2020.04.27.txt

==========New Papers==========
1, TITLE:       A Gamma-Poisson Mixture Topic Model for Short Text
http://arxiv.org/abs/2004.11464
AUTHORS:        Jocelyn Mazarura ; Alta de Waal ; Pieter de Villiers
COMMENTS:       26 pages, 14 Figures, to be published in Mathematical Problems in Engineering
HIGHLIGHT:      In this study, we focus on short text.

2, TITLE:       Debiasing Skin Lesion Datasets and Models? Not So Fast
http://arxiv.org/abs/2004.11457
AUTHORS:        Alceu Bissoto ; Eduardo Valle ; Sandra Avila
COMMENTS:       Accepted to the ISIC Skin Image Analysis Workshop @ CVPR 2020
HIGHLIGHT:      In this work we address this issue for skin-lesion classification models, with two objectives: finding out what are the spurious correlations exploited by biased networks, and debiasing the models by removing such spurious correlations from them. We perform a systematic integrated analysis of 7 visual artifacts (which are possible sources of biases exploitable by networks), employ a state-of-the-art technique to prevent the models from learning spurious correlations, and propose datasets to test models for the presence of bias.

3, TITLE:       Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
http://arxiv.org/abs/2004.11695
AUTHORS:        Hamed Jelodar ; Yongli Wang ; Rita Orji ; Hucheng Huang
HIGHLIGHT:      In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions.

4, TITLE:       A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
http://arxiv.org/abs/2004.11693
AUTHORS:        Arka Mitra ; Arunava Chakravarty ; Nirmalya Ghosh ; Tandra Sarkar ; Ramanathan Sethuraman ; Debdoot Sheet
COMMENTS:       accepted in EMBC 2020, 4 pages+2 page Appendix
HIGHLIGHT:      Over 63 experiments spanning 400 hours, executed on a 11:3 FP32 TensorTFLOPS compute system, we found the Xception and ResNet-18 architectures to be consistent performers in identifying co-existing disease conditions with an average AUC of 0.87 across nine pathologies.

5, TITLE:       Optic disc and fovea localisation in ultra-widefield scanning laser ophthalmoscope images captured in multiple modalities
http://arxiv.org/abs/2004.11691
AUTHORS:        Peter Robert Wakeford ; Enrico Pellegrini ; Gavin Robertson ; Michael Verhoek ; Alan Duncan Fleming ; Jano van Hemert ; Ik Siong Heng
COMMENTS:       Submitted to the 23rd Conference on Medical Image Understanding and Analysis (MIUA)
HIGHLIGHT:      We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina.

6, TITLE:       Upgrading the Newsroom: An Automated Image Selection System for News Articles
http://arxiv.org/abs/2004.11449
AUTHORS:        Fangyu Liu ; Rémi Lebret ; Didier Orel ; Philippe Sordet ; Karl Aberer
COMMENTS:       Accepted to ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)
HIGHLIGHT:      We propose an automated image selection system to assist photo editors in selecting suitable images for news articles.

7, TITLE:       Variance Reduction for Better Sampling in Continuous Domains
http://arxiv.org/abs/2004.11687
AUTHORS:        Laurent Meunier ; Carola Doerr ; Jeremy Rapin ; Olivier Teytaud
HIGHLIGHT:      We confirm this statement, provide explicit values for this reshaping of the search distribution depending on the population size $\lambda$ and the dimension $d$, and validate our results experimentally.

8, TITLE:       Proving $μ>1$
http://arxiv.org/abs/2004.11685
AUTHORS:        Laurent Meunier ; Yann Chevaleyre ; Jeremy Rapin ; Clément Royer ; Olivier Teytaud
HIGHLIGHT:      In the continuous unconstrained case, we prove mathematically that $\mu=1$ leads to a sub-optimal simple regret in the case of the sphere function.

9, TITLE:       Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise
http://arxiv.org/abs/2004.11443
AUTHORS:        Guru Swaroop Bennabhaktula ; Enrique Alegre ; Dimka Karastoyanova ; George Azzopardi
COMMENTS:       7 pages, 4 figures, conference paper
HIGHLIGHT:      In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras.

10, TITLE:      Roof material classification from aerial imagery
http://arxiv.org/abs/2004.11482
AUTHORS:        Roman Solovyev
HIGHLIGHT:      This paper describes an algorithm for classification of roof materials using aerial photographs.

11, TITLE:      Characterising User Content on a Multi-lingual Social Network
http://arxiv.org/abs/2004.11480
AUTHORS:        Pushkal Agarwal ; Kiran Garimella ; Sagar Joglekar ; Nishanth Sastry ; Gareth Tyson
COMMENTS:       Accepted at ICWSM 2020, please cite the ICWSM version
HIGHLIGHT:      In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages.

12, TITLE:      Pill Identification using a Mobile Phone App for Assessing Medication Adherence and Post-Market Drug Surveillance
http://arxiv.org/abs/2004.11479
AUTHORS:        david Prokop ; Joseph Babigumira ; Ashleigh Lewis
COMMENTS:       12 pages, 1 photo, 6 tables, 3 charts, 1 figure
HIGHLIGHT:      Here we conduct a software study of the usefulness and efficacy of a mobile phone app to measure medication adherence using photographs taken by a phone app of medications and self-reported health measures.

13, TITLE:      Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Surveillance Videos
http://arxiv.org/abs/2004.11475
AUTHORS:        Mamshad Nayeem Rizve ; Ugur Demir ; Praveen Tirupattur ; Aayush Jung Rana ; Kevin Duarte ; Ishan Dave ; Yogesh Singh Rawat ; Mubarak Shah
COMMENTS:       8 pages
HIGHLIGHT:      In this work we propose Gabriella, a real-time online system to perform activity detection on untrimmed surveillance videos.

14, TITLE:      Multiple Segmentations of Thai Sentences for Neural Machine Translation
http://arxiv.org/abs/2004.11472
AUTHORS:        Alberto Poncelas ; Wichaya Pidchamook ; Chao-Hong Liu ; James Hadley ; Andy Way
HIGHLIGHT:      In this work, we explore how to augment a set of English--Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training.

15, TITLE:      A Tool for Facilitating OCR Postediting in Historical Documents
http://arxiv.org/abs/2004.11471
AUTHORS:        Alberto Poncelas ; Mohammad Aboomar ; Jan Buts ; James Hadley ; Andy Way
HIGHLIGHT:      This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents.

16, TITLE:      Jealousy-freeness and other common properties in Fair Division of Mixed Manna
http://arxiv.org/abs/2004.11469
AUTHORS:        Martin Aleksandrov
COMMENTS:       13 pages, 1 table, 2 figures
HIGHLIGHT:      For this model, we study axiomatic concepts of allocations such as jealousy-freeness up to one item, envy-freeness up to one item and Pareto-optimality.

