October 2025 arXiv papers — page 157

Nataly Brukhim, Ariel Bruner, Orit E. Raz

We study a finite-field analogue of the Erd\H{o}s distinct distances problem under the Hamming metric. For a set \(S\subseteq \mathbb{F}_q^n\) let $\Delta(S)$ denote the set of Hamming distances determined by \(S\). We prove the lower bound \[ |\Delta(S)| \;\ge\; \frac{\log |S|}{2\log(2nq)}, \] and show this bound is tight when \(|S|=O(\text{poly}(n))\), whe

Chenxi Sui, Ching-Tai Fu, Guangxia Feng, Yuqi Li

The development of reversible metal anodes is a key challenge for advancing aqueous battery technologies, particularly for scalable and safe stationary energy storage applications. Here we demonstrate a strategy to realize epitaxial electrodeposition of iron (Fe) on single-crystal copper (Cu) substrates in aqueous electrolytes. We compare the electrodepositi

Zaixi Zhang, Souradip Chakraborty, Amrit Singh Bedi, Emilin Mathew

The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology. Yet this advancement is intrinsically linked to new vulnerabilities, as GenAI lowers the barrier to misuse and introduces novel biosecurity threats, such as generating synthetic viral proteins or toxins. These du

Soroush Mehraban, Andrea Iaboni, Babak Taati

Recent transformer-based models for 3D Human Mesh Recovery (HMR) have achieved strong performance but often suffer from high computational cost and complexity due to deep transformer architectures and redundant tokens. In this paper, we introduce two HMR-specific merging strategies: Error-Constrained Layer Merging (ECLM) and Mask-guided Token Merging (Mask-T

Shudong Sun, Hao Helen Zhang, Joseph C Watkins

Measuring dataset similarity is fundamental in machine learning, particularly for transfer learning and domain adaptation. In the context of supervised learning, most existing approaches quantify similarity of two data sets based on their input feature distributions, neglecting label information and feature-response alignment. To address this, we propose the

Ahmed Alanazi, Duy Ho, Yugyung Lee

Robots navigating dynamic, cluttered, and semantically complex environments must integrate perception, symbolic reasoning, and spatial planning to generalize across diverse layouts and object categories. Existing methods often rely on static priors or limited memory, constraining adaptability under partial observability and semantic ambiguity. We present GRI

Shuaicheng Zhang, Haohui Wang, Junhong Lin, Xiaojie Guo

Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - t

Aleksander Skenderi

Let $G$ be a connected algebraic semisimple real Lie group with finite center and no compact factors, and let $\Gamma$ be a Zariski dense discrete subgroup of $G$. We show that $\Gamma$ contains free, finitely generated subsemigroups whose critical exponents are arbitrarily close to that of $\Gamma$. Furthermore, these subsemigroups are Zariski dense in $G$

Samuel Yuan, Divyanshu Saxena, Jiayi Chen, Nihal Sharma

Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning from historical trends, and predicting future behaviors, such models promise to keep pace with ever-increasing workload dynamism and continuous hardware evolution. However, policies

Alina Chertock, Michael Herty, Arsen S. Iskhakov, Anna Iskhakova

In this paper, we study random dissipative weak solutions of the compressible Euler equations in the Kelvin-Helmholtz (KH) instability. Motivated by the fact that weak entropy solutions are not unique and can be viewed as inviscid limits of Navier-Stokes flows, we take a statistical approach following ideas from turbulence theory. Our aim is to identify solu

G. Krizman, A. Kazakov, C. -W. Cho, V. V. Volobuev

Two-dimensional quantum materials can host original electronic phases that arise from the interplay of electronic correlations, symmetry and topology. In particular, the spontaneous breaking of internal symmetry that acts simultaneously on the pseudospin and the spatial degree of freedom realizes a nematic ordering. We report evidence of a quantum Hall valle

Johann Pignat, Milena Vucetic, Christophe Gaudet-Blavignac, Jamil Zaghir

Developing natural language processing tools for clinical text requires annotated datasets, yet French oncology resources remain scarce. We present FRACCO (FRench Annotated Corpus for Clinical Oncology) an expert-annotated corpus of 1301 synthetic French clinical cases, initially translated from the Spanish CANTEMIST corpus as part of the FRASIMED initiative

