October 2025 arXiv papers — page 9

Rui Liu, Yifan Zhuang, Runsheng Zhang

This study addresses the challenges of dynamics and complexity in intelligent human-computer interaction and proposes a reinforcement learning-based optimization framework to improve long-term returns and overall experience. Human-computer interaction is modeled as a Markov decision process, with state space, action space, reward function, and discount facto

Chiho Kim, Wei Xiong, Akhlak Mahmood, Rampi Ramprasad

Poly(ethylene terephthalate) (PET), a widely used thermoplastic in packaging, textiles, and engineering applications, is valued for its strength, clarity, and chemical resistance. Increasing environmental impact concerns and regulatory pressures drive the search for alternatives with comparable or superior performance. We present an AI-driven polymer design

Luka Maisuradze, Corey S. O'Hern, Mark D. Shattuck

Motivation: Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on the chromatin interactions of individual cells.

Dmitry Zverevich, Alex Levchenko, A. V. Andreev

We develop a theory of drag in graphene double layers near charge neutrality. We work in the regime of electron hydrodynamics and account for interlayer correlations of charge puddle disorder. The drag resistivity is expressed in terms of the viscosity, intrinsic conductivity of the electron liquid, and the correlation function of the puddle disorder. The co

Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher

Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user profiling often overlooks the distinction between transitory short-term interests and stable long-term preferences. This paper examines the capability of leveraging Large Language Mod

A. Ciattoni

We investigate the quantum optical scattering of two-photon wavepackets by a macroscopic lossy sphere by means of macroscopic quantum electrodynamics in the form of modified Langevin noise formalism. The two ingoing photons with arbitrary frequency-polarization spectrum impinge onto the sphere along two different directions and, as consequence of matter loss

Chatum Sankalpa, Ghulam Mohy-ud-din, Erik Weyer, Maria Vrakopoulou

Aggregators of consumer energy resources (CERs) like rooftop solar and battery energy storage (BES) face challenges due to their inherent uncertainties. A sensible approach is to use stochastic optimization to handle such uncertainties, which can lead to infeasible problems or loss in revenues if not chosen appropriately. This paper presents three efficient

Fabian Koch, Tabita Wasmer, Tanja Schilling

We analyze the non-linear generalized Langevin equation which contains a thermodynamic force. We show that even for systems in thermal equilibrium the presence of the thermodynamic force implies that the auto-correlation function of the fluctuating force becomes non-stationary. We further illustrate that a standard coarse-graining procedure that neglects thi

Aon Safdar, Mohamed Saadeldin

Vision Transformers (ViTs) have demonstrated strong potential in medical imaging; however, their high computational demands and tendency to overfit on small datasets limit their applicability in real-world clinical scenarios. In this paper, we present CoMViT, a compact and generalizable Vision Transformer architecture optimized for resource-constrained medic

A. S. Barabash, L. Bergé, M. Buchynska, J. M. Calvo-Mozota

In preparation to the CROSS experiment at the Canfranc underground laboratory (Spain) $-$ aiming to search for neutrinoless double-beta ($0νββ$) decay of $^{100}$Mo using low-temperature detectors with heat-scintillation readout $-$ we report on development of a dedicated muon veto system. The need for the muon veto in CROSS is caused by a comparatively high

Milena Veneva

This paper presents a machine learning (ML)-based heuristic for finding the optimum sub-system size for the CUDA implementation of the parallel partition algorithm. Computational experiments for different system of linear algebraic equation (SLAE) sizes are conducted, and the optimum sub-system size for each of them is found empirically. To estimate a model

Yuchen Xu, Wenjun Mei, Ge Chen, Linyuan Lü

With the rapid development of the internet industry, online social networks have come to play an increasingly significant role in everyday life. In recent years, content-based emerging platforms such as TikTok, Instagram, and Bilibili have diverged fundamentally in their underlying logic from traditional connection-based social platforms like Facebook and Li

Eric W. Bridgeford, Iain Campbell, Zijao Chen, Zhicheng Lin

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide ten practical rules for AI-assisted coding that balance leveraging capabilities of AI with maintaining scientific and methodological rigor. We a

Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw

Principal component analysis (PCA) is a fundamental tool for analyzing multivariate data. Here the focus is on dimension reduction to the principal subspace, characterized by its projection matrix. The classical principal subspace can be strongly affected by the presence of outliers. Traditional robust approaches consider casewise outliers, that is, cases ge

Martin Ottens, Kai-Steffen Hielscher, Reinhard German

TheaterQ is a Linux qdisc designed for dynamic network emulation, addressing the limitations of static parameters in traditional tools like NetEm. By utilizing Trace Files containing timelines with network characteristics, TheaterQ achieves high-accuracy emulation of dynamic networks without involving the userspace and allows for resolutions of characteristi

Arman Bolatov, Alan Legg, Igor Melnykov, Amantay Nurlanuly

This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data distribution, a condition known as overspecification. We use a two-component Gaussian mixture model within each class to fit data generated from a single Gaussian, analyzing both the

Huseyin Goksu

Spectral Graph Neural Networks (GNNs) have achieved state-of-the-art results by defining graph convolutions in the spectral domain. A common approach, popularized by ChebyNet, is to use polynomial filters based on continuous orthogonal polynomials (e.g., Chebyshev). This creates a theoretical disconnect, as these continuous-domain filters are applied to inhe

Michał Zawalski, Meriem Boubdir, Klaudia Bałazy, Besmira Nushi

We present Contamination Detection via Context (CoDeC), a practical and accurate method to detect and quantify training data contamination in large language models. CoDeC distinguishes between data memorized during training and data outside the training distribution by measuring how in-context learning affects model performance. We find that in-context examp

Xiaofan Guo, Yaxuan Luan, Yue Kang, Xiangchen Song

This paper addresses the issues of insufficient coverage, unstable results, and limited reliability in retrieval-augmented generation under complex knowledge environments, and proposes a confidence control method that integrates multi-granularity memory indexing with uncertainty estimation. The method builds a hierarchical memory structure that divides knowl

Manohar Teja Kalluri, Andrew Hillier

The magnetic Rayleigh-Taylor instability (MRTI) governs plasma mixing and transport in a wide range of astrophysical and laboratory systems. Owing to computational constraints, MRTI is often studied using two-dimensional (2D) simulations, but the extent to which 2D captures the true three-dimensional (3D) dynamics remains unclear. In this work, we perform di

Ashley Lewis, Andrew Perrault, Eric Fosler-Lussier, Michael White

Hallucination--defined here as generating statements unsupported or contradicted by available evidence or conversational context--remains a major obstacle to deploying conversational AI systems in settings that demand factual reliability. Existing metrics either evaluate isolated responses or treat unverifiable content as errors, limiting their use for multi

Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, Dave Farris

Enterprise AI agents must continuously adapt to maintain accuracy, reduce latency, and remain aligned with user needs. We present a practical implementation of a data flywheel in NVInfo AI, NVIDIA's Mixture-of-Experts (MoE) Knowledge Assistant serving over 30,000 employees. By operationalizing a MAPE-driven data flywheel, we built a closed-loop system that s

Isaac Malsky, Tiffany Kataria, Natasha E. Batalha, Matthew Graham

Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General Circulation Models) that degrade the accuracy of the simulation. Radiative transfer calculations

Ponrawee Prasertsom, Andrea Silvi, Jennifer Culbertson, Moa Johansson

Much recent work has shown how cross-linguistic variation is constrained by competing pressures from efficient communication. However, little attention has been paid to the role of the systematicity of forms (regularity), a key property of natural language. Here, we demonstrate the importance of regularity in explaining the shape of linguistic systems by loo

Dengsheng Zhang

The integration of artificial intelligence (AI) into video lecture production has the potential to transform higher education by streamlining content creation and enhancing accessibility. This paper investigates a semi automated workflow that combines Google Gemini for script generation, Amazon Polly for voice synthesis, and Microsoft PowerPoint for video as

Mario Camarena, Het Patel, Fatemeh Nazari, Evangelos Papalexakis

This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a fine-tuned vision foundation model for semantic segmentation in autonomous driving (AD). AD-SAM extends the Segment Anything Model (SAM) with a dual-encoder and deformable decoder tailored to spatial and geometric complexity of road scenes. The dual-encoder produces multi-scale fus