17, TITLE:      Mining self-similarity: Label super-resolution with epitomic representations
http://arxiv.org/abs/2004.11498
AUTHORS:        Kolya Malkin ; Anthony Ortiz ; Caleb Robinson ; Nebojsa Jojic
COMMENTS:       Submitted to ECCV 2020
HIGHLIGHT:      We derive a new training algorithm for epitomes which allows, for the first time, learning from very large data sets and derive a label super-resolution algorithm as a statistical inference algorithm over epitomic representations.

18, TITLE:      UHH-LT & LT2 at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection
http://arxiv.org/abs/2004.11493
AUTHORS:        Gregor Wiedemann ; Seid Muhie Yimam ; Chris Biemann
HIGHLIGHT:      In this paper, we compare current pre-trained transformer networks with and without MLM fine-tuning on their performance for offensive language detection.

19, TITLE:      Adversarial Machine Learning: An Interpretation Perspective
http://arxiv.org/abs/2004.11488
AUTHORS:        Ninghao Liu ; Mengnan Du ; Xia Hu
HIGHLIGHT:      In this paper, we review recent work on adversarial attack and defense, particularly, from the perspective of machine learning interpretation.

20, TITLE:      On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
http://arxiv.org/abs/2004.11854
AUTHORS:        Biao Zhang ; Ivan Titov ; Rico Sennrich
HIGHLIGHT:      In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding.

21, TITLE:      Vision based hardware-software real-time control system for autonomous landing of an UAV
http://arxiv.org/abs/2004.11612
AUTHORS:        Krzysztof Blachut ; Hubert Szolc ; Mateusz Wasala ; Tomasz Kryjak ; Marek Gorgon
COMMENTS:       7 pages, 9 figures, submitted to MMAR 2020 conference
HIGHLIGHT:      In this paper we present a vision based hardware-software control system enabling autonomous landing of a multirotor unmanned aerial vehicle (UAV).

22, TITLE:      Learning Gaussian Maps for Dense Object Detection
http://arxiv.org/abs/2004.11855
AUTHORS:        Sonaal Kant
HIGHLIGHT:      In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other.

23, TITLE:      DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
http://arxiv.org/abs/2004.11853
AUTHORS:        Bill Cassidy ; Neil D. Reeves ; Pappachan Joseph ; David Gillespie ; Claire O'Shea ; Satyan Rajbhandari ; Arun G. Maiya ; Eibe Frank ; Andrew Boulton ; David Armstrong ; Bijan Najafi ; Justina Wu ; Moi Hoon Yap
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results.

24, TITLE:      Impact of different belief facets on agents' decision -- a refined cognitive architecture
http://arxiv.org/abs/2004.11858
AUTHORS:        Amir Hosein Afshar Sedigh ; Martin K. Purvis ; Bastin Tony Roy Savarimuthu ; Christopher K Frantz ; Maryam A. Purvis
COMMENTS:       Submitted to COINE 2020 workshop
HIGHLIGHT:      This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets.

25, TITLE:      Social Interactions or Business Transactions? What customer reviews disclose about Airbnb marketplace
http://arxiv.org/abs/2004.11604
AUTHORS:        Giovanni Quattrone ; Antonino Nocera ; Licia Capra ; Daniele Quercia
COMMENTS:       17 pages, 8 figures, Proceedings of The Web Conference 2020
HIGHLIGHT:      To answer these questions, we propose a novel market analysis approach that exploits customers' reviews.

26, TITLE:      Belief functions induced by random fuzzy sets: Application to statistical inference
http://arxiv.org/abs/2004.11638
AUTHORS:        Thierry Denoeux
HIGHLIGHT:      In this perspective, Dempster-Shafer theory deals with belief functions generated by random sets, while Possibility theory deals with belief functions induced by fuzzy sets.

27, TITLE:      A Survey on Visual Sentiment Analysis
http://arxiv.org/abs/2004.11639
AUTHORS:        Alessandro Ortis ; Giovanni Maria Farinella ; Sebastiano Battiato
COMMENTS:       This paper is a preprint of a paper accepted by IET Image Processing and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at the IET Digital Library
HIGHLIGHT:      Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions.

28, TITLE:      CQE in Description Logics Through Instance Indistinguishability (extended version)
http://arxiv.org/abs/2004.11870
AUTHORS:        Gianluca Cima ; Domenico Lembo ; Riccardo Rosati ; Domenico Fabio Savo
COMMENTS:       9 pages
HIGHLIGHT:      Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability.

29, TITLE:      Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
http://arxiv.org/abs/2004.11867
AUTHORS:        Biao Zhang ; Philip Williams ; Ivan Titov ; Rico Sennrich
COMMENTS:       ACL2020
HIGHLIGHT:      In this paper, we explore ways to improve them.

30, TITLE:      Low-latency hand gesture recognition with a low resolution thermal imager
http://arxiv.org/abs/2004.11623
AUTHORS:        Maarten Vandersteegen ; Wouter Reusen ; Kristof Van Beeck Toon Goedeme
HIGHLIGHT:      We recorded a new dataset of over 1300 video clips for training and evaluation and propose a light-weight low-latency prediction algorithm.

31, TITLE:      Dynamic Sampling for Deep Metric Learning
http://arxiv.org/abs/2004.11624
AUTHORS:        Chang-Hui Liang ; Wan-Lei Zhao ; Run-Qing Chen
COMMENTS:       11 pages, 4 figures
HIGHLIGHT:      In this paper, a dynamic sampling strategy is proposed to organize the training pairs in an easy-to-hard order to feed into the network.

32, TITLE:      Learning the grammar of prescription: recurrent neural network grammars for medication information extraction in clinical texts
http://arxiv.org/abs/2004.11622
AUTHORS:        Ivan Lerner ; Jordan Jouffroy ; Anita Burgun ; Antoine Neuraz
HIGHLIGHT:      In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts.

33, TITLE:      Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs
http://arxiv.org/abs/2004.11861
AUTHORS:        Tarcísio Souza Costa ; Simon Gottschalk ; Elena Demidova
HIGHLIGHT:      In this paper we present the Event-QA dataset for answering event-centric questions over knowledge graphs.

34, TITLE:      Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning
http://arxiv.org/abs/2004.11627
AUTHORS:        Zhongzhan Huang ; Xinjiang Wang ; Ping Luo
HIGHLIGHT:      Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning

35, TITLE:      Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
http://arxiv.org/abs/2004.11660
AUTHORS:        Yu Deng ; Jiaolong Yang ; Dong Chen ; Fang Wen ; Xin Tong
COMMENTS:       Accepted by CVPR2020(Oral)
HIGHLIGHT:      We propose an approach for face image generation of virtual people with disentangled, precisely-controllable latent representations for identity of non-existing people, expression, pose, and illumination.

