November 2025 arXiv papers — page 38

Moses O. Langa, Mark A. Thompson, Andrew J. Rigby, Gwenllian M. Williams

Massive stars (mass beyond 8 solMass) release vast amounts of energy into the interstellar medium through their stellar winds, photoionising radiation and supernova explosions. These processes may compress nearby regions, triggering further star formation, but the significance of triggered star formation across the Galactic disc is not well understood. This

Andrea Ranieri, Giorgio Palmieri, Silvia Biasotti

This paper addresses the critical need for automated crack detection in the preservation of cultural heritage through semantic segmentation. We present a comparative study of U-Net architectures, using various convolutional neural network (CNN) encoders, for pixel-level crack identification on statues and monuments. A comparative quantitative evaluation is p

Royer David Estrada-Esponda, Gerardo Matturro, Jose Reinaldo Sabogal-Pinilla

The technical knowledge and soft skills of entrepreneurial team members significantly impact the early stages of software startups. It is widely recognized that the success or failure of a startup is determined by the quality of the individuals who constitute the founding team. This article presents the findings of a study conducted within the Colombian entr

Adam Bjorndahl

The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to further our understanding of interdisciplinary is

Louis Ioos

We compute the full off-diagonal asymptotics of the equivariant and partial Bergman kernels associated with a circle action on a prequantized K\"ahler manifold with bounded geometry at infinity, then use these results to compute the asymptotics of the linear statistics of the associated determinantal point process as the number of points grows to infinity, s

Leonardo Colombo

We develop a unified geometric formulation of the Maxwell-Vlasov system using the infinite-dimensional Skinner-Rusk (SR) formalism. In this framework, particles and fields are treated simultaneously within a single presymplectic manifold, and the Gotay-Nester-Hinds algorithm recovers the full Maxwell-Vlasov equations as the compatibility conditions of a sing

Hunter Staiger, Endre Takacs, Steven A. Blundell, Naoki Kimura

We report a high-precision determination of the natural-abundance-averaged nuclear charge-radius difference between Yb and Lu using extreme ultraviolet (EUV) spectroscopy of highly charged ions (HCIs). By measuring the $D_1$ transition energies in Na- and Mg-like charge states of Lu and Yb confined in the Tokyo electron-beam ion trap, we extract meV-level en

Xianjing Dong, Yanda Lv

Zalcman's Lemma makes significant applications in normal families, complex dynamics and related problems in complex analysis. In the present paper, we are devoted to generalizing the classical Zalcman's lemma to complex Lie groups by means of exponential mappings defined by holomorphic one-parameter subgroups.

Jéfferson L. R. Bastos, Danilo Caprio, Oyran Raizzaro

In this work we study the backward filled Julia sets of a class of $p$-adic polynomial maps $f:\mathbb{Q}_p^2\longrightarrow \mathbb{Q}_p^2$ defined by $f(x,y)=(xy+c,x)$, where $c\in\mathbb{Q}_p$ is a $p$-adic number. In particular, if $|c|\leq 1$, then we proved that the backward filled Julia set of $f$ is a bounded subset in $\mathbb{Z}_p^2$. On the other

Wesley Bian, Xiaofeng Lin, Guang Cheng

Modern machine learning models for audio tasks often exhibit superior performance on English and other well-resourced languages, primarily due to the abundance of available training data. This disparity leads to an unfair performance gap for low-resource languages, where data collection is both challenging and costly. In this work, we introduce a novel data

Ilias Cherkaoui, Indrakshi Dey

Securing the Internet of Things (IoT) against quantum attacks requires public-key cryptography that (i) remains compact and (ii) runs efficiently on microcontrollers, capabilities many post-quantum (PQ) schemes lack due to large keys and heavy arithmetic. We address both constraints simultaneously with, to our knowledge, the first-ever isogeny framework that

Shamima Hossain

Visual Language Models (VLMs) are powerful generative tools but often produce factually inaccurate outputs due to a lack of robust reasoning capabilities. While extensive research has been conducted on integrating external knowledge for reasoning in large language models (LLMs), such efforts remain underexplored in VLMs, where the challenge is compounded by

Gleb A. Kotousov, Sergei L. Lukyanov, Daria A. Shabetnik

At the beginning of the 70's, Baxter introduced a multiparametric generalization of the six-vertex model. This integrable system has been found to exhibit a remarkable variety of critical behaviors. The work is part of a series of papers devoted to their systematic study. We focus on the case when the lattice model possesses an additional ${\cal Z}_r$ symmet

