November 2025 arXiv papers — page 123

Kanata Yatsuda, Masaki Fukunaga, Wenwei Yu, Jose Gomez-Tames

Background & Aim: Transcranial temporal interference stimulation (TIS) is a novel transcranial electrical stimulation modality that enables focused targeting of deep brain structures. When targeting deep regions, current pathways traverse the highly anisotropic white matter, making anisotropy a potentially critical factor. This study aimed to clarify how ani

Jun Liu, Linlin Li, Xiaofei Sun, Qiang Zhang

Peri-implant inflammation in orthodontic mini-implant may lead to patient discomfort and treatment failure. This study aims to evaluate the effects of diode laser application on the health of mini-implant, preventing peri-implantitis and promoting healing. A randomized controlled trial was conducted involving 30 orthodontic patients (12 males and 18 females,

Priyanka Mahalle, A. Kumar, Kumar Bharti, Dipanshu Bansal

We report a comprehensive investigation of the quasi-one-dimensional spin-chain compound Ca3CoIrO6 (CCIO) using a combination of structural, magnetic, thermodynamic, transport, Raman, and dielectric measurements. Temperature-dependent neutron powder diffraction confirms the rhombohedral R-3c structure down to 5 K without any structural phase transition. DC m

Yi Shi, Wenlong Meng, Zhenyuan Guo, Chengkun Wei

With the rapid rise of social media and Internet culture, memes have become a popular medium for expressing emotional tendencies. This has sparked growing interest in Meme Emotion Understanding (MEU), which aims to classify the emotional intent behind memes by leveraging their multimodal contents. While existing efforts have achieved promising results, two m

Salim Fares, Steffen Herbold

How to best use Large Language Models (LLMs) for software engineering is covered in many publications in recent years. However, most of this work focuses on widely-used general purpose programming languages. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in prop

Tuochao Chen, Bandhav Veluri, Hongyu Gong, Shyamnath Gollakota

Dialogue models falter in noisy, multi-speaker environments, often producing irrelevant responses and awkward turn-taking. We present AV-Dialog, the first multimodal dialog framework that uses both audio and visual cues to track the target speaker, predict turn-taking, and generate coherent responses. By combining acoustic tokenization with multi-task, multi

Andrei Muratov, Yevgeny Rakhmanov, Andrei Shilov, Igor Morozov

The optical properties of the superconducting K$_{0.8}$Fe$_{1.7}$(Se$_{0.73}$S$_{0.27}$)$_2$ single crystals with a critical temperature $T_c\approx 26$ K have been measured in the {\it ab} plane in a wide frequency range using both infrared Fourier-transform spectroscopy and spectroscopic ellipsometry at temperatures of 4--300 K. The normal-state reflectanc

Yuyang Huang, Dante Kalise, Hicham Kouhkouh

We develop a rigorous framework for global non-convex optimization by reformulating the minimization problem as a discounted infinite-horizon optimal control problem. For non-convex, continuous, and possibly non-smooth objective functions with multiple global minimizers, where classical gradient-based methods lack global convergence guarantees, we establish

Manvendra Somvanshi, D. Jaffino Stargen

The Aharonov-Bohm effect is a physical phenomenon in which the quantum state of a charged particle acquires a phase shift that is directly proportional to the magnetic flux, $\Phi$, due to a (classical) magnetic field, ${\mathbf B}$, which is confined in a spatial region from which the magnetic field cannot escape. Even though the charged particle is not all

Leeandro Boonzaaier, Sophie Marques, Daniella Moore

In this paper we develop a duality theory for all finite-dimensional near-vector spaces and introduce a notion of inner product tailored to the broad and natural class of strongly regular near-vector spaces. This generalized construction extends the classical inner product beyond the classical framework, yielding rich families of examples on multiplicative n

Xinyue Zhang, Haolong Li, Jiawei Ma, Chen Ye

Vectorized glyphs are widely used in poster design, network animation, art display, and various other fields due to their scalability and flexibility. In typography, they are often seen as special sequences composed of ordered strokes. This concept extends to the token sequence prediction abilities of large language models (LLMs), enabling vectorized charact

Ruchira Dhar, Ninell Oldenburg, Anders Soegaard

Benchmarks such as ARC, Raven-inspired tests, and the Blackbird Task are widely used to evaluate the intelligence of large language models (LLMs). Yet, the concept of intelligence remains elusive- lacking a stable definition and failing to predict performance on practical tasks such as question answering, summarization, or coding. Optimizing for such benchma

