December 2025 arXiv papers — page 81

Yi-Heng Feng, Che Ming Ko, Xiaofeng Luo, Yu-Gang Ma

Event-by-event fluctuations of the baryon number, which is mostly carried by protons and neutrons, in relativistic heavy-ion collisions provide a sensitive probe for locating the conjectured critical point in the quantum chromodynamics (QCD) phase diagram. Since current experiments have limited access to neutron fluctuations because detectors are largely ins

Cesare Straffelini

Generic absoluteness is the phenomenon that certain truths in the set-theoretic universe remain stable under forcing expansions. A classical result by Kripke asserts that every complete Boolean algebra completely embeds into a countably generated one, implying that any forcing extension can be realised inside one obtained via a collapse forcing. This observa

Alberto Salvadori, Mattia Serpelloni, Robert M. McMeeking

Building upon the classical chemo-mechanical theory of Larch{\'e} and Cahn for equilibrium, numerous studies have investigated the transport of species in solids, with or without trapping phenomena. In most applications -- such as the swelling of hydrogels, hydrogen embrittlement in metals, and the transport of lithium or sodium in battery electrodes -- the

O. Le Noan, E. Khan, S. Goriely, K. Sieja

The dipole response of a nuclear system, characterized by its photon strength function (PSF), is a key ingredient of many applications of nuclear structure, ranging from nuclear reactor design and nuclear waste transmutation to astrophysical models of nucleosynthesis and stellar evolution. While the majority of those applications require the knowledge of PSF

Zhi-Hang Yao, Hong-Hao Fan, Lie-Juan Li, Hai-Bo Sang

We investigate entropy of electron-positron pair production in time-dependent Sauter pulse electric field. Both cases of pair longitudinal momentum only and full momentum consideration are examined. We further examine three types of entropy, one is the usual entanglement entropy $S_{\text{E}}$, the other two extensions are thermal distribution entropy $S_{\t

Jozefien D'haeseleer, Sascha Kurz

We consider the geometric problem of determining the maximum number $n_q(r,h,f;s)$ of $(h-1)$-spaces in the projective space $\operatorname{PG}(r-1,q)$ such that each subspace of codimension $f$ does contain at most $s$ elements. In coding theory terms we are dealing with additive codes that have a large $f$th generalized Hamming weight. We also consider the

Nan Zhou, Zuxin Li, Fanhang Man, Xuecheng Chen

This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-age

Mostafa Bella, Shahram Hosseini, Thierry Contini, Hicham Saylani

This paper proposes four new methods to decontaminate spectra of stars and galaxies resulting from slitless spectroscopy used in many space missions such as Euclid. These methods are based on two distinct approaches and simultaneously take into account multiple dispersion directions of light. The first approach, called the local instantaneous approach, is ba

Hiroyuki Deguchi, Katsuki Chousa, Yusuke Sakai

Strongly human-correlated evaluation metrics serve as an essential compass for the development and improvement of generation models and must be highly reliable and robust. Recent embedding-based neural text evaluation metrics, such as COMET for translation tasks, are widely used in both research and development fields. However, there is no guarantee that the

Zhongpan Tang

Current Large Language Models (LLMs) face three major challenges: context length limitations, high inference costs, and catastrophic forgetting during continual learning. While Mixture-of-Experts (MoE) architectures mitigate some of these conflicts, their routing mechanisms typically rely on explicitly trained auxiliary classifiers. This not only increases s

Aryan Esmailpour, Xiao Hu, Jinchao Huang, Stavros Sintos

Subset sampling (also known as Poisson sampling), where the decision to include any specific element in the sample is made independently of all others, is a fundamental primitive in data analytics, enabling efficient approximation by processing representative subsets rather than massive datasets. While sampling from explicit lists is well-understood, modern

Itsuki Tazoe

We give an explicit and complete description of bubbling limits of a non-collapsing limit of polarized K3 surfaces in terms of the period mapping. In particular, we show that bubbling limits only depend on algebro-geometric data of the given family. As a corollary, this gives an affirmative answer to a conjecture of de Borbon--Spotti and confirms that Odaka'

