2020.03.13.txt

==========New Papers==========
1, TITLE:       Entropy of tropical holonomic sequences
http://arxiv.org/abs/2003.05466
AUTHORS:        Dima Grigoriev
HIGHLIGHT:      We introduce tropical holonomic sequences of a given order and calculate their entropy in case of the second order.

2, TITLE:       Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
http://arxiv.org/abs/2003.05856
AUTHORS:        Massimo Caccia ; Pau Rodriguez ; Oleksiy Ostapenko ; Fabrice Normandin ; Min Lin ; Lucas Caccia ; Issam Laradji ; Irina Rish ; Alexande Lacoste ; David Vazquez ; Laurent Charlin
HIGHLIGHT:      Accordingly, we propose a strong baseline: Continual-MAML, an online extension of the popular MAML algorithm.

3, TITLE:       Learning Compositional Rules via Neural Program Synthesis
http://arxiv.org/abs/2003.05562
AUTHORS:        Maxwell I. Nye ; Armando Solar-Lezama ; Joshua B. Tenenbaum ; Brenden M. Lake
HIGHLIGHT:      In this work, we present a neuro-symbolic model which learns entire rule systems from a small set of examples.

4, TITLE:       The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents
http://arxiv.org/abs/2003.05861
AUTHORS:        Pablo Barros ; Anne C. Bloem ; Inge M. Hootsmans ; Lena M. Opheij ; Romain H. A. Toebosch ; Emilia Barakova ; Alessandra Sciutti
COMMENTS:       Submitted to IROS2020
HIGHLIGHT:      In this paper, we propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in HRI scenarios, to provide a controllable and reproducible scenario for reinforcement-learning algorithms.

5, TITLE:       Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
http://arxiv.org/abs/2003.05473
AUTHORS:        Samuel Broscheit
COMMENTS:       Published at CoNLL 2019
HIGHLIGHT:      In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text-representations, i.e. BERT (Devlin et al., 2019)?

6, TITLE:       On the Complexity and Approximation of $λ_\infty\,,$ Spread Constant and Maximum Variance Embedding
http://arxiv.org/abs/2003.05582
AUTHORS:        Majid Farhadi ; Anand Louis ; Prasad Tetali
HIGHLIGHT:      We settle this question by proving that computing $\lambda_\infty$ is indeed NP-hard.

7, TITLE:       New Exponential Size Lower Bounds against Depth Four Circuits of Bounded Individual Degree
http://arxiv.org/abs/2003.05874
AUTHORS:        Suryajith Chillara
HIGHLIGHT:      In this paper, we make further progress by proving that for all large enough integers $n$ and $d$, and absolute constants $a$ and $b$ such that $\omega(\log^2n)\leq d\leq n^{a}$, any syntactic depth four circuit of bounded individual degree $\delta\leq n^{b}$ that computes $IMM_{n,d}$ must have size $n^{\Omega(\sqrt{d})}$.

8, TITLE:       Escaping Cannibalization? Correlation-Robust Pricing for a Unit-Demand Buyer
http://arxiv.org/abs/2003.05913
AUTHORS:        Moshe Babaioff ; Michal Feldman ; Yannai A. Gonczarowski ; Brendan Lucier ; Inbal Talgam-Cohen
HIGHLIGHT:      We devise a computationally efficient (polynomial in the support size of the marginals) algorithm that computes the worst-case joint distribution for any choice of item prices.

9, TITLE:       Semantic Holism and Word Representations in Artificial Neural Networks
http://arxiv.org/abs/2003.05522
AUTHORS:        Tomáš Musil
HIGHLIGHT:      We propose a more specific approach based on Frege's holistic and functional approach to meaning.

10, TITLE:      Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content
http://arxiv.org/abs/2003.05863
AUTHORS:        Han Yang ; Ruimao Zhang ; Xiaobao Guo ; Wei Liu ; Wangmeng Zuo ; Ping Luo
COMMENTS:       CVPR 2020
HIGHLIGHT:      To address this issue, we propose a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN).

11, TITLE:      CPS: Class-level 6D Pose and Shape Estimation From Monocular Images
http://arxiv.org/abs/2003.05848
AUTHORS:        Fabian Manhardt ; Manuel Nickel ; Sven Meier ; Luca Minciullo ; Nassir Navab
HIGHLIGHT:      In this paper, we propose the first deep learning approach for class-wise monocular 6D pose estimation, coupled with metric shape retrieval.

12, TITLE:      End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
http://arxiv.org/abs/2003.05855
AUTHORS:        Lei Li ; Siyu Zhu ; Hongbo Fu ; Ping Tan ; Chiew-Lan Tai
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds.

13, TITLE:      Top-1 Solution of Multi-Moments in Time Challenge 2019
http://arxiv.org/abs/2003.05837
AUTHORS:        Manyuan Zhang ; Hao Shao ; Guanglu Song ; Yu Liu ; Junjie Yan
HIGHLIGHT:      In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019.

14, TITLE:      Open Source Computer Vision-based Layer-wise 3D Printing Analysis
http://arxiv.org/abs/2003.05660
AUTHORS:        Aliaksei L. Petsiuk ; Joshua M. Pearce
COMMENTS:       29 pages, 19 figures
HIGHLIGHT:      The paper describes an open source computer vision-based hardware structure and software algorithm, which analyzes layer-wise the 3-D printing processes, tracks printing errors, and generates appropriate printer actions to improve reliability.

