October 2025 arXiv papers — page 208

A. Arango-Restrepo, J. M. Rubi

Self-propulsion of particles is typically explained by phoretic mechanisms driven by externally imposed chemical, electric, or thermal gradients. In contrast, chemical reactions can enhance particle diffusion even in the absence of such external gradients. We refer to this increase as active diffusivity, often attributed to self-diffusiophoresis or self-elec

Rohith Reddy Gangam, Shayan Taherijam, Vijay V. Vazirani

We study the classical rent division problem, where $n$ agents must allocate $n$ indivisible rooms and split a fixed total rent $R$. The goal is to compute an envy-free (EF) allocation, where no agent prefers another agent's room and rent to their own. This problem has been extensively studied under standard assumptions, where efficient algorithms for comput

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey, George Em Karniadakis

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural o

Shambhavi Mishra, Gaurav Sahu, Marco Pedersoli, Laurent Charlin

Can large language models solve AI research problems using only their parametric knowledge, without fine-tuning, retrieval, or other external aids? We introduce AInstein, a framework for testing whether LLM agents can generate and refine solutions to research problems through iterative critique loops. A blind study with 20 domain experts on held-out ICLR 202

Simina Brânzei

We present a simple local search algorithm for computing EFX (envy-free up to any good) allocations of $m$ indivisible goods among $n$ agents with additive valuations. EFX is a compelling fairness notion, and whether such allocations always exist remains a major open question in fair division. Our algorithm employs simulated annealing with the total number o

Tyler C. Sterling

The linear combination of atomic orbitals (LCAO) method uses a small basis set in exchange for expensive matrix element calculations. The most efficient approximation for the matrix element calculations is the two-center approximation (2CA) in tight binding (TB). In the 2CA, a variety of matrix elements are neglected with only "two-center integrals" (2CI) re

Vasily Tolstikov, Marcus Wentz, Joseph Schiarizzi, Derek Ding

We expand on the recent development of n-dimensional automated market makers for stablecoins by showing a way to build concentrated liquidity positions with ticks in polar coordinates in Rust, including the featured ability to skew said concentrated liquidity. We highlight the risk of stacking too many stablecoin pools and how to hedge said risk.

Kübra Benli, Greg Martin, Paul Péringuey

We provide a formula for the logarithmic density of the set of positive real numbers on which two prime counting functions $\psi(x;q,a)$ and $\psi(x;q,b)$ are simultaneously larger than their asymptotic main terms, as well as a method for calculating the numerical values of such densities with rigorously bounded errors. We apply these formulas to the pairwis

Anne Dranowski, Yura Kabkov, Daniel Tubbenhauer

Our goal is to one day take a photo of a knot and have a phone automatically recognize it. In this expository work, we explain a strategy to approximate this goal, using a mixture of modern machine learning methods (in particular convolutional neural networks and transformers for image recognition) and traditional algorithms (to compute quantum invariants li

R. Dastidar, G. Pignata, N. Dukiya, K. Misra

We present the analysis of the luminous Type II Supernova (SN) 2021tsz, which exploded in a low-luminosity galaxy. It reached a peak magnitude of -18.88 $\pm$ 0.13 mag in the $r$ band and exhibited an initial rapid decline of 4.05 $\pm$ 0.14 mag (100 d)$^{-1}$ from peak luminosity till $\sim$30 d. The photospheric phase is short, with the SN displaying bluer

Md Hasan Shahriar Rifat, Tanvir Khan, Md Arafat Hossain Shourov, Md Sahat Bin Sayed

The electronic and optical properties of CdGa2Te4 and ZnGa2Te4 were studied using first-principles DFT calculations. Band gaps were calculated using the GGA-PBESol functional. Both materials show promise for photovoltaic applications because of their large, near-unity absorption efficiencies (10^4 cm^-1) in the visible region. They exhibit low exciton bindin

Limin Zeng

We study the holographic dual of the extended thermodynamics of spherically symmetric, charged Gauss-Bonnet AdS black holes in the context of the AdS/CFT correspondence. Compared to Einstein's theory of gravity, Gauss-Bonnet gravity introduces higher-order curvature terms. The coupling constants of these higher-order curvature terms $\alpha$ can serve as new