Yuanyuan Lian, Filomena Pacella, Pieralberto Sicbaldi

We study an overdetermined eigenvalue problem for domains $\Omega$ contained in the half-cylinder $\Sigma=\omega \times (0, +\infty)$, based on a bounded regular domain $\omega \subset \mathbb{R}^{N-1}$. It is easy to see that in any bounded cylinder $\Omega_{t}=\omega \times (0, t)$, $t > 0$, the eigenvalue problem admits a one-dimensional positive eigenfun

Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki

Recursion is a fundamental concept in the design of filters and audio systems. In particular, artificial reverberation systems that use delay networks depend on recursive paths to control both echo density and the decay rate of modal components. The differentiable digital signal processing framework has shown promise in automatically tuning recursive and non

Arther Tian, Alex Ding, Frank Chen, Alan Wu

Decentralized large language model (LLM) inference promises transparent and censorship resistant access to advanced AI, yet existing verification approaches struggle to scale to modern models. Proof of Quality (PoQ) replaces cryptographic verification of computation with consensus over output quality, but the original formulation ignores heterogeneous comput

Zhao Su, Zhiyuan Li

Thousands of X-ray sources have been detected in the Galactic center (GC), most believed to be cataclysmic variables (CVs). As a potential probe of the old stellar population, in particular CVs, the existence and detectability of novae in the GC remain elusive, due to the prohibitive extinction toward the GC and their relatively low occurrence rate. Nova rem

Sheng Luo, Jiashu Xie, Yueling Che, Junmei Yao

Channel prediction is a key technology for improving the performance of various functions such as precoding, adaptive modulation, and resource allocation in MIMO-OFDM systems. Especially in high-mobility scenarios with fast time-varying channels, it is crucial for resisting channel aging and ensuring communication quality. However, existing methods suffer fr

Huayu Huang, Chen Chen, Banglei Guan, Ze Tan

Tracking and measuring targets using a variety of sensors mounted on UAVs is an effective means to quickly and accurately locate the target. This paper proposes a fusion localization method based on ridge estimation, combining the advantages of rich scene information from sequential imagery with the high precision of laser ranging to enhance localization acc

Haiyu Zhao, Yiwen Shan, Yuanbiao Gou, Xi Peng

Recent studies have explored all-in-one video restoration, which handles multiple degradations with a unified model. However, these approaches still face two challenges when dealing with time-varying degradations. First, the degradation can dominate temporal modeling, confusing the model to focus on artifacts rather than the video content. Second, current me

A. V. Kopylov, I. V. Orekhov, V. V. Petukhov, A. E. Solomatin

This paper presents the results of measurements of diurnal variations in the single electron count rate arising from dark photon conversion at the cathode of a gas proportional counter. Three experimental series were conducted, each consisting of 4 runs lasting approximately 60 days per run. One can expect an excess in the single electron count rate above th

Can Liu, Xing Yang, Youming Deng, Qingqing Duan

Real-time, bunch-by-bunch monitoring of transverse position, longitudinal phase, and bunch length is crucial for beam control in diffraction-limited storage rings, where complex collective dynamics pose unprecedented diagnostic challenges. This study presents a neural network framework that simultaneously predicts these parameters directly from beam position

Aleksandros Sobczyk, Anastasios Zouzias

In this work, we revisit prefix sums through the lens of linear algebra. We describe an identity that decomposes triangular all-ones matrices as a sum of two Kronecker products, and apply it to design recursive prefix sum algorithms and circuits. Notably, the proposed family of circuits is the first one that achieves the following three properties simultaneo

Haifa Fan, Qian Cao, Andy Chong, Qiwen Zhan

Optical vortices carrying orbital angular momentum offer additional degrees of freedom. According to the orientation of orbital angular momentum, optical vortices can be classified into spatial optical vortex beam carrying longitudinalorbital angular momentum and spatiotemporal optical vortices carrying transverse orbital angular momentum. As an emerging sub

Safwan Shaheer, G. M. Refatul Islam, Mohammad Rafid Hamid, Tahsin Zaman Jilan

In this fast-evolving area of LLMs, our paper discusses the significant security risk presented by prompt injection attacks. It focuses on small open-sourced models, specifically the LLaMA family of models. We introduce novel defense mechanisms capable of generating automatic defenses and systematically evaluate said generated defenses against a comprehensiv

