November 2025 arXiv papers — page 29

Yanmo Weng, Avantika Gori

This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu-Weather and Aurora) to reproduce observed TC track density, climatology o

Diana Singh, Maciej Ćwierzona, Régis Parvaud, Sebastian Maćkowski

Optical memristors are innovative devices that enable the integration of electro-optical functionalities - such as light modulation, multilevel optical memory, and nonvolatile reprogramming - into neuromorphic networks. Recently, their capabilities have expanded with the development of light-emitting memristors, which operate through various emission mechani

Tom Markvart

The paper considers a model for the solar cell as a mechanical open-cycle thermodynamic engine where the chemical potential is produced in an isochoric process corresponding to the thermalization of electron-hole pairs. Expansion of the beam under one-sun illumination and current generation are described as isothermal lost work. More generally, voltage produ

Yacine Mohamed Idir, Thomas Romary

Spatially varying coefficients (SVC) models allow for marginal effects to be non-stationary over space and thus offer a higher degree of flexibility with respect to standard geostatistical models with external drift. At the same time, SVC models have the advantage that they are easily interpretable. They offer a flexible framework for understanding how the r

Roi Bar-Zur, Aviv Tamar, Ittay Eyal

Blockchain security is threatened by selfish mining, where a miner (operator) deviates from the protocol to increase their revenue. Selfish mining is exacerbated by adverse conditions: rushing (network propagation advantage for the selfish miner), varying block rewards due to block contents, called miner extractable value (MEV), and petty-compliant miners wh

M. A. R. Griffith, S. Rufo, H. Caldas, F. Dinola Neto

High magnetic fields provide a direct route to probe the anisotropies that govern spin dynamics in layered magnets. Using the SU(3) bond operator framework for spin 1 systems, we derive analytic expressions for the magnon spectrum and the critical fields delimiting the field induced ordered phase. We show that the upper critical field $h_{c2}$ carries a simp

Thai-Khanh Nguyen, Uyen Vo, Tan M. Nguyen, Thieu N. Vo

Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers have advanced HAR but remain limited by vanishing or exloding gradients, high computational cost, and d

Jason Yik, Walter Gallego Gomez, Andrew Cheng, Benedetto Leto

Neuromorphic accelerators offer promising platforms for machine learning (ML) inference by leveraging event-driven, spatially-expanded architectures that naturally exploit unstructured sparsity through co-located memory and compute. However, their unique architectural characteristics create performance dynamics that differ fundamentally from conventional acc

Wen-Tai Hsu

We study the metastable behavior of diffusion processes in narrow tube domains, where the metastability is induced by entropic barriers. We identify a sequence of characteristic time scales $\{T_\epsilon^i\}_{1 \leq i \leq \abs{V'}}$ and characterize the asymptotic behavior of the diffusion process both at intermediate time scales and at the first critical t

Matheus Kunzler Maldaner, Wesley Hanwen Deng, Jason I. Hong, Kenneth Holstein

While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and utility. A small but growing line of research has explored tools and processes to better engage non-AI expert users in auditing generative AI systems. In this work, we present the design and evaluation of MIR

Maryam Roushan, Narges Rashidi

We study the cosmological consequences of the Extended Uncertainty Principle (EUP) by deriving modified Friedmann equations through thermodynamic arguments. The evolution of the effective equation of state induced by EUP corrections is analyzed and characterized using the Chevallier-Polarski-Linder (CPL) parametrization. We then examine the fulfillment of cl

Tao Feng, Koen Thas

We solve Ealy's conjecture from 1977 by showing that for each odd prime $p$, a finite generalized quadrangle each point of which admits a central symmetry of order $p$, is either a classical symplectic quadrangle in dimension $3$, or a Hermitian quadrangle in dimension $3$ or $4$. As a byproduct, we vastly generalize the aforementioned result by determining

Elana G. Alevy, Samuel D. Crossley, Lam T. Nguyen, Vu D. Phai

Diamonds offer unique benefits for optical technology development due to their optical, chemical, electrical, mechanical, and thermal properties. These attributes also contribute to their aesthetic appeal, high commercial value, and utility in geological studies. Thus, there is high demand for nondestructive techniques that enable rapid analysis of natural a

