November 2025 arXiv papers — page 160

Yubin Gao, Qikai Chen, Yaoguang Ma

The geometric phase is a universal concept in modern physics and has enabled the development of metasurfaces for versatile wavefront shaping. However, its realization in metasurfaces has been restricted to circularly polarized light, confining geometric phase metasurfaces to helicity-dependent operation and excluding them from the linear-polarization domain

Zihao Cheng, Yuheng Lu, Huaiqian Ye, Zeming Liu

Large Language Models (LLMs) have demonstrated remarkable capabilities in modern medicine, yet their application in Traditional Chinese Medicine (TCM) remains severely limited by the absence of standardized benchmarks and the scarcity of high-quality training data. To address these challenges, we introduce TCM-Eval, the first dynamic and extensible benchmark

Daniele Ugo Leonzio, Paolo Bestagini, Marco Marcon, Stefano Tubaro

Water is a critical resource that must be managed efficiently. However, a substantial amount of water is lost each year due to leaks in Water Distribution Networks (WDNs). This underscores the need for reliable and effective leak detection and localization systems. In recent years, various solutions have been proposed, with data-driven approaches gaining inc

Premabrata Manna, S. I. Mistakidis, P. G. Kevrekidis, Pankaj Kumar Mishra

We investigate the dynamical formation of nonlinear patterns in one-dimensional ring condensates under bichromatic periodic modulation of the interaction strength. The stability phase diagram of the condensate's homogeneous density state is analytically derived through a suitable biharmonic variant of the Mathieu equation and computing the associated Floquet

Linji Long, Jinjiang Li, Min Zhang, Rui Sun

Suppose that $c,d,\alpha,\beta$ are real numbers satisfying the inequalities $1<d<c<79/71$ and $1<\alpha<\beta<6^{1-d/c}$. In this paper, it is proved that, for sufficiently large real numbers $N_1$ and $N_2$ subject to $\alpha\leqslant N_2/N_1^{d/c}\leqslant\beta$, the following Diophantine inequalities system \begin{align*} \begin{cases} |p_1^c+p_2^c+p_3^c

Xinke Jian, Zhiyuan Ren, Wenchi Cheng

Current empirically driven research on semantic communication lacks a unified theoretical foundation, preventing quantifiable Quality of Service guarantees, particularly for transmitting minimal structural semantics in emergency scenarios. This deficiency limits its evolution into a predictable engineering science. To address this, we establish a complete th

Tommaso Bevilacqua, Axel Klawonn, Martin Lanser, Adam Wasiak

The Virtual Element Method (VEM) is used to perform the discretization of the Poisson problem on polygonal and polyhedral meshes. This results in a symmetric positive definite linear system, which is solved iteratively using overlapping Schwarz domain decomposition preconditioners, where to ensure robustness and parallel scalability a second level has to be

João Dionísio, Ambros Gleixner, João Pedro Pedroso, Ksenia Bestuzheva

This paper proposes a mixed-integer nonlinear programming approach for joint scheduling of long-term maintenance decisions and short-term production for groups of complex machines with multiple interacting components. We introduce an abstract model where the production and the condition of machines are described by convex functions, allowing the model to be

Anna Frances-Abellan, David Endesfelder, Alfredo Hernandez, Gemma Armengol

Purpose: Since its initial release, the aim of Biodose Tools was to offer an easy-to-use platform to perform the mathematical calculations needed in biological dosimetry. This update 3.7.1, mainly focuses on new features related to large-scale emergency responses, like criticality accidents dose estimation and laboratory networks. Material and Methods: Biodo

Xinyi Zhang, Daoyi Gao, Naiqi Li, Angela Dai

We introduce ProcGen3D, a new approach for 3D content creation by generating procedural graph abstractions of 3D objects, which can then be decoded into rich, complex 3D assets. Inspired by the prevalent use of procedural generators in production 3D applications, we propose a sequentialized, graph-based procedural graph representation for 3D assets. We use t

Humaira Zafar, Mauro Fernandes Pereira

This paper delivers the first report of a mid-infrared (MIR) waveguide array design that employs complementary and asymmetric tapered Euler-shaped bends. These provide greater fabrication flexibility to achieve subwavelength-pitch integration while reducing crosstalk to below 30 dB across the 3.1 to 3.6 micron wavelength range. Unlike previous designs, which