Adam Nowak, Paweł Plewa, Tomasz Z. Szarek

We give a unified and optimized proof of the sharp bounds for the Jacobi heat kernel, which were obtained gradually in several papers in recent years. We lay particular emphasis on tracing and estimating all constants appearing throughout the entire reasoning. This allows us to quantitatively control the multiplicative constants in the Jacobi heat kernel bou

P. Urdeitx, I. Alfaro, D. Gonzalez, F. Chinesta

It is well known that the lack of information about certain variables necessary for the description of a dynamical system leads to the introduction of historical dependence (lack of Markovian character of the model) and noise. Traditionally, scientists have made up for these shortcomings by designing phenomenological variables that take into account this his

Jiajun Yuan, Xiaochen Wang, Yuhang Xiao, Yulin Wu

Applying speech super-resolution (SR) to recordings with severely low sampling rates is a critical challenge in digital archiving and investigative audio recovery. In these scenarios, the input lacks essential acoustic cues. Consequently, existing generative models often fail; without sufficient context, they hallucinate phonetic content, guessing words base

Farida Mohsen, Ali Safa

Service robots in public spaces require real-time understanding of human behavioral intentions for natural interaction. We present a practical multimodal framework for frame-accurate human-robot interaction intent detection that fuses camera-invariant 2D skeletal pose and facial emotion features extracted from monocular RGB video. Unlike prior methods requir

Feng Liang, Sizhe Cheng, Chenqi Yi, Yong Wang

Omni-modal models that have multimodal input and output are emerging. However, benchmarking their multimodal generation, especially in image generation, is challenging due to the subtleties of human preferences and model biases. Many image generation benchmarks focus on aesthetics instead of the fine-grained generation capabilities of these models, failing t

Zixuan Chen, Chongkai Gao, Lin Shao, Jieqi Shi

One-shot imitation learning (OSIL) offers a promising way to teach robots new skills without large-scale data collection. However, current OSIL methods are primarily limited to short-horizon tasks, thus limiting their applicability to complex, long-horizon manipulations. To address this limitation, we propose ManiLong-Shot, a novel framework that enables eff

Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi, Zifeng Wang

Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learning with verifiable rewards can improve reasoning and tool use, and OpenClaw highlights a newer direction in which agents accumulate persistent memory and reusable skills. Yet the re

Ayan Biswas, Colin P. Folsom, James A. Barron, Gregg A. Wade

We present a spectropolarimetric study of the nearby M4.5V exoplanet host star YZ Cet, based on near-infrared observations obtained with the SpectroPolarim\`etre InfraRouge (SPIRou) at the Canada--France--Hawaii Telescope. We detect striking changes in the large-scale magnetic field strength and geometry over the course of just a few stellar rotations, a lev

Saksham Sahai Srivastava, Haoyu He

Large Language Model (LLM) agents increasingly rely on long-term memory and Retrieval-Augmented Generation (RAG) to persist experiences and refine future performance. While this experience learning capability enhances agentic autonomy, it introduces a critical, unexplored attack surface, i.e., the trust boundary between an agent's reasoning core and its own

Taozhao Chen, Linghan Huang, Kim-Kwang Raymond Choo, Huaming Chen

As large language models (LLMs) are increasingly adopted in safety-critical and regulated sectors, the retention of sensitive or prohibited knowledge introduces escalating risks, ranging from privacy leakage to regulatory non-compliance to to potential misuse, and so on. Recent studies suggest that machine unlearning can help ensure deployed models comply wi

Rositsa Miteva, Susan W. Samwel, Svetoslav Zabunov

Solar energetic protons (SEPs) in different energy channels from 10 to above 100 MeV are analyzed and their relationship to solar and geomagnetic activity is investigated. We performed temporal association analysis between the SEPs, solar flares (SFs), coronal mass ejections (CMEs) and geomagnetic storms (GSs) that occurred during solar cycles 23 and 24. The

Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang

With VLM-powered computer-using agents (CUAs) becoming increasingly capable at graphical user interface (GUI) navigation and manipulation, reliable step-level decision-making has emerged as a key bottleneck for real-world deployment. In long-horizon workflows, errors accumulate quickly and irreversible actions can cause unintended consequences, motivating cr

Amna Amir, Erchan Aptoula

Semantic overlap among land-cover categories, highly imbalanced label distributions, and complex inter-class co-occurrence patterns constitute significant challenges for multi-label remote-sensing image retrieval. In this article, Multi-Label Adaptive Contrastive Learning (MACL) is introduced as an extension of contrastive learning to address them. It integr

