857438 results (page 18 of 34298)
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The MeerKAT Fornax Survey VII. Characterisation of the Fornax cluster's magnetic field and new insights on magnetisation in large scale systems
Large scale magnetic fields in galaxy clusters can influence their physics and the evolution of the cluster galaxies. These properties remain poorly constrained due to a historical lack of high-sensitivity and high-resolution spectro-polarimetric data. Thanks to the advent of the SKA pathfinders and precursors this is now dramatically changing. By exploiting the densest RM grid produced to date wi…
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Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth Anything 3 provide excellent geometric priors, they lack an absolute metric scale. We propose a training-free framework that leverages depth foundation models as a structural prior, employing a robust local RANSAC-based alignment to fuse it with raw s…
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Polar Coded Quantization for Distributed Source Coding
Scalar quantization and probabilistic shaping are applied to the distributed source coding of Gaussian sources, with mean-square error distortion. A coding scheme with a modulo interval, dithering, and truncated Gaussian shaping is shown to achieve the corner points of the Berger-Tung region. The theory is illustrated by designing short-block-length multilevel 5G polar codes for Wyner-Ziv (WZ) pol…
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Reliability of AI Bots Footprints in GitHub Actions CI/CD Workflows
Continuous Integration and Deployment (CI/CD) workflows are central to modern software delivery, yet the reliability of agentic AI bots operating within these workflows remain underexplored. Using pull requests (PRs), commits, and repositories from the AIDev dataset, we retrieved associated CI/CD workflow runs via the GitHub Actions API and analyzed 61,837 runs from 2,355 repositories, all trigger…
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Dust characterization of halos -- The extended emission in protoplanetary disks
Extended, low surface brightness emission has been identified in a number of protoplanetary disks, in tension with predictions of radial drift theory. We aim to investigate the nature and origin of faint, extended dust emission in the outer regions of protoplanetary disks, which we define as the Halo, using multiwavelength (sub-)millimeter continuum observations of three systems: Elias 2-24, IM Lu…
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Will People Enjoy a Robot Trainer? A Case Study with Snoopie the Pacerbot
The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonstrate an exercise, and give intuitive feedback. Robot quadrupeds are also embodied agents with robust agility and athleticism. In our work, we investigate whether a robot quadruped can serve as an effective and enjoyable personal trainer device. We …
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From Gaia to GaiaNIR: II. A new view of the Milky Way bar
The Milky Way (MW) hosts a central bar whose pattern speed, orientation, and length remain uncertain, largely due to observational biases and selection effects, despite the transformative data provided by the Gaia mission. We aim to reassess the MW bar properties using Gaia DR3, explicitly accounting for incompleteness and astrometric uncertainties, and to quantify the expected improvements from f…
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FregeLogic at SemEval 2026 Task 11: A Hybrid Neuro-Symbolic Architecture for Content-Robust Syllogistic Validity Prediction
We present FregeLogic, a hybrid neuro-symbolic system for SemEval-2026 Task 11 (Subtask 1), which addresses syllogistic validity prediction while reducing content effects on predictions. Our approach combines an ensemble of five LLM classifiers, spanning three open-weights models (Llama 4 Maverick, Llama 4 Scout, and Qwen3-32B) paired with varied prompting strategies, with a Z3 SMT solver that ser…
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PARM: Pipeline-Adapted Reward Model
Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While most prior work focuses on single-step generation, real-world applications increasingly adopt multi-stage LLM pipelines, where effective reward guidance remains underexplored. We investigate this through code generation for combinatorial optimizatio…
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OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation
Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidelity human-centric videos in complex, real-world physical scenes remains a significant challenge. We identify that the root cause lies in the structural deficiencies of existing datasets across three dimensions: limited global scene and camera diver…
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An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees
We study a class of optimization problems on Riemannian manifolds, where the objective function consists of a smooth term and quasi-norm type penalties with exponent $p \in (0, 1]$. The essential difficulty lies in the fact that the objective function may not be locally Lipschitz continuous, which places this type of problems beyond the reach of existing Riemannian techniques. To overcome this obs…
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Which Small-Sample Correction Should Be Used When Analyzing Stepped-Wedge Designs with Time-Varying Treatment Effects?
