcQE1qLkh2IUVTwpduIr0jFhlieJUylopCtjV8XhUt+a5vQjYmCahRVCDLuHKpWY7AhSBB45eh52OhOuu+yzXks7u86ziYMxakA8Zy2xUgC7aOOK4r5BSKYkgXIQtFlEpUJD093//93DnnXfCPffcA0888QQ8//zzMDIywuJCIZWVldDW1gYXXXQRXHrppXDttddqElwRhKwH8pSG//yMjJcqzgYtAaZGi8eskYlS42vkzIJf8sgZ51l76UOcDTvGTfL5Q1nuSIy4t7MLdCflD4TtLoZ0CCHwEaAIhDZ4WmuIaPkxfHwvyIgQz7MkZI07AZrNKYpgM5MX7C4CwWkujgrqns7dgii+iLb+OsEyFM8jPp+TPS3J1yeG3A7SvooghMZWQVSK5uZmeMMb3sBeKebm5sDr9YLfT7FvCAcv9LRISouIfvCd5NJMRETb1JaKMScT0cgsOBXRxoz5rvmcW19HQd1EEFwYPpEt/JydGJGyZd1i2c4buy/ccR3GuDSZHHryPtvKQ6iAx75QoL0lewYEKo8RjMTIE56kc885hcsgQLkEKANBiIoQgqhChEJLmt4E4VRk1d61G1rYCVvGnVNi4hAWwldLD3/L3E0IcPAriKDFIkxA1DEoIePnjoNrSBocP1kGUeKNwbNHS8e1IgiCcNJ5BD005EH7A+HgvV6mzyMCcO7Y83l/i0cjtpSFIAjJBVEE4QZSrs8IgpDBJzvf5AgXwD0sQAIUxSvEBWzuRQhaiY4PZ2tmE4KRHjbGx48IY9ApnDm02+4iyAlddhIEIQG8rUZEsEIphqyuwh0PZ/d1qfOIiOvw+PBJ9iIIQlxIEEUQBEEQkmkgEnLA/bJepANfTlEovoPLEGgoEgRB2ALNg4Qdw47OI4Qgcx8pJZVqI1ogCEJ4QVQkEoF9+/bBiy++COPj4+zl8/mgtrYWGhsbYePGjbBy5Uq7i0kQUmspOQZqYsKWcUcDj+AxZoy55jOYBEeEKAShA9qnEAThRhQQw5UUQRiCziOE7VZ5qJzppX4gCEJOQdRTTz0F//Vf/wUPPPAAE0aVAgVSt99+O/zxH/8xdHR0WFZGgiAIgiCtL8L2Q2A6DfuFQMMnXrS7CIQO5qbGYG5qlEPb2T8GCRlJcvs9CVQJgnAjNPcROkZNgfFjYD0WSjGOIAjZ8NiV8cTEBLz73e9mQqV77rmnrBAKuXDhAnzlK1+BG264Ab75zW/SIkwQBEHYIAQgCAPjztC5j8YgoZ/I3BSL3cBDiEhjkSAI6VDIIopwALQXJDSPGd5DUBzFOIIg5MMWQRQKlF71qlfBL37xC12/R6HVP/3TP8F73/teOggThCuhTQ+hYpS4KDgwISpmuMKgsUjoY+zsMWo6wkHQmkzYDY1BwvqzBJ1HCM1jBngrxtF5hCAIiVzzoRDpL//yL+H48eNZf6+pqYGrrroK1q1bB8uWLYOqqiqIxWIwOTkJR44cgWeeeQYeeeQR9rcUd999N7S2tjKBFEEQBEGYCQUHJrQPmvwPhlw8Ll5gDB1+hjqD0EwiYw9NEHZg9AI16/ckAyDsgJSSCJuh8wihY9DwHoTUCQRByCOI+s53vgN79+5Nf66trYV3vOMd8OpXvxoCgUBZS6p/+7d/g+9///uQSCxI4b/xjW/AK1/5Sujr6zO97ARB2E90bgZmucS3IAiN0J6b0DxkzLGIGjl9iPqC0D5+EnFurTY/PU49QNgKxW0kCMKVkBCAMHQeMa4YR+svQRDSuOaLx+Pw5S9/Of25s7MTfvSjH8Edd9xRVgiFNDY2wkc+8hH4/Oc/D36/P53mf/zHf5haboIgjKEAP5/spw8+yS0twk0kITo/C2NnjxsWAhCEhkGT9XH0zBEY3LfLwBgkaSihn0Scj0UUjsPTB5+iriD0DB4YOX0YZsbPGx6DZBlKaEXhHCOK1mRCx6gxPu5IM47QPGiyx93w8b1w/NmHjKdH55ISTURnNoIQQhC1e/duGBkZYe+9Xi987nOfg+7ubs3pXH/99fC2t70t/fn3v/89E0gRBOFsEok4s4giCD0c3f0bOLn3Ef2NRxtKQiPxeDRLCHBq3+MwM2HgApbGICGAIIrGIaGXC4MHYHD/LpifnTTUiFMjZ6gTCF3niPHhk3DgMX1xqnOJRWapFwjrFdkW94LJRQ9BBKFm7ksRj0Vh6MizEJmfNjAEKUYUQRCSCKKefvrp9PvLLrsMNm7cqDutP/uzP4OKigr2fnx8HA4ePMiljARhGLooLMjUyBDse+Rnupv1/Ml9sP+Rnxm+vCDcefBDSyijYyelgZggxQdCB2ePPme43cgqj+B1EWFsHJKWJ2HfPIhE5vRfoBHu5sTzDxvaD6b2grOTIxCLznMsGUFoO98Mn1gKd0EQVgrQU/vA0weeoIYnCEJsQdT580tawDt27DCUFgqhNmzYUDBtgiAEQ1Hg6LO/hei8MWsmPPBx0+gmCK0sbrrPHt1DbUfYAgkACCPEYxHDDTg3NUYCUYIgwO17wcF9dAFLaGf87AlmyWREqS21F+Ql2CfcBbrHNcziGEQLU4IgCK34wEImJ5e0j+rq6gynl5nGxMSE4fQIgiAIohgT50+BLxhm1nkEYQeTF06D17cQI5Mg7ODgE7+CuhbtbrUJgguLl188Y48ShD7IMpTQM2qS8PxDPwBF0a8PPjF8Cnz+EDU/YRukGKeqlUzvB4KQFUsFUTU1Nen3qVhRRhgdHU2/r62tNZweQRAEQRRj4vwgexGEXaA13rljz1MHELYydu449QBh2yXu3PQ4tT5hGzMTF2D83Am6iCVsc7WMinH4IgjbIBfNBEHI4pqvqakp/f6xxx4zlNbU1BTs2bPkHqm5udlQegTBi5TvcMLEIK0EQRAuheZTgiDczMFd98DI6UN2F4NwKRgb6sQLf4C56TG7i0IQBGELZw49zSzzCIIghBdEbdu2Lf3+0Ucfhd27d+tO6xvf+AbMzc2lXfT19fVxKSNBEARBEARBEAQhJrNTS14xCIIgCIKwjunxYTj+/O+pyQmCEF8QtWnTprRVVCKRgHe9611w5MgRzen88pe/hC9+8Yvpz1dddRV4PJZWhSAIgiAIgiAIgiAIgiAIgiAWICdJBFEUS6U3KCx6y1vekv585swZeM1rXgPf+ta3YHZ2tuzvh4aG4AMf+AC8+93vhlgsxv7m8/ngr/7qr0wtN0EQBEEQBEEQBEEQBEEQBEEQBKEdH1jM6173Ovj5z3+eju80OTkJ//iP/wif+9zn4PLLL4d169ZBR0cHVFVVQTweZ/+OVlPPPPMMPPnkk8ySKpM3velN0NPTY3U1CKI4FLyRIAiCIAiCIAiCIAiCIAiCIOwRRPn9fvjSl74Ef/InfwKHDx9O/31mZgbuu+8+9lLL7bffDu985ztNKqmcDA4OwtmzZ2F8fBwaGxuhtbWVvQiCIAiCIAiCIAiCIAiCIAhzSJJvPoIQRxCFNDQ0wA9/+EO466674Mc//rHm34fDYXjve98Ld9xxhynlkw20GvvmN7/JLM1OnDiR9++rV6+G2267jQn/AoGALWUkCIIgCIIgCIIgCIIgCIIgCMJ92CKIQtD13j/90z8xYdJ3vvMduP/++2Fqaqrkbzo7O5kV1Gtf+1pobm62rKwis2vXLnj/+98Pp0+fLvqdffv2sddPfvIT+PSnPw39/f2WlpEgCIIgCIIgCIIgCIIgCIIgCHeiJJNiBLTB2E+HDh1iApOxsTFm5eP1eqGmpoYJnTZs2ABtbW12F1Modu/eDW94wxtgfn4+y/XhwMAANDU1wdDQEOzfvx8yuzhljdbV1WVKmS5cuABvectb2PsvfvGLzD2g2zh94Em4MHjQ7mIQBEEQBEEQBEEQBEEQBGER9e09sGz1DmpvghDJIioXj8fDLHXIWkcdw8PD8Dd/8zdZQqiXvexl8Ld/+7dZArujR4/CRz7yEXj88cfZ55GREXjzm98MP/vZz8hNH0EQBEEQBEFwRoknIDg2B5GqACSCwhy3CIIgCIIgCLMRw96DIITEY3cBCH184QtfYEKlFLfccgt87nOfy7MaW7lyJXz1q1+FHTuWpPFHjhyB7373u9T0BEEQBFeOxALw5akm+OJUM/xhvgqmEh5IJN19BjkZ88OzkTAMxvzsbyNxL+yar2Cv4bgPIknF7mISBMGLRBKa9pyD3p8fhGW/Owkrf3UYmp8eAv/kkuIYQRAEQRAEQRCEGyEVPQnBeFA/+tGPstztffjDHwZFKXyZFQgE4K677oKbb745bUGFbvMw1lY4HLas3ARBEIRzQaHT/5tshamkl31+MlKZ9e+b/TPQ75uD1f45WO6LsL+dj/vguzMNMJrwQo8vAi8Pj0GtJw4yczbug6cjFXAh4YODsRAMxgNlf1PviUGzJwYzSQ/0+OZhrX8WfJAEv5KE1b458JKsiiCkoPbIKNQdHk1/VpIAtcfH2WuysxourGuGWOWCUJogCIIgCIIgCMJNkCBKQu655x6IRqPpz6961augtra25G8wJtT1118Pd999N/s8OjoKDz/8MFx33XWml9dtCBJ2jSAIwlJQ8JISQhXimWgFe8EswHLvPNuAHIkH0/9+Mh6ER+Yrodc3D8u8EejwRmGNfw6avTHLvSjk6nVMJjxwKBaCg7EgBCAJDZ4Y1HniMJbwwgvRMKv7bNIDc0kFxpPat1ajCR97IafiAfjdfHX63zq9EahQEoBFavLEoNoTZ22Egj0PCagIQij800v781yqByfZa6a5AsZ76mC2sQISweJzpmqSSVDiC5NX0lfA2UUyCb6ZGCQ9AEmPAsHxefDOxyEe8kK0IgCxCl/+pCcQ3tko+GZjwCa8RJIJ90Ijs+Cdi0EUhXqKAt75GPt73O/FAMjgm4myv881hGC+NsTqmPRiAySFritBEARBEARBOBkSREnIAw88kPX5tttuU/U7/F5KEIX8+te/JkEUQRAEwYV2bxRaPFE4lyiv7X8iQwCVSRQ8sC8WZq8UPd45WOWfh25vBPZGQ5AAgGXeKLOc6vbNQ6snxu4Vz8T9sC8agrNxP8wkFSYoQoFNDBRmefVIpArGE14mLGv2RJlQp8YTh4CShPmkAsdjQTif8DGBD/5mi3+a+S9GwdBZFXUyi0yLqgM5/1alxKHNG2XWVOv8s3BxYJrd1dJdK0HYw1RnNdQeHWNCkWJUDM+wV4p4wAvztUGIB70QC/kgOLHgvQDjS0VqguCJJQA9ePpQeOT3QsKngDeaYA96xdlpCI3MsXkrlVYs5AUFBTbxJBM8eSPxhe+XAPONVvgg4fey7ye8CkSrAyw/nFQwHfa9sI+li4IszB8/42+wAJ5InH0PBT6YP5YbBUIonEPhkCeaYPl4o/GFv09FmeAM841WBiDpVdhv8BVebJ9S7aiKwwv/w2QSvoVyxSr8C/WtDiy2z4KyQzzgW/xOgv0f2x77ABcYbNdEwJMlxML6BsfnWOL479g//qkI+y3WIeHHuvlZnlhfrAvmO18fgnjIt5TG2BzEwn5WHoIgCIIgCIJwMj4zrHX27t0LVoNu59asWQNOZ2ZmBp599tn05/r6eujr61P12y1btoDX64V4fMHt0SOPPGJaOQmCIAh3gQKdt1adY/GhziT4XagdiYfYqxheSEI8fQ2rjuGEn71KsTua7VpQRFCodijmhUMA8GikCr493QghJQGTSS/UKnFY5Z9jQruAkoD5pAc8kIQaTwLq0LJKSTCXgNWeRNq1oldJQgiSzFViFBSIJhUmlKtSEswSC3//bLQCzsX9aSHYSl+EuRAkCAJgrqkCTl7VzYRRtcfGmQCk3OyEgp9MwVSKinP5fysHEzpFtLs39c3F2CuL87NgOovlDY2ZG0ML+8AbW5jrAlMR9oLz2to37vMwARIKqtBCy8i0h4I+z6IVW4po2AeTXTUw1xiGufoQJIKkLyoDSjzBBLMxtG70ephwEYWPKDxOBFBIq4BvOgL+mSh4IgnwRONMeMuEuAHvglVfChwSxUyd0fIRBbzRBHtmAhPzzNIRB3dSUdi4RCExfgfzRIE1CjhRIIrpojAY/596zqfbKiFWaY3wE4uE7pqPxQPQ5olCty/C9hP1njhULe5BCIIgCIJwB9x3uA8++GCW1Y1VrFq1yhWCqCNHjkAisbRh27Bhg+rfVlVVQW9vLxw4sKBTPTw8DOPj42Xd+hEEQRCEGjp9Ufj72tOwO1oBB6NBJiBCSyW0KkLXdSjU4I1WIZSV9PrmmFUWXrZEUKiTVJigqGnR3SBaaqHQ6FTcD0diQZgp4dpQDfPgYQInZDTpg12RKthV5jfoZnA64WG/1QPGskLXga3eKHOpiOlhj0wnPXAmHgCsKca92uKfYUIw2WOAEUQ5InUhGN7Sxl54eRw5NQs9z5yGUIzGvsygIMuLAiwO5AqhEP9sDBoOjGT9Dd04oiABLdMwthhaXjFrt0SSuSZMCRXwO0osyYQdKBjBf0eLrngQXRIuuDREwQf+u286BrFKH/s3tNiKVvkXLNIUtEhDl41lXDUu+rBV0FIPXUE6wNWhEk1Axblp1j5x/4I1HAqI8DPWD4U+KABKCfgFcTEAAN0MSURBVH1QmIQuLsPnZ5f6AAVIngWrvhQojGLplxFaZgqsUeCJ/YlCKmaxNxdLWwry1Plo3gMQqfTD5PIaJrBieaMwTVGYUA0tG+MBtO5DKRa6vfRAcCICvtko+x4K1Vg5/R72XRxLzApyNgbJeAIOeivhKaiCZ5NVMBrzwIzPD1WxKCyfmYbfe4JwLhSCGZ8P1s9NQP/cFOwLV0P73CxUJWIw7l9wuRkOAMx4vNCcjEJLIsIs4oPVXlgzPwl1k3OsfXBc45g9EaqAwagfls3NQMPcPDREIwtC4/k4a0P2zKDV4nRkobxVfohULfQz1gf/LTQ6t1g/HxMGY71Twj98hvC59U1FYN7jgfmKAMSrAzDnWRD0eZJJ8CaTMBrxQDSahJUTk1ARjwEG+cT8FtommrYYxbIv5O1jbv1xSkjOJcA3HYXZhAKj/gCgU+aY3wtDdVVwurYSQjU+WAMzUBMGUAoED40mAfyLf8b0cN+NLqQrPAlo4L33QsFoLLkwv1jpJzo3BEJq/sH2Z88KNmSSPZ++1LMTXbAqZooa83GIVgWYJS72RaQmAHMN2KA57gSK+erWWtbFdFN5+ycjaUtlLBuO3/maIJvfmRXzYr+y50hBa+SF9YJZ68YT6TolfF42brFe7N+9KIxeGKNsvOJ4wPG1aH1LEAQhEjQzScahQ6j3vMSyZcs0/b6zszMtiEqlt3XrVm7lIxDSDCcIwr3gmeuiwAx75WrE7o2G4clIBZyMB5jYY3zRcufy4CSzqvnf+Wo4HCtu/SQqjZ4YtHqizGqok1kJzUO/bw7vHzQzFPcxAc5w3AfPR9FNIbojNO+QP7IYm0ovKHaaSHphIuaFg0X67vloBfx8tj7dVgokIQkKDPhmYUdwGtb559iliSfjzM+UuplrQj9zmYjjA4V5OIb2x0LsfI+XKyjoDCpJ5prw5tA4s5DDv6eEfj4L7kewLBFQWPwwWKwDlv9oLAiH40E4EA3BTNLDXqgFjsI4dKe4yjcHq/1zaas0wjqwz347Xw33z9Uw4e32wDQTGq/wzTNhauoyTy3Y38OLsd6OxwLsmZhLeuBkzA9HKkPQftEKuPXMKVg7MQabx0fNqhaxyIzXC0G0VJF8T17IUk4tKDTQA1rS4OU4Xnwyax2cmxcFIXhJjheoqceDWf34PMzdIV5+4kUpXnzO1YWYkAovSicrgzDu8cHz3ip4PFgPh5Ihtg70K7Pw0snzsGJuCqrH55iVGhPioKvD1AUr5rt4CYuCDhTORKoDzMUhvmfuLPG7KgViofMzUHNigpUThW8oqMAL69D5WfDgQ2wA9vucNNQKjpRcgSe2A8ZFM5nAdBQaX7xgStrLAeCajM8oWKmORcGXIUhA0QiHKHmMbo3fR6ETwGTRf689CqYTvlDc6rQTppc+DA1n/RvuGMYCARgJBmHK54eG+XkmHDtRWQ2jgQDMBHywu7oe9lXXgT+RAG8yATNeH6yCGYj6vTAc98OO6BhsmBpn8fUGg2GYqAzBTEUAJhUfTMQ90BefgcpEDJbF56FnahLap2cgMB9jQtjkfAICszEIRGJMAILPI8KEKMnF/ysKRD0eiCsK+D0oYPHAucowTIKX7ZVQ4BicjQLEEjDiD8Kk1wfg90D9/Dx4w17whzwwk1CgGuIQSCaYK1eMDbgQL3DhumUhLqCHzQ/4d9XP8NmMtl0Ehfw4t6FlKgqEfIuWxVi/lMtZFBrhe5x3UFjN5iHfkpARBdP+jHlMSaW7GMNQDUyghL81OB9lpodzMs7l6Go2JZyaqw+zcmJ50VoS59dU/byRBITPz7D2xnrgXItzJdYFXdji91CAh3Uvq7jABLkLVqDFBJYoQMRyYN5sPQn7wRNPMLe+KbfAuB6kxhezcF4UyOHag//GXAaje2CmULGgjJFyJcysQRcVCJjFeCLJ1jj8HrrX9c7FmdJBKv/UmoYbRfwOthnGmkSBZa6L3lJQ3HiCKA4JoiRjcHAw63N7e7um33d0dGR9PnXqFAmiCIIgCNPBs8T6wCx7FWNncBpG4l5m5XQiHoD/nauGFzPiRRnKf9ENDMaxgkWLndPMYmfhQIFxo9CiB+NToRZpio3+GfYbjFOFLmVQuHEh4WNlrFQS7Dc8lUHbvDH2Qq4PT7AL86GEn5W/xRODQ7EgPDxfBX+IVIOMYNuleCRSzV6ZMCEAJNmlflkB3KL8ZiYJ8PB8NXtlgpfQLw1OwqbADBP66BVK4WUICr8wrhi6OsQz6Om4H341W8vilo0nl+qEF6xBJcFilRW11ltUTH5wvob9v0KJsxhrF/lnmCDzXMLHBIRoabYtMM3GKApsKwURWKUEbygARPC+BAWCKGBG60cUAqKFXIs3miGoXYjlhqDCMl4+TiY9cDgWhHFW1zj0+eZhbyzMNLjX+mehx6f+In0w5offz1cxgSUKlrBk+HxiX2D7YT74/OP4QmFmrtAU++LBHO9wfb45CCsJSCQVaPZGmaAqvuiyMuW2cgrzSgI8E60o6e7zTLgCvtzTz97XRxYu9jaNj7JX18w0HKusgta5WaiIx/Mua0txOhSGow21cDochrGkD9ZHJ2Hl1BT4IQG+oAc8fgUSi9YKLE5TPMniUeGlCl6c4SV84PwsJGdi7DLGk0jAcGUFuzxsmJmDmgi60MoHR6IaG8qEosBEZRDmvF6WfnU0AqFEAsZ9fniouRXmPV5YOT3FLAeqYjGojUYg4vHAhWCQXZwOB0Mw4fPD4apqmPb6ILY42V4IBOFIZTWsmpqA2mgUDldWwUggyN7HPQqM+wMQSMRh9cQ4NEXmoX12FirjUQjFEyyv7pkpWDM5wdLCC9zBcAU0RObBh5dXHgWqo1FuF+SygRYFntjCs4vjoxQ4BWD8MW/G99ACJfN3jYv/3wQArwMFhoNBmPX6YOXMFLdLexxn86EFC4eY3wPzCQUC0Tjr25jigYpoDKrxgltuuaTU1Efz53O3PmNGwbm3IRJhr0xWTRUXrPEU+mWVJZ5kAs1C5KomVUHhZ76lyN+LkrEVwvkG8GWQlEtbtEzNs17FV3TBGinNaXVlznN5q8Na1gjMQmp6IVajHne/5UBLUOb+c5FU/Mo0i4IjFBah4A6FQ+x7EXRpGmOCIqNKAFbDrGbDfpgNB+B8YyVEAj7mzhzXmYrYwqkS947BC16Ie1vB07McFD9duxNEJtyfiI9//OPw4Q9/GKwmHOZzUSU609PZGhxa3erV1NSUTK8cQ0NDJf99dHRJy/PMmTMwO2uBf3nBODN0FsaGzdEuIwiCcAudAHAHAMwmPOySNwwJFvMIL5fnkgqcT6DLPz9zZzefVNgLXb9dGpiCkCcBY3EvjCV84FOS7HK61hODUI5KIp73TsWDTLiEFk14esBLdHSZh9ZN7OI/53yLx+3MlfScBW3BDnGoxAkAKGq5cfGFFkKoWXou4YfH5ithfzQIU+AFtKFCl3l4hYxWZ+Wj1IhD6V2Gdu5hLw9UKkEm1LkqOMn6G4UffmVB1IXjIyVUwfhXe6Jh9nccU8djQTgaDy6m5odKxcOGxBxrVzzUZx/sz+goI14ljQHAi+wT06FNS6u+D6kYHn5o80QgrCRhOqHALHiYxQ2Wu0GJsRhtGOMLx28k6YGNgRk2ZrCuDYsxwfBgXOdVf2GDdwP7oyEmDJ5MeFgsD2wTFNZi3gulSjDLv4XPmGPmxXXqciIACvihRokzF5Cp3y6RKlNw8YYpsXhE8bHyV8HCs4h5owC5wxtlgiAUCqFgE2OlLVDq0jya835BCFGKwhFvi12YqI9xdHbx9WhNLQC+8rJYyMOfTIAvkWDCGhSeoBurmkgMWudnmRsoFM5EvLlXi1UA6SST0Oedh17/PKzwzkO3bx58TBoYhfhsFJ6LVMB+fx0829QBsSJiJcw/yPL2M0vGukgU5rweJiRCTfvKeBxCiRgrDwqMwvE4c6+FnAhXwVhgwcVWZt1C8ThEvB5IKIt51jeAZiLz7JWOmjs9yV6zOc/Vk/gG26iqKieBVqiMRlj5UdiFWtSZBOJxJiBEwZhnUXjYOjcHddEIxDweqIhFwZ9MwvM1dXCyopLVH9M4GwwxocicxwvBRALa5mYhmIjD8YoqZh3QPzkO7XMz0Dk7y/4tgHF9IMnaGT+XjlzoEKZw3gA4xTvdyeJPJF4Za7zqlpI4uh+T606XIAi3UFxOKh+LV54VhwEqCvwz7kVm4SiM3fMYQFsLhN70WlDQ5ajL0Oq9i3APPjMEQm4RCtnBzEz2hUcwmLocUUfu93PTK8eVV15Z8t/9fn86VtfFF1/sSkEUQRAEQRDi8QcA+Dy4A+ujtRIi8ozdBZCYcvH19PC/JqRJEARBEITA/NPfghsh94REMfRFpiZsY24O/RkvEQikNGXVEQplG0mToIggCIIgCIIgCIIgCIIgCIIgCLMgZ5WS4fNld1k8rs0nbjQaNSTIeuihh8q65ku5Zty1axfU1dWB2zhzaDeMnTtudzEIgiAIQlySAC9GQ7AvFmJaUQuxfRQWGwndrmGMqC5vlLlh6/fNMRdtGPunwhOHZk8sy9vgXEKBF6NhOJPwM1dyE0kvTKfdtZlLsxJlrs/QTeLR2MKeCt3PoXvE1f452B6YgQ4vRlQCVieM+4Ru5qKL3z8cC8Ex5v4vIJULxWJgXC58FY4sJAJJqFcwjleSuercGZhibgzR1SfGmXo2WgHjCS+LX1WvxGEm6YGRZPF4ZehWLcnixcXZOMXxinGp+n3z0LAY681gcVnMOozBhc9HkzfGXC/ieMPx/mykAkYTXhjj6IIT+w9j5qFrRgzJhHnh84QuHrHNcOxiWS6UaJdioHvJzYFZ6PREoNqTgHZ8NuQf9tpJAnP7+Uw0zOLMjSb9UKvEmOtZjKmGLmUrFt3QTiZ9MBz3svguNZ44TCa8MA0e1vJWzXP6SMIKmINV81PQl5yFhlgEOmZnITAfA28szlxNDlVVwPPVdfAbbz1MqnFMmExC4zy6SZyF1vk5FksNn0GML9YYmWP/RzeJGBcK3R5ivC90mRjxeGHc64Npvw9erK6D/dU17LfoZpHQRocnAm3eKHT5IlCtxFlMx05flI1N0cB5CgkuBqpBl82jCR97rtBF7GTSu+gMVmHrQqUnzvYZWL9mHvN3JkncGyisLDinRxbXDb2xK4vnkwT/TAwCE/MsDqCSir+jKJD0KOCdizH3ofEKP4sXGAv7ITA1D55IAvz4/VgCohhrLeiDCcUHEZ8XznsCMOgPwYg/wFzioovgOk+MrUtHYkHWZit882w8YIQcdIuNrrPrlASLFYltjOsGjpMB3xzrF4wViXM/PvVNniiMxLwsNmH17Dyc8Ibhgmchhiu2z+EsF8li4EkmoDoaY65X0S0txkGUYTELxWIsRmXn3CxM+3ws3uKkzweNkQhzPYuuc6d8PjgbCrHYlWkXuuiGDl3SJpJQH51nrmQx5iL+H13YYs1nvF6Y8vmZi9BUS+CzhX/D7zRH5lj8RXSJi985GwzDqD8Asz4vDIXC4E0mIbAYxxFTwBiYOFYxviWudBjnMRhPsPLh7xBME92Rznq9rC4YIRRdB+M60Dw/z76bUABqojEYCQSYK96qaBTa5ueY6+AzoYo817x5bRaPQf/kRNrVbtfMDDRG5qFrdhrqcu5WcRZEN7zeRJLNjXMeD9zTtgwueu3LYE19rotggnAvJIiSjIqKipIWUuWYn58vmV452trayrrmS9He3g6NjakQte5BmToNoaQbPJETBEEQhH5wR/HSvL8mF1+RjK1q+cPbivQ7PMAuXSAdiwXghzMNcCCWGzZbHUFIQJ9/fuFyGJLs0h9jIi3zRqDXNw8Dvih40gf13IDd3sWoXgvk7qC6AOAK9i4OM4k5GIwHWJwnFLThBdlZFgPNw2KZnU/44GQcYy0BVClxdmGNjCYXLtWmEl44ZtJFDV704uVZKuYUCg3xEq8dL+s8UXYptcy30B4Yaw1bA2OTTaCAJOGFwbgfhhMLdcE4URh/DYU9eIm+zj8L6/2zrJ8wPlcdu8xciHuGv0ehIl4soIASL++G4n52EYZgf+wMTrHLL8wT42RdEZxksayQC3EvHIyF2EXYikVhIKtP1p0DuhMPs5h0mwDgJlbLzAvIOCSTUfbXVN9PJzysXMu9ESZUTAlsFq5eUgpe+sZbMVYW+fvti/HEsAwoPMNYYCjw3BcNwe5oBeyNYnS90pcsXd55uDI4Bf3+OWjD/iv49fxg9PFklAXoXhCIeNIjfirhYf2IfY+XvvWeGNR64hmx2PCs4L5YCbngfLAj3ba57YutmhIypeKmpVh6j218Lu5jz8epeIBd/r4QDTPhqJlgbLjlvgh0evF5TzKh2rmEjz3TKJC9LDgJm/xzUOXB/q5cfGVTs/jqB4BXJqMQgyj4FWDx6HAuGE162eU1jh0cNzjGMbWJZAgmEpVszGEtUWCHLYJCBJxjTsUWxtYVoSlo8sTYuEu3XBJgB5uPAI7EQnA0FoTj8QCLNelWUMCM7TKWXLgWQoFoqzfG2hfn2FZvFFb55qHPNwfLvNHF+RP/I5ZgQA1lbjHAbeQ/ldmrV/3i/1eVvEZMrZdKxt8y93CZc5Gaub8aNuT9DfdIs2yPhPsg3C/g2o77CjtZiAO0gDbVbvvAufJQK8ChnL+fV3FRnNpdY51PgzXgHrMF5yPcV3ijbA8aTXqhIrEwZ/s8cdjsn4GAkmD7jdSagfvE++c6WMzfCk8C1vmn4VJflAlPcR+JwtB6UGA+tc/OGKe478c0cC+FeyLc9020N8E87oWVBJxTMGbxPAxBHKLzSbYHbUvMw4zihbOBEMyCF87EfTAYCcF5xQ+vGlgG161bblGLEYQckCBKMnIFR1pd6+XGhNIqiCIIgiAIgpCFFb4IvK9mCI7EAnAgGoILCR+7mERBEgpT/numPm29g5duFwWm4ZLANBM84eUn/t8KJdcKTxJWeZaUhaohwaxrtDKeQMGVwoQyeGBG4Q0KsfACBy3QhuN+JghCqxe8yMEDeK6gAg/VKBxq9kahwROHtf5Z9n8t1ClxJozAo/fG9FVNcdBKZulKpzQHo0F2mbAxMLsouCpMozcOjd5pMAr2v7KYHoJ5dmYIDgoLbqwFy1C7KJzES+RW7xRcGZpilzfH4gHYE6lgAkG0pkLrqWQSYGtgGq4PT2ju2xR4OYPjCAkqS2ngBQ6CQrrMdiL4g2Nx4RnAuWNprKOg91AsyKwVTsQC8GKsfPxmFHCjVQsKuVE4Ue+Js7kALwFxHsT5AC0fcJ5E4Q/PcZ+yjEDQUq46Yy7UTAn5CJYZrTJwXC7MOQvCKZxP0OoC50q0+ktZ6IaVBLMIRMtVvIDt8c1DrRJnigI4z+J6cjLuZ2sJtk1KOIaWHGpYEKInmQCv0G/qlBgT+GG+qKCAzxY+c1g2XJ9QcSG6+B7LiZep2HctnhgrLz7v2FdHFq1uUVgZgiR0L6aHCgRY3xQ4L0hg1EG4kNQeCQUAKWYSCrNmHor72DOJ+x58PhaE0wob97sjFUxIbrZwvhQpy+lMUMCBShr4V7QuxD0pClJQSIKCPNyXokAEv4FKSaj0gQoHqHhkZ13UgqVEiyCck/CF81wfU96aY/MO1h3np5GEl82duMbg33DfkFpjcF5NKRbp4bKgOsVwVPZasNrD/TBoW9+ydI7iMJCxDte1dkPHmp3gpUmVIMQURA0ODsJ9990Hzz77LOzbtw/GxsZgcnISPB4P1NTUQEtLC6xfvx62bt0K119/PVRWFtPfcD7Nzc1Zn4eGhjT9Pvf7TU1NXMpFLLGgM0wQBEEQhCj0sMu8lIbuEleHJplLtqmkB7q8EXbZITO1OYd2v5KAGs9C3S8vcCiPJoFpGKP1EV7k4MXHlsBslhWBaOBFVOZlFFEcvNRZ55mDdX5tHhQIuUGBxdbADHshaEHwfDTMBB94uYcCFhRArESBhAYXZOEMgaNTSAunfKWEphOq00NhDlqm4SU4XhjjpSa6ZDseCzChEQr/0Xp0R3CaCftSDMd9zMINBUkoHDZ6d8lsTxaF0+g+Fl/loPtSQiZwv7bcE2HC2kJcFJiBV1WMMjeAqG6EgmYUHuN+Bx8vtJZGi7+zi5akuA9EZR2cEauUBJsncb7E36CQJLQo6EKBFwqOUNmHKXckvEzph1ltJ/xwMhaANf5ZZjW9ITDLhL6oEICW4jgH4POPbnb1gEJoVCo5FVtwXYhPOFpCp4TnqHSEgvXUd1PrAZYZP2N90LobBXU456BCCZYb64ftkmkZlAnWG63gU4JudMXIhEyLlqpTSS8TIKHFEu7P2j3R9Jwym1SYUEqrG0rsAytYUKpZmi+5kcS0xRcaEoTrBFGHDx+GT33qU/C73/0OEon8Bx/jH124cIG9XnzxRfjRj34EH//4x+GP/uiP4O1vfztUVy+5O3ELPT09WZ/PnDmj6fe53+/t7eVSLoIgCIIgCBkpfQHpbNAVViEBHUEQzrIMxRdhPnjv2OXDGErZ68rlZTzZoUCQe1wigiDSAhC07sSXHfOjGkGwGtD6sZhi1QL6rcDROhSdGGOsVRS+pQRQKDBH172Z1pNaQPfNBEEQQgiivv71r8NnPvMZiOYEeCvH9PQ0/Od//ifce++97Pfbt28HN5ErOEILMrWgsO/gwYPpz+FwGDo70Ss+QRAEQRAEQRAEQRAEQRBuAq1D0XXnpkW3pQRBEGaRiq5sKZ/97Gfhk5/8ZEEhVGNjI6xbtw4uueQS2LZtGwwMDBSMY3Tu3Dn4y7/8S/jDH/4AbgLdFGYKj44cOQKjo6Oqfrt//36Ymlpyy7Jx40ZQyFyUIAiCIAiCIAiCIAiCIAjCEBSugyAEsoj67W9/C1/84hfzrHzuuOMOuPbaa6G1tbWoJc8999wD3/3ud2FiYsFP89zcHPzf//t/2d9RgOUWrr76avj2t7+d/vzAAw/Aq1/96rK/+/Wvf531+brrrjOlfK4HnYMTBEEQBEEQBEEQBEEQBEEQBGGtRRQKlDDGUyZ33nkn/PznP2eCqEJCKMTj8TDLqHe9611w3333wRVXXJH+t7GxMfjc5z4HbuKGG27I+vy9730PkmWEH5FIBP7nf/4nq01R8EcQZuLzl3GIThAEQRAEQRAEQRAEQRAOIDo3Y3cRCEJYLBVE7dq1CwYHB9Of0ZrpbW97G/h86g2zGhoa4D/+4z9gx44d6b/96le/YoIWt4BxsbZu3Zr+/MILL8APf/jDkr/5whe+AENDQ+nPt912G7S3t5taToIAWIwOSgiNotjipZUgCIIgCIfj84fsLgJBEARBEIRlzEych7mpMWpxgiiApbePTz31VPr9ihUr4E1vepOudFBw9bGPfSz9eXJyksU/chMoxMuM73TXXXfBvffeW/C73/nOd+BLX/pS+nMwGGSWaARhNhSDjCAIgiAIwr3QXpAgCFVzBSkwEgThIE4feNLuIhCEkFgaI+rs2bPp99dffz1zD6eXlStXwurVq2Hfvn3ptDds2ABuYdu2bUyY9P/+3/9jn+fn5+Ed73gHsxS76qqrWMwsbBMUTqHFVCYotOrs7LSp5ARBiGgRlUwmwKmHWgoWShAEQRAE4U78gTBEI7N2F4MoByrZOjTWMp1HCMJ9JOJxu4tAEEJiqSDK6/Wm33d1dRlOb/ny5WlBVGbabuGtb30ri5H1n//5n+m/Pf744+xVzJLsve99L9x6660WlpJwNRlWe0aobmiHYGUtnD+58LwTnCEPigRBEI7E4/FCIkEHYUL+vSBB0Bh0vvWkQ+VQBEG4kEQiZncRCEJILHXN19bWln6PFjxGmZ2dLZi2m/jABz4AX/7yl5mrw1KsX78evve978Gf//mfW1Y2guDlYiFQUQ2Vtc3UoCZBrjAIgiAIgqA9BuFEaJ8rCw4WWpNAniBcR5IUwQjCfouoTNd5e/fuNZRWIpFIx4WqrKxkrvrcypVXXslee/bsgcceewyGhoZgfHwcGhoaoL29nf1bb2+v3cUk3IjC7wCpuNDq0TKcfDhysJsPgiAIK1m1/UY4+sxvIRado4Yn1OPgLQYhCTQG5YD6iSAIB0Gu+QhCAEHUzp07WWyiwcFBuP/+++E973kPi2Wkh9/85jdw7tw59v6WW26BUCgEbmfjxo3sRdhLki69TZBEKcy9EGEOFEicIAiCKEeoqg48qBQSpbYiNO0yqLkIm6ExKANkuVYej8dH7r4IQhLINR9BCOCaDy8777zzTvZ+amoK3ve+9+ly0Xf8+HH42Mc+xt5XVFTAm970Ju5lJQhCvI03YRZ0QCcIgnAmfOd3hZRCCK1jhqPVNblpNg8nCwF4jcFguBrq29zrhcVsFMXSqykpWbn5pbBs9Q67i0G4mM6B7VDb3GV3MaQgGacYrQRRCMtX+1e+8pXwxje+kb1/+OGH4U/+5E80uen75S9/Ca973etgeHgYgsEgfO5zn4OuLpoICcLJBz9Mh1zzmQdZRBGE+PRtuwHqWkvHgySIPDjfLZN1snjUNC0DN+D1+qHnomtpDJqFk900c6KmpQuaulbbXQzn4uAhyO9M7AGPl5QzCfuob+uBYGUtdYEKkkDhAQiiENxXsdnZWYhGS/vseMtb3gLNzc3w+c9/nsU1uv3222H79u1wzTXXwJo1a6Cjo4PFfYrH4zA5OQnHjh2DZ599Fu699172HkEXf//wD/8A69evh4mJCQiHw+D3+3lXhyAIISDXfITOkaMoFCKKcAR46PP6aJ9D2AtZRImHa5RJFuu5YDVBWsaE9rFjeAjieYSEACair59QSWfs7MIdkeNRaB0WkaqGNpgaGQJXQCETCIIQTRD1oQ99CO6++27NMXV27drFXmrBOFP/5//8n/TnT33qU3DbbbdpypcgzIE0H9LwuhyhTbeQrjCq6lthavQs9/IQBFH4AoyEAITdKB5ynSScOzXRBVEchQBp95BxClQmChU1TTAzcR5c4XYQPTSQe1LhhOquEcanLKJoDAqHPxAGt4DPGwnkCYIwAp0mCcJmHO2TnVPdSAPRZHR2kz9YAeLDZwyiOyB0jUaIhatc1bELMNq2iYbo8UJ47zHoAkwwJLiA5ScEWPwfzYNiCQFk6A9ucii6gBUROWJLcRSGer180iJcNgb5IcW8TxCEsNAMQhB2o/PgF6yoAeHhdT9CJuAmQ5cP5fB4fOxFiIWbDkIsVp7LDroy4LY+IWsAQvug4Tb6XPnMWYeTrVG4HUhcte+wHBKGqmgidFdP5xHRcNu8QHtBgiCMwH0Vu+qqq1j8J6vp6+uzPE+CsBN/qALmZyYE7wRegVkXDn54+ZBMJrikSWS3r75+cc+mm41B0kAUDjkuwPhB1iji4bbLBxqDYoHrsPiW9fz2gm585oTvJgnWYW57BWVhDqTziDnonsskGIP8QKs8sogSDleNwQUFTYIgCL1wn0FuueUW9iII15LUfjhKJp0pBOB3SbyQDh7+4nESRImCFGOQoysMuoAVDxnGIE/oAlZARL984Fw+GoNiIYMwnncZaS0WCzetw6k9JZ1HzGpg5yrGcXNXj20kQX3dhgxjkCe0FyQIwgjumjEJQkjIFYbaJqLAmIJtniW4AOPFglUeaSCKhtv6xG31lQG3XT7QOizgZZDwS7HC9VmjeVCsi3IZhKE84/O4cd63CvLQoKaNPGSNIiBumxNoL0gQhBHcNWMShIOQYsPDzRNGyh0LXcKagl5PGDK4x+F0QbJw8BNv/EkxD5iI2+pPBz/xkGIe5IjH67e7CIRscyBf43jw+MglkCk42RqF215wIZ2kVvcXhKnIIQwFfh4ayDWfcLhtL+i2+hohmSBvPgSRC80gBGE3TvbJzlkDUURBgDNw7uWD0zfdocpacDMi9onbfLKHqxrAzYg+D/KOH0TCULEQffzxHYML6XhJGCoWEpxH+CFmXf3BCnC1MNRlinGkmCkerhKGCnoeERWKb04Q+UiwahOEXGjVknO0Kwxem+7FNiINRHMgVxhq2kjU5VKCecCV/WIOImrBSrEWmYgUF2AOH4Ouhj1/gj+DnK1RSBhqDnQeUT8GdQX3lUjhQL4x6KJ1WBFz30VKSS4ag6QcrIlEIm5WNxCEtLhrxiQIEXGwKwxupJpIsIOfY3CwBiJvqzzhELRYViHDGOSJkBewoj4bFuFRBBfMKC4Ygy7GbXMgQmNQMDfNbjqPEObi4DMxr/OIqHUVUThmJaL2i1koAiolhavF9NBArvkIIh93zZgEISBO1v7ityldtIhKko9dM3C0FiwnRK0racGKPw/yhFxhCIigc4NZkBBAvLVJ1PUpBbfyLaZDbqnEwlXnEVGfNUGLZVk/idovJiDsfC9quSzCbUohIoZLEPVMnCSLKILIw10zJkGIiINjRPEiHRxYMIuoYLgaHIGDNRD5haYQ9HkTtVxacbBVntOFAMJeilgAi9UgfP15x4jyc02PMIYiukWeCy6ZnIODhQCcXYUTZuHgvaDTzyMOQfcz7rJ+EVIhRNA+IIsogshHglWbICTDImGJ+JdfJmwIBBNEuR0pBFHkCsPRSDEPOtwVhqgHPytw2/gTVRiaSdeanZKsTS4ag9ysUUAKQpV14K54oZJ0DAeqmzoFjVnrkD7QLQNwSP1VIOr65pg+cLJypsMtojKpWZyrRYBiRBFEPu6aMQlCSGjDo3ZzJ5pFVC6rtt8IMqJbk9Aphw6ZcUgf6L8Ac9c2RkQhgGMuH3TgtvEnw+WDx+cXNk6AGUhhCcANOeaaFRuvBDchwzzIY53q33Ez+INhkIGKmiZw01hyl2KcoPNgRrkq61qgpmkZyAgJ5OXde2SWKVRVDy3d60AEyDUfQeQj3gxCEC7Dyf6w+flkX/y/aIKonPp5fQFwEyJuQs0ag3IccmXGwe5YHB8jSvy1yCxkmBd4X1qJKAx1NS7aC6bTEW0vKGGfuK++xssYrKhZ+pAU+xlbvu4ycBNuWYu71u4EGeqH+4T6tpUgJ3QecQyCrE0U45wg8hF/1SYIQtpNN4+Dn88fhNqWbvaeFnKxkGMMgqM1EJ3SB/o1EMW2zuCNkIK3jK6rbmgH1/WHoHODabitvoIjxxjkpZS0mI7w9ZUUJ1smu23ISPqMOFk50yiVtc1Q17oCxEVxRp/oHoISzIMcWLn5aruLIB3kmo8g8nHHjEkQIiOhT/ZQRa26L3IoYv8lt4LXtxAc3RcIgdABTWXddOvELZvuzL5NafgJU/eMIbfgCkMcn9iuFcy4TCCaOQ+GaxqhZcV6cA0C9ofZiLYOux1h1qJScHpM2no2sf83LRtg9SbrPJP3tGp/J8E8qLduxfCIGK/RzWdil+0FkdReS5iYdOJPA6pwtntIYzQvXwtV9a3pz17vwv2MKGSuRSKtS8lEwu4iEIRwOH/GJAinYuMCu3LL1ariRPA4+KWEUMiytTvZxUN1Y4fhdIklyB92aerbe9Jt1L5qK3N5smLTVUIMocxnjLnCaO8FKXGyJjYH+rbdALL0nUiHP7PBuvK+4BQdtFLGGDhtvZvVK6VYiJvGnyxzII9npHv95VDV0Mbe+0MVMLDz5TBwya0cSkcYRoZnjnMZu9ZdxsZ1RXUjCIEMfeDyedAMQVTf1htg2ZodIAJu2wvl4cJnsG/7y6BzYHtaScR+xDyPkCCKIPJx36pNEI7RQJTg8eW8AaioaYQ1l70SujdcwTVdQh9u0EDEgM/LVu/IEozWtiwXxypAkE22XbhhDKIGYri6If25trmL/T9UKZ4QwG1IsQ5zoqN/W/o9KoM0L1/jiudPdKQQhnJYp0JV2Vr//mBYnHXYKZBLKtWgVcDApbfByouuMbNHCJW4aS3OnPvDNQ3i1N0h5xHd9zIu3A8FwlXQ0NEHngylYVvJ6jpxxmMyEbe7CAQhHO6bMQnCMQc/OxdY+/JGqw9RNFwE2uMYRD6f7Fa5ohBmrKksn+jldaVA3ig5TYOWoau23wiNnf0gpisMOcegHlwx/gCgdeUGdtlAiIcbL79ER9Z1WO/cLcMYNKNPUBiqxjuEHXsoWccgeWjQ1WggAk45j+jFLftBuc6SYoxBihFFEPnQjEkQnEkmkxp/4dwLWPdtQt1WX4+t7gAI1guubgYZLsB4gxdfudYBhI3jT/RHkMO6FKyslWh9k6WcHNdhG6usyk2zNGPH3cgoBOjb9jKVwiAag07GjXtB8XD3M2Zr7G6r4oSJ3sWC7jXIIoog8qFVmyBk3bgYWGwNu3FR/XMxNwT8cEj9FPkOfmqfG+2CYZX5C9L3br/gs6sfguFqseM22YbimGlRFRI8f6LMVYQ52K2UVNXQruJbiiueNbdiZAwanZ8CoUp1+Th9/ORVz+H1zUWC/nXfeUSMcjndRSmeRzpXX2xL3kIjkIMGRRDLWYIQCRJEEYSkGDpUGdywL2x6y6fh+INfHu6qr90XYEQ2UrtF01tsSatLOGkOtGcQ+gNhW/IVHrfNCTbus8JVDRSnqSAuG4Q2nkeIdEM6pCnk8xKCMRMJMc//Vgrp7ByDoggjiXwq61rSsX0JgljCB5y5//77Yf/+/WA11113HaxevdryfAnCLmQVAmCMp0Q8Bk5AlI22Xbi6/oJUXcR5AA9ESUi6oO7WDALZDpiyldcIdlqFNnWthkQyDmeP7OGSns8fhFh03hF96qa1CcegXW5f2vo2w/i5E2W/56b+kBobXPPhbw0ZiqjN221jUNbqShg3ecXGK+G5334P3E7enlyEMYjjwiRLtPy8rMnGzrxl2guKUtbO/u3g9QfsLgZBuEMQdffdd4PVLF++nARRhCBo3PDY4ZrPqoNfAVZdfBPsf/Tn4EjE2PNYtlmzXxBACBmX1aKDn93jT+/FR6iiFuZmxsGRiDD+LMTuMajKKkplnwQraiA2Pmy4TIT+sZRMJvT9DuwRRC3kr++hRw3h8eGT4EhoHrS4sVzW4AVwu7DX7rWYF/5gBUTnZ7T9SJSuF6UchnFMRQgRoOFEEAVxxqpNEBITrqq34dBh36ro9TlJK8TluwuXH3zFQHFtPWx1RWhEEcDjhbqWbnAsLpoXcAw65gLQIfUQRQtW6yUqatXr+6199TWSd0f/Nsf3q2zobmNDrvn0/1RbNu4aP26rr6zrV+5eUO5+U4Sri1Xro51umhdLoN+9rj8EjgT7Xoh5QYQyEIR4kCCKIGyioqYJGjp6ob6j13rtL6vWRCE2ACaSUz0RNt2yuGOxEw+XoKFi1D23D9x38HMBgj9nmQG4RRh/vFBTFztd8xmhvW+L3UUgFqlr7YY1l70SKuqa9c+DOg1QbX1eBZ/XjLSjtPOgDW7RjLaVrPtQ/jijHfR7aJCz/nrvAEREyD7QUSYRz1Wmtb0CUNXQmvWnYLiaT6EIcZ8LgnCia76PfOQj8P73v7/s9+655x747Gc/C7Ozs+zz+vXrWZynNWvWQHt7O1RXV0MsFoPJyUk4duwY7NmzB371q1/B0NAQ+/6yZcvg4x//OKxatYp9rqmp4V0VgjCV1p6NUFXfCtN6XeEYcs3nMb6oqshehs0b4T5BQPfGK+HoMw/aXQwHY5WKs83WKJIfLtCtWzSysAfjiWPmfRUuJm2dA5lBoJq2VgrGatSWl0R9KlNZkSSw+AHJhHa3fPbXV6XGcYHvOGaecBTWu2m2bA4tULXOge0wuP8JcCSyzYMuPY/koi++qiLmmBNgDHJZZ0TfCxolp2qhqjqYn50s/F0B+lQLtM8gCBcJolCAhK9S/Nu//Rv867/+K3vf398PH/3oR2Hr1q1Fv7927Vq46aab4H3vex/893//N/zzP/8znDp1Ct761rfCF77wBdi5cyfvahAEF+1wUePz2Lkwi6AZsmz1Dji173HD6eS1owB1wzLpO8joycz++panfFsEgpUQmZ8GK2nr3QxDh5/h3AeCuCFQrJkXRJhLeFHTtAwmzp+yNM+VW66GA4//km+iDuoTVTF7HFRfQlLLZCNWeVYGci+Ut5nfNzEul1DtKAD2emjQn0Bd6wrbBVE9W66FI7sfMJ6QgEuRrvOIDXGTrSMp5Pmc1xlIyEt/DuNCzTrhKDfNNlmGj509zjVNYcYjjQuCKIjl4nu0hEoJobZt2wbf//73SwqhMvF4PPCa17wGvvOd70B9fT3MzMzAnXfeCWfOnDG51AQhHoY2PFYd/ATZA+QSrKwFx6JjXPj8QZ1ZSawBZhCjB46mrtVClMMMLNv821x3/fE0QAiCFTVQ29zFP2FB6mfFs2X3HMjzWRNxLnEDRhVH2BhUrHZNm8pcoMsenZ4JTEHSZ0m/eycj9eVhsWBRPibA9XkUDQufA7vXYjsxunb377yVU0GKfrANHuuTDHtBWdecFLW84ubmKoQI0Cwy75EIwkw8VluKoDs+pKKiAj7zmc9AZWWl5nTQiupDH/oQe4+u+77yla9wLytBmE5qsZTx4Cf5moqbgvq2lRwSUqQ+kKA2KFrm+EOVpudFmIVzNRDLfsfuuhvI3jKrxTI0LV9jdxGEJXMMFhtrC25q7XQPKfc87Q9W2F0EYSjXT8UurLX3r8JtbLDnQs0lXYGBKvK4dCvoksl613x2nkecMwZt3w/ZjJ1xymSH11xsuzCmEIpF9ZJ4CIlwHjFl7IjSJ7TXIYiCWLpi7Nq1C06cOMHeYzyo1tbs4HhaeNnLXgZNTU3s/d133w3z8/PcykkQVmKLaz4eF8Wqyq0IuzPo6N8OzkTR5KKw2chFtKwHPwFc6OAzWFXfxiUd0dBapkJzmao0xKu6o/pR/3MtX10aOvry/5jRJ4bcn0mD9f3Wv+MWy/MUDpXrkVJUEOVR4/Ep4/vyPZ+EuYSrGqChvbfwPGi6hwZrxqOw414BWLn5akfW19IySDAGRT2PmIUIY5CTJErFV+yMF4o7bxHa2gCmFV/ydiEIB2PprPnss8+m369bt85QWl6vF1avXnBtNDExAUePHjVcPoKQyx2LfZtuvRuegE7LG+4oGKzd68xNt5YiLJZXb7mNxQUQoK1spnvjFc4cg1qEoWsugf4dNxdIQq37T3vqK6vAJRfpD68c6NlyDXSs2qpLCGB/G8rbf8FwNVuHebkpFWsOFNeFV/a6bdQaxWaXbBzw+UPg9jHX2rsROldfrH9Pp7H+lXUtSz81OA6csIZV1etXzE0j4hgk13zZJK2NQU1om5tROXPgklvzxq06xTh5PTSIgBPmcYIgBBZEnT17Nv0eXfMZJTONzLQJQir0rr0aNz2Z1heGF3wdGy4Mhtpz0XXSb5ZER0vfGnbLY/fGW3KcGhtAy7hAF5mBcFX+wU+CicKq8S/Vc6ZIVt7FS9GCFk8Zlz5FL2htrSvn4NglkpKtT22/FDKBUsJQbQkVeW8m4hrHw4pNV3JPU7rnJYVilRV0xvcVG60BhOgmswohROWk2BdL+7xyZPm6y4wnkteOcrVrfXvPwnlEMsW4xcxBakxpO857ZL2lEKAMBAFuF0RFo9H0+8HBQcPpZaYRiUQMp0cQlpK+41LkO/jpyLO+oxf8wTCIgYMPfro2PLqlofpjrsi+MROl/Jnuw9h7QcplEHWa2RLXlTRbhSeZTJSPzyPDGBRlrrIMZ9a3qBU3B1eoejEy/kV5dsLVDeDx+sDVGDyPaP5d1pgVYxwQ/NHyjKOyYnVjBwQra/XlpXFe8/oCmb8GYbBJIaS2ZTk4EotCEYiynpl9HjFrDFbULIRbcSYSjw2CcIogqrm5Of3+gQceMGRijEKovXv3pj8biTdFEPZg0MRe62Yg68La2KMvvRCBEyK2g6WbYY1Zeb1+EOExy3OLKV43yg2P50JlGlIf/kSAe/M5pz8y96hFY0TZLvvllzmvZ0nIgOUCkzQaI8pOYZAhz3yKpvKYud8yPvadM+/pQvNxhJ9SkurfaxxvVsFrXOelY3/VNK0F/TtvhRUbr7TnXCVAWxlCkLNo5vPE+lGAYukZT3nzgtopxqYKM5tQQcaAEUUbHrHyMs/3wrSJIMUgCNGw9LR40UUXpd8fPHgQvvvd7+o+tH384x9PH97QRV9/fz+3chKEITQKWN3m3slJB7/8hM1JVroyOKugwo5jIwcm0dom9zeZz2ix9Gyf03TmH9Ks8StAp7rcIqqoIMpuBBwawraV5HBzWcV53lQ3Dyvizd+mIlvdksbihdpdX0ePJYnbQEORDM8HAlbfinjRhBUDQ3Hs+GvvW7qbtRuvzwZlVQuwfX0kCEGx9LR4ySWXQEvLUoDSu+66C77zne9oSmNmZgbe8573wG9/+9v032644QYIhYwHmyUIKzEadFTrwpalmWaVxYKIByOeCFk/RY7NlYhNRwh07lMcebGNbqDaereAoxFyXtRBlkVUMSGA7SZRFvWb+jp6FJtj3zmkO3JRirrm05hO5g8MP6v6fr96523gKBw65lRjwFW4VgvK1p6Nmr7vVsS4/NRTBuuFoWK0lf57g2Llb125kT1fNU2dYAcitCsPhQenKk30bb0BKuuWvFWpQ7K2EKHvRCgDQQiIpbc7Xq8X/vZv/zb9OR6Pw9///d/D6173OvjlL38J09PTRX87NDQE3/jGN5jQ6Re/+EX671VVVfDOd77T9LIThHAYWNhs3Rw6eEGWdtOtuGcMtqxYD44ipw9EGIM8DirZLj5KbFXsqi7zOqI9c3Q94QsEQRwUvgIbB83vmZrKRWNE2VxfEWMXFBWYOBA+bZs0aBGl2S9axi+tt8gPhCrBH6rQk4rqb9Y2d4GtWPjI8ehDexXjtOVVWbek0Kopz5yMei661tTziM8n0jrvAoz0pU3reM+Wa01Nv661G9a85HbrlJ+csv9TiterpGWyndXX0faBimoQC4eMH4IgVGF5dNabbroJnnrqKfiv//qv9N92797NXh6PB7q7u6Gjo4MJmGKxGExOTsKxY8fg3LlzeWn5/X745Cc/CW1tbRbXgiA4YpErDCMHP1noWLUVRk4fhoraJhg5fcju4hCCHVhQO7B5+RqYGj1rPDGnHLgEFYZm+flGi6g4h4IRliCGMJQvIljleTw+qG9fCRcGD2bEYBCvrX3+IFQ3tLN5Fss4PztZ8vu8hXnWxklUNLtj5h4jyoA1iuEQUbr6zvz+CVXVQeOyAeZe8+gzD1pi6To9ln9OlWUMZqVlORrHr97xo2TvBStrmw0L4ErR1rcZZidH2Ng4te9x09s8z62VAGsDr7kdFchGBg9C19pL4eizSx5xZFszMve1PVuuYdYokyNnzGtPRWHjIhaZA1sQYAxyKUOudXyi0IFEfKUkNyJCu9itsEYQomK5IAr50Ic+BNXV1fClL30JEoklH/z4/ujRo+xVjrq6OvjUpz4FV155pcmlJQiT/D0vbmx4HKq0ft+qYOJWbwD8oUpYdfGN7L0VgiivL5D9BxE2GxaWwcjmyo6DH2rSinChzJP8ANUCjEEeZFnZlOoz6+qLlyHnjj2fkbUipMUijnO8FA0EKyEyX9zSnAdo4XX+xIvgdIq75tPWp1X1bTA1OqSrDEwTl9P4CVZojFOmKVsFOldfzC55jz/3u7KCKKK0cs3omSNQ1dgO0bkZaioNY1C7yyH9dA5cDAce/4VQl1+Wovk8kmnxrN+iTzcWdA+eDzr6t7H3JQVRnGjoWAUT5wdBKDitV60rN7D9V6mxYuiyV+NPa5qWwcT5U8CFvCsDPW1W+Depeciqi3BucQw5om8uzv5NMuOuEutYXC/Ounl/2ZpL4NSLjxnKtui4KDVeNObVveEKdh8TqqyD4RN7wVKcch4mCIdi240cutP7wQ9+AFu3btX0O7SCeuUrXwn33HMPCaEIl6NtgfV4/aa5zlL5g4X/mbgxUJ22zjIEw9lm7A2dfeCETXeh31Q1LFiatnNy6bDq4ptAXOTYrOb6eceLDl8gDMLB4RnP1FQWRXhYVd8KMoAHPxQQtfZuKvtdo/MxapXnJwqOo+gFi8b2wws1u2nr3Vzwkp7b2mzBWp9JU9dqKZUx1BCsqIG+7S+Dtp7yz7JasgX72srf0N5rmSKTISycg0IVteAP2rcOcxmDRhXjNHtoyNKM05lnZu5q0lCsfX5Vps1DaNnetwU8OS5RRdDC5ymQ5VkftKzKSV3T75et3sGhFBb0j4ljoL69J+tzdWMHeP3Z7iiFEMhzLoLXHyicjcXPG7q4zSkAiHpuXbHxSghX11u0luUmCgIgRCEIQjhsPU1s3LgRvvvd77L4UG9729vgJS95CTQ3NzNhU4pQKARdXV1w4403wgc/+EH43e9+B5/4xCegsbHRzqITBD9LDQvWp4FLbuXqjkXctde8DNGNAm60sw5+Hp9wBz9eTdC9/gro23YDNHYNcEkvVJmjgS++BxhdVFQ3mipgyLwQX7HpqrzviHDw46GBmFnPon7MNT5veQc3WzC/f9AVCwrNrLowzrICFmEO5ERdS3f6fXWRYN8LY11dnVG47/HydUSgbs3J/g66JzUTs+egcFX2hQZPIY1mRBjvGssQqq7L+Km23+ZeNC7kbVEbKGKMQbR+4nMRLc4YRPdxxoqgVTHOp9/NuAB7HDWYfR5A69qM3EBIBC0Wxk7KxBarPB1odSVp5hjEOTBzny5u/F3jbRCsrGFnO6/XD8tWXwJioHBLg4eL0vLzsvnPDIv7l5WjIBOQIMUgCNGwxTVfLn19fXDnnXdm/W12dha8Xi8EAoU1DwhCdjCGQrEVqqV7HQyfeBFaVq6Hs0f2GN5gBsJVtgSothq1baKn/j5/KMftoqhtyMetA2pXGr2cKJ2lBFrVOli29hKYHj3HDi5Hdv+Ge/pZlkKijkGVxUq5rSkECp8q61tgdnIUOlZty3J7pHceXLHppUXTUYfC7ZCv5eBnmftWFTR2rkrHKFrIo1Amgo7LAqzc9NKScWZ6uwYWXLHEIoIKInjmX8LtUdF/Uyxtk3BVA3RvvAL2PfJTISwmeczB/lJWrSpcmGkpA7ZfVUM7jJ09rq2QS5nJEe/IxItlFKLka8Ur9lnlGewUdP/oD1WkEis4R46cOQwNHX1c4m31bb0BRoeOWN5W1iuKqfXQoD3l/h03s3U4282r3WtRIfh4aHAk6WqaF6fM7DFR9jwiQFeqPWtmejwp5PK8Z+t1kEzES+7dtcwxqFiIrotdF5aCI2svfxXs/f1/l/bQIMAgdM2cRhAaEfYmMBwOkxCKkBMVazG6TSqlGY0m72uveBUTSJmCxgNZ70XX6c1I5+/0Zmemmw2QAiGsskyySONH0rR29vqCzGUjxiuzBYkOfijUWPqNUtAHOsZ88wcrCuejsbLBYpZVutBpCVD0Pl+AjjMoPJSRlAvSgigKVNQ0MoGUEOAQ0XFJb+6aYO3lQ6iqtuz8Yumhn0Pbthq16NJQhOYVa7mWX3VbWzy/OXUfVBCDda3IurzLTytUXQ/L113GzTVtuKbBWJk5dK0Vc4SrxqDBNkCvHTIhS9+my+lYYa8KVBZpZZkzJtaNp0V7rqt1zeTJyjieRyRRjEPvD1KMSRHKQBACIqwgiiCcjC/DvUmxRbJs0E+NC1vm5kHrwlwqULue/G1HoKJwR2XfrtiY79ZNZHhYZxUbg1w3qqlznyX+3wXZZJtBZrUcWkUnguNeljHJ7RnVkgzvtuE5dUnSb04aQyiM9wWClvaJUHsx08ptpTCywJ8ksogqV1ZV6RuorygxILmjuk3kfB55wrx2pLBjHdKcp1h9VvQZtn1Ntzt/9fOj2jh/1NbG2tlSBCwSQRBLOHT3RxCSo2LzaGjRt31z6hB3bwI2o9pxUd3Ybu8GUvKDn5AuB62M11G6IOBIFBu1CXXgZq1vHnXPTsMCIbZDDv+mXyyXq7KVTSJE8xsQAhitgI4lx4pnxsw8MG2hnjuzhdEmnEeMtZ+YLsCsRZIKC7o/MDwe9VZLhWvtUnOX3nnNKsU43nCJxcdhDGa3uxhjOq9PFX6W7WaMF6HWTIux+5xAEKIiRIyomZkZ2L17N7zwwgswOjoK4+PjkEgk4LWvfS1s2bLF7uIRhOWYsmAb0VgseDjV+TszUZ2dnnJJspGQppgWFTTTlRXX8ShJQ9sBZ9c5RceK4pIxbKrLUfEsdDB+4uTIGTCdcnVX+I8DaQ/jRdtK0vpww+z6qxACaI6HpCl5DYmV+pbDx4mt1ROgbQ3pxTlgHbVZKYm1oQDDQNrn3sZiaokXquW7IijGGX22K2qbQCpsEzhYKKAU/jwiQyxvgiBsF0SdOHECvvzlL8PPfvYziETyA0Hv3LmzoCAKv/+d73yHvb/tttvgjjvusKS8BKGO8htFXyC09EGnBqIUBz+LsfzgJySilisHGw2itB7mdI1BS8aHIuS4VHO4KfusZtajVJ0kcoG0kEgqDesD+5oG56o0dg1YI4gqi5r+tlsayrGMkl2ilExRlpgYXIShnK1LSn5b0RWnzOrHxNYLWIsxOgZ9gbD1z5IBCwMR9jiqUBxWHz0IbjWemav1WVqgGGdptczIjLc1k+ofFS9H0aa29zm2O39hcfL8ShCSY5sg6qc//Sl87GMfY9ZQWrnsssvggx/8IBNenT59Gl7zmteA318+YB1B2E3z8jUQqqzLEkTp3zxYd/kgC2rbUvfBz0QBBi+sClhqHOszldU9hXSUGYN1rSvYXCg9dMAp1CiOaZesuZSXQZAzmiaDpOUXq+XncXkaOVxVb+maheklOaZp6SW6lrxMLZYz5rjWno0QqqjNjlGmu16cPS444KLWzPxYm0lwHpFlLtYcN1mWeqXK6YD5Sjec6y5M3+fWi4vAjSAIwjpsURn70Y9+BO9///sLCqHUbAaamprguuuuY++Hh4fhf//3f00pJ0Hwpq13M9S1rSj7PTXPgeaNc8b3+Rz8BNzAmGoxXsD6RMg2ELBMTtQCLaYVR1Z5Zfu2a+1OCFXV5TZc8aYu4RbMrmdQpmD0dqXtpDKVLKumsWCDAonlzSlP/xlG5zxQVd8KTV2roa5tpfG8NBQhiWIo2ddeVchdRxwfqtHZny3d66CmeZmaDFQUwZCLBnAislvlVTd2GE7DHXONtvnXlH1O0S2yhftUE/KyYvxU1DTBys1XcymHTHtXEeGueMMhnmPHqq3cykMQRDaW75JefPFFZgmVAi2ZMBYUutp7+umn4ZZbblGVzvXXX59+/8ADD5hSVoKwhIIbHJM3M5pjCui9ALNYA1Hyg58VeLw+aF6+Nuev4m+e9W4mMw9+lhhE0cHbhODAzq+Xkw6wstRFyxgTsk4cYjBkJccrdqLNTWW0TfyBMFTWNqvLS2dlW1ash/a+LeXLqkYpiVwCFWg2ua1fKmqbVe9neeZdMC3FIWcmte5+eSFau2mkadkAh1R0zF8ytJuI+1MR99F256+iDXq3XsdH8G9xVXnM+6m20eKu3u79htUEK2rsLgJBOBbLXfN95jOfgWg0yt7X1NTAF7/4Rdi6Vbu0+dJLL02//8Mf/sC1jARhe/wZxYwNHm1I3UC5TeLal/wRKB4S2JndznYcALVbZ+TTunIjnD26B2TA7kO2kfzNjFPm5j5Jo8J1UUPnqvJpqMnH1vYRpL0ddlERrmmE6fHh8l9UzH3+VbWlRoGqPTOP2GNC05i1qCq4T0vGEyq+aG6BeI9Bczw0iIdqxThO9Rdy3i1TpM6B7VBV3wa2Y8cYFLC7DOOQZ7nUs1TKQ4Od9ec6jzqjG51XF4JwGJbeRp49exZ+97vfpT9/4hOf0CWESgmxOjs70+75zp8/z62cBGE3Zh8orPKHbfnBSG29HLJZ1lM3YYRQFlnlCXk4F5i61m5bDkT5/STes6y7jCJjRhUEb5ZAsBLae7dA68oNlsfnsQPV+Sqcnmmzn0mh1m+z65r51rp6e71+8+JSWICbrOO5WtkWSsv0PrbmPEJYT7m+aujog0C4Ku9XVmPnmKqoaXSsYpwYz6oIZdAHxhRXg2OE+SJWwyltSxACYulO/eGHH06/X79+PVxzzTWGJtOurq70+6NHj3IoIUFYT8Exb7I7Fs2HdEnWYcs3Y5zzayynoV8Ef7DCUL62We9wrkdZ13yLeWpyw6Abq9Sms/MxfPBz+aYb3XLZQqlmF75LONtYmDwGK2qboGn5auamtHQ5ypfJ9gsA4ceGs8ns/7LjSRQU6+OcWL83c2xm6pEkjqHtc6hZyB5Xhke/CNy3We1eoJilziM8x6wvEIKuNTu5pecouIxBDr+xaRznC2kzUOw6c4JkSFdggnANlgqijh07ln5/xRVXGE6vuro6/X58fNxwegQhFSo3Rg3tvWAblm/eTDz4WVCXmuYl4boWMgUrwlg8Cbw3xAvDcsFppThgi1CGghgvl7qDvmKKSy7V2fHM3lGe+kQdl4WE08ax8iI116WbEbdZ+RdtnFxECTsvmUtN44KXBlWods2pcG1vX9AmQbvl48q8MVg4jpKNY95s13wcXZRW1DTp/u3S97V9vXAS5veXqfOgwHNsZV2L3UWQhKU+DFXVmTweLBgvBZVqDSbJ4yxh0X7U9n2PuFOCpWTtke3uE4IgSmLpjeXo6Gj6fUdHh+H0PBkXrpFIxHB6BGEPiqkbms7VFxfIUmv6cizmtm8EbUKaeDNKmXGvyy+3huwz0vD5g+BIBDj4cdFAlABhNZk1wbkOjpqDTRCGqmifYu4xea9vKzZeCeaguMcSJMO6nLeAcyF9fmOwpqmTuYHiOm8ZbOvuDcaVEu3YC4q01yzWn7pc5Jpcr0IKQNrHo7lnJn6oLBOvsgvSBpkKDmL2SyG0nUeIYq0oYJvxODuKWC+OZdSkRGMq/NtZnjmIINyHpYKoTMFRIpEwPGFkCrbq6gxqkxCEXSjma8Hmp26VxULhPMvF5tANxYgSctMVDC9Zr4pcTq7YUT0bmzQQqhL60MbfMtRYHdt6N2tITrz2LIXwz7ZKwb06S4D0f+zBQFujFnamIKV0fRXz+t+U5hPbTY0uoZXBKrX15Mw5AoxBFI6ZgpusUTKK07RsoPA/6ElMy69UtonH6+WVpfAIvw5agYDCUFvzTLptfCjOKLdqmTLf+lo1Rurbe8CJiHgWJQjCJkFUQ0ND+v3Zs2cNpRWPx+HFF18smDZBSI+qtdPAAqtViMR5M9SyYj3ItukolLY4m5z8OEgisXJLfjxAy5DEWIxfMGCjJlF6f8/ZHYJKIYCaucnj8UFb3xaV2SrWCAEqa6FWpytOQiCrAw3PW3P3Oq5rBo9g4E68DLN2Xc7IS4tlMs91SdH4Pbv73IL8xdmbWVGHpe/5Q86wRin5bc71WrX9RjAFE9tflvEti2JcwTEoXtH1Y0FdTBmTOsePPVZ56vOprG02IXsDyskeDzQu61f7bZAL2cpLEO7BUkFUd/eSu5EnnnjCUFqPPfYYTE5OsvfhcBh6epwpzSecjx0CDhEPB1xwar20oIjVbj5fEPwF41IIdPAzof6mPcOiumFUWayGjmKWSeZ1fkNnH3h9fuHmURHitXDX4GT96JB5mGPbNC9fC5V1ai8ftAhDQTxctA7rfX5UxwRRkb6dl9LW7iWtURKQbU+dadWY1UZ6yqS7GgYuQW2eLwzH55FcWMQfCRXjxCumHJQ6j6hUFJMDvvXoWncZ1/ScMoC5jxdnNAtBOBaflZlddtllbJLBeCYoiDp06BD09fVpnoTQGupf//Vf05+3bt0KgUDAlDIThOkXxQUDfCpZh5mCblxogS3QbM5qlL6tN8Chp+6T+vCbSMYL/4PJxfQHwtDasxFOH3gqI0/FWWNNEbRcBejZci3MTY1CXdtKbuNYrdCjWJsUnVsJW2PUqR3DlvZfsSJhWVUUN1xdv/R9GdEkG7NZkcZS2YgGLei6Fmjo6GOKGYWVMwokn1WZInnJOqZEQnMTCtTmxYZFgX9AF7XzMxNQ27JcZ1YmnEcsGL/47FmO2nVMl8BQZA8NGRQoUlPXapgePQctK83xjFHuXFBo7hWq7WRSjMvORJxpMWtPWrxQjZ2r+OXJsinfAM3L16he/0uSNPPsJ0pH2o+Q5xGCcAiWWkS1tLTAtm3b0hcXH/jAByASiWhKA3/3T//0T7B79+703175yldyLytBiI6hjaXmGA6ybEpMLKcNbRCuaYC6lsKB6zUdUBc1Zqsb2sFyilxSm30wWn3ZK+z1ey3NM1Manz+o6nuZG/BCG3fUOkbXD2ZYJsnaR2qfAVGFiwVR+I+/opeIJdqlVJupFZypF4ZaSW7Zy+dfUdukLmkZxplgZczsfzXjqq61W9uluFjV1V9AB1so2T0/a5mDgpU10HPRtUXdMOmdz4zMg2bPoQOX3AorNl0FVmP3uBCBQn1b3dgBfdtvMC8+XAmSyYSG+Ylv/6EArmieBseKU8caj3oVSqP3ouugc2B7fqzWIr+xon2FEoa6cKwZhtqFIMQWRCHvfOc70++feeYZeOMb3winT59W9dtTp07B3/zN38C3v/3t9N9WrlwJN910kyllJQghFvpiixsteqXbTW37GkGS/djy9S9hr2LuAMzcABe9nLPg4JefuiQdpgXengxy+qX/klv1pGI4XyG7ylRlNxErbD9eXwB6OMWYSwnkW1duUPkDdd8R1TUa1re+baVpMRmLZ2xi2qK5JhVkH6ZOE1ex7SJJrUJDSRT5+sUSip4ROKZlJoq5ZQyEq8Dj8RrLUxdqM3HRWLV5DPK23lZL/45bFoUeNq1fljzX8ozjYGUts072ePk5hmL7QIEtQ3EOXLHxyuLZS9R/6nFinQjCOVjqmg9Bi6jXv/718L3vfY993rVrF9x4441w/fXXM9d958+fT393dHQUnnrqKThw4AD8/ve/h4ceeghisVj639Ed3yc/+UnweCyXpxEEN+y4FNC64eCyQbFkH6zI7T6hQJpaTb0LpYFWKLa4JimlgSgQ+sa3CBtcxfQyqbZgyjjg85nTVKSh0i2aKZcPKquIQo+zR58D92DwMJ5KQ823FI+m+WX1pbdBPBaFYEW1jnLIZ2HR3L2Wjb9kImF4HtQ2R2Z815aLP759VdvcBePDJ1V8M2lqXYq6GLVT6FIm60CwEirqmqCqvi09D1px4WVmHrJohGfHjuKWaOFn2lCbyNGeQo8Ti84jOhIxJ12dqF0LeVNoz8F3jhL0GTLc1/p+n7Xn11UGM9tTQ9qcnhWfPwQDO2/lKnhzD+oVnEXTkyII0bFlRvrQhz4E586dg9/85jfs89zcHPz85z9nr0zQBV8xvF4vfPzjH4dNmzaZXl6CkEHjSqQ9K/pdxouH6fFhEBNBN+16yRpy1lsaOfkyRzpsOvgJ3d/F3EMWOjhwKideAqL2IQqAiwmivBlWAkK5LJQFjV3lC4TYy55CmGCNIugUauqzztK2tuLhmkYWb/DA478sUByTrXhVpa/ZpASsYsXml7JL2JmJC2ApZq83Fj97oYpamJsZL1yUrLpKcP4w+AwVvMBXk4bVZzO19VI4nTUFXA9E2+cXU1yxu5z+YEXW+fn0wYzYtlKhvh3RNePE+UFNqaNL0QunDmgskTxj0KwpCtN2oxCq3HPd3ncRnDn0NK/M+KRDEC7CFlMiFCL927/9G9x5553svVbq6urga1/7GrziFa8wpXwEYS36Fi9jG2eNv9WYV0f/NqhpXqbq4JfnMkM0DVDJ0Bf42IySiKIpmakZVzp/GSgXi6kU4aoGEBdFd4DYchS3mlFMdUVV1dAGSgmLbTxUh6vqoaKmCepaV4ATsDQ2i9muPFXUhX3HVosUlZaDxf8x/c4pgZbtskQoq4Rk2i2TynhmJsTbKJevmjwz3fbZfSEsKsvWXAJNywYK/6Mm990OUDShMSK5YlxpzHUVrj5GlJVjGhWRMHbbstU7mMs4Z5xHSrdfpiJWUXKS6Fi1VXXJShVDc9+q3GdZeR7J2q9xGKoVdc3p9+Fqkc+K9hEIVbG1WAaBJ0HIgG23tuhO721vexvcfffd8OpXvxpCoZAqARTGiLr//vth586dlpSTIIzSunIj2zyt2n5j4S/oXrtsXvQMbtJxMUdrAdx826ScbmGG5lLbsjz9ngkACU3ouoAt0/XWHWLVXwyGq+u1JmehKwyw4fKh0J+svXzo3XYD9G69rqTAykmgmzNumD7OyrtFM5Km9p+K9VwVhYeFA2EeXD1CGU8M94BoBYAB5EtmpaHg5iol8RDoaSyfojDLvML/pHB7rvTPc854hlEphBdq+9jaftJP19pLNf+GBMulGif7Y2VtM9S39+jbCwr6+PFz96uNQLja2FpgZntqUEixcm9U07QM2nu3sPuqygyhlPQoBucnJfv7GHe1SGI6CkcQ7sZ2O83e3l74h3/4B/jwhz8Me/bsgWeffZbFiRobG0sLn5qbm1lsqXXr1umyoCIIO0FzaNR810tRc21D91n2L5i4mBdd0HVi5uXDQj+IqTEeqqqDni3XsjLqu9C2fzxYg1vqaS+6Dk8mdk2x51aEC3AR5mIrQeWDsvF2VHs0UmfpI8aFvZbvlvhynjcmY4UMhKtgbnqsfFoaxqkIz1WpIuAYnB47xyVB059fNVZ5Nre30TYIVtQwK3qumO6ZT7E0dmbpNrZropKZ7Hr2bb0Bxs+fhMbOftj3yE/NyEJ66lq7YeL8KRg/d8LcjGxoN6fvw2Stn955FhVdTzz3e6isb+EiIFBdDhV5FY1TZrtusQJNy1ez97OToyAtWec953hoIAgnYrsgKkUgEGDCJnwRhJs2gHoXL/kWPXHKK27b6S9XSoNJlg2k3Qcjry+QdTE5PzPBeQzZVD+D7ar/2eDrJgI4ukXjFaDaXNdlfIQA5dygJhJxU9LOSNC6YOEiXDZrdvGiuzimpLd83WVw/LnfMTeS3MZCZjJmzPMGkwyEKmGaV1nMdg+pcgxqmZvs2P9YnWex/DoHLoa5qVG4MHgQHINS+IOlMUE0d6/1Y7DUMxKuaWAvnlj/nImxuNR39MLYuePsfV3byvKKJ0KgSJ2nKGfavHOdTec8jEu4asdN7L1o8baKxogq7KJBPEyPAWodWhVCSiJiXxGE4LjDFwxBiIzehVcCDSerY06YK9wQuL3L1VvIottwGZbRTv5gmLkgQFcYbT2bLC+Ls1GEGoRa4gLwscaxj2Llwr/377gF7IbrwU+APhB1HKhdF4OVNdB/yS2cLVIy8hPQirjcGNRk+ZzZtGbU1QyLqKw0xesfrW2BiiRqv1vXtgKUgnFJNbShjkd+4JJbwSwy+98fqmDu5VDpYNnaS/QkVvqfOboB5Ind+RfEpecRFPT3X3wz9O+4GfzBCqnKbjb2edWQtM2ttGYq9RtV+iDq8uGqjCUQQsaVUqx7HnM98lDMcoIoDwmiCEJ47N9A2m29YpS+bS8Dr9fPPV3Z28XWehSUVfAvR6mNJrrMxODAXv+SdRQ3lOJxvPjlofDvO53p1WTE/vFwcKGbexBMufFklhtaEdg1H3eK9F91Uye7pDSFrPhg+ZfE4aoGTQc/tf0igls0reXgO+YwLWvGsLb4PLb7mLHu8qFIuxSKNWM4X7vbdaEQ2Z9YmTRacyr8Y9c0d68tUC6rKJ+Xx+c3Mfvs/Hu2XAOrL31FwYtB09pFiLEpFlaOQTXzs5X7HVRwQJeb+nDueSQ7eYc9M1LVRxLFOJWIcJZBi1K0OG7qWnDvJ0q5jCrG6a1D79brWdytFRuv1PV7gnAD0giipqam4ODBg3Dy5EmIRCJ2F4cgLNuMpnz2av2d2di9wUDhUtOyAVXfDVfXg5PJ7Au7x4Us48cwGtsZXWCZjZ1939a7mR1Alq97CXg8Pu7joXP1Dna5Vt/Wk/EjdfXNc0e3lKl5aOyLUn3H51lJ2ucCUzHLFUbuBey1TADQtXYnvzzKlkHr9/leGNo+32vMvnXlBg5ZGqyzDoFQ0XbO+DtagOO+pGXFeli+/iVGSqixcNZlpTtzE8uI1s1o1RyqrCubIY+5lKVhw3On9lnHOKF6lWtk2JfZPucZJBiu5pSStnlsYOfLOWSpIk8J+0eGcc/TKwaPddjRz2le2RWbYtba04Z6zyMNHb1ZgqhS47I2Q3nRruear6twJe/eqXvD5VDd2MEvD4JwGLYIoqLRKMzPz7NXoswk8Pzzz8Mb3vAG2L59O9xyyy1w7bXXws6dO+GjH/0oTExoi+lBEJbA2fS+efla6BzYzrQriOxFvn3VRdY1SZ6lvgkbxEJJCujaiAsyH1IEodAYzD1A5LoHwE2xGWPX6/NDe98WqG3pMud5UxR24aiLos+QOAc/PYRrGoU7/OPYKjW+eAqicvPBOHm9W6+DutYVfNLPbMui7arSKonDPC7iZRnuTfLd2BQuJ2qGmmIZqnkMJk3dl+Aln+65StfzrFYYpFjuVln7mFX4WB4Ktr/Q3A4axmjWGm+028q2m8K/vQXrKz34fEHV312142bjHhoKGRqWFUaIKzy1fd0rUE4R11teoLKEHecR3C9YjlXjXqWHiuIWURrzk/huAD0ldFmgnFluvTTzPEIQhICCqJmZGbj66qth48aNcMkll8Dw8HDR7z722GPwx3/8x+z/mQIrtI763ve+B7fffnvJ3xOEE0A3Vw0dfVBR4NJRE7RGckDejZ8MGyqZNnLlDhyi1KWjf2vW5+4NV8Dqy14JTtFANHLwE+2yUs+lN7qlytZuLSUwkWQKVdkvwaraEmmoL5KaRDDmimVxARRx4i6WKxO6/0JN+9aVGzO+KshYKwLXyweeQoDCOWS84yME4DrvCdXVzrTW0Ia580NNc6fq76JiCloIuoHebdez+qpBmn2HzmIanf95tg8qclpqnWojHm9xTwSir8miPys8y1F8LyhGXa1oN/SUYFbfllI0Qld5oao6rhaeqTh4HXYIWQlCciwXRH3rW9+Cc+fOsfd/9md/Bq2trQW/Nzs7C+95z3uY1VQx0E3fO9/5TtPKShCygW6JrDC5J9SBm55MGjtXmRPAkrdQRJDNP3ecWq8syowFRQFfoLj2rnkxJDglo7J8Pn+IvwWEgBqIda3dTFEhjeBDvLqxHfwBY9Yi6JIVNSrb+6yziA2Eq6C+vQdClbUF4/+Ug6tLwkw4Pq96L6sw9lhWXDjB5tk8H/1cA1Rn1tXc+cGn0crK51dvpWEaVo4Fq6Zn1Hy3eqIt5SqJ5xxQJq32VVvZeqPG/Sla7FrmFlvzFlfhvj5kuaQiSjS+xsYx0FctK9erco1VKJ6a6XB+bgcuuZXFh7ONgvURay9gPkrB/QcqAOpRjOM2T9m2J7O5/8vU28thj7Tq4pvYs1dRq/1cQBBux1JBFF4G/fd///dCxh4PvPa1ry363R/84AdZ1k4NDQ3wqle9Ct70pjfBypULQcuRJ598Eu69916TS04Q5tK39QYu6bT1bVZnci+piwY70XPx0NG/Le/z2sv/CJwMuhypb1upLnaMBJQSYuAGdNmaSwr/oxXj3cZnqmT/8XDHoiaWDvuOUnA+7Vp3qS5rFNXzlJUWcRqSsl3ztVy7eLzMNVHfNv1rHrpkxQsXX2BJ2GgKOVVZtnoHe+ZR8FLqe0hn//YsN3SpywdLA9jLIgTQWk7NLqgUy1zzcSej6I2d/VBZ2wxVDW1lf4b7wIFLbyufqOHiqU3L/LGYZS0o2n4zpzx4MWlEKSlc1cAUm1jsycw4ZSaPbRRuonstNe5P1Y4Ny9YsgZ772tblWWuiVmxf5yWi6HOW0YQorMqL3SVZE+N+CBV0ZKK8UlLpTsCYRKGKWui56FptGZfp22BFtSrrMzXxcHFsZZ6Xip1HSlm0lcXK9a7s9ksR6hnKniuz1wB8XvC+IlhRY8gtPSoiEAQhuCBq7969cOrUKfYeXfN1dBTXUvnxj3+cfo/f+8lPfgJ33XUXs5L62c9+BhdffHH631PCLYIQAT2uc8I1NmhjGUWR6+CHAZxFwNBm06wNKcdNLLp9KyqcyTlA5JcDTIX34R2tI9QI3SxFMTeB5u61UFXfaq/mpYr5NPM5K+6Tne944BWbSDuZBz/rT36lLz+TBQ9uPDQR9QZUVp2+mjQKlAEvVxo6+7IsUrQFRVaku5DM3PcU7xe7ypq0xDrNDCFAZv+i1RleuK3c9NKcLxVu11x3kqaNFc3JmlSOjPbn5cbQLGqaOmHNZa/UFFsos928/gBTbLIi5pp4iNGHRmjr3cLcxnWvvzzbmtRCMH9jv1/DLLLz9oKqYivqp7a5q6S7XL3PPO5tHYtd816ZbPu2vwxWbr5ad/KNywZg1Y6bmIIG7+ezFCklxEzX5yX3oxn3D8X2H8tUWJlqQY/lPm8y1+FSd2Ohygx3eRaD/ZhnNa9HoUKQvQVByISlN7N79uxJv9+8eXPR7w0ODsL+/fvTn9EKqq1tSQMwGAzC3/3d36U/YwypaDRqSpkJQibMu5QqnS5qZooO+gnH9qmobgSPz2CgYJv6D4U3uLnk7fMcBSqlNlSp+B9qtO3UbsZQkwwtDJxKoXaoa+lm/8/TvJSI+taV7OCY5WebA6FS8X6yUHso8JQVAmB8JZ4YcovK6RBTbg7BZw7nwN6t16v6vlmUytcuK4vUBddS8oqqC++i5S1oqWCgzEqBOcayw2+ZfLKEMMW/6w9VMqvZlJsvO+CxX8FLUNRyruOsiFDyUpeb693sjys2XqXt91lpCXr5ooifFwqTeJSzlMY3ISaojNHWuwlqmrPdd5tJ3r6+ZyOs3lnMarI8eHmMeyjeVjihqvqS5e5cvYPl27PlWvms8mzOs9S5zy4rLlRs0219Y1IbovvyLNfPOWMwEKpkSoiexb1MRoF0n0fCVfWGLHNyyfQKYe26rl4QjYo16NK0e+OS60Jb4LKPEXQvRBACY7JqfjYnTpxIv+/sLB7w9Iknnki/93q9cOONN+Z9Z82aNSwNFFqhEOrw4cOwejX5ZyYERMCDOu/NG24kLgweBJHBwxK6qsHDvy7NlUK/KeeGinPfYx06By4umY+evkX/6Hg5V6wPUVMQNXjRymT/Y3eDtNj8LGIwU7x4MNN9huFn26Ymau3ZBCOnD2eUQ01B8CJehQVkEUuFyrpmZr0SjcyC2JRri0yXVKW/iXGO8OUIFP7P5/jwSW7pZV0+FNGCRYuG8XNLe2MjoHUFWmKdO/4CiGoJjgKc/ktuhVhkVrcw2+i6WtPcBcvXvQRGTh+EqdGzutIY2PlyNkfNTY0BT3ivDTgGy1mAYdy2vN8VnVhz08/+A1oBxqLFY/tah2LqmNLj+cC0dZ3zPNi4rB8unDpgPCGVbcrDOtZ1cHJ/nOdmNvPfbdoMYlwxjAN5/tSSQnKuEA8tsSKzUyaXRLyzu14KzW+ogDBy5nC+Ag7PfB3UhgVRdLR/MctpRdHkst7qNcgMi320ZONtzWbbOHX4UCcI6S2iJiYm0u+rq4trhD/99NPp9+vWrYP6+sLBTnt6li5Tzp7Vd5gkCCeCmjU8wUtddAeDmvS8rXGsxB8Mq3bn4KhNh4pDKwagzvhBzs+VhUtDnoKcgknpTz/PXREneAoT8QCNF8+mx7YRkHIbfbzAzNRANHowyOy3UhexaKEhO9kuwdS5wjALXjG2VCXBeRLGMYjxBjQWQpsgKuf75RQLsv6Oa5dA7m6zUGcQldY21i2E4uFyUVGgtqXLkOsanMNxvAjhjqXkGFQsvRysb+/NcklpZftkzoOl8sVLbvYdUKC+zW6hvML3Zxymhw6rLRUVgP6LbzaeiJtRxMyz0FyjZv7JtjQtb3EsZ/vZO2ZRCIlW/Ly8GwixFnKlvKvXbIpbx6txzadHgdTnD6XjpgqJZmNtvmPI0pisbl+DCEJ0i6jMQ0IkElEliNq6tfiGuKZmyYR1enqaSxkJwjB23hMtLrrLN1wO50/uK6rphBqIai0B0NoJL9AxQLaaINmEHWSawpuZi5qTpz2bMbPGpsiB7QtusvP+plEzvLwvDBNR0da5GvlFY4HwP/jJgCMuA1R75lNsinWUVYji/+Ip744F11Y14HjuWrMTJs6fyvitTuteHZTPR+uljQOfgXJrhY61xOgY5B2vRVtMI6tJqr6AXX3pK9JCUSvGl7ljUoPygaDrg1p32Xj5ylwaljmz4PnH8SS19q0oa4WFWFAWTRZFAp8nRGx/e88jmfkYVYwrvxdUd57LsS7fcTPEYxEIhKtK52/pnqywVpJt+0I1fVfqOzaeRwjC6VhqEZVpBXX69OmC3zl37hwcPLjkHmrbtm1F05ubm0u/DwTUm7MShKXYsPFE/8Wo1VjUzYsC6RghpQhXNUB7X+mgnUQpeG9MLNroWJKNczZtGO8KqW7sAPGQ9OCr6vBQ6ueZgqgkl3zxEgznVjxQYaB4vWXjjmiXG445kJWvR8kA1RljMNVHeg/jqy97BYRrGqBp+RpmmYxzTXVTZ5lLaHusUaTp/6LlTGr4bqblZVLsMbj0x7JpFhJooAVZXfvKdKzDpRKWSq+ARYRZ4yOpzToeX3ajqS0keazMunhE5TmMKVKuzbKt+wkpDf6yBOhWjC/t89Sq7Tey83Ehq2ZunjcIU1A1dlTpxakbg7wV41KKlyiULyqEsulcoNYy2RostIiyva4EIR+WWkR1dy8dXp566qmC37nvvvvS7z0eT0mLqLGxJd/stbX2B10kCJnIiqGiLwUprEa4klNN7ho+btvIqKgvXvwbCqaelZ057bvq4hthcuQMc7tnLQ4bLzynkUzXfEU0EDUn6fFA3/YbIRGPcb/ELDWXlBu3mXN5oKK422NrUFR1JI9nsWQaetPPEqjoS2KpCJlrpLExmHJ3hhZUvduuFzBugEhg7DiHzY0CrHm9W69j/2/v3wq+YLiIolOx8adyXBoobtbYt8gCydzfqPu5qvgjDhin6A6TLvgtRBALK1FBl3bF3NqtvuyVsPf3/w3uwt3jgVW/qFtlfYKoQucCtPhsXr4W7IIELgRBSGkRtXHjxvT7J598Ep555pmsf0d3fd/+9rfTn9euXVs0PhRy+PDhgm76CMKtaBOMqPmu+Ida1JBETV10W+TazVdW0Thc8Brwya62ndR+Dy0AgpU1aY1YxM5NeCFQI62xc1VWbAxbsGSMmpeHmkv0vLGp4uDH43IudemKggARNOlzL+hQCzxUWQvLVu8A+RFjri02DxaLyVX68iFpzjMqRlMxa8EUFTWN4BrMbn/DwtD8Y55RBZqFeH5boIZZ5ImJ3e4huayFIu85bQZjguKck4pZK/T+3ATErq9izXlEB6HKOkvaU43LXbH7kAMuOI/wdM2nFhRE6XEpa9kzJ6kyRHEc/pwShFssotavXw8rVqyAY8eOsUP5X//1X8N73vMe5n5vaGgI/vVf/xWOHz+e/v7LX/7yommdPHkyyyKqoWHpEEwQ9uK0RVhsKmub05q6wsHdYEqxZlPJ4QDRsmI9DB/fC50D2zVkWzrfvu0vg9nJEaisb3XxpjibXGtEEQ63Wn2dm2NFtHTwSyTjhtJEISOOZ5Ep9JzZchmrziDKOYe7EtWob+uBc8degGQizi4P1Kepvm2y+tjGuQ5dBbb2bGTlqWpoB1tR2XwizJWmx2XkaJWnGruGoYzuIZ2MiV2Arqn6BIxZi9b7g/t3QfPyNZbnLUw8nXLwejY1KMb1X3wzjA+fYGsyYRKKmvOI9YPQVOv7jL/j81fse5lKSRij0Ml4M4Rkrb2b7LXWzzmPFPPeI8NekCCciKWCKOStb30rvPe972XvR0ZG4O/+7u8Kfq+xsRH+6I/+qGg6Dz74YPo9Wk01NzebUFqCIIpBC7c4ZB5ARemX1pUb2GHc4/VxtfwwFodJjLbhhqrqWC+E1I0O3+LFvpfpwqdp2YChYjV3r2Njz5FjzeT+Luq2VdDm4RnrCP33D1xyKxMApMaPKPNzQQyUDevV0r0O3IbpgkA1l61q3XqmrfK4lMy98PTMx2M+yBI2lh6DdignoPXSyb2PQuOyVRblqCa4izntUN3YDqsvvQ3cQPHLee4ZGU4CvSq0VGpTJqprWwGzh0bz/l7b3AWiK2dOjw/bIgwtT1LK84hR8ByMXgumLpyBrrWXqv+hhGs1nr1w3xuZndKmNMoLhyufEoSTsFwQhVZOu3btgh/96EdFv+P1euGuu+6CqqoiAfgA4N57702/X71ag6YpQRD2bHBEvoBTRSErD945iNFGvMrBUwhFqCW37/huym2/SFcroPJ4oPei62B2ahTq21aCFYgWK88yDcRMASIoqnL1+gLg9fohHo+KN8bKzoPZ9S0njCLspfhzqdPyTDRKPC+8g6Rrzd9K6jt64dyx51W7xuKFaWMjxwVosXyy/y7WGpSyXlp92SvKzOuK6euraOuzMey3NLEKu+behs5VcObQ7uy/dfRB+6qLOOVgTr26N14Jc1OjzGW9/f1kbt/p3itq/p1By+RFrwU4B2kps8+vVQGuCIr17urxZfdeQu15pKSLbUH2NwThRCyNEZXi4x//OHzwgx9kVk+5oOu+L3/5y/DSl7606O/37t0LTz/9dPrzmjUian0QhA3Ytl4mHXXwwxgIwYoaAQ9Q5d0D6E86Iw1LXHvb5DbMQQh9OWpYcGL84FdR28Tc6mkRiGbFliJMAw93/ZfcKmALZ1pEqfi6aIdUjY+QakSrpyjzYqY1SpkrDz2CYaOXIB5vhkAmnX2mlRSYi4VdgbEjMXbVys1Xm68Eo3Fvq06I5DTEs9SXkYaOXi7zidaxpndsSjmmc8YnKi9lWtWnCFXVFfy7SMpBKITHmKZi7mUVafI0o6+0zIMYE7mmeRn3MshKx6qt5iUu4ZRFEE7AFnV1nIj/9E//FO644w54/vnnWXwo/Nvy5cuhv7+/7EQ9OjoKb3nLW9Kfr7nmGgtKTRDqkFX4QixR17aSXWg899vvlWgW2rnwpqqxnVmwZEHPkzZkHpZmXaJroK51BVwYPGhP5i7DzIDLfC7DFFvWeE1lF/yC1+hlTmf/dhg88ASYQsnQSmK3q1qWrdkBh5+6H4IVtRCoqDa9bvhslO9zc/L3eL3aYrHphOtFO+9pio4fjmuDYFilUpzlU5b6GE0mZ2MdPNd+kepVBrRex71GPBZxbB1FO490rb9M8gbkS6i6XvuPVM4/bHwTBGE5tvpN8ng8sHHjRvbSwmWXXcZeBEGYe5h2q1CtoDYtBmLNO+Gav0ksGlyTmZyr7B/NxSyivcvxAqvQ+MMYI8PH9wJPMtvILk1NFG6YQk5/8KifWZeUMlzs9my5BhQD2q6y1dcUZK+3O5c8W/EHiwfvrm/v0S6IsnAMZseIsizb7DKUqC9q8K++9BVsT6N2TmrvuwjOHt3DsYTyPlfoGikWnbO7GHl9J6XVSSkcVh2eFLRskbi9RBy7IpZJJDpX74Capk54/qEfOOI8IgXFwqu6tE14CYsKjUucYwd2vhz2P/pzLnkQBKEOEe12CYLQjf4NSteanY5q986Bi8HnC8Ky1TuKfgfdFxSCm1sX3htGB+8/UVjjtJhSGKOotWejLpcCocpaCIYXNNi1nFbyN9mKprFv70C1W2jo4AdMB90broCWFdoCfMtOluDahgM/i+ugM99cxQSez5CZzyO6QcqMZZSVr8fDYiuYgXMuH8vHKdPipqmpa4BDmZyBLxCCvm03GE+o6DNd0ixPbeLpd2WVkyz1WlmqbuX3Tlpo69uSfu8PVTrq2UflGHV7wXIJFa5ze+9S23FRTuTtKtwCxTg7sHoM1jZ3sXV2xcardFmbqltDyu1BZNJIsPs8QmSC4RJClXWmWeEHVKwbBEHwhQRRBEEw6tpMstrQuJHjdfGJftVXv+SVTKO6GMvXFbasNOID3FBbKPYG9VxK2oJNN4csei66lm1M0X2TujytPUxgjCK08sKLQK209W6G/ktuATOFDEKjoq9kv4gwm0zBrh43eKgB29BeOD4FYQ4rt1yt6fuZApzqxg6QEbzg6t5wueX5amqvYnONIuslG6F2bIarG7IvwDiuO5pigDh1vUsW3jup+mLGs9y79fpF6z9z9+92jMEVm4vHzTZKo17Bc1ZYWYeOTQfRte4yWHPZK6G6sV3zb3l5CigGxvWzEjPODmKnKcfzWUrYyfaJGwU/txIEoQkSRBEEdyS5jLDkUKu9LRo6+izbxKG2ayEKWebwPGjhgZkn1l3Ii7WZraxthlUX3wgNnfzGjNUUs5bSffBT6xPbp9PNgcnxRVR8Ccyk0LOk/vkSa+6vqm+DqvpWpk3dunJT0TkQL1qNYEUAbuuM8pK2XvxqVYLAS1e0+kWroo5V20A0MjX5dc85JoFu66yIKZTGrMdE6zA1UYlFRJiVYRnCNY0lBb1aLMq0wEPxKmt9ktSdttExhG1QUdMI/mAYnAauCbna+rnttTAG9LVh0f2NII+1LIpx2vNULG/HckpxxRTUdHuryKtj4TrjPpVP+iZRLB/B5lufP5huT0OhFUxo1kwvHM3L1xQtf9OyAVP3GkKeVwjCpZAgiiAchG0WAg5b19VuuvW2d7jG2MWvvJR3t+E2Gpf1F/y71G4KXdq1vsDSJVhD5ypDbabm8rRskorCNE1X7bi5qEUUfgdjY5VIRHOeeX/TOiAE0fwvWu6MA74IVnlo9btszSWLfWzSYqyzmq0rNzKFARSUFbvst6IcxSh4AaapCTPcotl0KSWyIKhg2RRr64KW7/iMdK8vbnXX0V9YiKtKAGXAeheFs2h5ahkO26s7qfLFXKYVc1sqVDxfW9ZBcec9WSk2F5ntJUTEYaL9mbJfIbR32w3MQwhav4kGCpkGLrkV+ra9rIjF6wKtPYWV5vTAZX+ekYbt8yxBOAzuN10//elPYc+e7CC3r3jFK2Djxo1F/50HmXkQhEjIvXAVL3vHwHY4+syDbHMRCFeBk1BjjSLCBeQS5coi50Wwm1Fz8DNmveME7K/r8nUvgQuDB9iFO14qYmyL+ZkJFvPMiBVJsKIaZibOcylj2THhqjHDH9KwLA5qYKMLVW77JFUhSqwbz9l52bTX0yos5jxvonu5M4d3s/cocBw9fRhEwh+qKBkrlH0nGGaXsBPnB8sKAbD9EpDgXk5CPHBdP3uU/51FIYq5TDPbLRpvyu83QECMFwqVfgb37YLGZRqVkBx8Jnb6eUS0vR9aTqY8hMSjEW7pomvauekxw+mIcFcksuIOQbgN7oKohx9+GO6+++6sv23atCktJCr07zzIzIMgiDIk+WgS91+MmvYhrm5LRNi4FrRGEaBcuXGclu7uLNoMc2wCLH/2hlCsDb3d6LKIssSDiYmZqLA0Ee3gV9vSxV4pUNOvlLYfIRGqhrr964IZtPZshLNHrLmAFR6plYnMBy+XVm2/EZLJBHN3iIKp0aGjhi/RcQ1MxGNgJ+m9rdVDIOkAjW0te2Y137WhahhrlocgCmN+nj38LLT1bdb824Lnq5z2EuHcZNo+0zTXgXzbDM/EAztvBd7g2Bk6/Aw47TzCYy9PQgWV7WRgfkDFuHB1PRdBlCpKFdXmec5p8yxB2A255iMI3gh0DjSbYGVNWZ/TMmKfWzQtm5xihzPFtYGHMb4Ylr1rzU5Dm8hicZusRPUFnoPmm+yDqTVjUN2YkPN5sAfF8IWDnvkHNTZ5kH2Rq04YKux8aeDQrMYdlC2ILByUZS42oXlQAJWKNdfWtwUaO1dB19qdLIaZ3kvHVRffBHZDF09uh8/DgjFR1lz+R7pi0hWdi3OEjuXGqvaxrGhSjJMZVn7OdUDrUKRz4GJD6fDa2xhBnWs+xdY10J/hGpsLGl0wy/4MOIasMWi8T7Ts71OeMMpZYBMEsYDEQSgIgtCC2ZdlKzZdBWcOPl38oKXILYjSs8nkszEtvLNfvv4yOLbnIQhV1PJ33SFRX+XGeUBN/lTAVr0EwtUgzcHPRnz+EMSic9ZmmvE40MGPwPhW4+dOQFNe8GO9z4bG+E+SzpXasbGisgh4bEK0eRDX32Ixl7Tg8dh/RBVGIJvbxxmfMy9gnagYZgo2PDLoulcPPD1OWA6HuUkkRQ8tFoedq3cw147oGlSGebsUyqJCAdc0Offryi3XMDfZRtxiS7E3kWfYuA6M1drWs6noM08QRDbcd/mf+tSn4BOf+ETW37wZC1ihf+dBZh4EQVhPdUM7VO+4WfpdltcXKO7HWCC3J9WNHdC/A10jhi070Ih0ICwEtkNpIZTY5Tf74MfFrV3GWOu56Bo4f3J/WvPTMNYbRJUoAMGr38ycNyrrWtjLUmiYCI/aNdGoK7NMJRBvwJgChIFSmPx992KFEMDoGGxc1s9iW6H1WV1Ld+m8LJy8hNsvSjpvF1JKsk+IUawRrS+PmeOLh4tL7CMuF9ICPEbCCORzyXgOMK4qT28Wdnho4IukEx5PFGvnBW7PPEG4BO6CKI/Hw156/50gCANoOJxYcpCRbB/Ut+0G8AcrVFqj2E+wokbV93i75OCCRReFsqLq4KfYP/46B7aLfwFlFIdVx/H9ZQQd041IccucNF+mx6WAy1fm5VdNUyfMToyocMFkYt84qN/TKPY/O8JewOZY8Pduvc7uYghFz5ZrWVydpq4BkB0ZxmBlfQtMXjit8sHVaGns2O2JPBXTZx2uuGB/mzSxnY2l7fUFsxRXCb2dQi1HEGZhv98DgnAYIl1KGcZBVVFDqKqes4BOW1r2umpwwW5LkacpzBgL4gsckirK6rJJSbJxiP1meOwqgghW1MQFEOyZwqDSKUKVtdzStWNtSlsbCfDIl6p/94Yr2Di1bf3WmK3dLqFEe2Yc6xbNxVTWNfMRztk8VAPByuIeGgSqDArhzxx6mlknp+LCSYVEbvKcgvB3JVoNoswYQwXSVLt++gJB5ipubnIUWlZuAFngvT+we79DEERxSBBFEAShF0dtcFTERuFYX5kuo+RBkfvg59hnSz4NbLcG201KLgytbemGuakx1odVDe0gMw2dfbp+l9dvFggz7bzskGItldRiSxRrFCn6OBcJiywS/RffDL5gWJcwlMd40TKn+YNhWL7uMsN5FilJkT/bMcA4zmOKs559WcrpJgt05jZdl+t0sfoS4xHrwY1jkiBkggRRBOEgtC26Vi/QqnfdtmHkMkmUDY/ucoi9n+aCKH3Esz5SCZd4H+os0kC0BkWYw+3ay18lp1YzbyQUhuIa1ta7GaQjY8x7PD5oXrEWmpbJ71bLsnGaO1YFvyCTBVEEUU5Hzd7bSXsdNQQqqkmb3yKcdjbgRUVNkyaFEC3PqGVtrnsfp0YpiTCbho5eaOpavdAPqvpSwzpB+ySCsBUSRBGE6bjr8OTmg2MetHctc3mmphGtH0N2HjrqWruhrnUFuP0Cv1gf2CGkcdshNFsIpdnvV/bn3P5S1X/WanNrLV/WGFSYM8Li/y4RorgwqWpohZbudfoTEKQehPzzbrimsfA/yPmI08VbGkWaMcgNG6pitWWW+bjLK4Qm95Ya+0mq+wEJ3TQ7hfKxN0vARUGH+pUgzIJUvQiCM2JtmgnZyb30NC2fzEMBjWHb6Fp7qfrAsnbEbbEqT5pGrcNwn0p0oWACUl2ouARfIJx+7/GKoXNnloCSzcmSCj9LzUV2PlcV1Y3Q3L0WaluWgxBIuSeTsczioHmvZfUY0ZmfkGdk1WVyyDzrEMwcS+rWa3PHQ0HhlsXPj6yKVSlY3LrFeKOtPRvtLg5BEBlwP5396Ec/gmeeeQas5tWvfjVs3iyhOxLCcTR09MHpg0/Zk7nInvlUIpNWkUxlJWS9zHERGg889PyJR9k+EfwZzIoR5aSLNq1YfPmQLXQw3n544TAzNgz+UAVU1rcaTk/YDRNhCi0r1xdXCKGhYALmN6obFQY6+7fDmUO7oa1PsvsRN3SV2n2CpPsJR+yDFqGzhpWNXWrcKJr7DRWRBna+HOKxKAQrqg0XjyAIgQVRjz/+ONx9991gNRdffDEJogghaFzWD5HZKTh/ar/dRSFsoGXFejh37Hn2f6k2rxkXj8XKberBwg0HT0IDijgaiITuwMLBylomEEgjunZlVvEUV2qQyg6uUz5/EPouvtFRl2GEIFjyeNMcIgSSzx8Y36e+vQcUjweSiQRIiUV9QHs/g+0n+bOiC776M4RJ+AIh9srDjWOWIARCDH8VBEEIiRs1CI3SunIDNLT3Mk1sWw5+PDZWijMPLarzlGRzmlsfa9rUIveQarCqmyQZD6agteoKQLi6ARo7V0FkbpoJ5I8981uQEhd3uxOeAb3zoeP3PXb3pd35F4OzABkF8rHoHLgV7vsREvDr6wcPvygMmpUsTJyDXSn4IHIgV+GEAWhNIQhnCaK8Xi97qSGRSOjWHPV4PFmbEPxMEIQDtL9kPVxklBuFUAQnHH4naBgnbaQzrfKKzQNJE2OSOKktLaRQu3X0bwM50egeUtb1SlrkudxGlzCJeMy09GXCUcI9lUNwxaar4OTeR8rHmaJ1x3Khv+PmbQVPdA6r0yJW1Ev1eOA4jTluDBaZy1LrIColSYXmedmB/Sl9XG07S0IQhOWCqE9+8pPsVY6vf/3r8PnPfx7m5ubA7/fDlVdeCddddx2sWbMG2tvboaqqCuLxOExOTsLRo0fhueeeg1/+8pewZ88e9nv8zj/+4z/CJZdcwrsKBGGczA2miefv7vWXw/Hnf5+ZMYiKIzfd5eokSp1VlCP7MrnY9wWpjwZClXUwNz0GbqKhoxfGho5D98YrmJtQgnDd3GzmBZiEF8d1rStg/Oxx6Fp3GciNPG3fe9F1cHT3gxCLzQtbdqsERP7gknJOVUMbyEdS8/wZrq6H/h03m1gmebFFaCLhvF2KkoozhcakTOu8qr4ysz7OGit20r/jFpidHGHz/tjQMZAFNXErua+fMj2jdqAU3ltE52dAUcgggSBkwxbXfB/60Ifghz/8IXuPgqSPfexjsGLFirzvoZVTQ0MDe23duhX+/M//HB566CH4yEc+AoODg/DGN74R/vmf/xluuukmG2pBEPZT07wMhMZhBz/TEWQT6qSL6eXrL4NTex+D6qZOcAudAxdD+6qt4PF4YYS3IErF0AhX1YOdh0PCzgtDMfqksWsAhg4/Y1CbUoy68GDZmkugfdVFLHaSkBfHWc0uYLvrKFKoqg6Wb3gJHNn9G3A7gVAlW5fmZyageflaEJ9yHU57W8I5BIKVEJmfzv4jh3kY1xtcOwxd2Au4HBDq8QfD4A/ad/6yaj8h5L7FBXXp2XINjA4dhbrW7gL/Kk89CMKNWC4+/u53v5sWQl177bXw1a9+taAQqhhoOfX9738fOjo6IBaLwf/3//1/cPjwYRNLTBBik6lpKpogCS9iUvhDlRwKRJTD6wvobKSk7k2grtwsElIGK2qgd9v10LJiHbgJFELZBQr9Wrq1t7dThQArN18NFTVNzIJV3cGPLjmN0risHzpWbQUzyLUeDYZrQHRwfJUXQsnwzMlQRqKYpW573xbwqHTfbjWlL8vlHHftvVtABpzqVk6Wy+LuTVdCZW0zmBGfauDS27inKyu4D3SyUMByVDRNQ8cq5h7QHFfh5u3V3eoqXO14T60ZgXAVi82NZ32CIOTCUkEUutr74he/yN7X1tbCXXfdxdzyaaWtrQ0++tGPsvfz8/NMmEUQhMYNqwWb24bOVVDfthKaulZDZV0LOApBDwd4ydO79XqDqSiq/+y4fiW4zEOtPRtNb0lZDuhV9a3Qu/U68S1YVSKDCwwUxNa391gyvnyBIHSt2WkoL4IwQri6wRENKK5Awt5LQb1rXdPy1dDYuYp7eQrm1bUaRKZl5Qa2dvl8Qahp7gLZybuoLjNGSo2hUGUt9Fx0LQRCVWCGRYzWPYMqV+EKP8U4q+hadylUN3ZIHEMzG6yL6OD+bGDnyy3Lr6GjD5y1fxdHICbu/oAgCD1Yepvw8MMPw9mzZ9n7G264AerqlqwltHLFFVewOFHIr371K5iezjEpJwhCiMtA5hKob4s0l8Y8NZTsqnNFTaN2ixhx9ppECWptvkAx9yCQdGR8HqkpMIf1bX8ZO2z3bbuhwNcln+e1jq/F6lbU8dcmFwrZ+9W2GEnWtJvol1/SUabbAuHq9PvaluVSxOkyG9RKN3POQOsao+4h8UK6/5JbwOtTpwQr8nqGlgAp9LpcdRp6FePUnu2MPqs4BldsvNIy4bDZLFu9g609y9e/BETGyuejc2C7uRkYPAPhvr2hvZeDwqr4iDx/EwRhsSDqxRdfTL8fGBgwPLn09/ez97Ozs3D8+HHD5SMIgnDiJb+adLMOWJbt3TSWyyJE3rx29G9nmsd4mDWKHLE6ytO19lK7i+AqUHsaD9tLVhj6n9GeLddCy4r1YA5Gn2Nx5wGzsX7eTdpSFjtdmPK+pM8TBBCmgVYe3RuuYO4/jQoBzbBCycKi/Qy6v8JYQ9pJqlZq49FvXr9e99VigftAtLaua+mGqoYFxVwpUDUeVbhFswF/IGxDruLUPxdfIMT2gjwU5Np6N2s+A5OFjL6QCZ2rL2YKq87EGYodBOEGLBVEDQ0Npd/rccmXS2YamWkTBFEekTb3MpSLsGfTX9NkX5BbUd1MoIUhD5cY6D5v4JJbQRhUeGMpROEgtYQMVNY1C3WBrlcgb+bcGKrU7z2AKE1VfRuEqxqs6V+J70fQnZnliCMLLbo3wVh0Ri11cB0uFDvGjDlF2LgjZfb9qy99hWPcT/ICrbow/iS6ezPj3CT6Jb/V5UNhi1HrR7eD7YcCa+y73DiyzcvXwJrL/wiERM3z5aC7CxQwpmhcZsxwQCaau9earxhCEIQ9gqjMDfCJEycMp5eZhrCba4JQSUV1o1TBhd0abDtFVV2rBJp0OgOzCnIARdcBFIDUGMHKpQCuuRpweHmR6d5FFZYdtsQYg1bhyXAVVNXQBk4iXLs07rwSuRBSMw+aPVeiq6GVW642NQ8ZSVleqLWmq2kqHJ8NhQi9266H/h23cC3fUgYgPSgkoTFortsojCGIFgHFUbL3aA669FSDfVZMzmznYsJ3YcZV1pWOGGVC93NGhc5uB4Wn6B5z4NLbCp491LrMtAI194pW3z2i4owVcyEKC/H8jVaoQrmRNHl+auvZBAM7BVLOJAiH47Mys9bWpYvb++67D971rneBz6evCIcOHYIDBw4UTJsgZAQP+vPTExCqri/pEmluelyKAKFOhF0Kbr4avF4/+EMVEIvMqRJeTQwPMjcuIpNtCWDOZi9UVQtTowttpuZAZ4frgAXBlxgHXx5U1jYz4XYsOg/1bT0gMk6JlaH3MhJdnMxOjkBbz2Y4c+hpcAqtKzdCPBphQtFgxVJsFdMx7JnP/jhlNY2dlsU3yJyTrXZZl3Who2L96d12A8xOjahWCEFXNJVnmgsKefHyVZgLWAHp3nB5loY0oV/ZbGbyAll5E7aBws7JC6ehc+BioXvBHlfhYvXThZP7oXP1DpCWImsq1/2MReu2CEpJ/mAFrNh0FVgFnr+d6L4P94C4DiO+oOAKwgThcCwVRF188dLG5+TJk/CFL3wB3v72t2tOJxKJwIc//OH057q6OsMxpwjC7stV1EAJ15R2PbFy8zUwNXoGqhuLuCujyxTTQZ/shZu+8CYUD3wd/UkbLrq05ReuWhKAFnWVZbAO2BYnXvgDE+jlHkbsFkL0XHQtjJ87AU3LBiA6PwtOomn5apCBzMtvVRfhOsZjSpivCYsED0ZjjYgKarl2rd2p+vtSCQVSZZWpzEXAuW/09GFWl/r2XjhzaDeI7KK0WkNcFFxvSs6DJvWfKNbFmXh9SxrV9W0rbS2LqJjRb3iJODV2lmm1Eznt7YD5UwbQ/Rm+rAb7l/s2yuCYQYucWHRRMU4Ry9IJ+whjgGG7TY8Pgyw4yzuRmrpYV1/m0pDmSS7u9xLxGFOMC4TKxzSkNicIhwiitm7dCt3d3XD8+HH2+d///d8hFovB2972NggE1Jmanjt3Dt73vvfBU089lf7bLbfcwiXmFEGIDl6+1LWu0Hdg1rGBCVipvZ7GeQfSQhsZFGhNjZ7N+aJZBVC3yV298zZ2MDPLFz8e/Pq23QCiWg7hC5FWECX5IQXdbKEwEC9Ka4oEPzZ60F2x8Sq4MHgQalvUB1dWvNZahxBiUOwAarfQ3CzQ5Us/xoxTrLeIEg9FwrlVUS0YxnlwbmqUxTgSia71l8HRZx6EUEWtDe7YzH2usT4YZ8bqC10jQrXUnshNoPeC0/ufgCYbBDZuQNV4tMBVOLodO77noQXFuEAwe/oUYImnC3A5Y9YSYoN3He2rLrK7GARBWC2IwkX1Ix/5CPzFX/xF+m9f+tKX4Fe/+hW87nWvg2uuuYYJqnIXX7SA2rt3L/veD3/4Q5iZmUn/W1NTE9x5551WVoMghELtpVhjZz8z9UcaOvtUuRHp7N8OVoDu7s4cfIppgLmFZWt2wvDxF9iluCigu0F8EYQdoPungZ23LbjKUuG6Uc8FBY7vtt5Nqr/fs+UaV1/Kq7oMEeDSRhZrFKRj1VY4j253BlSsrxZXwVNE6EqXYsbhKcBEF7LzMxO6f1/d2M5e6rBuEKKCzsAlt7K1QNwxJ2q5+IHr3sT5wZJ78pQyVVGrOkmtI2qaOpn7cxx/iUQcZIH78+LwYY5ugvsvMSk2IOEOSFglHnIuOwThSiwVRCGXXXYZvPvd74ZPf/rT6b+dOHECPvWpT7FXZWUltLW1QVVVFbOWmpqagtOnT0M0Gs1LC7/7+c9/nrnmIwiiNKjxhUFCETWaphjA2yrwQLvq4pvY+0RcnoOfkZOaPxiGjv5tMDUyBPOzk+pys/liJjP/QrE2CMKsi3A7Thl42YvasoTYgh2eoKUIaklX1JWwBEgaaxO0QhHNEoUw6QLdpDV7xcYr4dyx54tajspMoSD2YuH8my5c98qtfcvXXw4zE+ehslb7GpkZc8znFy/+mN17bRnKb2Yema7yPEU83mTmr8VNK0FoVxwpPNYz94k1TdrXYhTijw4dhWDYDu8zBEEQLhNEIW9+85uhpqYGPvGJT8DsbLYLpOnpaTh8+HDZNLq6uuAzn/kMbNy40cSSEoT4hxgtF2HWuzrRgdznP8ezbPUOJjxr69tid1EIG3yyy35BQ+hHOpd0OsYqWokMXHpbyYDaqEk9OzWSdvNB8MB8V0xOErKisAZdSxEuIW946h2v/OZwdPFYUgBQYv6t7+iFifOnWD3q2ilOGZFNqLqeeeVAJb2OVdtKn0dGz0Jb72ZqQsJyMOYsKoXEY1ENFsZLoDIqxvyuLKX4RKSh8ydBOAvbTtDoig+toz772c/C/fffX9DiqRD19fXw2te+Ft7ylrdAOBw2vZwEITrSXQ4KBmreJZMJEBa9ggaT7tXq23vYiyBsR667Y0lxXyOjtWop0L98ZG4aQlW1LOCxOzB3HIQqlzwbVNa3gpvBWHlo7dTQUd6FMmGHENt9cyLvvSu6u0WX3KIjm4BaXBTNF849W6+DZCJR0kqeziPufnbMFEx4vUuWeKXGILrx1AsqMmmJV0vYDd23EQRPbFXlTFk1jYyMwG9+8xt45plnYN++fTA6Ospc8nm9XmY51dzcDOvXr4etW7fClVdeCYGABFYdBCG5n3S3gL7wT+59FOoNa2WKfGAVuWwCQs0lLF7f0vrvC5AyipXQpVxq3IWgd+t12W1DloKGCFXVQefAxRCLzAmn6GBE2UfPM4OCqNqW5cw1qBAKSi5ZD622LiYIvljwoPJ4RlQWk8UKLeuqmRAL58yhzd3rYHz4JDtzONEFLkEQhN0I4VOkoaEBXv3qV7MXQRDuvhy0uj4VtU0wsPNWfT92VtNbDl3ecm5PSwakvYO+pmkZi08Wj0ZKBlInbEIggQzNL3LR0NFrX58q4lziYh3R5Q9BSI1AawFBEOpoXbkBzh59Dtpd7n6dxdW+5FY2j9FeUtL1hdYgghAaSwVRn/rUp+DBBx9kWmehUAi+9rWvQVNTk5VFIAiCIAhCUhSPB1ZueqndxXAsetyMkHtYgiiCTXfxTlNQIgzMs3ZdxjnBwszitkNXXYl4zNI8CSLXIrexs1+OmNImr3N43iAE7e8i6wudRwhCHiydYc+dOwdHjx6FY8eOwdq1a0kIRRAEQYh3aSFQUQhzIU3HBbo3XAGNnatY8O/sBqIRSNA4kA3DlzG0BmY3hwX7k5LCQ17ZU78KTc/ma5g1ZFvvZoMp0cJNbaYfOYRQBGE1NK8ShLQWUZWVlen3AwMDVmZNEAThAGgT5ESWrbkEzh19Dtp6t4h/s+TwIehWbbqapk72Eg939gdBiIJbrKsUZUk3kxQUCDsI1zTAqotv0vdjkR9TkctGEILi3vPIMpi8cBpWbLpKMEtfd/YHQThCENXS0pJ+Hw5TkHGjJBIJ2LNnDzzyyCPwwgsvwKFDh2BsbAzi8TjU1tZCe3s7bNq0CV760pfCtm3bDOdHaEEOVxi1zV0wcX4QVmy8EoRBIp++slzQ0KWK2NS3rWQvgpAdWeZEQiIEsJbVv4bS8yALHau2wqEn7wV/sAIqashtPEF7Z4IQ+ixPy6sjWb7+JcxFqdfnz/4H6m+CcBSWCqJ6enrS70+fPm1l1o4ChU8//vGP4d5774XR0dGC35mcnIRTp07BE088AV/96ldh3bp18JGPfIQJpggiRde6ywov9oR2eGyQkvw0omS4EFY83izf+ARhNzI8N1ZC7UHQOCDcQLCyBgYuvQ08Hh/FBiEI0+T/tMdyLPbrjEgNKY0utQPdSxGE87E0RhRa5qTc8z344INWZu0Yjhw5Aq9+9avhe9/7XlEhVCHQYur1r389fPvb3za1fITNptUaNZBosSfspLqhnfnD9/mD0NojkJBc1nOyBRqIjhZMCGB5QTgBGkdutZLOhS6W5AL3Ih7vkoKMqDh5XAWCS278CcIO3OoSjSCchKPPqwThACxVQQ+FQnD77bczYciBAwfgZz/7Gdx2221WFsFxAXO9Xi9ceumlcPnll8PWrVuZ+0N0e3j27Fnmsu9b3/oWs4xC0GXfXXfdBTU1NdTuTsUBF6lOPmAT2SgeD/RtvxGSiYQUlz8EQThz3SHcPb6csO9AIcrS+5Cm37asWA/njj0PbYUUQhzQNoT4F+ytKzfA+LmT0LV2p91FkRIrLl2zxw/NC4S9OFrQQPtqajeCcDiW+0J65zvfCY8//jgTRH3oQx+C6upquPrqq60uhvSgsAktnP7kT/4EOjvzA4xju/b19THrqfe9731w//33pwVZKIy64ooroL6+3oaSE4SDoAsaDk2ogEJCKIIgCHFx0Von8mV9KeraV8LYueNMsaOho0+zEKCxsx98gUVhlnu6WxVV9a0wNz2WJ/CzFDmHpSZhKL4IDtDzSxAEQRCEwFjqmg+pqqqCb37zm3DRRRfB/Pw8/PVf/zW8/e1vh8cee4xZ7BDlL21vvvlmFh/q/e9/f0EhVK7A6tOf/jSsWbMm/bfx8XH40Y9+RE1NEAQhO1ZfTrnoQprI7u/a1m5qEhPwB8LZfyBNWAERf97zeLzQs+Ua6N16nS4L47QQisijZeUGaOjohY5VW8EfqqAWKkJlfcvSePRS7FlCHsuVcNWScm51Y4etZSEIgiAIp2O5RdR3vvMdePLJJ9Mu5GZnZ+G+++5jL3Tdh1Y8DQ0NLJaUFlcZd9xxB2zbtg2cTk9PD3zmM5/R9JtAIABvfetb4W1ve1v6bxij681vfrMJJSQIQghIYOCYS+poZBYq65YueAh+NHWtganRs+x9uLqBtTWxBO7DujdcAdNj56Cle50lTeMLLLkVa+pa7fjuWLHpKhjc/wTUkaCvIJlzX3VTaeUr/Yh/UUrYBwZO7xy4mLqgDG19WyCZTEC4upEEm4RUblCDlTXQtfZSiEXmoK51hd3FIXKoqm/LbxNBxxJhvkC7qqHAeDABj29JoaKqod2SPAnCLVguiNq9ezfcc889Bf9tbm4Onn/+eV3pXnXVVa4QROll585sn9vHjx+3rSwEIQvN3Wth+PheCIarxdD4oz236+jdej1MXjgNNc1ddhdFaLrW7ITzp/ZDe98WTb/DwwxaEWBMFa8/QIKoAtQ0dbKXVXOUx+uDvm03wPzMBNQ2LwenE6qqY1YsRPH2QWFoMhGHaroIsAaHu4ETkpy5s61vM5za93haEKY7WQsva9FtIV7mEzZgQj+jlWUi4R5vNaQMUpoVG6+CC4MHiiolmeHWtv/im9kZqK5tJchCZ/92OH9yH7T3b9X0u6blpBhXLq70stU7YGp0CNp6tZ31jKxpmOfs5Ai0FoqhSRCEPIIowh7QJaLf74doNMo+j40t+DonTIIEBo6gdcUGqKprhVB1kXhq5EKJsWzNJTD44uPQ0KktLgVRHnQDJFK7iqrNWte2gr301IeszcQDrdPwRRCIakGoaGTMlxijlSDUghev/lAlU4TCCzjC3aBFCF7AWq0Ide7Y81Df3mtpvoSYVDe2s5fVlmr4kgk8s+k5t+Ez3rPlWmZNSopxhalv72EvVSg25EkQhLiCqGAwCBUV/P1ro5CFKM7IyEhaCIXU1Mi1qBOEHeDh33Tz75yL/UCoEiJz0yAT9W0rmU91ry9gd1EIgiAIwhCZ8ZJ8PoqdRFgPKklU1bfyTZSEodLStXYnjJw+ZGn8IrRGXb7+JYX/UUydJGhevhaGT+yFipomu4tCuFQxTi8LinHNdheDIAjCmYKou+66i70Ia9m1a1fW5+XLne/uRhhICZbQwMrNVzMXY2kf5TzGjwWbdTRfJ5xBY+cqGBk8BJ1rdthdFIKQg4w5Fq0YCMnIWSIbOlbB+LmT7GKorl2/SyBLXPYSBOF4MHZiy4r1cs47FgoMWlduYMI6FKIRBEEQBCEm5JrPJfzP//xP1ufLL7/ctrIQhCPhdNAKhKugY9VW48UR7SBKmIIZPtnbV22FlpUbSLhIEBoE8ei6Y3r0HNNcdxpO0zwuh8frzYrb5aY4KYQAkOUSYceYccA8j54syKqEIAiCIMSGBFEu4NFHH4Xf/e536c9erxduu+02W8tE8AOtFo4+8yAEQlWkiU0QBJdLZ7JwIwhtYEBjjAPkNqENQRBWQO4VCJuhIUiohPZBBEEQRClIEOVwJiYm4AMf+EDW31796ldDd3e3rvSGhkoHSh0dHdWVLqEf9CE/cMmt4PUHaeMnOWZYtxAEQViHu4UwdPkiJ6ZZEGclS+s7QRAE4VDIkpMgCIJQCQmiHEwikYD3ve99MDg4mP5bW1sbvOc979Gd5pVXXlny3/1+P6xZs0Z3+oR+d26E9ZDgiCAIt4NWQITTcbmA0eX1J2RmaezSnpUgCIJwB7RvIwiR8dhdAMI8/uVf/gV++9vfpj/7fD749Kc/DdXV1dTsJkOXFu7A41mS5de1rQBRofFIEISZ8XTScw25pSMIgiAIqSGhJUEQskGKcQQhD2QRpYGZmRnYsWMHmM1zzz1nOI1vfvOb8LWvfS3rbx/96Edh27ZthtJ96KGHyrrm+/CHP2woD4KQ6QJ2xcYrYWbiAjR1rRZLoESKQK6DhACEHSxbsxOOPP1rCFXWUZxCgsiCFmK3gHvACyf3Q8fAdruLQrgdkRVCRC4bYYja1m4YOvIse1/f3kOtSRAEQYgriIpGo/DEE0/Anj174ODBgzA+Pg5TU1MQiUQ0pfOOd7yjrNs4HlJ2reWyg5/+9KfwiU98Iutv6I4PY0MZBV37lXPNR+RCboucTHVjB3sRhC2QWzTCZsLV9bD60leAx+sjYShhC50DF8Pg/l1Q37bS+OUobdkIHbT3bYGW7nXg9Qeo/QjLEdmCibwyuINAqBL6d9zM7suCFTV2F4cgCIIQGNsEUSjQ+fKXvwzf//73YXh42HB6Y2NjXMolOw888AD83d/9XZZp6lve8hZ405veZGu5CILQi7iHS8J+fIFQwfcEYSV0+epwBFdib+joheqGdvAFwyASZKXqLmgeJNwi+CHhElEIEkARdtK4rB/OHHqavaczMUGIjS2CqNOnTzPhyP79+0EmKisrmeWWqPzhD3+Ad73rXRCPx9N/+9M//VP2N4IgCMJ51LWugLGzxyGZTEBDxyq7i0MQBGEL/lCFeYmTOynCdBTzkyW9Jsfi9QUgXNUAc1Oj0LX+MruLQxAEYTkNnX3g9fmZQBT/TxCEuFguiJqenoa/+qu/ggMHDmT9vaKiAtrb2+HChQtp66Zly5axv4+MjMD58+ezvt/c3AwtLS3pz3V1dZaUPxgMgog89dRT8Na3vjXLdeDtt98OH/jAB2wtF0EQAkCXaI5F8Xhg5eaX2l0MgiAIohQkBCB4QXs6Im9IKNBz0bUQi84xF2nmQJMYQViFx+NNv/cLZu0tcptRfDKCkAPLBVFf+9rXsoRQ/f398N73vhcuvfRS8Pl8LJbR3Xffzf7t7W9/O9x2223s/ZkzZ+BXv/oVfP3r/3979wEeRbk9fvykEALpIYEEAkQgEHoXURQVsFAEVCwgFkBEvYoFvepVuV69oui1YVcsiApiwT/qhWtFRUQRpClVqvQaQDr5P+f1t+vsZjfZTXZ2N7vfz/Psw8xmdubNbuZl9j1zzvuKKeWn80jdeuut0r9/f4l2S5YsMcG9AwcOOJ/r2bOn/Pvf/6YsSKjwJREhF+a1lAAAlQJlmAAgvMXGxUlCnF1BqMDg/xLANwnVkiUtu64cKNp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",
       "text/plain": [
-       "<Figure size 800x320 with 1 Axes>"
+       "<Figure size 900x320 with 2 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 344,
-       "width": 849
+       "height": 331,
+       "width": 977
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
+    "fig, (ax, ax_zoom) = plt.subplots(\n",
+    "    1, 2, figsize=(9, 3.2), layout=\"constrained\", gridspec_kw={\"width_ratios\": [3, 2]}\n",
+    ")\n",
     "for (w, label), color in zip(horizons, [\"C7\", \"C0\", \"C1\"]):\n",
     "    t, z = signed_z(stage2, w)\n",
     "    ax.plot(\n",
@@ -2473,25 +2493,35 @@
     "        alpha=0.5 if w == 1 else 1.0,\n",
     "        label=f\"{label} (w={w})\",\n",
     "    )\n",
-    "ax.axhline(0.0, color=\"k\", lw=0.8)\n",
-    "ax.set_xlabel(\"step within stage 2\")\n",
-    "ax.set_ylabel(r\"standardised block contrast $z_w$\")\n",
-    "ax.set_title(\"The same trace, three horizons: what a stop rule is allowed to see\")\n",
-    "ax.legend(fontsize=9);"
+    "    if w > 1:\n",
+    "        ax_zoom.plot(t, z, color=color, lw=1.6, label=f\"{label} (w={w})\")\n",
+    "for a in (ax, ax_zoom):\n",
+    "    a.axhline(0.0, color=\"k\", lw=0.8)\n",
+    "    a.set_xlabel(\"step within stage 2\")\n",
+    "ax.set_ylabel(r\"block contrast $z_w$\")\n",
+    "ax.set_title(\"Same trace, three horizons\", fontsize=11)\n",
+    "ax.legend(fontsize=8, loc=\"upper right\", frameon=True)\n",
+    "ax_zoom.set_ylim(-0.12, 0.12)\n",
+    "ax_zoom.set_title(\n",
+    "    f\"Aligned horizons only (control sd {spreads['1.5 epochs']:.2f} would fill this)\", fontsize=10\n",
+    ")\n",
+    "ax_zoom.legend(fontsize=8, loc=\"upper right\", frameon=True);"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a6aa147a",
+   "id": "7e5e9082",
    "metadata": {},
    "source": [
-    "Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised\n",
-    "contrast has unit spread — which is what the $\\sqrt{2/w}$ scaling is built to\n",
-    "produce — and a mean indistinguishable from zero: a single increment says\n",
-    "nothing about the trend.\n",
+    "Read the printout as three signal-to-noise ratios, and the figure as the same\n",
+    "thing twice: the left panel puts all three horizons on one scale, the right\n",
+    "panel zooms in on the two that are invisible at that scale. At $w = 1$ the\n",
+    "standardised contrast has unit spread (which is what the $\\sqrt{2/w}$ scaling\n",
+    "is built to produce) and a mean indistinguishable from zero: a single\n",
+    "increment says nothing about the trend.\n",
     "\n",
     "At the two epoch-aligned horizons the spread collapses far below what\n",
-    "independent noise would predict — and the control row explains why. The\n",
+    "independent noise would predict, and the control row explains why. The\n",
     "shuffle is done once on disk and replayed in the same order every epoch, so a\n",
     "window of exactly one or two passes averages over the *same rows* every time\n",
     "and the batch-composition noise, which dominates the per-step view, all but\n",
@@ -2502,14 +2532,12 @@
     "alignment changed.\n",
     "\n",
     "What survives is Monte-Carlo noise and the parameter drift\n",
-    "itself, and against that much smaller yardstick the drift becomes visible —\n",
-    "the ratio in the last column rises with the horizon. Two lessons sit on top of\n",
-    "each other here. The horizon is not a smoothing preference, it decides whether\n",
-    "there is anything to see at all; and with a fixed replay order the useful\n",
-    "horizons are the ones commensurate with an epoch, which is a property of how\n",
-    "the data was shuffled, not of the optimizer.\n",
-    "\n",
-    ":::{admonition} Why a naive rule does not merely stay silent — it fires\n",
+    "itself, and against that much smaller yardstick the drift becomes visible:\n",
+    "the ratio in the last column rises with the horizon. So, with a fixed replay\n",
+    "order, the useful horizons are the ones commensurate with an epoch. That is a\n",
+    "property of how the data was shuffled, not of the optimizer.\n",
+    "\n",
+    ":::{admonition} Why a naive rule fires instead of staying silent\n",
     ":class: warning\n",
     "It is tempting to accumulate per-step evidence with a one-sided cumulative-sum\n",
     "statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to\n",
@@ -2522,9 +2550,9 @@
     "deviation, so with $\\kappa$ above that value $S$ climbs at a roughly constant\n",
     "rate and crosses any fixed threshold after a roughly fixed number of steps. The\n",
     "rule therefore cannot tell \"converged\" from \"still improving, but too slowly\n",
-    "for this horizon to see\" — it announces the same thing at the same pace in\n",
-    "both cases. Which is why the fix is not a better threshold but a wider horizon,\n",
-    "plus a second yardstick asking whether the remaining improvement is negligible\n",
+    "for this horizon to see\"; it announces the same thing at the same pace in\n",
+    "both cases. So the fix is not a better threshold but a wider horizon, plus a\n",
+    "second yardstick that asks whether the remaining improvement is negligible\n",
     "relative to the reduction already achieved. That design is implemented in\n",
     "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this\n",
     "notebook shows the observation problem it exists to solve rather than shipping\n",
@@ -2532,20 +2560,20 @@
     ":::\n",
     "\n",
     "How much would an online rule be worth here? The retrospective answer is a\n",
-    "benchmark you can only compute afterwards — which is exactly why the online\n",
-    "version is needed:"
+    "benchmark you can only compute afterwards, which is why the online version is\n",
+    "needed:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "a54698c5",
+   "id": "8eae053d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.879250Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.879158Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.881551Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.881220Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.592375Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.592311Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.594684Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.594350Z"
     }
    },
    "outputs": [
@@ -2553,7 +2581,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "stage 2 smoothed reduction: 188.11 nats; 99% of it reached by step 1,947 of 2,400 (81% of the stage-2 budget)\n"
+      "stage 2 smoothed reduction: 188.11 nats;\n",
+      "99% of it reached by step 1,947 of 2,400 (81% of the stage-2 budget)\n"
      ]
     }
    ],
@@ -2568,7 +2597,7 @@
     "# steps from 1.\n",
     "t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch\n",
     "print(\n",
-    "    f\"stage 2 smoothed reduction: {total_drop:,.2f} nats; \"\n",
+    "    f\"stage 2 smoothed reduction: {total_drop:,.2f} nats;\\n\"\n",
     "    f\"99% of it reached by step {t99:,} of {len(stage2):,} \"\n",
     "    f\"({t99 / len(stage2):.0%} of the stage-2 budget)\"\n",
     ")"
@@ -2576,58 +2605,45 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa3d34bb",
+   "id": "31837366",
    "metadata": {},
    "source": [
-    "Two honest qualifications about any stop, this one included. What such a rule\n",
-    "detects is a *loss plateau* — a necessary signal, not a proof that the\n",
+    "Any stopping rule, this one included, comes with two qualifications. What such a rule\n",
+    "detects is a *loss plateau*, a necessary signal, not a proof that the\n",
     "posterior has converged; the recovery and predictive checks above are the kind\n",
     "of independent evidence a stop should be paired with. And a plateau in a noisy\n",
     "loss can only ever be established relative to a horizon: at any finite step, an\n",
     "improvement small enough is indistinguishable from none.\n",
     "\n",
-    "## Where the pieces live\n",
+    "## Related tooling in pymc-extras\n",
     "\n",
-    "The streaming stack this notebook exercises was contributed to pymc-extras\n",
-    "as four pull requests. The map is by role; each one's state — merged, open,\n",
-    "or closed — is on GitHub, and nothing on this page depends on it.\n",
+    "The loader used above is one of four streaming pieces in pymc-extras. The\n",
+    "others are not imported here; this is where each one fits.\n",
     "\n",
-    "| Component | Role | Form in this notebook |\n",
-    "| --- | --- | --- |\n",
-    "| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size` | Imported and used directly |\n",
-    "| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, while [#635](https://github.com/pymc-devs/pymc-extras/pull/635) develops the longer-term ADVI API | The `StreamAdvance` adapter above, owned by the notebook |\n",
-    "| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks | The observation problem it solves is shown; the implementation is not copied here |\n",
-    "| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | Quasi-Newton alternative returning a Gaussian proposal from a short optimizer run, with `pareto_k` as its own veto | Not run on this target — see below |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92c747e8",
-   "metadata": {},
-   "source": [
-    "### Choosing a path: why the fast one is not taken here\n",
+    "| Component | What it does |\n",
+    "| --- | --- |\n",
+    "| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size`; used directly above |\n",
+    "| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, the job the twenty-line `StreamAdvance` does here |\n",
+    "| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks; the observation problem it addresses is the previous section |\n",
+    "| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |\n",
     "\n",
-    "ADVI is not the only way to fit a model on a stream. Streaming Pathfinder runs\n",
-    "a short quasi-Newton trajectory on minibatch gradients — a few hundred steps\n",
-    "rather than thousands — and returns a Gaussian *proposal*, importance-corrected\n",
-    "against the exact full-data log-density, with the Pareto-$k$ diagnostic as its\n",
-    "own veto. It is the right tool when an approximately-placed starting point is\n",
-    "what you need, for example as an initialisation for MCMC.\n",
-    "\n",
-    "It is not run on this model, and the reason is a decision taken *before*\n",
-    "fitting rather than a result:"
+    "Pathfinder is the faster route when an approximately placed starting point is\n",
+    "what you need, for example to initialise MCMC. It is not run here, and the\n",
+    "reason is a decision taken before fitting rather than a result: its documented\n",
+    "operating range is non-hierarchical targets of at most a few tens of\n",
+    "parameters, and this target is hierarchical with"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "060693c5",
+   "id": "51c26323",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.882612Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.882531Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.904706Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.904333Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.595948Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.595828Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.614361Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.614004Z"
     }
    },
    "outputs": [
@@ -2648,21 +2664,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "245b9e0c",
+   "id": "f83d8d9f",
    "metadata": {},
    "source": [
-    "pymc-extras#722 states a measured operating range: non-hierarchical targets of\n",
-    "at most a few tens of parameters. This target is hierarchical and an order of\n",
-    "magnitude larger than that, so applying it here would be using a method\n",
-    "outside the range its author documented. That is an applicability decision,\n",
-    "not a prediction about what its diagnostic would report. The ADVI results\n",
+    "free parameters, an order of magnitude outside that range. The ADVI results\n",
     "above stand or fall on their own recovery and predictive checks, independently\n",
     "of this choice."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "97cc592a",
+   "id": "1ed48a73",
    "metadata": {},
    "source": [
     "## Authors\n",
@@ -2674,7 +2686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fafcd7d",
+   "id": "b1b9f4ca",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2686,7 +2698,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6505d9f",
+   "id": "302ffca4",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2695,13 +2707,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "840602e2",
+   "id": "24e61b83",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T03:55:36.905957Z",
-     "iopub.status.busy": "2026-08-18T03:55:36.905876Z",
-     "iopub.status.idle": "2026-08-18T03:55:36.940293Z",
-     "shell.execute_reply": "2026-08-18T03:55:36.939827Z"
+     "iopub.execute_input": "2026-08-18T04:24:29.615360Z",
+     "iopub.status.busy": "2026-08-18T04:24:29.615300Z",
+     "iopub.status.idle": "2026-08-18T04:24:29.645610Z",
+     "shell.execute_reply": "2026-08-18T04:24:29.644963Z"
     }
    },
    "outputs": [
@@ -2709,22 +2721,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wall time for every cell above, on this machine: 33 s\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "wall time for every cell above, on this machine: 28 s\n",
       "Last updated: Mon, 17 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
       "IPython version      : 9.16.1\n",
       "\n",
-      "pytensor   : 3.2.4\n",
-      "pymc_extras: 0.14.1.dev3+g8db1880d4\n",
-      "\n",
       "arviz      : 1.3.0\n",
       "logging    : 0.5.1.2\n",
       "matplotlib : 3.11.1\n",
@@ -2752,13 +2755,13 @@
    "source": [
     "%load_ext watermark\n",
     "print(f\"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s\")\n",
-    "%watermark -n -u -v -iv -w -p pytensor,pymc_extras\n",
+    "%watermark -n -u -v -iv -w\n",
     "%watermark -m"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4654151d",
+   "id": "18628d55",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 0b720c77a..0f3dac552 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -26,7 +26,7 @@ myst:
 
