2020.07.10.txt

==========New Papers==========
1, TITLE:       A Neuro-inspired Theory of Joint Human-Swarm Interaction
http://arxiv.org/abs/2007.04882
AUTHORS:        Jonas D. Hasbach ; Maren Bennewitz
COMMENTS:       ICRA Workshop on Human-Swarm Interaction 2020
HIGHLIGHT:      Here we apply a cognitive systems engineering perspective and introduce a neuro-inspired joint systems theory of HSI.

2, TITLE:       PIE-NET: Parametric Inference of Point Cloud Edges
http://arxiv.org/abs/2007.04883
AUTHORS:        Xiaogang Wang ; Yuelang Xu ; Kai Xu ; Andrea Tagliasacchi ; Bin Zhou ; Ali Mahdavi-Amiri ; Hao Zhang
HIGHLIGHT:      We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.

3, TITLE:       Attention Neural Network for Trash Detection on Water Channels
http://arxiv.org/abs/2007.04639
AUTHORS:        Mohbat Tharani ; Abdul Wahab Amin ; Mohammad Maaz ; Murtaza Taj
COMMENTS:       Object Detection, Trash Detection, Water Quality
HIGHLIGHT:      This paper proposes a method for the detection of visible trash floating on the water surface of the canals in urban areas. We also provide a large dataset, first of its kind, trash in water channels that contains object-level annotations.

4, TITLE:       IQ-VQA: Intelligent Visual Question Answering
http://arxiv.org/abs/2007.04422
AUTHORS:        Vatsal Goel ; Mohit Chandak ; Ashish Anand ; Prithwijit Guha
HIGHLIGHT:      To this end, we propose a model-independent cyclic framework which increases consistency and robustness of any VQA architecture. As a baseline for future works on consistency, we provide a new human annotated VQA-Implications dataset.

5, TITLE:       Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures
http://arxiv.org/abs/2007.04666
AUTHORS:        Steven Puttemans ; Timothy Callemein ; Toon Goedemé
HIGHLIGHT:      In this paper we explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines, using state-of-the-art open source deep learning frameworks, like Darknet.

6, TITLE:       Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound
http://arxiv.org/abs/2007.04480
AUTHORS:        Richard Droste ; Lior Drukker ; Aris T. Papageorghiou ; J. Alison Noble
COMMENTS:       Accepted at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020)
HIGHLIGHT:      We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning.

7, TITLE:       The Automation of Acceleration: AI and the Future of Society
http://arxiv.org/abs/2007.04477
AUTHORS:        Nicholas Kluge Corrêa
HIGHLIGHT:      In this article we present a review of several points, the risks and benefits of social modernization through AI, how human society has been preparing to deal with such changes, and finally, how the debate on such technologies is taking place in a way totally dominated by European and North American societies.

8, TITLE:       Animated GIF optimization by adaptive color local table management
http://arxiv.org/abs/2007.04717
AUTHORS:        Oliver Giudice ; Dario Allegra ; Francesco Guarnera ; Filippo Stanco ; Sebastiano Battiato
HIGHLIGHT:      In this paper a parametric optimization technique for animated GIFs will be presented.

9, TITLE:       ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
http://arxiv.org/abs/2007.04954
AUTHORS:        Chuang Gan ; Jeremy Schwartz ; Seth Alter ; Martin Schrimpf ; James Traer ; Julian De Freitas ; Jonas Kubilius ; Abhishek Bhandwaldar ; Nick Haber ; Megumi Sano ; Kuno Kim ; Elias Wang ; Damian Mrowca ; Michael Lingelbach ; Aidan Curtis ; Kevin Feigelis ; Daniel M. Bear ; Dan Gutfreund ; David Cox ; James J. DiCarlo ; Josh McDermott ; Joshua B. Tenenbaum ; Daniel L. K. Yamins
COMMENTS:       Project page: http://www.threedworld.org
HIGHLIGHT:      We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.

10, TITLE:      AI Assisted Apparel Design
http://arxiv.org/abs/2007.04950
AUTHORS:        Alpana Dubey ; Nitish Bhardwaj ; Kumar Abhinav ; Suma Mani Kuriakose ; Sakshi Jain ; Veenu Arora
HIGHLIGHT:      In this paper, we propose a system of AI assistants that assists designers in their design journey.

11, TITLE:      An Efficient Data Imputation Technique for Human Activity Recognition
http://arxiv.org/abs/2007.04456
AUTHORS:        Ivan Miguel Pires ; Faisal Hussain ; Nuno M. Garcia ; Eftim Zdravevski
COMMENTS:       8 Pages, 8 Figures, 1 Table. Accepted in 14th Multi Conference on Computer Science and Information Systems 2020 (MCCSIS 2020)
HIGHLIGHT:      Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities.

12, TITLE:      A Study on Encodings for Neural Architecture Search
http://arxiv.org/abs/2007.04965
AUTHORS:        Colin White ; Willie Neiswanger ; Sam Nolen ; Yash Savani
HIGHLIGHT:      In this work, we present the first formal study on the effect of architecture encodings for NAS, including a theoretical grounding and an empirical study.

13, TITLE:      EVO-RL: Evolutionary-Driven Reinforcement Learning
http://arxiv.org/abs/2007.04725
AUTHORS:        Ahmed Hallawa ; Thorsten Born ; Anke Schmeink ; Guido Dartmann ; Arne Peine ; Lukas Martin ; Giovanni Iacca ; Gusz Eiben ; Gerd Ascheid
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation.

14, TITLE:      InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning
http://arxiv.org/abs/2007.04589
AUTHORS:        Kwot Sin Lee ; Ngoc-Trung Tran ; Ngai-Man Cheung
COMMENTS:       Initial version was presented at NeurIPS 2019 Workshop on Information Theory and Machine Learning
HIGHLIGHT:      In this work, we propose a principled framework to simultaneously address two fundamental issues in GANs: catastrophic forgetting of the discriminator and mode collapse of the generator.

15, TITLE:      Auxiliary Tasks Speed Up Learning PointGoal Navigation
http://arxiv.org/abs/2007.04561
AUTHORS:        Joel Ye ; Dhruv Batra ; Erik Wijmans ; Abhishek Das
COMMENTS:       13 pages
HIGHLIGHT:      In this work, we develop a method to significantly increase sample and time efficiency in learning PointNav using self-supervised auxiliary tasks (e.g. predicting the action taken between two egocentric observations, predicting the distance between two observations from a trajectory,etc.).

16, TITLE:      Improving Style-Content Disentanglement in Image-to-Image Translation
http://arxiv.org/abs/2007.04964
AUTHORS:        Aviv Gabbay ; Yedid Hoshen
COMMENTS:       Project page: http://www.vision.huji.ac.il/style-content-disentanglement
HIGHLIGHT:      In this work, we propose a principled approach for improving style-content disentanglement in image-to-image translation.

17, TITLE:      Efficient detection of adversarial images
http://arxiv.org/abs/2007.04564
AUTHORS:        Darpan Kumar Yadav ; Kartik Mundra ; Rahul Modpur ; Arpan Chattopadhyay ; Indra Narayan Kar
COMMENTS:       10 pages, 3 figures, 3 algorithms, 8 tables. Extension of the Conference paper:- Kartik Mundra, Rahul Modpur, Arpan Chattopadhyay, and Indra Narayan Kar. Adversarial image detection in cyber-physical systems. In Proceedings of the 1st ACM Workshop on Autonomous and Intelligent Mobile Systems, pages 1-5, 2020. Can be found at https://dl.acm.org/doi/abs/10.1145/3377283.3377285
HIGHLIGHT:      In this paper, detection of deception attack on deep neural network (DNN) based image classification in autonomous and cyber-physical systems is considered.

18, TITLE:      SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
http://arxiv.org/abs/2007.04938
AUTHORS:        Kimin Lee ; Michael Laskin ; Aravind Srinivas ; Pieter Abbeel
HIGHLIGHT:      To mitigate these issues, we present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy RL algorithms.

19, TITLE:      A Reference Software Architecture for Social Robots
http://arxiv.org/abs/2007.04933
AUTHORS:        Luigi Asprino ; Paolo Ciancarini ; Andrea Giovanni Nuzzolese ; Valentina Presutti ; Alessandro Russo
HIGHLIGHT:      The ultimate goal of this work is to establish a common ground based on a reference software architecture to allow to easily reuse robotic software components in order to rapidly develop, implement, and personalise Social Robots.

20, TITLE:      Anyone here? Smart embedded low-resolution omnidirectional video sensor to measure room occupancy
http://arxiv.org/abs/2007.04934
AUTHORS:        Timothy Callemein ; Kristof Van Beeck ; Toon Goedemé
HIGHLIGHT:      In this paper, we present a room occupancy sensing solution with unique properties: (i) It is based on an omnidirectional vision camera, capturing rich scene info over a wide angle, enabling to count the number of people in a room and even their position.

21, TITLE:      Single architecture and multiple task deep neural network for altered fingerprint analysis
http://arxiv.org/abs/2007.04931
AUTHORS:        Oliver Giudice ; Mattia Litrico ; Sebastiano Battiato
HIGHLIGHT:      This paper proposes a method for detection of altered fingerprints, identification of types of alterations and recognition of gender, hand and fingers.

22, TITLE:      Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
http://arxiv.org/abs/2007.04557
AUTHORS:        Tadashi Ogura ; Aly Magassouba ; Komei Sugiura ; Tsubasa Hirakawa ; Takayoshi Yamashita ; Hironobu Fujiyoshi ; Hisashi Kawai
COMMENTS:       9 pages, 8 figures. accepted for IEEE Robotics and Automation Letters (RA-L) with presentation at IROS 2020
HIGHLIGHT:      In this paper, we propose the attention branch encoder--decoder network (ABEN), to generate sentences from visual inputs.

