2020.07.03.txt

==========New Papers==========
1, TITLE:       A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
http://arxiv.org/abs/2007.01126
AUTHORS:        Partoo Vafaeikia ; Khashayar Namdar ; Farzad Khalvati
HIGHLIGHT:      In this paper, we provide a brief review on the recent deep multi-task learning (dMTL) approaches followed by methods on selecting useful auxiliary tasks that can be used in dMTL to improve the performance of the model for the main task.

2, TITLE:       Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model ofMedical Disagreement
http://arxiv.org/abs/2007.00709
AUTHORS:        Hossein Hematialam ; Luciana Garbayo ; Seethalakshmi Gopalakrishnan ; Wlodek Zadrozny
COMMENTS:       39 pages, 4 figures
HIGHLIGHT:      Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening.

3, TITLE:       Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
http://arxiv.org/abs/2007.00708
AUTHORS:        Linnan Wang ; Rodrigo Fonseca ; Yuandong Tian
HIGHLIGHT:      In this paper, we coin LA-MCTS that extends LaNAS to other domains.

4, TITLE:       A Fully Polynomial Time Approximation Scheme For A NP-Hard Problem
http://arxiv.org/abs/2007.00912
AUTHORS:        Marius-Simion Costandin ; Bogdan Gavrea
HIGHLIGHT:      We present a novel feasibility criteria for the intersection of convex sets given by inequalities.

5, TITLE:       Fact-based Text Editing
http://arxiv.org/abs/2007.00916
AUTHORS:        Hayate Iso ; Chao Qiao ; Hang Li
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose a novel text editing task, referred to as \textit{fact-based text editing}, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples).

6, TITLE:       Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
http://arxiv.org/abs/2007.00914
AUTHORS:        Nuria Rodríguez-Barroso ; Goran Stipcich ; Daniel Jiménez-López ; José Antonio Ruiz-Millán ; Eugenio Martínez-Cámara ; Gerardo González-Seco ; M. Victoria Luzón ; Miguel Ángel Veganzones ; Francisco Herrera
COMMENTS:       47 pages, 5 figures
HIGHLIGHT:      Hence, we present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy.

7, TITLE:       Policy Improvement from Multiple Experts
http://arxiv.org/abs/2007.00795
AUTHORS:        Ching-An Cheng ; Andrey Kolobov ; Alekh Agarwal
HIGHLIGHT:      In this paper, we propose the state-wise maximum of the expert policies' values as a natural baseline to resolve conflicting advice from multiple experts.

8, TITLE:       Age-Oriented Face Synthesis with Conditional Discriminator Pool and Adversarial Triplet Loss
http://arxiv.org/abs/2007.00792
AUTHORS:        Haoyi Wang ; Victor Sanchez ; Chang-Tsun Li
HIGHLIGHT:      In this paper, we propose a method for the age-oriented face synthesis task that achieves a high synthesis accuracy with strong identity permanence capabilities.

9, TITLE:       Learning Surrogates via Deep Embedding
http://arxiv.org/abs/2007.00799
AUTHORS:        Yash Patel ; Tomas Hodan ; Jiri Matas
HIGHLIGHT:      This paper proposes a technique for training a neural network by minimizing a surrogate loss that approximates the target evaluation metric, which may be non-differentiable.

10, TITLE:      IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
http://arxiv.org/abs/2007.00924
AUTHORS:        Luxi Xing ; Yuqiang Xie ; Yue Hu ; Wei Peng
COMMENTS:       8 pages, 1 figure, 5 tables, SemEval-2020
HIGHLIGHT:      This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation.

11, TITLE:      High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
http://arxiv.org/abs/2007.00925
AUTHORS:        Elena Raponi ; Hao Wang ; Mariusz Bujny ; Simonetta Boria ; Carola Doerr
COMMENTS:       11 pages, 5 figures, conference paper accepted at PPSN2020
HIGHLIGHT:      In this paper, we propose to tackle the scalability of BO by hybridizing it with a Principal Component Analysis (PCA), resulting in a novel PCA-assisted BO (PCA-BO) algorithm.

12, TITLE:      Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
http://arxiv.org/abs/2007.00779
AUTHORS:        Thiago M. Paixão ; Rodrigo F. Berriel ; Maria C. S. Boeres ; Alessandro L. Koerich ; Claudine Badue ; Alberto F. de Souza ; Thiago Oliveira-Santos
COMMENTS:       Accepted for publication in Pattern Recognition
HIGHLIGHT:      The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.

13, TITLE:      Allocation of Multi-Robot Tasks with Task Variants
http://arxiv.org/abs/2007.00777
AUTHORS:        Zakk Giacometti ; Yu Zhang
HIGHLIGHT:      In this paper, we introduce a more general formulation of multi-robot task allocation problem that allows more than one option for specifying the set of task requirements--satisfying any one of the options will satisfy the task.

14, TITLE:      The Impact of Explanations on AI Competency Prediction in VQA
http://arxiv.org/abs/2007.00900
AUTHORS:        Kamran Alipour ; Arijit Ray ; Xiao Lin ; Jurgen P. Schulze ; Yi Yao ; Giedrius T. Burachas
COMMENTS:       Submitted to HCCAI 2020
HIGHLIGHT:      In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA).

15, TITLE:      NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification
http://arxiv.org/abs/2007.01030
AUTHORS:        Lukas Lange ; Heike Adel ; Jannik Strötgen
COMMENTS:       Published at IberLEF 2019. Winning System of the MEDDOCAN shared task
HIGHLIGHT:      In this paper, we describe our NLNDE system, with which we participated in the MEDDOCAN competition, the medical document anonymization task of IberLEF 2019.

16, TITLE:      RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
http://arxiv.org/abs/2007.01272
AUTHORS:        Sebastien Ehrhardt ; Oliver Groth ; Aron Monszpart ; Martin Engelcke ; Ingmar Posner ; Niloy Mitra ; Andrea Vedaldi
HIGHLIGHT:      We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects.

17, TITLE:      Signature-Based Abduction for Expressive Description Logics -- Technical Report
http://arxiv.org/abs/2007.00757
AUTHORS:        Patrick Koopmann ; Warren Del-Pinto ; Sophie Tourret ; Renate A. Schmidt
COMMENTS:       12 pages, 1 figure
HIGHLIGHT:      We present a method that performs signature-based abduction for observations expressed in the expressive description logic ALC, which can include TBox and ABox axioms.

18, TITLE:      ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network
http://arxiv.org/abs/2007.00992
AUTHORS:        Dongyoon Han ; Sangdoo Yun ; Byeongho Heo ; YoungJoon Yoo
HIGHLIGHT:      Based on the investigation, we propose simple yet effective design principles to mitigate the representational bottleneck.

19, TITLE:      Legends: Folklore on Reddit
http://arxiv.org/abs/2007.00750
AUTHORS:        Caitrin Armstrong ; Derek Ruths
HIGHLIGHT:      In this paper we introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit.

20, TITLE:      Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
http://arxiv.org/abs/2007.00991
AUTHORS:        Eugene Kharitonov ; Morgane Rivière ; Gabriel Synnaeve ; Lior Wolf ; Pierre-Emmanuel Mazaré ; Matthijs Douze ; Emmanuel Dupoux
HIGHLIGHT:      Here, we introduce WavAugment, a time-domain data augmentation library and find that applying augmentation in the past is generally more efficient and yields better performances than other methods.

