2020.05.13.txt

==========New Papers==========
1, TITLE:       Learning and Evaluating Emotion Lexicons for 91 Languages
http://arxiv.org/abs/2005.05672
AUTHORS:        Sven Buechel ; Susanna Rücker ; Udo Hahn
COMMENTS:       ACL 2020 Camera-Ready
HIGHLIGHT:      In order to break this bottleneck, we here introduce a methodology for creating almost arbitrarily large emotion lexicons for any target language.

2, TITLE:       Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
http://arxiv.org/abs/2005.05431
AUTHORS:        Neil Getty ; Thomas Brettin ; Dong Jin ; Rick Stevens ; Fangfang Xia
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data.

3, TITLE:       Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search
http://arxiv.org/abs/2005.05432
AUTHORS:        Prashant Pandey ; Prathosh AP ; Vinay Kyatham ; Deepak Mishra ; Tathagato Rai Dastidar
HIGHLIGHT:      In this paper, we propose a method for UDA that is devoid of the need for target data.

4, TITLE:       Delay-Aware Model-Based Reinforcement Learning for Continuous Control
http://arxiv.org/abs/2005.05440
AUTHORS:        Baiming Chen ; Mengdi Xu ; Liang Li ; Ding Zhao
HIGHLIGHT:      This paper proposes a formal definition of delay-aware Markov Decision Process and proves it can be transformed into standard MDP with augmented states using the Markov reward process.

5, TITLE:       On the Robustness of Language Encoders against Grammatical Errors
http://arxiv.org/abs/2005.05683
AUTHORS:        Fan Yin ; Quanyu Long ; Tao Meng ; Kai-Wei Chang
COMMENTS:       ACL 2020
HIGHLIGHT:      We use this approach to facilitate debugging models on downstream applications.

6, TITLE:       On the Generation of Medical Dialogues for COVID-19
http://arxiv.org/abs/2005.05442
AUTHORS:        Wenmian Yang ; Guangtao Zeng ; Bowen Tan ; Zeqian Ju ; Subrato Chakravorty ; Xuehai He ; Shu Chen ; Xingyi Yang ; Qingyang Wu ; Zhou Yu ; Eric Xing ; Pengtao Xie
HIGHLIGHT:      To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets -CovidDialog- (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19.

7, TITLE:       Improved Flight Time Predictions for Fuel Loading Decisions of Scheduled Flights with a Deep Learning Approach
http://arxiv.org/abs/2005.05684
AUTHORS:        Xinting Zhu ; Lishuai Li
HIGHLIGHT:      In this paper, we develop a novel spatial weighted recurrent neural network model to provide better flight time predictions by capturing air traffic information at a national scale based on multiple data sources, including Automatic Dependent Surveillance - Broadcast, Meteorological Airdrome Reports, and airline records.

8, TITLE:       Detecting Multiword Expression Type Helps Lexical Complexity Assessment
http://arxiv.org/abs/2005.05692
AUTHORS:        Ekaterina Kochmar ; Sian Gooding ; Matthew Shardlow
COMMENTS:       Accepted for publication at LREC 2020
HIGHLIGHT:      In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs. We release the MWE-annotated dataset with this paper, and we believe this dataset represents a valuable resource for the text simplification community.

9, TITLE:       Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation
http://arxiv.org/abs/2005.05451
AUTHORS:        Arjun Gupta ; Luca Carlone
COMMENTS:       Accepted to ITSC 2020
HIGHLIGHT:      Our first contribution is to present and evaluate model-based and learning-based monitors for a human-pose-and-shape reconstruction network, and assess their ability to predict the output loss for a given test input.

10, TITLE:      Luganda Text-to-Speech Machine
http://arxiv.org/abs/2005.05447
AUTHORS:        Irene Nandutu ; Ernest Mwebaze
HIGHLIGHT:      This study will enhance previous solutions to NLP gaps in Uganda, as well as provide raw data such that other research in this area can take place.

11, TITLE:      VIDIT: Virtual Image Dataset for Illumination Transfer
http://arxiv.org/abs/2005.05460
AUTHORS:        Majed El Helou ; Ruofan Zhou ; Johan Barthas ; Sabine Süsstrunk
COMMENTS:       Further information: https://github.com/majedelhelou/VIDIT
HIGHLIGHT:      We present a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods.

12, TITLE:      Neural Polysynthetic Language Modelling
http://arxiv.org/abs/2005.05477
AUTHORS:        Lane Schwartz ; Francis Tyers ; Lori Levin ; Christo Kirov ; Patrick Littell ; Chi-kiu Lo ; Emily Prud'hommeaux ;  Hyunji ;  Park ; Kenneth Steimel ; Rebecca Knowles ; Jeffrey Micher ; Lonny Strunk ; Han Liu ; Coleman Haley ; Katherine J. Zhang ; Robbie Jimmerson ; Vasilisa Andriyanets ; Aldrian Obaja Muis ; Naoki Otani ; Jong Hyuk Park ; Zhisong Zhang
HIGHLIGHT:      When we consider polysynthetic languages (those at the extreme of morphological complexity), approaches like stemming, lemmatization, or subword modelling may not suffice.

13, TITLE:      Schema-Guided Natural Language Generation
http://arxiv.org/abs/2005.05480
AUTHORS:        Yuheng Du ; Shereen Oraby ; Vittorio Perera ; Minmin Shen ; Anjali Narayan-Chen ; Tagyoung Chung ; Anu Venkatesh ; Dilek Hakkani-Tur
HIGHLIGHT:      In this paper, we present the novel task of Schema-Guided Natural Language Generation, in which we repurpose an existing dataset for another task: dialog state tracking.

14, TITLE:      Combining Deep Learning with Geometric Features for Image based Localization in the Gastrointestinal Tract
http://arxiv.org/abs/2005.05481
AUTHORS:        Jingwei Song ; Mitesh Patel ; Andreas Girgensohn ; Chelhwon Kim
HIGHLIGHT:      Considering these, we propose a novel approach to combine DL method with traditional feature based approach to achieve better localization with small training data.

15, TITLE:      Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)
http://arxiv.org/abs/2005.05487
AUTHORS:        Takashi Morita ; Hiroki Koda
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the ZeroSpeech Challenge 2020, in which participants proposed an end-to-end, unsupervised system that learned speech recognition and TTS together.

16, TITLE:      Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
http://arxiv.org/abs/2005.05490
AUTHORS:        David Suter ; Ruwan Tennakoon ; Erchuan Zhang ; Tat-Jun Chin ; Alireza Bab-Hadiashar
COMMENTS:       Parts under conference review, work in progress. Keywords: Monotone Boolean Functions, Consensus Maximisation, LP-Type Problem, Computer Vision, Robust Fitting, Matroid, Simplicial Complex, Independence Systems
HIGHLIGHT:      Indeed, this is our main motivation but we believe the results of the study of these connections are more widely applicable to LP-type problems (at least 'thresholded versions', as we describe), and perhaps even more widely.

17, TITLE:      Train and Deploy an Image Classifier for Disaster Response
http://arxiv.org/abs/2005.05495
AUTHORS:        Jianyu Mao ; Kiana Harris ; Nae-Rong Chang ; Caleb Pennell ; Yiming Ren
COMMENTS:       5 pages, 6 figures
HIGHLIGHT:      Our models and tutorials for working with the data set have created a foundation for others to classify other types of disasters contained in the images.

