October 2025 arXiv papers — page 42

Ting-Yang Hsiao, Alberto Maspero

We study the two-dimensional gravity-capillary water waves equations for a fluid of finite depth $\mathtt{h}>0$ under the combined effects of gravity and surface tension $\kappa \geq 0$. We analyze the linear stability and instability of small-amplitude, $2\pi$-periodic Stokes wave solutions, under the effect of longitudinal long-wave perturbations. The corr

Khoa Nguyen, Khang Tran, NhatHai Phan, Cristian Borcea

This paper proposes Stochastic Geographic Gradient Fusion (SGFusion), a novel training algorithm to leverage the geographic information of mobile users in Federated Learning (FL). SGFusion maps the data collected by mobile devices onto geographical zones and trains one FL model per zone, which adapts well to the data and behaviors of users in that zone. SGFu

Markus Herrmann-Wicklmayr, Kathrin Flaßkamp

Multi-objective model predictive control (MOMPC) for fixed point stabilization requires an automated a priori decision-making (DM) mechanism to translate a high-level preference into a single solution. To this aim, we introduce an approach called individual minima-informed DM. This class of methods can be implemented through two sequential optimizations, reg

Marco Grossi

This work is a commentary of the article \href{https://doi.org/10.18716/ojs/phai/2025.2801}{AI Survival Stories: a Taxonomic Analysis of AI Existential Risk} by Cappelen, Goldstein, and Hawthorne. It is not just a commentary though, but a useful reminder of the philosophical limitations of \say{linear} models of risk. The article will focus on the model empl

Tuğba Yavuz, Şahsene Altınkaya

The Mittag-Leffler function plays an important role in Geometric Function Theory, particularly in the study of analytic and meromorphic function classes. Among its various generalizations, the Barnes-Mittag-Leffler function stands out due to its intricate structure and applications in diverse mathematical fields. In this paper, our main focus is to investiga

Zhuoran Jin, Hongbang Yuan, Kejian Zhu, Jiachun Li

Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainly focused on text and image modalities, offering limited support for video, audio, and other modalities; and (2) Preference Rigidity, where training on fixed binary preference pair

Hannes Meinlschmidt, Joachim Rehberg

This article is about the (minimal) sector containing the numerical range of the principal part of a linear second-order elliptic differential operator defined by a form on closed subspaces V of the first-order Sobolev space $W^{1,2}(\Omega)$ incorporating mixed boundary conditions. We collect a comprehensive array of results on the angle of sectoriality and

M. M. Hammad

We introduce the Schrodinger Neural Network (SNN), a principled architecture for conditional density estimation and uncertainty quantification inspired by quantum mechanics. The SNN maps each input to a normalized wave function on the output domain and computes predictive probabilities via the Born rule. The SNN departs from standard parametric likelihood he

Xingtu Liu

In this work, we study out-of-distribution (OOD) generalization in meta-reinforcement learning from an information-theoretic perspective. We begin by establishing OOD generalization bounds for meta-supervised learning under two distinct distribution shift scenarios: standard distribution mismatch and a broad-to-narrow training setting. Building on this found

Anna Guerra, Francesco Guidi, Pau Closas, Davide Dardari

Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level proficiency. While proficiency self-assessment (PSA) has been studied for single agents, its extension to a team of agents remains underexplored. This letter addresses this gap by presenting a framework for

Mario Mally, Rafael Vázquez, Sebastian Schöps

For low-frequency electromagnetic problems, where wave-propagation effects can be neglected, eddy current formulations are commonly used as a simplification of the full Maxwell's equations. In this setup, time-domain simulations, needed to capture transient startup responses or nonlinear behavior, are often computationally expensive. We propose a novel teari

YuanDong Wang

Harnessing non-Markovian effects has emerged as a resource for quantum control, where a structured environment can act as a quantum memory. We investigate the quench dynamics from specific initial states to equilibrium steady states in strongly correlated quantum dot systems. The distance between quantum states is quantified using the Bures metric, which end

Fangtong Sun, Congyu Li, Ke Yang, Yuchen Pan

Low-light vision remains a fundamental challenge in computer vision due to severe illumination degradation, which significantly affects the performance of downstream tasks such as detection and segmentation. While recent state-of-the-art methods have improved performance through invariant feature learning modules, they still fall short due to incomplete mode

