2020.06.05.txt

==========New Papers==========
1, TITLE:       Height estimation from single aerial images using a deep ordinal regression network
http://arxiv.org/abs/2006.02801
AUTHORS:        Xiang Li ; Mingyang Wang ; Yi Fang
COMMENTS:       5 pages, 3 figures
HIGHLIGHT:      In this paper, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image.

2, TITLE:       A Computational Model of Early Word Learning from the Infant's Point of View
http://arxiv.org/abs/2006.02802
AUTHORS:        Satoshi Tsutsui ; Arjun Chandrasekaran ; Md Alimoor Reza ; David Crandall ; Chen Yu
COMMENTS:       Accepted by Annual Conference of the Cognitive Science Society (CogSci) 2020. (Oral Acceptance Rate = 177/811 = 22%)
HIGHLIGHT:      As the first model that takes raw egocentric video to simulate infant word learning, the present study provides a proof of principle that the problem of early word learning can be solved, using actual visual data perceived by infant learners.

3, TITLE:       Experiments on Paraphrase Identification Using Quora Question Pairs Dataset
http://arxiv.org/abs/2006.02648
AUTHORS:        Andreas Chandra ; Ruben Stefanus
HIGHLIGHT:      We tried several methods and algorithms and different approach from previous works.

4, TITLE:       Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
http://arxiv.org/abs/2006.02642
AUTHORS:        Jinseok Kim ; Kyungsu Kim ; Jae-Joon Kim
HIGHLIGHT:      In this work, we present a comparative study of the two methods and propose a new supervised learning method that combines them.

5, TITLE:       Using Self-Training to Improve Back-Translation in Low Resource Neural Machine Translation
http://arxiv.org/abs/2006.02876
AUTHORS:        Idris Abdulmumin ; Bashir Shehu Galadanci ; Abubakar Isa
COMMENTS:       8 pages, 5 figures, 4 tables
HIGHLIGHT:      This work proposes a self-training strategy where the output of the backward model is used to improve the model itself through the forward translation technique.

6, TITLE:       M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
http://arxiv.org/abs/2006.02635
AUTHORS:        Haoyang Huang ; Lin Su ; Di Qi ; Nan Duan ; Edward Cui ; Taroon Bharti ; Lei Zhang ; Lijuan Wang ; Jianfeng Gao ; Bei Liu ; Jianlong Fu ; Dongdong Zhang ; Xin Liu ; Ming Zhou
COMMENTS:       10 pages,2 figures
HIGHLIGHT:      This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing. We also build a new Multilingual Image-Language Dataset (MILD) by collecting large amounts of (text-query, image, context) triplets in 8 languages from the logs of a commercial search engine

7, TITLE:       The Importance of Prior Knowledge in Precise Multimodal Prediction
http://arxiv.org/abs/2006.02636
AUTHORS:        Sergio Casas ; Cole Gulino ; Simon Suo ; Raquel Urtasun
HIGHLIGHT:      In this paper we propose to incorporate these structured priors as a loss function.

8, TITLE:       FastReID: A Pytorch Toolbox for Real-world Person Re-identification
http://arxiv.org/abs/2006.02631
AUTHORS:        Lingxiao He ; Xingyu Liao ; Wu Liu ; Xinchen Liu ; Peng Cheng ; Tao Mei
HIGHLIGHT:      We present FastReID, as a widely used object re-identification (re-id) software system in JD AI Research.

9, TITLE:       Stopwords in Technical Language Processing
http://arxiv.org/abs/2006.02633
AUTHORS:        Serhad Sarica ; Jianxi Luo
HIGHLIGHT:      Here we address this gap by rigorously identifying generic, insignificant, uninformative stopwords in engineering texts beyond the stopwords in general texts, based on the synthesis of alternative data-driven approaches, and curating a stopword list ready for technical language processing applications.

10, TITLE:      Image Completion and Extrapolation with Contextual Cycle Consistency
http://arxiv.org/abs/2006.02620
AUTHORS:        Sai Hemanth Kasaraneni ; Abhishek Mishra
COMMENTS:       This paper has been accepted to 2020 IEEE International Conference on Image Processing (ICIP 2020)
HIGHLIGHT:      In this paper, we present a technique to train both completion and extrapolation networks concurrently while benefiting each other.

11, TITLE:      Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet
http://arxiv.org/abs/2006.02627
AUTHORS:        Sara Ranjbar ; Kyle W. Singleton ; Lee Curtin ; Cassandra R. Rickertsen ; Lisa E. Paulson ; Leland S. Hu ; J. Ross Mitchell ; Kristin R. Swanson
HIGHLIGHT:      In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with gadolinium contrast (T1Gd) and T2-weighted fluid attenuated inversion recovery (FLAIR) in patients with brain tumors.

12, TITLE:      Decomposition in Decision and Objective Space for Multi-Modal Multi-Objective Optimization
http://arxiv.org/abs/2006.02628
AUTHORS:        Monalisa Pal ; Sanghamitra Bandyopadhyay
COMMENTS:       29 pages, 5 figures, 5 tables, 1 supplementary document (22 pages, 1 figure, 13 tables)
HIGHLIGHT:      Subsequently, an evolutionary framework, called graph Laplacian based Optimization using Reference vector assisted Decomposition (LORD), is proposed, which is the first algorithm to use decomposition in both objective and decision space for dealing with MMMOPs.

13, TITLE:      Analogical Proportions
http://arxiv.org/abs/2006.02854
AUTHORS:        Christian Antić
HIGHLIGHT:      This paper contributes to the foundations of artificial general intelligence by introducing an abstract algebraic framework of analogical proportions of the form `$a$ is to $b$ what $c$ is to $d$' in the general setting of universal algebra.

14, TITLE:      Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments
http://arxiv.org/abs/2006.02610
AUTHORS:        Balagopal Unnikrishnan ; Pranshu Ranjan Singh ; Xulei Yang ; Matthew Chin Heng Chua
HIGHLIGHT:      In this study, we conduct a comprehensive study of heart sounds classification by using various supervised, semi-supervised and unsupervised approaches on the PhysioNet/CinC 2016 Challenge dataset.

