November 2025 arXiv papers — page 161

Yuxuan Zhou, Tao Yu, Wen Huang, Yuheng Zhang

The generalization capability of deepfake detectors is critical for real-world use. Data augmentation via synthetic fake face generation effectively enhances generalization, yet current SoTA methods rely on fixed strategies-raising a key question: Is a single static augmentation sufficient, or does the diversity of forgery features demand dynamic approaches?

Hui Lu, Yi Yu, Song Xia, Yiming Yang

Large-scale Video Foundation Models (VFMs) has significantly advanced various video-related tasks, either through task-specific models or Multi-modal Large Language Models (MLLMs). However, the open accessibility of VFMs also introduces critical security risks, as adversaries can exploit full knowledge of the VFMs to launch potent attacks. This paper investi

Hamed Arianfard, Tim Weiss, Yang Yang, Joshua Bader

We demonstrate Fano-like resonances in silicon-on-insulator (SOI) nanowire-based coupled Sagnac interferometers (SIs) formed by a self-coupled waveguide. By adjusting the reflectivity of the two SIs and coupling strength between them, we tailor coherent mode interference to achieve high-performance optical analogues of Fano resonance. The device is theoretic

Simone Bendazzoli, Antonios Tzortzakakis, Andreas Abrahamsson, Björn Engelbrekt Wahlin

Early cancer detection is crucial for improving patient outcomes, and 18F FDG PET/CT imaging plays a vital role by combining metabolic and anatomical information. Accurate lesion detection remains challenging due to the need to identify multiple lesions of varying sizes. In this study, we investigate the effect of adding anatomy prior information to deep lea

John Armstrong, Cristin Buescu, James Dalby, Rohan Hobbs

We use a neural network to identify the optimal solutions to a family of pension investment problems, where the parameters determining an investor's risk and consumption preferences are given as inputs to the neural network in addition to economic variables. Training a single network across such a family fails without modification. Our main contribution

Mihael Arcan, David-Paul Niland

Mental health disorders affect over one-fifth of adults globally, yet detecting such conditions from text remains challenging due to the subtle and varied nature of symptom expression. This study evaluates multiple approaches for mental health detection, comparing Large Language Models (LLMs) such as Llama and GPT with classical machine learning and transfor

Songmei Qin, Jing Zhong, Friedrich Anders, Lola Balaguer-Núñez

The high-precision {\it Gaia} data release 3 (DR3) enables the discovery of numerous open clusters in the Milky Way, providing an excellent opportunity to search for blue straggler stars in open clusters and investigate their formation and evolution in these environments. Using the member stars from literature open cluster catalogs, we visually inspected the

Arjo Dasgupta, Mateusz Łącki, Henning Korbmacher, Gustavo A. Domínguez-Castro

Dipoles in triangular optical ladders constitute a flexible platform for the study of the interplay between geometric frustration and long-range anisotropic interactions, and in particular for the observation of the spontaneous onset of chirality. Frustration magnifies the effect of the dipolar interactions in itinerant polarized dipolar bosons. As a result,

Ignacio Trujillo, Sergio Guerra Arencibia, Ignacio Ruiz Cejudo, Mireia Montes

We present deep optical imaging of the extremely isolated dwarf galaxy NGC 6789, obtained with the new 2-meter Two-meter Twin Telescope (TTT3) at Teide Observatory. Despite its location in the Local Void, NGC 6789 exhibits surprising recent central star formation equivalent to approximately 4% of its total stellar mass. The origin of the gas necessary for th

Yuanmin Huang, Wenxuan Li, Mi Zhang, Xiaohan Zhang

Deep neural networks have recently achieved notable progress in 3D point cloud recognition, yet their vulnerability to adversarial perturbations poses critical security challenges in practical deployments. Conventional defense mechanisms struggle to address the evolving landscape of multifaceted attack patterns. Through systematic analysis of existing defens

Zhenggang He, Longfu Shi, Shuangyan Li

Let $\mathcal{C}=(\mathcal{C},\mathbb{E},\mathfrak{s})$ be an extriangulated category with a proper class $\xi$ of $\mathbb{E}$-triangles. In this paper, we introduce and study quasi-resolving subcategories in $\mathcal{C}$. More precisely, we first introduce the notion of $\mathcal{X}$-resolution dimensions for a quasi-resolving subcategory $\mathcal{X}$ of