21, TITLE:      Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
http://arxiv.org/abs/2007.01282
AUTHORS:        Gautier Izacard ; Edouard Grave
HIGHLIGHT:      In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence.

22, TITLE:      Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
http://arxiv.org/abs/2007.00753
AUTHORS:        Samuel Henrique Silva ; Peyman Najafirad
HIGHLIGHT:      Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds.

23, TITLE:      4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes
http://arxiv.org/abs/2007.01044
AUTHORS:        Marcel Bengs ; Nils Gessert ; Alexander Schlaefer
COMMENTS:       Accepted at CURAC 2020
HIGHLIGHT:      In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.

24, TITLE:      Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification
http://arxiv.org/abs/2007.01042
AUTHORS:        Marcel Bengs ; Nils Gessert ; Wiebke Laffers ; Dennis Eggert ; Stephan Westermann ; Nina A. Mueller ; Andreas O. H. Gerstner ; Christian Betz ; Alexander Schlaefer
COMMENTS:       Accepted at MICCAI 2020
HIGHLIGHT:      In this work, we demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning.

25, TITLE:      Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning
http://arxiv.org/abs/2007.00760
AUTHORS:        Mason T. Chen ; Nicholas J. Durr
HIGHLIGHT:      To avoid this tradeoff, we introduce OxyGAN: a data-driven, content-aware method to estimate tissue oxygenation directly from single structured light images using end-to-end generative adversarial networks.

26, TITLE:      An encoder-decoder-based method for COVID-19 lung infection segmentation
http://arxiv.org/abs/2007.00861
AUTHORS:        Omar Elharrouss ; Nandhini Subramanian ; Somaya Al-Maadeed
HIGHLIGHT:      This paper proposes a multi-task deep-learning-based method for lung infection segmentation using CT-scan images.

27, TITLE:      Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
http://arxiv.org/abs/2007.00864
AUTHORS:        Shail Dave ; Riyadh Baghdadi ; Tony Nowatzki ; Sasikanth Avancha ; Aviral Shrivastava ; Baoxin Li
HIGHLIGHT:      Machine learning (ML) models are widely used in many domains including media processing and generation, computer vision, medical diagnosis, embedded systems, high-performance and scientific computing, and recommendation systems.

28, TITLE:      Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
http://arxiv.org/abs/2007.00862
AUTHORS:        Dapeng Zhao ; Jean Oh
COMMENTS:       submitted to IEEE Robotics and Automation Letters (RA-L) - CFP: Long-Term Human Motion Prediction (https://www.ieee-ras.org/publications/ra-l/special-issues/cfp-special-long-term-human-motion-prediction)
HIGHLIGHT:      We propose a novel way to learn, detect and extract patterns in sequential data, and successfully applied it to the problem of human trajectory prediction.

29, TITLE:      Automatic Horizontal Fusion for GPU Kernels
http://arxiv.org/abs/2007.01277
AUTHORS:        Ao Li ; Bojian Zheng ; Gennady Pekhimenko ; Fan Long
HIGHLIGHT:      We present automatic horizontal fusion, a novel optimization technique that complements the standard kernel fusion techniques for GPU programs.

30, TITLE:      Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems
http://arxiv.org/abs/2007.00737
AUTHORS:        Kyung Kim ; Robert C. Leishman ; Scott L. Nykl
HIGHLIGHT:      This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz.

31, TITLE:      Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
http://arxiv.org/abs/2007.00833
AUTHORS:        Guotai Wang ; Michael Aertsen ; Jan Deprest ; Sebastien Ourselin ; Tom Vercauteren ; Shaoting Zhang
COMMENTS:       10 pages, 4 figures, Accepted by MICCAI 2020
HIGHLIGHT:      To improve the efficiency of interactive refinement process, we propose an Uncertainty-Guided Interactive Refinement (UGIR) framework.

32, TITLE:      PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding
http://arxiv.org/abs/2007.00977
AUTHORS:        Kanish Garg ; Ajeet kumar Singh ; Dorien Herremans ; Brejesh Lall
COMMENTS:       Proceedings of IEEE International Conference on Imaging, Vision & Pattern Recognition, (IVPR 2020, Japan)
HIGHLIGHT:      Hence, we propose a method to provide good initialized images by incorporating perceptual understanding in the discriminator module.

33, TITLE:      Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank
http://arxiv.org/abs/2007.01251
AUTHORS:        Nicolas Basty ; Yi Liu ; Madeleine Cule ; E. Louise Thomas ; Jimmy D. Bell ; Brandon Whitcher
HIGHLIGHT:      Detection of fat-water swaps in the Dixon series is performed by a deep learning model and corrected automatically.

34, TITLE:      Human-centered collaborative robots with deep reinforcement learning
http://arxiv.org/abs/2007.01009
AUTHORS:        Ali Ghadirzadeh ; Xi Chen ; Wenjie Yin ; Zhengrong Yi ; Mårten Björkman ; Danica Kragic
HIGHLIGHT:      We present a reinforcement learning based framework for human-centered collaborative systems.

35, TITLE:      Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge
http://arxiv.org/abs/2007.00849
AUTHORS:        Pat Verga ; Haitian Sun ; Livio Baldini Soares ; William W. Cohen
HIGHLIGHT:      To address these problems, we develop a neural language model that includes an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge.

36, TITLE:      Learning ordered pooling weights in image classification
http://arxiv.org/abs/2007.01243
AUTHORS:        J. I. Forcen ; Miguel Pagola ; Edurne Barrenechea ; Humberto Bustince
HIGHLIGHT:      We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators.

37, TITLE:      Surface Denoising based on Normal Filtering in a Robust Statistics Framework
http://arxiv.org/abs/2007.00842
AUTHORS:        Sunil Kumar Yadav ; Martin Skrodzki ; Eric Zimmermann ; Konrad Polthier
HIGHLIGHT:      In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising.

38, TITLE:      PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors
http://arxiv.org/abs/2007.01001
AUTHORS:        Ziqiang Li ; Hong Pan ; Yaping Zhu ; A. K. Qin
COMMENTS:       Accepted by the 2020 International Joint Conference on Neural Networks (IJCNN 2020)
HIGHLIGHT:      To tackle such challenges, we propose a position-guided deformable UNet, namely PGD-UNet, which exploits the spatial deformation capabilities of deformable convolution to deal with the geometric transformation of both organs and tumors.

39, TITLE:      Low-light Environment Neural Surveillance
http://arxiv.org/abs/2007.00843
AUTHORS:        Michael Potter ; Henry Gridley ; Noah Lichtenstein ; Kevin Hines ; John Nguyen ; Jacob Walsh
COMMENTS:       Pre-print, accepted to IEEE International Workshop on Machine Learning for Signal Processing 2020 Conference Proceedings. Code and dataset are available at https://github.com/mcgridles/
HIGHLIGHT:      We design and implement an end-to-end system for real-time crime detection in low-light environments. We create a low-light action-recognition dataset, LENS-4, which will be publicly available.

40, TITLE:      Autonomous and cooperative design of the monitor positions for a team of UAVs to maximize the quantity and quality of detected objects
http://arxiv.org/abs/2007.01247
AUTHORS:        Dimitrios I. Koutras ; Athanasios Ch. Kapoutsis ; Elias B. Kosmatopoulos
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness.

