1025474 results (page 50 of 41019)
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Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning
Assessing chronic wound infection from photographs is challenging because visual appearance varies across wound etiologies, anatomical locations, and imaging conditions. Prior image-based deep learning methods have mainly focused on classification with limited interpretability, despite the need for evidence-grounded explanations to support point-of-care decision making. We present Infection-Reason…
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Generalization and Membership Inference Attack a Practical Perspective
With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previously accepted assumptions. In this paper, we revisit the longstanding debate regarding the correlation between MIA success rates and model generalization using an empirical approach. We focused on employing augmentation techniques and early stopping…
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A Hybrid Gauss Markov LSTM Mobility Model for Indoor OWC
Optical wireless communication (OWC) has emerged as a promising candidate for future high-capacity indoor wireless networks, driven by its large unregulated spectrum, high spatial reuse, and ability to support multi-gigabit data rates. However, OWC systems are highly sensitive to user mobility, as link performance depends strongly on the spatial alignment between transmitter and receiver. Accurate…
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Tracing Relational Knowledge Recall in Large Language Models
We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for relation classification via linear probes. Prior work shows how attention heads and MLPs interact to resolve subject, predicate, and object, but it remains unclear which representations support faithful linear relation classification and why some re…
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Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility
Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate …
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Hint-Writing with Deferred AI Assistance: Fostering Critical Engagement in Data Science Education
Generating hints for incorrect code is a cognitively demanding task that fosters learning and metacognitive development. This study investigates three designs for personalized, scalable, and reflective hint-writing activities within a data science course: (i) writing a hint independently, (ii) writing a hint with on-demand AI assistance, and (iii) deferred AI assistance, in which students first wr…
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Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential--Algebraic Systems
Neural surrogates for stiff differential-algebraic equations (DAEs) face two key challenges: soft-constraint methods leave algebraic residuals that stiffness amplifies into large errors, while hard-constraint methods require trajectory data from computationally expensive stiff integrators. We introduce an extended Newton implicit layer that enforces algebraic consistency and quasi-steady-state red…
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Resolving the Dust Budget Crisis at $z \sim 8$ with Optically Thick, High-Density Molecular Clumps: MACS0416_Y1
Dust plays a crucial role in galaxy evolution by shaping the spectral energy distribution (SED) and star formation history. However, standard models often underestimate the infrared luminosity of high-redshift galaxies ($z \sim 8$), leading to the so-called dust budget crisis. In this work, we modify the theoretical framework by focusing on compact star-forming clumps in the interstellar medium. M…
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CreativeGame:Toward Mechanic-Aware Creative Game Generation
Large language models can generate plausible game code, but turning this capability into \emph{iterative creative improvement} remains difficult. In practice, single-shot generation often produces brittle runtime behavior, weak accumulation of experience across versions, and creativity scores that are too subjective to serve as reliable optimization signals. A further limitation is that mechanics …
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Reinforcing privacy reasoning in LLMs via normative simulacra from fiction
Information handling practices of LLM agents are broadly misaligned with the contextual privacy expectations of their users. Contextual Integrity (CI) provides a principled framework, defining privacy as the appropriate flow of information within context-relative norms. However, existing approaches either double inference cost via supervisor-assistant architectures, or fine-tune on narrow task-spe…
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Behavioral Transfer in AI Agents: Evidence and Privacy Implications
AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused on their task performance and effects on human outcomes, but less is known about the relationship between agents and the specific individuals who deploy them. We ask whether agents systematically reflect the behavioral characteristics of their human…
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UniCon3R: Contact-aware 3D Human-Scene Reconstruction from Monocular Video
We introduce UniCon3R (Unified Contact-aware 3D Reconstruction), a unified feed-forward framework for online human-scene 4D reconstruction from monocular videos. Recent feed-forward methods enable real-time world-coordinate human motion and scene reconstruction, but they often produce physically implausible artifacts such as bodies floating above the ground or penetrating parts of the scene. The k…
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Measuring neutrino mass and asymmetry through galaxy pairwise peculiar velocity
Cosmic neutrinos are among the most abundant fermions in the Universe, yet the values of their masses and chemical potentials remain uncertain. In this Letter, we present the first constraints on the total neutrino mass $M_ν$ and the neutrino asymmetry parameter $η^2$ derived from the mean galaxy pairwise peculiar velocity in the quasi-linear and nonlinear regimes. We develop a simulation-based an…