15, TITLE:      Simple Unsupervised Multi-Object Tracking
http://arxiv.org/abs/2006.02609
AUTHORS:        Shyamgopal Karthik ; Ameya Prabhu ; Vineet Gandhi
HIGHLIGHT:      In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training.

16, TITLE:      Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach
http://arxiv.org/abs/2006.02834
AUTHORS:        Debayan Deb ; Anil K. Jain
HIGHLIGHT:      Given that face anti-spoofing is inherently a local task, we propose a face anti-spoofing framework, namely Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner.

17, TITLE:      Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map
http://arxiv.org/abs/2006.02824
AUTHORS:        Andrei Velichko
COMMENTS:       4 pages, 6 figures, 2 tables
HIGHLIGHT:      The study presents a neural network, which uses filters based on logistic mapping (LogNNet).

18, TITLE:      Pathological myopia classification with simultaneous lesion segmentation using deep learning
http://arxiv.org/abs/2006.02813
AUTHORS:        Ruben Hemelings ; Bart Elen ; Matthew B. Blaschko ; Julie Jacob ; Ingeborg Stalmans ; Patrick De Boever
COMMENTS:       18 pages, 2 figures, preprint to journal
HIGHLIGHT:      We propose a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea.

19, TITLE:      Refined Continuous Control of DDPG Actors via Parametrised Activation
http://arxiv.org/abs/2006.02818
AUTHORS:        Mohammed Hossny ; Julie Iskander ; Mohammed Attia ; Khaled Saleh
COMMENTS:       9 pages, 7 figures, 2 tables, submitted to Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by- nc-nd/4.0/
HIGHLIGHT:      In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load conditions during interaction with the environment.

20, TITLE:      CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning
http://arxiv.org/abs/2006.02814
AUTHORS:        Sameer Khurana ; Antoine Laurent ; James Glass
COMMENTS:       submitted to INTERSPEECH
HIGHLIGHT:      In this work, we provide a multimodal machine learning framework for speech representation learning by exploiting the correlations between the two modalities namely speech and its corresponding text translation.

21, 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:      This paper introduces a context-aware IoU-guided tracker that exploits an offline reference proposal generation strategy and a multitask two-stream network.

22, TITLE:      Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
http://arxiv.org/abs/2006.02598
AUTHORS:        Aditya Sanghi
HIGHLIGHT:      To solve these issues, we propose to extend the InfoMax and contrastive learning principles on 3D shapes.

23, TITLE:      Image Augmentations for GAN Training
http://arxiv.org/abs/2006.02595
AUTHORS:        Zhengli Zhao ; Zizhao Zhang ; Ting Chen ; Sameer Singh ; Han Zhang
HIGHLIGHT:      In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings.

24, TITLE:      An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP
http://arxiv.org/abs/2006.02591
AUTHORS:        Tae Jong Choi ; Chang Wook Ahn
HIGHLIGHT:      A new method for improving the optimization performance of a state-of-the-art differential evolution (DE) variant is proposed in this paper.

25, TITLE:      Meta Dialogue Policy Learning
http://arxiv.org/abs/2006.02588
AUTHORS:        Yumo Xu ; Chenguang Zhu ; Baolin Peng ; Michael Zeng
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains, such as dialogue acts and slots.

26, TITLE:      Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains
http://arxiv.org/abs/2006.02579
AUTHORS:        James Bannon ; Brad Windsor ; Wenbo Song ; Tao Li
HIGHLIGHT:      In this project we show how off-policy evaluation and the estimation of treatment effects in causal inference are two approaches to the same problem, and compare recent progress in these two areas.

27, TITLE:      An optimizable scalar objective value cannot be objective and should not be the sole objective
http://arxiv.org/abs/2006.02577
AUTHORS:        Isabel Kloumann ; Mark Tygert
COMMENTS:       13 pages
HIGHLIGHT:      This paper concerns the ethics and morality of algorithms and computational systems, and has been circulating internally at Facebook for the past couple years.

28, TITLE:      DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions
http://arxiv.org/abs/2006.02578
AUTHORS:        Sabbir Ahmed ; Uday Kamal ; Md. Kamrul Hasan
HIGHLIGHT:      In this paper, we look at the TSDR problem under CCs and focus on the performance degradation associated with them.

29, TITLE:      Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
http://arxiv.org/abs/2006.02570
AUTHORS:        Soumick Chatterjee ; Fatima Saad ; Chompunuch Sarasaen ; Suhita Ghosh ; Rupali Khatun ; Petia Radeva ; Georg Rose ; Sebastian Stober ; Oliver Speck ; Andreas Nürnberger
HIGHLIGHT:      Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoni{\ae} and healthy subjects using Chest X-Ray.

30, TITLE:      Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning
http://arxiv.org/abs/2006.02569
AUTHORS:        Yukun Guo ; Tristan T. Hormel ; Honglian Xiong ; Jie Wang ; Thomas S. Hwang ; Yali Jia
HIGHLIGHT:      Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume.

31, TITLE:      Extracting COVID-19 Events from Twitter
http://arxiv.org/abs/2006.02567
AUTHORS:        Shi Zong ; Ashutosh Baheti ; Wei Xu ; Alan Ritter
HIGHLIGHT:      We present a corpus of 7,500 tweets annotated with COVID-19 events, including positive test results, denied access to testing, and more.

32, TITLE:      Overcoming Overfitting and Large Weight Update Problem in Linear Rectifiers: Thresholded Exponential Rectified Linear Units
http://arxiv.org/abs/2006.02797
AUTHORS:        Vijay Pandey
HIGHLIGHT:      In this paper, we propose, "Thresholded exponential rectified linear unit" (TERELU) activation function that works better in alleviating in overfitting: large weight update problem.

33, TITLE:      A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
http://arxiv.org/abs/2006.02547
AUTHORS:        Sameer Khurana ; Antoine Laurent ; Wei-Ning Hsu ; Jan Chorowski ; Adrian Lancucki ; Ricard Marxer ; James Glass
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this work, we propose Convolutional Deep Markov Model (ConvDMM), a Gaussian state-space model with non-linear emission and transition functions modelled by deep neural networks.