41, TITLE:      Relevance-guided Supervision for OpenQA with ColBERT
http://arxiv.org/abs/2007.00814
AUTHORS:        Omar Khattab ; Christopher Potts ; Matei Zaharia
HIGHLIGHT:      We propose a weak supervision strategy that iteratively uses ColBERT to create its own training data.

42, TITLE:      NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection
http://arxiv.org/abs/2007.01022
AUTHORS:        Lukas Lange ; Heike Adel ; Jannik Strötgen
COMMENTS:       Published at BioNLP-OST@EMNLP 2019
HIGHLIGHT:      In this paper, we describe the system with which we participated in the first subtrack of the PharmaCoNER competition of the BioNLP Open Shared Tasks 2019.

43, TITLE:      Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
http://arxiv.org/abs/2007.01261
AUTHORS:        Luyu Yang ; Yogesh Balaji ; Ser-Nam Lim ; Abhinav Shrivastava
HIGHLIGHT:      In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS).

44, TITLE:      Lightme: Analysing Language in Internet Support Groups for Mental Health
http://arxiv.org/abs/2007.00824
AUTHORS:        Gabriela Ferraro ; Brendan Loo-Gee ; Shenjia Ji ; Luis Salvador-Carulla
HIGHLIGHT:      Lightme: Analysing Language in Internet Support Groups for Mental Health

45, TITLE:      Understanding Road Layout from Videos as a Whole
http://arxiv.org/abs/2007.00822
AUTHORS:        Buyu Liu ; Bingbing Zhuang ; Samuel Schulter ; Pan Ji ; Manmohan Chandraker
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we address the problem of inferring the layout of complex road scenes from video sequences.

46, TITLE:      Designing Environments Conducive to Interpretable Robot Behavior
http://arxiv.org/abs/2007.00820
AUTHORS:        Anagha Kulkarni ; Sarath Sreedharan ; Sarah Keren ; Tathagata Chakraborti ; David Smith ; Subbarao Kambhampati
HIGHLIGHT:      In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a type of interpretable behavior -- known in the literature as explicable behavior.

47, TITLE:      Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
http://arxiv.org/abs/2007.00808
AUTHORS:        Lee Xiong ; Chenyan Xiong ; Ye Li ; Kwok-Fung Tang ; Jialin Liu ; Paul Bennett ; Junaid Ahmed ; Arnold Overwijk
HIGHLIGHT:      In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in testing.

48, TITLE:      TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
http://arxiv.org/abs/2007.00801
AUTHORS:        Arindam Das ; Pavel Krizek ; Ganesh Sistu ; Fabian Burger ; Sankaralingam Madasamy ; Michal Uricar ; Varun Ravi Kumar ; Senthil Yogamani
COMMENTS:       Accepted for Oral Presentation at IEEE Intelligent Transportation Systems Conference (ITSC) 2020
HIGHLIGHT:      We propose a novel method to regress the area of each soiling type within a tile directly.

49, TITLE:      Query-Free Adversarial Transfer via Undertrained Surrogates
http://arxiv.org/abs/2007.00806
AUTHORS:        Chris Miller ; Soroush Vosoughi
HIGHLIGHT:      We show that optimizing a single surrogate model is a more effective method of improving adversarial transfer, using the simple example of an undertrained surrogate.

50, TITLE:      Hierarchically-Organized Latent Modules for Exploratory Search in Morphogenetic Systems
http://arxiv.org/abs/2007.01195
AUTHORS:        Mayalen Etcheverry ; Clement Moulin-Frier ; Pierre-Yves Oudeyer
HIGHLIGHT:      In this paper, we motivate the need for what we call Meta-diversity search, arguing that there is not a unique ground truth interesting diversity as it strongly depends on the final observer and its motives.

51, TITLE:      Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis
http://arxiv.org/abs/2007.01189
AUTHORS:        Avinash Madasu ; Vijjini Anvesh Rao
COMMENTS:       Accepted at 25th International Conference on Pattern Recognition, January 2021, Milan, Italy
HIGHLIGHT:      We propose a model-independent framework - Sequential Domain Adaptation (SDA).

52, TITLE:      Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
http://arxiv.org/abs/2007.01176
AUTHORS:        Brian Roark ; Lawrence Wolf-Sonkin ; Christo Kirov ; Sabrina J. Mielke ; Cibu Johny ; Isin Demirsahin ; Keith Hall
COMMENTS:       Published at LREC 2020
HIGHLIGHT:      This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages.

53, TITLE:      Am I Building a White Box Agent or Interpreting a Black Box Agent?
http://arxiv.org/abs/2007.01187
AUTHORS:        Tom Bewley
COMMENTS:       6 pages; pre-print
HIGHLIGHT:      The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa.

54, TITLE:      MPLP: Learning a Message Passing Learning Protocol
http://arxiv.org/abs/2007.00970
AUTHORS:        Ettore Randazzo ; Eyvind Niklasson ; Alexander Mordvintsev
COMMENTS:       Code at https://github.com/google-research/self-organising-systems/mplp
HIGHLIGHT:      We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP).

55, TITLE:      From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
http://arxiv.org/abs/2007.00730
AUTHORS:        Songyang Zhang ; Han Zhang ; Shuguang Cui ; Zhi Ding
HIGHLIGHT:      This work revisits the fundamentals of graph wavelet and explores the utility of spectral wavelet-kernels to signal propagation in the vertex domain.

56, TITLE:      JUMPS: Joints Upsampling Method for Pose Sequences
http://arxiv.org/abs/2007.01151
AUTHORS:        Lucas Mourot ; François Le Clerc ; Cédric Thébault ; Pierre Hellier
COMMENTS:       7 pages, 7 figures
HIGHLIGHT:      To this purpose, we propose a novel method called JUMPS for increasing the number of joints in 2D pose estimates and recovering occluded or missing joints.

57, TITLE:      Weakly Supervised Segmentation with Multi-scale Adversarial Attention Gates
http://arxiv.org/abs/2007.01152
AUTHORS:        Gabriele Valvano ; Andrea Leo ; Sotirios A. Tsaftaris
COMMENTS:       Project page: https://gvalvano.github.io/wss-multiscale-adversarial-attention-gates
HIGHLIGHT:      We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.

58, TITLE:      A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study
http://arxiv.org/abs/2007.00747
AUTHORS:        Yusuf Sermet ; Ibrahim Demir
COMMENTS:       11 pages, 6 figures
HIGHLIGHT:      This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology.

59, TITLE:      Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
http://arxiv.org/abs/2007.00748
AUTHORS:        Ostap Viniavskyi ; Mariia Dobko ; Oles Dobosevych
COMMENTS:       Accepted to AIME 2020
HIGHLIGHT:      In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision.

60, TITLE:      Double-Loop Unadjusted Langevin Algorithm
http://arxiv.org/abs/2007.01147
AUTHORS:        Paul Rolland ; Armin Eftekhari ; Ali Kavis ; Volkan Cevher
HIGHLIGHT:      This work proposes a new annealing step-size schedule for ULA, which allows to prove new convergence guarantees for sampling from a smooth log-concave distribution, which are not covered by existing state-of-the-art convergence guarantees.

61, TITLE:      RGB-D-based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments
http://arxiv.org/abs/2007.00981
AUTHORS:        Andrés Fuster-Guilló ; Jorge Azorín-López ; Marcelo Saval-Calvo ; Juan Miguel Castillo-Zaragoza ; Nahuel Garcia-DUrso ; Robert B Fisher
HIGHLIGHT:      The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment.