Jingwen Fu, Ming Xiao, Zhonghao Lyu, Mikael Skoglund

Semantic communications for multi-modal data can transmit task-relevant information efficiently over noisy and bandwidth-limited channels. However, a key challenge is to simultaneously compress inter-modal redundancy and improve semantic reliability under channel distortion. To address the challenge, we propose a robust and efficient multi-modal task-oriente

Cecilie Hermansen, Jens Paaske

Self-sustained oscillators play a central role in the stabilization and synchronization of complex dynamical systems. A number of different physical systems are currently being investigated to clarify the importance of such active components in the quantum realm. Here we explore the properties of a driven dissipative electron-photon hybrid system based on su

Jin Cheng, Xiangxiang Dai, Ningning Ding, John C. S. Lui

Vector data trading is essential for cross-domain learning with vector databases, yet it remains largely unexplored. We study this problem under online learning, where sellers face uncertain retrieval costs and buyers provide stochastic feedback to posted prices. Three main challenges arise: (1) heterogeneous and partial feedback in configuration learning, (

Theron Guo, Kento Kaneko, Claude Le Bris, Anthony T. Patera

We consider the thermal dunking problem, in which a solid body is suddenly immersed in a fluid of different temperature, and study both the temporal evolution of the solid and the associated Biot number -- a non-dimensional heat transfer coefficient characterizing heat exchange across the solid-fluid interface. We focus on the small-Biot-number regime. The p

Shiqi Jiang, Tianyi Liang, Huayuan Ye, Changbo Wang

Music induced painting is a unique artistic practice, where visual artworks are created under the influence of music. Evaluating whether a painting faithfully reflects the music that inspired it poses a challenging perceptual assessment task. Existing methods primarily rely on emotion recognition models to assess the similarity between music and painting, bu

Kang Lu

We introduce a minimalistic presentation for the twisted Yangian ${}^\imath\mathscr Y$ associated with split symmetric pairs (or Satake diagrams) introduced in arXiv:2406.05067 via a Drinfeld type presentation. As applications, we establish an injective algebra homomorphism from ${}^\imath\mathscr Y$ to the Yangian $\mathscr Y$, thereby identifying ${}^\imat

Meiying Melissa Chen, Zhenyu Wang, Zhiyao Duan

Voice conversion models modify timbre while preserving paralinguistic features, enabling applications like dubbing and identity protection. However, most VC systems require access to target utterances, limiting their use when target data is unavailable or when users desire conversion to entirely novel, unseen voices. To address this, we introduce a lightweig

Xian-Li Yin, Meixi Guo, Jian Huang, Heung-wing Joseph Lee

Quantum batteries (QBs), acting as energy storage devices, have potential applications in future quantum science and technology. However, the QBs inevitably losses energy due to their interaction with environment. How to enhance the performance of the QBs in the open-system case remains an important challenge. Here we propose a scheme to realize the driven-d

Haochen Yan, Xu Guo, Arghadeep Pal, Xiaoyuan Huang

Non-Hermitian physics can be used to break time reversal symmetry and is important for interactions in a wide range of systems, from active matter and neural networks to metamaterials and non-equilibrium thermodynamics. In integrated photonic devices, non-Hermitian physics can be used for direction-dependent light propagation, reconfigurable light paths, sel

Barath Chandran. C, Srinivas Anumasa, Dianbo Liu

Diffusion models, though successful, are known to suffer from hallucinations that create incoherent or unrealistic samples. Recent works have attributed this to the phenomenon of mode interpolation and score smoothening, but they lack a method to prevent their generation during sampling. In this paper, we propose a post-hoc adjustment to the score function d

Yi Cai, Jinjiang Li, Yankun Sui, Fei Xue

Let $-1/2<a<0$ be a fixed real number and \begin{equation*} \Delta_{a}(x)=\sideset{}{'}\sum_{n\leq x} \sigma_a(n)-\zeta(1-a)x-\frac{\zeta(1+a)}{1+a}x^{1+a}+\frac{1}{2}\zeta(-a). \end{equation*} In this paper, we investigate the higher--power moments of $\Delta_a(x)$ and give the corresponding asymptotic formula for the integral $\int_{1}^{T}\Delta_a^k(x)\mat

Ken Nagai

We construct a unified analytic framework connecting Bernoulli numbers, zeta-regularization, and Fredholm determinants associated with trigonometric selector kernels. Starting from the Bernoulli-Stirling algebra, Euler-Maclaurin corrections are reinterpreted as spectral traces of compact operators. This bridge transforms discrete combinatorial data into cont

