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
2111.14792	Classification-Regression for Chart Comprehension	Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most exi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,707
2312.12417	Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning	Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. The efficiency of FL can be improved by using over-the-air computation (AirComp) technology in the process of gradient aggregation. In this paper, we propose a relay-assisted large-scale FL framework, and i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,940
0910.0646	Digital Business Ecosystems: Natural Science Paradigms	A primary motivation for research in Digital Ecosystems is the desire to exploit the self-organising properties of natural ecosystems. Ecosystems arc thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	4,624
2401.05952	LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?	With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it potential risks, especially in terms of quality and integrity in fields like news, education, and science. Current research mainly focuses on pu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,972
2305.19926	Revisiting the Reliability of Psychological Scales on Large Language Models	Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administration of personality tests to LLMs has emerged as a noteworthy area in this context. However, the suitabilit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	369,741
1507.02177	Iris Recognition Using Scattering Transform and Textural Features	Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recogni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,952
0902.0899	Comparative concept similarity over Minspaces: Axiomatisation and Tableaux Calculus	We study the logic of comparative concept similarity $\CSL$ introduced by Sheremet, Tishkovsky, Wolter and Zakharyaschev to capture a form of qualitative similarity comparison. In this logic we can formulate assertions of the form " objects A are more similar to B than to C". The semantics of this logic is defined by s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,113
2407.08252	Spatially-Variant Degradation Model for Dataset-free Super-resolution	This paper focuses on the dataset-free Blind Image Super-Resolution (BISR). Unlike existing dataset-free BISR methods that focus on obtaining a degradation kernel for the entire image, we are the first to explicitly design a spatially-variant degradation model for each pixel. Our method also benefits from having a sign...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,089
2403.05457	Sparse dynamic network reconstruction through L1-regularization of a Lyapunov equation	An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes. A common approach is to use the elements of the correlation matrix or its inverse as proxies of the interaction strengths, but the reconstructed networks are ne...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	436,017
2302.14611	TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation	Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion. To tackle online settings, we propose TransAdapt, a framework that uses transfo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,368
1702.06941	An Algebraic Formalization of Forward and Forward-backward Algorithms	In this paper, we propose an algebraic formalization of the two important classes of dynamic programming algorithms called forward and forward-backward algorithms. They are generalized extensively in this study so that a wide range of other existing algorithms is subsumed. Forward algorithms generalized in this study s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	68,704
1909.13695	Non-native Speaker Verification for Spoken Language Assessment	Automatic spoken language assessment systems are becoming more popular in order to handle increasing interests in second language learning. One challenge for these systems is to detect malpractice. Malpractice can take a range of forms, this paper focuses on detecting when a candidate attempts to impersonate another in...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,493
2501.17842	From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning	Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-dir...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	528,479
2102.09964	Temporal Gaussian Process Regression in Logarithmic Time	The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in logarithmic O(log N) time, where N is the number of time steps. Our approach uses the state-space representation of GPs which in its origin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,940
2101.08413	MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping	Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	216,311
2108.11012	Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning	In this chapter, the regulation of Unmanned Aerial Vehicle (UAV) communication network is investigated in the presence of dynamic changes in the UAV lineup and user distribution. We target an optimal UAV control policy which is capable of identifying the upcoming change in the UAV lineup (quit or join-in) or user distr...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	252,060
2403.12335	Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems	Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,122
2310.03518	Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations	In real dialogue scenarios, as there are unknown input noises in the utterances, existing supervised slot filling models often perform poorly in practical applications. Even though there are some studies on noise-robust models, these works are only evaluated on rule-based synthetic datasets, which is limiting, making i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	397,315
2108.11974	Learning Cross-modal Contrastive Features for Video Domain Adaptation	Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition. Existing video domain adaptation methods mainly rely on adversarial feature alignment, which has been derived from the RGB image space. However, video data is usually associate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,350
1912.07076	Multilingual is not enough: BERT for Finnish	Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks. While most work on these...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	157,506
2310.17312	An Ensemble Method Based on the Combination of Transformers with Convolutional Neural Networks to Detect Artificially Generated Text	Thanks to the state-of-the-art Large Language Models (LLMs), language generation has reached outstanding levels. These models are capable of generating high quality content, thus making it a challenging task to detect generated text from human-written content. Despite the advantages provided by Natural Language Generat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,080