34, TITLE:      Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
http://arxiv.org/abs/2006.02786
AUTHORS:        Thilo von Neumann ; Christoph Boeddeker ; Lukas Drude ; Keisuke Kinoshita ; Marc Delcroix ; Tomohiro Nakatani ; Reinhold Haeb-Umbach
COMMENTS:       5 pages, submitted to INTERSPEECH 2020
HIGHLIGHT:      Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown.

35, TITLE:      Phasic dopamine release identification using ensemble of AlexNet
http://arxiv.org/abs/2006.02536
AUTHORS:        Luca Patarnello ; Marco Celin ; Loris Nanni
HIGHLIGHT:      In this paper, we present the use of convolutional neural networks (CNNs) for the identification of phasic dopamine releases.

36, TITLE:      The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
http://arxiv.org/abs/2006.03039
AUTHORS:        Annemarie Friedrich ; Heike Adel ; Federico Tomazic ; Johannes Hingerl ; Renou Benteau ; Anika Maruscyk ; Lukas Lange
COMMENTS:       Accepted for publication at ACL 2020
HIGHLIGHT:      This paper presents a new challenging information extraction task in the domain of materials science.

37, TITLE:      Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
http://arxiv.org/abs/2006.03031
AUTHORS:        Haichen Shen ; Jared Roesch ; Zhi Chen ; Wei Chen ; Yong Wu ; Mu Li ; Vin Sharma ; Zachary Tatlock ; Yida Wang
HIGHLIGHT:      This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms.

38, TITLE:      Visually Guided Sound Source Separation using Cascaded Opponent Filter Network
http://arxiv.org/abs/2006.03028
AUTHORS:        Lingyu Zhu ; Esa Rahtu
COMMENTS:       main paper 14 pages, ref 3 pages, and supp 6 pages
HIGHLIGHT:      The objective of this paper is to recover the original component signals from a mixture audio with the aid of visual cues of the sound sources.

39, TITLE:      Response to LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
http://arxiv.org/abs/2006.03022
AUTHORS:        Hao Wu ; Gareth J. F. Jones ; Francois Pitie
COMMENTS:       4 pages, 2 figures
HIGHLIGHT:      In this paper, we study these discrepancies in detail and propose an alternative baseline implementation as a reference for other researchers in this field.

40, TITLE:      Syntactic Search by Example
http://arxiv.org/abs/2006.03010
AUTHORS:        Micah Shlain ; Hillel Taub-Tabib ; Shoval Sadde ; Yoav Goldberg
HIGHLIGHT:      We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs.

41, TITLE:      A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition
http://arxiv.org/abs/2006.03001
AUTHORS:        Kexin Feng ; Theodora Chaspari
COMMENTS:       AffCon@AAAI-20; Presented at AAAI-20 W1: Affective Content Analysis
HIGHLIGHT:      In this paper, we propose a distance loss, which can be applied on the Siamese network fine-tuning, by optimizing the model based on the relevant distance between same and difference class pairs.

42, TITLE:      Differentiable Linear Bandit Algorithm
http://arxiv.org/abs/2006.03000
AUTHORS:        Kaige Yang ; Laura Toni
COMMENTS:       16 pages
HIGHLIGHT:      In this work, we aim at learning the confidence bound in a data-driven fashion, making it adaptive to the actual problem structure.

43, TITLE:      Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics
http://arxiv.org/abs/2006.03002
AUTHORS:        Guy Emerson
COMMENTS:       To be published in Proceedings of Probability and Meaning 2020
HIGHLIGHT:      In this paper, I show how the previous formulation gives trivial truth values when a precise quantifier is used with vague predicates.

44, TITLE:      A Polynomial Neural network with Controllable Precision and Human-Readable Topology II: Accelerated Approach Based on Expanded Layer
http://arxiv.org/abs/2006.02901
AUTHORS:        Gang Liu ; Jing Wang
COMMENTS:       some studies attempted to explain the existing NNs using Taylor series or polynomial.It is also troublesome. How about converting Taylor series to a network?
HIGHLIGHT:      Here, we presented an accelerated method based on an expanded order to optimize CR-PNN.

45, TITLE:      GAN-Based Facial Attractiveness Enhancement
http://arxiv.org/abs/2006.02766
AUTHORS:        Yuhongze Zhou ; Qinjie Xiao
HIGHLIGHT:      We propose a generative framework based on generative adversarial network (GAN) to enhance facial attractiveness while preserving facial identity and high-fidelity.

46, TITLE:      Seq2Seq AI Chatbot with Attention Mechanism
http://arxiv.org/abs/2006.02767
AUTHORS:        Abonia Sojasingarayar
COMMENTS:       18 pages 8 Figures 4 Tables 5 Equations
HIGHLIGHT:      Intelligent Conversational Agent development using Artificial Intelligence or Machine Learning technique is an interesting problem in the field of Natural Language Processing.

47, TITLE:      A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines
http://arxiv.org/abs/2006.02986
AUTHORS:        Callum Wilson ; Annalisa Riccardi ; Edmondo Minisci
COMMENTS:       Accepted for IJCNN/WCCI 2020
HIGHLIGHT:      Here we attempt to apply extreme learning machines to a reinforcement learning problem in the same manner as gradient based updates.

48, TITLE:      Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening
http://arxiv.org/abs/2006.02505
AUTHORS:        Christian F. Frasser ; Carola de Benito ; Vincent Canals ; Miquel Roca ; Pedro J. Ballester ; Josep L. Rossello
COMMENTS:       14 pages, 9 Figures, 3 Tables. Paper submitted to an IEEE journal
HIGHLIGHT:      In this work, we present a classification model describing each molecule with a single energy-based vector and propose a machine-learning system based on the use of ANNs.

49, TITLE:      Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1
http://arxiv.org/abs/2006.02964
AUTHORS:        Maria Nadejde ; Joel Tetreault
COMMENTS:       Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text
HIGHLIGHT:      We present the first results on adapting a general-purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences.

