November 2025 arXiv papers — page 94

Kangqiao Zhao, Shuo Huai, Xurui Song, Jun Luo

Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target mostly monocular perception. Therefore, the effectiveness of Physical Adversarial Examples (PAEs) on stereo-based binocular depth estimation remains largely unexplored. To this en

Mario Sanz-Guerrero, Katharina von der Wense

Large language models (LLMs) are powerful zero- and few-shot learners. However, when predicting over a set of candidate options, LLMs suffer from label biases, and existing calibration methods overlook biases arising from multi-token class labels. We tackle an issue we call label length bias, where labels of different lengths are treated inconsistently, even

N. A. Panchenko, S. I. Godunov

$BB$ pairs from $\Upsilon(4S)\to BB$ decays are in $C$-odd state. However, there is a small admixture of the $C$-even state and it modifies the time dependent $CP$-asymmetry. The $C$-even component appears due to soft photon emission, breaking the pure $C$-odd nature of the initial state. Using the two-particle wave function formalism, we derive analytical e

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be an excellent local complete intersection ring and let $I = (a_1, \ldots, a_r)$ be an ideal of positive height. Let $\mathcal{R}(I) = A[It]$ be the Rees algebra of $I$. Consider the map $\psi \colon S = A[X_1, \ldots, X_r] \rightarrow \mathcal{R}(I)$ which maps $X_i \mapsto a_it$ for all $i$. Let $J = \ker \psi$ and let $H_*(J)$ be t

Eknath Ghate

We start with background that goes into an Iwahori-theoretic reformulation of the mod $p$ Local Langlands Correspondence (\S 2). We then explain some classical $p$-adic functional analytic results (\S 3) that go into defining the $p$-adic Banach space (\S 4) attached to a two-dimensional semi-stable representation $V_{k,{\mathcal L}}$ of the Galois group of

Wei Kou, Xurong Chen

High-occupancy QCD matter enters a saturated regime when its entropy or occupancy approaches the unitarity bound $\sim 1/\alpha$, the ``saturon" criterion. We test this criterion for protons and nuclei at small $x$ using analytic and numerical solutions of the BK equation. From these solutions we construct the gluon occupancy $N_g(x)$ and a thermodynamic ent

Jia-Ming Xie, Zhi-Wei Liu, Jun-Xu Lu, Haozhao Liang

We present a comprehensive study of the axial-vector resonance $K_1(1270)$ within the unitarized chiral perturbation theory, focusing on its two-pole structure and manifestation in femtoscopic observables. By considering the dominant $\rho K$ and $K^*\pi$ coupled channels, we reproduce the well-established double-pole structure and trace the chiral evolution

Miha Brešar, Aleksandar Mijatović, Nikola Sandrić

This paper quantifies the ergodicity and the rate of decay of the tail of the stationary distribution for a broad class of storage models, encompassing constant, linear, and power-type release rates with both finite and infinite activity input process. Our results are expressed in terms of the asymptotics of the release rate, the tail-decay rate of the L\'ev

Raphael Korbmacher, Daniel Straub, Antoine Tordeux, Claudia Totzeck

Stop-and-go waves in traffic flow pose a persistent challenge, compromising safety, efficiency, and environmental sustainability. This paper introduces a novel mitigation strategy discovered through training multi-agent deep reinforcement learning (DRL) agents in a simulated ring-road environment. The agents autonomously develop a cooperative driving policy,

D. Alonso-López, S. P. O'Sullivan, A. Bonafede, L. M. Böss

Faraday Rotation Measure (RM) Grids provide a sensitive means to trace magnetized plasma across a wide range of cosmic environments. We study the RM signal from the Shapley Supercluster Core (SSC), in order to constrain the magnetic field properties of the gas. The SSC region consists of two galaxy clusters A3558 and A3562, and two galaxy groups between them

Alfonso Pedro Ridao

International standards for biometric identity documents mandate strict compliance with pose requirements, including the square presentation of a subject's shoulders. However, the literature on automated quality assessment offers few quantitative methods for evaluating this specific attribute. This paper proposes a Shoulder Presentation Evaluation (SPE) algo

