2020.04.21.txt

==========New Papers==========
1, TITLE:       Complex-Object Visual Inspection via Multiple Lighting Configurations
http://arxiv.org/abs/2004.09374
AUTHORS:        Maya Aghaei ; Matteo Bustreo ; Pietro Morerio ; Nicolo Carissimi ; Alessio Del Bue ; Vittorio Murino
COMMENTS:       8 pages, 7 figures, submitted to ICPR2020
HIGHLIGHT:      Along with an exhaustive set of experiments, in this paper, we demonstrate the suitability of the proposed setup for effective illumination of complex-objects, defined as manufactured items with variable surface characteristics that cannot be determined a priori.

2, TITLE:       Counterexamples to the Low-Degree Conjecture
http://arxiv.org/abs/2004.08454
AUTHORS:        Justin Holmgren ; Alexander S. Wein
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we refute the conjecture of Hopkins.

3, TITLE:       Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
http://arxiv.org/abs/2004.09305
AUTHORS:        Peiliang Li ; Jieqi Shi ; Shaojie Shen
COMMENTS:       cvpr2020
HIGHLIGHT:      To benefit from both the powerful object understanding skill from deep neural network meanwhile tackle precise geometry modeling for consistent trajectory estimation, we propose a joint spatial-temporal optimization-based stereo 3D object tracking method.

4, TITLE:       Reducing Commutativity Verification to Reachability with Differencing Abstractions
http://arxiv.org/abs/2004.08450
AUTHORS:        Eric Koskinen ; Kshitij Bansal
HIGHLIGHT:      In this paper, we describe techniques to automatically prove the correctness of method commutativity conditions from data structure implementations.

5, TITLE:       Adversarial Attack on Deep Learning-Based Splice Localization
http://arxiv.org/abs/2004.08443
AUTHORS:        Andras Rozsa ; Zheng Zhong ; Terrance E. Boult
COMMENTS:       This is a pre-print of the original paper accepted at the CVPR Workshop on Media Forensics 2020
HIGHLIGHT:      Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices.

6, TITLE:       Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills
http://arxiv.org/abs/2004.08449
AUTHORS:        Eric Michael Smith ; Mary Williamson ; Kurt Shuster ; Jason Weston ; Y-Lan Boureau
COMMENTS:       accepted to ACL 2020 (long paper)
HIGHLIGHT:      In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes.

7, TITLE:       Parallelization Techniques for Verifying Neural Networks
http://arxiv.org/abs/2004.08440
AUTHORS:        Haoze Wu ; Alex Ozdemir ; Aleksandar Zeljić ; Ahmed Irfan ; Kyle Julian ; Divya Gopinath ; Sadjad Fouladighaleh ; Guy Katz ; Corina Pasareanu ; Clark Barrett
HIGHLIGHT:      We introduce an algorithm based on partitioning the verification problem in an iterative manner and explore two partitioning strategies, that work by partitioning the input space or by case splitting on the phases of the neuron activations, respectively.

8, TITLE:       Robust Partial Matching for Person Search in the Wild
http://arxiv.org/abs/2004.09329
AUTHORS:        Yingji Zhong ; Xiaoyu Wang ; Shiliang Zhang
COMMENTS:       9 pages, 7 figures, accepted to CVPR 2020. The dataset will be released soon
HIGHLIGHT:      To alleviate this issue, this paper proposes an Align-to-Part Network (APNet) for person detection and re-Identification (reID).

9, TITLE:       Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
http://arxiv.org/abs/2004.09321
AUTHORS:        Marta B. M. Ranzini ; Irme Groothuis ; Kerstin Kläser ; M. Jorge Cardoso ; Johann Henckel ; Sébastien Ourselin ; Alister Hart ; Marc Modat
COMMENTS:       Accepted at IEEE International Symposium on Biomedical Imaging (ISBI) 2020
HIGHLIGHT:      In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI.

10, TITLE:      A Novel Multi-Agent System for Complex Scheduling Problems
http://arxiv.org/abs/2004.09312
AUTHORS:        Peter Hillmann ; Tobias Uhlig ; Gabi Dreo Rodosek ; Oliver Rose
HIGHLIGHT:      The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains.

11, TITLE:      How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study
http://arxiv.org/abs/2004.09317
AUTHORS:        D. B. de Jong ; F. Paredes-Vallés ; G. C. H. E. de Croon
COMMENTS:       15 pages, 15 figures
HIGHLIGHT:      Instead, in this article, we investigate how deep neural networks estimate optical flow.

12, TITLE:      Design and Control of Roller Grasper V2 for In-Hand Manipulation
http://arxiv.org/abs/2004.08499
AUTHORS:        Shenli Yuan ; Lin Shao ; Connor L. Yako ; Alex Gruebele ; J. Kenneth Salisbury
COMMENTS:       Under Review: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
HIGHLIGHT:      In this work, we present a novel non-anthropomorphic robot grasper with the ability to manipulate objects by means of active surfaces at the fingertips.

13, TITLE:      BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient
http://arxiv.org/abs/2004.08495
AUTHORS:        Behzad Hasani ; Pooran Singh Negi ; Mohammad H. Mahoor
COMMENTS:       To appear in IEEE Transactions on Affective Computing journal
HIGHLIGHT:      This paper introduces BReG-NeXt, a residual-based network architecture using a function wtih bounded derivative instead of a simple shortcut path (a.k.a. identity mapping) in the residual units for automatic recognition of facial expressions based on the categorical and dimensional models of affect.

14, TITLE:      ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
http://arxiv.org/abs/2004.09367
AUTHORS:        Jung-Woo Ha ; Kihyun Nam ; Jin Gu Kang ; Sang-Woo Lee ; Sohee Yang ; Hyunhoon Jung ; Eunmi Kim ; Hyeji Kim ; Soojin Kim ; Hyun Ah Kim ; Kyoungtae Doh ; Chan Kyu Lee ; Sunghun Kim
COMMENTS:       5 pages, 1 figure, The first two authors equally contributed to this work
HIGHLIGHT:      Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus.

15, TITLE:      Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
http://arxiv.org/abs/2004.09363
AUTHORS:        Shervin Minaee ; Rahele Kafieh ; Milan Sonka ; Shakib Yazdani ; Ghazaleh Jamalipour Soufi
HIGHLIGHT:      We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 97\%($\pm$ 5\%), while having a specificity rate of around 90\%.

16, TITLE:      Traffic Lane Detection using FCN
http://arxiv.org/abs/2004.08977
AUTHORS:        Shengchang Zhang ; Ahmed EI Koubia ; Khaled Abdul Karim Mohammed
COMMENTS:       6 pages, 6 figures
HIGHLIGHT:      In this project, we designed an Encoder- Decoder, Fully Convolutional Network for lane detection.

17, TITLE:      Spectral GUI for Automated Tissue and Lesion Segmentation of T1 Weighted Breast MR Images
http://arxiv.org/abs/2004.08960
AUTHORS:        Prajval Koul
HIGHLIGHT:      We present Spectral GUI, a multiplatform breast MR image analysis tool designed to facilitate the segmentation of fibro glandular tissues and lesions in T1 weighted breast MR images via a graphical user interface (GUI).

18, TITLE:      Machine Learning based Pallets Detection and Tracking in AGVs
http://arxiv.org/abs/2004.08965
AUTHORS:        Shengchang Zhang ; Jie Xiang ; Weijian Han
COMMENTS:       6 pages, 8 figures, 1 table
HIGHLIGHT:      In this project, we constructed a deep learning-based pallets detection and tracking architecture for pallets detection and position tracking.

19, TITLE:      Local Search is a Remarkably Strong Baseline for Neural Architecture Search
http://arxiv.org/abs/2004.08996
AUTHORS:        T. Den Ottelander ; A. Dushatskiy ; M. Virgolin ; P. A. N. Bosman
COMMENTS:       17 pages, 12 figures
HIGHLIGHT:      In this work we consider, for the first time, a simple Local Search (LS) algorithm for NAS.

20, TITLE:      Exclusive Hierarchical Decoding for Deep Keyphrase Generation
http://arxiv.org/abs/2004.08511
AUTHORS:        Wang Chen ; Hou Pong Chan ; Piji Li ; Irwin King
COMMENTS:       ACL 2020
HIGHLIGHT:      Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases.

21, TITLE:      Adversarial Training for Large Neural Language Models
http://arxiv.org/abs/2004.08994
AUTHORS:        Xiaodong Liu ; Hao Cheng ; Pengcheng He ; Weizhu Chen ; Yu Wang ; Hoifung Poon ; Jianfeng Gao
COMMENTS:       13 pages, 9 tables, 2 figures
HIGHLIGHT:      In this paper, we show that adversarial pre-training can improve both generalization and robustness.

22, TITLE:      JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
http://arxiv.org/abs/2004.08515
AUTHORS:        Keren Fu ; Deng-Ping Fan ; Ge-Peng Ji ; Qijun Zhao
HIGHLIGHT:      This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.

23, TITLE:      Semi-Supervised Semantic Segmentation via Dynamic Self-Training and Class-Balanced Curriculum
http://arxiv.org/abs/2004.08514
AUTHORS:        Zhengyang Feng ; Qianyu Zhou ; Guangliang Cheng ; Xin Tan ; Jianping Shi ; Lizhuang Ma
COMMENTS:       Code is available at https://github.com/voldemortX/DST-CBC
HIGHLIGHT:      In this work, we propose a novel and concise approach for semi-supervised semantic segmentation.

24, TITLE:      ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig
http://arxiv.org/abs/2004.08513
AUTHORS:        Hamid Reza Vaezi Joze ; Ilya Zharkov ; Karlton Powell ; Carl Ringler ; Luming Liang ; Andy Roulston ; Moshe Lutz ; Vivek Pradeep
HIGHLIGHT:      Herein, we are proposing a new data acquisition technique for gathering real image data set which could be used as an input for super resolution, noise cancellation and quality enhancement techniques.

25, 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 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.

26, TITLE:      A Formal Hierarchy of RNN Architectures
http://arxiv.org/abs/2004.08500
AUTHORS:        William Merrill ; Gail Weiss ; Yoav Goldberg ; Roy Schwartz ; Noah A. Smith ; Eran Yahav
COMMENTS:       To appear at ACL 2020. 10 pages with appendix
HIGHLIGHT:      We develop a formal hierarchy of the expressive capacity of RNN architectures.

27, TITLE:      Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective
http://arxiv.org/abs/2004.08519
AUTHORS:        Naoki Nishimura ; Noriyoshi Sukegawa ; Yuichi Takano ; Jiro Iwanaga
HIGHLIGHT:      We propose a shape-restricted optimization model to accurately estimate item-choice probabilities for all possible PV sequences.

28, TITLE:      Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical Image
http://arxiv.org/abs/2004.08522
AUTHORS:        Thanh Huy Nguyen ; Sylvie Daniel ; Didier Gueriot ; Christophe Sintes ; Jean-Marc Le Caillec
COMMENTS:       30 pages, 15 figures. Submitted to the MDPI Remote Sensing
HIGHLIGHT:      This paper presents an efficient snake model to overcome such challenge, called Super-Resolution-based Snake Model (SRSM).

29, TITLE:      Effect of Text Color on Word Embeddings
http://arxiv.org/abs/2004.08526
AUTHORS:        Masaya Ikoma ; Brian Kenji Iwana ; Seiichi Uchida
COMMENTS:       to appear at the 14th International Workshop on Document Analysis Systems (DAS) 2020
HIGHLIGHT:      In this paper, we try two tasks to verify the usefulness of text color in understanding the meanings of words, especially in identifying synonyms and antonyms.

30, TITLE:      Class Distribution Alignment for Adversarial Domain Adaptation
http://arxiv.org/abs/2004.09403
AUTHORS:        Wanqi Yang ; Tong Ling ; Chengmei Yang ; Lei Wang ; Yinghuan Shi ; Luping Zhou ; Ming Yang
HIGHLIGHT:      To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.

31, TITLE:      A Comprehensive Survey on Traffic Prediction
http://arxiv.org/abs/2004.08555
AUTHORS:        Xueyan Yin ; Genze Wu ; Jinze Wei ; Yanming Shen ; Heng Qi ; Baocai Yin
HIGHLIGHT:      The purpose of this paper is to provide a comprehensive survey for traffic prediction. Third, We collect and organize several related public datasets in the existing literature.

32, TITLE:      Learning to Dehaze From Realistic Scene with A Fast Physics Based Dehazing Network
http://arxiv.org/abs/2004.08554
AUTHORS:        Ruoteng Li ; Xiaoyi Zhang ; Shaodi You ; Yu Li
HIGHLIGHT:      In this paper, we complement the exiting datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from HD 3D videos.

33, TITLE:      The Notorious Difficulty of Comparing Human and Machine Perception
http://arxiv.org/abs/2004.09406
AUTHORS:        Christina M. Funke ; Judy Borowski ; Karolina Stosio ; Wieland Brendel ; Thomas S. A. Wallis ; Matthias Bethge
HIGHLIGHT:      Here, we highlight common shortcomings that can easily lead to fragile conclusions.

34, TITLE:      Accurate Tumor Tissue Region Detection with Accelerated Deep Convolutional Neural Networks
http://arxiv.org/abs/2004.08552
AUTHORS:        Gabriel Tjio ; Xulei Yang ; Jia Mei Hong ; Sum Thai Wong ; Vanessa Ding ; Andre Choo ; Yi Su
COMMENTS:       9 pages, 6 figures, 3 tables
HIGHLIGHT:      In computer vision approaches using deep learning methods, the input image is subdivided into patches which are separately passed through the neural network.