Kizito Salako, Rabiu Tsoho Muhammad

When using Bayesian inference to support conservative software reliability assessments, it is useful to consider a collection of Bayesian inference problems, with the aim of determining the worst-case value (from this collection) for a posterior predictive probability that characterizes how reliable the software is. Using a Bernoulli process to model the occ

Mohammad Mehdi Sadeghi

A novel idea, Quantum Thermodynamic Transformation Optics (QTTO), is introduced in this article. This theoretical framework integrates the geometric formalism of transformation optics with the thermodynamic principles found in quantum dissipative systems. This concept goes beyond traditional coordinate transformations by affecting the distribution of quantum

M. Leitzinger, P. Odert, R. Greimel, P. Kabáth

Flares and CMEs are known to be the dominating high-energy phenomena on cool stars. Superflares were thoroughly investigated using broadband photometry predominantly from Kepler, K2, and TESS. Here we present a spectroscopic investigation of superflares on the very active spectroscopic binary CC~Eri. We focus on spectroscopic signatures of (super)-flares and

Keyao Zhang, Yiquan Chen, Zhuo Hu, Wenhai Lin

The accuracy of large language models (LLMs) improves with increasing model size, but increasing model complexity also poses significant challenges to training stability. Periodic checkpointing is a key mechanism for fault recovery and is widely used in LLM training. However, traditional checkpointing strategies often pause or delay GPU computation during ch

Tobias Eckert, Daniel de las Heras, Enrique Velasco, Yuri Martínez-Ratón

We analyze the sedimentation behavior of a polydisperse two-dimensional liquid-crystal fluid using a local density functional theory based on scaled particle theory. Polydispersity is incorporated through variations in the roundness of hard rectangular particles interacting solely via excluded area effects. Despite its simplicity, the model displays a rich p

Yuanheng Li, Zhuoyang Chen, Xiaoyun Liu, Yuhao Wang

As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training data have grown, especially in the context of code. Open-source code, often protected by open source licenses (e.g, GPL), poses legal and ethical challenges when used in pretraining. Detecting whether specific

Yixuan Zhang, Jiabin Luo, Zhenggang Wang, Feng Zhou

Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the model level, they often suffer from high computational costs, limited scalability, and poor generalization. To address these challenges, we propose a Bayesian data selection framewor

Vivek Gusain, Mohd Zeeshan, B. K. Mani

Slack's phonon-glass and electron-crystal concept has been the guiding paradigm for designing new thermoelectric materials. Zintl phases, in principle, have been shown as great contenders of the concept and thereby good thermoelectric candidates. With this as motivation, we design new Zintl phases SrBaX (X = Si, Ge, Sn) using state-of-the-art computational m

Dan Goreac, Juan Li, Pangbo Wang

This paper has a double aim. One the one hand, we introduce a uni-nodal network model for cyber risks with firewalled edges and SIR intra-edge spreading. In connection to this, we formulate an insurance problem in which one seeks the running maximal reputation index against all control strategies of the companies represented by edges. On the other hand, we s

Liang Zhou, Qiming Wang, Tianze Chen

3D point cloud classification is a fundamental task in safety-critical applications such as autonomous driving, robotics, and augmented reality. However, recent studies reveal that point cloud classifiers are vulnerable to structured adversarial perturbations and geometric corruptions, posing risks to their deployment in safety-critical scenarios. Existing c

Huayang Xu, Huanhuan Yuan, Guanfeng Liu, Junhua Fang

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users' historical interaction data. Given that users' complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. Howe

Barry Smyth, Padraig Cunningham

Review papers have traditionally enjoyed a high status in academic publishing because of the important role they can play in summarising and synthesising a field of research. They can also attract significantly more citations than primary research papers presenting original research, making them attractive to authors. There has been a dramatic increase in th

Oluwayomi Akinfenwa, Niamh Cahill, Catherine Hurley

The World Development Indicators (WDI) database provides a wide range of global development data, maintained and published by the World Bank. Our \textit{wdiexplorer} package offers a comprehensive workflow that sources WDI data via the \textit{WDI} R package, prepares and explores country-level panel data of the WDI through computational functions to calcul

Mikhail Krasnov, Ljupcho Milosheski, Mihael Mohorčič, Carolina Fortuna

The proliferation of wireless devices necessitates more robust and reliable emitter detection and identification for critical tasks such as spectrum management and network security. Existing studies exploring methods for unknown emitters identification, however, are typically hindered by their dependence on labeled or proprietary datasets, unrealistic assump

