October 2025 arXiv papers — page 226

Tamás Dózsa, Matthias Voigt, Zoltán Szabó, József Bokor

The generalized operator-based Prony method is an important tool for describing signals which can be written as finite linear combinations of eigenfunctions of certain linear operators. On the other hand, Bernoulli's algorithm and its generalizations can be used to recover the parameters of rational functions belonging to finite-dimensional subspaces of $H_2

Kristi Topollai, Anna Choromanska

Few-shot classification and meta-learning methods typically struggle to generalize across diverse domains, as most approaches focus on a single dataset, failing to transfer knowledge across various seen and unseen domains. Existing solutions often suffer from low accuracy, high computational costs, and rely on restrictive assumptions. In this paper, we intro

Yuan Gao, Anton Rodomanov, Jeremy Rack, Sebastian Stich

Communication efficiency is a central challenge in distributed machine learning training, and message compression is a widely used solution. However, standard Error Feedback (EF) methods (Seide et al., 2014), though effective for smooth unconstrained optimization with compression (Karimireddy et al., 2019), fail in the broader and practically important setti

Tahar Benyoussef, Sami Mabrouk

In this article, we introduce mock-Lie superalgebras, we give some definitions, properties, constructions, and we study their representations. Moreover we introduce pseudo-euclidean mock-Lie superalgebras which are mock-Lie superalgebras with even non-degenerate supersymmetric and invariant bilinear forms. Finally, we study the double extensions and generali

John Nguyen, Marton Havasi, Tariq Berrada, Luke Zettlemoyer

We present OneFlow, the first non-autoregressive multimodal model that enables variable-length and concurrent mixed-modal generation. Unlike autoregressive models that enforce rigid causal ordering between text and image generation, OneFlow combines an insertion-based Edit Flow for discrete text tokens with Flow Matching for image latents. OneFlow enables co

Augustin Muster, Luis S. Froufe-Pérez

We propose a method to generate pure many-body interactions in colloidal systems by using optical forces induced by random optical fields with an optimized spectral energy density. To assess the feasibility in general settings, we develop a simple model for Lorentzian electric and magnetic dipole response. An optimization procedure is then introduced to desi

Ali Khairallah, Arkaitz Zubiaga

We introduce ALHD, the first large-scale comprehensive Arabic dataset explicitly designed to distinguish between human- and LLM-generated texts. ALHD spans three genres (news, social media, reviews), covering both MSA and dialectal Arabic, and contains over 400K balanced samples generated by three leading LLMs and originated from multiple human sources, whic

Lyes Saad Saoud, Loic Lesobre, Enrico Sorato, Irfan Hussain

Real-time animal detection and segmentation in natural environments are vital for wildlife conservation, enabling non-invasive monitoring through remote camera streams. However, these tasks remain challenging due to limited computational resources and the cryptic appearance of many species. We propose a mobile-optimized two-stage deep learning framework that

Alexander Somov, Vladimir Berdnikov

The Electromagnetic Calorimeter (ECAL), consisting of 1,596 lead tungstate scintillating crystals, has been recently constructed and installed in Experimental Hall D at Jefferson Lab (JLab). The calorimeter is a key component of the JLab Eta Factory Experiment, whose main goal is to measure the decays of eta and eta prime mesons into multi-photon final state

Marchelle Beougher, Nila Cibu, Kexin Ding, Steven DiSilvio

We study chip-firing games on multigraphs whose underlying simple graphs are trees, paths, and stars, denoted as banana trees, paths, and stars respectively. We present a polynomial time algorithm to compute the divisorial gonality of banana paths, and give combinatorial formulas for the related invariants of scramble number and screewidth for any banana tre

Elena Cáceres, Suman Das, Arnab Kundu, Harita Palani Balaji

Recent studies have demonstrated that an $\textit{ad hoc}$ Dirichlet boundary condition, placed outside but close to an event horizon, for probe degrees of freedom in an otherwise black hole geometry is capable of capturing non-trivial level-correlations of the corresponding spectrum of the probe sector. Much of the interesting physics stems from a hierarchy