Karola Köpferl, Albrecht Kurze

In the 15th century, printing revolutionized the dissemination of information. Innovations such as typewriters and computers have increased the speed and volume of information flows over time. More recent developments in large language models such as ChatGPT enable text to be generated in a matter of seconds. However, many people do not understand how this w

Jinghua Li, Zhiyong Yu

In this paper, we are concerned with the classical solvability of a class of second-order Hamilton-Jacobi-Bellman equations (HJB equations) arising from stochastic optimal control problems with linear dynamics and uniformly convex cost functionals. By introducing the Hamiltonian system and extending the gradient descent method to a Hilbert space, we prove th

Dingyi Zhao, Yingjie Peng

Understanding the quenching of star formation in central galaxies remains a core challenge in galaxy evolution. Two decades ago, the concept of halo quenching was introduced as a dominant mechanism, positing that massive central galaxy quenching is governed by the thermodynamics of gas predominantly influenced by dark matter halos. However, a vastly increasi

Isobel Romero-Shaw, Jakob Stegmann, Gonzalo Morras, Andris Dorozsmai

The gravitational-wave signal from the neutron star-black hole (NSBH) merger GW200105 is consistent with this binary having significant orbital eccentricity close to merger. This raises the question of how eccentric NSBHs form. Compact binaries that evolve in isolation radiate away any orbital eccentricity long before their gravitational-wave signal enters t

Bowen Liu

The score set of a tournament is defined as the set of its distinct out-degrees. In 1978, Reid proposed the conjecture that for any set of nonnegative integers $D$, there exists a tournament $T$ with a degree set $D$. In 1989, Yao presented an arithmetical proof of the conjecture, but a general polynomial-time construction algorithm is not known. This paper

Jimreeves David, Shashi Thutupalli

For non-motile microorganisms, spatial expansion in quiescent fluids is presumed to be limited by diffusion. We report that microbial colonies can explosively circumvent this constraint through a self-amplifying physical process. As non-motile yeast and bacteria metabolize dense nutrients into lighter waste within their fluid environment, they generate buoya

Yehor Tereshchenko, Mika Hämäläinen, Svitlana Myroniuk

The evaluation of Large Language Models (LLMs) for translation tasks has primarily focused on high-resource languages, leaving a significant gap in understanding their performance on low-resource and endangered languages. This study presents a comprehensive comparison of OpenAI's GPT models, specifically examining the differences between reasoning and non-re

Cassandre Lebot

This paper is devoted to the formal study of the low-Mach-number limit for solutions of the compressible Navier-Stokes or Euler equations for different types of fluids.We first review the different results obtained in the case of flows consisting of one phase. Then, we focus on the low-Mach-number limit for two-phase flows, considering different types of sys

Karola Köpferl, Albrecht Kurze

This essay explores a techno-artistic experiment that reanimates a 1980s East German typewriter using a contemporary AI language model. Situated at the intersection of media archaeology and speculative design, the project questions dominant narratives of progress by embedding generative AI in an obsolete, tactile interface. Through public exhibitions and aes

Milton Nicolás Plasencia Palacios, Alexander Boudewijn, Sebastiano Saccani, Andrea Filippo Ferraris

Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for pract

Yuan Wu

In this paper, we will prove the existence of full dimensional tori for 1-dimensional nonlinear Schr\"odinger equation \begin{eqnarray}\label{maineq0} \mathbf{i}u_{t}-u_{xx}+V*u+\epsilon f(x)|u|^{4}u=0,\ x\in\mathbb{T}=\mathbb{R}/2\pi\mathbb{Z}, \end{eqnarray} with boundary conditions, where $V*$ is the Fourier multiplier, and $f(x)$ is Gevrey smooth. Here t

Jinhao Zhang, Yunquan Zhang, Daning Chen, JunSun

Current mainstream post-training quantization methods for large language models typically apply a uniform quantization strategy across all network layers, overlooking the substantial differences in algorithmic suitability among layers. To address this limitation, we propose CALM (A CKA-guided Adaptive Layer-wise Modularization)a fine-tuning-free, plug-and-pl