62, TITLE:      Deep learning-based holographic polarization microscopy
http://arxiv.org/abs/2007.00741
AUTHORS:        Tairan Liu ; Kevin de Haan ; Bijie Bai ; Yair Rivenson ; Yi Luo ; Hongda Wang ; David Karalli ; Hongxiang Fu ; Yibo Zhang ; John FitzGerald ; Aydogan Ozcan
COMMENTS:       20 pages, 8 figures
HIGHLIGHT:      Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an existing holographic imaging system.

63, TITLE:      Quantifying causal contribution via structure preserving interventions
http://arxiv.org/abs/2007.00714
AUTHORS:        Dominik Janzing ; Patrick Blöbaum ; Lenon Minorics
HIGHLIGHT:      We introduce 'Causal Information Contribution (CIC)' and 'Causal Variance Contribution (CVC)' to quantify the influence of each variable in a causal directed acyclic graph on some target variable.

64, TITLE:      Higher-order Logic as Lingua Franca -- Integrating Argumentative Discourse and Deep Logical Analysis
http://arxiv.org/abs/2007.01019
AUTHORS:        David Fuenmayor ; Christoph Benzmüller
COMMENTS:       35 pages, 1 figure
HIGHLIGHT:      We present an approach towards the deep, pluralistic logical analysis of argumentative discourse that benefits from the application of state-of-the-art automated reasoning technology for classical higher-order logic.

65, TITLE:      A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
http://arxiv.org/abs/2007.01016
AUTHORS:        Boyu Zhang ; A. K. Qin ; Hong Pan ; Timos Sellis
COMMENTS:       Accepted by the 2020 International Joint Conference on Neural Networks (IJCNN 2020)
HIGHLIGHT:      To address these issues, we propose a novel DNN training framework.

66, TITLE:      Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
http://arxiv.org/abs/2007.01259
AUTHORS:        Hui Xie ; Zhe Pan ; Leixin Zhou ; Fahim A Zaman ; Danny Chen ; Jost B Jonas ; Yaxing Wang ; Xiaodong Wu
COMMENTS:       11 pages main content and reference, plus 10 pages appendix, total 21 pages
HIGHLIGHT:      In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.

67, TITLE:      A deep primal-dual proximal network for image restoration
http://arxiv.org/abs/2007.00959
AUTHORS:        Mingyuan Jiu ; Nelly Pustelnik
HIGHLIGHT:      In this work, we design a deep network, named DeepPDNet, built from primal-dual proximal iterations associated with the minimization of a standard penalized likelihood with an analysis prior, allowing us to take advantages from both worlds.

68, TITLE:      Verifiably Safe Exploration for End-to-End Reinforcement Learning
http://arxiv.org/abs/2007.01223
AUTHORS:        Nathan Hunt ; Nathan Fulton ; Sara Magliacane ; Nghia Hoang ; Subhro Das ; Armando Solar-Lezama
HIGHLIGHT:      This paper contributes a first approach toward enforcing formal safety constraints on end-to-end policies with visual inputs.

69, TITLE:      ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
http://arxiv.org/abs/2007.00711
AUTHORS:        Miguel Villarreal-Vasquez ; Bharat Bhargava
COMMENTS:       13 pages (excluding references), 7 figures, 7 tables
HIGHLIGHT:      In this work, we analyze the composition of the features learned by DNNs at training.

70, TITLE:      Project PIAF: Building a Native French Question-Answering Dataset
http://arxiv.org/abs/2007.00968
AUTHORS:        Rachel Keraron ; Guillaume Lancrenon ; Mathilde Bras ; Frédéric Allary ; Gilles Moyse ; Thomas Scialom ; Edmundo-Pavel Soriano-Morales ; Jacopo Staiano
COMMENTS:       LREC 2020
HIGHLIGHT:      Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset.

71, TITLE:      Learning Geocentric Object Pose in Oblique Monocular Images
http://arxiv.org/abs/2007.00729
AUTHORS:        Gordon Christie ; Rodrigo Rene Rai Munoz Abujder ; Kevin Foster ; Shea Hagstrom ; Gregory D. Hager ; Myron Z. Brown
COMMENTS:       CVPR 2020
HIGHLIGHT:      We exploit these attributes to rectify oblique images and remove observed object parallax to dramatically improve the accuracy of localization and to enable accurate alignment of multiple images taken from very different oblique viewpoints.

72, TITLE:      Iterative Bounding Box Annotation for Object Detection
http://arxiv.org/abs/2007.00961
AUTHORS:        Bishwo Adhikari ; Heikki Huttunen
COMMENTS:       Accepted at ICPR 2020
HIGHLIGHT:      In this paper, we propose a semi-automatic method for efficient bounding box annotation.

73, TITLE:      Adversarial Example Games
http://arxiv.org/abs/2007.00720
AUTHORS:        Avishek Joey Bose ; Gauthier Gidel ; Hugo Berrard ; Andre Cianflone ; Pascal Vincent ; Simon Lacoste-Julien ; William L. Hamilton
HIGHLIGHT:      In this work, we address this gap and lay the theoretical foundations for crafting transferable adversarial examples to entire function classes.

74, TITLE:      Estimating Blink Probability for Highlight Detection in Figure Skating Videos
http://arxiv.org/abs/2007.01089
AUTHORS:        Tamami Nakano ; Atsuya Sakata ; Akihiro Kishimoto
HIGHLIGHT:      Therefore, in this study, we propose a novel, automatic highlight detection method based on the blink rate.

75, TITLE:      Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
http://arxiv.org/abs/2007.01204
AUTHORS:        Jibin Wu ; Chenglin Xu ; Daquan Zhou ; Haizhou Li ; Kay Chen Tan
HIGHLIGHT:      In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.

76, TITLE:      Scene Graph Reasoning for Visual Question Answering
http://arxiv.org/abs/2007.01072
AUTHORS:        Marcel Hildebrandt ; Hang Li ; Rajat Koner ; Volker Tresp ; Stephan Günnemann
COMMENTS:       ICML Workshop Graph Representation Learning and Beyond (GRL+)
HIGHLIGHT:      We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.

77, TITLE:      Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
http://arxiv.org/abs/2007.01076
AUTHORS:        Remis Balaniuk ; Olga Isupova ; Steven Reece
COMMENTS:       Preprint submitted to Remote Sensing of Environment
HIGHLIGHT:      This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil.

78, TITLE:      Spot the conversation: speaker diarisation in the wild
http://arxiv.org/abs/2007.01216
AUTHORS:        Joon Son Chung ; Jaesung Huh ; Arsha Nagrani ; Triantafyllos Afouras ; Andrew Zisserman
COMMENTS:       The dataset will be available for download from http://www.robots.ox.ac.uk/~vgg/data/voxceleb/voxconverse.html . The development set will be released in July 2020, and the test set will be released in October 2020
HIGHLIGHT:      The goal of this paper is speaker diarisation of videos collected 'in the wild'.

79, TITLE:      Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
http://arxiv.org/abs/2007.01217
AUTHORS:        Leixin Zhou ; Xiaodong Wu
HIGHLIGHT:      In this paper, a novel model based on convolutional neural network (CNN) followed by a learnable surface smoothing block is proposed to tackle the surface segmentation problem with end-to-end training.