Hao Tu, Yebin Wang, Shaoshuai Mou, Huazhen Fang

Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging condi

Vadivelan Murugesan, Rajasundaram Mathiazhagan, Sanjana Joshi, Aliasghar Arab

Human-robot collaboration requires precise prediction of human motion over extended horizons to enable proactive collision avoidance. Unlike existing planners that rely solely on kinodynamic models, we present a prediction-driven safe planning framework that leverages granular, joint-by-joint human motion forecasting validated in a physics-based digital twin

Erik Pautsch, Tanmay Singla, Parv Kumar, Wenxin Jiang

LLM-based agents are rapidly proliferating, yet the infrastructure for discovering, evaluating, and governing them remains fragmented compared to mature ecosystems like software package registries (e.g., npm) and model hubs (e.g., Hugging Face). Existing efforts typically address naming, distribution, or protocol descriptors, but stop short of providing a re

Briley L. Lewis

Writing is a critical skill for modern science, enabling collaboration, scientific discourse, public outreach, and more. Accordingly, it is important to consider how physicists and astronomers are trained to write. This study aims to understand the landscape of science writing education, specifically in physics and astronomy, in higher education in the Unite

Nir Avni, Tsachik Gelander

We show that MIF (mixed-identity-free) linear groups are sharply MIF and linearly MIF. Along the way we provide a self contained proof of the strong approximation theorem, and a new (probabilistic) variant of the super approximation theorem.

Sarah-Michelle Hammer, Stefan Schmid, Rachee Singh, Vamsi Addanki

Efficient collective communication is critical for many distributed ML and HPC applications. In this context, it is widely believed that the Ring algorithm for the AllReduce collective communication operation is optimal only for large messages, while Recursive Doubling is preferable for small ones due to its logarithmic number of steps compared to the linear

Aneesha Sampath, Oya Aran, Emily Mower Provost

We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to test Large Language Models' (LLMs) ability to identify the specific spans of text that express emotion. Unlike traditional emotion recognition tasks that assign a single label to an entire sentence, SEER targets the underexplored task of emotion evidence detection: pinpointing which e

Taha M. Mahmoud, Naima Kaabouch

Electronic voting systems face growing risks from cyberattacks and data breaches, which are expected to intensify with the advent of quantum computing. To address these challenges, we introduce a quantum-secure voting framework that integrates Quantum Key Distribution (QKD), Dual-Key Symmetric Encryption, and verifiable receipt mechanisms to strengthen the p

Ryosuke Ota, Yuya Onishi, Daehee Lee, Yuki Ichikawa

Precise measurement of radiation has long played a vital role in a wide range of research and industrial fields, from fundamental physics beyond the Standard Model to medical imaging such as time-of-flight positron emission tomography. Developing radiation detectors that achieve high timing precision-on the order of a few tens of picoseconds-and energy measu

Aldrin Joar Rodrigo Taduran, Leo P. Piao

Residential and industrial areas are using rooftop grid-tied Photovoltaic (PV) systems, which are becoming increasingly popular. This is because solar energy reduces electrical consumption and provides free energy, while also lowering carbon emissions to create a more sustainable environment. This paper aims to analyze the 2.72kW p rooftop grid-tied PV syste

Anupam Panwar, Himadri Pal, Jiali Chen, Kyle Cho

Detecting anomalies in large, distributed systems presents several challenges. The first challenge arises from the sheer volume of data that needs to be processed. Flagging anomalies in a high-throughput environment calls for a careful consideration of both algorithm and system design. The second challenge comes from the heterogeneity of time-series datasets

Numan Saeed, Tausifa Jan Saleem, Fadillah Maani, Muhammad Ridzuan

Deep learning for medical imaging is hampered by task-specific models that lack generalizability and prognostic capabilities, while existing 'universal' approaches suffer from simplistic conditioning and poor medical semantic understanding. To address these limitations, we introduce DuPLUS, a deep learning framework for efficient multi-modal medical image an

Alex Bloedel, Tommaso Denti, Luciano Pomatto

We introduce a new cost function over experiments, f-information, based on the theory of multivariate statistical divergences, that generalizes Sims's classic model of rational inattention as well as the class of posterior-separable cost functions. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matejka a