2410.06420	ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments	The global shortage of healthcare workers has demanded the development of smart healthcare assistants, which can help monitor and alert healthcare workers when necessary. We examine the healthcare knowledge of existing Large Vision Language Models (LVLMs) via the Visual Question Answering (VQA) task in hospital setting...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	496,192
1807.10752	Dictionary Learning in Fourier Transform Scanning Tunneling Spectroscopy	Modern high-resolution microscopes, such as the scanning tunneling microscope, are commonly used to study specimens that have dense and aperiodic spatial structure. Extracting meaningful information from images obtained from such microscopes remains a formidable challenge. Fourier analysis is commonly used to analyze t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	104,023
2204.01156	Switched Max-Plus Linear-Dual Inequalities: Application in Scheduling of Multi-Product Processing Networks	P-time event graphs are discrete event systems suitable for modeling processes in which tasks must be executed in predefined time windows. Their dynamics can be represented by systems of linear dynamical inequalities in the max-plus algebra and its dual, the min-plus algebra, referred to as max-plus linear-dual inequal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,517
1504.03609	Output agreement in networks with unmatched disturbances and algebraic constraints	This paper considers a problem of output agreement in heterogeneous networks with dynamics on the nodes as well as on the edges. The control and disturbance signals entering the nodal dynamics are "unmatched" meaning that some nodes are only subject to disturbances, and are deprived of actuating signals. To further enr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	42,051
2409.07170	Learning Efficient Recursive Numeral Systems via Reinforcement Learning	The emergence of mathematical concepts, such as number systems, is an understudied area in AI for mathematics and reasoning. It has previously been shown Carlsson et al. (2021) that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems. However, it is a major ch...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	487,414
2206.09358	What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs	Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization mechanism. Moreover, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,535
2405.07847	SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling	We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that supports conveniently a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RGB-D/RGB-L/Mono//Depth-only reconstruction and SLAM. The workflow-centr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	453,888
2305.07037	On Expressivity of Height in Neural Networks	In this work, beyond width and depth, we augment a neural network with a new dimension called height by intra-linking neurons in the same layer to create an intra-layer hierarchy, which gives rise to the notion of height. We call a neural network characterized by width, depth, and height a 3D network. To put a 3D netwo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,756
2001.07408	Vector Single-Source Surface Integral Equation for TE Scattering From Cylindrical Multilayered Objects	A single-source surface integral equation (SS-SIE) for transverse electric (TE) scattering from cylindrical multilayered objects is proposed in this paper. By incorporating the differential surface admittance operator (DSAO) and recursively applying the surface equivalence theorem from innermost to outermost boundaries...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	161,029
1401.5657	Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids	Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	30,218
2310.10971	Context-Aware Meta-Learning	Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or fine-tu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	400,463
2502.05472	Robust Deep Signed Graph Clustering via Weak Balance Theory	Signed graph clustering is a critical technique for discovering community structures in graphs that exhibit both positive and negative relationships. We have identified two significant challenges in this domain: i) existing signed spectral methods are highly vulnerable to noise, which is prevalent in real-world scenari...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	531,628
2010.05495	Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers	For safety-critical applications such as autonomous driving, CNNs have to be robust with respect to unavoidable image corruptions, such as image noise. While previous works addressed the task of robust prediction in the context of full-image classification, we consider it for dense semantic segmentation. We build upon ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,156
2002.09441	Minimizing Localized Ratio Cut Objectives in Hypergraphs	Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data. Graph clustering has been studied extensively, and there are numerous methods for detecting small, localized clusters without having to explo...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	165,061
2404.11296	How to Exhibit More Predictable Behaviors	This paper looks at predictability problems, i.e., wherein an agent must choose its strategy in order to optimize the predictions that an external observer could make. We address these problems while taking into account uncertainties on the environment dynamics and on the observed agent's policy. To that end, we assume...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	447,451
2412.17933	BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism	We present BenCzechMark (BCM), the first comprehensive Czech language benchmark designed for large language models, offering diverse tasks, multiple task formats, and multiple evaluation metrics. Its scoring system is grounded in statistical significance theory and uses aggregation across tasks inspired by social prefe...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	520,175
2410.22790	Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation	Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while i...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	503,770
1705.00714	Characterization of Cross-posting Activity for Professional Users across Facebook, Twitter and Google+	Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	72,734
2405.16009	Streaming Long Video Understanding with Large Language Models	This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for video understanding, that capably understands arbitrary-length video with a constant number of video tokens streamingly encoded and adaptively selected. The challenge of video understanding in the vision language area mainly lies in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,209
2501.00709	KAN KAN Buff Signed Graph Neural Networks?	Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neural networks to model complex graph structures. Recently, the Kolmogorov-Arnold Neural Network (KAN) has emerged as a promising alternative to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,767