50, TITLE:      End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020
http://arxiv.org/abs/2006.02965
AUTHORS:        Marco Gaido ; Mattia Antonino Di Gangi ; Matteo Negri ; Marco Turchi
COMMENTS:       Accepted at IWSLT2020
HIGHLIGHT:      This paper describes FBK's participation in the IWSLT 2020 offline speech translation (ST) task.

51, TITLE:      RarePlanes: Synthetic Data Takes Flight
http://arxiv.org/abs/2006.02963
AUTHORS:        Jacob Shermeyer ; Thomas Hossler ; Adam Van Etten ; Daniel Hogan ; Ryan Lewis ; Daeil Kim
COMMENTS:       11 pages
HIGHLIGHT:      By doing so, we show the value of synthetic data for the task of detecting and classifying aircraft from an overhead perspective.

52, TITLE:      Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
http://arxiv.org/abs/2006.02713
AUTHORS:        Yixiao Ge ; Dapeng Chen ; Feng Zhu ; Rui Zhao ; Hongsheng Li
HIGHLIGHT:      To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory.

53, TITLE:      CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks
http://arxiv.org/abs/2006.02951
AUTHORS:        Gašper Beguš
HIGHLIGHT:      This paper proposes two neural network architectures for modeling unsupervised lexical learning from raw acoustic inputs, ciwGAN (Categorical InfoWaveGAN) and fiwGAN (Featural InfoWaveGAN), that combine a DCGAN architecture for audio data (WaveGAN; arXiv:1705.07904) with InfoGAN (arXiv:1606.03657), and propose a new latent space structure that can model featural learning simultaneously with a higher level classification.

54, TITLE:      Sparsity in Reservoir Computing Neural Networks
http://arxiv.org/abs/2006.02957
AUTHORS:        Claudio Gallicchio
COMMENTS:       This paper is currently under review
HIGHLIGHT:      In this paper, we empirically investigate the role of sparsity in RC network design under the perspective of the richness of the developed temporal representations.

55, TITLE:      Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game
http://arxiv.org/abs/2006.02716
AUTHORS:        Alexandr Grichshenko ; Luiz Jonata Pires de Araujo ; Susanna Gimaeva ; Joseph Alexander Brown
HIGHLIGHT:      Tabu Search (TS) metaheuristic improves simple local search algorithms (e.g. steepest ascend hill-climbing) by enabling the algorithm to escape local optima points.

56, TITLE:      Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue
http://arxiv.org/abs/2006.02708
AUTHORS:        Jia-Wang Bian ; Huangying Zhan ; Naiyan Wang ; Tat-Jun Chin ; Chunhua Shen ; Ian Reid
COMMENTS:       See codes, data, and demos in GitHub page (https://github.com/JiawangBian/Unsupervised-Indoor-Depth)
HIGHLIGHT:      In this work, we establish that the degenerate camera motions exhibited in handheld settings are a critical obstacle for unsupervised depth learning.

57, TITLE:      LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation
http://arxiv.org/abs/2006.02706
AUTHORS:        Weihao Jiang ; Zhaozhi Xie ; Yaoyi Li ; Chang Liu ; Hongtao Lu
COMMENTS:       To appear in icme2020workshop(MMC)
HIGHLIGHT:      This paper introduces a light-weighted network with an efficient reduced non-local module (LRNNet) for efficient and realtime semantic segmentation.

58, TITLE:      The growth and form of knowledge networks by kinesthetic curiosity
http://arxiv.org/abs/2006.02949
AUTHORS:        Dale Zhou ; David M. Lydon-Staley ; Perry Zurn ; Danielle S. Bassett
HIGHLIGHT:      Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification.

59, TITLE:      2D Image Features Detector And Descriptor Selection Expert System
http://arxiv.org/abs/2006.02933
AUTHORS:        Ibon Merino ; Jon Azpiazu ; Anthony Remazeilles ; Basilio Sierra
COMMENTS:       10 pages, 5 figures, 5 tables
HIGHLIGHT:      This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification.

60, TITLE:      Self-Training for End-to-End Speech Translation
http://arxiv.org/abs/2006.02490
AUTHORS:        Juan Pino ; Qiantong Xu ; Xutai Ma ; Mohammad Javad Dousti ; Yun Tang
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      Our approach is shown to be more effective than simply pre-training the encoder on the speech recognition task.

61, TITLE:      Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning
http://arxiv.org/abs/2006.02482
AUTHORS:        Numair Sani ; Daniel Malinsky ; Ilya Shpitser
HIGHLIGHT:      We propose to explain the behavior of black-box prediction methods (e.g., deep neural networks trained on image pixel data) using causal graphical models.

62, TITLE:      CircleNet: Anchor-free Detection with Circle Representation
http://arxiv.org/abs/2006.02474
AUTHORS:        Haichun Yang ; Ruining Deng ; Yuzhe Lu ; Zheyu Zhu ; Ye Chen ; Joseph T. Roland ; Le Lu ; Bennett A. Landman ; Agnes B. Fogo ; Yuankai Huo
HIGHLIGHT:      In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.

63, TITLE:      Problems of dataset creation for light source estimation
http://arxiv.org/abs/2006.02692
AUTHORS:        E. I. Ershov ; A. V. Belokopytov ; A. V. Savchik
HIGHLIGHT:      The paper describes our experience collecting a new dataset for the light source estimation problem in a single image.

64, TITLE:      Boundary-assisted Region Proposal Networks for Nucleus Segmentation
http://arxiv.org/abs/2006.02695
AUTHORS:        Shengcong Chen ; Changxing Ding ; Dacheng Tao
COMMENTS:       Early Acception by MICCAI 2020
HIGHLIGHT:      Accordingly, in this paper, we devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation.

65, TITLE:      Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
http://arxiv.org/abs/2006.02689
AUTHORS:        Dieqiao Feng ; Carla P. Gomes ; Bart Selman
COMMENTS:       8 pages, 6 figures, accepted by IJCAI 2020
HIGHLIGHT:      In contrast to prior efforts, which use carefully handcrafted pruning techniques, our approach automatically uncovers domain structure.