 +++
 
-Exchanges publish per-trade archives — Binance, for instance, distributes
+Exchanges publish per-trade archives; Binance, for instance, distributes
 aggregate trades as daily and monthly files at
 [data.binance.vision](https://data.binance.vision). Widen a pull across
 symbols and months and the feature matrix can outgrow the RAM of an ordinary
@@ -40,19 +40,19 @@ on 300,000 synthetic
 rows that are generated inside the notebook. It tries to teach three things:
 
 1. **When minibatch VI is even valid.** Two acceptance gates that most
-   time-series models fail — and a model class that passes both.
+   time-series models fail, and a model class that passes both.
 2. **The mechanics, end to end** on an archive small enough to rebuild here:
    an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
    what a stopping rule has to be able to see before it can fire.
-3. **Honest reporting.** With the truth known, recovery is checkable — but only
-   after a correction the fit forces on us: the cyclic replay puts the optimizer
-   on an orbit, and the last iterate misses the truth by several of its own
-   posterior standard deviations purely because of where in that orbit the step
-   budget ended. Averaged over one full pass, every identified row the
-   recovery table reports lands inside 1.2 — across three fits, one of them on
-   a different replay order. The widths themselves are a separate question this
-   notebook cannot settle on one dataset, and it says so rather than certifying
-   them.
+3. **Checking the recovery.** With the truth known, recovery is checkable once
+   one correction is made: the cyclic replay puts the optimizer on an orbit, and
+   the last iterate misses the truth by several of its own posterior standard
+   deviations purely because of where in that orbit the step budget ended.
+   Averaged over one full pass, every identified row the recovery table reports
+   lands inside 1.2 posterior standard deviations of its generating value,
+   across three fits, one of them on a different replay order. Whether the
+   widths themselves are calibrated is a separate question one dataset cannot
+   settle; no claim about them is made here.
 
 The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic
 data with known ground truth, which is what makes the recovery checks
@@ -65,10 +65,9 @@ possible: nothing here is a claim about any real market.
 Minibatch variational inference rests on one identity: if the likelihood
 factors over rows given the parameters, then the batch log-likelihood scaled by
 $N/B$ is an unbiased estimator of the full-data log-likelihood
-{cite:p}`hoffman2013stochastic`. Two gates shaped this notebook's model — the
-first is about *validity*, the second about whether streaming is doing any
-real work — and they are worth internalizing before reaching for `total_size`
-on your own data:
+{cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;
+the second is about whether streaming is doing any real work. Check both
+before reaching for `total_size` on your own data:
 
 **Gate 1 — validity: the likelihood must factor over rows, and the batching
 must weight every row equally.** No latent path coupling observations, no
@@ -76,7 +75,7 @@ label shared across rows, and every row given the same inclusion frequency by
 the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How
 the batches are *drawn* is a separate matter that this gate does not settle:
 fresh uniform batches make each step's scaled gradient conditionally unbiased
-for the full-data objective — the identity above — while a fixed shuffled
+for the full-data objective (the identity above), while a fixed shuffled
 order replayed every epoch visits each row once per pass, targets the same
 finite sum, and yields cyclic rather than unbiased updates. This notebook does
 the second, and returns to the distinction where the fit is described. The
@@ -86,24 +85,25 @@ stochastic volatility model of the {ref}`stochastic_volatility` notebook
 its neighbours, so a random subset of rows does not carry $B/N$ of the
 log-likelihood. Any model whose rows share one outcome (for example, every tick
 of a match sharing the final result) fails the same gate through
-pseudo-replication — the effective sample size is the number of outcomes, not
+pseudo-replication: the effective sample size is the number of outcomes, not
 the number of rows.
 
 **Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This
-one is not about validity but about honesty of the *showcase*: a Normal
+one is about whether the streaming machinery is needed at all, not about
+validity: a Normal
 likelihood with per-cell means and variances collapses to per-cell
 $(\sum y, \sum y^2, n)$, so one linear scan computes the exact posterior
-inputs and "streaming inference" degenerates into a glorified `groupby` —
-valid, but theater. A hurdle alone does not rescue it — an observed Bernoulli
+inputs and "streaming inference" degenerates into a glorified `groupby`:
+valid, but theater. A hurdle alone does not rescue it; an observed Bernoulli
 plus a Normal component still reduces to $(n_0, n_1, \sum y, \sum y^2)$. What
 does break the collapse is the combination used below: a Student-t component
 with an unknown $\nu$, and row-level continuous covariates entering through
 nonlinear links, so no fixed-dimensional summary of the rows suffices.
 
-The model below passes both gates. Each ingredient that gets it there is also
-the standard choice for the feature it addresses, which is the argument for
-it — not the synthetic data, which was written to contain those features in
-the first place.
+The model below passes both gates. The case for each ingredient is that it is
+the standard choice for the data feature it handles, not that it fits the
+synthetic data, which was written to contain those features in the first
+place.
 
 +++
 
@@ -118,11 +118,11 @@ notebook were produced against the merge commit itself, which is also the
 install line to use until the next release:
 
 ```
-pip install "pymc-extras @ git+https://github.com/pymc-devs/pymc-extras@8db1880"
+pip install git+https://github.com/pymc-devs/pymc-extras@8db1880
 ```
 
 That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull
-PyMC 6.2 and PyTensor 3.2 — the versions reported by the watermark at the
+PyMC 6.2 and PyTensor 3.2, the versions reported by the watermark at the
 bottom of this page. Once the module ships, `pip install pymc-extras` will do.
 :::
 
@@ -165,8 +165,8 @@ warnings.filterwarnings("ignore", message=r"Numba will use object mode")
 
 One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:
 the return $y_i = 10^4 \, (\log p_{i+1} - \log p_i)$ in basis points, and the
-move indicator $m_i = \mathbb{1}[y_i \neq 0]$. Two consequences of indexing by
-*events* rather than by clock time are worth stating before the algebra.
+move indicator $m_i = \mathbb{1}[y_i \neq 0]$. Indexing by *events* rather than
+by clock time has two consequences that matter before the algebra.
 
 First, prices move on a discrete grid, so an exact-zero return is not a
 measure-zero event the way it is for a continuous variable: consecutive trades
@@ -203,8 +203,8 @@ than engineered from a trade tape: only $\text{ylag}$ is produced
 sequentially, so it is the one covariate that could not see the future, and
 $q$ and $a$ are standardized once over the whole sample. On a real archive
 both would instead be trailing statistics built causally from the tape and
-standardized with constants frozen on a burn-in window — a construction that
-is out of scope here, and one that is easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
+standardized with constants frozen on a burn-in window, a construction that
+is out of scope here and easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
 a low-order version of the Fourier encoding of intraday periodicity in
 {cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
 back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
@@ -217,8 +217,8 @@ rows.
 The degrees of freedom are
 shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
 the floor of 1 rather than 2 leaves the support open to tails too heavy to
-carry a finite variance. Be precise about what that buys: under
-$\eta \sim \mathcal{N}(5, 1)$ the prior puts only about $4 \times 10^{-6}$ of
+carry a finite variance. Under $\eta \sim \mathcal{N}(5, 1)$ the prior puts
+only about $4 \times 10^{-6}$ of
 its mass on $\nu \le 2$, so this is a statement about support, not a serious
 prior belief in infinite variance. The estimand below is chosen so that the
 question does not arise at all.
@@ -227,7 +227,7 @@ The reported estimand is **event-return dispersion on the event clock**:
 $\pi_{s,h}$ together with the conditional-move 90% half-width
 $\sigma_{s,h} \cdot t^{-1}_{0.95}(\nu)$. A quantile is the safe choice here: it
 stays finite for any $\nu > 0$, whereas a moment-based dispersion requires
-$\nu > 2$ — a constraint the model does not impose, even though the generator
+$\nu > 2$, a constraint the model does not impose, even though the generator
 below happens to use $\nu = 3.5$.
 
 +++
@@ -332,8 +332,8 @@ print(
 )
 ```
 
-Each constant above is a setting, not a measurement, and it is worth reading
-them the way one would read a specification for a simulator:
+The constants above are settings, not measurements. Read them as the
+specification of the simulator:
 
 * `kappa0` = $\operatorname{logit}(0.7)$. If every other term in the logit were
   zero, the move probability would be 0.7. The harmonics, the covariates and
@@ -341,7 +341,7 @@ them the way one would read a specification for a simulator:
   printed above, not $30\%$ by construction.
 * `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional
   location: the next move is generated partly from the previous nonzero one. It
-  is a synthetic dependence parameter — note that the classical bid–ask bounce
+  is a synthetic dependence parameter; note that the classical bid–ask bounce
   of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial
   dependence, which this generator does not encode.
 * `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding
@@ -376,19 +376,17 @@ ax.legend();
 
 ## The on-disk global shuffle
 
-The trap in streaming ordered data is subtle enough to deserve its own
-section. A bounded runtime shuffle buffer only *block*-shuffles a strongly
+Streaming ordered data has one trap that is easy to miss. A bounded runtime shuffle buffer only *block*-shuffles a strongly
 ordered stream: with tick data sorted by symbol and time, early optimization
 steps would only ever see early dates and the first symbols, and an early
 stopping decision would be biased by construction. The fix is to shuffle
 **once, globally, on disk at ETL time**: every row gets a deterministic hash
 key, rows are scattered across shards by that key, and each shard is sorted by
 it. After that, sequential reads follow one fixed, data-independent
-permutation — pseudo-random, because it comes from a hash of the row index
-rather than from anything in the row, and replayed identically each epoch,
-which is single-shuffle SGD semantics rather than fresh per-step subsampling —
-and the loader can run with `shuffle=False`, without a copy through the
-shuffle buffer.
+permutation. It is pseudo-random (a hash of the row index, nothing from the
+row itself) and replayed identically each epoch, so this is single-shuffle SGD
+rather than fresh per-step subsampling. The loader can then run with
+`shuffle=False`, without a copy through the shuffle buffer.
 
 ```{code-cell} ipython3
 data_dir = tempfile.mkdtemp(prefix="ticks_")
@@ -434,15 +432,14 @@ print(f"first 10k rows of every shard cover all 24 hours and all {n_symbols} sym
 The assertion is the one to keep: the head of every shard must already mix
 every hour and every symbol, checked rather than assumed.
 
-Be clear about what this cell is and is not. It is the pedagogical miniature:
-it builds the whole table, the whole key array and the whole permutation in
-memory, which is exactly what one cannot do once the data stops fitting. What
-follows it — the inference — is genuinely disk-backed, but the preprocessing
-above is not. An out-of-core design with the same properties is two passes:
+This cell is a small-scale stand-in for the real ETL step: it builds the whole
+table, the whole key array and the whole permutation in memory, which is
+exactly what one cannot do once the data stops fitting. The inference that
+follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:
 assign each row to a shard from its hash key, appending row groups of bounded
 size, then sort each shard by key independently, so no step holds more than
 one shard. That yields a different permutation from the rank-based split used
-here — shard sizes come out approximately rather than exactly equal — with the
+here (shard sizes come out approximately rather than exactly equal), with the
 same guarantee that no shard's order depends on anything in the rows.
 
 So that the fit really does run against disk rather than against arrays still
@@ -452,23 +449,23 @@ resident from the generator, the row-scale objects are released first:
 del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves
 del logit_pi, log_sigma, t_draw, sigma
 gc.collect()
-print("row-scale generator arrays released; the fit reads from", data_dir)
+print("row-scale generator arrays released; the fit reads from", os.path.basename(data_dir))
 ```
 
 ## Streaming the model
 
-With `shuffle=False` the `DataLoader` passes source blocks through verbatim —
-one block per Parquet row group, in a frozen column order — which is why the
+With `shuffle=False` the `DataLoader` passes source blocks through verbatim,
+one block per Parquet row group, in a frozen column order. That is why the
 shards above were written with `row_group_size` equal to the batch size, and
-why the row counts were chosen to divide exactly. That is worth doing
-deliberately: on this path the loader hands you the row groups it finds, so a
+why the row counts were chosen to divide exactly. On this path the loader
+hands you the row groups it finds, so a
 ragged geometry gives ragged batches, `len(loader)` (which is
 `total_size // batch_size`) stops matching the number of blocks an epoch
 yields, and the recorded loss picks up a deterministic sawtooth that has
 nothing to do with convergence (the fitting section explains why the recorded
 value scales with the block size). The model reads
-one `pm.Data` placeholder; everything derived — the Fourier basis, the
-integer symbol index — is computed inside the graph, so advancing the stream
+one `pm.Data` placeholder; everything derived (the Fourier basis, the
+integer symbol index) is computed inside the graph, so advancing the stream
 is a single `set_value` per step.
 
 ```{code-cell} ipython3
@@ -489,22 +486,22 @@ assert set(block_rows) == {BATCH}
 assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH
 assert len(loader) == steps_per_epoch
 print(
-    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch, "
+    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch,\n"
     f"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}"
 )
 ```
 
-Two things that assertion pins down. First, **nothing is dropped**: verbatim
+That assertion pins down two things. First, **nothing is dropped**: verbatim
 pass-through streams every row exactly once per epoch. (Ragged geometry would
-still lose nothing — PyMC reads `b` from the batch actually installed, so a
-short block is weighted up by its own size — but it would cost the clean
+still lose nothing, since PyMC reads `b` from the batch actually installed and
+a short block is weighted up by its own size, but it would cost the clean
 diagnostics below, which is why the shards divide.) Only the shuffle-buffer
 path drops a trailing partial batch, and this notebook never uses it.
 
 Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a
 deterministic function of $t$, so what follows is single-shuffle, cyclic
-finite-sum optimization — the arrangement the epoch-scale diagnostics later in
-the notebook exploit, and the reason the fit has to be summarized with some
+finite-sum optimization, the arrangement the epoch-scale diagnostics later in
+the notebook exploit and the reason the fit has to be summarized with some
 care before anything is read off it.
 
 ```{code-cell} ipython3
@@ -599,19 +596,19 @@ def build_model(symbols, batch_init, total_size):
     return model
 ```
 
-Two implementation notes worth pausing on. The likelihood is a
+Two implementation notes. The likelihood is a
 {class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`
 gives the term *observed-RV semantics*, which is what makes PyMC's own
 `total_size` minibatch rescaling apply. And
-the hurdle's two logs are written with `softplus` —
-$\log \pi = -\operatorname{softplus}(-x)$,
-$\log(1-\pi) = -\operatorname{softplus}(x)$ — which is exact and stable at both
+the hurdle's two logs are written with `softplus`,
+$\log \pi = -\operatorname{softplus}(-x)$ and
+$\log(1-\pi) = -\operatorname{softplus}(x)$, which is exact and stable at both
 tails. The exact float comparison `value != 0` is safe here because the zeros are
 *structural*: the generator writes an exact 0.0 when the hurdle says the price
 did not move, and every nonzero draw is many orders of magnitude above the
 float32 subnormal range. On real data the same comparison is safe as long as
 returns are computed so that "no move" produces an exact zero rather than a
-rounding artefact — worth checking once, in the feature code.
+rounding artefact; check that once, in the feature code.
 
 ```{code-cell} ipython3
 model = build_model(
@@ -628,10 +625,9 @@ likelihood's `logp` and are not separate nodes; the equations above are where
 that structure is visible.
 
 The loop below is the whole streaming adapter: a `pm.fit` callback that pushes
-the next block into the placeholder after each step. pymc-extras#710 contains
-a wrapper around this same data-advance lifecycle and pymc-extras#635 a
-longer-term ADVI training API; the twenty lines here are deliberately the
-minimal version a notebook can own.
+the next block into the placeholder after each step; the twenty lines here are
+the minimal version a notebook can own (a library wrapper for the same
+lifecycle is listed at the end).
 
 ```{code-cell} ipython3
 class StreamAdvance:
@@ -681,14 +677,15 @@ approx = advi.fit(
 ```
 
 The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.
-The two-stage learning rate is deliberate. A constant step size that is
+The learning rate is cut once, at step 6,000. A constant step size that is
 comfortable early is too large near the optimum, where it keeps the
 variational mean bouncing instead of settling; dropping it once the descent
-has flattened is the cheapest fix. Do not look for the effect in the width of
+has flattened is the cheapest fix. The effect does not show up in the width of
 the loss band below: the band is dominated by batch-composition noise (the
-stopping section shows it all but vanishing at epoch-aligned horizons), the
-printed spread is the same on both sides of the change, and the effect is in
-the level and in the parameter trace examined after the fit.
+stopping section shows it all but vanishing at epoch-aligned horizons), and
+the printed spread is the same on both sides of the change. It shows up in the
+level, as the printed one-epoch means either side of the cut, and in the
+parameter trace examined after the fit.
 
 Before plotting it, one property of `approx.hist` that is easy to get wrong and
 that changes what the numbers mean. PyMC normalizes the minibatch objective
@@ -702,7 +699,7 @@ F_t = -\sum_{i \in B_t} \mathbb{E}_q\left[\log p(y_i \mid \theta)\right]
       + \frac{b}{N}\,\mathrm{KL}(q \Vert p),
 $$
 
-which lives on the scale of *one batch*, not of the full dataset — and which
+which lives on the scale of *one batch*, not of the full dataset, and which
 would move mechanically with $b$ if the blocks were ragged. Multiplying by
 $N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks
 that is one constant:
@@ -711,17 +708,31 @@ that is one constant:
 ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch
 loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE
 
-fig, ax = plt.subplots(figsize=(8, 3), layout="constrained")
-ax.plot(loss, lw=0.5)
-ax.axvline(6_000, color="C1", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
-ax.set_ylim(np.quantile(loss, 0.002), np.quantile(loss, 0.995))
+epoch_mean = np.convolve(loss, np.ones(steps_per_epoch) / steps_per_epoch, mode="valid")
+
+fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
+ax.plot(loss, lw=0.5, alpha=0.6, label="per step")
+ax.plot(
+    np.arange(steps_per_epoch - 1, len(loss)),
+    epoch_mean,
+    color="C1",
+    lw=1.5,
+    label="one-epoch moving mean",
+)
+ax.axvline(6_000, color="k", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
+# clip to the plateau: the first few hundred steps are orders of magnitude higher
+plateau = loss[1_000:]
+ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))
+ax.yaxis.set_major_formatter(plt.matplotlib.ticker.StrMethodFormatter("{x:,.0f}"))
 ax.set_xlabel("step")
-ax.set_ylabel("negative ELBO (full-data scale)")
-ax.set_title("Streaming ADVI loss (full-data scale, y clipped to the plateau)")
-ax.legend()
+ax.set_ylabel("negative ELBO, full-data scale")
+ax.set_title("Streaming ADVI loss on the plateau (steps before 1,000 are off scale)")
+ax.legend(loc="upper right", fontsize=9)
+before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]
 print(
-    "sd of the recorded loss over the last two passes at each learning rate: "
-    f"{loss[6_000 - 600 : 6_000].std():,.0f} (lr 0.02) vs {loss[-600:].std():,.0f} (lr 0.005)"
+    "recorded loss over the two passes either side of the cut:\n"
+    f"  spread (sd): {before.std():,.0f} before, {after.std():,.0f} after\n"
+    f"  level (mean): {before.mean():,.0f} before, {after.mean():,.0f} after"
 );
 ```
 
@@ -730,7 +741,7 @@ print(
 Before reading a single parameter off this fit, one property of the replay has
 to be dealt with. The batch at step $t$ is a deterministic function of $t$ with
 period one epoch, so the data term of every stochastic gradient repeats with
-that period — the parameters and the Monte-Carlo draw do not, so the realized
+that period. The parameters and the Monte-Carlo draw do not, so the realized
 gradients are not literally periodic, but the iterate inherits a component
 locked to the replay order. Two measurements separate that component from
 genuine progress: compare the same phase in consecutive epochs, and look at the
@@ -744,15 +755,14 @@ step = np.abs(np.diff(ends, axis=0)) / sd_unc  # (7 epoch pairs, 154 coordinates
 last = mu_t[-steps_per_epoch:]
 swing = (last.max(0) - last.min(0)) / sd_unc
 print(
-    "same phase, epoch to epoch — max over coordinates, per pair: "
+    "same phase, epoch to epoch, max over coordinates per pair:\n  "
     + " ".join(f"{v:.2f}" for v in step.max(1))
-    + f"\n                              median over coordinates, last pair: {np.median(step[-1]):.2f} sd"
-    f"\nwithin the last epoch:        median {np.median(swing):.2f}, max {swing.max():.1f} sd"
+    + f"\n  median over coordinates, last pair: {np.median(step[-1]):.2f} sd"
+    f"\nwithin the last epoch: median {np.median(swing):.2f}, max {swing.max():.1f} sd"
 )
 ```
 
-Read the two measurements against each other. Between the same phase of
-consecutive epochs the largest movement decays from about six posterior widths
+Between the same phase of consecutive epochs the largest movement decays from about six posterior widths
 in the first pair to about half a width in the last, and the median coordinate
 moves a tenth of a width per pass; within a single epoch the same coordinates
 swing by multiples of their width. So the optimizer is not converging to a
@@ -760,16 +770,13 @@ point. It is orbiting one, on a cycle locked to the order the rows are replayed
 in, and most of where the last iterate sits is the phase of that orbit at the
 step the budget ran out.
 
-That is not a subtlety to note and move past. It means the number you would
-report depends on where you stop, so the fit is summarized by averaging the
-variational parameters over the final whole pass. To the extent the order-locked
+The number you would report therefore depends on where you stop, so the fit
+is summarized by averaging the variational parameters over the final whole
+pass. To the extent the order-locked
 component repeats from one pass to the next, a window of exactly one pass
-averages it out — which is why the step budget above is a whole number of
-epochs — and a window that is not a whole number of passes leaves a residual of
-the order of one swing divided by the window length. This is Polyak–Ruppert
-averaging; [pymc-extras#722](https://github.com/pymc-devs/pymc-extras/pull/722)
-applies the same device to its own iterates for a neighbouring reason,
-minibatch noise in where the iterate sits. What the average does *not* remove
+averages it out (which is why the step budget above is a whole number of
+epochs), and a window that is not a whole number of passes leaves a residual of
+the order of one swing divided by the window length. This is Polyak–Ruppert averaging. What the average does *not* remove
 is the slow drift the first line still shows: a tenth of a width per pass in
 the median, and much more along the directions the recovery table is about to
 single out.
@@ -901,28 +908,29 @@ means = pd.DataFrame(
 spread = means.std(axis=1) / recovery.loc[identified, "sd"]
 print(
     "identified rows, max |z| against the truth, each fit in its own posterior sd:\n  "
-    + "; ".join(f"{k} {v:.2f}" for k, v in z_own.items())
-    + f"\nspread of the mean across the three tail-averaged fits, in seed-0 sd: median {spread.median():.2f}, max {spread.max():.2f}"
+    + "\n  ".join(f"{k}: {v:.2f}" for k, v in z_own.items())
+    + "\nspread of the mean across the three tail-averaged fits, in seed-0 sd:"
+    f"\n  median {spread.median():.2f}, max {spread.max():.2f}"
 )
 ```
 
-The table is a selection — the scalar globals and the three identified sums —
+The table is a selection, the scalar globals and the three identified sums,
 and the figure after it checks one of the five symbol-effect families; that,
 plus the two curves for one symbol further down, is the extent of the recovery
-evidence here. Read the table bottom-up. The raw intercepts look off by 0.3 and 1.3 — but a
-global coefficient and the mean of its group effects are only *jointly*
+evidence here. Read the table bottom-up. The raw intercepts look off by 0.3
+and 1.3, but a global coefficient and the mean of its group effects are only *jointly*
 pinned by the likelihood, which is exactly flat along
 $(\alpha_0 + d,\ b^{(\alpha)} - d)$. What tilts that direction at all is the
 hierarchical prior: shifting every symbol effect by $d$ costs
 $\sum_s (b_s - d)^2 / 2\tau^2$, which is minimized when the effects average
-to zero — and the generator standardized them to average exactly zero, so the
+to zero, and the generator standardized them to average exactly zero, so the
 prior points the split at the generating value. Conditional on everything
-else, that cost is a Gaussian in $d$ with standard deviation $\tau/\sqrt{12}$
-— a tenth for $\tau$ near the generator's $0.35$ — which is the scale of
+else, that cost is a Gaussian in $d$ with standard deviation $\tau/\sqrt{12}$,
+a tenth for $\tau$ near the generator's $0.35$, which is the scale of
 uncertainty the split actually carries. The same
 translation ridge exists for every global-coefficient/group-effect pair in
 this model (including the vector pairs $c$/$b^{(\pi h)}$ and
-$g$/$b^{(\sigma h)}$, whose sums we spare you).
+$g$/$b^{(\sigma h)}$, whose sums the table omits).
 
 Two things follow, and the table shows both. Along a direction that flat, a
 gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its
@@ -958,19 +966,18 @@ advi_more.fit(
 path = np.asarray(path)
 more_loss = np.asarray(advi_more.hist) * ELBO_SCALE
 print(
-    "kappa0 after +0 / +30 / +60 passes: "
-    + " / ".join(f"{v:.3f}" for v in path[[0, 30, 60], 0])
-    + f" (generating {truth['kappa0']:.3f}); alpha0: "
-    + " / ".join(f"{v:.3f}" for v in path[[0, 30, 60], 1])
-    + f" (generating {truth['alpha0']:.3f}); loss per pass over those 60: "
+    "after +0 / +30 / +60 passes\n"
+    f"  kappa0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 0])} (generating {truth['kappa0']:.3f})\n"
+    f"  alpha0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 1])} (generating {truth['alpha0']:.3f})\n"
+    "  loss per pass over those 60: "
     f"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats"
 )
 ```
 
 The ridge coordinates keep moving toward their generating values, at a rate the
 loss barely registers. The raw split rows are a fit still travelling along a
-nearly flat direction, not an ambiguity in the model. And their reported widths — a few thousandths —
-are the mean-field *conditional* width across the ridge, not the tenth just
+nearly flat direction, not an ambiguity in the model. And their reported
+widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just
 computed along it: mean-field has no correlation to spend,
 so it reports the narrow direction as if it were the wide one, which is why
 `alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the
@@ -979,23 +986,22 @@ matters: it removes the direction instead of asking the optimizer to find its
 way along it.
 
 The identified rows are a different story. Every row the table reports that
-*is* pinned by the likelihood — the three sums and the six standalone
-coefficients — lands inside $1.2$ posterior standard deviations of its
+*is* pinned by the likelihood, the three sums and the six standalone
+coefficients, lands inside $1.2$ posterior standard deviations of its
 generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.
 Read from the last iterate instead of the tail average, the same rows ran to
-$3.4$; that difference was the orbit, not the estimate. And on two refits —
-one with a different optimizer seed on the same replay order, one on a
-different on-disk order altogether — the identified rows land in the same
+$3.4$; that difference was the orbit, not the estimate. And on two refits,
+one with a different optimizer seed on the same replay order and one on a
+different on-disk order altogether, the identified rows land in the same
 range, each fit judged in its own posterior width, with the spread of the means
 across the three tail-averaged fits about a tenth of the reported width. That
-is the replication check the risk-management stance calls for, and it is worth
-stating what it does and does not buy: material disagreement between fits
-would have been enough to withhold any claim about a width; agreement only
-removes the instability warning — it does not validate calibration. Replication can veto a width; it can never certify one. The widths
-here are those of a mean-field approximation, and z-scores this small on one
-dataset are consistent with widths that are somewhat too narrow, somewhat too
-wide, or right — sizing that needs many simulated datasets, and this notebook
-fits one.
+is the replication check, and what it buys is limited. Material disagreement
+between fits would have been enough to withhold any claim about a width;
+agreement only removes the instability warning. It does not validate
+calibration. The widths here are those of a mean-field approximation, and
+z-scores this small on one dataset are consistent with widths that are
+somewhat too narrow, somewhat too wide, or right; sizing that needs many
+simulated datasets, and this notebook fits one.
 
 The more interesting check is hierarchical: what happened to the per-symbol
 effects of the two symbols with 1,800 and 900 rows?
@@ -1028,21 +1034,20 @@ ax.legend()
 
 order = np.argsort(-b_sd)
 print(
-    "posterior sd of the contrast, widest first: "
+    "posterior sd of the contrast, widest first:\n  "
     + ", ".join(f"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)" for i in order[:3])
-    + f" ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)"
+    + f"\n  ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)"
 )
 ```
 
-What the figure supports is narrower than the usual slogan, so it is worth
-being exact. The posterior spread is widest for the symbols with the least
-data — the printout orders them — which is the hierarchy expressing that it
+The posterior spread is widest for the symbols with the least data (the
+printout orders them), which is the hierarchy expressing that it
 knows less about those groups {cite:p}`gelman2006data`. The point estimates
 track their generating values across the board, including the thin ones.
 
 What the figure does *not* show is shrinkage in the strict sense: that would
 need an unpooled per-symbol fit to compare against, and this notebook does not
-run one. Nor are the bars calibrated intervals — the paragraph above declined
+run one. Nor are the bars calibrated intervals; the paragraph above declined
 to treat mean-field widths that way, and that applies to their ordering too.
 And whether pooling improves held-out prediction on real data is a separate
 question again, which nothing here tests.
@@ -1105,7 +1110,7 @@ fig.suptitle(f"SYM{s_idx:02d} on the event clock, at a = q = 0", fontsize=11);
 The half-width is a quantile of the *outcome* distribution, not a credible band
 for the curve: it answers "how far does a move go", and it stays finite for any
 $\nu > 0$, which is why the notebook reports it instead of a conditional
-standard deviation — the Student-t variance exists only for $\nu > 2$, and
+standard deviation: the Student-t variance exists only for $\nu > 2$, and
 nothing in this model guarantees that. Under this generator, the fit recovers
 both shapes; on real data the same two curves would be estimates, and would
 need out-of-sample evaluation before being used for anything.
@@ -1181,16 +1186,16 @@ ax.annotate(
 ax.set_xlabel("next-event return (bp)")
 ax.set_ylabel("ECDF")
 ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
-ax.legend(loc="lower right");
+ax.legend(loc="upper left");
 ```
 
 All three statistics sit inside their predictive 90% bands, though the zero
-share sits close to the lower edge — 0.300 observed against a band starting at
-0.299 — so it is a pass with almost no margin rather than a comfortable one.
-Two limits on what
-that establishes. The heading is literal — the evaluation batch was seen during
-the fit, so this is adequacy, not generalisation, and a held-out split is the
-next thing to add before any of this is used to compare models. And the check
+share sits close to the lower edge, 0.300 observed against a band starting at
+0.299, so it is a pass with almost no margin rather than a comfortable one.
+What that establishes is limited in two ways. The heading is literal: the
+evaluation batch was seen during the fit, so this is adequacy, not
+generalisation, and a held-out split is the next thing to add before any of
+this is used to compare models. And the check
 is one batch, one step ahead, with the observed `ylag` held fixed: it does not
 simulate a price path forward, and it says nothing about the hierarchy, the
 hourly curves, or the covariate responses, each of which would need its own
@@ -1219,7 +1224,7 @@ the loss fell. For noise without long memory, averaging divides it by
 $\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable
 drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,
 it sets what is visible at all. A fixed replay order is not noise of that kind,
-and the printout below shows where the generic scaling breaks — in the
+and the printout below shows where the generic scaling breaks, in the
 notebook's favour.
 
 ```{code-cell} ipython3
@@ -1255,7 +1260,9 @@ print(f"\ncontrol spread is {spreads['1.5 epochs'] / aligned:.0f}x the widest al
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
+fig, (ax, ax_zoom) = plt.subplots(
+    1, 2, figsize=(9, 3.2), layout="constrained", gridspec_kw={"width_ratios": [3, 2]}
+)
 for (w, label), color in zip(horizons, ["C7", "C0", "C1"]):
     t, z = signed_z(stage2, w)
     ax.plot(
@@ -1266,20 +1273,30 @@ for (w, label), color in zip(horizons, ["C7", "C0", "C1"]):
         alpha=0.5 if w == 1 else 1.0,
         label=f"{label} (w={w})",
     )
-ax.axhline(0.0, color="k", lw=0.8)
-ax.set_xlabel("step within stage 2")
-ax.set_ylabel(r"standardised block contrast $z_w$")
-ax.set_title("The same trace, three horizons: what a stop rule is allowed to see")
-ax.legend(fontsize=9);
+    if w > 1:
+        ax_zoom.plot(t, z, color=color, lw=1.6, label=f"{label} (w={w})")
+for a in (ax, ax_zoom):
+    a.axhline(0.0, color="k", lw=0.8)
+    a.set_xlabel("step within stage 2")
+ax.set_ylabel(r"block contrast $z_w$")
+ax.set_title("Same trace, three horizons", fontsize=11)
+ax.legend(fontsize=8, loc="upper right", frameon=True)
+ax_zoom.set_ylim(-0.12, 0.12)
+ax_zoom.set_title(
+    f"Aligned horizons only (control sd {spreads['1.5 epochs']:.2f} would fill this)", fontsize=10
+)
+ax_zoom.legend(fontsize=8, loc="upper right", frameon=True);
 ```
 
-Read the printout as three signal-to-noise ratios. At $w = 1$ the standardised
-contrast has unit spread — which is what the $\sqrt{2/w}$ scaling is built to
-produce — and a mean indistinguishable from zero: a single increment says
-nothing about the trend.
+Read the printout as three signal-to-noise ratios, and the figure as the same
+thing twice: the left panel puts all three horizons on one scale, the right
+panel zooms in on the two that are invisible at that scale. At $w = 1$ the
+standardised contrast has unit spread (which is what the $\sqrt{2/w}$ scaling
+is built to produce) and a mean indistinguishable from zero: a single
+increment says nothing about the trend.
 
 At the two epoch-aligned horizons the spread collapses far below what
-independent noise would predict — and the control row explains why. The
+independent noise would predict, and the control row explains why. The
 shuffle is done once on disk and replayed in the same order every epoch, so a
 window of exactly one or two passes averages over the *same rows* every time
 and the batch-composition noise, which dominates the per-step view, all but
@@ -1290,14 +1307,12 @@ even though it is itself *wider* than the one-epoch window. Nothing but the
 alignment changed.
 
 What survives is Monte-Carlo noise and the parameter drift
-itself, and against that much smaller yardstick the drift becomes visible —
-the ratio in the last column rises with the horizon. Two lessons sit on top of
-each other here. The horizon is not a smoothing preference, it decides whether
-there is anything to see at all; and with a fixed replay order the useful
-horizons are the ones commensurate with an epoch, which is a property of how
-the data was shuffled, not of the optimizer.
-
-:::{admonition} Why a naive rule does not merely stay silent — it fires
+itself, and against that much smaller yardstick the drift becomes visible:
+the ratio in the last column rises with the horizon. So, with a fixed replay
+order, the useful horizons are the ones commensurate with an epoch. That is a
+property of how the data was shuffled, not of the optimizer.
+
+:::{admonition} Why a naive rule fires instead of staying silent
 :class: warning
 It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
 statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to
@@ -1310,9 +1325,9 @@ Under symmetric noise $\mathbb{E}[\max(z,0)]$ is only a fraction of a standard
 deviation, so with $\kappa$ above that value $S$ climbs at a roughly constant
 rate and crosses any fixed threshold after a roughly fixed number of steps. The
 rule therefore cannot tell "converged" from "still improving, but too slowly
-for this horizon to see" — it announces the same thing at the same pace in
-both cases. Which is why the fix is not a better threshold but a wider horizon,
-plus a second yardstick asking whether the remaining improvement is negligible
+for this horizon to see"; it announces the same thing at the same pace in
+both cases. So the fix is not a better threshold but a wider horizon, plus a
+second yardstick that asks whether the remaining improvement is negligible
 relative to the reduction already achieved. That design is implemented in
 [pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this
 notebook shows the observation problem it exists to solve rather than shipping
@@ -1320,8 +1335,8 @@ a second copy of it.
 :::
 
 How much would an online rule be worth here? The retrospective answer is a
-benchmark you can only compute afterwards — which is exactly why the online
-version is needed:
+benchmark you can only compute afterwards, which is why the online version is
+needed:
 
 ```{code-cell} ipython3
 # t99: the first step at which a trailing average of width one epoch has covered
@@ -1334,45 +1349,36 @@ total_drop = smoothed[0] - smoothed.min()
 # steps from 1.
 t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch
 print(
-    f"stage 2 smoothed reduction: {total_drop:,.2f} nats; "
+    f"stage 2 smoothed reduction: {total_drop:,.2f} nats;\n"
     f"99% of it reached by step {t99:,} of {len(stage2):,} "
     f"({t99 / len(stage2):.0%} of the stage-2 budget)"
 )
 ```
 
-Two honest qualifications about any stop, this one included. What such a rule
-detects is a *loss plateau* — a necessary signal, not a proof that the
+Any stopping rule, this one included, comes with two qualifications. What such a rule
+detects is a *loss plateau*, a necessary signal, not a proof that the
 posterior has converged; the recovery and predictive checks above are the kind
 of independent evidence a stop should be paired with. And a plateau in a noisy
 loss can only ever be established relative to a horizon: at any finite step, an
 improvement small enough is indistinguishable from none.
 
-## Where the pieces live
-
-The streaming stack this notebook exercises was contributed to pymc-extras
-as four pull requests. The map is by role; each one's state — merged, open,
-or closed — is on GitHub, and nothing on this page depends on it.
-
-| Component | Role | Form in this notebook |
-| --- | --- | --- |
-| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size` | Imported and used directly |
-| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, while [#635](https://github.com/pymc-devs/pymc-extras/pull/635) develops the longer-term ADVI API | The `StreamAdvance` adapter above, owned by the notebook |
-| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks | The observation problem it solves is shown; the implementation is not copied here |
-| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | Quasi-Newton alternative returning a Gaussian proposal from a short optimizer run, with `pareto_k` as its own veto | Not run on this target — see below |
-
-+++
+## Related tooling in pymc-extras
 
-### Choosing a path: why the fast one is not taken here
+The loader used above is one of four streaming pieces in pymc-extras. The
+others are not imported here; this is where each one fits.
 
-ADVI is not the only way to fit a model on a stream. Streaming Pathfinder runs
-a short quasi-Newton trajectory on minibatch gradients — a few hundred steps
-rather than thousands — and returns a Gaussian *proposal*, importance-corrected
-against the exact full-data log-density, with the Pareto-$k$ diagnostic as its
-own veto. It is the right tool when an approximately-placed starting point is
-what you need, for example as an initialisation for MCMC.
+| Component | What it does |
+| --- | --- |
+| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size`; used directly above |
+| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, the job the twenty-line `StreamAdvance` does here |
+| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks; the observation problem it addresses is the previous section |
+| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |
 
-It is not run on this model, and the reason is a decision taken *before*
-fitting rather than a result:
+Pathfinder is the faster route when an approximately placed starting point is
+what you need, for example to initialise MCMC. It is not run here, and the
+reason is a decision taken before fitting rather than a result: its documented
+operating range is non-hierarchical targets of at most a few tens of
+parameters, and this target is hierarchical with
 
 ```{code-cell} ipython3
 from pymc.blocking import DictToArrayBijection
@@ -1381,11 +1387,7 @@ n_free = DictToArrayBijection.map(model.initial_point()).data.size
 print(f"free parameters in the unconstrained space: {n_free}")
 ```
 
-pymc-extras#722 states a measured operating range: non-hierarchical targets of
-at most a few tens of parameters. This target is hierarchical and an order of
-magnitude larger than that, so applying it here would be using a method
-outside the range its author documented. That is an applicability decision,
-not a prediction about what its diagnostic would report. The ADVI results
+free parameters, an order of magnitude outside that range. The ADVI results
 above stand or fall on their own recovery and predictive checks, independently
 of this choice.
 
@@ -1412,7 +1414,7 @@ of this choice.
 ```{code-cell} ipython3
 %load_ext watermark
 print(f"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s")
-%watermark -n -u -v -iv -w -p pytensor,pymc_extras
+%watermark -n -u -v -iv -w
 %watermark -m
 ```
 

From 8c9e2b105b9cf928aa944a7e2d20259489028468 Mon Sep 17 00:00:00 2001
From: Yicheng Yang <yy85@illinois.edu>
Date: Tue, 18 Aug 2026 14:14:29 -0500
Subject: [PATCH 42/42] Conform to the gallery style guide and the
 variational_inference siblings

Checked against the PyMC Jupyter style guide and the five notebooks in
the same section. Mechanics already matched (front matter, first cell,
extra_installs include, seed/rng, arviz-variat, citations, end-matter
order, watermark hook). What did not:

- "RVs" -> "random variables" (the guide's own example of an abbreviation
  to avoid); ETL, SGD, VI, ADVI, ELBO, MCMC spelled out at first use;
  "pp mean" / "lr" / "mean-field sd" labels expanded; ECDF expanded in
  the prose before the figure; `icpt` -> `intercept`.
- The batch size was written $B$ in the gates and $b$ in the objective,
  and $B$ also named the hour basis. It is $b$ throughout the prose now
  and the batch index set is $\mathcal{B}_t$; the symbols in the recorded
  objective are defined under the display, and the model equation gets
  a "where" list naming every coefficient, per the guide.
- `axes` -> `axs`; `plt.matplotlib.ticker` and the blocking bijection are
  imported at the top; `for a in (ax, ax_zoom)` no longer shadows the
  covariate `a`.
- British spellings (artefact, generalisation, standardised, favour,
  initialise, neighbours, travelling, Monte-Carlo) normalized to the
  American forms every sibling uses.
- "Notebooks in this collection do not download data at build time" was
  false (37 of them do); it now says only what this notebook does.
- Polyak-Ruppert averaging is cited (Polyak and Juditsky 1992, verified
  against CrossRef); the entry is added to references.bib in order.
- The GSoC credit moves out of the Authors bullet into an
  Acknowledgements section (the MOGP-Coregion-Hadamard precedent), and
  the Authors line follows the guide's pattern with the PR link.
- Watermark adds `-p xarray`, which is used without an import.

Not changed, on purpose: the impersonal voice (the siblings say "we"; the
guide is silent, and switching mid-notebook would be worse than either),
and the hand-rolled figures (each replaces an ArviZ function for a stated
reason in its cell comment).

Re-executed in the pinned environment; the machine was under load from
unrelated jobs, so the self-reported wall time reads 42 s against 28 s
idle.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 examples/references.bib                       |  10 +
 .../streaming_tick_data.ipynb                 | 988 +++++++++---------
 .../streaming_tick_data.myst.md               | 160 +--
 3 files changed, 623 insertions(+), 535 deletions(-)