80, TITLE:      ACFD: Asymmetric Cartoon Face Detector
http://arxiv.org/abs/2007.00899
AUTHORS:        Bin Zhang ; Jian Li ; Yabiao Wang ; Zhipeng Cui ; Yili Xia ; Chengjie Wang ; Jilin Li ; Feiyue Huang
COMMENTS:       1st place of IJCAI 2020 iCartoon Face Challenge (Detection Track)
HIGHLIGHT:      Aiming at the characteristics of cartoon faces, such as huge differences within the intra-faces, in this paper, we propose an asymmetric cartoon face detector, named ACFD.

81, TITLE:      Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey
http://arxiv.org/abs/2007.01127
AUTHORS:        Shivaji Alaparthi ; Manit Mishra
COMMENTS:       15 pages, 1 table
HIGHLIGHT:      The purpose of the study is to investigate the relative effectiveness of four different sentiment analysis techniques: (1) unsupervised lexicon-based model using Sent WordNet; (2) traditional supervised machine learning model using logistic regression; (3) supervised deep learning model using Long Short-Term Memory (LSTM); and, (4) advanced supervised deep learning models using Bidirectional Encoder Representations from Transformers (BERT).

82, TITLE:      Automatic Page Segmentation Without Decompressing the Run-Length Compressed Text Documents
http://arxiv.org/abs/2007.01142
AUTHORS:        Mohammed Javed ; P. Nagabhushan
COMMENTS:       Appeared in the Ph.D. Thesis (2016) of Dr. Mohammed Javed, entitled "On the Possibility of Processing Document Images in Compressed Domain" from Department of Studies in Computer Science, University of Mysore, Karnataka, India
HIGHLIGHT:      This research paper proposes demonstrating the possibility of carrying out a page segmentation operation directly in the run-length data of the CCITT Group-3 compressed text document, which could be single- or multi-columned and might even have some text regions in the inverted text color mode.

83, TITLE:      Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
http://arxiv.org/abs/2007.01293
AUTHORS:        Zhongzheng Ren ; Raymond A. Yeh ; Alexander G. Schwing
HIGHLIGHT:      In this paper we study how to use a different weight for every unlabeled example.

84, TITLE:      A Closer Look at Local Aggregation Operators in Point Cloud Analysis
http://arxiv.org/abs/2007.01294
AUTHORS:        Ze Liu ; Han Hu ; Yue Cao ; Zheng Zhang ; Xin Tong
COMMENTS:       Code available at https://github.com/zeliu98/CloserLook3D
HIGHLIGHT:      In this paper, we revisit the representative local aggregation operators and study their performance using the same deep residual architecture.

85, TITLE:      Implementing a Fast Unbounded Quantum Fanout Gate Using Power-Law Interactions
http://arxiv.org/abs/2007.00662
AUTHORS:        Andrew Y. Guo ; Abhinav Deshpande ; Su-Kuan Chu ; Zachary Eldredge ; Przemyslaw Bienias ; Dhruv Devulapalli ; Yuan Su ; Andrew M. Childs ; Alexey V. Gorshkov
COMMENTS:       6 pages, 1 figure
HIGHLIGHT:      Complementarily, we develop a new technique to give a general lower bound, linear in the size of the system, on the time required to implement the QFT and the fanout gate in systems that are constrained by a linear light cone.

86, TITLE:      Deep Single Image Manipulation
http://arxiv.org/abs/2007.01289
AUTHORS:        Yael Vinker ; Eliahu Horwitz ; Nir Zabari ; Yedid Hoshen
COMMENTS:       Project page: http://www.vision.huji.ac.il/deepsim/
HIGHLIGHT:      In this paper, we demonstrate that simply training a conditional adversarial generator on the single target image is sufficient for performing complex image manipulations.

87, TITLE:      Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?
http://arxiv.org/abs/2007.01062
AUTHORS:        Ella M. Gale ; Nicholas Martin ; Ryan Blything ; Anh Nguyen ; Jeffrey S. Bowers
COMMENTS:       Published in Vision Research 2020, 19 pages, 8 figures
HIGHLIGHT:      Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work.

88, TITLE:      Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
http://arxiv.org/abs/2007.01065
AUTHORS:        Ziyang Song ; Ziyi Yin ; Zejian Yuan ; Chong Zhang ; Wanchao Chi ; Yonggen Ling ; Shenghao Zhang
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this paper, we deeply explore the characteristics of the action recognition task in interaction scenarios and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenarios.

89, TITLE:      Efficient Document Exchange and Error Correcting Codes with Asymmetric Information
http://arxiv.org/abs/2007.00870
AUTHORS:        Kuan Cheng ; Xin Li
COMMENTS:       37 pages
HIGHLIGHT:      In this paper we study whether asymmetric partial information can help in these two problems.

90, TITLE:      Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification
http://arxiv.org/abs/2007.00875
AUTHORS:        Chetanya Rastogi ; Nikka Mofid ; Fang-I Hsiao
COMMENTS:       11 pages, 15 figures
HIGHLIGHT:      In this paper, we experiment with Easy Data Augmentation (EDA) and Backtranslation, as well as with three popular learning algorithms, Logistic Regression, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory Network (Bi-LSTM).

91, TITLE:      Image Analysis Based on Nonnegative/Binary Matrix Factorization
http://arxiv.org/abs/2007.00889
AUTHORS:        Hinako Asaoka ; Kazue Kudo
COMMENTS:       3 pages, 1 figure
HIGHLIGHT:      The NBMF algorithm converges in fewer iterations than those required for the convergence of nonnegative matrix factorization (NMF), although both techniques perform comparably in image classification.

92, TITLE:      Experience Report: Smuggling a Little Bit of Coq Inside a CAD Development Context (Extended Abstract)
http://arxiv.org/abs/2007.00695
AUTHORS:        Dimitur Nikolaev Krustev
COMMENTS:       Submitted to Coq Workshop 2020
HIGHLIGHT:      Experience Report: Smuggling a Little Bit of Coq Inside a CAD Development Context (Extended Abstract)

93, TITLE:      Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds
http://arxiv.org/abs/2007.00886
AUTHORS:        Qinbing Fu ; Shigang Yue
COMMENTS:       27 pages, 13 figures, been included in a future issue of the journal of Biological Cybernetics
HIGHLIGHT:      The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response.

94, TITLE:      MSA-MIL: A deep residual multiple instance learning model based on multi-scale annotation for classification and visualization of glomerular spikes
http://arxiv.org/abs/2007.00858
AUTHORS:        Yilin Chen ; Ming Li ; Yongfei Wu ; Xueyu Liu ; Fang Hao ; Daoxiang Zhou ; Xiaoshuang Zhou ; Chen Wang
HIGHLIGHT:      In this paper, we establish a visualized classification model based on the multi-scale annotation multi-instance learning (MSA-MIL) to achieve glomerular classification and spikes visualization.



==========Updates to Previous Papers==========
1, TITLE:       Extending Stan for Deep Probabilistic Programming
http://arxiv.org/abs/1810.00873
AUTHORS:        Javier Burroni ; Guillaume Baudart ; Louis Mandel ; Martin Hirzel ; Avraham Shinnar
HIGHLIGHT:      This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness.

2, TITLE:       Unique Geometry and Texture from Corresponding Image Patches
http://arxiv.org/abs/2003.08885
AUTHORS:        Dor Verbin ; Steven J. Gortler ; Todd Zickler
HIGHLIGHT:      We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process.