18, TITLE:      Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
http://arxiv.org/abs/2005.05496
AUTHORS:        Saeid Asgari Taghanaki ; Mohammad Havaei ; Alex Lamb ; Aditya Sanghi ; Ara Danielyan ; Tonya Custis
HIGHLIGHT:      To address this, we propose a regularization scheme for VAEs, which we show substantially addresses the feature imbalance problem.

19, TITLE:      Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
http://arxiv.org/abs/2005.05806
AUTHORS:        Bo Zheng ; Haoyang Wen ; Yaobo Liang ; Nan Duan ; Wanxiang Che ; Daxin Jiang ; Ming Zhou ; Ting Liu
COMMENTS:       ACL2020
HIGHLIGHT:      To address this issue, we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature, which are different levels of granularity: documents, paragraphs, sentences, and tokens.

20, TITLE:      A Report on the 2020 Sarcasm Detection Shared Task
http://arxiv.org/abs/2005.05814
AUTHORS:        Debanjan Ghosh ; Avijit Vajpayee ; Smaranda Muresan
COMMENTS:       2nd Workshop on Figurative Language Processing (FigLang2020) at ACL 2020
HIGHLIGHT:      In this paper we report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang2020) at ACL 2020.

21, TITLE:      One-Shot Recognition of Manufacturing Defects in Steel Surfaces
http://arxiv.org/abs/2005.05815
AUTHORS:        Aditya M. Deshpande ; Ali A. Minai ; Manish Kumar
COMMENTS:       Accepted for publication in NAMRC 48
HIGHLIGHT:      In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task.

22, TITLE:      A Novel Distributed Approximate Nearest Neighbor Method for Real-time Face Recognition
http://arxiv.org/abs/2005.05824
AUTHORS:        Aysan Aghazadeh ; Maryam Amirmazlaghani
HIGHLIGHT:      In this paper, we propose a novel distributed approximate nearest neighbor (ANN) method for real-time face recognition with a big data-set that involves a lot of classes.

23, TITLE:      A Survey of Behavior Trees in Robotics and AI
http://arxiv.org/abs/2005.05842
AUTHORS:        Matteo Iovino ; Edvards Scukins ; Jonathan Styrud ; Petter Ögren ; Christian Smith
HIGHLIGHT:      In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications.

24, TITLE:      Bayesian Fusion for Infrared and Visible Images
http://arxiv.org/abs/2005.05839
AUTHORS:        Zixiang Zhao ; Shuang Xu ; Chunxia Zhang ; Junmin Liu ; Jiangshe Zhang
HIGHLIGHT:      In this paper, a novel Bayesian fusion model is established for infrared and visible images.

25, TITLE:      Argument Schemes for Explainable Planning
http://arxiv.org/abs/2005.05849
AUTHORS:        Quratul-ain Mahesar ; Simon Parsons
HIGHLIGHT:      In this paper, we use argumentation to provide explanations in the domain of AI planning.

26, TITLE:      Neighborhood Matching Network for Entity Alignment
http://arxiv.org/abs/2005.05607
AUTHORS:        Yuting Wu ; Xiao Liu ; Yansong Feng ; Zheng Wang ; Dongyan Zhao
COMMENTS:       11 pages, accepted by ACL 2020
HIGHLIGHT:      This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.

27, TITLE:      Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
http://arxiv.org/abs/2005.05864
AUTHORS:        Wenyu Du ; Zhouhan Lin ; Yikang Shen ; Timothy J. O'Donnell ; Yoshua Bengio ; Yue Zhang
COMMENTS:       ACL20
HIGHLIGHT:      In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called "syntactic distances", where information between these two separate objectives shares the same intermediate representation.

28, TITLE:      Prta: A System to Support the Analysis of Propaganda Techniques in the News
http://arxiv.org/abs/2005.05854
AUTHORS:        Giovanni Da San Martino ; Shaden Shaar ; Yifan Zhang ; Seunghak Yu ; Alberto Barrón-Cedeño ; Preslav Nakov
COMMENTS:       propaganda, disinformation, fake news, media bias, COVID-19
HIGHLIGHT:      Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques.

29, TITLE:      Unified Framework for the Adaptive Operator Selection of Discrete Parameters
http://arxiv.org/abs/2005.05613
AUTHORS:        Mudita Sharma ; Manuel Lopez-Ibanez ; Dimitar Kazakov
HIGHLIGHT:      We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We make three sets of comparisons.

30, TITLE:      Probabilistic Semantic Segmentation Refinement by Monte Carlo Region Growing
http://arxiv.org/abs/2005.05856
AUTHORS:        Philipe A. Dias ; Henry Medeiros
COMMENTS:       Submitted to IEEE Transactions on Image Processing (April 2020)
HIGHLIGHT:      We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels into regions of low-confidence classification.

31, TITLE:      Neural Architecture Transfer
http://arxiv.org/abs/2005.05859
AUTHORS:        Zhichao Lu ; Gautam Sreekumar ; Erik Goodman ; Wolfgang Banzhaf ; Kalyanmoy Deb ; Vishnu Naresh Boddeti
COMMENTS:       17 pages
HIGHLIGHT:      In this paper, we propose Neural Architecture Transfer (NAT) to overcome this limitation.

32, TITLE:      Detecting CNN-Generated Facial Images in Real-World Scenarios
http://arxiv.org/abs/2005.05632
AUTHORS:        Nils Hulzebosch ; Sarah Ibrahimi ; Marcel Worring
COMMENTS:       Accepted to the workshop on Media Forensics at CVPR 2020
HIGHLIGHT:      In this work, we present a framework for evaluating detection methods under real-world conditions, consisting of cross-model, cross-data, and post-processing evaluation, and we evaluate state-of-the-art detection methods using the proposed framework.

33, TITLE:      Unsupervised Multi-label Dataset Generation from Web Data
http://arxiv.org/abs/2005.05623
AUTHORS:        Carlos Roig ; David Varas ; Issey Masuda ; Juan Carlos Riveiro ; Elisenda Bou-Balust
COMMENTS:       The 3rd Workshop on Visual Understanding by Learning from Web Data 2019
HIGHLIGHT:      This paper presents a system towards the generation of multi-label datasets from web data in an unsupervised manner.

34, TITLE:      Recurrent and Spiking Modeling of Sparse Surgical Kinematics
http://arxiv.org/abs/2005.05868
AUTHORS:        Neil Getty ; Zixuan Zhou ; Stephan Gruessner ; Liaohai Chen ; Fangfang Xia
COMMENTS:       5 pages, 8 figures
HIGHLIGHT:      In this study, we explore the possibility of using only kinematic data to predict surgeons of similar skill levels.

35, TITLE:      AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
http://arxiv.org/abs/2005.05642
AUTHORS:        Zewang Zhang ; Qiao Tian ; Heng Lu ; Ling-Hui Chen ; Shan Liu
COMMENTS:       Submitted to InterSpeech 2020
HIGHLIGHT:      To cope with this issue, we introduce AdaDurIAN by training an improved DurIAN-based average model and leverage it to few-shot learning with the shared speaker-independent content encoder across different speakers.