Chiara Bonfanti, Alessandro Druetto, Cataldo Basile, Tharindu Ranasinghe

The growing intersection of cybersecurity and law creates a complex information space where traditional legal research tools struggle to deal with nuanced connections between cases, statutes, and technical vulnerabilities. This knowledge divide hinders collaboration between legal experts and cybersecurity professionals. To address this important gap, this wo

Asmaa Abbas, Mohamed Gaber, Mohammed M. Abdelsamea

Identifying high-quality and easily accessible annotated samples poses a notable challenge in medical image analysis. Transfer learning techniques, leveraging pre-training data, offer a flexible solution to this issue. However, the impact of fine-tuning diminishes when the dataset exhibits an irregular distribution between classes. This paper introduces a no

Weicong Li, Hanlin Zou

This paper completes the classification of triply-transitive strongly regular graphs, a program recently initiated by Herman, Maleki, and Razafimahatratra. By proving that the collinearity graph of the polar space $\mathcal{Q}^{-}(5,q)$ and the affine polar graph $\mathrm{VO}^{\varepsilon}_{2m}(2)$ are triply-transitive, we resolve the final open cases in th

Donatella Darsena, Ivan Iudice, Vincenzo Galdi, Francesco Verde

Stacked intelligent metasurfaces (SIMs) represent a key enabler for next-generation wireless networks, offering beamforming gains while significantly reducing radio-frequency chain requirements. In conventional space-only SIM architectures, the rate of reconfigurability of the SIM is equal to the inverse of the channel coherence time. This paper investigates

Alberto Garilli, Diego Frezzato

For Markov jump processes on irreducible networks with finite number of sites, we derive a general and explicit expression of the squared coefficient of variation for the net number of transitions from one site to a connected site in a given time window of observation (i.e., an `integrated current' as dynamical output). Such expression, which in itself is pa

Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi, Runkai Yang

We study the problem of constructing coresets for $(k, z)$-clustering when the input dataset is corrupted by stochastic noise drawn from a known distribution. In this setting, evaluating the quality of a coreset is inherently challenging, as the true underlying dataset is unobserved. To address this, we investigate coreset construction using surrogate error

Yinling Zhang, Samuel D. Dunham, Curt A. Bronkhorst, Nan Chen

Polycrystalline metal failure often begins with stress concentration at grain boundaries. Identifying which microstructural features trigger these events is important but challenging because these extreme damage events are rare and the failure mechanisms involve multiple complex processes across scales. Most existing inference methods focus on average behavi

Samuel G. Fadel, Hrittik Roy, Nicholas Krämer, Yevgen Zainchkovskyy

Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power, and subpar calibration. Building upon recent work on repar

Stanislav Selitskiy, Chihiro Inoue

Discussion about the replacement of intellectual human labour by ``thinking machines'' has been present in the public and expert discourse since the creation of Artificial Intelligence (AI) as an idea and terminology since the middle of the twentieth century. Until recently, it was more of a hypothetical concern. However, in recent years, with the rise of Ge

K. Huynh, V. Bajaj, J. Mack, A. Calamida

In crowded fields, small-aperture photometry can reduce contamination errors from neighboring sources compared to larger aperture photometry. However, the UVIS encircled energy (EE) varies with detector position and focus variations on orbital timescales for aperture radii less than 10 pixels ($\sim$0.4 arcseconds). Using a set of focus-diverse empirical PSF

Mohammad Jannesari, Beatrice Ghitti, Bruce J. Gluckman, Francesco Costanzo

In conventional formulations of poroelasticity, when the porosity approaches zero or vanishes in some parts of the poroelastic domain, if only temporarily, the governing equations degenerate to those for the solid phase thereby inhibiting a suitable determination of the fluid velocity field. To address this challenge, we reformulated a poroelastic model base

Anti Maria Aader, Viktor Abramov, Olga Liivapuu

We propose an approach to extending the concept of a Lie algebra to ternary structures based on $\omega$-symmetry, where $\omega$ is a primitive cube root of unity. We give a definition of a corresponding structure, called a ternary Lie algebra at cube roots of unity, or a ternary $\omega$-Lie algebra. A method for constructing ternary associative algebras h

