Knowledge catalogue
arXiv:2603.12828v2 Announce Type: replace-cross Abstract: This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset-scale, actionable
arXiv:2608.01686v1 Announce Type: new Abstract: In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From c
arXiv:2608.02177v1 Announce Type: new Abstract: Existing rainfall simulation methods for autonomous driving remain limited in physical controllability and multi-view consistency. This paper presents G
arXiv:2608.00284v1 Announce Type: new Abstract: This paper presents an applied case study on hybrid visual attention estimation for human-robot interaction using an expressive robotic head based on th
arXiv:2608.02538v1 Announce Type: cross Abstract: This paper is concerned with one-bit mean estimation, where each independent sample is represented by a single binary message. We consider distributio
arXiv:2608.00956v1 Announce Type: new Abstract: This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-drive
arXiv:2608.02081v1 Announce Type: new Abstract: In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ra
arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In
arXiv:2608.01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmen
arXiv:2608.00464v1 Announce Type: new Abstract: Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robo
arXiv:2608.00272v1 Announce Type: new Abstract: This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signature
arXiv:2608.00991v1 Announce Type: cross Abstract: This paper introduces SCHEDBench, a natural-language benchmark for evaluating combinatorial scheduling constraint faithfulness under surface-form vari
arXiv:2608.01670v1 Announce Type: new Abstract: This paper answers the one-dimensional local root anti-concentration questions posed by Balcan, Pegden, and Sharma in the context of online optimization
arXiv:2608.02145v1 Announce Type: new Abstract: In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each
arXiv:2608.00984v1 Announce Type: new Abstract: This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited
arXiv:2607.28647v1 Announce Type: cross Abstract: This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum
arXiv:2607.29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equ
arXiv:2602.15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which coul
arXiv:2509.25146v2 Announce Type: replace-cross Abstract: This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Fe
arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not a
arXiv:2512.13677v2 Announce Type: replace Abstract: In this paper, we present JoVA, a streamlined framework that unifies joint video-audio generation and editing. While existing methods often rely on
arXiv:2607.28855v1 Announce Type: cross Abstract: Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propos
arXiv:2410.06665v4 Announce Type: replace-cross Abstract: This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike tradition
arXiv:2407.04521v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time q-learning in mean-field jump-diffusion models in a setting where the environment simulator does not pr
arXiv:2607.28637v1 Announce Type: new Abstract: This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subta
arXiv:2601.20797v1 Announce Type: cross Abstract: This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence
arXiv:2403.06494v2 Announce Type: replace-cross Abstract: This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of
arXiv:2607.28119v1 Announce Type: new Abstract: In this paper, we draw a comparison between linguists in training, a trained linguist, and annotations generated by large language models (LLMs) to find
arXiv:2512.04697v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time reinforcement learning (RL) for optimal switching problems across multiple regimes. We consider a type
arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines
arXiv:2607.28129v1 Announce Type: new Abstract: Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal f
arXiv:2607.28293v1 Announce Type: new Abstract: While depth sensors have the potential to complement RGB data for affordance segmentation in wearable robots, their usage seems to remain underexplored.
arXiv:2607.27807v1 Announce Type: new Abstract: This paper studies learning-augmented and randomized online aggregation with delays on a line metric. We consider advice given as online suggested servi
arXiv:2607.28036v1 Announce Type: new Abstract: It is well known that Newton's method converges faster when the initial guess is closer to a root of a system of nonlinear equations. In this paper, a t
arXiv:2607.18866v2 Announce Type: replace-cross Abstract: Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops a derivative theory of th
arXiv:2408.04227v2 Announce Type: replace-cross Abstract: Infrared imaging and turbulence strength measurements are in widespread demand in many fields. This paper introduces a Physical Prior Guided C
arXiv:2607.27209v1 Announce Type: cross Abstract: Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument. As submissions have scaled from thousands to
It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conferen
arXiv:2603.24800v2 Announce Type: replace Abstract: In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analy
arXiv:2607.27085v1 Announce Type: new Abstract: This paper presents the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) dataset and uses it to characterize mandatory lane-changi
arXiv:2607.27078v1 Announce Type: cross Abstract: In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output,
arXiv:2603.21139v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditi
arXiv:2607.26802v1 Announce Type: new Abstract: This paper presents a radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving. The propo
arXiv:2512.08574v2 Announce Type: replace Abstract: This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velo
arXiv:2607.25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language mo
arXiv:2511.03443v2 Announce Type: replace-cross Abstract: In this paper, we investigate optimization problems with nonnegative and orthogonal constraints, where any feasible matrix of size n imes p ex
arXiv:2607.25207v1 Announce Type: new Abstract: This paper investigates a hybrid reinforcement learning setting in tabular Markov Decision Processes (MDPs), where an agent aims to learn an optimal pol
arXiv:2607.25842v1 Announce Type: new Abstract: This paper describes the participation of team 'Go To Germany' in the ImageCLEF 2026 Deepfake Detection and Generation Task. For the image generation ta
arXiv:2607.25751v1 Announce Type: new Abstract: In this paper, we propose the Rule-based Visiting Circulation (RVC) model in tackling the challenge in the IEEE Big Data Cup 2022: Trip Destination Pred
arXiv:2409.12760v3 Announce Type: replace Abstract: To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLA
arXiv:2607.25605v1 Announce Type: new Abstract: This paper presents a set-theoretic formalization of the classical usuli method of al-Sabr wa al-Taqsim (Examination and Division) for extracting legal
arXiv:2607.25283v1 Announce Type: new Abstract: This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes th
arXiv:2607.25887v1 Announce Type: cross Abstract: This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based featur
arXiv:2507.00411v2 Announce Type: replace Abstract: Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of partial label learning (PLL) is
arXiv:2607.25069v1 Announce Type: cross Abstract: Automated verification of numerical claims is a challenging problem, as it requires both language understanding and quantitative reasoning. This paper
arXiv:2607.24757v1 Announce Type: cross Abstract: This paper reports on the rapid development and classroom deployment of a Thonny log visualizer built using AI-assisted ``vibe coding'' to make studen
arXiv:2607.25140v1 Announce Type: new Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and a
arXiv:2403.07598v2 Announce Type: replace Abstract: In this paper, we present Mondrian, an edge system that enables high-performance object detection on high-resolution video streams. Many lightweight
arXiv:2607.15508v2 Announce Type: replace Abstract: In this paper, we address the challenge of multi-objective motion planning for systems under kinodynamic constraints. We consider three problem clas
arXiv:2607.24755v1 Announce Type: cross Abstract: This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) cours