Chenyu Zhang, Xiaohang Luo

Formative feedback is widely recognized as one of the most effective drivers of student learning, yet it remains difficult to implement equitably at scale. In large or low-resource courses, instructors often lack the time, staffing, and bandwidth required to review and respond to every student reflection, creating gaps in support precisely where learners wou

Guirec de Tournemire, Nicolas Fares, Yacine Amarouchene, Thomas Salez

The encounter of diffusing entities underlies a wide range of natural phenomena. The dynamics of these first-passage dynamics are strongly influenced by confining geometries. Confinement modifies microscopic diffusion through conservative and hydrodynamic interactions, making it essential for realistic modeling. In this Letter, we investigate how confinement

Anne S. M. Buckner, Ana Duarte-Cabral, Angela Adamo, Sean Linden

We examine the spatial distribution of star clusters in NGC 628 using the statistical tool INDICATE to quantify clustering tendencies. Our sample, based on HST and JWST observations, is the most complete to date, spanning ages from 1 Myr to >100 Myr. We find cluster spatial behaviour varies with galactic position, age, and mass. Most emerging young clusters

Chiara Degan, Bart J. A. Mertens, Jelle Goeman, Nadine A. Ikelaar

In longitudinal studies, time-varying covariates are often endogenous, meaning their values depend on both their own history and that of the outcome variable. This violates key assumptions of Generalized Linear Mixed Effects Models (GLMMs), leading to biased and inconsistent estimates. Additionally, missing data and non-concurrent measurements between covari

Yifan Jiang, Yueying Wang, Rui Zhao, Toufiq Parag

Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framewo

Yihan Hou, Yilin Ye, Liangwei Wang, Huamin Qu

Multiple-view (MV) visualization provides a comprehensive and integrated perspective on complex data, establishing itself as an effective method for visual communication and exploratory data analysis. While existing studies have predominantly focused on designing explicit visual linkages and coordinated interactions to facilitate the exploration of MV visual

Xin Wang, Pietro Lodi Rizzini, Sourav Medya, Zhiling Lan

The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared network links. Parallel discrete event simulation (PDES) is commonly used to analyze workload interference. However, high-fidelity PDES is computationally expensive, making it imp

Marko Voutilainen, Pauliina Ilmonen, Lauri Viitasaari

In this article, we characterize continuous stationary fields via generalized Langevin dynamics. This gives natural connections between stationary fields, stationary increment fields, self-similar fields, and generalized Langevin dynamics. Our contribution extends some recently proved similar results for stochastic processes to the case of continuous random

Natalia Gorobey, Alexander Lukyanenko, A. V. Goltsev

A model of a two-sheeted universe in the quantum theory of gravity is proposed, based on the definition of 3D invariant and gauge-invariant proper time of the universe. A uniform time in a closed universe is introduced in the class of equivalent reference systems defined by the spectrum of a Hermitian 3D operator on the space of Dirac bi-spinors, the equalit

Ruban Goyal, Rohitash Chandra, Sonit Singh

Personal attacks have become a notable feature of U.S. presidential debates and play an important role in shaping public perception during elections. Detecting such attacks can improve transparency in political discourse and provide insights for journalists, analysts and the public. Advances in deep learning and transformer-based models, particularly BERT an

Zhonghua Jiang, Kui Chen, Kunxi Li, Keting Yin

Recent advancements in Audio-Video Large Language Models (AV-LLMs) have enhanced their capabilities in tasks like audio-visual question answering and multimodal dialog systems. Video and audio introduce an extended temporal dimension, resulting in a larger key-value (KV) cache compared to static image embedding. A naive optimization strategy is to selectivel

Jakov Budić, Serena Nasrallah, D. Santos-Cottin, F. Le Mardelé

We present an infrared spectroscopy study of the layered topological semimetal TaNiTe$_5$, a material with a quasi-one-dimensional structure and strong in-plane anisotropy. Despite its structural features, infrared reflectivity and electronic transport measurements along the $a$ and $c$ crystallographic axes show metallic behavior without evidence of reduced