15, TITLE:      Conditional Convolutions for Instance Segmentation
http://arxiv.org/abs/2003.05664
AUTHORS:        Zhi Tian ; Chunhua Shen ; Hao Chen
HIGHLIGHT:      We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation).

16, TITLE:      Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
http://arxiv.org/abs/2003.05653
AUTHORS:        Jiangke Lin ; Yi Yuan ; Tianjia Shao ; Kun Zhou
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.

17, TITLE:      ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
http://arxiv.org/abs/2003.05669
AUTHORS:        Mohammadreza Salehi ; Atrin Arya ; Barbod Pajoum ; Mohammad Otoofi ; Amirreza Shaeiri ; Mohammad Hossein Rohban ; Hamid R. Rabiee
HIGHLIGHT:      To address this problem, we propose a novel AE that can learn more semantically meaningful features.

18, TITLE:      SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates
http://arxiv.org/abs/2003.05559
AUTHORS:        Zhizhong Han ; Guanhui Qiao ; Yu-Shen Liu ; Matthias Zwicker
HIGHLIGHT:      To avoid dense and irregular sampling in 3D, we propose to represent shapes using 2D functions, where the output of the function at each 2D location is a sequence of line segments inside the shape.

19, TITLE:      ZSTAD: Zero-Shot Temporal Activity Detection
http://arxiv.org/abs/2003.05583
AUTHORS:        Lingling Zhang ; Xiaojun Chang ; Jun Liu ; Minnan Luo ; Sen Wang ; Zongyuan Ge ; Alexander Hauptmann
HIGHLIGHT:      To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected.

20, TITLE:      Extended Batch Normalization
http://arxiv.org/abs/2003.05569
AUTHORS:        Chunjie Luo ; Jianfeng Zhan ; Lei Wang ; Wanling Gao
HIGHLIGHT:      In this paper, we propose a simple but effective method, called extended batch normalization (EBN).

21, TITLE:      Memory-efficient Learning for Large-scale Computational Imaging
http://arxiv.org/abs/2003.05551
AUTHORS:        Michael Kellman ; Kevin Zhang ; Jon Tamir ; Emrah Bostan ; Michael Lustig ; Laura Waller
COMMENTS:       9 pages, 8 figures. See also relate NeurIPS 2019 presentation arXiv:1912.05098
HIGHLIGHT:      In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network's layers to enable data-driven design for large-scale computational imaging systems.

22, TITLE:      Frequency-Tuned Universal Adversarial Attacks
http://arxiv.org/abs/2003.05549
AUTHORS:        Yingpeng Deng ; Lina J. Karam
HIGHLIGHT:      Based on this, we propose a frequency-tuned universal attack method to compute universal perturbations and show that our method can realize a good balance between perceivability and effectiveness in terms of fooling rate by adapting the perturbations to the local frequency content.

23, TITLE:      VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
http://arxiv.org/abs/2003.05541
AUTHORS:        Oytun Ulutan ; A S M Iftekhar ; B. S. Manjunath
COMMENTS:       Accepted in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)
HIGHLIGHT:      VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions

24, TITLE:      Topology Dependent Bounds For FAQs
http://arxiv.org/abs/2003.05575
AUTHORS:        Michael Langberg ; Shi Li ; Sai Vikneshwar Mani Jayaraman ; Atri Rudra
COMMENTS:       A conference version was presented at PODS 2019
HIGHLIGHT:      In this paper, we prove topology dependent bounds on the number of rounds needed to compute Functional Aggregate Queries (FAQs) studied by Abo Khamis et al. [PODS 2016] in a synchronous distributed network under the model considered by Chattopadhyay et al. [FOCS 2014, SODA 2017].

25, TITLE:      Regular Intersection Emptiness of Graph Problems: Finding a Needle in a Haystack of Graphs with the Help of Automata
http://arxiv.org/abs/2003.05826
AUTHORS:        Petra Wolf ; Henning Fernau
HIGHLIGHT:      We consider the Int_reg-problem for a number of different graph problems and give general criteria that give decision procedures for these Int_reg-problems.

26, TITLE:      Option Discovery in the Absence of Rewards with Manifold Analysis
http://arxiv.org/abs/2003.05878
AUTHORS:        Amitay Bar ; Ronen Talmon ; Ron Meir
HIGHLIGHT:      In this paper, we present an approach based on spectral graph theory and derive an algorithm that systematically discovers options without access to a specific reward or task assignment.

27, TITLE:      Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning
http://arxiv.org/abs/2003.05886
AUTHORS:        Christopher Zach ; Huu Le
COMMENTS:       16 pages
HIGHLIGHT:      We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference.

28, TITLE:      Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
http://arxiv.org/abs/2003.05610
AUTHORS:        Chaochao Chen ; Ziqi Liu ; Peilin Zhao ; Jun Zhou ; Xiaolong Li
COMMENTS:       Accepted by AAAI'18
HIGHLIGHT:      To solve these, we present a Decentralized MF (DMF) framework for POI recommendation.

29, TITLE:      PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
http://arxiv.org/abs/2003.05602
AUTHORS:        Yuening Li ; Daochen Zha ; Praveen Kumar Venugopal ; Na Zou ; Xia Hu
COMMENTS:       In Companion Proceedings of the Web Conference 2020 (WWW 20)
HIGHLIGHT:      To fill this gap, we present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support, which automatically optimizes an outlier detection pipeline for a new data source at hand.