Don Vestal, Jonathan Sax

In 2001, Robertson and Schaal found the 2-color off-diagonal generalized Schur numbers: for two positive integers $k$ and $l$, they determined the smallest positive integer $S = S(k, l)$ such that for any coloring of the integers from 1 to $S$ using red and blue, there must be a red solution to the equation $x_1 + x_2 + \dots + x_k = x_0$ or a blue solution

Xinran Wang, Boran Zhu, Shujuan Zhou, Ziwen Long

Background: As large language models (LLMs) become increasingly integrated into digital health education and assessment workflows, their capabilities in supporting high-stakes, domain-specific certification tasks remain underexplored.In China, the national pharmacist licensure exam serves as a standardized benchmark for evaluating pharmacists' clinical and t

Yayuan Li, Aadit Jain, Filippos Bellos, Jason J. Corso

We introduce Mistake Attribution (MATT), a new task for fine-grained understanding of human mistakes in egocentric videos. While prior work detects whether a mistake occurs, MATT attributes the mistake to what part of the instruction is violated (semantic role), when in the video the deviation becomes irreversible (the Point-of-No-Return, PNR), and where the

Ana Paula Jeakel, Gabriel Vieira dos Santos, Valerio Marra, Rodrigo von Marttens

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14594 sources classified into extended (galaxies

H. Eberl, K. Hidaka, E. Ginina

We study the CP-even neutral Higgs boson decays $h^0 \to c \bar{c}, b \bar{b}, b \bar{s}, \gamma \gamma, g g$ in the Minimal Supersymmetric Standard Model (MSSM) with general quark flavor violation (QFV) due to squark generation mixings, identifying the $h^0$ as the Higgs boson with a mass of 125 GeV. We compute the widths of the $h^0$ decays to $c \bar c, b

Andrew Flynn, Cian McCafferty, Klaus Lehnertz, François David

Understanding how the brain switches from normal activity to an epileptic seizure is essential for improving seizure therapy, yet the underlying mechanisms remain largely unknown. In particular, seizure onset can be described as a critical transition (CT), but there is no consensus on whether (i) bifurcation-induced, (ii) noise-induced, or (iii) bifurcation/

C. Snodgrass, E. Mazzotta Epifani, C. Tubiana, J. P. Sánchez

Comet Interceptor is an ESA science mission with payload contributions from ESA Member States and with an international participation by JAXA. It is the first mission that is being designed, built, and potentially launched before its target is known. This approach will enable the spacecraft to perform the first mission to a Long Period Comet from the Oort Cl

Xiang Wang, Zhifei Zhang, He Zhang, Zhe Lin

Recent unified models integrate understanding experts (e.g., LLMs) with generative experts (e.g., diffusion models), achieving strong multimodal performance. However, recent advanced methods such as BAGEL and LMFusion follow the Mixture-of-Transformers (MoT) paradigm, adopting a symmetric design that mirrors one expert to another for convenient initializatio

Tillmann Bühler, Daniel Hug, Christoph Thäle

We consider a stationary Poisson process of $k$-planes in the $d$-dimensional hyperbolic space $\mathbb H^d$ of constant curvature $-1$, with $d \ge 4$ and $1 \le k \le d-1$. It is known that, after centring and normalization, the total $k$-volume of all intersections of $k$-planes with a geodesic ball of radius $R$ converges in distribution, as $R \to \inft

Stefano Longhi

The non-Hermitian skin effect (NHSE) -- the anomalous boundary accumulation of an extensive number of bulk modes -- has emerged as a hallmark of non-Hermitian physics, with broad implications for transport, sensing, and topological classification. A central open question is how magnetic or synthetic gauge fields influence this boundary phenomenon. Here, we d

Sam Laing, Antonio Orvieto

The Adam optimizer is a cornerstone of modern deep learning, yet the empirical necessity of each of its individual components is often taken for granted. This paper presents a focused investigation into the role of bias-correction, a feature whose contribution remains poorly understood. Through a series of systematic ablations on vision and language modellin

Kuniaki Saito, Risa Shinoda, Shohei Tanaka, Tosho Hirasawa

Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train VLMs. However, the generalizability