36, TITLE:      YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
http://arxiv.org/abs/2004.11657
AUTHORS:        Till Grenzdörffer ; Martin Günther ; Joachim Hertzberg
COMMENTS:       Accepted to ICRA-2020
HIGHLIGHT:      In this work, we present a dataset of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames.

37, TITLE:      Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
http://arxiv.org/abs/2004.11410
AUTHORS:        Giambattista Parascandolo ; Lars Buesing ; Josh Merel ; Leonard Hasenclever ; John Aslanides ; Jessica B. Hamrick ; Nicolas Heess ; Alexander Neitz ; Theophane Weber
HIGHLIGHT:      We propose a planning algorithm, Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS), for approximating the optimal plan by means of proposing intermediate sub-goals which hierarchically partition the initial tasks into simpler ones that are then solved independently and recursively.

38, TITLE:      End-to-end speech-to-dialog-act recognition
http://arxiv.org/abs/2004.11419
AUTHORS:        Viet-Trung Dang ; Tianyu Zhao ; Sei Ueno ; Hirofumi Inaguma ; Tatsuya Kawahara
HIGHLIGHT:      In this paper, we present an end-to-end model which directly converts speech into dialog acts without the deterministic transcription process.

39, TITLE:      Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
http://arxiv.org/abs/2004.11892
AUTHORS:        Alexander R. Fabbri ; Patrick Ng ; Zhiguo Wang ; Ramesh Nallapati ; Bing Xiang
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose an unsupervised approach to training QA models with generated pseudo-training data.

40, TITLE:      Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent Neural Networks
http://arxiv.org/abs/2004.11405
AUTHORS:        Nachum Dershowitz ; Ori Terner
HIGHLIGHT:      In this work we are trying to train a model that will automatically transliterate Judeo-Arabic into Arabic script; thus we aspire to enable Arabic readers to access those writings.

41, TITLE:      Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds
http://arxiv.org/abs/2004.11647
AUTHORS:        Artem Filatov ; Andrey Rykov ; Viacheslav Murashkin
COMMENTS:       Accepted to ICRA 2020
HIGHLIGHT:      In this work we propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation based on 3D point cloud sequence.

42, TITLE:      GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
http://arxiv.org/abs/2004.11648
AUTHORS:        Yi-Ju Lu ; Cheng-Te Li
COMMENTS:       To appear in Proceedings of The 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020. Code is available here https://github.com/l852888/GCAN
HIGHLIGHT:      Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern.

43, TITLE:      Lite Transformer with Long-Short Range Attention
http://arxiv.org/abs/2004.11886
AUTHORS:        Zhanghao Wu ; Zhijian Liu ; Ji Lin ; Yujun Lin ; Song Han
COMMENTS:       ICLR 2020. The first two authors contributed equally to this work
HIGHLIGHT:      In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices.

44, TITLE:      Revisiting Modulated Convolutions for Visual Counting and Beyond
http://arxiv.org/abs/2004.11883
AUTHORS:        Duy-Kien Nguyen ; Vedanuj Goswami ; Xinlei Chen
HIGHLIGHT:      In this paper, we propose a simple and effective alternative for visual counting by revisiting modulated convolutions that fuse query and image locally.

45, TITLE:      Robust testing of low-dimensional functions
http://arxiv.org/abs/2004.11642
AUTHORS:        Anindya De ; Elchanan Mossel ; Joe Neeman
HIGHLIGHT:      Following the surge of interest in noise-tolerant property testing, in this paper we prove a noise-tolerant (or robust) version of this result.

46, TITLE:      Understanding when spatial transformer networks do not support invariance, and what to do about it
http://arxiv.org/abs/2004.11678
AUTHORS:        Lukas Finnveden ; Ylva Jansson ; Tony Lindeberg
COMMENTS:       12 pages, 7 figures
HIGHLIGHT:      We present a simple proof for this and study the practical implications, showing that this inability is coupled with decreased classification accuracy.

47, TITLE:      Responsible AI and Its Stakeholders
http://arxiv.org/abs/2004.11434
AUTHORS:        Gabriel Lima ; Meeyoung Cha
COMMENTS:       4 pages, accepted to the Fair & Responsible AI Workshop at ACM CHI 2020
HIGHLIGHT:      Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems.

48, TITLE:      Style Your Face Morph and Improve Your Face Morphing Attack Detector
http://arxiv.org/abs/2004.11435
AUTHORS:        Clemens Seibold ; Anna Hilsmann ; Peter Eisert
COMMENTS:       Published at BIOSIG 2019
HIGHLIGHT:      In this paper, we propose a style transfer based method that improves the quality of morphed face images.

49, TITLE:      PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning
http://arxiv.org/abs/2004.11667
AUTHORS:        Guillaume Matheron ; Nicolas Perrin ; Olivier Sigaud
HIGHLIGHT:      In this paper, we propose a new algorithm called "Plan, Backplay, Chain Skills" (PBCS) that combines motion planning and reinforcement learning to solve hard exploration environments.

50, TITLE:      Retrofitting Parallelism onto OCaml
http://arxiv.org/abs/2004.11663
AUTHORS:        KC Sivaramakrishnan ; Stephen Dolan ; Leo White ; Sadiq Jaffer ; Tom Kelly ; Anmol Sahoo ; Sudha Parimala ; Atul Dhiman ; Anil Madhavapeddy
COMMENTS:       Submitted to ICFP 2020
HIGHLIGHT:      To this end, the paper presents a series of novel techniques and demonstrates that the new GC strikes a balance between performance and feature backwards compatibility for sequential programs and scales admirably on modern multicore processors.

51, TITLE:      Small circuits and dual weak PHP in the universal theory of p-time algorithms
http://arxiv.org/abs/2004.11582
AUTHORS:        Jan Krajicek
COMMENTS:       Preprint April 2020
HIGHLIGHT:      We prove, under a computational complexity hypothesis, that it is consistent with the true universal theory of p-time algorithms that a specific p-time function extending $n$ bits to $m \geq n^2$ bits violates the dual weak pigeonhole principle: every string $y$ of length $m$ equals to the value of the function for some $x$ of length $n$.

52, TITLE:      Customization and modifications of SignWriting by LIS users
http://arxiv.org/abs/2004.11583
AUTHORS:        Claudia S. Bianchini ; Fabrizio Borgia ; Margherita Castelli
COMMENTS:       in French. CORELA - COgnition, REpr{\'e}sentation, LAngage, CERLICO-Cercle Linguistique du Centre et de l'Ouest (France), A para{\^i}tre
HIGHLIGHT:      In this paper, we present the mechanisms adopted by signers of the Italian Sign Language (LIS), expert users of SW, to modify the standard SW glyphs and increase their writing skills and/or represent peculiar linguistic phenomena.