Khang Vo Huynh, David Parker, Lu Feng

We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs). While robust IMDP controller synthesis typically yields a single policy and most permissive-synthesis methods assume exact transition models, we synthesize multi-strategies that retain multiple actions while guaranteeing satisfaction of

Vali Tawosi, Salwa Alamir, Xiaomo Liu, Manuela Veloso

As a codebase expands over time, its library dependencies can become outdated and require updates to maintain innovation and security. However, updating a library can introduce breaking changes in the code, necessitating significant developer time for maintenance. To address this, we introduce a framework of LLM agents to be used in combination with migratio

Quan Nguyen

While Adam is one of the most effective optimizer for training large-scale machine learning models, a theoretical understanding of how to optimally set its momentum factors, $\beta_1$ and $\beta_2$, remains largely incomplete. Prior works have shown that Adam can be seen as an instance of Follow-the-Regularized-Leader (FTRL), one of the most important class

Eric Culf, Kieran Mastel, Connor Paddock, Taro Spirig

We show that the quantum smooth label cover problem is undecidable and RE-hard. This sharply contrasts the quantum unique label cover problem, which can be decided efficiently by a result of Kempe, Regev, and Toner (FOCS'08). On the other hand, our result aligns with the RE-hardness of the quantum label cover problem, which follows from the celebrated MIP* =

Abhijeet, Suman Chakravorty

This paper offers a unified perspective on different approaches to the solution of optimal control problems through the lens of constrained sequential quadratic programming. In particular, it allows us to find the relationships between Newton's method, the iterative LQR (iLQR), and Differential Dynamic Programming (DDP) approaches to solve the problem. It is

Fabian C. Quintero-Pareja, Diederik A. Montano-Burbano, Santiago Quintero-Pareja, D. Sierra-Porta

Sunspot numbers provide the longest continuous record of solar activity and remain a key index for heliophysical research and space-weather applications. Standard sunspot determination relies on visual inspection and algorithmic feature-detection pipelines, both of which involve methodological choices and can be sensitive to image quality and implementation

Yulun Zhang, Alexandre O. G. Barbosa, Federico Pecora, Jiaoyang Li

We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transporting them from induct workstations to eject chutes based on their shipping destinations (e.g. Los Angeles or Pittsburgh). The destination-to-chutes task mapping is used to determ

Dingqi Zhang, Ran Tao, Sheng Cheng, Naira Hovakimyan

Robust adaptive control methods are essential for maintaining quadcopter performance under external disturbances and model uncertainties. However, fragmented evaluations across tasks, simulators, and implementations hinder systematic comparison of these methods. This paper introduces an easy-to-deploy, modular simulation testbed for quadcopter control, built

Benoit Dherin, Michael Munn

Deep residual architectures, such as ResNet and the Transformer, have enabled models of unprecedented depth, yet a formal understanding of why depth is so effective remains an open question. A popular intuition, following Veit et al. (2016), is that these residual networks behave like ensembles of many shallower models. Our key finding is an explicit analyti

Keshav Ramani, Vali Tawosi, Salwa Alamir, Daniel Borrajo

We introduce a novel framework for evaluating the alignment between natural language plans and their expected behavior by converting them into Kripke structures and Linear Temporal Logic (LTL) using Large Language Models (LLMs) and performing model checking. We systematically evaluate this framework on a simplified version of the PlanBench plan verification

Kevin Charette, Tristan Boudreault

Accurate estimation of treatment effects in online A/B testing is challenging with zero-inflated and skewed metrics. Traditional tests, like Welch's t-test, often lack sensitivity with heavy-tailed data due to their reliance on means, as opposed to e.g., percentiles. The Controlled Experiments Using Pre-experiment Data (CUPED) technique improves sensitivity

Brendon Boldt, David Mortensen

In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measur

Xiaoli Li, Yang Chen, Xiao-Li Meng, David van Dyk

The C statistic is a widely used likelihood-ratio statistic for model fitting and goodness-of-fit assessments with Poisson data in high-energy physics and astrophysics. Although it enjoys convenient asymptotic properties, the statistic is routinely applied in cases where its nominal null distribution relies on unwarranted assumptions. Because researchers do