1810.01165	Semi-supervised Text Regression with Conditional Generative Adversarial Networks	Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervis...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	109,346
1809.09399	Non-Iterative Knowledge Fusion in Deep Convolutional Neural Networks	Incorporation of a new knowledge into neural networks with simultaneous preservation of the previous one is known to be a nontrivial problem. This problem becomes even more complex when new knowledge is contained not in new training examples, but inside the parameters (connection weights) of another neural network. Her...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,698
2502.06818	Globality Strikes Back: Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation	Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In CLIP, patch-wise image representations mainly encode the homogeneous image-level properties and thus are not discriminative enough, hindering its application to the dense prediction task. Previous works mak...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,264
2009.14137	A Comprehensive Multi-Period Optimal Power Flow Framework for Smart LV Networks	This paper presents an extensive multi-period optimal power flow framework, with new modelling elements, for smart LV distribution systems that rely on residential flexibility for combating operational issues. A detailed performance assessment of different setups is performed, including: ZIP flexible loads (FLs), varyi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,948
2111.11260	MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals	Identification of minerals in the field is a task that is wrought with many challenges. Traditional approaches are prone to errors where there is no enough experience and expertise. Several existing techniques mainly make use of features of the minerals under a microscope and tend to favour a manual feature extraction ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,603
1505.07726	Linear Codes from a Generic Construction	A generic construction of linear codes over finite fields has recently received a lot of attention, and many one-weight, two-weight and three-weight codes with good error correcting capability have been produced with this generic approach. The first objective of this paper is to establish relationships among some class...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,567
2305.01154	FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer	Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server commu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	361,577
1406.4784	Improved Densification of One Permutation Hashing	The existing work on densification of one permutation hashing reduces the query processing cost of the $(K,L)$-parameterized Locality Sensitive Hashing (LSH) algorithm with minwise hashing, from $O(dKL)$ to merely $O(d + KL)$, where $d$ is the number of nonzeros of the data vector, $K$ is the number of hashes in each h...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	33,973
2501.14844	Unmasking Conversational Bias in AI Multiagent Systems	Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings. However, the majority of existing methodologies for identifying biases in generated text consider the models in isolation and neglect their contextual applicat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	527,298
2310.20492	Log-based Anomaly Detection of Enterprise Software: An Empirical Study	Most enterprise applications use logging as a mechanism to diagnose anomalies, which could help with reducing system downtime. Anomaly detection using software execution logs has been explored in several prior studies, using both classical and deep neural network-based machine learning models. In recent years, the rese...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	404,417
1711.01432	Noise-induced synchronization of Hegselmann-Krause dynamics in full space	The Hegselmann-Krause (HK) model is a typical self-organizing system with local rule dynamics. In spite of its widespread use and numerous extensions, the underlying theory of its synchronization induced by noise still needs to be developed. In its original formulation, as a model first proposed to address opinion dyna...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	83,885
2112.09687	Light Field Neural Rendering	Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,225
2306.15318	Towards predicting Pedestrian Evacuation Time and Density from Floorplans using a Vision Transformer	Conventional pedestrian simulators are inevitable tools in the design process of a building, as they enable project engineers to prevent overcrowding situations and plan escape routes for evacuation. However, simulation runtime and the multiple cumbersome steps in generating simulation results are potential bottlenecks...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	375,980
2108.08214	Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks	Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,178
1806.09846	System Design in the Era of IoT --- Meeting the Autonomy Challenge	The advent of IoT is a great opportunity to reinvigorate Computing by focusing on autonomous system design. This certainly raises technology questions but, more importantly, it requires building new foundation that will systematically integrate the innovative results needed to face increasing environment and mission co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	101,442
1209.5805	Memoryless Control Design for Persistent Surveillance under Safety Constraints	This paper deals with the design of time-invariant memoryless control policies for robots that move in a finite two- dimensional lattice and are tasked with persistent surveillance of an area in which there are forbidden regions. We model each robot as a controlled Markov chain whose state comprises its position in the...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	18,765
1507.06565	Large scale lattice Boltzmann simulation for the coupling of free and porous media flow	In this work, we investigate the interaction of free and porous media flow by large scale lattice Boltzmann simulations. We study the transport phenomena at the porous interface on multiple scales, i.e., we consider both, computationally generated pore-scale geometries and homogenized models at a macroscopic scale. The...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	45,396
2204.08306	A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks	Momentum methods, including heavy-ball~(HB) and Nesterov's accelerated gradient~(NAG), are widely used in training neural networks for their fast convergence. However, there is a lack of theoretical guarantees for their convergence and acceleration since the optimization landscape of the neural network is non-convex. N...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,034
2107.08772	Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages	For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual data by using synthetic data generation techniques such as back-translation and noising, while self-supervised NMT (SSNMT) identifies parall...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	246,831