Christian Baker, Weidong Liang, Richard Colchester, Peng Lei

Many minimally invasive procedures, such as core needle biopsy of focal liver lesions, nerve blocks, and fetal and vascular interventions, are typically performed under ultrasound guidance, which provides real-time, high-resolution visualisation of tissue anatomy. Accurate and efficient localisation of the needle tip relative to patient anatomy is essential

Yi-Hao Peng, Jeffrey P. Bigham, Jason Wu

Generative models, such as large language models and text-to-image diffusion models, are increasingly used to create visual designs like user interfaces (UIs) and presentation slides. Finetuning and benchmarking these generative models have often relied on datasets of human-annotated design preferences. Yet, due to the subjective and highly personalized natu

Dmitry M. Anishchenko

BS4 is a natural Belnapian conservative extension of Lewis modal system S4 via strong negation. In [24] it was proved that the translation TB that naturally generalises the Godel-Tarski translation T embeds faithfully Nelsons logic N4 into BS4. So it is natural to define a modal companion of a logic extending N4 as an extension of BS4. In this paper we const

Eman Alqudah, Ashfaq Khokhar

The assignment of the pilot sequence is a critical challenge in massive MIMO systems, as sharing the same pilot sequence among multiple users causes interference, which degrades the accuracy of the channel estimation. This problem, equivalent to the NP-hard graph coloring problem, directly impacts real-time applications such as autonomous driving and industr

Futoshi Hamanoue

We present an emulator-based and hardware feasibility study of Aurora-DD, a phase-coherence compensation method that integrates a sign-based feedback update of a global phase offset (Delta phi) with a fixed-depth XY8 dynamical decoupling (DD) scaffold. The feedback optimization is performed offline on a calibrated emulator and the resulting Delta phi* is dep

Yuto Suzuki, Paul Awolade, Daniel V. LaBarbera, Farnoush Banaei-Kashani

Molecule generation using generative AI is vital for drug discovery, yet class-specific datasets often contain fewer than 100 training examples. While fragment-based models handle limited data better than atom-based approaches, existing heuristic fragmentation limits diversity and misses key fragments. Additionally, model tuning typically requires slow, indi

Mihaela Hudişteanu, Nikita P. Kalinin, Edwige Cyffers

Adaptive optimizers are the de facto standard in non-private training as they often enable faster convergence and improved performance. In contrast, differentially private (DP) training is still predominantly performed with DP-SGD, typically requiring extensive compute and hyperparameter tuning. We propose DP-MicroAdam, a memory-efficient and sparsity-aware

Elise Zhang, François Mirallès, Stéphane Dellacherie, Di Wu

Weather is a dominant external driver of residential electricity demand, but adding many meteorological covariates can inflate model complexity and may even impair accuracy. Selecting appropriate exogenous features is non-trivial and calls for a principled selection framework, given the direct operational implications for day-to-day planning and reliability.

I. Cherkaoui, C. Clarke, J. Horgan, I. Dey

The rapid deployment of AI models necessitates robust, quantum-resistant security, particularly against adversarial threats. Here, we present a novel integration of post-quantum cryptography (PQC) and zero trust architecture (ZTA), formally grounded in category theory, to secure AI model access. Our framework uniquely models cryptographic workflows as morphi

Nathan Roll, Jill Kries, Flora Jin, Catherine Wang

Large language models (LLMs) have emerged as a candidate "model organism" for human language, offering an unprecedented opportunity to study the computational basis of linguistic disorders like aphasia. However, traditional clinical assessments are ill-suited for LLMs, as they presuppose human-like pragmatic pressures and probe cognitive processes not inhere

Bilvin Varughese, Troy D. Loeffler, Suvo Banik, Aditya Koneru

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpreta

Jian Sheng Wang

Autonomous digital entities require deterministic identity mechanisms that avoid persistent storage of high-value master secrets, while supporting credential rotation and cryptographic agility across heterogeneous systems. Existing deterministic key hierarchies and centralized key management systems typically rely on long-lived root secrets, introducing stru

Vilius Čepaitis

The ATLAS experiment at the LHC employs comprehensive data quality monitoring procedures to ensure high-quality physics data. This contribution presents a long short-term memory autoencoder-based algorithm for detecting anomalies in ATLAS Liquid Argon calorimeter data, represented as multidimensional time series of statistical moments of energy cluster prope

