2020.06.10.txt

==========New Papers==========
1, TITLE:       Reliable Detection for Spatial Modulation Systems
http://arxiv.org/abs/2006.05084
AUTHORS:        Ibrahim Al-Nahhal ; Octavia A. Dobre ; Salama Ikki
COMMENTS:       5 pages, 7 figures, to be appeared on IEEE VTC-Fall 2020
HIGHLIGHT:      In this paper, a novel reliable sphere decoder (RSD) algorithm based on tree-search is proposed for the SM system.

2, TITLE:       Learning to Count up to Symmetry
http://arxiv.org/abs/2006.05080
AUTHORS:        Pierre Clairambault
HIGHLIGHT:      In this paper we develop the theory of how to count, in thin concurrent games, the configurations of a strategy witnessing that it reaches a certain configuration of the game.

3, TITLE:       Detection of Makeup Presentation Attacks based on Deep Face Representations
http://arxiv.org/abs/2006.05074
AUTHORS:        Christian Rathgeb ; Pawel Drozdowski ; Christoph Busch
HIGHLIGHT:      In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation.

4, TITLE:       SEKD: Self-Evolving Keypoint Detection and Description
http://arxiv.org/abs/2006.05077
AUTHORS:        Yafei Song ; Ling Cai ; Jia Li ; Yonghong Tian ; Mingyang Li
HIGHLIGHT:      Guided by these properties, a self-supervised framework, namely self-evolving keypoint detection and description (SEKD), is proposed to learn an advanced local feature model from unlabeled natural images.

5, TITLE:       Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
http://arxiv.org/abs/2006.05078
AUTHORS:        Samuel Daulton ; Maximilian Balandat ; Eytan Bakshy
HIGHLIGHT:      We derive a novel formulation of $q$-Expected Hypervolume Improvement ($q$EHVI), an acquisition function that extends EHVI to the parallel, constrained evaluation setting.

6, TITLE:       Learning Shared Filter Bases for Efficient ConvNets
http://arxiv.org/abs/2006.05066
AUTHORS:        Daeyeon Kim ; Woochul Kang
HIGHLIGHT:      In this paper, we propose to exploit the linear structure of convolution filters for effective and efficient sharing of parameters among iterative convolution layers.

7, TITLE:       Constrained episodic reinforcement learning in concave-convex and knapsack settings
http://arxiv.org/abs/2006.05051
AUTHORS:        Kianté Brantley ; Miroslav Dudik ; Thodoris Lykouris ; Sobhan Miryoosefi ; Max Simchowitz ; Aleksandrs Slivkins ; Wen Sun
HIGHLIGHT:      We propose an algorithm for tabular episodic reinforcement learning with constraints.

8, TITLE:       Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
http://arxiv.org/abs/2006.05281
AUTHORS:        Isar Nejadgholi ; Kathleen C. Fraser ; Berry De Bruijn
COMMENTS:       to appear at BioNLP2020
HIGHLIGHT:      For a domain-specific BERT-based NER system, we showed that 25% of the errors have the same labels and overlapping span with gold standard entities.

9, TITLE:       Policy-focused Agent-based Modeling using RL Behavioral Models
http://arxiv.org/abs/2006.05048
AUTHORS:        Osonde A. Osoba ; Raffaele Vardavas ; Justin Grana ; Rushil Zutshi ; Amber Jaycocks
HIGHLIGHT:      This paper examines the value of reinforcement learning (RL) models as adaptive, high-performing, and behaviorally-valid models of agent decision-making in ABMs.

10, TITLE:      Single Image Deraining via Scale-space Invariant Attention Neural Network
http://arxiv.org/abs/2006.05049
AUTHORS:        Bo Pang ; Deming Zhai ;  Member ;  IEEE ; Junjun Jiang ;  Member ;  IEEE ; Xianming Liu ;  Member ;  IEEE
HIGHLIGHT:      In this paper, we tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera.

11, TITLE:      Neural Physicist: Learning Physical Dynamics from Image Sequences
http://arxiv.org/abs/2006.05044
AUTHORS:        Baocheng Zhu ; Shijun Wang ; James Zhang
COMMENTS:       19 pages, 20 figures
HIGHLIGHT:      We present a novel architecture named Neural Physicist (NeurPhy) to learn physical dynamics directly from image sequences using deep neural networks.

12, TITLE:      High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation
http://arxiv.org/abs/2006.05030
AUTHORS:        Mohammad Hamghalam ; Baiying Lei ; Tianfu Wang
COMMENTS:       Will be published in Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)
HIGHLIGHT:      This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images.

13, TITLE:      Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding
http://arxiv.org/abs/2006.05033
AUTHORS:        Seongbin Oh ; Dongseok Kwon ; Gyuho Yeom ; Won-Mook Kang ; Soochang Lee ; Sung Yun Woo ; Jang Saeng Kim ; Min Kyu Park ; Jong-Ho Lee
HIGHLIGHT:      In this work, we train the SNN in which the firing time carries information using temporal backpropagation.

14, TITLE:      Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
http://arxiv.org/abs/2006.05018
AUTHORS:        Wei Wu ; Yu Shi ; Xukun Li ; Yukun Zhou ; Peng Du ; Shuangzhi Lv ; Tingbo Liang ; Jifang Sheng
HIGHLIGHT:      Two tasks were studied in this present paper.

15, TITLE:      Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?
http://arxiv.org/abs/2006.05015
AUTHORS:        Weixing Liu ; Jun Liu ; Bin Luo
COMMENTS:       5 pages, 5 figures
HIGHLIGHT:      In this letter, we propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection.

16, TITLE:      Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition
http://arxiv.org/abs/2006.05257
AUTHORS:        Gurunath Reddy Madhumani ; Sanket Shah ; Basil Abraham ; Vikas Joshi ; Sunayana Sitaram
COMMENTS:       5 pages (4 pages + 1 reference), 3 tables, 2 figures
HIGHLIGHT:      In this work, we present further improvements over our previous work by using domain adversarial learning to train task agnostic models.

17, TITLE:      DeepFair: Deep Learning for Improving Fairness in Recommender Systems
http://arxiv.org/abs/2006.05255
AUTHORS:        Jesús Bobadilla ; Raúl Lara-Cabrera ; Ángel González-Prieto ; Fernando Ortega
COMMENTS:       18 pages, 9 figures, 4 tables
HIGHLIGHT:      Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users.

18, TITLE:      HausaMT v1.0: Towards English-Hausa Neural Machine Translation
http://arxiv.org/abs/2006.05014
AUTHORS:        Adewale Akinfaderin
COMMENTS:       Accepted at 4th Widening NLP Workshop, Annual Meeting of the Association for Computational Linguistics, ACL 2020
HIGHLIGHT:      In this paper, we curated different datasets containing Hausa-English parallel corpus for our translation.

