Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2310.15656	Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks	Hypergraph Neural Networks (HGNNs) have been successfully applied in various hypergraph-related tasks due to their excellent higher-order representation capabilities. Recent works have shown that deep learning models are vulnerable to adversarial attacks. Most studies on graph adversarial attacks have focused on Graph ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,395
2402.07144	A Fundamental Analysis of the Impact on Traffic Assignment by Toll System of Electric Road System	Electric road system (ERS) is expected to make electric vehicles (EVs) more popular as EVs with Dynamic Wireless Power Transfer (DWPT) system can be charged while driving on ERS. Although some studies dealt with ERS implementation, its toll system has not been explored yet. This paper aims at a fundamental analysis on ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	428,581
1911.04058	Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation	We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but only limited labeled data in the target domain with the goal to train a good target model. A straightforward solution is to fine-tune a pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	152,872
1806.10040	Crowd Counting with Density Adaption Networks	Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different lightning, camera perspective and occlusion states. Previous approaches estimate head counts despite that they can vary dramatically in different...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,473
2409.16997	INT-FlashAttention: Enabling Flash Attention for INT8 Quantization	As the foundation of large language models (LLMs), self-attention module faces the challenge of quadratic time and memory complexity with respect to sequence length. FlashAttention accelerates attention computation and reduces its memory usage by leveraging the GPU memory hierarchy. A promising research direction is to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	491,609
2311.14465	DP-NMT: Scalable Differentially-Private Machine Translation	Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	410,118
1910.08292	Diversity in Fashion Recommendation using Semantic Parsing	Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing ...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	149,838
1709.01889	Polar Transformer Networks	Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combines ideas from the Spatial Transformer Network (STN) and c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,168
2111.10588	Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising	Vehicular visible light communications (V-VLC) is a promising intelligent transportation systems (ITS) technology for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications with the utilization of light-emitting diodes (LEDs). The main degrading factor for the performance of V-VLC systems is noise....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,371
1802.10280	Escoin: Efficient Sparse Convolutional Neural Network Inference on GPUs	Deep neural networks have achieved remarkable accuracy in many artificial intelligence applications, e.g. computer vision, at the cost of a large number of parameters and high computational complexity. Weight pruning can compress DNN models by removing redundant parameters in the networks, but it brings sparsity in the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	91,500
2411.15653	OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs	Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-constrained environments, often resulting in substantial target omissio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,705
2409.11143	Semformer: Transformer Language Models with Semantic Planning	Next-token prediction serves as the dominant component in current neural language models. During the training phase, the model employs teacher forcing, which predicts tokens based on all preceding ground truth tokens. However, this approach has been found to create shortcuts, utilizing the revealed prefix to spuriously...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	489,024
2102.10038	Going beyond p-convolutions to learn grayscale morphological operators	Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with erosions or dilations is particularly challenging because the min and max operations are not differentiable. Relying on the asymptotic behavior ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,959
2402.00011	Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide	The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivi...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	425,429
2009.00117	Energy-efficient Wireless Charging and Computation Offloading In MEC Systems	Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting mult...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	193,959
2211.10098	UnconFuse: Avatar Reconstruction from Unconstrained Images	The report proposes an effective solution about 3D human body reconstruction from multiple unconstrained frames for ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual avatars I (track1: Multi-View Based 3D Human Body Reconstruction). We reproduce the reconstruction method presented in MVP-Human as our ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,210
1508.05176	Efficient Representation of Uncertainty for Stochastic Economic Dispatch	Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher-order moments for quantities of interest, e.g., g...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	46,213
2411.16763	Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference	Membership inference attacks (MIAs) are critical tools for assessing privacy risks and ensuring compliance with regulations like the General Data Protection Regulation (GDPR). However, their potential for auditing unauthorized use of data remains under explored. To bridge this gap, we propose a novel clean-label backdo...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	511,165
2407.03772	CS3: Cascade SAM for Sperm Segmentation	Automated sperm morphology analysis plays a crucial role in the assessment of male fertility, yet its efficacy is often compromised by the challenges in accurately segmenting sperm images. Existing segmentation techniques, including the Segment Anything Model(SAM), are notably inadequate in addressing the complex issue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,283
2406.02030	Multimodal Reasoning with Multimodal Knowledge Graph	Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. Some approaches have sought to mitigate these issues by employing textual knowledge graphs, but their singular modality of knowledge limits comprehensive cross-modal ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	460,583
2301.10281	Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge	Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection. However, many other relevant applications of DL, especially at the edge, are based on time-series processing and require mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,755
1912.05480	$\Sigma$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction	We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in $\Sigma$-net are trained in a supervised way, including content and GAN losses, and with various ways...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	157,117
2409.13138	Learning to Compare Hardware Designs for High-Level Synthesis	High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs, enabling the rapid development of hardware accelerators. HLS relies on pragmas, which are directives inserted into the source code to guide the synthesis process, and pragmas have various settings and values...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	489,860
2111.00210	Mastering Atari Games with Limited Data	Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been significant progress in sample efficient image-based RL algorithms; however, co...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	264,146
1904.10161	A Novel Multi-layer Framework for Tiny Obstacle Discovery	For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e.g., noise and similar color distribution with background, it is still difficult to detect the edges of tiny obstacles at long distance. In this paper, we propose an obstacle-aware discover...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	128,570
1911.07527	SOGNet: Scene Overlap Graph Network for Panoptic Segmentation	The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene gra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,885
0908.3184	Location of Single Neuron Memories in a Hebbian Network	This paper reports the results of an experiment on the use of Kak's B-Matrix approach to spreading activity in a Hebbian neural network. Specifically, it concentrates on the memory retrieval from single neurons and compares the performance of the B-Matrix approach to that of the traditional approach.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	4,314
1609.04880	Die-out Probability in SIS Epidemic Processes on Networks	An accurate approximate formula of the die-out probability in a SIS epidemic process on a network is proposed. The formula contains only three essential parameters: the largest eigenvalue of the adjacency matrix of the network, the effective infection rate of the virus, and the initial number of infected nodes in the n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	61,047
2201.03364	High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM	Structure from Motion (SfM) techniques are being increasingly used to create 3D maps from images in many domains including environmental monitoring. However, SfM techniques are often confounded in visually repetitive environments as they rely primarily on globally distinct image features. Simultaneous Localization and ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	274,838
1708.07066	Single Reference Image based Scene Relighting via Material Guided Filtering	Image relighting is to change the illumination of an image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,431
2107.01787	Multi-View Correlation Distillation for Incremental Object Detection	In real applications, new object classes often emerge after the detection model has been trained on a prepared dataset with fixed classes. Due to the storage burden and the privacy of old data, sometimes it is impractical to train the model from scratch with both old and new data. Fine-tuning the old model with only ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,593
2310.04930	Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation	The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, $\textit{Diff-Transfer}...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	397,892
2001.01395	Accumulated Polar Feature-based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism	In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,483
2405.15564	Rethinking Independent Cross-Entropy Loss For Graph-Structured Data	Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learning simply sums up cross-entropy losses of the independent training nodes and applies the average lo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,995
1508.07265	Multiplex networks in metropolitan areas: generic features and local effects	Most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately: it is however important to understand the impact on urban systems of the coupling between them and we report in this paper an empirical analysis of the coupling between the stre...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	46,389
2303.14443	No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning	The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	354,097
1910.14544	Gaussian-Spherical Restricted Boltzmann Machines	We consider a special type of Restricted Boltzmann machine (RBM), namely a Gaussian-spherical RBM where the visible units have Gaussian priors while the vector of hidden variables is constrained to stay on an ${\mathbbm L}_2$ sphere. The spherical constraint having the advantage to admit exact asymptotic treatments, va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,678
2408.11824	AppAgent v2: Advanced Agent for Flexible Mobile Interactions	With the advancement of Multimodal Large Language Models (MLLM), LLM-driven visual agents are increasingly impacting software interfaces, particularly those with graphical user interfaces. This work introduces a novel LLM-based multimodal agent framework for mobile devices. This framework, capable of navigating mobile ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	482,452
2401.02511	Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation	To stabilize PDE models, control laws require space-dependent functional gains mapped by nonlinear operators from the PDE functional coefficients. When a PDE is nonlinear and its "pseudo-coefficient" functions are state-dependent, a gain-scheduling (GS) nonlinear design is the simplest approach to the design of nonline...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	419,741
2404.14977	Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case	This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, differ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,897
2304.04754	Por\'ownanie metod detekcji zaj\k{e}to\'sci widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez w\k{e}z{\l}a centralnego	Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms sup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	357,343
2109.06976	GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients	We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subproblem used in state-of-the-art robotic planning, control, and machine learning, which requires tens to hundreds of naturally parallel compu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	255,330
1811.08289	A Lagrangian Model to Predict Microscallop Motion in non Newtonian Fluids	The need to develop models to predict the motion of microrobots, or robots of a much smaller scale, moving in fluids in a low Reynolds number regime, and in particular, in non Newtonian fluids, cannot be understated. The article develops a Lagrangian based model for one such mechanism - a two-link mechanism termed a mi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	114,004
2010.15981	CoroBase: Coroutine-Oriented Main-Memory Database Engine	Data stalls are a major overhead in main-memory database engines due to the use of pointer-rich data structures. Lightweight coroutines ease the implementation of software prefetching to hide data stalls by overlapping computation and asynchronous data prefetching. Prior solutions, however, mainly focused on (1) indivi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	203,923
1909.11588	Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability	It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning. Despite considerable efforts and successes witnessed to solve Boolean satisfiability (SAT), it remains a mystery of GNN-based solvers for more complex predicate logic formulae. In this work,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	146,858
1612.04904	Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network	The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied "in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,603
2403.17933	SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic	SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model's outputs serve as an initial state for rule-based traffic simulation. The unique properties of the enti...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	441,702
2502.06800	Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach	Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,248
2306.15349	SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion	Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relations...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	375,995
2502.03004	MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation	Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks. However, their application to specialized domains such as medicine and biology requires further optimization to ensure factual accuracy, reliability, and contextual depth. We introduce MedBioLM, a domain-ada...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	530,559
2405.11100	Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations	Large language models (LLMs) have taken centre stage in debates on Artificial Intelligence. Yet there remains a gap in how to assess LLMs' conformity to important human values. In this paper, we investigate whether state-of-the-art LLMs, GPT-4 and Claude 2.1 (Gemini Pro and LLAMA 2 did not generate valid results) are m...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	455,003
2409.03381	CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks	Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	486,027
2205.10642	MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments	Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep n...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	297,792
2309.04037	SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks	The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scien...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	390,591
2307.07936	Joint Beam Management and SLAM for mmWave Communication Systems	The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	379,601
2501.16336	Runtime Analysis of Evolutionary Algorithms for Multiparty Multiobjective Optimization	In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multi-party multi-objective optimization problem (MPMOP). While numerous evolutionary algorithms have been propose...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	527,910
2010.16045	Machine Learning (In) Security: A Stream of Problems	Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenge...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	203,948
2009.01612	Evaluation of a Skill-based Control Architecture for a Visual Inspection-oriented Aerial Platform	The periodic inspection of vessels is a fundamental task to ensure their integrity and avoid maritime accidents. Currently, these inspections represent a high cost for the ship owner, in addition to the danger that this kind of hostile environment entails for the surveyors. In these situations, robotic platforms turn o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	194,353
1306.1271	Predictability of social interactions	The ability to predict social interactions between people has profound applications including targeted marketing and prediction of information diffusion and disease propagation. Previous work has shown that the location of an individual at any given time is highly predictable. This study examines the predictability of ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	25,030
2111.07044	Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization	Recently, the low-rank property of different components extracted from the image has been considered in man hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial self-similarity (NSS) and global spe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	266,259
2306.03013	Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning	Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side detectability of MS attacks were raised, questioning their practicality. In this work, for t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	371,153
1403.4303	A new network node similarity measure method and its applications	Network node similarity measure has been paid particular attention in the field of statistical physics. In this paper, we utilize the concept of information and information loss to measure the node similarity. The whole model is based on this idea that if two nodes are more similar than the others, then the information...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,637
1706.05356	Limits to rover miniaturisation and their implications for solar system exploration	Semiautonomous rover scaling is examined for exploration throughout the solar system. Communications to a relay orbiter is a major constraint, due to power requirements and decreasing antenna gain at small sizes. Also, analysis time scales adversely for power hungry Raman or surface abrasion, and also for low photon co...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	75,499
1606.07211	Toward a Deep Neural Approach for Knowledge-Based IR	This paper tackles the problem of the semantic gap between a document and a query within an ad-hoc information retrieval task. In this context, knowledge bases (KBs) have already been acknowledged as valuable means since they allow the representation of explicit relations between entities. However, they do not necessar...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	57,671
1510.04989	The Rationale for Second Level Adaptation	Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second level adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unkn...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,975
1503.08818	Founding Digital Currency on Imprecise Commodity	Current digital currency schemes provide instantaneous exchange on precise commodity, in which "precise" means a buyer can possibly verify the function of the commodity without error. However, imprecise commodities, e.g. statistical data, with error existing are abundant in digital world. Existing digital currency sche...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	41,626
2111.09564	LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model	The system log generated in a computer system refers to large-scale data that are collected simultaneously and used as the basic data for determining errors, intrusion and abnormal behaviors. The aim of system log anomaly detection is to promptly identify anomalies while minimizing human intervention, which is a critic...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	267,056
2012.05300	Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies	Cross-lingual word sense disambiguation (WSD) tackles the challenge of disambiguating ambiguous words across languages given context. The pre-trained BERT embedding model has been proven to be effective in extracting contextual information of words, and have been incorporated as features into many state-of-the-art WSD ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	210,729
2403.05332	Degradation Resilient LiDAR-Radar-Inertial Odometry	Enabling autonomous robots to operate robustly in challenging environments is necessary in a future with increased autonomy. For many autonomous systems, estimation and odometry remains a single point of failure, from which it can often be difficult, if not impossible, to recover. As such robust odometry solutions are ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	435,965
2309.06006	SoccerNet 2023 Challenges Results	The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	391,275
2106.05065	Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning	Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. The MuLaNE task is to allocate total random walk budget $B$ into each network l...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	239,956
2101.12241	Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search	Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions d...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	217,518
2405.12354	A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning	Quantum Computing aims to streamline machine learning, making it more effective with fewer trainable parameters. This reduction of parameters can speed up the learning process and reduce the use of computational resources. However, in the current phase of quantum computing development, known as the noisy intermediate-s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,484
2411.15742	PEnG: Pose-Enhanced Geo-Localisation	Cross-view Geo-localisation is typically performed at a coarse granularity, because densely sampled satellite image patches overlap heavily. This heavy overlap would make disambiguating patches very challenging. However, by opting for sparsely sampled patches, prior work has placed an artificial upper bound on the loca...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	510,753
2202.03577	Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees	Purpose - Inefficient hiring may result in lower productivity and higher training costs. Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. Also, employers typically spend a considerable amount of time managing employees who perform poorly. The purpose of this study is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,254
2305.19329	Mitigating Test-Time Bias for Fair Image Retrieval	We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the utility (performance) of the underlying vision-language (VL) model. Previous methods aim to disentangle learned representations of images a...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	369,474
1601.03316	Additive Approximation Algorithms for Modularity Maximization	The modularity is a quality function in community detection, which was introduced by Newman and Girvan (2004). Community detection in graphs is now often conducted through modularity maximization: given an undirected graph $G=(V,E)$, we are asked to find a partition $\mathcal{C}$ of $V$ that maximizes the modularity. A...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	50,904
2003.05115	Development of a Robotic System for Automated Decaking of 3D-Printed Parts	With the rapid rise of 3D-printing as a competitive mass manufacturing method, manual "decaking" - i.e. removing the residual powder that sticks to a 3D-printed part - has become a significant bottleneck. Here, we introduce, for the first time to our knowledge, a robotic system for automated decaking of 3D-printed part...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	167,774
1705.00341	Deriving Quests from Open World Mechanics	Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they coul...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	72,659
2008.01066	Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression	We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its gradients with different fidelity levels, namely, it is a Gradient-enhanced Cokriging method (GE-Cokriging). It provides the approximations o...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	190,195
1906.11010	Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm	The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,565
1810.00126	Resilient Structural Stabilizability of Undirected Networks	In this paper, we consider the structural stabilizability problem of undirected networks. More specifically, we are tasked to infer the stabilizability of an undirected network from its underlying topology, where the undirected networks are modeled as continuous-time linear time-invariant (LTI) systems involving symmet...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	109,099
2010.04893	Trust the Model When It Is Confident: Masked Model-based Actor-Critic	It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model errors. In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,906
2403.15749	Horoballs and the subgradient method	To explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces are manifolds and that the objectives are geodesically convex: the methods are described using tangent spaces and exponential maps. By contra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	440,727
2301.05708	A domain-decomposed VAE method for Bayesian inverse problems	Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,423
2104.03046	Multimodal Continuous Visual Attention Mechanisms	Visual attention mechanisms are a key component of neural network models for computer vision. By focusing on a discrete set of objects or image regions, these mechanisms identify the most relevant features and use them to build more powerful representations. Recently, continuous-domain alternatives to discrete attentio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	228,949
2306.08904	Enhancing Neural Rendering Methods with Image Augmentations	Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across computer vision applications, their potential remains underexplored when learning neural rendering methods (NRMs) for 3D scenes. This paper presen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,593
1411.6973	Uncovering Droop Control Laws Embedded Within the Nonlinear Dynamics of Van der Pol Oscillators	This paper examines the dynamics of power-electronic inverters in islanded microgrids that are controlled to emulate the dynamics of Van der Pol oscillators. The general strategy of controlling inverters to emulate the behavior of nonlinear oscillators presents a compelling time-domain alternative to ubiquitous droop c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	37,890
2406.16306	Cascade Reward Sampling for Efficient Decoding-Time Alignment	Aligning large language models (LLMs) with human preferences is critical for their deployment. Recently, decoding-time alignment has emerged as an effective plug-and-play technique that requires no fine-tuning of model parameters. However, generating text that achieves both high reward and high likelihood remains a sig...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	467,081
1701.06232	Election Bias: Comparing Polls and Twitter in the 2016 U.S. Election	While the polls have been the most trusted source for election predictions for decades, in the recent presidential election they were called inaccurate and biased. How inaccurate were the polls in this election and can social media beat the polls as an accurate election predictor? Polls from several news outlets and se...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	67,094
2410.02428	Collective Critics for Creative Story Generation	Generating a long story of several thousand words with narrative coherence using Large Language Models (LLMs) has been a challenging task. Previous research has addressed this challenge by proposing different frameworks that create a story plan and generate a long story based on that plan. However, these frameworks hav...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	494,272
1510.04597	Predictive partitioning for efficient BFS traversal in social networks	In this paper we show how graph structure can be used to drastically reduce the computational bottleneck of the Breadth First Search algorithm (the foundation of many graph traversal techniques). In particular, we address parallel implementations where the bottleneck is the number of messages between processors emitted...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	47,932
2401.10965	Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation	One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this pa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	422,835
2109.11790	Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation	Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequent...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	257,059
1702.07333	k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge	This abstract briefly describes a segmentation algorithm developed for the ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of the competition is to perform a segmentation (in the form of a binary mask image) of skin lesions in dermoscopic images as close as possible to a segmentation performe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,766
2206.12112	Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal	Seismic processing often requires suppressing multiples that appear when collecting data. To tackle these artifacts, practitioners usually rely on Radon transform-based algorithms as post-migration gather conditioning. However, such traditional approaches are both time-consuming and parameter-dependent, making them fai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,484
2304.03659	Probing Conceptual Understanding of Large Visual-Language Models	In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual grasp of the visual content. In this work we focus on conceptual understanding of these large V+L models. To facilitate this study, we propos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,897
2207.10169	Pediatric Bone Age Assessment using Deep Learning Models	Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and M...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	309,154
2412.19897	Surrogate Modeling for Explainable Predictive Time Series Corrections	We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series 'base model' is used. 'Explainability' of the correction is provided by fitting the base model again to the data from which the error predic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,014
1805.04310	Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer	Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated imp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,220

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