30, TITLE:      Toward certified quantum programming
http://arxiv.org/abs/2003.05841
AUTHORS:        Christophe Chareton ; Sébastien Bardin ; François Bobot ; Valentin Perrelle ; Benoit Valiron
HIGHLIGHT:      We propose QBRICKS, the first development environment for certified quantum programs featuring clear separation between code and proof, scale-invariance specification and proof, high degree of proof automation and allowing to encode quantum programs in a natural way, i.e. close to textbook style.

31, TITLE:      DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
http://arxiv.org/abs/2003.05523
AUTHORS:        Bharat Joshi ; Md Modasshir ; Travis Manderson ; Hunter Damron ; Marios Xanthidis ; Alberto Quattrini Li ; Ioannis Rekleitis ; Gregory Dudek
HIGHLIGHT:      In this paper, we propose a real-time deep-learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image.

32, TITLE:      Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
http://arxiv.org/abs/2003.05656
AUTHORS:        Han Wang ; Chen Wang ; Lihua Xie
COMMENTS:       Accepted in International Conference on Robotics and Automation (ICRA) 2020
HIGHLIGHT:      In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition.

33, TITLE:      AirSim Drone Racing Lab
http://arxiv.org/abs/2003.05654
AUTHORS:        Ratnesh Madaan ; Nicholas Gyde ; Sai Vemprala ; Matthew Brown ; Keiko Nagami ; Tim Taubner ; Eric Cristofalo ; Davide Scaramuzza ; Mac Schwager ; Ashish Kapoor
COMMENTS:       14 pages, 6 figures
HIGHLIGHT:      We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics.

34, TITLE:      Deciding the Consistency of Non-Linear Real Arithmetic Constraints with a Conflict Driven Search Using Cylindrical Algebraic Coverings
http://arxiv.org/abs/2003.05633
AUTHORS:        Erika {Á}brahám ; James H. Davenport ; Matthew England ; Gereon Kremer
HIGHLIGHT:      We present a new algorithm for determining the satisfiability of conjunctions of non-linear polynomial constraints over the reals, which can be used as a theory solver for satisfiability modulo theory (SMT) solving for non-linear real arithmetic.

35, TITLE:      Four heads are better than three
http://arxiv.org/abs/2003.05706
AUTHORS:        Ville Salo
COMMENTS:       14 pages
HIGHLIGHT:      We construct recursively-presented finitely-generated torsion groups which have a bounded torsion and whose word problem is conjunctive equivalent (in particular positive and Turing equivalent) to a given recursively enumerable set.

36, TITLE:      Learning word-referent mappings and concepts from raw inputs
http://arxiv.org/abs/2003.05573
AUTHORS:        Wai Keen Vong ; Brenden M. Lake
HIGHLIGHT:      In this paper, we present a neural network model trained from scratch via self-supervision that takes in raw images and words as inputs, and show that it can learn word-referent mappings from fully ambiguous scenes and utterances through cross-situational learning.

37, TITLE:      Sentiment Analysis with Contextual Embeddings and Self-Attention
http://arxiv.org/abs/2003.05574
AUTHORS:        Katarzyna Biesialska ; Magdalena Biesialska ; Henryk Rybinski
COMMENTS:       Accepted at the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS 2020)
HIGHLIGHT:      In this work, we propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism.

38, TITLE:      It Means More if It Sounds Good: Yet Another Hypotheses Concerning the Evolution of Polysemous Words
http://arxiv.org/abs/2003.05758
AUTHORS:        Ivan P. Yamshchikov ; Cyrille Merleau Nono Saha ; Igor Samenko ; Jürgen Jost
HIGHLIGHT:      This position paper looks into the formation of language and shows ties between structural properties of the words in the English language and their polysemy.

39, TITLE:      SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration
http://arxiv.org/abs/2003.05891
AUTHORS:        Jun Shi ; Jianfeng Xu ; Kazuyuki Tasaka ; Zhibo Chen
HIGHLIGHT:      In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization.

40, TITLE:      Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses
http://arxiv.org/abs/2003.05905
AUTHORS:        Rongliang Wu ; Gongjie Zhang ; Shijian Lu ; Tao Chen
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      To address these limitations, we propose Cascade Expression Focal GAN (Cascade EF-GAN), a novel network that performs progressive facial expression editing with local expression focuses.

41, TITLE:      Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information
http://arxiv.org/abs/2003.05684
AUTHORS:        Zhize Wu ; Thomas Weise ; Le Zou ; Fei Sun ; Ming Tan
HIGHLIGHT:      Differing from the previous studies, we propose a new method called Denoising Autoencoder with Temporal and Categorical Constraints (DAE_CTC)} to study the skeletal representation in a view of skeleton reconstruction.

42, TITLE:      Deformation Flow Based Two-Stream Network for Lip Reading
http://arxiv.org/abs/2003.05709
AUTHORS:        Jingyun Xiao ; Shuang Yang ; Yuanhang Zhang ; Shiguang Shan ; Xilin Chen
COMMENTS:       7 pages, FG 2020
HIGHLIGHT:      Specifically, we introduce a Deformation Flow Network (DFN) to learn the deformation flow between adjacent frames, which directly captures the motion information within the lip region.

43, TITLE:      SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images
http://arxiv.org/abs/2003.05712
AUTHORS:        Soumyajyoti Dey ; Soham Das ; Swarnendu Ghosh ; Shyamali Mitra ; Sukanta Chakrabarty ; Nibaran Das
HIGHLIGHT:      In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image.