19, TITLE:      RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
http://arxiv.org/abs/2006.05011
AUTHORS:        Etienne Dubeau ; Mathieu Garon ; Benoit Debaque ; Raoul de Charette ; Jean-François Lalonde
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.

20, TITLE:      A bio-inspired bistable recurrent cell allows for long-lasting memory
http://arxiv.org/abs/2006.05252
AUTHORS:        Nicolas Vecoven ; Damien Ernst ; Guillaume Drion
HIGHLIGHT:      In this work, we take inspiration from biological neuron bistability to embed RNNs with long-lasting memory at the cellular level.

21, TITLE:      What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time
http://arxiv.org/abs/2006.05249
AUTHORS:        Hanna Benoni ; Daniel Harari ; Shimon Ullman
COMMENTS:       7 pages, 2 figures, 1 table
HIGHLIGHT:      Rich empirical evidence has shown that visual object recognition in the brain is fast and effortless, with relevant brain signals reported to start as early as 80 ms. Here we study the time trajectory of the recognition process at the level of minimal recognizable images (termed MIRC).

22, TITLE:      Quantum Legendre-Fenchel Transform
http://arxiv.org/abs/2006.04823
AUTHORS:        David Sutter ; Giacomo Nannicini ; Tobias Sutter ; Stefan Woerner
COMMENTS:       25 pages, 3 figures
HIGHLIGHT:      We present a quantum algorithm to compute the discrete Legendre-Fenchel transform.

23, TITLE:      Towards an Intrinsic Definition of Robustness for a Classifier
http://arxiv.org/abs/2006.05095
AUTHORS:        Théo Giraudon ; Vincent Gripon ; Matthias Löwe ; Franck Vermet
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      In this paper, we point out that averaging the radius of robustness of samples in a validation set is a statistically weak measure.

24, TITLE:      PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
http://arxiv.org/abs/2006.05091
AUTHORS:        Yuecong Xu ; Haozhi Cao ; Jianfei Yang ; Kezhi Mao ; Jianxiong Yin ; Simon See
COMMENTS:       Single column, 26 pages, 6 figures
HIGHLIGHT:      To address the above limitations, we propose Pyramid Non-Local (PNL) module, which extends the non-local block by incorporating regional correlation at multiple scales through a pyramid structured module.

25, TITLE:      GAP++: Learning to generate target-conditioned adversarial examples
http://arxiv.org/abs/2006.05097
AUTHORS:        Xiaofeng Mao ; Yuefeng Chen ; Yuhong Li ; Yuan He ; Hui Xue
COMMENTS:       Accepted to IJCAI 2019 AIBS Workshop
HIGHLIGHT:      In this work, we propose a more general-purpose framework which infers target-conditioned perturbations dependent on both input image and target label.

26, TITLE:      SAL++: Sign Agnostic Learning with Derivatives
http://arxiv.org/abs/2006.05400
AUTHORS:        Matan Atzmon ; Yaron Lipman
HIGHLIGHT:      In this paper, we introduce SAL++: a method for learning implicit neural representations of shapes directly from such raw data.

27, TITLE:      Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
http://arxiv.org/abs/2006.04996
AUTHORS:        Xiang Jiang ; Qicheng Lao ; Stan Matwin ; Mohammad Havaei
COMMENTS:       Accepted at ICML2020. For code, see https://github.com/xiangdal/implicit_alignment
HIGHLIGHT:      We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective.

28, TITLE:      Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
http://arxiv.org/abs/2006.04998
AUTHORS:        Shikha Chaganti ; Abishek Balachandran ; Guillaume Chabin ; Stuart Cohen ; Thomas Flohr ; apl. Prof. ; Bogdan Georgescu ; Philippe Grenier ;  Prof. ; Sasa Grbic ; Siqi Liu ; François Mellot ; Nicolas Murray ; Savvas Nicolaou ; William Parker ; Thomas Re ; Pina Sanelli ; Alexander W. Sauter ; Zhoubing Xu ; Youngjin Yoo ; Valentin Ziebandt ; Dorin Comaniciu
HIGHLIGHT:      Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

29, TITLE:      Rethinking Classification Loss Designs for Person Re-identification with a Unified View
http://arxiv.org/abs/2006.04991
AUTHORS:        Zhizheng Zhang ; Cuiling Lan ; Wenjun Zeng ; Zhibo Chen ; Shif-Fu Chang
HIGHLIGHT:      In this paper, we rethink these loss functions within a generalized formulation and argue that triplet-based optimization can be viewed as a two-class subsampling classification, which performs classification over two sampled categories based on instance similarities.

30, TITLE:      Big GANs Are Watching You: Towards Unsupervised Object Segmentation with Off-the-Shelf Generative Models
http://arxiv.org/abs/2006.04988
AUTHORS:        Andrey Voynov ; Stanislav Morozov ; Artem Babenko
HIGHLIGHT:      In this work, we introduce an alternative, much simpler way to exploit generative models for unsupervised object segmentation.

31, TITLE:      5* Knowledge Graph Embeddings with Projective Transformations
http://arxiv.org/abs/2006.04986
AUTHORS:        Mojtaba Nayyeri ; Sahar Vahdati ; Can Aykul ; Jens Lehmann
HIGHLIGHT:      We propose a novel KGE model, which supports those transformations and subsumes other state-of-the-art models.

32, TITLE:      Pixel-Wise Motion Deblurring of Thermal Videos
http://arxiv.org/abs/2006.04973
AUTHORS:        Manikandasriram Srinivasan Ramanagopal ; Zixu Zhang ; Ram Vasudevan ; Matthew Johnson-Roberson
COMMENTS:       10 pages, 8 figures, Accepted to Robotics: Science and Systems 2020
HIGHLIGHT:      As described in this paper, this motion blur arises due to the thermal inertia of each pixel.

33, TITLE:      Knowledge-Aided Open-Domain Question Answering
http://arxiv.org/abs/2006.05244
AUTHORS:        Mantong Zhou ; Zhouxing Shi ; Minlie Huang ; Xiaoyan Zhu
HIGHLIGHT:      In this work, we propose a knowledge-aided open-domain QA (KAQA) method which targets at improving relevant document retrieval and candidate answer reranking by considering the relationship between a question and the documents (termed as question-document graph), and the relationship between candidate documents (termed as document-document graph).

34, TITLE:      Universal Vector Neural Machine Translation With Effective Attention
http://arxiv.org/abs/2006.05003
AUTHORS:        Satish Mylapore ; Ryan Quincy Paul ; Joshua Yi ; Robert D. Slater
COMMENTS:       15pages, 3 figures
HIGHLIGHT:      In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models.

35, TITLE:      A Review of Automatically Diagnosing COVID-19 based on Scanning Image
http://arxiv.org/abs/2006.05245
AUTHORS:        Delong Chen ; Fan Liu ; Zewen Li
COMMENTS:       under review of PRCV2020
HIGHLIGHT:      In this paper, we present a review of these recently emerging automatic diagnosing models.

