November 2025 arXiv papers — page 162

Daniel De Dios Allegue, Jinke He, Frans A. Oliehoek

Transformers have shown strong ability to model long-term dependencies and are increasingly adopted as world models in model-based reinforcement learning (RL) under partial observability. However, unlike natural language corpora, RL trajectories are sparse and reward-driven, making standard self-attention inefficient because it distributes weight uniformly a

Maria Emilia Maietti, Davide Trotta

Localic and realizability toposes are two central classes of toposes in categorical logic, both arising through the Hyland-Johnstone-Pitts tripos-to-topos construction. We investigate their shared geometric features by providing an algebraic abstraction of the notions of localic presheaves, sheafification and their connection to supercompactification of a lo

Dongsheng Hong, Chao Chen, Yanhui Chen, Shanshan Lin

Explanation-guided learning (EGL) has shown promise in aligning model predictions with interpretable reasoning, particularly in computer vision tasks. However, most approaches rely on external annotations or heuristic-based segmentation to supervise model explanations, which can be noisy, imprecise and difficult to scale. In this work, we provide both empiri

Fangqi Dai, Xingjian Jiang, Zizhuang Deng

To prevent misinformation and social issues arising from trustworthy-looking content generated by LLMs, it is crucial to develop efficient and reliable methods for identifying the source of texts. Previous approaches have demonstrated exceptional performance in detecting texts fully generated by LLMs. However, these methods struggle when confronting more adv

Kevin M. Hickson, Jean-Christophe Loison, Benjamin Benne, Michel Dobrijevic

The chemistry of planetary atmospheres containing molecular nitrogen as a major atmospheric component is strongly influenced by the reactions of atomic nitrogen. Although nitrogen atoms in their ground electronic state N(4S) are mostly unreactive towards stable molecules, electronically excited nitrogen atoms N(2D) are much more reactive and could play an im

Tim Heib, Andreea Silvia Goia, Sona Baghiyan, Robert Zeier

We study, classify, and explore the mathematical properties of finite-dimensional Lie algebras occurring in the quantum dynamics of single-mode and self-interacting bosonic systems. These Lie algebras are contained in the real skew-hermitian Weyl algebra $\hat{A}_1$, defined as the real subalgebra of the Weyl algebra $A_1$ consisting of all skew-hermitian po

Christophe Eyral, Masaharu Ishikawa, Mutsuo Oka

We investigate surface singularities defined by weighted-L\^e-Yomdin polynomials, with a particular focus on a specific subclass that we refer to as Newton weighted-L\^e-Yomdin polynomials. In particular, using polynomials in this subclass, we develop a method to construct new $\mu$-Zariski pairs of surface singularities.

Shunichiro Kittaka, Yohei Kono, Toshiro Sakakibara, Naoki Kikugawa

We performed high-resolution magnetostriction measurements on the Pauli-limited superconductor Sr$_2$RuO$_4$ using high-quality single crystals. A first-order superconducting transition, accompanied by pronounced hysteresis, was observed under in-plane magnetic fields, where the relative length change of the sample, $\Delta L/L$, was on the order of $10^{-8}

Yu Hou, Hua Li, Ha Young Kim, Won-Yong Shin

Diffusion models recently emerged as a powerful paradigm for recommender systems, offering state-of-the-art performance by modeling the generative process of user-item interactions. However, training such models from scratch is both computationally expensive and yields diminishing returns once convergence is reached. To remedy these challenges, we propose Re

Zhang Ban, Xiao-Bo Li, Xun Yang, Yu-Xi Jiang

This study presents a comprehensive end-to-end simulation analysis of the optical imaging performance of the China Survey Space Telescope (CSST) under in-orbit conditions. An integrated system model incorporating five static and two dynamic error sub-models was established. Wavefront errors were calculated for each sub-model and compared to the integrated sy

Nienke van der Marel, Niels F. W. Ligterink, Ryan van der Werf, Milou Temmink

Polycyclic Aromatic Hydrocarbons (PAHs) are commonly detected in protoplanetary disks, but it is unclear what causes the wide range of intensities across the samples. In this work, the measured PAH intensities of a range of disks are compared with ALMA dust continuum images, in order to test whether there is evidence that PAHs are frozen out on pebbles in du

Dennis Thumm

Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Form Games (CNFGs) to sequential settings, introducing Sequential Causal Multi-Agent Systems (S-CMAS) that incorporate Pearl's Causal Hierarchy across leader-follower interactions. W