Kexin Li, Guozhen Ding, Ilya Grishchenko, David Lie

Modern generative diffusion models rely on vast training datasets, often including images with uncertain ownership or usage rights. Radioactive watermarks -- marks that transfer to a model's outputs -- can help detect when such unauthorized data has been used for training. Moreover, aside from being radioactive, an effective watermark for protecting images f

Dilara Erdemir, Najib Mahdou, El Houssaine Oubouhou, Ünsal Tekir

Researchers introduced the notion of j-Artinian rings in [3] and obtained significant results concerning this new class of rings. Motivated by their definition and findings, we extend the study to modules by introducing the concept of j-Artinian modules. Recall from [9] that, if R is a commutative ring with identity, M is an R-module, and j is a submodule of

Zhihao Zhan, Jiaying Zhou, Likui Zhang, Qinhan Lv

Vision-Language-Action (VLA) models offer a unified framework for robotic manipulation by integrating visual perception, language understanding, and control generation. However, existing VLA systems still struggle to generalize across diverse tasks, scenes, and camera viewpoints, and often produce coarse or unstable actions. We argue that these limitations a

Xiaoyue Mi, Wenqing Yu, Jiesong Lian, Shibo Jie

Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges. Existing video reward models rely on vision-language models designed for pixel-space inputs, confining ReFL optimization to near-complete denoising steps after computationally expe

Arturo Perez-Romero, Mica Schwarm, Fabian Heidrich-Meisner

A generic question in the field of ultrafast dynamics is concerned with the relaxation dynamics and the subsequent thermalization of optically excited charge carriers. Among several possible relaxation channels available in a solid-state system, we focus on the coupling to magnetic excitations. In this paper, we study the real-time dynamics of a paradigmatic

Michael Puthawala

We call a family $\{Y_1,\dots,Y_I\}$ in Euclidean space an equidistance spacing if $\|y_i - y_j\| = 1$ whenever $y_i \in Y_i, y_j \in Y_j$ and $i \neq j$. In other words, choosing a representative from each set produces a complete distance graph (i.e. equilateral set). We say such a spacing is maximal if each $Y_i$ is maximal under inclusion. In this work we

Finnian Gray, David Kubiznak, Hryhorii Ovcharenko, Jiri Podolsky

Recently, a remarkable new class of spacetimes describing black holes immersed in a non-aligned electromagnetic field has been found. While still of type D, this class goes beyond the famous Pleba\'nski--Demia\'nski family. Here we demonstrate that the whole class admits a hidden symmetry encoded in the non-degenerate conformal Killing--Yano 2-form. Interest

Martin Rabel, Jakob Runge

Real-world problems, for example in climate applications, often require causal reasoning on spatially gridded time series data or data with comparable structure. While the underlying system is often believed to behave similarly at different Points in space and time, those variations that do exist are relevant twofold: They often encode important information

Nihir Chadderwala

Generative AI agents in life sciences face a critical challenge: determining the optimal approach for diverse queries ranging from simple factoid questions to complex mechanistic reasoning. Traditional methods rely on fixed rules or expensive labeled training data, neither of which adapts to changing conditions or user preferences. We present a novel framewo

K. Białas, J. Spiechowicz

Recently, a paradoxical effect has been demonstrated in which transport of a free Brownian particle driven by active fluctuations in the form of white Poisson shot noise can be significantly enhanced when it is additionally subjected to a periodic potential. This phenomenon can emerge in an overdamped system, but it may also be inertia-induced. Here, we cons

Morteza Sadeghi

The near-field (P2P) operator in the Multilevel Fast Multipole Algorithm (MLFMA) is a performance bottleneck on GPUs due to poor memory locality. This work introduces data redundancy to improve spatial locality by reducing memory access dispersion. For validation of results, we propose an analytical model based on a Locality metric that combines data volume

Husne Ara Rubaiyeat, Hasan Mahmud, Md Kamrul Hasan

Bangla Sign Language Translation (BdSLT) has been severely constrained so far as the language itself is very low resource. Standard sentence level dataset creation for BdSLT is of immense importance for developing AI based assistive tools for deaf and hard of hearing people of Bangla speaking community. In this paper, we present a dataset, IsharaKhobor , and

