astro-ph.GA - 星系天体物理学
    cs.AI - 人工智能
    cs.CL - 计算与语言
    cs.CR - 加密与安全
    cs.CV - 机器视觉与模式识别
    cs.CY - 计算与社会
    cs.DC - 分布式、并行与集群计算
    cs.ET - 新兴技术
    cs.HC - 人机接口
    cs.IR - 信息检索
    cs.IT - 信息论
    cs.LG - 自动学习
    cs.NE - 神经与进化计算
    cs.NI - 网络和互联网体系结构
    cs.RO - 机器人学
    cs.SE - 软件工程
    cs.SI - 社交网络与信息网络
    eess.IV - 图像与视频处理
    eess.SP - 信号处理
    math.CO - 组合数学
    math.NA - 数值分析
    math.PR - 概率
    math.ST - 统计理论
    physics.data-an - 数据分析、 统计和概率
    q-bio.NC - 神经元与认知
    q-bio.QM - 定量方法
    quant-ph - 量子物理
    stat.AP - 应用统计
    stat.CO - 统计计算
    stat.ME - 统计方法论
    stat.ML - (统计)机器学习

    • [astro-ph.GA]Bayesian Inference of Globular Cluster Properties Using Distribution Functions
    • [cs.AI]Artificial Intelligence Algorithms for Natural Language Processing and the Semantic Web Ontology Learning
    • [cs.AI]Phy-Q: A Benchmark for Physical Reasoning
    • [cs.AI]Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
    • [cs.AI]The Horn Non-Clausal Class and its Polynomiality
    • [cs.CL]A Generative Approach for Mitigating Structural Biases in Natural Language Inference
    • [cs.CL]A Search Engine for Discovery of Biomedical Challenges and Directions
    • [cs.CL]Automatic Rule Generation for Time Expression Normalization
    • [cs.CL]Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
    • [cs.CL]Cross-Lingual Text Classification of Transliterated Hindi and Malayalam
    • [cs.CL]Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
    • [cs.CL]Discretized Integrated Gradients for Explaining Language Models
    • [cs.CL]Dynamic Sliding Window for Meeting Summarization
    • [cs.CL]Effective Sequence-to-Sequence Dialogue State Tracking
    • [cs.CL]Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience
    • [cs.CL]Explaining Classes through Word Attribution
    • [cs.CL]Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
    • [cs.CL]Gray Cycles of Maximum Length Related to k-Character Substitutions
    • [cs.CL]How Does Adversarial Fine-Tuning Benefit BERT?
    • [cs.CL]Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
    • [cs.CL]Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
    • [cs.CL]Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control
    • [cs.CL]MELM: Data Augmentation with Masked Entity Language Modeling for Cross-lingual NER
    • [cs.CL]Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization
    • [cs.CL]Plan-then-Generate: Controlled Data-to-Text Generation via Planning
    • [cs.CL]Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions
    • [cs.CL]Robust Retrieval Augmented Generation for Zero-shot Slot Filling
    • [cs.CL]Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
    • [cs.CL]Semi-Supervised Exaggeration Detection of Health Science Press Releases
    • [cs.CL]T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
    • [cs.CL]TNNT: The Named Entity Recognition Toolkit
    • [cs.CL]TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
    • [cs.CL]Task-Oriented Dialogue System as Natural Language Generation
    • [cs.CL]The five Is: Key principles for interpretable and safe conversational AI
    • [cs.CL]Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools
    • [cs.CL]Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
    • [cs.CL]Unsupervised Open-Domain Question Answering
    • [cs.CL]Want To Reduce Labeling Cost? GPT-3 Can Help
    • [cs.CL]When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
    • [cs.CL]mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset
    • [cs.CR]Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
    • [cs.CR]Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs
    • [cs.CR]DLPFS: The Data Leakage Prevention FileSystem
    • [cs.CR]DeepTaskAPT: Insider APT detection using Task-tree based Deep Learning
    • [cs.CR]EG-Booster: Explanation-Guided Booster of ML Evasion Attacks
    • [cs.CR]Generalizing Weighted Trees: A Bridge from Bitcoin to GHOST
    • [cs.CR]Incorporating Deception into CyberBattleSim for Autonomous Defense
    • [cs.CR]Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings
    • [cs.CR]Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning
    • [cs.CV]A Novel Dataset for Keypoint Detection of quadruped Animals from Images
    • [cs.CV]AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks
    • [cs.CV]Attention-based Multi-Reference Learning for Image Super-Resolution
    • [cs.CV]Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams
    • [cs.CV]Automatic labelling of urban point clouds using data fusion
    • [cs.CV]Dead Pixel Test Using Effective Receptive Field
    • [cs.CV]Deep Learning on Edge TPUs
    • [cs.CV]DepthTrack : Unveiling the Power of RGBD Tracking
    • [cs.CV]Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge
    • [cs.CV]Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
    • [cs.CV]End-to-End Monocular Vanishing Point Detection Exploiting Lane Annotations
    • [cs.CV]Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
    • [cs.CV]InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images
    • [cs.CV]Is First Person Vision Challenging for Object Tracking?
    • [cs.CV]Module-Power Prediction from PL Measurements using Deep Learning
    • [cs.CV]One-shot domain adaptation for semantic face editing of real world images using StyleALAE
    • [cs.CV]PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
    • [cs.CV]Pruning with Compensation: Efficient Channel Pruning for Deep Convolutional Neural Networks
    • [cs.CV]Realistic Hands: A Hybrid Model for 3D Hand Reconstruction
    • [cs.CV]S4-Crowd: Semi-Supervised Learning with Self-Supervised Regularisation for Crowd Counting
    • [cs.CV]SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering
    • [cs.CV]ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets
    • [cs.CV]Scene Synthesis via Uncertainty-Driven Attribute Synchronization
    • [cs.CV]Self-Calibrating Neural Radiance Fields
    • [cs.CV]Self-balanced Learning For Domain Generalization
    • [cs.CV]SemIE: Semantically-aware Image Extrapolation
    • [cs.CV]Semi-supervised Image Classification with Grad-CAM Consistency
    • [cs.CV]SimulLR: Simultaneous Lip Reading Transducer with Attention-Guided Adaptive Memory
    • [cs.CV]Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
    • [cs.CV]Super-Resolution Appearance Transfer for 4D Human Performances
    • [cs.CY]Tracing app technology: An ethical review in the COVID-19 era and directions for post-COVID-19
    • [cs.CY]Why and How Governments Should Monitor AI Development
    • [cs.DC]A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays
    • [cs.DC]A log-linear 今日学术视野(2021.9.2) - 图1#card=math&code=%282%2B5%2F6%29&id=gnW4r)-approximation algorithm for parallel machine scheduling with a single orthogonal resource
    • [cs.DC]Building Time-Triggered Schedules for typed-DAG Tasks with alternative implementations
    • [cs.DC]ExaWorks: Workflows for Exascale
    • [cs.DC]PTRAIL — A python package for parallel trajectory data preprocessing
    • [cs.DC]Population Protocols: Beyond Runtime Analysis
    • [cs.ET]Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
    • [cs.HC]ConVIScope: Visual Analytics for Exploring Patient Conversations
    • [cs.IR]Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation
    • [cs.IR]Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
    • [cs.IR]Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems
    • [cs.IT]Energy Minimization for IRS-aided WPCNs with Non-linear Energy Harvesting Model
    • [cs.IT]Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups
    • [cs.IT]Robust Symbol-Level Precoding and Passive Beamforming for IRS-Aided Communications
    • [cs.IT]Spectral and Energy Efficiency of ACO-OFDM in Visible Light Communication Systems
    • [cs.IT]Structural Properties of Optimal Test Channels for Gaussian Multivariate Partially Observable Distributed Sources
    • [cs.IT]Successful Recovery Performance Guarantees of Noisy SOMP
    • [cs.IT]Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems
    • [cs.IT]Upper bounds on the length function for covering codes
    • [cs.LG]A New Approach to Multilinear Dynamical Systems and Control
    • [cs.LG]A manifold learning perspective on representation learning: Learning decoder and representations without an encoder
    • [cs.LG]APS: Active Pretraining with Successor Features
    • [cs.LG]Adaptive Label Smoothing To Regularize Large-Scale Graph Training
    • [cs.LG]Approximation Methods for Partially Observed Markov Decision Processes (POMDPs)
    • [cs.LG]Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms
    • [cs.LG]Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
    • [cs.LG]Canoe : A System for Collaborative Learning for Neural Nets
    • [cs.LG]Chi-square Loss for Softmax: an Echo of Neural Network Structure
    • [cs.LG]Communication-Computation Efficient Device-Edge Co-Inference via AutoML
    • [cs.LG]Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications
    • [cs.LG]Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
    • [cs.LG]DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
    • [cs.LG]Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
    • [cs.LG]Fast Multi-label Learning
    • [cs.LG]GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
    • [cs.LG]Heterogeneous Graph Neural Network with Multi-view Representation Learning
    • [cs.LG]Learning Optimal Prescriptive Trees from Observational Data
    • [cs.LG]Learning to Synthesize Programs as Interpretable and Generalizable Policies
    • [cs.LG]Machine Learning Methods for Management UAV Flocks — a Survey
    • [cs.LG]Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations
    • [cs.LG]Medical SANSformers: Training self-supervised transformers without attention for Electronic Medical Records
    • [cs.LG]Morphence: Moving Target Defense Against Adversarial Examples
    • [cs.LG]Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
    • [cs.LG]Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
    • [cs.LG]Time Series Prediction using Deep Learning Methods in Healthcare
    • [cs.LG]Towards Out-Of-Distribution Generalization: A Survey
    • [cs.LG]Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training
    • [cs.LG]WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
    • [cs.LG]When are Deep Networks really better than Random Forests at small sample sizes?
    • [cs.NE]Spike time displacement based error backpropagation in convolutional spiking neural networks
    • [cs.NI]A Hierarchical Stitching Algorithm for Coded Compressed Sensing
    • [cs.NI]Latency-Redundancy Tradeoff in Distributed Read-Write Systems
    • [cs.RO]A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
    • [cs.RO]Autonomous Rollator: A Case Study in the Agebots Project
    • [cs.RO]BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control
    • [cs.RO]Lane level context and hidden space characterization for autonomous driving
    • [cs.RO]Learning Practically Feasible Policies for Online 3D Bin Packing
    • [cs.RO]Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI
    • [cs.RO]Planning for Dexterous Ungrasping: Secure Ungrasping through Dexterous Manipulation
    • [cs.RO]Riemannian Optimization for Distance Geometric Inverse Kinematics
    • [cs.RO]Robotic Lime Picking by Considering Leaves as Permeable Obstacles
    • [cs.RO]SURENA IV: Towards A Cost-effective Full-size Humanoid Robot for Real-world Scenarios
    • [cs.RO]The Interaction Flow Editor: A New Human-Robot Interaction RapidPrototyping Interface
    • [cs.RO]Through the Looking Glass: Diminishing Occlusions in Robot Vision Systems with Mirror Reflections
    • [cs.SE]Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts
    • [cs.SI]Control Scenarios for Probabilistic SIR Epidemics on Social-Connection Graphs
    • [cs.SI]Modularity and Heavy-Tailed Degree Distributions
    • [cs.SI]Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review
    • [cs.SI]On Comparing and Enhancing Common Approaches to Network Community Detection
    • [cs.SI]The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset
    • [eess.IV]Iterative Filter Adaptive Network for Single Image Defocus Deblurring
    • [eess.IV]OARnet: Automated organs-at-risk delineation in Head and Neck CT images
    • [eess.IV]Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
    • [eess.IV]The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images
    • [eess.SP]Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
    • [math.CO]Graphs with minimum degree-based entropy
    • [math.NA]An FEA surrogate model with Boundary Oriented Graph Embedding approach
    • [math.PR]On the Number of Faces and Radii of Cells Induced by Gaussian Spherical Tessellations
    • [math.ST]New Highly Efficient High-Breakdown Estimator of Multivariate Scatter and Location for Elliptical Distributions
    • [math.ST]Uniform Consistency in Nonparametric Mixture Models
    • [physics.data-an]Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses
    • [q-bio.NC]Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network
    • [q-bio.QM]Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
    • [q-bio.QM]Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India
    • [quant-ph]Identifying optimal cycles in quantum thermal machines with reinforcement-learning
    • [quant-ph]Recent advances for quantum classifiers
    • [stat.AP]A practical guide to causal discovery with cohort data
    • [stat.AP]Comments on The clinical meaningfulness of a treatment’s effect on a time-to-event variable
    • [stat.AP]Optimal Daily Trading of Battery Operations Using Arbitrage Spreads
    • [stat.AP]Regional estimates of reproduction numbers with application to COVID-19
    • [stat.CO]A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example
    • [stat.ME]Comparison of cause specific rate functions of panel count data with multiple modes of recurrence
    • [stat.ME]Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
    • [stat.ME]Functional Data Representation with Merge Trees
    • [stat.ME]On Proximal Causal Inference With Synthetic Controls
    • [stat.ME]Spatial Blind Source Separation in the Presence of a Drift
    • [stat.ME]Variable Selection in Regression Model with AR(p) Error Terms Based on Heavy Tailed Distributions
    • [stat.ML]A Subsampling Based Method for Causal Discovery on Discrete Data
    • [stat.ML]Bayesian learning of forest and tree graphical models
    • [stat.ML]Decision Tree-Based Predictive Models for Academic Achievement Using College Students’ Support Networks
    • [stat.ML]Disentanglement Analysis with Partial Information Decomposition
    • [stat.ML]Evaluating the Robustness of Off-Policy Evaluation
    • [stat.ML]On the interpretation of black-box default prediction models: an Italian Small and Medium Enterprises case