Takuma Nakamura, William Groman, Qing-Xin Ji, Oguzhan Kara

Optically generated microwave signals exhibit some of the lowest phase noise and timing jitter of any microwave-generating technology to date. The success of octave-spanning optical frequency combs in down-converting ultrastable optical frequency references has motivated the development of compact, robust and highly manufacturable optical systems that mainta

Rui Tong

Generative modeling is typically framed as learning mapping rules, but from an observer's perspective without access to these rules, the task becomes disentangling the geometric support from the probability distribution. We propose that continuum percolation is uniquely suited to this support analysis, as the sampling process effectively projects high-dimens

Anna Jové, Mateo Mencía

Given a hyperbolic inner function $f \colon \mathbb{D} \to \mathbb{D}$ with Denjoy-Wolff point $p \in \partial \mathbb{D}$, it is well known that almost every point $\xi\in \partial \mathbb{D}$ converges to $p$ under iteration of the radial extension $f^* \colon \partial \mathbb{D} \to \partial \mathbb{D}$. We provide explicit bounds for the rate of this con

Muhammad Irfan, Nasir Rahim, Khalid Mahmood Malik

Accurate extraction and segmentation of the cerebral arteries from digital subtraction angiography (DSA) sequences is essential for developing reliable clinical management models of complex cerebrovascular diseases. Conventional loss functions often rely solely on pixel-wise overlap, overlooking the geometric and physical consistency of vascular boundaries,

Sidahmed Benabderrahmane, Talal Rahwan

Advanced Persistent Threats (APT) pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with class imbalance, high dimensional features, and scarce real world traces. They often lack transferability-performing well in the training domain but degrading in novel attack scenario

R. Iaria, T. Di Salvo, A. Anitra, F. Barra

A mysterious absorption feature at approximately 3.8 keV has been identified in the NICER spectrum of the low-mass X-ray binary system 4U 1820-30. We interpret this feature as a gravitationally redshifted iron absorption line. This interpretation is supported by the temporal proximity of the NICER observation to the detection of a carbon superburst by the X-

Van Tran, Shinan Liu, Tian Li, Nick Feamster

To address the scarcity and privacy concerns of network traffic data, various generative models have been developed to produce synthetic traffic. However, synthetic traffic is not inherently privacy-preserving, and the extent to which it leaks sensitive information, and how to measure such leakage, remain largely unexplored. This challenge is further compoun

Jiaxin Liu, Min Li, Wanting Xu, Liang Li

Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structures that invalidate rigid-body assumptions. This paper addresses this problem and enables the extension of existing rigid-body pose estimation methods to non-rigid systems. Our approach integrates two core componen

Jakub Hoscilowicz, Artur Janicki

We introduce the Adversarial Confusion Attack, a new class of threats against multimodal large language models (MLLMs). Unlike jailbreaks or targeted misclassification, the goal is to induce systematic disruption that makes the model generate incoherent or confidently incorrect outputs. Practical applications include embedding such adversarial images into we

Cyrill Püntener, Johann Schwabe, Dominique Garmier, Jonas Frey

We introduce Kleinkram, a free and open-source system designed to solve the challenge of managing massive, unstructured robotic datasets. Designed as a modular, on-premises cloud solution, Kleinkram enables scalable storage, indexing, and sharing of datasets, ranging from individual experiments to large-scale research collections. Kleinkram natively integrat

In Jun Park, Kamal Choudhary

As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a computationally tractable alternative to Density Functional Theory (DFT) for large-scale electronic structure calculations. H

Kiril Vasilev, Alexandre Misrahi, Eeshaan Jain, Phil F Cheng

Multimodal Large Language Models (LLMs) hold promise for biomedical reasoning, but current benchmarks fail to capture the complexity of real-world clinical workflows. Existing evaluations primarily assess unimodal, decontextualized question-answering, overlooking multi-agent decision-making environments such as Molecular Tumor Boards (MTBs). MTBs bring toget

Kanchan Chowdhury, Lixi Zhou, Lulu Xie, Xinwei Fu

Real-world AI/ML workflows often apply inference computations to feature vectors joined from multiple datasets. To avoid the redundant AI/ML computations caused by repeated data records in the join's output, factorized ML has been proposed to decompose ML computations into sub-computations to be executed on each normalized dataset. However, there is insuffic