diff --git a/examples/references.bib b/examples/references.bib
index 13499419b..8222dc48b 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -921,6 +921,16 @@ @article{penrose1985
   year          = {1985},
   pages         = {189}
 }
+@article{polyak1992acceleration,
+  title         = {Acceleration of Stochastic Approximation by Averaging},
+  author        = {Polyak, Boris T. and Juditsky, Anatoli B.},
+  journal       = {SIAM Journal on Control and Optimization},
+  volume        = {30},
+  number        = {4},
+  pages         = {838--855},
+  year          = {1992},
+  doi           = {10.1137/0330046}
+}
 @misc{quiroga2022bart,
   title         = {Bayesian additive regression trees for probabilistic programming},
   author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
index b7b8509b0..c35c9fa12 100644
--- a/examples/variational_inference/streaming_tick_data.ipynb
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9fa79e63",
+   "id": "fbd8fe51",
    "metadata": {},
    "source": [
     "(streaming_tick_data)=\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34e92bce",
+   "id": "85392e8c",
    "metadata": {},
    "source": [
     "Exchanges publish per-trade archives; Binance, for instance, distributes\n",
@@ -34,10 +34,11 @@
     "on 300,000 synthetic\n",
     "rows that are generated inside the notebook. It tries to teach three things:\n",
     "\n",
-    "1. **When minibatch VI is even valid.** Two acceptance gates that most\n",
+    "1. **When minibatch variational inference (VI) is even valid.** Two acceptance gates that most\n",
     "   time-series models fail, and a model class that passes both.\n",
     "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
-    "   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and\n",
+    "   an on-disk global shuffle, streaming automatic differentiation variational\n",
+    "   inference (ADVI) with `total_size` rescaling, and\n",
     "   what a stopping rule has to be able to see before it can fire.\n",
     "3. **Checking the recovery.** With the truth known, recovery is checkable once\n",
     "   one correction is made: the cyclic replay puts the optimizer on an orbit, and\n",
@@ -56,14 +57,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19825fb6",
+   "id": "7bc1b4b5",
    "metadata": {},
    "source": [
     "## Two gates: when minibatch VI is valid, and when it is worth it\n",
     "\n",
     "Minibatch variational inference rests on one identity: if the likelihood\n",
     "factors over rows given the parameters, then the batch log-likelihood scaled by\n",
-    "$N/B$ is an unbiased estimator of the full-data log-likelihood\n",
+    "$N/b$ is an unbiased estimator of the full-data log-likelihood\n",
     "{cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;\n",
     "the second is about whether streaming is doing any real work. Check both\n",
     "before reaching for `total_size` on your own data:\n",
@@ -71,7 +72,7 @@
     "**Gate 1 — validity: the likelihood must factor over rows, and the batching\n",
     "must weight every row equally.** No latent path coupling observations, no\n",
     "label shared across rows, and every row given the same inclusion frequency by\n",
-    "the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How\n",
+    "the batching scheme (unequal inclusion breaks the plain $N/b$ rescaling). How\n",
     "the batches are *drawn* is a separate matter that this gate does not settle:\n",
     "fresh uniform batches make each step's scaled gradient conditionally unbiased\n",
     "for the full-data objective (the identity above), while a fixed shuffled\n",
@@ -81,7 +82,7 @@
     "factorization requirement is exactly why the classic\n",
     "stochastic volatility model of the {ref}`stochastic_volatility` notebook\n",
     "*cannot* be minibatched: its latent volatility path ties every observation to\n",
-    "its neighbours, so a random subset of rows does not carry $B/N$ of the\n",
+    "its neighbors, so a random subset of rows does not carry $b/N$ of the\n",
     "log-likelihood. Any model whose rows share one outcome (for example, every tick\n",
     "of a match sharing the final result) fails the same gate through\n",
     "pseudo-replication: the effective sample size is the number of outcomes, not\n",
@@ -107,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3306be8a",
+   "id": "ca2e69c5",
    "metadata": {},
    "source": [
     ":::{include} ../extra_installs.md\n",
@@ -133,13 +134,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8c4c9bb8",
+   "id": "51568bd8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:23:59.666230Z",
-     "iopub.status.busy": "2026-08-18T04:23:59.666160Z",
-     "iopub.status.idle": "2026-08-18T04:24:01.389770Z",
-     "shell.execute_reply": "2026-08-18T04:24:01.389320Z"
+     "iopub.execute_input": "2026-08-18T19:12:39.355958Z",
+     "iopub.status.busy": "2026-08-18T19:12:39.355880Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.212539Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.212031Z"
     }
    },
    "outputs": [],
@@ -160,6 +161,8 @@
     "import pymc as pm\n",
     "import pytensor.tensor as pt\n",
     "\n",
+    "from matplotlib.ticker import StrMethodFormatter\n",
+    "from pymc.blocking import DictToArrayBijection\n",
     "from pymc_extras.variational.dataloader import DataLoader, parquet_source\n",
     "from scipy import stats\n",
     "\n",
@@ -169,13 +172,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "27ef0634",
+   "id": "d7d0dad0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:01.390970Z",
-     "iopub.status.busy": "2026-08-18T04:24:01.390821Z",
-     "iopub.status.idle": "2026-08-18T04:24:01.393767Z",
-     "shell.execute_reply": "2026-08-18T04:24:01.393492Z"
+     "iopub.execute_input": "2026-08-18T19:12:41.214022Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.213851Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.217141Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.216843Z"
     }
    },
    "outputs": [],
@@ -193,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b28210e",
+   "id": "9b1b9356",
    "metadata": {},
    "source": [
     "## The model: hierarchical hurdle–Student-t next-event returns\n",
@@ -231,6 +234,20 @@
     "\\end{aligned}\n",
     "$$\n",
     "\n",
+    "where\n",
+    "\n",
+    "* $\\kappa_0$ and $\\alpha_0$ are the global intercepts of the move probability\n",
+    "  and the log scale, and $b^{(\\kappa)}_s$, $b^{(\\alpha)}_s$ their per-symbol\n",
+    "  offsets;\n",
+    "* $B(h)$ is the hour-of-day Fourier basis (four columns), $c$ and $g$ its\n",
+    "  global coefficients, and $b^{(\\pi h)}_s$, $b^{(\\sigma h)}_s$ the per-symbol\n",
+    "  deviations from them;\n",
+    "* $\\lambda_a$, $\\lambda_q$, $\\beta_a$, $\\beta_q$ are the covariate slopes, with\n",
+    "  $b^{(\\beta a)}_s$ a per-symbol slope on activity in the scale;\n",
+    "* $\\theta_d$, $\\theta_r$ place the conditional location on the trade sign and\n",
+    "  the last nonzero return;\n",
+    "* every $b_s \\sim \\mathcal{N}(0, \\tau)$ with its own $\\tau$, and $\\nu$ is shared.\n",
+    "\n",
     "The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an\n",
     "activity-like $a_i$, and $\\text{ylag}_i$, the most recent previous nonzero\n",
     "return on that symbol. In the generator below they are drawn directly rather\n",
@@ -268,13 +285,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa18bf86",
+   "id": "1c838d65",
    "metadata": {},
    "source": [
     "## Synthetic data with known truth\n",
     "\n",
-    "Notebooks in this collection do not download data at build time, so the\n",
-    "executed path uses a seeded synthetic generator: the schema an exchange\n",
+    "This notebook neither ships nor downloads an exchange archive; the executed\n",
+    "path uses a seeded synthetic generator: the schema an exchange\n",
     "archive would have after feature construction, a hurdle at exactly zero,\n",
     "heavy conditional tails, and twelve symbols whose row counts span two orders\n",
     "of magnitude, so the hierarchy has both data-rich and data-poor groups to work\n",
@@ -286,13 +303,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6f581a9b",
+   "id": "9e033f86",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:01.394690Z",
-     "iopub.status.busy": "2026-08-18T04:24:01.394636Z",
-     "iopub.status.idle": "2026-08-18T04:24:01.397725Z",
-     "shell.execute_reply": "2026-08-18T04:24:01.397336Z"
+     "iopub.execute_input": "2026-08-18T19:12:41.218724Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.218500Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.222167Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.221590Z"
     }
    },
    "outputs": [],
@@ -334,13 +351,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "03090818",
+   "id": "76034f5c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:01.398871Z",
-     "iopub.status.busy": "2026-08-18T04:24:01.398814Z",
-     "iopub.status.idle": "2026-08-18T04:24:01.558916Z",
-     "shell.execute_reply": "2026-08-18T04:24:01.558359Z"
+     "iopub.execute_input": "2026-08-18T19:12:41.223540Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.223446Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.456631Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.455855Z"
     }
    },
    "outputs": [
@@ -408,7 +425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ed882f7",
+   "id": "eebd79a1",
    "metadata": {},
    "source": [
     "The constants above are settings, not measurements. Read them as the\n",
@@ -440,13 +457,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "02cc9bf7",
+   "id": "555034e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:01.560008Z",
-     "iopub.status.busy": "2026-08-18T04:24:01.559938Z",
-     "iopub.status.idle": "2026-08-18T04:24:01.986645Z",
-     "shell.execute_reply": "2026-08-18T04:24:01.986344Z"
+     "iopub.execute_input": "2026-08-18T19:12:41.458672Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.458538Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.128111Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.127491Z"
     }
    },
    "outputs": [
@@ -485,7 +502,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b99a253",
+   "id": "cc615068",
    "metadata": {},
    "source": [
     "## The on-disk global shuffle\n",
@@ -494,11 +511,11 @@
     "ordered stream: with tick data sorted by symbol and time, early optimization\n",
     "steps would only ever see early dates and the first symbols, and an early\n",
     "stopping decision would be biased by construction. The fix is to shuffle\n",
-    "**once, globally, on disk at ETL time**: every row gets a deterministic hash\n",
+    "**once, globally, on disk when the archive is written**: every row gets a deterministic hash\n",
     "key, rows are scattered across shards by that key, and each shard is sorted by\n",
     "it. After that, sequential reads follow one fixed, data-independent\n",
     "permutation. It is pseudo-random (a hash of the row index, nothing from the\n",
-    "row itself) and replayed identically each epoch, so this is single-shuffle SGD\n",
+    "row itself) and replayed identically each epoch, so this is single-shuffle stochastic gradient descent\n",
     "rather than fresh per-step subsampling. The loader can then run with\n",
     "`shuffle=False`, without a copy through the shuffle buffer."
    ]
@@ -506,13 +523,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "025ee8b7",
+   "id": "49e21042",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:01.987706Z",
-     "iopub.status.busy": "2026-08-18T04:24:01.987643Z",
-     "iopub.status.idle": "2026-08-18T04:24:02.201253Z",
-     "shell.execute_reply": "2026-08-18T04:24:02.200893Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.129376Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.129282Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.412920Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.412246Z"
     }
    },
    "outputs": [
@@ -567,13 +584,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e9bbfa7",
+   "id": "a8999f22",
    "metadata": {},
    "source": [
     "The assertion is the one to keep: the head of every shard must already mix\n",
     "every hour and every symbol, checked rather than assumed.\n",
     "\n",
-    "This cell is a small-scale stand-in for the real ETL step: it builds the whole\n",
+    "This cell is a small-scale stand-in for the real extract-transform-load step: it builds the whole\n",
     "table, the whole key array and the whole permutation in memory, which is\n",
     "exactly what one cannot do once the data stops fitting. The inference that\n",
     "follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:\n",
@@ -590,13 +607,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c443ae62",
+   "id": "a4653e9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:02.202378Z",
-     "iopub.status.busy": "2026-08-18T04:24:02.202310Z",
-     "iopub.status.idle": "2026-08-18T04:24:02.240544Z",
-     "shell.execute_reply": "2026-08-18T04:24:02.240261Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.414406Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.414289Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.464157Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.463290Z"
     }
    },
    "outputs": [
@@ -604,7 +621,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "row-scale generator arrays released; the fit reads from ticks_gki1p36z\n"
+      "row-scale generator arrays released; the fit reads from ticks_nihg_s5h\n"
      ]
     }
    ],
@@ -617,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fac6fdf6",
+   "id": "b6573ce7",
    "metadata": {},
    "source": [
     "## Streaming the model\n",
@@ -626,12 +643,11 @@
     "one block per Parquet row group, in a frozen column order. That is why the\n",
     "shards above were written with `row_group_size` equal to the batch size, and\n",
     "why the row counts were chosen to divide exactly. On this path the loader\n",
-    "hands you the row groups it finds, so a\n",
-    "ragged geometry gives ragged batches, `len(loader)` (which is\n",
-    "`total_size // batch_size`) stops matching the number of blocks an epoch\n",
-    "yields, and the recorded loss picks up a deterministic sawtooth that has\n",
-    "nothing to do with convergence (the fitting section explains why the recorded\n",
-    "value scales with the block size). The model reads\n",
+    "hands you the row groups it finds. A ragged geometry therefore gives ragged\n",
+    "batches; `len(loader)` (which is `total_size // batch_size`) stops matching the\n",
+    "number of blocks an epoch yields; and the recorded loss picks up a\n",
+    "deterministic sawtooth that has nothing to do with convergence (the fitting\n",
+    "section explains why the recorded value scales with the block size). The model reads\n",
     "one `pm.Data` placeholder; everything derived (the Fourier basis, the\n",
     "integer symbol index) is computed inside the graph, so advancing the stream\n",
     "is a single `set_value` per step."
@@ -640,13 +656,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "6a6e619d",
+   "id": "b1c4c88d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:02.241614Z",
-     "iopub.status.busy": "2026-08-18T04:24:02.241536Z",
-     "iopub.status.idle": "2026-08-18T04:24:02.291898Z",
-     "shell.execute_reply": "2026-08-18T04:24:02.291539Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.465684Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.465559Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.557073Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.556530Z"
     }
    },
    "outputs": [
@@ -684,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "795ac2d7",
+   "id": "229033e5",
    "metadata": {},
    "source": [
     "That assertion pins down two things. First, **nothing is dropped**: verbatim\n",
@@ -704,13 +720,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "91814adb",
+   "id": "c959355b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:02.292962Z",
-     "iopub.status.busy": "2026-08-18T04:24:02.292888Z",
-     "iopub.status.idle": "2026-08-18T04:24:02.297445Z",
-     "shell.execute_reply": "2026-08-18T04:24:02.297045Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.558484Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.558367Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.565543Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.564821Z"
     }
    },
    "outputs": [],
@@ -808,12 +824,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5600c9e5",
+   "id": "03837162",
    "metadata": {},
    "source": [
     "Two implementation notes. The likelihood is a\n",
     "{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`\n",
-    "gives the term *observed-RV semantics*, which is what makes PyMC's own\n",
+    "gives the term *observed random-variable semantics*, which is what makes PyMC's own\n",
     "`total_size` minibatch rescaling apply. And\n",
     "the hurdle's two logs are written with `softplus`,\n",
     "$\\log \\pi = -\\operatorname{softplus}(-x)$ and\n",
@@ -823,19 +839,19 @@
     "did not move, and every nonzero draw is many orders of magnitude above the\n",
     "float32 subnormal range. On real data the same comparison is safe as long as\n",
     "returns are computed so that \"no move\" produces an exact zero rather than a\n",
-    "rounding artefact; check that once, in the feature code."
+    "rounding artifact; check that once, in the feature code."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6586a912",
+   "id": "3350f3f4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:02.298353Z",
-     "iopub.status.busy": "2026-08-18T04:24:02.298294Z",
-     "iopub.status.idle": "2026-08-18T04:24:02.471869Z",
-     "shell.execute_reply": "2026-08-18T04:24:02.471397Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.567066Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.566950Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.773988Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.773477Z"
     }
    },
    "outputs": [
@@ -851,367 +867,367 @@
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        " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>cluster1000 x 8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M809.5,-8C809.5,-8 855.5,-8 855.5,-8 861.5,-8 867.5,-14 867.5,-20 867.5,-20 867.5,-94 867.5,-94 867.5,-100 861.5,-106 855.5,-106 855.5,-106 809.5,-106 809.5,-106 803.5,-106 797.5,-100 797.5,-94 797.5,-94 797.5,-20 797.5,-20 797.5,-14 803.5,-8 809.5,-8\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"835.5\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
        "</g>\n",
        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clusterharmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M945.5,-243.82C945.5,-243.82 1119.5,-243.82 1119.5,-243.82 1125.5,-243.82 1131.5,-249.82 1131.5,-255.82 1131.5,-255.82 1131.5,-353.63 1131.5,-353.63 1131.5,-359.63 1125.5,-365.63 1119.5,-365.63 1119.5,-365.63 945.5,-365.63 945.5,-365.63 939.5,-365.63 933.5,-359.63 933.5,-353.63 933.5,-353.63 933.5,-255.82 933.5,-255.82 933.5,-249.82 939.5,-243.82 945.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1087.87\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
        "<title>clustersymbol (12)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1151.5,-243.82C1151.5,-243.82 1425.5,-243.82 1425.5,-243.82 1431.5,-243.82 1437.5,-249.82 1437.5,-255.82 1437.5,-255.82 1437.5,-353.63 1437.5,-353.63 1437.5,-359.63 1431.5,-365.63 1425.5,-365.63 1425.5,-365.63 1151.5,-365.63 1151.5,-365.63 1145.5,-365.63 1139.5,-359.63 1139.5,-353.63 1139.5,-353.63 1139.5,-255.82 1139.5,-255.82 1139.5,-249.82 1145.5,-243.82 1151.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.12\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
        "<title>clustersymbol (12) x harmonic (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1468.5,-243.82C1468.5,-243.82 1642.5,-243.82 1642.5,-243.82 1648.5,-243.82 1654.5,-249.82 1654.5,-255.82 1654.5,-255.82 1654.5,-353.63 1654.5,-353.63 1654.5,-359.63 1648.5,-365.63 1642.5,-365.63 1642.5,-365.63 1468.5,-365.63 1468.5,-365.63 1462.5,-365.63 1456.5,-359.63 1456.5,-353.63 1456.5,-353.63 1456.5,-255.82 1456.5,-255.82 1456.5,-249.82 1462.5,-243.82 1468.5,-243.82\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.37\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
        "</g>\n",
        "<g id=\"clust5\" class=\"cluster\">\n",
        "<title>cluster1000</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M720.5,-114C720.5,-114 944.5,-114 944.5,-114 950.5,-114 956.5,-120 956.5,-126 956.5,-126 956.5,-223.82 956.5,-223.82 956.5,-229.82 950.5,-235.82 944.5,-235.82 944.5,-235.82 720.5,-235.82 720.5,-235.82 714.5,-235.82 708.5,-229.82 708.5,-223.82 708.5,-223.82 708.5,-126 708.5,-126 708.5,-120 714.5,-114 720.5,-114\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
        "</g>\n",
        "<!-- batch -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>batch</title>\n",
-       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M847.5,-98C847.5,-98 817.5,-98 817.5,-98 811.5,-98 805.5,-92 805.5,-86 805.5,-86 805.5,-52.5 805.5,-52.5 805.5,-46.5 811.5,-40.5 817.5,-40.5 817.5,-40.5 847.5,-40.5 847.5,-40.5 853.5,-40.5 859.5,-46.5 859.5,-52.5 859.5,-52.5 859.5,-86 859.5,-86 859.5,-92 853.5,-98 847.5,-98\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
        "</g>\n",
-       "<!-- lambda_q -->\n",
+       "<!-- alpha0 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>lambda_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"49.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- y_obs -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>y_obs</title>\n",
-       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"832.5\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"832.5\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- lambda_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>lambda_q&#45;&gt;y_obs</title>\n",
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+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
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-       "<!-- eta -->\n",
+       "<!-- theta_d -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>eta</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"478.5\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
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        "<!-- nu -->\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"478.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M258.52,-288.04C267.67,-272.31 281.32,-253.86 299.01,-243.82 366.19,-205.7 570.16,-193.61 705.67,-189.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"705.52,-193.33 715.42,-189.56 705.33,-186.33 705.52,-193.33\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"245.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- eta&#45;&gt;nu -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>eta&#45;&gt;nu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M478.5,-393.28C478.5,-381.58 478.5,-368.79 478.5,-357.14\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"482,-357.45 478.5,-347.45 475,-357.45 482,-357.45\"/>\n",
-       "</g>\n",
-       "<!-- tau_ba -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>tau_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1134.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1134.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M245.01,-393.28C245.01,-381.58 245.01,-368.79 245.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"248.51,-357.45 245.01,-347.45 241.51,-357.45 248.51,-357.45\"/>\n",
        "</g>\n",
-       "<!-- b_ba -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>b_ba</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1188.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1188.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"349.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- tau_ba&#45;&gt;b_ba -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>tau_ba&#45;&gt;b_ba</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1152.18,-395.53C1156.84,-385.58 1161.92,-374.73 1166.77,-364.38\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1169.8,-366.16 1170.87,-355.62 1163.46,-363.19 1169.8,-366.16\"/>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M366.44,-279.7C374.73,-266.27 385.95,-252.28 400.01,-243.82 450.19,-213.62 597.57,-199.6 706.51,-193.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.63,-196.73 716.41,-192.67 706.23,-189.74 706.63,-196.73\"/>\n",
        "</g>\n",
-       "<!-- kappa0 -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>kappa0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"158.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"158.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"458.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>kappa0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M175.27,-279.43C183.35,-265.97 194.4,-252.02 208.5,-243.82 250.39,-219.45 536.46,-202.03 706.37,-193.7\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.31,-197.21 716.13,-193.22 705.97,-190.21 706.31,-197.21\"/>\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M479.52,-279.99C489.31,-266.61 502.07,-252.57 517.01,-243.82 549.64,-224.7 636.56,-210.21 710.99,-200.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"711.35,-204.29 720.84,-199.59 710.49,-197.34 711.35,-204.29\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"567.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M584.74,-280.2C592.91,-267.01 603.76,-253.05 617.01,-243.82 644.99,-224.33 679.27,-211.66 712.21,-203.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"712.92,-206.86 721.85,-201.15 711.31,-200.04 712.92,-206.86\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
        "</g>\n",
        "<!-- lambda_a -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>lambda_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"266.5\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"266.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"675.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- lambda_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
        "<title>lambda_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M286.98,-279.88C296.57,-266.31 309.28,-252.16 324.5,-243.82 387.93,-209.04 576.7,-195.99 705.37,-191.09\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.47,-194.59 715.34,-190.72 705.22,-187.59 705.47,-194.59\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M698.56,-281.27C708.27,-268.53 720.24,-254.6 733.01,-243.82 740.22,-237.74 748.18,-232 756.39,-226.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"758.16,-229.71 764.8,-221.45 754.46,-223.76 758.16,-229.71\"/>\n",
        "</g>\n",
-       "<!-- beta_a -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>beta_a</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"374.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"374.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"783.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta_a&#45;&gt;y_obs -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>beta_a&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M391.47,-279.76C399.58,-266.35 410.6,-252.34 424.5,-243.82 470.61,-215.53 604.77,-201.24 706.77,-194.28\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"706.85,-197.78 716.59,-193.63 706.38,-190.8 706.85,-197.78\"/>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M797.61,-278.66C802.57,-265.99 808.19,-251.61 813.47,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"816.68,-239.52 817.06,-228.93 810.16,-236.97 816.68,-239.52\"/>\n",
        "</g>\n",
        "<!-- tau_k -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>tau_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1261.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1261.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_k -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
+       "<g id=\"node21\" class=\"node\">\n",
        "<title>b_k</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1288.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1288.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- tau_k&#45;&gt;b_k -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>tau_k&#45;&gt;b_k</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1270.71,-393.93C1272.69,-385.49 1274.8,-376.46 1276.86,-367.69\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.2,-368.78 1279.07,-358.24 1273.38,-367.18 1280.2,-368.78\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
        "</g>\n",
        "<!-- tau_al -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>tau_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
        "</g>\n",
        "<!-- b_al -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
+       "<g id=\"node19\" class=\"node\">\n",
        "<title>b_al</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1388.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1388.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- tau_al&#45;&gt;b_al -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>tau_al&#45;&gt;b_al</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1388.5,-393.28C1388.5,-385.5 1388.5,-377.25 1388.5,-369.16\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1392,-369.43 1388.5,-359.43 1385,-369.43 1392,-369.43\"/>\n",
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-       "<!-- tau_sh -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>tau_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1515.5\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
-       "</g>\n",
-       "<!-- b_sh -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>b_sh</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1505.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1505.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- tau_sh&#45;&gt;b_sh -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>tau_sh&#45;&gt;b_sh</title>\n",
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-       "<!-- nu&#45;&gt;y_obs -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>nu&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M492.74,-287.82C501.98,-272.43 515.48,-254.39 532.5,-243.82 562.29,-225.31 641.52,-211.06 711.09,-201.61\"/>\n",
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-       "<!-- theta_d -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>theta_d</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"582.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"582.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- theta_d&#45;&gt;y_obs -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>theta_d&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M600.55,-279.96C608.7,-266.9 619.46,-253.09 632.5,-243.82 656.78,-226.55 686.25,-214.63 715.01,-206.4\"/>\n",
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-       "<!-- beta_q -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>beta_q</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"682.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"682.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<!-- beta_q&#45;&gt;y_obs -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>beta_q&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702.03,-281.13C710.3,-268.37 720.75,-254.46 732.5,-243.82 739.38,-237.58 747.07,-231.77 755.06,-226.43\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"756.72,-229.53 763.27,-221.2 752.95,-223.63 756.72,-229.53\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
        "</g>\n",
-       "<!-- alpha0 -->\n",
+       "<!-- tau_ba -->\n",
        "<g id=\"node14\" class=\"node\">\n",
-       "<title>alpha0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"782.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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-       "<!-- alpha0&#45;&gt;y_obs -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>alpha0&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M797.09,-278.66C802.05,-265.99 807.68,-251.61 812.96,-238.11\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"816.16,-239.52 816.55,-228.93 809.65,-236.97 816.16,-239.52\"/>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
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        "<!-- theta_r -->\n",
        "<g id=\"node15\" class=\"node\">\n",
        "<title>theta_r</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"882.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"882.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- theta_r&#45;&gt;y_obs -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
        "<title>theta_r&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M867.9,-278.66C862.94,-265.99 857.32,-251.61 852.04,-238.11\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"855.35,-236.97 848.45,-228.93 848.83,-239.52 855.35,-236.97\"/>\n",
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        "<!-- tau_ph -->\n",
        "<g id=\"node16\" class=\"node\">\n",
        "<title>tau_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1642.5\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
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+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
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        "<!-- b_ph -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>b_ph</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1605.5\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1605.5\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "<!-- tau_ph&#45;&gt;b_ph -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>tau_ph&#45;&gt;b_ph</title>\n",
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        "<!-- g -->\n",
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        "<title>g</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"982.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"983.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- g&#45;&gt;y_obs -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
        "<title>g&#45;&gt;y_obs</title>\n",
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        "<!-- c -->\n",
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        "<title>c</title>\n",
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-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.5\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
-       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1082.5\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- c&#45;&gt;y_obs -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
        "<title>c&#45;&gt;y_obs</title>\n",
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-       "<!-- b_k&#45;&gt;y_obs -->\n",
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-       "<title>b_k&#45;&gt;y_obs</title>\n",
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        "<!-- b_al&#45;&gt;y_obs -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
        "<title>b_al&#45;&gt;y_obs</title>\n",
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        "<!-- b_ba&#45;&gt;y_obs -->\n",
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        "<title>b_ba&#45;&gt;y_obs</title>\n",
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+       "</g>\n",
+       "<!-- b_k&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>b_k&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.62,-280.44C1161.9,-266.96 1150.24,-252.73 1136.01,-243.82 1105.84,-224.92 1025.11,-210.63 954.56,-201.26\"/>\n",
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        "</g>\n",
        "<!-- b_sh&#45;&gt;y_obs -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
        "<title>b_sh&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1483.99,-281.95C1473.09,-267.68 1458.53,-252.43 1441.5,-243.82 1360.04,-202.63 1113.27,-191.66 960,-188.9\"/>\n",
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        "</g>\n",
        "<!-- b_ph&#45;&gt;y_obs -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
        "<title>b_ph&#45;&gt;y_obs</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1588.78,-279.34C1580.7,-265.87 1569.64,-251.93 1555.5,-243.82 1505.21,-214.97 1152.95,-198.82 959.33,-192.03\"/>\n",
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        "</g>\n",
        "<!-- y_obs&#45;&gt;batch -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>y_obs&#45;&gt;batch</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M832.5,-146.27C832.5,-134.38 832.5,-121.37 832.5,-109.53\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"836,-109.67 832.5,-99.67 829,-109.67 836,-109.67\"/>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x1323b5bb0>"
+       "<graphviz.graphs.Digraph at 0x117bf5460>"
       ]
      },
      "execution_count": 10,
@@ -1228,7 +1244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2493d7f6",
+   "id": "6dd8e399",
    "metadata": {},
    "source": [
     "The plate diagram is an inventory rather than a picture of the two links: five\n",
@@ -1247,13 +1263,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "da8e5940",
+   "id": "ce0fe1b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:02.473122Z",
-     "iopub.status.busy": "2026-08-18T04:24:02.473020Z",
-     "iopub.status.idle": "2026-08-18T04:24:08.293499Z",
-     "shell.execute_reply": "2026-08-18T04:24:08.292930Z"
+     "iopub.execute_input": "2026-08-18T19:12:42.775471Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.775327Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.294991Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.294432Z"
     }
    },
    "outputs": [],
@@ -1306,7 +1322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "760139ed",
+   "id": "5b9f2ddc",
    "metadata": {},
    "source": [
     "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
@@ -1328,26 +1344,31 @@
     "gets recorded per step is therefore\n",
     "\n",
     "$$\n",
-    "F_t = -\\sum_{i \\in B_t} \\mathbb{E}_q\\left[\\log p(y_i \\mid \\theta)\\right]\n",
+    "F_t = -\\sum_{i \\in \\mathcal{B}_t} \\mathbb{E}_q\\left[\\log p(y_i \\mid \\theta)\\right]\n",
     "      + \\frac{b}{N}\\,\\mathrm{KL}(q \\Vert p),\n",
     "$$\n",
     "\n",
-    "which lives on the scale of *one batch*, not of the full dataset, and which\n",
-    "would move mechanically with $b$ if the blocks were ragged. Multiplying by\n",
-    "$N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks\n",
+    "where $\\mathcal{B}_t$ is the set of rows in the batch at step $t$, $b$ its\n",
+    "size, $N$ the total row count (`loader.total_size`), $\\theta$ the parameters,\n",
+    "$q$ the variational approximation, $p$ the prior, and $\\mathrm{KL}$ the\n",
+    "Kullback–Leibler divergence. This lives on the scale of *one batch*, not of\n",
+    "the full dataset, and would move mechanically with $b$ if the blocks were\n",
+    "ragged. Multiplying by\n",
+    "$N/b$ puts it back on the full-data scale of the negative evidence lower bound\n",
+    "(ELBO), and with equal blocks\n",
     "that is one constant:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "65e7757b",
+   "id": "ee19b8c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:08.294680Z",
-     "iopub.status.busy": "2026-08-18T04:24:08.294605Z",
-     "iopub.status.idle": "2026-08-18T04:24:08.458073Z",
-     "shell.execute_reply": "2026-08-18T04:24:08.457789Z"
+     "iopub.execute_input": "2026-08-18T19:12:51.296919Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.296795Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.632724Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.631567Z"
     }
    },
    "outputs": [
@@ -1362,7 +1383,7 @@
     },
     {
      "data": {
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ddQu8Pi92iU8+VIV37yfz6j+nodKyaaPEo3nfq8VjWfPSUPig6dM5ZnsicpIgA1XdLQAgFArqyM/QV06c9+JYBgAqkeyKAAAAILnYefHAY0N9d2ED3blsE/UPj9h6gctBZSvuYucLOyv7Ezn7dcSCm/rNbnOqolehVmf6eBQt5cvByIGREZFRubmzx9H+c0U3LakzZbWKdOyxe/3LszTi81pYajaaBaX59GlzBzlB1cyw0qx0olaSh6LLUVVmBPhvfeEdU9riRtiaAZJjB+ehNt7dI9OXKQCAPvKmmwAAACjfweeRsvOg0GBGZyK+NLWanJCZmmo4e1Xm7Nw8byodUVVMx9WWkquYFHnxJbDPBR8GDqpwpjRffrp//E3zpqlSti86VUcNI1giPVnGMZxakON0E8CljIy3HhB0LFtYmi9dO6O9VZb9GwAkP8bpYOjULqipM4rwvQ8AapO3lw0AAMAhniT/nLEulhK9kCrO9I676DNrOceajkfxgMiXp9XE/Dsv00OrigKlO2O/Vt39gQOv0aTq/GB61u/E3NjlM62m97NZcZNGolktMu1Hstwk4b/BQet6rczOIKfJsP+DtoxN2V08tZq+OqvO6Wa4Ur6FN52YfcOeVWN0W5E5bPZ43QBmunHuRCxQk+VFOJaqcB5Ula3e+MIAAAhKAgAAJHCnpB0BNatFCzakWBQICe8wKsk0p7OHS8OarSE/m2RQk5tp2p36Tidd8bhUMwpz6aia6AHGRFTnRA/ceBLouJQpmJbqQDlKq5Rmpmt+rdWfemKcsRr1sLKtk/OzI3aQybJPy1Yq14m9xT17aGRmrqEJeVlUouM4YBuFx5T0qzPxmGYWT5TtSPbFffKEMmWOsXKP9+lJaHxsmRRZFJQ269wuS+r1n7g5xVrG87ZGpGtT0+soWXAMlP24CgAQiZpnBwAAAGBYwViJyHgXy7EudBLtVL5seg2d3VBBZjh8LIuuzMTOznMbKnAHsgVBf17nwZ3SHknKLpkxxpW++WkT7wYBj4Wfxehy9UTIlgb9zjHpOAn6yyeDeeyI7SR8xAqLPNnRuesxOfu6WkNWdcLVEhJ8o5Xr/LX7fkfP3/lT8dOvJsecTKEpBftuSsuNU4Ui0aBVeZY5WfDH1JaM+120tcXjzzlFpYDJgeWFlgf9rChhXODA0Bx6LCrTPhwCyiabz+7rHAAALSy/4lq8eDF1dHTEfM3atWt1vT7S+wAAAFTk5E3YRVGCBN6woKQVd4pX6+w8itWEdH97TbzeSktJoVxF7+yWpVNIy+x8DrQ97piSRDS7OI9WtnTumz85w8wxJe0oI/b75Vvivo5Ll8o+VmL4+ldtv5KdT/JOsvklefTi1iYqzvBSS/8gyUruvUhdvN5lWXecMbQi+FjkGd95f8OciSJ49sr2ZnKyLOphlUW0eHdr1L9zttzAyIgpbfroyUepo3EP5ZdX0LFfvWl0+haM/x0voS3RI9hR1cX02IZdZAWfjVVFtNL7lc9B28befquaE2fe+26cs+pUxazS/HqmwpVKVrd1kZsVpnupbcD872w5z1Qik/W8CgDAkaDkiy++KB5WvR4AAEBFsS4auIPpDyvid+5bJU1XGVsJLn48co1LlJmaSn3Dw5GnPfbTiaUmeRwohMfp5eAfT9ETOVBvlNZdrEJkU4QGxzw2bNenTCijVa3do/PTOMPcsDKjkeZ2TkMllWZ66b5V20hms4tyNQclQR6yZAmbhQNOF06por+u3k6qC66qIMN3EZ/nDEVpiCzrn502sSw0KBlBnqSZvZ4IZbPXtY9+r6jG7E1Cok3MdinkoRHcUmE7CQ6743nMr3JgNCgZ/LXA58L/2dpEsgalg9dpvO9Vs8vlAwCYwV0pAAAAACbQc9pu9LLC42AHU7S2zyzK1TwNpy9yZOzXiVWWyYz2nlBXasJURgNEZrp6Zh2pIO4WG3RlzwHmk+rKTFvmes0vzY85vqlVe99+pfmW7FvTC3NE+d6q7NiZ0nYfVcL7h5w+rmklWyvrcrMcOVbLPa5a4njfr4hRltMXVL7c7aYV5DhSRtCqQLhHY/BGhgCp1jZM0biOZAhKj+f8cpaVmUMjsKNNHlfcSjOKzDvuBJNhv9bqrEnmlJC3crgB1cYUNVKeNtoSK8qQ8+YUAIBYEJQEAICkp0oHtB0umlJNZUGli5gT185WzjLeuEN2bw0LdI4twyXQZFQatt3Iy6frrwvK8i0bV8jJThEn98VLp1fTt+dPIhVMLcihUyeWa359rI552TrgrAxoaUpIjvYqOaMWEKc0oFaJnFPkmnizllM3mbjZcbWlMTvFfWOZksyrUl1yoIagMTbtYvcWEnxMCv72KZQ0C9lO/nFrwX71Y8dMLfsGl64FAFCNJd+yxxxzDFVWmnv3fTyzZs2ydX4AAABOcKKrNrh/2Gegk50zMJc0d0S8yOJstBe3NZneQRGp89PJ7m63drVHK0Vk5hhSZtET7zA7IF+bk0nbu/tMnf8Z9eXU0jdIbQNDpMq2KraXGB9Opi7rL04evaZ5fktjxL+P+LSX5Uq2G2CijR9q1frVur9+Z0ED/eLTjSbNU6at1bnj5XG1JWIsxnf37BtT0BPtPMLk3cBfMt2KoD9nCa1tU7PkqGz2L8unuSW51D8k1zmBE9x91AA3SbZtVaWzNNy/BQCqsyQoeeyxx4oHAACAEiS/AtmvJJ+W7e0gGenpZOeytNfOnhDxb5yNFisoaeYqcjLT0CNpxlQsWvrcr5pZG/H3aSkeGojQ/8jbAY/TePeKrWS3SNuSx6ZAA2eT6glKajG7OE/8fGZz5KAZuFfwOKFOd07NKc6j9qDAOO9BZ06qoCc27h732rhNTXD/89g8npPTIq1zsz9ZKo+nG3mI5ND5ekbH83JCRVY6benqtWTafDMVP97f3UYyUOncgSJ8r4oAssNBydLMdGruG3DlMnYKj3+9p7ef3MDouPJOkf2mJ5d87ZpC65pSdFMEANAMufgAAABhZLtuOmViGR1dU2JL21S+aNTakXRMTTFNiDH2mZXcen3J4wTqLUma602TZh2kB5Wn4r8fW1siSluaWbrrsuk1tL8JY5mFZB+5dIvS+qmyUlOj3uggY2kzKzq7Obgvg9Pry8etwzxvgmWPJemJUzEL0qwlx1mPrMDAMXB2ceySrmYtXgVXkyO0LqbphbmiHKuZJNmlhRGZGuMSkfZBWfdLp849QQ6xxmlnMh0dZGoLAIAVEJQEAABQoLNS0mt7qe8on5QX+cKTA1D7lY5ml0XiVH/VpdNqnJmxAhaZEMzTcxd8dloqHVZVZOp+Xp6VYf7e4jPW8ay63PRUU8bcNLJe7D4CcsZ5sHMaKuikCWWO32iQSIaJzN8favDRnOJcKkj3Uk1OhqlTThurKOBRoPPfqu9spzI/4/FGqfZwfG1poNy0EVMLsqk629ztyUp1Om8y01ItweptK9mOfXoXo5WXWKWZ+s8ZkmttOb+tyhrQlm2/Cb4x0f9vLDsAUAmCkgAAAGGM9kGcNKGU6vOyLbkwwF2T2i+YL5xaFX05OrQgY20SVl+D+xQqbegxkI1mlExJFBHXhATrx2qaP6FE68ouwUFY/vgckFpQmh/4nVmB9GhZjlym1SxmZfoanYrKGceFGV762pwJlJk2ur5UODqIkrCiI8Kc1vosODTeNLeevjKzjlSyf3kBTS3IMWVaNbmZZDWzVpfec5aIZY9V2HEk5lGoJGr4ONDhIv1ZntaHUn2z9Ui4/u2g9ZxDpv0GAMAKct7+BwAAoLBJ+dniMRTjysfODhAr73K1qzM34TKAkn2m4DKhqkrzeGjI4gtlK/cPXOLrg85aeQVvy5ljx5bstBTLy7SuaOmM+Ld4AdErZtSKb6M3drTEHNMN+6g2kZZTfV4WtfQPRg1ot0b5m532L8sXJTQn5ZtTRt2Kr4scG885QL9JCw+inrYWmlhZSV+oKKTNnT2a34vji/w8jmafaX+t01QqMe7P8OZrUv91lhPLV4pAnwRNAACQgfo9YwAAAJCQSJeyHkmvnswYi89pF02ppvIESiLKojZnNHNiaqE5mRhOcfIuabP7j4I/yhFVRTS9MIcKMpLnnkOrVmXK2IriALxZreLs+QumGC+tGCuz6YIpVSFZk0Y656rH9nczVWZnUEV2Bp01qVy0VRbRbtyZX2JeVig7dmxsaKtwdt/xdaXSly7l0rCHVBaZHnhIdHrOn+Hoo04YIvq2NSU/mw6tKkp4Ghf89E664q4H6KUnHqP0VL2Zks6vcYViSa6jZf3nh5WelmV1Ob/lJu7QysLAeYDsyzAzVY6bUlRe3wAAWiAoCQAAoACPpJl/do01MlbtTbOq7MyQ7CEZ1MfJCrG6k8ruThVZL6ZL4ownZLTdscb0srIvlMs4ntNQaXr5XZnHvrKqrO+kvCw6uqaE9i/XHuCLh4PGVgT6gjXkc9lw4+vrtoWT4+4nRjr7udQotzWaRD+BmZs+L4MDKwppZlGuKeOvLiwrENMzNaPHNz67zx9QN5oNe0JtiTLfB7J+14Q7f3JVUn3eaHgbPW9KVULj1sq2/CJNS8txXt5vVfMkw2dMBmZ9r/rHKQ6ZNtkv/veqj+aXmntDEgAARCZPTx0AAADYciFmpEMmvNNZ1rG4Lp9RE3dcyWh8Dl3kW33zvtHJp41Fhr06IsQSJCRELGH4zf0mWTb9gigZRsHrXOZAn52iHT+0bjZWLUUO7B1cURixE238ay1qRBJL9LCR6H7F26E/EzzcWZMq6JyGCkomE3OzNG37wZw8phmds10tn1yQLdV3QPbYOKRWCT42WnWuGLwUtS7RuSaOi2u1CbnmlDi2/rsq/sRPmVCmxk0MEp63alnKNRbd9GTlZhNtm0z4xiRFMqWNVHZRqWQvAEA8CEoCAAAY4bHmbeVZGTQ7qOPEyCWU0c4vdS7fxitI15/tA5HV5WbRiXWlmkvpynbZfF5QBmPsuKr1W7yRDtpcE8c6m5QXPVsNFKDywRmUO4YaYWU/sJ4bZZJZ+FLK0RiUdNvSPbKmmPYrybctCGLExdOqY/7dXxnhwHJ9WdhWiBV05N1/0lh2fHBw3onjRaQbx2TdxvWUkD9tYhldO3sCJTMtm00im1aVjpKzWm4kMLJ5B1exUCi+CgAQEYKSAAAAQeX1yjLTpfhyvGpmLZ1RX04yCL7oyQu7mJfhbn+tgoO8sdh9kefvqJF9LEDPWAlCs8uDgj6HjI3Lw3w2ZMcYWdv+Msp6YQuzhhllSGNR9Q5+lb7Hghn9quKbn6LRuyoTudFCz/YS7ZUXTqmiogRKDQczdNOXx96sRI8DmT6aOvojtMlfXcEM0wpCx7L+8zWX0G/OO4mOPfZYstLs4tyYY8zpXRNmlPCeEXQc53Gkj64uoaNqiuO+z+rNZr844xnzWKKjpcHTRWZ6vCBmsFlF5n138bjLXJ49WHqUzPD6vCxxXahn/lzyOxqenh5fnVUX8jzWKuTsdq4EYvZNfGarypF/TMlgHOg9tlb7WM01Gj6f9qEHfJq3L/9NuDINWwIAEA+OWAAAAGMm5GXRV2bVSdmxKkOLuPNvRmGOVOVbZVguweJ1CERaXtypdOuCBsr1jnbYaM1esFt4y+eVxA7yOpnda4TRjjstbdf6+Txx7pzX29Zvz2+gy6ePlja2Q21uhiuDQbLQe/y1ugxprGCJHd+rTmxNVs7T6m/XSXHGOTaT0XMFX5ybesC8ZWrW9KIF8hJRnhU6/mTz1k3UuHE9rV271pTpHxtlDNUZRbnivMysAKsZN3UFBzR4fM6DKwvFdC+eWi3KTUejZ9YVMW5YMGNrunR6Tcwg5kjYd4kZY+UGqwz7fHW5mRGPVVlpqeK6MPyGzFhKolwH8LoJDihrUZDhTWBdaF9H0bbHKQU55NVZwvuGORPH/S687dfMmkDH1pSIYOt1JmV1cql9Py1bCQeZp4bd5BBuUVmBqCRSnZMhruti7f9aj3PzS/Ppy9NGz7+1LttI5VujTz+PLpteE/Nmo3BzFCplDQDuhKAkAAAkPVQ/0bZ8eLySaB3LCC2MLasENyatHfZm3umOdSZn53uDhZ3s3LFiNDj0RQvvoLfqmHy8jrvcVeKz8P2ylQXLMjHAEc7pm2vc0G6VbzDwWDgemxXsvnFOy9xkvJlPDztuBvv6vHpLs1z5xspYWXp6zkGunFlLTvIksO6MfEcEb7+8hk6rLzdlWQYLnp6RcsHhWZ43z6sX21YizNwctQRuizNHA3ycLVtoQlbnIRVF45ZHPBxfjHUe6xvL6r1wapW2ccUjvD+SI6qKKTdd3za6qCx29jG7fHotnTqxXGzDZmRiAwDYCUFJAAAAl9F6jWlmJ5I8XaPGmVVmzW38W4t/s+HOjIumVNMlccY8Uo3T27KVZbMSEX6YCL7DnEuwyRpU4Tv9D6+KX9JulInHQg0fSaXue8VjDUqTbdF/aWp1zH3Z6SCpkW11zliZTrvpyWpxagFpPqeMNQ2nv1htFG05ZAadW7rvuGruB+LKIVpZcUPBxLysmFmnRvlMzPLka5bgbSs6dTY6j4U3vTi5FPR+julFo+WZY21HXA43XuWa6O0BAHAWgpIAAABJmh2glb+j0X2dKJE1GChpp3UZxXpZ/ti4IFwuN7wzz8x14DPh9fX5WVSXa18JQPN4pJ2j2WXKEu8Ei/+OAT21pSwKhkRTkJ5GhZKP0+oErR24RjdDzggyNbNbQ3vc9h0Vd/HZHOgpMTh2o5PCx4zz2bwYo+0LiXQm2z2mpBnUa3HysmLzsmqTPaehkk6vLzd1mlbuXlaVsZaVLwnbpCmz3OI2AACoAkFJAAAA0HQF57HxAiuRzmUz2sDjfNhRhswXJ1OOA5IYK8sZMnaiRON0ZtKJdfaURVUpuCj79vNljWOKRuqYzQvLXok3hq4qHcVO3FwUPMdZRbl0YPm+cbFUZPUyNJqFV5ltY0aiAQvLCkg2HgmOfVqmbdapG0/n5AlldEhlkeltNXp+Kfv3i1Wife7C9DTTxvu0dVslOVom6408jo4rb8P7fZIsh2Q9ngCAPBCUBAAAsAhfKJ8dp/yPFZcbTlzKOXlhE+/znttQKcqMqiLHmxo1IKml81Vr573R7UTWzgynxepEUKsDIP4Krs21ZvwaMzYttZZ1YuxOmLph7sSYQUpVx5jTEtzXegPAsRrHtwqeWn3evmzz4HLYzpaZM5fP4WNv+BhiWscBg+Q0vzSfjqzWWv5bf5ZrMlRZsfsTyvKVg2PJPpKsEl1tUikR3ekbEwEAjEJQEgAAwEIzimKPU6TC5YRP8S7SaYU5osyoTMvSY6Cjxe5txqPwhXtCTTT4wczsJEgksCNjJ5Be4UswGTpwnWBpZpMKBwgLHFhhXsZjvCVo9xJOi3E8krFz9NvzJwX+nZEavdvDk0QBk2A+G/d3O7KPkoGEm5El6y+R/UWWrxyPS9Z/IICu6kYnMbMWqSzbPABAohCUBAAAcBmfRBdYN86dSF+bPcHm1riL0xedql7zHl9bSokMeehTKfvIZ2Vb5M/oiBu08Zee9sjd1m/PbwhZzodXmZ+hY4XTJpaRCoozvAmXYvSLth8kum2Fv02GbTSezLRUunSatvK/ZotdEjPy79PCxpGMNj3+twKLX4plfURV8bjxOSFUco4pp+qZolqfROa2WdleM296UW0ZWsl9xyEAUI2UZ5RdXV3U0dFBAwMDTjcFAAAgqS4IInXuBbL7EmhsrjeNCmwcdwzU6Ny24zPwGGLJmq2lhZElw2Wpr5xRS6p8xhSPR4zVagWjm+rX59WLUt+JdLo5vXXLdmyPFjjkYFqsUoxOBN7NWHc+B46/0Uo2OzoGmAkLwul9KViVReNfenRkvkYztTA74liyKpVrjkfm0waJm+b4Som1Cdbl2lctxeeSxeroPq3w4WRiUDl2AACQPCjZ1tZG999/P1177bV02GGH0ezZs2nRokV0wAEH0NNPPx3xPTt27KClS5eKR1NTk+1tBgAAd1HpAtIJ+8bAgWSk8nofGftp55ii7hlTMnZZ6gqLOs71bHMxX+fz0fDYsSvV46HJUcZpNcpnsK0cMAMNy8/CA5HTpUd9Dh179y8rMHUZOp1lbuXyMvrp9AS6Lo9xw4eZ+8G5DZHHPPfE+Ry+CGOh2n0D0DFX30infP02+v73v08yqc2xZpzlYGbtaadMMDPT3dqjldHvySOqtGfKy34zm1nrn6uJqDL+KVc7MIvZnyb8mFyela77PQAAySrNqRkPDQ3RXXfdRQ888AD19PToeu/69evpq1/9qvj3UUcdRffee69FrQQAAAA/O6+hcL0mL39JVM4+k73jZmSsU6U6J4O2dvWSrCbkZknYvtH168RqNiuLzL/+5d5SIZzZXcIyrn9PtFKiPj3liY0tqVjjLJqdLSNbP3/wpzLyCcPfy4G6zZ2yHcvNzXT2B0U4Y/7Xn20mO6SnhC7pA8+5UPz86oIG2tXTL81BojjTS3v7BuPPzjM+6NLSr+F9Ju5rPN76f7YmdoN/aWY6NfcFVzWzdgc/uqaYPmlqV+b4Ho3RpWTmZ+VtUGyHkh2bI7lkWg39bnn8Y42Wr6v4H9eaBRK8rGX5PpSkGQCQxFKcKs962WWX0d133607IMmOPPJImjp1qvj3m2++SZs323MyDAAAAPbAhZKBZTe28A6tLKLZxXmmL9N92WfxXxvWj2grbmYiAVQntr2Lp+nJ5PSY0latY0oa4WTHS1ZaamD+nCkZi5mB10n5xkp3XT2zjuwSb7lEc82sCfQVG9tpdFvSEnyLO3+LjwyydqzHC3z6xxfkbBqnPkMiayZ8TMl4tH66gysKdbdFuTEaffHH7Bz/HmP7T7Tym1qD5pfE+451+KTTm5JCDRqy+WUZM57L49t5/OTlo4pEv1dVuTayIxs4XI5XW6asLME+t58XAACYxfZv9+HhYfr6179OH3/8ccjJ5MyZM+ncc8+ladOmaZrOqaeeGrhQ+s9//mNZewEAAGTg73gxZ+wkn66Ot31jSnqiXnxzAMANZL8A9Om8Y19LGSG9hsYifakaIo76gm3m82/rdgZHnS4FaXb7nNgnjM5zv5J8EZT3B9B5/Vs1PlL4VBeW5kd8ndalXqpjnzWaKVeQnljRHD62lFlwbInm6OqSmH+PtBg40HPt7AmBrB5ZZael0ozCnMD4fkM+H3111mjAN94WK8ORZsJYCU/Zj3tOi3X44e303IbRMW+H/XfSOChuhu7YT74x6cz6ClvGnzM6dQ5q5nm1He+c2pbPn1xJx8UpqaklQG72qghfGidNKKOTJ5RaegONWR8hPz2NDqnUXrrVDJkxboLxmLD8nS6nWiLx92m462ZPoFsXNNB3FzQoc53HJtowBioHl1VYFgDgbrYHJZ944gl66623As8XLFhATz31lHjcfvvtNG/ePE3TOf744wP/fu211yxpKwAAQDyJntDr7fKQdYwTDkZyCa/jamN3Glu2XHFFZf4yjcMf6PF3osvaeVGU4Q1kSoa3NFbTZdjV7GqC3s5L3tdl+vzRXjedgzwpnsC6tLPUsNWd8xBr2Yf+m48BF0ypoiOriy1bbEbX9pemVovvUf9NHnzTh7/TWYJDUdztOVoZWjsDParvcZwFNKUgW9xMcVCFeeN8WiVQFjto24h3jmr22u9oaqT2Pbto165dmr+zZd9OeHkeUB5//Tt9A0BRuldkyYbeQOMx9bvQrE/IWf18/C/ISLNl3vGyJLVMO14Q1az1b+Rc96a59XRiXfwAeqIS3XLCFz9/t/J2yOeAp00sF7+zeu/REjSOtJlUjWUfZ6Vymw3MX+N7ZTi/AACwdUxJPlm95557As8POugg+vOf/0zp6fo7rCZPnky5ubmiFOzq1aupv7+fMjL0lZEAAADwfz/ZzX/NoHfOjozvFqeRM4pySTXcAViVnUm7evqUukjzGNhGfCZ3SMpcTuvGuRMp15sWsfM0ntnFxrbnRO9iP2csU0aPRDqngtun59DH2WZ693Wn43P++XOAUnac0dExMETJiDthedzXxzfsTmhbivU3LSURjezXZh1X/Td5+G/6sIKTe4GRT1WeFfsaf181B1IWd5qfMrGMVBC8Lp1a5nd/+WzqaNxDD9fU0Aer18cM+vsP/0a2QTsDgZw93TM0TLKItYqvnzOR7lqxxbGz53jbn/+7vyDdSweWF9KHjW1xp+nUdQBnoJZkeEX2+bu7WwO/90h6I8XCsgL6qLFd01iodon19am1coyV6z/Wurx8Ri11DgyRN8VDT27aY2ErAADkYWtv0qpVq2jHjh3i3xyI/MUvfjEuIKm104hf5y/1OjQ0ROvWrbOgxQAAANEdVllEZ04avfMynrIoGWOJXvycNamCTpmQWAeW06V/ZMB3z14+o4ZUNtuhYPDEvCw6vKqYDrQwoyPhu6TDnh9WVSQ6+PSYW7JvHE47cXYfP/R8fr2fzW6Jxlc8Jr1uUVk+HVNTQpPGSkw6yRPneuey6TXioYcdnacqfFtE2s5kyHi2e9xLu2kth+kkPvc6usZ4JQens9NkEf6d49/PnMwQjzVrPl+JNh6lk2Kdhxsdr9fsY5/PhLH+9LJiawq+N0nG/XlBaX6gHLYdtO6ysV7mLzXuJk6eO+Slp1Fm0Hjo8Vg7rjwAgPVsvZIIHkfyqKOOospK/XeEBysp2XeBsWfPHpozZ46h6QEAgJom5GWKn5PyzMmI0OpwjSXheNyV3LALd385Qc1lp8JeP9NAQErGi3FZyH6RFrzmonW0WX1BzdsuB/tkFP7ReT8xsq9YF5SK/Votq5DH9ONsUDfu/z7DGehjJYZTUuigikLN73MSr0u961P2NWl3+4LX44KyyGN7JirafmPWtjOnJI/6h0fEjQlt/c5nzGr5XNwh/czmRl3vsQuPKcq+IkmnuUpBck+MUpjcaR75JgDnPmC0WYcfT2XaPsMFnxNYFeizlsxLd5TmLVSyfVWy5oSwa6xuze/zxJ+OU1uq/HsIAIDLg5JNTU2Bf8+dO9fw9Lh8q19nZ6fh6QEAgJq4LM9tCydbVhrMqNBxV5y7OEk0GCHDBbGW0n0ql26zSrIuEtmzgc3ov81MtbfjUobjgMp8CXbgqxTMkMXs4jyRIasSHovswLEAuuYxoRzeNtLDsjtl2lRvmDtR+e+UaMvTqeWclZaScPlWe7K6QxvztTkTEmpDpP0qavlmkz5Ydc7ozZUy0jYmt0x7v3rniImU73cKV6BQAd/gE/4dJeuWytWPgqlyDgAAoFRQMjhwmJeXZ/hum+7u7sC/ExmXEgAA4MoZtVSY4bV9Qfi/7qzMWtqvJF+M07W3T/t4H05epnPJqj9/vi3h98e7OPMk2UUcf14ZPmq+N42KM63Zx8I/n1zdTKGcLG8XjZYmGWm21dufL4EORxn2CYBY7PqOMuOIJOv+pEo5XDszDLWu73gt4jHPAq+VaAMIP5/mQL+swpt2XK06N1EEl0GVdwkTlUQZNkNG/tL9suPz+Ypsa2/kNUuiVVJ8ErR1X1lsBxoDAODWoGR+/r67arq6ugxPb/fu3YF/FxXJWUYMAADk5vTF1YiFVz8nTygVgZDXtu9VotOMy1FaebczZ87kp6dRUYb842BFo+f6VJa+wutNzlhR4TPHE6+jweOyjIJzbc4CkL1ErQhQy9SbbxGzxgZ1M0+CWTVPbNwt+VYeyo7NXbbsp2CJdC5He4vT5QfPn1xJW7r6SDUeA98RZn+nhK9DMwOoZowVGKsscq3EWZ3Brp5Zm9D6l/38wU2cOA2S91tivCQ4TQSAJGdrr1zwGJDr1683NC0Oaq5atSrw3Oj4lBBZY2MjLV68mJYuXUqrV68WJXh52XPpXA4Ez5o1iw444AA68cQTKTPT2Alqb28vvf766/TSSy/R1q1bxTihPM2KigqaPXs2nX766TR//nzbV5Ud7XLLPABAf+eZI9cbLr3IidcRw+P0HKlxHNBYHTJfmlpND6zZTk6tutKxrMNEgrh2dvT6kjyYMTE3i7Z09Vq27Ix+djvjYuFj6jrJI1lZXEN8ck/aZ+O8kingJVNWjTxLxVpWBSpkzKCPpyDDS/PGKowo2Py4fIbGsHN+gRj9Xo/19oVl+cpss+HtdNP3kZ2CF6Mblo0Kn8HILqbG3gkA4EBQcr/99gv8mwNdAwMDCZddfeyxx2hoaEj8u6ysjCZNmkTJhIODW7ZsCTw2b94sfu7cuTOwXNitt95KF1xwga5pt7a20nPPPUdPP/00rVixImKGTEdHh5jXypUr6V//+hfdfvvtdM0119Dll19OKSn6y+W88cYb9L3vfY+am5vH/Y3n8+mnn9LDDz9MBx98MP3sZz+jqqoqsoMd7XLLPABAG19Y+SMrsxBV6TiIJDtNzsBAWoqHqnISz65NZG3X5mbSuvbukLGHvrHfpKjl6YJXuznl+VS4hB8l0xbPZaHDg5JmLkutU+IgW9/wsEOZS84w0jF8VVh2RbKToZNdZlbsRgp/dYNOsc4BzR5T0u3bVfCx6pv7je8b8llwXAtffSqdLyXCrk+n/cY0j9LXOsku2qpL9nUafFxBVi8AuFmK3UHJwsJC8W8Ojvz9739P6Ato27ZtdO+99waeH3744ZRM/vCHP9Bhhx1GF198Mf3P//yPWBac/caZjBws7OnpCTwGB7WPI+Z3/fXX009+8hNavny55s7ytrY2+vnPf05XXHGFmK8e9913H1177bURA2bh3n//fTrzzDNp7dq1ZDU72uWWeQCAfoHxDZ1YeDqv9Zxo4+n15XT2pIrA82S+PD1rUjldNr1G83hZbij3g0CIeW6ZZ6x8rpF9L+HOc4Ovi92JExuXeI7lq7PqKBHBbTXreGZlB3gyH3MjsTPYoPUY7lQA5PjaUjqgvMCReavKDd/Lkewb+8xDWWmpdFxtaczX2SVdkTFF2UEVo/1jspDh2K89q9Fn/piqEu+rRtqm970SLwZLl5XbbywIluSxXwCQgK1na5xB96UvfSnw/M4776R3331X1zQ2bNhAX/nKV0QQzH8CfOWVV1IysXpsr/DpT5gwgS677DIR3HrhhRfogw8+EGVAf//739ORRx45Lqh1ww030MjIiKZ5/ec//xHbQbDTTjuNHnroIZFN+8orr9CvfvUrmjFjRuDv7e3tIiuTs0WtYke73DIPANDHE3YTjswXv07izq0ZRbm63uPWa6u0lJS4gZJkDRBi91GjQ0LvNiTzei3JTKzKi8yfKZZk6qBLhJNHx4PKnQlm7F9eEDX4pCqnzsVi3ZDt9NiRifQfyBysNjKmpFWqsjPEeOdgPc3VJdLUCWjbbU6xvuuyZGTGeXVoxRvrvgnQBwEATrO9h+uqq64SpVc5a6u/v5+++tWvioAXP8rLy6O+j0tM/uMf/xAlJoMz8c444wyaOnUqJRO+eCktLaWJEycGHhw4rK+vF0FDDh6a4cADDxQB36OOOmrcBRNnvNbU1NAJJ5wgyrzedtttNDxWGuydd96hf//733TeeefFnH5nZyf98Ic/DAmCcoZm+Pv4s/F8vv71r9Nrr70W2B5++ctf0i9+8QtTPqvd7XLLPABAzkxJHm9qR3ffuN8HDgcuu1vW0axTiamQjWXVjVqROgXqcrOoJieT5pXkkUzOCsoItmX9yrs6Q8ZuNfJRYnXiyBBiN2sVHGxhho0Cm4mpn9WnWKfmbQsnk0pkWr6J0NoxrPsGjATKt8o8pqIZ09TiqnseopHhIbpuXoOj2XfJth9wENWO71Mnv6cXlOZLG7hyMrONl8uR1cXKBMUKEr2Z06fOtqr68QQAwPbbgHJzc+nuu++mzMxM8ZzLi/75z38WGXdcNpKzt/yef/55uvnmm+mUU06ho48+WgTbggOS06ZNE0GXZMPlVTnwx+Vv77jjDlGak5fRrFmzKCsry/D0OUh11113iWw6Xu7xSuryevvmN78Z8rs//elPcefD652z8vw4WBYtkJmRkSECZMFjHT777LOWlBm1o11umQcAJG7fodX8S4oDywvo1gUNCV3ERnqt1z8AJgS4LUtEVSM6dh8uefvl6TWGs04XlpmbCTJTZ0ZwPFoXSbzjgtHOIiOVPbj03tf3q6fphYktGyPlW1WSacPYu3ZkSuMbJhSyF9zPLWXznNpWy+obqGLyNJo+fbr5E/fZELy2cP1Hv1nH2PvPbag0/XzFKpFu2tD6+VNs2DmD95v6vGyyg96P5VF4rMeLplRTTe5of7Ne1TmJvc+q8s0y3xgKAGCUI7UJeGxJDkz6x5dkXO6Tx0TcsWNH4Hfvvfcevfjii6Jka7jZs2eLwFd2tj1f4jKx+mSAx4Y87rjjdL3ny1/+MhUX77tzasuWLRHXm9/Q0JDIfPXzer10yy23xA1oX3fddSHbzCOPPEJmsqNdbpkHACTGZyCooud7Qut3RQp5RFbS/jECLRdOqaJTJ0avZiALOy+V7S5RlkhgwKdw+VatgcORBD+lkU90Yp17AtJaOpSd6oPKTLU+4OZn90f0aLzDP8slZeTCs2vC+SQKynGlgVjmFpubaS17H68nydqwKMq5kNaO4WivC1/PSgaeZdgYLGq+KqvDyXZm2jlOp+wHxgRF2u8vmlqV0PunFuTo2kacGFPSqYBaQUbiNx+eYOE5vn+zDt66Ub4ZAJKZY1e6hx56KD3zzDN0zDHH6AqycXDlkksuEVmClZWVlrYRSNd6OeCAA0J+t3nz5qiv/+ijj0Ky+Dgjk0vSxnPqqaeGZINyuVEzx9i0o11umQcAGOO/E9fOPSzS1+035tfTzfMmxnxfYYZXupKXBeleS0oT8Z3gbsLrTkXTCnPo8um1cV8XLajv0bD/nR1WNpXn95WZdaS6aJ/d7q9zM26ic2e3pLZxmy6bXmP7HftW4dLJdki4JJ6OcsqnTiyjIkWPq044emy8PCuvJ8ycck1O7AC6nVQ4/mnf52KvpW/sN4ncQGsQxsqbvaKeA5BzrpwR/3zODWOee2yMuZZlJTa+tQo8Dr4/zYbqQGbti0bPRVwa+wcAhTh6+21FRQXdc889okwrj104Z84cSksbf1dLamqqyK7kLK5XX32Vvv/97wfKv4I8ioqKQp63tbVFfe0bb7wR8vywww7TNA/O5ps/f37geVNTEy1fvlx3W51sl1vmAQDG+K957Lzz2N8nOCFvXwexNyWF0lJSlLlL3K800/xO4ZMmlFF9vgmd5xZc5CV6tzFn/Zzmz3LV2ClsVt+xnuncOHfiuAvlKg2dw0Y6umeElSHj+VndySPFfT4at8/xmT32N96KOarQB8MBXbcEJGXdToKzTHyybaM2baTR5iM6+k0aKzBtbCZ2lESUQbQgSfjhU8vi8Lk8gBNeXl2PpS88Qx89+ai4Ud0MwUtGhq/pWO30b0vnT9aeYee0ijjZ8macG8uwdVu97fBxI948PBIeN74+rz7ytGVYaSYz4yNpOd3O96bhBikAUJ6xQXVMMnnyZPrud78r/j0wMEAtLS0ioMWdH1zitaSkhNLT3XsnkFtwACtYfn70QcI///zzkOcLFizQPJ9FixaJ0r7B05o3b56utjrZLrfMAwCMXaxwaULOyGjIt78M+XG1JXRIZewxLMA921pppvznULnetIQu6kcsaY27jMtY9GnrEHJyTEkrO3/8rTqnoZKe2Lg78PtTJpTR9u4+09oG9tPTefrFyZX0zq5WemtXSwLzIfffvGCS+aX5NDAyQlMKEj/XcdHiUJLHhOCE2eNwv/i7n1NH4x76sKaGPjjjHFOnnRmpbLbJG6EZxxB/mW+z5+nCOJEreRQ4jvqPGdHGvzZ8nqnot4MZ+1iqCRmdbjrXAAA1SRGUDMbBRy7LitKsauExDJcuXRryu7q66CXQgseb5M6y+vrId09FEv7aWGNX6mVHu9wyDwAwbmZYppZdOGMhPAgEJrJinFAzLmHHIlA8hih3Esd5mVmzs9QIrqhty66xekzz2G0w/7NyFjEH7Jv7BsTz/UrzxUMFPKZtjQKZlKVZXnEDztyS3Jjrz64ta0pBDlUazIYO3w+SvQPfo6EU3iGVodV0ZMDl2j9u2jfUhczLtio7k3b19EXNQlWB1nGiE2HO2Hej+EZBI+MZW52Nyu2Mt9qTNc4Q73O7YbnwKa9Hoc+jatDQCnqODJoy6bFoAcAFHC3fCu7x3//+l/bu3Rt4Xl5eTtOnT4/42o6OjpDX8liHejJhq6pCS5Vs2rQpoTY70S63zAMAEodrCPfhEjoc6JOez0e3LZxMX7AwS1Zvh5zRi+poY0q6SZkkma56Mx/1BDFVyOaNxomwAGcdabmphfd3J3Hn/tf3q6fyLDnG6TtvciUdXl1seDo+l5RvVYUV47QdX1dK182eoLstdm7L/m3r8hk1NC2o3LDf1+bEHg8c9It6JpdE+6SeY5qTNyuBvvXnxjUlc0lqAACIz/YeNCfGowFrccndO++8M+R3Z599dtST1Pb20LtSy8rKdM2PA56xppcoO9rllnkAgP323RlrzfdoUYY3oXF9nDI8dj6RKkGHyLVzJtDNUcZLkYGdi+hb8yfRjMLRYIkdp3wmVC9yhJ5mzy/No2tm6e88NyqR1Re8P/I5v3dsBcVbT2dPqqBZMYJsuHpwP1nGdFP0kCIF/3mEmx1eVUSHJZD5afR72P/+4O/VHG/iGX3hrBpP+cDywoRKt35lZh2dUR96TeqmfdbI9mDauZWKC87Ax7Hj49oxD5wPAQCAW9heu+2nP/0ptba2iqDVIYccQikqZBZATL/97W9DSnzyOKBXXXVV1Nd3d3eHPM/I0HfHafjre3p6TFlDdrTLLfOIZ/fufWM0RcLHAACQy/5l+VSTk0ElkmYshV/oD42lyHFpOKeJQExwMywcp092vD7MGOdEiyOqimn/8gJyo9qcTNo7VlaUb/IqzvSO68wsCfqdk52nJ9aVBjrJrpldR8v3dtHbY+P0HVpZRHneNCqP0uHt3x8kuLcgYTW5mbSytcvpZigpuAxdzEOjZNuHZM2RpvQsl77kGyjuXbWVVJDo51c1O8yJcq/H1pYkHCTlxzObG+O+1szTKp/OP9h9Shdyqml0dbr8fNTtjI4pKfNRzPBNHGY1RMu8LD6uRs18NW9UDwCA5AlK9vb20nPPPSceFRUVdOaZZ9JZZ51Fkyc7W14IEvPqq6/SX/7yl5Df/eAHP6CCguidhOFBrsxMfWPihL8+PAiXKDva5ZZ5xHPkkUfG/LvX66WZM2fqni4AWHtRVW3CGGXH15bSK9ubyUqnTSynT8bGguIgmIpVGJy8EIy3uOxcnGI5JDg/LkPL46NGnC6p7YS6UjqsqihqEPzy6bVUluW19LNrnc7Csn3nfAXpXqrO2XdzU3pqiimBY6vWpxnTXViaL7KDf7d8c8zXxRrHFZKPnm3vxrkT6ffLt5BdRu+zSWzvCL6BQo+DKwrp/T1tgef+kuh27ze6S5DH+NuFU6ron+t3jb7OZzB72IQbOPhYnJWWQq/t2DfMB8iJv+czI1Qu0bIZqX7+A/bQekhC4Eot2P8BQCWOpinu2bOH7rvvPjrllFPo/PPPp7///e8oFamQ1atX03e+852QzuBzzjmHTj31VEvvLgp/vVWd0Xa0yy3zAAAd+5jLFxZ3eqWY+ClPry8XQUi/iqwMmluSF8iUVK3eQqQSbADhONienx793sGqnAxKi1JtxL9pTc7Ppu8saNC0cGONiepx8f5gxmz5vClSGUXOSgouZ+tElpLsfA4F0mXYbvRMI9ebZuv4tHq+V/2BEyNjexWme+nomtCMupvnTRQPGdejXddGRucSfi7G3ysHVlg3prTsjqopFjfOxCLLUfob+01KeMxQFU8vT6ork3I9qLjsPUm0LKwa0sRpk/KzxU++8VKW9QkA4IpMyZKSyCU8li1bJh533HEHHXPMMSK4ddhhh1FqqnljJRh166230ksvvWTpPDjLkEvbym7btm30la98JSSL7oADDqAf/vCHcd+bnT36JevX19ena97hrw+fXqLsaJdb5hHPm2++Gbd8K2/rAMnInZdP1plTnBexE/CgikJ6bkvj6I0Usi3UGO2RIRgpU2xEhuXhZlrHXL1+zgT69WehmX5Orhqjnf4+ycpvXTGjliqz9ZXT98tKk+daKJ5ImT1aSXRYsqbdUbZpK7scef/3j7/MTqsvo0P6Cumvq7ebtj8cX1dK9flZpo5vyKLddJGsgm/guHhqNW3q7I37nu8ubKA7lmwwrw1RSo2zyQXmXI9baXZxnngYMbMoV4wret+qbYHf8Zjibf2D9Lc1O8gsZgxNcMqEMnp7Vyt1Dg5JfyxeUJZPL25rCjyPVgVDBWY0fVJeNm3qTGyIIrPP3eJ9nHMbKqkgxk10bsNDR7w1NkSBVQ4qLxBVOKweogTXYADgNNu/Pb7xjW+Ikq1PPvkkPfvss+PGnhsYGKAXX3xRPMrKyuj0008XQbpp06aR0/r7+00bvzCaoSF9J41OaGpqoiuvvFL89Js1axbdc889msYvDA9y8XLVI/z1VgUlrWiXW+YRT2VlZdzyrQAARnC2JD9UJXN/i8xtCyZLM7lkMZcTbukfJDcsn2yFgmCqSDQg6S+H6cQ4fYkEhk+rL6eNHT1iHDi97w5//WXTa2h3Tz+9tM3acuCJCCmrqfE9wz7t+ydnSdfnZdOhlcay2jhTOjgo5U1JoYoI2yLv8z1DwyFt0/q5MlJTAjcPqfad44mwj2oZq9fpMSV5HFt+yCAvPY1uW5hcw/CE30jA+0/34LBN89aXbbW2vYc624d0TUzvsXt6YY4ov2wmHquaS8G/YWKpYX/GOR+zgo93ZhLHThOigudNqaQRgxPi47I3xUOfNndE/LtZR7FphTmmTz9W9Y7QeYTPxfpj86FVRTGDkma0gL9j0lNHp4R7NwHAzRy5BZHHj/zWt75Fb7zxBv31r38VgcesrKxxr+Ogl//vnDn50EMPUUuLtXelQGzt7e0iILl1676OkYaGBjGuZF6etgvS8NfpXad794aenObn5+t6v5Ptcss8AABAgWhZGP+FbbR+jv1KcLxPtGTxNbMnkFtcM7vOts05eJw0j4md/h61d1XTxukzhUdfVqfRTCQ/HuM4x6SypdGYEVvSWlp1KGxsRF+cDMeLplZRicZyq0aFzyc4OUPmfeTL02pCnhtpa21uJn1r/qSIQVsZWJ7RoljPd15JGVVV18S9EVbektUee6oAhPzbmr35nIZKU8akD8aZ12YHOheV5YtxXo3u43ycsBp/B/BNJEbLdU/MG9/HaqsEjytTCrLprEkVlKW7ap79BzKPpIfS4gwvXTmj1hU3oQKAezmaZ5+SkkKHHnqoeHR1dYnSqE899RR99NFH4+7OXblypXj8/Oc/p6OOOorOOussOvLII23NuOJ5/+QnP7F0HloyDZ3CpVqvvvpqWrt2beB3tbW19MADD1BxcbHm6RQVFVFhYSG1tbWJ542NjSJDNC1N2+a4c+fOkOf19fWa5+10u9wyDwAAObn/6spn4lIJvhjlEmyHVBbSsr2R76iG5NhWctJSKdOhoRN8NpVvnW9jhvV5kytpTdu+oQ7ciDMaBsICbo4fjTVuP8EvO7C8gJ7cFGPIgyiTrMhKp6tm1tLjG/ZQ+0D0bOkhpeqk6cmVdI7Z2YJagwCJHJ/MqsKHTuRR1z/8VCBDc3uXvqFKjHLrOHYq0rMrcklY/1h9Zh4nvjZ7Ag2M+OjPn+8r6Svr8qnLzSJOwNusofyz4XnreO1FU6rpH+t3jiuV/LKElRLCcUB+TVsXyYZLoJdnpY/bdjl71qpMYQAAvaQp/p2bm0vnnnuueOzYsUMEJ59++mnasmVLyOsGBwfplVdeEQ8OhJ122mkii3LmzJmWtzE9PV08khGX9Lz22mvFuJ9+5eXlIiBZUVGhe3qcXblkyRLx7+HhYTFG5aRJ2u46C98mOPPWLHa0yy3zAABnoCvEWW4Ne146fV/GSfAYZABuYneZQe4ANaMTVGYckItUutinO4vHeJDASMBmRlEu3VaUm9B7y7Pi31Q6POJz5LskWtA4XgAtvNys1BT4zjI6Rl7wmJIgt1hr2oz93pfgawsz0ujo6hJa3dZNu3rsDeS6WUGG/iQJPdtBrjeVGk2KIV4yrZqW7+0UQUmZDiU8LnGeN033+KfjbxDwRF3ARRleEZQzuyrMGfVl1DVYTPestKbEvpnnCrfMqxdlgf9v2SYTpwoAkDgpR5Cvqamh66+/nl5++WX6xz/+QRdccEHEEpJclvLBBx8UWZMcxARrcCD4xhtvpA8//DDwOw4Ic0Cyri60xJdW06dPD3n+6aefan6vP9gWbVpG2NEut8wDAADG80g4fZk6Hmxbjm6NHjvBo95srG5yvsVlRVVSmOGlhgQDr5Ydm6xIJ0tgkvPGsnK5bOIXKoscOS7fPE9/xRO3Hj55bNbLp8cuZ6dFeHlpzvJZWJpvaVDSjnHSVBAroGDXIjZSBjVQvtWj/zhihoMrCykrTV8XoMxbbppZKcgmMns7PKO+nL44eXyZYvk+eYT22d7I6N+s6akpovRuVU6G6RmJfB5klUTPFTxR9hfZv4sAILlIGZQMtnDhQvrRj35E77zzDv36178WJVtTI5SV4sAZmI+z7Hj8zzfffDPwOw4Q81ifRjLouARvsHfffVdzxmZw0IzLlC5YsCDhdjjRLrfMAwAgmmS+3vFFuILkTjSjY8OYqW6s5F1JhlzVHwrSHRw3z4VU2A2NZv9E+4xWB3y+Miuxm/KSITs0UT4zg8MaNyi7vquunlUrxix0og1aOu3Dy5EaHdPVdhrby2OzJtoh7YuT5ROp7HVVdoYoic1kiJ3EagKXSjyproxkl861J6MwJYvUpm1fz1xOnVhOZ0+qsHw+fuHbqp7FGil4ZqW0oPWl3HFLx1jNkyPc+KOpGkGMRWLL0vLJewPlV2Zacx6nZbm6c0sFANBPnh6yOLhs6imnnEL33XcfvfHGG3TYYYc53STX4wvU733ve/Tiiy8GfpednU1/+tOfDJfLPeSQQ8S0/Lgcb3t7e9z3vfDCC2L8Ub9jjjkmYpBa5na5ZR4AANG4ubxYIh/tlIll4u5cM+60N2NMyQl5WfTdBQ2ig1Yml06rNpSBIKNLp+0ri6sqK9eJ0WNFqkOdkBmpKeIBzuF17y87reJxQ7YWH1RRSCqZXZwn/UlHZloq3TSWqWrW+rbykOcv3R4c6LFr3no9+dPv0XnnnUfXXHNNQu+flJdt2ZiSPotvtrJ6NRjJpJpakEP56fZVEkgNiqAaHYsazOXM2vBoPicoCxtv0WrIUAQAGE+pq+k1a9bQz3/+czHu5OLFi51ujuv99Kc/pSeeeCLwPDMzk+69916aP3++KUFmXo9+fX199Mc//jFuFl/4ay688ELDbbG7XW6ZBwDYa25JHs0pzqNpBTlJueiVugNagaZGuzjmMlGXBY0vaae89DSaECWbKDzzix+xtgmrgxRV2fHbyaJmR5F8HUQ8Lt5xtaXkBF6VpQl0EKGTxz307hOc2eXv/I4XJIhGVx+2L3Te7jDWgevx0DE1JREDKLJ+9fJ3lZ2CF8PUgmzHbqCwMu4yNDbuaXCgR1ZrFr9Bjz/+OD3//POB3+lZxLOKc+k7CxrIbmasv0THlNQqPBlV79YQfP41vzSfCi2sgpFo+VYeX9AqMsVGE2lLRZQxkq3+WJHGmI7W/hPqRs9VZapCo2Vblf0GKn9mPwCAXaQ/ivO4kX/729/o7LPPpjPOOEOUDW1qanK6Wa73u9/9jh566KHAc6/XS7///e/pwAMPNG0e1157bUg2H6/n1157LeJrR0ZG6Ic//CFt2rRvUObjjjuO9ttvv5jzWLVqlSg/6n9w5p8M7XLLPABAv9LMxO7M5Kyc0+vLxZgYesl9CaQWlZZlIhe/nH1SnaMt4JbMLp+hPXA7RZEbCa6aWSs6ap3yhYpCOrBcX7aWk53nsncuqS169yffkHDWpIpAp6QTDq8qGjfOW6KBUZlEyjSys4OdM/hVUJGdQZdMq7b8BooFZbHHqTTbUJxMSVmki0CE8TYGB4q9Y98lk/KyDB/7PVHbPP41Mh41wgPoRtp48oQyum7OBDLbBVOqxDINXq56bl7kMQBVOd6wSJ/M7L10emGuqO5x0dSqhKdxZHVx4N9WHkUGh0e3ykSuia0Wvr/kjZWZT/QmOisD6OEun2F8zGUAAD3kO4oT0cDAAL388st03XXX0eGHH0633367CC6FS0lJoYMPPlhkT5522mmOtNWNHnjgAbrrrrsCz9PS0sR4nkcccYSp8yktLaVvf/vbIeNX3njjjfSzn/1MBMf4eU9PD7333nt0xRVXhGRtFhUV0a233hp3Hv5p+B/d3d1StMst8wAA/c5tqHAsEw2s55Fo+m7oJHeDcxoq6Bv7hZbvdZLercKu7mnuUCzVWFKYS8RFzOiQuy8dTMySNKN8bqLbC2+rWjoYG8bGAcuOkX0g0yYbfFOCE+1SKfNZb7BW7/0THHw/0ebA+7A/U1Ly9XDzvImU50019VwnLSVFlNlfpDMQrGXePHbdNbNHx6+ze9F6NAZXuVKC/9pE9v2QK1XwsfWb8ycZaqtVnzN8sny+UjZ2Q6rdZ+Wxx5T0RKzuwWNYJnpDyiGVRXRIRVFIpqxWembnH36iJsExgu3kX56JmlGYk/D4l3qrDNlZehkAgEl11Pnss8/oqaeeEmU42traor6uvr6ezjzzTJE9WVWV+J08KuOMv9bW1nG/HxwcDHnOAaj/+7//G/e66urqkHInwX7729+Oy6L7zne+o7uNX/rSl0KCYtFes3nzZpHFxzhQdv/994sHj2XIz8NlZGSIoGldnTWDU9vVLrfMAwD0jzFUbXN5FEcughEPs4TbFuvZDRW0smXfWMaS94UlhDt4U8OjZxLwKLx9V2Zn0Lr27nGdijKVTQPteLXV52WJjuaNHT26t04Z1nukDl4OnnKHouzlx/0ZkkdVF4dkujDJm24bI5vYAeUFNL/E3qxHP84ofnlbs+ZhAj5v66KqKB39smwKHEC0ghWlID1hY9eFH6ucXKbBAVWulNDUOxAxKC3Levff4KGnUoXT+OaZL06ulLLSQnhAPcPk81Se+v7lBeKRyHtjff9w8JTLL5t5A0UKeWjEhKus4BZNyc+mkyeWUePYvpXQ9DyeiONfJlq6OHi6wfRWKgEAcEVQcvfu3fTMM8/Qk08+SRs3boz6ury8PDr55JPprLPOokWLFlGy44w/znKLh4OU4YFK1tvbq3leHJTUMq9IGa9a/M///A9NmTKF7rjjjpD5RAqYTZ8+nX75y1+Kn1qEf/bcXO1lyaxsl9vmAQAAkEgGwbTCHPEA+0kQx0lY5liW3JQExnaD6G6ZV0+DY9lSTgQauET5bz/bHNg6I5UTjcXSjl4DWZWq4LbK2FoZF6GeNjk1Vi9bVFZAy/d20q6efk3ZR9fONr/Uplb7leTTsr0dpNK5TcJ7zNjbEjnazijKoRUtnZQTlC1qhvSxIEd4xQJVzhVkO0wcXV1C+5WGlvk2A9+UxWaYOLYxl9l1eqxkreuPA+ixqg8k6pb96k2d3uT8bDpvymgCTe5YCVeznDShjBqilJrWes4RfH519cy6hMZ1BwBQMijJAbFXXnlFZEVy6UgOekXCmVxf+MIXREbk8ccfLzK4wJ3OP/98kf357LPP0ksvvURbt24V44ly6djy8nKaPXs2nX766XT00UeL8S31ZN8Gu/jii6VolxvnAQDysvtC3e6sFSc7LKvGOgeCx6lxa6es3gAByMmj4LYXKfsg0l3yMrZVJVxmLP6IataJtvrirVYZ1jvKZVu4bH3yBByibWuJrn98q+5zysQyJYKSZqx3I+F/LgfK5X21mJiXJaoKaFGQ4aUrZ9RSuaLBCaP70pn1FVSape0c/rSJ5fTclsaYr8lNTzVcujMSDshpXf88RiqPadg5OBTzdXrKrNop0mUHlxq2gp6S8JxVPRilD1sTgyctC3SsLy6p++7uVqnOlwAAHAtKPvLII6KcaKyx/SZPniwyIrlEa0VFha3tU8Ubb7xhqHOQx+OM5p133jGl41FvcIvHQOSxDfnBhoaGRGDayB3GH330UeDfxcXFdNFFF+mehhXtcus8AACSyTf3Cx3P5uKp1VSenU4f7Gk3fV6JfC2blfOS8Lhr0nQj28t/B3ddbibJSOZOcK1bDJetilS2C3Fzd24n8dpiy3qXaYGYTIVrAZ8kqybetibnopSyUcrSc27DpXtjT8ta+5flU8/QcEhQYt+8x8+9IuxGu9HXJYfgMXW1lDqOF5SUZcgQLnf7++VbbJ+3k9uNx4aqEr9cGr3Kn0y4JHuk/R8AICmDkqtWrYoYkCwoKKBTTjmFzjnnHJo3b57dzVJOdna2ktPWg7P4jODA6ieffBJ4zsG4rKwsx9uVTPMAgOTk1JiSiXQG8hgiFdmJ3RWeHnZX7YSwUjrc4cNjdKlOxUCPk50hXFKNA9bh24dsPdfOtyAxXApxOkr+mu6gikL5OhYl2E+ikbdl7ucvX2gntde3gl/iYSQ+FET17fkNccd989lwDNWTAeYkHtPuw8Y2p5vhWj6bbxzUu21PLcimJc1qZEobHc9RBjKfXwFA8nA0KsFBkcMOO0xkRR577LGUnq5mqQiQ07p166i1dfSuoMLCQt2lWwEAwP2+vWCSZZflF06povp8J4shKlzOUPFr5ZgBSbso3OGwb8vx6co8UfgjO+qkujJaUOZc+bZoqy3Ryi0qJU5ik9WwjIIW0jkNlaIkoWz8Hfwq3sSjAmmXq0+d/V7WReh3bG2JoaAkjqVqO76ulDoGh2m9xnLDAACgPkeCktOmTRPjRJ5xxhmipCSAFYJLt15++eWUk5ODBQ0AACGCy6/KLJFmmjXGmd55q7FEnWFrp6C0vbjWUeUjq3DY8UgU4PMosFzr87Los72dpCKVxgvO86Y6ctOHOksoEo8yxx2teEgWvvm4qKjI9uN+fnoaVWVn0q6ePiXGlHXRak9ayZDVxtdj6S7IQAQAAImDkt/+9rdF1hqA1T788MNAaeBLL70UCxwAABwnc8dVNAr1V4OOQDyXdOsbHon6Ol+CJb7Ks0Yrn0wpiH8zWPxNCx1UTh2XVN7tPQ5M9MS6MjF2k+bJSbBpy9AGrWRparRlpuJ3u7prgeiXv/xl4N9bO3ttL93I4/TdsWSDTItEw3c5JDO71z+2N7mpdDMSALiX7bcZIiAJdvn5z39OS5Ysobfffptyc7UPXA4AAPZJTzF+KoIL32jLBUtGNkbWSHGGV1fQI1aWx2kTy+mYmhKyQq43jW5bOJmqchwY902RTV6lviBFFmkInwPLhQMVvO27ZRuQvHnSUuUYBObuKHrPt5zYTrBPJ7dE13+i22oi88PhEwAguTg6piSAlTIzM7GAAQAkd+PciUnVUeJkoNDuDgkwbx2wa2ZPMG2Rzi3J0/0eK/ZTlTat2UW5tMnmjJxk45F5e3LRF9VZkypCSpfHLg3o7F56SEURzSnJpU+aOkgmuOnH5uXtUSOYLyOVvmdBPiruc7huibFsAssIRwYAcB6CkgAAAOAYJ8aGcpKKJd5U7JBwqqM/15tK0jDY4bCv44IcJM/Gd8akCtOmpUJfkJ1LPtrimFKQTfNL88Xj/T1thqcnEye3gZlFoxVkhmMc3D2S7INH1hjPDjdT+CLD96M8VNjvIX6wX8XzZAAAANAvuXoCAQAAwLVU6pBSqa1OZqFkp6Va2uFfmjk6/qFZHf11uVnkFj4Ht9lx88YO41jWl5OLPi0lhU6eUCbGP9UDXdrGx5aSbRnKdggID5yUjo2lm5PgdxbEWd5hG+SMGTMoPz9f/JRBpECarN9bMgfSEZCUL6tb1u0YAADUZ3qm5E9+8hN66qmnQn73ve99j84666yofzdD8DwAAAAAwBxOdkhcPqOGWvoGLZ3+8Ih9PXQydwZG41/9VjRdwcXhSugIdpBH9lmjRzpkaXiil3euzcmkwgyvchnJ1r3RuLLMdKrPzxq3/Lu6uqizs1P8hNiwB8t9A4is/C1VqMmgAVbn/9/efYBHVaWPH3/Te4VAQui9SRFFmiCylhW7YP2hiKuLdcUu1lV317KrrroW9L+6Ku6DHVEXK651RRGlqHSl95KEFiD8n/fgjDOTmWQmmXLL9/M882Ru5s49Jzdz79x73nPeIwCcHJTcuXOnuUD0tWfPnjpfjwbfMgAAAKyoScaB0RSZqckxa1TqUpgjM9dvkzZ5dhy15/+XFqSnmUespCUnSxp5Qywn8PPutEYx5qPz57B/L1zAcwx7gmQ6P1ekAUnbSOAB+rvurczPVVW7xEli+x2wP+YdimLF7ulbw52nj9GHB9j3Pw0AiAbmlAQAAIiTPk3zpGVupjSJMG1oJDfueempcknP1hKNORJXbXdWQ2CixaMhKjMlWXoU58ng0kK/3ze2oS8xoy0Y4+FGDQ1C82lBvDW2w0QiPrOZKSmya9++iN9nhUCK/TuoxDEzQ5S2Qyea6Act7fA51prut8hx7xR2DrgDgC2Ckr169ZIdO3b4/a5Vq1Z1vh4NvmUAAIDEOax5oXy+dgv/ghANEiW/zD1lBcNbNJH5W6pCzpGoj6UV0b9uCy06N+xWbUOJR0OUfsZObNssetv75Wc8m1J+LYsGHDdIsvqBG0NWaPAPd3RPIiXboI4NkYgz3AXdWkpF9d7w32CBXW/1f78VjuNf+dclyWb70s6skJ41WnWIR/rWWh9Fl3w43fFXAkACgpJnnHGGeTT0dQAAEBvxulUe1qLYPGB9A0oLzQPxRYNE8P0QKjhih3aq9ORkqa6pSXQ1LEPTSLtB4pugndGQXh8d+Z2anCRl2Rm1Xmto7dNSDuTubhph5oJoSOQpLT891TwQvv31/N8iHQGVyO80GxzuXn2a5svW3REE0C0k2H62w7nWCpy6lzydFxL599ngchqAi3A1CgCAw3EDYn/u+R+65y9F7YaZeDfYaZAjWjRl8t4aezSlDSotknZ5WbItktFSEbi2TztJsUMkGbaRmZoS9c5O2akpcmG3VlKcWf9ckAcV58mCrdulVW5mVMq2x5nCXyKPaDvGcpLCyUBgw78rnn7bukScKJZfj9Ee+W6nr3IrVdWqh7ZV6wXAnQhKAgAAhDkS6vAyRoDCZazUyhNlp7RvLou3RSc9cVZqitiFJ7gzd1NlTLafmnxgBJq9UyCGx341hq+mYaZT17maz+/akp0XhqFlxbJxV3VM9lWiAyT2aNC3Ry2deC72DQgm+rPqxk+q1T/5zCkJAP4ISgIA4PK0UwjP1X3asavgOvEMFMV7bruC9DTpV1IQ1zIBIJ5nungHRwaXFcVs24kaWZhkxxFzsds0EBcNPT6s0MEpnJHSMS0/8bsAAKwXlPzhhx9k1apV5nm3bt2kvLzcEtsCAMDprN6DtKHccN+VkZIsbfOyZGgMG/scwQ0fBhdpkZ1pUiZGM81qtFivRr9qn58tnW0wp2OzX0apdSzIsfx3Vc/iPJmxelOt31uxroke0aH/T8//Np7s1Agb6d7tWphrzoV6LVCfvLRUqdxjn3n4wpnTM9H/2/31/D75l2+EcL+rSN+KhkgO40Bw0ryV53RqIT9V7gx7fRt9BcScgz4GABws7kHJ559/Xl5++WXz/K677pLRo0dbYlsAADgVN2nxlZ924PIqnMbDSBoizurUQuwgofEjbsId5bDmBdKtOEcyU+yTGtUKzuhYJnaQm5YqNx7cIWHl926SL6u275Y2uVn1rjugtNA8PPheDW10h9Kw/wcHlxRIr+K8sNd3q+7FueYRjvO6lMvW6j1iB1f11vlnI3/f448/Ljt37pSsrPqP3WgKVdUmmWkyoryJ9G6aLy8uXhP1cstyMsxPnQu4sZdDx7UpkdkbKyTdgp19nCTaQaGjWjaVToXZ4b8hKfRnVZUkoONIQ9Jrh5tiO9EGNi80cx9bnedjUZRB0kQAiceZCAAA2JrV4lB9muaZm35PI5LbHFnexARmizMONHwkBo1tduAZTRUqgK/pXDXFqhpUWiTbdtujoT0WLu3ZJurzEaX/st/TGjgHpN2P3py0lIgCaG46U8Ur/d0xrZqKG8Vy7+r8l/qwg4Z23jr++OO9z5dHMJIqVvS7qn/zXzstRJt+D4bfgaPuT1d+eqp3XmG3O7dzeVijD8MxvEUTaZOXJat37JJYOKRZdFLNt8jJlAm92loigGbV77+GOKK8ifm5a+8+S96beujnXT/3zbPT5Z0VGxNdHQAuZ4+rVQAAAJvc3GrjVOsIerM7TVZqihzeyAavJLt/CGwi0cGo/s0KpFNBjhkxVx+3N6JqQ3K0dS7IluNal0iPYCOwEtiiZtXGPDeJdgAcVmDfL0irpiK05h7dn9CtntyumczdXCV2UJ6bGbVteUbSxyoo2dB/WLCYqxUCknWmRY7T8V6SmS4bdlWLHXQvyjUBZat97gHAtUHJmpoa7/Nki/XwBQAA0dcu373BPrfrXJAj5fXdkFu04dIKgjXyHNu6qbTNy6yzgSopxgH84l9SiSH+dP9rusH61olbfeJWkv1E0nh6eocyqYnSyZD/CezyWUhE/fZbuLKJ+n9p4CRawRM7S9Q8qIHf2XUF+CKpYriBQs81XbZFAp91GdOl3Duy0epOatc8ovUTPQ8vADg+KFlRUeF9np0dQX51AABgO5puyJNuEO5zWoLTLDqRNhrpnG7B7Lfq0BTExy8NWnwOrOH/OreQqj3hNZ52KOC+GL9yQtt0YAP7rFmzpLq6WtLT06Wkc3fzu1h+Y6X+Mv9itEasxys9MqI/Ws3qIvnOjsUx07M4V8qyM2wxF6Smj25oCun6cIQDgIODkjpKcu7cud7l4mJ3p3QCAMDpQo3m0vkxNLVjj6JceXT+8rjXyy7sFGOy8ugDAKjvvJoT5VEi+v1nlZR7cBcrBtBOOukkWbVqlZSXl8vnPyxq8HYOKs6TzoU59a6nKcbP6dTC1iMA7XANqJ80q1bz2j7tIpp/MuH7+5eqeqvcyAqF+6frSM1EByQZJRjmfvrlQ5LojyoA97JlUHLFihUyadIkWbdunfd3nTt3TmidAABA4gwqLWL3gztrIFpopWpwq+c1fdpZMIxTm6eOB5fUTuGbkZwsvZrkyYDmhTLp+x1iB1YJnvVuki/zNleKHTTLypCO9Yystcv8og357x/ftlnY60ZjrvBE7Ml4puB2stQwp4tKcmrgKg4fXivvuza5WZL2y4jpcNjjrAkADg1K3n333TJt2rRav6+qqvJb58EHH4xodOT27dtl9+7dfr/v27evFBXRGAkAgKV77SKh3NQuFYu/9ZR2zRs9R45dGncT3pDZyN3Euc5dDdpWO6rSwmy8tsL/9saDO4R8bWSb8AM2+NVxbUrMww4u6NYy0VVwpVieVXsU58mCrdslM9Ue5yFYk+d6NZbXrS1yMmRb9R6xg7M7twhvRQtcMg0tK5YexfWnGvb8by1QZQAuFbOgZGVlpWzcuLHOdTRA6RukbKiLL7640dsAAMCprDJ6AInlikBNDD/qXaM4l5AdjkjPvEQ2iElZ0kBGbwNwAK4h7aVLYU7IzgZWk5KUJPtccXF6gNX+0kgu72JxLXh8mxITQIs2t1+2Di5jwA4Ae7Bl+laP/Px8mThxogwbNizRVQEAALAkgkpo1OcnEbvP5i1K9TUI2/zPSwgdqTx/S+M7swKw3wj/ZlnpUpadKf1KCkKuQ/DUfi7t2Ub21tSIkyT6aMlIOTBCNuuXn1ZO36opcYszHTaiN9EfAACwkZgFJbt27SpHHHFErd8vWLBA1qxZY5536dJFysrKwt5mSkqK5OTkSGlpqfTs2dMEIzMz7TvZOAAAbmlQAsLh9GCNp7EoL83W/QJjj1OWK9XVgULncws1p1ub3ExZXGGPeQ/hDE7/rrJaKmcNXoztWu6Ya106ix2Qk6Yp8VOitFOjsxm7a5uXJae1L5VO9cwVC3gDzuwKAAkSsxaRMWPGmEegm266SV5++WXvOqNHj45VFQAAAOBCVm2bapKZLmd2LJM2eVkyY/WmRFfHMf83DfJW7tkb5drALk5p31x27K3xphyGu/Bvjy1GIMZinyLqLHL6T7JA54HOhTl22mWuk+jPCABYBd20AQBwOBqUEA00XkRHu3x79F4/oW0z+W5TpSTbYEjH+B6tzOfzq/XbEl0VJGgUVX56smzbvYf9D8CyCKADsWOJy9Uw6sD9FAAkKCh5xhlnyKBBg8zzXr16xbt4AAAAAPUozEiTYS2KpcYGragalIL9WaE9EXAqSzTYxxAd8GLL8pcCDv98x2K3OG2XWf4zajF2SnkNwJniHpTUQCTBSAAAAMD67NRoZae6AoiO9JQDR34yJwBYQJfCHFm5fVc930d8WBEfdgw7OT1YxtEPAAeQvhUAAFhGeU6mrNq+q8HvH1RaJCurGv5+APbl7GYs93DT/9FNf2usDC0rliYZ6VKckZboqiBOfvjhBzOHrM6ft9lix++hzQqkX7MCUzcnjVa1c93jKT891TvXdSDO9/H5jPJRDXN/sacAJBhBSQAAYBlndSqT3ftqGvx+TTdpFwcV50n7OMwvyE0nAFgNzabRkp6SLH1L8qO2PVhfXl6e9/nmyp1iJRqMTLFJeslT25dKdZjX3Faru1V1LMiRcV1bSvPsDLESO/377Hzfkpp0YDqBzgU5ia4KAFgeQUkAAGAZacnJ5uEGx7dtlugqWA498dGozw+7D7ZpfLVHLQGr47zfuFSzlrsGDHFq7FSQI32b2qPzQaiAZCI+q3WV2TovS5ZW7IhjbZwvNTlJbujbPuhrBPYBwKJByZUrV8rChQtl27ZtUllZKXv27Ino/UOGDJEuXbrErH4AAAB2ZKe5WaJ1w04jpTvZ55MOp7DT+RVwIo7AcPfTfst3GqvrGnBUh1KxO6t9Vk9r31x27G14dpp4f1abZKaLHQRL3QwAsFhQcunSpfLCCy/IW2+9JZs3N242gPz8fIKSAAAAAGDLYJt9GvKSXPS3IobieJg56RN3//33S0VFhWkDOu3CixNdHUuL1v+dUV7OOxZTk5MlPz3ZFvvl2j7tbZ9NxWr1T0k+UCGLVQuAiyQkKFlTUyOTJk2SRx55JOIRkQAAAHDm3CxWu2GHvXp826em9mGrfZpkp1iS1cbMwKkac1ikWzSdvgYlV61aJeXl5bYMSsbza3W/ja4F60rfWu97bfD3oW7760mLandWC+wPa1EkBemp0iQzLdFVAeBSCQlK3nvvvfL0008nomgAAAAAgNNYrMEPqJeF29nHdC6X/PRUeX7h6kRXxbL2Wy3KYPP0rUBUWPyzapVjKTMlRQY0L0x0NQC4WNyDkh999JFfQDI5OVmOP/54Oeqoo6R169by2GOPyfTp081rV111lRx55JGyZcsWmTt3rrz77rvy7bffmte0V9wdd9whrVq1MstNmjSJ958CAAAAwIWs0qgUK7lpB24Tc9JSEl0VS2rov/+g4jzJS0+VTbvIFgRrB9Jb5maKHZRlZ0jLnEzpU5Inn6/bInYQz68P0rfCLhx+WQUASHRQ8uGHH/Y+z83NNUHI/v37+/3Oo7i4WDp16mSe6zoXXHCBfPjhhzJx4kSTruOaa66RZ555Rrp27RrnvwIAAPvoWJBtGmpIzwKn9+gnfZczJGY+w8g0ZoCMHQKanQqyzUgpuwQmDBvs1+PbNjM/X126NtFVQQKM6lBKQDrK0lOSZUyX8mhvFjY6rwIAAPuJ6yQBK1askHnz5nmXb7vtNr+AZDh05OSzzz5rJjXXEZTjx4+XqqqqGNQWAABnKM/NlBsP7mBGZwBOZodgFmCXuURtFZBUHP6wuE4FOaTLQ0JwegQAAK4NSn711Vfe55qq9YQTTmjQdjp37iwTJkwwz9esWcP8lAAAAA5q0GrsiEcNqMC+nD7iNZXPJxz+GUfd+I6C1Tn9exjWw6URALhLXIOSK1eu9D4fMmRIvRfje/aEnmtj1KhRkp2dbZ5PnTo1irUEAABAIjHi0d3s8P9vaOPZb1uXyFmdWkS7OlC0ocMm9jcm/zNs69jWTaVLYY4tTo9X9m4jE3q1jVFtgNo4LQKAu8Q1KLlt2zbv8zZt2gRdJzX119Ry1dXVIbeVnp4uffv29aaFXbJkSVTrCgAAgF/R3g80Xp+m+VKcmcauBACXaZGTKae2L01I2ZGGwTNTUiQzNSVGtYHVZaTEtakYAOBCCZtcKiMjI+jvPaMf1ebNm+vcRtOmTb3P165dKx06dIhiDQEAQDKhKPyCcR3+BpUWSUX1Xj4fsLxzOrWQtGT7NDAyigxOH9VN+lZ3o5OXO5VkppufhRnW75g0tktLWb9zd1zLJH0rALhLXIOSRUVF3ueVlZVB1ykoKPA+X7VqVZ3b8x1JuXHjxqjUEQAAHHBlr7Y0nDhAVuqBYERp9oHGEKfKT4tvX7thLYrjWp4bpPzSImWHBvsjWhTLJ2u2iB20zstKdBUAR7HBKcrRDj74YGnVqpWUlJQkuiqwiNLsDNlWHXr6JyvoUJAtl/VsI3npCRsbEjbN6KCPDTtDZ6+zUgBVR3a+t5I2YQCwk7h+G7Zv395vZGN968yZM6fO7S1YsKDekZcAAKBhskjb5Ai5aammESQ3rXFpuOLRBntISYEsrdgR8aiu8T1aS2ZKskxetDpmdUPsdSnKkcN3F8tBxXmW3929m+abB6LPDkFpuJsd5z5z0lH1xhtvJLoKsJCLureSgvRUue/bKrE6OwQk7aYsxx5twem/pMRtl5ctC7ZuT3R1ACDh4vqN2KdPH+/zuXPnBl2ne/fu3ufLly+Xb7/91u99Hl9//bUsXbrUu9yiRYuo1xcAAMAJotEIsj9OvchvPDjydPxFNkiFhfBGSg4p+zWzSqSSHNXsbh2HNS+M6zFG+lbYBeec6Lr8oDZSY8OALxKryS9pUQEr09Gc1/RpJ6lJSTJ9xYZEVwcAEi6uk4to4LBbt27m+ffffx90zsiWLVv6BSYnTpwoq1f793rXYOR1113nXc7JyfFuFwAAANFDmAd2GRnU0PndULcjy5tI3ziMCmWAJOzGTuec/TbJ7JDvopFkdvifAIgezQRDNggAOCDuV3wjR46UH374Qfbu3StvvvmmnHvuubXWOeecc+Smm24yz5csWSLHHnusDBo0SJo1ayZr1qyRL774Qvbs+TVf/KhRoyQtjR7yAAAAbm40Y9QKANhbjg1SxxNAR+M+P3T3AmLGTjcuAOBicR0pqUaPHi3Z2dnm+dNPPy3V1bUnTj711FOlX79+3uXdu3fLjBkzZMqUKfLxxx/7BSTLy8vl0ksvjVPtAQAAYFV2GrUSb43dM3ZoQyUobW+ej1hOmntGSsHfhd1ayQXdWrFbUKcTTzxRBg4caH4mElccsAs+qwAAq4n7HV9hYaHcf//9smrVKrO8cePGWvNBJicny2OPPSbjx4+Xb775JuS2WrduLZMmTZKCgoKY1xsAAMCNbBCLQhyQvhWxlpyUJKe1L5XWeZmW39l2Oi8e0aJYllTsFDtommWPueE8HSB0bjCrG1RaJNN+Wm+rz2x9tI1I25O0gzqA0Jx03AMAnCUh3VCHDx9e7zoaaJw8ebK88cYbMm3aNDMH5datWyUrK0s6deokxxxzjJx99tmSmWn9m1YAAADEHiPl6to3gPV1Lsxp8Hs5/oMbWFpkHoie0ux0GVHeRPrEYa7VxupQkC1X9m7b4Pfz3eGMfWOHjkWwYJYMsQ871RUAkKCgZLh0xOTJJ59sHgAAAEBdSN9a175xPoJS7hbP4z8lmeZPt88J2L95obiBG747Gop9A7tIstFnvKEBdI5HALAXSwclAQAAACca2aaZ7GPoQnQRJ0KcDCktkuyUFCnLzmCfA7A8G2QaRgwRsAMAWA1BSQAAAISUmZJifuanWf+y0U4j5Xo1yUt0FRynX0m+VO+rkY4F2YmuChx+/KenJMuAUneMlIO72edbFXbHZ816+zTJRgF0Pj8AYC/Wb10CAABAwpTlZMg5nVpIq1zm8bYzNzTWpCUny9AWxWIH6cnJUl1Tk+hqAECdGGEVWpu8LNt8ekjMgAZ9bhKw20jfCgDuQFASAAAAdWptk4a3g0vyZfryDYmuhuUc06qptMq1x//QLc7v2lI27KpOdDUcJTctRXoU58nhZUWJrgoSqCgjjf2PmLu0ZxvJTj2QScLK7JS2tUNBtizYuj3R1UCC2CnbCQDAhkHJOXPmyIoVK6I2wXx2drbk5eVJaWmplJeXR2W7AGJr37593udbtmxhdwMAoqJ1kshFbQpl06ZN7FEfbZJFZEelbNrRsN1SXbFNdqWnyqZNOezXKGoqIps27WKfRtHgvBSpqaqQTVXW3q07tm6R6oodsnnTJtmTbu1+wnr8KzucV08qyTLBaavXddu2HWa/VmYlyaa0X++LrGh3xVaprt5rqX2anp4uWVlZ5qdvvQ7JTpKUpKS41fXAkdGI79VqrX9sOwxVbKkwZVVsSZVNEnlHmKqtW837t27OlE17YpsxY3Beqhyandfg/188z1W9M0VyUlMsX9e9NftNWW0L0xpU1tZd1eb921P2yaZcvZiMne3me7FKtmzOlqTM9Ijfv3PbFqmu3GW+VzNi3GHgjNIc2bm3xvL//3iXZTeFhYWS8ss0JQDiL2n//vgmcrjpppvk5Zdfjsm2i4uLpX///nLmmWfKwIEDY1IGgMZbvHix3HjjjexKAAAAAAAAAHHz+OOPS5MmTdjjQILEtqtLnG3evFmmT58uY8eOlTFjxsjatWsTXSUAAAAAAAAAAADA9Rw1UjKQ9nh46aWXSOsKWEx1dbUsX77cPM/Pz3d0yoT169fL6NGjzXM9HzVr1izRVQLgYJxzAHDOAeBUXOcA4JyDaCB9K5BYcZ/A4oILLpATTzxRfvjhB/n73/8uO3bs8AYQhw4dKl27dpWysjLJzc01885VVVWZ4MX8+fPlk08+kZ07d5r1W7RoIVdffbVJ2Vp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hYaH89ttvaht1dXXSuXNndZHcbbfdJC8vT7wKjhNi/JIlS2T9+vXKcw5iPhZuRo4cqW4w4gHn9tdffw2c7/z8fOnRo4dqT5hIeBmcU3g8GoG6QfJtIw9LeGPb0WeCwQ3SaaedZvj9jBkzlKXN559/bigmBnsTIFyF0WQMYTDQfn/++WdTb8lLLrnEttCt++23n6f7i90UFxfL1KlTVVvDuAtDG4x/CB+i9UOMv3aA9rhgwQI1EcJ+tXEX+0M7xTPqvnv37qpvop/G0v/d2o8fz0Gix2M/4VZduDX3cSPUOK61mL/gvgAGHhjDd9ppp4Qu9np5zhcvnDd7j3fffVe1LT0OPfTQmLfrt+uaH8Y7p8esZDhnbo7rTrcN3BsbgXORbPM5P63NxEOfPn3UWhvuF/FA2dBWvSpKYg7y7bffmnpewSA3WJDUOPXUU9V9tpHhC3jvvfcMDX5Tdd+pMqdMlT7v9rhrNKezeu0w+7/VbThBIuYoMMZA1AI4Y6Ft4XzhM6zPYizHuncscwsv90uv9b1DDjlE5b3XmyPBqeWMM84Q34mSCD2g5wLqNLfccosnQuDAItYMdKxYQAPEhN8IhGdAHHUQKewrRI1bb73V8Pt99tlHdt1115DPHn/8cTU46YGBCTGHATr6008/rUQIvXJEughiMEQoR3QADIpGAos2WUGsbUxMDjzwwKgGReR2wUIBPKTQ0SOJJeH73XnnneWf//ynHHTQQVENYLCEwzFGqkcMzDfffLOaSOiBCwEs5DCIhIMFU3jjIt653nGh/AiDCcsHq0K+1fMfz38x2CKvAKwyjLzrcHHCcceykIMy3H333Uocq6mpafM92g+sRVCevffeO5Af1sxLGaFC+/XrJ24BAU3r53qgL6Dv6YFjRi5KuOm7Cfoo2iP2e+WVV0b0nMRYjpsMozCuaLtmoiRCsVoVJSEyGOV5Ct5fsoMblddee03VHcaPSGAihBBCEKMRgjxa0A7feust+eOPP0wtg8Np3769subaZptt1Pjolf346Rx4ZTy2gtPzGqu4URduzX3iwWrbwZwKNzfwfofnvB5bb721uh5gwdAN3Jzzud1uk3nejHHx0UcfNfwecza9eXAwGFOxeGkE+i3GfKf44IMPDL/bf//9o96eE9e1ZG2zXhmz/DQXMcKtcd3JtoGQesHeAEbHoaW9MGrnWMvAOfHLfM4vazPxgvvMf/3rX1FFy/ECaCdm48Iee+xh6umIdmMmzmHuiuucXi7UVN13vCRqHZF9XhIy7tp17dDAOGoG5gsw/gkHYhRSatmN23MUrL9p+5w+fbqpgRCAKIm5Phy1YATltX5pJ5UJuN7iXkRPlNTuYdwQJdNazI40BpBcOJVFSRw76sDsYoIbNruBlb6ZWBENuICHi0xorLA20AMTBkwKcDFAiAizm3+zGNwIQYEOEW2+PDBixAglOMHSwQq4WLz00ksSLxiIEfMbYUGsAA8wI+Faq0e0IcRvhjVqJHCeLr/88sAghIHjhhtuiDi4A1iePf/88yq/aSSsnP94/ouLII7Dqpf1ySefrPqZ1cEXwiJulM1C9QSDseTGG29U/fWRRx4x/N2rr76qrFTc4rDDDpOFCxcans+vvvpKeRoaLSrBsOGVV16Jap+wUsSCvBG4gL/88suWtgWxfcKECYbCfPA+jRYa0S/Qj8y28frrrytv4kh8//33cs455xh+j5v6H374wVAgtbNuoiWePqmByRrKD2MiPaE+EpgMHXfccXLVVVcpa/lIYPHkwgsvNA1xbgVY6f35558J348duH0OvDAe+2le40ZduDn3iQcr9YVr0H/+8x/TG/ZgcD3AtTZS5Aer+/fCnM/pdptK8+ZVq1bJvvvua7oIHWnxB+3RLOw9vJ9hke0Es2bNkqOPPtrQuBDGg1Zx8rqWrG020WOWH+Yiia4jN9sGFtbMjBqtghx9ekKeF+dzflqbcRLM4XB/bAS+e/bZZyURYLzEvWY8a5wov9k8FWtLeo4RqbrveEnUOiL7vCRk3LXr2hEviHj39ddf27a9RMxRIEYi5DS8WKMFQiHusXG+/NYvrfBiAq+30KcQAcPIwAPbd5LoZoskInALN+Omm25SIV6TDYhgZ555pumkF+hp4BAXLrjgAjVAxdIJAcIS4CYIISrdBFYzyKeHRQ07gHUPQvxaESQBLsCaKAKrCoSpsCJIapY8OGdWbzKdAufs9NNPjyrsM4QehPq0Kkief/75lgVJrS7hSRqNhY3TIF+ikSAJIOJhMgWrLyMwYUAYg0SBcNRWLKlg5WRmlQULpnhCslr93eGHH24oSPodhLHAJBve27FM4jVLN4jcmBxi8dgMjGknnXRS3BPfSLi1Hz+eAy+Mx37C6brw89zHLHR2NHMKhFPDQoCXrrVuzvlixc9tx8469Fpoy3C+++47U6OlZLyu+anNOjlmJcM5c2tc92LbSIb5XKquzfgNhC40Ax7IkYj0G6N9pOq+k3VOmWp93ovjrl9we44Cj30IiphPxCJIatuI5GXqx3u9Sg/0vV122cXUicNpbPdnh0VLNDdadoEQPl4gUsxjCEEIB4jyjhs3Tj3D5dlrsbhjsRqEaBLLhQCDkx3WJ4gTjW3B+8/NNgg3d1iZwAoCnlWxsnbtWpk4caKpm7Yejz32mAo7AME7WmARAU9AN2LuG/UHiH/RhArQeOihh5TnoJkbP44PbSJS7HY9YBmc6IVHqyHAAOpCezbyVkfbgrUL2muigHgKId1sUoFcqGb5LxFawcwjHyF6MfHJysoy/A22b5bLQttPMoL+gMmPXRNR3PThJghhOIw8TuB1rJdE227c2o8fz0Gix2M/4XRdJMPcJ3z+cs0118QkLsIYC5adZt5VXsKuOV+sJEPbsasOvS5KIgVALAsAfr2u+anNOj1m+f2cuTWue7FtJMN8LtXXZvwC8pNHMnyzkk8NHlRmzJ49m/tOgTllKvV5r467fsHNOQrWH7HWHCllUire63ml7+28884qD6/Rvcy5554rvhIlkTgWj1QFLrpouJEmGEhaigdAeBOE0ER4AYiUCLFoJaSmV0DMZsTwtjooBS8iII9JpE6ImxqEa0T+Q+R2QseANYBRx7700ktVqKZoFkc7d+6s9oN6x2uIISgnLEMhbMHDzMilGcALD94R8Yh7kUJaml2U49kvxB3UmZVQMXYTqzWIZuGDsmNCYgSEqXg8QWGB5ZULo5nrP/JnaNaCWOgyG4MgbqLOErWYh/0i5BqsgYxAP4aVn1Femh122EHl8jTy+kR/xDhhFvYN4S/MrOpQn2a5LPwMDBjstoxbunSpEnH0wh9h3Dea6Gg5k5E7BOEyEAIEYxrGBoy5CPmLbaNNRMKt/fjxHHhhPPYTTteFV+Y+dhHr/EUD4WowXqNvuoEX5nyx4pW244U69LIoifmF3kJwcMhUK/jpuub1NuvWmJUM58ytcd2LbSMZ5nPJsDaTCuA8m4GxIicnJ+J2Ii2a6+0nVfftBF6YD6Van/fiuOsX3J6jPPDAAwkRJL3QLyPhlb430uSeBGuyaANO5tm0P/NvioOGDndfdD6rwAIRHRwPLe9Jjx49lEcRvCq32mor8TLhog86wJ577qkSCCNHCVzh0ZiRdDjYCxDWGWYXT4RpRG5FhFAMz1eCXH6INa9nvblx40Z59NFHI4aJRLJgePbut99+ShCOtLgBd2jk1DBarEQSZFzI7OiwyA+IMJxoBxBe4N1mNaQrRG7UPxLfon4gvpjli0D+QYQ4RT0kEni17b333qq9o9woE5IfR4pLbrTwi1xgkydPjrjf4cOHq5wEXbp0UUIe8hsgQbKXgNu8WfhZzUtSO/8HH3ywYUx1tCdchGH8kCiseAjAk9JIlERfxdholJQZoM+YiZL43oyjjjpKkhFMYszC4wZPgNAvME5izEN/0gxpjMCkCv0HUQCCQZx9WAMb5dVCDlCMdWbXGOz/l19+UV6wRiGq3dqPH89BosdjP2N3XSR67uM0uIFFbgrcBGIOiLEc1zCzOQxudhCO/s4773SsXF6e81kl0W0nGerQLWB1b5SnEWOKVeNTv1zXvNpmEzFm+f2cuTWu+6Ft+HU+59e1mVQjkgEc1iasEOl3evtJ1X3bhdfmQ6nU57067voFN+coWHe0KvLifI0ZM0a1XeT6xNon1rBxzqw6mXitX5rhpb43ePBgdW+iFyEKn6HPQVtwCoqSDnDKKaeosCXxuERjoHjhhRfUA4th//73v00tDdGZtEaJpLVPPvmk4W933XVX2WuvvQy/hxdSLCD3GjrJqaeeqgaScHAhDE56j85idMOuJasNFluCgfALywajQQ7WH+edd54Ka6oHBqsrr7wyKu9ADG7IkXH99dcbukzDKjreUMLYzz333CMdOnQIfAYB5vjjj4/4XxwP3PFhTaGBekC8dYTENGLWrFkJFSURmgTlDm7jcJmH6ITwskagj8EaPbiuNBAf3gy0xRtuuKFNvcIaBmUxE7zcRjNWMOLQQw8NeY9+Y5boGdtLpCiJSQfq3ywkFCz+zDjiiCNU2zAKd4zFEkwwsJASDmLZ//7774bbxkU5vE6TBYy7ZiGiceyYxOiJspikw/oKY50R6DfhE3mzyAGa8YUZsJbFdRAPhLg2Sqbu1n78eA4SPR7HghfmNU7URSLnPk6Dm3/MX8IXilBm5HY2suwEsKLF9Rc3lHbj5pzPyXbLeXMoXs75bNbWEWpP7z4pUde1ZG2ziRqz/DIXSfS47nbbwP0evD+CQ60bLXTif6eddprud0i745f5nJ/WZlKRSKKZ1YVws3QlRvtJ1X3bgZfXEVOhz7s97tp17dCAyAbjVSP+7//+T3fd366wsm7OUVBXkVKh4FhxvnbccUfd7yF6fvnll/LMM8/4tl/q4aW+l5WVpe5NjPQripI+BB0Vi1gQgozCC0bDd999pwQlNEyjhXIIoRpoTGY3lJhMRxosY3Wjx2BgRLC6jsFl0qRJhr+FZYNRJ9RA3G/ETNYTNjD4wXLj9NNPNxRFYr0xi3SRiWfQgjXTvffeqywjgoHVCM4bxEMzMAkJFiQBJiQYlMxEyUTmPcGF4+mnn9bNmXDRRRepsKVGnosYyBFCINzlHOcfkw4z4MWiJ/RiModFZ+RSMctb6BZYIDLLS4QwowjfGsx2222nLiywhNIDEyFcfO0SD6IFk2FMrMy8PyPdSCB8KyYvRnk/McbgOPXGJLQpM0EUC25WrTT9BCzaIyWrxmTOyEsUE3SE3cVk3gjksoCgjLFMw+zGAWMm+mukG8vg/mnkaevWfvx4DhI5HsdKouc1TtRFouc+ToIxGTdY4fMX0KdPH+UxgwggRiEB4VWDax2MsOzGzTmfU+020W3Hi/Pm4IU1rwGvBLNoKFZx47qWrG02UWOWH+Yiia6jRLSNcONbXN+NFpYhokZq516fz/ltbSYViRQi2eo4EUnE09tPqu7bDrw4H0qVPp+IcdfuawfuL81ESUQlgODnFG7NUXD9hyeiGbhPhiOJWbhm1Be8BdEWzUIye7lfhuPFvtezZ09DTcDsnsYO6CnpEFjEgqABl9tPP/3UtPNbARb38JbEQrlRSMNEguSoZhfAcGBRYRbyBTc4kcCgD1djhL3VA6E7rXZEdGYIOFhghJCMCw3qHI/gcxcphE6kXKKRgFWT3o2fNmibiZKdOnUy9KbEQAr3daN2iPjUiQJlNkvijrZlFk5Vr+wIm2XWvhBTHAO5GRdffLGyMjETr9wAC99mFkZGFyz0oSeeeEL3O7RvhPUNDwfgJohXbyZKRrKqAljkMBIlwUcffaQ7LuHzSNtNRlBXmCAagVj1J510kuk2EBoYRjdmIaF//fVXlTc0eLtmE72jjz5atVeExxw4cKAKHRKLB4xb+/HjOUjkeOxXnKgLr8197OTss882nL9oNzuw/kV+DiMQFteN8dcrc75o8Frb8UIdejmnpFm0h0h5sPx2XfNLm3VrzPLzOXOrjrzeNpJhPuf3tZlUwK5rWKR1Rr39pOq+ncAL86FU6fNeH3f9gFtzFOT8NPNIhWEhojBYyR+r/d4scqRX+6Vf+l4PEw/ZSBHs4oWipIMgoSq83v71r3+puL+wiDDrmJGA5f11112n3JetWi+4xXHHHRfV7yN5/OGCA0+1SCAfohGRcj5pAwLEY3jVwSMtXsxEFiuYWcVEWsSAh6SRxRgsTCBaGpXPKUsyK0SyAomUpFfPUmnu3LkRLWLMbrK1/cKazEo7cpIPPvjAdLJtdNEaP368oSipbTeRoqRZ3wVWQi8cdNBBKp46Jhdm40hw6IKFCxeahpqC4Uey5hFA+Akz4CFqJaQcLAbNJvLYT/BEHhParl27qhj3emBbwdvD9Q3/wcRzxIgRKtQw+mIki1i39uPHc5DI8divOFEXXpn7OAEse638xmzxOtK1O168NueLBq+0HS/VoZdFSbN5tdUFGL9c17zeZt0es/x8ztyqI6+3jWSYz/l1bSaVgJG0GWbiSzCRFtX1Upmk6r7txEvzoVTp814fd/2AW3MUeJxGWruORmT0a7/0S9/r2LGj4XdOawUUJV0AoW5uv/12lb8OSWERRhOdBZN0K95AwSB28FdffeW5fGdjx46N6vdGg6DGt99+G2eJWjspJjV6A2ZJSYnKoRLJ/T9a4lmQRShNhNyMdRED1ixmmAnZZvGsnQRlwsXNjEgT18bGRt3za8bw4cMtlQ9hABJ5A7VgwQLTyRcmBUZhwIYOHaqO02jCBeuZdevWRRVGzC7QLyP1FStx89E2MKlEDl89YAWF72DprfHhhx9GFCK8ZvRhF5EmZlbDXgwbNiyq/WDh+J///GfEXAAauC5CPMZDC2+C8Q/XPXiTo23r4dZ+/HgOEjke+xErdRHpmqxXF4me+zgFjDnMLG81It18OiXyeXHOFy2JbjvJUIduYmQsBaLJd+OH65pX22yixiw/nzO36sjLbSMZ5nN+XJtJRSLNqc08aaIR5/T2k6r7tgOvzodSoc97fdz1A27NUSK1L6xjpkK/9Evfa2/isBOPY50vREksGkNFR9JdLMBj4golNlqxDvkbYRnhZSA6wQNH88LBhRTHPnXqVPn999/Vw8px//LLL54SJeERCmuLaIgkGtkF9hPuzYCLDMKymYVdSwRwwTaz+o40oERKSpwo4dEM3OxGEoCsWDtF64UHr1GrbTuRvP/++6bfRxoHILAZiZIIaYAwpsGCnVvAOihSyBXkqrECJlVGoiQIFiWxT4TDjbS9ZCXSuGtXv9CbyJ9zzjlqgrVkyRKJdUL41ltvqT6B0MpG7dat/fjxHCRqPPYjVuoiFuOFRM59nMRqu430OyfC/3p1zhctnDf7Czu9OL1+XfPjeOf0mOXXc+ZWHXm5bUSzba/O5/y2NpOqRGojVs99pAVuPUPfVN13ss4pU6XPe3nc9RNuzFEi1WE0qQz82i/91PeaTVKWOR2ZJmGrSxBIENL05ZdfVrF+4yWRYQhjBZayO+64o3qgI2PS/uKLL6oY12aNwszVPBFYHfzDY0K7gZ5lA8I9enHAgmhtRqTBIJLXRqJzI8YawiqWQdCtXAVOj5FmYpsW990oITHYsGGD6f8xkUjEQgcMMCIRyUtJY9ddd1XenvD61APGLgjXiu3BU90sFAKs67beemtJViKNu1bbe6Tf6eUdwI3hCy+8oCzYkKchVmC4g7DosODXy5vh1n78eA4SNR77EafqIpFzHyexq906UT9enfNFC+fNsbU7p617Y7E4tuoF4pfrmh/HO6fHLL+eM7fqyMttIxnmc35bm0lVInkZw8sF64ORzmek+309T6ZU3XeyzilTpc97edz1E27MUSLVoZ1r017tl37qe7Um9yaRdApfipJQsi+44ALlIUhCLyawNIAYYZYLzs0cNlaIxR0fA5cbhF9wEP72008/jegJsssuu8jIkSOVFU2wZwjCMT3wwAOOlDXexV4/LhZbKXMsg3akiZnVPpRIK6mff/45YmJls3yTVoB11N9//y3bbbeduAUmL2+++WbExTyEvbYC2geMUp566inD3yBkK0RJeIamqpeklXHXqtVWpH5htB9Yaj3//PPK2x9tAG081huV+++/X4444gjd649b+/HjOUjEeOxHnKqLRM19nMZqu4107bXbqt3Lc75o4bw5trxTkRYtncJsHhpLbhYvX9f8ON65MWb58Zy5VUdebhvJMJ/z09pMKoMoWb179zY1mIXx8ZgxY0y3Y2agDDC/4b6Te06ZKn3ey+Ou33B6jhKpDo0cCpKpX/qp71WZnHsnvM4TKkriBvL8889XHj7hYbDgco7KqKioCLj0omFjcAm3dEVjCj6ZTicydhOEXDQTJZNhUTBSeIFTTjnFcvhGM8JzYUyaNMn090jii7ofNGiQ7veFhYWeWaAisbcveM9ZAXlfE0W8gqNV4C3ppigJD/lIiZv32GMPUy+DcI488khTURIhW2Hw8eWXXxr+BtZc48ePl1TuF3PmzLGlX0TaDxKb44GcewjlC09W3FDDwg0Ty5UrV0ZcbEbInr/++kt5yiZ6P348ByS15j5Og5Dpa9askS222ML0d5EifdjdbpNpzpeK8+ZIVtaRFk9gBIVxPxFgodkI1EmsePG65sfxzs0xy0/nzK068nLbSNX5XDKcEz+CCD1m98UYM8xESTgzRGpDRlGAUnXfsZJMc0q/9vlkG3e9gFNzlEh1OHnyZLnwwgtTsl96se8VmtybRJoH+k6UfOWVV0IEyX79+snll1+u8kF27NhRrr32WnnnnXfUd5dccknADRgdAwvKCPcKywbcaMLb0muLyAg1MH36dNlzzz1j9lyLFDYskrLuB4+57bff3vR7hFF0IkxNpFDB//3vfw0HLLBq1Srby0TsJ5KXHSyBkNMVIZSNwIXY6sTGbmCYEekCaxefffaZXHPNNa5YXyN86j333BPxdxMmTIhquwgDM2rUKJWbWI/169fL7bffHjB40WP33Xe3Nba9H8fdH374QYVuMBOEYXH11VdfmW7HqsgNKzWUKbxcmBSj7yEBu5mQjEmzlQU6t/bjx3PgF/wwr/Hy3McN0CZPO+20iL9xcwEp0XM+O9ttKs6bcV9oBhZLzEC+nESFCuzbt68joqTT17VkaLNeHbO8NBdJdB15vW2k4nwuGc6JH8Ea6DfffGP4/ddff60WqM0W9s3ub7GYPHz4cO47CeaUduPHPp9s466XsHuOMnr0aNP9/fnnn2rtDmt4qdYvvdj31pl4rprd09iBqy53sGh59tlnQyobAuQhhxwS8cYTi87wsITHy84776xuMq+88kp5++23xUtgUoAcbQcddJC89NJLMSUxjZRHDvnTzDATWqwkhXarI5qJr++9915c24egoxenOtL5MBuwwBdffBFXuYg7YGzJzc01DZ319NNPm05W7rzzTkkUEAohmrplSIGFO6fBWH3mmWcqgxIzMDGBp2S0wFvSjEghYyP9PxnAuGt2rYVV/KOPPmq6jddee8108ocFzXgXzTApRjuA1ZpZcu54Q5m7tR8/ngOv4Yd5jZfnPm6Aa6rZHGvp0qURjy/STVq0JHrOZ2e7TcV5M+ZxZtFhsEhiVD7cJ953332SKLbaaivD76xYnSfqupYMbdZvY1Yi5iKJriOvt41UnM8lwzlJBIjmduuttxo+IkU+Ovjgg03zdU2ZMkUt3hthFilIS01iZGySqvv265zSbvzY55Nt3PUDsc5RcK4ihf2Ec1qkdFUaEJvDo236tV96re/V1taaGnrqhQD3rSg5e/bskNwed999twrDGq2r65NPPhmw+LnpppsixlFPBPCywkQE3jewMETIQoSMhDBrBOIZ49geeugh020jHrIZkULZIpltLPlM7AThejEZMQL5Rl988cWotom6haXZUUcdpQRsvY4YyQvVLMwTLDneeOONqMpEEgMWsWAYYAYmLM8991ybPom+Ac9BN4Q6s5CqyRAqFnUJgRWWPtddd11EoRWTnhtuuCGmfR166KFqXIkFTJj23XdfSXZQP5H6BW7yHnnkkTYLphhPMYm/7bbbTP+PSbxeqAjk8zSz5DXqx2Y3rHo3JW7tx4/nwM/4YV7j5bmPG2B+f/rpp6tcxeHMnDlTzjjjDFMhBvMzRBmxk0TP+exst6k4b0b4VoRdMgJziquuuqpNHeLGGkZQuBdLpChpNCdBvUdz7+rmdS0Z2qwXxiyvz0USXUdebxupOJ9LhnOSCLCYC0cEowc8tyIZ35jVOwylsXCPddRg0KZuvvlm+f33303HFIhz3HdyzCntxo99PtnG3UThxhzFSmokGOlhjILjGTwyjcROtEM4suk5b/mxX3qt7y1cuNBQp4Kx4rBhwyRpwrcirKnGDjvsICNGjIgpMacW5hWhDOB1A+/LSINLokDnQlgFPABcySGoYqDr1KmTOhZYWMFtGJ0CE5tIg0GkRRsssOOm0miggWsuBnN4nCLBdrAVEUI8/Otf/xI3QPjdDz/80NBzCucUN0HnnXeeoXco6guxrxHaAoNZsOith9nihrbPoUOHqrDCwcDFH23OKctmYj8nnXRSIBS0Hhio4Q2JBM/oC+g3sNT57bffor5I28ny5ct1rYA0MGYgtHW0IGyqUV/76aefVN+JZQKG8sIAI9g7AVZqmGTAECOaC+IVV1wh2267rcQCDFz22Wcf07ASRmBSEMk7IFnAJAUTOjOP1YcffliFWke/gCEQzifyBETKBQouuugi3c/RRq6//noZN26cMtYZO3as9O/f3/TaiWu72aKyXnx7t/bjx3PgZ/wyr/Hq3Mct4LmGvOiY48MyFTc4uA5g8dpoTh/srW4W4SAWEj3ns7vdpuK8GSG09AQRjR9//FEZFWG8R/uB9Ts8LCJFZXAazCkwn8HCgh7oE2belIm6riVDm/XCmOWHuUii68jrbSMV53N+PicYaz///HPDMpkBj9/ge9lwrr76amU46xQXX3yx8tbBmqBRyO+jjz5adtppJ9UHca+N9YpIdYu1kPBrM/cdO4meDzmBH/t8so27icCtOQraDZwtjMY2gPVXrAHCyAIehD169FDjLaK5wYAPgpnZeqJf+6WX+t6sWbMMv0MY3lgdPzwpSgZX0o477qj7m+CbG7OBBt6CvXv3VgMLFqHhXWMWO9oroOH8/fffMf8fk/tIkwuAG13kbzPr/J9++qlu3ge3Fu8wcJ177rnqomEELBfeeustJWAjhC9ulmEZDYsJnHvcFEWz8ICB1ywsJxYztJttxE7GtiEQJdLamsQG+gBuojFAm4F8g5F+4yUvSQhvkfK76AHDiO+//95wooE6iGW7uFGCJWi8nHzyycoaOx4wPsYiSppZkSYbuH7Ash0Jv81AKIxo6/KII46QMWPGmF7/sE1tuxDYcXONsRZWbniPtog+ieukWZg4TI6Mwvy6tR8/ngM/44d5jVfnPm6CBWucJ7NzFQ5EEKQ+sBsvzPnsbLepOG+G0VCkeRGOzUvzOA0YkRqJkmgT//d//2d5W25e1/zeZr0yZnl9LpLoOvJD20i1+Zyfz8n8+fNjvh+Fd73Zf5EyyklREmual156qamTBQwAoumD2ja5b/tI9HzICfzY55Nt3E0UbsxRunfvHnFs04AIGcmzPJn6pZf63u8mHveoX6dxVZREQ9OAAq5HdnZ2SDhTM3DThJOBMDKLFi2SbbbZRpIZ1JmVyQU48MADo7pxSBQXXnih8rSCC7kRsIyAlSYe8YLODItqxFk2AoMvwhPpgYHMa0mqiTEIGWrFktBs0gOPP7fADYdZXwBmrv6R/mckSgIs+sUiSsYLDFFgkQZrITsWAGENF03OI1hXRUrEnWzgOgLLM1hV2QXq8JZbbonqP7CaQzii8JBEVjjmmGMsh393az9+PAd+wi/zGi/OfZymS5cu6qYo1hC6//nPfyLmS/frnM/udptq82YscgwePNjUW9IsukyfPn1cnceFG5EhB0+0CwCJvq75vc16dczy4lzESazUkRfbRqrP55LhnPgRGOdizcKO9DHw1EeEpEhhDVN9336bDzmFH/t8so27XsCpOcqpp56q5uEvv/yyOIGf+6UX+l5LS4uKMGN2L+M0ruaUDPZkNEo8HByLOFLS0+DEqVYTpPoVhMhBmEmz5LLhVh5Gwq/XgOUEQvHanYzaCHjVxlI3uLEyWlwg3gSLDogrj1DJ0YJJMvqRm2CRyizJMKyOY825td9++4UYfejFW587d664CRYLEQrCDkESwIoV3rHRkEpekhoYa3HThpjzdoA2CWtFs/ZlJzBA+ve//+3r/fj9HCQCP81rvDj3cRKE/oalZywRS+BJg5vZZJ3zOdFuU2neDGERYaaQmyYa8Hv8zygyjxsgXYdRqhJYniP8pdtYua4lQ5v185iVqDmP3tpDrMYi0dSR19pGtCTjfM7v58SP4Fp3//33q8X1eIAg9/jjj0d17UvVfftxTukUfuvzyTju+hUrc5RrrrlGCXDRzuVToV/eluC+hxSLRg48SKERKTyu70TJ4HxlcDmN9JtILrXBnpfBrxMJRMP77rtPWZnalZD+0EMPVWGJEAvZKhAvcIEPFm69Cty9Ec/5ySefVJYO8YI2hJBIRoMeBh8IvNF0MHjlvvrqq0pEIf4C4aReeOEFy+cb7eayyy5TXpaRYoybJXuOhQ8++CAuYdEM5HSJFPopUog0u0A/mjhxonz22Wfyj3/8w9ZtRyMy4uJ/+OGHSyqChbjbb79dhbuIddEJFnFXXXWVEv7dsODHjStuPmBp55QVrJv78eM5SCR+mtd4ce7jNLvttpuaW1m9KcTxX3755SqPiJMkes7nRLtNtXkzQmlh0cDq/AdiChaX7FqscmpOYqeFv53XtWRos34es9yeixiByCPvvfeeyjVllVjqyIttI9Xnc8lwTvwI2tEjjzyicljGso6IvK/vvvtuTPfWqbrvaEn0fMgp/Njnk23c9RvRzFHwW0RGw2+xNhsLmNsjr26y9cusBPc9s3sRpMZyA1fDt+LERxIchw0bFqLaIrSKXmJNuOAG52bEDZQXQFkhIuKBOM1//vmnSqqLB2LdG4mx4Z0WHQoWQ7iZjbVxwlIINxOwGsLif6RwuIkG8YphpfLLL7+oiQXqDPnqIoGb5lGjRqkJCXKNwvUedWgGBF7sA2LVa6+9ZhjuEYMmBtsJEyaoc1tcXBzz8ZHEMXLkSOUW//TTT6vzvmbNmja/wSQY7ulnnXVWwLI9krEDLo52hkyIFPc+1tCtGocccohpaAMYP2BiZnfeDEzyttxyS9U30cfRV52yBsJ1Bt4JGG8jgYUWvaTcqQQmG7heIT8UksYjxn5VVZXh77EojPH2gAMOkGOPPdbyzeOZZ56pzglCVOARjSEREq+jb8LqPniOkMj9+PEcJAN+m9d4be7jNBCPcF6ee+45dc3VC4eD8Fp77bWXXHzxxVEZ28VDoud8TrXbVJo3Y7z78MMPlTiJMG96OVSQQwuGRshP7ZXFJdTFgw8+qOZ4enMuGMFFmg8l4rqWDG02kWOWH+ciRsLkiy++qPI8IbIJcqTq9T07xnWvtY1YSLb5XDKcE6dB3joz4GkSDagnXMPQlnCtRTsyc9SAgTTEOCxCx5v7K1X3HS2Jng85iR/7fLKNu26QqDkK2gfmlmhXb7/9tkoVYBYlDmuSmI/i/I4fP97UIcTv/XJcAvoeQsNiDqwH9DW3nDfSWhBE1iUqKipk1113VZNZiG56C/Dl5eXKclGb8MKd9eijj27zO6jaN998c+D9F198oRKzeh10eHR8hO1BHgvcpOIUwLoAnkyw1kTnttsDC/WJRKjYN84DBFO9Uw9lXS/8IQYPnBujEJlOhbnEIKUNlNg/xGjUDeoLXqkYFOMVhlAPCF2JfDUQjbFt1APaU7jlBBLLvv7664bbQrvGTZkRSFSrtzBhpR5RRsTcNwIXB9SHWaJcnHc9UIdmg0485z/etoML5uTJkw2/33vvvWXAgAESzflGTG7ENsdFCKFd0ZYgXIZPSk488URlWGDEjBkz1I24HSA/rpkoiUWrE044QddIwypoe2iDZuCCj0UIDUzsgg1A9MCFD9Y4KBseuIjhAolFQYh+GNvcBOds1qxZEX+Hi/e2224b834i1Q2stpBY2wmcGpObmppk4cKFsm7dOjUeYlKPMRf9BMnKIdjbEdoE24ZxAK6D2If2wLUKlo/oi6g/GOXEEn7Z7f3YiZPnwGvjsZ/nNU7WhRtzn2jZf//9ZcWKFbrfYd4KgUVv3oJFfoSFQX9D2THmxnJNsGvMc3PO50S7TbV5s9FcBoaruJfCa+wb+8U8Llzg+/nnn2XRokWG28K8yulwXchRZJRLB4snuDf28nXNr2020WOWH+Yi0Y6rEKcRdhj1g2NCue2qI7fHM7N78ljbsp/mc7HixfmJlTlZPCCli5HXSfiaZPjiNxbe4/UWw8I02hGe0QexPbQh1DWMfp28hqXqvr04H2Kf98ZaRrzXjkhjFRyT+vXrJ26SyDkK0vBhno5rCuaYiFSHdUTUAc5VLGH2k6Vfrnb4evvjjz8qhxw9YCQC7/WkEyUBDhoHD2DJgAtKOOecc458//336jU6AMIoYqEcgwduhHBxh5u2JlyiwX7zzTduHgYhJMnBRRkWeEZCLqxxYJFFCCGEJDuxLPATQkSJXDBQ0vMwQwSMROezSVY4ZhFCnAZ50vTC38HD55133gmJFEcIIYR4hYsvvljXKQa6G65rsYZFjpb0RObWgFuqHuedd17A0hVWCEiMCo8W5EOD+y4sToNv7OB+TAghdmLmWQp23313VjghhBBCCDGkb9++KsyVHrjp10spQAghxNsg9N2UKVMMvUwoSBJCCPGqweQ3Bo59xx13nGuCZEJEyQMPPDDgMotFf4TeCWf77bdvIzTCjRe/DbcyRXgsxAMmhBA9fv31V8OY4mZhWR966KGIYxkhhBBCCCFmwOBWLwQVQjEZhXYlhBDiXZAOBqEB9cL9wYOSEEII8SIvvfSSCnUcDiKVInKpm7guSiIWOEIZTJo0SYU+NMo/cOWVV8pll11mmK8NOcyQ2BjCQXj+EEIICY7zDeOFq666So07Zt6PiNX91FNPyUknnaTiixuBuOJjxoxhJRNCCCGEEFOQ+8Uosg+MdJHjnBBCiH/47bff2nyGdcn//e9/hmuYhBBCSCLZuHGjyo2qx9lnn63yb7pJpiQAJOO0kpDz3HPPVeFeEdpm9uzZUlJSopJ6IpfbAQccoBKtEkJIJCAwfvjhh+qBHA+DBw9WiY6RRDkrK0slVUYS4blz5+rm/Am3Hvn3v//NSieEEEIIIZY466yz5P3335fVq1eHfI5UJU8//bRcffXVrElCCPEJkydPbvPZscceKzvvvHNCykMIIYREAk44NTU1bT7v16+fnHHGGeI2CRElo7UshdcSIYTYAUJlLViwQD2iBQImvLNhGEEIIYQQQogVEL71gQcekKlTp7b5rkOHDqxEQgjxCTBiDh/Lu3fvriIzEUIIIV5l8ODBMnHixDaf77DDDpKdne16eTwvShJCiBfo2rWr3H777bLHHnskuiiEEEIIIcRnjBo1Sj0IIYT4l6qqKrnkkktCPhs9erTk5eUlrEyEEEJIJCZMmCBegqIkIYRE8I48+OCDlTWJlbDThBBCCCGEEEIIST46d+4sp512WqKLQQghhPgaipKEkKTmwAMPVM8I17pkyZKIOSO1MFpbbbWVjBs3To455hjXk/0SQgghXuK8886T8vJy3e+6dOnienkIIcQMjlmEEEIIIYR4l7SWlpYWt3f6448/SmFhoXq95557qryR0TBnzhyZPXu2ej1y5EjZeuutHSknISS5gCC5YsUKKSkpkYqKCvWorKyUtLQ0yc/Pl9zcXDUeDRs2TDIyMhJdXEIIIYQQQgghhBBCCCEkaXBdlFy6dKmMHz9eiQMDBw6Ujz/+OOpkmitXrpRDDjlE6uvrpW/fvvL5558nJCEnIYQQQgghhBBCCCGEEEIIISQy6eIyjz/+eCB8IsKqxCIm9uvXT44++mj1etWqVfLee+/ZXk5CCCGEEEIIIYQQQgghhBBCiA9FSYRK/OKLL9RrhEo8+OCDY97WhAkTAq/feecdW8pHCCGEEEIIIYQQQgghhBBCCPG5KDllyhSpq6tTr3faaSdp165dzNvaaqutpFu3bur1rFmzpKyszLZyEkIIIYQQQgghhBBCCCGEEEJ8KkrOmTMn8HrkyJFxbw/CJEBazOBtE0K8TVNTk2zcuFE98JoQQgghhBBCCCGEEEIIIcmNq6JkSUlJ4HWXLl3i3l5BQYHutgkh3qa0tFTOPfdc9cBrQgghhBBCCCGEEEIIIYQkN5lu7iwtLS3wWgvjGg+1tbWB142NjXFvjxBCUp35pVXy3pJ1IZ/17NBOCmviH7Nj4eJtB0pOVkbg/e1TF6vnM7fqJ7nX3SXSsHnsb3fFWZI+oI96PWNjhXy6fL0c1K+7jO6eb1t5flhdLL8WthrBTBwzxJZtPjxzuVQ2NMa1Pa1cxw/dQgbmdwipK7vK6dQ2ndxuKvPmorWypLxaLhs1UNpnbu5DXsRP5x9lzUxLk3+PHpzoohBiSHNLi9w5bYn0zWkvJw9vvS4SQkiycN9990l5ebnk5+fL5ZdfnujiEEIIIYQQL4uSwd6Ra9asiXt7q1ev1vWaJIQQkgJkZYaIki00TiGkLZvtwUgK0dTSIr+uLZFde3WRzHQ2glQl2CCUEEKSSZTEWlCfPn0oShJCCCGE+BBXw7di0qjx888/x7UthGsNziPZs2fPuLZHCCHEZ2SG2dXUNySqJIQQ4inmllTKz+tKZGZxRaKLQkjSUFbfIKsrN0fqIYQQQgghhBDicVFy7NixgddLly6V7777LuZtPf/889Lc3Kxed+3aVYYOHWpLGQkhhPiDtKyskPct1TXiR+jHQpygpYX1mspo57+FDSElYfd3hidnr5SXFmyO1EMIIYQQQgghxOOiZK9evWTEiBGB9zfffLOsWxeau8wKU6ZMUaKkxl577cXwRIQQkmKkdQ7NFdmyfmPCykIIISQ21tfUyeR1rbl6vU5NY5PUNjUluhgkgWGRCSGEEEIIIYT4SJQE55xzTkheyZNOOkmmT59u+f8ffvih2kZ9fb16n5mZKWeffbYjZSWEEJJYWkz8PdJ6dQ/9bWm5pCpm9US8DTzZ1lbV2b5dppJLbfw0Iry+cK18v6ZY/MADM5bJwzOWi9ehBz4hzhglIDS2H3h27ip54O9liS4GIYQQQgghuoQl5HKegw8+WF577TXl7QhWrlwpxx9/vPJ2PPLII2X06NHSo0ePkP8sW7ZM/f6tt96SGTNmhHx3yimnyMCBA109BkIISSW8Km6kdekU8r6luFRSHa+eK2LMlPVlMmn1Rjlv6/7SuV1oSGJCiPdopLcc8QGPzFwuwzvnyP79uiW6KEnDJ8uLZFFZlarXdI9PuOCBnsxgbaipqUkyMjISXRRPooVuT/N4OyXET6yqrJX87Ez1IIQQEj+uj6aYGD300ENy7LHHyqpVq9RnyA357bffqgdo37695OXlqc/Ly8uloaFBd1vjxo2TK6+80tXyE0JIMuOn/GPhomTz/CUJKwshsVJS36ie65pa82TbhY+6MnEALkOmNn7q/qsra1WexstGDZT2mRQY7KKioVH+LCrzhShZ3dgkHX1w7msbGbrZCW6fulh279VF9tiiwPJ/evfu7UhZkoWvVm2UqUVlMnHMkEQXhSSAxuZmWVxeowwoiH28vGC1tM/IkMu2o1MMIYT4MnwrKCgoUN6SY8aM0f2+trZWioqKZOPGjYaCJETNhx9+mNZxhBCSoqR169Lms+aVaxNSFkIIIbFBAT21mbMpHGZxnf49H0luyuob5MEZy2R2cUWii0ISyNQNqZuCwQlmsD5Tmp/Wlsh7S9ZJKa+rtsO84oQQ4nNREvTs2VNefvllmThxYlSWbttuu6089thjcsstt0i7du0cLSMhhBCRTI+G/knv2/ba0byKoiTxJ3Z7Nnm02xKX8JOnHCEkdalqaPU+XFud3OFGif+pqG+UJ2avCLRZYg+NzS3S7APrJEQT+njZetlYWy9ep6axOVC3JDVZXlHjqwhYhJDUJKHBsDMzM+W0006TE088USZPnix//PGHzJw5U4qLi6WsrEx5QXbq1EkJmPCq3HXXXWXUqFGJLDIhhKQcIwtyZVVVrXiNtKxMSevaRVo2lgQ+Y15J4iQ1jU3ywIxlcvTgXrIlQyIRYgsU0AkhhETDU089JZWVlZKbmytnn322K5W3oKxKSuoaZEl5tWzbNc+VfaYCd09fIt3bZ8uZI/uJl0GahVnFFSrc9IShDB9sF1+u3CDLyqvlnK3727bNVGdddZ28tnCNHNivm4zpHpruhhBCvIQnMvRmZWXJnnvuqR6EEEK8RbqkqfwJXgxXkr71MGn68Y/A+5bS1vBPC0qrxC/Qoc0/VG6yjl9UVu15UZLGsakNx5XUhv2fEJLM3HzzzbJ69Wrp06ePa6IkcY4iH3gfEmdA7lNiL7WbPGUr6NVNCPE4nhAlCSGEkFhI65wf8l7zmlxY5h9RUgMhVtLoMkQISTEooBFC/EpRUZE0N7cuAHuJAS2tEU4KCwvF66CsWU3pUZX18MMPl/LycsnPz3fvGCtrVFmbykqksLFavEy/lloVEtUv5x94vawNzc2qrJ3rmj1f1o41laqs5RuLpCnL20u+fjn/fiprTW1D67haWSaFhamVrzs9PV26d++e6GIQQizi7SsUIYQQT+BVr5v07l1D3jcXbkhYWQjxEtS3UxtmkSGEEOfHVQiSTR6MJNI1O0M9e7FsemXNSEuPqqzDhw+XqqoqycnJce0Ys9Jay5olLZ6v14KsDGnxQTn91FZbmptVWXMz0jxf1o7paa316tHxyY/n309lzWhpbavt071fVkJIakNRkhBCiG8XuNO6dQn9oLJKWhobE1UcQjwDvc+IX6CATghJBjIyWhesvUB++3aeK5NZWTPS0qIqa+/evaW2tlbat2/v2jG2z8pUZW2Xlen5es1vn63u3bxeTj+11bT0dFXWDpkZni9rx+wsaUpLk6xMtNV08TJ+Of9+Kmt2VmtZO2Rneb6sdkHxlRB/4gtR8ocffpBvvvlG5Q3Izs6WIUOGqJAdsJAjhBCSuoSHb8UdeEtZRaKKQ0jMUEQkqeDdTgghfr32mY2rWPjt2bOneIUFzcXquWfPAvE6KCsEn549O1v+z6pVqwLhW92q96aqWiksr5a8TjnSs2OrOOFVFjWXSLO0+Ob8A6+XtbG5WRa1lEpmuyzpWZAnXqa8tEpKauqka7dO0jHL26KUX86/n8paWtcgK4srpENuB+mZ10FSAYTUpTBJiP9IiCj59NNPy8qVK9Xrs846S/r166f7u+rqarnsssvk+++/D/n8u+++k+eee04lNcf3hBBCUpSOHaQuK0vaNWzOl9CyrgjLQ5JK+GhNj7gtHrFxpCR+Ou1+EiUIIYQQQgghhBASH6778k+dOlXuueceefPNN2XBggWGgiS48cYb2wiSwfkbnnjiCXn88ccdLC0hhBAvu9ykpaXJhi6hIVyb16xv87uWlhapa2oWL9OS3KcqZkvPp+es9Py58yIMiUlUO0i6UYEQkkz46VpF+wlCUhf2f0IIIcTnouSzzz4beA0vSSNmzJghH374YZvFZ+QNCObRRx+VJUuWOFBSQgghfrgLK80PDaHTUlbe5jd/FZXLfX8vlXqKW77i7w0VsqG2XtZW1SW6KL6D3mdEtQOvD+A+EyUIIcT7oyohhBBCCCHexlVREnH/kR8SFBQUyLhx4wx/++qrr4aIkZdccon8+eefMn36dOUhmZfXugjd0NAgL774ogulJ4QQ4kUqcjqGvG8pLmvzm2UVNeq5oZlLSYSQ5Ic6X6rDax3xB34yoOG4Skjq4qf+76NhlRBCSArjqig5bdo0JSKCHXbYQTIz9VNa4jdff/114P348ePl/PPPl9zcXCVQ7r333nLdddcFvv/iiy9UaD5CCCGpdxdWnpsb8r5lbWHCykIIIV7AT7NiTuEJIYREQ9euXaV79+7qmRBCCCGE+A99VdAh5s+fH3g9fPhw09CtVVVVgffHHXdcm98cdthhcuuttyrvy9LSUlm1apVpfkpCCCH+paK+UfKyM3VDEq7vWhDy25aNpZLR2ChNBoYvnsPjoi9pC8NNEr/AnJKEEC/jp+upn4w9kp1TTz010UUgKYaf+r+PhlWS4m2VEJLauOopWVxcHHjdpUsXw9/99ddfIb8bPXp0m9/Ay3KrrbYKvGdeSUIIST7gQVNYXSePzFouC8s2G6sEU5GT0+azDnWx5SBsaG6WH9YUSzNdd0iEdkmIH2BOSeJ1OJwSv8E5ACGEEEIIIT4SJaurqzfvON1418gbqbHjjjsa/rZHjx6B1xUVFbaVkxBCiHcob2hUz0U19brf12VnS1OYqX2H2tqYFuT/KiqXX9eVyKKyzdcrPyzMltS11hHxL34Qj4h/oJU8IcQP+Eng47hKnGDRokXy9ttvq5RExLv4qf/7aFglKd5WCSGpjauiZMeOHQOvy8rKdH/T3Nwsf/75Z+D9mDFjDLeXkZEReN3YyAVZQghJSdLSpLZ9+5CPOtbWxXUX5zdPyfmllYkuAiEpA/Kbex0/jWA+G26JzXi/NxFCiHP89ttvct1118kDDzzAaiaEEEJIyuBqwq1u3brp5pcMD90aLFjCU9KI4N/l5eXZVk5CCCH+orp9e8mpqQm8zwnyzI8GTWtoSdTCPFdnfYEPNKkAdrblH9cUy9KKGjl1eB9JZVp8pKIxpyQhxMuk6vWUxMe7776ronDB6P3oo49mdRLH8VP/99GwSlK8rRJCUhtXRckRI0YEXv/0008q5Gq4mPjOO+8EXufn58vWW29tuL21a9cGXnfq1Mn28hJCCEk8Vtb/K3I6SveSksD7vCr9/JORSN+0Ouam5kDRwH/4SJOylV/Wbe5jhKSiKOEX/DRE+amshBBvsHz5cikvL1frRYQQQgghxH+4KkrusMMOkpOTI1VVVVJZWSnXXHON3H333dJ+U9i9r776Sj766KPA7/fee++QEK3B1NfXy+LFiwPvKUoSQkjqUpmTE/I+t3qz12Q0aGvjzVwmJS5BPYakOqkq8pNQaKCTmvip//N67Szz5s2TmTNnSpcuXWS//fZTny1YsECWLFkiDQ0NMnDgQNl2220tbw9rRfgv1p66du0qo0ePltzcXMv7XrZsmcr3WFpaKltuuaV0G7Kl5X1DMJ01a5asX79e2rVrJ7169VLlx/aD17M+/PBDmTZtmnqP9THklgwGxxts2G/n8cVTt6mIn/q/j4ZVkuJtlRCS2rgqSnbo0EGOOuooefnllwMi5JQpU2TUqFFSVFQkc+fODQmJdfzxxxtuC2FeMYEC6enp0qdPaocSI4SQZL2JseJFU9suO+R9u/r6mI4nEL7VixVBPAM9u1IbP+SUJMRvtHhyBkIIcYsff/xR7r33XhUpq3fv3sqAHWJaMBAH77jjDtPtfPPNN2o7ENyCyc7OlpNOOkkuu+wy9dpo33379pXrr79eZsyYEfj+zDPPlBPPjyxK1tbWyu23367Cy2prVRqZmZmyxx57yFVXXSWDBw9Wv0UuSQ2shwW/B1dccUUbUdLK8cVTt2aRygghhBBCfClKgosvvlgmTZoka9asUe9LSkrkhx9+aPO78ePHK2svIyBoavTv31/lEyCEEJKa1IUtLmTXhy4EWCV9k20hl0YJIcmQU5IQr0OJP7Xxk40HR353gHfhv/71LykrK1Pee4MGDZINGzYoY3Z4951yyily0EEHSVZWVpv/PvXUU0p80yJpjRkzRoV4hdfj33//Lc8995zyfnzyySeVYbvevrF9eDpuv/32yoMQYqJVL0IIjl9++aV6PWTIEBk6dKgqJ9IOLV26VL777ju1zgVREiLiMcccozwe4S2JtEYHHnhgyPaGDx8e0/GdcuMdyEkRU92++uqrht6ZqYyf+r+PhlWS4m2VEJLauC5KYtL04osvygUXXKAmPnrsvvvucvPNNxtuo6amRj777LPA+6222sqRshJCCPEHdWELE+0aWj0lY10ca06I6IB98jaSEJJa+EmU8At+0s19VFSS4m11M5yvOQk8BiHYPf7447LPPvsEPod4B49FGLd///33sv/++4f8b/LkyXLfffep1xMmTJCJEyeqSF0aP//8s1qDgtfga6+9prwK9fbduXNnJcwh9VAwa6pqTcu9evXqgCB55513ypFHHhnyfVNTkypDz5491XukMLr11lvllVdeUaJkt27d1Hsjojm+fh+8K3sfdUxMdQsvyvfee8/0WAkhhBBC4qWteZgLwLPx/fffl3vuuUcOP/xw2WmnnWTHHXdUoV0xQXrmmWdMPR8hSCIGP36DB+PfE0JIai1Y/VVUHvK+Pls/fGu0pG9aHfflGhkhxBUYvtVe/ClKELthTklvA2Otx2atkFWV5sJMMp9/75cweTj//PNDRDPN81AL3QrvvsLCwpDvH3roIRXJAN6DN954Y4hgpxm+n3XWWeo1jOSNgCgXLkhaYdWqVeo5JyenjSAJMjIyZNy4cTF7IUZzfF+/82bMdTt79mz57bffYipjMuOn/s9pVWrjp7ZKCEltMhO248xMFboCj2g5+uij1YMQQkhqMn1DuRzcv7thTskOtbVxTeIZnpEQQghxD+aU9DY1jc1SVt8gkwtL5djcXrZvPxXOf2ldg9Q2NYd8VtfYJA3NLZKbbe+yTEldaxqDddV1YjftM9Klc7u2oVPtNPyBJ6Aeu+yyiwo5ilCoCIk6bNgw9XlxcbFMnTpVvUYIUr3QrABi4cMPPywrVqxQno19+vQJ+b5du3Zy8MEHx1Ru5GoEVVVVyqtx1113FbuI9vjWr1klG9etFeldEFPdwuty7NixtpWfEEIIIcQzoiQhhBB/0CXbuYUHu0L7VeTkhLzvUFcvWQ0NcYRvjfqvhHgydCWbsgN1Stc+QmyDFv2pjZ/CN8dzPa1ubJInZq9sI75qomGvju3ETrTtzi6uFLtB/vWLRg2QjpkZ4gRbbLGFFBSEimnBbL311ko4Q95HjXnz5gVeQ2zUwo9q1+vgZwh6zc3NyrMxXJTs16+fCm8aS5tFNDAIefAyPP3009XrPfbYQ7bbbjsV2SvSds2I5fiK1q4VGb11THW7ZMmSmMuarPhpPu2jYZWkeFslhKQ2FCUJIYQYslvPLjIwPzQ8kBcn1eU5uW0+y6+sjGmhJZr92gFvHAkhqYyfRAliP1w88xk2G2Wkio0HBLxzt+7XxlPyj/Wl6nnnHp1t3Z9T29U8JZ0SJEF+fr7p9506dVLPDUHGh6WlrccL7r77bkv7qdS5T4CnZDxtFjkfb7jhBpk0aZL8+uuv6qFtd6+99pJzzjlHCX/REsvx1VZXxVy3FRUVUZeREEIIISQaKEoSQggxZPctuiS8dqwsWDVhcSQ/V6S8Mi5RUlscT4QnVIqsyxHie5hT0l5SRZQw4vapi2XvPl1lbE/7xQM/4YecgqmM08YDfjj/8ZZQL+Rpl02f2e0p6dR23aCmpsbS9927d1f5FUH79u0D3yPNj5XrNLwG7aZr167y6KOPKm/GX375RaZNm6bCri5btky+/PJLJVY+8MADsv/++0e13WiPr7C6Tgp69Iy5bjt27BhV+VIB749Qm0nxaVXK46e2SghJbShKEkII0WVIfkfJ8JELS3pBZ2kOESXbWggTQgghXuPP9WUpL0qmQk7BZKAlhc+/90uYHCCsam1tbYgQF8yiRYvU8/bbb6+8D7XQqRrnnntuyPtEgLCw//d//6ceYPHixXLVVVfJrFmz5I477ohalIz2+H5ZWyLNOi3Wat2Gh7UlhBBCCLEb/QzZhBBCiM9I6xrqZdKlbHOuGUJSZdE0nFT3QnOCVM0pieNuTtFjJ4Q4g49s34hLNDY2yhdffKH7HTwOZ86cqV7vtNNOgc+HDh0aENLeffddz7XZIUOGyAUXXBAQBuvqWnN+gqysVq/WpqYmw//bdXyx1C1pxU+zHw6rqY2f2iohJLWhKEkIIcTTWL35T+saGmp24OrV0tDULLWNxjf5XoJr/XbWZYs88PcyWVJebeNWCUltfl9fJndOW2L7dilK2L+A5McFKT+E7yT248e5jw+L7HnCe/8999wjK1euDPmsurparrnmGjXHQ+jWfffdN+R75GsEzz77rHzyySeG+4IgOGPGDNvbLEK24mEEwrhqeR2Dc1d269ZNPa9bt840vGo0x7dk7mzD72OpW0IIIYQQu2H4VkIIIUlBer/Q3DCdKyrlk1mL5LuCAhnWKSdh5SLu09TSIjVNTTJ5XakMzrcvLw4XzYlqBymqos0rcSYkth9FiVQEi9Vrq+tkixz9sH8kuXF61PPD9dX7JfQvwZeB3NxcKS4uliOPPFL++c9/yqBBg2TDhg3y4YcfKtEP1+Abb7xRsrOzQ7YxYcIEmTJlinz88cdyxRVXyPPPPy+777679OzZU4UsXb9+vSxfvlx+++03tc333nvP1mP4/fffZeLEiTJ8+HAVWhb77dKli5SXl6vvfv31V/W7448/PuR/o0ePlszMTKmvr5fTTz9d9ttvPyVcgm233VZGjBgR9fF179tfrn/q+TZljLVuib/6P6dVqY2f2iohJLWhKEkIIcTTWF2wTt9mS2np2EHSqjdbGXcrKZUNBQXOFY54Gt6UE+JAv2ppSVlhNpVZUFYt7y1ZJycM20IG5HWwfft+DI9NUuv8e7+EycGAAQPklFNOkZtuuklefvnlkO86duwo1113nfJ0/Pnnn5V4d/nllwe+v/vuu2XLLbeUp556SuVvxCMc5FPcbrvtbC933759VYjV+fPnq0c4EB5PPfVUueSSS0I+LygokAsvvFAeeOAB5U2peVQCiI+aKBnN8Q0euXXMdQtRlLSF/Z8QQgixF4qShBBCfMG0IvMckWkZGZIGb8n5iwOf5VU549kTL3VNzdIugxHUnSPNd4umdnuLpfriSVVDk2RnpElWuv39LFVzSlKHTG3K6xsDfYsQu+C4QvSAJx+8ACdNmiRLlixRuRAHDhwoBx54oPTo0UPuu+8+g/aUJmeffbaccMIJ8ssvv8icOXOkrKxMcnJyVFhSbGPs2LFKuAsG3o3HHHNMIG9jLG125513lm+//VYWLFgg06ZNk7Vr10pJSYkS+wYPHizjxo1TZdfjvPPOk7333lt+/PFHFcYVXpOYa6BcsRzfXyU10mwwE4xUtyTC+fdBBfmhjMQ5UvMuhRDiRyhKEkII8QXlDa0LoqZ0bg13pJFX6T1RsrC6Tp6bt8oxbxNCiMhDM5dJ747t5LQRfVkdHoeihL9wyjjDD+E7if340cbDj2X2Onq9H7kWEbI0FhCmFCIbHlaAYIiHHecfnox4RAs8IoO9IuM7PuPclPHWbarD7k8IIYTYA900CCGEJM+CdZgoOWrBQvEaxXUNAXEyGN7keh8/LZr7p6TOgfx3TmBn6FJ4Qvy9oVwam1tStk05scBf09hEjz6b4ZhCUv366v0S+hfvXwFJquOn/s/+lNr4qa0SQlIbipKEEEI8PaGNasE6P7fNR+lNTZ6+maOXkHPYHWbTDzmvNPxT0tRmXXW9fLaiSH4rLBW/4Ie29cCMZcpb1i/4KSSwUyX10/hKUvP8e7+EhBCnYP8nfoFtlRDiFyhKEkII8eyENupFqp7d2nzUqbLSk0KsF+rX62jnP5mFWz94hxDnaNokRiHPLCGpJPCQVkrrGqTSSnh6j8CWlbqsraqVik25ZVOZZJ6Tksj44fT7oYzEedgOCCFehzklCSGEJA1pQ/q3WTDrWFOboNIQkngoThBC/ECqGmg8PnuFep44Zoh4GafPTqqefz+xqLxaPe/Ru8D2bePsDx8+XI455hjp06ePeBkfObYH8Evd+gEfnn6SorCtEkK8DkVJQgghSQMWtUry8qRzRUXgs/Z1kfPKzdhYIfVNzZKbleFwCUksi5SxLgCl8s0Yl3cJIanK3JJKmVVcKccO6ZXoopAkgtdV58B8bdy4cepB7Id1m1qk8v0P4bWKEOIfGL6VEEJIUoVJqs/KCnmf3dAQ8T+fLl8vX6/aIImEHm3E3vZE/AAXuQmx/xr40dL1sqisilXrI/wwB/J+CYmWo3dDTb2vcvUSe1lfU8ew+CRl4chHCPELFCUJIYR4erEo2jWF+qzMqEXJROBEjfImhBDitIDKccYH8CQRQlKUioYmmVtaKWurI0dKiQXmlPQ+80urlMd8qhqT+aGMxHnYDgghXofhWwkhhCQVep6SrVlw/H+DAKvv79cUy849OktOUKjZ3h3bqcWXDK6UhNZXHHVNCCFkM9rlxSnnIztzCqqy8gLgK5hTMrWJtvcfddRR0tjYKJmZbZezmjYNUo3NzgwCdMD0B46df0e2Soj9sK0SQrxOwkVJLLDOnDlTZsyYIQsWLJDS0lKprKyUhig9W8444wzZa6+9HCsnIYSkMglbLIphNt0QQ/hWO6hrapYp68vkH706S1os4qCFYy2pa5TfCkulsqFJxg/sEfg8JzMjqT10Y9VaU3nhiNax/sIPoQtD+hUbGCHEoxTXNsikVRvkqCG9HDHW8s9o7R+irdOBAwfavs1EoIWYjem+gRCjdpXiNbOxtl6y09MlLzvhy90JgaMJIcQvJGyUbmpqkldffVVefvllWbFiRdzbO/zww20pFyGEEH9PaMM9JdvX6YdvqmxolNywUK/x8PPaEvljfakMzu8gW+S0t227JDWwewEh1Rck/ALXIQlgfyV+wSljHzsNM35ZVyKLyqulrK5RCtqHzgkJ8RIrK2tleWWN7NG7QLxOY3Ozes5MZwYou2hqbpEVlTUyIK+DpHNCaBtzNoXu9UO/cgLOKQkhfiEhomRxcbFccMEFMnXq1ETsnhBCSBJ78ZTl5oa871ZSKuGmL8vKa+T1RWvk1OF9dAXEWI622UW3PP+cjUTBGiKEEEJS2VLNT3NX4h/s1I6KauvFL0wuLE1poceJoXBNda2sqqqV/OxM6do+O5WH65QFkZay09Mc8ZZmOyCEeB3XRcn6+no5//zzZdq0aSGfZ2VlSdeuXaWqqkoqKirUZ927d5fs7GwpKSmR6urQjGCdO3eWLl26BN7n5eW5dASEEEK8vABV1DX0Zrnnho2Stsm6N3wRoLSuUbbIkcQQw1oZjWgJIST54MIRSXVszSlq25aIW0R7zpYtWxbIKWkllKudpHJqAGKv2aNznucObLOl1cyDHp320dDc3BplKa+j9Mm1P8oShypCiNdxXZR85ZVXQgTJfv36yeWXX67yQXbs2FGuvfZaeeedd9R3l1xyiRx77LHq9eLFi+XLL79U4V7haYmck/C2HD9+vNuHkPKsXr1apkyZom4GysrK1M1AQUGBEpUHDx4so0aNUmJyLEB8/vHHH1VI3/Xr10u7du2kZ8+esvXWW8uYMWMSlm/BjXIlyz4ISTTrCzYbrID0lhYpWL1WJL+zeAFH+lmS9t34FylT93YsOVsE8QZMKulEjRJCSKoS7Rj43nvvSXl5ueTn56u1JL/CuVpq4/jtm43bn1daJRtq61PeU9bu8L2goqHR1u1yXCGE+IVMt/NIPvvss4H3W265pRIZ4fUYiSFDhigPywkTJsill14qf/zxh1x55ZVSW1sbEC6Js0yaNEmefvrpNl6u4UCQhDD50EMPKaHSCripuOuuu+STTz6Rmpoa3d/06dNHzjnnHNUG3MKNciXLPkhyYGSxaacFu50Wm3oCX02HDlKcny8F5eWBz/KLNuiKkgzt5W14fuKpO+In6HlBvI73ZgEkmbDzep+kdlrEI7B9pSa+nKfZWGYIksQf+LGpEkJSE1ezVM+ePVs2bNgQeH/33XdbEiSDgcj15JNPyvDhw9X7m266SXlREueAoAVBWC/srlGI3j///FMqK1sTTEdi5syZyuP17bffNhTMNA/NG264Qc4991wlRjuNG+VKln2Q5Mdv4tC67t1C3nfYFBZcw4vrVVZqOM3oztiXd8rOw1ohfoFtlbA9ET/AsYo4gRfn5YT4ta36oYzEedgOCCFex1VPyenTpwde77DDDjJixIiIXjB6aGFeTznlFBXGFd6Xt912m+3lJSIbN26UU089VRYuXBioDngmjRw5UnbZZZdA3s+ioiIlWE6dOlWdE6usXLlSeedhPxoIw3LIIYfIoEGDlIj2999/yw8//CDNm3LCfffdd3LVVVfJgw8+6Fi4UTfKlSz7IMSLVOSEJorsWBYqSnppoY69jJDUxYte6IQQ4jYcC4lfBHTaAab4+Xdou6lWRuI8bAeEEK/jqigZ7CW544476v4mWAQxE7cgiPXu3VvWrl2rck3Cw6t9e/uTA6cySB4Pr7lgQXLnnXeW66+/XoXe1QP5Pt98882QML1mojPyhgYLZnvuuafcf//9kpubG/LbWbNmqbJA/AQ45y+++KKcdtppcRxh4sqVLPsgJJEsKa+Rtxav1f2uND8v5H1OSakkk/cpbzIs1hMrinVAbEObo7NbpTY8/yTV8Vv0ED9gZ43S1IcQdoZUhmMgIcQvuBq+taysLPC6R48eur+B152GWahJsNVWW6lnhAldtGiRbeUkrTz11FMyY8aMQHUcdthh8sILLxgKkqCgoEDOO+88+eqrr6Rbt9DwieF8/vnnKqSvBjxnH3vssTaCGdhmm23kmWeekaysrMBnjz/+uOUQsdHgRrmSZR8k+fCTc+zskkrDVY2yvNB23q662vHypEX5u1gWYHx0egghhCQRfvJk809JiZPQMImk+r1Sonj11VeVMfTZZ5+d6KL4CqzfoN4mTJhgzwZpP5GS8LQTQvyCq6JksCejUVhIhGbV0Ly2jEAYSqu/JdGxbNkyJUppQIi84447JCMjw9L/IU7mhIVPDCd4++DGG28MEcXCgah24oknBt6XlpbKa6+9JnbjRrmSZR8kyfH6XbfJalNNu1DP+az6BklvanKhUMQJ0jxyU+bkAic9L4hf4EI/ISmIj1Y5/SSgk1Z4xpKLqqoqKSwsDImUliykOTi8wikD9Rbv2ib7k79w6vLKdkAI8TquipLBnnMQPCL9BsKYVc/L4Nckfl566SWpr68PvEcOTzNBK1qWLFkiCxYsCPF6HT16dMT/HX/88SGC9hdffGFbmdwqV7Lsg5BET9LNvqvXGa+yGhslWYn1ZmZlZY2srqwVr+OjtVBCYoJtnBBCCEk1wxzKBqkdatiZ8885pU9wuPuzHRBCvI6roqQWbtVMcBw2bFjg9fTp0w3zSiLf4d9//x14n5cXmj+MxA4stD7++OPA+8GDB8vYsWNtrdJvvvkm5P2BBx5o6X8DBw6U4cOHB94jPOmaNWt8Va5k2QdJTpLFA6YhK9OSKOnXw7Wr3K8sWCMvLVht09aSHyech7Vt0rMjNfG6QzohfsQoIg8htuDXyaOHYZUSInLwwQfLDz/8IG+++aY91cFLoadx6t6Pp50Q4hdcFSW32267gLddcK7CYLbddtvAb+BN+dFHH+n+Dhfq4uLiEOGM2MOPP/4o5eXlIZMju5k6dWrI+x122MHyf8eMGRPyftq0ab4qV7LsgxAv05CZKc1hi5KdyyscXciMd0GFCzL20eJQSFSGbyWE+AFeTwhbF0l5HBoIafNAnKJDhw7Sq1cv6d69uz0b5GQgJeFpJ4T4hbauJA4Cb8Zdd91ViV7wlER4SeQqDM8T+Y9//EO+//579f6WW25Rz+PHj5fs7Gypra2Vt99+W+68887Af/r16yeDBg1y81CSGnioGolUyA8A662lS5eqHAHIG4mQuxC9tt56a0lPt6ZzI7yokRdtJEaOHBnyfvHixZb/64VyJcs+CPEyLenpsr6gQHpt3Bj4rO+6wja/K6yul20KJCEki1eqnbS0tPjCw8X7JSTEOTh0pShpm8dpQvwAW6p/CJ5XIS3PG2+8Ib/88ovKr5eRkaHWe/bee2856qij1JrQ5ZdfHvL/V199VZ588kkZMWKE3PXwozJ36p/y6pefyeqli1UUqC222EJFDvq///s/05Q0zc3N8vnnn8vXX3+t7qerq6ula9eusssuu8j+Rx0r0i4nruMM335JRaXkdu4i++2+m5x88snSo0ePNv8JPrannnpKRUSC4T7+j6hiiHT0z3/+Uw466CDTfVupV7Ny//n9tzLt5x/l3tUrpLKyUglnWIPDOh2iapnN3ydPnizvvPOOWv8LPx+xEF4n3333nXzwwQdq3QJ1ktdrC9nnsCNkp2OPDElLg2hgy5cvV7/B+sgZZ5yhnCKs1Nm6deukXtJkiz595fAD99ets3/961+qDBMmTJALLrjAcLuzZs2S888/X5pF5PL7H5WturRGqkLbuP3226V3795tvCXDj9msTs3uU7CO9+KLLyrDc7RvnMfdd99dTjrpJHWc2H/fvn3ltddes3AmrJ+TAQMGyDHHHCMHHHBAyDl54e13pXDVKsmWZkvnxKyP4hj0+hBYvXq16jt//fWXrF27Vjm45ObmqihhhxxyiOy1114Rj+vkm+5QY8sH336pW+92pruyA96vEkK8jquiJMCECaIkePfdd2XixIltfnPeeecp4Qs3vbjIXHPNNXLjjTdK586dpaSkpE1I1zPPPNO18qcCM2fODHkPsREX3Pvvv18JwjgnevTs2VPOPfdcdUHOzDRuWshVuWrVqhCxOprwu5ikOSGauVGuZNkHIX5YbFrdq2eIKNlzwwaVQ3FEl9zAZ3+sL5V9+3YV35CW3DcdKyprZUBeB8+vSHKhM7VJc2JbPhB6kmWcIYQQ4k3+/PNPJeYgYlb4/SiM1iGmQPCAmBYMDKchtMFY+t47bpe3Xns15PuVK1fK77//rrYBgUHPkBqiE4QipC8J/y+Mtl96+WU558ZbZcv9943p2Iy2v37Nanlqzix55ZVX5KGHHpI99tjD8Nj+85//yPvvvx/yPYz9cVwQB2G4D7HRrnrVxJyLLrpIt14QnUlb0zvttNN0jxtOBi+//LLh+TjpxtstG7br1cl1112n1qhCWLZMZv72q9StWqqO++KLL1YiWehPlimR6oknnlCinNU6W7t8mfz168+6dQZh7Ndff1XlwZqm0XFBrEP5h43YSrpv0SfwOdbc8LneWlrwMUeq0yvuvE93v1999ZVcccUVar1IY8WKFUqoe/3119VaLfbRrl07iZZI5wT1jTVerBfGek4i9VHUCfqQDN065Huk/TISwOfNmycffvihEvXvvffeNnUffFyvP3y/THr3rajHlkTh/TsLQkiq47ooCSuSPn36qMkNrI5gCRNuzbL99tsrofHpp58OfIYL5/r169tsD5ZdsEQi9hHsZde+fXs1OUIdw6rMDFysb7rpJmXx9PDDD0unTp10fwcPy6ampsB7hKiIhnDRTK9dxIIb5UqWfRDihwn1xs6dQ97nVtfIq0sLZWKQKOmlBXsrmoSWe8IH+kVM1DfDZngzLR4TQ5x04nTqnCZpU0korFNCvI1XrhmEaLRgflNV06ZCMjcZ+7ZURC8CmOHEdgPbbO4saTYvvLdsWtw/++yzlQgA7y2IFjvuuKM0NjYq8QJCBdYpsE4EYa5jx45ttjNnzhwlWGz/jz1lwvEnyE4jhiohA15fn332mTKOh4h27LHHhvwPnn+nnnqqEkUQueuss85S0bvwGp5lEBsgUj12w0TZcdib0nPkiKiOz2j7KxvSZOmypfLz26+p7UP8e++993RTE2nHNnr0aCV4wUuxqKhIranBcxJegPCavPDCC0P+F0+9wlPwlFNOUQbPWdnZss9Rx8qphx+q1u+w5oD/Yb9GHvRamffdd1/lxQYBL/x8DPr8E9nj0MOjqs/w7UNQOuGEE9S6B9as/nfX3bJi4QIlGiJt1G+//aaO47DDDlNrVBCiIOCuWbNGCWgQwoLFKL06227MDjKtsFgW/DFZ3n/ped06O/zww+WBBx5Qnnh//PGH8iANB/X+6aefqtf7Hzo+6omllTrd7pOPZLv9Dw65GEKA1gRJtB0cE5wP4HCA9btnn31W1Ve8GJ0TODjgu/Bz0nfsnpKTny+5G9eanhOrfRR96Nonn5de/QcE/qt5FB966KHKCxNlwjon6g1el+hzqANE8TPycA0eWy4581+69a43tiQCzoEIIX7BdVESllsIM6B528FlXo8rr7xSfffYY49JXV1dm+9hgQL3/+uvv94Xod78RHA+SYRnxQVfEyQRkmDcuHEqPyi+g2AFi6e5c+cG/gNLIYjNuEDrhQEJ97TEdqIh/AbEyHMzWtwoV7LsIxKYoJkBj2fifbLTEz+2/rCmWOaUVMb036qOoR53OTWR23Jx7WbLUaew45LVEmcOxQdnLJOD+tuUrySFSFYxmCQeNi1ia3tigyLEMzRNmy3173wmUlHV5rvtNz3X2rxPJ7Yb2GZejmQfc4hkjA71RooXiBYQgSAywGsr2AMNYsGoUaNUeEyIEvAqhGgUDsSxI485Vg678HLpl9NB+uV3UNvZaaedlAE1PMI++eSTNsLB448/rrYLoQLhGoNTDOH/u+22mxwz4TiZO2umPPvYo7LTIw9HdWxG268vKpecnr3kzEMPkBNPPFF5dD344IPqoXdsMN5/6aWXAmss/fv3V2l0UGeoEwgzEIKCZYl46hWG5hAkVcjc+x6WoduMkh16t+a9wHYgkB599NEhBtHhZYZx+8033xxSn8Hn4/dJX8UsSmL7xx9/vIqqpoEwoZd03UL+c9xR0lBfpzwXb7jhBlW/GhCoYIgN7zkIiBCzggVEvTqrb2qW9TldZJutRsj+Y3fSrTOE8sSxQZCE952eKPnTTz+p0KEQ3PY56CApjeGYI9XpD19+3ipKBs0F0KYgSKKMELIRhU4DYVNRJ/DEjRejc4IUQfvss49a2w0+Jz+tLVa/2WOn7UzPiZU+iraPPvTBc0/JuTfeGvge/ebLL79sU1aIs0gvhnRVV199tepbRh6uOK49xx8hp1zxH9ktqA9EGlsSAaeAhBC/kBDf8oKCAhWnHA89CzcNuPYjVjiER8Rsh1ckrJsuvfRSNeD/73//M419T6IHXpHB4XE3btwoixYtUq8xkYBV16OPPqomXphEXHbZZSr8BKyags8FQsBiMqdHuMgV7TkMDyfhlCjpRLmSZR+RgHBt9vDCZI2Y06NDOzmwX7eEV5OZIDk3glhZ1SFUlOxYWyfpYZ544fxeWCapQHVjk/yy1vvGAWkx3lxpC/JO3ZQ5IdfTvor4BavGDySKOmWVEpK01L/xka4g6VsqqlqPyUYa6mpVaElw+umn64YRhXig5aOD8TNEBoRMDDeAP//iSza9Cx1YEd4ULFy4MORzCGpvvdUakhHG2MFihwYEpLMuuli9/vn773SN5o2wun0tT+akSZMMtw/Dfb17e6zJdOjQQYlO8PzSqK2Nrl6xrqOBNSF4fgF4tEGQNEIvZKz2Ocqmh3Y+Vi/dHKUrWlBvWBsMp1NBgWy5bWt54RUHkSwcGNkj/VB4m4hUZ2kmdQaOPLI1jyXaJ7YVjvZ7hCftUhB9ChErdboiLP0O1nO+/fZb9fqcc84JESQ1ELoV+RXjxeicIPwpxL9Yzkm0fWj6Lz9JQ1AfMkstpdUbnC8Qqldb+9Sr96POPM/w/+Fl9gKJNy8nhBBzvBPw2gBclBCWAAmXEVoCcb5hvTJkyJBEFy1pRUk9YFH1wgsvKCsnPTD5gjAZDCyYENYjnOAY9iDahNDhE/FobgrMcKNcybIPkvyM6pon7TP1bzD9QrinJOhoMMYlAq5B+xcnzh1FCf/Am3xCCPEumpEPDSj8w/IFCwKG0TBENwIhKwE8qRAuETkNg8FahZ7goq1nhEeFAvPnzw98tueeexrue8Q226gIXSjnggULLB+b1e3DY9Fs+zDmR9hVPRBhDB6TAKExg0NORlOvCEmKMJkAkbA04+SDDz5YYgHno0uXLqbno2bT/mIBnqJG57tL99aIMNtss41hnj9NAAtuE5HqrMWkzrR0VRCI4WkJgTmYioqKQB7FI444Qpyq08rKipDPcS61YwrPWRqM2Xd2nBOtvqM9J9H2ocaGhjZiN4RNhFn997//rRxecP6wLTxwnhFW1yx1Eeo91yA9ldHYkmi41kAI8Tquh28l5rz55pshE0kngBWU0YQW4RD0wMXbKEekxiGHHKKs6X7++eeAmIUY+7BmMttHuIgWiXCRDJM+O3CjXMmyj0ggpG+k8K0I2UH8gx8Fk5p27aQpLU0yggqfU1MjzS0tUlK32SNclxiO140qohgSHX5otjyn/sMP7Yr4Cz94SsfquU6SCz/OB+0m2irIPu5ww/CtvmRT+FY7KS3eGHit580XLHho6Hmhmd23aoa6zWFRUyBwaiDPniaWaHkStefGoP8h/CZ45plnVMhHPSCAQCw02z5CguK+pF1GekheRm37wfTp08c0bRG+B8FG4cGvrdQryoD/oNwISamB0J4lFdGnuLB0PprMo9jEuv2MjMyIv9E86ILbRDx1BvAMwQuR3RDCFXkMNZC3EGsteXl56jdFDdEPqFG18bTQY0L70QQ0szYUD1bqO9pzYrWPBvehirLSkOhvWJNETshI6I0rsY4thBBCzKEo6TGQ8BkTWCdBTHUjURIWeLjIB19QIUbuv//+lraNsJyaKAkQTz9clAwP2RutJ174RMEsBHA0uFGuZNlHJBCSw4xovTcJiYm0NKnu0EHygkIQ51TXqDyVfxbph2lNxFpbbHmRU2tVMNb1+uCbUzvxgX5AfIbdLdWJtp+WmsOPK1DoISR5+xVyL7bfbiuRqrbROn5b3xpKf2wPfc+nWHFiu4FtDtpC0gy8nGIleO3BKBRo+Hd2XeeCDXiDxQ8zNK8zeL4Fi3d6xxTP9oMx8iwL/17z+ALBuR6t1qv2H6v/TTbiqTMNeEFClPzll1+UINa1a9eQ0K0HHXRQa8qchtZ1F8eGqpbQtog2Ynbf6dXzHEsfagz6z7XXXqsESQiLiMK3yy67KHEW77V+A29gu9JCeeVaxftVQojXcV2UhOcckhZH8thze1tkM1q4ieAwCFZFJC1sSHCoiHBycnJC3mMyHw3hvw/fXqy4Ua5k2QdJPfzgxQFadEK4hoiSNTUyvzTUUr0syGuyKYq7gsVlrdsd0ik64T7NJeEsGWDNEEKIN/DJNIA4BK/H8aFEvLy291SNla3Go2k638WDE9sNbNNmQRLk5W+OyITwicEekcEEh1bUy60YS5sNDoWJfIxGBrmF1XWyrKJGhnTqIMN6txrgwvhaLzcegDdcpO3PKq6QqoYm2alHJ0kPutnSC89ZVFRkehza98H/DQ6jabVetf9rQlpACOqgH5Iz2YhUZ2kmdabxj3/8Q7p3767OCcTJU089VVatWiV//fVXSN5Jt48J4im8cAsKCnR/F6mNJQqrfVTj98JSydsU5Q3Hq0Xxuueee2S//fbTFT2NPCSdhPMqQkiq47ooCRHxnXfeieix5/a2vMKECRNkt912c3Qf4cKhXtiG4DwGZiEewsHkCwKmZt2HZNF6Sa5xE6FZPFm1dtII/3005TPDjXIlyz5I6uFX3awqLNQKPCXDbwAem70ipm2/tbi1r0wcY0+OY+Y+8g8+7Q7Eg4sIThl8xOaBTQghhLhP/2HD1HULhnqIHGUknk2ePDlgNGuUdiZakN8OISPhYbhy5UoZN26c7u+aq2qlvEO1dO+U0+rltilUpxa2M5btr83sKO0aGqVXry4hoqQeEFcWLVokQ4cObfMdtq3l1xw5cmTg86222iqqekU+P02wwna0ciN/55ADD5dUIFKdtZjUWbDH4fjx4+W5555TIVwhSn700Udqm3379o24Hmc3w4cPD7yeMmWKynupB77zIlb7qEZBy2aDBYjBmqforrvuqvt7iMXJGHqV96uEEK/D8K0eY+zYseqRSIYNGxYiSkY74cfvNVESzxDHgi0ZMUlDXgJtH7BKQpx7iGlWWLNmTcj7IUPsEQTcKFey7IMkP8mynF0ZZkk59u8ZsnDXnRJaZ2lhv4xGPKDOEB28GaPY7Zd25VfDD5J6sKkSr5Msc9hUQIsWkpvfSXbeeWf5/fff5dlnn1WCTngON4gL7733nno9aNAg28oAURHeU8j39+CDD6pymOWPS+T2H3/8cbn33nvbfP7mm28G8lBiX2uDPOSiqddgsQrCL/Iefvnll/L000/Lf3bcTTp3tbae4GeM6qzFQp2Fh3CFKImwoYsXL1aiJDj88MPdMxzbtBuIpmPGjFHCNY4JbSQ8VOu0adPUMXuRePpQ8O+WLFki2267bcj3EDofeugh28tMCCEkMvbH3iC+B6JkMJWVlVHdWATHYsckQC+0SriF38yZMy3vY8aMGY6JZm6UK1n2QVKbwwb0ED9Q1DXUchU3lBlheT/8SKoIGF5dWPRquQhxNKfkpoafIsOPr6HnPXGubRHiDBdffLHK77Zs2TI588wzZeHChYHv/vjjDzn99NPVOkN+fr5st912tu77yiuvlE6dOikBCeFYv//+e6mraw1Xq62HLJw3Vz556Xm58+b/2rZ9bT5ZVVkpc+bMkccee0zlvzMCoUBvvvnmgACJbbz66qty++23q/eHHXaYDB48OOZ6PeOMM0L+e9lllylxEgbOd1xwtvz14/eBaEwQcyC2PfDAA8obMJkwq7M5U/8yrTONESNGBDwUb7nlFlm6dGmb0K1pLg7Y559/vnr++++/1fGtXr1avYeH4LfffisXXHCB6dwx3KjWbaz0Ua0PvXDXbSFrWYjmBtC3gtNLwYj/7LPPDngZJxu8XyWEeB1fe0oGJ/G2mvOQRAYx8DG51MBkzCrLly8PSfStTQDCQXLpzz77LCQU7957721pH1oeUYAwDjvtZJ/XkxvlSpZ9kNRayAw36PTiolRdU9uwKys25XzRwGHkl5XJ+o7moZZiwYt1QgjxNole5CGEEEK8AFLxXHfddUrAwf0oBDaIEFjzqapqzQePXHJYp4BHV3l5uW377tevnzz11FNy4YUXKtHinHPOUZ5k8JqrqakJMboeHhQeNd7tw0MUUY3qajZvf+utt9bdBsRGGHxDhHz99ddV2SoqKgIRqiCAXX/99XHVa69eofdN8Eh99NFHlYi1Yd1aefyGifLM/25Q+TLLysoC6z5XXXWVJBN6dZaf30nqGxukdlNbMKqzYCBA3nnnnfLrr7+q96NHj5YBAwZIIthjjz1Uu3vyySflm2++UQ/kakT7QxvHeuohhxyi1pDCvSi9QDR9dMCWIwKvIS6jff773/+W+fPnq3MCoR2fo/9o3q4///xzoD8QQghxB197SsJiSwMXFmIPCGnQu3fvwPtZs2ZZ9pbUYutrYOKlB0KBYCKggcmPlTjusGJCuAwNhG7ApNou3ChXsuyDpBZWnG56dWzNr5IoHpvVNjdkeV5em886l7TNdUu8CYVe4kX8ICP6MaekE96dhBBC/MOJJ54or732mjKmhQAH4QtCAYSHo446SnnkwYDaCbbffnv59NNP5aKLLpItt9xS3Ttv3LhRiR0Q4YaP3FrGn3qGXH3DTbZtv6ykWAmS2D7y5kFwue22zV5ewaA+XnrpJeWl17VrV+UtCUESdfOvf/1L1Rte212vyMMHD819j/4/KejRU+0T+4YghOOAN2Ww91+yEF5n5eVlSpCEkGy1LSL0a7DAh5CuieTyyy+X++67T3lxgpKSEiUsI0fjG2+8EVi7Q3v0IpH6qNaHTrvqmpD/IWQuPCgh+GN+jLaP9U3kD/3vf/8r999/f8jaWbLAWTUhxOv41lMSF9BgN3szCyUSHbhQ48INKyqAsAhvvfWWmgCbgQkNJm7B7LXXXrq/hQclvPk0ERN5DBEjHtZZZrzwwgttJnp24ka5kmUfJLXRW+7umJlYq8pag7Csy7foLQPWaNlVRPLKykX6iG8W4Gsam+S9JYVy9OCem/9ruE3r+/eiZmFXkXgTRpzED+2LAh9JZaHbR0UlPsQP1wA/A2HkiSeeaBXtysqUqIMwmcHA2wxCBLzVwAknnKDWL8yiZ8H77YcffjAdy2CMC1EDD4hv8MaE8Xv79u1lTVWtLC6vlqGdYjeGD9/+r8tWS2NWtuw9oLekWxi4kFvv6quvVg+IKpooaWV8tlKvRvTs2VOOv+gy9dihU3vlXQcvO6P9RnM+/lgfvbGole0ff/5FcvNVV5jmHoQXKMLRol4j1VlRSYlM31gpfboVyDYF1kQ7rMngGDWPUojJZhx88MGy22676XoqRlOnS8trRMWU0zk9hx56qHrAuxAPtB9NkHvllVfU88CBAy0dX7Tlg8ciBMV4zolZH9X4aW1reONwo308MG7guNH2g8sKsRNzZ7Rro+Oa0xq5OKaxxQxeUwghqYpjoiRilGux7sPFxODfRJPrDhcJTL4WLVqkknlrLvrIWRieB5HEB2Ljw1oKE1bw8MMPy+67764skoxAgmjEZQ8OsXDAAQcY/h5hQII9K2EVCM+8bt30E6hDVEOidQ1MljBBMANtDFZRGpiwXHNNqOVUIsqVLPsgyUc8oQSbPTqjLs3LkwESJEqWt4ZqSRTR3q/MLamUFZU1srCsWgblG9/EEaKLR/slaSXNhztgkyKEEOI0EErCBQKNo48+OuQ9BAxNxCirbw1nGg7WjKIxZIcIoSci2WX0gO136lIgFQ2bUxJFg5FoE0+9WgFCsCYGGxF8PozQzkeXluyoy2Bl+zl5+dKrp77nqEZBQYHlOuvcuYt0rI/+TtkonZEeEOuMBLto6rS8fZUU1tSZTtjC97Vy5Uq1HgRg3B4tVsoHQTFSFC6r58Ssj8bSfiG8RzwuHbEzlrHFLWifRQhJWVESQtA777xj+pvHH39cPeJlv/32C7GMIfGDycKll14qN93UGp4EAvApp5yiwhscdNBBIVZAiMWOkAfIb6CB75FIGjkJjRgzZoyyBvv888/V+6KiIpW0+t5775VRo0YFfod8B2+//bbceuutbSytzLYPIIwHt0NYgkUSJd0oV7Lsg6QuGbp35N5cqi7PC71BykmwKEmM8WYLIsTHsFP5Al+dJl8VlqQiXIglfoPDaoqS5u5mkQMTXsYIOwuxFGt28Hr98ccf5fbbb1cehPgcnpSEEEKI0/hecYDbPWKjE/tBqIKFCxcGQrLCyxVCJayIkHcSXoeFhYUqjC5CLASDsAyIvx8JCGHLli1TyarBihUr5Nhjj5WRI0eqxOoQQ2fPni3r169vs32EX3AKN8qVLPsgqcd+fbvJiC458uEyf3hKloVZbeaaiJJpHl8E08KzerSqCUkJnOjrLTb3amdCbbZuk6kfSTJHfSDJA+dKxFeNlcOWL2jxcRmxJqQ5hcC7D3kYERkNRuoA63twNjALr0r8gx/aKiEktfGtKAkPLohe8OZCmFDiDNdff70Sfp9++ulALHwIkXjogcnNddddJxMmTLC0fUx8sG2cx19//TXw+Zw5c9RD77xfcMEFcv755zu6SOdGuZJlHyT12KlHJ19NfMvCPCU7VlZKZmOjNHrEE9jONYii2nrpn8cbST/BNSj/4dWxLhjmlCSEpCwOJxWlYQYhxBeEDYVwMEC4U6wLwSh948aN6nN4R+61115y9tlnS//+/RNTVkIIISmHYyuyRxxxhGyzzTZtPv/oo4+UZx1AvjqEmLQKBBOILPDUGzFihHpNnAXx8y+77DIVIvf555+Xb7/9VoV1CAd5CQ488EA599xzpXfv3lHtA5Og5557ToVZRa5QvTyj7dq1UyL0WWedpduujICXYDBWvDfdKFey7YMkD/F463h1EbwkPz/EABnPPTdslNW99HNHxEq0R29nbWnb+mrlBtmhu3muDi9DgY4QdiriTTg+E0K8wosvviiVlZUq19upp56q6yntzbuS2KJXYd0M+fNI4vHVtTCsE2y11VZy2223qddwOCgtLVVpsLiummicGa181VYJISmJY6LkzjvvrB7hzJo1KyBK4nuEkiTeB+Fa77vvPhXuc9GiRcqyCjcCyD0JEXL48OFxhQrDf9EW8FizZo0KM4ociBDLevToIUOHDo0pmfuUKVMCrzMyMpRo6oVyJeM+CGnxaBkas7JkY+dO0q20LPDZkBUrbRcl4y2rW5quV8VjQvyCnTf52tTJsW7J7k4IsXEo4ZBCvAA8vMrLy6Wurk6SHdyj8z49elJ5rLIyT8XaGLwmo9lmKtcpIYQQ+/FG7DriGzp27CijRo1ydB9bbLGFetjBH3/8EXh92GGHqcTeXihXsu+DpCZezSkJFvfvFyJKdispEb+gW602KBi0niQkeXEmp6Qz+S+Jv+D5J36BNljEL/CqmprnKs2h6yrbE2E7IIT4AddFyf/+979yzTXXqNfw0iLEKTZs2CBLly4NhKGN1kuSEJI8C5VFBQUh77uWlIqfCa9pCozJh+bR6qS4RJIXP3pEB4fZJnHWpf9OPyGEEELshJMqQgghHibd7R1mZ2ermOV4IEckIU4RHLr1kEMOkcGDB7OyCUlRT8nizqF5FnNqayWjsckX94fx3k/WJOg4SXzcMW2JfLaiKO5q9HC3JG6cJxsXpLi2RfwyprCtpjY8/8QvsK36D1+dMz9csIlj+KqtEkJSEtdFSULcFiXhJXneeeex4glxmAwPe3RVt+/Q5rNupc6EcK1qbJIFpVWeuZ98b0mhpBp6XrvwHPtgaaFsqKkXL5Fmcss4Y2OFq2UhSQgXpAghKYqXI3gQQgghhBCSytBVkSQte+65p2y11VbSuXNnGTp0aKKLQ0jSs323PFm7ola8SG27bCnP6Sj5VdWBz7pvLJbCbt1sX8h6d3Gh+v/EMUPELcxKW9nQ6Fo5vExdU7PMLalUzxOG9havwaVTZymsrpOu7bMkMz09pUKi2omH7U4IIYQQH8MA5n4htWeCxE+wrRJCvA5FSZK07LXXXokuAiEpNXmFp2R2errUNzdLojDUDNLSVF7JYFEyr6rKmTLYfAsQrANoW+ZNBiHR8dy8VbJd13w5ZEB3T1adH8K3EkJSlxS3yQiBHph21iUh/oD9nhBCCEkyURJW5zNnzpQZM2bIggULpLS0VCorK6WhoSGq7ZxxxhkUoQghhBhSnpsb8j4vSKD0wkqMWwszXl4AMgtlGgtcRCXBrPdY6F6/wn7lQJ16emQmZDOp7jFOCPEHTo1UtPUifoFtlRDidRImSjY1Ncmrr74qL7/8sqxYsSLu7R1++OG2lIsQQkhyUtmxY8j7nOpqXwhvXP4jhBBCCLE6zyKEEP9AW4/Uhvf6hJBUJSGiZHFxsVxwwQUyderUROyeEEJIClKZEypKdimvMP39xtp66do+2/L2l5THJ3K+OH+1nD6ir+RkZcS1HbL57o458AghfsBuL3En0MZTPyyecuwnJLkZN26c1NfXS3a29Xk6IXbgg0sgIYQQ4gtcFyUxeTz//PNl2rRpIZ9nZWVJ165dpaqqSioqWheKu3fvriaaJSUlUh3m0dK5c2fp0qVL4H1eXp5LR0AIIcSPbOzcKeR9flWV8pasCvKg1G4055VWyfdriuVfI/pKr47tLG2/pC66sOPhVDY0yvzSStm+W74qB3J0Ar2l6mgWhdOCVmfX19RJp+ysuMpJSKqDvLkIYRjct0j8pPJCn93hW1O5Lgkhyc8OO+xg+J0fr8wcs30ATxIhhBDib1HylVdeCREk+/XrJ5dffrnKB9mxY0e59tpr5Z133lHfXXLJJXLssceq14sXL5Yvv/xShXuFpyVyTsLbcvz48W4fAiGEEB+yoUsXaUpLk4wgRS+nuiZElNQorm0ICIUi1kRJM8rrG6WmsUl6WhA4X5i3Wgpr6mTimCG23wM/O3eVbNUlNLdmMuMHjx4/lpWIVDU2SW6WjdNoNgBCCCGEEE/jJ9GbtxaEEEK8TLrbeSSfffbZwPstt9xSCZCHHHKIEiTNGDJkiPKw/OSTT2TnnXdWHpVXXnmlvP322y6UnBBCiO9vftLSpLZ9+5CPOtTWurLrR2ctl+fmrbLkGQNBEtw+dbHMK6m0vSzra+pt3ybxLp7uky4Br0ZbtmPLVvwP64GkWt8n/oJO7P5k2bJlam3ns88+s2V7fur+33z9tTr2hQsXJroohOjCcZUQQoivPSVnz54tGzZsCLy/++67VRjWaECI1yeffFKOO+44mT9/vtx0000yZswYJVoSQgghZlS3byc5NTWB9x3qWgVAM5o3rWqk23Q3Fs1m5pRUyYC8UCEVtPhskXZFRY10zMyQbh3czf0TTV3/vaFc+ua2jyqPKCGphJ+8A/yGnxbPCfEL7Ff+YurUqXLddddJnz59lNG6GUj3o4VRT4Y0Po8/9pjMnzdPJk6cKMOGDUt0cTzH559/LpWVlZbW/Zy+z2lxeV4VzbHbmXJr+vTpsm7dOunQoYNyJpF2+Y5sf2lto/QZNESkd4Gl/xYWFsrKlStl/fr1yumlR48ess0220hOTk5cZUpEPZs1Kr1zMGDAANt2Ge/2Y/n/vHnzZObMmRG3ve+++0pBgbX2QAjxN66Kkhi0gvMAjBgxIqYJhRbm9ZRTTlFhXOF9edttt9leXkIIIclFdZin5OAVK2XekMGm/3li9kqpb2qWS7cbaGtZWlJIYnh14Rr1rIWkdYto1iU+W1Ek7TMy5DKbzzMh/hgPEo/qr1Q+PQtPDSHEKzz99NNSXl4u+fn5KhUQSW7uu+8+WbFihdx4440p54zg5rFj/fWll16SRx55RPWvYLYctb2cfMXVlsXDaLf//o47ys0336x7jHBsefTRR+XXX39VHtXhtGvXTo444gg1FnTp0sV3bazFYh3taFJHlvcV5/bj+f+PP/4o9957b8QyQiegKElIauCqKBnsJYkBSw9Yu2lAcDRil112kd69e8vatWtVrskbbrhB2octNhNCCCHBlHTqJAPWrgu871FcErGCyuqNr0V2LOxGs9BrFPI1mv1R/DCmKancKpLpWAghhBBCkgtt7Wvf/faTbbfZptUjjXiaZDbQueuuu+S5555Tr+GxPHr0aCU8TZ48WRbMmC53XnSu7PTWmzJo0CDbtr+saKPMm/qn/Pnnn3LCCSfIG2+80Wb7q1atktdeey3wHv0Ewhe89ebOnStr1qyRt956S37//fdNv3M1S5lr58Csjtzavh3lg+C4zz77mH5PCEkNXBUly8rKAq/hZq9HdvbmsGk1QSH29Nhqq62UKAk3+0WLFim3fUIIIalLZUOj6ffFnTuFvM+tqpL05mZpTvfbzUtiBac7py6Rq8eYe5j6kYTkH3N4dSOpdNYkxI/nJxbjCOJ/0ny4FEvnW0KIHzjv/AskO8Nv9yIkmZgyZUpAbEKqLDh9ZGRkqPeLFy+WE045VUo3FKkQwxCd7Nr+T2uLZe3yZfLwvy9RIVmNtp+ZmSlHH320nH766TJw4OaoNo2NjfLiiy+q1GDLly+XO++8Uw6/fKIk4zk47bTTTOvI6e3bVb5+/frJrbfeGnX5CSHJh6uiZLAnY7BHZHhoVo2ioiLT7SFch9XfEkIISX6em7fK9PsFAwfIPr/9EXif2dwsA1avkaX9+opX8eIycDNFCR/gxZZDiLf5cGmhZKanyaED9I0nCSHex48COomOkpIS5R2VlZUlP/30k/LWyc3NNf0P8s9h4RxG7fg/fj98+HC1QB4pBxpCQo7cdXf12crly2XGqhVSWlqqPLZGjRolH3/8sdTW1qpoXv3791ceXNOmTVP51hBacuuttzbdz6RvvpbK8nLZfvvt2+SUjHfbwYZ3M2bMUMIN/gdPs6FDh6rvvv76a3U8qEftM6vole/vv/9Wuf/gZDB+/PiQdUDUPUJw4jzgnKzLaC8Dh28F/6g22549e7bMmTNHOSFoeUchTgVz1FFHBYSRYOpra5XnHEQSfA/PLTg12GHsgmNEXeIYsa7Zt29f1RbCI7dBMHv//fdN8uS1jlXff/2VpNfXhuQzjOfYY+GJJ55Qz6in66+/PmS7KNNJl1whj1z/H9X2fvvtNxk7dqxt2+89YKASuC688ELd7cOh5d1339VN/4U6OeOMM5THJNoiougdctGVkpmVZalc8dQz+iD+jxy3nTp1Uk4y3bt3F6fOgVkdubF9p8tHCEk9XBUlu3XrFniNSU+k3+jFCzfyvAx+TQghJIU8zaKgtn17Wdetq/TasDHw2YjFSwxFyfeWFEqi0a9Rby64/bm+TCYXlspF25onuvcqRgZThLjNM3NWSnpampy+lQcNJrw9zMfFnJLWRSm/i5Kp7s3Kkdx+/Nim/FdiEolvvvlG5SRbsmRJ4LPvvvtORds66aST5LLLLmsTuhGi1P33369+B0EsnG233Vb++9//qmejHGgQ/i7t2l1uu+lGWTp3TuD7M888U4mSd9xxh0pVhNCGEEkfeOCBNvnWDjzwQLntttt0xdPHH31M5s+fpzyMwkXJeLcNEFbx2muvbbO+huPC/5AfDgIshIZoRcng8kEQgnChCTxg3LhxSqz74osv5JVXXpG//vpLmpubQ7aRlZ0txx93nFx55ZVKMNX49ttvVdk0PvroI/UI5vDDDw8RR7Dv1x66T3765CNpqK8L+S3EFJzrXXfdVWKhpqpK3n/sWfn24w/aRHXLy8uTQw89VOUk1O4n6urq5LrrrouYJ++Zxx6RFcuWheQzjOXYYwXtCSISOOaYY9qIcmD07ntKp67dpGzjBnUuoxGcrGwfoi0EPTibhG9/iy22UA8zcE4hSqLOy0tKpMAgMl84sdQzjBtuueUWleMyGJz3vfbaS51zCNXRXKPiraNIxLt9p8tHCElNXBUlgy2TjATH4EnY9OnTVV5JWMCFA6sjWGAFTwIIIYQkDr/oOUv79g0RJYcvWy6fQ0xNS2sjqiZXjkHn+W71Rmn0QJ21JKmoHh3JdCypR1FtvXgNnwzxxGH8NLL4qax+gXXqz7DbycBTTz2lBEIA4QrG7IiyhQfWhRBWECl97nn4UfUb7TStWLFC3nvvPenQoYMS4Xr16qVew1MP/4MnJARNhBs08qSDsHn52WdKZUWFbD1qlAwbPFgtyocLmSgDxL3OnTvLfvvtp8RSeGFh7QuCHXjooYdiOv5Ytw0RECEvIdbgPzvvvLN07dpVCbs49lNPPVV3vS1ann76aVm4cKHyGEO+OHiXQqTRvAc/++wzFf4R4hIEG3i/VVVVyaz5C6RozWp56aWXlBfnk08+GRD1Ro4cqQQQCBwQG+FJOHhwaPqIYLEIovPJJ5+sygHgCQsPRng2QphdunSpEpIffvhh05x2emzcuFFuPe8MWbdieSCfHs4/9o+chwsWLFBtCMKunmATLdEeuz7WBit4+2F9FaB96JGeni5bjtpepnz3jfISjQar299pp51UO4l2+8FOKmg7HXNzHKvnlYsWyhVXXKT2h8932GEH6d27t2oD8BCE8QP61auvvhoSZjbRdRTv9u0sHzykNa9tnC+MBehLGJcJIamFq6LkdtttpyY8EBqNBikMRtpv4E0JKxXEDg/nzTfflOLi4sD78AsHIYQQokdJp82hvzUKysqkuHNnTwhqpthQPCcP0eO15+nj86MXSqrBM0QIIYS4z+TJk+W+++5TrydMmCA5OTnKUw3pfC6//HL5+eef5YILLlCejW+/8boMP3B84L89e/aUe+65R3nxBKcKAhAmr7rqKvnjjz/k9ttvV8KYHvD8ye/USa566HE5bI/dpEeHzd58wUA0RKhHeONpYhy8AuFNiLx3EA/hZaV5w0VDLNuGiADvSwiSEEieeeaZkFCv8BK75JJLlGgXLxACzTw2DznkEDn77LNViMtgflyzUf764Tt5/vab5YcfflBlwrkCeMYD5weCETzWjj/+eMMyXHPNNaocHXPz5MKbb5czDt0/8B1CzMKDDd50qJNJkyZFDPkbsu3/XK0EyXbt28st//ufKkswKB+2DWHGDqI99ngI9jxGCF4jem7y/oO468T2BwwYENP2YVSKugfwXG7fMceRekZ/e/qWG5UgCaMIGErA0CHYAODcc89VnsNXX321WrP2Sh3Fu307yzdr1iw59thjQz6DwcSRRx4pV1xxhTK8IISkBq5m04Y3oxYqARZdsCYKBxPLf/zjH4H3cItH/HBtooTJxMsvv6wmjRqYWCEUAyGEkMThdT1PY2nfPm0+617cNpyTn/BL3Sc7hdV1cvvUxVLZ0GpJmqx+bTjGmsYmcYtJqzbKjI0Vru2P+NwdnlBB3wSvjYQkB/AAhPAALyaEuAz37Nt9993lrLPOUq9ff+XlkO+wVoS8huGCJICHE8KOAogSeuFdNS688ioZtu12puWEVx7Wr4JzC0KkgnCKdS5tP7EQy7Yh8sEbCUDUDc89CW9BiCh2AA9U1KWR0HfQQQe1ESQBPKV23GsfOeGEE9R7zeszWiB0QNAEJ19xlWy9w04h36Pebr75ZnXO4fzwwQcfWN42vCx/2xSq86wrr24jSAIcN8Qsu0RJN9HCAaPsZoJQbqfW76qrq5UTid3bh3dtLNt/4YUXlNcwQE5Dp5j1+2RZs6xVnIP4HixIAnhNIq+iFvUP7cYrdRTv9u0sH8YKhHY94ogjZO+991b/wXr/W2+9pQwvkKuTEJIauOopCf75z38qCzYAsRFWSuGcd955agKFiScGM1g8YfKJwQ8TxfDBDSEYCCGEECs0ZGXJhs6dpFvp5lzEW6xfL/MH+8u4ZVl5jSyvqJFxffTzk2h4TUbQQqRGk79xXmmVdG2fLTlZ8edNcZIFpVXqeX1NveRmRTfFSovyTDU0N8uS8hoZ3tm6NbCdlNQ1SIdMd87HH+tb85CP6spQ/V6gxdEte23EIhrUpAmJD4hCmrefRv2m3HrZQUIGhDe9nGZTp06NuA8IVHhoVFRUGIYkDefDDz9Ui+oan3zySYhgpZX1P1deGbIPp0F0LO3YTznlFEPRB142CMu5auVK2bhurfTWmdcjlCtCvGKBPXhNCV46WBSHp+GOO+7Y5n8IF7vXAQfKihpzEQDegHohNSGIwYge4WILC2PLVx/LtrWcdxBPwgUUDXiexhpSNpiDDz44RDA1Ak4G8+fPl9WrV6u1vvmbcjkjDyDA+YmFr7/+Wj0jFOROe++n+xsI0/vvv7/yiIX3LcL2WgFelaBLt+6y18GHim14ZMqDc6L1AzOygvJ9wlPZathfq9sPbj9Wtw8vaXhCA4jCe+65p/y0dnNEPTuZNeV39Yzww8iVatRP4UCDcL/IAas3nrhdR3Zs347y4doGDSDcOAFjLxyPcB4xLvz73/9W7wkhyY/roiRCOiD+OgYbxFw/44wz1MQhmO23314JjYhLHzxQIZZ/OLCswESKEEIIscr8QYOk27TpgfdDlq+U78buEnjvB8fD1xetUc96ouS8TTf4boC8m2uqaqVfrrU8EHdMWyID8zrI8cO2sLyPv4rKpKimXk7c0vp/nMDZ0LfRbfzb1cUytahMLtp2gIEA6odWTAghhKRGWHQIYVgDiUS4N5sWPtTKfzVvlmBDMCv/A+EhPLGgrPff8H04DcKWaqA8yA8JUQvlwwL4O++8EzhWCJYIsVi0dq1IkCj5zTffKEEYoqMZRseGc6IW1yOIkvDCi+RBhFCqsRDLtrWQi8ibZwTCUGI9Tm+tLRoipVNCaMz7779fnT+IkUbE2r60doJ8mb98/qmkp6XJ6vwOIQaReNZSQCEHoFW0HJWDt97GV56QVkdATTjScgYa0Rgk5EOoF5u3HzwGWdk+wqVedNFFarsQCeHM4iSFK1cEQsQaAcMBGADAESeaEKtO1ZFd27ejfEYCLYRO6ALIMfvoo48qj2/k5QzP2UsIST5cFyUxSGPiqE1EjMI7XHnlleq7xx57THfihskAEhIjkXQ03haEEELI4v795B9BomRuTY20q6uTuk2TZ+3m1S+EXwbfX2psha0dmV2Xzp/WFMvkwlIljlllWUWN7udmZXIzXGgkvDDrqN1UH03N5m11VVWtFLS3ZkWbrNjVm302LBCSsvDeMLXx6tIAQmzCODuSp2T37t3b/Befhf/XaB/hfcHK//Q8YDp06BDyX62s4fuIByuXVYTa1Lj77rvbfI8IW+HUVrdGrgAI04n8btoxQqCDwIfj0wQmeInCcxKCph5WF/+dHHti2TYW+bU0SmZ06tQpblHSrI4gZJx++unKm1PL84lwtBBTixtbW0Fd0TqZMmWK4TmIBPL8gblz58rcubdE/D1EUqvA4xjk5neSZERrHzhPWKfVC3UMqisrAgJVNIKY1e1rgrSV7cN7GiGbsb099thDHnnkkYhefPFSs6k/RRoD0Z+C202i6sjO7TtdPgDPZYiSmuBMUZKQ5Md1URIUFBSoRyQQLgThXhGKYfbs2Sp0K5KaDx06VA444ICYEoQTQgghpZ3ypTktTdKDVIYBa9bKgkEDfVI53lFHSusbLYljfgH5C3fu0Unyso2nSFM3lMuB/dsuGrqJtjhlvHTT+v2ny9c7EvY02nCzhERiYVm1bNVF31iReAd/Ge0wJHAq47WmGh5aFWhhBvfobb42Eh7O1SpYSI7GIyyYww47LOS/VstqN8HhAI8++uiI4lxDc4sU9OipXkPg0oRMhO1EHjg9QeHjjz+WZESrOy30ohHwOnWSzz77TAmSECruvfdeFT0tvF2t/uErJUrGiiaAYK2w15ZbKU/JHh2yI3qXWkETYGptrqc0j1xXBwzYbFiKPg/BWK+sG9auafN7u7avfWdl+8jVePbZZyvRHflkIWQ5LUiC7PbtLPUX7XsYPiSqjuzevtPlA9AIsN6P82qW35cQkjwkRJSMBlhRWY31TgghJHFgjcBj6z+GNKenS1lurnQJsmAsCMoxqRY8bLpBRO6/ZGaujaFiw6vc7fbU2NKi8hdWNDTKkYNaF7S8iuZT0ZyghQyvhcXzK07XIvKb+qXMHywtpChJbIEmE84Rb/9fUl4tPTu083yOaOId+vfvH2K0Hvxej8qGRpm2oTwQvnTDhg3qNUI76gmSyMMYa0hVr49VmqerWZ5GeD2tRbhbB9HExr322itEkAxm+fLlce0D7eK3335T+f5OuuoayUxLl117dY5rm8HbRn7OlYsWRPW/4Hx6ZiFry4K8gRNBcN7Z6dOnGwhOabJ41kz1asSIEQ5sX2TatGkRt4/Qnuecc46qTwiSiKwXrUderHTt2SsknK8RCxa0thOrXuotNteRHvFu3+nyaWKuJuhCnCSEJD/+CYhOCCHEcSLpG/v06Zo0Z6E8L9QjJ9fkZhGsrjS3Mjbio6XrbV/sCw9/Go0u5aSY5DWvBDeBOAhR0y1gAd66X6NfpPDJIAGQd9QuKPQQJ5lX6l4uZJI43ly0Vt5dss7Xp4BXV3eB55u2uP/uu+9G8c+WECEo2OMymPfff1+SlR122CEQ6hLiqx6ff/65NDU5myJBOw9G4lFDfb0qRyRxz6yce+65p3qePHmyFMcZijYchAcFa5YtldlT/7L8P3jvaeKKkei6ePYsKd8UejbWY7fDEQR5EM28spfMnSPrNuVU3HfffW3f/owZMwI5GI22D9EZHpJOCJJW6nn49mPU85w5cwLCYzjoaytWtNbTzjvv7HodObV9p8sHMAZo4Zu1fRFCkhuKkoQQQtpgFBlph+7Jk0tjbVjOnvYRrKRfWrBa1lZHb0kdzX++Wtlqze0Ga6piE1ndbHeJEkFi0RbfW1IovxVGb+mcFmddJcpTkuizoqJGmpL8nCT54ZEEEcv4SfwJohEQorB4PYFnFHj22Wflk08+kZkzZ6rFfzwHA4/HWTNnBN7Da04L96onaELkgLCRrMArESF8kQcOnqL19aHREyCe3HfffY6Xo1+/fur5559/biOONjU2yot3327qrdmtWzf1rAkeekAEGTZsmGoDj/93opRsMBYmEWJyzZrWUKRW2HvvvWXQ4MHq9YP/+68sXrxY93fIZxkeinX77bdXzy+//HKbMLolxRvlpXtuN923lWMH8Ah+++231aNw07HVNVmP1nPKKacEvFqxjWAQTvPl++9Sr2EgsN9++7X5P9JtafsPb2eRtl9bXS033XST6fYhNmMcgCcdBOjHH3/cVg9JK/W8w7h9JLdTq/ftf/7znzY5I4uLi+X6669Xr7fYYgvVbqIh0jmIVEfaOfjw3XeU0G/39uP5/8aNG029obFNhNfWPJOjEXQJIf7F8+FbCSGEECeoC8s9kd3QEPE/tTZaqbZYUqVCb2xt0QISKChUNcRef/XNLepGP1IuoUQd4sKyKlf3p3lKGh+rs5KuEzklIej1z7Oef8VrlNc3yqsL18huvbrIuC3czblFSDDUjVOrHj5cWqiukccOaQ0tl+onJs2n1498k1zWegZvDU3Nvr5mRsOECRPUojVyP15xxRXSvXt36dGjh8oL+I9//EPWr1+vFrwhMg4YOEiueOyZQI6yffbZRyZNmiT33HOPCi244447Snp6uhI1v/76a5X3Dd45DRbuA/xGVUa2Ek+uvfZaJQiOHz9e5QpFvUB8ee+995SIp+VxszLHjoUjjzxSnnnmGSktLZUjjjhCjjrqKCVcQEh796OPpXDVSunUqZOUGXgM7rTTTvL7778rIQRlHDRoUCCHILaVkZGhzumDDz4oxx9/vCydO0euPvFYmbTffjJy5Eh1jiEYoZ0gt+W8efPkrrvuUmWxArZ/+113y2mnnCwb1q1T/zvggANk2223VftdvXq1/PXXXzJr1iwlDGVmbu7LJ554ovzyyy/Ksw7/O+SQQ5QAhvr/8KOPlNdfh44dpcYgYo+VYwfLli2T6667Tr3+z213ydDd9pA5JZWWc8AefvjhygMOZYWwhvYCT9vy8nL58MMPlYCdnp4h/73xRt38jd9++6088sgjAYEYbczK9metKZRfv/xcitasVsdyo8724ZmI0M0QddFOEAYYZTKiw8jR0ils/5GwUs/t2reXU668Wp7477XqPONcom337t1bCd0ffPCBEt9w/iGwBbcDu86BUR2Fn4P7P9hFpKCTrduP5/8IIQ1RE2Fgt9tuu8D4jf9ibMc2cZ8Pofn2228PCX1MCEleKEoSQgixjEP3qgmhLjsralEyGtZV10nPDm0n9K6vmnronD00c1nM/y2rb5Af15TIuD4Fjnt5tfggj6J2Wo09Jb1TVqtM31Du6wVWLX8sFpcJIYnHT169t09dLBPHDInpv1h4NgLXCM2IJdXq1E/8vbFcdurRSdpvEhgisWiTIZSfr5nRcvfdd6s8Zk899ZQUFRWpB8DCeHCI1m1GbRvyv1tuuUWJUfCqhDiJhwaEMWwXHlhWRcmaRv/kikcO12OOOUYJjhBlIVxpogWAFyWO/9FHH1WiAcQ7Jxg4cKDceeedMnHiRCkpKVEer8Hsst8BstvOO8v9t92i+/+TTz5ZPv30UyXkweMwXCjRhLkhQ4Yoj9iL/3ONzPnzD+VVi0c48KC1mu9PY8RWW8l/HnlSnr/jVlm+YJ4qDx7BQHCBSBkMBLrzzjtPnnjiCVX/wZ65BV27yoW33iXvPfagrFi2LK5jD/bQzIxB0EG5H3roISVgf/HFF4GHRk5+vpxyxX8CoWzt3n5+p05yy//+FwjDGwyOXfMyhXB98803m+7r6oeeiFqUtFrPY/bYS+649z6585b/qXEF41EwvXr1UmPOrrvuKnbXUefOndWx69WRHi02bz+e/0OIHzx4sPImxkOPbbbZRnlb4pkQkhrYLkrCsgQWJm5z7LHHyi677OL6fgkhhPiThrAbtux6e0XJ5+etkvEDe/hBK/QNi8qrYxclPV7h0a7zpm86Hi4QJwdc5yfEPpwe7pFjuk+ufn46r7GqsjalhCs/o+xarGmSlimqqZfuVgzkPACEKwho8J7RA95LyCd3wgknKO8/hN+EELPbbrspz0n8f+zYsdKYkSnTNpQH/gePrTfffFN++OEH+fPPP5VnGryt4OUGgQWeOfB2QljIcKFq+PDhqkzBn6+orJEBYX0KHogI5ThgwADD40MOPCzMjxo1qs13++y/n2y77TZKdA0nnm1rc8RTTz1VefZ9+eWXypsJxwyBACEWIST897//Vb/r2rWrRIuV8oFDDz1UealCxIBXK0QeeJh1HL6t9Bk8RGqXLjQ8/ygjPLHgCQYPNXhXabn/NLEoWHC8/J4HpXD5MqlZOFt5McIbFMcG7yyEU9WrZyv0HTxUrn/qeclZu0x5d0GUgkcYQnXi2CBK6nHppZeq40f5EaYW4i9+u82uu8vqRpGNC+ZKTWmxElVjPXZ8p4liY8buKsVN0c8sc3NzlbcpPEl//PFHWbdunRL6UV/52+4oWR07Gv4XHqk4f8AorKre9jc0ifQZNFjG7bOvjO3f2zD8r7ZtK+RHKUhG28b22W9/2X+vcWpMwW8rKyvbjCmxYnYO0F/xfaRzgLC9WdnZsrKyRgaGjVXxbD+e/6NtI2ckhHn0HfwPXqX4r9Z/UH5CSGphuyg5ffp0FVbDbSBIUpQkhBBilfpwUbJxsygZng8kVuL3mApdWo0lp2Ws1DY2yV9F5bJbr86OhXOKRKoKNVban+b50pxCtTS/tEqGd85JYAlSp67NmFJUJntvUSDZGambmt7voYaTHad76lerNsi/RvR1eC+pBTy69pXoBZFEUBFlqNVEUlrf4BtRcsyYMeoRCSx6Q1CDCJefny+XX355yPeVOjlLISogjCseemhhL8MZN26cekTKxQ6R1IonlhHnnn++oZdsvNvWgAB42mmntfkcDgUQYEAsooCV8mn07NlTCaTB/LS2WD1vs932sv9uxk4GEHoOPvhg9bDCFgMHyW5jI7cnJ9tqMMh3iUd4VB0pq5IzLrhQendsF9exI+ciQJhTCFJSE/s944gRI9QjZPvrSqWxxdhLGB6heES7fe38dwyLYBQMxHY9Md8IbZvREk0b69ixY1Tt0Y5zEAntHJTUNcis4grbt2/H/2E8ggchhIDUXU0ghBCSbM5mUVGflemop6TdOSWRj/GDpYURfiVSWtdgmvvP6mLtD2tL5Me1xbKh1v85dmoam+SZOas8GYJYrzhTgyz8I/0vqTwlI5yc95asc60oxJipRWVqcSqVcdNAxAqfLS+SOcXGITwJ8TobauvFL7y0YHWii+ArIBI2bgpvbkR9c7NtBoEkFIRuhXeeHoWFhQEvyZ133ll5nBL/0djYqLzP4NEbjUchIYQQkkhsN/GDdQmsRqxQX1+vLqB6ILEtEo5rLvN63wcnv402iTAhhJC2ZGwSBezMP+RVarNDLVKzGxslo7FRmmy8nqAW7VpiwYKNFYpsWtjTchW+tXitVNQ3yX/GDI74H6eXk6zmdAxf11pWUSMVOpbzXqWw2vnF2T/Xl6k8ZKcMjy6nDnEGv424DSbW8l7i57Ulsntv/VCAyZaPDo+RBaFhsxC6ixDiE/x2IbAIQqnmZWXK9t3yTX+HaJOZSVoHiQK3cxs2bJBDDjlERRWDZxM8JrHGNn/+fBVKFeFssa521VVXJbiwid29n0EYTHjs7b///pvWSP1j5OFHUmCZhBBCXMF2Je+GG25Qj0g888wzKkkuREnEYUcoDcS5R2x1WPhocbhh2YWY8zNmzJDPPvsskK8SMbuRQHjvvfe2+xAIISRlGdElR8rru8qornmS7FTmtDWgya+qkpJOnWzzjgn3Ugz9zj6i2VaLjSFon53b6n3oFLznc46vV22QVMEPvhd+KKMfQQgvhKD2uqGNn87/pNUbpVO7LNtCKTtx7N4+2/bltczJypDO7YxD3tmJU05sfmr7jpPElYFoHyQx5z4nJ0flUvzll1/UIxyIlLfddpvKh0f8CcLi3nrrrYkuBiGEEBIVCXEvvP766+Wtt95Sr3fddVf53//+p5IXG02iENsej+OOO04lJ7/22mtVgtzzzjtP/ffYY491+QgIISQ5wcLt2F6dJRVoyMqSmnbZ0qFuszVpfqW5KJnI9QcnF1nvnb5UtuuWJ/v17RbV/9ZHyFfS1NJi60Kmmcirh96uGR2MEELi44sVRQnO7+pcaOL5pdVy/LDenhc9tRCiE8cMSVheRRgmnbFVX8nzSX7FVMQLbTVFdVhPgDkvcm9+8803KrznvHnzVMjW2tpa6dKli8rTt9tuuykngWSBbSo1xxJCCCH+w/U7iDfeeCMgSMI7Et6SwWFYI7HjjjvK66+/Lscff7wSJm+88UYlWG699dYOlpoQQkgyUpGTKx3qigPv+69ZI8v7bBHVNpADJ83ICyet9Xsv3TTqlQehYaesL4talIwEFiw3xhFOtiXG8K2RmFZULn1y20mPDu0s7pn4OZdVh8wM27cbrUBuRHCbNh1LPIZdx+9XvH70fhrBnKxLGMZEw5crU8eDPF4WlVdLTVOTLCmvlu0ihOUkhCSWjIwMGTt2rHqQ5L+uEkIIIX4g3c2dIUfkk08+qV7n5eWpMBHRCJIaBQUFgfAECP/61FNP2V5WQgghyc/aHqEi3OCVzoYj9cONcmF1naUwW28vXhexDPEIkk4udn+xskhemNfqZeIV1lXXSaPFvKHEOg/PXO6L6ppbUinvLy0Uv2CXgQBppaG5WYpqvJ0DyutCrB4PzFimrmnEPtZW18qkVRvj2gZHD393sl/WliT88HNzcyU/P189E0Kch+M2IYQQX4uSv/32m6xZs0a9RhJmhIyIFXhMDho0SL2eNGmSlJaW2lZOQgghqcG8wa3XEY3O5RWSZqMwZGWBxepCtFseVM/NWyUvzl8dMczporIq8TPNHru7/mFNsXy1MraFXnjYzS6ucN0rl9jL/FJv9ymfrJf7lmfmrnR1f2b5gt3E6VFrvcfFXj/yx3red3uJ6sYmV40amj0gT5x99tly+eWXq2cjEl9KQszhvCq1oYEfISTVcVWURAx7jWHDhsW9vS233FI9NzQ0yJIlS+LeHiGEkHhDg/qrBsvy8tocX4faOl/fbIaHVQwvg5VTVFbfEMN+vXdzhdKEC3XxaLtOt+9YPUvnllTJR8vWWxa1mv3WUT0Ca20zqR6+1UlWVNQ43kdXV9bKo7OWq9CbVkm19o/Qr4kQbn9dV0LvziDK6hrklQVrlDdxLMY+XhHfnWT6hnKZV1qZ6GIQQpIYzvoIIYT4WpREUm2NzMz401kGb2PdutAwcoQQQkgkatq1k+YwlSqnpoYVFwNeXLCGuIq8lsEkox5Xt2mxtt6i++ed09wz5PpoaaE8PmtFQhc8kvGck+Qg3GiiuLZBXl24Rn4rLHW0zZdsMjzB/hKNVxc6EaIUwq3bQEh7beFaSSYQQjdWT/4/i8plZWWNFFZHb7SzqqpWvlhRJH5ixsaKqL3mo82dSkgqAYOGn9YWS0UKGCgQfwODNFzvGPmGEJIquCpKBrN8efw3ecuWLXM9rB0hhJDkoSU9Xarbtw/5LL+yMiqBzS9LQX68wUmzsBAXaZGhKMF5Ld2sm1jO8e1TF4uTzC6plNIYPG+doK6pSRo9FrfXh92SOIjmDWbFu+uZOSuj8nSMF7/eacV6i7i6qtbuoqQkaMsIC/97YZkkO7gGxzvXgjHV+hrmQQ2PIsEID94MGxyLB7PbVDc0qecNHr4fqG1sYhsnsq66TpZV1MhGDxiMEUJI0omSPXv2DLz+6quvpL4+9okBwrXOnj1bd9uEEEKIVYo7dwp532PjRtsWX2EwQ80hdiLV3VcrN8gjDniyWFlTLK3jDaPdrK12diG2uK5B3lpk3QPps+VF8uOaYklG6puaZYHHc1iCPwpLGcrSxODiu9X+b59OXyNj1YhimQe8NH+1vB3FGJMK1DW1ihYb6/Tv+70W9j0e5pVWyc/rShJdDE+ineVpG8rDrj0tEQWlOSWVsqoy1Ejg448/lrfeeks9k8TwV1GZOp8kfuDV7Yc5WTAldQ0xp51IhAe6H8qqzVeS56pICCEeEiXHjh0beL1+/Xq55557YtoOxMzrr78+8D4vL0+22WYbW8pICCEktdjQpXPI+11mzHJt307cIKVS4ADkXksUb5gsPL+5aK1UbbLMdorgG1Y/nHIr4p5ef1hYZu8izfJK623m743l8kvQArMXvBrhhQPv1tnFFXF5N7y/tFDeXbJOWed7mUmrNyqhh6Qujc3NvjECgXflIhe9V/1AYnJrt/U+nFcSOeciPOnXxOEh62VPLK9Q2dAohVF4gmqhaRvCohwsXLhQ5syZo56JuwR78mtGByR+Khy+b7CbWcUVymDAD+AasLjMg9dmD9xXEEJIyoiSI0eOlBEjRgTev/jii3LzzTdLdXV1VHkpzzrrLPnzzz8Dnx166KGSnZ1te3kJIYQkPxu6FLT5LLOx0ZXFuKfmrBS38YKwEjNhZTc6FDc8LyKFAbUafs2O8PPaFhJxasvqGiwt4gaLe1aBEPHOYndzhj/w9zL5ZW3iPF3WVtWZhtTVlv+mbYhdlHx4xvJA2M9o2kx4U4VV/7erN8ZcjmQEIQbdDJWdDF5mkUbAj5atl8dnW8tLayZseC10c6oRb7eIp62/tmCtMsSIxHerN8qL81crT3LiX3w9z/UBMNgizgCRNx7DCDeAuMdwyvaLpk4bsxJCiBdxPafkjTfeKOnpm3f76quvyoEHHiiPPPKIzJs3Txp1FoJramqUCPm///1PDjroIPntt98C3xUUFMill17qWvkJIYQkFyt792rz2eiZm8ODx4td66Buextw0cF/JGId7rHZK9QirhOEe0a4QU1Tk/y4NnEhMeeWOm913mxTS4Gn5e+FpbZsK1m4c9oS+WLlhpj/b+XMTF5XsjmXpIuhScNZXVkrj85cLs/NXeWox2142MZogZj+9JyVcs/0pbaViURX/3YISK8sWBNzO4N3uNUQ34AL7tZAdAM/iP0wFPF6VABCwPzSKlnscW/7dTV1KnQrsZepG5I/7zIhhISTKS4zevRoue6665TAqFkSI5Trww8/rB7weOzevbvk5uZKc3OzVFZWKu9IvA4nJydH/adLly5uHwYhhKQkZl5dfgghqUdFbo40paVJRtDKWM8N0S0qY2HWCC5u2UiCG1mLR6vCDm9L4hzwmn127iq5cJsBkpft+tQ7aUJ4Yu07OyM9Zsv+/OxM6d7B+cgqMzdWyMH9uzu2/e+DQiG3JHAsg3hf3tCoHssra2V455yElaUNYUNiPAuouF+1Msb+nEAP61iO6Y5pS+Sg/t1ldLd8a/+Jd582bGtjbYP0yc0Qp4jnStrU1CSZVa1iwuq19ZKZbm1rmZWbBYiNUieZm0T4wsIGS//D74Jfh39fV5cphbX2GLsEl3XdunpZWlEjuZkZ0qNju4j/Q20UFta3KSvmyJmbwqqvT6uX9LC+VtfYLJnVtVJbVyWFtZsjBPTt21dqa2ulffv2aq1I7z/asSPcKELr9sttL1lBxvHRUlnfIJm1DZbOj1Waquoks6lJNkidKlttY7M0NDfHPVfIqKxW3gdavdt9/o3aXTzbrWyskcKKTFvGN61NrW6pU/WZWV0nNfVVUlgTe5SJemzHYv+0SkV9o2RuCvuclp4mjWlpktnUHPf262vqJbOh0dayplfUSOam0busqUYya+ptPf9NGRlS2GiPIKttc+261jFFjUFpaVIodbZst7ilTqpinI8Go9pmkOFVVUO1ZNY1SHlTjTRnxd4XYIiDNm92fjD2ho+3fgbXYEKI/0jIysiJJ54o+fn5ctNNN0lFRUWbfJGrV0e2uB8wYIDKSTlq1CgHS0oIIcQqLR4TbaJhab++MnTF5lCqBaXWvX9wzFiU1SN8rv/n+jLJyXJuQc2P/BS0wF5c2yCDra2POkqLDb+JtS8gbFxRUF4qCFo9Opgv+Nmd7wn6fKQFMbRtWHQT/VynCBeZsWkAWLQpjw2sy70mSvphzG5saZEHZyxXC4ITxwyJaRtvLW7NARvr/90izSdhXcPLubKyxlCURFiyD5aul+OH9o5ZVPY6WNz7KYEe1vEI6FZFSb8SS5/Seg/ELBgzWKW8tnUReFZtnWzbNT+q/4CG9pmB90055tf94N/p/Uf7LLOlRZqy7Zl3hpS1Q7Z6j4Xzru0yLfwvTbesTc3Ngc8aO2RLRpiYW9/YpL5HNK3OWekBA4G1a9dKVVWVMlIPX4zW/qMde2lNnZTX1Utdu0xJz4y9LmrrGy2fH6tU1deredfs2joZ2ilHFm3Ko221/RhRXluvrgNpm+rd7vNv1O7i2W5uRprkZKTZMh4H98VB+R3V+2xpkaY47sEamlrbldExQwwtq2+UTtmZlo0Fa2HYs2mb2enpqv3XNDbFXafV9fVSvskYx7bzX1cvTS2tjiLoi3af/6bMTGlqb88cWdvmjE1jMd5D9G/qmG3Ldnu0y5RMydC9h8tKT7N8/hubNo9/oGN6mnrfKTNdOlg0bNGjPqhd6Z0fGGqtqqyREV1y4zLUIISQeEnYysj48eNll112kQcffFA+/fRTFaLVCt26dZPjjjtO5ZWEZRwhhBASL3+M2jZElMyvrJL2tXVS295eMejrVRvUTWci8ZoI8XNQrkHUz449Onmm8Mjtt2vPzrJXn66u7fPDpYUh+WSWlddYFyVb4s+p9PAmr18r4s17S9zN9+gXYKSAvJR7btE2X63XweJoWV2j9Mn11hwbgmSyjHl20uKR/U5ZXyb79e2m+9vpRRVqTFtdVasWh8Oxuuw2u7hCti7Ii62AUXo9ktREaxstm7yrYcxw2vC+0ttk0T04Lc3G+s3iWEaGNfEj+D/D0tID7yP9P/h3ev/RPmuf1aK7LRgS5GRmSkH7LEvlDC9r+qb9Nqelx1XW5rS0wGfYZrgo2dLUor7Ho312lvTP7aA+nz9/vpSXlytD9/D9b/5PjXTIzpLq5tb3QyyU1QyYi1k9P1Ypb2yRkk3bzKtttG37Gxua1HgHTyi7yhrevm0r66btdM/RLytCBEOrtDp2p7W0nm+NAZv6VW67OMu6qR0Bve0UVtfJrOJK2bYgL6L3sEaDbD7nHTJEsjLSpLy+Ke46rWq2v62WNDQpzz7QR6yPVZHQtmNlLIl2m8FttV2GfWXVG0sghv+6tkQG5HVQBgaWaAkta0GH1ve9Jb5+25TWOmYCve0U1dWo7xslTdrbVOdeIfiaTAjxPgk11+7Ro4fceuutMnHiRPnuu+/k77//lrlz50pJSYnyoMSAgokmwrlus802MmbMGNl9990lM9NbVuaEEEJCuXTUQHlgxrKI1ZKTmSFVHsjzUtIpX5pgoRq08D1o1SqZOzSyMPPiPGPv/jSdxfRY8+9wHTUxTNtQoStKaiHojYh12btwUzgkDStSTJpL3lHEOhUG3tN2gfYH4XOXnp0kMz1deTwN7dRROsThBQKQHxTeQU55FEJkf33RGrls1EBpH2dZE4lXhK1I45ARaUGGIGg3ndtZFyZi3pkBVo/go2XrrYuSBiBkabRtu0X3ELxx/q3gkaYaGr41xnbr1hUOxUO4T83T10yUxDqFxvLVm8Ok9uzZ09K+gv9zYLfusnx9raX/a//D71asrlLX/+D/aN937ZgvPXu2DSf93OrFWLqWiWP6WipneFm79+ghy9dWSVq7DlGVNfg1QK7H5WurA9sM96ZurqqT5UWtddItO1d26tlq/PDRRx+p6Fp9+vSRxx57zPA/y4vrlTfw8mqRfbt2k54WxSI9VqaVyfKypqjObyRKy5tleX3r8ffJ7STLK5tt2f7KNVXSJC2SmZamu621Va1hTfvntYq8Vghv3+HnMla07YzM7yI9dTy3YSA4qmueHDqgh6Xt4T5r+ZrNkTx279JVlm+ok945naRnz9iNDDfU1Jv2zzVF5bK8pEG2wXF0tXatKtxYIctLWz0au2RlSfuMdFnbUBd3nf5Zm6bavFFZY2H1uhqpaWlt/zt2LlB9y47tB9pVtv5YMrekUtplpMtgHaOmaNpqfkambWXVG0vqmppVu0tL6yD/sLifsroGWV642TlnaF5nWV7eJKM7F0jPLrkxl7O6vFqWbzQ+P1Pr0lX72K9rN1ej8RBCSDieUPeQPxKek3gQQgjxP1YXxod1zpHpG8ol0TRkZcmK3r1k0Oo1gc+2nb/QkihZWGOcn2JNVXy5K4j9+E62812BSTyeqlZZVVWrHggTtX23fPlk+XoZ3jlXjhoc3YLLgzOWyb+3HxzIfwZB0knmlFQGvEn9LEpGA6znP19RJLv36iKdLIp/WNxCOOtovJhiZXF5tezQ3cRD3aY2rkK8t7TIzOJKGdkl13LOvZiwZdMeUfN0wq41xWjcpDdHQb5WGDY4BcJHegWEQ8/PyjQce0JyNUtqgFCR8RqzuIXXp0MLSqvk3SXr5D+jB3vHcMXg8xfmr/JFSHONhaXVIgMSXQqSCD5YWuirtkoIIcQ69G0mhBBCsFgeJkBuUVQkvYqK4qobJJqPFy0fXbIv5iQbdtV3cxRb4jmOnW9Xb5Rfg0IJO1G/TgiV0CZagoSsWIAokWyibSJBPtGl5ZvH7Yr6RpmxsUK+D8qfq9FiIt4+OWdFxH21JOhcpMX4W4hgny5fL3+st5632Q3grTVvk2BuBwg16xRPzF4hT8/dHG4+HuBd9+XKDZIqPDt3lbyxiGHHw+uE2MMf68vUc1OMA7M3ZMxkIIknGIQQQkgSQVGSEEIIgbfIgP5S0TE0NMx2c+cntG6wWLwkaHHblm165GYd4ZVemm8c+tbreKMWW3HSIL++qVnWVfvd4zfy2fq9sFR+0BGN7NijkwuN6M9pHm+fXuKjpYW6IiyERC1XUizAqxEh5jTeXuxt4SNugT2KcSf4Z42bFNBgAd3thfiqhrbGQp8sL5L3N3lj2FFXCDXrF9B2NdAH4JUC7zkvWiKk2eQtqUdw/w+eJyX7WOp0qHGn5jLHH3+8nHHGGerZzwQfcrK3NeIdcB1cUbE5bCjRz7GORzKTzAaChBB/4InwrXpMnz5dli9frl6PHj1a+vfvn+giEUIISWIL32bkZRs+THab9nfgswEI54oZu42qT6zz/7QkuwEprquX1VWtuVncDqXoVUEX4p+2aB/YZ0tiz+2ny4tkXmllTGGT4r2Z94qArkf4kIAFnimbvCTCsfMows+xF2oIx94hM13SPRKuzqzOZpdUyrZd82RQWG6iNxeuleWVsS/QJWIsc3s8jzccodGl1O02/N3qjXLYwNDcZGYinKpjbzZtWwiu/8Vl1Sp/V6+O7WRsz86W/l/T1KzC8hq1D+1jW7x6HQoZu6qyVl5esFo6ZTsfLtnrIMdZTlams+GVLWKWe/Tuu++WZCDWfoH5xjerNsQd0hIGgl4414kGhjIZaWm21EWsOXPd5I1Fa5WBBkOiGvPwzNa16OjryPvnnxBCvIJnPSXffvttueqqq9Tj999/T3RxCCGEpADzBg8Ked+xrk66F9vnPRUtHl3j9zUrLAgPLTYIY7GcunBBMrwsibj13VBbb+l38DQL542Fa9t89v3qjSEeZfGGSERoTC/wxYoimRYhP65X+3O8beihmctU+Fs/E48g6UfcbIrxipl252bzaj/0KxB5/yoyHvv8UN1abvCy+obYt2ESUQDCDx52GVbFGqrbCo/NXqHELrcwqxW0Ky33YTioT4TFtksAgnFN8Lbw+ue1Ja56SkXTVyKFm7fKvdOXipvAIxnhsqNF7yxjO3ad//v+XqrygaYKpXWxj3Ve4c/1ZYae734hlta7tqquzTUA91WR7kH04HyIEJJoPCtKEkII8R/RCjheW6wqz8uTkvy8kM96r499ccZrxxcJ/4fp9A4tYYJdeYziWfiCC7wYcPNZUtdouZ2tccGLa5rOonSlTphEs8XrWEIkJmoRKXwdTE9QTkQ5EjX2LK8wbmN/FZW1EaLtWAhBLrTPlseX99eMBaVValHaLpLJdh6nz40mv6y8JunDpxkt3C8sq3Jk27imbAwK2WoHKyr1+/+L81fLvNLYjwPCk5MCnBGxtO3n5hnnZrz/76Xy6KxWr5t4+XjZeiWgBJMWpfgWyTAIuV+9ALx2zQQJ5KddrVPWaO9FENIbxjU/rimRNxetVZ7TVY1N8tPaYvlqpTWDG/xH87heGWbkghDJ8EK2a2yYubGiTflxvbLK9CDxIpq85bGyvKJGVm8aI3Ddvn/GMt3fRdOOcV3AdmYW25cLOFK6jJYoxkM/eEsmgt8KSw3Hn2jnhV+v2iAvz18jdrKxtl43xHt0hB4I2sL80irb2gQMNZCKQG9MjBabbGUIISRmKEoSQgghQWzsHBqyrM/6xOWGcvue9quV7lnHG4F8Vla83/CbEpctfWM9H1+v3GDbgqS2+LssKBeMXqi64KKWBgmYTmGXF0i01CZgwdq7/dmbedB+LyxzpC5gIf/3xuhFbqu7huCNRWk3ibZaEnWeY9mvkVBgtg75+qLYFhzNwndabXubF0gj/yHSL5C7KxqvbhiyvLN4nS2CXHhdTN9QIU/PXSluAIMYeFLG2mZeX7gmpj4erwWE3f0KRivVUXqHGXleYnE7nuOPxxvUS2h9Qy9KgxFGc0Zt/vJrYYkSpkLFUGvbf3DG8kBkCYTGDE4V8PSclfLW4raRI2LJKQnh9JPl60Pa0/dritX1yuoY8/kKY2MeCKvIJW4nry1cIy8taM0hH+u8vbapKST/bU1jcyD0slMg7z2MqqJlVnGFLCmPL/qCW6JmNCktoh1Kse1wr9i/Y/DmM8PuWnpqzkp51ubr48KyanlvyTqZW2KfodEGmwyLnp+3ygYRlhBCYoeiJCGEEBJEYbeuIfXRf82mvJIJJtocSX4Fi0GTC0sj/q7IYljReNFOPW6uvwgSbaNpEvEsmkSzNOREK02z+HmsYW796gUVzeKM0z3XC+GX9M4/vNxgEW+Wr4/YX+9eI003LLb+62j4cU2xWuizA6dr8dWgRflEL0xbDckdDfNL7fNWCmaViZc/Qti5GWbUbcw8L/3Cc3NXKUMz2wjqDyeP20Vu2nM72WeH7S3//f2lhZY8Cu0wugh+Z6c3IsS5cDRRIRqB1ogHZixTHs5eBEYKboJc0bEaawaLxgiVirHKrvF8aXl1yLawr/v/XhYi2lrlzmlLQt7DU/jxWStsKSfEcnizwuv/pzXxG3j9ti7yvWGswIMXgiSAl3Q8LKuoDomOoxlQ6PXdWME5x/waXsjxUtXoz/swQkhyQFGSEEIICWL+oIEh9dGhrl66lMVm2dniAbEBuZKselvEU94WOz1ZxHvgZh9hfVwnQmWkWWgnboTnipWHZxp7kHrAFiBmAcitonusikJYsCn8ZH2zt7xZ0zy83zSfNAAr4w4W4O6atkQKa5wZN39ZVxK911gCRfxYQ3i7SSB/Z4v3BXAITFNiCFdn9Vpjdy7TWIDwajd2X1frmlqkyKCP/z97dwEmSXX1j//0zPS4u667sYIFdwgQggRJAsFCgKCxN+GN/PKPAJEXkhAgEBIIEgLBJTgEWByWXVZY31mZmR13l/4/9850b3dPS8mtqnurv5+HZrZnuqtul3XVPXXOZeefsUqwmtHf20uDvb3U16tv+kYCN3ZxfosTe+MCu5lPZBUNp8rkm12BL+5q4ceqQQHnQiz7nGXi/nvbXh6UYjf2seAp+74Vsa+xTOGOKNnUehf/jolsUZb1v1LAGKhv1Fs3djmrvGHmGo8t/+DvIyuHFgg+rw4u8+5T8FgDAICgJAAAQJCu7GzqzUgPWSYHrfnM0DLaHSNDTsuYTtovAH2a79iV/JJeCC3LTbYOVr0BxYjZR7J32CQoJ9ZKcIeDE2OyySwR9pJYhwLW6cjGY7VyHkxr/zDP3PFn0clyeIoYgIr1eoPTFMFnwzTt+g6RZf1rySaT5fygvs/68aAj0fPpG/oG6B6bygGLoDewYXS7lWV7d9LDWxrod6tDM/H0BjWNjJNnBgv0GSnZGs5nUXDIfz63bWL8SyM3u2xo65HuZjHVvVnfbnu5f6NY9ZJ2G4b1AADQAkFJAACAYB4PbZk+PeRX83bUUlG7/rs8Y130bQ26uzFeptF4u+ReTauaBY4T4nRvjtPzB06CRBUJ+TRtrj5LxoqNvkISpby07AEmLZ6NEZBk4z+tEtAhG0/wNostR0IWrBTR5Xst/a6RoVGQ0FhwbKPOzDenNluWFRYp2L+rx1xpyXVtPfRKUIlmu07NWdDOEAdWgJFl8nRto2Pn5Hoy0FmWLdsPYpHlZsxRgRnBVmNlalmJWT9JFiEAJCgEJQEAAMJ8tN9iGvR6Q363YGvsCyOZsQsOq685PmwSN9aHCtdHutrocXZZyHTBqadTQ6Z2yyq4Qyba8hqwIFMyfD1qzSxCoDk2vZu86F3klT2t9NLuFsvGeo20/mXN9g5ko8vTJEu4/fOZXQZYPhYvex3L2olN1cndQ1R5Yqutae2ivRaUGh6JcWMnbmZxdlsN/i6PFzi0Yv3LJtaYsrF+Z3q+Pv3nT8HVkwAAnIagJAAAQJiB9HRqLcgP+V1Nw14hy8npsmAydfpGI38LxYnXsaJldUUq5QoWMNhxbbY0pFZ2rn8FDiPydGZ6JOtkD5tp8NPhiU5AK9avlkmuNZqhAiCQLIc3p46zCPgIWIYaFyJu1InOJ/hv7zd20F837FZiH+odGaUhjTeUJfL+qifzsmt4hAdQVbgO3trZZ2o8Vl3jSybyBgQAjkNQEgAAIIL1s2aGPC9u76D0AWfG99FjS+e+kix62XmdFmtMM9HtiDyGWPSrMOGLwWddEDvkLllyjvbxc3D1C8a2HePi38GulyxbsZOda1Z0ZBkZH8tq8ndfiluPgQ5eSz602InKsg/KAJ3K8rHz0JwoWd0itvOWgSHrbioNap8vUrt1rJ/b1u6kf2yqI1W0Dw5rep2eVWjV5qxC/uW6tm5N47G6rZoOACQeaYOS1157LT377LP8ccIJJzjdHAAASDCbp0+jsaAr4CSfjxZv3kKyizcemCzXHk394ks9Jap4dwrLss6d4kv0z2ZBjwM6wXXwuTPQonezYgFUJ25KsGKOKm3/e3oHxKxHnzVBdKOHJyuP62zaMqxiEdvZsIHy3W7+ztSCbZN2Hatija/rU+iAZMc2I3PwpHd4lPb0DAhtt5kAqt1UKTVsl0h7pPN7KQCAfKQNSpaVldGcOXP4Iy8vz+nmAABAghn2emnblJqQ3y1f/7lj7UkkokvcWtFfo6uNJuYfr3pPyGfzOZeRpfcjsjHrarv6TbUJxle5xP10mqCjxjir1r1T25QKZdUik7fdb9S1Ot0EZfgDUbJshiLasbatm2QW/Bk3d/Y62RQAwx7eWs+z25w42UnkCiRGzhkM3wij44023usDAKA0aYOSAACglvCLolOnltL5cypJZetmzwp5njE4SJl9xsujmmH2klOWTjYzxI6/50uY5WlHR7/28q3jHtpczztxwrFAZWOf/Fm0Y/GWqUXL3MhUFdpUXcPp44NPz7aq8ZvG9HcQqcnpcaDd8l2hZdL6tlVwm2djlPV3q1g3zakUarK7rbItm/YBbeVLRXPTEbO22903Klq9rty0LQBA4kBQEgAALLG4KIdqsjOUXrp7S0poNKzHYO6OWlKNap2q8bID7cSa8ujWBtpqYqxOq+0brkaiBWdw7DgWqPz7xj0ku3s31gU68c105Jvug5dllZsaN9XlPNYtCy3bz9M79HT0R5+gm8bidFtbhbOkukB8r+5pTejv0kTOeNLruzf/H3315j/Rr279k6Xz8Slws5cTwj+/zG0Vt873/XuLQlm9dtzs4ab1r8WwTBeqAAAKQ1ASAAAgjP/6bTAtlTpzc0L+dvjHqyh5ZNT2ZZZIlz/RPqvR7joz1+PDY2O0rauP3qiXowTeczubon62qB0pEm5/qm/PrPwss7q1m27+dDuNuLiDDqwrGW1HYtjGjh7+c2h0jFr6hzR1rlsdGokW9JFyv5GyURaPT+zQZ97Rpf3mH7vCd3YGKFW6scgJwUvnkONOpMXHn0zHnnSyI8fwDxoxhp6TfA5/rz6+rTF0nlq2JYd2789a5S7hbDcRQ3o8sHn8xkQVoAIBAMgsRfQEd+7cSc3NzWS3adOmUXFxse3zBQAAd3vzgP3pjFdfDzxP8vnoiI8/pjcOPohUsaa1mzoGx4MoSlRH09lI1qke6EyNcN3/aUuXvumR+nwu+zwy+PtEdmSwLR29geC107uG/0+dg8P0wq5mZdZ/cP/Q4OgYpSXjnkmt9Kzbp3c00tauPspITiYj7AgC+RSZR33vgICpgGUMbqz8NMKn7bu0oXeQKrLSjM0IpGLkmNA0MPkGDxmNKnHSP66+b5D2c7oRCouWvctuSJLlkOuJct1meL4GpsEXk0uS0rXs3W/Vt9FRVUU2tAYAQIKg5N13302PPfYY2e1Xv/oVnX322bbPFwAgEVh1SSvrNUFwx9SuygrqyMmm/O7xbBNmv42b6Y2DDhRzu6UNPmjsIJXs7hmgm1Zto/kF2UK2w1097hunpH/U/mxdkeLtOWz9H1FRKGReovrkOoecGTNIb2bNrh4HAiaCDoW9w6NKBiV9grY5K79RGnSM1WrFd75CfeMJxmfbXMSOC20dLdtqzwi70UtsUBLlWxWi0AGtb2SUMlOM3Yxip9UtXfTFKSWkAlmymlk74l2KqnKzj6H5KrQfRlpNdjTf7WN1AoDa1LvqBwAAqRxWXmD4vSpcSviSkujzmTMn/f4bTz7jSHsSQddEaczWAWeCQHaEmvtHRml7V1/czgSjbfGI2tEELww91QE3TWQhymzfXd/OzH+8o9/czIU03We880iF74HE6RgVf/TTFWixYWPQ+wm1NAnbsBg+RQIB4LxNa1fTrs9W0dpPV036m97vYzVuLzQ+Fqobyzdauc6ED1fgvsVvWiIukkT8zAAA8SAoCQAApi7+0hW4+9bsBeTHixZMek1hVxelDqlRvgki++NntTQy5jN14cjuQG/q156B5Pfk9kZ6ZGtD3M4KoxexGAMSVOTvVGUB6Y5B+TJT/V7Z02JJB6mVnVYiSqVZndEhSqzZoGNQLjIFGj06/i5ToEH1oFo0sRbxjy8+n/5y8Tn0ra+dI3w+IdUdcTMNKL6jSnSoAgdg/QNAQpVvZSVUDzzwQLLbsmXLbJ8nAAAkRkfKaEoKvbtsPzrk0zUhvy9vaaFdlZXkJL2la2K/OrEuXVhAsXdkhPJSvbrf619S/9hYRx1xynpGyhTyZ4Oq0unrRAespgwll2+yf/qslr44tYRm52VN2qbYeuUfP15g2yd2LCCjfDqm+8T2vZQl6IYXN5cuU6HMmb8aesjYfCosMJdybNELXulWnD+OTLSRB6UizMANm61sn0GmoHQwOVtlDI63zgocS9y0USUgn6T7WLxzO+z/AJBQQcmlS5fyB7hLd3c3vf/++7R27Vrau3cvf56SkkK5ubk0ffp0vs6XL19OSUnmkm/r6+vp5Zdfpt27d1NjYyOlpaVRWVkZLVq0iI455hhKT08X9plka5db5gHuhOsoog+XLJ4UlJy9Y6fjQUkIu6vdpjGc/BeB8QKScedvQ2Qe+6/9RC3z3pFReqehY1JQMjAfn3vLow6MjpEKarusG6+nZ3iEsr0pujubjHf0i99axiJM085tUssxPmZno4Z5oNNPwzIidcZclDVQBtbwSHdmCnwJYjeMv5UFnwNKttnEOo7K1lY3CL0ecLIlAAAOBiXBXdrb2+nWW2+lZ555hvr7Y3e6VFVV0Te/+U366le/qrtEVF1dHf2///f/aOXKlVE71bKysujCCy+kq666igdE7WBHu9wyDwDX83joneVL6dBVqwO/mtLQ4GiT3N5Rb0fHILqMnCFquTtckdIVfAk+fzPYuLCsDPPM3ExLpn/b2p10w/LJYxobEe3c3K7lr6WTTMZgUKx2s3Pq1+vaaGFhtp1NUp7RDlMj24fH4vbdtGobLSjA+leZkZsS5DtSgSgI6Ni0jF10/u7YtQg2VgBQHMaUhKg+++wzOuWUU+iRRx6JG5D0B77+v//v/6NLLrlE0+v93n77bTrttNP4z1jlB3p7e+mOO+6g8847jzo6Oixfc3a0yy3zAPdz0XWDKTvDsiJzevvIM6ZGNo/dnb6tg+LH22wZGKLWAbHTjViizeJFNDg6RqOCZsKWyactXTH3UzuuWd/d26GpPKTrqlhNbECxPs+6th5hszOz3P69bS/dsW4XWUFTNhm5swS0P6NVlu9ath/+7fM9DrbAOKe2kUifWusNlh82ddBzO5tIVT4b143qx4BoW8SGdnHHeJAD+vpBFR5JD7wRM9td2qGA4wUAgDFIh4KIWHlPlvXY2dkZ+J3X66Xjjz+ejxlaWlpKQ0NDtHPnTl4OdP369YHXvfvuu3TNNdfQPffcE3fpbty4ka699lrq6+sL/K6mpobOOussXhaWBTfXrFlDTz31VCDQyUrIsun/7W9/o9TUVEvWoB3tcss8IDHYcT1zbFURvVbXSjJ/3t7MjJDnST4fpQ8OUn9G6O/tVNc7SLXd1pUPdFKkC9q7N+wWljnklFvW7BA2rVf2tEwKSFizv8ae6vDYGLUMDFNJBr43IgWOoy9Vn6lx+vTY2tlLMnbaeFzQsSOq3VYGflzaFyjHcldgux3z+WhPz4DTzVB23x4ZG6P63sF9v3euSQnLqu8Htx8brVhs2P7jLB8HTmZkWyeRzm/1lOCW7fOoCssRAGSGoCREdOONN4YEJFkg6y9/+QvNmjVr0muvuOIKnk3585//nMYmMoZYVt7zzz/PMy2jGRkZoeuuuy4kYHbmmWfSL3/5y5DyoWeccQYPkF522WW0fft2/rsPP/yQt4cF3ESzo11umQeASEuLc6UJSkbTn54+aZSY7L5+R4OSD2yuEzathr59HW5mqRJcMDymJMnp7xvreDZpZZaYMYL1dSD4bO8EbB8c5p0/esvG2+GT5k56eXdo0Fg0bcvcZziI8cKuZjqiopByUtW9ZBB1LHo9wveTfFudfccun0uOmSqWlDXik+YuenVPC03JduZ8RfWxOV/e3eqabQFC+QwElGTeVkFNKp5PGOey+q0OwqEIAFSG8q0wSVNTE73xxhuB58nJybysZ6SApN+5555LV155ZcjvWKAylscff5xqa2sDzw844AAeDI00nmF1dTXdfffdlBHU8X/vvfdSa6v4AIYd7XLLPABMkzCQwERr1VhSEvVkho4dVtLWZkubEpHeDsC9fYPUOzyqVMfhm/Vit5+m/kFeGnZ3z77sWf/S6BgcDpSb1GtU8CIVObmd3XJm/2xs15+Z2DYwrPvQadXW3tw/RJ+1dtM7e9snzVOUdW3d1D86GvGzyNbp+0GjFaXt7f+QrHy0Uy3w3+Qg2zhtWjbtgbBjpx2ZMCLn0DU0EnH92ynSIpNsN49KdOl4u7Ht9f29HTSk2JADicTozVXx3qXSObEIMnxaGW+UU+Wmz8TYQsRwzycBgEQl/LZnVs6zubmZ7DZt2jQqLi62fb5u9NFHH4VcaB9++OE0Z86cuO+7+OKL6a677uIZeswnn3xCo6OjPKgZyV//+teQ5z/72c9insCxbM1LL72U/vznP/PnLAPwwQcf5NmAItnRLrfMAxKHrJcVTmgpyKec4JLIDXtpw+zoN22ANUJKPk78vHfjHspNTaHz54SO/RlLUgJt3Heu30XpUb6T4/nz2p1xX7OhrYdKq9Js76gZk+CyXFSn35Y4JVZZ9mLIfIOeWrUUIk03fC1qm/fkV722Bzc/qUr1Q+dztU1UnplG+5fmadp+79sUvSqB80egUJs6tN8QYVfw3+r59I+M0j821dHXZmv//jcV6JHorgn+vRqnPU39Q/RGfauwKgpuEmtd23kDhdFtCvEviLldWbC1emyYr0SHWCVEP1ZhQQJAAgUlWbbVY489Rnb71a9+RWeffbbt83Wj8KDy4sWLNb0vJyeHj1O4ZcsW/pwFJ1mmHRt/Mtznn3/Ox630W7FihabAJ1vHd955Jw92Mmw8S5FBMzva5ZZ5QGKx7HQ26IpDpovqWJ+3ubCQptfVB56XN1tbnjGh+Yxng9g8W03YmFQpSfYXqQju6BoIykrTQ0t2xXuNHXRUVRH/N8vWDM6AS8Q7wq04pD20pT6wfC0f63HiA2iZlUwd9Cqz42vQyHikIkTaRsLHObRzM1rb1s0fLCiptVS0keJz9b0Dtm8PT2zfS7LxRdkmPm7aN1yImdW/s7ufr6PP23tMTMW9xnyRb2wxStSueluEG55YG0Wcy5naNs0GVEy92+A8Xf417FNoTjKdE8k4pqg8S0dMm41+HgQMASDRoXwrTOIfF9IvPV37HZVaX/vaa6+FPD/22GM1va+8vJwWLFgQeL5169aQEqVm2dEut8wDIFHtqqwIeZ7X00PeYW0lF80Y9fcoKcLO1pq59veYmCfrBNXjj5/FzzZ0i+E4JQI1BbpibEV6Ovrt2BbD28o+nxU3WrCSqqHztW7/iFV6K9okR8Z8/BHvPXt6B+imVdtoOOy1VnTQrGrpIifJVsJMpm8SFpBiJaedFmsNfdDYybdVM2M5QvTsvW1d+ypPxCNRP79U+5EesZahLIGU/9a38coOsQKo97/5Hv3szU/plQ8/nfQ3OT6FRCU/VV0goPR6jnkOqaGtMt2srP/46OzKCDmXlmy7AAAIhqAkRBxfMFhjY6PmpRT82szMTCosLIz4ujVr1oQ8X758ueZ5sKy/WNMyw452uWUeAEIEXXFIdu0RVWNxEY0FtTvJ56MlmzZbPt+3GjB2ZTCR11hvGxjXkZUM/edE1ppWTo3lZMX16KPbjGXieAQ1nJXpM4plYHTbmIXhNKPr32MgUPjHz2rp9nXxg+9srEpmOGyfsKJP/I26VsfGSNRLpb4jI20N72TsGg7dD9l4h7u6+/k+2hP2Nzs/C+t89ASNe6r3/ZSA24NeLEM22hjHbHxoVg7cyWUm280EsXg0jH8pWwd/LPW94zcqxLoXLzM7h9Kzcyg7J4ec4XOsdKyMEiHjS9ZPGH6sYt9fAyNjlp5XiSrf+uqe6NWGJLlHIuB3q3eQCtj1zb8NXqMBAChfvpWVfzzwwAPJbsuWLbN9nm7F1l9qaioNDY1f0Lz11lt0ww03UFKcknNr166lpqamwPMvfOELlJISeRPbti30jmMtpUX95s6dG/J8+/btJIod7XLLPCCxKNSXYblhr5dnS04LKuG6eNMW+mThArV6fSzm6B33Pn0vZWVH3SBaJ6oVa8JswMDJvgZ/0OyG5TNNTYcFTwKZHBZ+oFiHFZ+AWRs5bHliBN6HosTeefYoJZ5IHXNtA8OUmuyh9OTQc+toQRrXifL9sKa1iz9kEu2rTGv5Voi87B7YXBd1jGM2PrQ0WWYKunvDviE87LK9q49m5GaSG4j4OpcpoCJRUyZZO3GDkhbYw7VZ19ZDm+OMT+6U8HX4UVD5bhCzf7cOTC41DwCQMEHJpUuX8geoKz8/n8455xx68MEH+XNWvvPee++lSy+9NOp7WADz17/+deA5C2BedtllEV87MDBA9fX1IfPLysrS3L6KioqYATij7GiXW+YBiSfRTmjj3d25et7ckKBkfnc3VTU2UV15GblV77BcneVh9wWH/GY00TbYeNutA8vDlwB31fvHd5RmPCmLxpTUUjla67zjvY6NlTkUp/SvG9y1YReleDz0nf2mhfz+ka0N5GYeF7WL7Tc+iz+QFZN3uqhcsPAxjuN9L2iNO4pcbtFaxEpP25nNqwXP8I2zkKxa/+zYZfZGHyPr7/Hte+l7S6eT1Vj26SsxsrqsYHQ7fn9vh9Bxp63MLH5u574b2uOR+azR7uUdy+6gcZqdFD7cQDAnl5bPRWc7PpddVwFA4hEelAR3+O53v0urV6+mdevW8ee/+93vqKGhgS6//HIqKSkJee0nn3xCN954Y+C1zLXXXhs1e7W1tTUkg6a0tFRX28rKQjv9W1rEXCDY0S63zAMg0dVWV1FXVhbl9u67E3XG7j2uDkr+aa2848uykjrBnWGrWhLzzttNHb3SRwKcaMo7e9sp0Y2YKB1sZ2lSM2V5VTNie6dxFIbHHDXTeWZd0MXQdM2+36J1KWqqLf1DUnWaGyXTR3heRzAFJvvz2p1UmZVueNE8etcdtHFvE6VnZdP3lt4odBFH2szCxxC3Y1s0OouVUc55ntjeSPsViSl3K9GuaJst0c6xHcRvyfS4Jyhl1Xe8/dRfFwAAdkBQEiJimXX/+Mc/6Be/+AU988wz/AThgQce4NmTM2bMoOLiYhoZGaGdO3eGBK1ycnLoBz/4AZ177rlRl2xfX1/Iczb2pB7hrw+fnlF2tMst84hn797Ydezb29FBDIrzeGjLtKm0Yv2GwK+KOtxRAlQUK3Od1rR0UU5q9FOYES2pXS68mOyRKJtVpiX4gUvK80YTr3N0V0+/7rFwVja004KC7Kh/7w8ar4hpHxyeFJhRJQjS2Dc+fpled67bRRkpkctPWjU2nZmxUGVbG2zcSBVEW25szKa0ZGPrlmXYDY36qDDdS3b46+e7JdguZNsC7aEnm9LKvnjZuvm7h0doU0eP4S3m0bvvoJa9DZRbWkZ0i7mgpM9g+WZZxBvb2P97trxZ2XkrqBwQC79miPRJWAWHaMFeM+yIv4kbZxecFukGqPBfqbwvAkDiQVASosrOzqbf/va3dPHFF9Mvf/lLnhHJvghZOc/wkp7Jycn09a9/na6++mrKy8uLuVTDg1xpaWm61kL463uDMpXMsKNdbplHPEceeWTMv3u9Xpo/f77u6YLbqXW501RUGPKclXP1jI2RL874uwmdsSfIf3Y105GVocvfDNljJ6wTKS05icoy9R3PA3zGAga5MQK/IB+R2/HbDW20sDA76oQjjfvHxjEKtrrF3NiAdu2WRsdx7Bga5o9InUBWtf2pHY22lE7e2zcYd/+PN5W+sNKcnrAxj9jYrtlecceY4bExqu3up9l5Wbo7eo2cfdR2G78p8ra1Ysa1DdfBbg4ga/g0Bvhf29NK582uoCQjve0OfRdv6RRzg2skTRFKGLKbPtgxNnz9mz2Gu2Gc08A4zRE40dkeb473b6qz9QYLVjYYxHp5d/yqUGMWbXum93k2VrfGnf7VPS2UnSLHeX20fdl/DDNT3UM09r2qxHFVgRsoAADCoecUomKZkH//+9/52JAsIBnL6Ogo3X///by863vvvRfztcPDoYMvp+g8OWIBrVjTM8qOdrllHgBA1JqfP2kxfPPRx+WPcNng8/bYd7+LMBqjY0a2NWDmYpZloLGxC/++cU/c15r53Det2kaPb9uX5c4CBuvbug1PT8RuIMt63N3THzebLnJblejGiMqjcz2wMbfidUIFpinLylXMUJwO6VvX1NIbda1Cxmkz67Gg4wnjC8uSYkSOyfd6XRufZ7zxj/tHRumz1tBj257eASWCO20Dsc/J71y/i/6yflfM1+gN7LB5Dmoc5/XN+jba2dPPXz85qzf+EmXZ1XacP2gKMFu4AUS6qcPqTnX/zReyZ9H85tPtPLvfaZOzjyKr6x0IHM9Ei7QJatk/wrOpgp/atfbNlLRm2yoL9rJjtR3Ctzc9TWcZlPHOfUSK1DSth6qPmjrpjfpWofPe90ed69sXO3OVVff4uMmaoTj0NpV9r94R5Xu1c3CYn3fYVRlkNPy4pGXMd5xwA4DE5LhVBgLuvvtu+uCDDyxdIhdddBEdfvjhcctrsgDjmjVrQsp6nnTSSXysyKKiIhoaGqL6+np655136N133+VfeJ9++inPrGTv/c53vhNx2hkZGSHPBwf1lc0aGBiIOT2j7GiXW+YRz5tvvhl3+/rZz36me7qgvrNnlvOSZ27Qlp9HPRkZlN2/72I2a2CATlj5Lr18+KFEiX7Hk8VBKbm71cS1dXuX9gwOsxeemzt7J2V4CO+XlbGnP44HN9eTKlgwQHSprg3tPXTaNH3jU8u8077T0E6v72mlby6osWwekTbzSPFE0YtkYHSUdzy6VaRgIeu4ZmV0eycCA7EyrZhtXX38oVW86fnbJcKGsGzjP35WG5JNt9mBMc3u2rCLqrPS6dQ4x4DmsIxAFlTQu5R6R0Y1ZQNbLbile3oG+PiRmRpKNcuKdaobtaOrj5I9HpqSI+Z6W0ay9tlL2ixNdpgoE7upvZcHe40OxaA38B5v/d+yZgctiTIWJ7uBh2XoG6U3ofyRrQ1Kby9aPq8/wLdVx/e0E9UymHcbO2hLZy+VZRisZKOTmT4UVsWAHc8BABI6KPn000/Hzboz4stf/jKtWLGCVPf555/TypUrLZ0HCyzGy5C88sorQwKSRx99NN10001UUFAw6fWXXnoprV69mgchWZCSdYr+5S9/ofz8fB6gjDdmodmgGRv/UgQ72uWWecRTXl6uKxsTEofIsbWcNpaURO8uX0onvBOaHb5g23baU15GG2bPokTlsWOQFIFUuJBPpM/hVizIm+NNoYosgZ0XQb1pI74x3nkm7DgbNBkznW5W6xwaprxU7ecVkbLi4gVb9GbCRJomG89rZKyEUnSW+GZBsa2dfTQnX8z5rtv8c0sDXTq/Ws07HsI8Xet8QE7LPhPJPZ/vppm5+65BuiLsM/79wtJsPVbOUHCmvNmOa61e29MS96aTWBk5LIgz6ouT1apz0f9rIggyqdRsgp9xxC+TbVM7HIqoxpttvIAiqz5hxz4lAtvvot3sU99rbExqFcU8ruq87usbGaO6CMvOia1ZpRu5zJSN/7CpQ2hbAACUDEp++OGH9Nhjjwmf7uLFi10RlJTBI488wjMe/dhyve2222IGkpYuXUp/+9vf6Mwzz6T+iayhW265hU455RQqLS2dNFZlsM5OfScCXV1dlgQl7WiXW+YBYAXF4lgcCzwu2/A5lbSHnuizQOVwSgptmT6NElGSDevynb3tJDPWUcHG1DtzRjk19TvbafFZWzftMHEha4RKHZZWlV0K3w3YGGKRxhETtaz9nYBGlz0bRzBYcHBTyzhW8UqLWuWt+nb6kohMzhjBlrg0fnS2HFN0Dp6xqrmLXtnTQt+YU2XJfiVqmls7e2lWXvRzxuC5mN3lgju8WyKM2Zeo5yRO03oIMHPjBFv3WrJXY3l3bzsdVJbPMwDjseKoFu3zh2cGDYSVzWWZ0LE8uaNRV3UFK5ldRyKPTbqnIsnpS33vAC8PmZfm1Zxp/W5juxJjcVo9JEEiYsuLXXeE73vsHNfyUriTSgbHHtTgie17qTlK2dt9QweoswX4JJn7zu7x8ukAAKpA+VbJsLKnZ5xxhqXzmDNnTsy//+tf/wp5/v3vf19TZtuMGTPo3HPPpfvuu48/Z+Vdn3jiCbriiitCXldcXMxLgvqDl3v37uUnLloza1g2ZrCaGjHlt+xol1vmARAs0ccqeOjLX6IzX3qFpjSEllQ55c236emUFNpRwzI4EgvrcFPpYtIKLPgkCzbeTWv8RBfls6XZ2D5MarK+qM/KBisC3Nq2f5GHTzb+qBlv1JnbZj9pjn0TlMgjQrx5uQkrackMjY053fMV00u7W2IGJUUaZstCweDiyNiY7kzZYDIFpV/c1Tzpd7FufmFjYa1pMT5Osd/DW+p55mZ+jOzo4E/ESutFGvuSZa0vjlKSUabzCPb99KPlM8Z/EWdV2RmQfL528vpf1dxJy0vyAmWyTdN8s8cYeYP2K73bdPgNOZH+Hv5dzYLj/ixaK61r66FDKyZXqtJSgl9ZNh+zJf5a1aW2q58+6O+IeBMGe8h0Lh8pk55xpEvBxDwlOr3gH+OfW+opJeykxy3bNwC4E4KSkpk3bx5/OKW7u5s2b94ceM5KsLIxJLU66qijAkFJZtWqVZNew4Jj06dPpw0bNgSCl01NTVRWVqZpHnv2hN5ZN3OmmIwDO9rllnkARMLOgVm2ihNjHjnt2WOOoqseCr2hg/nya2/Q80cennAZkyxTUqWLoPAOLJEXxT06yz6aIcP9AX/dsJsuizJWH+uQtyor0T/+Gpv6/yyb6MC1cR1JsOhNYZ30ZspC6dVoMnv45d2Tyxyyu8NZObgCjVklqrGj/DkrMWtU19AIbevso5l5ocMIyNh559T+vKa1m1ZMBG20YmUOyzLTxLXL4ETMHrpZAEdEECdaKVmPA5l8Rm6c0XvTjDNZbj5DATB2Y4I/KNkyMCy+XVGadeua2pjf+544QcxHt00ep88/K5Zhdu/GPZSeHDqmKPu+tPM7E+S5OcSPZbFajV1Tay3drudmHcfLG6t+0myTXd39usfzHcHCBQCFmP821unXv/41bdq0SfODjbH4wQcf0D//+U/69re/TUVFRYHsLVYG1v+6s88+2+6P4kotLaGdPKz0qp6xwcIDX+HT85s1K3SsteDxK+MJf234tMywo11umQckFq3ZkIsKc3i5SiNU6rAMN+z10t++cmbEv61YN36DQCIRNaZkS5TSPiJZfV1829qdZJfwUm9OiLXOfrd6h64AoN51wy7E9QQ9hWRyxBBrN2DtlCnLXMtd9Gb3aqs/Lcug+sv6XbrfZ9VqYOv4/b0d5oMfQW83OqVX97ToLq/LOm5ZoPemVdt4WUAtImWlRSzfqqsloe9jY4j2DmsvRSfRbmaoLVrKHEIoj8BpdA+Ju7HohQjZpTJi4+ia/X6yc7fTe7PTI1tDK5toCfTEK52rxRt1rSSaLIe31rAgtNHthwWYP2zUPvbdmtauyfOKM+s/fhb7vFxry+9Yv2u8goHFN4zF4raqNGzcQ3bzDmfRRxM12Xs27OblUq300JbQCmZGq6iw8zgAABnZHpTUKykpiWfrsXENr7vuOnrxxRfp2GOPpd27d9MFF1zAA5YgTnp6eshzlmWnR/jrw6fnd+ihh04aa1QLdtL58ccfB56npaXR/vvvT6LY0S63zAMAQnVnZ9ETxx87abGUt7ZSdm9vwp1cONkRzMZUMUumgJFWbuucsHodvDVRYteJVf3bT7fTWwKCou5a4+Y0xCnBZzcWyHujvpU2tPdEXV9atr2miUC/mbF6P2rqjNqpHq0J93y+JzAOFcuK0rKteWzYXu9Yt4v+qmWcTxsp+HVh2XenlTeZhQc+GKNB/1jvCp6kyKyTTpMBThEtifm5JT6ehpuzeD+qWbyUKuct1P1eO8quRvK+jmCbHvHuA7Tj+CQq4M6+p9omMhC1ntOy0tB6WB1IjMaK7GFZrGqJEBw2GIRlN1FZqaFXzLGNjYnp31YBAMClQclwubm59Mc//pEHStiYeddccw01NjY63SzXYJmoLBDsV1dXRz092ks5sczW8PENo5V5TUnZVz34+eef1xQAfffdd3kpUr9DDjmEsrLEjV9jR7vcMg9ILKIy39xuV1UlPXncMZN+f8iq1ZRIkrC9JDTWMaFSGWen4gmfBwWrEiHIrbe/yuogO7tzfK3/jnyTIrXUH8yIl+kQz1aHxgmLNUYpG+eyLkoZTS2MnlEY6fN8urbRNUFGS9rlmzwmIQumy+jF3ZMDH3etlytAHYtVm5XZUy5WepkdD3d07QvW6Uystt2N9z1EV973GH3j1rudbor0ntrRSH/7fI96N6/Z1FS7lskDm+ssvdZ2es3WBmUNOt2WWB7eWk/1Js5f9G5D/tXKKmdMep+ABSXzsgYAcF1QkvF6vfTTn/6U/7uzs5P+8pe/ON0k10hNTaVFixYFng8PD9OTTz6p+f2PPvpoyHOW4RoJy35lgTO/trY2euKJJ+JO/29/+1vI89NPP51EsqNdbpkHgAhuDHXurK6ibTXVIb9bsG07FbeZ65hWCWKSie3BzfV8bCm7fNzUSW7O6nTjcVIWHzZ1WtYh6VGwBK7ZkomN/UMxA73+t3QMicsuGCMfH6vPKjItcys/DyvXaxcRpTD925DRbclNx1WzXz+3rxsvZ7m2bd++a/64aH7PMVzmWV81zwjz9Vm2/1t5vuKLkdHVZHIcZ1FtkWUn9VcBsIPH5JAIqlRuCSnR7pP7yPuPTXW2BaX9y6LL4Pj1sW4QAwBQnZJBSWbevHk0Y8b4gObPPvssz5oEMb74xS+GPP/DH/5AGzbEHxPtzjvvpFWrVoUEj4877rior7/22mtDsjJ///vfU21tbdTXP/TQQ/TOO+8Eni9cuJBOPPHEmG1i07v00ksDj+uvvz7u57CjXW6ZByQOOy6I5LhMEePd5Usn/e78Z56nwnZrSjfJZntnX6A0porUuPyX1x7BdyDHs20iu0cm9b2Dgawj2WntkAkuQ2rlfOIRmYXrsTJ7KcLvVjWbK49pd+ek3u9llj353M59FTfs8lrQWG2il5B/TDk3f2ewrG025q8baD3O+Bw4J5X9pgT5s/fFaR0Y4mWto8/L7JiavqhlLq0LBMVvs9avEJY526EzGKJnzHBNfGLWydYI52L3bqyzdMcyMoYyW36RyiZ/Eue8wYobQfviBG0jfc/rGdtV1CIXUV5bSFs0ToSN060129Tvka0NjtxAAQBgB2WDksysWbP4z+7ubtqyZYvTzXGNr33ta1RZWRl4zsq3st/dc8891NU1+aSILfvvfOc7PHgZ7Ktf/SpVVVVFnc/cuXPp3HPPDTxn65G955VXXqGxoA4ANs9bb72VfvnLXwZ+x0qT3nDDDXHLXLBprly5MvB477334n5+O9rllnkAMBVZafxnUbo37gIpy0xNiIXWWlBAn82dM+n3B6/5jBIBuytbZexi/O/BHRYJTLavgZb+IfrUwk49UbpFd87FYFmcStIejse37xU2LTPjNBph5maNSPuipKvIdn3Do1IGetnxal1Q9tkre1poxERdTKtaGqkjVCWdAjNvRblz3S76y/pd1BNh23Tya1XrNvTUDnM3F1i1W7H9hwUU9dgZYft+eEtD1LHr7DyuegSeg4kc/9SfpRfN0zsml8W+be14xq1sPogwlqeMx4xoASujGXZmPBojEBYtaPnYtuBzM/uChS/GGc/0/qCsSLtE2xVvWbND902Wo3HOGRRJpAUAiGjfoHMKysjICPy7oaGBlixZ4mh73CI9PZ3uuOMOuuCCC3ggi2GZqL/73e/olltuoZkzZ1JhYSEv7bpnz56IY3oefPDB9IMf/CDuvH784x/Tjh076P333w+UGb366qv5WJRTp07l892+fTsNDIRmXfzkJz+hAw44QNhndqJdbpkHJAYeoI5y1luTnUE/WDqdUoIycyO5YflMSiRv77+clmzaHPK7ObU76bXBQRpMGw/kgvMibdaxOmLAWX/9PPL4Yf8NypRyG5Z9NubzYaxWS0gWdY/BY2FbWcZQtA56uUTvfdvUITbLy2xHHwt2Rjpe2V1G0V17QWT3fl5HJ08tIZn4y8o+H5RR1NxvrhRjpLf+8bNaGrKg9K7ZrDer+slZYH91Sxc98v0rqK2lhbIKCuOOKxmp/OGw7INmht3cwMZqL4xw82fwNsHGBiUTQU4t7/fbHGWs47fr22hxUQ5Z/R0YfGxlQeqyTP3XVa/taaWjqwonbasiqjp4Yszz2OoiEkXUVtw5OBxyHd8+aGz/Z+Xb2U3KpRmptt0MxG5UPLKykDJSkiP+3cz41zLwTWShJst2lygAQKJnSrJApB8LkIE48+fPp4cffpiX8Aw2OjpKmzdv5kGuTz75ZFJAkgUuzj//fD7OJxufMh5W4vX222+fNHZhS0sLnz4rGxscMMvKyqKbbrqJZ/xpEX7CwzIAtbC6XW6aBySGeBcP8QKSiWjY66Vnjz5y0u+vfPhRykDJcUs6bSBxvRfhTnjNHCr/xGa7tVNbidd45busMGDhOH2iGc16iHG/jWlWVJuwqhv9rfrYGZwsKM4CKrLqNpMpqeO30afh0xz0eHWPe2+gCLC577R/dJTemLgxZWhU27pzonuXBQ/NlsEOF6/Mot2f+7efbucBDhEircnGifKWO9avpd1rV1P9xvXWjGtq05CavjgZfR80dvIA3F0bdpls0MT8orSrrmeAPmk2P97lyr3t9GxtkyXfq9GChX/fuMfQ9D5s6qCGXmtuEvFNZPX2hn03sXnKeMPSHet3BcaWNYOVdWXjNdod8tdTOjaciLaGTyP49G+LxvP8WJVX2HGVnYfFbANSJgFAQcr2IrNg2Keffhp4XlQk7o4jGDd79mz697//zYNaxx57LOXn50ftdGFZdyyz8rnnnqOf/vSnIVms8WRnZ9NvfvMbXh72+OOPjxjMLCkpoYsuuoieeeYZOvPMMzVPO7ysb/h4mU61y23zAIDItk2dQlun1Ez6/eWPPIZFZlMGHajLrk4Nn4OZNO82tmt67YDOjmc9oiWMPBlUnk2WnJIdUcbpfHGXsSw/UR30diwfq4IJMfuxJma6qaOX3jY7TrCFC8lMZku0d1qVlOB0hpae8c1U0jYRCNN6TPXFyQL7T5ySgEaxDCR/FqUTfDYEByKN4Sdq2pHG3HOLSBmvcYOngr4b7t9cR++bubnLRh8KbKeVyWdPbt9Lf1pba3r9sLE9a7vil9g2G5MKDuyx7c7MeaeV5ZvZeKdbomTq6sGWabxAnwiRqjjcvWG37qEoFEruBgBwd/nWwcFBPt6dPzsyOTl5UkYfiMGW7XHHHccfTH19PbW2tvJxJlnWYU5ODh9/Mjc31/S8Dj/8cP5gmXt79+6l5uZmSktLo9LSUv5IMpCJ9dFHHwX+zYJxl112mRTtcus8AMxw6xim/z3oAJq1a3LA7Or7H6L7zziNunKsKXMEIKLzfK+LOwBVssfC8lMsq0FryTszRPT9/CvKOEdGy1pZFugjX0jZRmUJ7gQzWxbv39smDxlhtrMu2nZpdnuNtkkayWZg2Txs/G4rMiE6h0aoOULnKBsfzmjZfStLDWvRrqEM5Zv1bVTfOxix7Ou7e7UFNo0wE1QTUVbSDknkEZN9FLa9fy44y1RbG4y/9/Fte+OW1nXjlY+o6zlflG3itSjl+tk4rjNyM3gpT7PzESFWcJ5Vd8j1auuKZTcFsYfdw6C8XtdmWVlsLd/ZkV7iD9Kvadk3VnPwMmVBy6L0+NXamIe31tNROrcVPccNtq1GGw+UlRx+fU8rXTi3SvM0re4m2dzRy8ds/85+0yg9OXIpXACAhA5KskDKW2+9RX/729+otrY28PtDDz1USFAM4mMBSPawekzLadOm8YdZH374YeDfZ599NpWVlUnRLrfPAwD26cnKog+WLKaDPlsbslhSxsboksefoueOOoK2TpuKRQbSqVd0HBa95exUUNvdH7XTxmyH2q6e+Hfgy2xdWzeVZqQ52nFtB5fet6NLbXefnMGaCLP//eodEV9qpCjyfZv2WNYhHStoYlVpQ6utb5vcYR3pe2JNa5ftY1EGlzbVGmTe0NZD03K1VyGKRU9g23C2LI5VMcddBOPWtUX/fmcZyKtahnUFJSPeQCHo64RNm303bQsLTnYPjdAd63bRSTUl5E2Wd2cZsaAkqv88ZszkQo6UKcmWKaPnu7JneNSy8drZWN3RAuhGeKIFPyf+3Wvi2ueJ7Xt54JvpGx5DUBIA3BuU/P3vf0//+c9/dL1nZGSEuru7qa+vL+KYed/73vcEthDcgo05WldXF9hOjGRJAgCI8N7ypbRk0ybKGJw8Jtcx731AdeVl1J+ejoUNUtE4NJd0bl+rb1wcRT8mhN117vdUUNlZ2zI6dHTemQmaRpqNvyPJqO1dfdEDU/L2lwrcie09AthRLk7UtsqCoYH34UjpWEf/07WN/MaL06eXkp0e3679WBpM9do7wavIyVK72lh7PHlxVzMvM6kn0OOx8HjR74KbztgYuMze/kGqyRZ37WfFtsrOV9g51f8sm0EyfFeJzsK38mYvVSrN+AScRwIAKBOUbG9vDwSKzGLlOP/v//6P5s2bJ2R64C7BWZJsnMSKigpH2wMAie2u886hr7z4MlU3hpb0yxwcpIVbttLHixc51jZIbGx8Fhk7z+3o9LWiHCLYj5VvDR4PSV/nqb2Cg6Z65s0648I7HYfHxvgd7rKWBrZL8HJs7p98849MjJaa3djeg7CgC4wa3ADYuHJ2f10ZzZTkN3tEaOwzUUqFR6P34/YOj1JGipiQaCKdGbCyjdHoHffO6ntZtNxIpOt7lW+q9q5tp8tb6+Evk8rKouqhyk0slq4LG8pbqLGUAQBcdiMbG/uOjY331FNP0QknnOB0c0BS/vEkWZbk5Zdf7nRzACDReTz0xAnHUW3V5BLYX1i1mrwT4yQDyMJIkEc1bHwvER9T9PiLYA91ugadoUrnafANFI9uizzuqK5xywS0Ker8DB5wWAlA9x+R3S/4phk969OqEoOxBG+qeoISHhNldc3409pa+m99G8mOHQNYkFkWbBw5kXigj9QQsSSmgOmube3WFJiT/Rs2+LCjq7KEIhuAk2Xx9Q6/YNcixVABAODqTMnZs2fTYYcdpus9KSkplJ2dTUVFRTR//nw66KCDLB/XENR3zjnn8KA1G2+0qkr7INIAAFYZS06mp44/luZu30FffGtl4PfJPh9d9dC/6A8Xno+rAZfTe7exkxIhKMnGYHH/pzRPRCaBjMtZpc4XldpqNzPblojDnJ5JBBfKXdXcqUyA+O4Nux2dv1sY3d7Y0HMijqE+g+cAejKynQxK1Z5jTykAAOFDSURBVHb106zcTJK5YsP69h56traJLptfQ6pgZb7tFr4EVTol1VKBYHjMR3skGLc3eJxbmdi1ukVkdSq0aU6CU0sASKig5EUXXcQfAFZbsmQJFjIASGnblBrqycyg7L7+kN9f/48H6d4zT6fOnGz0QLvUv7bqy+JxktPxUz0dUEnkoTED3QJGS+mZYaQcmtNlsnqGR10ZsGOBHlVK+KLjKLrwVahnWfkczOp8aXeLvm08QmNV2X5h8rFqT8+ArhLVdgv+emSZunq+j+329kSGJAukrmm1NiNTi8e2RR+Ps2WixDRbpltNBvvs2vsf0XXuyr5XyTFdQ/rKDjvVVKszh7W6Y/2uuK9hQXSj5ZxFinYYDL5p56MmPTf7iKHy97C6LQcAN7A9KAkAAJDoRlJS6L8HHkCn/vetSX+7+Imn+M83DjqA1szHmMlus7snNBAtM5XGlIzWYS+jF3c1k2pe2aM9eKISZB/GJlOJwVhE7/p6D30+B8dcufnT7bqmo8hhMiE8tKVe6vKtRs8B7GjqYV+/hAZ6eyg9K5s/X7m3nf9sGRjiD6c19EUPOPsDKK/XtQqZl1U3YRg9VlgV6GGZha9qOBf5+8Y9Uu9XItl1mr6lM/q4o5H4bG5v8M15WraR4BsonKqXwsYEv+fz3dLdmKj2HgEAlOhByfvuu4/efvtt/m+WEcnGfgQAAIBQW6dOiblIjv7gI+rLyKAt06Zi0VnIjiwzVSXKhalKwVfVybZNCes8JXfSEzTRcqy1rEMybML6Ot4VWXuKd56DOUke+7dVoyXc7bhH6LDzL7Vs2oocETi2iqxq76rmLqkOVcocq8EyG9p6xE6QHVeDa6qLnbQSmicytwEAnBDtpkvDtmzZQitXruSPvXsnjz1w991301e/+lX+eOONN0TPHgAAFKLKCbslPB4+hmRzQX7Ul5zy37eooFN7GRoAN+kYsidLS/mxMxVpvowdigol2CImFYPPzNi9imwA49uqIo0F4ZzI6DL61egR1FYnv5rduqfpKTNpNFPeiWs7ldoqmozfC9FaZPbQILpqh1Xrf2dPP62LUp73BYHVUvhyNrn6n9vZJKg1AAASBCXj2blzJ61atYo/WlrcWQoKAAC0ke8yymYeDz305S/RU8cdE/UlFz75jK1NAgBQTfB4Qvre6HH/Z0xA3TaPfWXHeFLjmXKQyFS5f8YjwZjUsrLiKG7VN4Oumzsc9tcNuw2fAoTvV1Z/ak8CH6tkO4Y5cWPaaoPjysu27AAApAxKirozDgAAIFHUVlfRH7/xdXruqCMi/v2Az9ba3iYAlZjJdsSFvk0k7FDBVQtEHftKsm0OQWkQwY7vO9Yd1Dcyauk8Bnt7aKCnm/+0gkrnBVY1lY3hqEqgZ0ShFbamNXIGnWw8Cp0jrW7pdjarW8hUAAASj/CgZEZGRuDffX19oicPAADgSr6kJNo6bSo9duLxk/62Yt0Gyujvd6RdACAvhfrhpKXKMlSlc1A1kVa/jJsE7vsFVbajJEFHq1j74a1fOZF+ceQy/lM1nQIzufUeq/SNt2uQQoEeJ272+LCpQ4lzmMHRMdrW1edYSVk9n2tju/GbE2T8vgcASBTCg5JFRUWBf2/YsEH05AEAwEXQyTrZnopyemv/5SG/Sx8aossfeYw8Y2O2rRuARIEOCXDKmCoRUQHYJ5Xx06qyClQa/1TGMc4AZMAySNdHGWvOLfixSpUDq0LHVbuPtXW9A6SKQYPXx+iHwDIBAJcFJefPnx/49/PPP08vv/yyUiclAAAATlu1cEHE35/y37dsbwuAm7lh2AGcZZtdfvbe0R/sz2t3an6tCzZVy3gSYB8UlX0GiQ3dMs4aGsXNhTIZH6s39AiOvstxCg0pSsOjBksN42sVAMBRKaInePDBB1N+fj51dHTQ8PAwXXPNNZSVlUUlJSW846elpSXw2j/+8Y/0t7/9Tch8r7/+ejrppJOETAsAAMBRHg/dfe5X6BtPPsOzJP1m7dpN19/3AD197FG0o6Ym5C1pg4M0u3YnTaurp/KWFnr1CwfTzqpKXhYWACJ7Yvteqs5Kx+IBs4ds14+DZVb/yBhtMFFijRI9q0+lVEmIKlH6wIXc7IENXkmqbeOsTKmqfJKuRxGnNrbd7KPyeZgPR0kAUJvwoGRaWhpdddVV9Otf/zrwu97eXv4I19zczB8idHe7uwwGAAAklr6MDHp7/+V0/LvvT/rbl1/7L//58aKFtHLFMlq4Zeuk153+2hv85wNfPpVaCwpsarV6btORqQTu1DY47HQTEoKs3T6ytstt3m5oI1X0j4zyh0zY7UUjIjp6scErSbmYtIjGOvSBVcqUszIk4TFxk446SxASmmoRdAAAlxEelGS+8Y1vUFdXF9155500MiJuIG8AAIBEsn7ObJpaV09zdu6K+Pf9163nj1guePo5euzE42hPRYVFrQRQm0L9jyBpv1LvsFwBLDC3/9+5PvJ3rvOlpnGwSlR8TFaVVr/TB2WT3Jp/ZM82pM7KV6elalFp71Gprba0HTsFAKgelGSuvvpqOuuss+iFF16gdevWUVtbGw0ODtKuXbsCJVynTp1KRUVFQuZXXFwsZDoAAAAy+c9RR1DVo49TVn+/4Wl85aVXqTcjg+4//Us0mJYmtH0AAIluaMz68m/bu4x/B4A83DCOLSQGUx3eipRvVEnEDE4sJHApO24MwLcxAIBLg5JMRUUFXXLJJSG/+/GPf0yPPfYY//dll11GZ599tpVNAAAAUJvHQ3899ys0q3YnnfrftwxPhgU1r3z4Uf7v/xx5OG2eNtX4QGiQEBKl83xMkV691/a0Rvy9Gq2XE889U2QBvrpn/KZOiMDjcJVJRbYhpmvIhipGCi0PVYj6Nu4YQrlysFaSJ/GOq7CPR9TRyqfOcRXbKgCA8eEpAAAAQHJbp02lP1x4Pr1wxGExX/fPU0+mD5Ysjvmak998my5+/CmqbGzElRQkvEidCbKNJ8d82NRBKpOxf1HGNoGDnaAJQJWbMNzKzM0+bi0pGk1ifVpXD9NpiB33xfkUX0YyYscplY5V6rTUnvbjfAoAXJMpCQAAAAJ5PLRpxnTaNH0affHNt2lu7c6QP7+/3xJqKi7ij7qyUh58TB8aijipvJ4eOueFl/m/P50/l1buv4JGk5OxuiS2qxvlG+3yh89qbZsXyOemVducbgLoYKYDVETn6cCo9vK9ZvrZVerohcT16LYGyk1FN1OiQvaZfaTN0JO1XUrDQgUA97H9bPHb3/42nXfeefzfVVVVds8eAAAkkiDVIcXzeOiFo46gF4ho+u7dNLt2F22ePo1qq/d9r+6qqqS7zzubDlr9GR302dqYk1v2+SZavGkL3X/GadSVk2PDBwAjHtpSb+uC6xm2ocyfBMak7dXRRvHmOwpfQdb4x6Y6spOZO/vfrGszPX8ECwFC9Y+MWXoOcsEtd9Ho8BAle1OFL3pZv1KVyuqTtmFgNdUy7XAODQCQQEFJFohEMBIAAECMHTU1/BHJWFISvbd8KW2YNYPOeukVyu3tizqdlLExuuTxp6gtL5dGk5KppL2dBr1eShseprWzZ1FnTg71ZmbwLM0xZFSCi6DvzKbl7LPuThQzk8X6F6++d4BUsb69x9b5dSfIzR6qGBmz9wjgSZQMZIt7+qvmL6JEo0rwRKVt3A2s2iycPq9SZHN3lE+h4wIAQCSoqwEAAOBynbm59Pezz+L/zuzrp1P/+yZVNjVHfG1hZ1fg3ywgySzesjXwuxNXvkt7i4qotrqSdz1sm1JDzUWFln8GAACASDqHxr+r3Ozthjaam5/tdDNc5+8b95AKmgeGqFfCsY6jQT+5nMsFWd0AVuxX7oGbCgDATghKAgAAJJC+zAx69OSTyDM2Rkd++DEt3bhJ9zTKW1v5gzl4zWfUnZlJO6sqaUp9AzUVFdI7y5dRe36eBa0HEA93GQOACvb0YFxh0VoHIo+7LaOXd7U43YSEgfMCk8vPVWEaucm4rUrYJAAAkBCCkgAAAAnIl5RE/z34QNo0Yzqd+58XTU0rp6+PFk1kU+b29tKsXbtpT1kpPX7i8Xw+ADJD50lis7rMIIAoNlcaNaxnWJ2MPpWMKHSssrqpG996nYYHB8iblk7zjjhG6LTlXcrytsyKlnYkQAa8W4nY/+0ode6xaEgBAADQBkFJAACABNZQWkJ/uOgCWrxxM+2/bj3l9YgZX6u6sYnO+c9L9PJhhyBrEqSm8h39rO2qtN/KdvYpVNYQwCg19nSiv36+2+kmgMu31adu+il1NTVSbmkZ/UhwUFIlqhwTVLO3b9DpJoBC56aJUEIeAMAKCEoCAAAArZ03hz/47a1hd46yUq9pQ8N06KpVtHjzvvEl46loaaELn3qGnj/ycNoyfRqWMkCC2NmtRpnJzZ29lJqMbG5Qw5hCmXIQmZm8HJVWvyo3y0Syq7uf3t3bTrKxe4kmahJZ++Aw3avIWLMycuJmOV8CV8pQ+VgLAICgJAAAAMTshWAlWAfS0+i1Q77AH0ljY1TW0kKFHZ209PNN5PN4qLCjg1LGxiIuyVPefJvozbdp9by59M6KZTTs9WKJAwgwODpGHzR2Srcs/7mlftLvWDvXtHaRbLZ19jndBABIEF0mShKi69kee3oHSEaR4ifsd9JtFx5PxLay8xVIdNJtrUpzQUwVABIcgpIAABAXyyQZQXk8mDCWlEQNpaX8sX7O7JDlsmz9Bjryo08iLqulGzfxxyuHHDzpfQCg36aOXmUWm4wBSQZ9OqAKZEomNqx/UNkta3Y43QTX2drZJ+25lQrYmJII7AEAOAf1igAAIK4L5lTRqVNLsaQgrk8XLqD7v/ylmK855r0PqLC9A0sTAByH0legCgTQARJXpP2/e3iEWvqHSCqI8thG1oBkpE1gZGyMRiTcNiK16KZV2yhRybeGAMDNEJQEAIC4CtO9tLgoB0sKNGkryKc7v3pO1L8n+3x0zPsfYGkCgOMk7CMDiAjbamLDoQrCsbEPZS03qwLsU/a5dU0tNcsWQHdB9vyGth6nmwEAYBiCkgAAACDcYFoa/eGiC+jNA1ZE/Ht1YxOyJQHAceiUBFWMYWtNaD5EpROaMln9EcamB5AxS1IlkZbe5s5eeq2u1YHWAACIgaAkAAAAWFrOlQUn//z18yb97RtPP0uesTEsfQBwDDr6AUAF6NJXn6m4jCIbQFP/oCpNBYuotP5VaisAgNsgKAkAAACWG/F6adP0aZN+f939D1FlYyPWAAA4Ah1SAKACJBqJ92Z9G6mid2TU1vn1GZwfK9G5oR0lJUV7ageulZTPIgYAgBAISgIAAIAtVq5YRv1paZN+f84LL9PXnnmOlq9bT0mj9na6AAAAAMgO3e77pGZkUVpWFv9pxrt720mVEsz3b6onVXQMDpMKmhQa4/BzmwO9RoPSTgQJB0eNVd35b10rH5dRBesxdiQAuFCK0w0AAIDE5SGMO5JIurOz6a0DVtCJK9+d9LfStnb+OOLjVfz57vIyeuKE48iXhPunAMA6ivRHAUCCQzbQPt994mVKNAMK3bQ3psj36hPb9zrdBFfqHR5VZpzLHV39pIIXdjU73QQAAOEQlAQAAADbfD5rJqUNDdFRH34c83U1ext5aVdmT1kpbZtSQ2vnzOZlYAEAZMhcAQAAAIB9ntiuTqlZVTIl7YKbXwDATghKAgAAgK1WL5hPdWVl9PVnn9f0+urGJv448qNP+PNtNdW0Zt5caioqooH0yeVgAQAAAADAfghsJLahMWPlVAEAILEoGZR86KGH6J133uH/9ng8dNRRR9Hpp59OXmRPAADYZnpOJu3o7sMSB0Oaiwrptgu+Rke9/yEt3rJV13tn7t7DH37PHn0kz6QkD8oBAwAAAAA4BclnAAAA4Mqg5IYNG+i1114LPH/11VfpzjvvpMsvv5zOOOMMSk1NdbR9AACJ4Cszy2jA4MDyAMxocjK9dugX+IP1YJS0tdHx77xPpW1tuhbQl954k3ZWVtDzRx1BQzgHAAAAAHCtF/5wM/V3dVJGbh598fofOd0cAFAUyreGQTVbALBRErlEXV0d/exnP6MTTjiBZ1IODQ053SQAAFdLSUqibK+S97ZElETIsnOUx0PNRUX0z9NOobvPOYt2l5fpevvU+gb69j8foazePtyiDQAAAOBSa156lj5++t/8J8gHcQ1QBbZVAADnuKc3eUJDQwP94he/oLvuuov+8Ic/0PLly51uEgAASK4iM426hkaod2TU6aYAEfVlZtLjJ51AnrEx8iUl8SBj9d69NHPXHqpsaqLs3j7KGhiIuKwu+/fj/OcrhxxM62fPQklXAAAAAACb+FC/FQAAANwYlDzllFNo9uzZgee1tbX00Ucf0bZt2wInQI2Njfw5gpIAAKAF7pSUDw9IMh4P7amo4A+/suYWOurDj6iiuSXie49/933+WDd7Jq2dO4cai4vtajYAAAAAQELCNRWoAvFzAADnKBmUPOSQQ/gjXFtbGw9O+h9J/s5MAABQzjfmVNH9m+ucbgZIqrGkmB455Yu0eNNmOva9D6K+btGWbfzBgpNv77+CBtPSbG0nAAAAAAAAyAUBdAAA5ygZlIymsLCQTjzxRP4AAAC1VWWnO90EUADLgkwZGaEjP/ok5uv8wcnHTziO2vNyqS8jg8Zw8xIAAAAAgDDIPgNV+BCWBABwjKuCkgAAAJB4Pl24gD8y+/rpwiefprTh4aivPevlV/nPvrQ0evOgA2jTjOk2thQAAAAAwL2QfQYAAADxICgJAAAArtCXmUF3fv08yurto6qmJpq1cxfNqd0Z8bWZg4P0xbdW8seg10v96emUNjRIzx5zFNWXldnedgAAAAAA1SH7DEBNuKEAAOyEoCQAAAC4Sm9WJm2ePo0/6L9vRQ1M+rHMSn925TkvvEyNRYX04hGHUXtenk0tBgAAAAAAAAAAcD9pgpJ9fX38kZOTQ2lpaU43BwAAXOjEmmJ6aXeL080AG71wxGFUW1VFC7ZupdLWNkodGYn7nrLWNrrwyWf4vz9etJA+WryQBnFuAgAAAAAAAAAAoFZQ0ufz0ccff0wfffQRffLJJ7Rp0ybq6Oig4aDxnzIyMqiwsJAWLVpEy5cvp4MPPpjmzZtnd1MBAMBlUpKSnG4C2MyXlEQbZs/kDyZ5ZIS+8uIrVNGiLTi9/7r1/NGWl0vNhYW0o7qKttdU01BqqsUtBwAAAIBwcw87mvo7OygjLx8LBwAAAEBBtgUlBwYG6Mknn6T77ruPamtrY762v7+f6urq+OOll17iv9t///3p4osvpmOPPZY8Ho9NrQYAAAA3GU1JoUdOOYkqmluosKODlm3YSN3ZWTR9T13M9xV2dvHH3B21NObx0O6KckodHqb23Fxqy8+j7sxM2oFgJQAAAIClzvjxr7CEAQAAABRmS1Dys88+o+9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       "text/plain": [
        "<Figure size 800x320 with 1 Axes>"
       ]
@@ -1391,15 +1412,15 @@
     "    lw=1.5,\n",
     "    label=\"one-epoch moving mean\",\n",
     ")\n",
-    "ax.axvline(6_000, color=\"k\", ls=\"--\", lw=1, label=\"anneal: lr 0.02 to 0.005\")\n",
+    "ax.axvline(6_000, color=\"k\", ls=\"--\", lw=1, label=\"learning rate cut, 0.02 to 0.005\")\n",
     "# clip to the plateau: the first few hundred steps are orders of magnitude higher\n",
     "plateau = loss[1_000:]\n",
     "ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))\n",
-    "ax.yaxis.set_major_formatter(plt.matplotlib.ticker.StrMethodFormatter(\"{x:,.0f}\"))\n",
+    "ax.yaxis.set_major_formatter(StrMethodFormatter(\"{x:,.0f}\"))\n",
     "ax.set_xlabel(\"step\")\n",
     "ax.set_ylabel(\"negative ELBO, full-data scale\")\n",
     "ax.set_title(\"Streaming ADVI loss on the plateau (steps before 1,000 are off scale)\")\n",
-    "ax.legend(loc=\"upper right\", fontsize=9)\n",
+    "ax.legend(loc=\"upper right\", fontsize=9, frameon=True)\n",
     "before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]\n",
     "print(\n",
     "    \"recorded loss over the two passes either side of the cut:\\n\"\n",
@@ -1410,7 +1431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069add66",
+   "id": "c369e720",
    "metadata": {},
    "source": [
     "## The last iterate is not the answer\n",
@@ -1418,7 +1439,7 @@
     "Before reading a single parameter off this fit, one property of the replay has\n",
     "to be dealt with. The batch at step $t$ is a deterministic function of $t$ with\n",
     "period one epoch, so the data term of every stochastic gradient repeats with\n",
-    "that period. The parameters and the Monte-Carlo draw do not, so the realized\n",
+    "that period. The parameters and the Monte Carlo draw do not, so the realized\n",
     "gradients are not literally periodic, but the iterate inherits a component\n",
     "locked to the replay order. Two measurements separate that component from\n",
     "genuine progress: compare the same phase in consecutive epochs, and look at the\n",
@@ -1428,13 +1449,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "61900a5d",
+   "id": "07d51dca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:08.459240Z",
-     "iopub.status.busy": "2026-08-18T04:24:08.459160Z",
-     "iopub.status.idle": "2026-08-18T04:24:08.468200Z",
-     "shell.execute_reply": "2026-08-18T04:24:08.467838Z"
+     "iopub.execute_input": "2026-08-18T19:12:51.635695Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.635449Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.650726Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.649881Z"
     }
    },
    "outputs": [
@@ -1466,7 +1487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79e09ff7",
+   "id": "1152a3c1",
    "metadata": {},
    "source": [
     "Between the same phase of consecutive epochs the largest movement decays from about six posterior widths\n",
@@ -1483,7 +1504,8 @@
     "component repeats from one pass to the next, a window of exactly one pass\n",
     "averages it out (which is why the step budget above is a whole number of\n",
     "epochs), and a window that is not a whole number of passes leaves a residual of\n",
-    "the order of one swing divided by the window length. This is Polyak–Ruppert averaging. What the average does *not* remove\n",
+    "the order of one swing divided by the window length. This is Polyak–Ruppert averaging {cite:p}`polyak1992acceleration`. What the\n",
+    "average does *not* remove\n",
     "is the slow drift the first line still shows: a tenth of a width per pass in\n",
     "the median, and much more along the directions the recovery table is about to\n",
     "single out."
@@ -1492,13 +1514,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "ed2fc78e",
+   "id": "34d17006",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:08.469088Z",
-     "iopub.status.busy": "2026-08-18T04:24:08.469032Z",
-     "iopub.status.idle": "2026-08-18T04:24:08.471526Z",
-     "shell.execute_reply": "2026-08-18T04:24:08.471291Z"
+     "iopub.execute_input": "2026-08-18T19:12:51.652283Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.652164Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.655970Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.655304Z"
     }
    },
    "outputs": [
@@ -1519,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ecc8ccf",
+   "id": "d19f1039",
    "metadata": {},
    "source": [
     "Everything below reads from that averaged approximation."
@@ -1527,7 +1549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79e5ff2d",
+   "id": "26757243",
    "metadata": {},
    "source": [
     "## Did it recover the truth?"
@@ -1536,13 +1558,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1e854f70",
+   "id": "4d51fcc9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:08.472467Z",
-     "iopub.status.busy": "2026-08-18T04:24:08.472406Z",
-     "iopub.status.idle": "2026-08-18T04:24:08.607117Z",
-     "shell.execute_reply": "2026-08-18T04:24:08.606808Z"
+     "iopub.execute_input": "2026-08-18T19:12:51.657876Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.657765Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.871295Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.870626Z"
     }
    },
    "outputs": [
@@ -1713,9 +1735,9 @@
     "]\n",
     "rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}\n",
     "# the intercepts share a ridge with their group means; only the sums are identified\n",
-    "for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
-    "    s = post[icpt] + post[b].mean(\"symbol\")\n",
-    "    rows[f\"{icpt} + mean({b})\"] = [float(s.mean()), float(s.std())]\n",
+    "for intercept, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
+    "    s = post[intercept] + post[b].mean(\"symbol\")\n",
+    "    rows[f\"{intercept} + mean({b})\"] = [float(s.mean()), float(s.std())]\n",
     "recovery = pd.DataFrame(rows, index=[\"mean\", \"sd\"]).T\n",
     "recovery[\"truth\"] = [truth[p] for p in scalar_params] + [\n",
     "    truth[\"kappa0\"],\n",
@@ -1730,13 +1752,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "e8b5c59b",
+   "id": "f0630464",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:08.608686Z",
-     "iopub.status.busy": "2026-08-18T04:24:08.608576Z",
-     "iopub.status.idle": "2026-08-18T04:24:19.861421Z",
-     "shell.execute_reply": "2026-08-18T04:24:19.861068Z"
+     "iopub.execute_input": "2026-08-18T19:12:51.873195Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.872946Z",
+     "iopub.status.idle": "2026-08-18T19:13:09.175023Z",
+     "shell.execute_reply": "2026-08-18T19:13:09.174557Z"
     }
    },
    "outputs": [
@@ -1767,9 +1789,9 @@
     "def summarize(post_):\n",
     "    \"\"\"Posterior mean and sd of every row the recovery table reports.\"\"\"\n",
     "    out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}\n",
-    "    for icpt, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
-    "        total = post_[icpt] + post_[b].mean(\"symbol\")\n",
-    "        out[f\"{icpt} + mean({b})\"] = (float(total.mean()), float(total.std()))\n",
+    "    for intercept, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
+    "        total = post_[intercept] + post_[b].mean(\"symbol\")\n",
+    "        out[f\"{intercept} + mean({b})\"] = (float(total.mean()), float(total.std()))\n",
     "    return pd.DataFrame(out, index=[\"mean\", \"sd\"]).T\n",
     "\n",
     "\n",
@@ -1845,7 +1867,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25d61f56",
+   "id": "3b0c9524",
    "metadata": {},
    "source": [
     "The table is a selection, the scalar globals and the three identified sums,\n",
@@ -1868,20 +1890,20 @@
     "\n",
     "Two things follow, and the table shows both. Along a direction that flat, a\n",
     "gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its\n",
-    "way to $-3.00$. To see that it is still travelling, warm-start a fresh optimizer\n",
+    "way to $-3.00$. To see that it is still traveling, warm-start a fresh optimizer\n",
     "at the last iterate and give it sixty more passes at the same learning rate:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "95f47703",
+   "id": "ef1403c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:19.862649Z",
-     "iopub.status.busy": "2026-08-18T04:24:19.862560Z",
-     "iopub.status.idle": "2026-08-18T04:24:28.160181Z",
-     "shell.execute_reply": "2026-08-18T04:24:28.159900Z"
+     "iopub.execute_input": "2026-08-18T19:13:09.176608Z",
+     "iopub.status.busy": "2026-08-18T19:13:09.176497Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.319806Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.319322Z"
     }
    },
    "outputs": [
@@ -1935,11 +1957,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc1211fe",
+   "id": "c8381980",
    "metadata": {},
    "source": [
     "The ridge coordinates keep moving toward their generating values, at a rate the\n",
-    "loss barely registers. The raw split rows are a fit still travelling along a\n",
+    "loss barely registers. The raw split rows are a fit still traveling along a\n",
     "nearly flat direction, not an ambiguity in the model. And their reported\n",
     "widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just\n",
     "computed along it: mean-field has no correlation to spend,\n",
@@ -1974,13 +1996,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "e5df7a49",
+   "id": "dddf201a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:28.161367Z",
-     "iopub.status.busy": "2026-08-18T04:24:28.161288Z",
-     "iopub.status.idle": "2026-08-18T04:24:28.272311Z",
-     "shell.execute_reply": "2026-08-18T04:24:28.271883Z"
+     "iopub.execute_input": "2026-08-18T19:13:21.321755Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.321645Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.482179Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.481799Z"
     }
    },
    "outputs": [
@@ -1995,7 +2017,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 800x350 with 1 Axes>"
       ]
@@ -2023,7 +2045,13 @@
     "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
     "x = np.arange(n_symbols)\n",
     "ax.errorbar(\n",
-    "    x, b_mean, yerr=2 * b_sd, fmt=\"o\", color=\"C0\", capsize=3, label=\"mean ± 2 mean-field sd\"\n",
+    "    x,\n",
+    "    b_mean,\n",
+    "    yerr=2 * b_sd,\n",
+    "    fmt=\"o\",\n",
+    "    color=\"C0\",\n",
+    "    capsize=3,\n",
+    "    label=\"mean ± 2 posterior sd (mean-field)\",\n",
     ")\n",
     "ax.scatter(x, b_truth, marker=\"x\", color=\"C1\", s=60, zorder=3, label=\"truth\")\n",
     "for t in thin:\n",
@@ -2045,7 +2073,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf677a51",
+   "id": "b9c4eb60",
    "metadata": {},
    "source": [
     "The posterior spread is widest for the symbols with the least data (the\n",
@@ -2070,13 +2098,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "9411e0a6",
+   "id": "af541d05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:28.273666Z",
-     "iopub.status.busy": "2026-08-18T04:24:28.273555Z",
-     "iopub.status.idle": "2026-08-18T04:24:28.390958Z",
-     "shell.execute_reply": "2026-08-18T04:24:28.390637Z"
+     "iopub.execute_input": "2026-08-18T19:13:21.483708Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.483603Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.632988Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.632449Z"
     }
    },
    "outputs": [
@@ -2117,7 +2145,7 @@
     "    + pick(\"b_al\", \"symbol\")[s_idx][None, :]\n",
     "    + Bh @ (pick(\"g\", \"harmonic\") + pick(\"b_sh\", \"symbol\", \"harmonic\")[s_idx])\n",
     ")\n",
-    "# transform each draw, then summarise: the median of a function, not a function of medians\n",
+    "# transform each draw, then summarize: the median of a function, not a function of medians\n",
     "zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)\n",
     "half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick(\"nu\"))[None, :], axis=1)\n",
     "\n",
@@ -2132,27 +2160,27 @@
     "    + Bh @ (truth[\"g\"] + 0.12 * z_truth[\"z_sh\"][s_idx])\n",
     ")\n",
     "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(9, 3.2), layout=\"constrained\")\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.2), layout=\"constrained\")\n",
     "for ax, post_curve, truth_curve, ylab in [\n",
-    "    (axes[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), \"P(next event does not move)\"),\n",
-    "    (axes[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth[\"nu\"]), \"90% half-width (bp)\"),\n",
+    "    (axs[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), \"P(next event does not move)\"),\n",
+    "    (axs[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth[\"nu\"]), \"90% half-width (bp)\"),\n",
     "]:\n",
     "    ax.plot(hours, post_curve, color=\"C0\", lw=2, label=\"posterior median\")\n",
     "    ax.plot(hours, truth_curve, color=\"C1\", ls=\"--\", lw=1.5, label=\"generator truth\")\n",
     "    ax.set_xlabel(\"UTC hour\")\n",
     "    ax.set_ylabel(ylab)\n",
-    "axes[0].legend(fontsize=9)\n",
+    "axs[0].legend(fontsize=9)\n",
     "fig.suptitle(f\"SYM{s_idx:02d} on the event clock, at a = q = 0\", fontsize=11);"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9a5a1487",
+   "id": "3a858983",
    "metadata": {},
    "source": [
     "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
     "for the curve: it answers \"how far does a move go\", and it stays finite for any\n",
-    "$\\nu > 0$, which is why the notebook reports it instead of a conditional\n",
+    "$\\nu > 0$. That is why the notebook reports it instead of a conditional\n",
     "standard deviation: the Student-t variance exists only for $\\nu > 2$, and\n",
     "nothing in this model guarantees that. Under this generator, the fit recovers\n",
     "both shapes; on real data the same two curves would be estimates, and would\n",
@@ -2161,7 +2189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d10f7955",
+   "id": "025e4d72",
    "metadata": {},
    "source": [
     "## In-sample posterior predictive adequacy\n",
@@ -2171,19 +2199,21 @@
     "carries a `random` implementation alongside its `logp`, so\n",
     "{func}`~pymc.sample_posterior_predictive` works out of the box. The two\n",
     "features that matter for this model are the exact-zero share and the heavy\n",
-    "conditional tail:"
+    "conditional tail; the figure after the table shows the whole empirical\n",
+    "cumulative distribution function (ECDF) of one batch against sixty predictive\n",
+    "draws:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "b4bd5ced",
+   "id": "cd43c932",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:28.392158Z",
-     "iopub.status.busy": "2026-08-18T04:24:28.392076Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.282774Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.282454Z"
+     "iopub.execute_input": "2026-08-18T19:13:21.634373Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.634263Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.564992Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.564552Z"
     }
    },
    "outputs": [
@@ -2209,9 +2239,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>observed</th>\n",
-       "      <th>pp mean</th>\n",
-       "      <th>pp 5%</th>\n",
-       "      <th>pp 95%</th>\n",
+       "      <th>predictive mean</th>\n",
+       "      <th>predictive 5%</th>\n",
+       "      <th>predictive 95%</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2241,10 +2271,10 @@
        "</div>"
       ],
       "text/plain": [
-       "                    observed  pp mean  pp 5%  pp 95%\n",
-       "zero share             0.300    0.322  0.299   0.346\n",
-       "median |move| (bp)     0.039    0.040  0.037   0.043\n",
-       "q99 |move| (bp)        0.298    0.331  0.268   0.410"
+       "                    observed  predictive mean  predictive 5%  predictive 95%\n",
+       "zero share             0.300            0.322          0.299           0.346\n",
+       "median |move| (bp)     0.039            0.040          0.037           0.043\n",
+       "q99 |move| (bp)        0.298            0.331          0.268           0.410"
       ]
      },
      "execution_count": 20,
@@ -2284,7 +2314,7 @@
     "        check_row(np.median(y_nonzero), med_nonzero),\n",
     "        check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),\n",
     "    ],\n",
-    "    columns=[\"observed\", \"pp mean\", \"pp 5%\", \"pp 95%\"],\n",
+    "    columns=[\"observed\", \"predictive mean\", \"predictive 5%\", \"predictive 95%\"],\n",
     "    index=[\"zero share\", \"median |move| (bp)\", \"q99 |move| (bp)\"],\n",
     ").round(3)"
    ]
@@ -2292,19 +2322,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "f984820c",
+   "id": "d9992b9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.283785Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.283724Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.406850Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.406523Z"
+     "iopub.execute_input": "2026-08-18T19:13:22.566172Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.566077Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.728412Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.727895Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 800x350 with 1 Axes>"
       ]
@@ -2339,14 +2369,14 @@
     "    va=\"center\",\n",
     ")\n",
     "ax.set_xlabel(\"next-event return (bp)\")\n",
-    "ax.set_ylabel(\"ECDF\")\n",
+    "ax.set_ylabel(\"empirical CDF\")\n",
     "ax.set_title(\"Posterior predictive ECDF: the step at zero is the hurdle\")\n",
     "ax.legend(loc=\"upper left\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d2e54836",
+   "id": "1141078c",
    "metadata": {},
    "source": [
     "All three statistics sit inside their predictive 90% bands, though the zero\n",
@@ -2354,7 +2384,7 @@
     "0.299, so it is a pass with almost no margin rather than a comfortable one.\n",
     "What that establishes is limited in two ways. The heading is literal: the\n",
     "evaluation batch was seen during the fit, so this is adequacy, not\n",
-    "generalisation, and a held-out split is the next thing to add before any of\n",
+    "generalization, and a held-out split is the next thing to add before any of\n",
     "this is used to compare models. And the check\n",
     "is one batch, one step ahead, with the observed `ylag` held fixed: it does not\n",
     "simulate a price path forward, and it says nothing about the hierarchy, the\n",
@@ -2364,17 +2394,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0927d253",
+   "id": "55abcdf1",
    "metadata": {},
    "source": [
     "## What a stopping rule has to be able to see\n",
     "\n",
-    "A streamed ELBO trace is noisy: every value is a one-batch, one-Monte-Carlo\n",
+    "A streamed ELBO trace is noisy: every value is a one-batch, one-Monte Carlo\n",
     "estimate, so consecutive losses differ mostly because the batch changed. That\n",
     "makes \"has it converged?\" a signal-detection problem, and the horizon over\n",
     "which you look is the whole game.\n",
     "\n",
-    "Write the standardised *block contrast* at horizon $w$: average the $w$ losses\n",
+    "Write the standardized *block contrast* at horizon $w$: average the $w$ losses\n",
     "before a point, average the $w$ losses after it, and divide the difference by\n",
     "the noise scale of that difference,\n",
     "\n",
@@ -2389,19 +2419,19 @@
     "drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,\n",
     "it sets what is visible at all. A fixed replay order is not noise of that kind,\n",
     "and the printout below shows where the generic scaling breaks, in the\n",
-    "notebook's favour."
+    "notebook's favor."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "04e528be",
+   "id": "320fddcb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.408096Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.408024Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.411261Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.410957Z"
+     "iopub.execute_input": "2026-08-18T19:13:22.729856Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.729752Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.734093Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.733656Z"
     }
    },
    "outputs": [
@@ -2453,13 +2483,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "8ec971ec",
+   "id": "349f8ff2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.412482Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.412395Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.591338Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.591043Z"
+     "iopub.execute_input": "2026-08-18T19:13:22.735063Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.734993Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.003637Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.003064Z"
     }
    },
    "outputs": [
@@ -2495,9 +2525,9 @@
     "    )\n",
     "    if w > 1:\n",
     "        ax_zoom.plot(t, z, color=color, lw=1.6, label=f\"{label} (w={w})\")\n",
-    "for a in (ax, ax_zoom):\n",
-    "    a.axhline(0.0, color=\"k\", lw=0.8)\n",
-    "    a.set_xlabel(\"step within stage 2\")\n",
+    "for ax_ in (ax, ax_zoom):\n",
+    "    ax_.axhline(0.0, color=\"k\", lw=0.8)\n",
+    "    ax_.set_xlabel(\"step within stage 2\")\n",
     "ax.set_ylabel(r\"block contrast $z_w$\")\n",
     "ax.set_title(\"Same trace, three horizons\", fontsize=11)\n",
     "ax.legend(fontsize=8, loc=\"upper right\", frameon=True)\n",
@@ -2510,13 +2540,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e5e9082",
+   "id": "71cf8934",
    "metadata": {},
    "source": [
     "Read the printout as three signal-to-noise ratios, and the figure as the same\n",
     "thing twice: the left panel puts all three horizons on one scale, the right\n",
     "panel zooms in on the two that are invisible at that scale. At $w = 1$ the\n",
-    "standardised contrast has unit spread (which is what the $\\sqrt{2/w}$ scaling\n",
+    "standardized contrast has unit spread (which is what the $\\sqrt{2/w}$ scaling\n",
     "is built to produce) and a mean indistinguishable from zero: a single\n",
     "increment says nothing about the trend.\n",
     "\n",
@@ -2527,11 +2557,11 @@
     "and the batch-composition noise, which dominates the per-step view, all but\n",
     "drops out.\n",
     "Widen the window to one and a half passes and it does not: the printed ratio\n",
-    "puts the control an order of magnitude above the wider of its two neighbours,\n",
+    "puts the control an order of magnitude above the wider of its two neighbors,\n",
     "even though it is itself *wider* than the one-epoch window. Nothing but the\n",
     "alignment changed.\n",
     "\n",
-    "What survives is Monte-Carlo noise and the parameter drift\n",
+    "What survives is Monte Carlo noise and the parameter drift\n",
     "itself, and against that much smaller yardstick the drift becomes visible:\n",
     "the ratio in the last column rises with the horizon. So, with a fixed replay\n",
     "order, the useful horizons are the ones commensurate with an epoch. That is a\n",
@@ -2543,7 +2573,7 @@
     "statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to\n",
     "the standardized improvement: $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
     "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that\n",
-    "recursion behaves sensibly while there is signal: if the standardised\n",
+    "recursion behaves sensibly while there is signal: if the standardized\n",
     "improvement stays above $\\kappa$, the increment is negative and $S$ sits at\n",
     "zero. The difficulty is what happens when there is *no* resolvable signal.\n",
     "Under symmetric noise $\\mathbb{E}[\\max(z,0)]$ is only a fraction of a standard\n",
@@ -2567,13 +2597,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "8eae053d",
+   "id": "6899295a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.592375Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.592311Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.594684Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.594350Z"
+     "iopub.execute_input": "2026-08-18T19:13:23.005333Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.005204Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.008151Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.007755Z"
     }
    },
    "outputs": [
@@ -2605,7 +2635,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31837366",
+   "id": "9d1d4cb4",
    "metadata": {},
    "source": [
     "Any stopping rule, this one included, comes with two qualifications. What such a rule\n",
@@ -2628,7 +2658,7 @@
     "| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |\n",
     "\n",
     "Pathfinder is the faster route when an approximately placed starting point is\n",
-    "what you need, for example to initialise MCMC. It is not run here, and the\n",
+    "what you need, for example to initialize Markov chain Monte Carlo (MCMC). It is not run here, and the\n",
     "reason is a decision taken before fitting rather than a result: its documented\n",
     "operating range is non-hierarchical targets of at most a few tens of\n",
     "parameters, and this target is hierarchical with"
@@ -2637,13 +2667,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "51c26323",
+   "id": "fd8056a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.595948Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.595828Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.614361Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.614004Z"
+     "iopub.execute_input": "2026-08-18T19:13:23.009404Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.009302Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.036685Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.036298Z"
     }
    },
    "outputs": [
@@ -2656,15 +2686,13 @@
     }
    ],
    "source": [
-    "from pymc.blocking import DictToArrayBijection\n",
-    "\n",
     "n_free = DictToArrayBijection.map(model.initial_point()).data.size\n",
     "print(f\"free parameters in the unconstrained space: {n_free}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f83d8d9f",
+   "id": "9c9df534",
    "metadata": {},
    "source": [
     "free parameters, an order of magnitude outside that range. The ADVI results\n",
@@ -2674,19 +2702,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ed48a73",
+   "id": "1c2eb643",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "This notebook was written as part of the 2026\n",
+    "[Google Summer of Code](https://summerofcode.withgoogle.com/) project\n",
+    "*Streaming Variational Inference for Large Datasets* with PyMC and\n",
+    "[NumFOCUS](https://numfocus.org/), mentored by Rob Zinkov and Chris Fonnesbeck."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b74846ff",
    "metadata": {},
    "source": [
     "## Authors\n",
     "\n",
     "* Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August\n",
-    "  2026 for the Google Summer of Code project *Streaming Variational Inference\n",
-    "  for Large Datasets* (PyMC / NumFOCUS)."
+    "  2026 ([pymc-examples#892](https://github.com/pymc-devs/pymc-examples/pull/892))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1b9f4ca",
+   "id": "abd92be7",
    "metadata": {},
    "source": [
     "## References\n",
@@ -2698,7 +2738,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "302ffca4",
+   "id": "a133b6c3",
    "metadata": {},
    "source": [
     "## Watermark"
@@ -2707,13 +2747,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "24e61b83",
+   "id": "a63ff46e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-18T04:24:29.615360Z",
-     "iopub.status.busy": "2026-08-18T04:24:29.615300Z",
-     "iopub.status.idle": "2026-08-18T04:24:29.645610Z",
-     "shell.execute_reply": "2026-08-18T04:24:29.644963Z"
+     "iopub.execute_input": "2026-08-18T19:13:23.038391Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.038275Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.076341Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.075745Z"
     }
    },
    "outputs": [
@@ -2721,13 +2761,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wall time for every cell above, on this machine: 28 s\n",
-      "Last updated: Mon, 17 Aug 2026\n",
+      "wall time for every cell above, on this machine: 42 s\n",
+      "Last updated: Tue, 18 Aug 2026\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.12\n",
       "IPython version      : 9.16.1\n",
       "\n",
+      "xarray: 2026.7.0\n",
+      "\n",
       "arviz      : 1.3.0\n",
       "logging    : 0.5.1.2\n",
       "matplotlib : 3.11.1\n",
@@ -2755,13 +2797,13 @@
    "source": [
     "%load_ext watermark\n",
     "print(f\"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s\")\n",
-    "%watermark -n -u -v -iv -w\n",
+    "%watermark -n -u -v -iv -w -p xarray\n",
     "%watermark -m"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "18628d55",
+   "id": "beb2c98c",
    "metadata": {},
    "source": [
     ":::{include} ../page_footer.md\n",
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
index 0f3dac552..c2feab0c1 100644
--- a/examples/variational_inference/streaming_tick_data.myst.md
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -39,10 +39,11 @@ moves* by streaming minibatches from disk with pymc-extras'
 on 300,000 synthetic
 rows that are generated inside the notebook. It tries to teach three things:
 
