Concealed Handgun Detection using WiFi Signals
arXiv:2610.18736v1 Announce Type: new Abstract: While conventional camera-based surveillance systems and metal detectors can provide a sufficient level of security when used appropriately, they have f
Knowledge catalogue
arXiv:2610.18736v1 Announce Type: new Abstract: While conventional camera-based surveillance systems and metal detectors can provide a sufficient level of security when used appropriately, they have f
arXiv:2610.17098v1 Announce Type: new Abstract: The incorporation of footage from other sources has long been a concern from a copyright perspective, as such usage can harm the interests of rights hol
arXiv:2610.14333v1 Announce Type: new Abstract: We study decentralized bandit learning in discounted infinite-horizon, general-sum Markov games with continuous actions. Agents observe the public state
arXiv:2610.17083v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly trained on imperative programming languages, and existing work on LLM-based robot programming reflects th
arXiv:2512.14967v3 Announce Type: replace Abstract: We present a novel numerical method for solving McKean-Vlasov forward-backward stochastic differential equations (MV--FBSDEs) with common noise, com
arXiv:2610.16529v1 Announce Type: new Abstract: Plant fibers are increasingly used as sustainable reinforcements in composites, but the intrinsic variability of their microstructure causes substantial
arXiv:2610.15323v1 Announce Type: cross Abstract: Longitudinal health data often exhibit within-subject correlation, subject-specific heterogeneity, and complex nonlinear predictor--response relations
arXiv:2609.14421v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement infor
arXiv:2610.15932v1 Announce Type: new Abstract: Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something convent
arXiv:2610.15662v1 Announce Type: new Abstract: Generative policies offer expressive action distributions for online reinforcement learning (RL), but many diffusion- and flow-based policies require co
arXiv:2610.16792v1 Announce Type: new Abstract: Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally re
arXiv:2610.17980v1 Announce Type: new Abstract: Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random.
arXiv:2610.18856v1 Announce Type: new Abstract: Persistent homology summarizes the topological evolution of a graph as persistence diagrams (PDs). Generating graphs whose topology matches a target des
arXiv:2610.15241v1 Announce Type: cross Abstract: Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric cond
arXiv:2505.11884v3 Announce Type: replace Abstract: Face recognition performance based on deep learning heavily relies on large-scale training data, which is often difficult to acquire in practical ap
arXiv:2610.15921v1 Announce Type: new Abstract: With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' expe
arXiv:2601.22371v2 Announce Type: replace Abstract: Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggl
arXiv:2610.14098v1 Announce Type: new Abstract: Facade articulation at building scale is usually resolved manually or through parametric systems that encode design intent as explicit rules. This paper
arXiv:2610.14293v1 Announce Type: new Abstract: Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches addr
arXiv:2603.25072v2 Announce Type: replace Abstract: Video Large Language Models (VLMs) have achieved remarkable success in video understanding, but the significant computational cost from processing d
arXiv:2610.17927v1 Announce Type: new Abstract: Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless
arXiv:2610.18471v1 Announce Type: new Abstract: Weight-space learning treats the weights of neural networks as data, processed by other models called metanetworks. A key challenge is that different we
Workplace artificial intelligence security startup Harmonic Security Inc. today announced it has raised 50 million in new funding to grow its platform for governing how employees use AI tools. The com
arXiv:2610.18238v1 Announce Type: new Abstract: Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page
arXiv:2603.21862v2 Announce Type: replace Abstract: Scaling laws govern macroscopic resource allocation for LLMs, yet precise architectural configurations for Mixture-of-Experts (MoE) models remain gu
arXiv:2610.16855v1 Announce Type: new Abstract: Real-time bimanual humanoid teleoperation requires accurate wrist tracking while keeping arm configurations predictable. Because a 7-DoF arm admits mult
arXiv:2610.17151v1 Announce Type: cross Abstract: Variational inference is a flexible tool for modeling experimental data and has proven useful in crystallography. Pathwise gradient estimators provide
arXiv:2610.17671v1 Announce Type: cross Abstract: The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder regi
arXiv:2610.18855v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.
arXiv:2610.16757v1 Announce Type: new Abstract: Comparing machine-labelled sentiment across social media platforms is now routine: differences in classifier scores between platforms are read as differ
arXiv:2610.15356v1 Announce Type: cross Abstract: Beat tracking, chord estimation, and structure analysis are staple tasks of music information retrieval. Their datasets are drawn largely from Western
arXiv:2610.18195v1 Announce Type: cross Abstract: For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpo
arXiv:2610.17141v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions
arXiv:2610.16252v1 Announce Type: new Abstract: Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Exist
arXiv:2610.16285v1 Announce Type: cross Abstract: We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment
arXiv:2610.15205v1 Announce Type: cross Abstract: Efficient task scheduling in heterogeneous computing systems remains a fundamental challenge in embedded and high-level synthesis flows. This paper pr
arXiv:2610.18379v1 Announce Type: new Abstract: With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed
arXiv:2510.17719v3 Announce Type: replace Abstract: Adherent raindrops on camera lenses are a pervasive source of real-world image degradation, undermining outdoor visual perception for downstream app
arXiv:2610.14344v1 Announce Type: new Abstract: Scaling deep learning models efficiently requires predictable performance transfer across compute regimes. Establishing reliable hyperparameter transfer
arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl
arXiv:2602.08331v2 Announce Type: replace-cross Abstract: Network traffic classification underpins network security and management but faces growing challenges from pervasive encryption and evolving p
arXiv:2505.03539v4 Announce Type: replace Abstract: Panoramic imaging enables capturing 360{eg} images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception, which is critical to
arXiv:2610.15224v1 Announce Type: cross Abstract: Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding su
arXiv:2507.05291v2 Announce Type: replace Abstract: We propose a physics-informed machine learning framework called P-DivGNN to reconstruct local stress fields at the micro-scale, in the context of mu
arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets
arXiv:2610.17605v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of indiv
arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo
arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe
arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains
arXiv:2610.14519v1 Announce Type: new Abstract: Effectively eliciting the capabilities of large language models (LLMs) at inference time remains an important challenge. Existing studies address this c
arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me
arXiv:2610.14326v1 Announce Type: new Abstract: Traffic density estimation from camera feeds requires connecting visual perception with short-horizon forecasting. We present a two-stage pipeline that
arXiv:2610.14998v1 Announce Type: new Abstract: Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the numb
arXiv:2610.18292v1 Announce Type: new Abstract: Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that
arXiv:2610.18417v1 Announce Type: new Abstract: End-to-end deep learning pipelines for camera-radar 3D object detection are well established in driver assistance systems and autonomous vehicles; howev
arXiv:2610.15445v1 Announce Type: new Abstract: In recent years, the proliferation of smartphones has led to an upsurge in the digitization of document files via these portable devices. However, image
arXiv:2610.18838v1 Announce Type: cross Abstract: A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically o
arXiv:2610.14866v1 Announce Type: new Abstract: Cross-modal Thermal Geo-localization (TG) provides an illumination-robust positioning solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation
arXiv:2610.17458v1 Announce Type: cross Abstract: Unsupervised clustering is widely used to discover underlying group structure, after which researchers often seek to validate these groups via post-ho
arXiv:2601.07674v3 Announce Type: replace-cross Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing appl