Francesco Carenini, Matteo Cerruti

The identification of astrophysical sources responsible for high-energy cosmic neutrinos has long been a challenge. A significant milestone was achieved with the blazar TXS 0506+056, which was found to be in a flaring state of high gamma-ray emission and associated at the 3$\sigma$ level with a 290 TeV neutrino detected by IceCube in September 2017. This dis

Jin Pin, Krasowski Hanna, Vanneaux Elena

Obtaining safety guarantees for reinforcement learning is a major challenge to achieve applicability for real-world tasks. Safety shields extend standard reinforcement learning and achieve hard safety guarantees. However, existing safety shields commonly use random sampling of safe actions or a fixed fallback controller, therefore disregarding future perform

Xin Hong, Kaifeng Huang

Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately re

Jasper De Witte, Atefeh Shadmani, Zhe Liu, Andraz Debevc

Next-generation scalable quantum photonic technologies operating at the single photon level rely on bringing together optimized quantum building blocks with minimal optical coupling losses. Achieving this necessitates the heterogeneous integration of different elements onto a single interposer chip. Integrated quantum emitters are key enablers for generating

Marina E. Terzi, Vladislav V. Aleshin, Jules Ghesqui`ere, Vincent Tournat

A particle on a substrate supporting a surface acoustic wave can experience horizontal drift excited by the dry friction force. The effect is referred to as vibrational transportation, or as a surface acoustic wave motor. A traditional theory of vibrational transportation considers a particle as a material point moving on a rigid substrate. A more realistic

Andrei Agrachev, Ivan Beschastnyi, Michele Motta

We study the optimal control problem for a control-affine system, where we want to minimize the $L^1$ norm of the control. First, we show how Pontryagin Maximum Principle (PMP) applies to this problem and we divide the extremal trajectories into two categories: regular and singular extremals. Then, we obtain a strong generalized Legendre-Clebsch condition fo

Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen

A fundamental theoretical question in network analysis is to determine under which conditions community recovery is possible in polynomial time in the Stochastic Block Model (SBM). When the number $K$ of communities remains smaller than $\sqrt{n}$ --where $n$ denotes the number of nodes--, non-trivial community recovery is possible in polynomial time above,

Svetlana Barkanova, Gwen Grinyer, Juliette Mammei, Carolyn Sealfon

We discuss a number of new initiatives and events since 2020 which we hope will contribute to advancement of equity issues within the physics community in Canada. A recent analysis of high-school data shows that men are still over-represented in high-school physics courses, and the fraction has not changed in over a decade. Results from a national survey sho

Pierre Adorni, Minh-Tan Pham, Stéphane May, Sébastien Lefèvre

Recent advances in foundation models have shown great promise in domains such as natural language processing and computer vision, and similar efforts are now emerging in the Earth Observation community. These models aim to generalize across tasks with limited supervision, reducing the need for training separate models for each task. However, current strategi

Alex Bols, Boris Kjær

We classify the irreducible anyon sectors of Levin-Wen models over an arbitrary unitary fusion category $\mathcal{C}$, showing that they are in one-to-one correspondence with equivalence classes of simple objects of the Drinfeld center $Z(\mathcal{C})$. We achieve this by making explicit how the Levin-Wen Hamiltonian stabilizes subspaces isomorphic to state

Amir Amiri, Bastian Diaz Saez, Kilian Möhling

We investigate a low-reheating-temperature freeze-in scenario within a minimal model of fermionic dark matter interacting through a pseudoscalar mediator. In this setup, dark matter is produced via the decay of the pseudoscalar, which remains in thermal equilibrium with the Standard Model bath. We derive the thermalization and non-thermalization conditions f

Yiyang Jiang, Guangwu Qian, Jiaxin Wu, Qi Huang

Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sj\"ogren's syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern comb

Olivier Bilenne, Frédéric Meunier

A company provides a service at different time slots, each slot being endowed with a capacity. A non-atomic population of users is willing to purchase this service. The population is modeled as a continuous measure over the preferred times. Every user looks at the time slot that minimizes the sum of the price assigned by the company to this time slot and the

Gordon Fletcher, Saomai Vu Khan, Aldus Greenhill Fletcher

This paper examines the potential for generative artificial intelligence (GenAI) to assist with internal review processes for research quality evaluations in UK higher education and particularly in preparation for the Research Excellence Framework (REF). Using the lens of function substitution in the Viable Systems Model, we present an experimental methodolo