Pedro L. Esquinas, Fereshteh Yousefirizi, Ian Alberts, Nicolas A. Karakatsanis

Long-axial field-of-view (LAFOV) PET/CT has the potential to redefine the role of molecular imaging in theranostics by making multiparametric whole-body (MPWB) imaging and predictive dosimetry more clinically feasible. Compared to conventional PET systems, LAFOV scanners provide dramatic gains in sensitivity and coverage, allowing dynamic acquisitions, delay

Oscar Leong, Eliza O'Reilly, Yong Sheng Soh

Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliability and interpretability of downstream solutions. While recent work has characterized optimal regularizers for well-specified data distributions, practical deployments are often comp

Vali Tawosi, Keshav Ramani, Salwa Alamir, Xiaomo Liu

Multi-agent Large Language Model (LLM) systems have been leading the way in applied LLM research across a number of fields. One notable area is software development, where researchers have advanced the automation of code implementation, code testing, code maintenance, inter alia, using LLM agents. However, software development is a multifaceted environment t

Vinaya K. Kavatamane, Dewen Duan, Hadi Zadeh-Haghighi, Manh-Huong Phan

Precision sensing and imaging of weak static magnetic fields are crucial for a variety of emerging nanoscale applications. While nitrogen-vacancy (NV) centers in diamond provide exceptional AC magnetic field sensitivity with nanoscale spatial resolution, their sensitivity to static (DC) magnetic fields is fundamentally limited by the short dephasing time (T2

Sanjay Malakar, Michael D. Ernst, Martin Kellogg, Manu Sridharan

A resource leak occurs when a program fails to release a finite resource like a socket, file descriptor or database connection. While sound static analysis tools can detect all leaks, automatically repairing them remains challenging. Prior work took the output of a detection tool and attempted to repair only leaks from a hard-coded list of library resource t

Sibo Tian, Minghui Zheng, Xiao Liang

Rapid robot motion generation is critical in Human-Robot Collaboration (HRC) systems, as robots need to respond to dynamic environments in real time by continuously observing their surroundings and replanning their motions to ensure both safe interactions and efficient task execution. Current sampling-based motion planners face challenges in scaling to high-

Jeremy Beard

Originally introduced by Kolmann and Shelah as a surrogate for saturated models, limit models have been established as natural and useful objects when studying abstract elementary classes. Shelah began the study of when (multiple notions of) limit models exist for first order theories. In this paper we look at their structure. In superstable theories it is k

Mengyao Xu, Wenfei Zhou, Yauhen Babakhin, Gabriel Moreira

We present Omni-Embed-Nemotron, a unified multimodal retrieval embedding model developed to handle the increasing complexity of real-world information needs. While Retrieval-Augmented Generation (RAG) has significantly advanced language models by incorporating external knowledge, existing text-based retrievers rely on clean, structured input and struggle wit

Jianfeng Lin, Tianyu Wang, Baxi Chong, Matthew Fernandez

Elongate animals and robots use undulatory body waves to locomote through diverse environments. Geometric mechanics provides a framework to model and optimize such systems in highly damped environments, connecting a prescribed shape change pattern (gait) with locomotion displacement. However, the practical applicability of controlling compliant physical robo

Sabin Thapa, Biaogang Wu, Ramona Vogt, Ralf Rapp

We study bottomonium suppression in $p+ {\rm Pb}$ relative to $p+p$ collisions at center-of-mass energies of $\sqrt{s_{NN}}= 5.02$ and 8.16~TeV. Specifically, we combine cold nuclear matter effects (nuclear modifications of the parton densities, energy loss and momentum broadening) with those from hot nuclear matter (suppression and regeneration) by implemen

Sejuti Majumder, Saarthak Kapse, Moinak Bhattacharya, Xuan Xu

Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approaches rely on a small set of highly variable genes, which limits predictive scope and overlooks the coordinated biological programs that shape tissue phenotypes. We present PEaRL (Pa

Zhengxi Yan

This contribution reports STAR measurements of azimuthal anisotropies in produced particle distributions of the d+Au and $^{16}$O+$^{16}$O collisions at $\sqrt{s_{NN}} = 200$ GeV, probing the origin of collectivity in small systems. We test the hydrodynamic response of the produced medium by comparing these systems with vastly different initial geometries. T