Crystal Min Hui Poon, Pai Chet Ng, Xiaoxiao Miao, Immanuel Jun Kai Loh

Instruction-guided text-to-speech (TTS) research has reached a maturity level where excellent speech generation quality is possible on demand, yet two coupled biases persist in reducing perceived quality: accent bias, where models default towards dominant phonetic patterns, and linguistic bias, a misalignment in dialect-specific lexical or cultural informati

Maryna Kachanovska, Adrian Savchuk

In this work, we investigate time-dependent wave scattering by multiple small particles of arbitrary shape. To approximate the solution of the associated boundary-value problem, we derive an asymptotic model that is valid in the limit as the particle size tends to zero. Our method relies on a boundary integral formulation, semi-discretized in space using a G

Lennart Machill, Angkana Rüland

We investigate the scaling behaviour of a singular perturbation model within the geometrically linearized theory of elasticity involving data of higher lamination order. We study boundary data which are of staircase type and show rather general lower scaling bounds, both in the setting of prescribed Dirichlet data and for periodic configurations with a mean

Zaiyi Shen, Leilei Wang, Shishuang Zhang, Chenlu Li

Hydrodynamic interactions can generate rich emergent structures in active matter systems. Using large-scale hydrodynamic simulations, we demonstrate that hydrodynamic coupling alone can drive spontaneous self-organization across a hierarchy of spatial and temporal scales in confined suspensions of torque-driven particles at moderate Reynolds numbers. Spinner

Nobuhiro Maekawa, Kei Shibata, Masato Yamanaka

We investigate thermal leptogenesis within a supersymmetric grand unified theory (SUSY GUT) based on the $SO(10) \times U(1)_A$ symmetry, where both the doublet--triplet splitting problem and the unrealistic Yukawa relations are resolved under the natural assumption that all symmetry-allowed interactions appear with $\mathcal{O}(1)$ coefficients. In this fra

Vicenç Méndez, Rosa Flaquer-Galmés

We investigate the ergodic properties of Brownian motion in heterogeneous media through the statistics of occupation times. Using the Feynman-Kac formalism, we derive analytical expressions for the distributions, moments, and ergodicity breaking parameters of occupation times in two models with spatially varying diffusion coefficient: a piecewise-constant pr

Georgi Angelov, Alberto Domínguez Corella, Vladimir Veliov

The paper investigates the accuracy of the Model Predictive Control (MPC) method for finding online approximate optimal feedback control for Bolza type problems on a fixed finite horizon. The predictions for the dynamics, the state measurements, and the solution of the auxiliary open-loop control problems that appear at every step of the MPC method may be in

Mattia Ferrari, Giancarlo Papitto, Giorgio Deligios, Lorenzo Bruzzone

Bark beetle infestations represent a serious challenge for maintaining the health of coniferous forests. This paper proposes a few-shot learning approach leveraging contrastive learning to detect bark beetle infestations using satellite PRISMA hyperspectral data. The methodology is based on a contrastive learning framework to pre-train a one-dimensional CNN

Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen, Sheila A. McIlraith

Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set of training problems, for each problem, compute an optimal plan for each goal atom in some order, perform goal regression on the resulting plans, and lift the corresponding outputs

Lu-Meng Liu, Jinhui Chen, Xu-Guang Huang, Jiangyong Jia

The longitudinal structure of the quark-gluon plasma(QGP) remains a key challenge in heavy-ion physics. In this Letter, we propose a novel observable, event-by-event mean transverse momentum fluctuations Var$_{\langle p_{T} \rangle}$, which is sensitive to the local pressure gradients and serves as a probe of longitudinal dynamics in the initial state of QGP

Dylan Saeed, Ramtin Gharleghi, Susann Beier, Sonit Singh

Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally re

Jeffrey Seely

Predictive coding (PC) replaces global backpropagation with local optimization over weights and activations. We show that linear PC networks admit a natural formulation as cellular sheaves: the sheaf coboundary maps activations to edge-wise prediction errors, and PC inference is diffusion under the sheaf Laplacian. Sheaf cohomology then characterizes irreduc

Timothée Bénard, Weikun He

We establish an effective upper bound for the Brascamp-Lieb constant associated to a weighted family of linear maps.