Beyza Mevlüde Amir, Mohammad Sadek, Nermine El-Sissi

Let $f \in \mathbb Q[x]$ be a square-free polynomial of degree at least $3$, $m_i$, $i=1,2,3$, odd positive integers, and $a_i$, $i=1,2,3$, non-zero rational numbers. We show the existence of a rational function $D\in\mathbb{Q}(v_1,v_2,v_3,v_4)$ such that the Jacobian of the quadratic twist of $y^2=f(x)$ and the Jacobian of the $m_i$-twist, respectively $2m_

Nail Fatkullin

A fairly brief and complete presentation of the Zwanzig-Mori projection operator technique is given.

Seungeon Lee, Soumi Das, Manish Gupta, Krishna P. Gummadi

Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters are typically trained for a single task, limiting their applicability in real-world settings where inputs may span diverse and unpredictable domains. At inference time, existing approaches combine multiple LoRAs

Lorenzo Lazzari, Jérémie Schuhmann, Othmane Meskine, Martina Morassi

Hybrid photonic circuits, harnessing the complementary strengths of multiple materials, represent a key resource to enable compact, scalable platforms for quantum technologies. In particular, the availability of bright sources of tunable biphoton states is eagerly awaited to meet the variety of applications currently under development. In this work we demons

Linna Wang, Zhixuan You, Qihui Zhang, Jiunan Wen

Large Language Models (LLMs) and causal learning each hold strong potential for clinical decision making (CDM). However, their synergy remains poorly understood, largely due to the lack of systematic benchmarks evaluating their integration in clinical risk prediction. In real-world healthcare, identifying features with causal influence on outcomes is crucial

Tim Bohne, Anne-Kathrin Patricia Windler, Martin Atzmueller

This paper proposes a novel neuro-symbolic approach for sensor signal-based knowledge discovery, focusing on identifying latent subclasses in time series classification tasks. The approach leverages gradient-based saliency maps derived from trained neural networks to guide the discovery process. Multiclass time series classification problems are transformed

Andre Opris

Evolutionary algorithms are widely used for solving multi-objective optimization problems. A prominent example is NSGA-III, which is particularly well suited for solving problems involving more than three objectives, distinguishing it from the classical NSGA-II. Despite its empirical success, the theoretical understanding of NSGA III remains very limited, es

Zhikang Chen, Sen Cui, Deheng Ye, Yu Zhang

Large Language Models (LLMs) have demonstrated strong reasoning capabilities through \emph{Chain-of-Thought} (CoT) prompting, which enables step-by-step intermediate reasoning. However, explicit CoT methods rely on discrete token-level reasoning processes that are prone to error propagation and limited by vocabulary expressiveness, often resulting in rigid a

Shuangqing Xu, Yifeng Zheng, Zhongyun Hua

Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known to suffer from a communication bottleneck. Meanwhile, the gradients shared by clients as well as the trained model may

Changyue Shi, Chuxiao Yang, Xinyuan Hu, Minghao Chen

Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photorealistic reconstruction. In real-world scenarios, due to equipment constraints, sometimes only sparse frames are accessible. In this paper, we propose Sparse4DGS, the first method for

Zhen Guo, Jinjiang Li, Linji Long, Min Zhang

Suppose that $a$ and $b$ are positive integers subject to $(a,b)=1$. For $n\in\mathbb{Z}^+$, denote by $\tau_{a,b}(n;\ell_1,M_1,l_2,M_2)$ the asymmetric two--dimensional divisor function with congruence conditions, i.e., \begin{equation*} \tau_{a,b}(n;\ell_1,M_1,l_2,M_2)=\sum_{\substack{n=n_1^an_2^b\\ n_1\equiv\ell_1\!\!\!\!\!\pmod{M_1}\\ n_2\equiv\ell_2\!\!