    ·····································

    • [astro-ph.GA]Bayesian Inference of Globular Cluster Properties Using Distribution Functions
    Gwendolyn M. Eadie, Jeremy J. Webb, Jeffrey S. Rosenthal
    http://arxiv.org/abs/2108.13491v1

    • [cs.AI]Artificial Intelligence Algorithms for Natural Language Processing and the Semantic Web Ontology Learning
    Bryar A. Hassan, Tarik A. Rashid
    http://arxiv.org/abs/2108.13772v1

    • [cs.AI]Phy-Q: A Benchmark for Physical Reasoning
    Cheng Xue, Vimukthini Pinto, Chathura Gamage, Ekaterina Nikonova, Peng Zhang, Jochen Renz
    http://arxiv.org/abs/2108.13696v1

    • [cs.AI]Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
    Pavel Andreev, Alexander Fritzler, Dmitry Vetrov
    http://arxiv.org/abs/2108.13996v1

    • [cs.AI]The Horn Non-Clausal Class and its Polynomiality
    Gonzalo E. Imaz
    http://arxiv.org/abs/2108.13744v1

    • [cs.CL]A Generative Approach for Mitigating Structural Biases in Natural Language Inference
    Dimion Asael, Zachary Ziegler, Yonatan Belinkov
    http://arxiv.org/abs/2108.14006v1

    • [cs.CL]A Search Engine for Discovery of Biomedical Challenges and Directions
    Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope
    http://arxiv.org/abs/2108.13751v1

    • [cs.CL]Automatic Rule Generation for Time Expression Normalization
    Wentao Ding, Jianhao Chen, Jinmao Li, Yuzhong Qu
    http://arxiv.org/abs/2108.13658v1

    • [cs.CL]Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
    Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel
    http://arxiv.org/abs/2108.13854v1

    • [cs.CL]Cross-Lingual Text Classification of Transliterated Hindi and Malayalam
    Jitin Krishnan, Antonios Anastasopoulos, Hemant Purohit, Huzefa Rangwala
    http://arxiv.org/abs/2108.13620v1

    • [cs.CL]Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
    Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen
    http://arxiv.org/abs/2108.13161v2

    • [cs.CL]Discretized Integrated Gradients for Explaining Language Models
    Soumya Sanyal, Xiang Ren
    http://arxiv.org/abs/2108.13654v1

    • [cs.CL]Dynamic Sliding Window for Meeting Summarization
    Zhengyuan Liu, Nancy F. Chen
    http://arxiv.org/abs/2108.13629v1

    • [cs.CL]Effective Sequence-to-Sequence Dialogue State Tracking
    Jeffrey Zhao, Mahdis Mahdieh, Ye Zhang, Yuan Cao, Yonghui Wu
    http://arxiv.org/abs/2108.13990v1

    • [cs.CL]Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience
    George Chrysostomou, Nikolaos Aletras
    http://arxiv.org/abs/2108.13759v1

    • [cs.CL]Explaining Classes through Word Attribution
    Samuel Rönnqvist, Amanda Myntti, Aki-Juhani Kyröläinen, Sampo Pyysalo, Veronika Laippala, Filip Ginter
    http://arxiv.org/abs/2108.13653v1

    • [cs.CL]Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
    Faisal Ladhak, Esin Durmus, He He, Claire Cardie, Kathleen McKeown
    http://arxiv.org/abs/2108.13684v1

    • [cs.CL]Gray Cycles of Maximum Length Related to k-Character Substitutions
    Jean Néraud
    http://arxiv.org/abs/2108.13659v1

    • [cs.CL]How Does Adversarial Fine-Tuning Benefit BERT?
    Javid Ebrahimi, Hao Yang, Wei Zhang
    http://arxiv.org/abs/2108.13602v1

    • [cs.CL]Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
    Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren
    http://arxiv.org/abs/2108.13686v1

    • [cs.CL]Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
    Liesbeth Allein, Marie-Francine Moens, Domenico Perrotta
    http://arxiv.org/abs/2108.13892v1

    • [cs.CL]Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control
    Jacob Beel, Tong Xiang, Sandeep Soni, Diyi Yang
    http://arxiv.org/abs/2108.13556v1

    • [cs.CL]MELM: Data Augmentation with Masked Entity Language Modeling for Cross-lingual NER
    Ran Zhou, Ruidan He, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao
    http://arxiv.org/abs/2108.13655v1

    • [cs.CL]Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization
    Huy To Quoc, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, Anh Gia-Tuan Nguyen
    http://arxiv.org/abs/2108.13741v1

    • [cs.CL]Plan-then-Generate: Controlled Data-to-Text Generation via Planning
    Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier
    http://arxiv.org/abs/2108.13740v1

    • [cs.CL]Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions
    Diego Mollá, Urvashi Khanna, Dima Galat, Vincent Nguyen, Maciej Rybinski
    http://arxiv.org/abs/2108.12189v2

    • [cs.CL]Robust Retrieval Augmented Generation for Zero-shot Slot Filling
    Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo
    http://arxiv.org/abs/2108.13934v1

    • [cs.CL]Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
    Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou
    http://arxiv.org/abs/2108.12963v2

    • [cs.CL]Semi-Supervised Exaggeration Detection of Health Science Press Releases
    Dustin Wright, Isabelle Augenstein
    http://arxiv.org/abs/2108.13493v1

    • [cs.CL]T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
    Raymond Li, Wen Xiao, Lanjun Wang, Hyeju Jang, Giuseppe Carenini
    http://arxiv.org/abs/2108.13587v1

    • [cs.CL]TNNT: The Named Entity Recognition Toolkit
    Sandaru Seneviratne, Sergio J. Rodríguez Méndez, Xuecheng Zhang, Pouya G. Omran, Kerry Taylor, Armin Haller
    http://arxiv.org/abs/2108.13700v1