V. O. Galkin, Xian-Wei Kang

Exclusive semileptonic decays of $B$ mesons to the ground, radially and orbitally excited states of light mesons are investigated in the framework of the relativistic quark model based on the quasipotential approach. Such decays are very important for the determination of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{ub}|$ and testing the nature of the e

Puyun Hu, Wei Pan, Xun Jian, Zeqi Ma

Existing database benchmarks primarily focus on performance under ideal running environments. However, in real-world scenarios, databases probably face numerous adverse events. Quantifying the ability to cope with these events from a comprehensive perspective remains an open problem. We provide the definition of database resilience to describe its performanc

Huilin Wang Wenjun Zhang Weibing Deng

Link prediction in complex networks--identifying the missing or future connections--remains a cornerstone problem for understanding network evolution and function, yet existing methods struggle to balance computational efficiency with theoretical rigor across heterogeneous topologies. This work introduces a physically principled framework, Diffusion Distance

Sora Kadotani, Kosuke Nishida, Kyosuke Nishida

Large language models (LLMs) can generate fluent responses, but sometimes hallucinate facts. In this paper, we investigate whether LLMs can detect their own hallucinations. We formulate hallucination detection as a classification task of a sentence. We propose a framework for estimating LLMs' capability of hallucination detection and a classification method

Alexander Kagan, Peter W. MacDonald, Elizaveta Levina, Ji Zhu

Complex multilayer network datasets have become ubiquitous in various applications, including neuroscience, social sciences, economics, and genetics. Notable examples include brain connectivity networks collected across multiple patients or trade networks between countries collected across multiple goods. Existing statistical approaches to such data typicall

Nils Hausbrandt, Levin Nemesch, Stefan Ruzika

Matroid interdiction problems are well-researched in the field of combinatorial optimization. In the matroid $\ell$-interdiction problem, an interdiction strategy removes a subset of cardinality $\ell$ from the matroid's ground set. The goal is to maximize the weight of a remaining optimal basis. We examine the multi-parametric generalization of this problem

Ning-Ning Gao, Hui-Li Han, Ting-Yun Shi, Li-Yan Tang

We investigate resonances in positron-sodium scattering using the $R$-matrix propagation method formulated in hyperspherical coordinates. The interaction between the sodium core and the valence electron is described by analytical model potentials. High partial-wave resonances are calculated for collision energies up to the Na($4f$) threshold. Several resonan

Bingyu Hui, Lebin Yu, Quanming Yao, Yunpeng Qu

Zero-shot coordination(ZSC), a key challenge in multi-agent game theory, has become a hot topic in reinforcement learning (RL) research recently, especially in complex evolving games. It focuses on the generalization ability of agents, requiring them to coordinate well with collaborators from a diverse, potentially evolving, pool of partners that are not see

Francisco de la Hoz, Peru Muniain

In this paper, using a pseudospectral approach, we develop operational matrices based on the shifted Chebyshev polynomials to approximate numerically Caputo fractional derivatives and Riemann-Liouville fractional integrals. In order to make the generation of these matrices stable, we use variable precision arithmetic. Then, we apply the Caputo differentiatio

Jun Hu, Shangheng Chen, Yufei He, Yuan Li

Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world graphs. Pre-computation-based HGNNs address this by performing message passing only once during preprocessing, collecting neighbor in

M. Benfatto, L. De Paolis, L. Tonello, P. Grigolini

Ultra-weak photon emission (UPE) from living systems is widely hypothesized to reflect un-derlying self-organization and long-range coordination in biological dynamics. However, distin-guishing biologically driven correlations from trivial stochastic or instrumental effects requires a robust, multi-method framework. In this work, we establish and benchmark a

Sejin Kim, Hayan Choi, Seokki Lee, Sundong Kim

We present ARCTraj, a dataset and methodological framework for modeling human reasoning through complex visual tasks in the Abstraction and Reasoning Corpus (ARC). While ARC has inspired extensive research on abstract reasoning, most existing approaches rely on static input-output supervision, which limits insight into how reasoning unfolds over time. ARCTra

Youssef Haouchat, Sepand Kashani, Aleix Boquet-Pujadas, Philippe Thévenaz

We propose a method to efficiently compute tomographic projections of a 3D volume represented by a linear combination of shifted B-splines. To do so, we propose a ray-tracing algorithm that computes 3D line integrals with arbitrary projection geometries. One of the components of our algorithm is a neural network that computes the contribution of the basis fu