Yichen Wang, Zixuan Yang, Xiamiao Zhao, Yuhang Bai

Given a graph $F$, an $r$-uniform hypergraph $\mathcal{H}$ is a {\em Berge-$F$} if there is a bijection $\phi:E(F)\to E(\mathcal{H})$ such that $e\subseteq \phi(e)$ for each $e\in E(F)$. Given a family $\mathcal{F}$ of $r$-uniform hypergraphs, an $r$-uniform hypergraph is $\mathcal{F}$-free if it does not contain any member of $\mathcal{F}$ as a subhypergrap

Shrenik Bhansali, Larry Heck

Autoregressive (AR) decoding is a major latency bottleneck for large language models. Speculative decoding (SD) accelerates AR by letting a drafter propose multi-token blocks that a verifier accepts or rejects. However, many SD systems require heavy offline training or extra components. These choices raise data/compute cost and can yield brittle drafters und

Bruna O. Stahlhöfer, Edgar Huayra, Emmanuel G. de Oliveira

We study the dissociative associated production of a $J/\psi$ meson and a dimuon via double parton scattering (DPS) in nucleus--proton ultraperipheral collisions. This new channel, characterized by a rapidity gap, is sensitive to the photon and gluon distributions of the proton. We derive a DPS pocket formula for this process, together with a corresponding e

René Mayrhofer, Anja Lehmann, abhi shelat

This paper describes pseudonyms for the upcoming European Identity Wallet (EUDIW) architecture from both a cryptographic and an implementation perspective. Its main goal is to provide technical insights into the achievable properties and cryptographic realizations. In particular, we (1) outline the security and privacy requirements of EUDI pseudonyms as the

Srikanth B. Iyengar, Chandrashekhar B. Khare, Jeffrey Manning

In his proof of Fermat's Last Theorem, Wiles deployed a commutative algebra technique, namely a numerical criterion for detecting isomorphisms of rings. In our recent work we pick up on Wiles' work and generalize the numerical criterion to ``higher codimension''. A critical ingredient is a notion of congruence module in higher codimension: this has turned ou

Xinying Hou, Ruiwei Xiao, Runlong Ye, Michael Liut

The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun suppo

Shichen Wang, Peter D. Olmsted

We compare six elastic models for polymer networks in the context of phase separation within a gel, including a new model that combines the finite extensible Arruda-Boyce model and the slip tube model for entangled chains. We study incompressible uniaxial stretch and compression, and three volume-changing constrained-dimension deformations, in which the mate

Ziheng Geng, Jiachen Liu, Ran Cao, Lu Cheng

Large language models (LLMs) have recently been used to empower autonomous agents in engineering, significantly improving automation and efficiency in labor-intensive workflows. However, their potential remains underexplored in structural engineering, particularly for finite element modeling tasks requiring geometric modeling, complex reasoning, and domain k

Wonseok Lee, Levi D. Palmer, Thomas E. Gage, Scott K. Cushing

Time-resolved and ultrafast electron energy-loss spectroscopy (EELS) is an emerging technique for measuring photoexcited carriers, lattice dynamics, and near-fields across femtosecond to microsecond timescales. When performed in either a specialized scanning transmission electron microscope or ultrafast electron microscope (UEM), time-resolved and ultrafast

Matthew Elpers

For any homotopy class h in any compact orientable 3-manifold M which is closed or has exclusively torus boundary components, we produce infinitely many pairs of distinct knots representing h with orientation-preserving homeomorphic 0-surgeries.

Bruno Korbar, Andrew Zisserman

The goal of this paper is to be able to retrieve images using a compound query that combines object instance information from an image, with a natural text description of what that object is doing or where it is. For example, to retrieve an image of "Fluffy the unicorn (specified by an image) on someone's head". To achieve this we design a mapping network th

Junyi Fan, Li Sun, Negin Ashrafi, Kamiar Alaei

Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference Optimization (DPO) to adapt Mistral-7B, a locally deployable language

Paulo L. Dattori da Silva, André Pedroso Kowacs

We extend the directional Poincar\'e inequality on the torus, introduced by Steinerberger in [Ark. Mat. 54 (2016), pp. 555--569], to the setting of compact Lie groups. We provide necessary and sufficient conditions for the existence of such an inequality based on estimates on the eigenvalues of the global symbol of the corresponding vector field. We also pro