Guillaume Barraquand, Zikun Ouyang

A solvable model of directed polymer with matrix-valued disorder is introduced in arXiv:2203.14868. The disorder is made of $d\times d$ inverse-Wishart random matrices, so that the model nicely generalizes the well-studied log-gamma polymer, recovered when $d=1$. Much of the features of the log-gamma polymer seem to have analogues for higher $d$, although th

Yuhao Zhou

Solar prominences, or solar filaments, are cool and dense plasma structures in the hot solar corona, whose formation mechanisms have remained a fundamental challenge in solar physics. This review provides a comprehensive overview of the current theoretical, numerical, and observational understanding of prominence formation, with a focus on the origin of the

A. B. Batrakov, S. I. Fedotov, I. N. Onishchenko, E. G. Glushko

The paper reports the results of measuring the total dose of X-ray bremsstrahlung from a powerful X-ray source based on the high-current pulsed direct-action electron accelerator Temp-B. The parameters of the high-current, tubular relativistic electron beam from the accelerator were as follows: energy 600 keV, current 13.5 kA, and pulse duration 1.0 mks. Usi

Tao Wu, Zhao Guo, Yan Li

Context. The synergy between close binary stars and asteroseismology enables constraints on mass-transfer episodes and their consequences for internal structure, rotation profiles, and oscillation modes. Aims. We investigate how mass accretion and donation in close binaries affects the internal structure and oscillation modes of main-sequence stars. Methods.

Xiaolin Wang, Houzhang Fang, Qingshan Li, Lu Wang

Infrared unmanned aerial vehicle (UAV) target images often suffer from motion blur degradation caused by rapid sensor movement, significantly reducing contrast between target and background. Generally, detection performance heavily depends on the discriminative feature representation between target and background. Existing methods typically treat deblurring

Dushko Stavrov, Aneta Koseska, Tomislav Stankovski

The oscillatory dynamics of natural and man-made systems can be disrupted by their time-varying interactions, leading to oscillation quenching phenomena in which the oscillations are suppressed. We introduce a framework for analyzing, assessing, and controlling oscillation quenching using coupling functions. Specifically, by observing limit-cycle oscillators

Rui Zhu, Xue-Bing Wu, Yuxuan Pang, Yuming Fu

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is a deep wide-field multi-band imaging survey consisting of three layers (Wide, Deep, and UltraDeep), with the Wide layer covering $\sim 1470$ deg$^2$ to a depth of $i \sim 26$ mag. We present the QHSC catalog, a machine-learning selected sample of quasar candidates with photometric redshifts in the W

Rishi Gupta, Mukilan Karuppasamy, Shyam Marjit, Aditay Tripathi

While Large Vision Language Models (LVLMs) are increasingly deployed in real-world applications, their ability to interpret abstract visual inputs remains limited. Specifically, they struggle to comprehend hand-drawn sketches, a modality that offers an intuitive means of expressing concepts that are difficult to describe textually. We identify the primary bo

Dulaji Hidellaarachchi, Sebastian Baltes, John Grundy

Software engineering involves cognitively demanding activities impacted by individual differences. We investigate how cognitive capability and personality traits are associated with software problem solving accuracy. We assessed cognitive capability using Baddeley's three-minute grammatical reasoning test. Personality was measured using the IPIP-NEO-50 test.