44, TITLE:      Low-Rank and Total Variation Regularization and Its Application to Image Recovery
http://arxiv.org/abs/2003.05698
AUTHORS:        Pawan Goyal ; Hussam Al Daas ; Peter Benner
HIGHLIGHT:      In this paper, we study the problem of image recovery from given partial (corrupted) observations.

45, TITLE:      Fairness by Learning Orthogonal Disentangled Representations
http://arxiv.org/abs/2003.05707
AUTHORS:        Mhd Hasan Sarhan ; Nassir Navab ; Abouzar Eslami ; Shadi Albarqouni
HIGHLIGHT:      In this paper, we propose a novel disentanglement approach to invariant representation problem.

46, TITLE:      EDC3: Ensemble of Deep-Classifiers using Class-specific Copula functions to Improve Semantic Image Segmentation
http://arxiv.org/abs/2003.05710
AUTHORS:        Somenath Kuiry ; Nibaran Das ; Alaka Das ; Mita Nasipuri
HIGHLIGHT:      In the present work, we have utilized inter source statistical dependency among different classifiers for ensembling of different deep learning techniques for semantic segmentation of images.

47, TITLE:      Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
http://arxiv.org/abs/2003.05586
AUTHORS:        Pongpisit Thanasutives ; Ken-ichi Fukui ; Masayuki Numao ; Boonserm Kijsirikul
HIGHLIGHT:      In this paper, we proposed two modified neural network architectures based on SFANet and SegNet respectively for accurate and efficient crowd counting.

48, TITLE:      Arbitrary-Oriented Object Detection with Circular Smooth Label
http://arxiv.org/abs/2003.05597
AUTHORS:        Xue Yang ; Junchi Yan
COMMENTS:       18 pages, 7 figures, 6 tables
HIGHLIGHT:      In this paper, we show that existing regression-based rotation detectors suffer the problem of discontinuous boundaries, which is directly caused by angular periodicity or corner ordering.

49, TITLE:      Beyond the Camera: Neural Networks in World Coordinates
http://arxiv.org/abs/2003.05614
AUTHORS:        Gunnar A. Sigurdsson ; Abhinav Gupta ; Cordelia Schmid ; Karteek Alahari
HIGHLIGHT:      We propose a simple idea, WorldFeatures, where each feature at every layer has a spatial transformation, and the feature map is only transformed as needed.

50, TITLE:      Learning to Segment 3D Point Clouds in 2D Image Space
http://arxiv.org/abs/2003.05593
AUTHORS:        Yecheng Lyu ; Xinming Huang ; Ziming Zhang
COMMENTS:       Accepted in CVPR 2020
HIGHLIGHT:      In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.

51, TITLE:      Highly Efficient Salient Object Detection with 100K Parameters
http://arxiv.org/abs/2003.05643
AUTHORS:        Shang-Hua Gao ; Yong-Qiang Tan ; Ming-Ming Cheng ; Chengze Lu ; Yunpeng Chen ; Shuicheng Yan
HIGHLIGHT:      In this paper, we aim to relieve the contradiction between computation cost and model performance by improving the network efficiency to a higher degree.

52, TITLE:      Understanding Crowd Flow Movements Using Active-Langevin Model
http://arxiv.org/abs/2003.05626
AUTHORS:        Shreetam Behera ; Debi Prosad Dogra ; Malay Kumar Bandyopadhyay ; Partha Pratim Roy
HIGHLIGHT:      In this paper, a physics-based model is proposed to describe the movements in dense crowds.

53, TITLE:      Unified Image and Video Saliency Modeling
http://arxiv.org/abs/2003.05477
AUTHORS:        Richard Droste ; Jianbo Jiao ; J. Alison Noble
COMMENTS:       J. Jiao and R. Droste contributed equally to this work. The code is available at https://github.com/rdroste/unisal
HIGHLIGHT:      Here, we take a step back and ask: Can image and video saliency modeling be approached via a unified model, with mutual benefit?

54, TITLE:      Softmax Splatting for Video Frame Interpolation
http://arxiv.org/abs/2003.05534
AUTHORS:        Simon Niklaus ; Feng Liu
COMMENTS:       CVPR 2020, http://sniklaus.com/softsplat
HIGHLIGHT:      We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation.

55, TITLE:      Confidence Guided Stereo 3D Object Detection with Split Depth Estimation
http://arxiv.org/abs/2003.05505
AUTHORS:        Chengyao Li ; Jason Ku ; Steven L. Waslander
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      To tackle these two issues, we propose CG-Stereo, a confidence-guided stereo 3D object detection pipeline that uses separate decoders for foreground and background pixels during depth estimation, and leverages the confidence estimation from the depth estimation network as a soft attention mechanism in the 3D object detector.

56, TITLE:      Deep Vectorization of Technical Drawings
http://arxiv.org/abs/2003.05471
AUTHORS:        Vage Egiazarian ; Oleg Voynov ; Alexey Artemov ; Denis Volkhonskiy ; Aleksandr Safin ; Maria Taktasheva ; Denis Zorin ; Evgeny Burnaev
HIGHLIGHT:      We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.

57, TITLE:      Natural Language Interaction to Facilitate Mental Models of Remote Robots
http://arxiv.org/abs/2003.05870
AUTHORS:        Francisco J. Chiyah Garcia ; José Lopes ; Helen Hastie
COMMENTS:       In Workshop on Mental Models of Robots at HRI 2020
HIGHLIGHT:      We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well as facilitate the evaluation of operators' mental models.