Yunjian Peng

In this paper, we establish the Weyl bound for the Rankin-Selberg $L$-function in a certain joint ramification setting. To achieve this result, we employ the refined Petersson trace formula and develop a special Vorono\"i summation formula. Additionally, we obtain the sharp bound for the integral of products of Whittaker functions via the $p$-adic stationary

Zhidong Gao, Zimeng Pan, Yuhang Yao, Chenyue Xie

Diffusion models like Stable Diffusion (SD) drive a vibrant open-source ecosystem including fully fine-tuned checkpoints and parameter-efficient adapters such as LoRA, LyCORIS, and ControlNet. However, these adaptation components are tightly coupled to a specific base model, making them difficult to reuse when the base model is upgraded (e.g., from SD 1.x to

Leon Wehrhan, Lucien Walewski, Marie Bluntzer, Heloise Chomet

Machine-learned interatomic potentials (MLIPs) promise to significantly advance atomistic simulations by delivering quantum-level accuracy for large molecular systems at a fraction of the computational cost of traditional electronic structure methods. While model hubs and categorisation efforts have emerged in recent years, it remains difficult to consistent

Marcelo H. Alvarenga, Júlio C. Fabris

In this article, we propose an anisotropic Bianchi-I type cosmological model in non-conservative Unimodular Gravity ($\mathrm{NUG}$). We show that simply using the Bianchi-I type metric does not resolve a striking characteristic of the field equations in $\mathrm{NUG}$: their underdetermination. This fact led us to implement extra conditions on the combinati

Yurii Belov, Mikhail Mironov

We consider the sampling problem for two-sided small Fock spaces $\mathcal{F}^p_{\alpha}$, for the full range $0 < p \le \infty$. We establish a geometric description of shift-invariant sampling sequences, i.e., sequences $\Lambda$ such that $c \Lambda$ is sampling for all $c \in \mathbb{C} \setminus \{ 0 \}$.

María Clara Fittipaldi, Adrián González Casanova, Julio Ernesto Nava Trejo

We investigate the $\Lambda$-Seed-Bank-Wright-Fisher process, a model describing allele frequency dynamics in populations exhibiting both skewed offspring distributions and dormancy. By performing a change of measure, we condition this process on the eventual fixation of a specified genetic type. The resulting process is again a $\Lambda$-Wright-Fisher proce

Oem Trivedi, Venkat Venkatsubramanian

We present a game-theoretic statistical framework for cosmology, which we term \textit{Cosmological Teleodynamics}. We recast the dark sector, cosmic acceleration, large-scale structure, and cosmic tensions as emergent consequences of nonlocal memory and intrinsically persistent organization in a self-gravitating Universe. By introducing a maximum-caliber we

Mikihiro Fujii, Yang Li

In the present paper, we consider the real analyticity of the global solutions to the $3$D incompressible anisotropic Navier--Stokes equations. We show that if only the horizontal component of initial velocity is small and analytic in $x_3$, then there exists a unique global solution which is analytic in $t>0$ and $x\in \mathbb{R}^3$. Our functional framewor

Leonardo Cefalo, Crescenza Calculli, Alessio Pollice

In this study, we address the challenge of modelling the spatial variability in violence against women across municipalities in a Southern Italian region by proposing a Bayesian spatio-temporal Poisson regression model. Using data on access to Local Anti-Violence Centers in the Apulia region from 2021 to 2024, we investigate the impact of municipality-level

Sidahmed Benabderrahmane, James Cheney, Talal Rahwan

Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised learning methods require extensive labeled data, which is often scarce in real-world cybersecurity environments. In this paper, we propose an innovative approach that leverages AutoEncoders for unsupervised anomaly de

P. M. W. Kalberla

Context. Neutral atomic hydrogen (HI) absorption lines can be used to probe the cold neutral medium (CNM) at high Galactic latitudes. Cold HI with a significant optical depth from the GASKAP-HI survey is found to be located predominantly if not exclusively within filamentary structures that can be identified as caustics with the Hessian operator. Most of the

Kateryna Chumachenko, Amala Sanjay Deshmukh, Jarno Seppanen, Ilia Karmanov

We introduce Nemotron-Parse-1.1, a lightweight document parsing and OCR model that advances the capabilities of its predecessor, Nemoretriever-Parse-1.0. Nemotron-Parse-1.1 delivers improved capabilities across general OCR, markdown formatting, structured table parsing, and text extraction from pictures, charts, and diagrams. It also supports a longer output