Ke Ma, Yizhou Fang, Jean-Baptiste Weibel, Shuai Tan

Estimating the geometric and volumetric properties of transparent deformable liquids is challenging due to optical complexities and dynamic surface deformations induced by container movements. Autonomous robots performing precise liquid manipulation tasks, such as dispensing, aspiration, and mixing, must handle containers in ways that inevitably induce these

Andrey Pilipenko, Vadym Tkachenko

We provide necessary and sufficient conditions for explosion and implosion of birth-and-death (non-Markov) continuous-time random walks. In other words, we obtain conditions for $\infty$ to be accessible and for it to be an entrance point. We derive the analytical regularity criteria in terms of the appropriate scale function and the speed measure, which inv

WanYun Wu, Fei Xie, Long Ji, Mingyu Ge

Vela X-1 is one of the most archetypal wind-fed X-ray pulsars (XRPs), and the emergence of its orthogonal polarization states reveals distinctive polarimetric properties. Using data from Imaging X-ray Polarimetry Explorer (IXPE) observations of Vela X-1, we perform a polarization analysis of Vela X-1 using a triple power-law spectral model absorbed by varyin

Matthias Humt, Ulrich Hillenbrand, Rudolph Triebel

While generative models have seen significant adoption across a wide range of data modalities, including 3D data, a consensus on which model is best suited for which task has yet to be reached. Further, conditional information such as text and images to steer the generation process are frequently employed, whereas others, like partial 3D data, have not been

Jeremie Unterberger

Autocatalytic chemical reaction networks are dynamical systems whose linearization around zero, dX/dt = AX, is represented by a Perron-Frobenius matrix A with positive Lyapunov exponent; this exponent gives the growth rate of the species concentration vector X in the diluted regime, i.e. in a vicinity of zero. We introduce here a new, general recursive proce

Floris Vossebeld, Shenghui Wang

Generating complex, logically-sound SPARQL queries for multi-hop questions remains a critical bottleneck for Knowledge Graph Question Answering, as the brittle nature of one-shot generation by Large Language Models (LLMs) hinders reliable interaction with structured data. Current methods lack the adaptive policies needed to dynamically debug queries based on

Ziyi Li, Qingyu Mao, Shuai Liu, Qilei Li

Neural Video Representation~(NVR) is a promising paradigm for video compression, showing great potential in improving video storage and transmission efficiency. While recent advances have made efforts in architectural refinements to improve representational capability, these methods typically involve complex designs, which may incur increased computational o

Sangho Lim, Hyoungjin Lim, Wonyeol Lee, Xavier Rival

Probabilistic programming languages (PPLs) are a popular tool for high-level modelling across many fields. They provide a range of algorithms for probabilistic inference, which analyse models by learning their parameters from a dataset or estimating their posterior distributions. However, probabilistic inference is known to be very costly. One of the bottlen

Josef Rukavicka

Let $R(n)$ denote the number of rich words of length $n$ over a given finite alphabet. In 2017 it was proved that $\lim_{n\rightarrow\infty} \sqrt[n]{R(n)}=1$; it means the number of rich words has a subexponential growth. However, up to now, no subexponential upper bound on $R(n)$ has been presented. The current paper fills this gap. Let $\frac{1}{2}<\lambd

Pu-Zhao Kow, Janne Nurminen, Jesse Railo

This paper investigates the consistency of a posterior distribution in the single-measurement fractional Calder\'on problem with additive Gaussian noise. We consider a Bayesian framework with rescaled and Gaussian sieve priors, using a collection of noisy, discrete observations taken from a suitable exterior domain. Our main result shows that the posterior d

Axel Bücher, Johan Segers, Torben Staud

This paper explores strong and weak consistency of M-estimators for non-identically distributed data, extending prior work. Emphasis is given to scenarios where data is viewed as a triangular array, which encompasses distributional regression models with non-random covariates. Primitive conditions are established for specific applications, such as estimation

Jiechao Gao, Chang Liu, Yuangang Li

Radiology Report Generation (RRG) aims to automatically generate diagnostic reports from radiology images. To achieve this, existing methods have leveraged the powerful cross-modal generation capabilities of Multimodal Large Language Models (MLLMs), primarily focusing on optimizing cross-modal alignment between radiographs and reports through Supervised Fine

Muskaan Chopra, Lorenz Sparrenberg, Armin Berger, Sarthak Khanna

Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning has transformed DR screening, progressing from early convolutional neural networks trained on private datasets to advanced pipelines addressing class imbalance, label scarcity, domai

