October 2025 arXiv papers — page 86

Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa

Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research, resulting in frameworks with critical limitations such as centralization and inadequate trust and communication protocols. This paper introduces the Fetch.ai architecture, an industrial-strength platform designed to bridge this gap by

Taishan Yi, Xiao-Qiang Zhao

In this paper, under an abstract setting we establish the spreading properties and the existence, non-existence and global attractivity of spatially heterogeneous steady states for a large class of monotone evolution systems without the translational monotonicity under the assumption that one limiting system has both leftward and rightward spreading speeds a

F. Colangelo, F. Avitabile, Z. Makhdoumi Kakhaki, A. Kumar

W0.75Re0.25, in its bulk form, has been shown to be an interesting superconducting material due to its multiple crystalline phases, each exhibiting distinct superconducting characteristics. However, little is known about how these phases manifest in thin-film form, where deposition conditions and dimensionality are critical aspects. Here, we investigate supe

Prathap Kumar Jharapla, Nicolas Leconte, Zhiren He, Guru Khalsa

We demonstrate a finite twist-angle stabilization mechanism in lattice-mismatched 2D heterobilayers, which results from the geometric alignment between the flake edges and its moire pattern. Using atomistic simulations of graphene on hexagonal boron nitride flakes with diameters of up to $\sim 2500${\AA}, we identify robust metastable angles at $\sim 0.61^\c

Michael Creutz

The Wilson formulation of fermions in lattice gauge theory provides a unified description of the chiral anomalies in the standard model. The discrete Dirac operator diagonalizes into a series of two by two blocks. In each block the possible eigenvalues either form a complex pair or separate into two real eigenvalues that have specific chirality. The collisio

Weinan Jia, Yuning Lu, Mengqi Huang, Hualiang Wang

Long video generation with Diffusion Transformers (DiTs) is bottlenecked by the quadratic scaling of full attention with sequence length. Since attention is highly redundant, outputs are dominated by a small subset of query-key pairs. Existing sparse methods rely on blockwise coarse estimation, whose accuracy-efficiency trade-offs are constrained by block si

Feras AlMannaa, Talia Tseriotou, Jenny Chim, Maria Liakata

This study is the first to investigate LLM comprehension capabilities over long-context (LC), clinically relevant medical Question Answering (QA) beyond MCQA. Our comprehensive approach considers a range of settings based on content inclusion of varying size and relevance, LLM models of different capabilities and a variety of datasets across task formulation

M. Esfandiar, F. Shojai, O. Zamani Jamshidi, S. Zoorasna

This paper investigates the existence of cosmological black holes by analyzing the properties of trapping horizons in detail, based on Hayward's formalism of future outer and past inner trapping horizons, in several dynamical spacetimes embedded in an expanding universe. Through a detailed examination of the McVittie, Culetu, and Sultana--Dyer metrics, as we

Hiromi Ebisu, Bo Han, Weiguang Cao

Symmetries rigidly delimit the landscape of quantum matter. Recently uncovered spatially modulated symmetries, whose actions vary with position, enable excitations with restricted mobility, while Lieb-Schultz-Mattis (LSM) type anomalies impose sharp constraints on which lattice phases are realizable. In one dimensional a spin chain, gauging procedures have l

Jiangxu Huang, Chengjie Zhan, Zhenhua Chai, Changsheng Huang

In this work, we first propose a diffuse-interface model for the freezing processes of three-phase flows in complex geometries, and the core of the model to intergratge the Navier-Stokes equations for fluid flows, a modified phase-field equation for gas-liquid interfaces, and an enthalpy approach for solid-liquid phase-change processes in a unified diffuse-i

Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu

Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models. In many real-world applications, such as energy management and stock investment, agents can access multi-step predictions of future states, which provide additional advantages for decision making. However, multi

Zhuoming Li, Aitong Liu, Mengxi Jia, Yubi Lu

Free-form gesture understanding is highly appealing for human-computer interaction, as it liberates users from the constraints of predefined gesture categories. However, the sole existing solution GestureGPT suffers from limited recognition accuracy and slow response times. In this paper, we propose Gestura, an end-to-end system for free-form gesture underst

