1614491 results (page 15 of 64580)
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Quadratic Forms for Measuring Geometric Trees in 3-dimensional Space
Tree-like structures appear in many areas of science, and their shapes can help understand the underlying processes they drive or that give rise to them. By thinking of these structures as geometric graphs in $\mathbb{R}^3$, we gain access to tools from computational geometry and topology to study them. In this paper, we adopt the theory of quadratic forms to measure the directional spread of …
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Stitching and dimensionality effects on large artificially generated volume datasets
Generating large images via deep learning requires patching input data to accommodate hardware memory limitations, then assembling output patches, a process that can introduce stitching artifacts when neighboring patches do not align at borders. While these artifacts are known to affect segmentation tasks, their impact on generative models for style-transfer remains poorly understood. We investiga…
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MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer
Makeup transfer models enable fun augmented reality (AR) experiences as well as virtual try-on (VTO) for online makeup shopping. While recent state-of-the-art diffusion based solutions such as Stable-Makeup dramatically improve the accuracy and realism of makeup transfer, they still face limitations in identity and skin color preservation, making production-level VTO for makeup shopping unrealisti…
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Self-Preference Is Weak or Absent in Verifiable Instruction-Following Revision: A Four-Model Test Under Genuine Authorship
Large language models (LLMs) increasingly review and revise text, including their own. A documented self-preference bias (models favoring their own generations when acting as judges) raises the question of whether models also resist valid corrections to their own writing. We test this in a setting where "valid" is decided not by another model but by a deterministic verifier: instruction-following …
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EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-relevant cues become occluded or unobservable over time. While existing memory-augmented methods utilize historical context, they either suffer from severe information bottlenecks, incur high latency via decoupled dual systems, or rely on unselective bu…
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IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources
Persian pretrained language models (PLMs) are still limited by the scarcity of large-scale, high-quality pretraining corpora and by insufficient evaluation beyond standard classification and NER tasks. We present IHUBERT, a monolingual Persian PLM trained from scratch with the RoBERTa-base encoder (125M parameters) on a 45 GB curated subset of the Sepahr-Danesh collection (about 7-8B tokens). To i…
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On the impact of the carbon fusion rate over the properties of superbursts -- Numerical simulations of superbursts with MESA
Context: Superbursts are very energetic explosions in the crust of neutron stars in Low-Mass X-ray Binaries (LMXBs). These are triggered by unstable carbon burning at $T\leq 10^{9}$ K. In recent years, there has been a re-examination of the carbon fusion rate, finding that at these temperatures it might be either smaller or higher with respect to the classic rate from Caughler \& Fowler (1988) by …
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Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity. Traditional reinforcement learning (RL) approaches using discrete action spaces suffer from slow convergence and susceptibility to local optima, limiting their effectiveness for high-precision manufacturing tasks. This study addresses these limitations by employing a continuous acti…
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A FAST search for radio pulsations during the dormant state of the AMSPs IGR J00291+5934 and MAXI J1957+032
Accreting millisecond pulsars (AMSPs) and transitional millisecond pulsars (tMSPs) are neutron star low-mass X-ray binaries which can evolve into "recycled" radio millisecond pulsars. In both types of systems, X-ray pulsations have been detected during phases of X-ray activity when matter accretion through a disc is turned on. On the other hand, when accretion stops, and these systems enter the qu…
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Structure and properties of large cross-intersecting families
The study of intersecting families, initiated by Erdős, Ko, and Rado, is a central topic in extremal combinatorics. A classical stability result of Hilton and Milner determines the largest non-trivial intersecting family, and in subsequent works researchers developed structural stability results via the notion of diversity. In this paper, we study cross-intersecting families. We establish a stru…
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Residual-Space Evolutionary Optimization via Flow-based Generative Models
Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these assumptions break down in flow-based settings, where edits are performed through forward and backward integration and often involve non-differentiable or black-box objectives. We introduce residual-space evolutionary optimization, a model-agnostic framework that addresses thi…
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Holo-World: Unified Camera, Object and Weather Control for Video World Model
Video world models are moving toward preserving an observed world under controllable camera and object motion while allowing its environmental state to change. Yet these controls remain isolated, and weather generation typically relies on a source video or reconstructed scene that already specifies future structure. We study a first-frame-anchored source-to-state setting, where the model starts fr…