36, TITLE:      MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
http://arxiv.org/abs/2005.05402
AUTHORS:        Jie Lei ; Liwei Wang ; Yelong Shen ; Dong Yu ; Tamara L. Berg ; Mohit Bansal
COMMENTS:       ACL 2020 (12 pages)
HIGHLIGHT:      Towards this goal, we propose a new approach called Memory-Augmented Recurrent Transformer (MART), which uses a memory module to augment the transformer architecture.

37, TITLE:      Counting Query Answers over a DL-Lite Knowledge Base (extended version)
http://arxiv.org/abs/2005.05886
AUTHORS:        Diego Calvanese ; Julien Corman ; Davide Lanti ; Simon Razniewski
COMMENTS:       Extended version of an article to appear in the proceedings of IJCAI 2020
HIGHLIGHT:      In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration.

38, TITLE:      SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
http://arxiv.org/abs/2005.05635
AUTHORS:        Hao Tian ; Can Gao ; Xinyan Xiao ; Hao Liu ; Bolei He ; Hua Wu ; Haifeng Wang ; Feng Wu
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks.

39, TITLE:      Latent Fingerprint Registration via Matching Densely Sampled Points
http://arxiv.org/abs/2005.05878
AUTHORS:        Shan Gu ; Jianjiang Feng ; Jiwen Lu ; Jie Zhou
HIGHLIGHT:      In this paper, we propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints through a dense fingerprint patch alignment and matching procedure.

40, TITLE:      A Frobenius Algebraic Analysis for Parasitic Gaps
http://arxiv.org/abs/2005.05639
AUTHORS:        Michael Moortgat ; Mehrnoosh Sadrzadeh ; Gijs Wijnholds
COMMENTS:       SemSpace 2019, submitted to J, of Applied Logics
HIGHLIGHT:      We identify two types of parasitic gapping where the duplication of semantic content can be confined to the lexicon.

41, TITLE:      Invertible Image Rescaling
http://arxiv.org/abs/2005.05650
AUTHORS:        Mingqing Xiao ; Shuxin Zheng ; Chang Liu ; Yaolong Wang ; Di He ; Guolin Ke ; Jiang Bian ; Zhouchen Lin ; Tie-Yan Liu
HIGHLIGHT:      In this work, we propose to solve this problem by modeling the downscaling and upscaling processes from a new perspective, i.e. an invertible bijective transformation, which can largely mitigate the ill-posed nature of image upscaling.

42, TITLE:      Identifying Mechanical Models through Differentiable Simulations
http://arxiv.org/abs/2005.05410
AUTHORS:        Changkyu Song ; Abdeslam Boularias
COMMENTS:       to be published in Learning for DynamIcs & Control (L4DC), June 10-11th, 2020
HIGHLIGHT:      This paper proposes a new method for manipulating unknown objects through a sequence of non-prehensile actions that displace an object from its initial configuration to a given goal configuration on a flat surface.

43, TITLE:      Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks
http://arxiv.org/abs/2005.05652
AUTHORS:        Thomas Tilak ; Arnaud Braun ; David Chandler ; Nicolas David ; Sylvain Galopin ; Amélie Lombard ; Michaël Michaud ; Camille Parisel ; Matthieu Porte ; Marjorie Robert
COMMENTS:       8 pages, 14 figures, ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
HIGHLIGHT:      This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class.

44, TITLE:      Efficient and Interpretable Infrared and Visible Image Fusion Via Algorithm Unrolling
http://arxiv.org/abs/2005.05896
AUTHORS:        Zixiang Zhao ; Shuang Xu ; Chunxia Zhang ; Junmin Liu ; Jiangshe Zhang
HIGHLIGHT:      In this paper, an interpretable deep network fusion model is proposed.

45, TITLE:      Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning
http://arxiv.org/abs/2005.05420
AUTHORS:        Binyu Wang ; Zhe Liu ; Qingbiao Li ; Amanda Prorok
HIGHLIGHT:      To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information.

46, TITLE:      Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
http://arxiv.org/abs/2005.05414
AUTHORS:        Soumya Banerjee ; Debarshi Kumar Sanyal ; Samiran Chattopadhyay ; Plaban Kumar Bhowmick ; Parthapratim Das
COMMENTS:       to appear in the proceedings of JCDL'2020
HIGHLIGHT:      In this paper, we address this problem using transfer learning.

47, TITLE:      Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics
http://arxiv.org/abs/2005.05659
AUTHORS:        Max Schwarz ; Sven Behnke
COMMENTS:       Accepted for ICRA 2020
HIGHLIGHT:      We describe a synthesis pipeline capable of producing training data for cluttered scene perception tasks such as semantic segmentation, object detection, and correspondence or pose estimation.

48, TITLE:      Optimizing Vessel Trajectory Compression
http://arxiv.org/abs/2005.05418
AUTHORS:        Giannis Fikioris ; Kostas Patroumpas ; Alexander Artikis
HIGHLIGHT:      In this paper, our goal is to fine-tune the selection of these parameter values.

49, TITLE:      High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
http://arxiv.org/abs/2005.05550
AUTHORS:        Seyed Amir Hossein Hosseini ; Burhaneddin Yaman ; Steen Moeller ; Mehmet Akçakaya
HIGHLIGHT:      In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach.

50, TITLE:      Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
http://arxiv.org/abs/2005.05552
AUTHORS:        Chengcheng Ma ; Baoyuan Wu ; Shibiao Xu ; Yanbo Fan ; Yong Zhang ; Xiaopeng Zhang ; Zhifeng Li
HIGHLIGHT:      In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i.e., shape factor, mean, and variance).

51, TITLE:      List homomorphism problems for signed graphs
http://arxiv.org/abs/2005.05547
AUTHORS:        Jan Bok ; Richard Brewster ; Tomás Feder ; Pavol Hell ; Nikola Jedličková
HIGHLIGHT:      We consider homomorphisms of signed graphs from a computational perspective.

52, TITLE:      Simultaneous paraphrasing and translation by fine-tuning Transformer models
http://arxiv.org/abs/2005.05570
AUTHORS:        Rakesh Chada
COMMENTS:       Accepted to ACL 2020 4th workshop on Neural Generation and Translation
HIGHLIGHT:      This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020.

53, TITLE:      Spike-Triggered Descent
http://arxiv.org/abs/2005.05572
AUTHORS:        Michael Kummer ; Arunava Banerjee
HIGHLIGHT:      We introduce a technique, called spike-triggered descent (STD), which can be used alone or in conjunction with STA to increase precision and yield success in scenarios where STA fails.

54, TITLE:      Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
http://arxiv.org/abs/2005.05576
AUTHORS:        Sampa Misra ; Seungwan Jeon ; Seiyon Lee ; Ravi Managuli ; Chulhong Kim
COMMENTS:       7 pages, 3 figures, 1 Table
HIGHLIGHT:      To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray.

55, TITLE:      Enabling Language Models to Fill in the Blanks
http://arxiv.org/abs/2005.05339
AUTHORS:        Chris Donahue ; Mina Lee ; Percy Liang
COMMENTS:       To appear in the proceedings of ACL 2020
HIGHLIGHT:      We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document.