Zheng-Yuan Xue, Cheng-Yun Ding

The geometric phase stands as a foundational concept in quantum physics, revealing deep connections between geometric structures and quantum dynamical evolution. Unlike dynamical phases, geometric phases exhibit intrinsic resilience to certain types of perturbation, making them particularly valuable for quantum information processing, where maintaining coher

Matteo Pettenó, Alessandro Ilic Mezza, Alberto Bernardini

Explicit latent variable models provide a flexible yet powerful framework for data synthesis, enabling controlled manipulation of generative factors. With latent variables drawn from a tractable probability density function that can be further constrained, these models enable continuous and semantically rich exploration of the output space by navigating thei

Jannik Nitschke

Ensemble techniques in recommender systems have demonstrated accuracy improvements of 10-30%, yet their environmental impact remains unmeasured. While deep learning recommendation algorithms can generate up to 3,297 kg CO2 per paper, ensemble methods have not been sufficiently evaluated for energy consumption. This thesis investigates how ensemble techniques

Andong Li, Tong Lei, Rilin Chen, Kai Li

This paper revisits the neural vocoder task through the lens of audio restoration and propose a novel diffusion vocoder called BridgeVoC. Specifically, by rank analysis, we compare the rank characteristics of Mel-spectrum with other common acoustic degradation factors, and cast the vocoder task as a specialized case of audio restoration, where the range-spac

Luiz H. G. Tizei, Yves Auad, Florian Castioni, Mathieu Kociak

Electron spectroscopy implemented in electron microscopes provides high spatial resolution, down to the atomic scale, of the chemical, electronic, vibrational and optical properties of materials. In this review, we will describe how temporal coincidence experiments in the nanosecond to femtosecond range between different electron spectroscopies involving pho

Jérôme Amiaux, Fabien Malbet, Florence Ardellier-Desages, Eric Doumayrou

This study presents a comprehensive system analysis for an instrument onboard the Habitable Worlds Observatory (HWO), designed for high-precision, high-accuracy differential astrometry, with the primary scientific goal to determine the mass of Earth-like planets around the nearest Sun-like stars. The analysis integrates the definition of the mission profile,

Khashayar Alavi, Zhastay Yeltay, Lucie Flek, Akbar Karimi

When LLM agents work together, they seem to be more powerful than a single LLM in mathematical question answering. However, are they also more robust to adversarial inputs? We investigate this question using adversarially perturbed math questions. These perturbations include punctuation noise with three intensities (10%, 30%, 50%), plus real-world and human-

Lucas Acito, Matías N. Sempé

We provide a detailed derivation of the Schwarzian modes in the full geometry of the Ba\~nados-Teitelboim-Zanelli (BTZ) black hole at finite temperature, establishing the precise conditions under which they emerge from the general solution, thereby clarifying the absence of rotational modes in the full geometry. In addition, we demonstrate that the same mode

Tianhao Fu, Xinxin Xu, Weichen Xu, Jue Chen

Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more attempts are being made to apply LLMs to financial areas. A simple, direct application of LLM as an agent shows significant performance. Such methods are hindered by their slow in

Linlin Liu, Huihui Zheng

Family algebraic structures indexed by a semigroup arise naturally in renormalizations of quantum field theory. In this paper, we first define the notion of $\Omega$-associative $H$-pseudoalgebra, where the operations are indexed by pairs of elements from a semigroup $\Omega$. Then we construct $\Omega$-associative $H$-pseudoalgebras from associative $H$-pse

Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Ming-Hsuan Yang

Three-dimensional feature extraction is a critical component of autonomous driving systems, where perception tasks such as 3D object detection, bird's-eye-view (BEV) semantic segmentation, and occupancy prediction serve as important constraints on 3D features. While large image encoders, high-resolution images, and long-term temporal inputs can significantly

Cem Bingol, Matias Duran-Matute, Eckart Meiburg, Herman J. H. Clercx

This paper describes the evolution of two-dimensional (2D) gravity currents that flow against a horizontally uniform laminar pulsating flow. We study the effect of opposing mean flow amplitude and the oscillatory velocity amplitude on the evolution of the gravity current, the emergence of instabilities due to shear at the interface of heavy and light fluid a

Junji Hou, Junzhou Zhao, Shuo Zhang, Pinghui Wang

Motivated by the increasing risks of data misuse and fabrication, we investigate the problem of identifying synthetic time series generated by Time-Series Large Models (TSLMs) in this work. While there are extensive researches on detecting model generated text, we find that these existing methods are not applicable to time series data due to the fundamental

Sirui Wang, Jiang He, Natàlia Blasco Andreo, Xiao Xiang Zhu

Improving the quality of hyperspectral images (HSIs), such as through super-resolution, is a crucial research area. However, generative modeling for HSIs presents several challenges. Due to their high spectral dimensionality, HSIs are too memory-intensive for direct input into conventional diffusion models. Furthermore, general generative models lack an unde

Andrew Kresch, Sho Tanimoto, Yuri Tschinkel

We propose new invariants in equivariant birational geometry, combining equivariant intermediate Jacobians and the Burnside formalism, for smooth rationally connected threefolds with actions of finite groups.