Paul S. Rosenbloom

Evaluation is a critical activity associated with any theory. Yet this has proven to be an exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be challenging for theories based on generative neural architectures. This dual challenge is approached here by leveraging a broad p

Allison Davis, Yezhi Shen, Xiaoyu Ji, Fengqing Zhu

Structured illumination (SI) enhances image resolution and contrast by projecting patterned light onto a sample. In two-phase optical-sectioning SI (OS-SI), reduced acquisition time introduces residual artifacts that conventional denoising struggles to suppress. Deep learning offers an alternative to traditional methods; however, supervised training is limit

Benjamin Seeger

The question of optimally approximating an arbitrary probability measure in the Wasserstein distance by a discrete one with uniform weights is considered. Estimates are obtained for the optimal approximation distance, with an explicit rate of convergence to $0$ as the number of points tends to infinity that depends on the moment order, the parameter in the W

Alexander J. Silenko

For twisted particles in arbitrary gravitational fields, the problems of the rotation of intrinsic orbital angular momentum and the orbital Hall effect are solved in the general case. We need not use the Maxwell equations in curved spacetimes for a description of twisted photons. The exact general equation rigorously defining the OAM dynamics in any Riemanni

Parsa Jamshidian, Donatello Telesca

We introduce BLAST, Bayesian Linear regression with Adaptive Shrinkage for Transfer, a Bayesian multi-source transfer learning framework for high-dimensional linear regression. The proposed analytical framework leverages global-local shrinkage priors together with Bayesian source selection to balance information sharing and regularization. We show how Bayesi

Vade Shah, Yohan John, Ethan Freifeld, Lily Y. Chen

Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting--strategic overcooling in anticipation of future demands--offers substantial efficiency gains. This work seeks to rigorously quantify these

Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh, Leo Ardon

Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently, a new game-theoretic approach provides a more holistic view of risk also considering the reward-variance. However, these variance-based approaches measure variance of the reward

Ion I. Cotaescu

New time-dependent metric tensors with spherical symmetry satisfying the Einstein-Maxwell equations in space-times with FLRW asymptotic behaviour are derived here for the first time. These geometries describe dynamical charged non-rotating black holes hosted by the perfect fluid of the asymptotic FLRW space-times. Their gravitational sources are the stress-e

M. M. Chems-Eddin, B. Feryouch, A. Tamoussit

Let $D$ be an integral domain with quotient field $K,$ $E$ a subset of $K$ and $X$ an indeterminate over $K$. The set $\mathrm{Int}(E,D):=\{f\in K[X];\; f(E)\subseteq D\}$, of integer-valued polynomials on $E$ over $D$, is known to be an integral domain. The purpose of this note is to calculate the Krull dimension of $\mathrm{Int}(E,D)$ across various classe

Ege Cakar, Per Ola Kristensson

Humans are black boxes -- we cannot observe their neural processes, yet society functions by evaluating verifiable arguments. AI explainability should follow this principle: stakeholders need verifiable reasoning chains, not mechanistic transparency. We propose using structured argumentation to provide a level of explanation and verification neither interpre

Chashi Mahiul Islam, Oteo Mamo, Samuel Jacob Chacko, Xiuwen Liu

Vision-language models (VLMs) have advanced multimodal reasoning but still face challenges in spatial reasoning for 3D scenes and complex object configurations. To address this, we introduce SpatialViLT, an enhanced VLM that integrates spatial features like depth maps, 3D coordinates, and edge maps through a multi-task learning framework. This approach enric

Jan Šťovíček

We investigate the relation between partial silting modules, Gabriel topologies, and ring epimorphisms, with a particular emphasis on commutative rings. We show that a ring epimorphism of commutative rings is flat if and only if it is a silting ring epimorphism.