56, TITLE:      Data-driven Algorithm for Scheduling with Total Tardiness
http://arxiv.org/abs/2005.05579
AUTHORS:        Michal Bouška ; Antonín Novák ; Přemysl Šůcha ; István Módos ; Zdeněk Hanzálek
HIGHLIGHT:      In this paper, we investigate the use of deep learning for solving a classical NP-Hard single machine scheduling problem where the criterion is to minimize the total tardiness.

57, TITLE:      Discriminative Multi-modality Speech Recognition
http://arxiv.org/abs/2005.05592
AUTHORS:        Bo Xu ; Cheng Lu ; Yandong Guo ; Jacob Wang
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we propose a two-stage speech recognition model.

58, TITLE:      Understanding and Correcting Low-quality Retinal Fundus Images for Clinical Analysis
http://arxiv.org/abs/2005.05594
AUTHORS:        Ziyi Shen ; Huazhu Fu ; Jianbing Shen ; Ling Shao
HIGHLIGHT:      Due to the special optical beam of fundus imaging and retinal structure, the natural image enhancement methods cannot be utilized directly.

59, TITLE:      Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
http://arxiv.org/abs/2005.05906
AUTHORS:        Sandra Wachter ; Brent Mittelstadt ; Chris Russell
HIGHLIGHT:      We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement that aligns with the European Court of Justice's "gold standard."

60, TITLE:      Constraint Synchronization with Two or Three State Partial Constraint Automata
http://arxiv.org/abs/2005.05907
AUTHORS:        Stefan Hoffmann
COMMENTS:       12 pages paper, including 6 tables and figures + 26 pages appendix
HIGHLIGHT:      To derive our results, we generalize the known polynomial time algorithm from the unconstrained setting to broaden the range of constraint problems that could be solved in PTIME.

61, TITLE:      Localized convolutional neural networks for geospatial wind forecasting
http://arxiv.org/abs/2005.05930
AUTHORS:        Arnas Uselis ; Mantas Lukoševičius ; Lukas Stasytis
HIGHLIGHT:      In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets.

62, TITLE:      Intersectional Bias in Hate Speech and Abusive Language Datasets
http://arxiv.org/abs/2005.05921
AUTHORS:        Jae Yeon Kim ; Carlos Ortiz ; Sarah Nam ; Sarah Santiago ; Vivek Datta
HIGHLIGHT:      Algorithms are widely applied to detect hate speech and abusive language in social media.

63, TITLE:      Semantic Scaffolds for Pseudocode-to-Code Generation
http://arxiv.org/abs/2005.05927
AUTHORS:        Ruiqi Zhong ; Mitchell Stern ; Dan Klein
HIGHLIGHT:      We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program.

64, TITLE:      Training spiking neural networks using reinforcement learning
http://arxiv.org/abs/2005.05941
AUTHORS:        Sneha Aenugu
HIGHLIGHT:      In this project, we propose biologically-plausible alternatives to backpropagation to facilitate the training of spiking neural networks.

65, TITLE:      MOReL : Model-Based Offline Reinforcement Learning
http://arxiv.org/abs/2005.05951
AUTHORS:        Rahul Kidambi ; Aravind Rajeswaran ; Praneeth Netrapalli ; Thorsten Joachims
COMMENTS:       First two authors contributed equally. 18 pages of main text. 2 sections of appendix
HIGHLIGHT:      In this work, we present MOReL, an algorithmic framework for model-based RL in the offline setting.

66, TITLE:      RetinotopicNet: An Iterative Attention Mechanism Using Local Descriptors with Global Context
http://arxiv.org/abs/2005.05701
AUTHORS:        Thomas Kurbiel ; Shahrzad Khaleghian
COMMENTS:       7 pages, 23 figures
HIGHLIGHT:      In this paper we develop an efficient solution by reproducing how nature has solved the problem in the human brain.

67, TITLE:      CapablePtrs: Securely Compiling Partial Programs using the Pointers-as-Capabilities Principle
http://arxiv.org/abs/2005.05944
AUTHORS:        Akram El-Korashy ; Stelios Tsampas ; Marco Patrignani ; Dominique Devriese ; Deepak Garg ; Frank Piessens
HIGHLIGHT:      We prove for a model of such a compiler that it is fully abstract. We provide performance benchmarks that show how performance overhead is proportional to the number of cross-compilation-unit function calls.

68, TITLE:      Fostering Event Compression using Gated Surprise
http://arxiv.org/abs/2005.05704
AUTHORS:        Dania Humaidan ; Sebastian Otte ; Martin V. Butz
COMMENTS:       submitted to ICANN 2020
HIGHLIGHT:      Here, we introduce a hierarchical, surprise-gated recurrent neural network architecture, which models this process and develops compact compressions of distinct event-like contexts.

69, TITLE:      Automatic clustering of Celtic coins based on 3D point cloud pattern analysis
http://arxiv.org/abs/2005.05705
AUTHORS:        Sofiane Horache ; Jean-Emmanuel Deschaud ; François Goulette ; Katherine Gruel ; Thierry Lejars
HIGHLIGHT:      In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins.

70, TITLE:      IterDet: Iterative Scheme for ObjectDetection in Crowded Environments
http://arxiv.org/abs/2005.05708
AUTHORS:        Danila Rukhovich ; Konstantin Sofiiuk ; Danil Galeev ; Olga Barinova ; Anton Konushin
HIGHLIGHT:      In this work we develop an alternative iterative scheme, where a new subset of objects is detected at each iteration.

71, TITLE:      Planning to Explore via Self-Supervised World Models
http://arxiv.org/abs/2005.05960
AUTHORS:        Ramanan Sekar ; Oleh Rybkin ; Kostas Daniilidis ; Pieter Abbeel ; Danijar Hafner ; Deepak Pathak
COMMENTS:       Videos and code at https://ramanans1.github.io/plan2explore/
HIGHLIGHT:      We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration.

72, TITLE:      Preference Elicitation in Assumption-Based Argumentation
http://arxiv.org/abs/2005.05721
AUTHORS:        Quratul-ain Mahesar ; Nir Oren ; Wamberto W. Vasconcelos
HIGHLIGHT:      In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences over assumptions could lead to a given set of conclusions being drawn.

73, TITLE:      Goal Recognition over Imperfect Domain Models
http://arxiv.org/abs/2005.05712
AUTHORS:        Ramon Fraga Pereira
COMMENTS:       Ph. D. Thesis defended in February of 2020, PUCRS, Porto Alegre, Brazil
HIGHLIGHT:      In this thesis, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well-defined.

74, TITLE:      COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19
http://arxiv.org/abs/2005.05954
AUTHORS:        Junaed Younus Khan ; Md. Tawkat Islam Khondaker ; Iram Tazim Hoque ; Hamada Al-Absi ; Mohammad Saifur Rahman ; Tanvir Alam ; M. Sohel Rahman
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining.

75, TITLE:      Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
http://arxiv.org/abs/2005.05957
AUTHORS:        Rafael Valle ; Kevin Shih ; Ryan Prenger ; Bryan Catanzaro
COMMENTS:       10 pages, 7 pictures
HIGHLIGHT:      %auto-ignore In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer.