Vadim A. Naumov, Dmitry S. Shkirmanov

In a quantum field approach to neutrino oscillations, the neutrino is treated as a propagator, while the external initial and final particle states are described by covariant wave packets. For the asymptotic behavior on short and long macroscopic baselines, the wave packet modified neutrino propagator is expressed through asymptotic series in powers of dimen

Gilad Gressel, Rahul Pankajakshan, Shir Rozenfeld, Ling Li

Romance-baiting scams have become a major source of financial and emotional harm worldwide. These operations are run by organized crime syndicates that traffic thousands of people into forced labor, requiring them to build emotional intimacy with victims over weeks of text conversations before pressuring them into fraudulent cryptocurrency investments. Becau

Yiliu Yang, Yilei Jiang, Qunzhong Wang, Yingshui Tan

Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural language are ambiguous and context dependent, so they map poorly to machine-checkable rules, and runtime enforcement is unreliable. Expressing safety policies as sequents, we propose

Juntao Bai, Shi Dai, Na Wang, Stefan Osłowski

With the installation of next-generation phased array feed (PAF) receivers on radio telescopes, there is an urgent need to develop effective and computationally efficient radio frequency interference (RFI) mitigation methods for large-scale surveys. Here we present a new RFI mitigation package, called mRAID (multi-beam RAdio frequency Interference Detector),

Jialiang Wang, Xueyan Bao, Hao Wu

Second-order Latent Factor (SLF) model, a class of low-rank representation learning methods, has proven effective at extracting node-to-node interaction patterns from High-dimensional and Incomplete (HDI) data. However, its optimization is notoriously difficult due to its bilinear and non-convex nature. Sharpness-aware Minimization (SAM) has recently propose

Ji-Xiang Zhao

The general solutions with free variable to the second-kind Abel equation, a nonlinear ordinary differential equation that has remained unsolved for nearly two centuries, are presented for the first time by using elementary quadrature method.

Yuta Hayashida, Shonosuke Sugasawa

Mixture regression models are powerful tools for capturing heterogeneous covariate-response relationships, yet classical finite mixtures and Bayesian nonparametric alternatives often suffer from instability or overestimation of clusters when component separability is weak. Recent repulsive priors improve parsimony in density mixtures by discouraging nearby c

G. P. Malik, V. S. Varma

The unified approach based on the Generalized BCS equations incorporating chemical potential employed to deal with the critical temperature, gap(s) and coherence length(s) of any superconductor (SC) in an earlier paper is shown here to be also applicable when the SC is in an applied field. Presented herein are the calculated values of the following parameter

Ce Zheng, Ke Zhang, Chen Sun, Wenqi Zhang

Speculative decoding accelerates large language model (LLM) inference by allowing a small draft model to predict multiple future tokens for verification by a larger target model. In AI-native radio access networks (AI-RAN), this enables device-edge collaborative inference but introduces significant uplink overhead, as existing distributed speculative decodin

Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich, Raviv Gal

Large Language Models are increasingly deployed as judges (LaaJ) in code generation pipelines. While attractive for scalability, LaaJs tend to overlook domain specific issues raising concerns about their reliability in critical evaluation tasks. To better understand these limitations in practice, we examine LaaJ behavior in a concrete industrial use case: le

Geofrey Owino, Bernard Shibwabo Kasamani, Ahmed M. Abdelmoniem, Edem Wornyo

Accurate and interpretable classification of infant cry paralinguistics is essential for early detection of neonatal distress and clinical decision support. However, many existing deep learning methods rely on correlation-driven acoustic representations, which makes them vulnerable to noise, spurious cues, and domain shifts across recording environments. We

Rui Gui, Yang Wan, Haochen Han, Dongxing Mao

Text rendering has recently emerged as one of the most challenging frontiers in visual generation, drawing significant attention from large-scale diffusion and multimodal models. However, text editing within images remains largely unexplored, as it requires generating legible characters while preserving semantic, geometric, and contextual coherence. To fill

Khaoula El Maddah, Matti Lassas, Teemu Tyni

We introduce a numerical framework for reconstructing the potential in two dimensional semilinear elliptic PDEs with power type nonlinearities from the nonlinear Dirichlet to Neumann map. By applying higher order linearization method, we compute the Fourier data of the unknown potential and then invert it to recover $q$. Numerical experiments show accurate r