Brendon Boldt, David Mortensen

We introduce CSAR, an algorithm for inducing morphemes from emergent language corpora of parallel utterances and meanings. It is a greedy algorithm that (1) weights morphemes based on mutual information between forms and meanings, (2) selects the highest-weighted pair, (3) removes it from the corpus, and (4) repeats the process to induce further morphemes (i

Grace Ra Kim, Duncan Eddy, Vedant Srinivas, Mykel J. Kochenderfer

Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce communication gaps between access windows. Traditional ground station selection typically begins by choosing from a fixed set of locations offered by Ground Station-as-a-Service (GSaaS)

Jairo Diaz-Rodriguez, Mumin Jia

Kernel change-point detection (KCPD) has become a widely used tool for identifying structural changes in complex data. While existing theory establishes consistency under independence assumptions, real-world sequential data such as text exhibits strong dependencies. We establish new guarantees for KCPD under $m$-dependent data: specifically, we prove consist

A. Emran

Europa's surface composition and physical characteristics are commonly constrained using spectral deconvolution through linear mixture (LM) modeling and radiative transfer-based (RT) intimate mixture modeling. Here, I compared the results of these two spectral modeling- LM versus RT- against laboratory spectra of water (H$_{2}$O) ice and sulfuric acid octahy

Robert Conte

We build several matrix Lax pairs of ${\rm q-P_{\rm VI}}$ valid even when the two eigenvalues of the residue of the monodromy matrix at infinity are equal. Their elements are rational functions of the dependent variables.

Nirmal Elamon, Rouzbeh Davoudi

The field of object detection and understanding is rapidly evolving, driven by advances in both traditional CNN-based models and emerging multi-modal large language models (LLMs). While CNNs like ResNet and YOLO remain highly effective for image-based tasks, recent transformer-based LLMs introduce new capabilities such as dynamic context reasoning, language-

Áron Samuel Kovács, Pedro Hermosilla, Renata G. Raidou

We introduce Style Brush, a novel style transfer method for textured meshes designed to empower artists with fine-grained control over the stylization process. Our approach extends traditional 3D style transfer methods by introducing a novel loss function that captures style directionality, supports multiple style images or portions thereof, and enables smoo

Jiajun Shen, Yufei Jin, Yi He, Xingquan Zhu

Learning from large heterogeneous graphs presents significant challenges due to the scale of networks, heterogeneity in node and edge types, variations in nodal features, and complex local neighborhood structures. This paper advocates for ensemble learning as a natural solution to this problem, whereby training multiple graph learners under distinct sampling

Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark A. Anastasio

Quantitative photoacoustic computed tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing robust qPACT reconstruction methods remains challenging due to computational demands, modeling difficulties, and experimental uncertainties. Learning-based methods have been propos

Pham Ngoc Anh

We discuss interrelations between: Cohn localizations of full square matrices; a Leavitt localization of a row; and the Jacobson quasi-inverses of quasi-regular elements. The latter Jacobson localizations appear naturally and easily in rings which are Hausdorff topological spaces with respect to an ideal topology, pointing out also a connection to specific G

Diksha Garg, Laksha Pradip Das, Mary Hall Reno

Cosmic rays interacting with the Earth's atmosphere generate extensive air showers, which produce Cherenkov, fluorescence and radio emissions. These emissions are key signatures for detection by ground-based, sub-orbital, and satellite-based telescopes aiming to study high energy cosmic ray and neutrino events. However, detectors operating at ground and ball

Hossein Gholizadeh, Yonggang Jiang

In this paper, we discuss the maximum flow problem in the two-party communication model, where two parties, each holding a subset of edges on a common vertex set, aim to compute the maximum flow of the union graph with minimal communication. We show that this can be solved with $\tilde{O}(n^{1.5})$ bits of communication, improving upon the trivial $\tilde{O}

Franz A. Heinsen, Leo Kozachkov

Many domains, from deep learning to finance, require compounding real numbers over long sequences, often leading to catastrophic numerical underflow or overflow. We introduce generalized orders of magnitude (GOOMs), a principled extension of traditional orders of magnitude that incorporates floating-point numbers as a special case, and which in practice enab

Congzheng Song, Xinyu Tang

Fine-tuning large language models (LLMs) with backpropagation\textemdash even for a subset of parameters such as LoRA\textemdash can be much more memory-consuming than inference and is often deemed impractical for resource-constrained mobile devices. Alternative methods, such as zeroth-order optimization (ZO), can greatly reduce the memory footprint but come