Cong Lin, Oliver T. Schmidt

We show that both temporal and spatial symmetry breaking in canonical K-type transition arise as organized hydrodynamic structures rather than stochastic fluctuations. Before the skin-friction maximum, the flow is fully described by a periodic, spanwise symmetric, harmonic response to the Tollmien-Schlichting wave, forming a spatially compact coherent struct

Siyu Duan

The connection between texts is referred to as intertextuality in literary theory, which served as an important theoretical basis in many digital humanities studies. Over the past decade, advancements in natural language processing have ushered intertextuality studies into the quantitative age. Large-scale intertextuality research based on cutting-edge metho

Md Tanvirul Alam, Nidhi Rastogi

Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities; however, its ability to foster genuine reasoning remains unclear. We investigate RL

Hao Jiang, Xiaojun Yuan, Yinuo Huang, Qinghua Guo

In this paper, we propose a novel blind multi-input multi-output (MIMO) semantic communication (SC) framework named Blind-MIMOSC that consists of a deep joint source-channel coding (DJSCC) transmitter and a diffusion-based blind receiver. The DJSCC transmitter aims to compress and map the source data into the transmitted signal by exploiting the structural c

Michael Kleinman, Matthew Trager, Alessandro Achille, Wei Xia

Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users may prefer to allocate different amounts of reasoning effort depending on how they value output quality versus latency and cost. To leverage this tradeoff effectively, users need fine-grained control over the amoun

Mohammed-Adnane Garab

We introduce a geometric-arithmetic approach to the analysis of the Flint Hills series, linking its convergence behavior to the irrationality measure of pi. The framework highlights the interplay between the distribution of near-multiples of pi and the growth rate of denominator sequences, offering new insights into the arithmetic structure underlying this f

Dian Chen, Yunkai Chen, Tong Lin, Sijie Chen

Multimodal approaches that integrate protein structure and sequence have achieved remarkable success in protein-protein interface prediction. However, extending these methods to protein-peptide interactions remains challenging due to the inherent conformational flexibility of peptides and the limited availability of structural data that hinder direct trainin

Zhuo Zheng, Lingran Meng, Ziyu Lin

Accurate traffic flow forecasting is essential for the development of intelligent transportation systems (ITS), supporting tasks such as traffic signal optimization, congestion management, and route planning. Traditional models often fail to effectively capture complex spatial-temporal dependencies in large-scale road networks, especially under the influence

Fatima Adam Muhammad, Shamsuddeen Muhammad Hassan, Isa Inuwa-Dutse

Sexism reinforces gender inequality and social exclusion by perpetuating stereotypes, bias, and discriminatory norms. Noting how online platforms enable various forms of sexism to thrive, there is a growing need for effective sexism detection and mitigation strategies. While computational approaches to sexism detection are widespread in high-resource languag

Shang Wang

As large pre-trained language models become increasingly critical to natural language understanding (NLU) tasks, their substantial computational and memory requirements have raised significant economic and environmental concerns. Addressing these challenges, this paper introduces the Elastic Language Model (ELM), a novel neural architecture search (NAS) meth

Nicolò Dalmasso, Peter J. Watson, Tommaso Treu, Michele Trenti

We analyze JWST NIRISS and NIRSpec spectroscopic observations in the Abell 2744 galaxy cluster field. From approximately 120 candidates, we identify 12 objects with at least a prominent emission lines among \Oii, \Hb, \Oiiia, \Oiiib, and \Ha that are spectroscopically confirmed by both instruments. Our key findings reveal systematic differences between the t

Yanbing Mao, Yihao Cai, Lui Sha

This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL) agent to develop safe and high-performance action policies in real plants (i.e., real physical systems to be controlled), while prioritizing safety! The Real-DRL consists of three interactive components: a DRL-

Noah Gorgichuk, Mohammad Ayyash, Matteo Mariantoni, Sahel Ashhab

We present a protocol for preparing oscillator states with $n$-fold rotational symmetry, which include many logical codewords for bosonic quantum error correction codes. The protocol relies on a multiphoton interaction between the oscillator and an auxiliary qubit. Further, we achieve arbitrary control over the oscillator's Hilbert space by using a combinati