3, TITLE:       Analyzing the Interpretability Robustness of Self-Explaining Models
http://arxiv.org/abs/1905.12429
AUTHORS:        Haizhong Zheng ; Earlence Fernandes ; Atul Prakash
HIGHLIGHT:      Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.

4, TITLE:       DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance
http://arxiv.org/abs/2004.07532
AUTHORS:        Ruben Tolosana ; Sergio Romero-Tapiador ; Julian Fierrez ; Ruben Vera-Rodriguez
HIGHLIGHT:      This study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance.

5, TITLE:       Meta-Embeddings Based On Self-Attention
http://arxiv.org/abs/2003.01371
AUTHORS:        Qichen Li ; Yuanqing Lin ; Luofeng Zhou ; Jian Li
HIGHLIGHT:      In this paper, we devise a new meta-embedding model based on the self-attention mechanism, namely the Duo.

6, TITLE:       Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
http://arxiv.org/abs/1912.11936
AUTHORS:        Yen-Chia Hsu ; Jennifer Cross ; Paul Dille ; Michael Tasota ; Beatrice Dias ; Randy Sargent ; Ting-Hao 'Kenneth' Huang ; Illah Nourbakhsh
COMMENTS:       Accepted by ACM Transactions on Interactive Intelligent Systems on 2020. This is an extended version of the arXiv:1810.11143, which was accepted by the ACM IUI 2019 conference. arXiv admin note: substantial text overlap with arXiv:1810.11143
HIGHLIGHT:      Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated.

7, TITLE:       An Abstraction-guided Approach to Scalable and Rigorous Floating-Point Error Analysis
http://arxiv.org/abs/2004.11960
AUTHORS:        Arnab Das ; Ian Briggs ; Ganesh Gopalakrishnan ; Pavel Panchekha ; Sriram Krishnamoorthy
COMMENTS:       A more informative and updated version of this paper has been accepted for publication at SuperComputing 2020
HIGHLIGHT:      In this work, we present Satire, a new tool that sheds light on how scalability and bound-tightness can be attained through a combination of incremental analysis, abstraction, and judicious use of concrete and symbolic evaluation.

8, TITLE:       Digital Social Contracts: A Foundation for an Egalitarian and Just Digital Society
http://arxiv.org/abs/2005.06261
AUTHORS:        Luca Cardelli ; Liav Orgad ; Gal Shahaf ; Ehud Shapiro ; Nimrod Talmon
HIGHLIGHT:      Here, we present a formal definition of a digital social contract as agents that communicate asynchronously via crypto-speech acts, where the output of each agent is the input of all the other agents.

9, TITLE:       Learning to Read through Machine Teaching
http://arxiv.org/abs/2006.16470
AUTHORS:        Ayon Sen ; Christopher R. Cox ; Matthew Cooper Borkenhagen ; Mark S. Seidenberg ; Xiaojin Zhu
HIGHLIGHT:      We used a cognitively interesting neural network architecture to examine whether the sequence of learning trials could be structured to facilitate learning.

10, TITLE:      Liquid Resource Types
http://arxiv.org/abs/2006.16233
AUTHORS:        Tristan Knoth ; Di Wang ; Adam Reynolds ; Jan Hoffmann ; Nadia Polikarpova
HIGHLIGHT:      This article presents liquid resource types, a technique for automatically verifying the resource consumption of functional programs.

11, TITLE:      Deep Involutive Generative Models for Neural MCMC
http://arxiv.org/abs/2006.15167
AUTHORS:        Span Spanbauer ; Cameron Freer ; Vikash Mansinghka
COMMENTS:       13 pages, 6 figures. Revised discussion of the Jacobian determinant factor in the acceptance ratio
HIGHLIGHT:      We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.

12, TITLE:      Augmenting Visual Place Recognition with Structural Cues
http://arxiv.org/abs/2003.00278
AUTHORS:        Amadeus Oertel ; Titus Cieslewski ; Davide Scaramuzza
COMMENTS:       8 pages, accepted for publication in RA-L & IROS 2020
HIGHLIGHT:      In this paper, we propose to augment image-based place recognition with structural cues.

13, TITLE:      Curriculum By Smoothing
http://arxiv.org/abs/2003.01367
AUTHORS:        Samarth Sinha ; Animesh Garg ; Hugo Larochelle
HIGHLIGHT:      In this paper, we propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters.

14, TITLE:      Secure and Scalable Data Classification
http://arxiv.org/abs/2006.14109
AUTHORS:        Paulo Tanaka ; Sameet Sapra ; Nikolay Laptev
HIGHLIGHT:      Secure and Scalable Data Classification

15, TITLE:      Drug discovery with explainable artificial intelligence
http://arxiv.org/abs/2007.00523
AUTHORS:        José Jiménez-Luna ; Francesca Grisoni ; Gisbert Schneider
HIGHLIGHT:      This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.

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

17, TITLE:      Towards in-store multi-person tracking using head detection and track heatmaps
http://arxiv.org/abs/2005.08009
AUTHORS:        Aibek Musaev ; Jiangping Wang ; Liang Zhu ; Cheng Li ; Yi Chen ; Jialin Liu ; Wanqi Zhang ; Juan Mei ; De Wang
HIGHLIGHT:      In this paper, we study the problem of computer vision based customer tracking in retail industry. To this end, we introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket.

18, TITLE:      A Polynomial Neural Network with Controllable Precision and Human-Readable Topology for Prediction and System Identification
http://arxiv.org/abs/2004.03955
AUTHORS:        Gang Liu ; Jing Wang
COMMENTS:       Add initialization notes
HIGHLIGHT:      This paper presents a controllable and readable polynomial neural network (CR-PNN) for approximation, prediction, and system identification.

19, TITLE:      LiDAR Iris for Loop-Closure Detection
http://arxiv.org/abs/1912.03825
AUTHORS:        Ying Wang ; Zezhou Sun ; Cheng-Zhong Xu ; Sanjay Sarma ; Jian Yang ; Hui Kong
COMMENTS:       IROS 2020
HIGHLIGHT:      In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection.

20, TITLE:      ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion
http://arxiv.org/abs/1910.00969
AUTHORS:        Andreas Hinterreiter ; Peter Ruch ; Holger Stitz ; Martin Ennemoser ; Jürgen Bernard ; Hendrik Strobelt ; Marc Streit
COMMENTS:       Changes compared to previous version: Reintroduced NN pruning use case; restructured Evaluation section; several additional minor revisions. Submitted as Minor Revision to IEEE TVCG on 2020-07-02
HIGHLIGHT:      To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time.

21, TITLE:      A Framework for Reinforcement Learning and Planning
http://arxiv.org/abs/2006.15009
AUTHORS:        Thomas M. Moerland ; Joost Broekens ; Catholijn M. Jonker
HIGHLIGHT:      Therefore, this paper presents a unifying framework for reinforcement learning and planning (FRAP), which identifies the underlying dimensions on which any planning or learning algorithm has to decide.

22, TITLE:      Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
http://arxiv.org/abs/2003.00380
AUTHORS:        Jieshan Chen ; Chunyang Chen ; Zhenchang Xing ; Xiwei Xu ; Liming Zhu ; Guoqiang Li ; Jinshui Wang
COMMENTS:       Accepted to 42nd International Conference on Software Engineering
HIGHLIGHT:      To overcome these challenges, we develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play.