35, TITLE:      Kernels for time series with irregularly-spaced multivariate observations
http://arxiv.org/abs/2004.08545
AUTHORS:        Ahmed Guecioueur ; Franz J. Király
COMMENTS:       11 pages
HIGHLIGHT:      In this manuscript, we show that a "series kernel" that is general enough to represent irregularly-spaced multivariate time series may be built out of well-known "vector kernels".

36, TITLE:      Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means
http://arxiv.org/abs/2004.08547
AUTHORS:        Narayana Reddy A ; Ranjita Das
COMMENTS:       4 pages, 2 figures, Included in conference proceedings of "International conference in Recent Trends on Electronics & Computer Science (ICRTECS-2019)" organised by National Institute of Technology, Silchar
HIGHLIGHT:      This paper presents a new image segmentation algorithm called Adaptive Particle Swarm Optimization and Fuzzy C-means Clustering Algorithm (APSOF), which is based on Adaptive Particle Swarm Optimization (APSO) and Fuzzy C-means clustering.

37, TITLE:      FedNAS: Federated Deep Learning via Neural Architecture Search
http://arxiv.org/abs/2004.08546
AUTHORS:        Chaoyang He ; Murali Annavaram ; Salman Avestimehr
COMMENTS:       accepted to CVPR 2020 workshop on neural architecture search and beyond for representation learning
HIGHLIGHT:      We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy.

38, TITLE:      Moire Image Restoration using Multi Level Hyper Vision Net
http://arxiv.org/abs/2004.08541
AUTHORS:        D. Sabari Nathan ; M. Parisa Beham ; S. M. Md Mansoor Roomi
COMMENTS:       8 pages , 5 figures
HIGHLIGHT:      These challenges makes the demoireing more difficult than many other image restoration tasks.

39, TITLE:      Autonomous task planning and situation awareness in robotic surgery
http://arxiv.org/abs/2004.08911
AUTHORS:        Michele Ginesi ; Daniele Meli ; Andrea Roberti ; Nicola Sansonetto ; Paolo Fiorini
COMMENTS:       Submitted to IROS 2020 conference
HIGHLIGHT:      In this paper, we propose a framework to implement surgical task automation.

40, TITLE:      The Cost of Training NLP Models: A Concise Overview
http://arxiv.org/abs/2004.08900
AUTHORS:        Or Sharir ; Barak Peleg ; Yoav Shoham
HIGHLIGHT:      We review the cost of training large-scale language models, and the drivers of these costs.

41, TITLE:      Tree Echo State Autoencoders with Grammars
http://arxiv.org/abs/2004.08925
AUTHORS:        Benjamin Paassen ; Irena Koprinska ; Kalina Yacef
COMMENTS:       accepted at the 2020 International Joint Conference on Neural Networks (IJCNN 2020)
HIGHLIGHT:      In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing.

42, TITLE:      3D rectification with Visual Sphere perspective: an algebraic alternative for P4P pose estimation
http://arxiv.org/abs/2004.08933
AUTHORS:        Jakub Maksymilian Fober
COMMENTS:       12 pages, 5 figures
HIGHLIGHT:      Presented algorithm solves co-planar P4P problem of parallel lines viewed in perspective with algebraic equation.

43, TITLE:      MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
http://arxiv.org/abs/2004.08915
AUTHORS:        Ling Lo ; Hong-Xia Xie ; Hong-Han Shuai ; Wen-Huang Cheng
COMMENTS:       Accepted by IEEE MIPR 2020
HIGHLIGHT:      Inspired by the nodes relationship building Graph Convolutional Networks (GCN), we propose an end-to-end AU-oriented graph classification network, namely MER-GCN, which uses 3D ConvNets to extract AU features and applies GCN layers to discover the dependency laying between AU nodes for ME categorization.

44, TITLE:      MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification
http://arxiv.org/abs/2004.08914
AUTHORS:        Seyed Ahmad Mirsalari ; Sima Sinaei ; Mostafa E. Salehi ; Masoud Daneshtalab
COMMENTS:       To appear in IEEE International Symposium on Circuits & Systems in 2020. arXiv admin note: text overlap with arXiv:1807.04093 by other authors
HIGHLIGHT:      In this paper, we propose a multi-level binarized LSTM, which significantly reduces computations whereas ensuring an accuracy pretty close to the full precision LSTM.

45, TITLE:      A practical approach to testing random number generators in computer algebra systems
http://arxiv.org/abs/2004.08913
AUTHORS:        Migran N. Gevorkyan ; Dmitry S. Kulyabov ; Anastasia V. Demidova ; Anna V. Korolkova
COMMENTS:       in English, in Russian
HIGHLIGHT:      Up to now, a large number of libraries and weakly supported mathematical packages use outdated algorithms for random number generation.

46, TITLE:      Quantum algorithms for computational geometry problems
http://arxiv.org/abs/2004.08949
AUTHORS:        Andris Ambainis ; Nikita Larka
COMMENTS:       10 pages
HIGHLIGHT:      We study quantum algorithms for problems in computational geometry, such as POINT-ON-3-LINES problem.

47, TITLE:      Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
http://arxiv.org/abs/2004.08947
AUTHORS:        Sebastián Salazar-Colores ; Hugo Alberto-Moreno ; César Javier Ortiz-Echeverri ; Gerardo Flores
HIGHLIGHT:      In this paper, a novel computational approach to remove the smoke effects is introduced.

48, TITLE:      ResNeSt: Split-Attention Networks
http://arxiv.org/abs/2004.08955
AUTHORS:        Hang Zhang ; Chongruo Wu ; Zhongyue Zhang ; Yi Zhu ; Zhi Zhang ; Haibin Lin ; Yue Sun ; Tong He ; Jonas Mueller ; R. Manmatha ; Mu Li ; Alexander Smola
HIGHLIGHT:      We present a simple and modular Split-Attention block that enables attention across feature-map groups.

49, TITLE:      Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
http://arxiv.org/abs/2004.08945
AUTHORS:        Seyma Yucer ; Samet Akçay ; Noura Al-Moubayed ; Toby P. Breckon
COMMENTS:       CVPR 2020 - Fair, Data Efficient and Trusted Computer Vision Workshop
HIGHLIGHT:      In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features.

50, TITLE:      Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation
http://arxiv.org/abs/2004.09251
AUTHORS:        Luca Ciampi ; Carlos Santiago ; Joao Paulo Costeira ; Claudio Gennaro ; Giuseppe Amato
COMMENTS:       Submitted to the ECAI 2020: "1st International Workshop on New Foundations for Human-Centered AI"
HIGHLIGHT:      We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.

51, 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.

52, TITLE:      An Efficient Method for Computing Liouvillian First Integrals of Planar Polynomial Vector Fields
http://arxiv.org/abs/2004.09298
AUTHORS:        L. G. S. Duarte ; L. A. C. P. da Mota
HIGHLIGHT:      Here we present an efficient method to compute Darboux polynomials for polynomial vector fields in the plane.

53, TITLE:      MPNet: Masked and Permuted Pre-training for Language Understanding
http://arxiv.org/abs/2004.09297
AUTHORS:        Kaitao Song ; Xu Tan ; Tao Qin ; Jianfeng Lu ; Tie-Yan Liu
HIGHLIGHT:      In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations.

54, TITLE:      A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT
http://arxiv.org/abs/2004.09205
AUTHORS:        Chi-Liang Liu ; Tsung-Yuan Hsu ; Yung-Sung Chuang ; Hung-Yi Lee
HIGHLIGHT:      In this work, we provide an in-depth experimental study to supplement the existing literature of cross-lingual ability.

55, TITLE:      End-to-End Learning for Video Frame Compression with Self-Attention
http://arxiv.org/abs/2004.09226
AUTHORS:        Nannan Zou ; Honglei Zhang ; Francesco Cricri ; Hamed R. Tavakoli ; Jani Lainema ; Emre Aksu ; Miska Hannuksela ; Esa Rahtu
HIGHLIGHT:      In this paper, we propose an end-to-end learned system for compressing video frames.

56, TITLE:      Unsupervised Person Re-identification via Multi-label Classification
http://arxiv.org/abs/2004.09228
AUTHORS:        Dongkai Wang ; Shiliang Zhang
COMMENTS:       CVPR2020
HIGHLIGHT:      This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels.

57, TITLE:      Towards Understanding Normalization in Neural ODEs
http://arxiv.org/abs/2004.09222
AUTHORS:        Julia Gusak ; Larisa Markeeva ; Talgat Daulbaev ; Alexandr Katrutsa ; Andrzej Cichocki ; Ivan Oseledets
HIGHLIGHT:      This paper investigates how different normalization techniques affect the performance of neural ODEs.

58, TITLE:      4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
http://arxiv.org/abs/2004.09216
AUTHORS:        Nils Gessert ; Marcel Bengs ; Julia Krüger ; Roland Opfer ; Ann-Christin Ostwaldt ; Praveena Manogaran ; Sven Schippling ; Alexander Schlaefer
COMMENTS:       Accepted at MIDL 2020
HIGHLIGHT:      In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance.

59, TITLE:      CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition
http://arxiv.org/abs/2004.09215
AUTHORS:        Zhengwei Wang ; Qi She ; Tejo Chalasani ; Aljosa Smolic
COMMENTS:       CVPR 2020 Workshop at Continual Learning (CLVISION)
HIGHLIGHT:      In this work, we demonstrate a lifelong 3D convolutional framework -- c(C)la(a)ss increment(t)al net(Net)work (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples.

60, TITLE:      Learning Geometric Word Meta-Embeddings
http://arxiv.org/abs/2004.09219
AUTHORS:        Pratik Jawanpuria ; N T V Satya Dev ; Anoop Kunchukuttan ; Bamdev Mishra
HIGHLIGHT:      We propose a geometric framework for learning meta-embeddings of words from different embedding sources.

61, TITLE:      A Practical Guide to Studying Emergent Communication through Grounded Language Games
http://arxiv.org/abs/2004.09218
AUTHORS:        Jens Nevens ; Paul Van Eecke ; Katrien Beuls
COMMENTS:       This paper was officially published at the 'Language Learning for Artificial Agents (L2A2) Symposium' of the 2019 Artificial Intelligence and Simulation of Behaviour (AISB) Convention
HIGHLIGHT:      The aim of this paper is twofold.

62, TITLE:      CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
http://arxiv.org/abs/2004.09249
AUTHORS:        Shinji Watanabe ; Michael Mandel ; Jon Barker ; Emmanuel Vincent
HIGHLIGHT:      This paper provides a baseline description of the CHiME-6 challenge for both segmented multispeaker speech recognition (Track 1) and unsegmented multispeaker speech recognition (Track 2).

63, TITLE:      The complexity of approximating averages on bounded-degree graphs
http://arxiv.org/abs/2004.09238
AUTHORS:        Andreas Galanis ; Daniel Stefankovic ; Eric Vigoda
HIGHLIGHT:      Our work extends to the antiferromagnetic Ising model and generalizes to all 2-spin antiferromagnetic models, establishing hardness of computing the average magnetization in the tree non-uniqueness region.

64, TITLE:      Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
http://arxiv.org/abs/2004.08878
AUTHORS:        Qianyu Zhou ; Zhengyang Feng ; Guangliang Cheng ; Xin Tan ; Jianping Shi ; Lizhuang Ma
HIGHLIGHT:      In this paper, we propose an uncertainty-aware consistency regularization method to tackle this issue for semantic segmentation.

65, TITLE:      Make E Smart Again
http://arxiv.org/abs/2004.08858
AUTHORS:        Zarathustra Amadeus Goertzel
COMMENTS:       8 pages, 2 figures, IJCAR2020
HIGHLIGHT:      In this work in progress, we demonstrate a new use-case for the ENIGMA system.

66, TITLE:      Role-Wise Data Augmentation for Knowledge Distillation
http://arxiv.org/abs/2004.08861
AUTHORS:        Jie Fu ; Xue Geng ; Zhijian Duan ; Bohan Zhuang ; Xingdi Yuan ; Adam Trischler ; Jie Lin ; Chris Pal ; Hao Dong
HIGHLIGHT:      We compare our approach with existing KD methods on training popular neural architectures and demonstrate that role-wise data augmentation improves the effectiveness of KD over strong prior approaches.

67, TITLE:      Space Debris Ontology for ADR Capture Methods Selection
http://arxiv.org/abs/2004.08866
AUTHORS:        Marko Jankovic ; Mehmed Yüksel ; Mohammad Mohammadzadeh Babr ; Francesca Letizia ; Vitali Braun
COMMENTS:       32 pages, 7 figures and 6 tables
HIGHLIGHT:      To bridge this gap we present a domain-ontology of intact derelict objects, i.e. payloads and rocket bodies, for ADR capture methods selection.

68, TITLE:      A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Hyperspectral Imagery Classification
http://arxiv.org/abs/2004.08886
AUTHORS:        Yue Shi ; Liangxiu Han ; Wenjiang Huang ; Sheng Chang ; Yingying Dong ; Darren Dancey ; Lianghao Han
COMMENTS:       13 pages, 11 figures
HIGHLIGHT:      This study proposes an interpretable deep learning model -- a biologically interpretable two-stage deep neural network (BIT-DNN), by integrating biochemical and biophysical associated information into the proposed framework, capable of achieving both high accuracy and interpretability on HSI based classification tasks.

69, TITLE:      A Weighted Population Update Rule for PACO Applied to the Single Machine Total Weighted Tardiness Problem
http://arxiv.org/abs/2004.08433
AUTHORS:        Daniel Abitz ; Tom Hartmann ; Martin Middendorf
HIGHLIGHT:      In this paper a new population update rule for population based ant colony optimization (PACO) is proposed.

70, TITLE:      Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
http://arxiv.org/abs/2004.08423
AUTHORS:        Xin Chen ; Lingxi Xie ; Jun Wu ; Longhui Wei ; Yuhui Xu ; Qi Tian
COMMENTS:       17 pages, 2 figures and 3 tables
HIGHLIGHT:      To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated.