Ramūnas Garunkštis, Jokūbas Putrius

In this paper, we demonstrate the existence of the second moment of the Selberg zeta function for a cofinite Fuchsian group at $σ=1$. Note that by employing the recent approach of Broucke and Hilberdink in proving the second moment theorem, we can circumvent the separation condition introduced by Landau for general Dirichlet series.

Zhisheng Zhang, Derui Wang, Yifan Mi, Zhiyong Wu

Recent advancements in speech synthesis technology have enriched our daily lives, with high-quality and human-like audio widely adopted across real-world applications. However, malicious exploitation like voice-cloning fraud poses severe security risks. Existing defense techniques struggle to address the production large language model (LLM)-based speech syn

Yuanshao Zhu, Xiangyu Zhao, Zijian Zhang, Xuetao Wei

Fine-grained urban flow inference is crucial for urban planning and intelligent transportation systems, enabling precise traffic management and resource allocation. However, the practical deployment of existing methods is hindered by two key challenges: the prohibitive computational cost of over-parameterized models and the suboptimal performance of conventi

Diego Gosmar, Anna Chiara Pallotta, Giovanni Zenezini

This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three s

Christian Bressen Pipper, Andreas Nordland, Klaus Kähler Holst

Testing intersections of null-hypotheses is an integral part of closed testing procedures for assessing multiple null-hypotheses under family-wise type 1 error control. Popular intersection tests such as the minimum p-value test are based on marginal p-values and are typically evaluated conservatively by disregarding simultaneous behavior of the marginal p-v

Meghyn Bienvenu, Quentin Manière

In this paper, we study the data complexity of querying inconsistent weighted description logic (DL) knowledge bases under recently-introduced cost-based semantics. In a nutshell, the idea is to assign each interpretation a cost based upon the weights of the violated axioms and assertions, and certain and possible query answers are determined by considering

Necati Sefercioglu, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim

Deep learning-based low-dose computed tomography reconstruction methods already achieve high performance on standard image quality metrics like peak signal-to-noise ratio and structural similarity index measure. Yet, they frequently fail to preserve the critical anatomical details needed for diagnostic tasks. This fundamental limitation hinders their clinica

Jānis Lazovskis, Ran Levi, Juliano Morimoto

We give bounds for dimension 0 persistent homology and codimension 1 homology of Vietoris--Rips, alpha, and cubical complex filtrations from finite sets related by enrichment (adding new elements), sparsification (removing elements), and aligning to a grid (uniformly discretizing elements). For enrichment we use barycentric subdivision, for sparsification we

Wei-You Liao, Ge Yan, Yujin Song, Tian-Ci Tian

The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal. However, QEM techniques incur substanti

Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen

Text-to-image generative models often exhibit bias related to sensitive attributes. However, current research tends to focus narrowly on single-object prompts with limited contextual diversity. In reality, each object or attribute within a prompt can contribute to bias. For example, the prompt "an assistant wearing a pink hat" may reflect female-inclined bia

Cornelius Grunwald, Gudrun Hiller, Kevin Kröninger, Lara Nollen

We present a global analysis of lepton-flavor-specific operators in the Standard Model Effective Field Theory (SMEFT), combining data from collider and flavor physics experiments. We systematically explore various lepton-flavor scenarios, including flavor-specific, universal, and democratic patterns, while employing a minimal flavor violation (MFV) ansatz in

Jean Philip Filling, Felix Post, Michael Wand, Denis Andrienko

We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focus on regressing scalar quantities and derive tensorial properties from their derivatives, our approach maintains $SO(3)$-equivariance through the use of local coordinate frames. Ou

Marcel Pehlke, Marc Jansen

We present a modular, explainable LLM-agent pipeline for decision support that externalizes reasoning into auditable artifacts. The system instantiates three frameworks: Vester's Sensitivity Model (factor set, signed impact matrix, systemic roles, feedback loops); normal-form games (strategies, payoff matrix, equilibria); and sequential games (role-condition