23, TITLE:      Challenges of AI in Wireless Networks for IoT
http://arxiv.org/abs/2007.04705
AUTHORS:        Ijaz Ahmad ; Shahriar Shahabuddin ; Tanesh Kumar ; Erkki Harjula ; Marcus Meisel ; Markku Juntti ; Thilo Sauter ; Mika Ylianttila
HIGHLIGHT:      In this article, the main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted with potential generalized solution and future research directions.

24, TITLE:      Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model
http://arxiv.org/abs/2007.04574
AUTHORS:        Haojie Liu ; Ming Lu ; Zhan Ma ; Fan Wang ; Zhihuang Xie ; Xun Cao ; Yao Wang
HIGHLIGHT:      In this paper, we propose an end-to-end deep neural video coding framework (NVC), which uses variational autoencoders (VAEs) with joint spatial and temporal prior aggregation (PA) to exploit the correlations in intra-frame pixels, inter-frame motions and inter-frame compensation residuals, respectively.

25, TITLE:      A Generative Graph Method to Solve the Travelling Salesman Problem
http://arxiv.org/abs/2007.04949
AUTHORS:        Amal Nammouchi ; Hakim Ghazzai ; Yehia Massoud
COMMENTS:       5 pages, 2 figures, 2 tables, conference
HIGHLIGHT:      In this paper, we propose to use the novel Graph Learning Network (GLN), a generative approach, to approximately solve the TSP.

26, TITLE:      Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
http://arxiv.org/abs/2007.04571
AUTHORS:        Majid Ramezani ; Mohammad-Reza Feizi-Derakhshi ; Mohammad-Ali Balafar ; Meysam Asgari-Chenaghlu ; Ali-Reza Feizi-Derakhshi ; Narjes Nikzad-Khasmakhi ; Mehrdad Ranjbar-Khadivi ; Zoleikha Jahanbakhsh-Nagadeh ; Elnaz Zafarani-Moattar ; Taymaz Rahkar-Farshi
HIGHLIGHT:      The major objective of this study is to enhance the accuracy of APP from the text.

27, TITLE:      Client Adaptation improves Federated Learning with Simulated Non-IID Clients
http://arxiv.org/abs/2007.04806
AUTHORS:        Laura Rieger ; Rasmus M. Th. Høegh ; Lars K. Hansen
COMMENTS:       11 pages, 11 figures. To appear at International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020
HIGHLIGHT:      We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients.

28, TITLE:      Medical Instrument Detection in Ultrasound-Guided Interventions: A Review
http://arxiv.org/abs/2007.04807
AUTHORS:        Hongxu Yang ; Caifeng Shan ; Alexander F. Kolen ; Peter H. N. de With
COMMENTS:       Draft paper
HIGHLIGHT:      This article reviews medical instrument detection methods in the ultrasound-guided intervention.

29, TITLE:      The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization
http://arxiv.org/abs/2007.04940
AUTHORS:        Jingjing Shen ; Thomas J. Cashman ; Qi Ye ; Tim Hutton ; Toby Sharp ; Federica Bogo ; Andrew William Fitzgibbon ; Jamie Shotton
HIGHLIGHT:      To solve model-fitting problems for HoloLens 2 hand tracking, where the computational budget is approximately 100 times smaller than an iPhone 7, we introduce a new surface model: the `Phong surface'.

30, TITLE:      Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
http://arxiv.org/abs/2007.04942
AUTHORS:        Robin Schrijvers ; Steven Puttemans ; Timothy Callemein ; Toon Goedemé
HIGHLIGHT:      We solve this challenge by implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector, on a Jetson TX2 hardware platform.

31, TITLE:      A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings
http://arxiv.org/abs/2007.04782
AUTHORS:        Igor André Pegoraro Santana ; Marcos Aurelio Domingues
COMMENTS:       15 pages
HIGHLIGHT:      A systematic review adopts a formal and systematic method to perform a bibliographic review, and it is used to identify and evaluate all the research in certain area of study, by analyzing the relevant research published.

32, TITLE:      Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
http://arxiv.org/abs/2007.04542
AUTHORS:        Colby L. Wight ; Jia Zhao
HIGHLIGHT:      In this paper, we focus on using the deep neural network to design an automatic numerical solver for the Allen-Cahn and Cahn-Hilliard equations by proposing an improved physics informed neural network (PINN).

33, TITLE:      Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
http://arxiv.org/abs/2007.04780
AUTHORS:        Anna Volokitin ; Ertunc Erdil ; Neerav Karani ; Kerem Can Tezcan ; Xiaoran Chen ; Luc Van Gool ; Ender Konukoglu
COMMENTS:       accepted for publication at MICCAI 2020. Code available https://github.com/voanna/slices-to-3d-brain-vae/
HIGHLIGHT:      We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.

34, TITLE:      Attention-based Residual Speech Portrait Model for Speech to Face Generation
http://arxiv.org/abs/2007.04536
AUTHORS:        Jianrong Wang ; Xiaosheng Hu ; Li Liu ; Wei Liu ; Mei Yu ; Tianyi Xu
HIGHLIGHT:      To this end, in this paper, we propose a novel Attention-based Residual Speech Portrait Model (AR-SPM) by introducing the ideal of the residual into a hybrid encoder-decoder architecture, where face prior features are merged with the output of speech encoder to form the final face feature.

35, TITLE:      Point Set Voting for Partial Point Cloud Analysis
http://arxiv.org/abs/2007.04537
AUTHORS:        Junming Zhang ; Weijia Chen ; Yuping Wang ; Ram Vasudevan ; Matthew Johnson-Roberson
HIGHLIGHT:      This paper proposes a general model for partial point clouds analysis wherein the latent feature encoding a complete point clouds is inferred by applying a local point set voting strategy.

36, TITLE:      Long Short-Term Memory Spiking Networks and Their Applications
http://arxiv.org/abs/2007.04779
AUTHORS:        Ali Lotfi Rezaabad ; Sriram Vishwanath
HIGHLIGHT:      In this paper, we present a novel framework for training recurrent SNNs.

37, TITLE:      EPI-based Oriented Relation Networks for Light Field Depth Estimation
http://arxiv.org/abs/2007.04538
AUTHORS:        Kunyuan Li ; Jun Zhang ; Rui Sun ; Xudong Zhang ; Jun Gao
HIGHLIGHT:      Based on the observation that the similar linear structure between the oriented lines and their neighboring pixels, we propose an end-to-end fully convolutional network (FCN) to estimate the depth value of the intersection point on the horizontal and vertical EPIs.

38, TITLE:      Long-Term Residual Blending Network for Blur Invariant Single Image Blind deblurring
http://arxiv.org/abs/2007.04543
AUTHORS:        Sungkwon An ; Hyungmin Roh ; Myungjoo Kang
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      We present a novel, blind, single image deblurring method that utilizes information regarding blur kernels.

39, TITLE:      Wandering Within a World: Online Contextualized Few-Shot Learning
http://arxiv.org/abs/2007.04546
AUTHORS:        Mengye Ren ; Michael L. Iuzzolino ; Michael C. Mozer ; Richard S. Zemel
HIGHLIGHT:      We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual experience of an agent wandering within a world.

40, TITLE:      Words as Art Materials: Generating Paintings with Sequential GANs
http://arxiv.org/abs/2007.04383
AUTHORS:        Azmi Can Özgen ; Hazım Kemal Ekenel
HIGHLIGHT:      As the network architecture, we proposed a sequential Generative Adversarial Network model.

41, TITLE:      EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization
http://arxiv.org/abs/2007.04681
AUTHORS:        Lorenzo Federici ; Boris Benedikter ; Alessandro Zavoli
COMMENTS:       2020 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Glasgow, UK
HIGHLIGHT:      This paper presents the main characteristics of the evolutionary optimization code named EOS, Evolutionary Optimization at Sapienza, and its successful application to challenging, real-world space trajectory optimization problems.

42, TITLE:      How low can you go? Privacy-preserving people detection with an omni-directional camera
http://arxiv.org/abs/2007.04678
AUTHORS:        Timothy Callemein ; Kristof Van Beeck ; Toon Goedemé
HIGHLIGHT:      In this work, we use a ceiling-mounted omni-directional camera to detect people in a room.

43, TITLE:      NASGEM: Neural Architecture Search via Graph Embedding Method
http://arxiv.org/abs/2007.04452
AUTHORS:        Hsin-Pai Cheng ; Tunhou Zhang ; Shiyu Li ; Feng Yan ; Meng Li ; Vikas Chandra ; Hai Li ; Yiran Chen
HIGHLIGHT:      To enable quick search of more sophisticated neural architectures while preserving graph information, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.

44, TITLE:      Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset
http://arxiv.org/abs/2007.04690
AUTHORS:        Sebastiano Battiato ; Alessandro Ortis ; Francesca Trenta ; Lorenzo Ascari ; Mara Politi ; Consolata Siniscalco
COMMENTS:       This paper is a preprint of a paper accepted at the IEEE International Conference on Image Processing 2020
HIGHLIGHT:      This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects.

45, TITLE:      Kanren Light: A Dynamically Semi-Certified Interactive Logic Programming System
http://arxiv.org/abs/2007.04691
AUTHORS:        Marco Maggesi ; Massimo Nocentini
COMMENTS:       Accepted for communication to miniKanren 2020 - miniKanren and Relational Programming Workshop
HIGHLIGHT:      We present an experimental system strongly inspired by miniKanren, implemented on top of the tactics mechanism of the HOL~Light theorem prover.