66, TITLE:      Uncertainty quantification in medical image segmentation with Normalizing Flows
http://arxiv.org/abs/2006.02683
AUTHORS:        Raghavendra Selvan ; Frederik Faye ; Jon Middleton ; Akshay Pai
COMMENTS:       12 pages
HIGHLIGHT:      In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow).

67, TITLE:      Automatic Verification of LLVM Code
http://arxiv.org/abs/2006.02670
AUTHORS:        Axel Legay ; Dirk Nowotka ; Danny Bøgsted Poulsen
HIGHLIGHT:      In this work we present our work in developing a software verification tool for llvm-code - Lodin - that incorporates both explicit-state model checking, statistical model checking and symbolic state model checking algorithms.

68, TITLE:      Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
http://arxiv.org/abs/2006.02666
AUTHORS:        Yesheng Xu ; Ming Kong ; Wenjia Xie ; Runping Duan ; Zhengqing Fang ; Yuxiao Lin ; Qiang Zhu ; Siliang Tang ; Fei Wu ; Yu-Feng Yao
COMMENTS:       Accepted by Engineering
HIGHLIGHT:      In this paper, we propose a sequential-level deep learning model to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images.

69, TITLE:      Evaluation of Deep Segmentation Models for the Extraction of Retinal Lesions from Multi-modal Retinal Images
http://arxiv.org/abs/2006.02662
AUTHORS:        Taimur Hassan ; Muhammad Usman Akram ; Naoufel Werghi
HIGHLIGHT:      In this paper, we present a detailed evaluation of RAGNet, PSPNet, SegNet, UNet, FCN-8 and FCN-32 for the extraction of retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen, and other chorioretinal anomalies from retinal fundus and OCT scans.

70, TITLE:      MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
http://arxiv.org/abs/2006.02659
AUTHORS:        Qing Yang ; Xia Zhu ; Yun Ye ; Jong-Kae Fwu ; Ganmei You ; Yuan Zhu
HIGHLIGHT:      In this work, we address the Explainable AI problem of black-box classifiers which take images as input and output probabilities of classes.

71, TITLE:      Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation
http://arxiv.org/abs/2006.02655
AUTHORS:        AbdElRahman ElSaid ; Joshua Karns ; Alexander Ororbia II ; Daniel Krutz ; Zimeng Lyu ; Travis Desell
HIGHLIGHT:      This work introduces network-aware adaptive structure transfer learning (N-ASTL), an advancement over prior efforts to remove this restriction.



==========Updates to Previous Papers==========
1, TITLE:       Towards A Controllable Disentanglement Network
http://arxiv.org/abs/2001.08572
AUTHORS:        Zengjie Song ; Oluwasanmi Koyejo ; Jiangshe Zhang
COMMENTS:       arXiv admin note: text overlap with arXiv:1912.11675
HIGHLIGHT:      This paper addresses two crucial problems of learning disentangled image representations, namely controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality.

2, TITLE:       Spatial Action Maps for Mobile Manipulation
http://arxiv.org/abs/2004.09141
AUTHORS:        Jimmy Wu ; Xingyuan Sun ; Andy Zeng ; Shuran Song ; Johnny Lee ; Szymon Rusinkiewicz ; Thomas Funkhouser
COMMENTS:       To appear at Robotics: Science and Systems (RSS), 2020. Project page: https://spatial-action-maps.cs.princeton.edu
HIGHLIGHT:      In this work, we present "spatial action maps," in which the set of possible actions is represented by a pixel map (aligned with the input image of the current state), where each pixel represents a local navigational endpoint at the corresponding scene location.

3, TITLE:       Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks
http://arxiv.org/abs/2003.10566
AUTHORS:        Alan B. Cannaday II ; Curt H. Davis ; Grant J. Scott ; Blake Ruprecht ; Derek T. Anderson
COMMENTS:       9 pages, 9 figures, 9 tables, pre-published expansion of IGARSS2019 conference paper "Improved Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks"
HIGHLIGHT:      Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature.

4, TITLE:       Neural networks-based backward scheme for fully nonlinear PDEs
http://arxiv.org/abs/1908.00412
AUTHORS:        Huyen Pham ; Xavier Warin ; Maximilien Germain
HIGHLIGHT:      We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs).

5, TITLE:       Training Keyword Spotting Models on Non-IID Data with Federated Learning
http://arxiv.org/abs/2005.10406
AUTHORS:        Andrew Hard ; Kurt Partridge ; Cameron Nguyen ; Niranjan Subrahmanya ; Aishanee Shah ; Pai Zhu ; Ignacio Lopez Moreno ; Rajiv Mathews
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model.

6, TITLE:       Pyramid Attention Networks for Image Restoration
http://arxiv.org/abs/2004.13824
AUTHORS:        Yiqun Mei ; Yuchen Fan ; Yulun Zhang ; Jiahui Yu ; Yuqian Zhou ; Ding Liu ; Yun Fu ; Thomas S. Huang ; Humphrey Shi
HIGHLIGHT:      To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid.

7, TITLE:       Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles
http://arxiv.org/abs/1804.08666
AUTHORS:        Christopher Mitcheltree ; Skyler Wharton ; Avneesh Saluja
COMMENTS:       Equal contribution from first two authors. Accepted for publication in the NAACL 2018 Industry Track
HIGHLIGHT:      In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks.

8, TITLE:       Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots
http://arxiv.org/abs/2006.01286
AUTHORS:        Corneliu Arsene
HIGHLIGHT:      This paper presents fast procedures for thermal infrared remote sensing in dark, GPS-denied environments, such as those found in industrial plants such as in High-Voltage Direct Current (HVDC) converter stations.

9, TITLE:       Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
http://arxiv.org/abs/2004.05488
AUTHORS:        Lyes Khacef ; Laurent Rodriguez ; Benoit Miramond
COMMENTS:       Preprint, 24 pages, 11 figures, 4 tables
HIGHLIGHT:      [...] In this paper, we build a brain-inspired neural system based on the Reentry principles, using Self-Organizing Maps and Hebbian-like learning.