53, TITLE:      Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
http://arxiv.org/abs/2004.11579
AUTHORS:        Yi Liao ; Xin Jiang ; Qun Liu
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we propose a probabilistic masking scheme for the masked language model, which we call probabilistically masked language model (PMLM).

54, TITLE:      A Review of an Old Dilemma: Demosaicking First, or Denoising First?
http://arxiv.org/abs/2004.11577
AUTHORS:        Qiyu Jin ; Gabriele Facciolo ; Jean-Michel Morel
HIGHLIGHT:      In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic.

55, TITLE:      High performance SIMD modular arithmetic for polynomial evaluation
http://arxiv.org/abs/2004.11571
AUTHORS:        Pierre Fortin ; Ambroise Fleury ; François Lemaire ; Michael Monagan
HIGHLIGHT:      In this article, we focus on the efficient computation of such polynomial evaluations on one single CPU core.

56, TITLE:      Efficient Algorithms for Approximating Quantum Partition Functions
http://arxiv.org/abs/2004.11568
AUTHORS:        Ryan L. Mann ; Tyler Helmuth
COMMENTS:       6 pages, 0 figures
HIGHLIGHT:      We establish a polynomial-time approximation algorithm for partition functions of quantum spin models at high temperature.

57, TITLE:      Deep 3D Portrait from a Single Image
http://arxiv.org/abs/2004.11598
AUTHORS:        Sicheng Xu ; Jiaolong Yang ; Dong Chen ; Fang Wen ; Yu Deng ; Yunde Jia ; Xin Tong
COMMENTS:       Accepted by CVPR2020; Code: https://github.com/sicxu/Deep3dPortrait
HIGHLIGHT:      In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image.

58, TITLE:      Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
http://arxiv.org/abs/2004.11721
AUTHORS:        Arunava Chakravarty ; Tandra Sarkar ; Nirmalya Ghosh ; Ramanathan Sethuraman ; Debdoot Sheet
COMMENTS:       accepted in EMBC 2020, 4pg+2pg Supplementary Material
HIGHLIGHT:      To address this issue, we propose a Graph Neural Network (GNN) based solution to obtain ensemble predictions which models the dependencies between different diseases.

59, TITLE:      A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
http://arxiv.org/abs/2004.11726
AUTHORS:        Sarath Chandra K ; Arunava Chakravarty ; Nirmalya Ghosh ; Tandra Sarkar ; Ramanathan Sethuraman ; Debdoot Sheet
COMMENTS:       accepted in EMBC 2020, 4 pg+1 pg Supplementary
HIGHLIGHT:      To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework.

60, TITLE:      Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
http://arxiv.org/abs/2004.11727
AUTHORS:        Zihan Liu ; Genta Indra Winata ; Peng Xu ; Pascale Fung
COMMENTS:       Accepted in ACL 2020
HIGHLIGHT:      In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling.

61, TITLE:      Ultra Fast Structure-aware Deep Lane Detection
http://arxiv.org/abs/2004.11757
AUTHORS:        Zequn Qin ; Huanyu Wang ; Xi Li
HIGHLIGHT:      Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at extremely fast speed and challenging scenarios.

62, TITLE:      Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
http://arxiv.org/abs/2004.11514
AUTHORS:        Loc Truong ; Chace Jones ; Brian Hutchinson ; Andrew August ; Brenda Praggastis ; Robert Jasper ; Nicole Nichols ; Aaron Tuor
HIGHLIGHT:      Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger" that provides a predetermined response advantageous to the adversary.

63, TITLE:      PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
http://arxiv.org/abs/2004.11744
AUTHORS:        Qing Yang ; Xia Zhu ; Jong-Kae Fwu ; Yun Ye ; Ganmei You ; Yuan Zhu
COMMENTS:       Accepted to appear in CVPR2020 WMF
HIGHLIGHT:      In this paper, we propose a novel pipeline-based multi-stream CNN architecture called PipeNet for multi-modal face anti-spoofing.

64, TITLE:      ST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning
http://arxiv.org/abs/2004.11742
AUTHORS:        Xiwen Chen ; Kenny Q. Zhu
COMMENTS:       9 pages, 11 figures
HIGHLIGHT:      In this work, we develop a meta-learning framework to transfer between any kind of text styles, including personal writing styles that are more fine-grained, share less content and have much smaller training data.

65, TITLE:      What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
http://arxiv.org/abs/2004.11500
AUTHORS:        Jiahua Dong ; Yang Cong ; Gan Sun ; Bineng Zhong ; Xiaowei Xu
COMMENTS:       This paper is accepted by IEEE Conference on Computer Vision and Pattern Recognition 2020 (CVPR 2020)
HIGHLIGHT:      To address these challenges, we develop a new unsupervised semantic transfer model including two complementary modules (i.e., T_D and T_F ) for endoscopic lesions segmentation, which can alternatively determine where and how to explore transferable domain-invariant knowledge between labeled source lesions dataset (e.g., gastroscope) and unlabeled target diseases dataset (e.g., enteroscopy).

66, TITLE:      Automatic low-bit hybrid quantization of neural networks through meta learning
http://arxiv.org/abs/2004.11506
AUTHORS:        Tao Wang ; Junsong Wang ; Chang Xu ; Chao Xue
COMMENTS:       7 pages, 3 figures
HIGHLIGHT:      In this paper, we employ the meta learning method to automatically realize low-bit hybrid quantization of neural networks.

67, TITLE:      Deep Global Registration
http://arxiv.org/abs/2004.11540
AUTHORS:        Christopher Choy ; Wei Dong ; Vladlen Koltun
COMMENTS:       Accepted for CVPR'20 oral presentation
HIGHLIGHT:      We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans.

68, TITLE:      Quantization of Deep Neural Networks for Accumulator-constrained Processors
http://arxiv.org/abs/2004.11783
AUTHORS:        Barry de Bruin ; Zoran Zivkovic ; Henk Corporaal
COMMENTS:       20 pages, 13 figures
HIGHLIGHT:      We introduce an Artificial Neural Network (ANN) quantization methodology for platforms without wide accumulation registers.

69, TITLE:      Exploring Explainable Selection to Control Abstractive Generation
http://arxiv.org/abs/2004.11779
AUTHORS:        Wang Haonan ; Gao Yang ; Bai Yu ; Mirella Lapata ; Huang Heyan
HIGHLIGHT:      In this paper, we target using a select and generate paradigm to enhance the capability of selecting explainable contents (i.e., interpret the selection given its semantics, novelty, relevance) and then guiding to control the abstract generation.