Jia-Yi Lu, Jia-Xin Li, Xin-Yu Zhao, Ya-Nan Zhang

The discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ under high pressure has sparked a surge of research into Ruddlesden-Popper (RP) nickelates. Currently, stabilizing the bilayer RP phases with smaller $A$-site ions remains a significant challenge. In this work, we have successfully synthesized medium- and high-entropy bilayer nickelates

Bangya Liu, Chengpo Yan, Chenghao Jiang, Suman Banerjee

Cooperative perception between vehicles is poised to offer robust and reliable scene understanding. Recently, we are witnessing experimental systems research building testbeds that share raw spatial sensor data for cooperative perception. While there has been a marked improvement in accuracies and is the natural way forward, we take a moment to consider the

F. Patat, S. Piranomonte, S. Benetti, A. Bonforte

The coming decades will see gravitational-wave (GW) astronomy expand decisively into the mHz-Hz frequency range, opening access to a population of compact binaries that are currently invisible or only detectable moments before merger. The Lunar Gravitational Wave Antenna (LGWA) concept is designed to probe this gap, enabling continuous observation of compact

Damian Maingi

We prove stability of the kernel bundle and prove that the cohomology bundle is simple for vector bundles associated to monads on $X = (\mathbb{P}^{n_1})^2\times\cdots\times(\mathbb{P}^{n_s})^2$ for an ample line bundle $\mathscr{L}=\mathcal{O}_X(\alpha_1,\alpha_1,\cdots,\alpha_s,\alpha_s)$.

Bosen Yang, Kang Ma, Jin Lin, Yonghua Song

This paper proposes a black-start method for an off-grid wind-to-hydrogen (W2H) system comprising a wind farm based on Doubly-Fed Induction Generators (DFIGs), proton exchange membrane fuel cells (PEMFCs) serving as the black-start power source, and a hydrogen production industry. The PEMFC is installed within the hydrogen industry to facilitate direct acces

Yifei She, Ping Zhang, He Liu, Yanmin Jia

Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \textit{Temporal Gap} between action initiation and completion. Existing environment-side solutions, such as blocking wrappers or frequent polling, either limit scalability or dilute the agent's con

Georgios A. Tritsaris

The Fourth Industrial Revolution commonly refers to the accelerating technological transformation that has been taking place in the 21st century. Economic growth theories which treat the accumulation of knowledge and its effect on production endogenously remain relevant, yet they have been evolving to explain how the current wave of advancements in automatio

Jin-Xing Hou, Chuang Li, Lun-Hui Hu, Song-Bo Zhang

The recent realization of superconducting proximity effect in chiral antiferromagnets (cAFMs) opens a new route to nonreciprocal superconducting transport of fundamental interest and practical importance. Using microscopic modeling and symmetry analysis, we show that Josephson junctions formed by conventional $s$-wave superconductors (SCs) and cAFMs on the k

Zeyu Huang, Wei Meng, Quan Liu, Kun Chen

Spiking neurons, the fundamental information processing units of Spiking Neural Networks (SNNs), have the all-or-zero information output form that allows SNNs to be more energy-efficient compared to Artificial Neural Networks (ANNs). However, the hard reset mechanism employed in spiking neurons leads to information degradation due to its uniform handling of

Roman Cherniha, Vasyl' Davydovych

A mathematical model for description of the viscous fingering induced by a chemical reaction is under study. This complicated five-component model is reduced to a three-component diffusive Lotka-Volterra system with convection by introducing a stream function. The system obtained is examined by the classical Lie method. A complete Lie symmetry classification

Adriana Celeste Alvarado Leon, Osmar Denilson Herrera Cueva, Rosario Mercedes Morales Orvezo, Daniella Alexandra Crysti Vargas Saldana

The accelerating global development of quantum technologies strengthens the case for introducing quantum computing concepts before university. Yet in Latin America, there is no consolidated, region wide integration of quantum computing into secondary education, and the feasibility conditions for doing so remain largely unexamined. This paper proposes a quali

Rahul Thakur, Abhijith Ajith, Sukanta Panda, Archit Vidyarthi

We study general $f(R,\phi)$ theories in Palatini formalism and attempt to constrain the behavior of ones that could support both inflationary and late-time expansion era in a unified model. In particular, we find conditions for which the theories remain consistent in weak gravity regimes as well as cosmic expansion eras in both early and late universe. Assu