2212.04831	Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models	Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy. Instead, in this work, we propose to quantify the uncertainty associated with clean speech estimates in neural network-based speech enhancement. Predictive uncertainty...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,592
2312.14499	Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks	Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computation...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	417,663
2205.00869	Topology Analysis of the XRP Ledger	XRP Ledger is one of the oldest, well-established blockchains. Despite the popularity of the XRP Ledger, little is known about its underlying peer-to-peer network. The structural properties of a network impact its efficiency, security and robustness. We aim to close the knowledge gap by providing a detailed analysis of...	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	294,407
2202.08837	Adiabatic Quantum Computing for Multi Object Tracking	Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	281,001
2502.10582	Named entity recognition for Serbian legal documents: Design, methodology and dataset development	Recent advancements in the field of natural language processing (NLP) and especially large language models (LLMs) and their numerous applications have brought research attention to design of different document processing tools and enhancements in the process of document archiving, search and retrieval. Domain of offici...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,951
2212.11431	Local Policy Improvement for Recommender Systems	Recommender systems predict what items a user will interact with next, based on their past interactions. The problem is often approached through supervised learning, but recent advancements have shifted towards policy optimization of rewards (e.g., user engagement). One challenge with the latter is policy mismatch: we ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	337,790
2403.03111	Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection	Perception is a key element for enabling intelligent autonomous navigation. Understanding the semantics of the surrounding environment and accurate vehicle pose estimation are essential capabilities for autonomous vehicles, including self-driving cars and mobile robots that perform complex tasks. Fast moving platforms ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	435,067
2402.15608	Machine Learning-Based Completions Sequencing for Well Performance Optimization	Establishing accurate field development parameters to optimize long-term oil production takes time and effort due to the complexity of oil well development, and the uncertainty in estimating long-term well production. Traditionally, oil and gas companies use simulation software that are inherently computationally expen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	432,210
2109.13754	Deep Generative Modeling for Protein Design	Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein fa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	257,736
2106.09711	Visual Correspondence Hallucination	Given a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view. Local feature matching methods are only able to identify the correspondent's location when it is visible, whil...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,766
2404.19195	Evaluation of Thermal Performance of a Wick-free Vapor Chamber in Power Electronics Cooling	Efficient thermal management in high-power electronics cooling can be achieved using phase-change heat transfer devices, such as vapor chambers. Traditional vapor chambers use wicks to transport condensate for efficient thermal exchange and to prevent "dry-out" of the evaporator. However, wicks in vapor chambers presen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	450,538
2410.11886	Are Grid Cells Hexagonal for Performance or by Convenience?	This paper investigates whether the hexagonal structure of grid cells provides any performance benefits or if it merely represents a biologically convenient configuration. Utilizing the Vector-HaSH content addressable memory model as a model of the grid cell -- place cell network of the mammalian brain, we compare the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	498,766
1907.07167	Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression	Linear regression in $\ell_p$-norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processing. Generic convex optimization algorithms for solving $\ell_p$-regression are slow in practice. Iteratively Reweighted Least Squares (IRLS)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	138,797
2305.12586	Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies	In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions. In this paper, we aim to extend this method to question answerin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,076
2501.08676	FlexiClip: Locality-Preserving Free-Form Character Animation	Animating clipart images with seamless motion while maintaining visual fidelity and temporal coherence presents significant challenges. Existing methods, such as AniClipart, effectively model spatial deformations but often fail to ensure smooth temporal transitions, resulting in artifacts like abrupt motions and geomet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	524,868
2001.10223	BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks	Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	161,770
1912.01106	MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices	Despite the blooming success of architecture search for vision tasks in resource-constrained environments, the design of on-device object detection architectures have mostly been manual. The few automated search efforts are either centered around non-mobile-friendly search spaces or not guided by on-device latency. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,978
1705.00823	STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset	In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few dat...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	72,757
2310.02692	Clustering-based Image-Text Graph Matching for Domain Generalization	Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	396,955
2401.16981	Selection of gamma events from IACT images with deep learning methods	Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the EASs. The ability to segregate gamma rays images from the hadronic cosmic ray backg...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,051
1303.7054	Wireless Broadcast with Physical-Layer Network Coding	This work investigates the maximum broadcast throughput and its achievability in multi-hop wireless networks with half-duplex node constraint. We allow the use of physical-layer network coding (PNC). Although the use of PNC for unicast has been extensively studied, there has been little prior work on PNC for broadcast....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	23,316