46, TITLE:      Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery
http://arxiv.org/abs/2007.04449
AUTHORS:        Daniil Pakhomov ; Nassir Navab
COMMENTS:       MICCAI 2020
HIGHLIGHT:      In this work, we design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images.

47, TITLE:      Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features
http://arxiv.org/abs/2007.04686
AUTHORS:        Ali Basirat ; Joakim Nivre
COMMENTS:       This paper was originally submitted to EMNLP 2015 and has not been previously published
HIGHLIGHT:      In this way, we achieve the best results for greedy transition-based parsing with supertag features with $88.6\%$ LAS and $90.9\%$ UASon the English Penn Treebank converted to Stanford Dependencies.

48, TITLE:      Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
http://arxiv.org/abs/2007.04916
AUTHORS:        Salomón Wollenstein-Betech ; Christian Muise ; Christos G. Cassandras ; Ioannis Ch. Paschalidis ; Yasaman Khazaeni
COMMENTS:       Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Rhodes, Greece, 2020. (In Press)
HIGHLIGHT:      We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system.

49, TITLE:      Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
http://arxiv.org/abs/2007.04687
AUTHORS:        Peng Wu ; Jing Liu ; Yujia Shi ; Yujia Sun ; Fangtao Shao ; Zhaoyang Wu ; Zhiwei Yang
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      To address this problem, in this work we first release a large-scale and multi-scene dataset named XD-Violence with a total duration of 217 hours, containing 4754 untrimmed videos with audio signals and weak labels.

50, TITLE:      Automation Strategies for Unconstrained Crossword Puzzle Generation
http://arxiv.org/abs/2007.04663
AUTHORS:        Charu Agarwal ; Rushikesh K. Joshi
COMMENTS:       28 pages, 28 figures, category: cs, preprint
HIGHLIGHT:      An end-to-end algorithm that combines these strategies is presented, and its performance is analyzed.

51, TITLE:      Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
http://arxiv.org/abs/2007.04928
AUTHORS:        Sontje Ihler ; Max-Heinrich Laves ; Tobias Ortmaier
HIGHLIGHT:      To achieve high accuracy at high processing rates, we propose patient-specific fine-tuning of a fast model.

52, TITLE:      The autonomous hidden camera crew
http://arxiv.org/abs/2007.04657
AUTHORS:        Timothy Callemein ; Wiebe Van Ranst ; Toon Goedemé
COMMENTS:       4 pages, 6 figures
HIGHLIGHT:      This paper will present an approach to follow people in their day-to-day lives, for long periods of time (months to years), while being as unobtrusive as possible.

53, TITLE:      Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
http://arxiv.org/abs/2007.04655
AUTHORS:        Jennifer Maier ; Marlies Nitschke ; Jang-Hwan Choi ; Garry Gold ; Rebecca Fahrig ; Bjoern M. Eskofier ; Andreas Maier
COMMENTS:       10 pages, 2 figures, 2 tables, accepted at MICCAI 2020
HIGHLIGHT:      We propose to attach an inertial measurement unit (IMU) containing an accelerometer and a gyroscope to the leg of the subject in order to measure the motion during the scan and correct for it.

54, TITLE:      Automated analysis of eye-tracker-based human-human interaction studies
http://arxiv.org/abs/2007.04671
AUTHORS:        Timothy Callemein ; Kristof Van Beeck ; Geert Brône ; Toon Goedemé
HIGHLIGHT:      In this paper we will show that the use of this single-pipeline framework provides robust results, which are both more accurate and faster than previous work in the field.

55, TITLE:      Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete Data Sets
http://arxiv.org/abs/2007.04908
AUTHORS:         Rustam ; Koredianto Usman ; Mudyawati Kamaruddin ; Dina Chamidah ;  Nopendri ; Khaerudin Saleh ; Yulinda Eliskar ; Ismail Marzuki
COMMENTS:       13 pages, 13 figures, submitted to Acta Polytechnica as scientific journal published by the Czech Technical University in Prague
HIGHLIGHT:      Therefore, in this study, we propose a modification of the PFCM algorithm that can be applied to incomplete data sets clustering.

56, TITLE:      Multi-Granularity Modularized Network for Abstract Visual Reasoning
http://arxiv.org/abs/2007.04670
AUTHORS:        Xiangru Tang ; Haoyuan Wang ; Xiang Pan ; Jiyang Qi
HIGHLIGHT:      Inspired by cognitive studies, we propose a Multi-Granularity Modularized Network (MMoN) to bridge the gap between the processing of raw sensory information and symbolic reasoning.

57, TITLE:      Learning to Prune Deep Neural Networks via Reinforcement Learning
http://arxiv.org/abs/2007.04756
AUTHORS:        Manas Gupta ; Siddharth Aravindan ; Aleksandra Kalisz ; Vijay Chandrasekhar ; Lin Jie
COMMENTS:       Accepted at the ICML 2020 Workshop on Automated Machine Learning (AutoML 2020)
HIGHLIGHT:      This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks.

58, TITLE:      Uncertainty Quantification in Deep Residual Neural Networks
http://arxiv.org/abs/2007.04905
AUTHORS:        Lukasz Wandzik ; Raul Vicente Garcia ; Jörg Krüger
HIGHLIGHT:      In this work, we address the problem of uncertainty quantification in deep residual networks by using a regularization technique called stochastic depth.

59, TITLE:      Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing
http://arxiv.org/abs/2007.04514
AUTHORS:        Rui Zhao ; Tianshan Liu ; Jun Xiao ; Daniel P. K. Lun ; Kin-Man Lam
COMMENTS:       ICPR 2020
HIGHLIGHT:      To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis.

60, TITLE:      Aligning Videos in Space and Time
http://arxiv.org/abs/2007.04515
AUTHORS:        Senthil Purushwalkam ; Tian Ye ; Saurabh Gupta ; Abhinav Gupta
COMMENTS:       To appear at the European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      In this paper, we focus on the task of extracting visual correspondences across videos.

61, TITLE:      Cross-Modal Weighting Network for RGB-D Salient Object Detection
http://arxiv.org/abs/2007.04901
AUTHORS:        Gongyang Li ; Zhi Liu ; Linwei Ye ; Yang Wang ; Haibin Ling
COMMENTS:       Accepted in ECCV2020. Code: https://github.com/MathLee/CMWNet
HIGHLIGHT:      In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.

62, TITLE:      Discourse Coherence, Reference Grounding and Goal Oriented Dialogue
http://arxiv.org/abs/2007.04428
AUTHORS:        Baber Khalid ; Malihe Alikhani ; Michael Fellner ; Brian McMahan ; Matthew Stone
COMMENTS:       Accepted for Publishing at SemDial 2020
HIGHLIGHT:      In this paper, we argue for a new approach, inspired by coherence-based models of discourse such as SDRT \cite{asher-lascarides:2003a}, in which utterances attach to an evolving discourse structure and the associated knowledge graph of speaker commitments serves as an interface to real-world reasoning and conversational strategy.

63, TITLE:      JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering
http://arxiv.org/abs/2007.04754
AUTHORS:        Mayank Patwari ; Ralf Gutjahr ; Rainer Raupach ; Andreas Maier
COMMENTS:       10 pages, 4 figures, 1 table. Accepted at MICCAI2020
HIGHLIGHT:      In this study we introduce JBFnet, a neural network for low dose CT denoising.

64, TITLE:      Generalized Many-Way Few-Shot Video Classification
http://arxiv.org/abs/2007.04755
AUTHORS:        Yongqin Xian ; Bruno Korbar ; Matthijs Douze ; Bernt Schiele ; Zeynep Akata ; Lorenzo Torresani
HIGHLIGHT:      In this work we thus develop a simple 3D CNN baseline, surpassing existing methods by a large margin. Our results saturate current 5-way benchmarks for few-shot video classification and therefore we propose a new challenging benchmark involving more classes and a mixture of classes with varying supervision.

65, TITLE:      Computing the Largest Bond and the Maximum Connected Cut of a Graph
http://arxiv.org/abs/2007.04513
AUTHORS:        Gabriel L. Duarte ; Hiroshi Eto ; Tesshu Hanaka ; Yasuaki Kobayashi ; Yusuke Kobayashi ; Daniel Lokshtanov ; Lehilton L. C. Pedrosa ; Rafael C. S. Schouery ; Uéverton S. Souza
COMMENTS:       This paper resulted from a merge of two papers submitted to arXiv (arXiv:1908.03389 and arXiv:1910.01071). Both preliminary versions were presented at the 14th International Symposium on Parameterized and Exact Computation (IPEC 2019)
HIGHLIGHT:      In this paper, we aim to reduce this gap on the complexity of computing the largest bond, and the maximum connected cut of a graph.

66, TITLE:      PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
http://arxiv.org/abs/2007.04525
AUTHORS:        Saeid Asgari Taghanaki ; Kaveh Hassani ; Pradeep Kumar Jayaraman ; Amir Hosein Khasahmadi ; Tonya Custis
COMMENTS:       Accepted to ICML 2020 WHI
HIGHLIGHT:      In this paper, we investigate both of these strategies on deep models operating on point clouds.

67, TITLE:      Less is More: Rejecting Unreliable Reviews for Product Question Answering
http://arxiv.org/abs/2007.04526
AUTHORS:        Shiwei Zhang ; Xiuzhen Zhang ; Jey Han Lau ; Jeffrey Chan ; Cecile Paris
COMMENTS:       ECML-PKDD 2020
HIGHLIGHT:      In this paper, we focus on the issue of answerability and answer reliability for PQA using reviews.