58, TITLE:      Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation
http://arxiv.org/abs/2003.05746
AUTHORS:        Meghyn Bienvenu ; Camille Bourgaux
HIGHLIGHT:      In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of facts, and a priority relation between conflicting facts.

59, TITLE:      Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers
http://arxiv.org/abs/2003.05817
AUTHORS:        M. Naya-Varela ; A. Faina ; R. J. Duro
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      To this end, we present the results of some initial experiments on the application of morpho-logical development to learning to walk in three cases, that of a quadruped, a hexapod and that of an octopod.

60, TITLE:      Control-flow Flattening Preserves the Constant-Time Policy (Extended Version)
http://arxiv.org/abs/2003.05836
AUTHORS:        Matteo Busi ; Pierpaolo Degano ; Letterio Galletta
COMMENTS:       Extended version of ITASEC20 camera ready paper
HIGHLIGHT:      In this paper we start addressing this problem: we consider control-flow flattening, a popular obfuscation technique used in industrial compilers, and a specific security policy, namely constant-time.

61, TITLE:      Optimal HDR and Depth from Dual Cameras
http://arxiv.org/abs/2003.05907
AUTHORS:        Pradyumna Chari ; Anil Kumar Vadathya ; Kaushik Mitra
HIGHLIGHT:      In this work, we explore an optimal method for capturing the scene HDR and disparity map using dual camera setups.



==========Updates to Previous Papers==========
1, TITLE:       Rational proofs for quantum computing
http://arxiv.org/abs/1804.08868
AUTHORS:        Tomoyuki Morimae ; Harumichi Nishimura
COMMENTS:       17 pages, 4 figures
HIGHLIGHT:      In this paper, we show that the client can be completely classical if the server is rational (i.e., economically motivated), following the "rational proofs" framework of Azar and Micali.

2, TITLE:       Knowledge Graphs on the Web -- an Overview
http://arxiv.org/abs/2003.00719
AUTHORS:        Nicolas Heist ; Sven Hertling ; Daniel Ringler ; Heiko Paulheim
COMMENTS:       Nicolas Heist, Sven Hertling, Daniel Ringler, Heiko Paulheim: Knowledge Graphs on the Web -- an Overview. In: Ilaria Tiddi, Freddy Lecue, Pascal Hitzler (eds.), Knowledge Graphs for eXplainable AI -- Foundations, Applications and Challenges. Studies on the Semantic Web, IOS Press, Amsterdam, 2020, to appear. [extended version]
HIGHLIGHT:      In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.

3, TITLE:       A Neural Approach to Discourse Relation Signal Detection
http://arxiv.org/abs/2001.02380
AUTHORS:        Amir Zeldes ; Yang Liu
COMMENTS:       33 pages, 7 figures. Submitted to Dialogue & Discourse (D&D); Addressed reviewers' comments: strengthened arguments, added references, corrected typos etc
HIGHLIGHT:      In this paper we present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Delta s (or 'delta-softmax'), to quantify signaling strength.

4, TITLE:       The Complexity of Approximately Counting Retractions
http://arxiv.org/abs/1807.00590
AUTHORS:        Jacob Focke ; Leslie Ann Goldberg ; Stanislav Zivny
HIGHLIGHT:      Our first contribution is to give a complete trichotomy for approximately counting retractions to graphs of girth at least $5$.

5, TITLE:       HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
http://arxiv.org/abs/1908.10357
AUTHORS:        Bowen Cheng ; Bin Xiao ; Jingdong Wang ; Honghui Shi ; Thomas S. Huang ; Lei Zhang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids.

6, TITLE:       Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks
http://arxiv.org/abs/1801.10585
AUTHORS:        Timo Hackel ; Mikhail Usvyatsov ; Silvano Galliani ; Jan D. Wegner ; Konrad Schindler
COMMENTS:       Updated to IJCV version
HIGHLIGHT:      In this work we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights, and thereby allow for significantly lower memory footprints and computation times than the conventional dense framework when processing data with a high degree of sparsity.

7, TITLE:       BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
http://arxiv.org/abs/1906.03786
AUTHORS:        A. Sufian ; Anirudha Ghosh ; Avijit Naskar ; Farhana Sultana ; Jaya Sil ; M M Hafizur Rahman
COMMENTS:       23 pages, 11 figures, 7 tables, Accepted Manuscript
HIGHLIGHT:      The model has achieved the test accuracy of 99.775%(baseline was 99.40%) on the test dataset of ISI Bengali handwritten numerals. Here we have also created a dataset of 1000 images of Bengali handwritten numerals to test the trained model, and it giving promising results.

8, TITLE:       3D Hand Pose Estimation in the Wild via Graph Refinement under Adversarial Learning
http://arxiv.org/abs/1912.01875
AUTHORS:        Yiming He ; Wei Hu ; Siyuan Yang ; Xiaochao Qu ; Pengfei Wan ; Zongming Guo
HIGHLIGHT:      To this end, we propose a hand-model regularized graph refinement paradigm under an adversarial learning framework, aiming to explicitly capture structural inter-dependencies of hand joints for the learning of intrinsic patterns.

9, TITLE:       Object-Oriented Video Captioning with Temporal Graph and Prior Knowledge Building
http://arxiv.org/abs/2003.03715
AUTHORS:        Fangyi Zhu ; Jenq-Neng Hwang ; Zhanyu Ma ; Jun Guo
HIGHLIGHT:      We propose a novel task, named object-oriented video captioning, which focuses on understanding the videos in object-level.