Angela Davey, Arthur Leroy, Eliana Vasquez Osorio, Kate Vaughan

Survivors of childhood cancer need lifelong monitoring for side effects from radiotherapy. However, longitudinal data from routine monitoring is often infrequently and irregularly sampled, and subject to inaccuracies. Due to this, measurements are often studied in isolation, or simple relationships (e.g., linear) are used to impute missing timepoints. In thi

Wietse M. Boon, Sarah Gasda, Tor Harald Sandve, Svenn Tveit

Finite volume methods are prevalent in reservoir simulation due to their mass conservation properties and their ability to handle complex grids. However, a simple and consistent finite volume method for elasticity was unavailable until the recently developed two-point stress approximation finite volume method (TPSA). In this work, we show how to couple TPSA

Titus Pinta

The purpose of this work is to investigate root finding problems defined on (quasi-)metric spaces, and ranging in Euclidean spaces. The motivation for this line of inquiry stems from recent models in biology and phylogenetics, where problems of great practical significance are cast as optimization problems on (quasi-)metric spaces. We investigate a minimal a

Vadim Gorin, Sergei Korotkikh

We study a problem with three equivalent formulations: describing Gibbs measures for five-vertex model in quadrant; classifying coherent systems on a p-deformation of the Gelfand-Tsetlin graph related to Grothendieck polynomials; finding the Martin boundary for discrete time TASEP with p-geometric jumps. We find a wide family of the Gibbs measures, parameter

Ahmet Serdar Karadeniz, Dimitrios Mallis, Danila Rukhovich, Kseniya Cherenkova

Computer-Aided Design (CAD) plays a foundational role in modern manufacturing and product development, often requiring designers to modify or build upon existing models. Converting 3D scans into parametric CAD representations--a process known as CAD reverse engineering--remains a significant challenge due to the high precision and structural complexity of CA

Michael L. Parker, Samar Mahmoud, Bailey Montefiore, Mario Öeren

We explore a "best-of-both" approach to modelling molecular properties by combining learned molecular descriptors from a graph neural network (GNN) with general-purpose descriptors and a mixed ensemble of machine learning (ML) models. We introduce a MetaModel framework to aggregate predictions from a diverse set of leading ML models. We present a featurisati

Luca Melis, Matthew Grange, Iden Kalemaj, Karan Chadha

The increasing deployment of Machine Learning (ML) models in sensitive domains motivates the need for robust, practical privacy assessment tools. PrivacyGuard is a comprehensive tool for empirical differential privacy (DP) analysis, designed to evaluate privacy risks in ML models through state-of-the-art inference attacks and advanced privacy measurement tec

C. E. P. Robin, M. J. Savage

Non-local non-stabilizerness and anti-flatness provide a measure of the quantum complexity in the wavefunction of a physical system. Supported by entanglement, they cannot be removed by local unitary operations, thus providing basis-independent measures, and sufficiently large values underpin the need for quantum computers in order to perform precise simulat

Xiaojing Dong, Yibing Han, Yunqing Huang

Based on the Stokes complex with vanishing boundary conditions and its dual complex, we reinterpret a grad-curl problem arising from the quad-curl problem as a new vector potential formulation of the three-dimensional Stokes system. By extending the analysis to the corresponding non-homogeneous problems and the accompanying trace complex, we construct a nove

Elouanes Khelifi, Amir Saki, Usef Faghihi

Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often leads to the acquisition of spurious correlations, hindering their problem-solving capabilities. In this paper, we introduce a novel approach to integrate causal principles into DQNs, leveraging the PEACE (Probabi

Diederik van Engelenburg, Christophe Garban, Romain Panis, Franco Severo

We study the probability that the origin is connected to the boundary of the box of size $n$ (the one-arm probability) in several percolation models related to the Ising model. We prove that different universality classes emerge at criticality. - For the FK-Ising measure in a box of size $n$ with wired boundary conditions, we prove that this probability deca

Lu Xia, Kaiqi Zhao, Sunil Kadam, M. Dolores Blanco-González

Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, m

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AI

S. Bonvicini, T. Pisanski, A. Žitnik

A bicirculant is a regular graph that admits a semi-regular automorphism with two vertex-orbits of the same size. By $m$ we denote the size of vertex-orbits and by $d$ the valence of a bicirculant. Furthermore, we denote by $s$ the valence of the bipartite graph joining the two vertex-orbits. In 1983, Brian Alspach proved that the only non-hamiltonian genera