71, TITLE:      Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
http://arxiv.org/abs/2004.08426
AUTHORS:        Dazhou Guo ; Dakai Jin ; Zhuotun Zhu ; Tsung-Ying Ho ; Adam P. Harrison ; Chun-Hung Chao ; Jing Xiao ; Alan Yuille ; Chien-Yu Lin ; Le Lu
HIGHLIGHT:      This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.

72, TITLE:      A Chinese Corpus for Fine-grained Entity Typing
http://arxiv.org/abs/2004.08825
AUTHORS:        Chin Lee ; Hongliang Dai ; Yangqiu Song ; Xin Li
COMMENTS:       LREC 2020
HIGHLIGHT:      In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing.

73, TITLE:      Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination
http://arxiv.org/abs/2004.08830
AUTHORS:        Muhammad Burhan Hafez ; Cornelius Weber ; Matthias Kerzel ; Stefan Wermter
HIGHLIGHT:      In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model.

74, TITLE:      Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning
http://arxiv.org/abs/2004.08833
AUTHORS:        Hongcai Xu ; Junpeng Bao ; Gaojie Zhang
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      This paper proposes a dynamic Knowledge graph-based dialogue generation method with improved adversarial Meta-Learning (KDAD).

75, TITLE:      Graph-Structured Referring Expression Reasoning in The Wild
http://arxiv.org/abs/2004.08814
AUTHORS:        Sibei Yang ; Guanbin Li ; Yizhou Yu
COMMENTS:       CVPR 2020 Accepted Oral Paper. Data and code are available at https://github.com/sibeiyang/sgmn
HIGHLIGHT:      In this paper, we propose a scene graph guided modular network (SGMN), which performs reasoning over a semantic graph and a scene graph with neural modules under the guidance of the linguistic structure of the expression.

76, TITLE:      Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey
http://arxiv.org/abs/2004.08821
AUTHORS:        Florenc Demrozi ; Graziano Pravadelli ; Azra Bihorac ; Parisa Rashidi
COMMENTS:       31 Pages, 11 Figures, 6 Tabels Paper under review process
HIGHLIGHT:      This survey focuses on critical applications of Machine Learning (ML) in the fields of HAR, largely oriented to Daily Life Activities by presenting an overview of the publications on HAR based on ML and inertial, physiological and environmental sensors.

77, TITLE:      Cosmetic-Aware Makeup Cleanser
http://arxiv.org/abs/2004.09147
AUTHORS:        Yi Li ; Huaibo Huang ; Junchi Yu ; Ran He ; Tieniu Tan
COMMENTS:       Accepted by BTAS 2019 (the 10th IEEE International Conference on Biometrics: Theory, Applications and Systems)
HIGHLIGHT:      With the rapid development of deep generative models, this paper proposes a semanticaware makeup cleanser (SAMC) to remove facial makeup under different poses and expressions and achieve verification via generation.

78, TITLE:      Spatial Action Maps for Mobile Manipulation
http://arxiv.org/abs/2004.09141
AUTHORS:        Jimmy Wu ; Xingyuan Sun ; Andy Zeng ; Shuran Song ; Johnny Lee ; Szymon Rusinkiewicz ; Thomas Funkhouser
COMMENTS:       Project webpage: https://spatial-action-maps.cs.princeton.edu
HIGHLIGHT:      This paper proposes a new action representation for learning to perform complex mobile manipulation tasks.

79, TITLE:      Recurrent Convolutional Neural Networks help to predict location of Earthquakes
http://arxiv.org/abs/2004.09140
AUTHORS:        Roman Kail ; Alexey Zaytsev ; Evgeny Burnaev
HIGHLIGHT:      Our data-based classification model aims to predict if an earthquake with the magnitude above a threshold takes place at a given area of size $10 \times 10$ kilometers in $30$-$180$ days from a given moment.

80, TITLE:      Transformer Reasoning Network for Image-Text Matching and Retrieval
http://arxiv.org/abs/2004.09144
AUTHORS:        Nicola Messina ; Fabrizio Falchi ; Andrea Esuli ; Giuseppe Amato
COMMENTS:       Submitted to ICPR 2020
HIGHLIGHT:      In this work, we consider the problem of accurate image-text matching for the task of multi-modal large-scale information retrieval.

81, TITLE:      Variational Inference for Learning Representations of Natural Language Edits
http://arxiv.org/abs/2004.09143
AUTHORS:        Edison Marrese-Taylor ; Machel Reid ; Yutaka Matsuo
HIGHLIGHT:      With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process.

82, TITLE:      Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients
http://arxiv.org/abs/2004.09134
AUTHORS:        Florenc Demrozi ; Graziano Pravadelli ; Patrick J Tighe ; Azra Bihorac ; Parisa Rashidi
COMMENTS:       Accepted for Publication in EMBC 2020
HIGHLIGHT:      In this study, we collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device.

83, TITLE:      Pose Manipulation with Identity Preservation
http://arxiv.org/abs/2004.09169
AUTHORS:        Andrei-Timotei Ardelean ; Lucian Mircea Sasu
COMMENTS:       9 pages, journal article
HIGHLIGHT:      This paper describes a new model which generates images in novel poses e.g. by altering face expression and orientation, from just a few instances of a human subject.

84, TITLE:      VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
http://arxiv.org/abs/2004.09164
AUTHORS:        Xiangyu Zhu ; Zhenbo Luo ; Pei Fu ; Xiang Ji
HIGHLIGHT:      In this work, we focus on the failure cases caused by similar background and shape.

85, TITLE:      CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
http://arxiv.org/abs/2004.09167
AUTHORS:        Akshay Smit ; Saahil Jain ; Pranav Rajpurkar ; Anuj Pareek ; Andrew Y. Ng ; Matthew P. Lungren
HIGHLIGHT:      In this work, we investigate BERT-based approaches to medical image report labeling that exploit both the scale of available rule-based systems and the quality of expert annotations.

86, TITLE:      Invariant Integration in Deep Convolutional Feature Space
http://arxiv.org/abs/2004.09166
AUTHORS:        Matthias Rath ; Alexandru Paul Condurache
COMMENTS:       Accepted at ESANN 2020 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)
HIGHLIGHT:      In this contribution, we show how to incorporate prior knowledge to a deep neural network architecture in a principled manner.

87, TITLE:      On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
http://arxiv.org/abs/2004.09189
AUTHORS:        Chen Wu ; Prince Zizhuang Wang ; William Yang Wang
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.

88, TITLE:      Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
http://arxiv.org/abs/2004.09188
AUTHORS:        Anh Viet Do ; Jakob Bossek ; Aneta Neumann ; Frank Neumann
COMMENTS:       11 pages, 3 tables, 3 figures, to be published in GECCO '20
HIGHLIGHT:      With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP).

89, TITLE:      GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples
http://arxiv.org/abs/2004.09179
AUTHORS:        Julia Lust ; Alexandru Paul Condurache
COMMENTS:       Accepted at ESANN 2020 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)
HIGHLIGHT:      This paper therefore proposes GraN, a time- and parameter-efficient method that is easily adaptable to any DNN.

90, TITLE:      Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model
http://arxiv.org/abs/2004.09190
AUTHORS:        Juyong Zhang ; Hongrui Cai ; Yudong Guo ; Zhuang Peng
COMMENTS:       The code is available at https://github.com/Juyong/CaricatureFace
HIGHLIGHT:      Based on the constructed dataset and the nonlinear parametric model, we propose a neural network based method to regress the 3D face shape and orientation from the input 2D caricature image. To this end, we first build a dataset with various styles of 2D caricatures and their corresponding 3D shapes, and then build a parametric model on vertex based deformation space for 3D caricature face.

91, TITLE:      LSM: Learning Subspace Minimization for Low-level Vision
http://arxiv.org/abs/2004.09197
AUTHORS:        Chengzhou Tang ; Lu Yuan ; Ping Tan
COMMENTS:       To be presented at CVPR2020
HIGHLIGHT:      We study the energy minimization problem in low-level vision tasks from a novel perspective.

92, TITLE:      Generative Feature Replay For Class-Incremental Learning
http://arxiv.org/abs/2004.09199
AUTHORS:        Xialei Liu ; Chenshen Wu ; Mikel Menta ; Luis Herranz ; Bogdan Raducanu ; Andrew D. Bagdanov ; Shangling Jui ; Joost van de Weijer
COMMENTS:       Accepted at CVPR2020: Workshop on Continual Learning in Computer Vision
HIGHLIGHT:      We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars.

93, TITLE:      CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
http://arxiv.org/abs/2004.08697
AUTHORS:        Mengyue Yang ; Furui Liu ; Zhitang Chen ; Xinwei Shen ; Jianye Hao ; Jun Wang
HIGHLIGHT:      We thus propose a new VAE based framework named CausalVAE, which includes causal layers to transform independent factors into causal factors that correspond to causally related concepts in data.

94, TITLE:      A fast semi-automatic method for classification and counting the number and types of blood cells in an image
http://arxiv.org/abs/2004.08690
AUTHORS:        Hamed Sadeghi ; Shahram Shirani ; David W. Capson
HIGHLIGHT:      A novel and fast semi-automatic method for segmentation, locating and counting blood cells in an image is proposed.

95, TITLE:      Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
http://arxiv.org/abs/2004.08694
AUTHORS:        Kaustubh D. Dhole ; Christopher D. Manning
COMMENTS:       In Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs.

96, TITLE:      Attention, please: A Spatio-temporal Transformer for 3D Human Motion Prediction
http://arxiv.org/abs/2004.08692
AUTHORS:        Emre Aksan ; Peng Cao ; Manuel Kaufmann ; Otmar Hilliges
HIGHLIGHT:      In this paper, we propose a novel architecture for the task of 3D human motion modelling.

97, TITLE:      A Large Dataset of Historical Japanese Documents with Complex Layouts
http://arxiv.org/abs/2004.08686
AUTHORS:        Zejiang Shen ; Kaixuan Zhang ; Melissa Dell
COMMENTS:       8 pages, 8 figures, accepted at CVPR2020 Workshop on Text and Documents in the Deep Learning Era
HIGHLIGHT:      To this end, we present HJDataset, a Large Dataset of Historical Japanese Documents with Complex Layouts.

98, TITLE:      Compositionality and Generalization in Emergent Languages
http://arxiv.org/abs/2004.09124
AUTHORS:        Rahma Chaabouni ; Eugene Kharitonov ; Diane Bouchacourt ; Emmanuel Dupoux ; Marco Baroni
HIGHLIGHT:      In this paper, we study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations, and whether it accomplishes this feat by strategies akin to human-language compositionality.

99, TITLE:      SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
http://arxiv.org/abs/2004.08728
AUTHORS:        Masoud Jalili Sabet ; Philipp Dufter ; Hinrich Schütze
HIGHLIGHT:      We propose word alignment methods that require no parallel data.

100, TITLE:     Pro-Russian Biases in Anti-Chinese Tweets about the Novel Coronavirus
http://arxiv.org/abs/2004.08726
AUTHORS:        Autumn Toney ; Akshat Pandey ; Wei Guo ; David Broniatowski ; Aylin Caliskan
COMMENTS:       6 pages, 2 tables
HIGHLIGHT:      We introduce extensions of the Word Embedding Association Test from Caliskan et.

101, TITLE:     Enhancing Pharmacovigilance with Drug Reviews and Social Media
http://arxiv.org/abs/2004.08731
AUTHORS:        Brent Biseda ; Katie Mo
HIGHLIGHT:      This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs).

102, TITLE:     Fewer colors for perfect simulation of proper colorings
http://arxiv.org/abs/2004.08716
AUTHORS:        Mark Huber
COMMENTS:       13 pages
HIGHLIGHT:      Here a new randomized algorithm is presented based upon the randomness recycler protocol introduced by the author and Fill at FOCS 2000.

103, TITLE:     Device Authentication Codes based on RF Fingerprinting using Deep Learning
http://arxiv.org/abs/2004.08742
AUTHORS:        Joshua Bassey ; Xiangfang Li ; Lijun Qian
HIGHLIGHT:      In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures.

104, TITLE:     An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network
http://arxiv.org/abs/2004.08740
AUTHORS:        Yong Wang ; Qi Liu ; Hongyu Zu ; Xiao Liu ; Ruichao Xie ; Feng Wang
HIGHLIGHT:      In this paper, a novel end-to-end CNN framework for polarization vision tasks is proposed, which enables the networks to take full advantage of polarimetric images.

105, TITLE:     Tensor completion using enhanced multiple modes low-rank prior and total variation
http://arxiv.org/abs/2004.08747
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
HIGHLIGHT:      In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor.

106, TITLE:     Learning to Evaluate Perception Models Using Planner-Centric Metrics
http://arxiv.org/abs/2004.08745
AUTHORS:        Jonah Philion ; Amlan Kar ; Sanja Fidler
COMMENTS:       CVPR 2020 poster
HIGHLIGHT:      In this paper, we propose a principled metric for 3D object detection specifically for the task of self-driving.

107, TITLE:     Are we pretraining it right? Digging deeper into visio-linguistic pretraining
http://arxiv.org/abs/2004.08744
AUTHORS:        Amanpreet Singh ; Vedanuj Goswami ; Devi Parikh
COMMENTS:       23 pages, 6 figures. First two authors contributed equally. More info at https://github.com/facebookresearch/pythia
HIGHLIGHT:      In this work, we question some of the default choices made in literature.