Ritesh Mehta

Biomedical Named Entity Recognition (BioNER), task6 in BioASQ (A challenge in large-scale biomedical semantic indexing and question answering), is crucial for extracting information from scientific literature but faces hurdles such as distinguishing between similar entity types like genes and chemicals. This study evaluates the GLiNER-BioMed model on a BioAS

Aditya Manuwal, Vladimir Avila-Reese, Daniel Montenegro-Taborda, Vicente Rodriguez-Gomez

Extragalactic stars within galaxy clusters contribute to the intracluster light (ICL), which is thought to be a promising tracer of the underlying dark matter (DM) distribution. In this study, we employ the TNG300 simulation to investigate the prospect of recovering the dark matter distribution of galaxy clusters from deep, wide-field optical images. For thi

Ethan Foss, Simone D'Amico

This work presents a novel algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints

Taekwon Hong, Wenbin Lu, Shu Yang, Pulak Ghosh

The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing policies that balance public health and the economic and social impacts. We utilize extensive scanner dat

Cassandra Masschelein, Michelle Richer, Paul W. Ayers

Evaluating the permanent of a matrix is a fundamental computation that emerges in many domains, including traditional fields like computational complexity theory, graph theory, many-body quantum theory and emerging disciplines like machine learning and quantum computing. While conceptually simple, evaluating the permanent is extremely challenging: no polynom

Manh Tuan Hoang, Matthias Ehrhardt

In this work, we propose a generalized, second-order, nonstandard finite difference (NSFD) method for non-autonomous dynamical systems. The proposed method combines the NSFD framework with a new non-local approximation of the right-hand side function. This method achieves second-order convergence and unconditionally preserves the positivity of solutions for

Joseph Rawson, Domniki Ladopoulou, Petros Dellaportas

Neural diffusion processes provide a scalable, non-Gaussian approach to modelling distributions over functions, but existing formulations are limited to single-task inference and do not capture dependencies across related tasks. In many multi-task regression settings, jointly modelling correlated functions and enabling task-aware conditioning is crucial for

Ananya Mantravadi, Shivali Dalmia, Olga Pospelova, Abhishek Mukherji

Retrieval-Augmented Generation (RAG) integrates large language models (LLMs) with external sources, but unresolved contradictions in retrieved evidence often lead to hallucinations and legally unsound outputs. Benchmarks currently used for contradiction detection lack domain realism, cover only limited conflict types, and rarely extend beyond single-sentence

Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol, Eduardo Blanco

Large Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn jailbreaks that distribute malicious intent across benign exchanges and bypass alignment mechanisms. Existing approaches often explore the adversarial space poorly, rely on hand-crafted heuristics, or lack systematic

Ashley Lenau, Dennis Dimiduk, Stephen R. Niezgoda

A successful deep learning network is highly dependent not only on the training dataset, but the training algorithm used to condition the network for a given task. The loss function, dataset, and tuning of hyperparameters all play an essential role in training a network, yet there is not much discussion on the reliability or reproducibility of a training alg

Amierul Aqil Khairi, Elyse Lian, Uri Shumlak

The ionizations per photon, or S/XB, method uses spectroscopic measurements of radiating impurity ions to determine the influx from a solid surface. It is highly useful as a non-perturbing, in-situ measure of the gross erosion flux of plasma-facing components (PFCs). In sheared-flow-stabilized (SFS) Z-pinch devices, the electrode supplies the plasma current

Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash, Mike Bernhardt

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collab

Pasquale Ambrosio, Simone Ciani

We prove the local boundedness of local weak solutions to the parabolic equation \[ \partial_{t}u\,=\,\sum_{i=1}^{n}\partial_{x_{i}}\left[(\vert u_{x_{i}}\vert-\delta_{i})_{+}^{p-1}\frac{u_{x_{i}}}{\vert u_{x_{i}}\vert}\right]\,\,\,\,\,\,\,\,\,\,\mathrm{in}\,\,\,\Omega_{T}=\Omega\times(0,T]\,, \] where $\Omega$ is a bounded domain in $\mathbb{R}^{n}$ with $n