Lina Conti, Dennis Fucci, Marco Gaido, Matteo Negri

Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bias concerns. For example, in speech translation (ST), when translating from languages with notional gender, such as English, into languages where gender-ambiguous terms referring to the speaker are assigned gramm

Linbo Wang, Thomas Richardson, James Robins

Causal inference is a central goal across many scientific disciplines. Over the past several decades, three major frameworks have emerged to formalize causal questions and guide their analysis: the potential outcomes framework, structural equation models, and directed acyclic graphs. Although these frameworks differ in language, assumptions, and philosophica

Hiroshi Haramoto, Kosuke Suzuki

Massively parallel molecular simulations require pseudorandom number streams that are provably non-overlapping and reproducible across thousands of compute units in parallel computing environments. In the widely used LAMMPS package, the standard RANMAR generator lacks a mathematically exact mechanism to jump ahead; distinct seeds are typically assigned inste

Hui Gong, Akash Sedai, Francesca Medda

Classical correlation and rolling PCA summarize market dependence through covariance spectra, but they do not provide a unified operator representation for entropy, purity-based mixing, and standardized structural deviations built from rolling multi-feature trajectories. We propose the Quantum Network of Assets (QNA), a quantum-inspired but non-physical dens

Matīss Kalnāre, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein

Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes understanding their internal decision-making challenging. Existing explainability methods often focus on input-output attributions, leaving the internal mechanisms largely opaque. This paper addresses

Sara L. Ellison, Leonardo Ferreira

Galaxy mergers are known to trigger bursts of central star formation, which should therefore lead to stellar mass growth in their inner regions. However, observational measurements of this `burst mass fraction' are scant. Here, we assemble a large sample of ~14,000 post-coalescence galaxies that have recently completed their merger-induced star formation, an

Han He, Ali Luo, Haotong Zhang, Song Wang

The variability of H$\alpha$ chromospheric activity of solar-like stars is investigated by using the time-domain data of LAMOST Medium-Resolution Spectroscopic Survey (MRS). We use $R_\mathrm{H\alpha}$ index (ratio of H$\alpha$ luminosity to bolometric luminosity) to measure the H$\alpha$ activity intensity of a spectrum, and utilize the median of the $R_\ma

Ke Zhang, Xiaoning Zhao, Ce Zheng, Jiahong Ning

This study proposes Tool-RoCo, a novel benchmark for evaluating large language models (LLMs) in long-term multi-agent cooperation based on RoCo, a multi-robot cooperative benchmark. Recent research on LLM-based multi-agent systems has relied on predefined orchestration, while ignoring agent autonomy. Tool-RoCo treats other agents as tools and introduces coop

Dirk Beyer, Gidon Ernst, Martin Jonáš, Marian Lingsch-Rosenfeld

In the past two decades, significant research and development effort went into the development of verification tools for individual languages, such asC, C++, and Java. Many of the used verification approaches are in fact language-agnostic and it would be beneficial for the technology transfer to allow for using the implementations also for other programming

Da-Wu Xiao, Chong Chen

The dissipative quantum Rabi model exhibits rich non-equilibrium physics, including a dissipative phase transition from the normal phase to the superradiant phase. In this work, we investigate the stability of the superradiant phase in the presence of a weak spin relaxation. We find that even a weak spin relaxation can render the superradiant phase a superra

Lorenzo Pellegrini, Serafino Pandolfini, Davide Maltoni, Matteo Ferrara

The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and optimization techniques. These factors make it difficult to fairly compare detectors and to understand which factors truly contribute to their performance. To address this, we syst

Marc Kegel, Misha Schmalian

We show that there is no analogue of characterising slopes for multi-component links. Concretely, we show that for any ordered link L in S3 with n>1 components and any rational slopes r_1, ..., r_n, there are infinitely many links L_i with non-homeomorphic complements such that the Dehn fillings L(r_1, ..., r_n) and L_i(r_1, ..., r_n) are homeomorphic.