Kebin Contreras, Luis Toscano-Palomino, Mauro Dalla Mura, Jorge Bacca

Recovering the past from present observations is an intriguing challenge with potential applications in forensics and scene analysis. Thermal imaging, operating in the infrared range, provides access to otherwise invisible information. Since humans are typically warmer (37 C -98.6 F) than their surroundings, interactions such as sitting, touching, or leaning

Ram Manohar, S. M. Mallikarjuaniah

We propose and analyze an adaptive finite element method for a phase-field model of dynamic brittle fracture. The model couples a second-order hyperbolic equation for elastodynamics with the Ambrosio-Tortorelli regularization of the Francfort-Marigo variational fracture energy, which circumvents the need for explicit crack tracking. Our numerical scheme comb

Sashank Makanaboyina

Incremental brain tumor segmentation is critical for models that must adapt to evolving clinical datasets without retraining on all prior data. However, catastrophic forgetting, where models lose previously acquired knowledge, remains a major obstacle. Recent incremental learning frameworks with knowledge distillation partially mitigate forgetting but rely h

Benjamin Jarvis-Frain, Andy Schang, Fernando Quijandría, Ibrahim Nsanzineza

The generation of entangled photons through Spontaneous Parametric Down-Conversion (SPDC) is a critical resource for many key experiments and technologies in the domain of quantum optics. Historically, SPDC was limited to the generation of photon pairs. However, the use of the strong nonlinearities in circuit quantum electrodynamics has recently enabled the

Martin Milanič, Đorđe Mitrović

It was shown by Beisegel, Chudnovsky, Gurvich, Milani\v{c}, and Servatius in 2022 that every induced $2$-edge path in a vertex-transitive graph closes to an induced cycle. Similar results were obtained for 3-edge paths closing to cycles in edge-transitive graphs, where the cycle can be assumed to be induced if the path is induced. Motivated by these results,

R. R. S. Oliveira

In the present comment, we show that the fundamental equation worked by Dvornikov in his paper, which is the Dirac equation for a massive neutrino interacting with linearly accelerated matter, is incorrect. In particular, Dvornikov incorrectly wrote/defined the effective external current in a curved space-time. In other words, Dvornikov wrote/defined such an

Ahmad Alsheikh, Andreas Fischer

Predicting the final hardness of steel after heat treatment is a challenging regression task due to the many-to-one nature of the process -- different combinations of input parameters (such as temperature, duration, and chemical composition) can result in the same hardness value. This ambiguity makes the inverse problem, estimating input parameters from a de

Yuxiang Gao, Kevin Allen, Rose Albu Mustaf, Yichen Zhang

Recent interest in quantum materials has focused on systems exhibiting both superconductivity and non-trivial band topology as material candidates to realize topological or unconventional superconducting states. So far, superconductivity in most topological materials has been identified as type II. In this work, we present magnetotransport studies on the qua

Kangwoo Yi, Bo Shen, Qin Li, Haimin Wang

Solar Proton Events (SPEs) cause significant radiation hazards to satellites, astronauts, and technological systems. Accurate forecasting of their proton flux time profiles is crucial for early warnings and mitigation. This paper explores deep learning sequence-to-sequence (seq2seq) models based on Long Short-Term Memory networks to predict 24-hour proton fl

Kazuumi Fujioka, Rui Sun

We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wid

Nicolas Lanchier, Max Mercer, Hyunsik Yun

This paper considers a natural variant of the $d$-dimensional multitype contact process in which individuals can be fertile or sterile. Fertile individuals of type $i$ give birth to an offspring of their own type at rate $\lambda_i$, the offspring being fertile with probability $p_i$ and sterile with probability $1 - p_i$, whereas sterile individuals can't g

Nilesh Gupta, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar

In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, candidate documents, and the query into the model's input prompt and tasking the LLM to identify relevant document(s). While it is effective, efficiency is a significant challenge in

Martin Chuaqui, Iason Efraimidis, Rodrigo Hernández

We consider normalized univalent functions with prescribed second Taylor coefficient $a_2$. For convex functions $f$ we study the Hardy spaces to which $f$ and $f'$ belong, refining in particular on a theorem of Eenigenburg and Keogh, and give a sharp asymptotic estimate and an explicit uniform bound for their coefficients. Relating the lower order of a conv