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Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding
Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural language understanding tasks. Commonsense knowledge, on the other hand, despite being a well-studied topic, lacks investigations involving negation. In this work, we show that commonsense knowledge with negation is challenging for models to underst…
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Black Hole Interiors as a Laboratory for Time-Dependent Classical Double Copy
The classical double copy provides a powerful bridge between gravity and gauge theory, but its most explicit realizations remain concentrated in stationary or highly symmetric settings. We show that trapped regions of black-hole geometries furnish an exact setting for time-dependent classical double copy. In the static, spherically symmetric case, each trapped interval admits a local single-copy d…
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Neural Simulation-based Inference with Hierarchical Priors for Detached Eclipsing Binaries
Detached eclipsing binaries (DEBs) enable direct inference of stellar and orbital properties across diverse stellar populations. However, inference typically requires computationally intensive forward modeling and radial velocity (RV) measurements, limiting homogeneous analyses to relatively small samples. The growing number of photometrically identified DEBs from modern time-domain surveys motiva…
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Stability of Multiplanet Systems Through Hot Jupiter Destruction
Recent observational and theoretical work suggests that the sub-Jovian desert (periods ${\lesssim}3$ days, masses ${\sim}10{-}100 \ M_{\oplus}$) hosts the remains of destroyed hot Jupiters (``desert dwellers"). In this work, we explore how differing hot Jupiter destruction mechanisms -- Roche lobe overflow (RLO) vs. tidal disruption during high eccentricity migration (HEM) -- may be discerned obse…
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A giant solution to the disk mass budget problem of planet formation
Understanding how dust evolves in protoplanetary disks is crucial to constraining the initial conditions of planet formation. The apparent "mass budget problem", which stems from the comparison of the observed disk masses to the ones inferred for exoplanets, remains debated, as it is unclear whether the discrepancy arises from limitations in interpreting disk observations, from evolutionary proces…
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Search for Anisotropic Pair Halos Associated with Blazar Jets
The origin of intergalactic magnetic fields (IGMFs) remains one of the key open questions in cosmology. Gamma-ray pair halos produced by electromagnetic cascades from TeV-emitting blazars provide a powerful indirect probe of these fields. In this work, we present a novel search for pair halos that explicitly exploits their expected anisotropic morphology, aligning with the projected orientation of…
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DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with domain-specific knowledge is underexplored. This paper demonstrates a critical vulnerability where available standard cell library layouts (SCLL) can be exploited to compromise the privacy of sensitive integrated circuit (IC…
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AI Incident Monitoring through a Public Health Lens
Artificial intelligence systems are now deployed at scale across sectors, accompanied by a growing number of real-world incidents ranging from misinformation and cybercrime to autonomous-system failures. Databases of AI incidents index these events, but they cannot measure ``risk'' (i.e., a joint measure of likelihood and severity) without additional data regarding the prevalence of risk-associate…
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A Proximal Primal-Dual Approach to Generalized JKO Schemes for Doubly Nonlinear Parabolic Equations
Variational methods based on optimization strategies are proposed to numerically solve a large family of nonlinear partial differential equations. They are all particular instances of gradient flows with general costs, including the $p$-Laplace equation and flux-limited equations such as the relativistic heat equation. This is achieved by computing explicit formulas for proximal operators with gen…
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Finite-Length Empirical Comparison of Polar, PAC, and Invertible-Extractor Secrecy Codes over the Wiretap BSC
We compare three secrecy-coding schemes for the degraded wiretap binary symmetric channel (BSC) in the finite-blocklength regime: (i) polar wiretap coset codes, (ii) PAC codes used as wiretap coset codes, and (iii) the invertible-extractor (IE) framework of Bellare-Tessaro. Our comparison is empirical and uses a common semantic-secrecy metric (distinguishing advantage). For polar coset codes, we c…
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SceneOrchestra: Efficient Agentic 3D Scene Synthesis via Full Tool-Call Trajectory Generation
Recent agentic frameworks for 3D scene synthesis have advanced realism and diversity by integrating heterogeneous generation and editing tools. These tools are organized into workflows orchestrated by an off-the-shelf LLM. Current approaches typically adopt an execute-review-reflect loop: at each step, the orchestrator executes a tool, renders intermediate results for review, and then decides on t…
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Going MLIR-native: Demonstrating a Future for DSL compilers on a NumPy-like Example
Compilers for general-purpose languages have been shown to be at a disadvantage when it comes to specialized application domains as opposed to their Domain-Specific Language (DSL) counterparts. However, the field of DSL compilers features little consolidation in terms of compiler frameworks and adjacent software ecosystems. As a result, considerable work is duplicated, lost to maintenance issues, …