10, TITLE:      Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
http://arxiv.org/abs/2003.05037
AUTHORS:        Ophir Gozes ; Maayan Frid-Adar ; Hayit Greenspan ; Patrick D. Browning ; Adam Bernheim ; Eliot Siegel
COMMENTS:       19 pages, 6 figures
HIGHLIGHT:      We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding.

11, TITLE:      JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
http://arxiv.org/abs/2002.08397
AUTHORS:        Abhijeet Shenoi ; Mihir Patel ; JunYoung Gwak ; Patrick Goebel ; Amir Sadeghian ; Hamid Rezatofighi ; Roberto Martin-Martin ; Silvio Savarese
COMMENTS:       9 pages, 2 figures, 2 tables
HIGHLIGHT:      In this work we present JRMOT, a novel 3D MOT system that integrates information from 2D RGB images and 3D point clouds into a real-time performing framework. As part of our work, we release the JRDB dataset, a novel large scale 2D+3D dataset and benchmark annotated with over 2 million boxes and 3500 time consistent 2D+3D trajectories across 54 indoor and outdoor scenes.

12, TITLE:      Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
http://arxiv.org/abs/2003.00273
AUTHORS:        Runfa Chen ; Wenbing Huang ; Binghui Huang ; Fuchun Sun ; Bin Fang
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      To tackle this issue, we develop a decoupled training strategy by which the encoder is only trained when maximizing the adversary loss while keeping frozen otherwise.

13, TITLE:      Cross-modality Person re-identification with Shared-Specific Feature Transfer
http://arxiv.org/abs/2002.12489
AUTHORS:        Yan Lu ; Yue Wu ; Bin Liu ; Tianzhu Zhang ; Baopu Li ; Qi Chu ; Nenghai Yu
COMMENTS:       To appear at CVPR2020
HIGHLIGHT:      In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance.

14, TITLE:      Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks
http://arxiv.org/abs/1812.00879
AUTHORS:        Saúl Alonso-Monsalve ; Leigh H. Whitehead
HIGHLIGHT:      We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images.

15, TITLE:      Learning in the Frequency Domain
http://arxiv.org/abs/2002.12416
AUTHORS:        Kai Xu ; Minghai Qin ; Fei Sun ; Yuhao Wang ; Yen-Kuang Chen ; Fengbo Ren
COMMENTS:       Accepted to CVPR 2020; https://github.com/calmevtime/DCTNet
HIGHLIGHT:      Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss.

16, TITLE:      Channel Pruning via Optimal Thresholding
http://arxiv.org/abs/2003.04566
AUTHORS:        Yun Ye ; Ganmei You ; Jong-Kae Fwu ; Xia Zhu ; Qing Yang ; Yuan Zhu
COMMENTS:       9 pages, 9 figures, 4 tables
HIGHLIGHT:      In this paper, we present a simple yet effective method, termed Optimal Thresholding (OT), to prune channels with layer dependent thresholds that optimally separate important from negligible channels.

17, TITLE:      Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
http://arxiv.org/abs/1902.07430
AUTHORS:        Muhammad Usman ; Muhammad Umar Farooq ; Siddique Latif ; Muhammad Asim ; Junaid Qadir
COMMENTS:       This paper has been published in Scientific Reports Journal
HIGHLIGHT:      In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI.

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

19, TITLE:      Automatically Batching Control-Intensive Programs for Modern Accelerators
http://arxiv.org/abs/1910.11141
AUTHORS:        Alexey Radul ; Brian Patton ; Dougal Maclaurin ; Matthew D. Hoffman ; Rif A. Saurous
COMMENTS:       10 pages; Machine Learning and Systems 2020
HIGHLIGHT:      We present a general approach to batching arbitrary computations for accelerators such as GPUs.

20, TITLE:      Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
http://arxiv.org/abs/1910.02653
AUTHORS:        Paras Jain ; Ajay Jain ; Aniruddha Nrusimha ; Amir Gholami ; Pieter Abbeel ; Kurt Keutzer ; Ion Stoica ; Joseph E. Gonzalez
COMMENTS:       In Proceedings of 3rd Conference Machine Learning and Systems 2020 (MLSys 2020)
HIGHLIGHT:      We introduce Checkmate, a system that solves for optimal rematerialization schedules in reasonable times (under an hour) using off-the-shelf MILP solvers or near-optimal schedules with an approximation algorithm, then uses these schedules to accelerate millions of training iterations.

21, TITLE:      Online Learned Continual Compression with Adaptive Quantization Modules
http://arxiv.org/abs/1911.08019
AUTHORS:        Lucas Caccia ; Eugene Belilovsky ; Massimo Caccia ; Joelle Pineau
HIGHLIGHT:      We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a representative dataset from a non i.i.d data stream, while only observing each sample once.

22, TITLE:      Interpretable CNNs for Object Classification
http://arxiv.org/abs/1901.02413
AUTHORS:        Quanshi Zhang ; Xin Wang ; Ying Nian Wu ; Huilin Zhou ; Song-Chun Zhu
HIGHLIGHT:      This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part.