36, TITLE:      An Efficient Accelerator Design Methodology for Deformable Convolutional Networks
http://arxiv.org/abs/2006.05238
AUTHORS:        Saehyun Ahn ; Jung-Woo Chang ; Suk-Ju Kang
COMMENTS:       IEEE International Conference on Image Processing (ICIP) 2020
HIGHLIGHT:      In this paper, we present a novel approach to accelerate deformable convolution on FPGA.

37, TITLE:      audino: A Modern Annotation Tool for Audio and Speech
http://arxiv.org/abs/2006.05236
AUTHORS:        Manraj Singh Grover ; Pakhi Bamdev ; Yaman Kumar ; Mika Hama ; Rajiv Ratn Shah
COMMENTS:       Submitted to 28th ACM International Conference on Multimedia
HIGHLIGHT:      In this paper, we introduce a collaborative and modern annotation tool for audio and speech: audino.

38, TITLE:      Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
http://arxiv.org/abs/2006.05220
AUTHORS:        Xiaolin Zhang ; Yunchao Wei ; Yi Yang ; Fei Wu
HIGHLIGHT:      We propose a two-stage approach to generate the localization maps by simply comparing the similarity of point-wise features between the high-activation and the rest pixels.

39, TITLE:      SANOM Results for OAEI 2019
http://arxiv.org/abs/2006.05219
AUTHORS:        Majid Mohammadi ; Amir Ahooye Atashin ; Wout Hofman ; Yao-Hua Tan
HIGHLIGHT:      This paper contains the configuration of SANOM and its results on the anatomy and conference tracks.

40, TITLE:      Super-resolution Variational Auto-Encoders
http://arxiv.org/abs/2006.05218
AUTHORS:        Ioannis Gatopoulos ; Maarten Stol ; Jakub M. Tomczak
COMMENTS:       13 pages, 11 figures, 3 tables. Code available at: https://github.com/ioangatop/srVAE
HIGHLIGHT:      Here, we propose to enhance VAEs by adding a random variable that is a downscaled version of the original image and still use the log-likelihood function as the learning objective.

41, TITLE:      Graph-Aware Transformer: Is Attention All Graphs Need?
http://arxiv.org/abs/2006.05213
AUTHORS:        Sanghyun Yoo ; Young-Seok Kim ; Kang Hyun Lee ; Kuhwan Jeong ; Junhwi Choi ; Hoshik Lee ; Young Sang Choi
HIGHLIGHT:      To tackle this incompatibility, we propose GRaph-Aware Transformer (GRAT), the first Transformer-based model which can encode and decode whole graphs in end-to-end fashion.

42, TITLE:      Neural Network Activation Quantization with Bitwise Information Bottlenecks
http://arxiv.org/abs/2006.05210
AUTHORS:        Xichuan Zhou ; Kui Liu ; Cong Shi ; Haijun Liu ; Ji Liu
HIGHLIGHT:      Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Information Bottleneck approach for quantizing and encoding neural network activations.

43, TITLE:      Re-evaluating phoneme frequencies
http://arxiv.org/abs/2006.05206
AUTHORS:        Jayden L. Macklin-Cordes ; Erich R. Round
COMMENTS:       24pp (2 figures, 3 tables). This article has been submitted but not yet accepted for publication. Supplementary information, data and code available at http://doi.org/10.5281/zenodo.3886212
HIGHLIGHT:      We infer the fit of power laws and three alternative distributions to 168 Australian languages, using a maximum likelihood framework.

44, TITLE:      The Tragedy of the AI Commons
http://arxiv.org/abs/2006.05203
AUTHORS:        Travis LaCroix ; Aydin Mohseni
COMMENTS:       22 Pages, 4 Figures
HIGHLIGHT:      In this paper, we use stochastic evolutionary game dynamics to model this social dilemma in the context of the ethical development of artificial intelligence.

45, TITLE:      Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges
http://arxiv.org/abs/2006.05415
AUTHORS:        Edgar Galván ; Peter Mooney
COMMENTS:       20 pages (double column), 2 figures, 3 tables, 157 references
HIGHLIGHT:      This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs.

46, TITLE:      D-VPnet: A Network for Real-time Dominant Vanishing Point Detection in Natural Scenes
http://arxiv.org/abs/2006.05407
AUTHORS:        Yin-Bo Liu ; Ming Zeng ; Qing-Hao Meng
COMMENTS:       18 pages, 6 figures, under review
HIGHLIGHT:      In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet).

47, TITLE:      Automatic Code Summarization via Multi-dimensional Semantic Fusing in GNN
http://arxiv.org/abs/2006.05405
AUTHORS:        Shangqing Liu ; Yu Chen ; Xiaofei Xie ; Jing Kai Siow ; Yang Liu
HIGHLIGHT:      To this end, in this paper, we combine diverse representations of the source code (i.e., AST, CFG and PDG)into a joint code property graph. To evaluate our proposed approach, we release a new challenging benchmark, crawledfrom200+diversified large-scale open-source C/C++projects.

48, TITLE:      Multi-spectral Facial Landmark Detection
http://arxiv.org/abs/2006.05196
AUTHORS:        Jin Keong ; Xingbo Dong ; Zhe Jin ; Khawla Mallat ; Jean-Luc Dugelay
HIGHLIGHT:      In this paper, we propose a robust neural network enabled facial landmark detection, namely Deep Multi-Spectral Learning (DMSL).

49, TITLE:      A Note on Deepfake Detection with Low-Resources
http://arxiv.org/abs/2006.05183
AUTHORS:        Piotr Kawa ; Piotr Syga
HIGHLIGHT:      In this paper we present two methods that allow detecting Deepfakes for a user without significant computational power.

50, TITLE:      Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs
http://arxiv.org/abs/2006.05181
AUTHORS:        Miguel de Prado ; Andrew Mundy ; Rabia Saeed ; Maurizio Denna ; Nuria Pazos ; Luca Benini
HIGHLIGHT:      Building on this knowledge, we present an automated exploration framework to ease the deployment of DNNs for industrial applications by automatically exploring the design space and learning an optimised solution that speeds up the performance and reduces the memory on embedded CPU platforms. Thus, we present a set of results for state-of-the-art DNNs on a range of Arm Cortex-A CPU platforms achieving up to 4x improvement in performance and over 2x reduction in memory with negligible loss in accuracy with respect to the BLAS floating-point implementation.

51, TITLE:      Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
http://arxiv.org/abs/2006.05180
AUTHORS:        Naoto Yokoya ; Kazuki Yamanoi ; Wei He ; Gerald Baier ; Bruno Adriano ; Hiroyuki Miura ; Satoru Oishi
HIGHLIGHT:      We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation.