Yauhen Babakhin, Radek Osmulski, Ronay Ak, Gabriel Moreira

We introduce llama-embed-nemotron-8b, an open-weights text embedding model that achieves state-of-the-art performance on the Multilingual Massive Text Embedding Benchmark (MMTEB) leaderboard as of October 21, 2025. While recent models show strong performance, their training data or methodologies are often not fully disclosed. We aim to address this by develo

João N. C. Especial, Beatriz P. Teixeira, Ana Nunes, Miguel Machuqueiro

For several decades, experimental and computational studies have been used to investigate the potential functional role of knots in protein structures. A property that has attracted considerable attention is thermal stability, i.e., the extent to which a protein retains its native conformation and biological activity at high temperatures, without undergoing

Junjun Pan, Yixin Liu, Chuan Zhou, Fei Xiong

Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume identical training and testing distributions, which is rarely valid in practice. In real-world scenarios, unseen but normal samples may emerge during deployment, leading to a normalit

Hadi Hosseini, Sanjukta Roy, Aditi Sethia

House Allocations concern with matchings involving one-sided preferences, where houses serve as a proxy encoding valuable indivisible resources (e.g. organs, course seats, subsidized public housing units) to be allocated among the agents. Every agent must receive exactly one resource. We study algorithmic approaches towards ensuring fairness in such settings

Yuri Yu. Tarasevich, Andrei V. Eserkepov, Irina V. Vodolazskaya

Using the mean-field approximation, a formula for the effective electrical conductivity of a two-dimensional system of randomly arranged conducting sticks with a given orientation distribution was obtained. Both the resistance of the sticks themselves and the resistance of the contacts between them were taken into account. The accuracy in the resulting formu

Dean Crnković, Ronan Egan, Andrea Švob

The concept of switching has arisen in several different areas within combinatorics. The act of switching usually transforms a combinatorial object into a non-isomorphic object of the same type, in a way that some key property is preserved. Godsil-McKay switching of graphs preserves the spectrum, switching of designs preserves their parameters, and switching

Peter Wriggers

The third medium contact approach has been successfully employed in structural applications and extended to various optimization problems. This discretization technique replaces classical contact formulations and algorithms by introducing a compliant interfacial layer - referred to as the third medium - between the contacting bodies. Unlike traditional conta

Sean Eberhard, Elena Maini

Building on work of Wilson, we show that if $G$ is a finitely generated residually soluble group whose growth function $\gamma$ satisfies $(\log \gamma(n))/ n^{1/4} \to 0$ as $n \to \infty$ then $G$ is virtually nilpotent. This shows that Grigorchuk's Gap Conjecture holds for all exponents $\beta < 1/4$ within the class of residually soluble groups (improvin

Ruida Hu, Xinchen Wang, Xin-Cheng Wen, Zhao Zhang

Code review is a cornerstone of software quality assurance, and recent advances in Large Language Models (LLMs) have shown promise in its automation. However, existing benchmarks for LLM-based code review face three major limitations. Lack of semantic context: most benchmarks provide only code diffs without textual information such as issue descriptions, whi

Mugdha Mahesh Pokharanakar

The higher-order Cheeger inequalities were established for graphs by Lee, Oveis Gharan and Trevisan. We prove analogous inequalities for graphons in this article.

So-Yoon Cho, Jin-Young Kim, Kayoung Ban, Hyeng Keun Koo

Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierar

Louis Pagot, Sébastien Merlet, Leonid A Sidorenkov, Franck Pereira dos Santos

One of the main residual limitations of inertial sensors based on atom interferometry stems from laser beam distortions, which cause parasitic phase shifts and non-homogeneous matter-light couplings. Here we present numerical simulations, accompanied by analytical calculations, which quantify the impact of these effects in a cold atom gradiometer. We demonst

Jan Rozman, Sumesh P. Thampi, Julia M. Yeomans

Active nematic models explain the topological defects and flow patterns observed in epithelial tissues, but the nature of active stress-whether it is extensile or contractile, a key parameter of the theory-is not well established experimentally. Individual cells are contractile, yet tissue-level behavior often resembles extensile nematics. To address this di

Anastasiia Tokareva, Judith Dineley, Zoe Firth, Pauline Conde

Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date has largely used non-clinical, cross-sectional written language and complex machine learning (ML) approaches with limited interpretability. Methods: We used linear mixed-effect models to identify interpretable le