108, TITLE:     Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
http://arxiv.org/abs/2004.08763
AUTHORS:        Homanga Bharadhwaj ; Kevin Xie ; Florian Shkurti
COMMENTS:       L4DC 2020; Accepted for presentation in the 2nd Annual Conference on Learning for Dynamics and Control
HIGHLIGHT:      We propose a method to solve this planning problem by interleaving CEM and gradient descent steps in optimizing the action sequence.

109, TITLE:     TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
http://arxiv.org/abs/2004.08769
AUTHORS:        Subhankar Roy ; Aliaksandr Siarohin ; Enver Sangineto ; Nicu Sebe ; Elisa Ricci
HIGHLIGHT:      In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.

110, TITLE:     Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems
http://arxiv.org/abs/2004.08761
AUTHORS:        Hao Li ; Aozhou Wu ; Wen Fang ; Qingqing Zhang ; Mingqing Liu ; Qingwen Liu ; Wei Chen
HIGHLIGHT:      Thus, we propose a machine learning detection approach which provides a lighter and faster model based on traditional Mask R-CNN.

111, TITLE:     Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning
http://arxiv.org/abs/2004.08798
AUTHORS:        Hongcai Xu ; Junpeng Bao ; Junqing Wang
COMMENTS:       15 pages,7 figures
HIGHLIGHT:      To overcome this drawback, we propose a knowledge graph based proactive dialogue generation model (KgDg) with three components, improved model-agnostic meta-learning algorithm (MAML), knowledge selection in knowledge triplets embedding, and knowledge aware proactive response generator.

112, 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.

113, TITLE:     Extractive Summarization as Text Matching
http://arxiv.org/abs/2004.08795
AUTHORS:        Ming Zhong ; Pengfei Liu ; Yiran Chen ; Danqing Wang ; Xipeng Qiu ; Xuanjing Huang
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.

114, TITLE:     On the Unusual Effectiveness of Type-aware Mutations for Testing SMT Solvers
http://arxiv.org/abs/2004.08799
AUTHORS:        Dominik Winterer ; Chengyu Zhang ; Zhendong Su
HIGHLIGHT:      We propose type-aware mutation testing, a simple and effective approach for testing SMT solvers.The key idea is to mutate operators of same type in SMT formulas to generate type-correct mutant formulas.

115, TITLE:     UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
http://arxiv.org/abs/2004.08790
AUTHORS:        Huimin Huang ; Lanfen Lin ; Ruofeng Tong ; Hongjie Hu ; Qiaowei Zhang ; Yutaro Iwamoto ; Xianhua Han ; Yen-Wei Chen ; Jian Wu
HIGHLIGHT:      In this paper, we propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions.

116, TITLE:     Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews
http://arxiv.org/abs/2004.08793
AUTHORS:        Gino V. H. Mangnoesing ; Maria Mihaela Trusca ; Flavius Frasincar
COMMENTS:       Accepted for publication in the 25th International Conference on Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken Germany, June 24-26 2020
HIGHLIGHT:      In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement.

117, TITLE:     AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
http://arxiv.org/abs/2004.08787
AUTHORS:        Yunpeng Zhai ; Shijian Lu ; Qixiang Ye ; Xuebo Shan ; Jie Chen ; Rongrong Ji ; Yonghong Tian
HIGHLIGHT:      This paper presents a novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters.

118, TITLE:     BanFakeNews: A Dataset for Detecting Fake News in Bangla
http://arxiv.org/abs/2004.08789
AUTHORS:        Md Zobaer Hossain ; Md Ashraful Rahman ; Md Saiful Islam ; Sudipta Kar
COMMENTS:       LREC 2020
HIGHLIGHT:      In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla.

119, TITLE:     Decision Problems in Information Theory
http://arxiv.org/abs/2004.08783
AUTHORS:        Mahmoud Abo Khamis ; Phokion G. Kolaitis ; Hung Q. Ngo ; Dan Suciu
HIGHLIGHT:      Here, we initiate an investigation of decision problems about constraints on entropies by placing several different such problems into levels of the arithmetical hierarchy.

120, TITLE:     Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging
http://arxiv.org/abs/2004.08782
AUTHORS:        Ali Hariri ; Kamran Alipour ; Yash Mantri ; Jurgen P. Schulze ; Jesse V. Jokerst
COMMENTS:       submitted to Biomedical Optics Express journal
HIGHLIGHT:      Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map.

121, TITLE:     Efficient Synthesis of Compact Deep Neural Networks
http://arxiv.org/abs/2004.08704
AUTHORS:        Wenhan Xia ; Hongxu Yin ; Niraj K. Jha
HIGHLIGHT:      In this paper, we review major approaches for automatically synthesizing compact, yet accurate, DNN/LSTM models suitable for real-world applications.

122, TITLE:     Adaptive Attention Span in Computer Vision
http://arxiv.org/abs/2004.08708
AUTHORS:        Jerrod Parker ; Shakti Kumar ; Joe Roussy
COMMENTS:       6 pages with 1 Algorithm, 4 figures, 1 Table and 1 page appendix
HIGHLIGHT:      In this work we propose a novel method for learning the local self-attention kernel size.

123, TITLE:     Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
http://arxiv.org/abs/2004.09015
AUTHORS:        Frank F. Xu ; Zhengbao Jiang ; Pengcheng Yin ; Bogdan Vasilescu ; Graham Neubig
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation.

124, TITLE:     DeepSDF x Sim(3): Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
http://arxiv.org/abs/2004.09048
AUTHORS:        Oladapo Afolabi ; Allen Yang ; Shankar S. Sastry
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we present a formulation that overcomes this issue by jointly estimating the shape and similarity transformation parameters.

125, TITLE:     Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
http://arxiv.org/abs/2004.09043
AUTHORS:        Eric Zelikman ; William Yin ; Kenneth Wang
COMMENTS:       Originally completed as part of Stanford's CS 234 "Reinforcement Learning." Presented at the California Cognitive Science Conference 2019
HIGHLIGHT:      A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards.

126, TITLE:     Taming the Expressiveness and Programmability of Graph Analytical Queries
http://arxiv.org/abs/2004.09045
AUTHORS:        Lu Qin ; Longbin Lai ; Kongzhang Hao ; Zhongxin Zhou ; Yiwei Zhao ; Yuxing Han ; Xuemin Lin ; Zhengping Qian ; Jingren Zhou
COMMENTS:       22 pages
HIGHLIGHT:      We focus on analytical queries in this paper.

127, TITLE:     Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
http://arxiv.org/abs/2004.09044
AUTHORS:        Yixin Zhu ; Tao Gao ; Lifeng Fan ; Siyuan Huang ; Mark Edmonds ; Hangxin Liu ; Feng Gao ; Chi Zhang ; Siyuan Qi ; Ying Nian Wu ; Joshua B. Tenenbaum ; Song-Chun Zhu
COMMENTS:       For high quality figures, please refer to http://wellyzhang.github.io/attach/dark.pdf
HIGHLIGHT:      In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data.

128, TITLE:     X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions
http://arxiv.org/abs/2004.09039
AUTHORS:        Michael Danielczuk ; Anelia Angelova ; Vincent Vanhoucke ; Ken Goldberg
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      For mechanical search, we introduce X-Ray, an algorithm based on learned occupancy distributions.

129, TITLE:     Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
http://arxiv.org/abs/2004.09036
AUTHORS:        Yefei Zha ; Ruobing Li ; Hui Lin
COMMENTS:       10 pages, 4 figures and 5 tables
HIGHLIGHT:      In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts.

130, TITLE:     Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
http://arxiv.org/abs/2004.09034
AUTHORS:        Damien Teney ; Ehsan Abbasnedjad ; Anton van den Hengel
HIGHLIGHT:      We propose an auxiliary training objective that improves the generalization capabilities of neural networks by leveraging an overlooked supervisory signal found in existing datasets.

131, TITLE:     OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
http://arxiv.org/abs/2004.09033
AUTHORS:        Xiaoxu Li ; Dongliang Chang ; Zhanyu Ma ; Zheng-Hua Tan ; Jing-Hao Xue ; Jie Cao ; Jingyi Yu ; Jun Guo
COMMENTS:       TIP 2020. Code available at https://github.com/dongliangchang/OSLNet
HIGHLIGHT:      To this end, this paper aims to find a subspace of neural networks that can facilitate a large decision margin.

132, TITLE:     Semantic Correspondence via 2D-3D-2D Cycle
http://arxiv.org/abs/2004.09061
AUTHORS:        Yang You ; Chengkun Li ; Yujing Lou ; Zhoujun Cheng ; Lizhuang Ma ; Cewu Lu ; Weiming Wang
HIGHLIGHT:      In this paper, we propose a new method on predicting semantic correspondences by leveraging it to 3D domain and then project corresponding 3D models back to 2D domain, with their semantic labels.

133, TITLE:     Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation
http://arxiv.org/abs/2004.09050
AUTHORS:        Archna Bhatia ; Adam Dalton ; Brodie Mather ; Sashank Santhanam ; Samira Shaikh ; Alan Zemel ; Tomek Strzalkowski ; Bonnie J. Dorr
COMMENTS:       Accepted at STOC
HIGHLIGHT:      We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation.

134, TITLE:     Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
http://arxiv.org/abs/2004.09057
AUTHORS:        Congcong Wen ; Xiang Li ; Xiaojing Yao ; Ling Peng ; Tianhe Chi
HIGHLIGHT:      In this paper, we present a graph attention convolution neural network (GACNN) that can be directly applied to the classification of unstructured 3D point clouds obtained by airborne LiDAR.

135, TITLE:     Colonoscope tracking method based on shape estimation network
http://arxiv.org/abs/2004.09056
AUTHORS:        Masahiro Oda ; Holger R. Roth ; Takayuki Kitasaka ; Kazuhiro Furukawa ; Ryoji Miyahara ; Yoshiki Hirooka ; Nassir Navab ; Kensaku Mori
COMMENTS:       Accepted paper as an oral presentation at SPIE Medical Imaging 2019, San Diego, CA, USA
HIGHLIGHT:      This paper presents a colonoscope tracking method utilizing a colon shape estimation method.

136, TITLE:     Deep Exposure Fusion with Deghosting via Homography Estimation and Attention Learning
http://arxiv.org/abs/2004.09089
AUTHORS:        Sheng-Yeh Chen ; Yung-Yu Chuang
COMMENTS:       ICASSP 2020
HIGHLIGHT:      This paper proposes a deep network for exposure fusion.

137, TITLE:     CatSIM: A Categorical Image Similarity Metric
http://arxiv.org/abs/2004.09073
AUTHORS:        Geoffrey Z. Thompson ; Ranjan Maitra
COMMENTS:       17 pages, 16 figures, 10 tables
HIGHLIGHT:      We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes.

138, TITLE:     Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks
http://arxiv.org/abs/2004.08572
AUTHORS:        Sudeep Kondal ; Viraj Kulkarni ; Ashrika Gaikwad ; Amit Kharat ; Aniruddha Pant
COMMENTS:       5 pages, 3 figures, 5 tables
HIGHLIGHT:      In this paper, we propose a novel method using convolutional neural networks to automatically grade knee radiographs on the KL scale.

139, TITLE:     On the Synergies between Machine Learning and Stereo: a Survey
http://arxiv.org/abs/2004.08566
AUTHORS:        Matteo Poggi ; Fabio Tosi ; Konstantinos Batsos ; Philippos Mordohai ; Stefano Mattoccia
COMMENTS:       Paper version of our CVPR 2019 tutorial: "Learning-based depth estimation from stereo and monocular images: successes, limitations and future challenges" (https://sites.google.com/view/cvpr-2019-depth-from-image/home)
HIGHLIGHT:      In this paper, we review recent research in the field of learning-based depth estimation from images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future.

140, TITLE:     Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network
http://arxiv.org/abs/2004.09412
AUTHORS:        Ji Gan ; Weiqiang Wang ; Ke Lu
COMMENTS:       8 pages, 4 figures. A full version of this paper has been submitted to an international journal
HIGHLIGHT:      Instead of viewing characters as either static images or temporal trajectories, here we propose to represent characters as geometric graphs, retaining both spatial structures and temporal orders.

141, TITLE:     Shape-Oriented Convolution Neural Network for Point Cloud Analysis
http://arxiv.org/abs/2004.09411
AUTHORS:        Chaoyi Zhang ; Yang Song ; Lina Yao ; Weidong Cai
COMMENTS:       8 pages, 6 figures, AAAI2020
HIGHLIGHT:      Shape-Oriented Convolution Neural Network for Point Cloud Analysis

142, TITLE:     VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
http://arxiv.org/abs/2004.09416
AUTHORS:        Hyeryung Jang ; Nicolas Skatchkovsky ; Osvaldo Simeone
COMMENTS:       14 pages, submitted for possible conference publication
HIGHLIGHT:      In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits.

143, TITLE:     PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
http://arxiv.org/abs/2004.09447
AUTHORS:        Vivek Srivastava ; Mayank Singh
HIGHLIGHT:      This paper presents a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. We are releasing the parallel corpus to facilitate future research opportunities in code-mixed machine translation.

144, TITLE:     Three Modern Roles for Logic in AI
http://arxiv.org/abs/2004.08599
AUTHORS:        Adnan Darwiche
COMMENTS:       To be published in PODS 2020
HIGHLIGHT:      We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.

145, TITLE:     A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations
http://arxiv.org/abs/2004.09443
AUTHORS:        Ning Zhang ; Susan Francis ; Rayaz Malik ; Xin Chen
COMMENTS:       4 pages, accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 2020
HIGHLIGHT:      In this paper, we propose a spatially constrained deep convolutional neural network (DCNN) to achieve smooth and robust image segmentation using inaccurately annotated labels for training.