52, TITLE:      Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
http://arxiv.org/abs/2006.05187
AUTHORS:        Qingdong He ; Zhengning Wang ; Hao Zeng ; Yijun Liu ; Shuaicheng Liu ; Bing Zeng
HIGHLIGHT:      In this paper, we propose the Stereo RGB and Deeper LIDAR (SRDL) framework which can utilize semantic and spatial information simultaneously such that the performance of network for 3D object detection can be improved naturally.

53, TITLE:      Optimal Continual Learning has Perfect Memory and is NP-hard
http://arxiv.org/abs/2006.05188
AUTHORS:        Jeremias Knoblauch ; Hisham Husain ; Tom Diethe
COMMENTS:       Accepted for publication at ICML (International Conference on Machine Learning) 2020; 13 pages, 8 Figures
HIGHLIGHT:      The current paper develops a theoretical approach that explains why.

54, TITLE:      Hand-crafted Attention is All You Need? A Study of Attention on Self-supervised Audio Transformer
http://arxiv.org/abs/2006.05174
AUTHORS:        Tsung-Han Wu ; Chun-Chen Hsieh ; Yen-Hao Chen ; Po-Han Chi ; Hung-yi Lee
HIGHLIGHT:      In this paper, we seek to reduce the computation complexity of transformer-based models for speech representation learning.

55, TITLE:      Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation
http://arxiv.org/abs/2006.05165
AUTHORS:        Qiu Ran ; Yankai Lin ; Peng Li ; Jie Zhou
COMMENTS:       This work has been accepted for publication at ACL2020
HIGHLIGHT:      To alleviate this problem, we propose a novel semi-autoregressive model RecoverSAT in this work, which generates a translation as a sequence of segments.

56, TITLE:      ConfNet2Seq: Full Length Answer Generation from Spoken Questions
http://arxiv.org/abs/2006.05163
AUTHORS:        Vaishali Pal ; Manish Shrivastava ; Laurent Besacier
COMMENTS:       Accepted at Text, Speech and Dialogue, 2020
HIGHLIGHT:      We propose a novel system to generate full length natural language answers from spoken questions and factoid answers. We release a large-scale dataset of 259,788 samples of spoken questions, their factoid answers and corresponding full-length textual answers.

57, TITLE:      Physically constrained short-term vehicle trajectory forecasting with naive semantic maps
http://arxiv.org/abs/2006.05159
AUTHORS:        Albert Dulian ; John C. Murray
HIGHLIGHT:      In this paper we propose the model based on a combination of the CNN and LSTM encoder-decoder architecture that learns to extract a relevant road features from semantic maps as well as general motion of agents and uses this learned representation to predict their short-term future trajectories.

58, TITLE:      Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
http://arxiv.org/abs/2006.05398
AUTHORS:        Danny Driess ; Jung-Su Ha ; Marc Toussaint
COMMENTS:       Robotics: Science and Systems (R:SS) 2020
HIGHLIGHT:      In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image.

59, TITLE:      A t-distribution based operator for enhancing \\ out of distribution robustness of neural network classifiers
http://arxiv.org/abs/2006.05389
AUTHORS:        Niccolò Antonello ; Philip N. Garner
COMMENTS:       5 pages, 5 figures, to be published in IEEE Signal Processing Letters, reproducible code https://github.com/idiap/tsoftmax
HIGHLIGHT:      A t-distribution based operator for enhancing \\ out of distribution robustness of neural network classifiers

60, TITLE:      Smooth Proxy-Anchor Loss for Noisy Metric Learning
http://arxiv.org/abs/2006.05142
AUTHORS:        Carlos Roig ; David Varas ; Issey Masuda ; Juan Carlos Riveiro ; Elisenda Bou-Balust
COMMENTS:       The 4th Workshop on Visual Understanding by Learning from Web Data (CVPR 2020)
HIGHLIGHT:      In this work, we propose a Metric Learning method that is able to overcome the presence of noisy labels using our novel Smooth Proxy-Anchor Loss.

61, TITLE:      A Survey on Generative Adversarial Networks: Variants, Applications, and Training
http://arxiv.org/abs/2006.05132
AUTHORS:        Abdul Jabbar ; Xi Li ; Bourahla Omar
HIGHLIGHT:      Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning.

62, TITLE:      Contestable Black-Boxes
http://arxiv.org/abs/2006.05133
AUTHORS:        Andrea Aler Tubella ; Andreas Theodorou ; Virginia Dignum ; Loizos Michael
COMMENTS:       Accepted at RuleML 2020 as a short paper
HIGHLIGHT:      This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved, opening new questions about the interplay of contestability and explainability.

63, TITLE:      On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
http://arxiv.org/abs/2006.05129
AUTHORS:        Balázs Tarján ; György Szaszák ; Tibor Fegyó ; Péter Mihajlik
COMMENTS:       8 pages, 2 figures, accepted for publication at TSD 2020
HIGHLIGHT:      In the present paper we analyze the amount of transferable knowledge and demonstrate that the neural augmented LM (RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by dropping the second decoding pass and making the system real-time capable.

64, TITLE:      Over-crowdedness Alert! Forecasting the Future Crowd Distribution
http://arxiv.org/abs/2006.05127
AUTHORS:        Yuzhen Niu ; Weifeng Shi ; Wenxi Liu ; Shengfeng He ; Jia Pan ; Antoni B. Chan
HIGHLIGHT:      In this paper, we formulate a novel crowd analysis problem, in which we aim to predict the crowd distribution in the near future given sequential frames of a crowd video without any identity annotations.

65, TITLE:      A Self-supervised Approach for Adversarial Robustness
http://arxiv.org/abs/2006.04924
AUTHORS:        Muzammal Naseer ; Salman Khan ; Munawar Hayat ; Fahad Shahbaz Khan ; Fatih Porikli
COMMENTS:       CVPR-2020 (Oral). Code this http https://github.com/Muzammal-Naseer/NRP}
HIGHLIGHT:      In this paper, we take the first step to combine the benefits of both approaches and propose a self-supervised adversarial training mechanism in the input space.

66, TITLE:      What Matters in Unsupervised Optical Flow
http://arxiv.org/abs/2006.04902
AUTHORS:        Rico Jonschkowski ; Austin Stone ; Jonathan T. Barron ; Ariel Gordon ; Kurt Konolige ; Anelia Angelova
COMMENTS:       Source code is available at https://github.com/google-research/google-research/tree/master/uflow
HIGHLIGHT:      By combining the results of our investigation with our improved model components, we are able to present a new unsupervised flow technique that significantly outperforms the previous unsupervised state-of-the-art and performs on par with supervised FlowNet2 on the KITTI 2015 dataset, while also being significantly simpler than related approaches.