M. N. Sabry, A. E. Hussin

Although transient convection is ubiquitous in natural and manmade phenomena, few research works attempted to make a compact model for it, altogether, others attempted a compact model that contradicts problem physics. The correct modelling pattern is deduced here analytically for a simple geometry, but it can readily be used for many common applications such

D. Urošević, B. Vukotić, M. Anđelić, N. Mladenović

We present a new selected sample of 69 Galactic supernova remnants (SNRs) for calibration of radio $\Sigma-D$ relation at 1 GHz. Calibrators with the most reliable distances were selected through an extensive literature search. The calibration is performed using kernel smoothing of the selected sample of calibrators in $\Sigma-D$ plane and an orthogonal offs

A. Viitanen, G. Mountrichas, H. Stiele, F. J. Carrera

The XMM-Newton X-ray observatory has played a prominent role in astrophysics, conducting precise and thorough observations of the X-ray sky for the past two decades. The most recent iteration of the 4XMM catalogue and one of its latest data releases DR11 mark significant improvements over previous XMM-Newton catalogues, serving as a cornerstone for comprehen

Sebastian Acosta, Benjamin Palacios

We investigate the inverse problem of recovering an initial source for the wave equation with fractional attenuation, motivated by photoacoustic tomography (PAT). The attenuation is modeled by a Caputo fractional derivative of order $\alpha\in(0,1)$. We establish uniqueness under a geometric foliation condition via an adaptation of two types of Carleman esti

Boniface M. Sindala, Ragib Hasan

Research management applications (RMA) are widely used in clinical research environments to collect, transmit, analyze, and store sensitive data. This data is so valuable making RMAs susceptible to security threats. This analysis, analyzes RMAs' security, focusing on Research Electronic Data Capture (REDCap) as an example. We explore the strengths and vulner

Maryam A. Nasir, W. A. Atkinson

Head-to-head ferroelectric domain walls are intrinsically charged, and are typically compensated by a mix of oppositely charged defects and free electrons. The free electrons form a two-dimensional electron gas (2DEG) along the domain wall. In many cases, inversion symmetry is broken at the wall, which implies that the 2DEG is subject to nontrivial spin-orbi

Sanket Badhe

We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space (for example, discovery requests, motions, meet-and-confer, sanctions) governed by a JSON rules engine, while a stochasti

Martin Ammon, Jette Germerodt, Christoph Sieling, Julio Virrueta

We provide a holographic prescription to compute real-time thermal correlators with arbitrary operator ordering. In field theory, these correlation functions are captured by a multi-fold Schwinger-Keldysh time contour. We propose a holographic dual for these contours, which generalizes the gravitational Schwinger-Keldysh geometry previously advocated in the

A. A. Chrimes, P. G. Jonker, A. J. Levan, A. Mummery

Luminous Fast Blue Optical Transients (LFBOTs) are rare extragalactic events of unknown origin. Tidal disruptions of white dwarfs by intermediate mass black holes (BHs), mergers of BHs and Wolf-Rayet stars, and failed supernovae are among the suggestions. We explore the viability of very massive star core-collapse events as the origin of LFBOTs. The appeal o

Christina Schoettler, James E Owen

Young planetary systems are subjected to different dynamical effects that can influence their orbital structure over time. In systems with more than one planet, other planets can internally influence each other, e.g. via planet-planet scattering. External perturbing effects also need to be taken into account, as stars do not form by themselves but together w

David DePalma, Neeraj Gupta, Hsiao-Wen Chen, Robert A. Simcoe

We report statistically significant detection of H I 21-cm emission from intermediate-redshift ($z\approx0.2$-0.6) galaxies. By leveraging multi-sightline galaxy survey data from the Cosmic Ultraviolet Baryon Survey (CUBS) and deep radio observations from the MeerKAT Absorption Line Survey (MALS), we have established a sample of $\approx6000$ spectroscopical

Xiaoyan Bai, Aryan Shrivastava, Ari Holtzman, Chenhao Tan

Self-recognition is a crucial metacognitive capability for AI systems, relevant not only for psychological analysis but also for safety, particularly in evaluative scenarios. Motivated by contradictory interpretations of whether models possess self-recognition (Panickssery et al., 2024; Davidson et al., 2024), we introduce a systematic evaluation framework t