Omkar Dhungel, Saravanan Sengottuvel, Mariusz Mrozek, Till Lenz

Nitrogen-vacancy centers in nanodiamond offer a microwave-free, noninvasive platform for probing superconductors via near zero-field cross-relaxation magnetometry. We demonstrate this by depositing nanodiamonds on YBCO thin films to measure critical parameters: transition temperature and penetration field. This method leverages nanodiamond fluorescence modul

Federico Berra, Matías Rubén Bolaños, Alberto De Toni, Kannan Vijayadharan

In recent decades, there has been an increasing demand for faster modulation schemes. Electro-optic modulators are essential components in modern photonic systems, enabling high-speed control of light for applications ranging from telecommunications to quantum communication. Conventional inline and Mach-Zehnder modulators, while widely adopted, are limited b

Daniel Dalsgaard, Michael Kuffmeier, Troels Haugbølle

The attracting properties of gravity enable matter present in cores to collapse into stars with seven orders of magnitude change in space and time making modelling of star formation a challenging multi-scale process. To circumvent this scale problem stars are replaced by a sub-grid sink particle at a much larger scale. Sink particles are created above a thre

Shane O'Brien, Amber Wong, Te Han, Paul Robertson

We confirm the planetary nature of (1) TOI-5916 b and (2) TOI-6158 b, two Exoplanets Transiting M-dwarf Stars (GEMS), both discovered by the Transiting Exoplanet Survey Satellite (TESS). Both systems were confirmed with ground-based photometry (Red Buttes Observatory and Swope, respectively) and radial velocity data from the Habitable-zone Planet Finder. The

Antonio O. Bouzas

We give an integral expression for the vector potential of a time-independent, steady azimuthal current density. Our derivation is substantially simpler and somewhat more general than others given in the literature. As an illustration, we recover the results for the vector potential of a circular current loop as an orthogonal expansion in spherical and cylin

Anne-Marie Toparkus, Rafael Weissbach

With double-truncated lifespans, we test the hypothesis of a parametric distribution family for the lifespan. The typical finding from demography is an instationary behaviour of the life expectancy, and a copula models the resulting weak dependence of lifespan and the age at truncation. Our main example is the Farlie-Gumbel-Morgenststern copula. The test is

Sheng Jin, Haoming Wang, Zhiqi Gao, Yongbo Yang

Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics. Addressing the fragmented issues in teaching process modeling and the

Sujan Chakraborty, Rahul Bordoloi, Anindya Sengupta, Olaf Wolkenhauer

Graph-based learning is a cornerstone for analyzing structured data, with node classification as a central task. However, in many real-world graphs, nodes lack informative feature vectors, leaving only neighborhood connectivity and class labels as available signals. In such cases, effective classification hinges on learning node embeddings that capture struc

Masafumi Udagawa, Roderich Moessner

Fractional excitations provide a key to identifying sought-after topological quantum spin liquid states in realistic materials. Their single-particle dynamics already presents a challenging many-body problem on account of the coupling to their emergent gauge field. Here, we study the spinon excitations of kagome ice, realized at the $2/3$ magnetization plate

H. Sakai, C. Tabata, K. Kaneko, Y. Tokiwa

alpha-UTe3, a van der Waals (vdW) actinide compound with a monoclinic ZrSe3-type structure, is a narrow-gap semiconductor with 5f moments. 125Te NMR reveals strongly anisotropic, layer-confined spin fluctuations below about 20 K, with the a-axis component enhanced, and a signal wipeout at the antiferromagnetic (AFM) transition at TN = 5 K. Single-crystal neu

Yuhao Tong, Vanessa Rossetto Marcelino, Robert Turnbull, Heroen Verbruggen

Genome-resolved metagenomics has contributed largely to discovering prokaryotic genomes. When applied to microscopic eukaryotes, challenges such as the high number of introns and repeat regions found in nuclear genomes have hampered the mining and discovery of novel protistan lineages. Organellar genomes are simpler, smaller, have higher abundance than their

Max T. Birch, Sebastian Wintz, Yuhan Sun, Akiko Kikkawa

Novel antiferromagnets with broken time reversal symmetry (TRS) have launched a new direction in spintronics research, combining the advantageous dynamical properties of conventional antiferromagnets with the controllability typically associated with ferromagnets. However, antiferromagnetic domains are notoriously challenging to image in real-space. X-ray ma

Paul Leask

The interactions of anyonic quasi-particles (vortices) in the Chern--Simons extension of the Ginzburg--Landau model is investigated and we show that it manifestly realizes a hybridization of type I/II superconductivity. Through Gauss' law, each vortex simultaneously carries a flux quantum and a proportional Noether charge, thereby realizing an anyonic ex

Rocío Díaz Martín, Linda Saal

Given a compact subgroup K of the orthogonal group acting on the Euclidean space Rn, Gerald Schwarz proved that every smooth K-invariant function on Rn can be expressed as a smooth function of a generating set of $K$-invariant polynomials on n variables. The goal of this work is to provide an alternative and more straightforward proof of this result, based o

Pok Man Chiu

We study the degree of band flatness and anisotropic quantum geometry in magic-angle twisted bilayer graphene by varying the twist angle and the lattice relaxation through optical conductivity. We show that the degree of band flatness and its quantum geometry can be revealed through optical absorption and its resulting optical bounds, which are based on the

Samuele Silveravalle, Andrea Lapi, Francesco Benetti, Stefano Liberati

We explore a cosmological model in which dark matter is non-minimally coupled to gravity at the fluid level. While typically subdominant compared to Standard Model forces, such couplings may dominate dark matter dynamics. We show that this interaction modifies the early-time Friedmann equations, driving a phase of accelerated expansion that can resolve the h

Andrew T. Rozema, James C. Davis

Social engineering attacks delivered via email, commonly known as phishing, represent a persistent cybersecurity threat leading to significant organizational incidents and data breaches. Although many organizations train employees on phishing, often mandated by compliance requirements, the real-world effectiveness of this training remains debated. To contrib

Souvik Dey, Jian Liu, Yuki Mifune, Yuya Otake

Let R be a commutative Noetherian ring. We establish a close relationship between the strong generation of the singularity category of R and the nonvanishing of the annihilator of the singularity category of R. As an application, we prove that the singularity category of R has a strong generator if and only if the annihilator of the singularity category of R

F. J. Collings, N. J. Fitch, R. A. Jenkins, J. M. Dyne

We present the design and characterization of a low-noise environment for measuring the electron's electric dipole moment (EDM) with a beam of molecules. To minimize magnetic Johnson noise from metals, the design features ceramic electric field plates housed in a glass vacuum chamber. To suppress external magnetic noise the apparatus is enclosed within a

Chiara Gabellieri, Lars Teeuwen, Yaolei Shen, Antonio Franchi

This work considers a large class of systems composed of multiple quadrotors manipulating deformable and extensible cables. The cable is described via a discretized representation, which decomposes it into linear springs interconnected through lumped-mass passive spherical joints. Sets of flat outputs are found for the systems. Numerical simulations support

András Grabarits, Federico Balducci, Adolfo del Campo

We investigate the efficiency of approximate counterdiabatic driving (CD) in accelerating adiabatic passage through exponentially small gaps. First, we analyze a minimal spin-glass bottleneck model that is analytically tractable and exhibits both an exponentially small gap at the transition point and a change in the ground state that involves a macroscopic r

Tyafur Rahman Pathan, Daryoosh Vashaee

The coherence and fidelity of quantum dot (QD) spin qubits are fundamentally limited by charge noise arising from electrically active trap states at oxide interfaces, heterostructure boundaries, and within the bulk semiconductor. These traps introduce electrostatic fluctuations that couple to the qubit via spin-orbit interactions or charge-sensitive confinem

Antonios Zitridis

Motivated by recent developments in the calibration of stochastic volatility models (SVMs for short), we study continuous-time formulations of martingale optimal transport and martingale Schr\"odinger bridge problems. We establish duality formulas and also provide alternative proofs, via different techniques, of duality results previously established in the

Negin Esmaeili, Paul T. Grogan

This work presents an objective, repeatable, automatic, and fast methodology for assessing the representativeness of geophysical variables sampled by Earth-observing satellites. The primary goal is to identify and mitigate potential sampling biases attributed to orbit selection during pre-Phase A mission studies. This methodology supports current incubation

Chris Su, Harrison Li, Matheus Marques, George Flint

Recent work reports that Large Reasoning Models (LRMs) undergo a collapse in performance on solving puzzles beyond certain perplexity thresholds. In subsequent discourse, questions have arisen as to whether the nature of the task muddles an evaluation of true reasoning. One potential confound is the requirement that the model keep track of the state space on

Guanli Liu, Renata Borovica-Gajic

Data and workload drift are key to evaluating database components such as caching, cardinality estimation, indexing, and query optimization. Yet, existing benchmarks are static, offering little to no support for modeling drift. This limitation stems from the lack of clear definitions and tools for generating data and workload drift. Motivated by this gap, we

Suhas S. Jain

In this work, we propose a novel transport model for soluble surfactants in two-phase flows. In a two-phase flow, the soluble surfactants can adsorb/desorb from/into the bulk of any of the phases to the interface and can modify the interface properties. This results in sharp gradients in the surfactant concentration on the interface and also between the two

Dirk Lauinger, Luc Coté, Andy Sun

Electricity storage is used for intertemporal price arbitrage and for ancillary services that balance unforeseen supply and demand fluctuations via frequency regulation. We present an optimization model that computes bids for both arbitrage and frequency regulation and ensures that storage operators can honor their market commitments at all times for all flu

Muhammad Afifurrahman, Valentio Iverson, Gian Cordana Sanjaya

We establish several asymptotic formulae and upper bounds for the count of multiplicatively dependent integer vectors that lie on a fixed hyperplane and have bounded height. This work constitutes a direct extension of the results obtained by Pappalardi, Sha, Shparlinski, and Stewart.

Daniel R. Johnston

In number theory, many major results related to the additive properties of primes are proven using the methods of sieve theory. However, in nearly every case, the existing proofs of these results are ineffective, in that explicit values for which they hold cannot be computed. The reason for this ineffectivity is due to the reliance on the Bombieri--Vinogrado

Tanay Saha, Shiroman Prakash

Magic state distillation is a leading but costly approach to fault-tolerant quantum computation, and it is important to explore all possible ways of minimizing its overhead cost. The number of ancillae required to produce a magic state within a target error rate $\epsilon$ is $O(\log^{\gamma} (\epsilon^{-1}))$ where $\gamma$ is known as the yield parameter.

Tingxuan Leng, Yushi Wang, Tinglong Zheng, Changsheng Luo

Humanoid locomotion requires not only accurate command tracking for navigation but also compliant responses to external forces during human interaction. Despite significant progress, existing RL approaches mainly emphasize robustness, yielding policies that resist external forces but lack compliance particularly challenging for inherently unstable humanoids.

Donald Loveland, Yao-An Yang, Danai Koutra

Learning on text-attributed graphs has motivated the use of Large Language Models (LLMs) for graph learning. However, most fusion strategies are applied uniformly across all nodes and attain only small overall performance gains. We argue this result stems from aggregate metrics that obscure when LLMs provide benefit, inhibiting actionable signals for new str

Ilya Fokin

Lattice QCD (LQCD) calculations predict that chiral symmetry is restored in a smooth crossover transition between a quark-gluon plasma and a hadron resonance gas (HRG) at vanishing net-baryon density, a condition realized in heavy-ion collisions at the LHC. In this regime, the net-baryon number cumulants computed using the HRG and LQCD partition functions ar

Kaixuan Ren, Preslav Nakov, Usman Naseem

As vision-language models (VLMs) become increasingly capable, maintaining a balance between safety and usefulness remains a central challenge. Safety mechanisms, while essential, can backfire, causing over-refusal, where models decline benign requests out of excessive caution. Yet, there is currently a significant lack of benchmarks that have systematically

Hillary Davis, Thor Tepper-Garcia, Naomi McClure-Griffiths, Joss Bland-Hawthorn

The development of an N-body/hydrodynamic `surrogate' model of the Milky Way (MW) - a model that resembles the MW in several key aspects after many Gyrs of evolution - would be extremely beneficial for Galactic Archaeology. Here we present four new `surrogate' models, all built with the Nexus framework. The simulations contain stars, dark matter and gas. Our

Ryan E. G. Bushling

Let $1 \leq m < s \leq n$ and let $A \subseteq \mathbb{R}^n$ be a Borel set of with $s$-dimensional Hausdorff measure $\mathcal{H}^s(A) > 0$. The classical Marstrand slicing theorem states that, for almost every $m$-dimensional subspace $V \subset \mathbb{R}^n$, there is a positive-measure set of $x \in V$ such that $x + V^\perp$ intersects $A$ in a set of H

Jared Grinberg, Yanran Ding

This paper presents a method for detecting and localizing contact along robot legs using distributed joint torque sensors and a single hip-mounted force-torque (FT) sensor using a generalized momentum-based observer framework. We designed a low-cost strain-gauge-based joint torque sensor that can be installed on every joint to provide direct torque measureme

Davide A. Bignamini, Paolo De Fazio

In this paper we prove the well-posedness of non-autonomous deterministic and stochastic reaction-diffusion equations with a polynomial reaction term. Concerning the stochastic problem, we also prove a new result on the space-time regularity of the non-autonomous stochastic convolution.

Chen Wang, Mengtan Lin

The Fire We Share proposes a care-centered, consequence-aware visualization framework for engaging with wildfire data not as static metrics, but as living archives of ecological and social entanglement. By combining plants-inspired data forms, event-based mapping, and narrative layering, the project foregrounds fire as a shared temporal condition-one that cu

Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli

An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational

Hamza Harraf, Noura Chabar, Mohamed Amazioug, Rachid Ahl Laamara

Nonreciprocal physics is attracting significant interest in quantum information processing. In this work, we propose a scheme to investigate the nonreciprocity of bi- and tripartite entanglement and generate squeezed states in a magnomechanical system. This is achieved through the Barnett effect, which originates from the rotation of the first magnon mode. T

Anne Lonjou

This expository article builds on lecture notes from a minicourse entitled "Cremona groups and CAT(0) cube complexes" and given by the author as part of the 2023 Riverside Workshop on Geometric Group Theory. It presents recent constructions of actions of Cremona groups on median graphs aimed at both geometric group theorists and algebraic geometers.

Thomas Brüstle, Justin Desrochers, Samuel Leblanc

Let $\mathcal{C}$ be a small, connected category with finite hom-sets. We show that if the embedding of a connected subcategory $\mathcal{J}$ is both initial and final, then the restriction of any $\mathcal{C}$-module along $\mathcal{J}$ preserves the generalized rank-or equivalently, the multiplicity of the ``entire&#34; interval modules for $\mathcal{C}$ a

Partha Nandi, Partha Ghose, Francesco Petruccione

Spin networks in loop quantum gravity provide a kinematical picture of quantum geometry but lack a natural mechanism for dynamical Dirac-type evolution, while the Wheeler--DeWitt equation typically enters only as an imposed constraint. We propose a stochastic framework in which each spin-network edge carries helicity-resolved amplitudes -- two-state internal

Yichen Xu, Yiqing Zhou, James P. Sethna, Eun-Ah Kim

The threshold theorem promises a path to fault-tolerant quantum computation, provided the physical error rate is below a critical threshold. While transversal gates efficiently implement logical operations, they propagate errors and can lower this threshold relative to a static quantum memory. In this work, we generalize the statistical-mechanical (stat-mech

Jeffrey Shallit

We show that there is no automaton accepting the Tribonacci representations of $n$ and $x$ in parallel, where $\psi = 1.839\cdots$ is the Tribonacci constant, and $x= \lfloor n \psi \rfloor$. Similarly, there is no Tribonacci automaton generating the Sturmian characteristic word with slope $\psi-1$.

Mehdi Zekriyapanah Gashti

In this paper we represent a new framework for integrated distributed and reliable systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyze previous frameworks related to integrated systems, then represent new proposed framework in order to improving previous framework, and we

Baptiste Rabecq, Thomas Lee, Andy Sun

As power grids experience increasing renewable penetration and rapid load growth from AI data centers and electrification, alleviating line congestion becomes critical to unlocking additional grid capacity. This work investigates Dynamic Line Rating (DLR), a congestion mitigation method that adjusts power line current limits in response to meteorological con

Anikeya Aditya, Ayu Irie, Nabankur Dasgupta, Rajiv K. Kalia

The structures and properties of moire patterns in twisted bilayers of two-dimensional (2D) materials are known to depend sensitively on twist angle, yet their dependence on stacking order remains comparatively underexplored. In this study, we use molecular dynamics simulations to systematically investigate the combined effects of stacking order and rotation

Gabriel Smithline, Scott Nivison

Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph

Juan Carlos Muñoz Grajales, Deissy Marcela Pizo

In this paper, we investigate the well-posedness of a nonlinear dispersive model with variable coefficients that describes the evolution of surface waves propagating through a one-dimensional shallow water channel of finite length with irregular bottom topography. To complement the theoretical analysis, we utilize the numerical solver developed by the author

Zhenghan Tai, Hanwei Wu, Qingchen Hu, Jijun Chi

Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as te

Shudong Sun, Aki Hara, Laurel Johnstone, Brian Hallmark

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in r

Haeji Jung, Jinju Kim, Kyungjin Kim, Youjeong Roh

Transliteration has emerged as a promising means to bridge the gap between various languages in multilingual NLP, showing promising results especially for languages using non-Latin scripts. We investigate the degree to which shared script, overlapping token vocabularies, and shared phonology contribute to performance of multilingual models. To this end, we c

Emeline Bolmont, Edward Galantay, Sergi Blanco-Cuaresma, Apurva V. Oza

We investigate the origin and stability of extrasolar satellites orbiting close-in gas giants, focusing on whether these satellites can survive planetary migration within a protoplanetary disk. To address this question, we used Posidonius, an N-Body code with an integrated tidal model, which we expanded to account for the migration of a gas giant within a di

Rodrigo Rey Carvalho, Matheus Duzi, Vinicius de Oliveira Rodrigues

We provide new results on combinatorial characterizations of covering properties in end spaces and ray spaces. In particular, we characterize the Lindel\"of degree, the extent, the Rothberger property, $\sigma$-compactness and the Menger property for ray, end and edge-end spaces. We show that $\sigma$-compactness and the Menger property are equivalent for th

Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela

We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by le

Daniel Howard

We present a framework for characterizing neurosis in embodied AI: behaviors that are internally coherent yet misaligned with reality, arising from interactions among planning, uncertainty handling, and aversive memory. In a grid navigation stack we catalogue recurrent modalities including flip-flop, plan churn, perseveration loops, paralysis and hypervigila

Clemence Mottez, Louisa Fay, Maya Varma, Sophie Ostmeier

Deep learning models have shown promise in improving diagnostic accuracy from chest X-rays, but they also risk perpetuating healthcare disparities when performance varies across demographic groups. In this work, we present a comprehensive bias detection and mitigation framework targeting sex, age, and race-based disparities when performing diagnostic tasks w

Fupeng Zhang, Pau Amaro Seoane

We explore extreme mass-ratio inspirals (EMRIs) in the co-evolution of massive black holes (MBHs) and nuclear star clusters (NSCs), which host diverse stellar populations across a wide range of masses. The dynamics are simulated self-consistently with GNC, which we have updated to incorporate gravitational wave orbital decay, the loss cone of a spinning MBH,

Maarten van der Hulst, Rodrigo A. González, Koen Classens, Paul Tacx

Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems fr

Vivek Acharya

Generative AI is reshaping how software is designed, written, and maintained. Advances in large language models (LLMs) are enabling new development styles - from chat-oriented programming and 'vibe coding' to agentic programming - that can accelerate productivity and broaden access. This paper examines how AI-assisted techniques are changing software enginee

Kevin Chalmers, Jan Bækgaard Pedersen

We present the first spin-free, kernel-lock-free mutex that cooperates with user-mode schedulers and is formally proven FIFO-fair and linearizable using CSP/FDR. Our fairness oracle and stability-based proof method are reusable across coroutine runtime designs. We designed the claim/release protocol for a process-oriented language -- ProcessJ -- to manage th

Boutros Pierre Embaid

Recently a new meteoritic mineral, Zolenskyite (Fe0.99Mn0.04Ca0.01Cr1.99S3.98), was discovered from the Indarch meteorite. Zolenskyite was structurally indexed as the monoclinic C2/m CrNb2Se4 - Cr3S4 type structure of synthetic FeCr2S4, with unit cell parameters a = 12.84(1) {\AA}, b = 3.44(1) {\AA}, c = 5.94(1) {\AA} and \b{eta} = 117(1){\deg}. Zolenskyite

Tiarnaigh Downey-Webb, Olamide Jogunola, Oluwaseun Ajao

This paper presents a systematic security assessment of four prominent Large Language Models (LLMs) against diverse adversarial attack vectors. We evaluate Phi-2, Llama-2-7B-Chat, GPT-3.5-Turbo, and GPT-4 across four distinct attack categories: human-written prompts, AutoDAN, Greedy Coordinate Gradient (GCG), and Tree-of-Attacks-with-pruning (TAP). Our compr

Oliver Braunling

Every LCA group has a Haar measure unique up to rescaling by a positive scalar. Clausen has shown that the Haar measure describes the universal determinant functor of the category LCA in the sense of Deligne. We show that when only working with LCA groups without allowing real vector spaces, any conceivable determinant functor is unique up to rescaling by at

Meiru Zhang, Philipp Borchert, Milan Gritta, Gerasimos Lampouras

Automating the formalization of mathematical statements for theorem proving remains a major challenge for Large Language Models (LLMs). LLMs struggle to identify and utilize the prerequisite mathematical knowledge and its corresponding formal representation in languages like Lean. Current retrieval-augmented autoformalization methods query external libraries

Allen Juntao Fang, Elena Giorgi, Jingbo Wan

The resolution of the nonlinear stability of black holes as solutions to the Einstein equations relies crucially on imposing the right geometric gauge conditions. In the vacuum case, the use of Generally Covariant Modulated (GCM) spheres and hypersurfaces has been successful in the proof of stability for slowly rotating Kerr spacetime. For the charged settin

Enric Junque de Fortuny, Veronica Roberta Cappelli

Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents&#39; behavior, such as negotiation, policy design, and market simulation. However, can we trust LLMs to think strategically in complex situations? Existing research mostly evaluates LLMs&#39; adherence to equilibrium play or their exhibited depth of rea

Ananth Hariharan

In the rapidly evolving educational landscape, the integration of technology has shifted from an enhancement to a cornerstone of educational strategy worldwide. This transition is propelled by advancements in digital technology, especially the emergence of artificial intelligence as a crucial tool in learning environments. This research project critically ev

William Acero, Isabel Molina, J. Miguel Marín

We propose small area estimators of general indicators in off-census years, which avoid the use of deprecated census microdata, but are nearly optimal in census years. The procedure is based on replacing the obsolete census file with a larger unit-level survey that adequately covers the areas of interest and contains the values of useful auxiliary variables.

Allen Juntao Fang, Elena Giorgi, Jingbo Wan

The nonlinear stability problem for black hole solutions of the Einstein equations critically depends on choosing an appropriate geometric gauge. In the vacuum setting, the use of Generally Covariant Modulated (GCM) spheres and hypersurfaces has played a central role in the proof of stability for slowly rotating Kerr spacetime. In this work, we develop an al

Omar Islam Laskar, Fatemeh Ramezani Khozestani, Ishika Nankani, Sohrab Namazi Nia

Data sharing ecosystems connect providers, consumers, and intermediaries to facilitate the exchange and use of data for a wide range of downstream tasks. In sensitive domains such as healthcare, privacy is enforced as a hard constraint, any shared data must satisfy a minimum privacy threshold. However, among all masking configurations that meet this requirem

Gheehyun Nahm

In a recent breakthrough, Ren and Willis gave the first analysis-free proof of the existence of exotic compact, orientable 4-manifolds; their main tool is the Khovanov skein lasagna module defined by Morrison, Walker, and Wedrich. In this paper, we introduce a new, simple way of using Khovanov homology to distinguish certain exotic compact, orientable 4-mani

Francisco M. Fernández

When a sheared potential is deformed in such a way that the distance between the classical turning points remains constant the eigenvalues of the Schr\"{o}dinger equation oscillate with respect to the potential parameter responsible for the deformation. We show that such an oscillation is intimately related to the passing of the nodes of the corresponding ei

Ali Atiah Alzahrani

We examine whether regime-conditioned generative scenarios combined with a convex CVaR allocator improve portfolio decisions under regime shifts. We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shr

Mihir Gupte, Paolo Giusto, Ramesh S

Large Language Models (LLMs) are adept at generating responses based on information within their context. While this ability is useful for interacting with structured data like code files, another popular method, Retrieval-Augmented Generation (RAG), retrieves relevant documents to augment the model's in-context learning. However, it is not well-explored how

Soraya S. Anvari, Rina R. Wehbe

Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and personalization, their integration into mental health care brings privacy and safety challenges that have not been well-examined.

Alessandro Albini, Mohsen Kaboli, Giorgio Cannata, Perla Maiolino

Robotic tactile perception is a complex process involving several computational steps performed at different levels. Tactile information is shaped by the interplay of robot actions, the mechanical properties of its body, and the software that processes the data. In this respect, high-level computation, required to process and extract information, is commonly

Javier García-Sigüenza, Mirco Nanni, Faraón Llorens-Largo, José F. Vicent

This work addresses the challenge of using a deep learning model to prune graphs and the ability of this method to integrate explainability into spatio-temporal problems through a new approach. Instead of applying explainability to the model's behavior, we seek to gain a better understanding of the problem itself. To this end, we propose a novel model that i

Md Abdullah Al Mazid, Liangdong Deng, Naphtali Rishe

Clouds remain a major obstacle in optical satellite imaging, limiting accurate environmental and climate analysis. To address the strong spectral variability and the large scale differences among cloud types, we propose MSCloudCAM, a novel multi-scale context adapter network with convolution based cross-attention tailored for multispectral and multi-sensor c

Bahar İlgen, Georges Hattab

Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Processing (NLP), such as BLEU, FKGL, and SARI, mainly capture surface-level features and fail to account for human-centered qualities like clarity

Davide Rolino, Paolo Perinotti, Alessandro Tosini

We propose an operational definition of complementarity, pinning down the concept originally introduced by Bohr. Two properties of a system are considered complementary if they cannot be simultaneously well defined. We further show that, within quantum theory, this notion is equivalent to the incompatibility of operations -- that is, their inability to be pe

Wanshu Nie, Sujay V. Kumar, Junyu Chen, Long Zhao

Recent advances in machine learning such as Long Short-Term Memory (LSTM) models and Transformers have been widely adopted in hydrological applications, demonstrating impressive performance amongst deep learning models and outperforming physical models in various tasks. However, their superiority in predicting land surface states such as terrestrial water st

Juan Antonio Barceló, Salvador Peréz-Esteva, Emilio Marmolejo-Olea, Mari Cruz Vilela

We study, for $1 \leq p \leq \infty$, the Hardy space $\bm{h}_e^p(\B)$, the elastic analogue of the classical Hardy spaces of harmonic functions in the unit ball of $\mathbb{R}^3$. The space consists of vector-field solutions of the Lam\'e system satisfying the standard integrability condition on concentric spheres centered at the origin. Using the elastic P

Aleksandra Melnikova, Petr Matula

High-quality, publicly available segmentation annotations of image and video datasets are critical for advancing the field of image processing. In particular, annotations of volumetric images of a large number of targets are time-consuming and challenging. In (Melnikova, A., & Matula, P., 2025), we presented the first publicly available full 3D time-lapse se

Raheleh Biglari, Joachim Denil

Model validity is as critical as the model itself, especially when guiding decision-making processes. Traditional approaches often rely on predefined validity frames, which may not always be available or sufficient. This paper introduces the Decision Oriented Technique (DOTechnique), a novel method for determining model validity based on decision consistency