67, TITLE:      Reposing Humans by Warping 3D Features
http://arxiv.org/abs/2006.04898
AUTHORS:        Markus Knoche ; István Sárándi ; Bastian Leibe
COMMENTS:       Accepted at CVPR 2020 Workshop on Human-Centric Image/Video Synthesis
HIGHLIGHT:      Based on the recent success in deep learning-based volumetric representations, we propose to implicitly learn a dense feature volume from human images, which lends itself to simple and intuitive manipulation through explicit geometric warping.

68, TITLE:      Probabilistic Semantic Mapping for Urban Autonomous Driving Applications
http://arxiv.org/abs/2006.04894
AUTHORS:        David Paz ; Hengyuan Zhang ; Qinru Li ; Hao Xiang ; Henrik Christensen
COMMENTS:       6 pages, 10 figures, submitted to IROS 2020
HIGHLIGHT:      Experiments from data collected in an urban environment show that this model can be extended for automatically incorporating road features into HD maps with potential future work directions.

69, TITLE:      Quantum Logspace Algorithm for Powering Matrices with Bounded Norm
http://arxiv.org/abs/2006.04880
AUTHORS:        Uma Girish ; Ran Raz ; Wei Zhan
HIGHLIGHT:      We give a quantum logspace algorithm for powering contraction matrices, that is, matrices with spectral norm at most~1.

70, TITLE:      KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
http://arxiv.org/abs/2006.04878
AUTHORS:        Jeya Maria Jose ; Vishwanath Sindagi ; Ilker Hacihaliloglu ; Vishal M. Patel
COMMENTS:       Provisionally Accepted at MICCAI 2020
HIGHLIGHT:      We analyze this issue in detail, and address it by proposing an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions (in the spatial sense).

71, TITLE:      Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
http://arxiv.org/abs/2006.04874
AUTHORS:        Jane Wu ; Zhenglin Geng ; Hui Zhou ; Ronald Fedkiw
HIGHLIGHT:      We present a novel learning framework for cloth deformation by embedding virtual cloth into a tetrahedral mesh that parametrizes the volumetric region of air surrounding the underlying body.

72, TITLE:      Integer Programming for Multi-Robot Planning: A Column Generation Approach
http://arxiv.org/abs/2006.04856
AUTHORS:        Naveed Haghani ; Jiaoyang Li ; Sven Koenig ; Gautam Kunapuli ; Claudio Contardo ; Julian Yarkony
HIGHLIGHT:      We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints.

73, TITLE:      Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation
http://arxiv.org/abs/2006.04859
AUTHORS:        Suryansh Saxena ; Isaac K Isukapati
HIGHLIGHT:      This paper introduces a novel perception framework that has the ability to identify and track objects in autonomous vehicle's field of view.

74, TITLE:      Evaluation of Quantum Approximate Optimization Algorithm based on the approximation ratio of single samples
http://arxiv.org/abs/2006.04831
AUTHORS:        Jason Larkin ; Matías Jonsson ; Daniel Justice ; Gian Giacomo Guerreschi
HIGHLIGHT:      Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality.

75, TITLE:      Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences
http://arxiv.org/abs/2006.05367
AUTHORS:        Huaying Hao ; Huazhu Fu ; Yanwu Xu ; Jianlong Yang ; Fei Li ; Xiulan Zhang ; Jiang Liu ; Yitian Zhao
COMMENTS:       Accepted to MICCAI 2020
HIGHLIGHT:      To address this, we propose a novel sequence multi-scale aggregation deep network (SMA-Net) for open-narrow-synechiae ACA classification based on an AS-OCT sequence.

76, TITLE:      Orientation Attentive Robot Grasp Synthesis
http://arxiv.org/abs/2006.05123
AUTHORS:        Nikolaos Gkanatsios ; Georgia Chalvatzaki ; Petros Maragos ; Jan Peters
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      On top of that, we build the ORientation AtteNtive Grasp synthEsis (ORANGE) framework that jointly solves a bin classification problem and a real-value regression.

77, TITLE:      Vocal markers from sustained phonation in Huntington's Disease
http://arxiv.org/abs/2006.05365
AUTHORS:        Rachid Riad ; Hadrien Titeux ; Laurie Lemoine ; Justine Montillot ; Jennifer Hamet Bagnou ; Xuan Nga Cao ; Emmanuel Dupoux ; Anne-Catherine Bachoud-Lévi
COMMENTS:       submitted to INTERSPEECH 2020. 1 pages of supplementary material appear only in the arxiv version
HIGHLIGHT:      We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up.

78, TITLE:      Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?
http://arxiv.org/abs/2006.05121
AUTHORS:        Corentin Kervadec ; Grigory Antipov ; Moez Baccouche ; Christian Wolf
HIGHLIGHT:      In this work, we design a new benchmark based on a fine-grained reorganization of the GQA dataset [1], which allows to precisely answer these questions.

79, TITLE:      Combination of abstractive and extractive approaches for summarization of long scientific texts
http://arxiv.org/abs/2006.05354
AUTHORS:        Vladislav Tretyak
COMMENTS:       11 pages, 2 figures, 3 table, submitted to 23rd International Conference on Discovery Science
HIGHLIGHT:      In this research work, we present a method to generate summaries of long scientific documents that uses the advantages of both extractive and abstractive approaches.

80, TITLE:      Human brain activity for machine attention
http://arxiv.org/abs/2006.05113
AUTHORS:        Lukas Muttenthaler ; Nora Hollenstein ; Maria Barrett
HIGHLIGHT:      We devise a method for finding such EEG features to supervise machine attention through combining theoretically motivated cropping with random forest tree splits.

81, TITLE:      A Hybrid Framework for Matching Printing Design Files to Product Photos
http://arxiv.org/abs/2006.05355
AUTHORS:        Alper Kaplan ; Erdem Akagunduz
HIGHLIGHT:      Using this image set, we benchmark various hand-crafted and deep features for matching performance and propose a framework in which deep learning is utilized with highest contribution, but without disabling real-time operation using an ordinary desktop computer.

82, TITLE:      MeshWalker: Deep Mesh Understanding by Random Walks
http://arxiv.org/abs/2006.05353
AUTHORS:        Alon Lahav ; Ayellet Tal
HIGHLIGHT:      In this paper we look at the most popular representation of 3D shapes in computer graphics - a triangular mesh - and ask how it can be utilized within deep learning.

83, TITLE:      Towards Good Practices for Data Augmentation in GAN Training
http://arxiv.org/abs/2006.05338
AUTHORS:        Ngoc-Trung Tran ; Viet-Hung Tran ; Ngoc-Bao Nguyen ; Trung-Kien Nguyen ; Ngai-Man Cheung
HIGHLIGHT:      We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the JS divergence w.r.t. the original distribution and it leverages the augmented data to improve the learnings of discriminator and generator.

84, TITLE:      mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation
http://arxiv.org/abs/2006.05327
AUTHORS:        Roberto Daza ; Aythami Morales ; Julian Fierrez ; Ruben Tolosana
HIGHLIGHT:      This work presents mEBAL, a multimodal database for eye blink detection and attention level estimation.