Fredrik Hasselgren, Max O. Al-Hasso, Amy Searle, Joseph Tindall

Spin glass systems as lattices of disordered magnets with random interactions have important implications within the theory of magnetization and applications to a wide-range of hard combinatorial optimization problems. Nevertheless, despite sustained efforts, algorithms that attain both high accuracy and efficiency remain elusive. Due to their topologies bei

Gokturk Aytug Akarlar

Large language models show promise as autonomous decision-making agents, yet their deployment in high-stakes domains remains fraught with risk. Without architectural safeguards, LLM agents exhibit catastrophic brittleness: identical capabilities produce wildly different outcomes depending solely on prompt framing. We present Chimera, a neuro-symbolic-causal

Lukas Worch, James O. Douglas, Kavin Arunasalam, Baptiste Gault

Porous microstructures, while central to many functional materials, remain difficult to characterize quantitatively by atom probe tomography (APT). Although several strategies have been proposed over the past decade, most remain constrained by significant practical or technical limitations. Here, we introduce an in situ pore filling approach that integrates

Marco Antonio Ortiz Rincon, Yihui Yang, Christoph Holst

This study presents a novel workflow designed to efficiently and accurately register large-scale mobile laser scanning (MLS) point clouds to a target model point cloud in urban street scenarios. This workflow specifically targets the complexities inherent in urban environments and adeptly addresses the challenges of integrating point clouds that vary in dens

Moona Mazher, Geoff J. M. Parker, Daniel C. Alexander

Foundation models in artificial intelligence (AI) are transforming medical imaging by enabling general-purpose feature learning from large-scale, unlabeled datasets. In this work, we introduce BrainFound, a self-supervised foundation model for brain MRI, built by extending DINO-v2, a vision transformer originally designed for 2D natural images. BrainFound ad

Ivan Sipiran, Gustavo Santelices, Lucas Oyarzún, Andrea Ranieri

Unlike image or text domains that benefit from an abundance of large-scale datasets, point cloud learning techniques frequently encounter limitations due to the scarcity of extensive datasets. To overcome this limitation, we present Symmetria, a formula-driven dataset that can be generated at any arbitrary scale. By construction, it ensures the absolute avai

Alessandro Di Giorgio, Pawel Sobocinski, Niels Voorneveld

Many algorithms are specified with respect to a fixed but unspecified parameter. Examples of this are especially common in cryptography, where protocols often feature a security parameter such as the bit length of a secret key. Our aim is to capture this phenomenon in a more abstract setting. We focus on resource theories -- general calculi of processes with

Carlos Rodriguez, Anna-Laura Sattelberger

Border bases are a generalization of Gr\"obner bases for zero-dimensional ideals in polynomial rings. In this article, we introduce border bases for a non-commutative ring of linear differential operators, namely the rational Weyl algebra. We elaborate on their properties and present algorithms to compute with them. We apply this theory to represent integrab

Jiahao Ji, Tianyu Wang, Yeshu Li, Yushen Huo

Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments for better ad performance, it has limitations in generalizability across environments since these models are typically tailored for specific bidding scenarios. To this end, we app

Youngjun Choi, Joonseong Kang, Sungjun Lim, Kyungwoo Song

Data valuation has become central in the era of data-centric AI. It drives efficient training pipelines and enables objective pricing in data markets by assigning a numeric value to each data point. Most existing data valuation methods estimate the effect of removing individual data points by evaluating changes in model validation performance under in-distri

Abolfazl Younesi, Zahra Najafabadi Samani, Thomas Fahringer

Data pipelines are essential in stream processing as they enable the efficient collection, processing, and delivery of real-time data, supporting rapid data analysis. In this paper, we present AutoStreamPipe, a novel framework that employs Large Language Models (LLMs) to automate the design, generation, and deployment of stream processing pipelines. AutoStre

Yi Lu, Xiaoming Xue, Kai Zhang, Liming Zhang

Expensive optimization problems (EOPs) present significant challenges for traditional evolutionary optimization due to their limited evaluation calls. Although surrogate-assisted search (SAS) has become a popular paradigm for addressing EOPs, it still suffers from the cold-start issue. In response to this challenge, knowledge transfer has been gaining popula