-1. **When minibatch VI is even valid.** Two acceptance gates that most
+1. **When minibatch variational inference (VI) is even valid.** Two acceptance gates that most
    time-series models fail, and a model class that passes both.
 2. **The mechanics, end to end** on an archive small enough to rebuild here:
-   an on-disk global shuffle, streaming ADVI with `total_size` rescaling, and
+   an on-disk global shuffle, streaming automatic differentiation variational
+   inference (ADVI) with `total_size` rescaling, and
    what a stopping rule has to be able to see before it can fire.
 3. **Checking the recovery.** With the truth known, recovery is checkable once
    one correction is made: the cyclic replay puts the optimizer on an orbit, and
@@ -64,7 +65,7 @@ possible: nothing here is a claim about any real market.
 
 Minibatch variational inference rests on one identity: if the likelihood
 factors over rows given the parameters, then the batch log-likelihood scaled by
-$N/B$ is an unbiased estimator of the full-data log-likelihood
+$N/b$ is an unbiased estimator of the full-data log-likelihood
 {cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;
 the second is about whether streaming is doing any real work. Check both
 before reaching for `total_size` on your own data:
@@ -72,7 +73,7 @@ before reaching for `total_size` on your own data:
 **Gate 1 — validity: the likelihood must factor over rows, and the batching
 must weight every row equally.** No latent path coupling observations, no
 label shared across rows, and every row given the same inclusion frequency by
-the batching scheme (unequal inclusion breaks the plain $N/B$ rescaling). How
+the batching scheme (unequal inclusion breaks the plain $N/b$ rescaling). How
 the batches are *drawn* is a separate matter that this gate does not settle:
 fresh uniform batches make each step's scaled gradient conditionally unbiased
 for the full-data objective (the identity above), while a fixed shuffled
@@ -82,7 +83,7 @@ the second, and returns to the distinction where the fit is described. The
 factorization requirement is exactly why the classic
 stochastic volatility model of the {ref}`stochastic_volatility` notebook
 *cannot* be minibatched: its latent volatility path ties every observation to
-its neighbours, so a random subset of rows does not carry $B/N$ of the
+its neighbors, so a random subset of rows does not carry $b/N$ of the
 log-likelihood. Any model whose rows share one outcome (for example, every tick
 of a match sharing the final result) fails the same gate through
 pseudo-replication: the effective sample size is the number of outcomes, not
@@ -143,6 +144,8 @@ import pyarrow.parquet as pq
 import pymc as pm
 import pytensor.tensor as pt
 