Sarah J. James, Marcus A. Rodriguez, David P. Miller

Over the last four decades, the way knowledge is created in academia has transformed dramatically: research teams have grown larger, scholars draw from ever-wider pools of prior work, and the most influential discoveries increasingly emerge from complex collaborative efforts. Using a massive dataset of over 15 million publications spanning 1970-2010 and cove

Florin Manea

We compute the first-order correction in the quartic coupling $\alpha$ to the entanglement entropy of a massive, non-minimally coupled scalar across the horizon of a four-dimensional Schwarzschild black hole, treating the non-minimal coupling $\xi$ as a free parameter. Combining the replica trick on the conical manifold $\mathcal{M}_n$ with heat-kernel metho

Shizhe Sun, Wataru Ohyama

We propose Cross-Attention-based Non-local Knowledge Distillation (CanKD), a novel feature-based knowledge distillation framework that leverages cross-attention mechanisms to enhance the knowledge transfer process. Unlike traditional self-attention-based distillation methods that align teacher and student feature maps independently, CanKD enables each pixel

D. L. Moutard, L. R. Corrales, R. Tomaru, C. Done

Low mass X-ray binaries (LMXBs) occasionally show signs of outflowing material from the accretion disk. Studying these outflows can inform the understanding of the geometry of the systems, as well as the dynamics and energetics of accretion. One key variable for determining the location of these disk winds is the density of the outflowing material. In this p

Qian Hong, Cheng Bian, Xiao Zhou, Xiaoyu Li

Translating non-invasive signals such as photoplethysmography (PPG) and ballistocardiography (BCG) into clinically meaningful signals like arterial blood pressure (ABP) is vital for continuous, low-cost healthcare monitoring. However, temporal misalignment in multimodal signal transformation impairs transformation accuracy, especially in capturing critical f

Wenkang Xin, Andrew Mummery

The tidal forces experienced on an orbit contain, in principle, information about the underlying spacetime an object is moving through. Astronomical observations often probe the properties of tidal forces in the relativistic regime, and could thus in principle be leveraged to examine the properties of strong-field gravity, provided that a general procedure f

Anna Mazzucato, Anping Pan

In this manuscript, we extend Constantin-Iyer's Lagrangian formulation of Navier-Stokes Equation to a wider class of hydrodynamic models. Moreover, we prove that such Lagrangian formulation is naturally derived from a stochastic Hamilton-Pontryagin type variational principle. Generalized version of Kelvin circulation theorem in viscous fluids is also discuss

Andrew Golightly, Sarah E. Heaps, Chris Sherlock, Laura E. Wadkin

The ensemble Kalman filter (EnKF) is a popular technique for performing inference in state-space models (SSMs), particularly when the dynamic process is high-dimensional. Unlike reweighting methods such as sequential Monte Carlo (SMC, i.e. particle filters), the EnKF leverages either the linear Gaussian structure of the SSM or an approximation thereof, to ma

Masahisa Ebina, Ivan Nourdin, Giovanni Peccati

This paper investigates a local central limit theorem for a normalized sequence of random variables belonging to a fixed order Wiener chaos and converging to the standard normal distribution. We prove, without imposing any additional conditions, that the optimal rate of convergence of their density functions to the standard normal density in the Sobolev spac

Philipp Stark, Alexandros Sopasakis, Ola Hall, Markus Grillitsch

We transform large-scale Swedish register data into textual life trajectories to address two long-standing challenges in data analysis: high cardinality of categorical variables and inconsistencies in coding schemes over time. Leveraging this uniquely comprehensive population register, we convert register data from 6.9 million individuals (2001-2013) into se

Ziying Xiang, Jun-Wei Luo, Shu-Shen Li

We present a unified bond dipole theory for metal-semiconductor interfaces to explain the microscopic origin of interface dipoles and Fermi level pinning (FLP) in terms of Harrison's bond-orbital model. By combining first-principles calculations with tight-binding analysis, we show that localized bonding between semiconductor surface dangling bonds and metal

Xuhang Jiang

The Fourier transform of two-point momentum-space Feynman integrals with massless propagators and two off-shell legs can be used to prove identities between their periods, exemplified by the glue-and-cut identity. We generalize this framework to massless momentum-space Feynman integrals with three off-shell legs and obtain a similar family of identities that