    • [cs.CL]TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
    Po-Wei Lin, Shang-Yu Su, Yun-Nung Chen
    http://arxiv.org/abs/2108.13811v1

    • [cs.CL]Task-Oriented Dialogue System as Natural Language Generation
    Weizhi Wang, Zhirui Zhang, Junliang Guo, Boxing Chen, Weihua Luo
    http://arxiv.org/abs/2108.13679v1

    • [cs.CL]The five Is: Key principles for interpretable and safe conversational AI
    Mattias Wahde, Marco Virgolin
    http://arxiv.org/abs/2108.13766v1

    • [cs.CL]Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools
    Nils Feldhus, Robert Schwarzenberg, Sebastian Möller
    http://arxiv.org/abs/2108.13961v1

    • [cs.CL]Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
    Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder
    http://arxiv.org/abs/2108.13530v1

    • [cs.CL]Unsupervised Open-Domain Question Answering
    Pengfei Zhu, Xiaoguang Li, Jian Li, Hai Zhao
    http://arxiv.org/abs/2108.13817v1

    • [cs.CL]Want To Reduce Labeling Cost? GPT-3 Can Help
    Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng
    http://arxiv.org/abs/2108.13487v1

    • [cs.CL]When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
    Zixian Huang, Ao Wu, Yulin Shen, Gong Cheng, Yuzhong Qu
    http://arxiv.org/abs/2108.13875v1

    • [cs.CL]mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset
    Luiz Henrique Bonifacio, Israel Campiotti, Roberto Lotufo, Rodrigo Nogueira
    http://arxiv.org/abs/2108.13897v1

    • [cs.CR]Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
    Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, Ruotian Ma, Xipeng Qiu
    http://arxiv.org/abs/2108.13888v1

    • [cs.CR]Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs
    Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, Gholamreza Haffari
    http://arxiv.org/abs/2108.13873v1

    • [cs.CR]DLPFS: The Data Leakage Prevention FileSystem
    Stefano Braghin, Marco Simioni, Mathieu Sinn
    http://arxiv.org/abs/2108.13785v1

    • [cs.CR]DeepTaskAPT: Insider APT detection using Task-tree based Deep Learning
    Mohammad Mamun, Kevin Shi
    http://arxiv.org/abs/2108.13989v1

    • [cs.CR]EG-Booster: Explanation-Guided Booster of ML Evasion Attacks
    Abderrahmen Amich, Birhanu Eshete
    http://arxiv.org/abs/2108.13930v1

    • [cs.CR]Generalizing Weighted Trees: A Bridge from Bitcoin to GHOST
    Ignacio Amores-Sesar, Christian Cachin, Anna Parker
    http://arxiv.org/abs/2108.13502v1

    • [cs.CR]Incorporating Deception into CyberBattleSim for Autonomous Defense
    Erich Walter, Kimberly Ferguson-Walter, Ahmad Ridley
    http://arxiv.org/abs/2108.13980v1

    • [cs.CR]Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings
    Mazda Moayeri, Soheil Feizi
    http://arxiv.org/abs/2108.13797v1

    • [cs.CR]Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning
    Doha Al Bared, Mohamed Nassar
    http://arxiv.org/abs/2108.13617v1

    • [cs.CV]A Novel Dataset for Keypoint Detection of quadruped Animals from Images
    Prianka Banik, Lin Li, Xishuang Dong
    http://arxiv.org/abs/2108.13958v1

    • [cs.CV]AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks
    Jingfei Chang, Yang Lu, Ping Xue, Yiqun Xu, Zhen Wei
    http://arxiv.org/abs/2108.13591v1

    • [cs.CV]Attention-based Multi-Reference Learning for Image Super-Resolution
    Marco Pesavento, Marco Volino, Adrian Hilton
    http://arxiv.org/abs/2108.13697v1

    • [cs.CV]Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams
    Florian Stinner, Martin Wiecek, Marc Baranski, Alexander Kümpel, Dirk Müller
    http://arxiv.org/abs/2108.13912v1

    • [cs.CV]Automatic labelling of urban point clouds using data fusion
    Daan Bloembergen, Chris Eijgenstein
    http://arxiv.org/abs/2108.13757v1

    • [cs.CV]Dead Pixel Test Using Effective Receptive Field
    Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Dong Gu Lee, Wonseok Jeong, Sang Woo Kim
    http://arxiv.org/abs/2108.13576v1

    • [cs.CV]Deep Learning on Edge TPUs
    Andreas M Kist
    http://arxiv.org/abs/2108.13732v1

    • [cs.CV]DepthTrack : Unveiling the Power of RGBD Tracking
    Song Yan, Jinyu Yang, Jani Käpylä, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen
    http://arxiv.org/abs/2108.13962v1

    • [cs.CV]Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge
    Jingtang Liang, Cheng Wang, Yujie Cheng, Zheng Wang, Fang Wang, Liyu Huang, Zhibin Yu, Yubo Wang
    http://arxiv.org/abs/2108.13983v1

    • [cs.CV]Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
    Baisong Zhang, Weiqing Min, Jing Wang, Sujuan Hou, Qiang Hou, Yuanjie Zheng, Shuqiang Jiang
    http://arxiv.org/abs/2108.13775v1

    • [cs.CV]End-to-End Monocular Vanishing Point Detection Exploiting Lane Annotations
    Hiroto Honda, Motoki Kimura, Takumi Karasawa, Yusuke Uchida
    http://arxiv.org/abs/2108.13699v1

    • [cs.CV]Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
    Bo Li, Xinyang Jiang, Donglin Bai, Yuge Zhang, Ningxin Zheng, Xuanyi Dong, Lu Liu, Yuqing Yang, Dongsheng Li
    http://arxiv.org/abs/2108.13465v1

    • [cs.CV]InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images
    Anoop Cherian, Goncalo Dias Pais, Siddarth Jain, Tim K. Marks, Alan Sullivan
    http://arxiv.org/abs/2108.13865v1

    • [cs.CV]Is First Person Vision Challenging for Object Tracking?
    Matteo Dunnhofer, Antonino Furnari, Giovanni Maria Farinella, Christian Micheloni
    http://arxiv.org/abs/2108.13665v1

    • [cs.CV]Module-Power Prediction from PL Measurements using Deep Learning
    Mathis Hoffmann, Johannes Hepp, Bernd Doll, Claudia Buerhop-Lutz, Ian Marius Peters, Christoph Brabec, Andreas Maier, Vincent Christlein
    http://arxiv.org/abs/2108.13640v1