Hengxing Pan, Matt J. Jarvis, Ian Heywood, Tariq Yasin

The relationship between the already formed stellar mass in a galaxy and the gas reservoir of neutral atomic hydrogen, is a key element in our understanding of how gas is turned into stars in galaxy haloes. In this paper, we measure the $M_{\rm HI}-M_{\star}$ relation based on a stellar-mass selected sample at $0.25 < z < 0.5$ and the MIGHTEE-HI DR1 spectral

Nicholas Miesch, Edward Shuryak, Ismail Zahed

In previous papers we developed the light front formulation for Hamiltonians and wave functions (WFs) for mesons and baryons, with both confinement and chiral symmetry breaking. For baryons limited to the lowest Fock component with three quarks, the longitudinal WF is valued in an equilateral triangle with momentum fractions $x_i,i=1,2,3$. The WF was develop

Carl Hvarfner, David Eriksson, Eytan Bakshy, Max Balandat

Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the surrogate model is crucial for good optimization performance, especially in the few-shot setting where only a small number of batches of points can be evaluated. In this setting,

Ş. Ekmen, H. Lee

Recently, the Spherical Wavelet Framework (SWF) was proposed to combine the benefits of Ambisonics and Object-Based Audio (OBA) by utilising highly localised basis functions. SWF can enhance the sweet-spot area and reduce localisation blur while still enabling a sparse representation of the complete sound field, making storage and transmission more efficient

Xinming Wang, Jian Xu, Bin Yu, Sheng Lian

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning-answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby

Thinh Hoang, Daniel Delahaye

Air Traffic Flow Management (ATFM) traffic regulations are being increasingly used as rising demand meets persistent workforce shortages. This operational strain has amplified a critical phenomenon that we call \emph{regulation cascading}: the compounding, non-linear interactions that occur when multiple regulations influence one another in unpredictable way

S. Riggi, P. Romano, A. Pilzer, U. Becciani

We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent foundational models - SigLIP2 for image encoding, VideoMAE for spatio-temporal video representation, and Moirai2 for multivariat

Yun Kai Zhuang

The project has carried out the re-optimization of image coloring in accordance with the existing Autocolorization direction model DDColor. For the experiments on the existing weights of DDColor, we found that it has limitations in some frequency bands and the color cast problem caused by insufficient input dimension. We construct two optimization schemes an

Yakov Solomons, Roni Ben-Maimon, Arpit Behera, Ofer Firstenberg

We present a practical approach for interfacing light with a two-dimensional atomic tweezer array. Typical paraxial fields are poorly matched to the array's multi-diffraction-order radiation pattern, thus severely limiting the interface coupling efficiency. Instead, we propose to design a field mode that naturally couples to the array: it consists of a uniqu

Pratik N. Kalamkar, Anupama G. Phakatkar

Opinion mining, also called sentiment analysis, is the field of study that analyzes people opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. Holistic lexicon-based approach does not consider the strength of each opinion,

Lu Dong, Haiyu Zhang, Han Lin, Ziang Yan

Video temporal grounding (VTG) aims to locate precise segments in videos based on language queries, which is a fundamental challenge in video understanding. While recent Multimodal Large Language Models (MLLMs) have shown promise in tackling VTG through reinforcement learning (RL), they overlook the challenges arising from both the quality and difficulty of

Musleh Alharthi, Kaleel Mahmood, Sarosh Patel, Ausif Mahmood

The immense success of the Transformer architecture in Natural Language Processing has led to its adoption in Time Se ries Forecasting (TSF), where superior performance has been shown. However, a recent important paper questioned their effectiveness by demonstrating that a simple single layer linear model outperforms Transformer-based models. This was soon s

Evy Beijen, Pien Pieterse, Yusuf Çelik, Willem Th. van Peursen

Religious language continues to permeate contemporary discourse, even in ostensibly secular domains such as environmental activism and climate change debates. This paper investigates how explicit and implicit forms of religious language appear in climate-related texts produced by secular and religious nongovernmental organizations (NGOs). We introduce a dual

Lucrezia Berghenti, Elisa Damiani, Margherita Marsili, Maria Clelia Righi

The increasing complexity and volume of data generated by high-throughput computational materials science require robust tools to ensure their accessibility, reproducibility, and reuse. In particular, integrating the FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable) into computational workflows is essential to enable open science pr