Chrysanthi Kosyfaki, Nikos Mamoulis, Reynold Cheng, Ben Kao

Analyzing flow of objects or data at different granularities of space and time can unveil interesting insights or trends. For example, transportation companies, by aggregating passenger travel data (e.g., counting passengers traveling from one region to another), can analyze movement behavior. In this paper, we study the problem of finding important trends i

Mohamed Tolba, Olivia Kendall, Daniel Tudball Smith, Alexander Gregg

Educational videos are widely used across various instructional models in higher education to support flexible and self-paced learning. However, student engagement with these videos varies significantly depending on how they are designed. While several studies have identified potential influencing factors, there remains a lack of scalable tools and open data

Nigel T. Bishop, Vishnu Kakkat, Monos Naidoo

The interaction of gravitational waves (GWs) with matter is normally treated as being insignificant. However, recent work has shown that the interaction with a viscous fluid may be astrophysically important when the distance between the matter and GW source is somewhat smaller than the GW wavelength. Previous work has mainly considered perturbations on a Min

Kazuto Akiba, Yuzuki Sega, Yuichi Akahama, Yuta Seo

We investigated the out-of-plane magnetoresistance of pressurized black phosphorus (BP) with a longitudinal field configuration. Despite the absence of the Lorentz force in the present configuration, we observed a significant enhancement of magnetoresistance marked with a clear onset field in both the semiconducting (1.1 GPa) and semimetallic (1.3 GPa) phase

Changeun Kim, Younwoo Jeong, Bong-Gyu Jang

We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), which embeds aggregate analyst consensus as a structural bottleneck, treating professional beliefs as a sufficient statistic for the market's high-dimensional information set. Unlike post-hoc explainability approaches, CB-APM achieves interpretability-by-design: the bottleneck constraint fun

Sanjoy Chowdhury, Karren D. Yang, Xudong Liu, Fartash Faghri

Recent multimodal large language models (MLLMs) such as GPT-4o and Qwen3-Omni show strong perception but struggle in multi-speaker, dialogue-centric settings that demand agentic reasoning tracking who speaks, maintaining roles, and grounding events across time. These scenarios are central to multimodal audio-video understanding, where models must jointly rea

Federico Ledda, Daniele Torsello, Davide Gambino, Flyura Djurabekova

Radiation damage in high-temperature cuprate superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors and accelerator facilities. Their complex crystal structure makes modeling irradiation effects in this class of materials a particularly demanding task, for which existing damage

Qingguo Hu, Zhenghao Lin, Ziyue Yang, Yucheng Ding

Mixture-of-Experts (MoE) has emerged as a promising paradigm for foundation models due to its efficient and powerful scalability. In this work, we present Sigma-MoE-Tiny, an MoE language model that achieves the highest sparsity compared to existing open-source models. Sigma-MoE-Tiny employs fine-grained expert segmentation with up to 96 experts per layer, wh

Ruiyu Li, Peige Zhao, Guangxia Li, Pengcheng Wu

One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from different organizations, raising privacy concerns. Federated graph learning (FGL) addresses this by enabling collaborative GNN model training without sharing private data. However

Carmen Amarra, Alice Devillers, Cheryl E. Praeger

We study block designs which admit an automorphism group that is transitive on blocks and points, and leaves invariant every partition in a given finite poset of partitions of the point set. The full stabiliser $G$ of all the partitions in the poset is a generalised wreath product. We use the theory of generalised wreath products to give necessary and suffic

Aniruddha Roy, Jyoti Patel, Aman Chadha, Vinija Jain

Merging large language models (LLMs) is a practical way to compose capabilities from multiple fine-tuned checkpoints without retraining. Yet standard schemes (linear weight soups, task vectors, and Fisher-weighted averaging) can preserve loss while quietly destroying alignment. We argue that merging is not a numerical trick but a geometry-constrained operati

Xueqi Ma, Xingjun Ma, Sarah Monazam Erfani, Danilo Mandic

Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neural networks (GNNs) in open-world scenarios. Existing methods typically treat all OOD samples as a single class, despite real-world applications, especially high-stake settings such

Qi Zhang, Yunfei Gong, Zhidan Xie, Zhizi Wang

Multi-view crowd counting has been proposed to deal with the severe occlusion issue of crowd counting in large and wide scenes. However, due to the difficulty of collecting and annotating multi-view images, the datasets for multi-view counting have a limited number of multi-view frames and scenes. To solve the problem of limited data, one approach is to coll