    • [cs.CV]One-shot domain adaptation for semantic face editing of real world images using StyleALAE
    Ravi Kiran Reddy, Kumar Shubham, Gopalakrishnan Venkatesh, Sriram Gandikota, Sarthak Khoche, Dinesh Babu Jayagopi, Gopalakrishnan Srinivasaraghavan
    http://arxiv.org/abs/2108.13876v1

    • [cs.CV]PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
    Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan
    http://arxiv.org/abs/2108.13828v1

    • [cs.CV]Pruning with Compensation: Efficient Channel Pruning for Deep Convolutional Neural Networks
    Zhouyang Xie, Yan Fu, Shengzhao Tian, Junlin Zhou, Duanbing Chen
    http://arxiv.org/abs/2108.13728v1

    • [cs.CV]Realistic Hands: A Hybrid Model for 3D Hand Reconstruction
    Michael Seeber, Martin R. Oswald, Roi Poranne
    http://arxiv.org/abs/2108.13995v1

    • [cs.CV]S4-Crowd: Semi-Supervised Learning with Self-Supervised Regularisation for Crowd Counting
    Haoran Duan, Yu Guan
    http://arxiv.org/abs/2108.13969v1

    • [cs.CV]SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering
    Enxu Li, Ryan Razani, Yixuan Xu, Liu Bingbing
    http://arxiv.org/abs/2108.13588v1

    • [cs.CV]ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets
    Vitaliy Kinakh, Olga Taran, Svyatoslav Voloshynovskiy
    http://arxiv.org/abs/2108.13939v1

    • [cs.CV]Scene Synthesis via Uncertainty-Driven Attribute Synchronization
    Haitao Yang, Zaiwei Zhang, Siming Yan, Haibin Huang, Chongyang Ma, Yi Zheng, Chandrajit Bajaj, Qixing Huang
    http://arxiv.org/abs/2108.13499v1

    • [cs.CV]Self-Calibrating Neural Radiance Fields
    Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
    http://arxiv.org/abs/2108.13826v1

    • [cs.CV]Self-balanced Learning For Domain Generalization
    Jin Kim, Jiyoung Lee, Jungin Park, Dongbo Min, Kwanghoon Sohn
    http://arxiv.org/abs/2108.13597v1

    • [cs.CV]SemIE: Semantically-aware Image Extrapolation
    Bholeshwar Khurana, Soumya Ranjan Dash, Abhishek Bhatia, Aniruddha Mahapatra, Hrituraj Singh, Kuldeep Kulkarni
    http://arxiv.org/abs/2108.13702v1

    • [cs.CV]Semi-supervised Image Classification with Grad-CAM Consistency
    Juyong Lee, Seunghyuk Cho
    http://arxiv.org/abs/2108.13673v1

    • [cs.CV]SimulLR: Simultaneous Lip Reading Transducer with Attention-Guided Adaptive Memory
    Zhijie Lin, Zhou Zhao, Haoyuan Li, Jinglin Liu, Meng Zhang, Xingshan Zeng, Xiaofei He
    http://arxiv.org/abs/2108.13630v1

    • [cs.CV]Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
    Junjun Jiang, Chenyang Wang, Kui Jiang, Xianming Liu, Jiayi Ma
    http://arxiv.org/abs/2108.13584v1

    • [cs.CV]Super-Resolution Appearance Transfer for 4D Human Performances
    Marco Pesavento, Marco Volino, Adrian Hilton
    http://arxiv.org/abs/2108.13739v1

    • [cs.CY]Tracing app technology: An ethical review in the COVID-19 era and directions for post-COVID-19
    Saleh Afroogh, Amir Esmalian, Ali Mostafavi, Ali Akbari, Kambiz Rasoulkhani, Shahriar Esmaeili, Ehsan Hajiramezanali
    http://arxiv.org/abs/2108.12673v2

    • [cs.CY]Why and How Governments Should Monitor AI Development
    Jess Whittlestone, Jack Clark
    http://arxiv.org/abs/2108.12427v2

    • [cs.DC]A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays
    Martin Karp, Artur Podobas, Tobias Kenter, Niclas Jansson, Christian Plessl, Philipp Schlatter, Stefano Markidis
    http://arxiv.org/abs/2108.12188v2

    • [cs.DC]A log-linear 今日学术视野(2021.9.2) - 图2#card=math&code=%282%2B5%2F6%29&id=dTHrS)-approximation algorithm for parallel machine scheduling with a single orthogonal resource
    Adrian Naruszko, Bartłomiej Przybylski, Krzysztof Rzadca
    http://arxiv.org/abs/2108.13716v1

    • [cs.DC]Building Time-Triggered Schedules for typed-DAG Tasks with alternative implementations
    Houssam-Eddine Zahaf, Nicola Capodieci
    http://arxiv.org/abs/2108.13871v1

    • [cs.DC]ExaWorks: Workflows for Exascale
    Aymen Al-Saadi, Dong H. Ahn, Yadu Babuji, Kyle Chard, James Corbett, Mihael Hategan, Stephen Herbein, Shantenu Jha, Daniel Laney, Andre Merzky, Todd Munson, Michael Salim, Mikhail Titov, Matteo Turilli, Justin M. Wozniak
    http://arxiv.org/abs/2108.13521v1

    • [cs.DC]PTRAIL — A python package for parallel trajectory data preprocessing
    Salman Haidri, Yaksh J. Haranwala, Vania Bogorny, Chiara Renso, Vinicius Prado da Fonseca, Amilcar Soares
    http://arxiv.org/abs/2108.13202v1

    • [cs.DC]Population Protocols: Beyond Runtime Analysis
    Javier Esparza
    http://arxiv.org/abs/2108.13449v1

    • [cs.ET]Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
    Humberto Inzunza Velarde, Jheel Nagaria, Zihan Yin, Ajey Jacob, Akhilesh Jaiswal
    http://arxiv.org/abs/2108.12684v1

    • [cs.HC]ConVIScope: Visual Analytics for Exploring Patient Conversations
    Raymond Li, Enamul Hoque, Giuseppe Carenini, Richard Lester, Raymond Chau
    http://arxiv.org/abs/2108.13514v1

    • [cs.IR]Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation
    Ioannis Partalas
    http://arxiv.org/abs/2108.13824v1

    • [cs.IR]Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
    HongChien Yu, Chenyan Xiong, Jamie Callan
    http://arxiv.org/abs/2108.13454v1

