Knowledge catalogue
arXiv:2609.31731v1 Announce Type: new Abstract: In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems.
arXiv:2609.32064v1 Announce Type: new Abstract: This paper presents Grasp2Twist, a bimanual dexterous manipulation system that learns to grasp and twist open jar lids using reinforcement learning. Lea
arXiv:2609.34670v1 Announce Type: cross Abstract: In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation
arXiv:2609.33637v1 Announce Type: cross Abstract: In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under com
arXiv:2609.32346v1 Announce Type: new Abstract: This paper compares three human motion prediction methods - GMM-based clustering, TransFusion, and Graph-Mixer - evaluating their effectiveness in human
arXiv:2609.33416v1 Announce Type: new Abstract: In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The g
arXiv:2609.32820v1 Announce Type: cross Abstract: This paper studies learning of shuffle ideals with membership queries as well as with contrastive queries---a form of membership query that reveals no
arXiv:2510.22654v3 Announce Type: replace Abstract: This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like
arXiv:2511.18038v2 Announce Type: replace-cross Abstract: Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a mult
arXiv:2609.35431v1 Announce Type: new Abstract: This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server
arXiv:2609.31975v1 Announce Type: new Abstract: This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With
arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit
arXiv:2609.34426v1 Announce Type: new Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline
arXiv:2609.33892v1 Announce Type: new Abstract: This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external distur
arXiv:2609.35562v1 Announce Type: new Abstract: This paper is a Track 2 reproducibility companion to an ICPR 2026 study on risky tackle detection in American football prac- tice videos. The original w
arXiv:2609.34310v1 Announce Type: new Abstract: K-means is a widely adopted clustering approach in signal processing and machine learning. In this paper, we study K-means clustering through a cardinal
arXiv:2609.31634v1 Announce Type: new Abstract: This paper develops a theorem-level pipeline in symmetry-quotient settings: quotient linear stability implies quotient flatness, quotient flatness impli
arXiv:2609.33280v1 Announce Type: cross Abstract: Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text co
arXiv:2609.33915v1 Announce Type: new Abstract: Progress in machine learning cannot outpace our ability to verify it. With an explosion in papers today, every scientific claim rests initially on trust
arXiv:2609.31810v1 Announce Type: cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we investiga
arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed
In this paper, we study federated optimization for solving stochastic variational inequalities (VIs), a problem that has attracted growing attention in recent years. Despite substantial progress, a si
The paper “Gap‑free Differentially Private PCA for Gaussian Data” by Alina Ene and Huy L. Nguyen proposes a differentially private algorithm for principal component analysis that does not rely on a sp
arXiv:2609.30846v1 Announce Type: new Abstract: In this paper, we propose I-Parakeet, an integer-only implementation of NVIDIA's Parakeet-CTC (0.6B parameters) that runs on a smartphone NPU without an
arXiv:2506.22497v2 Announce Type: replace-cross Abstract: This paper reconceptualises peer review as structured public commentary. Traditional academic validation is hindered by anonymity, latency, an
arXiv:2609.30446v1 Announce Type: new Abstract: This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on th
arXiv:2602.06042v2 Announce Type: replace-cross Abstract: The Moore-Penrose Pseudo-inverse (PInv) serves as the fundamental solution for linear systems. In this paper, we propose a natural generalizat
arXiv:2609.31282v1 Announce Type: cross Abstract: This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle (SDLC) while also
arXiv:2509.14952v3 Announce Type: replace Abstract: This paper considers the smooth bilevel optimization in which the lower-level problem is strongly convex and the upper-level problem is possibly non
arXiv:2603.07096v2 Announce Type: replace Abstract: Robots that share space with people need human motion predictions with reliable uncertainty, not just accurate means. This paper presents a structur
arXiv:2609.30784v1 Announce Type: cross Abstract: This paper proposes an architecture for equipping large language models (LLMs) with audio-understanding capabilities without fine-tuning their weights
arXiv:2609.29150v1 Announce Type: new Abstract: This paper investigates the joint optimization of the transmit precoder and the transmission/reflection coefficients of a simultaneously transmitting an
arXiv:2609.29340v1 Announce Type: new Abstract: This paper presents a grasping model for cloth manipulation specifically tailored to ease the deployment of robotic control methods. The model is robust
arXiv:2609.29371v1 Announce Type: new Abstract: This paper presents BanglaTurn, a corpus for end-of-turn detection in Bangla conversational speech, and a model trained on it. The corpus holds 35,374 s
arXiv:2609.29798v1 Announce Type: cross Abstract: This paper presents an end-to-end study of automatic speech recognition (ASR) for adolescent health communication in three Ghanaian languages (Twi, Da
arXiv:2505.11909v2 Announce Type: replace-cross Abstract: This paper addresses cross-modal medical image segmentation, focusing on MRI-CT transfer in a source-only domain generalization setting. Durin
arXiv:2609.30140v1 Announce Type: new Abstract: In this paper, we study the joint selection of an environmental support contact and a whole-body configuration for a prescribed loco-manipulation task.