Sujoy Majumder, Nabadwip Sarkar, Molla Basir Ahamed

In this paper, we study the Bohr phenomenon for differential operators $D$ and $\mathscr{D}$ of stable harmonic mappings involving multiple Schwarz functions in $\mathcal{B}_n$, using distance formulations. By constructing suitable combinations of multiple Schwarz functions, we establish sharp and improved Bohr-type inequalities for these mappings. The corre

Rahul Dalal, Mathilde Gerbelli-Gauthier, Simon Marshall

We prove two results on the growth of dimensions of fixed vectors of representations $\pi$ of $p$-adic ${\rm GL}_N$ under principal congruence subgroups: First, a uniform bound on the growth of fixed vectors in terms of the GK-dimension $\pi$, which we extend to a uniform bound on the Harish-Chandra--Howe coefficients. Second, for $\pi$ unitary, a quantitati

Dor Shmilovich, Tony Wu, Aviad Dahan, Yuval Domb

Diffusion Transformers, particularly for video generation, achieve remarkable quality but suffer from quadratic attention complexity, leading to prohibitive latency. Existing acceleration methods face a fundamental trade-off: dynamically estimating sparse attention patterns at each denoising step incurs high computational overhead and estimation errors, whil

Jan Vorberger, Gabriel J. Smith, William Z. Van Benschoten, Hayley R. Petras

We investigate basic hydrogen quantities like the molecular bond length, the molecular dissociation energy and the van-der-Waals interaction in idealized situations in an effort to discern a suitable exchange-correlation functional for the molecular to metal transition in warm dense hydrogen. The best reproduction of bond length and dissociation energy is gi

Jiaxuan Chen, Bo Zhang, Qingdong He, Jinlong Peng

Image composition aims to seamlessly insert foreground object into background. Despite the huge progress in generative image composition, the existing methods are still struggling with simultaneous detail preservation and foreground pose/view adjustment. To address this issue, we extend the existing generative composition model to multi-reference version, wh

Yoshiki Fukusumi, Taishi Kawamoto

We study conformal field theories (CFTs) and their classifications from a modern perspective based on the abstract algebraic formalism of symmetries or conserved charges, known as symmetry topological field theories (SymTFTs). By studying the algebraic structure of the SymTFTs in detail, we found a natural generalization of the quantum dimensions associated

V. Bach, A. F. M. ter Elst, J. Rehberg

We provide a new short proof for the Birman--Solomyak theorem for Hilbert--Schmidt operators and give an application to a Schr\"odinger--Poisson system.

Kotaro Kimura, Tomohiro I

Nishimoto and Tabei [CPM, 2021] proposed r-enum, an algorithm to enumerate various characteristic substrings, including maximal repeats, in a string $T$ of length $n$ in $O(r)$ words of compressed working space, where $r \le n$ is the number of runs in the Burrows-Wheeler transform (BWT) of $T$. Given the run-length encoded BWT (RLBWT) of $T$, r-enum runs in

Takaaki Aoki, Shumpei Masuda

We investigate the fast-forward and time-scaling properties of superconducting resonators under an off-resonant coherent drive. We propose a scheme for perfect displacement of a superconducting resonator by modulating the drive amplitude based on fast-forward scaling theory. Furthermore, we propose a scheme exploiting both the fast-forward and time-scaling p

Michael Schwarz, Julian Erhard

Sound static analysis can prove the absence of data races by establishing that no two conflicting memory accesses can occur at the same time. We repurpose the concept of digests -- summaries of computational histories originally introduced to bring tunable concurrency-sensitivity to thread-modular value analysis by abstract interpretation, extending this ide

Martina Alutto, Sofia Bellotti, Fabrizio Dabbene, Chiara Ravazzi

Understanding how sustainable behaviors spread within heterogeneous societies requires the integration of behavioral data, social influence mechanisms, and structured approaches to control. In this paper, we propose a data-driven computational framework for coupled opinion-adoption dynamics in social systems. Each node in the multilayer network represents a

Jinxuan Zhu, Chenrui Tie, Xinyi Cao, Yuran Wang

Non-prehensile (NP) manipulation, in which robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manipulation capabilities when grasping is infeasible or insufficient. However, enabling a unified framework that generalizes across different tasks, objects, and environments while sea

P. Zhang, H. Ma, W. Dai, M. Jing

In China Jinping Underground Laboratory (CJPL), the deepest and largest underground laboratory globally, the cosmic-ray muon flux is significantly reduced due to the substantial shielding provided by the overlying mountain. From 2016 to 2020, we measured the muon flux in the second phase of CJPL (CJPL-II) with a plastic scintillator muon telescope system, de

Han Wang, Deyi Ji, Junyu Lu, Lanyun Zhu

Accurate detection of offensive content on social media demands high-quality labeled data; however, such data is often scarce due to the low prevalence of offensive instances and the high cost of manual annotation. To address this low-resource challenge, we propose a self-training framework that leverages abundant unlabeled data through collaborative pseudo-

Xiangru Wang, Zekun Jiang, Heng Yang, Cheng Tan

Molecular property prediction is fundamental to chemical engineering applications such as solvent screening. We present Socrates-Mol, a framework that transforms language models into empirical Bayesian reasoners through context engineering, addressing cold start problems without model fine-tuning. The system implements a reflective-prediction cycle where ini

Xin Yan, Zhi-Kang Zhuang, Fu-Jia Du, Wen Duan

Reflective membrane mirrors provide a lightweight, low-cost alternative to traditional optics for next-generation large-aperture telescopes, but their non-rigid, thin structure poses challenges for surface metrology. We present a phase-measuring deflectometry (PMD) system enhanced with tailored ray-tracing and iterative reconstruction to enable non-contact m

Sun Jo, Seok Young Hong, JinHyun Kim, Seungmin Kang

4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving suc

Sebastian Halbig, Christian Lomp

The aim of this note is to provide a self-contained classification of the irreducible representations of generalised Kac--Paljutkin Hopf algebras, recently introduced by the second author.

Wenrui Li, Yidan Lu, Yeyu Chai, Rui Zhao

With the daily influx of 3D data on the internet, text-3D retrieval has gained increasing attention. However, current methods face two major challenges: Hierarchy Representation Collapse (HRC) and Redundancy-Induced Saliency Dilution (RISD). HRC compresses abstract-to-specific and whole-to-part hierarchies in Euclidean embeddings, while RISD averages noisy f

Ho Yin Au, Junkun Jiang, Jie Chen

Traditional text-to-motion frameworks often lack precise control, and existing approaches based on joint keyframe locations provide only positional guidance, making it challenging and unintuitive to specify body part orientations and motion timing. To address these limitations, we introduce the Salient Orientation Symbolic (SOS) script, a programmable symbol

P. Zhang, Z. Zeng, J. Cheng, H. Ma

China Jinping Underground Laboratory (CJPL), the deepest and largest underground laboratory worldwide, provides a low radiation background environment, which is necessary to frontier scientific research, such as the experimental studies of rare-event physics. Due to the almost filled space of CJPL-I and the requirement of future physics experiments, the cons

Yu Zhang, Bing-Zhao Li

To address the limitations of conventional critically sampled graph filter banks in joint time-vertex signal processing, which require decomposing the joint graph into bipartite subgraphs and thus cannot fully exploit all temporal and spatial edges in a single-stage transform, we introduce the joint time-vertex oversampled graph Laplacian matrix. This operat

Asen Nachkov, Jan-Nico Zaech, Danda Pani Paudel, Xi Wang

Planning allows an agent to safely refine its actions before executing them in the real world. In autonomous driving, this is crucial to avoid collisions and navigate in complex, dense traffic scenarios. One way to plan is to search for the best action sequence. However, this is challenging when all necessary components - policy, next-state predictor, and cr

Amisha Priyadarshini, Sergio Gago-Masague

Despite recent advances in fairness-aware machine learning, predictive models often exhibit discriminatory behavior towards marginalized groups. Such unfairness might arise from biased training data, model design, or representational disparities across groups, posing significant challenges in high-stakes decision-making domains such as college admissions. Wh

Qian Zhang, Yan Zheng, Jinyi Liu, Hebin Liang

Recent studies on LLM agent scaling have highlighted the potential of Multi-Agent Debate (MAD) to enhance reasoning abilities. However, the critical aspect of role allocation strategies remains underexplored. In this study, we demonstrate that allocating roles with differing viewpoints to specific positions significantly impacts MAD's performance in reasonin

Hualei Wang, Yiming Li, Shuo Ma, Hong Liu

Recent Large Audio-Language Models (LALMs) exhibit impressive capabilities in understanding audio content for conversational QA tasks. However, these models struggle to accurately understand timestamps for temporal localization (e.g., Temporal Audio Grounding) and are restricted to short audio perception, leading to constrained capabilities on fine-grained t

Sagar Sudhakara, Pankaj Rajak

Modern networks support network slicing, which partitions physical infrastructure into virtual slices tailored to different service requirements (for example, high bandwidth or low latency). Optimally allocating users to slices is a constrained optimization problem that traditionally requires complex algorithms. In this paper, we explore the use of Large Lan

Jiaming Huang, Yi Gao, Fuchang Pan, Renjie Li

With the rapid growth of the Internet of Things (IoT), integrating artificial intelligence (AI) on extremely weak embedded devices has garnered significant attention, enabling improved real-time performance and enhanced data privacy. However, the resource limitations of such devices and unreliable network conditions necessitate error-resilient device-edge co

Sergei Nokhrin, Mikhail Patrakeev

Tournament ranking is a function that assigns each vertex of a tournament (i.e., a directed graph without loops, in which each pair of different vertexes is connected by exactly one arc) a number called the rank of the vertex. One of approaches to constructing tournament rankings suggests choosing a ranking that satisfies a fixed set of axioms. In another ap

Peter S. Morfe

This paper analyzes a class of recursive distributional equations (RDE's) proposed by Gurel-Gurevich [17] and involving a bias parameter $p$, which includes the logarithm of the resistance of the series-parallel graph. A discrete-time evolution equation resembling a quasilinear Fisher-KPP equation is derived to describe the CDF's of solutions. When the bias

Biqing Zeng, Mengquan Liu, Zongwei Zhen

The advent of Intelligent Tutoring Systems (ITSs) has marked a paradigm shift in education, enabling highly personalized learning pathways. However, true personalization requires adapting to learners' complex knowledge states (multi-source) and diverse goals (multi-sink); existing ITSs often lack the necessary structural-reasoning capability and knowledge dy

Pooja Singh, Siddhant Ujjain, Tapan Kumar Gandhi, Sandeep Kumar

Recent advances in multimodal large language models have enabled unified processing of visual and textual inputs, offering promising applications in general-purpose medical AI. However, their ability to generalize compositionally across unseen combinations of imaging modality, anatomy, and task type remains underexplored. We introduce CrossMed, a benchmark d

S. Kimura, M. Wada, H. Haba, Y. Hirayama

Nuclear mass is a key indicator of how the nuclear shell structure evolves. The recent mass measurement study of neutron-rich lanthanum isotopes [A. Jaries, $et~al$., Phys. Rev. Lett. {\bf 134}, 042501(2025)] reveals the presence of a distinct prominence in their two-neutron separation energies. However, its presence has been called into question based on th

Wei Wang, Feng Jiang, Xin Wang

Automatic segmentation methods of polyps is crucial for assisting doctors in colorectal polyp screening and cancer diagnosis. Despite the progress made by existing methods, polyp segmentation faces several challenges: (1) small-sized polyps are prone to being missed during identification, (2) the boundaries between polyps and the surrounding environment are

Lin Liu, Huixia Ben, Shuo Wang, Jinda Lu

Controllable generative models have been widely used to improve the realism of synthetic visual content. However, such models must handle control conditions and content generation computational requirements, resulting in generally low generation efficiency. To address this issue, we propose a Hybrid-Grained Cache (HGC) approach that reduces computational ove

Chi-Yu Chen, Rawan Abulibdeh, Arash Asgari, Sebastián Andrés Cajas Ordóñez

Artificial intelligence is revealing what medicine never intended to encode. Deep vision models, trained on chest X-rays, can now detect not only disease but also invisible traces of social inequality. In this study, we show that state-of-the-art architectures (DenseNet121, SwinV2-B, MedMamba) can predict a patient's health insurance type, a strong proxy for

Liu Cao, Weizheng Wang, Qipeng Xie, Dongyu Wei

Vehicle-to-Everything (V2X) communication faces a critical authentication dilemma: traditional public-key schemes like ECDSA provide strong security but impose 2 ms verification delays unsuitable for collision avoidance, while symmetric approaches like TESLA achieve microsecond-level efficiency at the cost of 20-100 ms key disclosure latency. Neither meets 5