    • [cs.IR]Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems
    Philip J. Feng, Pingjun Pan, Tingting Zhou, Hongxiang Chen, Chuanjiang Luo
    http://arxiv.org/abs/2108.13592v1

    • [cs.IT]Energy Minimization for IRS-aided WPCNs with Non-linear Energy Harvesting Model
    Piao Zeng, Qingqing Wu, Deli Qiao
    http://arxiv.org/abs/2108.13603v1

    • [cs.IT]Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups
    Tung T. Vu, Hien Quoc Ngo, Duy T. Ngo, Minh N Dao, Erik G. Larsson
    http://arxiv.org/abs/2108.13512v1

    • [cs.IT]Robust Symbol-Level Precoding and Passive Beamforming for IRS-Aided Communications
    Guangyang Zhang, Chao Shen, Bo Ai, Zhangdui Zhong
    http://arxiv.org/abs/2108.13782v1

    • [cs.IT]Spectral and Energy Efficiency of ACO-OFDM in Visible Light Communication Systems
    Shuai Ma, Ruixin Yang, Xiong Deng, Xintong Ling, Xun Zhang, Fuhui Zhou, Shiyin Li, Derrick Wing Kwan Ng
    http://arxiv.org/abs/2108.13906v1

    • [cs.IT]Structural Properties of Optimal Test Channels for Gaussian Multivariate Partially Observable Distributed Sources
    Michail Gkagkos, Evagoras Stylianou, Charalambos D. Charalambous
    http://arxiv.org/abs/2108.13488v1

    • [cs.IT]Successful Recovery Performance Guarantees of Noisy SOMP
    Wei Zhang, Taejoon Kim
    http://arxiv.org/abs/2108.13855v1

    • [cs.IT]Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems
    Xu Shi, Jintao Wang, Guozhi Chen, Jian Song
    http://arxiv.org/abs/2108.13765v1

    • [cs.IT]Upper bounds on the length function for covering codes
    Alexander A. Davydov, Stefano Marcugini, Fernanda Pambianco
    http://arxiv.org/abs/2108.13609v1

    • [cs.LG]A New Approach to Multilinear Dynamical Systems and Control
    Randy C. Hoover, Kyle Caudle, Karen Braman
    http://arxiv.org/abs/2108.13583v1

    • [cs.LG]A manifold learning perspective on representation learning: Learning decoder and representations without an encoder
    Viktoria Schuster, Anders Krogh
    http://arxiv.org/abs/2108.13910v1

    • [cs.LG]APS: Active Pretraining with Successor Features
    Hao Liu, Pieter Abbeel
    http://arxiv.org/abs/2108.13956v1

    • [cs.LG]Adaptive Label Smoothing To Regularize Large-Scale Graph Training
    Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
    http://arxiv.org/abs/2108.13555v1

    • [cs.LG]Approximation Methods for Partially Observed Markov Decision Processes (POMDPs)
    Caleb M. Bowyer
    http://arxiv.org/abs/2108.13965v1

    • [cs.LG]Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms
    Albert Jiménez Sanfiz, Mohamed Akrout
    http://arxiv.org/abs/2108.13446v1

    • [cs.LG]Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
    Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John Pearson
    http://arxiv.org/abs/2108.13941v1

    • [cs.LG]Canoe : A System for Collaborative Learning for Neural Nets
    Harshit Daga, Yiwen Chen, Aastha Agrawal, Ada Gavrilovska
    http://arxiv.org/abs/2108.12124v2

    • [cs.LG]Chi-square Loss for Softmax: an Echo of Neural Network Structure
    Zeyu Wang, Meiqing Wang
    http://arxiv.org/abs/2108.13822v1

    • [cs.LG]Communication-Computation Efficient Device-Edge Co-Inference via AutoML
    Xinjie Zhang, Jiawei Shao, Yuyi Mao, Jun Zhang
    http://arxiv.org/abs/2108.13009v2

    • [cs.LG]Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications
    Lorenzo Cazzella, Dario Tagliaferri, Marouan Mizmizi, Damiano Badini, Christian Mazzucco, Matteo Matteucci, Umberto Spagnolini
    http://arxiv.org/abs/2108.13831v1

    • [cs.LG]Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
    Tobias Alt, Karl Schrader, Joachim Weickert, Pascal Peter, Matthias Augustin
    http://arxiv.org/abs/2108.13993v1

    • [cs.LG]DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
    Nazanin Alipourfard, Keith Burghardt, Kristina Lerman
    http://arxiv.org/abs/2108.13581v1

    • [cs.LG]Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
    Linus Scheibenreif, Michael Mommert, Damian Borth
    http://arxiv.org/abs/2108.13902v1

    • [cs.LG]Fast Multi-label Learning
    Xiuwen Gong, Dong Yuan, Wei Bao
    http://arxiv.org/abs/2108.13570v1

    • [cs.LG]GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
    Yen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song, Hongyan Li
    http://arxiv.org/abs/2108.13858v1

    • [cs.LG]Heterogeneous Graph Neural Network with Multi-view Representation Learning
    Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu
    http://arxiv.org/abs/2108.13650v1

    • [cs.LG]Learning Optimal Prescriptive Trees from Observational Data
    Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos
    http://arxiv.org/abs/2108.13628v1

    • [cs.LG]Learning to Synthesize Programs as Interpretable and Generalizable Policies
    Dweep Trivedi, Jesse Zhang, Shao-Hua Sun, Joseph J. Lim
    http://arxiv.org/abs/2108.13643v1

    • [cs.LG]Machine Learning Methods for Management UAV Flocks — a Survey
    Rina Azoulay, Yoram Haddad, Shulamit Reches
    http://arxiv.org/abs/2108.13448v1

    • [cs.LG]Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations
    Shameem A. Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith, Sean MacAvaney, Evangelos Zervas
    http://arxiv.org/abs/2108.13810v1

    • [cs.LG]Medical SANSformers: Training self-supervised transformers without attention for Electronic Medical Records
    Yogesh Kumar, Alexander Ilin, Henri Salo, Sangita Kulathinal, Maarit K. Leinonen, Pekka Marttinen
    http://arxiv.org/abs/2108.13672v1

    • [cs.LG]Morphence: Moving Target Defense Against Adversarial Examples
    Abderrahmen Amich, Birhanu Eshete
    http://arxiv.org/abs/2108.13952v1

    • [cs.LG]Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
    Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
    http://arxiv.org/abs/2108.13577v1