Thomas Dedieu

The goal of this text is to present the computation by Salmon, in the second half of the XIXth century, of various numbers enumerating planes with a prescribed tangency pattern with a sufficiently general surface $S$ in $\mathbf{P}^3$ (or, equivalently, of hyperplane sections of $S$ with prescribed singularities). Emblematic among these are the number of tri

Lars O. Jernkvist, Ali R. Massih

Computational models are used here for simulating diffusion-controlled redistribution of hydrogen that is picked up by zirconium-base nuclear fuel cladding during light water reactor operation. Axial localization of hydrogen, leading to localized precipitation of zirconium hydrides at lower-temperature regions near interpellet gaps, is studied with a bespoke

Mohamed Nabih Ali, Daniele Falavigna, Alessio Brutti

Multilingual automatic speech recognition (ASR) remains a challenging task, especially when balancing performance across high- and low-resource languages. Recent advances in sequence modeling suggest that architectures beyond Transformers may offer better scalability and efficiency. In this work, we introduce MLMA (Multilingual Language Modeling with Mamba f

Samuel J. Barrett, Docko Sow

Earth Observation (EO) Foundation Modelling (FM) holds great promise for simplifying and improving the use of EO data for diverse real-world tasks. However, most existing models require additional adaptation before they can be used and are structured rigidly around particular data sources or training approaches. To address this, we take inspiration from larg

Changfeng Gui, Amir Moradifam

The Sphere Covering Inequality was introduced in \cite{GM} (\emph{Invent. Math.}, 2018) as a sharp geometric inequality that provides a lower bound for the total area of two distinct surfaces of Gaussian curvature 1. These surfaces are assumed to be conformal to the Euclidean unit disk and share the same conformal factor along the boundary. In this paper, we

Philippe Formont, Maxime Darrin, Banafsheh Karimian, Jackie CK Cheung

Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, input modalities and datasets, each capturing unique aspects of the input. Multi-teacher distillation leverages this diversity to enrich representations but often remains tailored t

Georgios Baziotis, Alexandros Chatzinikolaou, Gage Hoefer

We develop an algebraic and operational framework for quantum isomorphisms of hypergraphs, using tools from compact quantum group theory. We introduce a new synchronous version of the hypergraph isomorphism game whose game algebra uniformly encodes multiple notions of quantum isomorphisms of hypergraphs. We show that there exist hypergraphs that are quantum

Alberto Sanchez-Delgado, João Carlos Virgolino Soares, David Omar Al Tawil, Alessia Li Noce

Legged rovers provide enhanced mobility compared to wheeled platforms, enabling navigation on steep and irregular planetary terrains. However, traditional legged locomotion might be energetically inefficient and potentially dangerous to the rover on loose and inclined surfaces, such as crater walls and cave slopes. This paper introduces a preliminary study t

Vishal Jindal, Scott T. Milner

Charge carriers in organic semiconductors form polarons, which are self-localized states stabilized by interactions with their environment. Using a dielectric-stabilized tight-binding model parameterized from first-principles calculations, we compute ground and excited polaron states in poly(3-hexylthiophene) (P3HT). Our results quantitatively reproduce key

Jordan Collard, Scott B. Lindstrom

Under conditions that prevent tangential intersection, we prove quadratic convergence of a projection algorithm for the feasibility problem of finding a point in the intersection of a smooth curve and line in $\mathbb{R}^2$. This nonconvex problem has been studied in the literature for both Douglas-Rachford algorithm (DR) and circumcentered reflection method

Su Hu, Min-Soo Kim

In this paper, we construct a $p$-adic path integral via $p$-adic multiple integrals. This integral describes the evolution of a wave function $\Psi(x)$, which is defined as a map from a domain in $\mathbb{C}_{p}$ to $\mathbb{C}_{p}$. We also compute the Feynman propagator for free particles, demonstrating that the result obtained is similar to the classical

Alexander Nemecek, Zebin Yun, Zahra Rahmani, Yaniv Harel

As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems, ensuring their privacy and trustworthiness has emerged as an imperative challenge for the healthcare sector. Fine-tuning LLMs on sensitive electronic health record (EHR) data improves domain alignment but also raise

Sungtae Cho, Su Houng Lee

We study charm-strange mesons, or $D_s$, $D_s^*$, $D_{s0}^*(2317)$, and $D_{s1}(2460)$ mesons by focusing on their production by coalescence from a quark-gluon plasma in relativistic heavy ion collisions at $\sqrt{s_{NN}}=5.02$ TeV. Starting from the investigation of the transverse momentum distribution of both charm and strange quarks through transverse mom