Lorenzo Pellegrino

In this work we investigate constant angle surfaces in the Lorentzian Heisenberg group $\htt$. After providing a complete description of the geometry of the ambient space, we perform the full classification of minimal and CMC helix surfaces in $\htt$, giving their explicit parametrizations. In addition, we investigate the constant angle spacelike and timelik

Guange Su, Xiaosen Han

Vortices represent a class of topological solitons arising in gauge theories coupled with complex scalar fields, holding significant importance across various domains of modern physics. In this paper we establish the existence of vortex solutions for a mixed boundary-value problem derived from the Abelian-Higgs model incorporating a neutral scalar field, a s

ShahZeb Khan, Chiara Pallara, Barbara Monti, Alexis Molina

Alzheimers disease (AD), a progressive neuro-degenerative disorder, currently lacks effective therapeutic strategies that can modify disease progression. Recent studies have highlighted the circadian rhythm critical role in AD pathophysiology, implicating circadian clock kinases, such as the Salt-Inducible Kinase 3 (SIK3), as promising therapeutic target. Ge

Mahmoud Nabil, Isel Grau-García, Ricardo Grau-Crespo, Said Hamad

Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift-diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible. This work uses DD modelling to generate a large

Yushan Xie, Xiaokai Chen, Shuai Feng, Zhaojun Yan

The Multi-Channel Imager (MCI), one of the instruments aboard the China Survey Space Telescope (CSST), is designed to simultaneously observe the sky in three filters, covering wavelengths from the near-ultraviolet (NUV) to the near-infrared (NIR). With its large field of view ($7.5^{\prime}\times7.5^{\prime}$), MCI is particularly well-suited for observing g

Shuai Feng, Shiyin Shen, Wei Chen, Zhaojun Yan

We developed a Python package GEHONG to mock the three-dimensional spectral data cube under the observation of an ideal telescope for the Integral Field Spectrograph of the Chinese Space Station Telescope (CSST-IFS). This package can generate one-dimensional spectra corresponding to local physical properties at specific positions according to a series of two

Kajal Madaan, Guido Roma, Jasurbek Gulomov, Pascal Pochet

Halide perovskites have emerged in the last decade as a new important class of semiconductors for a variety of optoelectronic applications. A lot of previous studies were thus devoted to the characterisation of their point defects. Positron annihilation spectroscopy is a well recognized tool for probing vacancies in materials. Recent applications of this tec

Zhicheng Li, Kunyang Sun, Rui Yao, Hancheng Zhu

Video shadow detection confronts two entwined difficulties: distinguishing shadows from complex backgrounds and modeling dynamic shadow deformations under varying illumination. To address shadow-background ambiguity, we leverage linguistic priors through the proposed Vision-language Match Module (VMM) and a Dark-aware Semantic Block (DSB), extracting text-gu

Giovanni Calvaruso, Lorenzo Pellegrino, Amirhesam Zaeim

In the framework of the study of homogeneous Lorentzian three-manifolds, we consider here the only class of examples which admit a four-dimensional group of isometries but are neither Lorentzian Bianchi-Cartan-Vranceanu spaces nor plane waves. We obtain an explicit description in global coordinates of these special homogeneous Lorentzian manifolds. We then p

Konstantinos Alexoudis, Jasper Müller, Sai Kireet Patri, Vincent A. J. M. Sleiffer

We demonstrate an experimental phase optical time-domain reflectometry (OTDR) system capable of simultaneous detection and classification of various environmental events, such as wind-induced fiber movement, vehicle movement, and audio signatures, with real-time visualization.

Siddhartha Mahajan, Harsh Raj, Sonam Tanwar

This project investigates traffic congestion within North Campus, Delhi University (DU), using continuous time simulations implemented in UXSim to model vehicle movement and interaction. The study focuses on several key intersections, identifies recurring congestion points, and evaluates the effectiveness of conventional traffic management measures. Implemen

Yiyuan Wang

This paper proposes a novel iterative algorithm to compute the stabilizing solution of regime-switching stochastic game-theoretic Riccati differential equations with periodic coefficients. The method decomposes the original complex stochastic problem into a sequence of deterministic subproblems. By sequentially solving for the minimal solutions of the Riccat

Luis Diener, Jens Kalkkuhl, Markus Enzweiler

Accurate ego-motion estimation in consumer-grade vehicles currently relies on proprioceptive sensors, i.e. wheel odometry and IMUs, whose performance is limited by systematic errors and calibration. While visual-inertial SLAM has become a standard in robotics, its integration into automotive ego-motion estimation remains largely unexplored. This paper invest

Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca

This document represents the proceedings of the 2025 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'25 (31st International Conference on Principles and Practice of Constraint Programming).