Siddharth Betala, Kushan Raj, Vipul Betala, Rohan Saswade

In this paper, we describe our system under the team name BLEU Monday for the English-to-Indic Multimodal Translation Task at WAT 2025. We participate in the text-only translation tasks for English-Hindi, English-Bengali, English-Malayalam, and English-Odia language pairs. We present a two-stage approach that addresses quality issues in the training data thr

Jack Richings, Margaux Leblanc, Ian Groves, Victoria Nockles

The continually advancing quality of deepfake technology exacerbates the threats of disinformation, fraud, and harassment by making maliciously-generated synthetic content increasingly difficult to distinguish from reality. We introduce a simple yet effective two-stage detection method that achieves an AUROC of over 99.8% on contemporary deepfakes. However,

Stefan Michel

The Strip Packing Problem is a classical optimization problem in which a given set of rectangles must be packed, without overlap, into a strip of fixed width and infinite height, while minimizing the total height of the packing. A straightforward and widely studied approach to this problem is the Bottom-Left Heuristic. It consists of iteratively placing each

Duc Nguyen, Yan-Ling Lai, Qilin Zhang, Prabin Gyawali

3D semantic scene understanding remains a long-standing challenge in the 3D computer vision community. One of the key issues pertains to limited real-world annotated data to facilitate generalizable models. The common practice to tackle this issue is to simulate new data. Although synthetic datasets offer scalability and perfect labels, their designer-crafte

Bowei He, Bowen Gao, Yankai Chen, Yanyan Lan

Virtual screening (VS) is an essential task in drug discovery, focusing on the identification of small-molecule ligands that bind to specific protein pockets. Existing deep learning methods, from early regression models to recent contrastive learning approaches, primarily rely on structural data while overlooking protein sequences, which are more accessible

Ambashri Purkayastha, Camille Delezoide, Vinod Bajaj, Mounia Lourdiane

We propose a physics-based digital twin to predict the statistical QoT distribution of a realistic optical lightpath. We demonstrate up to 0.73 dB accuracy improvement in worst-case SNR prediction for short distance transmissions in linear regime.

Christofer Meinecke, Estelle Guéville, David Joseph Wrisley

We aim to theorize the medieval manuscript page and its contents more holistically, using state-of-the-art techniques to segment and describe the entire manuscript folio, for the purpose of creating richer training data for computer vision techniques, namely instance segmentation, and multimodal models for medieval-specific visual content.

Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, Murun Yang

Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging. In this paper, we identify a failure mode of multilingual supervised fine-tuning (SFT) on multi-way parallel data: when such data are reused symmetrically around a pivot la

Zhenliang Zhang, Xinyu Hu, Xiaojun Wan

Large language models sometimes inadvertently reproduce passages that are copyrighted, exposing downstream applications to legal risk. Most existing studies for inference-time defences focus on surface-level token matching and rely on external blocklists or filters, which add deployment complexity and may overlook semantically paraphrased leakage. In this wo

Sayantan Ghosh, Sugata Paul, Tamoghna Chattoraj, Ritesh Kumar

Comprehensive study using DC transport, specific heat, magnetization, and two-coil mutual inductance measurements unveils an understanding of three temperature regimes in SmB$_6$: (i) $T \geq T^{*}$ ($\sim66$K), (ii) $T_g$ ($\sim40$ K) $\leq T < T^{*}$, and (iii) $T < T_g$. Onset of Kondo breakdown below $T^{*}$ releases disorder-driven magnetic fluctuations

Jessica Renz, Frederik Witt, Iain G. Johnston

We present an algebraic approach to evolutionary accumulation modelling (EvAM). EvAM is concerned with learning and predicting the order in which evolutionary features accumulate over time. Our approach is complementary to the more common optimisation-based inference methods used in this field. Namely, we first use the natural underlying polynomial structure

Javier Castillo-Martínez, Raul Baños, Francisco G. Montoya

Classical phasor analysis is fundamentally limited to sinusoidal single-frequency conditions, which poses challenges when working in the presence of harmonics. Furthermore, the conventional solution, which consists of decomposing signals using Fourier series and applying superposition, is a fragmented process that does not provide a unified solution in the f