70, TITLE:      GAPS: Generator for Automatic Polynomial Solvers
http://arxiv.org/abs/2004.11765
AUTHORS:        Bo Li ; Viktor Larsson
HIGHLIGHT:      We demonstrate in this report the main approach and enhancement features of GAPS.

71, TITLE:      Molecular Inverse-Design Platform for Material Industries
http://arxiv.org/abs/2004.11521
AUTHORS:        Seiji Takeda ; Toshiyuki Hama ; Hsiang-Han Hsu ; Victoria A. Piunova ; Dmitry Zubarev ; Daniel P. Sanders ; Jed W. Pitera ; Makoto Kogoh ; Takumi Hongo ; Yenwei Cheng ; Wolf Bocanett ; Hideaki Nakashika ; Akihiro Fujita ; Yuta Tsuchiya ; Katsuhiko Hino ; Kentaro Yano ; Shuichi Hirose ; Hiroki Toda ; Yasumitsu Orii ; Daiju Nakano
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      In this paper, we present a material industry-oriented web platform of an AI-driven molecular inverse-design system, which automatically designs brand new molecular structures rapidly and diversely.

72, TITLE:      The Two Kinds of Free Energy and the Bayesian Revolution
http://arxiv.org/abs/2004.11763
AUTHORS:        Sebastian Gottwald ; Daniel A. Braun
HIGHLIGHT:      The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature.

73, TITLE:      Deep Feature-preserving Normal Estimation for Point Cloud Filtering
http://arxiv.org/abs/2004.11563
AUTHORS:        Dening Lu ; Xuequan Lu ; Yangxing Sun ; Jun Wang
COMMENTS:       accepted to Computer Aided Design (Symposium on Solid and Physical Modeling 2020)
HIGHLIGHT:      In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.

74, TITLE:      FLAT: Chinese NER Using Flat-Lattice Transformer
http://arxiv.org/abs/2004.11795
AUTHORS:        Xiaonan Li ; Hang Yan ; Xipeng Qiu ; Xuanjing Huang
COMMENTS:       Accepted to the ACL 2020
HIGHLIGHT:      In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans.

75, TITLE:      Optimal Streaming Approximations for all Boolean Max-2CSPs
http://arxiv.org/abs/2004.11796
AUTHORS:        Chi-Ning Chou ; Alexander Golonev ; Santhoshini Velusamy
HIGHLIGHT:      We prove tight upper and lower bounds on approximation ratios of all Boolean Max-2CSP problems in the streaming model.

76, TITLE:      G-DAUG: Generative Data Augmentation for Commonsense Reasoning
http://arxiv.org/abs/2004.11546
AUTHORS:        Yiben Yang ; Chaitanya Malaviya ; Jared Fernandez ; Swabha Swayamdipta ; Ronan Le Bras ; Ji-Ping Wang ; Chandra Bhagavatula ; Yejin Choi ; Doug Downey
HIGHLIGHT:      We investigate G-DAUG, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting.

77, TITLE:      Dropout as an Implicit Gating Mechanism For Continual Learning
http://arxiv.org/abs/2004.11545
AUTHORS:        Seyed-Iman Mirzadeh ; Mehrdad Farajtabar ; Hassan Ghasemzadeh
COMMENTS:       CVPR 2020 Workshops
HIGHLIGHT:      In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active.

78, TITLE:      Algebra-based Loop Synthesis
http://arxiv.org/abs/2004.11787
AUTHORS:        Andreas Humenberger ; Laura Kovács
HIGHLIGHT:      We present an algorithm for synthesizing program loops satisfying a given polynomial loop invariant.

79, TITLE:      DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
http://arxiv.org/abs/2004.11784
AUTHORS:        Dahlia Urbach ; Yizhak Ben-Shabat ; Michael Lindenbaum
HIGHLIGHT:      We introduce a new deep learning method for point cloud comparison.

80, TITLE:      Self-Paced Deep Reinforcement Learning
http://arxiv.org/abs/2004.11812
AUTHORS:        Pascal Klink ; Carlo D'Eramo ; Jan Peters ; Joni Pajarinen
HIGHLIGHT:      In this paper, we consider recently established results in Curriculum Learning for episodic RL, proposing an extension that is easily integrated with well-known RL algorithms and providing a theoretical formulation from an RL-as-Inference perspective.

81, TITLE:      Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention
http://arxiv.org/abs/2004.11814
AUTHORS:        Feng Li ; Runming Cong ; Huihui Bai ; Yifan He
COMMENTS:       Accepted by the IJCAI-PRICAI 2020
HIGHLIGHT:      In this paper, to tackle this problem, we propose a deep interleaved network (DIN) to learn how information at different states should be combined for image SR where shallow information guides deep representative features prediction.

82, TITLE:      Detecting Unsigned Physical Road Incidents from Driver-View Images
http://arxiv.org/abs/2004.11824
AUTHORS:        Alex Levering ; Martin Tomko ; Devis Tuia ; Kourosh Khoshelham
COMMENTS:       Preprint to T-IV paper
HIGHLIGHT:      In this paper we propose a system based on an off-the-shelf deep neural network architecture that is able to detect and recognize types of unsigned (non-placarded, such as traffic signs), physical (visible in images) road incidents. After selecting eight target types of incidents, we collect a dataset of twelve thousand images gathered from publicly-available web sources.



==========Updates to Previous Papers==========
1, TITLE:       Reinforcement Learning in Healthcare: A Survey
http://arxiv.org/abs/1908.08796
AUTHORS:        Chao Yu ; Jiming Liu ; Shamim Nemati
HIGHLIGHT:      Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure.

2, TITLE:       Scalable learning for bridging the species gap in image-based plant phenotyping
http://arxiv.org/abs/2003.10757
AUTHORS:        Daniel Ward ; Peyman Moghadam
COMMENTS:       Under review. Abstract modified to meed arXiv requirements. Dataset available at: https://csiro-robotics.github.io/UPGen_Webpage/
HIGHLIGHT:      In this paper, we investigate the use of synthetic data for leaf instance segmentation.

3, TITLE:       Proposal Learning for Semi-Supervised Object Detection
http://arxiv.org/abs/2001.05086
AUTHORS:        Peng Tang ; Chetan Ramaiah ; Yan Wang ; Ran Xu ; Caiming Xiong
HIGHLIGHT:      In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled and unlabeled data.

4, TITLE:       Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
http://arxiv.org/abs/2004.02325
AUTHORS:        Dmitrii Shadrin ; Mariia Pukalchik ; Anastasia Uryasheva ; Evgeny Tsykunov ; Grigoriy Yashin ; Nikita Rodichenko ; Dzmitry Tsetserukou
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      This research proposes a modern approach for analyzing the spectral data in Near-Infrared and Mid-Infrared ranges of the apple tree diseases at different stages.