Xin Zhang, Yue-dong Fang, Cheng-liang Wei, Guo-liang Li

The China Space Station Telescope (CSST), slated to become China's largest space-based optical telescope in the coming decade, is designed to conduct wide-field sky surveys with high spatial resolution. Among its key observational modes, slitless spectral observation allows simultaneous imaging and spectral data acquisition over a wide field of view, offerin

Nasrin Sadeghzadeh, Meshkat Yavari

The development of projective invariant Weyl metrics in this paper offers a fresh perspective, as we establish the characteristics of both weakly-Weyl and generalized weakly-Weyl Finsler metrics. We thoroughly examine the connections between these metrics and various projective invariants, highlighting their significance in the context of generalized Sakaguc

Kawshik Kumar Paul, Mahdi Hasnat Siyam, Khandokar Md. Rahat Hossain

This paper presents a low-cost, stand-alone embedded system that automates patient queue handling and basic health data acquisition for small private medical chambers. The proposed design separates interaction into two physically distinct modules: a patient's self-service corner for entering basic details and measuring vital signs, and a doctor's corner for

Mahdi Salmani, Pratik Worah, Meisam Razaviyayn, Vahab Mirrokni

In the training of large deep neural networks, there is a need for vast amounts of training data. To meet this need, data is collected from multiple domains, such as Wikipedia and GitHub. These domains are heterogeneous in both data quality and the diversity of information they provide. This raises the question of how much we should rely on each domain. Seve

Abdullah Aloufi, Kevin Wilson, Nina Wilson, Lisa Shaw

The paper considers two different designs; a two-arm superiority cluster randomised controlled trial (RCT) with a continuous outcome, and a twoarm superiority cluster RCT with a binary outcome. From a Bayesian perspective, for the analysis of the trial we use a (generalised) linear mixed effects model. We summarise the inference for the treatment effect for

Yazeed Tawalbeh, Marwa Ghazi, Mauro Fernandes Pereira

We present an analytical framework that predicts and controls nanoparticle size through external magnetic fields, uniting first-principles thermodynamics with a sphere packing approach. Calibrated to diamagnetic silver nanoparticles (20 nm at zero field and 5 nm at 250 mT), the model yields a closed-form relation between radius and field that reproduces the

Maissâ Boughrara

This work investigates a mathematical model arising in the study of MEMS devices, described by the following parabolic equation on $[0,T)\times\Omega$: $$\partial_t v = \Delta v + \frac{\lambda}{(1-v)^2\left( 1 + \gamma \int_{\Omega} \frac{1}{1-v}\, dx \right)^{2}} , \qquad 0 \leq v \leq 1,$$ where $\Omega \subset \mathbb{R}^N$ is a bounded domain and $\lamb

Ze Wang, Tianyu Xu, Yuanlei Wang, Kaixuan Zhu

Soliton microcombs generated in optical microresonators are accelerating the transition of optical frequency combs from laboratory instruments to industrial platforms. Self injection locking (SIL) enables direct driving of soliton microcombs by integrated lasers, providing turnkey initiation and improved coherence, but it also pins the pump close to resonanc

Yuzhen Li, Min Liu, Zhaoyang Li, Yuan Bian

Monocular 3D Visual Grounding (Mono3DVG) is an emerging task that locates 3D objects in RGB images using text descriptions with geometric cues. However, existing methods face two key limitations. Firstly, they often over-rely on high-certainty keywords that explicitly identify the target object while neglecting critical spatial descriptions. Secondly, genera

Ilias Papalamprou, Dimosthenis Masouros, Ioannis Loudaros, Francky Catthoor

General Matrix Multiplication (GEMM) is a fundamental operation in many scientific workloads, signal processing, and particularly deep learning. It is often a bottleneck for performance and energy efficiency, especially in edge environments with tight resource and power constraints. AMD's Versal ACAP offers heterogeneous components (AIEs, PL, PS) that can ad