68, TITLE:      Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization
http://arxiv.org/abs/2007.04768
AUTHORS:        Mayank Patwari ; Ralf Gutjahr ; Rainer Raupach ; Andreas Maier
COMMENTS:       4 pages, 5 figures, 1 table. Accepted at CT Meeting 2020
HIGHLIGHT:      In this paper, we use a Joint Bilateral Filter (JBF) to denoise our CT images.

69, TITLE:      Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
http://arxiv.org/abs/2007.04395
AUTHORS:        Xiang Ling ; Lingfei Wu ; Saizhuo Wang ; Tengfei Ma ; Fangli Xu ; Alex X. Liu ; Chunming Wu ; Shouling Ji
COMMENTS:       17 pages
HIGHLIGHT:      In this paper, we propose a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects.

70, TITLE:      One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
http://arxiv.org/abs/2007.04976
AUTHORS:        Wenlong Huang ; Igor Mordatch ; Deepak Pathak
COMMENTS:       Accepted at ICML 2020. Videos and code at https://huangwl18.github.io/modular-rl/
HIGHLIGHT:      We propose to express this global policy as a collection of identical modular neural networks, dubbed as Shared Modular Policies (SMP), that correspond to each of the agent's actuators.

71, TITLE:      Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network
http://arxiv.org/abs/2007.04734
AUTHORS:        Nan Wang ; Chengwei Chen ; Yuan Xie ; Lizhuang Ma
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      Aiming at the imbalance of brain tumor data and the rare amount of labeled data, we propose an innovative brain tumor abnormality detection algorithm.

72, TITLE:      Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
http://arxiv.org/abs/2007.04978
AUTHORS:        Jie Yang ; Elsa D. Angelini ; Pallavi P. Balte ; Eric A. Hoffman ; John H. M. Austin ; Benjamin M. Smith ; R. Graham Barr ; Andrew F. Laine
HIGHLIGHT:      In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location, and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtypes.

73, TITLE:      Quaternion Capsule Networks
http://arxiv.org/abs/2007.04389
AUTHORS:        Barış Özcan ; Furkan Kınlı ; Furkan Kıraç
HIGHLIGHT:      In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions.

74, TITLE:      A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks
http://arxiv.org/abs/2007.04979
AUTHORS:        Unnat Jain ; Luca Weihs ; Eric Kolve ; Ali Farhadi ; Svetlana Lazebnik ; Aniruddha Kembhavi ; Alexander Schwing
COMMENTS:       Accepted to ECCV 2020 (spotlight); Project page: https://unnat.github.io/cordial-sync
HIGHLIGHT:      Addressing this, we introduce the novel task FurnMove in which agents work together to move a piece of furniture through a living room to a goal.

75, TITLE:      Temporal aggregation of audio-visual modalities for emotion recognition
http://arxiv.org/abs/2007.04364
AUTHORS:        Andreea Birhala ; Catalin Nicolae Ristea ; Anamaria Radoi ; Liviu Cristian Dutu
HIGHLIGHT:      In this paper, we propose a multimodal fusion technique for emotion recognition based on combining audio-visual modalities from a temporal window with different temporal offsets for each modality.

76, TITLE:      Contrastive Code Representation Learning
http://arxiv.org/abs/2007.04973
AUTHORS:        Paras Jain ; Ajay Jain ; Tianjun Zhang ; Pieter Abbeel ; Joseph E. Gonzalez ; Ion Stoica
HIGHLIGHT:      We propose Contrastive Code Representation Learning (ContraCode), a self-supervised algorithm for learning task-agnostic semantic representations of programs via contrastive learning.

77, TITLE:      Journey Towards Tiny Perceptual Super-Resolution
http://arxiv.org/abs/2007.04356
AUTHORS:        Royson Lee ; Łukasz Dudziak ; Mohamed Abdelfattah ; Stylianos I. Venieris ; Hyeji Kim ; Hongkai Wen ; Nicholas D. Lane
COMMENTS:       Accepted at the 16th European Conference on Computer Vision (ECCV), 2020
HIGHLIGHT:      In this work, we propose a neural architecture search (NAS) approach that integrates NAS and generative adversarial networks (GANs) with recent advances in perceptual SR and pushes the efficiency of small perceptual SR models to facilitate on-device execution.

78, TITLE:      Cultural Cartography with Word Embeddings
http://arxiv.org/abs/2007.04508
AUTHORS:        Dustin S. Stoltz ; Marshall A. Taylor
HIGHLIGHT:      Word embedding models overcome this problem by constructing a standardized meaning space where words are assigned a location based on relations of similarity to, and difference from, other words based on how they are used in natural language samples.

79, TITLE:      Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks
http://arxiv.org/abs/2007.04505
AUTHORS:        Daniil Pakhomov ; Wei Shen ; Nassir Navab
COMMENTS:       IROS 2020
HIGHLIGHT:      Since generated annotations will not directly correspond to endoscopic images due to errors, we formulate the problem as an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation using an adversarial model.

80, TITLE:      DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and Affective Labels
http://arxiv.org/abs/2007.04626
AUTHORS:        Alberto Barbado ; Víctor Fresno ; Ángeles Manjarrés Riesco ; Salvador Ros
COMMENTS:       24 pages, 3 figures, 17 tables
HIGHLIGHT:      This article presents a study over a labeled corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words in order to predict their GAM.

81, TITLE:      Attention or memory? Neurointerpretable agents in space and time
http://arxiv.org/abs/2007.04862
AUTHORS:        Lennart Bramlage ; Aurelio Cortese
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      We design a model incorporating a self-attention mechanism that implements task-state representations in semantic feature-space, and test it on a battery of Atari games.

82, TITLE:      Treewidth-Aware Complexity in ASP: Not all Positive Cycles are Equally Hard
http://arxiv.org/abs/2007.04620
AUTHORS:        Markus Hecher ; Jorge Fandinno
HIGHLIGHT:      In this paper, we refine the above result and show that the consistency problem for ASP can be solved in exponential time in k \cdot log({\lambda}) where {\lambda} is the minimum between the treewidth and the size of the largest strongly-connected component in the positive dependency graph of the program.

83, TITLE:      Logic of computational semi-effects and categorical gluing for equivariant functors
http://arxiv.org/abs/2007.04621
AUTHORS:        Yuichi Nishiwaki ; Toshiya Asai
COMMENTS:       32 pages
HIGHLIGHT:      In this paper, we revisit Moggi's celebrated calculus of computational effects from the perspective of logic of monoidal action (actegory).

84, TITLE:      VisImages: A Large-scale, High-quality Image Corpus in Visualization Publications
http://arxiv.org/abs/2007.04584
AUTHORS:        Dazhen Deng ; Yihong Wu ; Xinhuan Shu ; Mengye Xu ; Jiang Wu ; Siwei Fu Yingcai Wu
HIGHLIGHT:      This study presents VisImages, a high-quality and large-scale image corpus collected from visualization publications.

85, TITLE:      A Deep Joint Sparse Non-negative Matrix Factorization Framework for Identifying the Common and Subject-specific Functional Units of Tongue Motion During Speech
http://arxiv.org/abs/2007.04865
AUTHORS:        Jonghye Woo ; Fangxu Xing ; Jerry L. Prince ; Maureen Stone ; Arnold Gomez ; Timothy G. Reese ; Van J. Wedeen ; Georges El Fakhri
HIGHLIGHT:      In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech.

86, TITLE:      Invertible Zero-Shot Recognition Flows
http://arxiv.org/abs/2007.04873
AUTHORS:        Yuming Shen ; Jie Qin ; Lei Huang
COMMENTS:       ECCV2020
HIGHLIGHT:      Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently.

87, TITLE:      Lightweight Image Super-Resolution with Enhanced CNN
http://arxiv.org/abs/2007.04344
AUTHORS:        Chunwei Tian ; Ruibin Zhuge ; Zhihao Wu ; Yong Xu ; Wangmeng Zuo ; Chen Chen ; Chia-Wen Li
HIGHLIGHT:      To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCN-N) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB).

88, TITLE:      CompRes: A Dataset for Narrative Structure in News
http://arxiv.org/abs/2007.04874
AUTHORS:        Effi Levi ; Guy Mor ; Shaul Shenhav ; Tamir Sheafer
COMMENTS:       Accpted to the First Joint Workshop on Narrative Understanding, Storylines, and Events, ACL 2020
HIGHLIGHT:      We introduce CompRes -- the first dataset for narrative structure in news media.

89, TITLE:      Maximum Entropy Regularization and Chinese Text Recognition
http://arxiv.org/abs/2007.04651
AUTHORS:        Changxu Cheng ; Wuheng Xu ; Xiang Bai ; Bin Feng ; Wenyu Liu
COMMENTS:       15 pages
HIGHLIGHT:      We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters and modification of a model.

90, TITLE:      On the Reliability and Generalizability of Brain-inspired Reinforcement Learning Algorithms
http://arxiv.org/abs/2007.04578
AUTHORS:        Dongjae Kim ; Jee Hang Lee ; Jae Hoon Shin ; Minsu Abel Yang ; Sang Wan Lee
HIGHLIGHT:      We show that the computational model combining model-based and model-free control, which we term the prefrontal RL, reliably encodes the information of high-level policy that humans learned, and this model can generalize the learned policy to a wide range of tasks.

91, TITLE:      Guru, Partner, or Pencil Sharpener? Understanding Designers' Attitudes Towards Intelligent Creativity Support Tools
http://arxiv.org/abs/2007.04848
AUTHORS:        Angus Main ; Mick Grierson
HIGHLIGHT:      Individuals develop personal approaches to creativity, particularly in the context of commercial design where signature styles and techniques are valuable commodities.