10, TITLE:      Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades
http://arxiv.org/abs/2006.02322
AUTHORS:        Aifu Han ; Yongze Zhang ; Ajuan Li ; Changjin Li ; Fengying Zhao ; Qiujie Dong ; Qin Liu ; Yanting Liu ; Ximei Shen ; Sunjie Yan ; Shengzong Zhou
COMMENTS:       11 pages with 11 figures
HIGHLIGHT:      In this study, we proposed the real-time detection and location method for Wagner grades of DF based on refinements on YOLOv3.

11, TITLE:      Hierarchical Quantized Autoencoders
http://arxiv.org/abs/2002.08111
AUTHORS:        Will Williams ; Sam Ringer ; Tom Ash ; John Hughes ; David MacLeod ; Jamie Dougherty
HIGHLIGHT:      This leads us to introduce a novel objective for training hierarchical VQ-VAEs.

12, TITLE:      Toward Metrics for Differentiating Out-of-Distribution Sets
http://arxiv.org/abs/1910.08650
AUTHORS:        Mahdieh Abbasi ; Changjian Shui ; Arezoo Rajabi ; Christian Gagne ; Rakesh Bobba
COMMENTS:       Workshop on Safety and Robustness in Decision Making, NeurIPS 2019
HIGHLIGHT:      Instead, we propose three novel computationally-efficient metrics for differentiating between OOD sets according to their "protection" level of in-distribution sub-manifolds.

13, TITLE:      Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation
http://arxiv.org/abs/2005.02545
AUTHORS:        Kaouther Messaoud ; Nachiket Deo ; Mohan M. Trivedi ; Fawzi Nashashibi
HIGHLIGHT:      To better model the interdependence of the two cues, we propose a multi-head attention-based model that uses a joint representation of the static scene and agent configuration for generating both keys and values for the attention heads.

14, TITLE:      Active Preference-Based Gaussian Process Regression for Reward Learning
http://arxiv.org/abs/2005.02575
AUTHORS:        Erdem Bıyık ; Nicolas Huynh ; Mykel J. Kochenderfer ; Dorsa Sadigh
COMMENTS:       Proceedings of Robotics: Science and Systems (RSS), July 2020
HIGHLIGHT:      To address these challenges, we present a preference-based learning approach, where as an alternative, the human feedback is only in the form of comparisons between trajectories.

15, TITLE:      RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
http://arxiv.org/abs/2004.14798
AUTHORS:        Jiawei Du ; Hanshu Yan ; Vincent Y. F. Tan ; Joey Tianyi Zhou ; Rick Siow Mong Goh ; Jiashi Feng
HIGHLIGHT:      We propose a novel defense framework, \emph{\underline{R}obust and \underline{A}ccurate \underline{I}mage classificatio\underline{N}} (RAIN), to improve the robustness of given CNN classifiers and, at the same time, preserve their high prediction accuracies.

16, TITLE:      Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
http://arxiv.org/abs/2003.03241
AUTHORS:        Theodore Papamarkou ; Hayley Guy ; Bryce Kroencke ; Jordan Miller ; Preston Robinette ; Daniel Schultz ; Jacob Hinkle ; Laura Pullum ; Catherine Schuman ; Jeremy Renshaw ; Stylianos Chatzidakis
HIGHLIGHT:      This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel.

17, TITLE:      Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
http://arxiv.org/abs/2004.01279
AUTHORS:        Shikha Chaganti ; Abishek Balachandran ; Guillaume Chabin ; Stuart Cohen ; Thomas Flohr ; Bogdan Georgescu ; Philippe Grenier ; Sasa Grbic ; Siqi Liu ; François Mellot ; Nicolas Murray ; Savvas Nicolaou ; William Parker ; Thomas Re ; Pina Sanelli ; Alexander W. Sauter ; Zhoubing Xu ; Youngjin Yoo ; Valentin Ziebandt ; Dorin Comaniciu
HIGHLIGHT:      Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

18, TITLE:      Exorcising Spectres with Secure Compilers
http://arxiv.org/abs/1910.08607
AUTHORS:        Marco Patrignani ; Marco Guarnieri
HIGHLIGHT:      In this paper, we formally prove the security (or insecurity) of compiler-level countermeasures for Spectre.

19, TITLE:      Effect of Annotation Errors on Drone Detection with YOLOv3
http://arxiv.org/abs/2004.01059
AUTHORS:        Aybora Koksal ; Kutalmis Gokalp Ince ; A. Aydin Alatan
HIGHLIGHT:      In this work, different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector, YOLOv3, with erroneous annotations during training and testing stages is examined.

20, TITLE:      MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
http://arxiv.org/abs/1909.04761
AUTHORS:        Julian Martin Eisenschlos ; Sebastian Ruder ; Piotr Czapla ; Marcin Kardas ; Sylvain Gugger ; Jeremy Howard
COMMENTS:       Proceedings of EMNLP-IJCNLP 2019
HIGHLIGHT:      We propose Multi-lingual language model Fine-Tuning (MultiFiT) to enable practitioners to train and fine-tune language models efficiently in their own language.

21, TITLE:      Description Based Text Classification with Reinforcement Learning
http://arxiv.org/abs/2002.03067
AUTHORS:        Duo Chai ; Wei Wu ; Qinghong Han ; Fei Wu ; Jiwei Li
COMMENTS:       Accepted by ICML 2020
HIGHLIGHT:      Inspired by the current trend of formalizing NLP problems as question answering tasks, we propose a new framework for text classification, in which each category label is associated with a category description.

22, TITLE:      Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
http://arxiv.org/abs/2003.04377
AUTHORS:        Olivier Vincent ; Charley Gros ; Joseph Paul Cohen ; Julien Cohen-Adad
COMMENTS:       Presented at OHBM 2020 (v2-3 : corrected typos)
HIGHLIGHT:      To tackle this challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowledge within the segmentation model and learn the characteristics of each contrast.

23, TITLE:      CNN Denoisers as Non-Local Filters: The Neural Tangent Denoiser
http://arxiv.org/abs/2006.02379
AUTHORS:        Julián Tachella ; Junqi Tang ; Mike Davies
HIGHLIGHT:      We introduce a novel interpretation of denoising networks with no clean training data in the context of the neural tangent kernel (NTK), elucidating the strong links with well-known non-local filtering techniques, such as non-local means or BM3D.