Keita Kinjo

Currently, machine learning is widely used across various domains, including time series data analysis. However, some machine learning models function as black boxes, making interpretability a critical concern. One approach to address this issue is counterfactual explanation (CE), which aims to provide insights into model predictions. This study focuses on t

Xiaokun Zhang, Zhaochun Ren, Bowei He, Ziqiang Cui

Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. However, current understanding of this crucial resource remains limited. Specifically, a quantitative definition of collaborative information is missing, its manifestation within user-i

Janani G, Deepak Bhat

In biological cells, DNA replication is carried out by the replisome, a protein complex encompassing multiple DNA polymerases. DNA replication is semi-discontinuous: a DNA polymerase synthesizes one (leading) strand of the DNA continuously, and another polymerase synthesizes the other (lagging) strand discontinuously. Complex dynamics of the lagging-strand p

Pauline Baudat

We show that, for $n \geq 3 $, 1-cocycles of degree zero on the Lie algebra of derivations of the free associative algebra $T(A_n)$ with values in $ \rvert T(A_n) \rvert \otimes \rvert T(A_n) \rvert $ are linear combinations of the non-commutative divergence and its switch, when restricted to finite-degree quotients. Here, $ \rvert T(A_n) \rvert $ denotes th

Xu Liu, Na Xia, Jinxing Zhou, Jingyuan Xu

Spiking Neural Networks (SNNs) become popular due to excellent energy efficiency, yet facing challenges for effective model training. Recent works improve this by introducing knowledge distillation (KD) techniques, with the pre-trained artificial neural networks (ANNs) used as teachers and the target SNNs as students. This is commonly accomplished through a

Leonard Saur, Marc von Pawlowski, Ulrich Gengenbach, Ingo Sieber

The detection and classification of microplastics in water remain a significant challenge due to their diverse properties and the limitations of traditional optical methods. Standard spectroscopic techniques often suffer from the strong infrared absorption of water, while many emerging optical approaches rely on transmission geometries that require sample tr

Ricardo Suárez

In previous work, we associated to $\textrm{SU(3)}$, $\mathrm{G}_2$, and $\textrm{Spin(7)}$-structures minimal left ideals for the Clifford algebras $\mathbb{R}_{0,6},\mathbb{R}_{0,7}$, and $\mathbb{R}_{0,8}$, respectively. In this paper, we continue to analyze the link between Berger's classification theorem and the structure theorem of minimal left ideals

Haofeng Wang, Yu Zhang

Large Vision-Language Models (LVLMs) excel in multimodal reasoning and have shown impressive performance on various multimodal benchmarks. However, most of these benchmarks evaluate models primarily through multiple-choice or short-answer formats, which do not take the reasoning process into account. Although some benchmarks assess the reasoning process, the

Boyan Tang, Xuanhao Ren, Peng Xiao, Shunbo Lei

Accurate day-ahead electricity price forecasting (DAEPF) is critical for the efficient operation of power systems, but extreme condition and market anomalies pose significant challenges to existing forecasting methods. To overcome these challenges, this paper proposes a novel hybrid deep learning framework that integrates a Distilled Attention Transformer (D

Joaquim Reizi Higuchi

This paper introduces the concept of distorted monoidal categories, a generalization of monoidal and braided monoidal categories that supports non-reversible and direction-sensitive tensor structures. Unlike the classical setting, where the braiding symmetry is required to be invertible, distorted monoidal categories admit non-invertible binary distortions a

Zhenxi Zhang, Fuchen Zheng, Adnan Iltaf, Yifei Han

Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases.Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular

Giacomo Graziani

One of the most powerful ideas in the study and classification of algebraic varieties is the notion of a model: that is, to single out an object, in the appropriate isomorphism class, with nice properties. This survey aims to define and study suitable models of rational and, more generally, ruled surfaces in the smooth complex case, and to use them to study

Viktor Veselý, Aleksandar Todorov, Matthia Sabatelli

The double descent (DD) paradox, where over-parameterized models see generalization improve past the interpolation point, remains largely unexplored in the non-stationary domain of Deep Reinforcement Learning (DRL). We present preliminary evidence that DD exists in model-free DRL, investigating it systematically across varying model capacity using the Actor-

Wenlong Shang, Shihao Tian, Xutong Wan, Peng Chang

Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean Squared Error (MSE) loss results in statistically flawed reconstruction residuals. This fundamental weakness leads to noisy, unstable anomaly scores, hindering reliable detection. To address this, we propose Constrained

