diff --git a/CUTS_Plus/README.md b/CUTS_Plus/README.md
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## 🎄CUTS+: High-dimensional Causal Discovery from Irregular Time-series
-[arXiv](https://arxiv.org/abs/2305.05890) | [Tutorial (Coming Soon) ](https://colab.research.google.com/)
+[arXiv](https://arxiv.org/abs/2305.05890) | [Tutorial (Coming Soon) ](https://colab.research.google.com/) | [Supplementary Materials](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf)
### ✍️ Paper summary
diff --git a/README.md b/README.md
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| Algorithm | Summary | Paper | Code |
|--------|---------------------------------------------------------------------------|----| ----|
| CUTS | EM-Style joint causal graph learning and missing data imputation for irregular temporal data | [ICLR 2023](https://openreview.net/forum?id=UG8bQcD3Emv)
[Latest Version](CUTS/CUTS_ver0324_camera5.pdf) |[Code](CUTS/)
-| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | Accepted to AAAI-24
[arXiv](https://arxiv.org/abs/2305.05890) |[Code](CUTS_Plus/)
+| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | Accepted to AAAI-24
[Supplements](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf)
[arXiv](https://arxiv.org/abs/2305.05890)|[Code](CUTS_Plus/)
| CausalTime Benchmark| A novel pipeline capable of generating realistic time-series along with a ground truth causal graph that is generalizable to different fields. [Official Website.](https://www.causaltime.cc/) | [arXiv](https://arxiv.org/abs/2310.01753) | [Code](CausalTime/)
| REACT | A causal deep learning approach that combines neural networks with causal discovery to develop a reliable and generalizable model to predict a patient's risk of developing CSA-AKI within the next 48 hours. | [medRxiv](https://www.medrxiv.org/content/10.1101/2023.12.04.23299332v1) | [Code](REACT/)