Yingjie Zhou, Xiaoqian Wang, Tao Li

We investigate the distributed online economic dispatch problem for power systems with time-varying coupled inequality constraints. The problem is formulated as a distributed online optimization problem in a multi-agent system. At each time step, each agent only observes its own instantaneous objective function and local inequality constraints; agents make d

Leo Kurata, Kensei Nakamura

This paper studies preference aggregation under ambiguity when agents have incomplete preference relations due to imprecise beliefs. We introduce the "dual" of the Pareto principle, which respects unanimity among individuals, including those with unexpressed opinions. Our first theorem shows that, in most cases, this principle leads to a dictatorial rule in

Bohan Wu, Eli N. Weinstein, David M. Blei

Empirical Bayes (EB) improves the accuracy of simultaneous inference "by learning from the experience of others" (Efron, 2012). Classical EB theory focuses on latent variables that are iid draws from a fitted prior (Efron, 2019). Modern applications, however, feature complex structure, like arrays, spatial processes, or covariates. We propose a gener

Yifeng Cai, Zhida An, Yuhan Meng, Houqian Liu

Future improvements in large language model (LLM) services increasingly hinge on access to high-value professional knowledge rather than more generic web data. However, the data providers of this knowledge face a skewed tradeoff between income and risk: they receive little share of downstream value yet retain copyright and privacy liability, making them relu

Zhi Helu, Huang Jingjing, Xu Wang, Xu Yangbin

Embodied intelligence, a grand challenge in artificial intelligence, is fundamentally constrained by the limited spatial understanding and reasoning capabilities of current models. Prevailing efforts to address this through enhancing Vision-Language Models (VLMs) are trapped in a dilemma: template-based datasets are scalable but structurally rigid, while man

Tejul Pandit, Sakshi Mahendru, Meet Raval, Dhvani Upadhyay

Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This paper is a thorough guide to examine the changing reranker landscape and offer a clear view of the advancements made in reranking methods. We present a comprehensive survey of rer

Satya Narayana Panda, Vaishnavi Kukkala, Spandana Iyer

Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes. This research addresses the critica

Zichen Geng, Zeeshan Hayder, Wei Liu, Hesheng Wang

3D human reaction generation faces three main challenges:(1) high motion fidelity, (2) real-time inference, and (3) autoregressive adaptability for online scenarios. Existing methods fail to meet all three simultaneously. We propose ARMFlow, a MeanFlow-based autoregressive framework that models temporal dependencies between actor and reactor motions. It cons

Noriaki Sato, Marco Scutari, Shuichi Kawano, Rui Yamaguchi

We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constraint. Our Zero-Inflated Continuous Optimization (ZICO) approach uses node-wise likelihoods with canonical links and enforces acyclicity through

Xin Wang, Zhao-Min Gao

Parametric amplification offers a powerful means to enhance quantum interactions through field squeezing, yet it typically introduces additional noise which accelerates quantum decoherence, a major obstacle for scalable quantum information processing. The squeezing field is implemented in cavities rather than continuous waveguides, thereby limiting its scala

Luke Hagar, Andrew J. Martin

In many settings, robust data analysis involves computational methods for uncertainty quantification and statistical inference. To design frequentist studies that leverage robust analysis methods, suitable sample sizes to achieve desired power are often found by estimating sampling distributions of p-values via intensive simulation. Moreover, most sample siz

Baochang Shi, Xiaolei Yuan, Zhenhua Chai

We develop a unified multi-relaxation-time lattice Boltzmann (MRT-LB) framework based on discrete Hermite polynomials (Hermite matrices) for the Navier-Stokes equations (NSEs) and nonlinear convection-diffusion equations (NCDEs), using multispeed rectangular lattice (rD$d$Q$b$) models. For NSEs, the proposed MRT-LB model simulates incompressible and compress

Chenkai Xu, Yijie Jin, Jiajun Li, Yi Tu

Diffusion Large Language Models (dLLMs) have demonstrated significant potential for high-speed inference. However, current confidence-driven decoding strategies are constrained by limited parallelism, typically achieving only 1--3 tokens per forward pass (TPF). In this work, we identify that the degree of parallelism during dLLM inference is highly sensitive