146, TITLE:     DAPnet: A double self-attention convolutional network for segmentation of point clouds
http://arxiv.org/abs/2004.08596
AUTHORS:        Li Chen ; Zewei Xu ; Yongjian Fu ; Haozhe Huang ; Shaowen Wang ; Haifeng Li
COMMENTS:       13 pages, 9 figures
HIGHLIGHT:      In this study, we propose a double self-attention convolutional network, called DAPnet, by combining geometric and contextual features to generate better segmentation results.

147, TITLE:     Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton
http://arxiv.org/abs/2004.08595
AUTHORS:        Jiang-Jiang Liu ; Qibin Hou ; Ming-Ming Cheng
HIGHLIGHT:      In this paper, we solve three low-level pixel-wise vision problems, including salient object segmentation, edge detection, and skeleton extraction, within a unified framework.

148, TITLE:     Improving correlation method with convolutional neural networks
http://arxiv.org/abs/2004.09430
AUTHORS:        Dmitriy Goncharov ; Rostislav Starikov
COMMENTS:       8 pages, 3 figures, 2 tables, 1 formula
HIGHLIGHT:      We present a convolutional neural network for the classification of correlation responses obtained by correlation filters.

149, TITLE:     BiFNet: Bidirectional Fusion Network for Road Segmentation
http://arxiv.org/abs/2004.08582
AUTHORS:        Haoran Li ; Yaran Chen ; Qichao Zhang ; Dongbin Zhao
HIGHLIGHT:      Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud.

150, TITLE:     StereoSet: Measuring stereotypical bias in pretrained language models
http://arxiv.org/abs/2004.09456
AUTHORS:        Moin Nadeem ; Anna Bethke ; Siva Reddy
COMMENTS:       9 pages, 6 tables, and 3 figures
HIGHLIGHT:      We present StereoSet, a large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion.

151, TITLE:     The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case
http://arxiv.org/abs/2004.09002
AUTHORS:        Edward Farhi ; David Gamarnik ; Sam Gutmann
COMMENTS:       19 pages, no figures
HIGHLIGHT:      The Quantum Approximate Optimization Algorithm can naturally be applied to combinatorial search problems on graphs.

152, TITLE:     Bringing Old Photos Back to Life
http://arxiv.org/abs/2004.09484
AUTHORS:        Ziyu Wan ; Bo Zhang ; Dongdong Chen ; Pan Zhang ; Dong Chen ; Jing Liao ; Fang Wen
COMMENTS:       CVPR 2020 Oral, project website: http://raywzy.com/Old_Photo/
HIGHLIGHT:      We propose to restore old photos that suffer from severe degradation through a deep learning approach.

153, TITLE:     Music Gesture for Visual Sound Separation
http://arxiv.org/abs/2004.09476
AUTHORS:        Chuang Gan ; Deng Huang ; Hang Zhao ; Joshua B. Tenenbaum ; Antonio Torralba
COMMENTS:       CVPR 2020. Project page: http://music-gesture.csail.mit.edu
HIGHLIGHT:      To address this, we propose "Music Gesture," a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music.

154, TITLE:     Attention Routing: track-assignment detailed routing using attention-based reinforcement learning
http://arxiv.org/abs/2004.09473
AUTHORS:        Haiguang Liao ; Qingyi Dong ; Xuliang Dong ; Wentai Zhang ; Wangyang Zhang ; Weiyi Qi ; Elias Fallon ; Levent Burak Kara
HIGHLIGHT:      In this work, we propose a new router: attention router, which is the first attempt to solve the track-assignment detailed routing problem using reinforcement learning.

155, TITLE:     Accumulator Bet Selection Through Stochastic Diffusion Search
http://arxiv.org/abs/2004.08607
AUTHORS:        Nassim Dehouche
HIGHLIGHT:      We address this relatively under-studied combinatorial aspect of sports betting, and propose a binary optimization model for the problem of selecting the most promising combinations of matches, in terms of their total potential payout and probability of a win, to form an accumulator bet.

156, TITLE:     Feathers dataset for Fine-Grained Visual Categorization
http://arxiv.org/abs/2004.08606
AUTHORS:        Alina Belko ; Konstantin Dobratulin ; Andrey Kuznetsov
COMMENTS:       6 pages, 6 figures, 3 tables
HIGHLIGHT:      This paper introduces a novel dataset FeatherV1, containing 28,272 images of feathers categorized by 595 bird species.

157, TITLE:     Underwater image enhancement with Image Colorfulness Measure
http://arxiv.org/abs/2004.08609
AUTHORS:        Hui Li ; Xi Yang ; ZhenMing Li ; TianLun Zhang
HIGHLIGHT:      To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model.

158, TITLE:     Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships
http://arxiv.org/abs/2004.08614
AUTHORS:        Kuldeep Kulkarni ; Tejas Gokhale ; Rajhans Singh ; Pavan Turaga ; Aswin Sankaranarayanan
COMMENTS:       Image synthesis, GAN, Scene completion, Label maps
HIGHLIGHT:      We address the new problem of complex scene completion from sparse label maps.

159, TITLE:     Time Adaptive Reinforcement Learning
http://arxiv.org/abs/2004.08600
AUTHORS:        Chris Reinke
COMMENTS:       ICLR 2020 Workshop: Beyond Tabula Rasa in Reinforcement Learning
HIGHLIGHT:      We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble.

160, TITLE:     Single-step Adversarial training with Dropout Scheduling
http://arxiv.org/abs/2004.08628
AUTHORS:        Vivek B. S. ; R. Venkatesh Babu
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, (i) we show that models trained using single-step adversarial training method learn to prevent the generation of single-step adversaries, and this is due to over-fitting of the model during the initial stages of training, and (ii) to mitigate this effect, we propose a single-step adversarial training method with dropout scheduling.

161, TITLE:     Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module
http://arxiv.org/abs/2004.08656
AUTHORS:        Yanlu Wei ; Renshuai Tao ; Zhangjie Wu ; Yuqing Ma ; Libo Zhang ; Xianglong Liu
COMMENTS:       10 pages, 8 figures; for data and code, see https://github.com/OPIXray-author/OPIXray
HIGHLIGHT:      We evaluate our method on the OPIXray dataset and compare it to several baselines, including popular methods for detection and attention mechanisms. Second, we present a well-directed dataset, of which the images in the dataset are annotated manually by the professional inspectors from Beijing Capital International Airport.

162, TITLE:     Motion Segmentation using Frequency Domain Transformer Networks
http://arxiv.org/abs/2004.08638
AUTHORS:        Hafez Farazi ; Sven Behnke
COMMENTS:       28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2020
HIGHLIGHT:      To address this task, we propose a novel end-to-end learnable architecture that predicts the next frame by modeling foreground and background separately while simultaneously estimating and predicting the foreground motion using Frequency Domain Transformer Networks.

163, TITLE:     A Deep Learning Approach to Object Affordance Segmentation
http://arxiv.org/abs/2004.08644
AUTHORS:        Spyridon Thermos ; Petros Daras ; Gerasimos Potamianos
COMMENTS:       5 pages, 4 figures, ICASSP 2020
HIGHLIGHT:      In this paper, we propose a novel approach that exploits the spatio-temporal nature of human-object interaction for affordance segmentation. For evaluation purposes, we introduce the SOR3D-AFF corpus, which consists of human-object interaction sequences and supports 9 types of affordances in terms of pixel-wise annotation, covering typical manipulations of tool-like objects.

164, TITLE:     Modeling Survival in model-based Reinforcement Learning
http://arxiv.org/abs/2004.08648
AUTHORS:        Saeed Moazami ; Peggy Doerschuk
HIGHLIGHT:      To that end, a substitute model for the reward function approximator is introduced that learns to avoid terminal states rather than to maximize accumulated rewards from safe states.

165, TITLE:     Macro-Action-Based Deep Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2004.08646
AUTHORS:        Yuchen Xiao ; Joshua Hoffman ; Christopher Amato
HIGHLIGHT:      This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case.

166, TITLE:     A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention
http://arxiv.org/abs/2004.08673
AUTHORS:        Maria Mihaela Trusca ; Daan Wassenberg ; Flavius Frasincar ; Rommert Dekker
COMMENTS:       Accepted for publication in the 20th International Conference on Web Engineering (ICWE 2020), Helsinki Finland, 9-12 June 2020
HIGHLIGHT:      In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions.

167, TITLE:     iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots
http://arxiv.org/abs/2004.08672
AUTHORS:        Shiqi Zhang ; Peter Stone
HIGHLIGHT:      In this article, we present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers.

168, TITLE:     Dual Embedding Expansion for Vehicle Re-identification
http://arxiv.org/abs/2004.08665
AUTHORS:        Clint Sebastian ; Raffaele Imbriaco ; Egor Bondarev ; Peter H. N. de With
HIGHLIGHT:      We propose an efficient approach for combining the outputs of multiple models at various scales while exploiting tracklet and neighbor information, called dual embedding expansion (DEx).

169, TITLE:     The $(1+(λ,λ))$ Genetic Algorithm for Permutations
http://arxiv.org/abs/2004.08664
AUTHORS:        Anton Bassin ; Maxim Buzdalov
COMMENTS:       This contribution was submitted to the GECCO 2020 workshop on permutation-based problems
HIGHLIGHT:      We aim at improving this situation by proposing an adaptation of the $(1+(\lambda,\lambda))$ genetic algorithm to permutation-based problems.

170, TITLE:     The State and Fate of Linguistic Diversity and Inclusion in the NLP World
http://arxiv.org/abs/2004.09095
AUTHORS:        Pratik Joshi ; Sebastin Santy ; Amar Budhiraja ; Kalika Bali ; Monojit Choudhury
COMMENTS:       Accepted at ACL 2020 (10 pages + 3 pages Appendix)
HIGHLIGHT:      In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time.



==========Updates to Previous Papers==========
1, TITLE:       Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning
http://arxiv.org/abs/1912.08167
AUTHORS:        Matteo Rucco ; Lorenzo Falsetti ; Giovanna Viticchi
COMMENTS:       22 pages; 16 figures
HIGHLIGHT:      In the present investigation, we intend to demonstrate by means of numerical experiments that topological features combined with textural features can be enrolled for GBM analysis and morphological characterization on FLAIR.

2, TITLE:       Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda
http://arxiv.org/abs/2001.05065
AUTHORS:        Jake Gutierrez ; Jacob Schrum
COMMENTS:       Congress on Evolutionary Computation 2020
HIGHLIGHT:      This paper combines a GAN approach to generating individual rooms with a graph grammar approach to combining rooms into a dungeon.

3, TITLE:       Track to Reconstruct and Reconstruct to Track
http://arxiv.org/abs/1910.00130
AUTHORS:        Jonathon Luiten ; Tobias Fischer ; Bastian Leibe
COMMENTS:       RA-L 2020 and ICRA 2020
HIGHLIGHT:      We propose a novel method that closes this loop, first tracking to reconstruct, and then reconstructing to track.

4, TITLE:       XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
http://arxiv.org/abs/2004.01401
AUTHORS:        Yaobo Liang ; Nan Duan ; Yeyun Gong ; Ning Wu ; Fenfei Guo ; Weizhen Qi ; Ming Gong ; Linjun Shou ; Daxin Jiang ; Guihong Cao ; Xiaodong Fan ; Bruce Zhang ; Rahul Agrawal ; Edward Cui ; Sining Wei ; Taroon Bharti ; Ying Qiao ; Jiun-Hung Chen ; Winnie Wu ; Shuguang Liu ; Fan Yang ; Rangan Majumder ; Ming Zhou
HIGHLIGHT:      In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.

5, TITLE:       Verification of Markov Decision Processes with Risk-Sensitive Measures
http://arxiv.org/abs/1803.00091
AUTHORS:        Murat Cubuktepe ; Ufuk Topcu
COMMENTS:       7 pages, to appear in ACC 2018
HIGHLIGHT:      Specifically, we use a particular risk-sensitive measure from cumulative prospect theory, which has been previously adopted in psychology and economics.

6, TITLE:       MulayCap: Multi-layer Human Performance Capture Using A Monocular Video Camera
http://arxiv.org/abs/2004.05815
AUTHORS:        Zhaoqi Su ; Weilin Wan ; Tao Yu ; Lingjie Liu ; Lu Fang ; Wenping Wang ; Yebin Liu
HIGHLIGHT:      We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning.

7, TITLE:       A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting
http://arxiv.org/abs/1910.03617
AUTHORS:        Manish Bhattarai ; Manel Martínez-Ramón
HIGHLIGHT:      The objective of this research is to create an automated system that is capable of real-time, intelligent object detection and recognition and facilitates the improved situational awareness of firefighters during an emergency response.

8, TITLE:       TypeNet: Scaling up Keystroke Biometrics
http://arxiv.org/abs/2004.03627
AUTHORS:        Alejandro Acien ; John V. Monaco ; Aythami Morales ; Ruben Vera-Rodriguez ; Julian Fierrez
HIGHLIGHT:      We study the suitability of keystroke dynamics to authenticate 100K users typing free-text.

9, TITLE:       Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
http://arxiv.org/abs/1905.00719
AUTHORS:        Rongpeng Li ; Zhifeng Zhao ; Xing Xu ; Fei Ni ; Honggang Zhang
COMMENTS:       6 figures; accepted by IEEE Wireless Commun with the title "The Collective Advantage for Advancing Communications and Intelligence"
HIGHLIGHT:      In particular, we introduce a regular language (i.e., the information economy metalanguage) supporting the future collective communications to augment human intelligence and explain its potential applications in naming Internet information and pushing information centric networks forward.

10, TITLE:      Non-Adversarial Video Synthesis with Learned Priors
http://arxiv.org/abs/2003.09565
AUTHORS:        Abhishek Aich ; Akash Gupta ; Rameswar Panda ; Rakib Hyder ; M. Salman Asif ; Amit K. Roy-Chowdhury
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.