Qi Li, Junpan Wu, Xiang Liu, Yuxin Wang

The reasoning large language model (RLLM) has been proven competitive in solving complex reasoning tasks such as mathematics, coding, compared to general LLM. However, the serving performance and behavior of RLLM remains unexplored, which may undermine the deployment and utilization of RLLM in real-world scenario. To close this gap, in this paper, we conduct

Hanif Rasyidi, Moshiur Farazi

This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition diff

Luciano Petruzziello, Martin B. Plenio

In this paper, we revisit the interpretation of the circular Unruh effect. To this aim, we rely on the principle of general covariance applied to the decay properties of non-inertial particles. Specifically, we show how the tree-level decay rate of an inverse-$\beta$ process involving scalar fields does not require the introduction of a thermal (or non-therm

M. Zakradze, Z. Tabagari, N. Koblishvili, T. Davitashvili

This paper describes the application of the method of probabilistic solutions (MPS) to numerically solve the Dirichlet generalized and classical harmonic problems for irregular n sided pyramidal domains. Here, generalized means that the boundary function has a finite number of first kind discontinuity curves, with the pyramid edges acting as these curves. Th

Brendon Madison

Future electron-positron ($\ee$) colliders, operating as Higgs factories or Z factories, promise unprecedented precision electroweak measurements that are vital to testing the Standard Model (SM) and exploring physics beyond it. Here we present work on the precision of integrated luminosity ($\mathcal{L}$) and center-of-mass energy ($\sqrt{s}$), measurements

Lucy L. Hale, Daniele De Bernardis, Stephan Lempereur, Lianhe H. Li

We investigate the coupling of a multi-mode metal-insulator-metal cavity to a two-dimensional electron gas (2DEG) in a quantum well in the presence of a strong magnetic field. The TM cavity mode is strongly hybridized with an intersubband transition of the 2DEG, forming a polaritonic mode in the ultrastrong coupling regime, while the TE mode remains an almos

Helmut Prodinger

Peca suggested in a recent paper on the arxiv to consider binary butterfly trees and their Horton-Strahler numbers. The trees are obtained by glueing two binary trees together in a special way; the results are again binary trees but with a different probability distribution. A thorough combinatorial analysis is provided and leads asymptotically to the same r

Yan-Heng Yu, Zhe Chang, Sai Wang

Dissipation is an intrinsic property of the cosmic fluid, leading to the damping of curvature perturbations at small scales. In this paper, we comprehensively study dissipative effects in gravitational waves induced by curvature perturbations, known as induced gravitational waves (IGWs). We find dissipative effects become especially significant at wavenumber

Shama Siddiqui, Anwar Ahmed Khan, Indrakshi Dey

Simulation studies are conducted at different levels of details for assessing the performance of Media Access Control (MAC) protocols in Wireless Sensor Networks (WSN). In the present-day scenario where hundreds of MAC protocols have been proposed, it is important to assess the quality of performance evaluation being conducted for each of the proposed protoc

Hichem Sahbi

This paper devises a novel interactive satellite image change detection algorithm based on active learning. Our framework employs an iterative process that leverages a question-and-answer model. This model queries the oracle (user) about the labels of a small subset of images (dubbed as display), and based on the oracle's responses, change detection model is

Dong Yun, Marco Schouten, Dim Papadopoulos

User queries in information retrieval are often ambiguous, making it challenging for systems to identify a user's target from a single query. While recent dialogue-based interactive retrieval systems can clarify user intent, they are inefficient as they often lack an explicit strategy to ask the most informative questions. To address this limitation, we prop

Gabrielle Browne, Mengfei Liu, Eitan Grinspun, Otman Benchekroun

Non-rigid registration is a crucial task with applications in medical imaging, industrial robotics, computer vision, and entertainment. Standard approaches accomplish this task using variations on the Non-Rigid Iterative Closest Point (NRICP) algorithms, which are prone to local minima and sensitive to initial conditions. We instead formulate the non-rigid r