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Beyond Averaging in John Ellipsoid Approximation: High-Accuracy Algorithms in the Leverage-Score Model
The John ellipsoid of a symmetric polytope $P=\{\mathbf{x}\in\mathbb{R}^d:\|\mathbf{A}\mathbf{x}\|_\infty\le1\}$, $\mathbf{A}\in\mathbb{R}^{n\times d}$, is computed by a long line of leverage-score algorithms, from Cohen, Cousins, Lee and Yang (COLT 2019) to its successors [WY24, CLS+25], all reaching a $(1+\varepsilon)$-approximation in $Θ(\varepsilon^{-1}\log(n/d))$ iterations. We separate this …
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The Hidden Evolution of Disguised Visual Context inside the VLM
Visual tokens enter Large Language Models (LLMs) as raw, foreign signals. How they are transformed into meaningful representations and interact with the language space depends entirely on the integration architecture. Whether by treating visual tokens as in-context prompts within the input sequence or injecting them directly into the LLM's intermediate layers. A controlled comparison and understan…
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Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio. Variable-length tokenizers (VLTs) promise adaptive compression by varying token counts, allowing diffusion models to flexibly balance quality and compute. However, conventional VLTs modulate length by truncating ordered toke…
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What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
Latent Chain-of-Thought (CoT) internalizes reasoning within continuous hidden states, offering a promising alternative to verbose discrete reasoning traces. However, robust latent reasoning remains difficult because outcome supervision provides weak learning signals and leaves latent trajectories prone to semantic drift. In this work, we analyze Latent CoT from an information-theoretic perspective…
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Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU
Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs). Automatic burst detection remains challenging because BS patterns vary substantially between patients and annotated datasets are scarce. Recently, EEG Foundation Models (F…
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A posteriori error bounds for pseudo-parabolic equations using $C_0$ semigroups
A class of pseudo-parabolic partial differential equations is considered. We derive a posteriori error bounds for approximations obtained by FEMs in space and a BDF formula in time. The analysis is based on the $C_0$ semigroup theory and an adaptation of the concept of elliptic reconstruction to pseudo-parabolic problems. The analysis is complemented with numerical experiments.
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Source-Grounded Data Generation for Text-to-JSON Learning
From financial filings to clinical records, legacy industries rely heavily on long, unstructured documents to store high-value information. Reliably extracting this information into structured, machine-readable representations is a key prerequisite to making the contents accessible to automated systems. JSON is a natural target for such structured extraction, yet constructing reliable and scalable…
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A minimum-risk and cost-efficient two-sample sequential testing framework for the shifted exponential models with application to precipitation data
This paper investigates the problem of comparing the location parameters of two shifted exponential models through a novel double sequential sampling framework. The proposed hypothesis testing procedure is developed by controlling the type I error probability at a preassigned level while minimizing a loss function that incorporates both the type II error probability and the associated sampling cos…
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Process-Verified Reinforcement Learning for Theorem Proving via Lean
While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assista…
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VASTER: The ASKAP real-time fast-imaging pipeline -- overview and discovery of two long period transients
Recent developments in widefield radio telescopes have enabled searches of a new region of parameter space in the time domain: timescales of seconds to minutes, that have been overlooked in traditional surveys. These searches have revealed a new population of sources: long period transients, which typically show periodic behaviour of minutes to hours. In addition they have detected phenomena rangi…
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Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
People increasingly get answers straight from AI search engines like ChatGPT, Claude, Perplexity, and Gemini rather than scrolling search results. Brands that once focused on search engine optimization (SEO) must now optimize for how these engines represent, cite, and recommend them -- a shift variously called Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI Search Vi…
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AI Conversational Interviewing: Scaling Up Semi-Structured and In-depth Interviews
Public opinion research has long faced a trade-off between depth and scale: standardized surveys enable large-scale measurement but restrict respondents to researcher-defined categories, obscuring the diversity of unexpected considerations that underlie public sentiment. More conversational interviews provide richer insights through open-ended probing, but their reliance on trained human interview…
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Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning
We introduce optimal coarse correlated equilibria for continuous-time mean field games. A coarse correlated equilibrium is a randomized recommendation scheme from which no player can gain by ignoring the recommendation and switching to an alternative strategy. The problem is as follows: a moderator selects, among all mean-field coarse correlated equilibria, one that optimizes a prescribed performa…