Daojun Liang, Jing Chen, Xiao Wang, Yinglong Wang

Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first analysing concept drift through a bias-variance lens and proving that weighted ensemble reduces variance without increasin

Kailin Tong, Selim Solmaz, Kenan Mujkic, Gottfried Allmer

Conventional road-situation detection methods achieve strong performance in predefined scenarios but fail in unseen cases and lack semantic interpretation, which is crucial for reliable traffic recommendations. This work introduces a multi-agent AI framework that combines multimodal large language models (MLLMs) with vision-based perception for road-situatio

I. M. Moiseenko, E. Titova, M. Kashchenko, D. Svintsov

We present a combined experimental and theoretical study of photovoltage generation in a bilayer graphene (BLG) transistor structure exposed to subterahertz radiation. The device features a global bottom and split top gate, enabling independent control of the band gap and Fermi level, thereby enabling the formation of a tunable p-n junction in graphene. Meas

Yilin Jiang, Mingzi Zhang, Xuanyu Yin, Sheng Jin

Large Language Models for Simulating Professions (SP-LLMs), particularly as teachers, are pivotal for personalized education. However, ensuring their professional competence and ethical safety is a critical challenge, as existing benchmarks fail to measure role-playing fidelity or address the unique teaching harms inherent in educational scenarios. To addres

Federico Herrero-Hervás, Mihaela Negreanu

This work studies the following system of parabolic partial differential equations \begin{equation*} \begin{cases} \displaystyle \frac{\partial u}{\partial t} = D\Delta u + \chi \nabla \cdot(u \nabla v) + ru(1-u) - u v, \quad & x \in \Omega, ~t > 0, \\ \displaystyle \frac{\partial v}{\partial t} = \Delta v + a u -v+ f(x,t), \quad & x \in \Omega, ~t > 0, \end

Jan-Hendrik Koch, Jonas Krumme, Konrad Gadzicki

Text-to-image diffusion models exhibit remarkable generative capabilities, but lack precise control over object counts and spatial arrangements. This work introduces a two-stage system to address these compositional limitations. The first stage employs a Large Language Model (LLM) to generate a structured layout from a list of objects. The second stage uses

Beatriz Viña-Bausá, Antonio. T. Costa, Joao Henriques, Eva Cortés-del Río

Magnetic interactions between localized spins-1/2 play a central role in quantum magnetism, spin-based quantum computing, and quantum simulation. The range and strength of these interactions are key figures of merit. Here, we probe exchange interactions in pairs and trimers of spins-1/2 introduced by chemisorption of individual hydrogen atoms on graphene. Us

Neelesh Kumar Shukla, Sanasam Ranbir Singh

Most of the Natural Language Processing systems are involved in entity-based processing for several tasks like Information Extraction, Question-Answering, Text-Summarization and so on. A new challenge comes when entities play roles according to their act or attributes in certain context. Entity Role Detection is the task of assigning such roles to the entiti

Davis Byamugisha, Francis Kamuganga, Adones Rukundo, John Businge

Effective treatment of cancer requires early diagnosis which involves the patient's awareness of the early signs and symptoms, leading to a consultation with a health provider, who would then promptly refer the patient for confirmation of the diagnosis and thereafter treatment. However, this is not always the case because of delays arising from limited skill

N. J. Ayres, G. Ban, G. Bison, K. Bodek

We present a novel method for extracting the energy spectrum of ultracold neutrons from magnetically induced spin depolarization measurements using the n2EDM apparatus. This method is also sensitive to the storage properties of the materials used to trap ultracold neutrons, specifically, whether collisions are specular or diffuse. We highlight the sensitivit

E D Hunter, M Bumbar, C Amsler, M Bayo

We measure the time-dependent temperature and density distribution of antiprotons and positrons while slowly combining them to make antihydrogen atoms in a nested Penning-Malmberg trap. The total antihydrogen yield and the number of atoms escaping the trap as a beam are greatest when the positron temperature is lowest and when antiprotons enter the positron

Zhipeng Li, Wenjie Ma

This paper investigates streaming codes for three-node relay networks under burst packet erasures with a delay constraint $T$. In any sliding window of $T+1$ consecutive packets, the source-to-relay and relay-to-destination channels may introduce burst erasures of lengths at most $b_1$ and $b_2$, respectively. Let $u = \max\{b_1, b_2\}$ and $v = \min\{b_1, b