Adamu Issifu, Andreas Konstantinou, Franciele M. da Silva, Tobias Frederico

We investigate the rotational properties of self-bound strange quark stars using two representative quark matter equations of state (EOS): the vector MIT bag model and the density-dependent quark mass (DDQM) model. Through general-relativistic calculations of uniformly rotating sequences, we analyze their mass--radius relations, moments of inertia, quadrupol

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search is presented for the two-body charmed baryonic decays $\overline{B}_{(s)}^{0}\to\Lambda_{c}^{+}\overline{\Lambda}_{c}{}^{-}$, using a data sample collected by the LHCb experiment during 2011--2012 and 2015--2018 corresponding to an integrated luminosity of $9\,\mathrm{fb}^{-1}$. The first observation of the $\overline{B}_{s}^{0}\to\Lambda_{c}^{+}\ov

Sujoy Majumder, Debabrata Pramanik

The purpose of this paper is to investigate the non-constant entire as well as meromorphic solutions of the Fermat-type partial differential-difference equation: \[\left(\sum_{j=1}^m\frac{\partial f(z_1, z_2, \ldots, z_m)}{\partial z_j}\right)^{m_1} + f^{m_2}(z_1 + c_1, z_2 + c_2, \ldots, z_m + c_m ) = 1,\] where $m_1$ and $m_2$ are positive integers such th

Jake Masters

Orbit-finite models of computation generalise the standard models of computation, to allow computation over infinite objects that are finite up to symmetries on atoms, denoted by $\mathbb{A}$. Set theory with atoms is used to reason about these objects. Recent work assumes that $\mathbb{A}$ is countable and that the symmetries are the automorphisms of a stru

Fabrizio Battistelli, Francesca Farruggia

This article examines the changing relationship between the public and nuclear weapons in a context of increasing international insecurity. It discusses the erosion of the nuclear taboo, understood as a normative aversion to nuclear use. Standard surveys capture abstract and rational opinions, while experimental surveys place respondents in simulated strateg

Akshit Pramod Anchan, Jewelith Thomas, Sritama Roy

Developing comprehensive assistive technologies requires the seamless integration of visual and auditory perception. This research evaluates the feasibility of a modular architecture inspired by core functionalities of perceptive systems like 'Smart Eye.' We propose and benchmark three independent sensing modules: a Convolutional Neural Network (CNN) for eye

David Alfaya, Indranil Biswas, Tomás L. Gómez

We classify all isomorphisms between moduli stacks of vector bundles of fixed determinant on a smooth complex projective of genus at least 4. It is shown that each isomorphism between two different moduli stacks can be described as a composition of a pullback using an isomorphism of curves, dualization of vector bundles and tensoring with the pullback of a l

Roman Vasyltsiv, Allison L. Matous, Natasha Mulenga, Megan A. Clark

Background: In vivo dosimetry is essential for treatment verification in modern radiotherapy, but existing techniques are limited by spatiotemporal resolution and performance on non-uniform anatomy. Scintillation imaging dosimetry shows potential to address several of these limitations. Here, translation to conventional photon external beam radiotherapy was

Yuto Suzuki, Farnoush Banaei-Kashani

Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-Thought (CoT) prompting technique, numerous reasoning methods have emerged that decompose problems into smaller, sequential steps (or thoughts). However, existi

Genís Plaja-Roglans, Yun-Ning Hung, Xavier Serra, Igor Pereira

Extracting individual elements from music mixtures is a valuable tool for music production and practice. While neural networks optimized to mask or transform mixture spectrograms into the individual source(s) have been the leading approach, the source overlap and correlation in music signals poses an inherent challenge. Also, accessing all sources in the mix

Ben Hamscher, Arnold Brosch, Nicolas Binninger, Maksymilian Jan Dejna

Dance is an essential component of human culture and serves as a tool for conveying emotions and telling stories. Identifying and distinguishing dance genres based on motion data is a complex problem in human activity recognition, as many styles share similar poses, gestures, and temporal motion patterns. This work presents a lightweight framework for classi

Yuanhao Li, Mingshan Liu, Hongbo Wang, Yiding Zhang

Large Language Models (LLMs) have shown impressive capabilities in multi-step reasoning and problem-solving.Recent works introduce multi-agent reflection frameworks where multiple LLM agents critique and refine each other's outputs using reinforcement learning (RL). However, these approaches often rely on single-shot responses and lack structural diversity i