92, TITLE:      JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
http://arxiv.org/abs/2007.04646
AUTHORS:        Linpu Fang ; Xingyan Liu ; Li Liu ; Hang Xu ; Wenxiong Kang
COMMENTS:       Accepted by ECCV2020 as a Spotlight paper
HIGHLIGHT:      In this paper, a novel pixel-wise prediction-based method is proposed to address the above issues.

93, TITLE:      DeepSinger: Singing Voice Synthesis with Data Mined From the Web
http://arxiv.org/abs/2007.04590
AUTHORS:        Yi Ren ; Xu Tan ; Tao Qin ; Jian Luan ; Zhou Zhao ; Tie-Yan Liu
COMMENTS:       Accepted by KDD2020 research track
HIGHLIGHT:      In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites.

94, TITLE:      Monocular Vision based Crowdsourced 3D Traffic Sign Positioning with Unknown Camera Intrinsics and Distortion Coefficients
http://arxiv.org/abs/2007.04592
AUTHORS:        Hemang Chawla ; Matti Jukola ; Elahe Arani ; Bahram Zonooz
COMMENTS:       Accepted at 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
HIGHLIGHT:      In this work, we demonstrate an approach to computing 3D traffic sign positions without knowing the camera focal lengths, principal point, and distortion coefficients a priori.

95, TITLE:      Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
http://arxiv.org/abs/2007.04350
AUTHORS:        Yongkang Liu ; Ziran Wang ; Kyungtae Han ; Zhenyu Shou ; Prashant Tiwari ; John H. L. Hansen
COMMENTS:       Accepted by the 31st IEEE Intelligent Vehicles Symposium
HIGHLIGHT:      To advance the development of visual guidance systems, we introduce a novel sensor fusion methodology, integrating camera image and Digital Twin knowledge from the cloud.

96, TITLE:      Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
http://arxiv.org/abs/2007.04349
AUTHORS:        Sophia Bano ; Francisco Vasconcelos ; Luke M. Shepherd ; Emmanuel Vander Poorten ; Tom Vercauteren ; Sebastien Ourselin ; Anna L. David ; Jan Deprest ; Danail Stoyanov
COMMENTS:       Accepted at MICCAI 2020
HIGHLIGHT:      We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.

97, TITLE:      ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID
http://arxiv.org/abs/2007.04644
AUTHORS:        Chaoping Tu ; Yin Zhao ; Longjun Cai
HIGHLIGHT:      In this paper we propose an entropy based semantic feature alignment model, which takes advantages of the detailed information of the human semantic feature. We construct a new re-ID dataset based on content videos with many cases of occlusion and body part missing, which will be released in future.

98, TITLE:      Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing
http://arxiv.org/abs/2007.04645
AUTHORS:        Prem Raj ; Vinay P. Namboodiri ; L. Behera
COMMENTS:       Accepted in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2020). For video visit - https://youtu.be/GSG20lmWDUo
HIGHLIGHT:      In this paper, we explore switching of CNNs to improve the precision of visual servo control.

99, TITLE:      Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning
http://arxiv.org/abs/2007.04614
AUTHORS:        Lei Zhang ; Wei Bai ; Shize Guo ; Shiming Xia ; Hongmei Li ; Zhisong Pan
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      In this work, we present a learning-based approach to analysis cyberspace configuration.

100, TITLE:     Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models
http://arxiv.org/abs/2007.04824
AUTHORS:        Fabrice Muhlenbach ; Long Nguyen Phuoc ; Isabelle Sayn
COMMENTS:       Extended version of the poster accepted at the first ICML Workshop on "Law and Machine Learning". https://sites.google.com/view/icml-law-and-ml-2020/
HIGHLIGHT:      From this perspective, we present an explainable AI model designed in this purpose by combining a classification with random forest and a regression model, as a complementary tool to existing decision-making scales or guidelines created by practitioners.

101, TITLE:     Identifying efficient controls of complex interaction networks using genetic algorithms
http://arxiv.org/abs/2007.04853
AUTHORS:        Victor-Bogdan Popescu ; Krishna Kanhaiya ; Iulian Năstac ; Eugen Czeizler ; Ion Petre
COMMENTS:       The submission contains 34 pages, 9 figures and 6 tables
HIGHLIGHT:      We propose in this article a new solution for this problem based on genetic algorithms.

102, TITLE:     Principal Word Vectors
http://arxiv.org/abs/2007.04629
AUTHORS:        Ali Basirat ; Christian Hardmeier ; Joakim Nivre
HIGHLIGHT:      We generalize principal component analysis for embedding words into a vector space.



==========Updates to Previous Papers==========
1, 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:       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).

2, TITLE:       Object Detection under Rainy Conditions for Autonomous Vehicles
http://arxiv.org/abs/2006.16471
AUTHORS:        Mazin Hnewa ; Hayder Radha
COMMENTS:       Accepted in IEEE Signal Processing Magazine / Special Issue on Autonomous Driving
HIGHLIGHT:      The main objective of this paper is to present a tutorial on state-of-the-art and emerging techniques that represent leading candidates for mitigating the influence of rainy conditions on an autonomous vehicle\textsc{'}s ability to detect objects.

3, TITLE:       SegFix: Model-Agnostic Boundary Refinement for Segmentation
http://arxiv.org/abs/2007.04269
AUTHORS:        Yuhui Yuan ; Jingyi Xie ; Xilin Chen ; Jingdong Wang
COMMENTS:       ECCV 2020. Project Page: https://github.com/openseg-group/openseg.pytorch
HIGHLIGHT:      We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model.

4, TITLE:       Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles
http://arxiv.org/abs/1911.12199
AUTHORS:        Ana Lucic ; Harrie Oosterhuis ; Hinda Haned ; Maarten de Rijke
HIGHLIGHT:      We introduce a novel approximation technique that is effective for finding counterfactual explanations for predictions of the original model and show that our counterfactual examples are significantly closer to the original instances compared to other methods specifically designed for tree ensembles.

5, TITLE:       On the Enumeration and Counting of Bicriteria Temporal Paths
http://arxiv.org/abs/1812.02507
AUTHORS:        Petra Mutzel ; Lutz Oettershagen
HIGHLIGHT:      We introduce two bicriteria temporal min-cost path problems in which we are interested in the set of all efficient paths with low cost and short duration or early arrival time, respectively.

6, TITLE:       Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images
http://arxiv.org/abs/2004.05717
AUTHORS:        Eduardo Luz ; Pedro Lopes Silva ; Rodrigo Silva ; Ludmila Silva ; Gladston Moreira ; David Menotti
COMMENTS:       31 pages, 9 figures
HIGHLIGHT:      Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays.

7, TITLE:       Cortical surface registration using unsupervised learning
http://arxiv.org/abs/2004.04617
AUTHORS:        Jieyu Cheng ; Adrian V. Dalca ; Bruce Fischl ; Lilla Zollei
COMMENTS:       cortical surface registration, deep network, unsupervised learning, registration, deep learning, cortical, spherical, invertible
HIGHLIGHT:      In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues.

8, TITLE:       Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
http://arxiv.org/abs/2004.02684
AUTHORS:        Hao Li ; Xiaopeng Zhang ; Hongkai Xiong ; Qi Tian
HIGHLIGHT:      In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples.

9, TITLE:       Object-Contextual Representations for Semantic Segmentation
http://arxiv.org/abs/1909.11065
AUTHORS:        Yuhui Yuan ; Xilin Chen ; Jingdong Wang
COMMENTS:       ECCV 2020 Spotlight. Project Page: https://github.com/openseg-group/openseg.pytorch; https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR
HIGHLIGHT:      In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy.

10, TITLE:      Verification of Flat FIFO Systems
http://arxiv.org/abs/1908.07282
AUTHORS:        Alain Finkel ; M. Praveen
HIGHLIGHT:      We construct a trace-flattable counter system that is bisimilar to a given flat FIFO system, which allows to model-check the original flat FIFO system.

11, TITLE:      Pedestrian Detection: The Elephant In The Room
http://arxiv.org/abs/2003.08799
AUTHORS:        Irtiza Hasan ; Shengcai Liao ; Jinpeng Li ; Saad Ullah Akram ; Ling Shao
COMMENTS:       17 pages, 1 figure
HIGHLIGHT:      To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation.

12, TITLE:      RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
http://arxiv.org/abs/1910.01508
AUTHORS:        Krzysztof Rusek ; José Suárez-Varela ; Paul Almasan ; Pere Barlet-Ros ; Albert Cabellos-Aparicio
COMMENTS:       12 pages
HIGHLIGHT:      In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss.

13, TITLE:      Human Assisted Artificial Intelligence Based Technique to Create Natural Features for OpenStreetMap
http://arxiv.org/abs/2007.02149
AUTHORS:        Piyush Yadav ; Dipto Sarkar ; Shailesh Deshpande ; Edward Curry
COMMENTS:       3 pages, 2 Figures, Submitted to FOSS4G Europe 2020 Academic Track (Postponed to 2021)
HIGHLIGHT:      In this work, we propose an AI-based technique using freely available satellite images like Landsat and Sentinel to create natural features over OSM in congruence with human editors acting as initiators and validators.

14, TITLE:      Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
http://arxiv.org/abs/1910.01741
AUTHORS:        Denis Yarats ; Amy Zhang ; Ilya Kostrikov ; Brandon Amos ; Joelle Pineau ; Rob Fergus
HIGHLIGHT:      Following these findings, we propose effective techniques to improve training stability.