23, TITLE:      SAR2SAR: a self-supervised despeckling algorithm for SAR images
http://arxiv.org/abs/2006.15037
AUTHORS:        Emanuele Dalsasso ; Loïc Denis ; Florence Tupin
COMMENTS:       Article submitted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Code is made available at https://github.com/emanueledalsasso/SAR2SAR
HIGHLIGHT:      Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR.

24, TITLE:      Integrating Multimodal Information in Large Pretrained Transformers
http://arxiv.org/abs/1908.05787
AUTHORS:        Wasifur Rahman ; Md. Kamrul Hasan ; Sangwu Lee ; Amir Zadeh ; Chengfeng Mao ; Louis-Philippe Morency ; Ehsan Hoque
HIGHLIGHT:      In this paper, we proposed an attachment to BERT and XLNet called Multimodal Adaptation Gate (MAG).

25, TITLE:      Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models
http://arxiv.org/abs/2006.15994
AUTHORS:        Viet Bui The ; Oanh Tran Thi ; Phuong Le-Hong
HIGHLIGHT:      This paper describes our study on using mutilingual BERT embeddings and some new neural models for improving sequence tagging tasks for the Vietnamese language.

26, TITLE:      SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
http://arxiv.org/abs/2006.15559
AUTHORS:        Emanuele Dalsasso ; Xiangli Yang ; Loïc Denis ; Florence Tupin ; Wen Yang
COMMENTS:       Article submitted to Remote Sensing, MDPI. Notebook with Colab compatibility is available at https://github.com/emanueledalsasso/SAR-CNN
HIGHLIGHT:      To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data.

27, TITLE:      Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice
http://arxiv.org/abs/1811.04786
AUTHORS:        Brandon Fain ; Ashish Goel ; Kamesh Munagala ; Nina Prabhu
COMMENTS:       Conference version Published in AAAI 2019 (https://aaai.org/Conferences/AAAI-19/)
HIGHLIGHT:      Our primary contribution is the first social choice mechanism with constant sample complexity \textit{and} constant Squared Distortion (which also implies constant Distortion).

28, TITLE:      A Survey on Bayesian Deep Learning
http://arxiv.org/abs/1604.01662
AUTHORS:        Hao Wang ; Dit-Yan Yeung
COMMENTS:       To appear in ACM Computing Surveys (CSUR) 2020
HIGHLIGHT:      This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, etc.

29, TITLE:      Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
http://arxiv.org/abs/1909.09741
AUTHORS:        Ricardo Augusto Borsoi ; Tales Imbiriba ; José Carlos Moreira Bermudez ; Cédric Richard
HIGHLIGHT:      In this paper, we propose a library augmentation strategy to increase the diversity of existing spectral libraries, thus improving their ability to represent the materials in real images.

30, TITLE:      Mitosis Detection Under Limited Annotation: A Joint Learning Approach
http://arxiv.org/abs/2006.09772
AUTHORS:        Pushpak Pati ; Antonio Foncubierta-Rodriguez ; Orcun Goksel ; Maria Gabrani
COMMENTS:       2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
HIGHLIGHT:      We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning.

31, TITLE:      Fundamentals of Computing
http://arxiv.org/abs/2005.06436
AUTHORS:        Leonid A. Levin
COMMENTS:       21 page. minor changes
HIGHLIGHT:      The notes can be used by an instructor designing a course or by students who either know the material and want to refresh the memory or are exceptionally bright and have access to an instructor for questions.

32, TITLE:      Revisiting Few-sample BERT Fine-tuning
http://arxiv.org/abs/2006.05987
AUTHORS:        Tianyi Zhang ; Felix Wu ; Arzoo Katiyar ; Kilian Q. Weinberger ; Yoav Artzi
COMMENTS:       Code available at https://github.com/asappresearch/revisit-bert-finetuning
HIGHLIGHT:      We study the problem of few-sample fine-tuning of BERT contextual representations, and identify three sub-optimal choices in current, broadly adopted practices.

33, TITLE:      Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset
http://arxiv.org/abs/2006.16670
AUTHORS:        Kutsev Bengisu Ozyoruk ; Kagan Incetan ; Gulfize Coskun ; Guliz Irem Gokceler ; Yasin Almalioglu ; Faisal Mahmood ; Nicholas J. Durr ; Eva Curto ; Luis Perdigoto ; Marina Oliveira ; Helder Araujo ; Henrique Alexandrino ; Hunter B. Gilbert ; Mehmet Turan
COMMENTS:       27 pages, 16 figures
HIGHLIGHT:      With this paper, we introduce a comprehensive endoscopic SLAM dataset containing both capsule and standard endoscopy recordings.

34, TITLE:      Is Robustness To Transformations Driven by Invariant Neural Representations?
http://arxiv.org/abs/2007.00112
AUTHORS:        Syed Suleman Abbas Zaidi ; Xavier Boix ; Neeraj Prasad ; Sharon Gilad-Gutnick ; Shlomit Ben-Ami ; Pawan Sinha
HIGHLIGHT:      In this paper, we analyze the conditions under which invariance emerges.

35, TITLE:      Adversarial Neural Pruning with Latent Vulnerability Suppression
http://arxiv.org/abs/1908.04355
AUTHORS:        Divyam Madaan ; Jinwoo Shin ; Sung Ju Hwang
COMMENTS:       Accepted to ICML 2020. Code available at https://github.com/divyam3897/ANP_VS
HIGHLIGHT:      In this paper, we conjecture that the leading cause of adversarial vulnerability is the distortion in the latent feature space, and provide methods to suppress them effectively.

36, TITLE:      FathomNet: An underwater image training database for ocean exploration and discovery
http://arxiv.org/abs/2007.00114
AUTHORS:        Océane Boulais ; Ben Woodward ; Brian Schlining ; Lonny Lundsten ; Kevin Barnard ; Katy Croff Bell ; Kakani Katija
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      FathomNet: An underwater image training database for ocean exploration and discovery

37, TITLE:      Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay
http://arxiv.org/abs/2006.08419
AUTHORS:        Ruosi Wan ; Zhanxing Zhu ; Xiangyu Zhang ; Jian Sun
HIGHLIGHT:      We comprehensively reveal the learning dynamics of deep neural networks (DNN) with batch normalization (BN) and weight decay (WD), named as Spherical Motion Dynamics (SMD).

38, TITLE:      Medical idioms for clinical Bayesian network development
http://arxiv.org/abs/2007.00364
AUTHORS:        Evangelia Kyrimi ; Mariana Raniere Neves ; Scott McLachlan ; Martin Neil ; William Marsh ; Norman Fenton
HIGHLIGHT:      This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs.

39, TITLE:      Synthesizing Tasks for Block-based Programming
http://arxiv.org/abs/2006.16913
AUTHORS:        Umair Z. Ahmed ; Maria Christakis ; Aleksandr Efremov ; Nigel Fernandez ; Ahana Ghosh ; Abhik Roychoudhury ; Adish Singla
COMMENTS:       longer version
HIGHLIGHT:      In this paper, we formalize the problem of synthesizing visual programming tasks.

40, TITLE:      Wavesplit: End-to-End Speech Separation by Speaker Clustering
http://arxiv.org/abs/2002.08933
AUTHORS:        Neil Zeghidour ; David Grangier
HIGHLIGHT:      We introduce Wavesplit, an end-to-end source separation system.