Xijie Zhang, Fengliang He, Hong-Ning Dai

Natural and efficient interaction remains a critical challenge for virtual reality and augmented reality (VR/AR) systems. Vision-based gesture recognition suffers from high computational cost, sensitivity to lighting conditions, and privacy leakage concerns. Acoustic sensing provides an attractive alternative: by emitting inaudible high-frequency signals and

Filip Beránek, Václav Diviš, Ivan Gruber

We present Pandar128, the largest public dataset for lane line detection using a 128-beam LiDAR. It contains over 52,000 camera frames and 34,000 LiDAR scans, captured in diverse real-world conditions in Germany. The dataset includes full sensor calibration (intrinsics, extrinsics) and synchronized odometry, supporting tasks such as projection, fusion, and t

Marc Jansen, Marcel Pehlke

This paper introduces an approach to increasing the explainability of artificial intelligence (AI) systems by embedding Large Language Models (LLMs) within standardized analytical processes. While traditional explainable AI (XAI) methods focus on feature attribution or post-hoc interpretation, the proposed framework integrates LLMs into defined decision mode

A. M. Baldini, L. Bianco, H. Benmansour, G. Cavoto

The use of Oxygen in gas mixtures for drift chambers is highly discouraged because Oxygen, being strongly electronegative, is generally believed to lead, even in very small quantities, to extremely reduced drift electron survival probability, thus preventing the detector's operation.The drift chamber of the MEG II experiment at PSI has been operating for sev

Guanghu Xie, Mingxu Li, Songwei Wu, Yang Liu

Depth perception of transparent and reflective objects has long been a critical challenge in robotic manipulation.Conventional depth sensors often fail to provide reliable measurements on such surfaces, limiting the performance of robots in perception and grasping tasks. To address this issue, we propose a novel depth completion network,HDCNet,which integrat

Khalil Hennara, Ahmad Bastati, Muhammad Hreden, Mohamed Motasim Hamed

The performance of large language models (LLMs) and large multimodal models (LMMs) depends heavily on the quality and scale of their pre-training datasets. Recent research shows that large multimodal models trained on natural documents where images and text are interleaved outperform those trained only on image-text pairs across a wide range of benchmarks, l

Roman Galactic Plane Survey Definition Committee

The Roman Galactic Plane Survey (RGPS) is a 700-hour program approved for early definition as a community-designed General Astrophysics Survey. It was selected following a proposal call for science programs that would benefit from an early community-based definition (Sanderson et al 2024). The community was invited to submit white papers and science pitches

Masaki Gen, Takuya Nomoto, Hiraku Saito, Taro Nakajima

We investigate uniaxial-stress effects on the magnetic phase diagram of the square-lattice itinerant magnet EuAl$_{4}$, where strong coupling among spin, lattice, and charge produces a variety of helimagnetic phases, including rhombic and square skyrmion lattices. Combining resistivity and magnetization measurements with neutron scattering, we find that comp

Luanyuan Dai, Xiaoyu Du, Jinhui Tang

Two-view correspondence pruning aims to accurately remove incorrect correspondences (outliers) from initial ones and is widely applied to various computer vision tasks. Current popular strategies adopt multilayer perceptron (MLP) as the backbone, supplemented by additional modules to enhance the network ability to handle context information, which is a known

Abdullah Al Maruf, Aditi Golder, Zakaria Masud Jiyad, Abdullah Al Numan

Emotion detection from text seeks to identify an individual's emotional or mental state - positive, negative, or neutral - based on linguistic cues. While significant progress has been made for English and other high-resource languages, Bengali remains underexplored despite being the world's fourth most spoken language. The lack of large, standardized datase

Leander Grech, Matthias G. Krauss, Mirko Consiglio, Tony J. G. Apollaro

Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits. Central to realizing the potential of quantum computing are perfect entangling (PE) two-qubit gates, which serve as a critical building block for universal quantum computation. I

Shunyu Wu, Tianyue Li, Yixuan Leng, Jingyi Suo

Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the quality of time series data is crucial to TSFM performance, rendering an accurate and efficient data valuation of time series for TSFMs indispensable. However, traditional data valu

Tingyu Jiang, Shen Li, Yiyao Song, Lan Zhang

Instruction tuning plays a critical role in enhancing the performance and efficiency of Large Language Models (LLMs). Its success depends not only on the quality of the instruction data but also on the inherent capabilities of the LLM itself. Some studies suggest that even a small amount of high-quality data can achieve instruction fine-tuning results that a