23, TITLE:      The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective
http://arxiv.org/abs/2003.05012
AUTHORS:        Stephanie Milani ; Nicholay Topin ; Brandon Houghton ; William H. Guss ; Sharada P. Mohanty ; Oriol Vinyals ; Noboru Sean Kuno
COMMENTS:       10 pages, 2 figures
HIGHLIGHT:      We describe the competition and provide an overview of the top solutions, each of which uses deep reinforcement learning and/or imitation learning.

24, TITLE:      A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation
http://arxiv.org/abs/2002.11898
AUTHORS:        Jamal Lottier Molin ; Chetan Singh Thakur ; Ralph Etienne-Cummings ; Ernst Niebur
COMMENTS:       16 pages, 6 figures, 5 tables, journal
HIGHLIGHT:      To address this, we introduce a Field-Programmable Gate Array implementation of the model on an Opal Kelly 7350 Kintex-7 board.

25, TITLE:      A (Simplified) Supreme Being Necessarily Exists, says the Computer: Computationally Explored Variants of Gödel's Ontological Argument
http://arxiv.org/abs/2001.04701
AUTHORS:        Christoph Benzmüller
COMMENTS:       10 pages, 10 figures
HIGHLIGHT:      An approach to universal (meta-)logical reasoning in classical higher-order logic is employed to explore and study simplifications of Kurt G\"odel's modal ontological argument.

26, TITLE:      Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
http://arxiv.org/abs/2001.08434
AUTHORS:        Sourav Garg ; Michael Milford
COMMENTS:       8 pages, 4 figures, Accepted for oral presentation at the 2020 IEEE International Conference on Robotics and Automation
HIGHLIGHT:      In this paper we present a novel place recognition system which enables for the first time the combination of ultra-compact place representations, near sub-linear storage scaling and extremely lightweight compute requirements.

27, TITLE:      Invariant Transform Experience Replay
http://arxiv.org/abs/1909.10707
AUTHORS:        Yijiong Lin ; Jiancong Huang ; Matthieu Zimmer ; Juan Rojas ; Paul Weng
COMMENTS:       8 pages, 9 figures
HIGHLIGHT:      Based on this data augmentation idea, we formulate a general framework, called Invariant Transform Experience Replay that we present with two techniques.

28, TITLE:      Mask Mining for Improved Liver Lesion Segmentation
http://arxiv.org/abs/1908.05062
AUTHORS:        Karsten Roth ; Jürgen Hesser ; Tomasz Konopczyński
HIGHLIGHT:      We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models.

29, TITLE:      On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
http://arxiv.org/abs/2001.07362
AUTHORS:        Geoffrey Pettet ; Ayan Mukhopadhyay ; Mykel Kochenderfer ; Yevgeniy Vorobeychik ; Abhishek Dubey
COMMENTS:       Accepted at AAMAS 2020 (International Conference on Autonomous Agents and Multiagent Systems)
HIGHLIGHT:      We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem.

30, TITLE:      Model Assertions for Monitoring and Improving ML Models
http://arxiv.org/abs/2003.01668
AUTHORS:        Daniel Kang ; Deepti Raghavan ; Peter Bailis ; Matei Zaharia
HIGHLIGHT:      We propose methods of using model assertions at all stages of ML system deployment, including runtime monitoring, validating labels, and continuously improving ML models.

31, TITLE:      Attention-Privileged Reinforcement Learning
http://arxiv.org/abs/1911.08363
AUTHORS:        Sasha Salter ; Dushyant Rao ; Markus Wulfmeier ; Raia Hadsell ; Ingmar Posner
HIGHLIGHT:      In this paper, we introduce Attention-Privileged Reinforcement Learning (APRiL) which uses a self-supervised attention mechanism to significantly alleviate these drawbacks: by focusing on task-relevant aspects of the observations, attention provides robustness to distractors as well as significantly increased learning efficiency.

32, TITLE:      r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
http://arxiv.org/abs/1911.03854
AUTHORS:        Kai Nakamura ; Sharon Levy ; William Yang Wang
COMMENTS:       Accepted LREC 2020
HIGHLIGHT:      We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news.

33, TITLE:      Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
http://arxiv.org/abs/2003.00330
AUTHORS:        Luis Lamb ; Artur Garcez ; Marco Gori ; Marcelo Prates ; Pedro Avelar ; Moshe Vardi
COMMENTS:       Updated version
HIGHLIGHT:      In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing.

34, TITLE:      Learning Conceptual-Contextual Embeddings for Medical Text
http://arxiv.org/abs/1908.06203
AUTHORS:        Xiao Zhang ; Dejing Dou ; Ji Wu
HIGHLIGHT:      We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations.

35, TITLE:      Orderless Recurrent Models for Multi-label Classification
http://arxiv.org/abs/1911.09996
AUTHORS:        Vacit Oguz Yazici ; Abel Gonzalez-Garcia ; Arnau Ramisa ; Bartlomiej Twardowski ; Joost van de Weijer
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence.

36, TITLE:      Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
http://arxiv.org/abs/2002.00786
AUTHORS:        Sravan Mylavarapu ; Mahtab Sandhu ; Priyesh Vijayan ; K Madhava Krishna ; Balaraman Ravindran ; Anoop Namboodiri
COMMENTS:       Under review in IV (IEEE Intelligent Vehicles Symposium) 2020
HIGHLIGHT:      In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video.

37, TITLE:      Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
http://arxiv.org/abs/2001.04692
AUTHORS:        Marco Toldo ; Umberto Michieli ; Gianluca Agresti ; Pietro Zanuttigh
COMMENTS:       11 pages, 3 figures, 3 tables
HIGHLIGHT:      In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations.