11, TITLE:      Fixing the train-test resolution discrepancy: FixEfficientNet
http://arxiv.org/abs/2003.08237
AUTHORS:        Hugo Touvron ; Andrea Vedaldi ; Matthijs Douze ; Hervé Jégou
HIGHLIGHT:      This note complements the paper "Fixing the train-test resolution discrepancy" that introduced the FixRes method.

12, TITLE:      SpaRCe: Sparse reservoir computing
http://arxiv.org/abs/1912.08124
AUTHORS:        Luca Manneschi ; Andrew C. Lin ; Eleni Vasilaki
HIGHLIGHT:      Inspired by neuroscience, here we introduce sparseness into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to give output but silencing outputs from neurons with high thresholds.

13, TITLE:      Fine-grained Fact Verification with Kernel Graph Attention Network
http://arxiv.org/abs/1910.09796
AUTHORS:        Zhenghao Liu ; Chenyan Xiong ; Maosong Sun ; Zhiyuan Liu
COMMENTS:       Accepted to ACL 2020, 10 pages, 6 figures
HIGHLIGHT:      This paper presents Kernel Graph Attention Network (KGAT), which conducts more fine-grained fact verification with kernel-based attentions.

14, TITLE:      Runners in action
http://arxiv.org/abs/1910.11629
AUTHORS:        Danel Ahman ; Andrej Bauer
COMMENTS:       ESOP 2020 final version + online appendix
HIGHLIGHT:      Runners of algebraic effects, also known as comodels, provide a mathematical model of resource management.

15, TITLE:      Residual-driven Fuzzy C-Means Clustering for Image Segmentation
http://arxiv.org/abs/2004.07160
AUTHORS:        Cong Wang ; Witold Pedrycz ; ZhiWu Li ; MengChu Zhou
COMMENTS:       14 pages, 13 figures, 6 tables
HIGHLIGHT:      We propose a residual-driven FCM framework by integrating into FCM a residual-related fidelity term derived from the distribution of different types of noise.

16, TITLE:      SPECTER: Document-level Representation Learning using Citation-informed Transformers
http://arxiv.org/abs/2004.07180
AUTHORS:        Arman Cohan ; Sergey Feldman ; Iz Beltagy ; Doug Downey ; Daniel S. Weld
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.

17, TITLE:      Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
http://arxiv.org/abs/2002.09253
AUTHORS:        Cédric Colas ; Tristan Karch ; Nicolas Lair ; Jean-Michel Dussoux ; Clément Moulin-Frier ; Peter Ford Dominey ; Pierre-Yves Oudeyer
COMMENTS:       Contains main article and supplementaries
HIGHLIGHT:      We present experiments in a simulated domain where the agent interacts with procedurally generated scenes containing objects of various types and colors, discovers goals, imagines others and learns to achieve them.

18, TITLE:      4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
http://arxiv.org/abs/2002.07089
AUTHORS:        Samaneh Abbasi-Sureshjani ; Sina Amirrajab ; Cristian Lorenz ; Juergen Weese ; Josien Pluim ; Marcel Breeuwer
COMMENTS:       Accepted to MIDL 2020
HIGHLIGHT:      We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images.

19, TITLE:      A Re-evaluation of Knowledge Graph Completion Methods
http://arxiv.org/abs/1911.03903
AUTHORS:        Zhiqing Sun ; Shikhar Vashishth ; Soumya Sanyal ; Partha Talukdar ; Yiming Yang
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem.

20, TITLE:      IrisNet: Deep Learning for Automatic and Real-time Tongue Contour Tracking in Ultrasound Video Data using Peripheral Vision
http://arxiv.org/abs/1911.03972
AUTHORS:        M. Hamed Mozaffari ; Md. Aminur Rab Ratul ; Won-Sook Lee
HIGHLIGHT:      In this paper, we proposed a deep learning model comprises of a convolutional module mimicking the peripheral vision ability of the human eye to handle real-time, accurate, and fully automatic tongue contour tracking tasks, applicable for almost all primary ultrasound tongue datasets.

21, TITLE:      Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseases
http://arxiv.org/abs/2001.05859
AUTHORS:        Ayaka Suzuki ; Yoshiro Suzuki
HIGHLIGHT:      As long as there is a risk of overlooking a disease with a technique, ophthalmologists must double-check even those images that the technique classifies as normal.

22, TITLE:      NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names
http://arxiv.org/abs/1911.09279
AUTHORS:        Guang Jiang ; Mengzhen Shi ; Ying Su ; Pengcheng An ; Brian Y. Lim ; Yunlong Wang
COMMENTS:       DIS '20 Companion
HIGHLIGHT:      This paper presents the system design, the pilot feasibility test, and our plan for the following study, which aims to evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.

23, TITLE:      Learning to Determine the Quality of News Headlines
http://arxiv.org/abs/1911.11139
AUTHORS:        Amin Omidvar ; Hossein Poormodheji ; Aijun An ; Gordon Edall
COMMENTS:       10 Pages, Accepted at the 12th International Conference on Agents and Artificial Intelligence (ICAART) 2020
HIGHLIGHT:      In this paper, we propose four indicators to determine the quality of published news headlines based on their click count and dwell time, which are obtained by website log analysis.

24, TITLE:      Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2004.01098
AUTHORS:        Weichao Mao ; Kaiqing Zhang ; Erik Miehling ; Tamer Başar
COMMENTS:       Submitted to CDC 2020
HIGHLIGHT:      In this work, we investigate a partially observable MARL problem in which agents are cooperative.

25, TITLE:      Hair Segmentation on Time-of-Flight RGBD Images
http://arxiv.org/abs/1903.02775
AUTHORS:        Yuanxi Ma ; Cen Wang ; Shiying Li ; Jingyi Yu
HIGHLIGHT:      We present a novel computational imaging solution that tackles the problem from both input and processing fronts. We collect the first ToF RGBD hair dataset with 20k+ head images captured on 30 human subjects with a variety of hairstyles at different view angles.

26, TITLE:      Fast Hardware-Aware Neural Architecture Search
http://arxiv.org/abs/1910.11609
AUTHORS:        Li Lyna Zhang ; Yuqing Yang ; Yuhang Jiang ; Wenwu Zhu ; Yunxin Liu
HIGHLIGHT:      Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware.

27, TITLE:      Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
http://arxiv.org/abs/2004.05471
AUTHORS:        Han Lin Aung ; Burak Uzkent ; Marshall Burke ; David Lobell ; Stefano Ermon
HIGHLIGHT:      In this paper, we break down this task using satellite imaging into two approaches: 1) Segmentation of parcel boundaries, and 2) Segmentation of parcel areas.

28, TITLE:      ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
http://arxiv.org/abs/2004.05861
AUTHORS:        Fatima Haouari ; Maram Hasanain ; Reem Suwaileh ; Tamer Elsayed
COMMENTS:       Switched abstract from math mode to normal text
HIGHLIGHT:      In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 31st of March 2020.

29, TITLE:      Unsupervised Opinion Summarization as Copycat-Review Generation
http://arxiv.org/abs/1911.02247
AUTHORS:        Arthur Bražinskas ; Mirella Lapata ; Ivan Titov
COMMENTS:       ACL 2020
HIGHLIGHT:      We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the "amount of novelty" going into the new review or, equivalently, vary the extent to which it deviates from the input.

30, TITLE:      Disentangle, align and fuse for multimodal and zero-shot image segmentation
http://arxiv.org/abs/1911.04417
AUTHORS:        Agisilaos Chartsias ; Giorgos Papanastasiou ; Chengjia Wang ; Scott Semple ; David E. Newby ; Rohan Dharmakumar ; Sotirios A. Tsaftaris
HIGHLIGHT:      We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no annotations (zero-shot) are available for this specific modality.

31, TITLE:      Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
http://arxiv.org/abs/1911.12012
AUTHORS:        Shuo Cheng ; Zexiang Xu ; Shilin Zhu ; Zhuwen Li ; Li Erran Li ; Ravi Ramamoorthi ; Hao Su
COMMENTS:       Accepted to CVPR 2020 (Oral)
HIGHLIGHT:      We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images.

32, TITLE:      Role of Spatial Context in Adversarial Robustness for Object Detection
http://arxiv.org/abs/1910.00068
AUTHORS:        Aniruddha Saha ; Akshayvarun Subramanya ; Koninika Patil ; Hamed Pirsiavash
COMMENTS:       CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision
HIGHLIGHT:      In this paper, we examine this problem and design category specific adversarial patches which make a widely used object detector like YOLO blind to an attacker chosen object category.

33, TITLE:      No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
http://arxiv.org/abs/1909.06927
AUTHORS:        Ece Calikus ; Slawomir Nowaczyk ; Anita Sant'Anna ; Onur Dikmen
COMMENTS:       Submitted to Expert Systems with Applications
HIGHLIGHT:      In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure to overcome the limitations of one-size-fits-all solutions.

34, TITLE:      Instant recovery of shape from spectrum via latent space connections
http://arxiv.org/abs/2003.06523
AUTHORS:        Riccardo Marin ; Arianna Rampini ; Umberto Castellani ; Emanuele Rodolà ; Maks Ovsjanikov ; Simone Melzi
HIGHLIGHT:      We introduce the first learning-based method for recovering shapes from Laplacian spectra.

35, TITLE:      The Complexity of Reachability in Affine Vector Addition Systems with States
http://arxiv.org/abs/1909.02579
AUTHORS:        Michael Blondin ; Mikhail Raskin
COMMENTS:       17 pages, 7 figures
HIGHLIGHT:      More specifically, we provide a trichotomy on the complexity of integer reachability in VASS extended with affine operations (affine VASS).

36, TITLE:      Question Classification with Deep Contextualized Transformer
http://arxiv.org/abs/1910.10492
AUTHORS:        Haozheng Luo ; Ningwei Liu ; Charles Feng
HIGHLIGHT:      We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions.

37, TITLE:      Gated Texture CNN for Efficient and Configurable Image Denoising
http://arxiv.org/abs/2003.07042
AUTHORS:        Kaito Imai ; Takamichi Miyata
COMMENTS:       code is available: https://github.com/mdipcit/GTCNN
HIGHLIGHT:      To achieve high performance and computational efficiency, we propose a gated texture CNN (GTCNN), which is designed to carefully exclude the texture information from each intermediate feature map of the CNN by incorporating gating mechanisms.

38, TITLE:      Does Gender Matter? Towards Fairness in Dialogue Systems
http://arxiv.org/abs/1910.10486
AUTHORS:        Haochen Liu ; Jamell Dacon ; Wenqi Fan ; Hui Liu ; Zitao Liu ; Jiliang Tang
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we perform the initial study about the fairness issues in dialogue systems. In particular, we construct the first dataset and propose quantitative measures to understand fairness in dialogue models. We will release the dataset and the measurement code later to foster the fairness research in dialogue systems.

39, TITLE:      Is Supervised Learning With Adversarial Features Provably Better Than Sole Supervision?
http://arxiv.org/abs/1910.13993
AUTHORS:        Litu Rout
HIGHLIGHT:      In this paper, we intend to theoretically analyze whether supervised learning with adversarial features can outperform sole supervision, or not.

40, TITLE:      Spectral Sparsification via Bounded-Independence Sampling
http://arxiv.org/abs/2002.11237
AUTHORS:        Dean Doron ; Jack Murtagh ; Salil Vadhan ; David Zuckerman
COMMENTS:       37 pages
HIGHLIGHT:      We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs.

41, TITLE:      A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
http://arxiv.org/abs/2003.07074
AUTHORS:        Rohan Pandey ; Vaibhav Gautam ; Chirag Jain ; Priyanka Syal ; Himanshu Sharma ; Kanav Bhagat ; Ridam Pal ; Lovedeep Singh Dhingra ;  Arushi ; Lajjaben Patel ; Mudit Agarwal ; Samprati Agrawal ; Manan Arora ; Bhavika Rana ; Ponnurangam Kumaraguru ; Tavpritesh Sethi
COMMENTS:       7 pages, 5 figures, 3 tables
HIGHLIGHT:      In this work, we address this gap by creating a life-long learning application that delivers authentic information to users in Hindi and English, the most widely used languages in India.

42, TITLE:      An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems
http://arxiv.org/abs/2004.02603
AUTHORS:        Florian Fontan ; Luc Libralesso
HIGHLIGHT:      [libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php).

43, TITLE:      Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
http://arxiv.org/abs/1810.10974
AUTHORS:        Simone Palazzo ; Concetto Spampinato ; Isaak Kavasidis ; Daniela Giordano ; Joseph Schmidt ; Mubarak Shah
HIGHLIGHT:      This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines.

44, TITLE:      Meta-Learning Initializations for Image Segmentation
http://arxiv.org/abs/1912.06290
AUTHORS:        Sean M. Hendryx ; Andrew B. Leach ; Paul D. Hein ; Clayton T. Morrison
HIGHLIGHT:      We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks. We also construct a small benchmark dataset, FP-k, for the empirical study of how meta-learning systems perform in both few- and many-shot settings.

45, TITLE:      Learning Physics-guided Face Relighting under Directional Light
http://arxiv.org/abs/1906.03355
AUTHORS:        Thomas Nestmeyer ; Jean-François Lalonde ; Iain Matthews ; Andreas M. Lehrmann
COMMENTS:       CVPR 2020 (Oral)
HIGHLIGHT:      To train and evaluate our model, we collected a portrait database of 21 subjects with various expressions and poses.

46, TITLE:      Graph isomorphism in quasipolynomial time parameterized by treewidth
http://arxiv.org/abs/1911.11257
AUTHORS:        Daniel Wiebking
COMMENTS:       52 pages, 1 figure
HIGHLIGHT:      We extend Babai's quasipolynomial-time graph isomorphism test (STOC 2016) and develop a quasipolynomial-time algorithm for the multiple-coset isomorphism problem.