15, TITLE:      Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies
http://arxiv.org/abs/2002.06198
AUTHORS:        Yu Chen ; S. Yusef Shafi ; Yi-fan Chen
COMMENTS:       37 pages, 9 figures
HIGHLIGHT:      In this paper, we build a traffic simulation pipeline to explore the above problems, covering many aspects of evacuation, including map creation, demand generation, vehicle behavior, bottleneck identification, traffic management policy improvement, and results analysis.

16, TITLE:      A convolutional neural network reaches optimal sensitivity for detecting some, but not all, patterns
http://arxiv.org/abs/1911.05055
AUTHORS:        Fabian H. Reith ; Brian A. Wandell
COMMENTS:       22 pages, 8 figures, pre-print
HIGHLIGHT:      We investigate the performance of modern convolutional neural networks (CNN) and a linear support vector machine (SVM) with respect to spatial contrast sensitivity.

17, TITLE:      Normalizador Neural de Datas e Endereços
http://arxiv.org/abs/2007.04300
AUTHORS:        Gustavo Plensack ; Paulo Finardi
COMMENTS:       7 pages, in Portuguese, 5 tables
HIGHLIGHT:      To circumvent this challenge, we present a solution with deep neural networks state of art T5 that treats non-preconfigured formats of dates and addresses with accuracy above 90% in some cases.

18, TITLE:      Best-First Beam Search
http://arxiv.org/abs/2007.03909
AUTHORS:        Clara Meister ; Tim Vieira ; Ryan Cotterell
COMMENTS:       TACL 2020
HIGHLIGHT:      In this work, we show that standard beam search is a computationally inefficient choice for many decoding tasks; specifically, when the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation.

19, TITLE:      Pruned Wasserstein Index Generation Model and wigpy Package
http://arxiv.org/abs/2004.00999
AUTHORS:        Fangzhou Xie
COMMENTS:       fix typos and errors
HIGHLIGHT:      I hereby propose a Lasso-based shrinkage method to reduce dimensionality for the vocabulary as a pre-processing step prior to fitting the WIG model.

20, TITLE:      Self-supervising Fine-grained Region Similarities for Large-scale Image Localization
http://arxiv.org/abs/2006.03926
AUTHORS:        Yixiao Ge ; Haibo Wang ; Feng Zhu ; Rui Zhao ; Hongsheng Li
COMMENTS:       Accepted in ECCV 2020 (Spotlight), code is available at https://github.com/yxgeee/SFRS
HIGHLIGHT:      To tackle this challenge, we propose to self-supervise image-to-region similarities in order to fully explore the potential of difficult positive images alongside their sub-regions.

21, 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.

22, TITLE:      Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
http://arxiv.org/abs/2003.04719
AUTHORS:        Junhui Yin ; Siqing Zhang ; Dongliang Chang ; Zhanyu Ma ; Jun Guo
COMMENTS:       Accepted by the 25th International Conference on Pattern Recognition (ICPR 2020)
HIGHLIGHT:      In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL.

23, TITLE:      Efficient convolutional neural networks for multi-planar lung nodule detection: improvement on small nodule identification
http://arxiv.org/abs/2001.04537
AUTHORS:        Sunyi Zheng ; Ludo J. Cornelissen ; Xiaonan Cui ; Xueping Jing ; Raymond N. J. Veldhuis ; Matthijs Oudkerk ; Peter M. A. van Ooijen
HIGHLIGHT:      Methods: We propose a multi-planar detection system using convolutional neural networks.

24, TITLE:      Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
http://arxiv.org/abs/2003.11013
AUTHORS:        Pietro Astolfi ; Ruben Verhagen ; Laurent Petit ; Emanuele Olivetti ; Jonathan Masci ; Davide Boscaini ; Paolo Avesani
COMMENTS:       Accepted at MICCAI2020
HIGHLIGHT:      In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties.

25, TITLE:      Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves
http://arxiv.org/abs/2007.01921
AUTHORS:        Ruisen Liu ; Manisha Natarajan ; Matthew Gombolay
HIGHLIGHT:      We introduce a novel resource coordination algorithm that enables robots to explore the relative strengths and learning abilities of their human teammates, by constructing schedules that are robust to stochastic and time-varying human task performance.

26, TITLE:      Learning to Understand Child-directed and Adult-directed Speech
http://arxiv.org/abs/2005.02721
AUTHORS:        Lieke Gelderloos ; Grzegorz Chrupała ; Afra Alishahi
COMMENTS:       ACL 2020 Anthology: https://www.aclweb.org/anthology/2020.acl-main.1/
HIGHLIGHT:      This study explores the effect of child-directed speech when learning to extract semantic information from speech directly.

27, TITLE:      COMET: Context-Aware IoU-Guided Network for Small Object Tracking
http://arxiv.org/abs/2006.02597
AUTHORS:        Seyed Mojtaba Marvasti-Zadeh ; Javad Khaghani ; Hossein Ghanei-Yakhdan ; Shohreh Kasaei ; Li Cheng
HIGHLIGHT:      To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.

28, TITLE:      Language (Re)modelling: Towards Embodied Language Understanding
http://arxiv.org/abs/2005.00311
AUTHORS:        Ronen Tamari ; Chen Shani ; Tom Hope ; Miriam R. L. Petruck ; Omri Abend ; Dafna Shahaf
COMMENTS:       Accepted to ACL2020 Theme Track. Extended bibliography version
HIGHLIGHT:      This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL).

29, TITLE:      Weakly supervised multiple instance learning histopathological tumor segmentation
http://arxiv.org/abs/2004.05024
AUTHORS:        Marvin Lerousseau ; Maria Vakalopoulou ; Marion Classe ; Julien Adam ; Enzo Battistella ; Alexandre Carré ; Théo Estienne ; Théophraste Henry ; Eric Deutsch ; Nikos Paragios
COMMENTS:       Accepted MICCAI 2020; added code + results url; 10 pages, 3 figures
HIGHLIGHT:      In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems.

30, TITLE:      Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
http://arxiv.org/abs/2006.16581
AUTHORS:        Qunliang Xing ; Mai Xu ; Tianyi Li ; Zhenyu Guan
COMMENTS:       Accepted by ECCV 2020
HIGHLIGHT:      In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images.

31, TITLE:      Technical Report of "Deductive Joint Support for Rational Unrestricted Rebuttal"
http://arxiv.org/abs/2005.03620
AUTHORS:        Marcos Cramer ; Meghna Bhadra
COMMENTS:       New version with some minor corrections based on the reviews of the associated paper
HIGHLIGHT:      In this paper we propose that ASPIC-style argumentation can benefit from keeping track not only of the attack relation between arguments, but also the relation of deductive joint support that holds between a set of arguments and an argument that was constructed from that set using a strict rule.

32, TITLE:      FinBERT: A Pretrained Language Model for Financial Communications
http://arxiv.org/abs/2006.08097
AUTHORS:        Yi Yang ; Mark Christopher Siy UY ; Allen Huang
COMMENTS:       https://github.com/yya518/FinBERT
HIGHLIGHT:      In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora.

33, TITLE:      SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
http://arxiv.org/abs/2003.03653
AUTHORS:        Tiago Cortinhal ; George Tzelepis ; Eren Erdal Aksoy
HIGHLIGHT:      In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.

34, TITLE:      Motion-Attentive Transition for Zero-Shot Video Object Segmentation
http://arxiv.org/abs/2003.04253
AUTHORS:        Tianfei Zhou ; Shunzhou Wang ; Yi Zhou ; Yazhou Yao ; Jianwu Li ; Ling Shao
COMMENTS:       AAAI 2020. Code: https://github.com/tfzhou/MATNet
HIGHLIGHT:      In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.

35, TITLE:      Ocean: Object-aware Anchor-free Tracking
http://arxiv.org/abs/2006.10721
AUTHORS:        Zhipeng Zhang ; Houwen Peng ; Jianlong Fu ; Bing Li ; Weiming Hu
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this paper, we propose a novel object-aware anchor-free network to address this issue.

36, TITLE:      Computing Maximum Matchings in Temporal Graphs
http://arxiv.org/abs/1905.05304
AUTHORS:        George B. Mertzios ; Hendrik Molter ; Rolf Niedermeier ; Viktor Zamaraev ; Philipp Zschoche
HIGHLIGHT:      We introduce and study the complexity of a natural temporal extension of the classical graph problem Maximum Matching, taking into account the dynamic nature of temporal graphs.

37, TITLE:      Neural Architecture Design for GPU-Efficient Networks
http://arxiv.org/abs/2006.14090
AUTHORS:        Ming Lin ; Hesen Chen ; Xiuyu Sun ; Qi Qian ; Hao Li ; Rong Jin
HIGHLIGHT:      To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.

38, TITLE:      Semi-supervised semantic segmentation needs strong, varied perturbations
http://arxiv.org/abs/1906.01916
AUTHORS:        Geoff French ; Samuli Laine ; Timo Aila ; Michal Mackiewicz ; Graham Finlayson
COMMENTS:       19 pages, 5 figures, submitted to BMVC 2020
HIGHLIGHT:      We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.

39, TITLE:      Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction
http://arxiv.org/abs/1905.11503
AUTHORS:        Hosnieh Sattar ; Katharina Krombholz ; Gerard Pons-Moll ; Mario Fritz
HIGHLIGHT:      We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image.

40, TITLE:      TLIO: Tight Learned Inertial Odometry
http://arxiv.org/abs/2007.01867
AUTHORS:        Wenxin Liu ; David Caruso ; Eddy Ilg ; Jing Dong ; Anastasios I. Mourikis ; Kostas Daniilidis ; Vijay Kumar ; Jakob Engel
COMMENTS:       Corrected typo on author affiliation and removed redundant acknowledgements in the footnote
HIGHLIGHT:      In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation.