    • [cs.LG]Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
    Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu
    http://arxiv.org/abs/2108.13886v1

    • [cs.LG]Time Series Prediction using Deep Learning Methods in Healthcare
    Mohammad Amin Morid, Olivia R. Liu Sheng, Joseph Dunbar
    http://arxiv.org/abs/2108.13461v1

    • [cs.LG]Towards Out-Of-Distribution Generalization: A Survey
    Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui
    http://arxiv.org/abs/2108.13624v1

    • [cs.LG]Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training
    Maximus Mutschler, Andreas Zell
    http://arxiv.org/abs/2108.13880v1

    • [cs.LG]WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
    Tian Lan, Sunil Srinivasa, Stephan Zheng
    http://arxiv.org/abs/2108.13976v1

    • [cs.LG]When are Deep Networks really better than Random Forests at small sample sizes?
    Haoyin Xu, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Sambit Panda, Joshua T. Vogelstein
    http://arxiv.org/abs/2108.13637v1

    • [cs.NE]Spike time displacement based error backpropagation in convolutional spiking neural networks
    Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh, Timothée Masquelier
    http://arxiv.org/abs/2108.13621v1

    • [cs.NI]A Hierarchical Stitching Algorithm for Coded Compressed Sensing
    Yi-Jheng Lin, Chia-Ming Chang, Cheng-Shang Chang
    http://arxiv.org/abs/2108.13760v1

    • [cs.NI]Latency-Redundancy Tradeoff in Distributed Read-Write Systems
    Saraswathy Ramanathan, Gaurav Gautam, Vikram Srinivasan, Parimal Parag
    http://arxiv.org/abs/2108.13949v1

    • [cs.RO]A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
    Zichen He, Jiawei Wang, Chunwei Song
    http://arxiv.org/abs/2108.13619v1

    • [cs.RO]Autonomous Rollator: A Case Study in the Agebots Project
    Jonas Frei, Anina Havelka, Markus Wüst, Einar Nielsen, Andreas Ziltner, Katrin S. Lohan
    http://arxiv.org/abs/2108.13648v1

    • [cs.RO]BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control
    Mark Selden, Jason Zhou, Felipe Campos, Nathan Lambert, Daniel Drew, Kristofer S. J. Pister
    http://arxiv.org/abs/2108.13606v1

    • [cs.RO]Lane level context and hidden space characterization for autonomous driving
    Alexandre Armand, Corentin Sanchez, Philippe Xu, Alexandre Arm, Philippe Bonnifait
    http://arxiv.org/abs/2108.13664v1

    • [cs.RO]Learning Practically Feasible Policies for Online 3D Bin Packing
    Hang Zhao, Chenyang Zhu, Xin Xu, Hui Huang, Kai Xu
    http://arxiv.org/abs/2108.13680v1

    • [cs.RO]Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI
    Alim Kerem Erdogmus, Assist. Prof. Dr. Ugur Yayan
    http://arxiv.org/abs/2108.13803v1

    • [cs.RO]Planning for Dexterous Ungrasping: Secure Ungrasping through Dexterous Manipulation
    Chung Hee Kim, Ka Hei Mak, Jungwon Seo
    http://arxiv.org/abs/2108.13580v1

    • [cs.RO]Riemannian Optimization for Distance Geometric Inverse Kinematics
    Filip Marić, Matthew Giamou, Adam W. Hall, Soroush Khoubyarian, Ivan Petrović, Jonathan Kelly
    http://arxiv.org/abs/2108.13720v1

    • [cs.RO]Robotic Lime Picking by Considering Leaves as Permeable Obstacles
    Heramb Nemlekar, Ziang Liu, Suraj Kothawade, Sherdil Niyaz, Barath Raghavan, Stefanos Nikolaidis
    http://arxiv.org/abs/2108.13889v1

    • [cs.RO]SURENA IV: Towards A Cost-effective Full-size Humanoid Robot for Real-world Scenarios
    Aghil Yousefi-Koma, Behnam Maleki, Hessam Maleki, Amin Amani, Mohammad Ali Bazrafshani, Hossein Keshavarz, Ala Iranmanesh, Alireza Yazdanpanah, Hamidreza Alai, Sahel Salehi, Mahyar Ashkvari, Milad Mousavi, Milad Shafiee Ashtiani
    http://arxiv.org/abs/2108.13515v1

    • [cs.RO]The Interaction Flow Editor: A New Human-Robot Interaction RapidPrototyping Interface
    Matthew Huggins, Anastasia K. Ostrowski, Andrew Rapo, Eric Woudenberg, Cynthia Breazeal, Hae Won Park
    http://arxiv.org/abs/2108.13838v1

    • [cs.RO]Through the Looking Glass: Diminishing Occlusions in Robot Vision Systems with Mirror Reflections
    Kentaro Yoshioka, Hidenori Okuni, Tuan Thanh Ta, Akihide Sai
    http://arxiv.org/abs/2108.13599v1

    • [cs.SE]Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts
    Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß
    http://arxiv.org/abs/2108.13837v1

    • [cs.SI]Control Scenarios for Probabilistic SIR Epidemics on Social-Connection Graphs
    Jan B. Broekaert, Davide La Torre
    http://arxiv.org/abs/2108.13714v1

    • [cs.SI]Modularity and Heavy-Tailed Degree Distributions
    Larry Wilson
    http://arxiv.org/abs/2108.13450v1

    • [cs.SI]Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review
    Massimo Stella
    http://arxiv.org/abs/2108.13800v1

    • [cs.SI]On Comparing and Enhancing Common Approaches to Network Community Detection
    Niko Motschnig, Alexander Ramharter, Oliver Schweiger, Philipp Zabka, Klaus-Tycho Foerster
    http://arxiv.org/abs/2108.13482v1

    • [cs.SI]The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset
    Wenjie Yin, Rabab Alkhalifa, Arkaitz Zubiaga
    http://arxiv.org/abs/2108.13898v1

    • [eess.IV]Iterative Filter Adaptive Network for Single Image Defocus Deblurring
    Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee
    http://arxiv.org/abs/2108.13610v1

    • [eess.IV]OARnet: Automated organs-at-risk delineation in Head and Neck CT images
    Mumtaz Hussain Soomro, Hamidreza Nourzadeh, Victor Gabriel Leandro Alves, Wookjin Choi, Jeffrey V. Siebers
    http://arxiv.org/abs/2108.13987v1

    • [eess.IV]Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
    Adam J. Shephard, Simon Graham, R. M. Saad Bashir, Mostafa Jahanifar, Hanya Mahmood, Syed Ali Khurram, Nasir M. Rajpoot
    http://arxiv.org/abs/2108.13904v1

    • [eess.IV]The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images
    Wei-Chih Huang, Mads Svanborg Peters, Mads Juul Ahlebaek, Mads Toudal Frandsen, René Lynge Eriksen, Bjarke Jørgensen
    http://arxiv.org/abs/2108.13458v1

    • [eess.SP]Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
    Shuai Wang, Dachuan Li, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng
    http://arxiv.org/abs/2108.13669v1

    • [math.CO]Graphs with minimum degree-based entropy
    Yanni Dong, Maximilien Gadouleau, Pengfei Wan, Shenggui Zhang
    http://arxiv.org/abs/2108.13884v1

    • [math.NA]An FEA surrogate model with Boundary Oriented Graph Embedding approach
    Xingyu Fu, Fengfeng Zhou, Dheeraj Peddireddy, Zhengyang Kang, Martin Byung-Guk Jun, Vaneet Aggarwal
    http://arxiv.org/abs/2108.13509v1

    • [math.PR]On the Number of Faces and Radii of Cells Induced by Gaussian Spherical Tessellations
    Eric Lybrand, Anna Ma, Rayan Saab
    http://arxiv.org/abs/2108.13523v1

    • [math.ST]New Highly Efficient High-Breakdown Estimator of Multivariate Scatter and Location for Elliptical Distributions
    Justin A. Fishbone, Lamine Mili
    http://arxiv.org/abs/2108.13567v1

    • [math.ST]Uniform Consistency in Nonparametric Mixture Models
    Bryon Aragam, Ruiyi Yang
    http://arxiv.org/abs/2108.14003v1

    • [physics.data-an]Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses
    Kristina Dingel, Thorsten Otto, Lutz Marder, Lars Funke, Arne Held, Sara Savio, Andreas Hans, Gregor Hartmann, David Meier, Jens Viefhaus, Bernhard Sick, Arno Ehresmann, Markus Ilchen, Wolfram Helml
    http://arxiv.org/abs/2108.13979v1

    • [q-bio.NC]Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network
    Yuliya Tsybina, Innokentiy Kastalskiy, Mikhail Krivonosov, Alexey Zaikin, Victor Kazantsev, Alexander Gorban, Susanna Gordleeva
    http://arxiv.org/abs/2108.13414v1

    • [q-bio.QM]Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
    Gary K. Nave Jr., Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Nirmish Shah, Daniel M. Abrams
    http://arxiv.org/abs/2108.13963v1

    • [q-bio.QM]Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India
    Hanuman Verma, Saurav Mandal, Akshansh Gupta
    http://arxiv.org/abs/2108.13823v1

    • [quant-ph]Identifying optimal cycles in quantum thermal machines with reinforcement-learning
    Paolo Andrea Erdman, Frank Noé
    http://arxiv.org/abs/2108.13525v1

    • [quant-ph]Recent advances for quantum classifiers
    Weikang Li, Dong-Ling Deng
    http://arxiv.org/abs/2108.13421v1

    • [stat.AP]A practical guide to causal discovery with cohort data
    Ryan M. Andrews, Ronja Foraita, Vanessa Didelez, Janine Witte
    http://arxiv.org/abs/2108.13395v2

    • [stat.AP]Comments on The clinical meaningfulness of a treatment’s effect on a time-to-event variable
    Christos Argyropoulos
    http://arxiv.org/abs/2108.13575v1

    • [stat.AP]Optimal Daily Trading of Battery Operations Using Arbitrage Spreads
    Ekaterina Abramova, Derek Bunn
    http://arxiv.org/abs/2108.13511v1

    • [stat.AP]Regional estimates of reproduction numbers with application to COVID-19
    Jan Pablo Burgard, Stefan Heyder, Thomas Hotz, Tyll Krueger
    http://arxiv.org/abs/2108.13842v1

    • [stat.CO]A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example
    Fernando Alarid-Escudero, Eline M. Krijkamp, Eva A. Enns, Alan Yang, M. G. Myriam Hunink, Petros Pechlivanoglou, Hawre Jalal
    http://arxiv.org/abs/2108.13552v1

    • [stat.ME]Comparison of cause specific rate functions of panel count data with multiple modes of recurrence
    Sankaran P. G., Ashlin Mathew, P. M., Sreedevi E. P.
    http://arxiv.org/abs/2108.13967v1

    • [stat.ME]Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
    Corinne Emmenegger, Peter Bühlmann
    http://arxiv.org/abs/2108.13657v1

    • [stat.ME]Functional Data Representation with Merge Trees
    Matteo Pegoraro, Piercesare Secchi
    http://arxiv.org/abs/2108.13147v2

    • [stat.ME]On Proximal Causal Inference With Synthetic Controls
    Xu Shi, Wang Miao, Mengtong Hu, Eric Tchetgen Tchetgen
    http://arxiv.org/abs/2108.13935v1

    • [stat.ME]Spatial Blind Source Separation in the Presence of a Drift
    Christoph Muehlmann, Peter Filzmoser, Klaus Nordhausen
    http://arxiv.org/abs/2108.13813v1

    • [stat.ME]Variable Selection in Regression Model with AR(p) Error Terms Based on Heavy Tailed Distributions
    Yetkin Tuaç, Olcay Arslan
    http://arxiv.org/abs/2108.13755v1

    • [stat.ML]A Subsampling Based Method for Causal Discovery on Discrete Data
    Austin Goddard, Yu Xiang
    http://arxiv.org/abs/2108.13984v1

    • [stat.ML]Bayesian learning of forest and tree graphical models
    Edmund Jones
    http://arxiv.org/abs/2108.13992v1

    • [stat.ML]Decision Tree-Based Predictive Models for Academic Achievement Using College Students’ Support Networks
    Anthony Frazier, Joethi Silva, Rachel Meilak, Indranil Sahoo, David Chan, Michael Broda
    http://arxiv.org/abs/2108.13947v1

    • [stat.ML]Disentanglement Analysis with Partial Information Decomposition
    Seiya Tokui, Issei Sato
    http://arxiv.org/abs/2108.13753v1

    • [stat.ML]Evaluating the Robustness of Off-Policy Evaluation
    Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, Kei Tateno
    http://arxiv.org/abs/2108.13703v1

    • [stat.ML]On the interpretation of black-box default prediction models: an Italian Small and Medium Enterprises case
    Lisa Crosato, Caterina Liberati, Marco Repetto
    http://arxiv.org/abs/2108.13914v1