De-Fu Bu, Xiao-Hong Yang, Liang Chen, Chenwei Yang

Winds from the accretion disk in tidal disruption events (TDEs) play a key role in determining the radiation of TDEs. The winds from the super-Eddington accretion phase in TDEs have recently been studied. However, properties of the winds from the sub-Eddington accretion disk in TDEs are not clear. We aim to investigate properties of winds from the circulariz

John Ellis, Marcos A. G. Garcia, Keith A. Olive, Sarunas Verner

We analyze the latest cosmic microwave background (CMB) constraints on the scalar spectral index $n_s$ and tensor-to-scalar ratio $r$ from Planck 2018, BICEP/Keck 2018, the Atacama Cosmology Telescope Data Release 6 (ACT DR6), and the South Pole Telescope (SPT-3G) data, focusing on their implications for attractor models of inflation. We compare systematical

Han Cui

This paper aims to establish the global well-posedness of the Euler-Poisson system for ions in 2D. The difficulties arising from time resonance at low frequencies and slow decay will be overcome by applying the method developed for the gravity-capillary water waves and Euler-Maxwell systems.

Daniel Csillag, Diego Mesquita

E-values have gained prominence as flexible tools for statistical inference and risk control, enabling anytime- and post-hoc-valid procedures under minimal assumptions. However, many real-world applications fundamentally rely on sensitive data, which can be leaked through e-values. To ensure their safe release, we propose a general framework to transform non

Pavel Mnev, Konstantin Wernli

We study the perturbative path integral of Chern-Simons theory (the effective BV action on zero-modes) in Lorenz gauge, expanded around a (possibly non-acyclic) flat connection, as a family over the smooth irreducible stratum $\mathcal{M}' \subset \mathcal{M}$ of the moduli space of flat connections. We prove that it is horizontal with respect to the Grothen

Yohei Ibe, Yutaka Hirano, Yasuo Ozu, Toru Kawakubo

We propose a measurement-based FTQC (MB-FTQC) architecture for high-connectivity platforms such as trapped ions and neutral atoms. The key idea is to use verified logical ancillas combined with Knill's error-correcting teleportation, eliminating repeated syndrome measurements and simplifying decoding to logical Pauli corrections, thus keeping classical overh

Uraz Odyurt, Ömer Sayilir, Mariëlle Stoelinga, Vadim Zaytsev

Industrial cyber-physical systems generate vast amounts of semi-structured time-series data that require careful preprocessing before they can be effectively used for machine learning applications such as fault detection and identification. Raw sensor datasets are often corrupted or incomplete, making it challenging to develop reliable solutions without prop

Reggie C. Pantig

Is it possible to find imprints of a black hole ringdown through gravitational lensing? To address this question, we formulate an analytic description of weak-field and strong-deflection lensing of light in a time-dependent, perturbed Schwarzschild spacetime. The spacetime dynamics are modeled by a single, axisymmetric, even-parity quasinormal mode with \(\e

Madeline Navarro, Lisa O'Bryan, Santiago Segarra

We propose a flexible probabilistic model for predicting turn-taking patterns in group conversations based solely on individual characteristics and past speaking behavior. Many models of conversation dynamics cannot yield insights that generalize beyond a single group. Moreover, past works often aim to characterize speaking behavior through a universal formu

Miro Miranda, Marcela Charfuelan, Matias Valdenegro Toro, Andreas Dengel

Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivity and food security. Crop simulation models, which align with physical processes, offer intrinsic explainability but often perform poorly. Conversely, machine learning models for

Anwar Ahmed Khan, Shama Siddiqui, Indrakshi Dey

Managing delay is one of the core requirements of industrial automation applications due to the high risk associated for equipment and human lives. Using efficient Media Access Control (MAC) schemes guarantees the timely transmission of critical data, particularly in the industrial environments where heterogeneous data is inherently expected. This paper comp

Sribalaji C. Anand, Anh Tung Nguyen, André M. H. Teixeira, Henrik Sandberg

Networked Control Systems (NCSs) are integral in critical infrastructures such as power grids, transportation networks, and production systems. Ensuring the resilient operation of these large-scale NCSs against cyber-attacks is crucial for societal well-being. Over the past two decades, extensive research has been focused on developing metrics to quantify th

Bejoys Jacob, João Azevedo, João Lourenço, Filipe Camarneiro

III-V semiconductor nanolight sources with deep-subwavelength dimensions ($<<$1 ${\mu}$m) are essential for miniaturized photonic devices such as nanoLEDs and nanolasers. However, these nanoscale emitters suffer from substantial non-radiative recombination at room temperature, resulting in low efficiency and ultrashort lifetimes ($<$100 ps). Previous works h

Mattias Trende, Petter Ögren

Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest is often non-trivial, especially for systems having low computational resources, higher-order dynamics, and moving close to obstacles of complex shape. A common solution to this problem is to use a purely distance

Tiffany MG Baptiste, Cristobal Rodero, Charles P Sillett, Marina Strocchi

The deformation of the left atrium (LA), or its biomechanical function, is closely linked to the health of this cardiac chamber. In atrial fibrillation (AF), atrial biomechanics are significantly altered but the underlying cause of this change is not always clear. Patient-specific models of the LA that replicate patient atrial motion can allow us to understa

Ru-Yu Lai, Yi-Hsuan Lin, Lili Yan

We examine inverse problems for the variable-coefficient nonlocal parabolic operator $(\partial_t - \Delta_g)^s$, where $0 < s < 1$. This article makes two primary contributions. First, we introduce a novel entanglement principle for these operators under suitable smoothness conditions. Second, we prove that lower-order perturbations can be uniquely determin

Nils Japke, Sebastian Koch, Helmut Lukasczyk, David Bermbach

Performance regressions in large-scale software systems can lead to substantial resource inefficiencies, making their early detection critical. Frequent benchmarking is essential for identifying these regressions and maintaining service-level agreements (SLAs). Performance benchmarks, however, are resource-intensive and time-consuming, which is a major chall

Samuel Perreault, Silvana M. Pesenti, Daniyal Shahzad

Risk assessment in casualty insurance, such as flood risk, traditionally relies on extreme-value methods that emphasizes rare events. These approaches are well-suited for characterizing tail risk, but do not capture the broader dynamics of environmental variables such as moderate or frequent loss events. To complement these methods, we propose a modelling fr

Yanna Ding, Songtao Lu, Yingdong Lu, Tomasz Nowicki

Transformer architectures can solve unseen tasks based on input-output pairs in a given prompt due to in-context learning (ICL). Existing theoretical studies on ICL have mainly focused on linear regression tasks, often with i.i.d. inputs. To understand how transformers express ICL when modeling dynamics-driven functions, we investigate Markovian function lea

Sheida Rahnamai Kordasiabi, Damian Dalle Nogare, Florian Jug

Semantic segmentation of electron microscopy (EM) images of biological samples remains a challenge in the life sciences. EM data captures details of biological structures, sometimes with such complexity that even human observers can find it overwhelming. We introduce {\epsilon}-Seg, a method based on hierarchical variational autoencoders (HVAEs), employing c

Baptiste Bauvin, Loïc Baret, Ola Ahmad

Neural network compression has gained increasing attention in recent years, particularly in computer vision applications, where the need for model reduction is crucial for overcoming deployment constraints. Pruning is a widely used technique that prompts sparsity in model structures, e.g. weights, neurons, and layers, reducing size and inference costs. Struc

Paolo Seghetti, Matthias Gsell, Anton Prassk, Martin Bishop

Simulations of Cardiac Electrophysiology are gaining momentum beyond basic mechanistic studies, as an approach for supporting clinical decision making. The potential for in silico technologies observed from the research community is immense, with studies demonstrating significantly improved therapeutical outcome with little to no additional burden for patien

Satwik Bhattamishra, Phil Blunsom, Varun Kanade

We study the learnability of languages in the Next Symbol Prediction (NSP) setting, where a learner receives only positive examples from a language together with, for every prefix, (i) whether the prefix itself is in the language and (ii) which next symbols can lead to an accepting string. This setting has been used in prior works to empirically analyze neur

Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas

Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer. Efficiently selecting informative documents requires balancing a key trade-off: (i) retrieving broadly enough to capture all the relevant material,

Zhangquan Chen, Manyuan Zhang, Xinlei Yu, Xufang Luo

Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited rep

Carlo Proietti, Antonio Yuste-Ginel

Modelling qualitative uncertainty in formal argumentation is essential both for practical applications and theoretical understanding. Yet, most of the existing works focus on \textit{abstract} models for arguing with uncertainty. Following a recent trend in the literature, we tackle the open question of studying plausible instantiations of these abstract mod

Sam Kirkham, Patrycja Strycharczuk

The control of speech can be modelled as a dynamical system in which articulators are driven toward target positions. These models are typically evaluated using fleshpoint data, such as electromagnetic articulography (EMA), but recent methodological advances make ultrasound imaging a promising alternative. We evaluate whether the parameters of a linear harmo

Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis, Mehdi Sabiri

We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data. Those rules are leveraged to increase the predictive performance of tree-based models (decision trees and random forests) used for a classification task. They are also used to imp

Kenya S. Andrews, Deborah Dormah Kanubala, Kehinde Aruleba, Francisco Enrique Vicente Castro

Course syllabi set the tone and expectations for courses, shaping the learning experience for both students and instructors. In computing courses, especially those addressing fairness and ethics in artificial intelligence (AI), machine learning (ML), and algorithmic design, it is imperative that we understand how approaches to navigating barriers to fair out

Navaneeth P K, Gopal Bhatta, Sangeetha Kizhakkekalam

We present a comprehensive multi-wavelength investigation of BL Lac object OJ 287 using Swift and Fermi observatories spanning 2008-2025. The source exhibits significant flux variability across optical-UV, X-ray, and $\gamma$-ray regimes, with outbursts observed in optical, UV and X-ray frequencies. Variability and correlation analyses of the long-term light

Eric DeMarbre, Jay Henderson, J. Felipe Gonzalez, Rob Teather

We present an experiment exploring how the controller's virtual representation impacts target acquisition performance across MR and VR contexts. Participants performed selection tasks comparing four visual configurations: a virtual controller, a virtual hand, both the controller and the hand, and neither representation. We found performance comparable betwee

Li Zhu, Hongfeng Wu

Let $f(X)$ be a nonconstant polynomial over $\mathbb{F}_{q}$, with a nonzero constant term. The order of $f(X)$ is a classical notion in the theory of polynomials over finite fields, and recently the definition of freeness of binomials of $f(X)$ was given in \cite{Mart\'{i}nez}. Generalizing these two notions, we introduce the definition of the minimal binom

Somasekhara Goud Sontti, Arnab Atta

Formation and dynamics of Taylor bubble in power-law liquids flowing through a circular co-flow microchannel are numerically investigated using coupled level set and volume-of-fluid method. Aqueous solutions of polyacrylamide (PAAm) are used as power-law liquids. Influences of PAAm concentration, gas-liquid velocities, and surface tension on bubble character

Adith Ramamurti, Joseph F. Lingevitch, Jonathan C. Lighthall, Michael D. Collins

Modern parabolic equation (PE) methods for wave propagation rely on application of a variety of fractional-powered differential operators. Rational approximations of these operators need to properly map their spectra onto the complex plane, accurately handling propagating modes while annihilating evanescent ones. Standard approaches for stable and accurate r

Alexander Avdoshkin, Max Geier, Liang Fu

We present an approach to solving the ground state of Fermi systems that contain spin or other discrete degrees of freedom in addition to continuous coordinates. The approach combines a Markov chain Monte Carlo sampling for energy estimation that we adapted to cover the extended configuration space with a transformer-based wavefunction to represent fermionic

Chukwudalu Okafor, Kohei Takahara, Svetlana Korneychuk, Isabel Huck

Point defect engineering is widely used to tailor the electronic and transport properties of complex oxides, yet its influence on dislocation plasticity remains poorly understood. Here, we establish how donor (Nb) doping modifies dislocation nucleation, multiplication, and mobility in single-crystal SrTiO3 by bridging nano-, meso-, and macroscale deformation

Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang

Multimodal Large Language Models (MLLMs) frequently hallucinate due to their reliance on fragile, linear reasoning and weak visual grounding. We propose Visual Attention Reasoning (VAR), a reinforcement learning framework that reformulates reasoning as a hierarchical search with self-verification. VAR enforces traceable evidence grounding by generating expli

Kangkang Deng, Jiachen Jin, Jiang Hu, Hongxia Wang

We study the problem of minimizing the sum of a smooth function and a nonsmooth convex regularizer over a compact Riemannian submanifold embedded in Euclidean space. By introducing an auxiliary splitting variable, we propose an adaptive Riemannian alternating direction method of multipliers (ARADMM), which, for the first time, achieves convergence without re

Marta Contreiras Silva, Daniel Faria, Laura Balbi, Susana Nunes

Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating the disease not the patient. Achieving this vision requires the integration and interpretation of vast, heterogeneous biomedical data within a meaningful scientific framework. Knowl

Somasekhara Goud Sontti, Arnab Atta

In the present study, Taylor bubble formation in two-phase gas-non-Newtonian Carreau liquid flowing through a confined co-flow microchannel is investigated. Systematic analysis are carried out to explore the influences of rheological properties, inlet velocities, and surface tension on Taylor bubble length, shape, velocity and liquid film thickness. Aqueous

Sijie Zhang

Jet quenching is a well-established probe of the QGP in large collision systems such as Au+Au and Pb+Pb, but is absent in smaller p+A collisions despite the presence of collectivity. This makes it important to study its dependence on system size. O+O collisions offer an ideal opportunity, bridging the gap between small and large systems. In these proceedings

Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis, Mehdi Sabiri

We present a new approach for distilling boosted trees into decision trees, in the objective of generating an ML model offering an acceptable compromise in terms of predictive performance and interpretability. We explain how the correction approach called rectification can be used to implement such a distillation process. We show empirically that this approa

René Coignard, Anton Rygin

We present Qatsi, a hierarchical key derivation scheme using Argon2id that generates reproducible cryptographic secrets without persistent storage. The system eliminates vault-based attack surfaces by deriving all secrets deterministically from a single high-entropy master secret and contextual layers. Outputs achieve 103-312 bits of entropy through memory-h

Netta Karjalainen, Greta Lupi, Rouven Koch, Adolfo O. Fumega

Nanoscale engineered spin systems, ranging from spins on surfaces to nanographenes, provide flexible platforms to realize entangled quantum magnets from a bottom up approach. However, assessing the quantum many-body Hamiltonian realized in a specific experiment remains an exceptional open challenge, due to the difficulty of disentangling competing terms acco

Muhammad Hassan, Maria Mushtaq, Jaan Raik, Tara Ghasempouri

RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM. The first line of work to detect flush+fault-based microarchite

Fayad Ali Banna, Antoine Caradot, Eduardo Brandao, Jean-Philippe Colombier

Identifying from observation data the governing differential equations of a physical dynamics is a key challenge in machine learning. Although approaches based on SINDy have shown great promise in this area, they still fail to address a whole class of real world problems where the data is sparsely sampled in time. In this article, we introduce Unrolled-SINDy

Adeel A. Khan, Tasuki Kinjo, Hyeonjun Park, Pavel Safronov

We construct period sheaves for Hamiltonian spaces, as conjectured in the work of Ben-Zvi, Sakellaridis and Venkatesh, using the perverse pullback functors introduced in the authors' previous work. We prove a dimensional reduction isomorphism (generalizing the results of Davison and Kinjo in cohomological Donaldson--Thomas theory) which implies that perverse

Aren A. Nahapetyan, Linda A. Khachatryan

Different aspects of application of non-Euclidian geometries in architectural compositions are considered. These aspects are illustrated by examples of Armenian (medieval and contemporary) architectural compositions.

Md. Mohiuddin, Alamgir Kabir

In this study, we investigate the structural properties, chemical stability, and electronic, optical, and thermoelectric properties of $\mathrm{Sr_2PrSbO_6}$ using first-principles calculations based on Density Functional Theory (DFT). The goal of this study is to evaluate its potential contribution to next-generation electrocatalysts, optoelectronic devices

Stephen Griffeth, David Guevara

We compute the graded rank of the cohomology of the hyperplane complement associated with a quaternionic reflection group, and observe that it factors into irreducible factors with positive integer coefficients. For an irreducible group, these irreducible factors are all linear except at most one irreducible quadratic factor, which occurs for precisely three

Chunyu Qiao, Tong Liu, Yucheng Zhang, Zhiwei Fan

In large scale short video platforms, CDN resource selection plays a critical role in maintaining Quality of Experience (QoE) while controlling escalating traffic costs. To better understand this phenomenon, we conduct in the wild network measurements during video playback in a production short video system. The results reveal that CDNs delivering higher ave

Zian Meng, Qiang Li, Wenqian Tang, Mingdie Yan

Deep learning-based semantic communication has largely relied on analog or semi-digital transmission, which limits compatibility with modern digital communication infrastructures. Recent studies have employed vector quantization (VQ) to enable discrete semantic transmission, yet existing methods neglect channel state information during codebook optimization,

Heather Smith Blake, Jędrzej Hodor, Piotr Micek, Michał T. Seweryn

The dimension of a partially ordered set $P$ (poset for short) is the least positive integer $d$ such that $P$ is isomorphic to a subposet of $\mathbb{R}^d$ with the natural product order. Dimension is arguably the most widely studied measure of complexity for posets, and standard examples in posets are the canonical structure forcing dimension to be large.

Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein

Mobile apps often embed authentication secrets, such as API keys, tokens, and client IDs, to integrate with cloud services. However, developers often hardcode these credentials into Android apps, exposing them to extraction through reverse engineering. Once compromised, adversaries can exploit secrets to access sensitive data, manipulate resources, or abuse

Shubham Vyas, Franek Stark, Rohit Kumar, Hannah Isermann

Missions such as the Ingenuity helicopter have shown the advantages of using novel locomotion modes to increase the scientific return of planetary exploration missions. Legged robots can further expand the reach and capability of future planetary missions by traversing more difficult terrain than wheeled rovers, such as jumping over cracks on the ground or t

Peiyi Zhou

Volatility clustering and spillovers are key features of real-world financial time series when there are a lot of cross-sectional financial assets. While network analysis helps connect stocks that are 'similar' or 'correlated', which is effective to link volatility spillovers between stocks, contemporary multivariate ARCH-GARCH formulations struggle to repre

Lujia Bai, Holger Dette

Most of the work on checking spherical symmetry assumptions on the distribution of the $p$-dimensional random vector $Y$ has its focus on statistical tests for the null hypothesis of exact spherical symmetry. In this paper, we take a different point of view and propose a measure for the deviation from spherical symmetry, which is based on the minimum distanc

Vincent Delecroix, Oscar Fontaine, Francis Lazarus

The geometry of a graph $G$ embedded on a closed oriented surface $S$ can be probed by counting the intersections of $G$ with closed curves on $S$. Of special interest is the map $c \mapsto \mu_G(c)$ counting the minimum number of intersections between $G$ and any curve freely homotopic to a given curve $c$. Schrijver [On the uniqueness of kernels, 1992] cal

Zhiheng Xi, Xin Guo, Yang Nan, Enyu Zhou

Reinforcement learning (RL) has recently become the core paradigm for aligning and strengthening large language models (LLMs). Yet, applying RL in off-policy settings--where stale data from past policies are used for training--improves sample efficiency, but remains challenging: policy entropy declines sharply, optimization often becomes unstable and may eve

Haojia Lin, Xiaoyu Tan, Yulei Qin, Zihan Xu

Computer-using agents (CUAs) enable task completion through natural interaction with operating systems and software interfaces. While script-based verifiers are widely adopted for evaluation, they suffer from limited scalability and inability to provide step-wise assessment. Reward models offer promising alternatives, but their effectiveness on CUA evaluatio

Guillermo Martínez-Somonte, Airam Marcos-Caballero, Enrique Martínez-González, Antonio L. Maroto

We investigate the capability of the J-PAS survey to constrain the primordial power spectrum using a non-parametric Bayesian method. Specifically, we analyze simulated power spectra generated by a local oscillatory primordial feature template motivated by non-standard inflation. The feature is placed within the range of scales where the signal-to-noise ratio

Sardor Yakupov

Symplectic Lefschetz fibrations can be described via classifying maps with values in the Deligne-Mumford compactification of the moduli space of curves, by means of constructions relying on symplectic geometry. In this note we prove the existence of classifying maps for achiral Lefschetz fibrations using Riemannian geometry. We further extend the Smith signa

Yuval Gil, Merav Parter

We provide new communication-efficient distributed interactive proofs for planarity. The notion of a \emph{distributed interactive proof (DIP)} was introduced by Kol, Oshman, and Saxena (PODC 2018). In a DIP, the \emph{prover} is a single centralized entity whose goal is to prove a certain claim regarding an input graph $G$. To do so, the prover communicates