Agniprabha Chakraborty

The paradigm of learning-based robotics holds immense promise, yet its translation to real-world applications is critically hindered by the sample inefficiency and brittleness of conventional model-free reinforcement learning algorithms. In this work, we address these challenges by introducing DREAMer-VXS, a model-based framework for Autonomous Ground Vehicl

Ahmad Alsheikh, Andreas Fischer

Accurate and efficient temperature prediction is critical for optimizing the preheating process of PET preforms in industrial microwave systems prior to blow molding. We propose a novel deep learning framework for generalized temperature prediction. Unlike traditional models that require extensive retraining for each material or design variation, our method

Samuel Bouaziz--Ermann, Minki Hhan, Garazi Muguruza, Quoc-Huy Vu

There are various notions of quantum pseudorandomness, such as pseudorandom unitaries (PRUs), pseudorandom state generators (PRSGs), pseudorandom function-like state generators (PRFSGs) and quantum-computable PRGs. Unlike the different notions of classical pseudorandomness, which are known to be existentially equivalent to each other, the relations among qua

Andrea Valassi

The first production release of the CUDACPP plugin for the Madgraph5_aMC@NLO generator, which speeds up matrix element (ME) calculations for leading-order (LO) processes using a data parallel approach on vector CPUs and GPUs, was delivered in October 2024. This was described in previous publications by the team behind that effort. In this paper, I describe m

Rakhat-Bi Abdyssagin, Bob Coecke

We initiate the development of a new language and theory for quantum music, to which we refer as Quantum Concept Music (QCM). This new music formalism is based on Categorical Quantum Mechanics (CQM), and more specifically, its diagrammatic incarnation Quantum Picturalism (QPict), which is heavily based on ZX-calculus. In fact, it is naturally inherited from

Felicity Anderson, Julien Sindt, Neil Chue Hong

We describe data-driven RSE personas: an approach combining software repository mining and data-driven personas applied to research software (RS), an attempt to describe and identify common and rare patterns of Research Software Engineering (RSE) development. This allows individuals and RS project teams to understand their contributions, impact and repositor

Robert Hołyst, Paweł Jan Żuk, Konrad Giżyński, Anna Maciołek

Classical thermodynamics describes physical systems in thermodynamic equilibrium, characterized in particular by the absence of macroscopic motion. Global non-equilibrium thermodynamics extends this framework to include physical systems in stationary states (Ho{\l}yst et al., EPL 149, 30001 (2025)). Here, we demonstrate that this extended theory captures mac

Marissa N. Perry, Anthony J. Taylor, Oscar A. Chavez Ortiz, Steven L. Finkelstein

We present an analysis of bursty star-formation histories (SFHs) of 346 star-forming galaxies at $1\lesssim z<7$, selected from JWST/NIRSpec G395M and PRISM spectroscopy provided by the CEERS and RUBIES surveys. We analyze the correlation of star-formation rate vs. stellar mass (the star-forming main sequence, SFMS) for our sample and find no significant dif

Ananth Kandala, Ratna Kandala, Akshata Kishore Moharir, Niva Manchanda

Mental health communication in India is linguistically fragmented, culturally diverse, and often underrepresented in clinical NLP. Current health ontologies and mental health resources are dominated by diagnostic frameworks centered on English or Western culture, leaving a gap in representing patient distress expressions in Indian languages. We propose cross

Mikil Foss, Andrew Lamperski

Estimating the Kullback-Leibler (KL) divergence between random variables is a fundamental problem in statistical analysis. For continuous random variables, traditional information-theoretic estimators scale poorly with dimension and/or sample size. To mitigate this challenge, a variety of methods have been proposed to estimate KL divergences and related quan

Rohan Arni, Carlos Blanco

Physics-Informed Neural Networks (PINNs) are a useful framework for approximating partial differential equation solutions using deep learning methods. In this paper, we propose a principled redesign of the PINNsformer, a Transformer-based PINN architecture. We present the Spectral PINNSformer (S-Pformer), a refinement of encoder-decoder PINNSformers that add

Felipe Almeida, Peter Barker

Optically levitated and cooled nanoparticles are a new quantum system whose application to the creation of non-classical states of motion and quantum limited sensing is fundamentally limited by recoil and bulk heating. We study the creation of stable 3D optical traps using optical cylindrically polarized vortex beams with radial and azimuthal polarization an

David F. Anderson, Jingyi Ma, Praful Gagrani

We study a stochastic model of a copolymerization process that has been extensively investigated in the physics literature. The main questions of interest include: (i) what are the criteria for transience, null recurrence, and positive recurrence in terms of the system parameters; (ii) in the transient regime, what are the limiting fractions of the different

Zhuowei Xu, Zilin Si, Kevin Zhang, Oliver Kroemer

Tactile sensing holds great promise for enhancing manipulation precision and versatility, but its adoption in robotic hands remains limited due to high sensor costs, manufacturing and integration challenges, and difficulties in extracting expressive and reliable information from signals. In this work, we present a low-cost, easy-to-make, adaptable, and compa

Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali

Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. This gap is commonly attributed to retrieval failures -- the models' inability to identify relevant information in the long inputs. Accordingly, recent efforts often focus on evaluating and improving LLMs' retrie

Grégoire Sergeant-Perthuis, Léo Boitel

In graphical models, factor graphs, and more generally energy-based models, the interactions between variables are encoded by a graph, a hypergraph, or, in the most general case, a partially ordered set (poset). Inference on such probabilistic models cannot be performed exactly due to cycles in the underlying structures of interaction. Instead, one resorts t

Xiaogeng Liu, Chaowei Xiao

Recent advancements in jailbreaking large language models (LLMs), such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery. AutoDAN-Turbo employs a lifelong learning agent to build a rich library of attack strategies from scratch. While highly effective, its test-time generation process involves sampling a strategy and generating a

Reza Habibi, Seung Wan Ha, Zhiyu Lin, Atieh Kashani

Meaningful human-AI collaboration requires more than processing language; it demands a deeper understanding of symbols and their socially constructed meanings. While humans naturally interpret symbols through social interaction, AI systems often miss the dynamic interpretations that emerge in conversation. Drawing on Symbolic Interactionism theory, we conduc

Bibhas Adhikari

In this work, we consider weighted signed network representations of financial markets derived from raw or denoised correlation matrices, and examine how negative edges can be exploited to reduce portfolio risk. We then propose a discrete optimization scheme that reduces the asset selection problem to a desired size by building a time series of signed networ

Andrea C Burgess, Peter Danziger, Diane Donovan, Tara Kemp

A weak $c$-colouring of a design is an assignment of colours to its points from a set of $c$ available colours, such that there are no monochromatic blocks. A colouring of a design is block-equitable, if for each block, the number of points coloured with any available pair of colours differ by at most one. Weak and block-equitable colourings of balanced inco

Alben Rome Bagabaldo, Jürgen Hackl

Intelligent intersections play a pivotal role in urban mobility, demanding innovative solutions such as digital twins to enhance safety and efficiency. This literature review investigates the integration and application of digital twins for intelligent intersections, a critical area within smart urban traffic systems. The review systematically categorizes ex

Utkarsh Saxena, Kaushik Roy

Quantizing the key-value (KV) cache is a promising strategy for improving the inference efficiency of large language models (LLMs). However, aggressive quantization to very low precision (e.g., 2 bits) introduces significant errors in the stored key and value tensors, which propagate through the dot-product attention mechanism and ultimately degrade generati

J. W. Moffat, E. J. Thompson

We present the invariant structure of a Holomorphic Unified Field Theory in which gravity and gauge interactions arise from a single geometric framework. The theory is formulated using a product principal bundle, with one connection, and curvature equipped with a Hermitian field on a complexification of spacetime. From a single $\mathrm{Diff}(M)\times G$-inv

Karen L. Collins, David Galvin, Christine A. Kelley, Emily McMillon

Graphs that are squares under the gluing algebra arise in the study of homomorphism density inequalities such as Sidorenko's conjecture. Recent work has focused on these homomorphism density applications. This paper takes a new perspective and focuses on the graph properties of arbitrary square graphs, not only those relevant to homomorphism conjectures and

Lorenzo Riva, Martina Rovelli

We construct an explicit combinatorial model of the functor which adds right adjoints to the morphisms of an $\infty$-category, and we speculate on possible extensions to higher dimensions.

Xin Wang, Xialu Liu

Factor analysis is a widely used technique for dimension reduction in high-dimensional data. However, a key challenge in factor models lies in the interpretability of the latent factors. One intuitive way to interpret these factors is through their associated loadings. Liu and Wang proposed a novel framework that redefines factor models with sparse loadings

R. da Rocha, P. H. O. Silva

Light-flavor baryon resonances in the $J^P=3/2^+$, $J^P=5/2^+$, and $J^P=5/2^-$ families are investigated in a soft-wall AdS/QCD model at finite temperature, including the zero temperature limit. Regge-like trajectories relating the configurational entropy underlying these resonances to both the radial quantum number and the baryon mass spectra are construct

Nicolás A. Barnafi, Felipe Lepe, Francisca Muñoz Riquelme

In two and three dimensions, we analyze a finite element method to approximate the solutions of an eigenvalue problem arising from neutron transport. We derive the eigenvalue problem of interest, which results to be non-symmetric. Under a standard finite element approximation based on piecewise polynomials of degree $k \geq 1$, and under the framework of the

Yang Xiao, Gen Li, Kaiyuan Deng, Yushu Wu

Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. In this paper, we decompose the inference process into the encoding, denoising, and decoding stages, and observe that cache-based acceleratio

Yassine Kebbati, Andreas Rauh, Naima Ait-Oufroukh, Dalil Ichalal

This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed co

Irtaza Sajid Qureshi, Zhen Ming, Jiang

Large Language Models (LLMs) are increasingly applied to automated software testing, yet their ability to generalize beyond memorized patterns and reason about natural language bug reports remains unclear. We present a systematic evaluation of LLM reasoning in test case generation, structured around the cognitive layers of Bloom's taxonomy: \textit{Remember}

Chenyang Li, Qin Li, Haimin Wang, Bo Shen

High-resolution (HR) solar imaging is crucial for capturing fine-scale dynamic features such as filaments and fibrils. However, the spatial resolution of the full-disk H$\alpha$ images is limited and insufficient to resolve these small-scale structures. To address this, we propose a GAN-based superresolution approach to enhance low-resolution (LR) full-disk

Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey

Responsible use of Authorship Verification (AV) systems not only requires high accuracy but also interpretable solutions. More importantly, for systems to be used to make decisions with real-world consequences requires the model's prediction to be explainable using interpretable features that can be traced to the original texts. Neural methods achieve high a

Alex Iacob, Andrej Jovanovic, Mher Safaryan, Meghdad Kurmanji

Training large models with distributed data parallelism (DDP) requires frequent communication of gradients across workers, which can saturate bandwidth. Infrequent communication strategies (e.g., Local SGD) reduce this overhead but, when applied to adaptive optimizers, often suffer a performance gap relative to fully synchronous DDP. We trace this gap to a t

Daozhi Han, Xiaoming Wang

We develop and analyze a highly efficient, second-order time-marching scheme for infinite-dimensional nonlinear geophysical fluid models, designed to accurately approximate invariant measures-that is, the stationary statistical properties (or climate) of the underlying dynamical system. Beyond second-order accuracy in time, the scheme is particularly well su

Taha Ondogan, Ran Jing, Andrew P. Sabelhaus, Roberto Tron

Although Koopman operators provide a global linearization for autonomous dynamical systems, nonautonomous systems are not globally linear in the inputs. State (or output) feedback controller design therefore remains nonconvex in typical formulations, even with approximations via bilinear control-affine terms. We address this gap by introducing the Koopman Co

Ethan Addison, Tedi Draghici, Mehdi Lejmi

Using an integral identity proved by Sekigawa \cite{Sek87} on compact almost Hermitian 4-manifolds, we naturally obtain a global characterization of the class $\mathcal{AH}_1$ of almost Hermitian 4-manifolds satisfying the first Gray curvature condition from apparently weaker conditions. Then we take steps towards a classification of almost Hermitian 4-manif

Alexander Blackston, Alexandra Fonseca Montenegro, Sevim Polat Genlik, Maryam Ghazisaeidi

Recent \textit{ab initio} calculations predict 1D dispersive electronic bands confined to the atomic scale cores of dislocations in the wide bandgap (3.84 eV) semiconductor ZnS. We test these predictions by correlating sub-bandgap optical transitions with the density of dislocations formed during strain relaxation in epitaxial ZnS grown on GaP. The densities

Saud Lingawi, Garrett Frank, Benedictus H. Kartawidjaja, Mahsa Khalili

Photoplethysmography (PPG) is a common tool for monitoring cardiopulmonary health. Relying on absorption or reflectance of light by hemoglobin in the blood, the measured PPG waveform can be analyzed per heart beat using physiological assumptions to extract metrics ranging from heart rate to specific blood oxygenation (SpO2). This has led to the widespread us

Sagnik Saha, Hector O. Silva

We present a comprehensive study of the Kerr-Newman quasinormal mode spectrum in the Dudley-Finley approximation, where the linear gravitoelectromagnetic perturbations are decoupled by "freezing" either one of the fields to its background value. First, we reassess the accuracy of this approximation by comparing it to calculations that solve the coupled syste

Abid Hussain, Touqeer Ahmad

A fundamental challenge in comparing two survival distributions with right censored data is the selection of an appropriate nonparametric test, as the power of standard tests like the Log rank and Wilcoxon is highly dependent on the often unknown nature of the alternative hypothesis. This paper introduces a new, distribution free two sample test designed to

Jhon F. Puerres, Valdivino V. Junior, Pablo M. Rodriguez

The vertices of a tree represent individuals in one of three states: ignorant, spreader, or stifler. A spreader transmits the rumor to any of its nearest ignorant neighbors at rate one. At the same rate, a spreader becomes a stifler after contacting nearest-neighbor spreaders or stiflers. The rumor survives if, at all times, there exists at least one spreade

Jinghao Cao, Qin Li, Mengnan Du, Haimin Wang

We propose Physics-informed Attention-enhanced Fourier Neural Operator (PIANO) to solve the Nonlinear Force-Free Field (NLFFF) problem in solar physics. Unlike conventional approaches that rely on iterative numerical methods, our proposed PIANO directly learns the 3D magnetic field structure from 2D boundary conditions. Specifically, PIANO integrates Efficie

Zohaib Hasan Siddiqui, Dayam Nadeem, Mohammad Masudur Rahman, Mohammad Nadeem

Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-scale data. We present an evaluation protocol for healthcare settings that quantifies associated biases and assesses their operational risk. Our methodology (i) defines a taxonomy

Ian Moore, Anthony Gruber, Chris Wentland, Irina Tezaur

We propose a novel hybrid domain decomposition method that couples sub-domain-local high-fidelity finite element (FE) models with reduced order models (ROMs) using the Schwarz alternating method. By integrating the noninstrusive Operator Inference (OpInf) ROM, our approach accelerates the Schwarz process while allowing for geometry and mesh flexibility. We d

Sihe Chen, Kevin Batzinger, Manuel Smeu

Decrease in the size of integrated circuits (IC) and metal interconnects raise resistivity due the amplification of electron scattering effects, which decreases the efficiency of chiplets. While previous studies have investigated the electron scattering due to a roughened surface, the effect of thermal induced atomic motion on the roughened surface remains u

George Stavisskii, Leonid Fedichkin

Measurement of a charge qubit via point contacts with complex internal structures is considered. In this context, a fully formalized derivation of the many-body wave function method is presented, together with the corresponding master equations for point contacts possessing an arbitrary number of internal states. The focus is placed on the current noise powe

Alim Yolalmaz, Jeroen Kalkman

The phase unwrapping plays a key role in obtaining a ground-truth phase of the wrapped phase. High-accurate unwrapped phases are demanded in various research fields such as optical holography, optical diffraction tomography, and magnetic resonance imaging. Unfortunately, the ground-truth phase is not accessible due to 2pi ambiguity which arises from phase ju

Addie McCurdy, Andrew Gusty, Emily Jensen

The optimal controller design problem for a linear, first-order spatially-invariant distributed parameter system is considered. Through a case study of the Linear Quadratic Regulator (LQR) problem for the diffusion equation over the torus, it is illustrated that the optimal controller design problem can be equivalently formulated as an optimization problem o

Aladin Şura, F. Ömer Ilday

Predictively steering self-organising systems with hierarchical structure toward intended outcomes across widely separated dynamical scales remains a fundamental challenge. Despite decades of progress, hierarchy remains a descriptive property rather than a mechanism for control. Here, we show how a high-dimensional stochastic system can be steered toward pre

Mingyu Kim, Pronoy Sarker, Seungmo Kim, Daniel J. Stilwell

This paper studies sensor placement when detection performance varies stochastically due to environmental factors over space and time and false alarms are present, but a filter is used to attenuate the effect. We introduce a unified model that couples detection and false alarms through an availability function, which captures how false alarms reduce effectiv

Zijian Wang, Andreas Hauptmann, Lu Lu, John C. Schotland

We propose a method to reconstruct the optical absorption of a highly-scattering medium probed by diffuse light. The method consists of learning the optical detection system and then using this result to reconstruct the absorption. Our results are illustrated by numerical simulations.

Christos G. Tsagas, Leandros Perivolaropoulos, Kerkyra Asvesta

Observations have repeatedly confirmed the presence of large-scale peculiar motions in the universe, commonly referred to as ``bulk flows''. These are vast regions of the observable universe, typically spanning scales of several hundred Mpc, that move coherently with speeds of the order of several hundred km/sec. While there is a general consensus on the dir

Abrar Faiyad, Ashlie Martini

Dopants can tune the performance of MoS2 in various applications, but use of molecular dynamics simulations for doped MoS2 materials discovery is limited by the lack of multi-dopant interatomic potentials. Universal machine learning interatomic potentials (MLIPs) could be a solution, but the accuracy of these potentials must first be evaluated. Here, we eval

Asli Karacelik

In this review, we assess the use of Bayesian methods in model predictive control (MPC), focusing on neural-network-based modeling, control design, and uncertainty quantification. We systematically analyze individual studies and how they are implemented in practice. While Bayesian approaches are increasingly adopted to capture and propagate uncertainty in MP

Joseph Palmer

This article presents an overview of the theory of integrable systems with symmetries, focusing on toric systems, semitoric systems, and their classifications via decorated polygons. We discuss certain one-parameter families of integrable systems called semitoric families, and explain how deforming systems through controlled bifurcations in such families (an

Lawrence Feldman, Matthew Bomparola

This document describes a system for selecting teams to the NCAA Men's Division I Lacrosse Championship Tournament called "Safe3Step" (S3S) that was developed in conversation with the NCAA Lacrosse Selection Criteria and Ranking Committee (SCR) with the objective of improving on the Ratings Percentage Index (RPI). S3S employs three steps that: 1) evaluate th

Yongan Yu, Xianda Du, Qingchen Hu, Jiahao Liang

Historical archives on weather events are collections of enduring primary source records that offer rich, untapped narratives of how societies have experienced and responded to extreme weather events. These qualitative accounts provide insights into societal vulnerability and resilience that are largely absent from meteorological records, making them valuabl

Oskar Wysocki, Magdalena Wysocka, Mauricio Jacobo, Harriet Unsworth

We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain, with a focus on cancer research. M-Reason leverages recent advances in large language models (LLMs) and modular agent orchestration to automate evidence retrieval, appraisal, and synthesis across diverse biomedical data source

Nishu Karna, Tatiana Niembro

In this study, we present a comprehensive observational and modeling study of a geo-effective event with D_ST index of -80 nT observed on 2011 May 28 when a coronal hole was bordering an active region. We analyze HMI and EUV images and found that this event involved two filament eruptions ~8 hours apart from two different active region closed to each other.

Michelle Bucher, Alessio Savini

Monod proved that any continuous cohomology of a semisimple Lie group $G$ can be represented by a measurable cocycle on the associated Furstenberg boundary, which we upgraded to an alternating cocycle. In the current paper we improve that result by showing that we can actually take a representing cocycle which is continuous on an explicit subset of generic t

M. J. Hamel-Bravo, D. B. Fisher, D. A. Berg, A. J. Cameron

We present VLT/X-shooter spectroscopy of the extremely metal-poor starburst galaxy SBS 0335-052E, a nearby (D $\sim$54 Mpc) analog of high-redshift systems, probing its outflow up to a distance of $\sim$2.6 kpc. Using direct-method oxygen abundances, we find a complex metallicity profile that generally declines with distance, decreasing by 0.37 dex from the