76, TITLE:      Skeleton-Aware Networks for Deep Motion Retargeting
http://arxiv.org/abs/2005.05732
AUTHORS:        Kfir Aberman ; Peizhuo Li ; Dani Lischinski ; Olga Sorkine-Hornung ; Daniel Cohen-Or ; Baoquan Chen
COMMENTS:       SIGGRAPH 2020. Project page: https://deepmotionediting.github.io/retargeting , Video: https://www.youtube.com/watch?v=ym8Tnmiz5N8
HIGHLIGHT:      We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs.

77, TITLE:      Dynamic Memory Induction Networks for Few-Shot Text Classification
http://arxiv.org/abs/2005.05727
AUTHORS:        Ruiying Geng ; Binhua Li ; Yongbin Li ; Jian Sun ; Xiaodan Zhu
COMMENTS:       8 pages, 2 figures
HIGHLIGHT:      This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification.

78, TITLE:      ReadNet:Towards Accurate ReID with Limited and Noisy Samples
http://arxiv.org/abs/2005.05740
AUTHORS:        Yitian Li ; Ruini Xue ; Mengmeng Zhu ; Qing Xu ; Zenglin Xu
HIGHLIGHT:      To address these challenges, this paper proposes ReadNet, an adversarial camera network (ACN) with an angular triplet loss (ATL).

79, TITLE:      3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
http://arxiv.org/abs/2005.05501
AUTHORS:        Yancheng Wang ; Yang Xiao ; Fu Xiong ; Wenxiang Jiang ; Zhiguo Cao ; Joey Tianyi Zhou ; Junsong Yuan
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      3DV: 3D Dynamic Voxel for Action Recognition in Depth Video

80, TITLE:      Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019
http://arxiv.org/abs/2005.05738
AUTHORS:        Antonio Toral
COMMENTS:       Accepted at the 22nd Annual Conference of the European Association for Machine Translation (EAMT 2020)
HIGHLIGHT:      Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019

81, TITLE:      Unpaired Motion Style Transfer from Video to Animation
http://arxiv.org/abs/2005.05751
AUTHORS:        Kfir Aberman ; Yijia Weng ; Dani Lischinski ; Daniel Cohen-Or ; Baoquan Chen
COMMENTS:       SIGGRAPH 2020. Project page: https://deepmotionediting.github.io/style_transfer , Video: https://www.youtube.com/watch?v=m04zuBSdGrc , Code: https://github.com/DeepMotionEditing/deep-motion-editing
HIGHLIGHT:      In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.

82, TITLE:      Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering
http://arxiv.org/abs/2005.05754
AUTHORS:        Angrosh Mandya ; James O'Neill ; Danushka Bollegala ; Frans Coenen
HIGHLIGHT:      In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems.

83, TITLE:      MathZero, The Classification Problem, and Set-Theoretic Type Theory
http://arxiv.org/abs/2005.05512
AUTHORS:        David McAllester
HIGHLIGHT:      We propose the foundation of set-theoretic dependent type theory and an objective defined in terms of the classification problem -- the problem of classifying concept instances up to isomorphism.

84, TITLE:      Deep Learning: Our Miraculous Year 1990-1991
http://arxiv.org/abs/2005.05744
AUTHORS:        Juergen Schmidhuber
COMMENTS:       37 pages, 188 references, based on work of 4 Oct 2019
HIGHLIGHT:      Deep Learning: Our Miraculous Year 1990-1991

85, TITLE:      A Framework for Hierarchical Multilingual Machine Translation
http://arxiv.org/abs/2005.05507
AUTHORS:        Ion Madrazo Azpiazu ; Maria Soledad Pera
HIGHLIGHT:      In this paper, we present a hierarchical framework for building multilingual machine translation strategies that takes advantage of a typological language family tree for enabling transfer among similar languages while avoiding the negative effects that result from incorporating languages that are too different to each other.

86, TITLE:      Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
http://arxiv.org/abs/2005.05509
AUTHORS:        Mohammad Rami Koujan ; Luma Alharbawee ; Giorgos Giannakakis ; Nicolas Pugeault ; Anastasios Roussos
COMMENTS:       to be published in 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
HIGHLIGHT:      This paper proposes a novel method for human emotion recognition from a single RGB image. We construct a large-scale dataset of facial videos (\textbf{FaceVid}), rich in facial dynamics, identities, expressions, appearance and 3D pose variations.

87, TITLE:      WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
http://arxiv.org/abs/2005.05763
AUTHORS:        Hongming Zhang ; Xinran Zhao ; Yangqiu Song
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC).

88, TITLE:      Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic
http://arxiv.org/abs/2005.05513
AUTHORS:        Baani Leen Kaur Jolly ; Palash Aggrawal ; Amogh Gulati ; Amarjit Singh Sethi ; Ponnurangam Kumaraguru ; Tavpritesh Sethi
HIGHLIGHT:      In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India.

89, TITLE:      A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes
http://arxiv.org/abs/2005.05519
AUTHORS:        Kaijie Xu ; Witold Pedrycz ; Zhiwu Li ; Yinghui Quan ; Weike Nie
HIGHLIGHT:      Therefore, we propose a novel bi-clustering method by involving here the theory of Granular Computing.

90, TITLE:      Approximating Boolean Functions with Disjunctive Normal Form
http://arxiv.org/abs/2005.05773
AUTHORS:        Yunhao Yang ; Andrew Tan
HIGHLIGHT:      This paper will demonstrate this theorem in detail by showing how this theorem is generated and proving its correctness.

91, TITLE:      Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
http://arxiv.org/abs/2005.05776
AUTHORS:        Xiyang Liu ; Jie Yang ; Tieqiang Wang ; Wenrui Ding
HIGHLIGHT:      To solve this problem, we introduce a new target, named local counting map (LCM), to obtain more accurate results than density map based approaches.

92, TITLE:      DiscreTalk: Text-to-Speech as a Machine Translation Problem
http://arxiv.org/abs/2005.05525
AUTHORS:        Tomoki Hayashi ; Shinji Watanabe
COMMENTS:       Submitted to INTERSPEECH 2020. The demo is available on https://kan-bayashi.github.io/DiscreTalk/
HIGHLIGHT:      This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT).

93, TITLE:      Making Robots Draw A Vivid Portrait In Two Minutes
http://arxiv.org/abs/2005.05526
AUTHORS:        Fei Gao ; Jingjie Zhu ; Zeyuan Yu ; Peng Li ; Tao Wang
COMMENTS:       7 pages, 7 figures
HIGHLIGHT:      Besides, we propose a componential-sparsity constraint to reduce the number of brush-strokes over insignificant areas.

94, TITLE:      PSDet: Efficient and Universal Parking Slot Detection
http://arxiv.org/abs/2005.05528
AUTHORS:        Zizhang Wu ; Weiwei Sun ; Man Wang ; Xiaoquan Wang ; Lizhu Ding ; Fan Wang
COMMENTS:       Accpeted to IV 2020, i.e., the 31st IEEE Intelligent Vehicles Symposium
HIGHLIGHT:      Driven by the observation of various parking lots in our benchmark, we propose the circular descriptor to regress the coordinates of parking slot vertexes and accordingly localize slots accurately. Thus, we annotate a large-scale benchmark for training the network and release it for the benefit of community.