5, TITLE:       Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya
http://arxiv.org/abs/2004.04081
AUTHORS:        Andrew Hobbs ; Stacey Svetlichnaya
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      This paper introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance.

6, TITLE:       Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
http://arxiv.org/abs/2004.10547
AUTHORS:        Shuting He ; Hao Luo ; Weihua Chen ; Miao Zhang ; Yuqi Zhang ; Fan Wang ; Hao Li ; Wei Jiang
COMMENTS:       Solution for AI City Challenge, CVPR2020 Workshop. Codes are at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID
HIGHLIGHT:      This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20).

7, TITLE:       Accelerating temporal action proposal generation via high performance computing
http://arxiv.org/abs/1906.06496
AUTHORS:        Tian Wang ; Shiye Lei ; Youyou Jiang ; Choi Chang ; Hichem Snoussi ; Guangcun Shan
COMMENTS:       11 pages, 12 figures
HIGHLIGHT:      In this work, one novel high performance ring parallel architecture based on Message Passing Interface (MPI) is further introduced into temporal action proposal generation, which is a reliable communication protocol, in order to respond to the requirements of large memory occupation and a large number of videos.

8, TITLE:       Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
http://arxiv.org/abs/1911.09487
AUTHORS:        Cong Sun ; Zhihao Yang ; Leilei Su ; Lei Wang ; Yin Zhang ; Hongfei Lin ; Jian Wang
COMMENTS:       8 pages, 4 figures, Bioinformatics manuscript
HIGHLIGHT:      Results: In this paper, we propose a novel neural network-based approach to improve CPI extraction.

9, TITLE:       A note on the parametric integer programming in the average case: sparsity, proximity, and FPT-algorithms
http://arxiv.org/abs/2002.01307
AUTHORS:        D. V. Gribanov ; D. S. Malyshev ; P. M. Pardalos
HIGHLIGHT:      We consider the Integer Linear Programming (ILP) problem $\max\{c^\top x : A x \leq b,\, x \in Z^n \}$, parameterized by a right-hand side vector $b \in Z^m$, where $A \in Z^{m \times n}$ is a matrix of the rank $n$.

10, TITLE:      Designing Precise and Robust Dialogue Response Evaluators
http://arxiv.org/abs/2004.04908
AUTHORS:        Tianyu Zhao ; Divesh Lala ; Tatsuya Kawahara
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models.

11, TITLE:      Pre-trained Models for Natural Language Processing: A Survey
http://arxiv.org/abs/2003.08271
AUTHORS:        Xipeng Qiu ; Tianxiang Sun ; Yige Xu ; Yunfan Shao ; Ning Dai ; Xuanjing Huang
COMMENTS:       Invited Review of Science China Technological Sciences
HIGHLIGHT:      In this survey, we provide a comprehensive review of PTMs for NLP.

12, TITLE:      Computing the k Densest Subgraphs of a Graph
http://arxiv.org/abs/2002.07695
AUTHORS:        Riccardo Dondi ; Danny Hermelin
HIGHLIGHT:      In this paper we hope to remedy this situation by analyzing three natural variants of the k densest subgraphs problem.

13, TITLE:      On the Importance of Delexicalization for Fact Verification
http://arxiv.org/abs/1909.09868
AUTHORS:        Sandeep Suntwal ; Mithun Paul ; Rebecca Sharp ; Mihai Surdeanu
COMMENTS:       published in the proceedings at EMNLP2019
HIGHLIGHT:      In this work we aim to understand and estimate the importance that a neural network assigns to various aspects of the data while learning and making predictions.

14, TITLE:      Time-Delay Feedback Neural Network for Fast-Moving Small Target Discrimination Against Complex Dynamic Environments
http://arxiv.org/abs/2001.05846
AUTHORS:        Hongxin Wang ; Huatian Wang ; Jiannan Zhao ; Cheng Hu ; Jigen Peng ; Shigang Yue
COMMENTS:       13 pages, 16 figures
HIGHLIGHT:      In this paper, we propose a STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to lower layers to mediate neural responses.

15, TITLE:      End-to-End Bias Mitigation by Modelling Biases in Corpora
http://arxiv.org/abs/1909.06321
AUTHORS:        Rabeeh Karimi Mahabadi ; Yonatan Belinkov ; James Henderson
COMMENTS:       Accepted in ACL 2020 as a long paper
HIGHLIGHT:      We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets.

16, TITLE:      Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors
http://arxiv.org/abs/2004.08501
AUTHORS:        Peri Akiva ; Kristin Dana ; Peter Oudemans ; Michael Mars
COMMENTS:       to be published in proceeding of CVPR 2020 in the Agriculture Vision Workshop
HIGHLIGHT:      In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. To train and evaluate the network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the largest dataset of aerial drone imagery from cranberry fields.

17, TITLE:      Colouring $(sP_1+P_5)$-Free Graphs: a Mim-Width Perspective
http://arxiv.org/abs/2004.05022
AUTHORS:        Nick Brettell ; Jake Horsfield ; Daniel Paulusma
HIGHLIGHT:      For this problem, we may assume that the input graph is $K_{k+1}$-free.

18, TITLE:      When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks
http://arxiv.org/abs/2004.08796
AUTHORS:        Zhiyu Zhu ; Zhen-Peng Bian ; Junhui Hou ; Yi Wang ; Lap-Pui Chau
HIGHLIGHT:      To handle these challenging issues, we propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations.

19, TITLE:      In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
http://arxiv.org/abs/2004.07691
AUTHORS:        Florin Condrea ; Victor-Andrei Ivan ; Marius Leordeanu
COMMENTS:       Computer Vision and Pattern Recognition (CVPR) Workshop on Computer Vision for Physiological Measurement (CVPM) 2020
HIGHLIGHT:      In this paper we address this limitation through a novel deep learning approach, in which a recurrent deep neural network is trained to detect vital signs in the infrared thermal domain from purely synthetic data.

20, TITLE:      Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
http://arxiv.org/abs/2004.01019
AUTHORS:        Philipp Terhörst ; Jan Niklas Kolf ; Naser Damer ; Florian Kirchbuchner ; Arjan Kuijper
HIGHLIGHT:      In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment.

21, TITLE:      Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
http://arxiv.org/abs/1906.00363
AUTHORS:        Cunxiang Wang ; Shuailong Liang ; Yue Zhang ; Xiaonan Li ; Tian Gao
COMMENTS:       This paper has been accepted by ACL2019
HIGHLIGHT:      In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense.