41, TITLE:      Bayesian Hierarchical Mixture Clustering using Multilevel Hierarchical Dirichlet Processes
http://arxiv.org/abs/1905.05022
AUTHORS:        Weipeng Huang ; Nishma Laitonjam ; Guangyuan Piao ; Neil Hurley
HIGHLIGHT:      We develop a novel Bayesian nonparametric method combining the nested Chinese Restaurant Process (nCRP) and the Hierarchical Dirichlet Process (HDP).

42, TITLE:      A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
http://arxiv.org/abs/2006.04045
AUTHORS:        Risheng Liu ; Pan Mu ; Xiaoming Yuan ; Shangzhi Zeng ; Jin Zhang
COMMENTS:       Accepted at ICML 2020
HIGHLIGHT:      Theoretically, we derive a new methodology to prove the convergence of BDA without the LLS condition.

43, TITLE:      Better Document-Level Machine Translation with Bayes' Rule
http://arxiv.org/abs/1910.00553
AUTHORS:        Lei Yu ; Laurent Sartran ; Wojciech Stokowiec ; Wang Ling ; Lingpeng Kong ; Phil Blunsom ; Chris Dyer
COMMENTS:       Accepted by TACL
HIGHLIGHT:      We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available.

44, TITLE:      Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network
http://arxiv.org/abs/1901.00361
AUTHORS:        Bowen Lin ; Shujun Fu ; Caiming Zhang ; Fengling Wang ; Yuliang Li
HIGHLIGHT:      To deal with this problem, we propose a filtering method based on deep learning, called optical fringe patterns denoising convolutional neural network (FPD-CNN), for directly removing speckle from the input noisy fringe patterns.

45, TITLE:      Predicting Temporal Sets with Deep Neural Networks
http://arxiv.org/abs/2006.11483
AUTHORS:        Le Yu ; Leilei Sun ; Bowen Du ; Chuanren Liu ; Hui Xiong ; Weifeng Lv
COMMENTS:       9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '2020)
HIGHLIGHT:      In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction.

46, TITLE:      Deep Mixture Density Network for Probabilistic Object Detection
http://arxiv.org/abs/1911.10614
AUTHORS:        Yihui He ; Jianren Wang
COMMENTS:       IROS 2020 oral
HIGHLIGHT:      In this paper, we measure the uncertainties of object localization to minimize this kind of risk.

47, TITLE:      Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
http://arxiv.org/abs/1909.13231
AUTHORS:        Yu Sun ; Xiaolong Wang ; Zhuang Liu ; John Miller ; Alexei A. Efros ; Moritz Hardt
COMMENTS:       ICML 2020
HIGHLIGHT:      In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

48, TITLE:      Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
http://arxiv.org/abs/2003.08441
AUTHORS:        Yingda Xia ; Qihang Yu ; Wei Shen ; Yuyin Zhou ; Elliot K. Fishman ; Alan L. Yuille
COMMENTS:       The first two authors contributed equally to this work. Accepted to MICCAI 2020
HIGHLIGHT:      In this paper, we investigate the problem of automated detecting PDACs in multi-phase (arterial and venous) CT scans.

49, TITLE:      Distributional Discrepancy: A Metric for Unconditional Text Generation
http://arxiv.org/abs/2005.01282
AUTHORS:        Ping Cai ; Xingyuan Chen ; Peng Jin ; Hongjun Wang ; Tianrui Li
HIGHLIGHT:      Thus, we propose a method for estimating the DD by training a neural-network-based text classifier.

50, TITLE:      Unifying Model Explainability and Robustness via Machine-Checkable Concepts
http://arxiv.org/abs/2007.00251
AUTHORS:        Vedant Nanda ; Till Speicher ; John P. Dickerson ; Krishna P. Gummadi ; Muhammad Bilal Zafar
COMMENTS:       22 pages, 12 figures, 11 tables
HIGHLIGHT:      In this paper, we propose a robustness-assessment framework, at the core of which is the idea of using machine-checkable concepts.

51, TITLE:      On Affine Reachability Problems
http://arxiv.org/abs/1905.05114
AUTHORS:        Stefan Jaax ; Stefan Kiefer
HIGHLIGHT:      We analyze affine reachability problems in dimensions 1 and 2.

52, TITLE:      Object Goal Navigation using Goal-Oriented Semantic Exploration
http://arxiv.org/abs/2007.00643
AUTHORS:        Devendra Singh Chaplot ; Dhiraj Gandhi ; Abhinav Gupta ; Ruslan Salakhutdinov
COMMENTS:       Winner of the CVPR 2020 AI-Habitat Object Goal Navigation Challenge. See the project webpage at https://devendrachaplot.github.io/projects/semantic-exploration.html
HIGHLIGHT:      We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.

53, TITLE:      Toward Standardized Classification of Foveated Displays
http://arxiv.org/abs/1905.06229
AUTHORS:        Josef Spjut ; Ben Boudaoud ; Jonghyun Kim ; Trey Greer ; Rachel Albert ; Michael Stengel ; Kaan Aksit ; David Luebke
COMMENTS:       9 pages, 8 figures, presented at IEEE VR 2020
HIGHLIGHT:      Toward Standardized Classification of Foveated Displays

54, TITLE:      Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
http://arxiv.org/abs/1807.07279
AUTHORS:        Lutfi Kerem Senel ; Ihsan Utlu ; Furkan Şahinuç ; Haldun M. Ozaktas ; Aykut Koç
COMMENTS:       14 pages, 5 figures
HIGHLIGHT:      The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget's Thesaurus.

55, TITLE:      Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling
http://arxiv.org/abs/1910.08242
AUTHORS:        Risheng Liu ; Pan Mu ; Jian Chen ; Xin Fan ; Zhongxuan Luo
COMMENTS:       11 pages, Accepted at IEEE TIP
HIGHLIGHT:      In this work, we provide a new perspective, named Task-driven Latent Feasibility (TLF), to incorporate specific task information to narrow down the solution space for the optimization-based image modeling problem.

56, TITLE:      On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach
http://arxiv.org/abs/2003.06492
AUTHORS:        Renyan Feng ; Erman Acar ; Stefan Schlobach ; Yisong Wang ; Wanwei Liu
HIGHLIGHT:      On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach

57, TITLE:      Default Disambiguation for Online Parsers
http://arxiv.org/abs/1909.08557
AUTHORS:        Lukas Diekmann ; Laurence Tratt
COMMENTS:       14 pages, 6 tables, 8 figures. Note: This reverts this paper back to v1 (which was accidentally replaced with a different paper)
HIGHLIGHT:      In this paper, we show that default disambiguation, which is inappropriate for batch parsing, works well for online parsing, where it can be overridden by the user if necessary.

58, TITLE:      Trajectory Poisson multi-Bernoulli filters
http://arxiv.org/abs/2003.12767
AUTHORS:        Ángel F. García-Fernández ; Lennart Svensson ; Jason L. Williams ; Yuxuan Xia ; Karl Granström
HIGHLIGHT:      This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step.

59, TITLE:      Efficient Adversarial Training with Transferable Adversarial Examples
http://arxiv.org/abs/1912.11969
AUTHORS:        Haizhong Zheng ; Ziqi Zhang ; Juncheng Gu ; Honglak Lee ; Atul Prakash
COMMENTS:       Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020 (CVPR 2020)
HIGHLIGHT:      Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs.