October 2025 arXiv papers — page 22

Nihat Sadik Deger, Ceren Ayse Deral

We classify and construct supersymmetric solutions of D=3, N=2 gauged supergravity extended with a Fayet-Iliopoulos term, and null and timelike warped AdS spacetimes are among them. From the first one, it is possible to obtain a well-defined black hole by periodic identification. We also find charged string solutions that interpolate between two supersymmetr

Yucheng Huo, Kexin Guo, Massimo Paradiso, K. Jimmy Hsia

Collective behaviors in cellular systems are regulated not only by biochemical signalling pathways but also by intercellular mechanical forces, whose quantification in contractile monolayers remains poorly understood. Here, by integrating traction force microscopy and numerical simulations, we reconstruct the stress distribution in C2C12 myoblast monolayers

Ahmad Kokhahi, Mary Kurz

Multi-Agent Path Finding (MAPF) has gained significant attention, with most research focusing on minimizing collisions and travel time. This paper also considers energy consumption in the path planning of automated guided vehicles (AGVs). It addresses two main challenges: i) resolving collisions between AGVs and ii) assigning tasks to AGVs. We propose a new

Shanzhong Sun, Zhifu Xie, Peng You

The degeneracy of central configurations in the planar $N$-body problem makes their enumeration problem hard and the related dynamics appealing. To truly understand the bifurcations of central configurations, we should work in the FULL configuration space which also facilitates the computer-aided methods. The degeneracy is always intertwined with the symmetr

Ishfaq Aziz, Mohamad Alipour

Estimating subsurface dielectric properties is essential for applications ranging from environmental surveys of soils to nondestructive evaluation of concrete in infrastructure. Conventional wave inversion methods typically assume few discrete homogeneous layers and require dense measurements or strong prior knowledge of material boundaries, limiting scalabi

M. Llerena, L. Pentericci, R. Amorín, A. Ferrara

We investigate the properties of extreme emission line galaxies (EELGs) at $z=4-9$ and their role in reionization. Compact, low-mass galaxies with intense optical emission lines are linked to elevated specific star formation rates (sSFRs) and recent bursts of star formation. Feedback in these systems may enable the leakage of ionizing radiation into the inte

Xinyi Wang, Devansh R. Agrawal, Dimitra Panagou

Stability analysis of the Kalman filter under randomly lost measurements has been widely studied. We revisit this problem in a general continuous-time framework, where both the measurement matrix and noise covariance evolve as random processes, capturing variability in sensing locations. Within this setting, we derive a closed-form upper bound on the expecte

Elias Fink, Carlo Contaldi, Giorgio Mentasti

We investigate the cross-correlation between astrometric and timing-residual observables for distant sources, such as pulsars and galaxies, and equivalent observables for nearby solar system bodies. Using the unified spin-weighted formalism introduced in [1], we derive the angular correlation functions-generalised Hellings-Downs curves-that describe the resp

Ferran Larroya, Isabelle Bonhoure, Femke Min, Josep Perelló

We present a dataset generated to investigate urban heat and thermal perception across five neighborhoods in the Barcelona metropolitan area. In collaboration with 14 non-academic partner organizations, we conducted a series of citizen science campaigns involving 439 residents as co-researchers engaged throughout all stages of the research process. Participa

Claudio Bonanno, Margarita García Pérez, Antonio González-Arroyo, Ken-Ichi Ishikawa

We investigate the universal features of chiral symmetry breaking in large-$N$ QCD by comparing non-perturbative determinations of the low-lying Dirac spectrum with chiral Random Matrix Theory (RMT) predictions. Our numerical Monte Carlo calculations are based on a chiral lattice discretization of the Dirac operator, and exploit twisted volume reduction to r

Karl Welzel, Yang Liu, Raphael A. Hauser, Coralia Cartis

Optimization methods that make use of derivatives of the objective function up to order $p > 2$ are called tensor methods. Among them, ones that minimize a regularized $p$th-order Taylor expansion at each step have been shown to possess optimal global complexity, which improves as $p$ increases. The local convergence of such optimization algorithms on functi

Promila Biswas, Subhajit Pal, Sukanya Dutta, Ritabrata Biswas

We investigate the evolution of black hole mass within a cosmological background modeled by a Modified Chaplygin Gas (MCG) under various dark energy equation of state parametrizations, including Linear, Logarithmic, CPL, JBP models. The logarithmic mass ratio $\log_{10}[M(z)/M_0]$ is found to be highly sensitive to the redshift-dependent evolution of $\omega

Ignacio Muñoz-Escobar, Patricia B. Tissera, Jenny Gonzalez-Jara, Emanuel Sillero

Context. Bulges, located at the central regions of galaxies, are complex structures, expected to be shaped by the physical processes involved in the assembly history of their host galaxy, such as gravitational collapse, mergers, interactions, and bars. As a consequence a variety of bulges with distinct morphology and chemistry could be produced. Aim. We aim

Austin Marga, Venkat Chandrasekhar

Mesoscopic systems possess shot noise in their currents due to the quantization of the conducting quasiparticles. Measurements of this shot noise are useful to study phenomena that do not manifest themselves in standard conductance or resistance measurements, such as the statistics of the conducting quasiparticles or quantum entanglement via Bell tests. The

Laurin E. Fischer, Daniel Bultrini, Ivano Tavernelli, Francesco Tacchino

Quantum subspace expansion (QSE) offers promising avenues to perform spectral calculations on quantum processors but comes with a large measurement overhead. Informationally complete (IC) measurements, such as classical shadows, were recently proposed to overcome this bottleneck. Here, we report the first large-scale implementation of QSE with IC measurement

Sławomir Dinew, Dan Popovici

Starting from the notion of $m$-plurisubharmonic function introduced recently by Dieu and studied, in particular, by Harvey and Lawson, we consider $m$-(semi-)positive $(1,\,1)$-currents and Hermitian holomorphic line bundles on complex Hermitian manifolds and prove two kinds of results: vanishing theorems and $L^2$-estimates for the $\bar\partial$-equation

Renjie Luo, Tanner J. Legvold, Gage Eichman, Henry Navarro

The spin Seebeck effect is useful for probing the spin correlations and magnetic order in magnetic insulators. Here, we report a strong longitudinal spin Seebeck effect (LSSE) in antiferromagnetic V2O3 thin films. The LSSE response at cryogenic temperatures increases as a function of the external magnetic field until it approaches saturation. The response at

Siqi Zhao, Huirong Yan, Terry Z. Liu, Chuanpeng Hou

Turbulence is a ubiquitous process that transfers energy across many spatial and temporal scales, thereby influencing particle transport and heating. Recent progress has improved our understanding of the anisotropy of turbulence with respect to the mean magnetic field; however, its exact form and implications for magnetic topology and energy transfer remain

Ivy Li, Peter Gaemers, Juehang Qin, Naija Bruckner

Accurate position reconstruction in noble-element time-projection chambers (TPCs) is critical for rare-event searches in astroparticle physics, yet is systematically limited by electric field distortions arising from charge accumulation on detector surfaces. Conventional data-driven field corrections suffer from three fundamental limitations: discretization

Van-Vi Vo, Tien-Dung Nguyen, Duc-Tai Le, Hyunseung Choo

Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their stat

J. Barranco, Emiliano Durán

We revisited the scenario of a resonant enhancement in the oscillation probability due to an interaction potential between neutrinos and dark matter with the novelty of the inclusion of the generalized uncertainty principle. It is shown that a new resonant conversion appears at higher energies. This effect could be tested with future neutrino data as new dip

Weikang Wan, Fabio Ramos, Xuning Yang, Caelan Garrett

Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms. In this paper, we introduce a hierarchical framework that frames this challenge as an integrated skill planning & scheduling problem, going beyond purely sequenti

Wuqiushi Yao, Or Hadas, Yohai Kaspi

The midlatitude climate and weather are shaped by storms, yet the factors governing their predictability remain insufficiently understood. Here, we use a Convolutional Neural Network (CNN) to predict and quantify uncertainty in the intensity growth and trajectory of over 200,000 storms simulated in a 200-year aquaplanet GCM. This idealized framework provides

Jannatul Shefa, Taylan G. Topcu

Complex engineered systems routinely face schedule and cost overruns, along with poor post-deployment performance. Championed by both INCOSE and the U.S. Department of Defense (DoD), the systems engineering (SE) community has increasingly looked to Digital Engineering (DE) as a potential remedy. Despite this growing advocacy, most of DE's purported benefits

Sweta Das, Andrii Dmytryshyn, Volker Mehrmann

We derive the canonical forms for a pair of $n\times n$ complex matrices $(E,Q)$ under transformations $(E,Q) \rightarrow (UEV,U^{-T}QV)$, and $(E,Q) \rightarrow (UEV,U^{-*}QV)$, where $U$ and $V$ are nonsingular complex matrices. We, in particular, consider the special cases of $E^TQ$ and $E^*Q$ being (skew-)symmetric and (skew-)Hermitian, respectively, tha

Irmak Balçık

Let $q$ be a prime with $q \geq 5$. We show that the average rank of elliptic curves over a function field $\mathbb{F}_{q}(t)$, when ordered by naive height, is bounded above by $25/14 \approx 1.8$. Our result improves the previous upper bound of $2.3$ proven by Brumer. The upper bound obtained is less than $2$, which shows that a positive proportion of elli

Yusheng Liao, Chaoyi Wu, Junwei Liu, Shuyang Jiang

Electronic Health Records (EHRs) contain rich yet complex information, and their automated analysis is critical for clinical decision-making. Despite recent advances of large language models (LLMs) in clinical workflows, their ability to analyze EHRs remains limited due to narrow task coverage and lack of EHR-oriented reasoning capabilities. This paper aims

Sabber Khandoozi, Siddharth Gautam, Craig Dietsch, Muhammad Sahimi

Capillary heterogeneity is increasingly recognized as a first-order control on gas plume migration and trapping in aquifers and storage formations. We show that spatial variability in the water-methane contact angle, determined by mineralogy and salinity, alters capillary entry pressures and migration pathways. Using molecular dynamics simulations, we estima

Andreas Opedal, Yanick Zengaffinen, Haruki Shirakami, Clemente Pasti

Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: efficiency. In real-world reasoning scenarios, much of the available information is irrelevant, and effective deductive inference requires identifying and ignoring such distractions. We prop

Guillermo Barragán, Ashton Hetherington, Arindam Sengupta, Rodrigo Abadía-Heredia

We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The methodology leverages High-Order Singular Value Decomposition (HOSVD) to obtain a structured multilinear representation of multi-dimensional datasets, separating physical parameters, s

Nils B. de Vries, Adrian J. Barker, Rainer Hollerbach

Tidal dissipation in star-planet systems occurs through various mechanisms, including the precessional instability. This is an instability of laminar flows (``Poincar\'{e} flows") forced by axial precession of a rotating, oblate, spin-orbit misaligned fluid planet or star, which excites inertial waves in convective regions if the dimensionless precession rat

Davide Romano, Jonathan Schwarz, Daniele Giofré

Test-time scaling (TTS) techniques can improve the performance of large language models (LLMs) at the expense of additional computation and latency. While TTS has proven effective in formal domains such as mathematics and programming, its value in argumentative domains such as law remains underexplored. We present an empirical study of verifier-based TTS met

Yi Xu, Moyu Zhang, Chaofan Fan, Jinxin Hu

Item IDs form the backbone of industrial recommender systems, but suffer from representation instability and poor long-tail generalization in large, dynamic item corpora. Semantic IDs (SIDs) mitigate these issues by enabling knowledge sharing through quantization of item content features. Existing methods attempt to enhance SID expressiveness by incorporatin

Zihao Wan, Pau Tong Lin Xu, Fuwen Luo, Ziyue Wang

While Vision-language Models (VLMs) have demonstrated strong semantic capabilities, their ability to interpret the underlying geometric structure of visual information is less explored. Pictographic characters, which combine visual form with symbolic structure, provide an ideal test case for this capability. We formulate this visual recognition challenge in

Mohammad Aghajani Asl, Behrooz Minaei Bidgoli

The advent of Large Language Models (LLMs) has revolutionized Natural Language Processing, yet their application in high-stakes, specialized domains like religious question answering is hindered by challenges like hallucination and unfaithfulness to authoritative sources. This issue is particularly critical for the Persian-speaking Muslim community, where ac

Francesca Benini, Paolo Restuccia, Sophie Loehlé, Quentin Arnoux

The performance of phosphorus-based lubricant additives is governed by their adsorption, stability, and reactivity at the metal interface. In this study, we investigate the adsorption behavior and tribochemical stability of three additives: Octyl Acid Phosphate (OAP), Dibutyl Hydrogen Phosphite (DBHP), and Amine Neutralized Acid Phosphate (ANAP). These addit

A. C. Ulibarri, D. J. McCloskey, D. Wang, N. Dontschuk

Electrical detection methods for solid-state spins are attractive for quantum technologies, being readily chip-scalable and not subject to the small photon budgets of single emitters. However, realising electrical spin readout in wide-bandgap materials with similar fidelity and bandwidth to optical approaches remains challenging. Here, we introduce a photoel

Dongze Sun, Leo C. Stein

We investigate how commonly used parameter definitions in Post-Newtonian (PN) theory compare with those from Numerical Relativity (NR) for binary black hole (BBH) systems. In NR, masses and spins of each companion are measured quasi-locally from apparent horizon geometry, whereas in PN they are attributes of point particles defined via asymptotic matching in

Carlos E. Máximo, Romain Bachelard, Tobias Donner

Radiation-matter hybridization allows atoms to serve as mediators of effective interactions between light modes and, conversely, to interact among themselves via light. Here we exploit the spatial structure of atomic ensembles to control the coupling between modes of distinct cavities, thereby reshaping the resulting atom-photon spectra. We show that extende

Nikita Kachaev, Mikhail Kolosov, Daniil Zelezetsky, Alexey K. Kovalev

The growing success of Vision-Language-Action (VLA) models stems from the promise that pretrained Vision-Language Models (VLMs) can endow agents with transferable world knowledge and vision-language (VL) grounding, laying a foundation for action models with broader generalization. Yet when these VLMs are adapted to the action modality, it remains unclear to

Elhabib Jaloum, Mohamed Amazioug

Quantum coherence, a cornerstone of quantum mechanics, is of paramount importance for quantum information protocols. However, maintaining coherence in elementary particle systems presents significant challenges. In this work, we investigate quantum coherence and quantum correlations in the $e^{+}e^{-} \to \Lambda\bar{\Lambda}$ processes at BESIII using exper

Niklas Jost, Adolfo Escobedo, Alice Kirchheim

This paper investigates why and when the edge-based districting problem becomes computationally intractable. The overall problem is represented as an exact mathematical programming formulation consisting of an objective function and several constraint groups, each enforcing a well-known districting criterion such as balance, contiguity, or compactness. While

Amit Giloni, Chiara Picardi, Roy Betser, Shamik Bose

An Agentic AI Workflow (AAW), also known as an LLM-based multi-agent system, is an autonomous system that assembles several LLM-based agents to work collaboratively towards a shared goal. The high autonomy, widespread adoption, and growing interest in such AAWs highlight the need for a deeper understanding of their operations, from both quality and security

Thomas E. Cecil, Patrick J. Ryan

This is an updated version of a paper which appeared in the proceedings of the 1979 Berlin Colloquium on Global Differential Geometry. This paper contains the original exposition together with some notes by the authors made in 2025 (as indicated in the text) that give references to descriptions of progress made in the field since the time of the original ver

Maurits Flos, Bastien François, Irene Schicker, Kirien Whan

Weather predictions are often provided as ensembles generated by repeated runs of numerical weather prediction models. These forecasts typically exhibit bias and inaccurate dependence structures due to numerical and dispersion errors, requiring statistical postprocessing for improved precision. A common correction strategy is the two-step approach: first adj

Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad, Ben Liang

We propose an alternative to the standard GAN training approach, in which the discriminator is a binary classifier trained by cross-entropy to distinguish real samples from generated ones. Instead, we directly target the discrimination Bayes error rate (BER). To this end, we use the recently proposed Bayes optimal learning threshold (BOLT) loss and train the

Zeyu Rao, Yue-Xin Huang, Guang-Can Guo, Ming Gong

The vibration of the mechanical nanowire coupled to photons via photon pressure and coupled to charges via the capacity has been widely explored in experiments in the past decades. This system is electrically neutral, thus its coupling to the other degrees of freedom is always challenging. Here, we show that the vibration can slightly change the nanowire len

Xiaohong Chen, Zhenxiao Chen, Wayne Yuan Gao

We provide theoretical results for the estimation and inference of a class of welfare and value functionals of the nonparametric conditional average treatment effect (CATE) function under optimal treatment assignment, i.e., treatment is assigned to an observed type if and only if its CATE is nonnegative. For the optimal welfare functional defined as the aver

Steven Finch

The simple continued fractions for the Golden & Silver means are well-known. It is astonishing that, as far as we know, no one has published half-iterates (let alone quarter-iterates) for the corresponding algorithms. We also examine the cosine and logistic maps (with parameter $2 < \lambda < 3$).

Oleg Andreev

We propose a stringy description of a system composed of two heavy quarks and two heavy antiquarks, mimicking that in pure $SU(3)$ gauge theory. We present both analytical and numerical studies of the string configurations for rectangular geometries. As an application, we analyze the two lowest Born-Oppenheimer potentials. Our results suggest that, depending

Krishna Chaythanya KV, Saqib Abbas Baba, Anurag Kumar, Arpan Chattopadhyay

Motivated by Industry 4.0 applications, we consider quickest change detection (QCD) of an abrupt change in a process when its measurements are transmitted by a sensor over a lossy wireless link to a decision maker (DM). The sensor node samples measurements using a Bernoulli sampling process, and places the measurement samples in the transmit queue of its tra

Matthew Bradshaw, Titus de Jong, Wencai Liu, Audrey Wang

We establish quantum dynamical upper bounds for quasi-periodic Schr\"odinger operators with Liouville frequencies. Our approach combines semi-algebraic discrepancy estimates for the Kronecker sequence $\{n\alpha\}$ with quantitative Green's function estimates adapted to the Liouville setting.

Mengzhao Chen, Meng Wu, Hui Jin, Zhihang Yuan

Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Large Language Models (LLMs). Despite this industry trend, a unified comparison of FP and integer (INT) quantization across varying granularities has been missing, leaving algorithm an

Trivikram Satharasi, S Sitharama Iyengar

Artificial Intelligence, especially Large Language Models (LLMs), has transformed domains such as software engineering, journalism, creative writing, academia, and media (Naveed et al. 2025; arXiv:2307.06435). Diffusion models like Stable Diffusion generate high-quality images and videos from text. Evidence shows rapid expansion: 74.2% of newly published web

Zhonghua Jiang, Kunxi Li, Yiyun Zhou, Sihao Liu

Vision-Language Large Models (VLLMs) face significant efficiency challenges when processing high-resolution inputs. The quadratic complexity in attention and autoregressive generation, as well as the constantly growing key value (KV) cache size, severely hinder the prefilling and decoding stages. Recent efforts have attempted to compress KV cache by identify

Anton S. Galaev, Thomas Leistner, Felipe Leitner

We study the holonomy that is associated to a sub-Riemannian structure defined on the kernel of a global contact form. This includes the holonomy of Schouten's horizontal connection as well as of the adapted connection, both canonical invariants of the structure. Under a condition on the torsion of the structure, we show that they are either equal or that th

Siddhartha Upadhyay, Ratnangshu Das, Pushpak Jagtap

This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to achieve prescribed-time reach-avoid-stay tasks in dynamic environments. Each agent is assigned a social awareness index that quantifies its level of cooperation or self-interest, allowing heterogeneous social behaviors wi

Jiachuan Xu, Tim Eifler, Elisabeth Krause, Vivian Miranda

We combine weak lensing, galaxy clustering, cosmic microwave background (CMB) lensing, and their cross-correlations (so-called 6$\times$2pt) to constrain cosmology and baryonic feedback scenarios using data from the Dark Energy Survey (DES) Y3 Maglim catalog and the Planck satellite PR4 data release. We include all data points in the DES Y3 cosmic shear two-

Run Peng, Ziqiao Ma, Amy Pang, Sikai Li

While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by language descriptions), their abilities to collaborate with each other to achieve a joint goal are not well explored. To address this limitation, this paper studies LLM agents in task collaboration, particularly under

Arani Roy, Marco P. Apolinario, Shristi Das Biswas, Kaushik Roy

Training deep neural networks (DNNs) with backpropagation (BP) achieves state-of-the-art accuracy but requires global error propagation and full parameterization, leading to substantial memory and computational overhead. Direct Feedback Alignment (DFA) enables local, parallelizable updates with lower memory requirements but is limited by unstructured feedbac

Aymen Hamrouni, Sofie Pollin, Hazem Sallouha

This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-Things (IoT) networks that integrate Optical Wireless Communication (OWC) with Radio Frequency (RF). We first present an optimization formulation that jointly maximizes throughput and minimizes delivery-based Age of Information (AoI) between access points and IoT nodes under

Pavlo Bazilinskyy, Md Shadab Alam, Roberto Merino-Martınez

The growing adoption of electric vehicles, known for their quieter operation compared to internal combustion engine vehicles, raises concerns about their detectability, particularly for vulnerable road users. To address this, regulations mandate the inclusion of exterior sound signals for electric vehicles, specifying minimum sound pressure levels at low spe

Hagai Berend, Ohad Elishco, Moshe Schwartz

We consider multidimensional codes capable of correcting a burst error of weight at most $2$. When two positions are in error, the burst limits their relative position. We study three such limitations: the $L_\infty$ distance between the positions is bounded, the $L_1$ distance between the positions is bounded, or the two positions are on an axis-parallel li

Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu

We study the Sobolev IPM problem for measures supported on a graph metric space, where critic function is constrained to lie within the unit ball defined by Sobolev norm. While Le et al. (2025) achieved scalable computation by relating Sobolev norm to weighted $L^p$-norm, the resulting framework remains intrinsically bound to $L^p$ geometric structure, limit

Pengtao Chen, Xianfang Zeng, Maosen Zhao, Mingzhu Shen

Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this di

S. Haemmerich, A. Gokus, F. McBride, P. Weber

Aims. eROSITA, on board the Spectrum Roentgen Gamma (SRG) spacecraft, performed its first X-ray all-sky survey (eRASS1) between December 2019 and June 2020. It detected about 930000 sources, providing us with an unprecedented opportunity for a detailed blazar census. We present the properties of blazars and blazar candidates in eRASS1 and the compilation of

Guangzhi Su, Shuchang Huang, Yutong Ke, Zhuohang Liu

Multimodal large language models (MLLMs) have achieved impressive performance across diverse tasks by jointly reasoning over textual and visual inputs. Despite their success, these models remain highly vulnerable to adversarial manipulations, raising concerns about their safety and reliability in deployment. In this work, we first generalize an approach for

Eranga Bandara, Ross Gore, Atmaram Yarlagadda, Anita H. Clayton

The diagnosis of most mental disorders, including psychiatric evaluations, primarily depends on dialogues between psychiatrists and patients. This subjective process can lead to variability in diagnoses across clinicians and patients, resulting in inconsistencies and challenges in achieving reliable outcomes. To address these issues and standardize psychiatr

Omar AbdelGafar, Selin Palaz, Yihui Yang, Christoph Holst

Recent advancements in technology have established terrestrial laser scanners (TLS) as a powerful instrument in geodetic deformation analysis. As TLS becomes increasingly integrated into this field, it is essential to develop a comprehensive stochastic model that accurately captures the measurement uncertainties. A key component of this model is the construc

Hernando Maldonado Colmán, Michael E. Mueller

Ammonia (NH3) is a zero-carbon fuel that has been receiving increasing attention for power generation and even transportation. Compared to H2, NH3's volumetric energy density is higher, is not as explosive, and has well established transport and storage technologies. Yet, NH3 has poor flammability and flame stability characteristics and more reactive nitroge

Ryan Dickmann, Palani Lideros, Akash Narayanan

We completely classify the bijections of the Thurston geometries that preserve geodesics as sets. For Riemannian manifolds that satisfy a certain technical condition, we prove that a totally geodesic subset is a submanifold. We also classify the geodesic-preserving bijections of the Euclidean cylinder $\mathbb{S}^1 \times \mathbb{R}$ and the bijections of th

Nienke C. Balz, Florian Herren, Bastian Kubis, Simon Mutke

The rich analytic structure of hadronic form factors makes a theoretically consistent yet easily applicable parametrisation cumbersome. Consequently, most parametrisations are limited to reproducing the simplest analytic features sufficient to describe form factors on their first Riemann sheet. Here, we introduce two novel form factor parametrisations that a

Kenta Kasai

We study finite-field extensions that preserve the same support as the parity-check matrices defining a given binary CSS code. Here, an LDPC-CSS code refers to a CSS code whose parity-check matrices are orthogonal in the sense that each pair of corresponding rows overlaps in an even (possibly zero) number of positions, typically at most twice in sparse const

Spyros Angelopoulos, Bertrand Simon

Online bidding is a classic optimization problem, with several applications in online decision-making, the design of interruptible systems, and the analysis of approximation algorithms. In this work, we study online bidding under learning-augmented settings that incorporate stochasticity, in either the prediction oracle or the algorithm itself. In the first

Leticia Barchini, Nicolas Arancibia Robert, Paul Mezo

In their study of Arthur's conjectures for real groups, Adams, Barbasch, and Vogan introduced the notion of micro-packets. Micro-packets are finite sets of irreducible representations defined using microlocal geometric methods and characteristic cycles. We explore an action of the Weyl group on characteristic cycles to compute all micro-packets of real group

Harm Lameris, Shree Harsha Bokkahalli Satish, Joakim Gustafson, Éva Székely

Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation. However, how these models interpret non-lexical cues remains largely unstudied. We introduce VQ-Bench, a controlled evaluation suite featuring a parallel dataset of synthesized modal, breathy, creaky, and en

Julián Pozuelo, Simone Verzellesi, Giacomo Vianello

We study the variational behavior of the total inverse mean curvature of curves with prescribed boundary in the half-plane. We characterize the existence of critical points with prescribed area. We show that such critical points are strongly stable. As an application, we prove a local minimality property.

Alessandro Padella, Francesco Vinci, Massimiliano de Leoni

Process mining analyzes and improves processes by examining transactional data stored in event logs, which record sequences of events with timestamps. However, the effectiveness of process mining, especially when combined with machine or deep learning, depends on having large event logs. Event log augmentation addresses this limitation by generating addition

Yixin Lan, Yumeng Wu, Jie Xiao

According to the Hall algebras of quivers with automorphisms under Lusztig's construction, the polynominal forms of several structure coefficients for quantum groups of all finite types are presented in this note. We first provide a geometric realization of the coefficients between PBW basis and the canonical basis via standard sheaves on quiver moduli space

Katsumi Ishikawa, Takayuki Morifuji, Masaaki Suzuki

In our previous work, we introduced the notion of the twisted Alexander vanishing order of knots, defined as the order of the smallest finite group for which the corresponding twisted Alexander polynomial vanishes. In this paper, we explore several properties of this invariant in detail and present a list of twisted Alexander vanishing groups of order less t

Urtzi Ayesta, Sergey Foss, Matthieu Jonckheere, Vittorio Puricelli

We investigate the challenges of applying model-free reinforcement learning algorithms, like online Q-learning, to infinite state space Markov Decision Processes (MDPs). We first introduce the notion of Local Learning Processes (LLPs), where agents make decisions based solely on local information, and we show that Q-learning can be seen as a specific instanc

Yilong Lu, Si Chen, Songyan Gao, Han Liu

Traditional non-biological storage media, such as hard drives, face limitations in both storage density and lifespan due to the rapid growth of data in the big data era. Mirror-image peptides composed of D-amino acids have emerged as a promising biological storage medium due to their high storage density, structural stability, and long lifespan. The sequenci

Phuc Tran, Nisheeth K. Vishnoi

Low-rank pseudoinverses are widely used to approximate matrix inverses in scalable machine learning, optimization, and scientific computing. However, real-world matrices are often observed with noise, arising from sampling, sketching, and quantization. The spectral-norm robustness of low-rank inverse approximations remains poorly understood. We systematicall

Vadim V. Bobylev, Nazar R. Ikhsanov, Anisa T. Bajkova

A review of publications devoted to the study of the characteristics of the Radcliffe wave has been given. The advent of mass measurements of radial velocities of stars has recently led to a number of interesting results obtained from the analysis of spatial velocities of stars and open star clusters. An important place in the study has been given to issues

Benjamin Leblanc, Pascal Germain

PAC-Bayes is a popular and efficient framework for obtaining generalization guarantees in situations involving uncountable hypothesis spaces. Unfortunately, in its classical formulation, it only provides guarantees on the expected risk of a randomly sampled hypothesis. This requires stochastic predictions at test time, making PAC-Bayes unusable in many pract

Qianhui Li, Weiya Wang, Qianqi Zhao, Tong Qu

Quarter level cell (QLC) 3D NAND flash memory is emerging as the predominant storage solution in the era of artificial intelligence. QLC 3D NAND flash stores 4 bit per cell to expand the storage density, resulting in narrower read margins. Constrained to read margins, QLC always suffers from lateral charge migration (LCM), which caused by non-uniform charge

Richard Osuagwu, Thomas Cook, Maraim Masoud, Koustav Ghosal

While Large Language Models (LLMs) excel at tool calling, deploying these capabilities in regulated enterprise environments such as fintech presents unique challenges due to on-premises constraints, regulatory compliance requirements, and the need to disambiguate large, functionally overlapping toolsets. In this paper, we present a comprehensive study of too

Abdelkrim Moussaoui

We establish the existence of three solutions for sign-coupled Gierer-Meinhardt type system with Neumann boundary conditions. Two solutions are of opposite constant-sign while the third solution is nodal with synchronous sign components. The approach combines sub-supersolutions method and Leray-Schauder topological degree involving perturbation argument.

Svetlana Churina, Niranjan Chebrolu, Kokil Jaidka

We show that continual pretraining on plausible misinformation can overwrite specific factual knowledge in large language models without degrading overall performance. Unlike prior poisoning work under static pretraining, we study repeated exposure to counterfactual claims during continual updates. Using paired fact-counterfact items with graded poisoning ra

Jorge Martinez-Gil, Mario Pichler, Nefeli Bountouni, Sotiris Koussouris

We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or colla

Yeganeh Bahoo, Ahmad Kamaludeen

We explore the problem of $M$-guarding polygons with holes using $k$-visibility guards, where a set of guards is said to $M$-guard a polygon if every point in the polygon is visible to at least $M$ guards, with the constraint that there may only be 1 guard on each edge. A $k$-visibility guard can see through up to $k$ walls, with $k \geq 2$. We present a the

Diego Torres, Axel Roebel, Nicolas Obin

We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach enforces disentanglement through a simple perturbation: during training, F0 contours are flattened and randomly shifted, while the true F0 is provided as conditioning. A vector-quantization bottleneck prevents pitch recovery, and a flow-based decoder gen

Adrian Llamas-Jaramillo, Ivan Latella, David Reguera

We present a simulation technique to evaluate the most important quantity for nucleation processes: the nucleation barrier, i.e. the free energy of formation of the critical cluster. The method is based on stabilizing a small cluster by simulating it in the NVT ensemble and using the thermodynamics of small systems to convert the properties of this stable cl

Thiago S. Gomides, Evangelos Kranakis, Ioannis Lambadaris, Yannis Viniotis

Efficient truck platooning is a key strategy for reducing freight costs, lowering fuel consumption, and mitigating emissions. Deadlines are critical in this context, as trucks must depart within specific time windows to meet delivery requirements and avoid penalties. In this paper, we investigate the optimal formation and dispatch of truck platoons at a high

Víctor Medina, Giovanny A. Cuervo-Londoño, Javier Sánchez

The accurate prediction of oceanographic variables is crucial for understanding climate change, managing marine resources, and optimizing maritime activities. Traditional ocean forecasting relies on numerical models; however, these approaches face limitations in terms of computational cost and scalability. In this study, we adapt Aurora, a foundational deep

Kaiqiang Lin, Kangchun Zhao, Yijie Mao

Reliable downlink communication in satellite-to-underground networks remains challenging due to severe signal attenuation caused by underground soil and refraction in the air-soil interface. To address this, we propose a novel cooperative rate-splitting (CRS)-aided transmission framework, where an aboveground relay decodes and forwards the common stream to u

Antonin Gagnere, Slim Essid, Geoffroy Peeters

Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different listeners may adopt various rhythmic interpretations, none of which would necessarily be incorrect. To address this, we propose a contrastive self-supervised pre-training approach that

Luis Avilés, Patrick Concha, Javier Matulich, Evelyn Rodríguez

We construct generalized sets of asymptotic conditions for both three-dimensional Maxwell Chern-Simons gravity and a novel extension that incorporates torsion through a deformation of the Maxwell algebra. These boundary conditions include the most general temporal components of the gauge fields that consistently preserve the corresponding asymptotic Maxwell

Dmitrii Pirozhkov

This is mostly an expository note about an example communicated to the author by Aise Johan de Jong. In a triangulated category $T$ an object $G$ is said to be a classical generator when the smallest triangulated subcategory containing $G$ coincides with the whole $T$, and it is said to be a generator when the orthogonal complement to $G$ in $T$ is zero, i.e

Wenduan Xu

We present a hybrid quantum-classical recurrent neural network (QRNN) architecture in which the recurrent core is realized as a parametrized quantum circuit (PQC) controlled by a classical feedforward network. The hidden state is the quantum state of an $n$-qubit PQC in an exponentially large Hilbert space $\mathbb{C}^{2^n}$, which serves as a coherent recur