22, TITLE:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
http://arxiv.org/abs/2004.11051
AUTHORS:        Yannis Kalantidis ; Laura Sevilla-Lara ; Ernest Mwebaze ; Dina Machuve ; Hamed Alemohammad ; David Guerena
COMMENTS:       14 papers accepted, 4 as oral, 10 as spotlights
HIGHLIGHT:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020

23, TITLE:      Learning Formation of Physically-Based Face Attributes
http://arxiv.org/abs/2004.03458
AUTHORS:        Ruilong Li ; Karl Bladin ; Yajie Zhao ; Chinmay Chinara ; Owen Ingraham ; Pengda Xiang ; Xinglei Ren ; Pratusha Prasad ; Bipin Kishore ; Jun Xing ; Hao Li
COMMENTS:       12 pages, 16 figures
HIGHLIGHT:      Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering.

24, TITLE:      Not All Multi-Valued Partial CFL Functions Are Refined by Single-Valued Functions
http://arxiv.org/abs/1610.07175
AUTHORS:        Tomoyuki Yamakami
COMMENTS:       (A4 size, 10 pt, 18 pages, 2 figures) This is a complete and corrected version of an extended abstract that appeared in the Proceedings of the 8th IFIP International Conference on Theoretical Computer Science (IFIP TCS 2014), Rome, Italy, September 1-3, 2014, Lecture Notes in Computer Science, Springer, vol. 8705, pp. 136-150, 2014
HIGHLIGHT:      We give an answer to a fundamental question, raised by Konstantinidis, Santean, and Yu [Act.

25, TITLE:      Med7: a transferable clinical natural language processing model for electronic health records
http://arxiv.org/abs/2003.01271
AUTHORS:        Andrey Kormilitzin ; Nemanja Vaci ; Qiang Liu ; Alejo Nevado-Holgado
COMMENTS:       16 pages, 1 figure, 15 tables
HIGHLIGHT:      In this work we introduced a named-entity recognition model for clinical natural language processing.

26, TITLE:      Model-based actor-critic: GAN + DRL (actor-critic) => AGI
http://arxiv.org/abs/2004.04574
AUTHORS:        Aras Dargazany
COMMENTS:       arXiv admin note: text overlap with arXiv:1610.01945, arXiv:1903.04411, arXiv:1910.01007 by other authors
HIGHLIGHT:      To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents.

27, TITLE:      Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect
http://arxiv.org/abs/1901.07858
AUTHORS:        Bo Liu ; Wenhao Chi ; Xinran Li ; Peng Li ; Wenhua Liang ; Haiping Liu ; Wei Wang ; Jianxing He
COMMENTS:       We have substantially revised the article. The previous version had 74 pages and 2 figures, and the lateset version had 66 pages and 6 figures
HIGHLIGHT:      The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over 3 decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques.

28, TITLE:      Inception-inspired LSTM for Next-frame Video Prediction
http://arxiv.org/abs/1909.05622
AUTHORS:        Matin Hosseini ; Anthony S. Maida ; Majid Hosseini ; Gottumukkala Raju
HIGHLIGHT:      In this paper, we provide a novel unsupervised deep-learning method called Inception-based LSTM for video frame prediction.

29, TITLE:      Entropy of tropical holonomic sequences
http://arxiv.org/abs/2003.05466
AUTHORS:        Dima Grigoriev
HIGHLIGHT:      We introduce tropical holonomic sequences of a given order and calculate their entropy in case of the second order.

30, TITLE:      From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds
http://arxiv.org/abs/2001.07360
AUTHORS:        Christiane Sommer ; Yumin Sun ; Leonidas Guibas ; Daniel Cremers ; Tolga Birdal
COMMENTS:       Accepted to IEEE Robotics and Automation Letters 2020 | Video: https://youtu.be/nHWJrA6RcB0 | Code: https://github.com/c-sommer/orthogonal-planes
HIGHLIGHT:      We propose a new method for segmentation-free joint estimation of orthogonal planes, their intersection lines, relationship graph and corners lying at the intersection of three orthogonal planes.

31, TITLE:      Structured Landmark Detection via Topology-Adapting Deep Graph Learning
http://arxiv.org/abs/2004.08190
AUTHORS:        Weijian Li ; Yuhang Lu ; Kang Zheng ; Haofu Liao ; Chihung Lin ; Jiebo Luo ; Chi-Tung Cheng ; Jing Xiao ; Le Lu ; Chang-Fu Kuo ; Shun Miao
HIGHLIGHT:      In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.

32, TITLE:      Finding Black Cat in a Coal Cellar -- Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments
http://arxiv.org/abs/2004.01549
AUTHORS:        Manikandan Ravikiran
COMMENTS:       v1 preprint. Working paper. More results to be added. Text overlap with arXiv:2003.07019
HIGHLIGHT:      As such in this paper we aim to quantify the effectiveness of supervised and unsupervised approaches for the task for keyphrase extraction and generic/specific keyphrase-rubric relationship extraction.

33, TITLE:      Learning Visual Features Under Motion Invariance
http://arxiv.org/abs/1909.00350
AUTHORS:        Alessandro Betti ; Marco Gori ; Stefano Melacci
COMMENTS:       73 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:1801.07110
HIGHLIGHT:      In this paper, we claim that processing visual streams naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of learning that originates from variational principles, just like in physics.

34, TITLE:      A Revised Generative Evaluation of Visual Dialogue
http://arxiv.org/abs/2004.09272
AUTHORS:        Daniela Massiceti ; Viveka Kulharia ; Puneet K. Dokania ; N. Siddharth ; Philip H. S. Torr
COMMENTS:       16 pages, 5 figures
HIGHLIGHT:      We propose a revised evaluation scheme for the VisDial dataset leveraging metrics from the NLP literature to measure consensus between answers generated by the model and a set of relevant answers. We construct these relevant answer sets using a simple and effective semi-supervised method based on correlation, which allows us to automatically extend and scale sparse relevance annotations from humans to the entire dataset. We release these sets and code for the revised evaluation scheme as DenseVisDial, and intend them to be an improvement to the dataset in the face of its existing constraints and design choices.

35, TITLE:      PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
http://arxiv.org/abs/1911.10949
AUTHORS:        Rundi Wu ; Yixin Zhuang ; Kai Xu ; Hao Zhang ; Baoquan Chen
COMMENTS:       Accepted to CVPR 2020. Code available at https://github.com/ChrisWu1997/PQ-NET
HIGHLIGHT:      We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.

36, TITLE:      Overcoming Small Minirhizotron Datasets Using Transfer Learning
http://arxiv.org/abs/1903.09344
AUTHORS:        Weihuang Xu ; Guohao Yu ; Alina Zare ; Brendan Zurweller ; Diane Rowland ; Joel Reyes-Cabrera ; Felix B Fritschi ; Roser Matamala ; Thomas E. Juenger
HIGHLIGHT:      In this paper, we investigate the use of deep neural networks based on the U-net architecture for automated, precise pixel-wise root segmentation in minirhizotron imagery.