==========Updates to Previous Papers==========
1, TITLE:       A fast and memory-efficient algorithm for smooth interpolation of polyrigid transformations: application to human joint tracking
http://arxiv.org/abs/2005.02159
AUTHORS:        K. Makki ; B. Borotikar ; M. Garetier ; S. Brochard ; D. Ben Salem ; F. Rousseau
HIGHLIGHT:      In this paper, we propose an algorithm using a matrix diagonalization based method for smooth interpolation of homogeneous polyrigid transformations of human joints during motion.

2, TITLE:       On Catastrophic Interference in Atari 2600 Games
http://arxiv.org/abs/2002.12499
AUTHORS:        William Fedus ; Dibya Ghosh ; John D. Martin ; Marc G. Bellemare ; Yoshua Bengio ; Hugo Larochelle
COMMENTS:       First two authors contributed equally. Code available to reproduce experiments at https://github.com/google-research/google-research/tree/master/memento
HIGHLIGHT:      By synthetically controlling for interference, we demonstrate performance boosts across architectures, learning algorithms and environments.

3, TITLE:       Parameterized Complexity of Fair Vertex Evaluation Problems
http://arxiv.org/abs/1803.06878
AUTHORS:        Dušan Knop ; Tomáš Masařík ; Tomáš Toufar
COMMENTS:       25 pages, 5 figures, presented at MFCS 2019
HIGHLIGHT:      In this work, we are changing the measure to the fair measure [Lin&Sahni: Fair edge deletion problems.

4, TITLE:       Ordered Functional Decision Diagrams
http://arxiv.org/abs/2003.09340
AUTHORS:        Joan Thibault ; Khalil Ghorbal
HIGHLIGHT:      In this paper, we introduce a novel framework, termed $\lambda$ Decision Diagram ($\lambda$DD), that revisits BDD from a purely functional point of view.

5, TITLE:       Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift
http://arxiv.org/abs/2005.00853
AUTHORS:        Benjamin Doerr
COMMENTS:       Author generated version of a paper accepted for publication in the proceedings of PPSN 2020 with appendix containing the full proofs
HIGHLIGHT:      We propose a simple negative drift theorem for multiplicative drift scenarios and show that it can simplify existing analyses.

6, TITLE:       TResNet: High Performance GPU-Dedicated Architecture
http://arxiv.org/abs/2003.13630
AUTHORS:        Tal Ridnik ; Hussam Lawen ; Asaf Noy ; Itamar Friedman ; Emanuel Ben Baruch ; Gilad Sharir
COMMENTS:       11 pages, 5 figures
HIGHLIGHT:      In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.

7, TITLE:       Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
http://arxiv.org/abs/2003.08040
AUTHORS:        Zhonghao Wang ; Mo Yu ; Yunchao Wei ; Rogerio Feris ; Jinjun Xiong ; Wen-mei Hwu ; Thomas S. Huang ; Humphrey Shi
COMMENTS:       CVPR 2020
HIGHLIGHT:      We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.

8, TITLE:       A Robust Attentional Framework for License Plate Recognition in the Wild
http://arxiv.org/abs/2006.03919
AUTHORS:        Linjiang Zhang ; Peng Wang ; Hui Li ; Zhen Li ; Chunhua Shen ; Yanning Zhang
HIGHLIGHT:      In this work, we propose a robust framework for license plate recognition in the wild. Moreover, we released a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China.

9, TITLE:       Instance Shadow Detection
http://arxiv.org/abs/1911.07034
AUTHORS:        Tianyu Wang ; Xiaowei Hu ; Qiong Wang ; Pheng-Ann Heng ; Chi-Wing Fu
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results.

10, TITLE:      Template-based Question Answering using Recursive Neural Networks
http://arxiv.org/abs/2004.13843
AUTHORS:        Ram G Athreya ; Srividya Bansal ; Axel-Cyrille Ngonga Ngomo ; Ricardo Usbeck
HIGHLIGHT:      We propose a neural network-based approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks.

11, TITLE:      Fewer colors for perfect simulation of proper colorings
http://arxiv.org/abs/2004.08716
AUTHORS:        Mark Huber
COMMENTS:       The paper contained an error in Lemma 5. The weight of the recycled state is $(k_v - 1) / k_v$, which depends on the neighboring colors in the state. That prevents the output of the algorithm from being uniform
HIGHLIGHT:      Here a new randomized algorithm is presented based upon the randomness recycler protocol introduced by the author and Fill at FOCS 2000.

12, TITLE:      Shaping Visual Representations with Language for Few-shot Classification
http://arxiv.org/abs/1911.02683
AUTHORS:        Jesse Mu ; Percy Liang ; Noah Goodman
COMMENTS:       ACL 2020. Version 1 appeared at the NeurIPS 2019 Workshop on Visually Grounded Interaction and Language (ViGIL)
HIGHLIGHT:      Instead, we propose language-shaped learning (LSL), an end-to-end model that regularizes visual representations to predict language.

13, TITLE:      Learning Discriminative Model Prediction for Tracking
http://arxiv.org/abs/1904.07220
AUTHORS:        Goutam Bhat ; Martin Danelljan ; Luc Van Gool ; Radu Timofte
HIGHLIGHT:      We develop an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction.

14, TITLE:      Host-Pathongen Co-evolution Inspired Algorithm Enables Robust GAN Training
http://arxiv.org/abs/2006.04720
AUTHORS:        Andrei Kucharavy ; El Mahdi El Mhamdi ; Rachid Guerraoui
COMMENTS:       8 pages, 10 figures
HIGHLIGHT:      Here, we explore that similarity to propose a more robust algorithm for GANs training.

15, TITLE:      ARID: A New Dataset for Recognizing Action in the Dark
http://arxiv.org/abs/2006.03876
AUTHORS:        Yuecong Xu ; Jianfei Yang ; Haozhi Cao ; Kezhi Mao ; Jianxiong Yin ; Simon See
COMMENTS:       6 pages, 7 figures, Data available at https://xuyu0010.github.io/arid
HIGHLIGHT:      In this paper, we explored the task of action recognition in dark videos.

16, TITLE:      ResKD: Residual-Guided Knowledge Distillation
http://arxiv.org/abs/2006.04719
AUTHORS:        Xuewei Li ; Songyuan Li ; Bourahla Omar ; Xi Li
HIGHLIGHT:      In this paper, we see knowledge distillation in a fresh light, using the knowledge gap between a teacher and a student as guidance to train a lighter-weight student called res-student.

17, TITLE:      The Hessian Estimation Evolution Strategy
http://arxiv.org/abs/2003.13256
AUTHORS:        Tobias Glasmachers ; Oswin Krause
HIGHLIGHT:      We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy.