Guang Yang, Lixia Luo, Qiongxiu Li

Federated clustering allows multiple parties to discover patterns in distributed data without sharing raw samples. To reduce overhead, many protocols disclose intermediate centroids during training. While often treated as harmless for efficiency, whether such disclosure compromises privacy remains an open question. Prior analyses modeled the problem as a so-

Annie Millet, Svetlana Roudenko

We study the focusing $L^2$-critical and supercritical stochastic nonlinear Schr\"odinger equation subject to additive or multiplicative noise. We investigate global or long time behavior of solutions in $H^1$, which would correspond to global well-posedness in the deterministic case, with either deterministic or random initial data, and establish quantitati

Shien Zhu, Samuel Bohl, Robin Oester, Gustavo Alonso

Mixture-of-Experts (MoE) Large Language Models (LLMs) efficiently scale-up the model while keeping relatively low inference cost. As MoE models only activate part of the experts, related work has proposed expert prediction and caching methods to prefetch the experts for faster inference. However, existing approaches utilize the activations from the previous

Marcel Müller

This dissertation explores the application of multi-agent reinforcement learning (MARL) for handling deadlocks in intralogistics systems that rely on autonomous mobile robots (AMRs). AMRs enhance operational flexibility but also increase the risk of deadlocks, which degrade system throughput and reliability. Existing approaches often neglect deadlock handlin

Fei Zhao, Chonggang Lu, Haofu Qian, Fangcheng Shi

As a key medium for human interaction and information exchange, social networking services (SNS) pose unique challenges for large language models (LLMs): heterogeneous workloads, fast-shifting norms and slang, and multilingual, culturally diverse corpora that induce sharp distribution shift. Supervised fine-tuning (SFT) can specialize models but often trigge

Nikolas Adaloglou, Diana Petrusheva, Mohamed Asker, Felix Michels

Large-scale visual out-of-distribution (OOD) detection has witnessed remarkable progress by leveraging vision-language models such as CLIP. However, a significant limitation of current methods is their reliance on a pre-defined set of in-distribution (ID) ground-truth label names (positives). These fixed label names can be unavailable, unreliable at scale, o

Ruijie Zhang, Bixin Zeng, Shengpeng Wang, Fuhui Zhou

Millimeter-wave radar offers a promising sensing modality for autonomous systems thanks to its robustness in adverse conditions and low cost. However, its utility is significantly limited by the sparsity and low resolution of radar point clouds, which poses challenges for tasks requiring dense and accurate 3D perception. Despite that recent efforts have show

Euihyeok Lee, Seonghyeon Kim, SangHun Im, Heung-Seon Oh

Self-talk-an internal dialogue that can occur silently or be spoken aloud-plays a crucial role in emotional regulation, cognitive processing, and motivation, yet has remained largely invisible and unmeasurable in everyday life. In this paper, we present MutterMeter, a mobile system that automatically detects vocalized self-talk from audio captured by earable

Brage Eilertsen, Røskva Bjørgfinsdóttir, Francielle Vargas, Ali Ramezani-Kebrya

The opaque nature of deep learning models presents significant challenges for the ethical deployment of hate speech detection systems. To address this limitation, we introduce Supervised Rational Attention (SRA), a framework that explicitly aligns model attention with human rationales, improving both interpretability and fairness in hate speech classificatio

Tom Bachmann, Anton Engelmann, Klaus Mattis

We extend the work of Bousfield and Kan on monadic resolutions of spaces to $\infty$-topoi, with applications to genuine $G$-equivariant spaces ($G$ a finite group) and motivic spaces over a perfect field. In particular, we give a proof of the principal fibration lemma in this context. We apply the principal fibration lemma to prove convergence of several ki

Marc Jansen, Christophe Verdot

This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into w

Alexander B. Shick, Evgenia A. Tereshina-Chitrova

The intermediate valence of Ce in CeCo$_5$ challenges standard density functional theory (DFT) and static DFT+$U$ approaches, which fail to capture its magnetic properties. By combining DFT+$U$ with exact diagonalization of the Anderson impurity model for the Ce 4$f$ shell, we find a substantial reduction of Ce spin and orbital moments, consistent with DFT+D