37, TITLE:      Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic
http://arxiv.org/abs/1910.11797
AUTHORS:        Thibault Gauthier
HIGHLIGHT:      The paper describes a deep reinforcement learning framework based on self-supervised learning within the proof assistant HOL4.

38, TITLE:      Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
http://arxiv.org/abs/1910.02624
AUTHORS:        Weifeng Ge ; Sheng Guo ; Weilin Huang ; Matthew R. Scott
COMMENTS:       Rectifiy some typos in Arxiv title
HIGHLIGHT:      Unlike previous methods which are composed of multiple offline stages, we propose Sequential Label Propagation and Enhancement Networks (referred as Label-PEnet) that progressively transform image-level labels to pixel-wise labels in a coarse-to-fine manner.

39, TITLE:      TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
http://arxiv.org/abs/2004.07493
AUTHORS:        Bill Yuchen Lin ; Dong-Ho Lee ; Ming Shen ; Ryan Moreno ; Xiao Huang ; Prashant Shiralkar ; Xiang Ren
COMMENTS:       Accepted to the ACL 2020. Camera-ready version. The first two authors contributed equally. Code and data: https://github.com/INK-USC/TriggerNER
HIGHLIGHT:      In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.

40, TITLE:      Unsupervised Domain Adaptation with Progressive Domain Augmentation
http://arxiv.org/abs/2004.01735
AUTHORS:        Kevin Hua ; Yuhong Guo
HIGHLIGHT:      In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation.

41, TITLE:      Image Retrieval using Multi-scale CNN Features Pooling
http://arxiv.org/abs/2004.09695
AUTHORS:        Federico Vaccaro ; Marco Bertini ; Tiberio Uricchio ; Alberto Del Bimbo
COMMENTS:       Accepted at ICMR 2020
HIGHLIGHT:      In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network.

42, TITLE:      General Value Function Networks
http://arxiv.org/abs/1807.06763
AUTHORS:        Matthew Schlegel ; Andrew Jacobsen ; Muhammad Zaheer ; Andrew Patterson ; Adam White ; Martha White
HIGHLIGHT:      In this work, we explore how to use multi-step predictions, as a simple and general approach to inject prior knowledge, while retaining much of the generality and learning power behind RNNs.

43, TITLE:      Self-explaining AI as an alternative to interpretable AI
http://arxiv.org/abs/2002.05149
AUTHORS:        Daniel C. Elton
COMMENTS:       12pgs. 10pg version to appear in Proceedings of the 13th Annual Conference on Artificial General Intelligence (AGI-2020)
HIGHLIGHT:      To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI.

44, TITLE:      TransSent: Towards Generation of Structured Sentences with Discourse Marker
http://arxiv.org/abs/1909.05364
AUTHORS:        Xing Wu ; Dongjun Wei ; Liangjun Zang ; Jizhong Han ; Songlin Hu
COMMENTS:       5 figures
HIGHLIGHT:      Therefore, we propose a task that mimics this process, called discourse transfer.

45, TITLE:      Porous Lattice-based Transformer Encoder for Chinese NER
http://arxiv.org/abs/1911.02733
AUTHORS:        Xue Mengge ; Yu Bowen ; Liu Tingwen ; Wang Bin ; Meng Erli ; Li Quangang
COMMENTS:       9 pages, 4 figures
HIGHLIGHT:      In this paper, we propose a porous lattice-based transformer encoder for Chinese named entity recognition, which is capable to better exploit the GPU parallelism and batch the computation owing to the mask mechanism in transformer.

46, TITLE:      Social Bias Frames: Reasoning about Social and Power Implications of Language
http://arxiv.org/abs/1911.03891
AUTHORS:        Maarten Sap ; Saadia Gabriel ; Lianhui Qin ; Dan Jurafsky ; Noah A. Smith ; Yejin Choi
COMMENTS:       ACL 2020 Camera Ready; Data available at http://tinyurl.com/social-bias-frames
HIGHLIGHT:      We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups.

47, TITLE:      Few-Shot Class-Incremental Learning
http://arxiv.org/abs/2004.10956
AUTHORS:        Xiaoyu Tao ; Xiaopeng Hong ; Xinyuan Chang ; Songlin Dong ; Xing Wei ; Yihong Gong
COMMENTS:       Accepted by CVPR 2020 (oral)
HIGHLIGHT:      On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework.

48, TITLE:      Deep Learning for Screening COVID-19 using Chest X-Ray Images
http://arxiv.org/abs/2004.10507
AUTHORS:        Sanhita Basu ; Sushmita Mitra ; Nilanjan Saha
HIGHLIGHT:      Therefore, we propose a new concept called domain extension transfer learning (DETL).

49, TITLE:      Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network
http://arxiv.org/abs/1904.06823
AUTHORS:        Feng Xiao ; Dapeng Zhang ; Gang Kou ; Lu Li
HIGHLIGHT:      In this paper, we propose a novel deep learning framework called LC-ST-FCN (locally connected spatiotemporal fully-convolutional neural network) to address the unique challenges of the region-level demand forecasting problem within one end-to-end architecture (E2E).

50, TITLE:      The Multivariate Schwartz-Zippel Lemma
http://arxiv.org/abs/1910.01095
AUTHORS:        M. Levent Doğan ; Alperen A. Ergür ; Jake D. Mundo ; Elias Tsigaridas
COMMENTS:       Revised the paper to improve presentation, fixed some typos and a minor error
HIGHLIGHT:      Motivated by applications in combinatorial geometry, we consider the following question: Let $\lambda=(\lambda_1,\lambda_2,\ldots,\lambda_m)$ be an $m$-partition of a positive integer $n$, $S_i \subseteq \mathbb{C}^{\lambda_i}$ be finite sets, and let $S:=S_1 \times S_2 \times \ldots \times S_m \subset \mathbb{C}^n$ be the multi-grid defined by $S_i$.

51, TITLE:      ERASER: A Benchmark to Evaluate Rationalized NLP Models
http://arxiv.org/abs/1911.03429
AUTHORS:        Jay DeYoung ; Sarthak Jain ; Nazneen Fatema Rajani ; Eric Lehman ; Caiming Xiong ; Richard Socher ; Byron C. Wallace
COMMENTS:       Accepted as a long paper to ACL2020 Website and leaderboard available at http://www.eraserbenchmark.com/ Code available at https://github.com/jayded/eraserbenchmark
HIGHLIGHT:      We propose the Evaluating Rationales And Simple English Reasoning (ERASER) benchmark to advance research on interpretable models in NLP.