Yuma Murata, Rina Tazai, Youichi Yamakawa, Seiichiro Onari

The interplay between unconventional density waves and exotic superconductivity has attracted growing interest. Kagome superconductors $A\rm{V}_3\rm{Sb}_5$ ($A = \rm{K}, \rm{Rb}, \rm{Cs}$) offer a platform for studying quantum phase transitions and the resulting symmetry breaking. Among these quantum phases, the $4a_0$ stripe charge-density-wave (CDW) has be

Emil Björnson, Murat Babek Salman

This paper presents the first experimental validation of reflective near-field beamfocusing using a reconfigurable intelligent surface (RIS). While beamfocusing has been theoretically established as a key feature of large-aperture RISs, its practical realization has remained unexplored. We derive new analytical expressions for the array gain achieved with a

Xuan Liu, Sebastien Ourselin, Tianrui Zhao

Precise light delivery through biological tissue is essential for deep-tissue imaging and phototherapeutic applications. Wavefront shaping enables control over scattered light by modulating the incident wavefront, but its application in living tissue is hindered by tissue-induced temporal decorrelation. This study systematically investigated the real-valued

M. Omar Nadeem, Arslan Sikandar

We study the purely leptonic decay of the charged $B$-meson within the $U_1$ Vector Leptoquark model at both leading-order and with one-loop QCD corrections. The structure of this amplitude is characterised by direct quark-lepton couplings which allow for lepton flavour universality violation (LFUV) and additional loop-level topologies. The leptoquark channe

Siqi Huang, Sida Huang, Hongyuan Zhang

Large models have achieved remarkable performance across a range of reasoning and understanding tasks. Prior work often utilizes model ensembles or multi-agent systems to collaboratively generate responses, effectively operating in a server-to-server paradigm. However, such approaches do not align well with practical deployment settings, where a limited numb

Xingcheng Liu, Bin Rao, Yanchen Guan, Chengyue Wang

Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hinder real-world deployment: (1) noisy or degraded sensory inputs from weather, motion blur, or hardware limitations, and (2) the need to issue timely yet reliable predictions that

Yang Wang, Marta Zagorowska, Riccardo M. G. Ferrari

Lithium-ion (Li-ion) batteries are ubiquitous in electric vehicles (EVs) as efficient energy storage devices. The reliable operation of Li-ion batteries depends critically on the accurate estimation of battery capacity. However, conventional estimation methods require extensive training datasets from costly battery tests for modeling, and a full cycle of cha

Aditya Sneh, Nilesh Kumar Sahu, Anushka Sanjay Shelke, Arya Adyasha

Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. Large-scale data collection remains costly and time-consuming, restricting accessibility. To address this, we introduce the Hyperbolic Curvature Few-Shot Learning Network (HCFSLN), a

Maria Lugaro, Marco Pignatari, René Reifarth, Michael Wischer

Neutron captures produce the vast majority of abundances of elements heavier than iron in the Universe. Beyond the classical slow (s) and rapid (r) processes, there is observational evidence for neutron-capture processes that operate at neutron densities in between, at different distances from the valley of $\beta$ stability. Here, we review the main propert

M. C. Mooij, H. L. Bethlem, W. Ubachs, P. Aggarwal

High-resolution spectroscopy on the $A^2\Pi$ - $X^2\Sigma^+$ electronic system of $^{138}$Ba$^{19}$F is performed using a cold molecular beam produced by a buffer gas source. The hyperfine structure in both $X^2\Sigma^+$ ground and $A^2\Pi$ excited states is fully resolved and absolute transition frequencies of individual components are measured at the sub-M

Alaa Mohammed Alqaied, Tarek Saanouni

This paper studies a non-linear biharmonic Sch\"odinger equation with an unbounded inhomogeneous term. The main goal is to develop a local theory but also a global theory for small data, in the energy space. Moreover, we develop a local theory in Sobolev spaces with lower regularity. The challenge is to deal with the inhomogeneous unbounded term, which broke

Si-Qi Liu, Paolo Rossi, Di Yang, Youjin Zhang

Given a semisimple Frobenius manifold, we construct a class of integrable deformations of its hierarchy of topological type. We show that these integrable deformations have polynomial tau-structures, and conjecture that for the one-dimensional Frobenius manifold they give a universal object for integrable deformations of the Riemann--Hopf hierarchy having a

Robin Sjökvist, Yining Xie, Zabeada Aslam, Andy P. Brown

Stacking faults and other topological defects in ferroics can have a significant influence on the electronic and mechanical properties of the material. Here, regular stacking faults in the tetragonal tungsten bronze material Sr$_2$NaNb$_5$O$_{15}$ are investigated through transmission electron microscopy, symmetry mode analysis and machine-learned force-fiel