41, TITLE:      Making DensePose fast and light
http://arxiv.org/abs/2006.15190
AUTHORS:        Ruslan Rakhimov ; Emil Bogomolov ; Alexandr Notchenko ; Fung Mao ; Alexey Artemov ; Denis Zorin ; Evgeny Burnaev
HIGHLIGHT:      In this work, we target the problem of redesigning the DensePose R-CNN model's architecture so that the final network retains most of its accuracy but becomes more light-weight and fast.

42, TITLE:      Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields
http://arxiv.org/abs/2006.01645
AUTHORS:        Genta Kobayashi ; Hayaru Shouno
COMMENTS:       10 pages
HIGHLIGHT:      In this research, we investigate the receptive fields of a ResNet on the classification task in ImageNet.

43, TITLE:      Provable Self-Play Algorithms for Competitive Reinforcement Learning
http://arxiv.org/abs/2002.04017
AUTHORS:        Yu Bai ; Chi Jin
COMMENTS:       Appearing at ICML 2020. Fixed typos from v1
HIGHLIGHT:      We introduce a self-play algorithm---Value Iteration with Upper/Lower Confidence Bound (VI-ULCB)---and show that it achieves regret $\tilde{\mathcal{O}}(\sqrt{T})$ after playing $T$ steps of the game, where the regret is measured by the agent's performance against a \emph{fully adversarial} opponent who can exploit the agent's strategy at \emph{any} step.

44, TITLE:      A Vision-based Social Distancing and Critical Density Detection System for COVID-19
http://arxiv.org/abs/2007.03578
AUTHORS:        Dongfang Yang ; Ekim Yurtsever ; Vishnu Renganathan ; Keith A. Redmill ; Ümit Özgüner
HIGHLIGHT:      Against this backdrop, we propose using a monocular camera and deep learning-based real-time object detectors to measure social distancing.

45, TITLE:      Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft
http://arxiv.org/abs/1912.08664
AUTHORS:        Alexey Skrynnik ; Aleksey Staroverov ; Ermek Aitygulov ; Kirill Aksenov ; Vasilii Davydov ; Aleksandr I. Panov
HIGHLIGHT:      We present a structured task-dependent replay buffer and adaptive prioritizing technique that allow the HDQfD agent to gradually erase poor-quality expert data from the buffer.

46, TITLE:      An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
http://arxiv.org/abs/2003.13502
AUTHORS:        Mohamed Abdelhack
HIGHLIGHT:      This manuscript introduces an open-source tool that allows the implementation of image augmentation for hyperspectral images in Tensorflow.

47, TITLE:      Texture Hallucination for Large-Factor Painting Super-Resolution
http://arxiv.org/abs/1912.00515
AUTHORS:        Yulun Zhang ; Zhifei Zhang ; Stephen DiVerdi ; Zhaowen Wang ; Jose Echevarria ; Yun Fu
COMMENTS:       Accepted to ECCV 2020. Supplementary material contains more visual results and is available at http://yulunzhang.com/papers/PaintingSR_supp_arXiv.pdf
HIGHLIGHT:      We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8X, 16X).

48, TITLE:      Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
http://arxiv.org/abs/1912.10557
AUTHORS:        Vishal Monga ; Yuelong Li ; Yonina C. Eldar
HIGHLIGHT:      In this article, we review algorithm unrolling for signal and image processing.

49, TITLE:      MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
http://arxiv.org/abs/2001.06902
AUTHORS:        Simon Vandenhende ; Stamatios Georgoulis ; Luc Van Gool
COMMENTS:       Accepted at ECCV2020 (spotlight) - Code: https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch
HIGHLIGHT:      In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup.

50, TITLE:      Set-Invariant Constrained Reinforcement Learning with a Meta-Optimizer
http://arxiv.org/abs/2006.11419
AUTHORS:        Chuangchuang Sun ; Dong-Ki Kim ; Jonathan P. How
COMMENTS:       Accepted to ICML 2020 Workshop Theoretical Foundations of RL
HIGHLIGHT:      To address this, we propose to learn a neural network-based meta-optimizer to optimize the objective while satisfying such linear constraints.

51, TITLE:      Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
http://arxiv.org/abs/1909.02553
AUTHORS:        Yichun Hu ; Nathan Kallus ; Xiaojie Mao
HIGHLIGHT:      We study a nonparametric contextual bandit problem where the expected reward functions belong to a H\"older class with smoothness parameter $\beta$.

52, TITLE:      COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions
http://arxiv.org/abs/2006.10532
AUTHORS:        Petrônio C. L. Silva ; Paulo V. C. Batista ; Hélder S. Lima ; Marcos A. Alves ; Frederico G. Guimarães ; Rodrigo C. P. Silva
COMMENTS:       37 pages, 17 figures
HIGHLIGHT:      This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government.

53, TITLE:      Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
http://arxiv.org/abs/2005.00527
AUTHORS:        Ruosong Wang ; Simon S. Du ; Lin F. Yang ; Sham M. Kakade
HIGHLIGHT:      Our analysis introduces two ideas: (i) the construction of an $\varepsilon$-net for optimal policies whose log-covering number scales only logarithmically with the planning horizon, and (ii) the Online Trajectory Synthesis algorithm, which adaptively evaluates all policies in a given policy class using sample complexity that scales with the log-covering number of the given policy class.

54, TITLE:      Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
http://arxiv.org/abs/2006.13811
AUTHORS:        Esther Puyol-Antón ; Chen Chen ; James R. Clough ; Bram Ruijsink ; Baldeep S. Sidhu ; Justin Gould ; Bradley Porter ; Mark Elliott ; Vishal Mehta ; Daniel Rueckert ; Christopher A. Rinaldi ; Andrew P. King
COMMENTS:       MICCAI 2020 conference
HIGHLIGHT:      In this paper we address both of these issues.

55, TITLE:      A Speech Act Classifier for Persian Texts and its Application in Identify Speech Act of Rumors
http://arxiv.org/abs/1901.03904
AUTHORS:        Zoleikha Jahanbakhsh-Nagadeh ; Mohammad-Reza Feizi-Derakhshi ; Arash Sharifi
COMMENTS:       Published Link: http://jscit.nit.ac.ir/article_103557.html
HIGHLIGHT:      This study presents a dictionary-based statistical technique for Persian SA recognition.

56, TITLE:      Learning and Evaluating Contextual Embedding of Source Code
http://arxiv.org/abs/2001.00059
AUTHORS:        Aditya Kanade ; Petros Maniatis ; Gogul Balakrishnan ; Kensen Shi
COMMENTS:       Published in ICML 2020. This version (v.2) is the conference-time camera-ready version of the paper. There will be a subsequent update with the archival version
HIGHLIGHT:      Future work on source-code embedding can benefit from reusing our benchmark, and comparing against CuBERT models as a strong baseline. Specifically, first, we curate a massive, deduplicated corpus of 6M Python files from GitHub, which we use to pre-train CuBERT, an open-sourced code-understanding BERT model; and, second, we create an open-sourced benchmark that comprises five classification tasks and one program-repair task, akin to code-understanding tasks proposed in the literature before.

57, TITLE:      Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
http://arxiv.org/abs/2004.04498
AUTHORS:        Danielle Saunders ; Bill Byrne
COMMENTS:       ACL 2020
HIGHLIGHT:      During inference we propose a lattice-rescoring scheme which outperforms all systems evaluated in Stanovsky et al (2019) on WinoMT with no degradation of general test set BLEU, and we show this scheme can be applied to remove gender bias in the output of `black box` online commercial MT systems.

58, TITLE:      Accurate Optimization of Weighted Nuclear Norm for Non-Rigid Structure from Motion
http://arxiv.org/abs/2003.10281
AUTHORS:        José Pedro Iglesias ; Carl Olsson ; Marcus Valtonen Örnhag
HIGHLIGHT:      In this paper we show that more accurate results can in many cases be achieved with 2nd order methods.

59, TITLE:      CCNet: Criss-Cross Attention for Semantic Segmentation
http://arxiv.org/abs/1811.11721
AUTHORS:        Zilong Huang ; Xinggang Wang ; Yunchao Wei ; Lichao Huang ; Humphrey Shi ; Wenyu Liu ; Thomas S. Huang
COMMENTS:       IEEE TPAMI 2020 & ICCV 2019
HIGHLIGHT:      We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way.

60, TITLE:      Quantum Lower and Upper Bounds for 2D-Grid and Dyck Language
http://arxiv.org/abs/2007.03402
AUTHORS:        Andris Ambainis ; Kaspars Balodis ; Jānis Iraids ; Kamil Khadiev ; Vladislavs Kļevickis ; Krišjānis Prūsis ; Yixin Shen ; Juris Smotrovs ; Jevgēnijs Vihrovs
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:1911.12638
HIGHLIGHT:      We give an algorithm with $O\left(\sqrt{n}(\log{n})^{0.5k}\right)$ quantum queries for $Dyck_{k,n}$ for all $k$.

61, TITLE:      Rethinking Positional Encoding in Language Pre-training
http://arxiv.org/abs/2006.15595
AUTHORS:        Guolin Ke ; Di He ; Tie-Yan Liu
COMMENTS:       refine writing; update down-stream results due to bug fix
HIGHLIGHT:      In this work, we investigate the problems in the previous formulations and propose a new positional encoding method for BERT called Transformer with Untied Positional Encoding (TUPE).

62, TITLE:      Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
http://arxiv.org/abs/2007.03643
AUTHORS:        Keegan Lensink ; Issam Laradji ; Marco Law ; Paolo Emilio Barbano ; Savvas Nicolaou ; William Parker ; Eldad Haber
COMMENTS:       9 pages, 5 figures. Fix typo in delimiter between author names in arXiv metadata
HIGHLIGHT:      In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection.