arXiv:2609.29887v1 Announce Type: cross Abstract: This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions. Specifically, we propos
arXiv:2609.29065v1 Announce Type: new Abstract: Grasping is a fundamental robotic capability that bridges perception and physical task execution. This paper studies grasp pose recovery under a percept
arXiv:2609.29163v1 Announce Type: new Abstract: This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedde
arXiv:2609.29409v1 Announce Type: cross Abstract: In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired b
arXiv:2609.29028v1 Announce Type: new Abstract: In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassi
arXiv:2609.28784v1 Announce Type: new Abstract: In this paper, we investigate how script knowledge is distributed across the layers of LLMs using two complementary interpretability methods: logistic r
arXiv:2609.29031v1 Announce Type: new Abstract: Torque observations in reinforcement learning remain challenging because simulated and measured torque differ in scale, offset, and noise. In this paper
arXiv:2609.29417v1 Announce Type: new Abstract: This paper deals with image-based visual servoing and pose estimation by observing four and five lines. Our main interest is to determine the relative c
arXiv:2609.28716v1 Announce Type: cross Abstract: This paper investigates temporal neural networks for mbox{end-effector} position mbox{estimation} of an aerial continuum manipulator (ACM) operating u
arXiv:2609.27240v1 Announce Type: new Abstract: In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with
arXiv:2609.27022v1 Announce Type: new Abstract: In this paper, we investigate the impact of adversarial attacks on identity encoders within a realistic de-identification framework. Our experiments sho
arXiv:2609.26945v1 Announce Type: new Abstract: Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the
arXiv:2412.02575v3 Announce Type: replace Abstract: Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question A
arXiv:2509.14959v4 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and a
arXiv:2609.26806v1 Announce Type: cross Abstract: This paper presents a complete, structure-preserving port to Lean 4 of the Isabelle/HOL dataset accompanying Benzmuller and Scott's study of Godel's m
arXiv:2609.26816v1 Announce Type: cross Abstract: This paper develops a data-driven framework for long-term prediction of fluid--structure interaction (FSI) dynamics, focusing on the flow-induced vibr
arXiv:2609.27489v1 Announce Type: cross Abstract: This paper introduces Passing, an interactive audiovisual installation that generates an endless journey from a single continuous monorail-window reco
arXiv:2609.27982v1 Announce Type: cross Abstract: In this paper, we are concerned with matrices formed by block-diagonal factors interleaved with fixed permutations -- a flexible family of structured
arXiv:2609.28117v1 Announce Type: cross Abstract: In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps.
arXiv:2606.13295v3 Announce Type: replace-cross Abstract: In the era of Explainable Artificial Intelligence, there is a renewed focus on single trees for their ease of interpretation. This paper intro
arXiv:2609.27917v1 Announce Type: cross Abstract: Neural combinatorial optimization solvers generate many candidate solutions per instance and report the best one found, using the same sample budget f
arXiv:2410.11113v4 Announce Type: replace-cross Abstract: This paper develops theory for deep neural network (DNN) estimators under dependent data. To provide theory applicable to a variety of DNN-bas
arXiv:2609.27221v1 Announce Type: new Abstract: This paper studies multivariate conformal prediction (CP), a distribution-free uncertainty quantification framework with finite-sample coverage guarante