Liangkai Liu, Weisong Shi, Kang G. Shin

This paper presents pNav, a novel power-management system that significantly enhances the power/energy-efficiency of Autonomous Mobile Robots (AMRs) by jointly optimizing their physical/mechanical and cyber subsystems. By profiling AMRs' power consumption, we identify three challenges in achieving CPS (cyber-physical system) power-efficiency that involve bot

Alexis Boulin, Erik Haufs

Understanding complex dependencies and extrapolating beyond observations are key challenges in modeling environmental space-time extremes. To address this, we introduce a simplifying approach that projects a wide range of multivariate exceedance problems onto a univariate peaks-over-threshold problem. In this framework, an estimator is computed by minimizing

Mingfu Shao, Suo Liu, Haiqing Xu, Peng Jia

Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in space weather research. In recent years, substantial progress has been made in the field of solar flare forecasting, driv

Feyzollah Younesizadeh, Davoud Kamani

We investigate the cosmic inflation within a class of the scalar-tensor model with the scalar-dependent non-minimal kinetic couplings. The inflationary dynamical potential will be applied. Using the slow-roll approximation, we compute theoretical predictions for the key observables, like the spectral indexes $n_s$, scalar-to-tensor ratio $r$ and the running

Amy K. Strong, Ali Kashani, Claus Danielson, Leila Bridgeman

Barrier functions (BFs) characterize safe sets of dynamical systems, where hard constraints are never violated as the system evolves over time. Computing a valid safe set and BF for a nonlinear (and potentially unmodeled), non-autonomous dynamical system is a difficult task. This work explores the design of BFs using data to obtain safe sets with determinist

Jiatao Gu, Ying Shen, Tianrong Chen, Laurent Dinh

Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-ba

Marek Matas, Filip Krizek, Carl P. Romao

We investigate a new quantum sensor for dark matter direct detection with sub-eV sensitivity, focusing on several candidate materials that potentially host chiral phonons with large magnetic moments that can be directly read out with an external magnetometer. We focus on metal-organic frameworks (MOFs) as possible candidate materials for single chiral phonon

Yunqi Zhou, Chengjie Jiang, Chun Yuan, Jing Li

With advances in satellite constellations, sensor technologies, and imaging pipelines, ultra-high-resolution (Ultra-HR) remote sensing imagery is becoming increasingly widespread. However, current remote sensing foundation models are ill-suited to such inputs: full-image encoding exhausts token and memory budgets, while resize-based preprocessing loses fine-

Mosab Rezaei, Mina Rajaei Moghadam, Abdul Rahman Shaikh, Hamed Alhoori

Recent advances in large language models have created new opportunities for stylometry, the study of writing styles and authorship. Two challenges, however, remain central: training generative models when no paired data exist, and evaluating stylistic text without relying only on human judgment. In this work, we present a framework for both generating and ev

Sebastian Vander Ploeg Fallon, James Halverson, Liam McAllister, Yunhao Zhu

We compute the couplings of Ramond-Ramond four-form axions in three ensembles of F-theory compactifications, with up to 181,200 axions. We work in the stretched K\"ahler cone, where $\alpha'$ corrections are plausibly controlled, and we use couplings to certain non-Abelian sectors as a proxy for couplings to photons. The axion masses, decay constants, and co

Afra Kilic, Kim Batselier

Modeling nonlinear systems with Volterra series is challenging because the number of kernel coefficients grows exponentially with the model order. This work introduces Bayesian Tensor Network Volterra kernel machines (BTN-V), extending the Bayesian Tensor Network framework to Volterra system identification. BTN-V represents Volterra kernels using canonical p

Shreevanth Krishnaa Gopalakrishnan, Stephen Hailes

Machine learning drives Channel State Information (CSI)-based human sensing in modern wireless networks, enabling applications like device-free human activity recognition (HAR) and identification (HID). However, the susceptibility of these models to adversarial perturbations raises security concerns that must be quantified prior to edge deployment. We presen

Neil Ashton, Johannes Brandstetter, Siddhartha Mishra

Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. Our main contribution is to propose the first scaling law that incorpora

Xutian Jing, Kaiwen Wei, Chenglin Gu, Xiong Qin

Advances in mid-infrared (MIR) dual-comb spectroscopy (DCS) have significantly enhanced molecular detection in recent years. The capability of DCS to precisely identify and quantify atmospheric trace gases makes it attractive for field applications across the environmental, agricultural, energy, and industrial sectors. In particular, there is a growing deman