Farid Bagirov, Mikhail Arkhipov, Ksenia Sycheva, Evgeniy Glukhov

The application of Reinforcement Learning with Verifiable Rewards (RLVR) to mathematical and coding domains has demonstrated significant improvements in the reasoning and problem-solving abilities of Large Language Models. Despite its success in single generation problem solving, the reinforcement learning fine-tuning process may harm the model's exploration

Yuri Latushkin, Vyacheslav Pivovarchik, Alesia Supranovych

We study characteristic functions and describe asymptotics of the eigenvalues for the spectral Sturm-Liouville problem on graphs equipped with Robin-Kirhhoff boundary conditions. Also, we show how to recover the coefficients in the Robin conditions for the quantum graphs provided the shape of the graphs and some Robin eigenvalues are known.

Li Zhaoping

A critical visual computation is to construct global scene properties from activities of early visual cortical neurons which have small receptive fields. Such a computation is enabled by contextual influences, through which a neuron's response to visual inputs is influenced by contextual inputs outside its classical receptive fields. Accordingly, neurons can

Adolfo S. Carvalho, Lynne A. Hillenbrand, Gregory J. Herczeg, Kevin France

We present the results of the first high-sensitivity NUV (1800 to 3200 \AA) survey of FU Ori objects, using the \textit{Hubble Space Telescope} (HST) STIS spectrograph. We compare new low resolution spectra for 6 sources with predictions from accretion disk models and find that all show emission in excess of the disk model spectrum. The physical properties o

Edoardo Manino, Bruno Farias, Rafael Sá Menezes, Fedor Shmarov

The behaviour of neural network components must be proven correct before deployment in safety-critical systems. Unfortunately, existing neural network verification techniques cannot certify the absence of faults at the software level. In this paper, we show how to specify and verify that neural networks are safe, by explicitly reasoning about their floating-

Kento Akamatsu, Takuya Hirose, Nobuhito Maru, Akio Nago

We study a six-dimensional U(1)$_\chi$ gauge theory compactified on a magnetized torus, where the zero mode of the extra-dimensional gauge field (a Wilson-line (WL) scalar field) plays the role of a pseudo-Nambu-Goldstone (pNG) dark matter (DM) candidate. The pNG DM is naturally included by construction without introducing an additional scalar field. We show

Alvaro Paz, Mahdi Hejrati, Pauli Mustalahti, Jouni Mattila

Heavy-duty hydraulic manipulators (HHMs) operate under strict physical and safety-critical constraints due to their large size, high power, and complex nonlinear dynamics. Ensuring that both joint-level and end-effector trajectories remain compliant with actuator capabilities, such as force, velocity, and position limits, is essential for safe and reliable o

Jan Van den Berghe, Miguel A. Mendez, Yann Bartosiewicz

Ejectors are passive devices used in refrigeration, propulsion, and process industries to compress a secondary stream without moving parts. The engineering modeling of choking in these devices remains an open question, with two mechanisms-Fabri and compound choking-proposed in the literature. This work develops a unified one-dimensional framework that implem

Pratik N. Kalamkar, A. G. Phakatkar

Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opinions (or sentiments) about an Entity, opinion mining aims to

Qiuyang Chen, Huiqi Yang, Qingyan Meng, Zhengyu Ma

Spiking Neural Networks (SNNs) are gaining attention as energy-efficient alternatives to Artificial Neural Networks (ANNs), especially in resource-constrained settings. While ANN-to-SNN conversion (ANN2SNN) achieves high accuracy without end-to-end SNN training, existing methods rely on large time steps, leading to high inference latency and computational co

Songxi Yang, Tang Sui, Qunying Huang

Super resolution offers a way to harness medium even lowresolution but historically valuable remote sensing image archives. Generative models, especially diffusion models, have recently been applied to remote sensing super resolution (RSSR), yet several challenges exist. First, diffusion models are effective but require expensive training from scratch resour

Chi-An Chen, Chun Liu, Ming Zhong

We propose a unified learning framework for identifying the profile function in discrete Keller-Segel equations, which are widely used mathematical models for understanding chemotaxis. Training data are obtained via either a rigorously developed particle method designed for stable simulation of high-dimensional Keller-Segel systems, or stochastic differentia