24, TITLE:      Evaluating Adversarial Robustness for Deep Neural Network Interpretability in fMRI Decoding
http://arxiv.org/abs/2004.11114
AUTHORS:        Patrick McClure ; Dustin Moraczewski ; Ka Chun Lam ; Adam Thomas ; Francisco Pereira
HIGHLIGHT:      In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI) data from multiple tasks per subject to create ground truth saliency maps.

25, TITLE:      MTRNet++: One-stage Mask-based Scene Text Eraser
http://arxiv.org/abs/1912.07183
AUTHORS:        Osman Tursun ; Simon Denman ; Rui Zeng ; Sabesan Sivapalan ; Sridha Sridharan ; Clinton Fookes
COMMENTS:       This paper is under CVIU review (after major revision)
HIGHLIGHT:      To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++.

26, TITLE:      Efficient Algorithms for Outlier-Robust Regression
http://arxiv.org/abs/1803.03241
AUTHORS:        Adam Klivans ; Pravesh K. Kothari ; Raghu Meka
COMMENTS:       27 pages. Appeared in COLT 2018. This update removes Lemma 6.2 that erroneously claimed an information-theoretic lower bound on error rate as a function of fraction of outliers
HIGHLIGHT:      We give the first polynomial-time algorithm for performing linear or polynomial regression resilient to adversarial corruptions in both examples and labels.

27, TITLE:      Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing
http://arxiv.org/abs/2004.03755
AUTHORS:        Goonmeet Bajaj ; Bortik Bandyopadhyay ; Daniel Schmidt ; Pranav Maneriker ; Christopher Myers ; Srinivasan Parthasarathy
HIGHLIGHT:      As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs.

28, TITLE:      Fair comparison of skin detection approaches on publicly available datasets
http://arxiv.org/abs/1802.02531
AUTHORS:        Alessandra Lumini ; Loris Nanni
HIGHLIGHT:      In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets.

29, TITLE:      A Logic that Captures $β$P on Ordered Structures
http://arxiv.org/abs/1912.03841
AUTHORS:        Kexu Wang ; Xishun Zhao
COMMENTS:       15 pages. This article was reported with a title "Logarithmic-Bounded Second-Order Quantifiers and Limited Nondeterminism" in National Conference on Modern Logic 2019, on November 9 in Beijing
HIGHLIGHT:      We extend the inflationary fixed-point logic, IFP, with a new kind of second-order quantifiers which have (poly-)logarithmic bounds.

30, TITLE:      No-Rainbow Problem and the Surjective Constraint Satisfaction Problem
http://arxiv.org/abs/2003.11764
AUTHORS:        Dmitriy Zhuk
HIGHLIGHT:      In this paper we show that one of the most popular variants of the SCSP, called No-Rainbow Problem, is NP-Hard.

31, TITLE:      Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks
http://arxiv.org/abs/1907.00670
AUTHORS:        Bruno Magalhães ; Michael Hines ; Thomas Sterling ; Felix Schuermann
HIGHLIGHT:      We introduce a distributed fully-asynchronous execution model that removes global synchronization, allowing for longer variable timestep interpolations.

32, TITLE:      ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
http://arxiv.org/abs/2004.12832
AUTHORS:        Omar Khattab ; Matei Zaharia
COMMENTS:       Accepted at SIGIR 2020
HIGHLIGHT:      To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval.

33, TITLE:      Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
http://arxiv.org/abs/1912.07398
AUTHORS:        Jacqueline G. Cavazos ; P. Jonathon Phillips ; Carlos D. Castillo ; Alice J. O'Toole
HIGHLIGHT:      To illustrate how these issues apply, we present data from four face recognition algorithms (a previous-generation algorithm and three deep convolutional neural networks, DCNNs) for East Asian and Caucasian faces.

34, TITLE:      Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
http://arxiv.org/abs/1808.07647
AUTHORS:        Michele Polese ; Rittwik Jana ; Velin Kounev ; Ke Zhang ; Supratim Deb ; Michele Zorzi
COMMENTS:       15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computing
HIGHLIGHT:      In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks.

35, TITLE:      High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning
http://arxiv.org/abs/2001.01680
AUTHORS:        Mingyuan Meng ; Xingyu Yang ; Lei Bi ; Jinman Kim ; Shanlin Xiao ; Zhiyi Yu
COMMENTS:       16 pages, 10 figures, 6 tables, submitted to Neurocomputing
HIGHLIGHT:      In this paper, to overcome these limitations, we: 1) designed a high-parallelism network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature; 2) extended a widely used vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding; 3) proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to enhance the spiking activities and thus further accelerate the network's learning.

36, TITLE:      A Common Operating Picture Framework Leveraging Data Fusion and Deep Learning
http://arxiv.org/abs/2001.05982
AUTHORS:        Benjamin Ortiz ; David Lindenbaum ; Joseph Nassar ; Brendan Lammers ; John Wahl ; Robert Mangum ; Margaret Smith ; Marc Bosch
HIGHLIGHT:      In this work, we present a data fusion framework for accelerating solutions for Processing, Exploitation, and Dissemination (PED).

37, TITLE:      Generic bivariate multi-point evaluation, interpolation and modular composition with precomputation
http://arxiv.org/abs/2003.12468
AUTHORS:        Vincent Neiger ; Johan Rosenkilde ; Grigory Solomatov
COMMENTS:       ISSAC 2020. 8 pages, 7 algorithms
HIGHLIGHT:      We introduce a technique called \emph{reshaping} which allows us to design quasi-linear algorithms for both: computing the evaluations of an input polynomial $f \in \mathbb{K}[x,y]$ at all points of $\mathcal{P}$; and computing an interpolant $f \in \mathbb{K}[x,y]$ which takes prescribed values on $\mathcal{P}$ and satisfies an input $y$-degree bound.