Stepped-wedge cluster randomized trials (SW-CRTs) evaluate interventions rolled out across clusters over time. Standard analyses typically use immediate-treatment (IT) models, which assume effects begin at crossover and remain constant thereafter. When effects vary with exposure duration, IT models may misrepresent target effects. Exposure-time indicator (ETI) models address this by allowing treat…
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Accuracy Certificates for Convex Optimization at Accelerated Rates via Primal-Dual Averaging
Many works in convex optimization provide rates for achieving a small primal gap. However, this quantity is typically unavailable in practice. In this work, we show that solving a regularized surrogate with algorithms based on simple primal-dual averaging provides non-asymptotic convergence guarantees for a \textit{computable} optimality certificate. We first analyze primal and dual methods based …
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EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degradation as model predictions drift, while template-based methods are confined to a static set of transformations that cannot adapt in difficulty or diversity. We contend that robust, continuous self-improvement requires not only deterministic exter…
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in epidemiological or survey settings, individuals with certain outcomes may be more likely to be included, resulting in biased prevalence estimates with potentially s…
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Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics
The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory, cognition, and behavior. It is characterized by the accumulation of abnormal proteins in the brain, including amyloid-beta plaques and neurofibrillary tangles of tau protein, which disrupt neuronal communication and lead to neuronal death. Early man…
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Embarrassingly Causal: Causal Use of Associational Data in Magic The Gathering Drafts
Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the criterion of embarrassingly causal scenario…
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Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection
Open-Vocabulary Temporal Action Detection (OV-TAD) aims to localize and classify action segments of unseen categories in untrimmed videos, where effective alignment between action semantics and video representations is critical for accurate detection. However, existing methods struggle to mitigate the semantic imbalance between concise, abstract action labels and rich, complex video contents, inev…
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Scale-free adaptive planning for deterministic dynamics & discounted rewards
We address the problem of planning in an environment with deterministic dynamics and stochastic rewards with discounted returns. The optimal value function is not known, nor are the rewards bounded. We propose Platypoos, a simple scale-free planning algorithm that adapts to the unknown scale and smoothness of the reward function. We provide a sample complexity analysis for Platypoos that improves …
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On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. The integration of Natural Language Generation into XAI aims to deliver explanations in textual form, making them more accessible to practitioners. Current approaches, however, largely yield static lists of feature importances. Although such explana…
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Symmetry Guarantees Statistic Recovery in Variational Inference
Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of the resulting approximation are crucial for understanding which of its properties VI can faithfully capture. Recent wor…
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From Program Slices to Causal Clarity: Evaluating Faithful, Actionable LLM-Generated Failure Explanations via Context Partitioning and LLM-as-a-Judge
Large language model (LLM)-based debugging systems can generate failure explanations, but these explanations may be incomplete or incorrect. Misleading explanations are harmful for downstream tasks (e.g., bug triage, bug fixing). We investigate how explanation quality is affected by various LLM context configurations. Existing work predominantly treats LLM-generated failure explanations as an ad h…
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Reasoning Models Know What's Important, and Encode It in Their Activations
Language models often solve complex tasks by generating long reasoning chains, consisting of many steps with varying importance. While some steps are crucial for generating the final answer, others are removable. Determining which steps matter most, and why, remains an open question central to understanding how models process reasoning. We investigate if this question is best approached through mo…
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CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We propose a novel modeling approach named ContextAware ARLLM CAARL that provides an interpretable framework to decode the contextual dynamics influencing changes in coevolving series CAARL decomposes time series into autoregr…
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Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support
Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-…