Sricharan Raghavan-Chitra, Arghadip Koner, Joel Yuen-Zhou

Classical linear optics posits that at sufficiently low intensities, light propagation in dielectric media is governed solely by their linear susceptibilities. Here, we demonstrate a departure from this paradigm in high-Q microresonators, where prolonged photon confinement enables rare quantum electrodynamical (QED) events, mediated by the quantum vacuum, to

Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall, Hamid Rezatofighi

Visual reasoning, particularly spatial reasoning, is a challenging cognitive task that requires understanding object relationships and their interactions within complex environments, especially in robotics domain. Existing vision_language models (VLMs) excel at perception tasks but struggle with fine-grained spatial reasoning due to their implicit, correlati

Grant Merz, Ming-Yang Zhuang, Junyao Li, Qian Yang

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this

Eric Jovinelly, Brian Lehmann, Eric Riedl

We show that klt Fano varieties and certain lc Fano varieties contain free higher-genus curves in their smooth loci. Our methods also allow us to find free curves on varieties in positive characteristic and on quasiprojective varieties, under a natural positivity condition on the tangent bundle. We then use the existence of free curves to deduce finiteness o

Chris Jennings-Shaffer, Ziyue, Chen, Julia A Palacios

Phylogenetic tree shapes capture fundamental signatures of evolution. We consider ``ranked'' tree shapes, which are equipped with a total order on the internal nodes compatible with the tree graph. Recent work has established an elegant bijection between ranked tree shapes and a class of integer matrices, called \textbf{F}-matrices, defined by simple inequal

Philipp V. Rouast

This report introduces VitalLens 2.0, a new deep learning model for estimating physiological signals from face video. This new model demonstrates a significant leap in accuracy for remote photoplethysmography (rPPG), enabling the robust estimation of not only heart rate (HR) and respiratory rate (RR) but also Heart Rate Variability (HRV) metrics. This advanc

Martin Ottens, Kai-Steffen Hielscher, Reinhard German

The increasing prevalence LEO satellite mega-constellations for global Internet coverage requires new approaches to evaluate the behavior of existing Internet protocols and applications. Traditional discrete event simulators like Hypatia allow for modeling these environments but fall short in evaluating real applications. This paper builds upon our previous

Chenyang Li, Ping Lin, Haibiao Zheng

This paper investigates a Gauge-Uzawa finite element method (GU-FEM) for the two-dimensional chemo-repulsion-Navier-Stokes (CRNS) system. The proposed approach establishes a fully discrete projection framework that integrates the advantages of both canonical and Uzawa-type formulations while preserving variational consistency. The method possesses two notabl

D. Chloe Griffin, Chi-Wang Shu

We develop a simple, high-order, conservative and robust positivity-preserving sweeping procedure for the density and the nonlinear pressure function in the compressible Euler equations. Using the scaling limiter in Zhang and Shu (2010), we obtain a nontrivial extension of the scalar sweeping technique in Liu, Cheng, and Shu (2016) for the positivity of pres

Maria D. Kazachenko, Yuhong Fan, Andrey N. Afanasyev

Understanding the three-dimensional evolution of coronal magnetic fields during solar flares remains challenging due to the lack of direct coronal field measurements. Here we combine data-driven MHD simulations of NOAA AR 11158 (Fan et al., 2024) with flare-ribbon and coronal-dimming observations to investigate realistic coronal magnetic-field evolution duri

Rui Liu, Jan Hannig, J. S. Marron

Smoothing methods find signals in noisy data. A challenge for Statistical inference is the choice of smoothing parameter. SiZer addressed this challenge in one-dimension by detecting significant slopes across multiple scales, but was not a completely valid testing procedure. This was addressed by the development of an advanced distribution theory that ensure

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

Gravitational-wave detectors can probe the existence of dark matter with exquisite sensitivity. Here, we perform a search for three kinds of dark matter -- dilatons (spin-0), dark photons (spin-1) and tensor bosons (spin-2) -- using three independent methods on the first part of the most recent data from the fourth observing run of LIGO--Virgo--KAGRA. Each f