Yun Liu, Chen Cui, Shi Shu, Zhen Wang

The numerical solution of partial differential equations (PDEs) is fundamental to scientific and engineering computing. In the presence of strong anisotropy, material heterogeneity, and complex geometries, however, classical iterative solvers often suffer from reduced efficiency and require substantial problem-dependent tuning. The Fourier neural solver (FNS

Taehoon Kim, Donghwan Jang, Bohyung Han

We present a novel training approach, named Merge-and-Bound (M&B) for Class Incremental Learning (CIL), which directly manipulates model weights in the parameter space for optimization. Our algorithm involves two types of weight merging: inter-task weight merging and intra-task weight merging. Inter-task weight merging unifies previous models by averaging th

Pierluigi Colli, Elisabetta Rocca, Jürgen Sprekels

In this paper, we study a phase field model for a tumor growth model of Cahn--Hilliard type in which the often assumed parabolic relaxation of the chemical potential is replaced by a hyperbolic one. We show that the resulting initial-boundary value problem is well posed and that its solutions depend continuously on two given functions: one appearing in the m

Mircea Bejan, Pieter W. Claeys, Jiangtian Yao

Nonstabilizerness, or quantum magic resource, presents a valuable resource in quantum error correction and computation. We study the dynamics of locally injected nonstabilizerness in unitary Clifford circuits, where the total nonstabilizerness is conserved. However, the absence of physical observables quantifying nonstabilizerness precludes a direct microsco

Biao Zhang

Studies of strong interactions between hadrons provide a valuable opportunity to test Quantum Chromodynamics at nucleon-scale distances. The femtoscopy technique has proven to be an effective tool for studying interactions between unstable hadrons by measuring the correlation function of hadron pairs in momentum space. While several measurements of the stron

M. P. Kozlovskii, O. A. Dobush, R. V. Romanik, I. V. Pylyuk

Thermodynamic response functions, including the isothermal compressibility, the thermal pressure coefficient, and the thermal expansion coefficient, isochoric and isobaric heat capacities are explicitly derived for a many-particle system interacting through a Curie-Weiss-type potential. These calculations are based on an exact equation of state previously ob

Manuel Terradillos Perea, Olga Alonso Gonzalez, Cristina Soguero Ruiz, David Gutierrez

The equinus foot is a neuromuscular condition that affects ankle dorsiflexion, impairing gait and reducing quality of life. This study presents EquiSay, a dynamic anti-equinus orthosis equipped with an anterior elastic tension system and an electromyography (EMG) sensor to quantify muscle activation, particularly of the tibialis anterior. EquiSay provides dy

Xiang-Qian Li, Hao-Peng Yan, Xiao-Jun Yue

The direct imaging of black holes by the Event Horizon Telescope (EHT) enables strong-field tests of gravity. We study the cosmological evolution and the black-hole shadow radius in 4D Einstein-Gauss-Bonnet (EGB) gravity coupled to Kaniadakis holographic dark energy (KHDE), adopting the future event horizon as the infrared cutoff. Using Cosmic Chronometers,

Hao Yang, Angela Yao, Eric Chang, Hexiang Wang

Demographic inference plays a crucial role in understanding the representativeness and equity of social media-based research. However, existing methods typically rely on a single modality, such as text, image, or network, and are limited to predicting one or two demographic attributes, constraining their generalizability and robustness across populations. Th

Kai-Lei Wang, Ya-Mei Cao, Hui-Xiao Duan, Xian-Hui Zhong

In this work, to establish a more abundant $\Lambda_c$ baryon spectrum, we discuss the production potentials of the excited $\Lambda_c$ baryons through $\Lambda_b$ hadronic weak decays within a constituent quark model. Based on our successful explanations of the existing experimental data for the $\Lambda_b \to \Lambda_c(\pi^-, K^{-}, D^{-}, D^{-}_s, D_s^{*-

Lorenzo Carlucci, Giordano Celli

We study the relations under Weihrauch reducibility of the well-ordering preservation principle for the operator $X \mapsto X^\omega$ and the Ordered Ramsey Theorem. Both principles are known to be equivalent to $\Sigma^0_2$-induction in Reverse Mathematics. We show that the Ordered Ramsey Theorem is Weihrauch-equivalent to the parallel product of the well-o