38, TITLE:      Spatial-Angular Interaction for Light Field Image Super-Resolution
http://arxiv.org/abs/1912.07849
AUTHORS:        Yingqian Wang ; Longguang Wang ; Jungang Yang ; Wei An ; Jingyi Yu ; Yulan Guo
COMMENTS:       In this version, we have revised the paper and compared our LF-InterNet to the most recent LF-ATO method (CVPR2020). Codes and pre-trained models are available at https://github.com/YingqianWang/LF-InterNet
HIGHLIGHT:      In this paper, we propose a spatial-angular interactive network (namely, LF-InterNet) for LF image SR.

39, TITLE:      Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
http://arxiv.org/abs/2006.01372
AUTHORS:        Takuma Kato ; Kaori Abe ; Hiroki Ouchi ; Shumpei Miyawaki ; Jun Suzuki ; Kentaro Inui
COMMENTS:       Accepted by ACL SRW 2020
HIGHLIGHT:      In this work, we propose to integrate label component information as embeddings into models.

40, TITLE:      Arbitrary Scale Super-Resolution for Brain MRI Images
http://arxiv.org/abs/2004.02086
AUTHORS:        Chuan Tan ; Jin Zhu ; Pietro Lio'
COMMENTS:       12 pages, 8 figures, 1 table, to appear as a full paper with oral contribution in AIAI 2020
HIGHLIGHT:      A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network.

41, TITLE:      pyBART: Evidence-based Syntactic Transformations for IE
http://arxiv.org/abs/2005.01306
AUTHORS:        Aryeh Tiktinsky ; Yoav Goldberg ; Reut Tsarfaty
COMMENTS:       Accepted ACL2020 system demonstration paper
HIGHLIGHT:      We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit.

42, TITLE:      Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models
http://arxiv.org/abs/2005.13780
AUTHORS:        Dharani Punithan ; Byoung-Tak Zhang
HIGHLIGHT:      We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model.

43, TITLE:      Deep Semantic Segmentation of Natural and Medical Images: A Review
http://arxiv.org/abs/1910.07655
AUTHORS:        Saeid Asgari Taghanaki ; Kumar Abhishek ; Joseph Paul Cohen ; Julien Cohen-Adad ; Ghassan Hamarneh
COMMENTS:       45 pages, 16 figures. Accepted for publication in Springer Artificial Intelligence Review
HIGHLIGHT:      In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.

44, TITLE:      HistoNet: Predicting size histograms of object instances
http://arxiv.org/abs/1912.05227
AUTHORS:        Kishan Sharma ; Moritz Gold ; Christian Zurbruegg ; Laura Leal-Taixé ; Jan Dirk Wegner
HIGHLIGHT:      We propose to predict histograms of object sizes in crowded scenes directly without any explicit object instance segmentation. We also provide a new data set for this task, the FlyLarvae data set, which consists of 11,000 larvae instances labeled pixel-wise.

45, TITLE:      A divide-and-conquer algorithm for computing Gröbner bases of syzygies in finite dimension
http://arxiv.org/abs/2002.06404
AUTHORS:        Simone Naldi ; Vincent Neiger
COMMENTS:       ISSAC 2020. 8 pages, 4 algorithms
HIGHLIGHT:      We address the problem of computing a Gr\"obner basis of the module of syzygies of $(f_1,\ldots,f_m)$, that is, of vectors $(p_1,\ldots,p_m) \in R^m$ such that $p_1 f_1 + \cdots + p_m f_m = 0$.

46, TITLE:      A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics
http://arxiv.org/abs/2002.07505
AUTHORS:        Hatem Khalloof ; Wilfried Jakob ; Shadi Shahoud ; Clemens Duepmeier ; Veit Hagenmeyer
HIGHLIGHT:      In the present paper, a new generic and highly scalable parallel method for unit commitment of distributed energy resources using metaheuristic algorithms is presented.

47, TITLE:      HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
http://arxiv.org/abs/2005.14446
AUTHORS:        Zhaohui Yang ; Yunhe Wang ; Dacheng Tao ; Xinghao Chen ; Jianyuan Guo ; Chunjing Xu ; Chao Xu ; Chang Xu
HIGHLIGHT:      We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact that the effects of the architecture often proceed from the vital few blocks.

48, TITLE:      Quantum Complexity of Time Evolution with Chaotic Hamiltonians
http://arxiv.org/abs/1905.05765
AUTHORS:        Vijay Balasubramanian ; Matthew DeCross ; Arjun Kar ; Onkar Parrikar
COMMENTS:       35+11 pages, 13 figures, improved motivation of cost factors, improved discussion of superoperator corrections
HIGHLIGHT:      We study the quantum complexity of time evolution in large-$N$ chaotic systems, with the SYK model as our main example.

49, TITLE:      Explainable Artificial Intelligence: a Systematic Review
http://arxiv.org/abs/2006.00093
AUTHORS:        Giulia Vilone ; Luca Longo
COMMENTS:       78 pages, 18 figures, journal paper to be submitted to Information Fusion
HIGHLIGHT:      This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation.

50, TITLE:      BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
http://arxiv.org/abs/2006.01174
AUTHORS:        Maria de la Iglesia Vayá ; Jose Manuel Saborit ; Joaquim Angel Montell ; Antonio Pertusa ; Aurelia Bustos ; Miguel Cazorla ; Joaquin Galant ; Xavier Barber ; Domingo Orozco-Beltrán ; Francisco García-García ; Marisa Caparrós ; Germán González ; Jose María Salinas
HIGHLIGHT:      This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests.

51, TITLE:      Unsupervised Image Noise Modeling with Self-Consistent GAN
http://arxiv.org/abs/1906.05762
AUTHORS:        Hanshu Yan ; Xuan Chen ; Vincent Y. F. Tan ; Wenhan Yang ; Joe Wu ; Jiashi Feng
HIGHLIGHT:      To ameliorate this problem, we propose a self-consistent GAN (SCGAN), that can directly extract noise maps from noisy images, thus enabling unsupervised noise modeling.

52, TITLE:      Directly Mapping RDF Databases to Property Graph Databases
http://arxiv.org/abs/1912.02127
AUTHORS:        Renzo Angles ; Harsh Thakkar ; Dominik Tomaszuk
COMMENTS:       This work has been accepted and published at the IEEE Access Journal DOI: 10.1109/ACCESS.2020.2993117
HIGHLIGHT:      This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema.