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
2106.15339	SpreadsheetCoder: Formula Prediction from Semi-structured Context	Spreadsheet formula prediction has been an important program synthesis problem with many real-world applications. Previous works typically utilize input-output examples as the specification for spreadsheet formula synthesis, where each input-output pair simulates a separate row in the spreadsheet. However, this formula...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	243,722
2406.02336	Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation	We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with a polynomial approximant. PANNs combine the strengths of DNNs (flexibility and efficiency in higher-dimensional approximation) with those of polynomial approximation (rapid conve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	460,725
2406.06465	AID: Adapting Image2Video Diffusion Models for Instruction-guided Video Prediction	Text-guided video prediction (TVP) involves predicting the motion of future frames from the initial frame according to an instruction, which has wide applications in virtual reality, robotics, and content creation. Previous TVP methods make significant breakthroughs by adapting Stable Diffusion for this task. However, ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	462,590
2405.13300	FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting	Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. However, despite their considerable advantages over traditional statistical approaches, current deep learning-based predictive models often exh...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,884
2003.02929	Flexible Bayesian Nonlinear Model Configuration	Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described throug...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,071
2409.18108	Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot	Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation that encodes both appearance and semantics in a unified representatio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	492,108
1709.00268	Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity	Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here we investigate the application of a simplicity bias based on a natural but alg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	79,876
1903.11916	Intelligent Processing in Vehicular Ad hoc Networks: a Survey	The intelligent Processing technique is more and more attractive to researchers due to its ability to deal with key problems in Vehicular Ad hoc networks. However, several problems in applying intelligent processing technologies in VANETs remain open. The existing applications are comprehensively reviewed and discussed...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	125,607
2409.10141	PSHuman: Photorealistic Single-view Human Reconstruction using Cross-Scale Diffusion	Detailed and photorealistic 3D human modeling is essential for various applications and has seen tremendous progress. However, full-body reconstruction from a monocular RGB image remains challenging due to the ill-posed nature of the problem and sophisticated clothing topology with self-occlusions. In this paper, we pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	488,633
2102.09462	Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data	We present a rotation-equivariant unsupervised learning framework for the sparse deconvolution of non-negative scalar fields defined on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tis...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	220,782
1910.01863	Template-free Data-to-Text Generation of Finnish Sports News	News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics. This poses a challenge for automated dat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	148,063
1811.10158	Reinforcement Learning for Uplift Modeling	Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,427
1902.01999	Testing Markov Chains without Hitting	We study the problem of identity testing of markov chains. In this setting, we are given access to a single trajectory from a markov chain with unknown transition matrix $Q$ and the goal is to determine whether $Q = P$ for some known matrix $P$ or $\text{Dist}(P, Q) \geq \epsilon$ where $\text{Dist}$ is suitably define...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	120,787
2102.09728	On a Variational Definition for the Jensen-Shannon Symmetrization of Distances based on the Information Radius	We generalize the Jensen-Shannon divergence by considering a variational definition with respect to a generic mean extending thereby the notion of Sibson's information radius. The variational definition applies to any arbitrary distance and yields another way to define a Jensen-Shannon symmetrization of distances. When...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	220,868
2402.10062	Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection	For a machine learning model deployed in real world scenarios, the ability of detecting out-of-distribution (OOD) samples is indispensable and challenging. Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidenc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,791
2006.11395	Deep Learning Based Single Sample Per Person Face Recognition: A Survey	Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	183,208
2207.13345	Traffic Sign Detection With Event Cameras and DCNN	In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions -- parameters that are parti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,279
1709.06669	A textual transform of multivariate time-series for prognostics	Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	81,147
1903.08858	Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network	We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	124,926
2405.13555	A Perspective Analysis of Handwritten Signature Technology	Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear wi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,987
2008.11337	Comparison of Centralized and Decentralized Approaches in Cooperative Coverage Problems with Energy-Constrained Agents	A multi-agent coverage problem is considered with energy-constrained agents. The objective of this paper is to compare the coverage performance between centralized and decentralized approaches. To this end, a near-optimal centralized coverage control method is developed under energy depletion and repletion constraints....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	193,242
1910.02235	Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes	Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN). The first stage preliminarily locate the kidney...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,182
2111.01231	Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching	Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated dat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	264,491
2303.02484	Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries	Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempted to promote further diversity in DE via hyperparameters or regularizing loss functions, these methods primarily still rely on a stochastic ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	349,379
2402.00620	Actor Identification in Discourse: A Challenge for LLMs?	The identification of political actors who put forward claims in public debate is a crucial step in the construction of discourse networks, which are helpful to analyze societal debates. Actor identification is, however, rather challenging: Often, the locally mentioned speaker of a claim is only a pronoun ("He proposed...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	425,658
2211.10418	Sample-efficient Quantum Born Machine through Coding Rate Reduction	The quantum circuit Born machine (QCBM) is a quantum physics inspired implicit generative model naturally suitable for learning binary images, with a potential advantage of modeling discrete distributions that are hard to simulate classically. As data samples are generated quantum-mechanically, QCBMs encompass a unique...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,301
2412.04735	A dynamical measure of algorithmically infused visibility	This work focuses on the nature of visibility in societies where the behaviours of humans and algorithms influence each other - termed algorithmically infused societies. We propose a quantitative measure of visibility, with implications and applications to an array of disciplines including communication studies, politi...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	514,539
2203.12376	A Fast Diagnostic to Inform Screening of Discarded or Retired Batteries	With the increased pervasiveness of Lithium-ion batteries, there is growing concern for the amount of retired batteries that will be entering the waste stream. Although these batteries no longer meet the demands of their first application, many still have a significant portion of their initial capacity remaining for us...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	287,249
2102.06282	A reproduction of Apple's bi-directional LSTM models for language identification in short strings	Language Identification is the task of identifying a document's language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog p...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	219,700
2403.13914	Database Dependencies and Formal Concept Analysis	This is an account of the characterization of database dependencies with Formal Concept Analysis.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	439,844
2107.09949	Online structural kernel selection for mobile health	Motivated by the need for efficient and personalized learning in mobile health, we investigate the problem of online kernel selection for Gaussian Process regression in the multi-task setting. We propose a novel generative process on the kernel composition for this purpose. Our method demonstrates that trajectories of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,174
2109.04581	A Unified Model with Inertia Shaping for Highly Dynamic Jumps of Legged Robots	To achieve highly dynamic jumps of legged robots, it is essential to control the rotational dynamics of the robot. In this paper, we aim to improve the jumping performance by proposing a unified model for planning highly dynamic jumps that can approximately model the centroidal inertia. This model abstracts the robot a...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	254,451
2104.13133	Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms	In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	232,409
2312.00372	Event-driven Real-time Retrieval in Web Search	Information retrieval in real-time search presents unique challenges distinct from those encountered in classical web search. These challenges are particularly pronounced due to the rapid change of user search intent, which is influenced by the occurrence and evolution of breaking news events, such as earthquakes, elec...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	412,028
2302.12784	STA: Self-controlled Text Augmentation for Improving Text Classifications	Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural Language Processing (NLP) which can enrich the training data with new ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	347,692
2405.15879	Global Output-Feedback Extremum Seeking Control with Source Seeking Experiments	This paper discusses the design of an extremum seeking controller that relies on a monitoring function for a class of SISO uncertain nonlinear systems characterized by arbitrary and uncertain relative degree. Our demonstration illustrates the feasibility of achieving an arbitrarily small proximity to the desired optima...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	457,141
1604.04789	A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids	Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making fo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	54,708
2101.01336	Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays	The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	214,340
2011.13726	AdS/Deep-Learning made easy: simple examples	Deep learning has been widely and actively used in various research areas. Recently, in the gauge/gravity duality, a new deep learning technique so-called the AdS/Deep-Learning (DL) has been proposed [1, 2]. The goal of this paper is to describe the essence of the AdS/DL in the simplest possible setups, for those who w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,574
2303.10725	SIESTA: Efficient Online Continual Learning with Sleep	In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any distributional assumptions about the data stream. Ideally, only one pass through the dataset is needed for computational efficiency. However, e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	352,559
2203.16941	A unified theory of learning	Recently machine learning using neural networks (NN) has been developed, and many new methods have been suggested. These methods are optimized for the type of input data and work very effectively, but they cannot be used with any kind of input data universally. On the other hand, the human brain is universal for any ki...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	288,988
1808.09270	Models for Predicting Community-Specific Interest in News Articles	In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communit...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	106,147
1109.1409	A georeferenced Agent-Based Model to analyze the climate change impacts on the Andorra winter tourism	This study presents a georeferenced agent-based model to analyze the climate change impacts on the ski industry in Andorra and the effect of snowmaking as future adaptation strategy. The present study is the first attempt to analyze the ski industry in the Pyrenees region and will contribute to a better understanding o...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	12,026
2303.18021	A flatness-based saturated controller design for a quadcopter with experimental validation	Using the properties of differential flatness, a controllable system, such as a quadcoper model, may be transformed into a linear equivalent system via a coordinate change and an input mapping. This is a straightforward advantage for the quadcopter's controller design and its real-time implementation. However, one sign...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	355,439
2209.05738	RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments	We present a novel reinforcement learning based algorithm for multi-robot task allocation problem in warehouse environments. We formulate it as a Markov Decision Process and solve via a novel deep multi-agent reinforcement learning method (called RTAW) with attention inspired policy architecture. Hence, our proposed po...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	317,192
2112.04981	PE-former: Pose Estimation Transformer	Vision transformer architectures have been demonstrated to work very effectively for image classification tasks. Efforts to solve more challenging vision tasks with transformers rely on convolutional backbones for feature extraction. In this paper we investigate the use of a pure transformer architecture (i.e., one wit...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,706
2306.03647	Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks	An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,427
1401.2101	NoSQL Databases	In this document, I present the main notions of NoSQL databases and compare four selected products (Riak, MongoDB, Cassandra, Neo4J) according to their capabilities with respect to consistency, availability, and partition tolerance, as well as performance. I also propose a few criteria for selecting the right tool for ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	29,714
2209.13094	Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix	With the advent of sophisticated cameras, the urge to capture high-quality images has grown enormous. However, the noise contamination of the images results in substandard expectations among the people; thus, image denoising is an essential pre-processing step. While the algebraic image processing frameworks are someti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	319,767
1907.04481	Tails of Lipschitz Triangular Flows	We investigate the ability of popular flow based methods to capture tail-properties of a target density by studying the increasing triangular maps used in these flow methods acting on a tractable source density. We show that the density quantile functions of the source and target density provide a precise characterizat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,117
2404.05997	Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis	The black-box nature of deep learning models has raised concerns about their interpretability for successful deployment in real-world clinical applications. To address the concerns, eXplainable Artificial Intelligence (XAI) aims to provide clear and understandable explanations of the decision-making process. In the med...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,292
2108.01246	AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments	Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	248,966
2406.07393	Large Language Models are Limited in Out-of-Context Knowledge Reasoning	Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. However, previous work challenges their out-of-context reasoning ability, i.e., the ability to infer information from their training data, instead of from the context or prompt. This paper focuses on a s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	463,026
2210.10352	Temporal Action Segmentation: An Analysis of Modern Techniques	Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid gro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,888
2502.13722	Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve	Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market participants. However, achieving VWAP is inherently challenging due to its dependence on two dynamic factors, volumes and prices. Traditional appr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,485
2501.12274	Making it to First: The Random Access Problem in DNA Storage	We study the Random Access Problem in DNA storage, which addresses the challenge of retrieving a specific information strand from a DNA-based storage system. Given that $k$ information strands, representing the data, are encoded into $n$ strands using a code. The goal under this paradigm is to identify and analyze code...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,238
2212.14421	Timestomping Vulnerability of Age-Sensitive Gossip Networks	We consider gossip networks consisting of a source that maintains the current version of a file, $n$ nodes that use asynchronous gossip mechanisms to disseminate fresh information in the network, and an oblivious adversary who infects the packets at a target node through data timestamp manipulation, with the intent to ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	338,608
1907.00452	Detecting Spiky Corruption in Markov Decision Processes	Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem within the formalism of Corrupt Reward Markov Decision Processes (CRMDPs). We show that if the reward corruption in a CRMDP is sufficiently "...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,046
2201.10017	Online Convex Optimization Using Coordinate Descent Algorithms	This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	276,850
1806.00699	Quantifying the dynamics of topical fluctuations in language	The availability of large diachronic corpora has provided the impetus for a growing body of quantitative research on language evolution and meaning change. The central quantities in this research are token frequencies of linguistic elements in texts, with changes in frequency taken to reflect the popularity or selectiv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	99,372
1211.7326	Repeated Root Constacyclic Codes of Length $mp^s$ over $\mathbb{F}_{p^r}+u \mathbb{F}_{p^r}+...+ u^{e-1}\mathbb{F}_{p^r}$	We give the structure of $\lambda$-constacyclic codes of length $p^sm$ over $R=\mathbb{F}_{p^r}+u \mathbb{F}_{p^r}+...+ u^{e-1}\mathbb{F}_{p^r}$ with $\lambda \in \F_{p^r}^*$. We also give the structure of $\lambda$-constacyclic codes of length $p^sm$ with $\lambda=\alpha_1+u\alpha_2+...+u^{e-1} \alpha_{e-1}$, where $\...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,044
1910.08965	Learning GANs and Ensembles Using Discrepancy	Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions. The choice of that divergence is therefore critical. We argue that the divergence must take into account the hypothesis set and the loss function used in a subsequent learning task, where the data generated ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,033
2407.01299	Preserving Full Degradation Details for Blind Image Super-Resolution	The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to absence of true degradation models in real-world scenarios, previous methods learn distinct representations by distinguishing different degradations in a batch. However, the most ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,234
2401.16920	Sparse Portfolio Selection via Topological Data Analysis based Clustering	This paper uses topological data analysis (TDA) tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction. Our asset selection strategy exploits the topological features of stock price movements to select a subset of topologically similar (different) assets ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,031
2111.00979	Parabola-Inscribed Poncelet Polygons Derived from the Bicentric Family	We study loci and properties of a Parabola-inscribed family of Poncelet polygons whose caustic is a focus-centered circle. This family is the polar image of a special case of the bicentric family with respect to its circumcircle. We describe closure conditions, curious loci, and new conserved quantities.	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	264,418
1905.05891	Crowd Density Estimation using Novel Feature Descriptor	Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crow...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,842
1111.1555	A scheme to protect against multiple quantum erasures	We present a scheme able to protect k >= 3 qubits of information against the occurrence of multiple erasures, based on the code proposed by Yang et al. (2004 JETP Letters 79 236). In this scheme redundant blocks are used and we restrict to the case that each erasure must occur in distinct blocks. We explicitly characte...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,938
2405.11601	How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure	The combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data and provide employees and customers easy access to these cloud data. These massive collections of complex data sets are facing security challenges. While cloud platf...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,209
2110.06661	A Primer on Near-Field Beamforming for Arrays and Reconfigurable Intelligent Surfaces	Wireless communication systems have almost exclusively operated in the far-field of antennas and antenna arrays, which is conventionally characterized by having propagation distances beyond the Fraunhofer distance. This is natural since the Fraunhofer distance is normally only a few wavelengths. With the advent of acti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	260,705
2205.06226	The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning	Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated the research of non-contrastive self-supervised learning. It is mysterious why even when ther...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,177
2102.05983	Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model	Real-world applications have been dealing with large amounts of data that arrive over time and generally present changes in their underlying joint probability distribution, i.e., concept drift. Concept drift can be subdivided into two types: virtual drift, which affects the unconditional probability distribution p(x), ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,601
2007.09859	Novel Approach to Use HU Moments with Image Processing Techniques for Real Time Sign Language Communication	Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn't have a clear idea of sign language. "Sign Language Communicator" (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The mai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,088
1910.10461	A Novel Generalized Artificial Neural Network for Mining Two-Class Datasets	A novel general neural network (GNN) is proposed for two-class data mining in this study. In a GNN, each attribute in the dataset is treated as a node, with each pair of nodes being connected by an arc. The reliability is of each arc, which is similar to the weight in artificial neural network and must be solved using ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	150,494
1804.02872	Variational 3D-PIV with Sparse Descriptors	3D Particle Imaging Velocimetry (3D-PIV) aim to recover the flow field in a volume of fluid, which has been seeded with tracer particles and observed from multiple camera viewpoints. The first step of 3D-PIV is to reconstruct the 3D locations of the tracer particles from synchronous views of the volume. We propose a ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,515
2303.03539	A Study on Multirobot Quantile Estimation in Natural Environments	Quantiles of a natural phenomena can provide scientists with an important understanding of different spreads of concentrations. When there are several available robots, it may be advantageous to pool resources in a collaborative way to improve performance. A multirobot team can be difficult to practically bring togethe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	349,760
2409.05601	Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation	This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on short segments containing only lowercase or partial punctuation and capitalization (PnC) sentences, we propose training on longer utterances t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	486,834
2112.00006	Towards algorithm-free physical equilibrium model of computing	Our computers today, from sophisticated servers to small smartphones, operate based on the same computing model, which requires running a sequence of discrete instructions, specified as an algorithm. This sequential computing paradigm has not yet led to a fast algorithm for an NP-complete problem despite numerous attem...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	269,013
2403.02297	Uncertainty-Aware Prediction and Application in Planning for Autonomous Driving: Definitions, Methods, and Comparison	Autonomous driving systems face the formidable challenge of navigating intricate and dynamic environments with uncertainty. This study presents a unified prediction and planning framework that concurrently models short-term aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and epistemic uncertainty (E...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,758
2205.14412	Design, Modelling, and Control of a Reconfigurable Rotary Series Elastic Actuator with Nonlinear Stiffness for Assistive Robots	In assistive robots, compliant actuator is a key component in establishing safe and satisfactory physical human-robot interaction (pHRI). The performance of compliant actuators largely depends on the stiffness of the elastic element. Generally, low stiffness is desirable to achieve low impedance, high fidelity of force...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	299,355
2307.14272	Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing	Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods have demonstrated impressive learning capabilities using visual input, a lack of tactile sensing limits their capability for fine and reliable...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	381,864
2203.11373	Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset	Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices for their benefits during UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper pres...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	true	false	false	286,885
2106.06529	The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective	Large width limits have been a recent focus of deep learning research: modulo computational practicalities, do wider networks outperform narrower ones? Answering this question has been challenging, as conventional networks gain representational power with width, potentially masking any negative effects. Our analysis in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,515
1906.00271	GLAD: Learning Sparse Graph Recovery	Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formulation to recover the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,332
1004.4460	Handling Overload Conditions In High Performance Trustworthy Information Retrieval Systems	Web search engines retrieve a vast amount of information for a given search query. But the user needs only trustworthy and high-quality information from this vast retrieved data. The response time of the search engine must be a minimum value in order to satisfy the user. An optimum level of response time should be main...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	6,277
2309.12476	Differentially Private Reward Functions in Policy Synthesis for Markov Decision Processes	Markov decision processes often seek to maximize a reward function, but onlookers may infer reward functions by observing the states and actions of such systems, revealing sensitive information. Therefore, in this paper we introduce and compare two methods for privatizing reward functions in policy synthesis for multi-...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	393,807
1911.04660	Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification	In this work, we analyse the random projections of Mel-spectrograms as low-level features for music genre classification. This approach was compared to handcrafted features, features learned using an auto-encoder and features obtained from a transfer learning setting. Tests in five different well-known, publicly availa...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	153,048
2005.07037	Training conformal predictors	Efficiency criteria for conformal prediction, such as \emph{observed fuzziness} (i.e., the sum of p-values associated with false labels), are commonly used to \emph{evaluate} the performance of given conformal predictors. Here, we investigate whether it is possible to exploit efficiency criteria to \emph{learn} classif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,175
2307.14009	Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images	Compositional neural scene graph studies have shown that radiance fields can be an efficient tool in an editable autonomous driving simulator. However, previous studies learned within a sequence of autonomous driving datasets, resulting in unsatisfactory blurring when rotating the car in the simulator. In this letter, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,784
1609.08438	Flows Generating Nonlinear Eigenfunctions	Nonlinear variational methods have become very powerful tools for many image processing tasks. Recently a new line of research has emerged, dealing with nonlinear eigenfunctions induced by convex functionals. This has provided new insights and better theoretical understanding of convex regularization and introduced new...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	61,596
2312.15322	Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization	Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,972
2112.05593	A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications	The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	270,888
2410.15460	Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training	As large language models (LLMs) are increasingly deployed across various industries, concerns regarding their reliability, particularly due to hallucinations - outputs that are factually inaccurate or irrelevant to user input - have grown. Our research investigates the relationship between the training process and the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	500,549
2011.10804	BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures	Binary Neural Networks (BNNs) have received significant attention due to their promising efficiency. Currently, most BNN studies directly adopt widely-used CNN architectures, which can be suboptimal for BNNs. This paper proposes a novel Binary ARchitecture Search (BARS) flow to discover superior binary architecture in ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	207,627
2412.06209	Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment	How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation task is challenging due to the significant information gap between auditory and visual signals. We address this challenge by designing a mode...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	515,152
2110.11223	Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking	Many road accidents are caused by drowsiness of the driver. While there are methods to detect closed eyes, it is a non-trivial task to detect the gradual process of a driver becoming drowsy. We consider a simple real-time detection system for drowsiness merely based on the eye blinking rate derived from the eye aspect ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	262,399
cs/0504028	On Extrinsic Information of Good Codes Operating Over Discrete Memoryless Channels	We show that the Extrinsic Information about the coded bits of any good (capacity achieving) code operating over a wide class of discrete memoryless channels (DMC) is zero when channel capacity is below the code rate and positive constant otherwise, that is, the Extrinsic Information Transfer (EXIT) chart is a step fun...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,649
2305.19339	Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses	A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	369,478
2407.15589	Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models	Object-centric (OC) representations, which represent the state of a visual scene by modeling it as a composition of objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning. However, these claims have not been thoroughly analyzed yet....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	475,243
1711.10658	Deep-Person: Learning Discriminative Deep Features for Person Re-Identification	Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,637
2106.12406	Mitigating the Impact of Distributed Generations on Relay Coordination Using Fault Current Limiters	The use of distributed generation resources, in addition to considerable benefits, causes some problems in the power system. One of the most critical problems in the case of disruption is increasing short-circuit current level in grids, which leads to change the protection devices settings in the downstream and upstrea...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	242,718

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