95, TITLE:      HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning
http://arxiv.org/abs/2005.05777
AUTHORS:        Axel Barroso-Laguna ; Yannick Verdie ; Benjamin Busam ; Krystian Mikolajczyk
HIGHLIGHT:      We propose a dense descriptor that uses a multi-scale approach and a hybrid combination of hand-crafted and learned features to obtain rotation and scale robustness by design.

96, TITLE:      DeepFaceLab: A simple, flexible and extensible face swapping framework
http://arxiv.org/abs/2005.05535
AUTHORS:        Ivan Petrov ; Daiheng Gao ; Nikolay Chervoniy ; Kunlin Liu ; Sugasa Marangonda ; Chris Umé ; Jian Jiang ; Luis RP ; Sheng Zhang ; Pingyu Wu ; Weiming Zhang
HIGHLIGHT:      In this paper, we detail the principles that drive the implementation of DeepFaceLab and introduce the pipeline of it, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose, and it's noteworthy that DeepFaceLab could achieve results with high fidelity and indeed indiscernible by mainstream forgery detection approaches.

97, TITLE:      Dynamic Models Applied to Value Learning in Artificial Intelligence
http://arxiv.org/abs/2005.05538
AUTHORS:        Nicholas Kluge Corrêa ; Nythamar de Oliveira
COMMENTS:       18 pages, no figures, submited to publication, pre-printed version
HIGHLIGHT:      It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis.



==========Updates to Previous Papers==========
1, TITLE:       The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective
http://arxiv.org/abs/1807.09000
AUTHORS:        Robert D. Hawkins ; Hyowon Gweon ; Noah D. Goodman
HIGHLIGHT:      We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking.

2, TITLE:       SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
http://arxiv.org/abs/2005.04114
AUTHORS:        Da Yin ; Tao Meng ; Kai-Wei Chang
COMMENTS:       ACL-2020
HIGHLIGHT:      We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.

3, TITLE:       Duality in Persistent Homology of Images
http://arxiv.org/abs/2005.04597
AUTHORS:        Adélie Garin ; Teresa Heiss ; Kelly Maggs ; Bea Bleile ; Vanessa Robins
COMMENTS:       This is an extended abstract for the SoCG Young Researchers Forum 2020
HIGHLIGHT:      Applied to greyscale digital images, we obtain an algorithm to convert barcodes between the two different (dual) topological models of pixel connectivity.

4, TITLE:       Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
http://arxiv.org/abs/1909.13561
AUTHORS:        Yizhe Wu ; Sudhanshu Kasewa ; Oliver Groth ; Sasha Salter ; Li Sun ; Oiwi Parker Jones ; Ingmar Posner
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      In this paper we explore the richness of information captured by the latent space of a vision-based generative model -- and how to exploit it.

5, TITLE:       TVQA+: Spatio-Temporal Grounding for Video Question Answering
http://arxiv.org/abs/1904.11574
AUTHORS:        Jie Lei ; Licheng Yu ; Tamara L. Berg ; Mohit Bansal
COMMENTS:       ACL 2020 camera-ready (15 pages)
HIGHLIGHT:      We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos.

6, TITLE:       Challenge of Spatial Cognition for Deep Learning
http://arxiv.org/abs/1908.04396
AUTHORS:        Xi Zhang ; Xiaolin Wu ; Jun Du
HIGHLIGHT:      Challenge of Spatial Cognition for Deep Learning

7, TITLE:       O-Minimal Invariants for Discrete-Time Dynamical Systems
http://arxiv.org/abs/1802.09263
AUTHORS:        Shaull Almagor ; Dmitry Chistikov ; Joël Ouaknine ; James Worrell
HIGHLIGHT:      In this paper, we introduce the class of \emph{o-minimal invariants}, which is broader than any previously considered, and study the decidability of the existence and algorithmic synthesis of such invariants as certificates of non-termination for linear loops equipped with a large class of halting conditions.

8, TITLE:       Meta-Meta-Classification for One-Shot Learning
http://arxiv.org/abs/2004.08083
AUTHORS:        Arkabandhu Chowdhury ; Dipak Chaudhari ; Swarat Chaudhuri ; Chris Jermaine
COMMENTS:       8 pages without references, 2 figures
HIGHLIGHT:      We present a new approach, called meta-meta-classification, to learning in small-data settings.

9, TITLE:       Parallel Mapper
http://arxiv.org/abs/1712.03660
AUTHORS:        Mustafa Hajij ; Basem Assiri ; Paul Rosen
HIGHLIGHT:      In this paper, we study the parallel analysis of the construction of Mapper.

10, TITLE:      MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
http://arxiv.org/abs/2005.00812
AUTHORS:        Jakob D. Havtorn ; Jan Latko ; Joakim Edin ; Lasse Borgholt ; Lars Maaløe ; Lorenzo Belgrano ; Nicolai F. Jacobsen ; Regitze Sdun ; Željko Agić
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      We propose a novel multimodal approach to real-time sequence labeling in speech.

11, TITLE:      Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
http://arxiv.org/abs/2003.11645
AUTHORS:        Tosin P. Adewumi ; Foteini Liwicki ; Marcus Liwicki
COMMENTS:       6 pages, 7 figures, 6 tables
HIGHLIGHT:      The objective of this work is to empirically show optimal combination of hyper-parameters exists and evaluate various combinations.

12, TITLE:      Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI
http://arxiv.org/abs/2004.09788
AUTHORS:        Jinyoung Kim ; Remi Patriat ; Jordan Kaplan ; Oren Solomon ; Noam Harel
COMMENTS:       56 pages (one column), 13 figures, 5 tables, supplementary materials, Under review
HIGHLIGHT:      In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI.

13, TITLE:      Normalized Convolutional Neural Network
http://arxiv.org/abs/2005.05274
AUTHORS:        Dongsuk Kim ; Geonhee Lee
COMMENTS:       6pages typo errors ,errata are fixed.(p1,2,4,5)
HIGHLIGHT:      In this paper, we propose Normalized Convolutional Neural Network(NCNN).

14, TITLE:      Linear predictor on linearly-generated data with missing values: non consistency and solutions
http://arxiv.org/abs/2002.00658
AUTHORS:        Marine Le Morvan ; Nicolas Prost ; Julie Josse ; Erwan Scornet ; Gaël Varoquaux
HIGHLIGHT:      We consider building predictors when the data have missing values.

15, TITLE:      Generalized Entropy Regularization or: There's Nothing Special about Label Smoothing
http://arxiv.org/abs/2005.00820
AUTHORS:        Clara Meister ; Elizabeth Salesky ; Ryan Cotterell
COMMENTS:       Published as long paper at ACL 2020
HIGHLIGHT:      We introduce a parametric family of entropy regularizers, which includes label smoothing as a special case, and use it to gain a better understanding of the relationship between the entropy of a model and its performance on language generation tasks.

16, TITLE:      The Safari of Update Structures: Visiting the Lens and Quantum Enclosures
http://arxiv.org/abs/2005.05293
AUTHORS:        Matthew Wilson ; James Hefford ; Guillaume Boisseau ; Vincent Wang
COMMENTS:       Submitted to ACT2020, typesetting error fixed
HIGHLIGHT:      We build upon our recently introduced concept of an update structure to show that they are a generalisation of very-well-behaved lenses, that is, there is a bijection between a strict subset of update structures and vwb lenses in cartesian categories.