Jiaxuan Hou, Lifeng Wei

We develop a continuous-time entropy-regularized reinforcement learning framework under model uncertainty. By applying Sion's minimax theorem, we transform the intractable robust control problem into an equivalent standard entropy-regularized stochastic control problem, facilitating reinforcement learning algorithms. We establish sufficient conditions for th

Giacomo Graziani

The Riemann-Roch Theorem is one of the cornerstones of algebraic geometry, connecting algebraic data (sheaf cohomology) with geometric ones (intersection theory). This survey paper provides a self-contained introduction and a complete proof of the Hirzebruch-Riemann-Roch (HRR) Theorem for smooth projective varieties over an algebraically closed field. Starti

Amir Zablotsky, Subham Biswas, Laura Schaedel, Karin John

The structural integrity of microtubules is paramount for cellular function. We present a theoretical analysis of their lattice fracture, focusing on the influence of multi-seam structures arising from monomer defects and aiming to provide a more accurate estimation of GDP lattice parameters. Our findings reveal that seams function as pre-existing pathways t

Tirumala Chakradhar, Katie Gittins, Georges Habib, Norbert Peyerimhoff

In this paper, we introduce the magnetic Steklov operator on differential forms and show that the underlying boundary value problem is well-posed. Moreover, we show that an analogue of the Diamagnetic Inequality does not always hold for this operator, and we present some spectral computations of magnetic Steklov operators for $2$-dimensional and $4$-dimensio

Eyal Gutflaish, Eliran Kachlon, Hezi Zisman, Tal Hacham

Text-to-image models have rapidly evolved from casual creative tools to professional-grade systems, achieving unprecedented levels of image quality and realism. Yet, most models are trained to map short prompts into detailed images, creating a gap between sparse textual input and rich visual outputs. This mismatch reduces controllability, as models often fil

Zhizhong Kong, Jerry Zhijian Yang, Cheng Yuan, Xiaofei Zhao

We propose an unsupervised deep learning approach for computing the ground state (GS) of rotating Bose-Einstein condensation. To minimize the energy under a mass constraint, our approach introduces two key and novel ingredients: a normalized loss function that exactly enforces the mass constraint, and a training strategy named virtual rotation acceleration t

Ruohan Wang, Siyuan Liu, Zhiyong Sun, Sofie Haesaert

Discrete-time stochastic systems with continuous spaces are hard to verify and control, even with MDP abstractions due to the curse of dimensionality. We propose an abstraction-based framework with robust dynamic programming mappings that deliver control strategies with provable lower bounds on temporal-logic satisfaction, quantified via approximate stochast

R. Runthala, N. Grover, P. Arora

This work presents a high-performance, multilayered surface plasmon resonance (SPR)-based sensor designed to enhance performance parameters in the near-infrared (NIR) region through angular interrogation. The multi-layered sensor consists of a bimetallic layer (Aluminum (Al) & Gold (Au)), a dielectric layer (MgF2), and an optimized number of 2D nanomaterial

Ethan Leeman, Pasin Manurangsi

Steinke (2025) recently asked the following intriguing open question: Can we solve the differentially private selection problem with nearly-optimal error by only (adaptively) invoking Gaussian mechanism on low-sensitivity queries? We resolve this question positively. In particular, for a candidate set $\mathcal{Y}$, we achieve error guarantee of $\tilde{O}(\

Tim Kutta, Holger Dette, Shixuan Wang

We study the problem of detecting and localizing multiple changes in the mean parameter of a Banach space-valued time series. The goal is to construct a collection of narrow confidence intervals, each containing at least one (or exactly one) change, with globally controlled error probability. Our approach relies on a new class of weighted scan statistics, ca

Alexis Front, Tapio Ala-Nissila, Miguel A. Caro

Understanding the sodium-storage mechanism in hard carbon (HC) anodes is crucial for advancing sodium-ion battery (SIB) technology. However, the intrinsic complexity of HC microstructures and their interactions with sodium remain not fully elucidated. We present a multiscale methodology that integrates grand-canonical Monte Carlo (GCMC) simulations with a ma

Si-Qi Zhou, Jin-Min Liang, Ziheng Ding, Zhihua Chen

Quantum algorithms have demonstrated provable speedups over classical counterparts, yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge. In this work, we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms. We propose