Ankit Mazumder, Srikanta Bedathur

Link prediction is a pivotal task in graph mining with wide-ranging applications in social networks, recommendation systems, and knowledge graph completion. However, many leading Graph Neural Network (GNN) models often neglect the valuable semantic information aggregated at the class level. To address this limitation, this paper introduces CGLE (Class-label

Diogo V. Saraiva, Lotte Polling, Ivo R. Vermaire, Sander J. W. Vonk

Cellulose nanocrystals (CNCs) form cholesteric architectures that can have color specific reflectivity and enable sustainable photonic films. However, achieving uniform color, suppressing iridescence, and accessing ordered defect structures such as focal conic domains remain challenging. Here, we control the photonic properties of CNC films by steering the s

Ziyu Liu

We consider a free group extension of a subshift of finite type $\sigma:\Sigma\rightarrow\Sigma$, and consider three sets of points in $\Sigma$ to which the corresponding trajectories on the free group escape to a given point in the Gromov boundary of the free group in three different senses. Under very mild conditions, we provide a common positive lower bou

Xinpeng Lv, Yunxin Mao, Haoxuan Li, Ke Liang

Strategic classification~(SC) explores how individuals or entities modify their features strategically to achieve favorable classification outcomes. However, existing SC methods, which are largely based on linear models or shallow neural networks, face significant limitations in terms of scalability and capacity when applied to real-world datasets with signi

Di Zhang

Bayesian inference, while foundational to probabilistic reasoning, is often hampered by the computational intractability of posterior distributions, particularly through the challenging evidence integral. Conventional approaches like Markov Chain Monte Carlo (MCMC) and Variational Inference (VI) face significant scalability and efficiency limitations. This p

Youjie Xu, Steffen J. Schmidt, Nikolaus A. Adams

Velocity and temperature distributions are both crucial for modeling compressible wall-bounded turbulent flows. The compressible law of the wall for velocity has been extensively examined through velocity transformations. However, a well-established temperature transformation remains an open issue. We propose new Van Driest type (VD-type) and semi-local type

Liheng Yu, Zhe Zhao, Xucong Wang, Di Wu

Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning models while ignoring the underlying chemical rules. More importantly, experiments show that they face a serious sub-property confusion SPC problem. To address the above challenges,

Jean-Bernard Bru, Walter de Siqueira Pedra, Artur O. Lopes

Let $\Omega =\{1,2,\ldots ,d\}^{\mathbb{N}}$, $T$ be the shift acting on $\Omega $, $\mathcal{P}(T)$ the set of $T$-invariant probabilities. Given a H\"{o}lder potential $A$ and a continuous function $F$, we investigate the probabilities $\rho _{F,A}$ that are maximizers of the nonlinear pressure $\mathfrak{P}_{F,A}:=\sup_{\rho \in \mathcal{P}(T)}\{ F(\int A

Rafael Diaz Fuentes, Fatma Gamze Duzgun, Silvia Frassu, Giuseppe Viglialoro

This paper studies a chemotaxis system where cells move in response to a chemical signal within a confined habitat. The model includes external source terms that combine local and nonlocal growth with dampening effects. The main focus is on conditions under which solutions exist for all time and remain uniformly bounded, preventing cell aggregation. Two type

Anand Krishnakumar, Vengadesh Ravikumaran

Traditional methods for identifying structurally similar spreadsheets fail to capture the spatial layouts and type patterns defining templates. To quantify spreadsheet similarity, we introduce a hybrid distance metric that combines semantic embeddings, data type information, and spatial positioning. In order to calculate spreadsheet similarity, our method co

Jan Gavranovič, Lara Čalić, Jernej Debevc, Else Lytken

In a high-energy physics data analysis, the term "fake" backgrounds refers to events that would formally not satisfy the (signal) process selection criteria, but are accepted nonetheless due to mis-reconstructed particles. This can occur, e.g., when leptons from secondary decays are incorrectly identified as originating from the hard-scatter interaction poin

Qiushi Liang, Yeyue Cai, Jianhua Mo, Meixia Tao

Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a freque

Cheng-Liang Wei, Guo-Liang Li, Yue-Dong Fang, Xin Zhang

The Chinese Space Station Survey Telescope (CSST) is a flagship space-based observatory. Its main survey camera is designed to conduct high spatial resolution near-ultraviolet to near-infrared imaging and low-resolution spectroscopic surveys. To maximize the scientific output of CSST, we have developed a comprehensive, high-fidelity simulation pipeline for r

Nilanjan Bag, Dwaipayan Mazumder

This paper is devoted to finding moments of double exponential sums with monomials over arbitrary sets and intervals in finite fields. The study of such sums dates back to the work of Heath-Brown, who studied such sums in a work on least square-free numbers in an arithmetic progression.