17, TITLE:      One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
http://arxiv.org/abs/1810.05241
AUTHORS:        Xingdi Yuan ; Tong Wang ; Rui Meng ; Khushboo Thaker ; Peter Brusilovsky ; Daqing He ; Adam Trischler
COMMENTS:       ACL 2020
HIGHLIGHT:      In this study, we address this problem from both modeling and evaluation perspectives.

18, TITLE:      Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
http://arxiv.org/abs/2004.13826
AUTHORS:        Yufeng Zhang ; Xueli Yu ; Zeyu Cui ; Shu Wu ; Zhongzhen Wen ; Liang Wang
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      In this work, to overcome such problems, we propose TextING for inductive text classification via GNN.

19, TITLE:      SAC-Net: Spatial Attenuation Context for Salient Object Detection
http://arxiv.org/abs/1903.10152
AUTHORS:        Xiaowei Hu ; Chi-Wing Fu ; Lei Zhu ; Tianyu Wang ; Pheng-Ann Heng
HIGHLIGHT:      This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.

20, TITLE:      A Tale of a Probe and a Parser
http://arxiv.org/abs/2005.01641
AUTHORS:        Rowan Hall Maudslay ; Josef Valvoda ; Tiago Pimentel ; Adina Williams ; Ryan Cotterell
HIGHLIGHT:      To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation.

21, TITLE:      Synchronous Bidirectional Learning for Multilingual Lip Reading
http://arxiv.org/abs/2005.03846
AUTHORS:        Mingshuang Luo ; Shuang Yang ; Xilin Chen ; Zitao Liu ; Shiguang Shan
COMMENTS:       12 pages,2 figures,4 tables
HIGHLIGHT:      To make the learning process more targeted at each particular language, we introduce an extra task of predicting the language identity in the learning process.

22, TITLE:      Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
http://arxiv.org/abs/2004.07443
AUTHORS:        Weiyi Xie ; Colin Jacobs ; Jean-Paul Charbonnier ; Bram van Ginneken
HIGHLIGHT:      In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module.

23, TITLE:      Towards Anomaly Detection in Dashcam Videos
http://arxiv.org/abs/2004.05261
AUTHORS:        Sanjay Haresh ; Sateesh Kumar ; M. Zeeshan Zia ; Quoc-Huy Tran
COMMENTS:       To appear at IV 2020
HIGHLIGHT:      We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes.

24, TITLE:      Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention
http://arxiv.org/abs/2005.03867
AUTHORS:        Myunghun Jung ; Youngmoon Jung ; Jahyun Goo ; Hoirin Kim
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information.

25, TITLE:      Open Diagrams via Coend Calculus
http://arxiv.org/abs/2004.04526
AUTHORS:        Mario Román
COMMENTS:       Clarifies section 4, fixes typos, changes examples and diagrams and adds references. 17 pages, 12 figures
HIGHLIGHT:      We propose a description of these non-square boxes, which we call open diagrams, using the monoidal bicategory of profunctors.

26, TITLE:      MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
http://arxiv.org/abs/2005.00383
AUTHORS:        Yue Qian ; Junhui Hou ; Yiming Zeng ; Qijian Zhang ; Sam Kwong ; Ying He
COMMENTS:       12 pages, 11 figures, 7 tables
HIGHLIGHT:      We propose MOPS-Net, a novel end-to-end deepneural network which is designed from the perspective of matrixoptimization, making it fundamentally different from the existing deep learning-based methods.

27, TITLE:      Hyperspectral Images Classification Based on Multi-scale Residual Network
http://arxiv.org/abs/2004.12381
AUTHORS:        Xiangdong Zhang ; Tengjun Wang ; Yun Yang
HIGHLIGHT:      Because hyperspectral remote sensing images contain a lot of redundant information and the data structure is highly non-linear, leading to low classification accuracy of traditional machine learning methods.

28, TITLE:      Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL
http://arxiv.org/abs/2005.04544
AUTHORS:        Baihan Lin ; Guillermo Cecchi ; Djallel Bouneffouf ; Jenna Reinen ; Irina Rish
COMMENTS:       This article supersedes and extends our work arXiv:1706.02897 (MAB) and arXiv:1906.11286 (RL) into the Contextual Bandit (CB) framework. It generalized extensively into multi-armed bandits, contextual bandits and RL settings to create a unified framework of human behavioral agents
HIGHLIGHT:      Motivated by clinical literature of a wide range of neurological and psychiatric disorders, we propose here a more general and flexible parametric framework for sequential decision making that involves a two-stream reward processing mechanism.

29, TITLE:      A combination of 'pooling' with a prediction model can reduce by 73% the number of COVID-19 (Corona-virus) tests
http://arxiv.org/abs/2005.03453
AUTHORS:        Tomer Cohen ; Lior Finkelman ; Gal Grimberg ; Gadi Shenhar ; Ofer Strichman ; Yonatan Strichman ; Stav Yeger
HIGHLIGHT:      We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.

30, TITLE:      A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
http://arxiv.org/abs/1912.11186
AUTHORS:        Lyndon Chan ; Mahdi S. Hosseini ; Konstantinos N. Plataniotis
COMMENTS:       23 pages; submitted to International Journal of Computer Vision (IJCV). Associated code available at https://github.com/lyndonchan/wsss-analysis. To view Supplementary Materials, please download pdf file listed under "Ancillary files"
HIGHLIGHT:      We present some practical techniques for these methods on unseen datasets and argue that more work is needed for a generalizable approach to weakly-supervised semantic segmentation.

31, TITLE:      Compiling Spiking Neural Networks to Neuromorphic Hardware
http://arxiv.org/abs/2004.03717
AUTHORS:        Shihao Song ; Adarsha Balaji ; Anup Das ; Nagarajan Kandasamy ; James Shackleford
COMMENTS:       10 pages, 17 figures, accepted at 21st ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES 2020)
HIGHLIGHT:      We propose an approach to analyze and compile SNNs on a resource-constrained neuromorphic hardware, providing guarantee on key performance metrics such as execution time and throughput.

32, TITLE:      Universal Gödel statements and computability of intelligence
http://arxiv.org/abs/2001.07592
AUTHORS:        Yasha Savelyev
COMMENTS:       16 pages, added a complete formalization of fundamental soundness, small title change
HIGHLIGHT:      We then completely re-frame the argument in the language of Turing machines, and by defining our subject just enough, we show that a certain analogue of a G\"odel statement, or a G\"odel string as we call it in the language of Turing machines, can be readily constructed directly, without appeal to the G\"odel incompleteness theorem.

33, TITLE:      Empowering Active Learning to Jointly Optimize System and User Demands
http://arxiv.org/abs/2005.04470
AUTHORS:        Ji-Ung Lee ; Christian M. Meyer ; Iryna Gurevych
COMMENTS:       To appear as a long paper in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). Download our code and simulated user models at github: https://github.com/UKPLab/acl2020-empowering-active-learning
HIGHLIGHT:      In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances).

34, TITLE:      Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks
http://arxiv.org/abs/1908.04250
AUTHORS:        Indrajit Mazumdar
HIGHLIGHT:      Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net.