Ahmet Kaplan, Diana P. M. Osorio, Erik G. Larsson

Future Internet-of-Things networks are envisioned to use small and cheap sensor nodes with extremely low power consumption to avoid the extensive use of batteries. To provide connectivity to a massive number of these nodes, backscatter communication (BC) is emerging as an energy- and cost-efficient technology exploiting the reflection of radio frequency sign

Chang-Chao Liu, Jing Li, Ji-Yong Liu, Jia-Yi Lu

We report the crystal growth, structure, physical properties, and first-principles calculations of a vanadium-based oxytelluride Cs$_{1-\delta}$V$_2$Te$_2$O. The material possesses two-dimensional V$_2$O square nets sandwiched by tellurium layers, with local crystallographic symmetry satisfying the spin symmetry for a $d$-wave altermagnet. An antiferromagnet

Pallav Jain, Yuvraj Agrawal, Ashutosh Nigam, Pushpak Patil

In modern, large-scale software development, engineering leaders face the significant challenge of gaining a holistic and data-driven view of team performance and system health. Data is often siloed across numerous disparate tools, making manual report generation time-consuming and prone to inconsistencies. This paper presents the architecture and implementa

Sicheng Yang, Xing Hu, Qiang Wu, Dawei Yang

Vector quantization (VQ) transforms continuous image features into discrete representations, providing compressed, tokenized inputs for generative models. However, VQ-based frameworks suffer from several issues, such as non-smooth latent spaces, weak alignment between representations before and after quantization, and poor coherence between the continuous an

Huan Sun, David Sanán, Jingyi Wang, Yongwang Zhao

Ensuring compliance with Information Flow Security (IFS) is known to be challenging, especially for concurrent systems with large codebases such as multicore operating system (OS) kernels. Refinement, which verifies that an implementation preserves certain properties of a more abstract specification, is promising for tackling such challenges. However, in ter

Wietse M. Boon, Alessio Fumagalli, Jan M. Nordbotten, Ivan Yotov

We propose mixed finite element methods for Cosserat materials that use suitable quadrature rules to eliminate the Cauchy and coupled stress variables locally. The reduced system consists of only the displacement and rotation variables. Four variants are proposed for which we show stability and convergence using a priori estimates. Numerical experiments veri

Hung-Yang Sung, Chien-Chun Wang, Kuan-Tang Huang, Tien-Hong Lo

Automatic speech recognition (ASR) for low-resource languages such as Taiwanese Hokkien is difficult due to the scarcity of annotated data. However, direct fine-tuning on Han-character transcriptions often fails to capture detailed phonetic and tonal cues, while training only on romanization lacks lexical and syntactic coverage. In addition, prior studies ha

Qifeng Lei, Zhiyong Yang, Qianqian Xu, Cong Hua

Efficiently fine-tuning pre-trained models for downstream tasks is a key challenge in the era of foundation models. Parameter-efficient fine-tuning (PEFT) presents a promising solution, achieving performance comparable to full fine-tuning by updating only a small number of adaptation weights per layer. Traditional PEFT methods typically rely on a single expe

Ali Övgün

We develop a quantum optical framework for probing black hole quasinormal modes (QNMs) using two-level atoms in the spirit of the horizon-brightened acceleration radiation (HBAR) program. Starting from the QNM contribution to the Wightman function of a scalar field on a static, spherically symmetric black hole background, we derive the response function of a

Xiang Shuwen, Luo Enquan, Yang Yanlong

As Aumann stated, cooperation and non-cooperation are different ways of viewing the same game, with the main difference being whether players can reach a binding cooperative agreement. In the real world, many games often coexist competition and cooperation. Based on the above reasons, we propose a method to transform strategic games into a biform game model,

Fanding Li, Xiangyu Li, Xianghe Su, Xingyu Qiu

A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncated diffusion probabilistic models (TDPMs) hold strong potential with a paradigm optimization, existing TDPMs suffer from entangled accuracy and diversity of predictions with insuff

Andrea Testa, Søren Hauberg, Tamim Asfour, Leonel Rozo

The Schr\"odinger Bridge provides a principled framework for modeling stochastic processes between distributions; however, existing methods are limited by energy-conservation assumptions, which constrains the bridge's shape preventing it from model varying-energy phenomena. To overcome this, we introduce the non-conservative generalized Schr\"odinger bridge