38, TITLE:      Neural Network Compression Framework for fast model inference
http://arxiv.org/abs/2002.08679
AUTHORS:        Alexander Kozlov ; Ivan Lazarevich ; Vasily Shamporov ; Nikolay Lyalyushkin ; Yury Gorbachev
COMMENTS:       9 pages, 1 figure
HIGHLIGHT:      In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).

39, TITLE:      Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention
http://arxiv.org/abs/1908.07236
AUTHORS:        Cristian Rodriguez-Opazo ; Edison Marrese-Taylor ; Fatemeh Sadat Saleh ; Hongdong Li ; Stephen Gould
COMMENTS:       Winter Conference on Applications of Computer Vision 2020
HIGHLIGHT:      This paper studies the problem of temporal moment localization in a long untrimmed video using natural language as the query.

40, TITLE:      Regularized Adaptation for Stable and Efficient Continuous-Level Learning on Image Processing Networks
http://arxiv.org/abs/2003.05145
AUTHORS:        Hyeongmin Lee ; Taeoh Kim ; Hanbin Son ; Sangwook Baek ; Minsu Cheon ; Sangyoun Lee
HIGHLIGHT:      In this paper, we propose a novel continuous-level learning framework using a Filter Transition Network (FTN) which is a non-linear module that easily adapt to new levels, and is regularized to prevent undesirable side-effects.

41, TITLE:      Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
http://arxiv.org/abs/2001.01173
AUTHORS:        Jie Cao ; Huaibo Huang ; Yi Li ; Ran He ; Zhenan Sun
HIGHLIGHT:      In this paper, we reveal that improving the sample selection strategy is an effective solution.

42, TITLE:      Adversarial Multimodal Network for Movie Question Answering
http://arxiv.org/abs/1906.09844
AUTHORS:        Zhaoquan Yuan ; Siyuan Sun ; Lixin Duan ; Xiao Wu ; Changsheng Xu
COMMENTS:       We will revise the paper
HIGHLIGHT:      In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering.

43, TITLE:      RevealNet: Seeing Behind Objects in RGB-D Scans
http://arxiv.org/abs/1904.12012
AUTHORS:        Ji Hou ; Angela Dai ; Matthias Nießner
COMMENTS:       CVPR 2020
HIGHLIGHT:      Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry.

44, TITLE:      Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
http://arxiv.org/abs/2002.09951
AUTHORS:        Rodolfo Quispe ; Darwin Ttito ; Adín Ramírez Rivera ; Helio Pedrini
COMMENTS:       https://github.com/RQuispeC/multi-stream-crowd-counting-extended , The International Journal of Electrical and Computer Engineering Systems 2020
HIGHLIGHT:      A Multi-Stream Convolutional Neural Network is developed and evaluated in this work, which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion.

45, TITLE:      MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech
http://arxiv.org/abs/1909.07208
AUTHORS:        Emna Rejaibi ; Ali Komaty ; Fabrice Meriaudeau ; Said Agrebi ; Alice Othmani
COMMENTS:       14 pages, 7 figures, 9 tables
HIGHLIGHT:      In this paper, a deep recurrent neural network-based framework is presented to detect depression and to predict its severity level from speech.

46, TITLE:      EdgeNets:Edge Varying Graph Neural Networks
http://arxiv.org/abs/2001.07620
AUTHORS:        Elvin Isufi ; Fernando Gama ; Alejandro Ribeiro
COMMENTS:       submitted in the IEEE Transactions on Pattern Analysis and Machine Intelligence
HIGHLIGHT:      In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance.

47, TITLE:      Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops
http://arxiv.org/abs/1905.05334
AUTHORS:        Yan Ru Pei ; Haik Manukian ; Massimiliano Di Ventra
COMMENTS:       38 pages, 9 figures
HIGHLIGHT:      Here, we propose a method of generating weighted MAX-2-SAT instances inspired by the frustrated-loop algorithm used by the quantum annealing community.

48, TITLE:      Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
http://arxiv.org/abs/1910.12969
AUTHORS:        Qin Lin ; Wenshuo Wang ; Yihuan Zhang ; John Dolan
COMMENTS:       ACC final version
HIGHLIGHT:      This paper proposes a general approach for measuring spatiotemporal similarity of interactive behaviors using a multivariate matrix profile technique.

49, TITLE:      Improvements to Target-Based 3D LiDAR to Camera Calibration
http://arxiv.org/abs/1910.03126
AUTHORS:        Jiunn-Kai Huang ; Jessy W. Grizzle
HIGHLIGHT:      This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession.

50, TITLE:      The Evolution of Sex Chromosomes through the Baldwin Effect
http://arxiv.org/abs/1808.03471
AUTHORS:        Larry Bull
COMMENTS:       14 pages
HIGHLIGHT:      Using the well-known NK model of fitness landscapes it is here shown that the emergence of sex determination systems can also be explained under this view of eukaryotic evolution.

51, TITLE:      Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
http://arxiv.org/abs/2003.01966
AUTHORS:        Ren Yang ; Fabian Mentzer ; Luc Van Gool ; Radu Timofte
COMMENTS:       CVPR 2020 Camera-Ready
HIGHLIGHT:      In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and recurrent enhancement.

52, TITLE:      Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
http://arxiv.org/abs/2003.04989
AUTHORS:        Daniel Otero Baguer ; Johannes Leuschner ; Maximilian Schmidt
HIGHLIGHT:      In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime.