November 2025 arXiv papers — page 171

Md. Faiaad Rahman, Md. Ashaduzzaman Niloy, Ehsanur Rahman, Ahmed Zubair

The widespread use of lead-based materials in tandem solar cells raises critical environmental and health concerns due to their inherent toxicity and risk of contamination. To address this challenge, we focused on lead-free tandem architectures based on non-toxic, environmentally benign materials such as tin-based perovskites and kesterites, which are essent

Jacob Fields, David Radice

We present new numerical-relativity simulations of a magnetized binary neutron star merger performed with AthenaK. The simulations employ a temperature- and composition-dependent tabulated nuclear equation of state, with initially dipolar fields with a maximum initial strength of ${\sim}10^{16}\ {\rm G}$ which extend outside the stars. We employ adaptive mes

Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li

Multimodal Stance Detection (MSD) is a crucial task for understanding public opinion on social media. Existing methods predominantly operate by learning to fuse modalities. They lack an explicit reasoning process to discern how inter-modal dynamics, such as irony or conflict, collectively shape the user's final stance, leading to frequent misjudgments. To ad

Esteban M. Ruiz, Juan E. Tapia, Reinel T. Soto, Christoph Busch

Remote identity verification is essential for modern digital security; however, it remains highly vulnerable to sophisticated Presentation Attacks (PAs) that utilise forged or manipulated identity documents. Although Deep Learning (DL) has driven advances in Presentation Attack Detection (PAD), the field is fundamentally limited by a lack of data and the poo

Animesh Karnewar, Denis Korzhenkov, Ioannis Lelekas, Adil Karjauv

We introduce Neodragon, a text-to-video system capable of generating 2s (49 frames @24 fps) videos at the 640x1024 resolution directly on a Qualcomm Hexagon NPU in a record 6.7s (7 FPS). Differing from existing transformer-based offline text-to-video generation models, Neodragon is the first to have been specifically optimised for mobile hardware to achieve

Alessio Arcudi, Alessandro Ferreri, Francesco Borsatti, Gian Antonio Susto

Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branchin

Yan He, Adetunmise C. Dada

The 1.8-2.3 {\mu}m band lies within the short-wavelength infrared (SWIR) region and serves as a key window for a wide range of applications, including quantum sensing, molecular spectroscopy, and free-space quantum and classical optical communication. Despite its significance, optical devices operating in this band still face two major challenges: chromatic

Philipp Schaad, Tal Ben-Nun, Patrick Iff, Torsten Hoefler

Scientific computing applications heavily rely on multi-level loop nests operating on multidimensional arrays. This presents multiple optimization opportunities from exploiting parallelism to reducing data movement through prefetching and improved register usage. HPC frameworks often delegate fine-grained data movement optimization to compilers, but their lo

Mahmoud El-Bahnasawi

This paper addresses the critical challenge of developing computationally efficient hate speech detection systems that maintain competitive performance while being practical for real-time deployment. We propose a novel three-layer framework that combines rule-based pre-filtering with a parameter-efficient LoRA-tuned BERTweet model and continuous learning cap

V. Roatti, G. Picogna, F. Marzari

We investigate the formation of dust gaps in circumstellar disks driven by the presence of multiple low-mass planets, focusing on the distinct physical mechanisms that operate across different gas-dust coupling regimes. We performed 2D hydrodynamical simulations of multiple planets embedded in a circumstellar disk using the PLUTO code, with the addition of d

Nabil Baouche, Amine Ahriche

We perform a comprehensive phenomenological analysis of the Scale Invariant Two Higgs Doublet Model (\textit{SI2HDM})~\cite{Lee:2012jn}. In this framework, the electroweak symmetry breaking is triggered radiatively, and the entire scalar mass spectrum, including that of the $125$ \textrm{GeV} Higgs boson, is generated at the one loop level. After imposing st

Xinyuan Yan, Shusen Liu, Kowshik Thopalli, Bei Wang

Sparse autoencoders (SAEs) have emerged as a powerful tool for uncovering interpretable features in large language models (LLMs) through the sparse directions they learn. However, the sheer number of extracted directions makes comprehensive exploration intractable. While conventional embedding techniques such as UMAP can reveal global structure, they suffer

Teije Kuijper

We construct a Brownian motion on complex partial flag manifolds with blocks of equal size as a matrix-valued diffusion from a Brownian motion on the unitary group. This construction leads to an explicit expression for the characteristic function of the joint distribution of the stochastic areas on these manifolds. The limit law of these stochastic areas is

Zhihui Ke, Yuyang Liu, Xiaobo Zhou, Tie Qiu

Streaming free-viewpoint video~(FVV) in real-time still faces significant challenges, particularly in training, rendering, and transmission efficiency. Harnessing superior performance of 3D Gaussian Splatting~(3DGS), recent 3DGS-based FVV methods have achieved notable breakthroughs in both training and rendering. However, the storage requirements of these me

Hanwen Liu, Yixuan Ma, Shi Jin, Yuguang Wang

Attention mechanism is a significant part of Transformer models. It helps extract features from embedded vectors by adding global information and its expressivity has been proved to be powerful. Nevertheless, the quadratic complexity restricts its practicability. Although several researches have provided attention mechanism in sparse form, they are lack of t

Alejandro J. Garza, Jonte R. Hance

The precision with which we can measure operators that do not commute with conserved quantities is limited by the need to preserve the associated global symmetries. We show how to construct a local hidden-variable model that violates Bell inequalities by interpreting this measurement error as altering the measure space of the hidden variables. This provides

Fanghui Song, Zhongjian Wang, Jiebao Sun

Optimal transport (OT) provides a principled framework for learning mappings between probability distributions, and has found broad applications in generative modeling, inverse problems and scientific computing. Recently, flow matching methods have emerged as an efficient paradigm for learning continuous-time transport dynamics. However, existing OT-based fl

Yanfei Xiang, Yuan Gao, Hao Wu, Quan Zhang

Accurate and efficient global ocean state estimation remains a grand challenge for Earth system science, hindered by the dual bottlenecks of computational scalability and degraded data fidelity in traditional data assimilation (DA) and deep learning (DL) approaches. Here we present an AI-driven Data Assimilation Framework for Ocean (ADAF-Ocean) that directly

Zhangsong Li

We study the computational task of detecting and estimating correlated signals in a pair of spiked matrices $$ X=\tfrac{\lambda}{\sqrt{n}} xu^{\top}+W, \quad Y=\tfrac{\mu}{\sqrt{n}} yv^{\top}+Z $$ where the spikes $x,y$ have correlation $\rho$. Specifically, we consider two fundamental models: (1) Correlated spiked Wigner model with signal-to-noise ratio $\l

Mehul Srivastava, Trishna Gunna, Makarand Kandiyaped Serkad, Manu Sebastian

Compliant mechanisms utilise elastic deformation of their segments to transmit motion or force. The utility and behaviour of specific compliant mechanisms can be enhanced by introducing an engaging and disengaging ability with its elastic segments. Towards this, we present an engaging-disengaging compliant mechanism (EDCM) that can switch its stiffness betwe

Rumen Bachev, Milen Minev, Anton Strigachev, Alexander Kurtenkov

BL Lacertae is not only archetypical of an entire class of jet-dominated active galactic nuclei, blazars, but also one of the most active and rapidly changing objects in this class. In the fall of 2024 (September--November), BL Lacertae underwent another episode of strong optical activity, reaching an R-band magnitude of about 12 and showing extremely rapid

Huiling Liu, Yan Lu, Hui Hong, Huiyuan Wang

Radio active galactic nuclei (AGNs) eject a huge amount of energy into the surrounding medium and are thought to potentially prevent gas cooling and maintain the quiescence of massive galaxies. The short-lived, sporadic, and anisotropic nature of radio activities, coupled with the detection of abundant cold gas around some massive quiescent galaxies, raise q

Hassan Hizeh, Rim Chighri, Muhammad Mahboob Ur Rahman, Mohamed A. Bahloul

The current body of research on Parkinson's disease (PD) screening, monitoring, and management has evolved along two largely independent trajectories. The first research community focuses on multimodal sensing of PD-related biomarkers using noninvasive technologies such as inertial measurement units (IMUs), force/pressure insoles, electromyography (EMG), ele

Vincenzo Mottola, Alessandro Sardellitti, Filippo Milano, Luigi Ferrigno

The Buckingham's $\pi$, theorem has been recently introduced in the context of Non destructive Testing \& Evaluation (NdT\&E) , giving a theoretical basis for developing simple but effective methods for multi-parameter estimation via dimensional analysis. Dimensional groups, or $\pi-$groups, allow for the reduction of the number of parameters affecting the d

Ali Ghalavand, Qing Jie, Zemin Jin, Xueliang Li

According to a study by Erd\H{o}s et al. in 1975, the anti-Ramsey number of a graph \(G\), denoted as \(AR(n, G)\), is defined as the maximum number of colors that can be used in an edge-coloring of the complete graph \(K_n\) without creating a rainbow copy of \(G\). In this paper, we investigate the anti-Ramsey number under edge deletion and demonstrate tha

Zihui Zhao, Yifei Zhang, Zheng Wang, Yang Li

The raw depth images captured by RGB-D cameras using Time-of-Flight (TOF) or structured light often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts, which limit their applications in downstream vision tasks. Existing methods address this problem through depth completion in the image domain, but they overlook the p

Wang-Tao Zhou, Zhao Kang, Ke Yan, Ling Tian

Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure t

Diantong Li, Qiaoling Wei, Meirong Zhang, Zhe Zhou

We study planar bicycle dynamics via the rotation number function associated with a closed front track and bicycle length R. We prove that mode-locking plateaus occur only at integer rotation numbers and that the rotation number function is real-analytic off resonance. From the rotation number function we introduce two new geometric invariants: the critical

Barbara Łupińska, Piotr Rybka

We improve the time decay estimates of solutions to the one-dimensional fractional diffusion equation involving the Caputo derivative. The equation is considered on the half-line. Depending on the boundary condition, we show that solutions converge in $L^p$, $p>1$ to a multiple of the self-similar solutions or decay to zero. The convergence rate is provided.

Hui Zeng, Daming Zhao, Pengfei Yang, WenXuan Hou

Generative reasoning with large language models (LLMs) often involves long decoding sequences, leading to substantial memory and latency overheads from accumulating key-value (KV) caches. While existing KV compression methods primarily focus on reducing prefill memory from long input sequences, they fall short in addressing the dynamic and layer-sensitive na

Enis Golaszewski, Alan T. Sherman, Edward Zieglar, Jonathan D. Fuchs

As a case study in cryptographic binding, we present a formal-methods analysis of the cryptographic channel binding mechanisms in the Fast IDentity Online (FIDO) Universal Authentication Framework (UAF) authentication protocol, which seeks to reduce the use of traditional passwords in favor of authentication devices. First, we show that UAF's channel binding

Muzakkiruddin Ahmed Mohammed, John R. Talburt, Leon Claasssens, Adriaan Marais

Industrial part specification extraction from unstructured text remains a persistent challenge in manufacturing, procurement, and maintenance, where manual processing is both time-consuming and error-prone. This paper introduces a retrieval-augmented multi-LLM ensemble framework that orchestrates nine state-of-the-art Large Language Models (LLMs) within a st

Susovan Pal, Roger P. Woods, Suchit Panjiyar, Elizabeth Sowell

We present a Riemannian framework for linear and quadratic discriminant classification on the tangent plane of the shape space of curves. The shape space is infinite dimensional and is constructed out of square root velocity functions of curves. We introduce the idea of mean and covariance of shape-valued random variables and samples from a tangent space to

Yi Gao, Xi Xiong, Karl H. Johansson, Li Jin

This paper considers coordination of platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks in the face of three practically important factors, viz. time-varying traffic demand, random CAV platoon sizes, and capacity breakdowns. Platoon coordination is essential to smoothen the interaction between CAV platoons and non-CAV traffic.

Derya Sürgit, Mücahit Kuz, Volkan Bakış, Chris Engelbrecht

As part of an ongoing programme of observing detached eclipsing binary stars in the southern sky, we present the first analysis of spectroscopic observations of the Algol-type binary system DG Mic. A spectroscopic analysis of mid-resolution spectra allowed us to constrain the effective temperature of the primary component and to test the consistency of the s

Feng Lu, Tong Jin, Canming Ye, Yunpeng Liu

Visual place recognition (VPR) is typically regarded as a specific image retrieval task, whose core lies in representing images as global descriptors. Over the past decade, dominant VPR methods (e.g., NetVLAD) have followed a paradigm that first extracts the patch features/tokens of the input image using a backbone, and then aggregates these patch features i

Deng Yixuan, Ji Xiaoqiang

Large Language Models (LLMs) often exhibit implicit biases and discriminatory tendencies that reflect underlying social stereotypes. While recent alignment techniques such as RLHF and DPO have mitigated some of these issues, they remain limited in addressing culturally specific and multi-dimensional forms of discrimination. This paper proposes a Multi-Reward

Thomas Jannaud, Henrik N. Latter

The magnetorotational instability (MRI) plays a crucial role in the evolution of many types of accretion disks. It is often studied using ideal-MHD numerical simulations. In principle, such simulations should be numerically converged, i.e. their properties should not change with resolution. Convergence is often assessed via the MRI quality factor, $Q$, the r

Ziying Chen, Yong Li

We consider the spatio-temporal periodic problem for the Navier-Stokes equations with a small external force in the rotational framework. We prove the existence and uniqueness of the rotating periodic, spiral-like almost periodic and spiral-like almost automorphic solutions of Navier-Stokes equations with the Coriolis force.

Si Zuo, Yuqing Song, Sahar Golipoor, Ying Liu

Recent research has demonstrated the complementary nature of camera-based and inertial data for modeling human gestures, activities, and sentiment. Yet, despite its growing importance for environmental sensing as well as the advance of joint communication and sensing for prospective WiFi and 6G standards, a dataset that integrates these modalities with radio

Priyansh Srivastava, Romit Chatterjee, Abir Sen, Aradhana Behura

Video Frame Interpolation (VFI) remains a cornerstone in video enhancement, enabling temporal upscaling for tasks like slow-motion rendering, frame rate conversion, and video restoration. While classical methods rely on optical flow and learning-based models assume access to dense ground-truth, both struggle with occlusions, domain shifts, and ambiguous moti

Alexander F. Zakharov

It would be reasonable to recall some critical issues in physical cosmology development. GR was created by A. Einstein in 1915. In 1917 Einstein proposed the first (static) cosmological model. Soon after the A. Eddington proved that the model is unstable therefore it can not be realizable in nature. In 1922 and 1924 A. A. Friedmann found non-stationary solut

Longhua Li, Lei Qi, Xin Geng

Edge computing in person re-identification (ReID) is crucial for reducing the load on central cloud servers and ensuring user privacy. Conventional compression methods for obtaining compact models require computations for each individual student model. When multiple models of varying sizes are needed to accommodate different resource conditions, this leads t

Kristine Kate Torres, Isabela G. Huckabee, Patrizia Phem Odo, Danielle Baldono

We present updated transit timing measurements for the hot Jupiter WASP-135 b using three new ground-based transit observations obtained with Leia, a 0.6-meter telescope operated by NASA's Exoplanet Watch at the Table Mountain Facility. These observations, conducted as part of Exoplanet Watch citizen science initiative, were analyzed with the EXOplanet Trans

Jinhong Jia, Chuanting Jiang, Yiqun Li, Mengmeng Liu

We investigate a local modification of a variable-order fractional wave equation, which describes the propagation of diffusive wave in viscoelastic media with evolving physical property. We incorporate an equivalent formulation to prove the well-posedness of the model as well as its high order regularity estimates. To accommodate the convolution term in the

Quanlong Wang, Richard D. P. East, Razin A. Shaikh, Lia Yeh

We introduce the Spin-ZX calculus as an elevation of Penrose's diagrams and associated binor calculus to the level of a formal diagrammatic language. The power of doing so is illustrated by the variety of scientific areas we apply it to: permutational quantum computing, quantum machine learning, condensed matter physics, and quantum gravity. Respectively, we

Myunghyun Rhee, Sookyung Choi, Euiseok Kim, Joonseop Sim

The escalating context length in Large Language Models (LLMs) creates a severe performance bottleneck around the Key-Value (KV) cache, whose memory-bound nature leads to significant GPU under-utilization. This paper introduces Mixture of Shared KV Attention (MoSKA), an architecture that addresses this challenge by exploiting the heterogeneity of context data

Kenta Sudo, Yuki Yanagi, Mitsuru Akaki, Hiroshi Tanida

Spontaneous breaking of time-reversal and spatial-inversion symmetries in solids triggers diverse intriguing phenomena. Although these phenomena have been extensively studied in insulators, similar investigations for metals remain limited. Herein, we report the observation and properties of spontaneous (i.e., zero-magnetic field) nonreciprocal charge transpo

Jin Sun, Zhi-Peng Xing, Xiao-Gang He

Motivated by the recent 3.9$\sigma$ evidence for CP violation from the LHCb collaboration in decays of an anti-triplet beauty baryon to a charmonium, an octet baryon, and a pseudoscalar meson, we perform, for the first time, a systematic analysis of this class of decays within the framework of flavor $SU(3)$ symmetry. Several predictions for branching ratios

Márcio Cavalcante, Chulkwang Kwak, José Marques

In this paper, we establish local well-posedness for the Cauchy problem associated with the Kawahara equation on a general metric star graph. Initially, we identify suitable boundary conditions that produce a well-behaved dynamics for the linear equation. Subsequently, we derive the integral formula using the forcing operator method, previously applied to th

Sulaimon Oyeniyi Adebayo, Ayaz H. Khan

Medical image denoising is essential for improving image quality while minimizing the exposure of sensitive information, particularly when working with large-scale clinical datasets. This study explores distributed deep learning for denoising chest X-ray images from the NIH Chest X-ray14 dataset, using additive Gaussian noise as a lightweight obfuscation tec

Adit Desai, Sudipta Roy, Mohna Chakraborty

Vision Language Models (VLMs) are increasingly deployed across downstream tasks, yet their training data often encode social biases that surface in outputs. Unlike humans, who interpret images through contextual and social cues, VLMs process them through statistical associations, often leading to reasoning that diverges from human reasoning. By analyzing how

N. P. Konstantinidis

The ground state of the classical antiferromagnetic XX model in a magnetic field is calculated for spins mounted on the vertices of the icosahedron. The magnetization is characterized by two discontinuities as a function of the external field. For a wide field range above the first discontinuity the ground state is degenerate, with two spins related by spati

Atsushi Miki, Toshiyasu Matsushima

Private information retrieval (PIR) is a mechanism for efficiently downloading messages while keeping the index of the desired message secret from the servers. PIR schemes have been extended to various scenarios with adversarial servers: PIR schemes where some servers are unresponsive or return noisy responses are called robust PIR and Byzantine PIR, respect

Shivank Saxena, Dhruv Srivastava, Makarand Tapaswi

Recent advances in text-to-image models have enabled a new era of creative and controllable image generation. However, generating compositional scenes with multiple subjects and attributes remains a significant challenge. To enhance user control over subject placement, several layout-guided methods have been proposed. However, these methods face numerous cha

Favour Yahdii Aghaebe, Tanefa Apekey, Elizabeth Williams, Nafise Sadat Moosavi

Clinical interventions often hinge on age: medications and procedures safe for adults may be harmful to children or ineffective for older adults. However, as language models are increasingly integrated into biomedical evidence synthesis workflows, it remains uncertain whether these systems preserve such crucial demographic distinctions. To address this gap,

Chennan Wang, Yuhang Zhang, Zhen Zhao, Zhouyouwei Lu

Using the muon as a sensitive local magnetic probe, we investigated the layered kagome superconductor Cs(V$_{0.95}$Ti$_{0.05}$)$_3$Sb$_5$, a material notably devoid of both static magnetic moments and long-range charge order. Our transverse-field $\mu$SR measurements reveal that the local magnetic susceptibility, obtained via the muon Knight shift, is domina

Amandine Doliva-Dolinsky, N. F. Martin, Michelle L. M. Collins

The arrangement of M31's dwarf galaxies exhibits anisotropy, with the majority located in the hemisphere between the Milky Way and the host galaxy. This study aims to investigate whether M31's present location is aligned with the center of its distribution of dwarf galaxies. We use forward modeling to infer the center of the M31 satellite 3D spatial distribu

Aleksandr Rotkevich

We construct an example of a Lebesgue space with variable exponent on which Cauchy-Leray-Fantappi\`{e} operator associated with a complex ellipsoid is not bounded. This result extends previous counterexamples for the unit ball and demonstrates that the logarithmic continuity condition for the exponent function $p(\cdot)$ is sharp even for non-strictly convex

Xianhui Meng, Yukang Huo, Li Zhang, Liu Liu

Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored problem due to their inherent kinematic constraints. To address these challenges, this work proposes a novel point-pair-based pose tracking framework, termed \textbf{PPF-Tracker}. T

Jianbo Yuan, Jing Dai, Yerui Fan, Yaxiong Wu

The human arm exhibits remarkable capabilities, including both explosive power and precision, which demonstrate dexterity, compliance, and robustness in unstructured environments. Developing robotic systems that emulate human-like operational characteristics through musculoskeletal structures has long been a research focus. In this study, we designed a novel

Jose M. Campos-Salazar, Felipe Santander, Eduardo Keim

Dryers play a central role in the processing of phosphate rock, where moisture removal is essential for downstream handling and energy efficiency. Due to the inherently nonlinear and multivariable nature of these systems, accurate modeling and control remain industrial challenges. This article presents a comprehensive nonlinear dynamic model of a phosphate-p

P. A. G. Monteiro, C. J. A. P. Martins

The discovery of the accelerated expansion of the universe highlighted General Relativity's inability to naturally account for dark energy without invoking a finely tuned cosmological constant. In response, a wide range of alternative paradigms have been proposed. Among these, Teleparallel Gravity and Symmetric Teleparallel Gravity, which depart from the Rie

Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han

Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, the entropy of LLMs usually collapses during RLVR training, leading to premature convergence to suboptimal local minima and hindering further performance improvement. Although various app

Tiago da Cruz, Bernardo Tavares, Francisco Belo

Retrieval-Augmented Generation (RAG) systems combine Large Language Models (LLMs) with external knowledge, and their performance depends heavily on how that knowledge is represented. This study investigates how different Knowledge Graph (KG) construction strategies influence RAG performance. We compare a variety of approaches: standard vector-based RAG, Grap

Idoia Cortes Garcia, P. Förster, W. Schilders, S. Schöps

Stiff ordinary differential equations (ODEs) play an important role in many scientific and engineering applications. Often, the dependence of the solution of the ODE on additional parameters is of interest, e.g.\ when dealing with uncertainty quantification or design optimization. Directly studying this dependence can quickly become too computationally expen

Prateek Singh, Moumita Dholey, P. K. Vinod

In breast ultrasound images, precise lesion segmentation is essential for early diagnosis; however, low contrast, speckle noise, and unclear boundaries make this difficult. Even though deep learning models have demonstrated potential, standard convolutional architectures frequently fall short in capturing enough global context, resulting in segmentations tha

G. Górski, K. Kucab, T. Domański

We study the charge transport and thermoelectric properties of the junction, comprising double quantum dot embedded in T-shaped geometry on the interface between two normal/ferromagnetic electrodes and superconducting lead. We show that the interdot coupling plays major role in controlling the local and nonlocal transport properties of this setup. For the we

Guanlue Li, Xufeng Zhao, Fang Wu, Sören Laue

Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target receptors remains a significant challenge in computational protein design. In this work, we introduce PepBridge, a n

Xing-Peng Yang, Wen-Cong Chen

Magnetic braking (MB) mechanism plays a vital role throughout the evolution of low-mass X-ray binaries (LMXBs). Considering the standard MB prescription, the initial orbital periods of LMXBs that can evolve into binary millisecond pulsar (MSP) with He white dwarfs (WDs) and short orbital periods ($2-9~\rm hours$) are within an extremely narrow interval, whic

Rui Jia, Min Zhang, Fengrui Liu, Bo Jiang

In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirements while supporting adaptive assessment and learning. Existing LLM-based single-agent and multi-agent methods improve generation flexibility, but they still tend to rely on aggregated feedback or model randomness, ma

Theofanis Vergos, Polykarpos Vergos, Mehdi B. Tahoori, Georgios Zervakis

Flexible electronics offer unique advantages for conformable, lightweight, and disposable healthcare wearables. However, their limited gate count, large feature sizes, and high static power consumption make on-body machine learning classification highly challenging. While existing bendable RISC-V systems provide compact solutions, they lack the energy effici

E. F. Zhou, J. M. Yao

We present a microscopic study of the low-lying states of five odd-mass nuclei of particular interest for experimental searches of atomic electric dipole moments (EDMs): $^{129}$Xe, $^{199}$Hg, $^{225}$Ra, $^{229}$Th, and $^{229}$Pa. The analysis is performed within the recently developed multi-reference covariant density functional theory (MR-CDFT), which i

Connor Simpson, Ricardo J. G. B. Campello, Elizabeth Stojanovski

Validation plays a crucial role in the clustering process. Many different internal validity indexes exist for the purpose of determining the best clustering solution(s) from a given collection of candidates, e.g., as produced by different algorithms or different algorithm hyper-parameters. In this study, we present a comprehensive benchmark study of 26 inter

Albert Schotschneider, Svetlana Pavlitska, J. Marius Zöllner

Deep neural networks (DNNs) are widely used in perception systems for safety-critical applications, such as autonomous driving and robotics. However, DNNs remain vulnerable to various safety concerns, including generalization errors, out-of-distribution (OOD) inputs, and adversarial attacks, which can lead to hazardous failures. This survey provides a compre

S. V. Talalov

In this study, we propose a new approach to describing certain macroscopic objects that can arise in a quantum fluid. These objects are formed by means of quantum entanglement from the circular-shaped mesoscale and microscale vortices, and can be interpreted as a vortex filaments with any shape and size. The method is based on a quantization scheme for class

Etienne Le Naour, Tahar Nabil, Adrien Petralia, Ghislain Agoua

Foundation models for time series imputation remain largely unexplored. Recently, two such models, TabPFN-TS and MoTM, have emerged. These models share a common philosophy that places them within the family of time-indexed foundation models. This paper presents the first large-scale empirical study of these models for zero-shot imputation, which enables miss

Koushik Ray

A simple scheme to express the Mellin transform of $D$-dimensional Euclidean conformal bootstrap equation is presented by relating conformal blocks to a Gauss-Grassmann (GG) system due to Gelfand-Graev, associated to conformal integrals, which, in turn, are generalised hypergeometric functions. Usefulness of the expression for obtaining bounds on the spectru

Yuyang Liu, Jingjing Cai, Jiayi Ren, Peng Zhou

Anomaly troubleshooting for large model distributed inference (LMDI) remains a critical challenge. Resolving anomalies such as inference performance degradation or latency jitter in distributed system demands significant manual efforts from domain experts, resulting in extremely time-consuming diagnosis processes with relatively low accuracy. In this paper,

Pavel Naumov, Alexandra Pavlova

The paper proposes to treat agent awareness as a form of knowledge, breaking the tradition in the existing literature on awareness. It distinguishes the de re and de dicto forms of such knowledge. The work introduces two modalities capturing these forms and formally specifies their meaning using a version of 2D-semantics. The main technical result is a sound

Mile Stankovic

Chunking quality determines RAG system performance. Current methods partition documents individually, but complex queries need information scattered across multiple sources: the knowledge fragmentation problem. We introduce Cross-Document Topic-Aligned (CDTA) chunking, which reconstructs knowledge at the corpus level. It first identifies topics across docume

Seyit Okan Kara

We develop a minimal and fully anomaly-free realization of a universal leptophilic $U(1)'_{\ell}$ gauge symmetry, under which all Standard-Model leptons carry a common charge while quarks remain neutral. Gauge consistency is restored by introducing one vectorlike lepton family and singlet scalars whose vacuum expectation values both break $U(1)'_{\ell}$ and

Florio M. Ciaglia, Giuseppe Marmo, Marco Pacelli, Luca Schiavone

Contrast functions play a fundamental role in information geometry, providing a means for generating the geometric structures of a statistical manifold: a pseudo-Riemannian metric and a pair of torsion-free conjugate affine connections. Conventional contrast-based approaches become indeed insufficient within settings where torsion is naturally present, such

Filippus S. Roux

The definition of quantum states in terms of tagged vector spaces is generalized to incorporate the spatiotemporal and spin degrees of freedom. Considering a tagged vector space where the index space is a function space, representing the additional degrees of freedom, we obtained axioms for the tags that include a completeness condition expressed in terms of

Alice Ragonesi, Stefania Fresca, Karli Gillette, Stefan Kurath-Koller

Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faster ventricular activation rate, and provides a substrate for atrio-ventricular reentrant tachycardia (AVRT). Accurate localization of the AP is critical for planning and guiding cat

Jing Han, Bernard Pire, Qin-Tao Song

$\gamma^* \gamma \to B \bar{B}$ is the golden process to access chiral-even di-baryon generalized distribution amplitudes (GDAs) as deeply virtual Compton scattering has proven to be for the generalized parton distributions. In the framework of colinear QCD factorization where the leading twist amplitude is the convolution of GDAs and a perturbatively calcul

Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood, Dong Hye Ye

The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods often rely on resource-heavy Large Language Models (LLMs) or static knowledge graphs and struggle with two fundamental challenges in real-world clinical data: (1) missing modalities, s

Yuxuan Lin, Hanjing Yan, Xuan Tong, Yang Chang

Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small nu

Zhixin Cheng, Xiaotian Yin, Jiacheng Deng, Bohao Liao

Typical detection-free methods for image-to-point cloud registration leverage transformer-based architectures to aggregate cross-modal features and establish correspondences. However, they often struggle under challenging conditions, where noise disrupts similarity computation and leads to incorrect correspondences. Moreover, without dedicated designs, it re

Huanyu Zhang, Bo-Hui Tang, Tian Hu, Yun Jiang

Land surface temperature (LST) is a fundamental parameter in thermal infrared remote sensing, while current LST products are often constrained by the trade-off between spatial and temporal resolutions. To mitigate this limitation, numerous studies have been conducted to enhance the resolutions of LST data, with a particular emphasis on the spatial dimension

Kamillo Ferry

Max-linear Bayesian networks are recursive max-linear structural equation models represented by an edge weighted directed acyclic graph (DAG). The identifiability and estimation of max-linear Bayesian networks is an intricate issue as Gissibl, Kl\"uppelberg, and Lauritzen have shown. As such, a max-linear Bayesian network is generally unidentifiable and stan

Julian Gurs, Nils Sueltmann, Christian Darsow-Fromm, Sebastian Steinlechner

Quantum technologies with quantum correlated light require photodiodes with near-perfect `true' quantum efficiency, the definition of which adequately accounts for the photodiode dark noise. Future squeezed-light-enhanced gravitational wave detectors could in principle achieve higher sensitivities with a longer laser wavelength around 2 {\mu}m. Photodiodes m

Enrico Manzini, Thomas Gonzalez Saito, Joan Escudero, Ana Génova

Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of hospitalizations, strongly associated with decreased survival, yet predicting the timing of these events remains challenging and has received limited attention in the literature. In this study, we performed survival analysis to predict hospitalization and death in COPD patie

Ali Eghbali, Meysam Hosseinpour-Sadid, Adel Rezaei-Aghdam

In superdimension $(2|2)$ there are only three non-Abelian Lie superalgebras admitting non-degenerate ad-invariant supersymmetric metric, the well-known Lie superalgebra $gl(1|1)$, and two more, $({\C}^3 + \A)$ and $({\C}_0^5 +{\A})$. After a brief review of the construction of the Wess-Zumino-Witten (WZW) models based on the $GL(1|1)$ and $(C^3 + A)$ Lie su

Stepan Vanecek, Manuel Walter Mussbacher, Dominik Groessler, Urvij Saroliya

Understanding GPU topology is essential for performance-related tasks in HPC or AI. Yet, unlike for CPUs with tools like hwloc, GPU information is hard to come by, incomplete, and vendor-specific. In this work, we address this gap and present MT4G, an open-source and vendor-agnostic tool that automatically discovers GPU compute and memory topologies and conf

Dong-Ping Xuan, Zhong-Xi Shen, Wen Zhou, Shao-Ming Fei

The quantum speed limit sets a fundamental restriction on the evolution time of quantum systems. We explore the relationship between quantum imaginarity and the quantum speed limit by utilizing measures such as relative entropy, trace distance, and geometric imaginarity. These speed limits define the fundamental constraints on the minimum time necessary for

Daomin Cao, Jie Wan

In this article, we consider clustered solutions to a semilinear elliptic equation in divergence form \begin{equation*} \begin{cases} -\varepsilon^2\text{div}(K(x)\nabla u)= (u-q|\ln\varepsilon|)^{p}_+,\ \ &x\in \Omega,\\ u=0,\ \ &x\in\partial \Omega \end{cases} \end{equation*} for small values of $ \varepsilon $. Using Green's function of the elliptic opera

Surbhi Madan, Shreya Ghosh, Ramanathan Subramanian, Abhinav Dhall

A person's gaze offers valuable insights into their focus of attention, level of social engagement, and confidence. In this work, we investigate how contextual cues combined with visual scene and facial information can be effectively utilized to predict and interpret social gaze patterns during conversational interactions. We introduce CSGaze, a context awar

Srikar Sharma Sadhu, Praful D. Mankar, Santosh Nannuru

Near-field localization is expected to play a crucial role in enabling a plethora of applications under the paradigm of 6G networks. The conventional localization methods rely on complex infrastructure for providing cooperative anchor nodes that often contribute to higher network overload and energy consumption. To address this, the passive reconfigurable in

Atharva Mehta, Shivam Chauhan, Megha Sharma, Gus Xia

In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems (music-AI systems) which affect stakeholders including creators, distributors, an