+from matplotlib.ticker import StrMethodFormatter
+from pymc.blocking import DictToArrayBijection
 from pymc_extras.variational.dataloader import DataLoader, parquet_source
 from scipy import stats
 
@@ -196,6 +199,20 @@ y_i \mid m_i = 1 &\sim \text{StudentT}(\nu,\ \mu_i,\ \sigma_i) \\
 \end{aligned}
 $$
 
+where
+
+* $\kappa_0$ and $\alpha_0$ are the global intercepts of the move probability
+  and the log scale, and $b^{(\kappa)}_s$, $b^{(\alpha)}_s$ their per-symbol
+  offsets;
+* $B(h)$ is the hour-of-day Fourier basis (four columns), $c$ and $g$ its
+  global coefficients, and $b^{(\pi h)}_s$, $b^{(\sigma h)}_s$ the per-symbol
+  deviations from them;
+* $\lambda_a$, $\lambda_q$, $\beta_a$, $\beta_q$ are the covariate slopes, with
+  $b^{(\beta a)}_s$ a per-symbol slope on activity in the scale;
+* $\theta_d$, $\theta_r$ place the conditional location on the trade sign and
+  the last nonzero return;
+* every $b_s \sim \mathcal{N}(0, \tau)$ with its own $\tau$, and $\nu$ is shared.
+
 The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an
 activity-like $a_i$, and $\text{ylag}_i$, the most recent previous nonzero
 return on that symbol. In the generator below they are drawn directly rather
@@ -234,8 +251,8 @@ below happens to use $\nu = 3.5$.
 
 ## Synthetic data with known truth
 
-Notebooks in this collection do not download data at build time, so the
-executed path uses a seeded synthetic generator: the schema an exchange
+This notebook neither ships nor downloads an exchange archive; the executed
+path uses a seeded synthetic generator: the schema an exchange
 archive would have after feature construction, a hurdle at exactly zero,
 heavy conditional tails, and twelve symbols whose row counts span two orders
 of magnitude, so the hierarchy has both data-rich and data-poor groups to work
@@ -380,11 +397,11 @@ Streaming ordered data has one trap that is easy to miss. A bounded runtime shuf
 ordered stream: with tick data sorted by symbol and time, early optimization
 steps would only ever see early dates and the first symbols, and an early
 stopping decision would be biased by construction. The fix is to shuffle
-**once, globally, on disk at ETL time**: every row gets a deterministic hash
+**once, globally, on disk when the archive is written**: every row gets a deterministic hash
 key, rows are scattered across shards by that key, and each shard is sorted by
 it. After that, sequential reads follow one fixed, data-independent
 permutation. It is pseudo-random (a hash of the row index, nothing from the
-row itself) and replayed identically each epoch, so this is single-shuffle SGD
+row itself) and replayed identically each epoch, so this is single-shuffle stochastic gradient descent
 rather than fresh per-step subsampling. The loader can then run with
 `shuffle=False`, without a copy through the shuffle buffer.
 
@@ -432,7 +449,7 @@ print(f"first 10k rows of every shard cover all 24 hours and all {n_symbols} sym
 The assertion is the one to keep: the head of every shard must already mix
 every hour and every symbol, checked rather than assumed.
 
-This cell is a small-scale stand-in for the real ETL step: it builds the whole
+This cell is a small-scale stand-in for the real extract-transform-load step: it builds the whole
 table, the whole key array and the whole permutation in memory, which is
 exactly what one cannot do once the data stops fitting. The inference that
 follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:
@@ -458,12 +475,11 @@ With `shuffle=False` the `DataLoader` passes source blocks through verbatim,
 one block per Parquet row group, in a frozen column order. That is why the
 shards above were written with `row_group_size` equal to the batch size, and
 why the row counts were chosen to divide exactly. On this path the loader
-hands you the row groups it finds, so a
-ragged geometry gives ragged batches, `len(loader)` (which is
-`total_size // batch_size`) stops matching the number of blocks an epoch
-yields, and the recorded loss picks up a deterministic sawtooth that has
-nothing to do with convergence (the fitting section explains why the recorded
-value scales with the block size). The model reads
+hands you the row groups it finds. A ragged geometry therefore gives ragged
+batches; `len(loader)` (which is `total_size // batch_size`) stops matching the
+number of blocks an epoch yields; and the recorded loss picks up a
+deterministic sawtooth that has nothing to do with convergence (the fitting
+section explains why the recorded value scales with the block size). The model reads
 one `pm.Data` placeholder; everything derived (the Fourier basis, the
 integer symbol index) is computed inside the graph, so advancing the stream
 is a single `set_value` per step.
@@ -598,7 +614,7 @@ def build_model(symbols, batch_init, total_size):
 
 Two implementation notes. The likelihood is a
 {class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`
-gives the term *observed-RV semantics*, which is what makes PyMC's own
+gives the term *observed random-variable semantics*, which is what makes PyMC's own
 `total_size` minibatch rescaling apply. And
 the hurdle's two logs are written with `softplus`,
 $\log \pi = -\operatorname{softplus}(-x)$ and
@@ -608,7 +624,7 @@ tails. The exact float comparison `value != 0` is safe here because the zeros ar
 did not move, and every nonzero draw is many orders of magnitude above the
 float32 subnormal range. On real data the same comparison is safe as long as
 returns are computed so that "no move" produces an exact zero rather than a
-rounding artefact; check that once, in the feature code.
+rounding artifact; check that once, in the feature code.
 
 ```{code-cell} ipython3
 model = build_model(
@@ -695,13 +711,18 @@ that same constant, because `scale_cost_to_minibatch` is on by default. What
 gets recorded per step is therefore
 
 $$
-F_t = -\sum_{i \in B_t} \mathbb{E}_q\left[\log p(y_i \mid \theta)\right]
+F_t = -\sum_{i \in \mathcal{B}_t} \mathbb{E}_q\left[\log p(y_i \mid \theta)\right]
       + \frac{b}{N}\,\mathrm{KL}(q \Vert p),
 $$
 
-which lives on the scale of *one batch*, not of the full dataset, and which
-would move mechanically with $b$ if the blocks were ragged. Multiplying by
-$N/b$ puts it back on the full-data negative-ELBO scale, and with equal blocks
+where $\mathcal{B}_t$ is the set of rows in the batch at step $t$, $b$ its
+size, $N$ the total row count (`loader.total_size`), $\theta$ the parameters,
+$q$ the variational approximation, $p$ the prior, and $\mathrm{KL}$ the
+Kullback–Leibler divergence. This lives on the scale of *one batch*, not of
+the full dataset, and would move mechanically with $b$ if the blocks were
+ragged. Multiplying by
+$N/b$ puts it back on the full-data scale of the negative evidence lower bound
+(ELBO), and with equal blocks
 that is one constant:
 
 ```{code-cell} ipython3
@@ -719,15 +740,15 @@ ax.plot(
     lw=1.5,
     label="one-epoch moving mean",
 )
-ax.axvline(6_000, color="k", ls="--", lw=1, label="anneal: lr 0.02 to 0.005")
+ax.axvline(6_000, color="k", ls="--", lw=1, label="learning rate cut, 0.02 to 0.005")
 # clip to the plateau: the first few hundred steps are orders of magnitude higher
 plateau = loss[1_000:]
 ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))
-ax.yaxis.set_major_formatter(plt.matplotlib.ticker.StrMethodFormatter("{x:,.0f}"))
+ax.yaxis.set_major_formatter(StrMethodFormatter("{x:,.0f}"))
 ax.set_xlabel("step")
 ax.set_ylabel("negative ELBO, full-data scale")
 ax.set_title("Streaming ADVI loss on the plateau (steps before 1,000 are off scale)")
-ax.legend(loc="upper right", fontsize=9)
+ax.legend(loc="upper right", fontsize=9, frameon=True)
 before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]
 print(
     "recorded loss over the two passes either side of the cut:\n"
@@ -741,7 +762,7 @@ print(
 Before reading a single parameter off this fit, one property of the replay has
 to be dealt with. The batch at step $t$ is a deterministic function of $t$ with
 period one epoch, so the data term of every stochastic gradient repeats with
-that period. The parameters and the Monte-Carlo draw do not, so the realized
+that period. The parameters and the Monte Carlo draw do not, so the realized
 gradients are not literally periodic, but the iterate inherits a component
 locked to the replay order. Two measurements separate that component from
 genuine progress: compare the same phase in consecutive epochs, and look at the
@@ -776,7 +797,8 @@ pass. To the extent the order-locked
 component repeats from one pass to the next, a window of exactly one pass
 averages it out (which is why the step budget above is a whole number of
 epochs), and a window that is not a whole number of passes leaves a residual of
-the order of one swing divided by the window length. This is Polyak–Ruppert averaging. What the average does *not* remove
+the order of one swing divided by the window length. This is Polyak–Ruppert averaging {cite:p}`polyak1992acceleration`. What the
+average does *not* remove
 is the slow drift the first line still shows: a tenth of a width per pass in
 the median, and much more along the directions the recovery table is about to
 single out.
@@ -811,9 +833,9 @@ scalar_params = [
 ]
 rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}
 # the intercepts share a ridge with their group means; only the sums are identified
-for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
-    s = post[icpt] + post[b].mean("symbol")
-    rows[f"{icpt} + mean({b})"] = [float(s.mean()), float(s.std())]
+for intercept, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
+    s = post[intercept] + post[b].mean("symbol")
+    rows[f"{intercept} + mean({b})"] = [float(s.mean()), float(s.std())]
 recovery = pd.DataFrame(rows, index=["mean", "sd"]).T
 recovery["truth"] = [truth[p] for p in scalar_params] + [
     truth["kappa0"],
@@ -838,9 +860,9 @@ identified = [name for name in recovery.index if name not in raw_splits]
 def summarize(post_):
     """Posterior mean and sd of every row the recovery table reports."""
     out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}
-    for icpt, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
-        total = post_[icpt] + post_[b].mean("symbol")
-        out[f"{icpt} + mean({b})"] = (float(total.mean()), float(total.std()))
+    for intercept, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
+        total = post_[intercept] + post_[b].mean("symbol")
+        out[f"{intercept} + mean({b})"] = (float(total.mean()), float(total.std()))
     return pd.DataFrame(out, index=["mean", "sd"]).T
 
 
@@ -934,7 +956,7 @@ $g$/$b^{(\sigma h)}$, whose sums the table omits).
 
 Two things follow, and the table shows both. Along a direction that flat, a
 gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its
-way to $-3.00$. To see that it is still travelling, warm-start a fresh optimizer
+way to $-3.00$. To see that it is still traveling, warm-start a fresh optimizer
 at the last iterate and give it sixty more passes at the same learning rate:
 
 ```{code-cell} ipython3
@@ -975,7 +997,7 @@ print(
 ```
 
 The ridge coordinates keep moving toward their generating values, at a rate the
-loss barely registers. The raw split rows are a fit still travelling along a
+loss barely registers. The raw split rows are a fit still traveling along a
 nearly flat direction, not an ambiguity in the model. And their reported
 widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just
 computed along it: mean-field has no correlation to spend,
@@ -1020,7 +1042,13 @@ b_truth = b_truth - b_truth.mean()
 fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
 x = np.arange(n_symbols)
 ax.errorbar(
-    x, b_mean, yerr=2 * b_sd, fmt="o", color="C0", capsize=3, label="mean ± 2 mean-field sd"
+    x,
+    b_mean,
+    yerr=2 * b_sd,
+    fmt="o",
+    color="C0",
+    capsize=3,
+    label="mean ± 2 posterior sd (mean-field)",
 )
 ax.scatter(x, b_truth, marker="x", color="C1", s=60, zorder=3, label="truth")
 for t in thin:
@@ -1079,7 +1107,7 @@ log_sigma = (
     + pick("b_al", "symbol")[s_idx][None, :]
     + Bh @ (pick("g", "harmonic") + pick("b_sh", "symbol", "harmonic")[s_idx])
 )
-# transform each draw, then summarise: the median of a function, not a function of medians
+# transform each draw, then summarize: the median of a function, not a function of medians
 zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)
 half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick("nu"))[None, :], axis=1)
 
@@ -1094,22 +1122,22 @@ t_logsig = (
     + Bh @ (truth["g"] + 0.12 * z_truth["z_sh"][s_idx])
 )
 
-fig, axes = plt.subplots(1, 2, figsize=(9, 3.2), layout="constrained")
+fig, axs = plt.subplots(1, 2, figsize=(9, 3.2), layout="constrained")
 for ax, post_curve, truth_curve, ylab in [
-    (axes[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), "P(next event does not move)"),
-    (axes[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth["nu"]), "90% half-width (bp)"),
+    (axs[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), "P(next event does not move)"),
+    (axs[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth["nu"]), "90% half-width (bp)"),
 ]:
     ax.plot(hours, post_curve, color="C0", lw=2, label="posterior median")
     ax.plot(hours, truth_curve, color="C1", ls="--", lw=1.5, label="generator truth")
     ax.set_xlabel("UTC hour")
     ax.set_ylabel(ylab)
-axes[0].legend(fontsize=9)
+axs[0].legend(fontsize=9)
 fig.suptitle(f"SYM{s_idx:02d} on the event clock, at a = q = 0", fontsize=11);
 ```
 
 The half-width is a quantile of the *outcome* distribution, not a credible band
 for the curve: it answers "how far does a move go", and it stays finite for any
-$\nu > 0$, which is why the notebook reports it instead of a conditional
+$\nu > 0$. That is why the notebook reports it instead of a conditional
 standard deviation: the Student-t variance exists only for $\nu > 2$, and
 nothing in this model guarantees that. Under this generator, the fit recovers
 both shapes; on real data the same two curves would be estimates, and would
@@ -1124,7 +1152,9 @@ model to reproduce the data features it exists to describe. The `CustomDist`
 carries a `random` implementation alongside its `logp`, so
 {func}`~pymc.sample_posterior_predictive` works out of the box. The two
 features that matter for this model are the exact-zero share and the heavy
-conditional tail:
+conditional tail; the figure after the table shows the whole empirical
+cumulative distribution function (ECDF) of one batch against sixty predictive
+draws:
 
 ```{code-cell} ipython3
 eval_batch = next(iter(loader))
@@ -1158,7 +1188,7 @@ pd.DataFrame(
         check_row(np.median(y_nonzero), med_nonzero),
         check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),
     ],
-    columns=["observed", "pp mean", "pp 5%", "pp 95%"],
+    columns=["observed", "predictive mean", "predictive 5%", "predictive 95%"],
     index=["zero share", "median |move| (bp)", "q99 |move| (bp)"],
 ).round(3)
 ```
@@ -1184,7 +1214,7 @@ ax.annotate(
     va="center",
 )
 ax.set_xlabel("next-event return (bp)")
-ax.set_ylabel("ECDF")
+ax.set_ylabel("empirical CDF")
 ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
 ax.legend(loc="upper left");
 ```
@@ -1194,7 +1224,7 @@ share sits close to the lower edge, 0.300 observed against a band starting at
 0.299, so it is a pass with almost no margin rather than a comfortable one.
 What that establishes is limited in two ways. The heading is literal: the
 evaluation batch was seen during the fit, so this is adequacy, not
-generalisation, and a held-out split is the next thing to add before any of
+generalization, and a held-out split is the next thing to add before any of
 this is used to compare models. And the check
 is one batch, one step ahead, with the observed `ylag` held fixed: it does not
 simulate a price path forward, and it says nothing about the hierarchy, the
@@ -1205,12 +1235,12 @@ stratified check.
 
 ## What a stopping rule has to be able to see
 
-A streamed ELBO trace is noisy: every value is a one-batch, one-Monte-Carlo
+A streamed ELBO trace is noisy: every value is a one-batch, one-Monte Carlo
 estimate, so consecutive losses differ mostly because the batch changed. That
 makes "has it converged?" a signal-detection problem, and the horizon over
 which you look is the whole game.
 
-Write the standardised *block contrast* at horizon $w$: average the $w$ losses
+Write the standardized *block contrast* at horizon $w$: average the $w$ losses
 before a point, average the $w$ losses after it, and divide the difference by
 the noise scale of that difference,
 
@@ -1225,7 +1255,7 @@ $\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable
 drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,
 it sets what is visible at all. A fixed replay order is not noise of that kind,
 and the printout below shows where the generic scaling breaks, in the
-notebook's favour.
+notebook's favor.
 
 ```{code-cell} ipython3
 # Stage 2 only, on the full-data scale: after the last planned optimizer change,
@@ -1275,9 +1305,9 @@ for (w, label), color in zip(horizons, ["C7", "C0", "C1"]):
     )
     if w > 1:
         ax_zoom.plot(t, z, color=color, lw=1.6, label=f"{label} (w={w})")
-for a in (ax, ax_zoom):
-    a.axhline(0.0, color="k", lw=0.8)
-    a.set_xlabel("step within stage 2")
+for ax_ in (ax, ax_zoom):
+    ax_.axhline(0.0, color="k", lw=0.8)
+    ax_.set_xlabel("step within stage 2")
 ax.set_ylabel(r"block contrast $z_w$")
 ax.set_title("Same trace, three horizons", fontsize=11)
 ax.legend(fontsize=8, loc="upper right", frameon=True)
@@ -1291,7 +1321,7 @@ ax_zoom.legend(fontsize=8, loc="upper right", frameon=True);
 Read the printout as three signal-to-noise ratios, and the figure as the same
 thing twice: the left panel puts all three horizons on one scale, the right
 panel zooms in on the two that are invisible at that scale. At $w = 1$ the
-standardised contrast has unit spread (which is what the $\sqrt{2/w}$ scaling
+standardized contrast has unit spread (which is what the $\sqrt{2/w}$ scaling
 is built to produce) and a mean indistinguishable from zero: a single
 increment says nothing about the trend.
 
@@ -1302,11 +1332,11 @@ window of exactly one or two passes averages over the *same rows* every time
 and the batch-composition noise, which dominates the per-step view, all but
 drops out.
 Widen the window to one and a half passes and it does not: the printed ratio
-puts the control an order of magnitude above the wider of its two neighbours,
+puts the control an order of magnitude above the wider of its two neighbors,
 even though it is itself *wider* than the one-epoch window. Nothing but the
 alignment changed.
 
-What survives is Monte-Carlo noise and the parameter drift
+What survives is Monte Carlo noise and the parameter drift
 itself, and against that much smaller yardstick the drift becomes visible:
 the ratio in the last column rises with the horizon. So, with a fixed replay
 order, the useful horizons are the ones commensurate with an epoch. That is a
@@ -1318,7 +1348,7 @@ It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
 statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to
 the standardized improvement: $S \leftarrow \max(0,\ S + (\kappa -
 \max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that
-recursion behaves sensibly while there is signal: if the standardised
+recursion behaves sensibly while there is signal: if the standardized
 improvement stays above $\kappa$, the increment is negative and $S$ sits at
 zero. The difficulty is what happens when there is *no* resolvable signal.
 Under symmetric noise $\mathbb{E}[\max(z,0)]$ is only a fraction of a standard
@@ -1375,14 +1405,12 @@ others are not imported here; this is where each one fits.
 | [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |
 
 Pathfinder is the faster route when an approximately placed starting point is
-what you need, for example to initialise MCMC. It is not run here, and the
+what you need, for example to initialize Markov chain Monte Carlo (MCMC). It is not run here, and the
 reason is a decision taken before fitting rather than a result: its documented
 operating range is non-hierarchical targets of at most a few tens of
 parameters, and this target is hierarchical with
 
 ```{code-cell} ipython3
-from pymc.blocking import DictToArrayBijection
-
 n_free = DictToArrayBijection.map(model.initial_point()).data.size
 print(f"free parameters in the unconstrained space: {n_free}")
 ```
@@ -1393,11 +1421,19 @@ of this choice.
 
 +++
 
+## Acknowledgements
+
+This notebook was written as part of the 2026
+[Google Summer of Code](https://summerofcode.withgoogle.com/) project
+*Streaming Variational Inference for Large Datasets* with PyMC and
+[NumFOCUS](https://numfocus.org/), mentored by Rob Zinkov and Chris Fonnesbeck.
+
++++
+
 ## Authors
 
 * Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August
-  2026 for the Google Summer of Code project *Streaming Variational Inference
-  for Large Datasets* (PyMC / NumFOCUS).
+  2026 ([pymc-examples#892](https://github.com/pymc-devs/pymc-examples/pull/892))
 
 +++
 
@@ -1414,7 +1450,7 @@ of this choice.
 ```{code-cell} ipython3
 %load_ext watermark
 print(f"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s")
-%watermark -n -u -v -iv -w
+%watermark -n -u -v -iv -w -p xarray
 %watermark -m
 ```