63, TITLE:      Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
http://arxiv.org/abs/1909.07543
AUTHORS:        Thang Doan ; Bogdan Mazoure ; Moloud Abdar ; Audrey Durand ; Joelle Pineau ; R Devon Hjelm
HIGHLIGHT:      In this work, we present a novel approach to population-based RL in continuous control that leverages properties of normalizing flows to perform attractive and repulsive operations between current members of the population and previously observed policies.

64, TITLE:      AI safety: state of the field through quantitative lens
http://arxiv.org/abs/2002.05671
AUTHORS:        Mislav Juric ; Agneza Sandic ; Mario Brcic
COMMENTS:       2020 43rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
HIGHLIGHT:      In this paper, bibliometric analysis of the literature finds significant increase in research activity since 2015.

65, TITLE:      Anysize GAN: A solution to the image-warping problem
http://arxiv.org/abs/2003.03233
AUTHORS:        Connah Kendrick ; David Gillespie ; Moi Hoon Yap
HIGHLIGHT:      We propose a new type of General Adversarial Network (GAN) to resolve a common issue with Deep Learning.

66, TITLE:      Automatized Evaluation of Formalization Exercises in Mathematics
http://arxiv.org/abs/2006.01800
AUTHORS:        Merlin Carl
HIGHLIGHT:      We describe two systems for supporting beginner students in acquiring basic skills in expressing statements in the formalism of first-order predicate logic; the first, called "math dictations", presents users with the task of formalizing a given natural-language sentence, while the second, called "Game of Def", challenges users to give a formal description of a set of a geometric pattern displayed to them.

67, TITLE:      MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop
http://arxiv.org/abs/2004.14858
AUTHORS:        Lukas Stappen ; Alice Baird ; Georgios Rizos ; Panagiotis Tzirakis ; Xinchen Du ; Felix Hafner ; Lea Schumann ; Adria Mallol-Ragolta ; Björn W. Schuller ; Iulia Lefter ; Erik Cambria ; Ioannis Kompatsiaris
COMMENTS:       Baseline Paper MuSe 2020, MuSe Workshop Challenge, ACM Multimedia
HIGHLIGHT:      In this paper, we provide detailed information on MuSe-CaR, the first of its kind in-the-wild database, which is utilised for the challenge, as well as the state-of-the-art features and modelling approaches applied.

68, TITLE:      Deep Active Learning via Open Set Recognition
http://arxiv.org/abs/2007.02196
AUTHORS:        Jaya Krishna Mandivarapu ; Blake Camp ; Rolando Estrada
HIGHLIGHT:      Here, we formulate active learning as an open-set recognition problem.

69, TITLE:      Multi-view Drone-based Geo-localization via Style and Spatial Alignment
http://arxiv.org/abs/2006.13681
AUTHORS:        Siyi Hu ; Xiaojun Chang
COMMENTS:       9 pages 9 figures. arXiv admin note: text overlap with arXiv:2002.12186 by other authors
HIGHLIGHT:      In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag.

70, TITLE:      Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations
http://arxiv.org/abs/2007.03777
AUTHORS:        Huaiyi Huang ; Yuqi Zhang ; Qingqiu Huang ; Zhengkui Guo ; Ziwei Liu ; Dahua Lin
COMMENTS:       To appear in ECCV 2020. Dataset is available at: https://hahehi.github.io/placepedia.html
HIGHLIGHT:      In this work, we contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places.

71, TITLE:      Hierarchical nucleation in deep neural networks
http://arxiv.org/abs/2007.03506
AUTHORS:        Diego Doimo ; Aldo Glielmo ; Alessio Ansuini ; Alessandro Laio
HIGHLIGHT:      In this work we study the evolution of the probability density of the ImageNet dataset across the hidden layers in some state-of-the-art DCNs.

72, TITLE:      Computer Analysis of Architecture Using Automatic Image Understanding
http://arxiv.org/abs/1807.04892
AUTHORS:        Fan Wei ; Yuan Li ; Lior Shamir
HIGHLIGHT:      Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion.

73, TITLE:      Heuristic-Based Weak Learning for Automated Decision-Making
http://arxiv.org/abs/2005.02342
AUTHORS:        Ryan Steed ; Benjamin Williams
COMMENTS:       5 pages, 3 figures. Camera-ready version for Participatory Approaches to Machine Learning @ ICML 2020
HIGHLIGHT:      Instead of creating a simplified labeling task for a crowd, we suggest collecting ranked decision-making heuristics from a focused sample of affected users.

74, TITLE:      The Abstract Machinery of Interaction (Long Version)
http://arxiv.org/abs/2002.05649
AUTHORS:        Beniamino Accattoli ; Ugo Dal Lago ; Gabriele Vanoni
COMMENTS:       Accepted at PPDP 2020
HIGHLIGHT:      This paper revisits the Interaction Abstract Machine (IAM), a machine based on Girard's Geometry of Interaction, introduced by Mackie and Danos & Regnier.

75, TITLE:      Is Japanese gendered language used on Twitter ? A large scale study
http://arxiv.org/abs/2006.15935
AUTHORS:        Tiziana Carpi ; Stefano Maria Iacus
HIGHLIGHT:      This study analyzes the usage of Japanese gendered language on Twitter.

76, TITLE:      Emergence of Separable Manifolds in Deep Language Representations
http://arxiv.org/abs/2006.01095
AUTHORS:        Jonathan Mamou ; Hang Le ; Miguel Del Rio ; Cory Stephenson ; Hanlin Tang ; Yoon Kim ; SueYeon Chung
COMMENTS:       9 pages. 10 figures. Accepted to ICML 2020. Included supplemental materials
HIGHLIGHT:      In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience that connects geometry of feature representations with linear separability of classes, to analyze language representations from large-scale contextual embedding models.

77, TITLE:      Faithful Embeddings for Knowledge Base Queries
http://arxiv.org/abs/2004.03658
AUTHORS:        Haitian Sun ; Andrew O. Arnold ; Tania Bedrax-Weiss ; Fernando Pereira ; William W. Cohen
HIGHLIGHT:      \emph{Query embedding} (QE) techniques have been recently proposed where KB entities and KB queries are represented jointly in an embedding space, supporting relaxation and generalization in KB inference.

78, TITLE:      Implicit Geometric Regularization for Learning Shapes
http://arxiv.org/abs/2002.10099
AUTHORS:        Amos Gropp ; Lior Yariv ; Niv Haim ; Matan Atzmon ; Yaron Lipman
COMMENTS:       37th International Conference on Machine Learning, Vienna, Austria, 2020
HIGHLIGHT:      In this paper we offer a new paradigm for computing high fidelity implicit neural representations directly from raw data (i.e., point clouds, with or without normal information).

79, TITLE:      Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
http://arxiv.org/abs/2007.01546
AUTHORS:        Yunpeng Zhai ; Qixiang Ye ; Shijian Lu ; Mengxi Jia ; Rongrong Ji ; Yonghong Tian
COMMENTS:       Accepted by ECCV'20
HIGHLIGHT:      In this paper, we propose a multiple expert brainstorming network (MEB-Net) for domain adaptive person re-ID, opening up a promising direction about model ensemble problem under unsupervised conditions.

80, TITLE:      Which Tasks Should Be Learned Together in Multi-task Learning?
http://arxiv.org/abs/1905.07553
AUTHORS:        Trevor Standley ; Amir R. Zamir ; Dawn Chen ; Leonidas Guibas ; Jitendra Malik ; Silvio Savarese
COMMENTS:       Accepted to ICML 2020 See project website at http://taskgrouping.stanford.edu/
HIGHLIGHT:      We study task cooperation and competition in several different learning settings and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks.

81, TITLE:      Estimation of Ground Contacts from Human Gait by a Wearable Inertial Measurement Unit using machine learning
http://arxiv.org/abs/2007.02433
AUTHORS:        Muhammad Junaid Umer ; Qaiser Riaz
COMMENTS:       Not completely discussed with supervisor need some improvements in article to prepare a final draft
HIGHLIGHT:      This article addresses the estimation and classification of right and left foot during the healthy human gait based on the IMU sensor data of chest and lower back. For this purpose we have collected an IMU data of 48 subjects by using two smartphones at chest and lower back of the human body and one smart watch at right ankle of the body.

82, TITLE:      Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
http://arxiv.org/abs/1208.6335
AUTHORS:        Aman Chadha ; Sushmit Mallik ; Ravdeep Johar
COMMENTS:       8 pages, 16 figures, 11 tables
HIGHLIGHT:      Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper.

83, TITLE:      On Basing One-way Permutations on NP-hard Problems under Quantum Reductions
http://arxiv.org/abs/1804.10309
AUTHORS:        Nai-Hui Chia ; Sean Hallgren ; Fang Song
COMMENTS:       27 pages
HIGHLIGHT:      In this work, we initiate a study of the quantum analogues of these questions.

84, TITLE:      How Does Topology of Neural Architectures Impact Gradient Propagation and Model Performance?
http://arxiv.org/abs/1910.00780
AUTHORS:        Kartikeya Bhardwaj ; Guihong Li ; Radu Marculescu
HIGHLIGHT:      In this paper, we address two fundamental questions in neural architecture design research: (i) How does an architecture topology impact the gradient flow during training?

85, TITLE:      Intrinsic Reward Driven Imitation Learning via Generative Model
http://arxiv.org/abs/2006.15061
AUTHORS:        Xingrui Yu ; Yueming Lyu ; Ivor W. Tsang
HIGHLIGHT:      To address this challenge, we propose a novel reward learning module to generate intrinsic reward signals via a generative model.