18, TITLE:      Preference Neural Network
http://arxiv.org/abs/1904.02345
AUTHORS:        Ayman Elgharabawy
COMMENTS:       The current content is inappropriate and requires to be comprehensively reviewed again
HIGHLIGHT:      This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function.

19, TITLE:      AGVNet: Attention Guided Velocity Learning for 3D Human Motion Prediction
http://arxiv.org/abs/2005.12155
AUTHORS:        Xiaoli Liu ; Jianqin Yin ; Huaping Liu ; Jun Liu
HIGHLIGHT:      In this paper, we propose a novel attention-guided velocity learning network, AGVNet, that utilizes multi-order information such as positions and velocities derived from the dynamic states of the human body for predicting human motion.

20, TITLE:      WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
http://arxiv.org/abs/2006.04598
AUTHORS:        Hyeongju Kim ; Hyeonseung Lee ; Woo Hyun Kang ; Sung Jun Cheon ; Byoung Jin Choi ; Nam Soo Kim
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis.

21, TITLE:      The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
http://arxiv.org/abs/2005.04790
AUTHORS:        Douwe Kiela ; Hamed Firooz ; Aravind Mohan ; Vedanuj Goswami ; Amanpreet Singh ; Pratik Ringshia ; Davide Testuggine
HIGHLIGHT:      This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.

22, TITLE:      Generating new concepts with hybrid neuro-symbolic models
http://arxiv.org/abs/2003.08978
AUTHORS:        Reuben Feinman ; Brenden M. Lake
COMMENTS:       Published in Proceedings of the 42nd Annual Meeting of the Cognitive Science Society, July 2020
HIGHLIGHT:      In this paper, we explore a synthesis of these two traditions through a novel neuro-symbolic model for generating new concepts.

23, TITLE:      Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
http://arxiv.org/abs/2006.04323
AUTHORS:        Zhen Zhao ; Bingyu Liu ; Yuhong Guo ; Jieping Ye
HIGHLIGHT:      In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.

24, TITLE:      Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review
http://arxiv.org/abs/2004.14143
AUTHORS:        Mahdi Rezaei ; Mahsa Shahidi
COMMENTS:       Version 2.0
HIGHLIGHT:      In this review paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well as our recommended solution, motivations behind each approach, and their advantages over each category to guide the researchers to proceed with the best techniques and practices based on their applications.

25, TITLE:      FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
http://arxiv.org/abs/2006.04558
AUTHORS:        Yi Ren ; Chenxu Hu ; Xu Tan ; Tao Qin ; Sheng Zhao ; Zhou Zhao ; Tie-Yan Liu
HIGHLIGHT:      In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs.

26, TITLE:      Service mining for Internet of Things
http://arxiv.org/abs/2005.06895
AUTHORS:        Bing Huang ; Athman Bouguettaya
HIGHLIGHT:      A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up. We present a set of metrics to evaluate the interestingness of discovered service relationships.

27, TITLE:      End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera
http://arxiv.org/abs/2006.04082
AUTHORS:        Zhenbo Song ; Jianfeng Lu ; Tong Zhang ; Hongdong Li
HIGHLIGHT:      In this paper, we propose a monocular camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network.

28, TITLE:      Adaptive Geo-Topological Independence Criterion
http://arxiv.org/abs/1810.02923
AUTHORS:        Baihan Lin ; Nikolaus Kriegeskorte
HIGHLIGHT:      We propose a class of adaptive (multi-threshold) test statistics, which form the basis for permutation tests.

29, TITLE:      Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning
http://arxiv.org/abs/1910.11102
AUTHORS:        Xinxin Zhu ; Longteng Guo ; Peng Yao ; Shichen Lu ; Wei Liu ; Jing Liu
COMMENTS:       4 pages,2 figure
HIGHLIGHT:      This report describes our solution for the VATEX Captioning Challenge 2020, which requires generating descriptions for the videos in both English and Chinese languages.

30, TITLE:      Provable Robust Learning Based on Transformation-Specific Smoothing
http://arxiv.org/abs/2002.12398
AUTHORS:        Linyi Li ; Maurice Weber ; Xiaojun Xu ; Luka Rimanic ; Tao Xie ; Ce Zhang ; Bo Li
COMMENTS:       58 pages, 7 figures
HIGHLIGHT:      In this paper, we aim to provide a unified framework for certifying ML robustness against general adversarial transformations.

31, TITLE:      Policy Gradient from Demonstration and Curiosity
http://arxiv.org/abs/2004.10430
AUTHORS:        Jie Chen ; Wenjun Xu
HIGHLIGHT:      In this work, an integrated policy gradient algorithm was proposed to boost exploration and facilitate intrinsic reward learning from only limited number of demonstrations.

32, TITLE:      Testing Robustness Against Unforeseen Adversaries
http://arxiv.org/abs/1908.08016
AUTHORS:        Daniel Kang ; Yi Sun ; Dan Hendrycks ; Tom Brown ; Jacob Steinhardt
HIGHLIGHT:      We address this discrepancy between research and reality by proposing a new evaluation framework called ImageNet-UA.

33, TITLE:      Learning to Branch for Multi-Task Learning
http://arxiv.org/abs/2006.01895
AUTHORS:        Pengsheng Guo ; Chen-Yu Lee ; Daniel Ulbricht
COMMENTS:       Accepted at ICML 2020
HIGHLIGHT:      In this work, we present an automated multi-task learning algorithm that learns where to share or branch within a network, designing an effective network topology that is directly optimized for multiple objectives across tasks.

34, TITLE:      FakeLocator: Robust Localization of GAN-Based Face Manipulations
http://arxiv.org/abs/2001.09598
AUTHORS:        Yihao Huang ; Felix Juefei-Xu ; Run Wang ; Qing Guo ; Xiaofei Xie ; Lei Ma ; Jianwen Li ; Weikai Miao ; Yang Liu ; Geguang Pu
COMMENTS:       9 pages
HIGHLIGHT:      Based on this basic observation, we have proposed a novel approach to obtain high localization accuracy, at full resolution, on manipulated facial images.

35, TITLE:      What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
http://arxiv.org/abs/2004.10899
AUTHORS:        Irene Li ; Yixin Li ; Tianxiao Li ; Sergio Alvarez-Napagao ; Dario Garcia-Gasulla ; Toyotaro Suzumura
COMMENTS:       7 pages, 7 figures
HIGHLIGHT:      In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets.

36, TITLE:      An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation
http://arxiv.org/abs/2006.03810
AUTHORS:        Deepan Das ; Haley Massa ; Abhimanyu Kulkarni ; Theodoros Rekatsinas
HIGHLIGHT:      We present a novel Class-Discrimination metric to quantitatively measure this dichotomy in performance and link it to the discriminative capacity induced by the different strategies on a network's latent space.