Ya-jin Zhou

We review recent theoretical developments and experimental measurements of azimuthal modulations in photon-induced processes, covering both ultra-peripheral heavy-ion collisions (UPCs) and $e^+e^-$ colliders. The azimuthal asymmetries $\cos(n\phi)$ ($n=1,2,3,4$) serve as precision diagnostics that probe the linear polarization of coherent photons, final-stat

Jiexi Liu, Meng Cao, Songcan Chen

Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS modeling primarily rely on observed values to impute unobserved ones or infer latent dynamics. However, these methods overlook a critical source of learning signal: the reconstruc

Qiushi Li, Celi Lou, Yanfang Cheng, Bilang Gong

The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intrinsically restricted to shallow imaging d

Peng Zhang, Peijie Sun

Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neural processes: the detection of harm and the execution of a re

Yuichi Itto

Two recent topics on a formal thermodynamic analogy of intracellular diffusivity fluctuations observed experimentally in normal/anomalous diffusion are reported. Not only the analogs of the quantity of heat and work as well as the internal energy but also that of the Clausius inequality are identified. Then, the analog of the heat engine is constructed to ch

Dariush Salami, Ramin Hashemi, Parham Kazemi, Mikko A. Uusitalo

This paper presents a novel and sustainable approach for improving beam selection in 5G and beyond networks using transfer learning and Reinforcement Learning (RL). Traditional RL-based beam selection models require extensive training time and computational resources, particularly when deployed in diverse environments with varying propagation characteristics

Yinxing Li, Tsukasa Ishigaki

Product line extension is a strategically important managerial decision that requires anticipating how consumer segments and purchasing contexts may respond to hypothetical product designs that do not yet exist in the market. Such decisions are inherently uncertain because managers must infer future outcomes from historical purchase data without direct marke

Lu Zhou, Zhi-Qiang You, Lu Li, Xiao-Jin Liu

Among the $\sim 4000$ known pulsars in our Galaxy, $\lesssim 10\%$ are found in globular clusters, but none has been confirmed in any open clusters yet, although they outnumber globular clusters by about 20 times. In this work, we make use of the Gaia DR3 catalog of Galactic open clusters and conduct a pulsar census, in order to identify pulsars that are eit

Ved Datar, Vamsi Pritham Pingali, Harish Seshadri

We prove that a complete noncompact K\"ahler surface with positive and bounded sectional curvature is biholomorphic to $\mathbb{C}^2$. This result confirms a special case of Yau's conjecture that a complete noncompact K\"ahler $n$-manifold with positive holomorphic bisectional curvature is biholomorphic to $\mathbb{C}^n$. In contrast to all known results on

Huiyuan Tian, Bonan Xu, Shijian Li

While feature-based knowledge distillation has proven highly effective for compressing CNNs, these techniques unexpectedly fail when applied to Vision Transformers (ViTs), often performing worse than simple logit-based distillation. We provide the first comprehensive analysis of this phenomenon through a novel analytical framework termed as "distillation dyn

Jonas Stange

We examine a thermodynamically consistent diffuse interface model for bulk-surface viscous fluid mixtures. This model consists of a Navier--Stokes--Cahn--Hilliard model in the bulk coupled to a surface Navier--Stokes--Cahn--Hilliard system on the boundary. In this paper, we address the global well-posedness of strong solutions in the two-dimensional setting,

Federico Vasile, Ri-Zhao Qiu, Lorenzo Natale, Xiaolong Wang

System identification involving the geometry, appearance, and physical properties from video observations is a challenging task with applications in robotics and graphics. Recent approaches have relied on fully differentiable Material Point Method (MPM) and rendering for simultaneous optimization of these properties. However, they are limited to simplified o

Si-Qi Zhou, Jin-Min Liang, Jiayin Peng, Zhihua Chen

Quantum entanglement and coherence are crucial resources in quantum information theory. In some scenarios, however, it is not necessary to directly estimate entanglement or coherence measures to quantify the capabilities of a state in quantum information processing. Instead, fully entangled fraction and coherence fraction are two alternatives for entanglemen

Mu Yang, Yue Li, Mingtao Xu, Wei Yi

Open systems feature a variety of phenomena that arise from non-Hermitian physics. Recent theoretical studies have offered much insights into these phenomena through the non-Bloch band theory, though many of the theory's key features are experimentally elusive. For instance, the correspondence between complex momenta and non-Hermitian bands, while central to