Hajime Kaneko, Bill Mance

In this paper, we consider recurrence sequences $x_n=\xi_1 \alpha_1^n+\xi_2 \alpha_2^n$ ($n=0,1,\ldots$) with companion polynomial $P(X)$. For example, the sequence $x_n=\xi_1(4+\sqrt{2})^n+\xi_2(4-\sqrt{2})^n$ satisfies the recurrence $x_{n+2}-8x_{n+1}+14x_n=0$ and has companion polynomial $P(X)=X^2-8X+14=(X-4-\sqrt{2})(X-4+\sqrt{2})$. We call $(\xi_1,\xi_2

Yu Yao, Xuejie Liu, Xiaoyun Chen, Yuheng Wu

In this work, we investigate the resonance structures in the $\Sigma(1/2^-)$ system from both three-quark and five-quark perspectives within the framework of the chiral quark model. An accurate few-body computational approach, the Gaussian Expansion Method, is employed to construct the orbital wave functions of multiquark states. To reduce the model dependen

M. Meo

The interplay between string theory and early-universe cosmology offers promising avenues to explore high-energy regimes where the standard single-field slow-roll model may no longer provide an accurate description. One intriguing scenario emerges from certain non-supersymmetric string models, where supersymmetry breaking induces a non-trivial vacuum energy,

Yumeng Luo, Yuehong Li, Mengfan Jiang, Muwei Wu

Using stochastic series expansion quantum Monte Carlo and density matrix renormalization group methods, we investigate the ground-state phase diagram of the $S=1/2$ Heisenberg model on the two-dimensional square-hexagon-octagon (SHO) lattice. The model incorporates nearest-neighbor interactions $J_1$ (intrahexagon interaction) and $J_2$ (interhexagon), as we

Lu Li, Zhaozhou Li, Zhengyi Shao

The thousands of open cluster (OC) candidates identified by the Gaia mission are significantly contaminated by false positives from field star fluctuations, posing a major validation challenge. Based on the Mixture Model for OCs (MiMO), we present a Bayesian framework for validating OC candidates in the color--magnitude diagram. The method compares the Bayes

Lu Li, Zhengyi Shao, Zhaozhou Li, Xiaoting Fu

We present a homogeneous catalog of 1,232 open clusters with precisely determined ages, metallicities, distances, extinctions, and stellar mass function (MF) slopes, derived from Gaia DR3 data. The parameters are inferred using the Mixture Model for Open clusters (MiMO), a novel Bayesian framework for modeling clusters in the color-magnitude diagram. By expl

Ondřej Draganov, Herbert Edelsbrunner, Sophie Rosenmeier, Morteza Saghafian

Let $c$ be the constant such that the expected length of the Euclidean minimum spanning tree of $n$ random points in the unit square is $c \sqrt{n}$ in the limit, when $n$ goes to infinity. We improve the prior best lower bound of $0.6008 \leq c$ by Avram and Bertsimas to $0.6289 \leq c$. The proof is a by-product of studying the persistent homology of rando

ATLAS Collaboration

Events with $t$-channel single top quarks are used to probe effective field theory operators in $\sqrt{s}=13$ TeV proton-proton collision data corresponding to 140 fb$^{-1}$ recorded by the ATLAS detector at the Large Hadron Collider. An analysis method leveraging Fourier techniques applied to quadruple-differential decay rates based on observables containin

Hyunseung Kim, Dae-Woong Jeong, Changyoung Park, Won-Ji Lee

Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection and retraining for each new property. Here we introduce and validate GATE (Geometrically Aligned Transfer Encoder) -- a generalizable AI framework that jointly learns 34 physicochemi

Kai-sa Qiao, Bing-song Zou

We investigate the production of hyperons, charmed baryons, and potential hadronic molecular states in neutrino-proton $(\bar{\nu}_\mu p)$ reaction, a process characterized by a particularly clean final state. Employing effective Lagrangians, chiral perturbation theory, and a hadronic molecular model, we perform theoretical calculations for several relevant

Yifan Jiao, Xinran Liu, Xiaoqiong Liu, Xiaohui Yuan

Planar tracking has drawn increasing interest owing to its key roles in robotics and augmented reality. Despite recent great advancement, further development of planar tracking, particularly in the deep learning era, is largely limited compared to generic tracking due to the lack of large-scale platforms. To mitigate this, we propose PlanarTrack, a large-sca

Leonard Busch, Tony Liimatainen, Mikko Salo, Leo Tzou

We study a generalized boundary rigidity problem, which investigates whether the areas of embedded minimal surfaces can uniquely determine a Riemannian manifold with boundary. We prove that for a conformal perturbation of an analytic metric in dimension $n+1$ ($n \geq 2$), the metric is determined by these volumes under an ampleness condition. Furthermore, w

Inhyeok Choi, Dongryul M. Kim

This is an expository/teaser version of [arXiv:2601.22668] for a very special case of products of $\mathrm{CAT}(-1)$ spaces. We keep this on arXiv as many parts of the argument of [arXiv:2601.22668] are simplified in this special case.