37, TITLE:      Adaptive Conditional Neural Movement Primitives via Representation Sharing Between Supervised and Reinforcement Learning
http://arxiv.org/abs/2003.11334
AUTHORS:        M. Tuluhan Akbulut ; M. Yunus Seker ; Ahmet E. Tekden ; Yukie Nagai ; Erhan Oztop ; Emre Ugur
COMMENTS:       8 pages, 9 figures, IROS 2020 review
HIGHLIGHT:      In this study, to improve the applicability of CNMP to changing tasks and/or environments, we couple it with a reinforcement learning agent that exploits the formed representations by the original CNMP network, and learns to generate synthetic demonstrations for further learning.

38, TITLE:      Semi-Supervised Semantic Segmentation with Cross-Consistency Training
http://arxiv.org/abs/2003.09005
AUTHORS:        Yassine Ouali ; Céline Hudelot ; Myriam Tami
COMMENTS:       Published at CVPR 2020
HIGHLIGHT:      In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation.

39, TITLE:      Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
http://arxiv.org/abs/2006.04660
AUTHORS:        Rajdeep Mukherjee ; Hari Chandana Peruri ; Uppada Vishnu ; Pawan Goyal ; Sourangshu Bhattacharya ; Niloy Ganguly
COMMENTS:       4 pages, accepted in the Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020
HIGHLIGHT:      In this work, we argue the need and propose a solution for generating personalized aspect-based opinion summaries from large collections of online tourist reviews.

40, TITLE:      Strategic and Crowd-Aware Itinerary Recommendation
http://arxiv.org/abs/1909.07775
AUTHORS:        Junhua Liu ; Kristin L. Wood ; Kwan Hui Lim
HIGHLIGHT:      In this work, we propose the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR) algorithm which optimizes social welfare in real-world situations.

41, TITLE:      Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
http://arxiv.org/abs/1909.10707
AUTHORS:        Yijiong Lin ; Jiancong Huang ; Matthieu Zimmer ; Yisheng Guan ; Juan Rojas ; Paul Weng
COMMENTS:       8 pages, 11 figures
HIGHLIGHT:      Based on this data augmentation idea, we formulate a general framework, called Invariant Transform Experience Replay that we present with two techniques: (i) Kaleidoscope Experience Replay exploits reflectional symmetries and (ii) Goal-augmented Experience Replay which takes advantage of lax goal definitions.

42, TITLE:      Robust Numerical Tracking of One Path of a Polynomial Homotopy on Parallel Shared Memory Computers
http://arxiv.org/abs/2002.09504
AUTHORS:        Simon Telen ; Marc Van Barel ; Jan Verschelde
COMMENTS:       Corrected some mistakes, added efficiency plots
HIGHLIGHT:      We consider the problem of tracking one solution path defined by a polynomial homotopy on a parallel shared memory computer.

43, TITLE:      Learning and Planning in the Feature Deception Problem
http://arxiv.org/abs/1905.04833
AUTHORS:        Zheyuan Ryan Shi ; Ariel D. Procaccia ; Kevin S. Chan ; Sridhar Venkatesan ; Noam Ben-Asher ; Nandi O. Leslie ; Charles Kamhoua ; Fei Fang
HIGHLIGHT:      (2) We propose an approximation algorithm for finding the optimal deception strategy given the learned preferences and show that the problem is NP-hard. In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.

44, TITLE:      Beyond Universal Person Re-ID Attack
http://arxiv.org/abs/1910.14184
AUTHORS:        Wenjie Ding ; Xing Wei ; Yunfeng Qiu ; Rongrong Ji ; Xiaopeng Hong ; Yihong Gong
HIGHLIGHT:      In this paper, we make the first attempt to examine the vulnerability of current person Re-ID models against a dangerous attack method, \ie, the universal adversarial perturbation (UAP) attack, which has been shown to fool classification models with a little overhead.

45, TITLE:      Fast In-place Algorithms for Polynomial Operations: Division, Evaluation, Interpolation
http://arxiv.org/abs/2002.10304
AUTHORS:        Pascal Giorgi ; Bruno Grenet ; Daniel S. Roche
HIGHLIGHT:      We demonstrate new in-place algorithms for the aforementioned polynomial computations which require only constant extra space and achieve the same asymptotic running time as their out-of-place counterparts.

46, TITLE:      Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
http://arxiv.org/abs/1909.05311
AUTHORS:        Shangwen Lv ; Daya Guo ; Jingjing Xu ; Duyu Tang ; Nan Duan ; Ming Gong ; Linjun Shou ; Daxin Jiang ; Guihong Cao ; Songlin Hu
COMMENTS:       8 pages, 7 figure, AAAI 2020
HIGHLIGHT:      In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence.

47, TITLE:      Efficient Architecture Search for Continual Learning
http://arxiv.org/abs/2006.04027
AUTHORS:        Qiang Gao ; Zhipeng Luo ; Diego Klabjan
COMMENTS:       12 pages, 11 figures
HIGHLIGHT:      To reach these goals, we propose a novel approach named as Continual Learning with Efficient Architecture Search, or CLEAS in short.

48, TITLE:      Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
http://arxiv.org/abs/2005.00033
AUTHORS:        Firoj Alam ; Shaden Shaar ; Fahim Dalvi ; Hassan Sajjad ; Alex Nikolov ; Hamdy Mubarak ; Giovanni Da San Martino ; Ahmed Abdelali ; Nadir Durrani ; Kareem Darwish ; Preslav Nakov
HIGHLIGHT:      Thus, here we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic.

49, TITLE:      Proving P!=NP in first-order PA
http://arxiv.org/abs/2005.10080
AUTHORS:        Rupert McCallum
HIGHLIGHT:      We show that it is provable in PA that there is an arithmetically definable sequence $\{\phi_{n}:n \in \omega\}$ of $\Pi^{0}_{2}$-sentences, such that - PRA+$\{\phi_{n}:n \in \omega\}$ is $\Pi^{0}_{2}$-sound and $\Pi^{0}_{1}$-complete - the length of $\phi_{n}$ is bounded above by a polynomial function of $n$ with positive leading coefficient - PRA+$\phi_{n+1}$ always proves 1-consistency of PRA+$\phi_{n}$.

50, TITLE:      Real-Time Model Calibration with Deep Reinforcement Learning
http://arxiv.org/abs/2006.04001
AUTHORS:        Yuan Tian ; Manuel Arias Chao ; Chetan Kulkarni ; Kai Goebel ; Olga Fink
COMMENTS:       18 pages, 10 figures
HIGHLIGHT:      In this paper, we propose a novel framework for inference of model parameters based on reinforcement learning.

51, TITLE:      Pre-training Polish Transformer-based Language Models at Scale
http://arxiv.org/abs/2006.04229
AUTHORS:        Sławomir Dadas ; Michał Perełkiewicz ; Rafał Poświata
HIGHLIGHT:      In this study, we present two language models for Polish based on the popular BERT architecture.