47, TITLE:      Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
http://arxiv.org/abs/2003.12025
AUTHORS:        Saeed Khaki ; Hieu Pham ; Ye Han ; Andy Kuhl ; Wade Kent ; Lizhi Wang
COMMENTS:       14 pages, 9 figures
HIGHLIGHT:      In this paper, we propose a kernel detection and counting method based on a sliding window approach.

48, TITLE:      Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions
http://arxiv.org/abs/1909.08608
AUTHORS:        Renos Karamanis ; Eleftherios Anastasiadis ; Panagiotis Angeloudis ; Marc Stettler
COMMENTS:       12 pages, Ride-Sharing, Combinatorial Double Auctions, Assignment, Dynamic Pricing
HIGHLIGHT:      To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions.

49, TITLE:      Few-Shot NLG with Pre-Trained Language Model
http://arxiv.org/abs/1904.09521
AUTHORS:        Zhiyu Chen ; Harini Eavani ; Wenhu Chen ; Yinyin Liu ; William Yang Wang
COMMENTS:       ACL 2020
HIGHLIGHT:      In this work, we propose the new task of \textit{few-shot natural language generation}.

50, TITLE:      Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
http://arxiv.org/abs/1911.04974
AUTHORS:        Benjamin Lengerich ; Sarah Tan ; Chun-Hao Chang ; Giles Hooker ; Rich Caruana
COMMENTS:       AISTATS 2020
HIGHLIGHT:      To compute this decomposition, we present a fast, exact algorithm that transforms any piecewise-constant function (such as a tree-based model) into a purified, canonical representation.

51, TITLE:      Self-supervised Multi-view Person Association and Its Applications
http://arxiv.org/abs/1805.08717
AUTHORS:        Minh Vo ; Ersin Yumer ; Kalyan Sunkavalli ; Sunil Hadap ; Yaser Sheikh ; Srinivasa Narasimhan
COMMENTS:       Accepted to IEEE TPAMI
HIGHLIGHT:      We present a self-supervised framework to adapt a generic person appearance descriptor to the unlabeled videos by exploiting motion tracking, mutual exclusion constraints, and multi-view geometry.

52, TITLE:      Deep Learning Training with Simulated Approximate Multipliers
http://arxiv.org/abs/2001.00060
AUTHORS:        Issam Hammad ; Kamal El-Sankary ; Jason Gu
COMMENTS:       Presented at: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019, Dali, China, December 2019. WINNER OF THE MOZI BEST PAPER IN AI AWARD
HIGHLIGHT:      This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs).

53, TITLE:      DebFace: De-biasing Face Recognition
http://arxiv.org/abs/1911.08080
AUTHORS:        Sixue Gong ; Xiaoming Liu ; Anil K. Jain
HIGHLIGHT:      We present a novel de-biasing adversarial network that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation.

54, TITLE:      Self-Attention Gazetteer Embeddings for Named-Entity Recognition
http://arxiv.org/abs/2004.04060
AUTHORS:        Stanislav Peshterliev ; Christophe Dupuy ; Imre Kiss
COMMENTS:       Preprint
HIGHLIGHT:      In this work, we present GazSelfAttn, a novel gazetteer embedding approach that uses self-attention and match span encoding to build enhanced gazetteer embeddings.

55, TITLE:      Neural Architecture Search by Estimation of Network Structure Distributions
http://arxiv.org/abs/1908.06886
AUTHORS:        Anton Muravev ; Jenni Raitoharju ; Moncef Gabbouj
COMMENTS:       14 pages, 4 figures, 3 tables
HIGHLIGHT:      We propose a probabilistic representation of a neural network structure under the assumption of independence between layer types.

56, TITLE:      High-precision Estimation of Random Walks in Small Space
http://arxiv.org/abs/1912.04524
AUTHORS:        AmirMahdi Ahmadinejad ; Jonathan Kelner ; Jack Murtagh ; John Peebles ; Aaron Sidford ; Salil Vadhan
COMMENTS:       51 pages
HIGHLIGHT:      We provide a deterministic $\tilde{O}(\log N)$-space algorithm for estimating random walk probabilities on undirected graphs, and more generally Eulerian directed graphs, to within inverse polynomial additive error ($\epsilon=1/\mathrm{poly}(N)$) where $N$ is the length of the input.

57, TITLE:      Face X-ray for More General Face Forgery Detection
http://arxiv.org/abs/1912.13458
AUTHORS:        Lingzhi Li ; Jianmin Bao ; Ting Zhang ; Hao Yang ; Dong Chen ; Fang Wen ; Baining Guo
COMMENTS:       Accepted to CVPR 2020 (Oral)
HIGHLIGHT:      In this paper we propose a novel image representation called face X-ray for detecting forgery in face images.

58, TITLE:      Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
http://arxiv.org/abs/1907.04224
AUTHORS:        Yonatan Belinkov ; Ahmed Ali ; James Glass
COMMENTS:       Corrected dataset statistics
HIGHLIGHT:      In this paper, we analyze the learned internal representations in an end-to-end ASR model.

59, TITLE:      MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
http://arxiv.org/abs/1810.00278
AUTHORS:        Paweł Budzianowski ; Tsung-Hsien Wen ; Bo-Hsiang Tseng ; Iñigo Casanueva ; Stefan Ultes ; Osman Ramadan ; Milica Gašić
COMMENTS:       Accepted for publication at EMNLP 2018
HIGHLIGHT:      The contribution of this work apart from the open-sourced dataset labelled with dialogue belief states and dialogue actions is two-fold: firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.

60, TITLE:      Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS
http://arxiv.org/abs/2004.07965
AUTHORS:        Pradeeban Kathiravelu ; Ashish Sharma ; Saptarshi Purkayastha ; Priyanshu Sinha ; Alexandre Cadrin-Chenevert ; Imon Banerjee ; Judy Wawira Gichoya
COMMENTS:       Preprint submitted to Machine Learning for Healthcare 2020 (under review)
HIGHLIGHT:      We propose Niffler, an integrated ML framework that runs in research clusters that receives radiology images in real-time from hospitals' Picture Archiving and Communication Systems (PACS).

61, TITLE:      Probabilistic Oriented Object Detection in Automotive Radar
http://arxiv.org/abs/2004.05310
AUTHORS:        Xu Dong ; Pengluo Wang ; Pengyue Zhang ; Langechuan Liu
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      In this paper, we propose a deep-learning based algorithm for radar object detection. We created a new multimodal dataset with 102544 frames of raw radar and synchronized LiDAR data.

62, TITLE:      Unsupervised Image-to-Image Translation with Self-Attention Networks
http://arxiv.org/abs/1901.08242
AUTHORS:        Taewon Kang ; Kwang Hee Lee
COMMENTS:       Accepted by 2020 IEEE International Conference on Big Data and Smart Computing (IEEE BigComp 2020). This paper has been accepted as a REGULAR paper presented at IEEE International Conference on BigComp 2020. 7 pages, 11 figures; v4: corrected typos, figures
HIGHLIGHT:      In this paper, we propose an unsupervised image-to-image translation with self-attention networks, in which long range dependency helps to not only capture strong geometric change but also generate details using cues from all feature locations.

63, TITLE:      Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction
http://arxiv.org/abs/2004.03842
AUTHORS:        Hayoung Kim ; Dongchan Kim ; Gihoon Kim ; Jeongmin Cho ; Kunsoo Huh
COMMENTS:       6 pages, 5 figures, 2020 IEEE Intelligent Vehicles Symposium (IV)
HIGHLIGHT:      This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction.

64, TITLE:      Non-Signaling Proofs with $O(\sqrt{\log n})$ Provers are in PSPACE
http://arxiv.org/abs/1910.02590
AUTHORS:        Dhiraj Holden ; Yael Kalai
HIGHLIGHT:      Our main technical contribution (which is used in both our proofs) is a reduction showing how to convert any sub-no-signaling strategy with value at least $1-2^{-\Omega(k^2)}$ into a no-signaling one with value at least $2^{-O(k^2)}$.

65, TITLE:      Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images
http://arxiv.org/abs/2004.06930
AUTHORS:        D. Sabari Nathan ; K. Uma ; D Synthiya Vinothini ; B. Sathya Bama ; S. M. Md Mansoor Roomi
COMMENTS:       6pages,4 figures
HIGHLIGHT:      This work proposes a novel light weight network with very less number of parameters about 233,059 parameters based on Residual dense model with attention mechanism to obtain this solution.

66, TITLE:      PanNuke Dataset Extension, Insights and Baselines
http://arxiv.org/abs/2003.10778
AUTHORS:        Jevgenij Gamper ; Navid Alemi Koohbanani ; Ksenija Benes ; Simon Graham ; Mostafa Jahanifar ; Syed Ali Khurram ; Ayesha Azam ; Katherine Hewitt ; Nasir Rajpoot
COMMENTS:       Work in progress
HIGHLIGHT:      We study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images.

67, TITLE:      A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
http://arxiv.org/abs/1907.02731
AUTHORS:        Elena Burceanu ; Marius Leordeanu
COMMENTS:       6+1 pages, 6 figures, 3 tables, accepted at IJCAI220
HIGHLIGHT:      We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods.

68, TITLE:      $P\neq NP$
http://arxiv.org/abs/2003.09791
AUTHORS:        Tianrong Lin
COMMENTS:       Readability improved, comments are welcome. arXiv admin note: text overlap with arXiv:1305.4029 by other authors
HIGHLIGHT:      The main contribution of the present paper is that a series of results are obtained.

69, TITLE:      C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
http://arxiv.org/abs/1912.09628
AUTHORS:        Qihang Yu ; Dong Yang ; Holger Roth ; Yutong Bai ; Yixiao Zhang ; Alan L. Yuille ; Daguang Xu
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we propose coarse-to-fine neural architecture search (C2FNAS) to automatically search a 3D segmentation network from scratch without inconsistency on network size or input size.

70, TITLE:      Train, Learn, Expand, Repeat
http://arxiv.org/abs/2003.08469
AUTHORS:        Abhijeet Parida ; Aadhithya Sankar ; Rami Eisawy ; Tom Finck ; Benedikt Wiestler ; Franz Pfister ; Julia Moosbauer
COMMENTS:       Published as a workshop paper at AI4AH, ICLR 2020
HIGHLIGHT:      We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations.

71, TITLE:      Multi-task self-supervised learning for Robust Speech Recognition
http://arxiv.org/abs/2001.09239
AUTHORS:        Mirco Ravanelli ; Jianyuan Zhong ; Santiago Pascual ; Pawel Swietojanski ; Joao Monteiro ; Jan Trmal ; Yoshua Bengio
COMMENTS:       In Proc. of ICASSP 2020
HIGHLIGHT:      This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments.

72, TITLE:      Improved Binary Artificial Bee Colony Algorithm
http://arxiv.org/abs/2003.11641
AUTHORS:        Rafet Durgut
HIGHLIGHT:      In this paper, we improve the ABC algorithm to solve binary optimization problems and call it the improved binary Artificial Bee Colony (ibinABC).

73, TITLE:      Mixture Dense Regression for Object Detection and Human Pose Estimation
http://arxiv.org/abs/1912.00821
AUTHORS:        Ali Varamesh ; Tinne Tuytelaars
HIGHLIGHT:      To this end, we devise a framework for spatial regression using mixture density networks.

74, TITLE:      Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
http://arxiv.org/abs/2003.13815
AUTHORS:        Asmaa Abbas ; Mohammed M. Abdelsamea ; Mohamed Medhat Gaber
HIGHLIGHT:      In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images.

75, TITLE:      Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings
http://arxiv.org/abs/2004.08243
AUTHORS:        Pratik Jawanpuria ; Mayank Meghwanshi ; Bamdev Mishra
COMMENTS:       Accepted as a short paper in ACL 2020
HIGHLIGHT:      We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages.

76, TITLE:      Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
http://arxiv.org/abs/1911.10425
AUTHORS:        Nibraas Khan ; Joshua Phillips
COMMENTS:       8 pages
HIGHLIGHT:      We propose a new model, PONOWMtk, which combines both approaches, ATRs and input storage, with a static or dynamic number of ATRs.

77, TITLE:      Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
http://arxiv.org/abs/2004.06089
AUTHORS:        Ted Xiao ; Eric Jang ; Dmitry Kalashnikov ; Sergey Levine ; Julian Ibarz ; Karol Hausman ; Alexander Herzog
COMMENTS:       Published as a conference paper at ICLR 2020
HIGHLIGHT:      We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must "think while moving".

78, TITLE:      Fast DenseNet: Towards Efficient and Accurate Text Recognition with Fast Dense Networks
http://arxiv.org/abs/1912.07016
AUTHORS:        Zhao Zhang ; Zemin Tang ; Yang Wang ; Haijun Zhang ; Shuicheng Yan ; Meng Wang
HIGHLIGHT:      In this paper, we propose an efficient convolutional block called Fast Dense Block (FDB).

79, TITLE:      Learning a Neural Solver for Multiple Object Tracking
http://arxiv.org/abs/1912.07515
AUTHORS:        Guillem Brasó ; Laura Leal-Taixé
COMMENTS:       Accepted to CVPR 2020 (oral)
HIGHLIGHT:      In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks (MPNs).

80, TITLE:      Regularized Context Gates on Transformer for Machine Translation
http://arxiv.org/abs/1908.11020
AUTHORS:        Xintong Li ; Lemao Liu ; Rui Wang ; Guoping Huang ; Max Meng
COMMENTS:       Published in ACL 2020
HIGHLIGHT:      Extensive experiments on 4 translation datasets demonstrate that the proposed model obtains an averaged gain of 1.0 BLEU score over a strong Transformer baseline.

81, TITLE:      Neural Data-to-Text Generation with Dynamic Content Planning
http://arxiv.org/abs/2004.07426
AUTHORS:        Kai Chen ; Fayuan Li ; Baotian Hu ; Weihua Peng ; Qingcai Chen ; Hong Yu
COMMENTS:       25 pages, 1 figure and 6 tables
HIGHLIGHT:      To alleviate these problems, we propose a Neural data-to-text generation model with Dynamic content Planning, named NDP for abbreviation.

82, TITLE:      Validating Mathematical Structures
http://arxiv.org/abs/2002.00620
AUTHORS:        Kazuhiko Sakaguchi
COMMENTS:       Preprint of the paper accepted in proceedings of IJCAR 2020, LNCS, Springer, including appendix
HIGHLIGHT:      We identify two hierarchy invariants that ensure modularity of reasoning and predictability of inference with packed classes, and propose algorithms to check these invariants.

83, TITLE:      Contextual Two-Stage U-Nets for Robust Pulmonary Lobe Segmentation in CT Scans of COVID-19 and COPD Patients
http://arxiv.org/abs/2004.07443
AUTHORS:        Weiyi Xie ; Colin Jacobs ; Jean-Paul Charbonnier ; Bram van Ginneken
HIGHLIGHT:      In this paper, we propose a contextual two-stage U-net (CTSU-Net) that leverages global context by introducing a first stage in which the receptive field encompasses the entire scan and by using a novel non-local neural network module.

84, TITLE:      Toward A Neuro-inspired Creative Decoder
http://arxiv.org/abs/1902.02399
AUTHORS:        Payel Das ; Brian Quanz ; Pin-Yu Chen ; Jae-wook Ahn ; Dhruv Shah
HIGHLIGHT:      Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space.

85, TITLE:      Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network
http://arxiv.org/abs/2004.07807
AUTHORS:        Md. Rezaul Karim ; Bharathi Raja Chakravarthi ; John P. McCrae ; Michael Cochez
COMMENTS:       This paper is under review in the Journal of Natural Language Engineering
HIGHLIGHT:      In this paper, we provide several classification benchmarks for Bengali, an under-resourced language.

86, TITLE:      A Novel Method of Extracting Topological Features from Word Embeddings
http://arxiv.org/abs/2003.13074
AUTHORS:        Shafie Gholizadeh ; Armin Seyeditabari ; Wlodek Zadrozny
HIGHLIGHT:      In this paper, we introduce a novel algorithm to extract topological features from word embedding representation of text that can be used for text classification.

87, TITLE:      Comparing recurrent and convolutional neural networks for predicting wave propagation
http://arxiv.org/abs/2002.08981
AUTHORS:        Stathi Fotiadis ; Eduardo Pignatelli ; Mario Lino Valencia ; Chris Cantwell ; Amos Storkey ; Anil A. Bharath
HIGHLIGHT:      In this work, we investigate the performance of recurrent and convolutional deep neural network architectures to predict the surface waves.

88, TITLE:      SynSin: End-to-end View Synthesis from a Single Image
http://arxiv.org/abs/1912.08804
AUTHORS:        Olivia Wiles ; Georgia Gkioxari ; Richard Szeliski ; Justin Johnson
COMMENTS:       Project page: www.robots.ox.ac.uk/~ow/synsin.html
HIGHLIGHT:      We propose a novel end-to-end model for this task; it is trained on real images without any ground-truth 3D information.

89, TITLE:      Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
http://arxiv.org/abs/1909.02339
AUTHORS:        Anne Lauscher ; Ivan Vulić ; Edoardo Maria Ponti ; Anna Korhonen ; Goran Glavaš
HIGHLIGHT:      In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining.

90, TITLE:      Controllable Sentence Simplification
http://arxiv.org/abs/1910.02677
AUTHORS:        Louis Martin ; Benoît Sagot ; Éric de la Clergerie ; Antoine Bordes
COMMENTS:       Code and models: https://github.com/facebookresearch/access
HIGHLIGHT:      We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models.

91, TITLE:      Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
http://arxiv.org/abs/1903.06694
AUTHORS:        Kirthevasan Kandasamy ; Karun Raju Vysyaraju ; Willie Neiswanger ; Biswajit Paria ; Christopher R. Collins ; Jeff Schneider ; Barnabas Poczos ; Eric P. Xing
COMMENTS:       Journal of Machine Learning Research 2020, Special Issue on Bayesian Optimization
HIGHLIGHT:      In this work, we present Dragonfly, an open source Python library for scalable and robust BO.

92, TITLE:      k-decay: A New Method For Learning Rate Schedule
http://arxiv.org/abs/2004.05909
AUTHORS:        Tao Zhang
HIGHLIGHT:      In this paper, we put forward the k-decay method for the learning rate schedule, which impacts its k-order derivative to the rate of change of the learning rate.

93, TITLE:      Multi-task Learning for Voice Trigger Detection
http://arxiv.org/abs/2001.09519
AUTHORS:        Siddharth Sigtia ; Pascal Clark ; Rob Haynes ; Hywel Richards ; John Bridle
HIGHLIGHT:      In this study, we address two major challenges. Next, we collect a much smaller dataset of examples that are challenging for the baseline system.

94, TITLE:      Multi-Scale Learned Iterative Reconstruction
http://arxiv.org/abs/1908.00936
AUTHORS:        Andreas Hauptmann ; Jonas Adler ; Simon Arridge ; Ozan Öktem
HIGHLIGHT:      As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction scheme that computes iterates on discretisations of increasing resolution.

95, TITLE:      Alternative Function Approximation Parameterizations for Solving Games: An Analysis of $f$-Regression Counterfactual Regret Minimization
http://arxiv.org/abs/1912.02967
AUTHORS:        Ryan D'Orazio ; Dustin Morrill ; James R. Wright ; Michael Bowling
COMMENTS:       11 pages, includes appendix
HIGHLIGHT:      We derive approximation error-aware regret bounds for $(\Phi, f)$-regret matching, which applies to a general class of link functions and regret objectives.

96, TITLE:      A Non-Dominated Sorting Based Customized Random-Key Genetic Algorithm for the Bi-Objective Traveling Thief Problem
http://arxiv.org/abs/2002.04303
AUTHORS:        Jonatas B. C. Chagas ; Julian Blank ; Markus Wagner ; Marcone J. F. Souza ; Kalyanmoy Deb
HIGHLIGHT:      In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP).

97, TITLE:      CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries
http://arxiv.org/abs/2003.08560
AUTHORS:        Han Yang ; Xingjian Zhen ; Ying Chi ; Lei Zhang ; Xian-Sheng Hua
COMMENTS:       This work is done by Xingjian Zhen during internship in Alibaba Damo Academy
HIGHLIGHT:      Motivated by the wide application of the graph neural network in structured data, in this paper, we propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration, since CT image contains abundant information such as branch size and spanning direction.

98, TITLE:      Class-dependent Compression of Deep Neural Networks
http://arxiv.org/abs/1909.10364
AUTHORS:        Rahim Entezari ; Olga Saukh
HIGHLIGHT:      Motivated by the success of the lottery ticket hypothesis, in this paper we propose an iterative deep model compression technique, which keeps the number of false negatives of the compressed model close to the one of the original model at the price of increasing the number of false positives if necessary.

99, TITLE:      COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
http://arxiv.org/abs/2004.03747
AUTHORS:        Md Zahangir Alom ; M M Shaifur Rahman ; Mst Shamima Nasrin ; Tarek M. Taha ; Vijayan K. Asari
COMMENTS:       11 pages, 15 figures
HIGHLIGHT:      In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods.

100, TITLE:     A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot
http://arxiv.org/abs/1912.11856
AUTHORS:        Issam Hammad ; Kamal El-Sankary ; Jason Gu
COMMENTS:       Accepted and presented at IEEE International Conference on Robotics and Biomimetics (ROBIO) -2019
HIGHLIGHT:      In this paper, two primary research contributions are presented.

101, TITLE:     Kinship Identification through Joint Learning Using Kinship Verification Ensemble
http://arxiv.org/abs/2004.06382
AUTHORS:        Wei Wang ; Shaodi You ; Sezer Karaoglu ; Theo Gevers
COMMENTS:       18 pages, 8 figures
HIGHLIGHT:      To solve it, we propose a novel kinship identification approach through the joint training of kinship verification ensembles and a Joint Identification Module.

102, TITLE:     The Computational Complexity of Evil Hangman
http://arxiv.org/abs/2003.10000
AUTHORS:        Jérémy Barbay ; Bernardo Subercaseaux
HIGHLIGHT:      We show that a greedy strategy for Evil Hangman can perform arbitrarily far from optimal, and most importantly, that playing optimally as an Evil Hangman setter is computationally difficult.

103, TITLE:     Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation Discretization
http://arxiv.org/abs/1911.11854
AUTHORS:        Erfan Ebrahim Esfahani ; Alireza Hosseini
HIGHLIGHT:      Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform.

104, TITLE:     Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling
http://arxiv.org/abs/2003.01989
AUTHORS:        Fabian Wolf ; Gernot A. Fink
COMMENTS:       Accepted to Workshop on Document Analysis Systems (DAS) 2020
HIGHLIGHT:      In this work, we present an annotation-free method that still employs machine learning techniques and therefore outperforms other learning-free approaches.

105, TITLE:     Generating 3D People in Scenes without People
http://arxiv.org/abs/1912.02923
AUTHORS:        Yan Zhang ; Mohamed Hassan ; Heiko Neumann ; Michael J. Black ; Siyu Tang
COMMENTS:       cvpr'20, camera ready version with appendix
HIGHLIGHT:      We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene.

106, TITLE:     AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
http://arxiv.org/abs/1903.10297
AUTHORS:        Chenyang Zhu ; Kai Xu ; Siddhartha Chaudhuri ; Li Yi ; Leonidas Guibas ; Hao Zhang
COMMENTS:       CVPR 2020
HIGHLIGHT:      We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds.

107, TITLE:     Relationship-Embedded Representation Learning for Grounding Referring Expressions
http://arxiv.org/abs/1906.04464
AUTHORS:        Sibei Yang ; Guanbin Li ; Yizhou Yu
COMMENTS:       This paper is going to appear in TPAMI. Code is available at https://github.com/sibeiyang/sgmn/tree/master/lib/cmrin_models
HIGHLIGHT:      In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph.

108, TITLE:     Controllable Person Image Synthesis with Attribute-Decomposed GAN
http://arxiv.org/abs/2003.12267
AUTHORS:        Yifang Men ; Yiming Mao ; Yuning Jiang ; Wei-Ying Ma ; Zhouhui Lian
COMMENTS:       Accepted by CVPR 2020 (Oral). Project Page: https://menyifang.github.io/projects/ADGAN/ADGAN.html
HIGHLIGHT:      This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e.g., pose, head, upper clothes and pants) provided in various source inputs.

109, TITLE:     On the Language Neutrality of Pre-trained Multilingual Representations
http://arxiv.org/abs/2004.05160
AUTHORS:        Jindřich Libovický ; Rudolf Rosa ; Alexander Fraser
COMMENTS:       11 pages, 3 figures. arXiv admin note: text overlap with arXiv:1911.03310
HIGHLIGHT:      We instead focus on the language-neutrality of mBERT with respect to lexical semantics.

110, TITLE:     ASLFeat: Learning Local Features of Accurate Shape and Localization
http://arxiv.org/abs/2003.10071
AUTHORS:        Zixin Luo ; Lei Zhou ; Xuyang Bai ; Hongkai Chen ; Jiahui Zhang ; Yao Yao ; Shiwei Li ; Tian Fang ; Long Quan
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available
HIGHLIGHT:      In this paper, we present ASLFeat, with three light-weight yet effective modifications to mitigate above issues.

111, TITLE:     Two new results about quantum exact learning
http://arxiv.org/abs/1810.00481
AUTHORS:        Srinivasan Arunachalam ; Sourav Chakraborty ; Troy Lee ; Manaswi Paraashar ; Ronald de Wolf
COMMENTS:       v3: 21 pages. Small corrections and clarifications
HIGHLIGHT:      We present two new results about exact learning by quantum computers.

112, TITLE:     A Simple Approach to Learning Unsupervised Multilingual Embeddings
http://arxiv.org/abs/2004.05991
AUTHORS:        Pratik Jawanpuria ; Mayank Meghwanshi ; Bamdev Mishra
HIGHLIGHT:      In contrast, we propose a simple, two-stage framework in which we decouple the above two sub-problems and solve them separately using existing techniques.

113, TITLE:     #MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement
http://arxiv.org/abs/1912.06927
AUTHORS:        Akash Gautam ; Puneet Mathur ; Rakesh Gosangi ; Debanjan Mahata ; Ramit Sawhney ; Rajiv Ratn Shah
COMMENTS:       Preprint of paper accepted at ICWSM 2020
HIGHLIGHT:      In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts.

114, TITLE:     Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
http://arxiv.org/abs/2004.07703
AUTHORS:        Fei Pan ; Inkyu Shin ; Francois Rameau ; Seokju Lee ; In So Kweon
COMMENTS:       Accepted to CVPR 2020 as an Oral Presentation. Code is available at https://github.com/feipan664/IntraDA
HIGHLIGHT:      In this work, we propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.

115, TITLE:     Mining International Political Norms from the GDELT Database
http://arxiv.org/abs/2003.14027
AUTHORS:        Rohit Murali ; Suravi Patnaik ; Stephen Cranefield
COMMENTS:       16 pages, 2 figures, pre-print for International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2020
HIGHLIGHT:      This work investigates this issue in the context of international politics.