Yecun Wu, Yan-Kai Tzeng, Hao Chen, Kun Xu

The practical implementation of lithium-metal anodes has been hindered by uncontrollable dendrite formation and interfacial instability. This study presents a defect-engineering approach of a chemically stable and electrically insulating interfacial layer of hexagonal boron nitride (h-BN) that markedly enhances ionic conductivity through argon ion irradiatio

Sophie Wenning

This master thesis extends the formal model of the GCS algorithm as presented by (Fan and Lynch 2004, 325), (Lenzen, Locher and Wattenhofer 2008, 510) and (F\"ugger et al. 2023) to operate under implementation-near assumptions by replacing the one-way measurement paradigm assumed in prior work by the two-way measurement paradigm. With this change of paradigm

Yana Wei, Zeen Chi, Chongyu Wang, Yu Wu

In open-world environments, human-object interactions (HOIs) evolve continuously, challenging conventional closed-world HOI detection models. Inspired by humans' ability to progressively acquire knowledge, we explore incremental HOI detection (IHOID) to develop agents capable of discerning human-object relations in such dynamic environments. This setup confr

Abel C. H. Chen

As quantum computing hardware continues to advance, the integration of such technology with quantum algorithms is anticipated to enable the decryption of ciphertexts produced by RSA and Elliptic Curve Cryptography (ECC) within polynomial time. In response to this emerging threat, the U.S. National Institute of Standards and Technology (NIST) finalized a seri

YingQiao Wang, Eric Bigelow, Boyi Li, Tomer Ullman

We propose a novel cognitively-inspired method to improve and interpret physical simulation in vision-language models. Our ``Chain of Time" method involves generating a series of intermediate images during a simulation, and it is motivated by in-context reasoning in machine learning, as well as mental simulation in humans. Chain of Time is used at inference

Jingyao Zhu, Yasmeen Asali, Mary Putman, Tobias Westmeier

We present a sample of 127 gas-bearing dwarf galaxies around 56 late-type host galaxies within 30 Mpc using 21-cm HI data from the WALLABY, MHONGOOSE, and ALFALFA surveys. We characterize the environment of each dwarf galaxy based on its host galaxy halo and derive optical properties using the DESI Legacy Surveys for 110. The gaseous satellites span $\log (M

Alexander Heinlein, Taniya Kapoor

Approximating the solutions of boundary value problems governed by partial differential equations with neural networks is challenging, largely due to the difficult training process. This difficulty can be partly explained by the spectral bias, that is, the slower convergence of high-frequency components, and can be mitigated by localizing neural networks via

Ayoub Hammal, Pierre Zweigenbaum, Caio Corro

Several previous works concluded that the largest part of generation capabilities of large language models (LLM) are learned (early) during pre-training. However, LLMs still require further alignment to adhere to downstream task requirements and stylistic preferences, among other desired properties. As LLMs continue to scale in terms of size, the computation

Jayden Serenari, Stephen Lee

With the increasing use of conversational AI systems, there is growing concern over privacy leaks, especially when users share sensitive personal data in interactions with Large Language Models (LLMs). Conversations shared with these models may contain Personally Identifiable Information (PII), which, if exposed, could lead to security breaches or identity t

Elliott Wen, Sean Ma, Ewan Tempero, Jens Dietrich

While NVIDIA remains the dominant provider of AI accelerators within cloud data center, emerging vendors such as AMD, Intel, Mac, and Huawei offer cost-effective alternatives with claims of compatibility and performance. This paper presents the first empirical study investigating divergence in machine learning model across heterogeneous AI accelerators. Util

Zachary Izzo, Eshaan Nichani, Jason D. Lee

We study the problem of length generalization (LG) in transformers: the ability of a model trained on shorter sequences to maintain performance when evaluated on much longer, previously unseen inputs. Prior work by Huang et al. (2025) established that transformers eventually achieve length generalization once the training sequence length exceeds some finite

Sean Patten, Pin-Yu Chen, Christina Schweikert, D. Frank Hsu

This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072\%. CFA leverages the concept of cognitive diversity, which utilizes rank-score character