Florent Hivert, Vincent Pilaud, Ludovic Schwob

We prove that the excedance relation on permutations defined by N. Bergeron and L. Gagnon actually extends to a congruence of the lattice on alternating sign matrices. Motivated by this example, we study all lattice congruences of the lattice on alternating sign matrices whose quotient is isomorphic to the Stanley lattice on Dyck paths, which we call catalan

Yuxin Gou, Aming Wu, Richang Hong, Meng Wang

A comprehensive understanding of molecular structures is important for the prediction of molecular ground-state conformation involving property information. Meanwhile, state space model (e.g., Mamba) has recently emerged as a promising mechanism for long sequence modeling and has achieved remarkable results in various language and vision tasks. However, towa

Emanuele Aliverti

Ordinal categorical data are routinely encountered in many practical applications. When the primary goal is to construct a regression model for ordinal outcomes, cumulative link models represent one of the most popular choices to link the cumulative probabilities of the response with a set of covariates through a parsimonious linear predictor, shared across

Liqun Qi, Chunfeng Cui, Yi Xu

In this paper, we study structured symmetric tensors. We introduce several new classes of structured symmetric tensors: completely decomposable (CD) tensors, strictly sum of squares (SSOS) tensors and SOS$^*$ tensors. CD tensors have applications in data analysis and signal processing. Complete Hankel tensors are CD tensors. SSOS tensors are defined as SOS t

Ambika Saxena, Vaibhav Pant, Tom Van Doorsselaere, M. Saleem Khan

Decades-long studies of asymmetric spectral lines in the solar corona suggest mass and energy transport from lower atmospheric layers to the corona. While slow magnetoacoustic waves and plasma flows are recognized as drivers of these spectral line asymmetries, the role of transverse MHD waves remains largely unexplored. Previous simulations have shown that u

Giuseppina Simone

Ultrafast permittivity modulation in epsilon-near-zero (ENZ) media provides a pathway for real-time control of non-Hermitian photonic topology. We model ultrafast topological dynamics in an ITO/SiO$_2$/Ag multilayer supporting hybrid epsilon-near-zero (ENZ)-plasmon modes. Using a time-dependent Drude-Lorentz permittivity for ITO and rigorous coupled-wave ana

Yolanda Dube, Bikash R. Dinda, Sheean Jolicoeur, Roy Maartens

The turnover at the peak of the Fourier matter power spectrum encodes a fundamental signature of matter-radiation equality in the early Universe. This delivers a potential standard ruler, independent of baryon acoustic oscillations and therefore able to break parameter degeneracies and improve precision. Furthermore, the turnover scale is independent of reds

Erel Naor, Ofir Lindenbaum

Deep neural networks often under-perform on tabular data due to their sensitivity to irrelevant features and a spectral bias toward smooth, low-frequency functions. These limitations hinder their ability to capture the sharp, high-frequency signals that often define tabular structure, especially under limited labeled samples. While self-supervised learning (

Yigit Sozen, Thomas Pucher, Bhagyanath Paliyottil Kesavan, Nuria Jimenez-Arevalo

In this study, we demonstrate an improved version of the roll-to-roll mechanical exfoliation method, incorporating a controlled sliding motion into the exfoliation process to achieve uniform nanosheet films of two-dimensional materials at wafer-scale. This scalable technique enables the fabrication of high-quality films suitable for electronic and optoelectr

B. Jia, Q. Zhong, Y. Li, Y. Xiao

Using the averaged magnetic drift model and a first-order finite Larmor radius (FLR) expansion, the eigenvalue equation for the ion temperature gradient (ITG) mode in tokamak plasmas is reduced to a Schr\"odinger-type differential equation. By invoking generalized translational invariance, the model is extended to reversed magnetic shear (RMS) configurations