Hyeongkyun Kim, Orestis Oikonomou

Flood hazard mapping is essential for disaster prevention but remains challenging in data-scarce regions, where traditional hydrodynamic models require extensive geophysical inputs. This paper introduces \textit{ZeroFlood}, a framework that leverages Geo-Foundation Models (GeoFMs) to predict flood hazard maps using single-modality Earth Observation (EO) data

Sean Fletcher, Gabby Scott, Douglas Currie, Xin Zhang

Medical image analysis is central to drug discovery and preclinical evaluation, where scalable, objective readouts can accelerate decision-making. We address classification of paclitaxel (Taxol) exposure from phase-contrast microscopy of C6 glioma cells -- a task with subtle dose differences that challenges full-image models. We propose a simple tiling-and-a

Hanzhang Wang, Zonglin Liu, Jingyi Xu, Chenyang Wang

Proximal gradient algorithms (PGA), while foundational for inverse problems like image reconstruction, often yield unstable convergence and suboptimal solutions by violating the critical non-negativity constraint. We identify the gradient descent step as the root cause of this issue, which introduces negative values and induces high sensitivity to hyperparam

Fleur Seuren, Santiago A. Triana, Jérémy Rekier, Véronique Dehant

Evidence from seismic studies, mineral physics, thermal evolution models and geomagnetic observations is inconclusive about the presence of a stably stratified layer at the top of the Earth's fluid outer core. Such a convectively stable layer could have a strong influence on the internal fluid waves propagating underneath the core-mantle boundary (CMB) that

André Rocha, Claudia Manini, José I López, Annick Laruelle

We study the effect of intratumor heterogeneity in the likelihood of cancer cells moving from a primary tumor to other sites in the human body, generating a metastatic process. We model different scenarios of competition between tumor cells using a static evolutionary game in which cells compete for nutrients and oxygen and might choose to stay and prolifera

Chungeng Tian, Ning Hao, Fenghua He

This paper presents a novel approach to address the inconsistency problem caused by observability mismatch in visual-inertial navigation systems (VINS). The key idea involves applying a linear time-varying transformation to the error-state within the Error-State Kalman Filter (ESKF). This transformation ensures that \textrr{the unobservable subspace of the t

Sheri Osborn, Rohit Valecha, H. Raghav Rao, Dan Sass

Artificial intelligence is reshaping labor markets, yet we lack tools to systematically forecast its effects on employment. This paper introduces a benchmark for evaluating how well large language models (LLMs) can anticipate changes in job demand, especially in occupations affected by AI. Existing research has shown that LLMs can extract sentiment, summariz

Shaohan Bian, Ying Zhang, Guohui Tian, Zhiqiang Miao

With the rapid advancement of large language models (LLMs) and robotics, service robots are increasingly becoming an integral part of daily life, offering a wide range of services in complex environments. To deliver these services intelligently and efficiently, robust and accurate task planning capabilities are essential. This paper presents a comprehensive

Sandra Coello Suarez, V. Sanchez Padilla, Ronald Ponguillo-Intriago, Albert Espinal

Parameter monitoring and control systems are crucial in the industry as they enable automation processes that improve productivity and resource optimization. These improvements also help to manage environmental factors and the complex interactions between multiple inputs and outputs required for production management. This paper proposes an automation system

Pengyu Gao, Qu Luo, Jing Zhu, Gaojie Chen

In this paper, a novel uncoordinated random access (URA) protocol is presented to address the pressing demand for massive connectivity with low access latency in future massive machine type communication (mMTC) scenarios. The proposed URA scheme integrates the classical slotted ALOHA (S-ALOHA) protocol with sparse code multiple access (SCMA) technique, refer