Ziqin Zhou, Hui Chen, Gerhard Steinböck, Henk Wymeersch

High-precision wireless localization in urban canyons is challenged by noisy measurements and severe non-line-of-sight (NLOS) propagation. This paper proposes a robust three-stage algorithm synergizing a digital twin (DT) model with the random sample consensus (RANSAC) algorithm to overcome these limitations. The method leverages the DT for geometric path as

Samy Kaci, Gwenael Giacinti, Dmitri Semikoz

Pulsar wind nebulae (PWNe) are the dominant Ultra-high-energy (UHE) gamma-ray sources in the LHAASO catalog suggesting that they are the dominant leptonic PeVatrons in our Galaxy. Despite this, still very little is known about their UHE gamma-ray emission, their number in the Galaxy, or their contribution to the gamma-ray emission of our Galaxy. In this work

Xiang Fang, Juncheng Wei, Youquan Zheng

We consider the nonlinear heat equations with Neumann boundary conditions $$ \begin{cases} u_{t}=\Delta u & \text{in}\ \mathbb{R}_{+}^{4} \times(0, T) ,\\ -\frac{d u}{d x_{4}}(\tilde{x}, 0, t) \ =u^2(\tilde{x}, 0, t)& \text{in}\ \mathbb{R}^{3} \times(0, T). \end{cases} $$ We establish the existence of a finite-time blow-up solution. Specifically, for any suf

Dmitry V. Fedorov, Nikita E. Rybin, Mikhail A. Averyanov, Alexander V. Shapeev

Ubiquitous van der Waals (vdW) interactions play a subtle yet crucial role in determining the precise atomic arrangements in solids, particularly in molecular crystals where these weak forces are the primary link between constituent building blocks. Within density functional (DF) theory, the most natural approach for addressing vdW forces is the use of vdW-i

Melchior Wirth

We resolve a conjecture of De Palma and Trevisan by proving the triangle inequality for a quantum 2-Wasserstein distance. The proof relies on complex analysis methods to establish a new integral representation of the cost in the optimal transport problem.

Damien Rigutto, Manuel Ratz, Miguel A. Mendez

Constrained radial basis function (RBF) regression has recently emerged as a powerful meshless tool for reconstructing continuous velocity fields from scattered flow measurements, particularly in image-based velocimetry. However, existing formulations based on isotropic kernels often suffer from spurious oscillations in regions with sharp gradients or strong

Srijon Ghosh, Sagnik Chakraborty, Rosario Lo Franco

We present a quantum thermometric protocol for the estimation of multiple temperatures within the collisional model framework. Employing the formalism of multiparameter quantum metrology, we develop a systematic strategy to estimate the temperatures of several thermal reservoirs with minimal estimation error. We prove a necessary and sufficient condition for

Misha Padidar, Teresa Huang, Andrew Giuliani, Marina Spivak

Stellarators are a prospective class of fusion-based power plants that confine a hot plasma with three-dimensional magnetic fields. Typically framed as a PDE-constrained optimization problem, stellarator design is a time-consuming process that can take hours to solve on a computing cluster. Developing fast methods for designing stellarators is crucial for ad

M. A. Balcewicz, C. Y. Tan

For the PIP-II program, transverse emittance in the Fermilab Booster must remain well controlled at higher bunch intensities. 4-plate beam position monitors (BPMs) have a small but measurable quadrupole moment, making it possible to infer transverse emittance. By compositing many BPMs together, it becomes possible to improve the quality of the quadrupole sig

Amy K. Strong, Samuel Akinwande, Leila Bridgeman

Continuous piecewise affine (CPA) Lyapunov function synthesis is one method to perform Lyapunov stability analysis for nonlinear systems. This method first generates a mesh over the region of interest in the system's state space and then solves a linear program (LP), which enforces constraints on each vertex of the mesh, to synthesize a Lyapunov function. Fi

Anne Boutet de Monvel, Iryna Karpenko, Dmitry Shepelsky, Lech Zielinski

This work addresses the development of the Riemann-Hilbert problem (RHP) formalism (the Fokas method) for the Camassa-Holm equation under periodic boundary conditions. Particularly, we present a representation of the solution to this problem in terms of the solution of the associated Riemann-Hilbert problem, the data for which are determined by the initial d