Raneen Younis, Louay Hamdi, Lukas Chavez, Zahra Ahmadi

Whole-slide images are central to digital pathology, yet their extreme size and scarce annotations make self-supervised learning essential. Masked Autoencoders (MAEs) with Vision Transformer backbones have recently shown strong potential for histopathology representation learning. However, conventional random patch sampling during MAE pretraining often inclu

Markus Penz, Michael F. Herbst, Trygve Helgaker, Andre Laestadius

Within density-functional theory, Moreau-Yosida regularization enables both a reformulation of the theory and a mathematically well-defined definition of the Kohn-Sham approach. It is further employed in density-potential inversion schemes and, through the choice of topology for the density and potential space, can be directly linked to classical field theor

Xian Jing-Tian, Lin Lin, Fang Yue-Dong, Zhang Xin

Stray light significantly influences the detection capabilities of astronomical telescopes. The actual stray-light level during observations depends not only on the telescope's inherent stray-light suppression capability but also on its operational orbit conditions. Accurate estimation of stray-light levels is crucial for assessing image quality and performi

Arya Parameshwara, Santosh Hanamappa Mokashi

Edge AI deployment faces critical challenges balancing computational performance, energy efficiency, and resource constraints. This paper presents FPGA-accelerated RISC-V instruction set architecture (ISA) extensions for efficient neural network inference on resource-constrained edge devices. We introduce a custom RISC-V core with four novel ISA extensions (

Yuchong Zhang, Yong Ma, Di Fu, Stephanie Zubicueta Portales

Conversational agents (CAs) are increasingly embedded in daily life, yet their ability to navigate user emotions efficiently is still evolving. This study investigates how users with varying traits -- gender, personality, and cultural background -- adapt their interaction strategies with emotion-aware CAs in specific emotional scenarios. Using an emotion-awa

Siyue Teng, Ge Gao, Duolikun Danier, Yuxuan Jiang

3D Gaussian Splatting (3DGS) enhances 3D scene reconstruction through explicit representation and fast rendering, demonstrating potential benefits for various low-level vision tasks, including video compression. However, existing 3DGS-based video codecs generally exhibit more noticeable visual artifacts and relatively low compression ratios. In this paper, w

Alireza HosseiniArani, Stefano Bertone, Daniel Arnold, William Desprats

The European Space Agency's BepiColombo mission continues its pioneering voyage to Mercury, the innermost planet of the Solar System. Among the advanced instruments onboard the Mercury Planetary Orbiter (MPO) is the Italian Spring Accelerometer (ISA), whose scientific objectives are closely linked to the Mercury Orbiter Radio-Science Experiment (MORE). Toget

Anna Katharina Holl-Etten, Nina Schnaderbeck, Elizaveta Kosareva, Leonhard Aron Prattke

The rapid development of language-based artificial intelligence (AI) offers new possibilities for psychotherapy and assistive systems, particularly benefitting autistic individuals who often respond well to technology. Parents of autistic persons emphasize the importance of appropriate and context-specific communication behavior. This study investigated whet

Márcio Cavalcante, Aílton C. Nascimento

We study special regularity properties of solutions to the initial-boundary value problem associated with the Korteweg-de Vries equations posed on the positive half-line. In particular, for initial data $u_0 \in H^{\frac{3}{4}^{+}}(\mathbb{R}^+)$ and boundary data $f\in H^{\frac32^+}(\R^+)$, where the restriction of $u_0$ to some subset of $(b,\infty)$ has a

M. Doostmohammadian, U. A. Khan, N. Meskin

In this paper, the problem of distributed state estimation of human-driven vehicles (HDVs) by connected autonomous vehicles (CAVs) is investigated in mixed traffic transportation systems. Toward this, a distributed observable state-space model is derived, which paves the way for estimation and observability analysis of HDVs in mixed traffic scenarios. In thi

Trung Kien Pham, Hoang Minh Vu, Anh Duc Chu, Dac Thai Nguyen

Attenuation artifacts remain a significant challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often compromising diagnostic accuracy and reducing clinical interpretability. While hybrid SPECT/CT systems mitigate these artifacts through CT-derived attenuation maps, their high cost, limited accessi

Yulin Chen, Zeyuan Wang, Tianyuan Yu, Yingmei Wei

The well-aligned attribute of CLIP-based models enables its effective application like CLIPscore as a widely adopted image quality assessment metric. However, such a CLIP-based metric is vulnerable for its delicate multimodal alignment. In this work, we propose \textbf{FoCLIP}, a feature-space misalignment framework for fooling CLIP-based image quality metri