Amin Shafiee, Zahra Ghanaatian, Benoit Charbonnier, Mahdi Nikdast

In this paper, we propose a novel fully programmable linear photonic processor, which we call LightPro, with improved scalability, performance, and footprint. At the heart of LightPro are compact, low-loss, and programmable silicon photonic (SiPh) directional coupler (DC) devices that deploy phase-change material (PCM) for programming the DC's splitting rati

William E. Heap, Yimeng Qin, Kai Hammond, Anish Bayya

Rehabilitation of aging pipes requires accurate condition assessment and mapping far into the pipe interiors. Soft growing vine robot systems are particularly promising for navigating confined, sinuous paths such as in pipes, but are currently limited by complex subsystems and a lack of validation in real-world industrial settings. In this paper, we introduc

Jared Junkin, Samuel Nathanson

Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential linearizations are instead used. Yet the question of whether it is viable to accept the information loss introduced by causal masking on nonsequential data has received little direc

Fabian Raoul Pieroth, Ole Petersen, Martin Bichler

Predatory pricing -- where a firm strategically lowers prices to undermine competitors -- is a contentious topic in dynamic oligopoly theory, with scholars debating practical relevance and the existence of predatory equilibria. Although finite-horizon dynamic models have long been proposed to capture the strategic intertemporal incentives of oligopolists, th

H. C. I. Wichern, G. Leloudas, M. Pursiainen, A. Cikota

Tidal disruptions of stars by supermassive black holes produce multi-wavelength emission, of which the optical emission is of ambiguous origin. A unification scenario of tidal disruption events (TDEs) has been proposed to explain the different classes of X-ray and optically selected events by introducing a dependence on the viewing angle and geometry. This w

Giuseppe M. Ferro, Edwin T. Pos, Andrea Somazzi

The classical Maximum-Entropy Principle (MEP) based on Shannon entropy is widely used to construct least-biased probability distributions from partial information. However, the Shore-Johnson axioms that single out the Shannon functional hinge on strong system independence, an assumption often violated in real-world, strongly correlated systems. We provide a

I. D. Moore, B. M. White, B. Graner, J. D. Siverns

Photonic interconnects are a key technology for scaling up atomic based quantum computers. By facilitating the connection of multiple systems, high-performance modular quantum processing units may be constructed to perform deeper and more useful algorithms. Most previous implementations of photonic interconnects in trapped ions utilize the scheme of preparin

Hongbo Li, Qinhang Wu, Sen Lin, Yingbin Liang

Mixture-of-Experts (MoE) models improve transformer efficiency but lack a unified theoretical explanation, especially when both feed-forward and attention layers are allowed to specialize. To this end, we study the Mixture-of-Transformers (MoT), a tractable theoretical framework in which each transformer block acts as an expert governed by a continuously tra

Muhammad Zeeshan Mazhar, Tolga Buz, Yiran Su

Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad

Mihir Mahajan, Alfred Nguyen, Franz Srambical, Stefan Bauer

While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastructure for world modeling remains nascent. We introduce Jasmine, a performant JAX-based world modeling codebase that scales from single hosts to hundreds of accelerators with minimal code changes. Jasmine achieves an orde

Elise Wolf

Multi-armed bandit (MAB) problems serve as a fundamental building block for more complex reinforcement learning algorithms. However, evaluating and comparing MAB algorithms remains challenging due to the lack of standardized conditions and replicability. This is particularly problematic for variance-aware extensions of classical methods like UCB, whose perfo

Andri M. Gretarsson, Ambroise L. M. Juston, Benjamin Nicolai, Naomi Borg

Using a custom-built scanning system, we generated maps of birefringence on reflection at $\lambda=1064$~nm from single-crystal GaAs/Al$_{0.92}$Ga$_{0.08}$As Bragg reflectors (henceforth ``AlGaAs coatings''). Ten coatings were bonded to fused silica substrates and one remained on the epitaxial growth wafer. The average phase difference on reflection between

Adithya Neelakantan, Pratik Satpute, Prerna Shinde, Tejas Manjunatha Devang

The AIoT-Based Smart Education System integrates Artificial Intelligence and IoT to address persistent challenges in contemporary classrooms: attendance fraud, lack of personalization, student disengagement, and inefficient resource use. The unified platform combines four core modules: (1) a dual-factor authentication system leveraging RFID-based ID scans an