William Da Silva, Xingjian Hu, Ellen Powell, Mo Dick Wong

We establish the first scaling limit for FK($q$)-weighted planar maps in the critical case $q=4$, resolving a problem that has remained open since Sheffield's seminal work arXiv:1108.2241. In that work, Sheffield proved a scaling limit for $q<4$ via the celebrated hamburger-cheeseburger bijection, which initiated the peanosphere (mating-of-trees) approach to

Weixiang Yu, John J. Ruan, Colin J. Burke, Roberto J. Assef

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor tens of millions of active galactic nuclei (AGNs) for a period of 10 years with an average cadence of 3 days in six broad photometric bands. This unprecedented dataset will enable robust characterizations of AGN UV/optical variability across a wide range of AGN physical prope

Alexis Metz-Donnadieu

We study a broad class of random labelled trees in which integer-valued labels evolve along the edges according to increments in $\{-1, 0, 1\}$. These models include e.g. branching random walks, embedded complete and incomplete binary trees, random Cayley and plane trees with uniform displacements along edges. Motivated by recent work suggesting a Markovian

Dong-Jae Lee, Jiwan Hur, Jaehyun Choi, Jaemyung Yu

Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a significant challenge. To address this, token reduction methods have been widely explored. However, existing approaches often overlook the frequency characteristics of self-attentio

Filippo Stocco, Michele Garibbo, Noelia Ferruz

Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such approaches given their abundant data and the versatility of their representations, ranging from sequences to structures and functions. This vers

Shuai Zhang, Bao Tang, Siyuan Yu, Yueting Zhu

Recently, video generation has witnessed rapid advancements, drawing increasing attention to image-to-video (I2V) synthesis on mobile devices. However, the substantial computational complexity and slow generation speed of diffusion models pose significant challenges for real-time, high-resolution video generation on resource-constrained mobile devices. In th

Fabian Paischer, Leo Cotteleer, Yann Dreze, Richard Kurle

The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such

Yurui Zheng, Yijun Chen, Shaohong Zhang

Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism t

Marco Musacchio, Markus Felber, Matteo Paoluzzi, Andrea Gnoli

We investigate magnetic active matter in confined geometries using both experiments with magnetic toy robots Hexbugs and simulations of elongated magnetic active Brownian particles in circular domains. Standard active particles tend to accumulate at boundaries, forming clusters even at relatively low densities. In the presence of magnetic interactions, we pr

Peiran Xu, Sudong Wang, Yao Zhu, Jianing Li

Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language models (MLLMs) have made significant strides, existing benchmarks often oversimplify spatial cognition, reducing it to a single-dimensional metric, which fails to capture the hierarchic

Stephen Brittain, Robert S. Coulter, Alice Man Wa Hui

Hughes introduced the projective planes that bear his name in 1957 and they have since been studied extensively. However, until now, no polynomial representation of a planar ternary ring that represents them has been determined. In this paper, we rectify this omission by determining a reduced PTR polynomial for any Hughes plane defined over a regular nearfie

Xinye Xiao, Haomin Huang

This paper develops a comprehensive Hamilton-Jacobi framework to analyze asymptotic propagation dynamics in a field-road system featuring unidirectional advection and Wentzell-type boundary conditions. We rigorously derive a Hamilton-Jacobi variational inequality as the singular limit of a reaction-diffusion system in the upper half-plane, where the road is

Marc Alemany-Gotor, Cristian Viglione, Pablo Fosalba, Isaac Tutusaus

Stage-IV surveys will enable unprecedented tests of gravity on cosmological scales. However, assuming General Relativity in the analysis of large-scale structure could introduce systematic biases if gravity deviates from GR at these scales. Modified gravity theories, such as the Hu-Sawicki formulation of $f(R)$ gravity, offer an alternative explanation for c

Tomas Dohnal, Maximilian Hanisch, Runan He

We study the existence of polychromatic solutions of cubically nonlinear Maxwell equations in the whole space and with dispersive media, i.e., with a time delayed polarization. Due to the complex nature of the dielectric function, the frequencies are complex, resulting in a decay in time. The geometry is that of a waveguide in $x$ with the propagation direct

Muhammad Sukri Bin Ramli

New methods are needed to monitor environmental treaties, like the Montreal Protocol, by reviewing large, complex customs datasets. This paper introduces a framework using unsupervised machine learning to systematically detect suspicious trade patterns and highlight activities for review. Our methodology, applied to 100,000 trade records, combines several ML