Yimei Zhang, Guojiang Shen, Kaili Ning, Tongwei Ren

Region representation learning plays a pivotal role in urban computing by extracting meaningful features from unlabeled urban data. Analogous to how perceived facial age reflects an individual's health, the visual appearance of a city serves as its "portrait", encapsulating latent socio-economic and environmental characteristics. Recent studies have explored

Xinran Li, Yu Liu, Jiaqi Qiao, Xiujuan Xu

Emotion Recognition in Conversation (ERC) is a crucial task for understanding human emotions and enabling natural human-computer interaction. Although Large Language Models (LLMs) have recently shown great potential in this field, their ability to capture the intrinsic connections between explicit and implicit emotions remains limited. We propose a novel ERC

Lorenzo Andreaus

Let $X\subseteq \mathbb{P}^3$ be a smooth projective surface of degree $d\ge 4$ defined over a number field $K$, and let $N_{X^{\prime}}(B)$ be the number of rational points of $X$ of height at most $B$ that do not lie on lines contained in $X$. Assuming a suitable hypothesis on the size of the rank of Abelian varieties, we show that $N_{X^{\prime}}(B)\ll_{K

Serhii Zabolotnii

Classical estimators for ARIMA parameters (MLE, CSS, OLS) assume Gaussian innovations, an assumption frequently violated in financial and economic data exhibiting asymmetric distributions with heavy tails. We develop and validate the second-order polynomial maximization method (PMM2) for estimating ARIMA$(p,d,q)$ models with non-Gaussian innovations. PMM2 is

Moreno Invitti

We prove a linearization theorem for pre-rings of endogenies acting on a definable abelian group of finite dimension. Observe that no assumptions on the connectivity of A are made. We also prove a similar result when one of the two pre-rings is of quasi-endomorphisms. A corollary of these results is a generalization of Zilber's Field Theorem for finite-dimen

Zidong Chen, Fadratul Hafinaz Hassan

Deploying lightweight medical image segmentation models on edge devices presents two major challenges: 1) efficiently handling the stark contrast between lesion boundaries and background regions, and 2) the sharp drop in accuracy that occurs when pursuing extremely lightweight designs (e.g., <0.5M parameters). To address these problems, this paper proposes T

Moreno Invitti

We prove that a finite-dimensional omega-categorical group is finite-by-abelian-by-finite and that a finite-dimensional omega-categorical ring is virtually finite-by-null.

Frédéric Jaron, Valentí Bosch-Ramon

The $\gamma$-ray emitting binary LS I +61 303 exhibits periodic emission across the electromagnetic spectrum, from radio up to the very-high-energy regime. The most prominent features are the three periods $P_1 = 26.5$ d, $P_2 = 26.9$ d, and $P_{\rm long} = 4.6$ years. Occasionally, a fourth period of 26.7 d is also detected. Mathematically, these four perio

Thomas Compton

Community Unionism has served as a pivotal concept in debates on trade union renewal since the early 2000s, yet its theoretical coherence and political significance remain unresolved. This article investigates why CU has gained such prominence -- not by testing its efficacy, but by mapping how it is constructed, cited, and contested across the scholarly lite

Katharina Beckh, Sven Heuser, Stefan Rüping

High-stakes decisions informed by decision support systems require explicit evidence. While prior work focuses on short sufficient evidence, regulatory compliance and medical billing call for complete evidence: all relevant input tokens that support a decision. We formulate complete evidence extraction as a task and study it in a medical coding setting. Moti

Pradeep M, Twinkle Tripathy

The objective of this paper is to investigate graph-theoretic conditions for structural herdability of an LTI system. In particular, we are interested in the structural sign (SS) herdability of a system wherein the underlying digraph representing it is signed. Structural herdability finds applications in various domains like power networks, biological networ

Jacek Komasa

The quantum electrodynamic Araki-Sucher correction arising from the interaction between electrons and nuclei is calculated for rovibrational energy levels of the hydrogen molecule and its isotopologues. The corresponding expectation value $\langle r_{en}^{-3}\rangle$ is evaluated across a wide range of internuclear distances using the Born-Oppenheimer approx

S. Gokul Krishnan, Mohd Asim Aftab, Shehab Ahmed, Charalambos Konstantinou

The growing integration of renewable energy sources (RESs) in modern power systems has intensified the need for resilient and efficient microgrid solutions. DC microgrids have gained prominence due to their reduced conversion losses, simplified interfacing with DC-based RESs, and improved reliability. To manage the inherent variability of RESs and ensure sta