John J. Vastola, Samuel J. Gershman, Kanaka Rajan

Learning rules -- prescriptions for updating model parameters to improve performance -- are typically assumed rather than derived. Why do some learning rules work better than others, and under what assumptions can a given rule be considered optimal? We propose a theoretical framework that casts learning rules as policies for navigating (partially observable)

Arghavan Rezvani, Xiangyi Yan, Anthony T. Wu, Kun Han

In this study, we propose MoME, a Mixture of Visual Language Medical Experts, for Medical Image Segmentation. MoME adapts the successful Mixture of Experts (MoE) paradigm, widely used in Large Language Models (LLMs), for medical vision-language tasks. The architecture enables dynamic expert selection by effectively utilizing multi-scale visual features tailo

Elliot L. Epstein, John Winnicki, Thanawat Sornwanee, Rajat Dwaraknath

Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own answers and find that they are systematically overconfident. To evaluate this behavior, we introduce FermiEval, a benchmark of Fermi-style estimation questions with a rigorous scorin

Zheng Nie, Peijie Sun

Recent advances in large language models (LLMs) promise more effective information extraction for review-based recommender systems, yet current methods still (i) mine free-form reviews without scope control, producing redundant and noisy representations, (ii) lack principled metrics that link LLM hallucination to downstream effectiveness, and (iii) leave the

Peter Boyle, Norman H. Christ, Xu Feng, Taku Izubuchi

In this work, we perform a lattice QCD calculation of the branching ratios and the form factors of radiative leptonic decays $P \to \ell \nu_\ell \gamma$ ($P = \pi, K$) using $N_f=2+1$ domain wall fermion ensembles generated by the RBC and UKQCD collaborations at the physical pion mass. We adopt the infinite volume reconstruction (IVR) method, which extends

C. O. Ascencio, D. J. P. de Sousa, Tony Low

Topological chiral crystals have emerged as a fertile material platform for investigating optical phenomena derived from the distinctive Fermi surface Berry curvature and orbital magnetic moment textures around multifold chiral band crossings pinned at the time-reversal invariant momenta. In this work, by means of tight-binding model and first principles bas

Vasileios Fotopoulos, Alexander L. Shluger

Hydrogen embrittlement remains a critical challenge in structural and electronic applications of copper (Cu) but its mechanism is still not fully understood. In this study, we combine density functional theory (DFT) and bond-order potential (BOP) simulations to determine the atomistic pathways for hydrogen adsorption/incorporation and fast interfacial diffus

Mainak Singha, Sangeeta Malhotra, James Ely Rhoads

The origin of the ionizing photons that completed hydrogen reionization remains debated. Using recent JWST and ground-based surveys at 4.5 <= z <= 6.5, we construct a unified rest-UV AGN luminosity function that separates unobscured Type I and obscured Type II populations, and show that "little red dots" and X-ray selected sources are magnitude-filtered subs

Agorakis Bompotas, Konstantinos Koutras, Nikitas Rigas Kalogeropoulos, Panagiotis Kechagias

The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot

Fardad Vakilipoor, Johannes Konrad, Maximilian Schäfer

Cellular intelligence enables cells to process environmental signals and make context-dependent decisions, as exemplified by chemotaxis, where cells navigate chemical gradients despite noisy signaling pathways. To investigate how cells deal with uncertainty, we apply an information-theoretic framework based on rate distortion theory (RDT). The Blahut-Arimoto

Karl Dilcher, Christophe Vignat

Departing from a class of infinite series with central binomial coefficients in the numerator and depending on a positive integer parameter, we first extend known identities to all complex parameters. Then we use various methods, including exponential Bell polynomials and integral representations, to further extend these results. Throughout the paper, we mak

C. J. O. Reichhardt, D. McDermott, C. Reichhardt

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equili

Tobias Blickhan, Julianne Stratton, Alan A. Kaptanoglu

This article introduces a new 3D magnetohydrodynamic (MHD) equilibrium solver, based on the concept of admissible variations of B, p that allows for magnetic relaxation of a magnetic field in a perturbed/non-minimum energy state to a lower energy state. We describe the mathematical theory behind this method, including ensuring certain bounds on the magnetic