35, TITLE:      PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
http://arxiv.org/abs/2003.09696
AUTHORS:        Adarsha Balaji ; Prathyusha Adiraju ; Hirak J. Kashyap ; Anup Das ; Jeffrey L. Krichmar ; Nikil D. Dutt ; Francky Catthoor
COMMENTS:       10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 2020
HIGHLIGHT:      We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN).

36, TITLE:      Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
http://arxiv.org/abs/1904.07387
AUTHORS:        Po-Yu Kao ; Angela Zhang ; Michael Goebel ; Jefferson W. Chen ; B. S. Manjunath
COMMENTS:       8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge 2019; Added NDA
HIGHLIGHT:      In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents.

37, TITLE:      Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
http://arxiv.org/abs/1902.03932
AUTHORS:        Ruqi Zhang ; Chunyuan Li ; Jianyi Zhang ; Changyou Chen ; Andrew Gordon Wilson
COMMENTS:       Published at ICLR 2020
HIGHLIGHT:      In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode.

38, TITLE:      Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
http://arxiv.org/abs/2002.00786
AUTHORS:        Sravan Mylavarapu ; Mahtab Sandhu ; Priyesh Vijayan ; K Madhava Krishna ; Balaraman Ravindran ; Anoop Namboodiri
COMMENTS:       To appear in IV (IEEE Intelligent Vehicles Symposium) 2020
HIGHLIGHT:      In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video.

39, TITLE:      LRCN-RetailNet: A recurrent neural network architecture for accurate people counting
http://arxiv.org/abs/2004.09672
AUTHORS:        Lucas Massa ; Adriano Barbosa ; Krerley Oliveira ; Thales Vieira
HIGHLIGHT:      We introduce LRCN-RetailNet: a recurrent neural network architecture capable of learning a non-linear regression model and accurately predicting the people count from videos captured by low-cost surveillance cameras.

40, TITLE:      Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
http://arxiv.org/abs/2004.01738
AUTHORS:        Elizabeth K. Cole ; Joseph Y. Cheng ; John M. Pauly ; Shreyas S. Vasanawala
HIGHLIGHT:      In this work, we investigate end-to-end complex-valued convolutional neural networks - specifically, for image reconstruction in lieu of two-channel real-valued networks.

41, TITLE:      Review of Text Style Transfer Based on Deep Learning
http://arxiv.org/abs/2005.02914
AUTHORS:        Xiangyang Li ; Guo Pu ; Keyu Ming ; Pu Li ; Jie Wang ; Yuxuan Wang
HIGHLIGHT:      This article summarizes the research on the text style transfer model based on deep learning in recent years, and summarizes, analyzes and compares the main research directions and progress. In addition, the article also introduces public data sets and evaluation indicators commonly used for text style transfer.

42, TITLE:      Weakly Supervised Object Localization with Inter-Intra Regulated CAMs
http://arxiv.org/abs/1911.07160
AUTHORS:        Guofeng Cui ; Ziyi Kou ; Shaojie Wang ; Wentian Zhao ; Chenliang Xu
COMMENTS:       14 pages, 4 figures
HIGHLIGHT:      In this work, instead of following one of the two main approaches before, we analyze their internal relationship and propose a novel intra-sample strategy which regulates two CAMs of the same sample, generated from different classifiers, to dynamically adapt each of their pixels involved in adversarial or cooperative process based on their own values.

43, TITLE:      Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
http://arxiv.org/abs/2002.10310
AUTHORS:        Ayan Kumar Bhunia ; Yongxin Yang ; Timothy M. Hospedales ; Tao Xiang ; Yi-Zhe Song
COMMENTS:       IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020 [Oral Presentation] Code: https://github.com/AyanKumarBhunia/on-the-fly-FGSBIR
HIGHLIGHT:      In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible.

44, TITLE:      A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
http://arxiv.org/abs/2002.11843
AUTHORS:        Ruthvik Vaila ; John Chiasson ; Vishal Saxena
COMMENTS:       A section of of this work is Submitted to IEEE TETCI 2020 Journal
HIGHLIGHT:      In this work we approach this using spiking neural networks.

45, TITLE:      CONFIG: Controllable Neural Face Image Generation
http://arxiv.org/abs/2005.02671
AUTHORS:        Marek Kowalski ; Stephan J. Garbin ; Virginia Estellers ; Tadas Baltrušaitis ; Matthew Johnson ; Jamie Shotton
COMMENTS:       includes supplementary materials
HIGHLIGHT:      To this end we propose ConfigNet, a neural face model that allows for controlling individual aspects of output images in semantically meaningful ways and that is a significant step on the path towards finely-controllable neural rendering.

46, TITLE:      In-Domain GAN Inversion for Real Image Editing
http://arxiv.org/abs/2004.00049
AUTHORS:        Jiapeng Zhu ; Yujun Shen ; Deli Zhao ; Bolei Zhou
COMMENTS:       31 pages, 23 figures, 2 tables
HIGHLIGHT:      To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing.

47, TITLE:      Scope Head for Accurate Localization in Object Detection
http://arxiv.org/abs/2005.04854
AUTHORS:        Geng Zhan ; Dan Xu ; Guo Lu ; Wei Wu ; Chunhua Shen ; Wanli Ouyang
HIGHLIGHT:      To tackle these issues, in this paper, we propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship.

48, TITLE:      Tight Polynomial Worst-Case Bounds for Loop Programs
http://arxiv.org/abs/1906.10047
AUTHORS:        Amir M. Ben-Amram ; Geoff Hamilton
HIGHLIGHT:      This paper shows how to obtain asymptotically-tight, multivariate, disjunctive polynomial bounds for this class of programs.

49, TITLE:      SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
http://arxiv.org/abs/1810.09091
AUTHORS:        Xiaolin Zhang ; Yunchao Wei ; Yi Yang ; Thomas Huang
HIGHLIGHT:      In this paper, we propose a simple yet effective Similarity Guidance network to tackle the One-shot (SG-One) segmentation problem.

50, TITLE:      Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications
http://arxiv.org/abs/2005.04425
AUTHORS:        Kiyoharu Aizawa ; Azuma Fujimoto ; Atsushi Otsubo ; Toru Ogawa ; Yusuke Matsui ; Koki Tsubota ; Hikaru Ikuta
COMMENTS:       10 pages, 8 figures
HIGHLIGHT:      In this article, we describe the details of the dataset and present a few examples of multimedia processing applications (detection, retrieval, and generation) that apply existing deep learning methods and are made possible by the dataset.

51, TITLE:      Shared task: Lexical semantic change detection in German (Student Project Report)
http://arxiv.org/abs/2001.07786
AUTHORS:        Adnan Ahmad ; Kiflom Desta ; Fabian Lang ; Dominik Schlechtweg
HIGHLIGHT:      We present the results of the first shared task on unsupervised lexical semantic change detection (LSCD) in German based on the evaluation framework proposed by Schlechtweg et al. (2019).

52, TITLE:      A localized version of the basic triangle theorem
http://arxiv.org/abs/1908.03327
AUTHORS:        Nihar Gargava ; Hoang Ngoc Minh ; Pierre Simonnet ; Gérard Duchamp
HIGHLIGHT:      In this short note, we give a localized version of the basic triangle theorem, first published in 2011 (see [4]) in order to prove the independence of hyperlogarithms over various function fields.