1408.0848	Multilayer bootstrap networks	Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	35,115
2311.00566	CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders	A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	404,698
2501.06235	NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data	4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics ,combining semantic and instance segmentation with temporal consistency.Current methods, like 4D-PLS and 4D-STOP, use a tracking-by-detection methodology, employing deep learning networks to perform semantic and insta...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	523,895
1812.05721	Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint	In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to large-scale graphs. Our contribution consists of reformulating spect...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,461
1806.00040	Efficient Algorithms and Lower Bounds for Robust Linear Regression	We study the problem of high-dimensional linear regression in a robust model where an $\epsilon$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution $\mathcal{N}(0, \Sigma)$ on $\mathbb{R}^d$. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	99,215
1902.05388	Face Recognition using Compressive Sensing	This paper deals with the Compressive Sensing implementation in the Face Recognition problem. Compressive Sensing is new approach in signal processing with a single goal to recover signal from small set of available samples. Compressive Sensing finds its usage in many real applications as it lowers the memory demand an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,530
2404.10907	Causal Effect Estimation Using Random Hyperplane Tessellations	Matching is one of the simplest approaches for estimating causal effects from observational data. Matching techniques compare the observed outcomes across pairs of individuals with similar covariate values but different treatment statuses in order to estimate causal effects. However, traditional matching techniques are...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	447,300
1704.07693	Coding for Arbitrarily Varying Remote Sources	We study a lossy source coding problem for a memoryless remote source. The source data is broadcast over an arbitrarily varying channel (AVC) controlled by an adversary. One output of the AVC is received as input at the encoder, and another output is received as side information at the decoder. The adversary is assumed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,403
2007.10653	Accounting for Unobserved Confounding in Domain Generalization	This paper investigates the problem of learning robust, generalizable prediction models from a combination of multiple datasets and qualitative assumptions about the underlying data-generating model. Part of the challenge of learning robust models lies in the influence of unobserved confounders that void many of the in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,333
2311.15113	NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies	Single cell analysis of human skeletal muscle (SM) tissue cross-sections is a fundamental tool for understanding many neuromuscular disorders. For this analysis to be reliable and reproducible, identification of individual fibres within microscopy images (segmentation) of SM tissue should be automatic and precise. Biom...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	410,390
1902.05492	Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification	Zero-shot learning (ZSL) is one of the most extreme forms of learning from scarce labeled data. It enables predicting that images belong to classes for which no labeled training instances are available. In this paper, we present a new ZSL framework that leverages both label attribute side information and a semantic lab...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,557
2209.11497	Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions	Latent variables often mask cause-effect relationships in observational data which provokes spurious links that may be misinterpreted as causal. This problem sparks great interest in the fields such as climate science and economics. We propose to estimate confounded causal links of time series using Sequential Causal E...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,210
1509.07087	Deep Temporal Sigmoid Belief Networks for Sequence Modeling	Deep dynamic generative models are developed to learn sequential dependencies in time-series data. The multi-layered model is designed by constructing a hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden state, inherit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	47,228
2203.11562	A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis	Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech. However, most of the TTS research focuses on using adult speech data and there has been very limited work done on child speech synthesis. This study developed and validated a training pipeline for...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,960
2301.09578	A Multi-stack Power-to-Hydrogen Load Control Framework for the Power Factor-Constrained Integration in Volatile Peak Shaving Conditions	Large-scale power-to-hydrogen (P2H) systems formed by multi-stack are potentially powerful peak-shaving resources of power systems. However, due to the research gap in connecting the grid-side performance with the inherent operation control, the continuous operation of P2H loads is limited by the PF assessment under vo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	341,539
1606.05381	Deep Image Set Hashing	In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets. These methods are slow to compute and not ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	57,398
2206.08273	Concentration of Data Encoding in Parameterized Quantum Circuits	Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical data, such algorithms generally begin with quantum circuits for data encoding and ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	303,067
2209.14454	CompNet: A Designated Model to Handle Combinations of Images and Designed features	Convolutional neural networks (CNNs) are one of the most popular models of Artificial Neural Networks (ANN)s in Computer Vision (CV). A variety of CNN-based structures were developed by researchers to solve problems like image classification, object detection, and image similarity measurement. Although CNNs have shown ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	320,251
1801.06357	On the Modeling and Performance Assessment of Random Access with SIC	In this paper, we review the key figures of merit to assess the performance of advanced random access (RA) schemes exploiting physical layer coding, repetitions and collision resolution techniques. We then investigate RA modeling aspects and their impact on the figures of merit for the exemplary advanced RA schemes: Co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,593

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