Mostafa Darvishi

This paper presents an in-depth analysis of timing closure challenges and constraints in Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). We examine core timing principles, architectural distinctions, and design methodologies influencing timing behavior in both technologies. A case study comparing the Xilinx Kintex

Enzo Duflot, Stanislas Robineau

We study opportunistic optimal liquidation over fixed deadlines on BTC-USD limit-order books (LOB). We present RL-Exec, a PPO agent trained on historical replays augmented with endogenous transient impact (resilience), partial fills, maker/taker fees, and latency. The policy observes depth-20 LOB features plus microstructure indicators and acts under a sell-

Jean-Benoît Bost, Frédéric Paulin

Let ${\bf C}$ be a smooth geometrically connected projective curve over a finite field, and let $A$ be the affine algebra of its regular functions outside a fixed place of ${\bf C}$. We give precise relationships between the Mahler successive minima of normed $A$-lattices and the Harder-Narasimhan slopes of vector bundles over ${\bf C}$ using their category

Katherine Rossella Foglia, Francesco Sergio Pisani, Vittorio Colao

We introduce LMLRSGA, a limited memory variant of Low Rank Symplectic Gradient Adjustment (LRSGA) for differentiable games. It is an iterative scheme for approximating Nash equilibria with first order like cost while retaining the stabilizing effect of symplectic second order corrections via low rank information. By storing only a limited history of curvatur

Ziling Ma, Ángel López-Oriona, Hernando Ombao, Ying Sun

Fuzzy clustering provides a natural framework for modeling partial memberships, particularly important in multivariate time series (MTS) where state boundaries are often ambiguous. For example, in EEG monitoring of driver alertness, neural activity evolves along a continuum (from unconscious to fully alert, with many intermediate levels of drowsiness) so cri

Zhichao Hou, Weizhi Gao, Xiaorui Liu

This work tackles a critical challenge in AI safety research under limited compute: given a fixed computation budget, how can one maximize the strength of iterative adversarial attacks? Coarsely reducing the number of attack iterations lowers cost but substantially weakens effectiveness. To fulfill the attainable attack efficacy within a constrained budget,

Joanne Steiner, Philippe Gondret, Alban Sauret, Cyprien Morize

Unsteady flows generated when a body approaches or departs from a granular bed arise in swimming, burrowing, and maneuvering devices. Yet, the threshold for grain motion in such transients remains poorly modeled due to the complexity of the flow. In this study, we report laboratory measurements of the onset of erosion when a rigid circular disk is subjected

A. C. Schröder

We present a homogeneous 2MASX galaxy catalogue at low Galactic latitudes (|b| <= 10.0 deg, called Zone of Avoidance, ZoA) which is complete to a Galactic extinction-corrected magnitude of Ko <= 11.75 mag. Also included are galaxies at higher latitudes in areas of high foreground extinctions (E(B-V) > 0.95). This catalogue supersedes the previously presented

Anam Fatima, Yi Yu, Janak Kapuriya, Julien Lalanne

Live commenting on video streams has surged in popularity on platforms like Twitch, enhancing viewer engagement through dynamic interactions. However, automatically generating contextually appropriate comments remains a challenging and exciting task. Video streams can contain a vast amount of data and extraneous content. Existing approaches tend to overlook

Kehao Zhuang, Linbin Huang, Huanhai Xin, Xiuqiang He

This work introduces a novel dispatchable current source virtual oscillator control (dCVOC) scheme for grid-following (GFL) converters, which exhibits duality with dispatchable virtual oscillator control (dVOC) in two ways: a) the current frequency is generated through reactive power control, similar to a PLL ; b) the current magnitude reference is generated

J. W. Zhou, Sami Dib

We used CO (2-1) and CO (1-0) data cubes to identify molecular clouds and study their kinematics and dynamics in three nearby galaxies and the inner Milky Way. When observed at similar spatial and velocity resolutions, molecular clouds in the same mass range across these galaxies show broadly comparable physical properties and similar star formation rates (S