874710 results (page 21 of 34989)
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AgenTEE: Confidential LLM Agent Execution on Edge Devices
Large Language Model (LLM) agents provide powerful automation capabilities, but they also create a substantially broader attack surface than traditional applications due to their tight integration with non-deterministic models and third-party services. While current deployments primarily rely on cloud-hosted services, emerging designs increasingly execute agents directly on edge devices to reduce …
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ToFiE, a Topology-aware Fiber Extraction workflow for 3D reconstruction of dense and heterogeneous biological fiber networks from microscopy images
Fibrous networks are ubiquitous structural components in biology, spanning cellulose in plant cell walls, fibrin in blood clots, and collagen in the extracellular matrix of animal tissues. Theoretical models predict that network connectivity critically influences their mechanical behavior. However, accurately reconstructing network topology from 3D image data remains a major challenge as current s…
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Inference for Functional Data under Markov Constraints
Smoothness has long been the dominant form of parsimony in functional data analysis, to the point of occasionally being conflated with the very notion of functional data. However, many core inferential tasks depend on the inverse covariance, where sparsity--rather than smoothness--emerges as the more natural structural constraint. In this paper, we explore Markovianity as an alternative to smoothn…
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Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications
Early-stage specifications of safety-critical systems are typically expressed in natural language, making it difficult to derive formal properties suitable for verification and needed to guarantee safety. While recent Large Language Model (LLM)-based approaches can generate formal artifacts from text, they mainly focus on syntactic correctness and do not ensure semantic alignment between informal …
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FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the curse of dimensionality by removing the redundant features, while unlike dimensionality reduction methods, preserving explainability. Feature selection is conducted in both supervised and unsupervised settings, with differe…
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Model in Distress: Sentiment Analysis on French Synthetic Social Media
Automated analysis of customer feedback on social media is hindered by three challenges: the high cost of annotated training data, the scarcity of evaluation sets, especially in multilingual settings, and privacy concerns that prevent data sharing and reproducibility. We address these issues by developing a generalizable synthetic data generation pipeline applied to a case study on customer distre…
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Is SAM3 ready for pathology segmentation?
Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales, where traditional methods often suffer from high annotation costs and poor generalization. SAM3 introduces Promptable Concept Segmentation, offering a potential automated interface via text prompts. With this work, we propose a systematic evaluat…
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WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
Large language models are rapidly evolving into interactive coding agents capable of end-to-end web coding, yet existing benchmarks evaluate only narrow slices of this capability, typically text-conditioned generation with static-correctness metrics, leaving visual fidelity, interaction quality, and codebase-level reasoning largely unmeasured. We introduce WebCompass, a multimodal benchmark that p…
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Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation
Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dynamic entanglement between language and observations: the meaning of instruction shifts as the agent's field of view and spatial context evolve. However, many existing models encode the instruction as a static global representation, limiting their a…
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Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling
We develop two deep learning surrogate autoregressive models for the prediction of the temporal evolution of two-dimensional ideal magnetohydrodynamic (MHD) Kelvin-Helmholtz instabilities across a range of magnetic field strengths. Using two neural network architectures, a Koopman-based Transformer model and a ConvLSTM-UNet, our approach enables simultaneous prediction of vorticity and current den…
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EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit their reliability. Here, we propose a novel framework that models EEG signals as mixtures of independent blind sources and identifies those strongly correlated with braking action. Our method employs independent component analysis to decompose EEG into d…
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A Counterexample to EFX; $n \ge 3$ Agents, $m \ge n + 5$ Items, Monotone Valuations; via SAT-Solving
SAT solving has recently been proven effective in tackling open combinatorial problems. We contribute two additional results in the context of fair distribution of indivisible goods. Specifically, we demonstrate that EFX (envy-freeness up to any good) allocations always exist for three agents and seven goods, while we provide a counterexample for the case of $n \ge 3$ agents and $m \ge n +…
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Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation
Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing methods mostly entangle memory modeling with video generation, leading to inconsistent content during scene revisits and diminished generative capacity when exploring novel regions, even trained on extensive annotated data. To address these limitations…
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Inductive Dual-Polarity Modeling via Static-Dynamic Disentanglement for Dynamic Signed Networks
Dynamic signed networks (DSNs) are common in online platforms, where time-stamped positive and negative relations evolve over time. A core task in DSNs is dynamic edge prediction, which forecasts future relations by jointly modeling edge existence and polarity (positive, negative, or non-existent). However, existing dynamic signed network embedding (DSNE) methods often entangle positive and negati…
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TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains limited. Bridging this gap is essential, as prediction reveals what is likely to occur, whereas tactic generation determines what should o…
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Galaxy Populations in the IllustrisTNG Caustic Skeleton
The caustic skeleton is a parameter-free and mathematically rigorous formalism for tracing the hierarchical formation history of the multiscale cosmic web from the singularities in the underlying dark matter flow. In the present study, we explicitly use the multistreaming nature of the cosmic mass distribution to address the influence of the weblike embedding on the galaxy populations and discern …
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Towards Symmetry-sensitive Pose Estimation: A Rotation Representation for Symmetric Object Classes
Symmetric objects are common in daily life and industry, yet their inherent orientation ambiguities that impede the training of deep learning networks for pose estimation are rarely discussed in the literature. To cope with these ambiguities, existing solutions typically require the design of specific loss functions and network architectures or resort to symmetry-invariant evaluation metrics. In c…
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A Novel Piecewise Atmospheric Attenuation Model for Free Space Optical Links in Vertical Heterogeneous Networks
Free-space optical (FSO) communication is emerging as a key backhaul technology for next-generation vertical heterogeneous networks (VHetNets), whose architecture spans satellites, high-altitude platform stations (HAPS), unmanned aerial vehicles (UAVs), and terrestrial nodes. Along these vertical and slant paths, optical beams traverse successive atmospheric layers that may contain clouds, fog, ra…
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A Control Architecture for Training-Free Memory Use
Prompt-injected memory can improve reasoning without updating model weights, but it also creates a control problem: retrieved content helps only when it is applied in the right state. We study this problem in a strict training-free setting and formulate it as applicability control: when to trigger a memory-assisted second pass, when to trust it, and how to maintain the memory bank over time. Our m…
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A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting
Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual quality of generated images, they often overlook the fidelity of surface geometry. This limitation is particularly critical in robotics, where accurate geometry is essential for tasks such as grasping and object manipulation. In this paper, we present…
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Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages
We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages, Archi and Rutul, based on curated and standardized speech-transcript resources totaling approximately 50 minutes and 1 hour 20 minutes of audio, respectively. Existing recordings and transcriptions are consolidated and processed into a form suitable f…
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Asteroid Mining to Sustain a Mars Colony: A Logistics Point of View
Asteroid mining can become an enabling technology to establish a sustainable manned colony on Mars, which requires metallic materials more often than they are readily available in shipments from Earth. This paper describes a feasibility study of a supply chain that delivers metals extracted from metallic asteroids to Mars. The asteroids are selected to respect the $ΔV$ limits imposed by up-to-date…
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Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs
Multimodal LLMs can accurately perceive numerical content across modalities yet fail to perform exact multi-digit multiplication when the identical underlying arithmetic problem is presented as numerals, number words, images, or in audio form. Because existing benchmarks often lack systematically paired instances across modalities, it remains difficult to compare genuine arithmetic limits within a…
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Centre manifold theorem for maps along manifolds of fixed points
We prove a centre manifold theorem for a map along a manifold-with-boundary of fixed points, and provide an application to the study of gradient descent with large step size on two-layer matrix factorisation problems.
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DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery
Diffusion models have emerged as powerful tools for a wide range of vision tasks, including text-guided image generation and editing. In this work, we explore their potential for object grounding in remote sensing imagery. We propose a hybrid pipeline that integrates diffusion-based localization cues with state-of-the-art segmentation models such as RemoteSAM and SAM3 to obtain more accurate bound…