Enes Bektas, Fazli Can

Ensemble learning improves classification performance by combining multiple base classifiers. While increasing the number of classifiers generally enhances accuracy, excessively large ensembles can lead to computational inefficiency and diminishing returns. This paper investigates the relationship between ensemble size and performance through the lens of lin

Mfuphi Ntshatsha, Markus Böttcher, Soebur Razzaque

Among active galactic nuclei (AGNi), blazars are the brightest emitters of high-energy (HE, $E \geq 100$ MeV) to very-high-energy (VHE, $E \geq 100$ GeV) $\gamma$-rays from their jets. Radio galaxies, being the misaligned parent population of the blazar class, were historically not detected at these frequencies. However, advances in experiments and observato

Jonas Elmiger, Fabian Stuber, Oscar Castañeda, Gian Marti

We present the first single-input multiple-output (SIMO) receiver ASIC that jointly performs jammer mitigation, channel estimation, and data detection. The ASIC implements a recent algorithm called siMultaneous mitigAtion, Estimation, and Detection (MAED). MAED mitigates smart jammers via spatial filtering using a nonlinear optimization problem that unifies

Junjian Wang, Lidan Zhao, Xi Sheryl Zhang

Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where dangerous instructions pose significant risks. Existing methods often suffer from either high computational costs due to preference alignment training or over-rejection when using single-agent safety prompts. To add

Lorenzo De Rebotti, Emanuele Giacomini, Giorgio Grisetti, Luca Di Giammarino

Efficient and scalable 3D surface reconstruction from range data remains a core challenge in computer graphics and vision, particularly in real-time and resource-constrained scenarios. Traditional volumetric methods based on fixed-resolution voxel grids or hierarchical structures like octrees often suffer from memory inefficiency, computational overhead, and

Guilherme J. Inacio, Juan José Esteve-Paredes, Maurício F. C. Martins Quintela, Wendel S. Paz

We develop a practical approach to electrically tuning the nonlinear photoresponse of two-dimensional semiconductors by explicitly incorporating a static out-of-plane electric field into the electronic ground state prior to optical excitation, as a gate bias. The method is implemented by dressing a Wannier-interpolated Hamiltonian with the field through its

Victor de Vries

In this document we let $U$ be a smooth variety of pure dimension $d$ over a local field $k_v$ with unit ball $\mathcal{O}_v$ and residue field $\mathbb{F}$ of characteristic $p>0$ and we set $n$ to be a positive integer such that $p\nmid n$. For various $u\in U(k_v)$ we study the evaluation map $u^*:\mathrm{H}^2(U,\mu_n)\to \mathrm{H}^2(k_v,\mu_n)$. We supp

Md. Raihan Tapader, Md. Mostafizer Rahman, Ariful Islam Shiplu, Md Faizul Ibne Amin

In today's world, the focus of programmers has shifted from writing complex, error-prone code to prioritizing simple, clear, efficient, and sustainable code that makes programs easier to understand. Code refactoring plays a critical role in this transition by improving structural organization and optimizing performance. However, existing refactoring methods

Sagnik Ghosh, Sandip Chakraborty

Conventional methods for water pollutant detection, such as chemical assays and optical spectroscopy, are often invasive, expensive, and unsuitable for real-time, portable monitoring. In this paper, we introduce VibraWave, a novel non-invasive sensing framework that combines mmWave radar with controlled acoustic excitation, tensor decomposition, and deep lea

Marcus Michelen, Oren Yakir

Let $f_n$ be a random polynomial of degree $n$ with i.i.d. mean-zero and finite variance random coefficients. It is well known that the roots of $f_n$ cluster uniformly around the unit circle as $n$ grows large. We give a simple and self-contained proof of local universality for the correlation functions of the roots at the microscopic scale $1/n$ around a f

Rahul Arora, Liton Majumdar

Modeling the atmospheres of exoplanets is fundamental to understanding their atmospheric physics and chemical processes. While one-dimensional (1D) atmospheric models with 1D radiative transfer (RT) have been widely used, advances in three-dimensional (3D) general circulation models (GCMs) and 3D RT methods now allow quantitative comparisons of these approac