Hongwei Liu, Junnan Liu, Shudong Liu, Haodong Duan

The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fide

Prathamesh Kalamkar, Ned Letcher, Meissane Chami, Sahger Lad

The application of large language models (LLMs) to chemistry is frequently hampered by a "tokenization bottleneck", where tokenizers tuned on general-domain text tend to fragment chemical representations such as SMILES into semantically uninformative sub-tokens. This paper introduces a principled methodology to resolve this bottleneck by unifying the represe

Christina Bowers, Deviprasath Palani, John Barta, Tyler Guglielmo

We demonstrate a method for generating entanglement between trapped atomic ions based on adiabatically ramped state-dependent forces. By ramping both the amplitude of the state-dependent force and the motional mode frequencies, we realize an entangling operation that is robust to motional mode occupation and drifts in the mode frequencies. We measure Bell st

Rong-Hang Chen, Jing Dong, Wen Yang, Qing Ai

Many living organisms can exploit quantum mechanical effects to gain distinct biological advantages. In plants, photosynthesis uses quantum coherence to achieve near 100% efficiency in energy transfer. With advances in experimental techniques, two-dimensional electronic spectroscopy can reveal dynamic processes such as coherence and coupling within a system,

Ana Diakvnishvili, Giorgi Khimshiashvili

We are concerned with the Steiner chains consisting of four circles. More precisely, we deal with the so-called complex moments of Steiner 4-chains introduced in a recent paper by J.Lagarias, C.Mallows and A.Wilks. We compute the invariant complex moments of poristic Steiner 4-chains and establish certain algebraic relations between those invariants. To this

Hang Ding, Yilun Zhao, Tiansheng Hu, Manasi Patwardhan

The accelerating growth of scientific publications has intensified the need for scalable, trustworthy systems to synthesize knowledge across diverse literature. While recent retrieval-augmented generation (RAG) methods have improved access to scientific information, they often overlook citation graph structure, adapt poorly to complex queries, and yield frag

Fabio Rapallo, Enrico Scalas, Pietro Terna

In this work, we study the finite-population behaviour of the Reed-Frost epidemic model. Our analysis relies on the exact expression for the final epidemic size, replaced by Monte Carlo simulations in cases where the exact formula becomes numerically unstable. When the initial reproduction number is greater than a critical threshold, the distribution of the

Gustavo Adolpho Bonesso, Carlos Marcelo Gurjão de Godoy, Tammy Hentona Osaki, Midori Hentona Osaki

Blinking is a vital physiological process that protects and maintains the health of the ocular surface. Objective assessment of eyelid movements remains challenging due to the complexity, cost, and limited clinical applicability of existing tools. This study presents the Bapp (Blink Application), a mobile application developed using the Flutter framework and

Sihan Jiao, Jingwen Wu, Hauyu Baobab Liu, Chao-Wei Tsai

We have carried out unprecedentedly deep, nearly confusion-limited JCMT-SCUBA2 mapping observations on the nearest spiral galaxy, M31 (Andromeda). The 850 $\mu$m image with a $\sim$50 pc resolution yields a comprehensive catalog of 383 giant molecular clouds (GMCs) that are associated with the spiral arms. In addition, it unveiled a population of 189 compact

Sebastian Lubos, Alexander Felfernig, Damian Garber, Viet-Man Le

Usability is a key factor in the effectiveness of recommender systems. However, the analysis of user interfaces is a time-consuming process that requires expertise. Recent advances in multimodal large language models (LLMs) offer promising opportunities to automate such evaluations. In this work, we explore the potential of multimodal LLMs to assess the usab

Kai Ren, Maryam Kamgarpour

We address cost identification in a finite-horizon linear quadratic Gaussian game. We characterize the set of cost parameters that generate a given Nash equilibrium policy. We propose a backpropagation algorithm to identify the time-varying cost parameters. We derive a probabilistic error bound when the cost parameters are identified from finite trajectories

Hoang Chuong Nguyen, Wei Mao, Jose M. Alvarez, Miaomiao Liu

3D Gaussian Splatting (3DGS) has recently emerged as a fast, high-quality method for novel view synthesis (NVS). However, its use of low-degree spherical harmonics limits its ability to capture spatially varying color and view-dependent effects such as specular highlights. Existing works augment Gaussians with either a global texture map, which struggles wit

M. E. Ramos-Ceja, L. Fiorino, E. Bulbul, V. Ghirardini

The soft X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) mission, eROSITA (extended ROentgen Survey with an Imaging Telescope Array), has produced the largest sample to date of galaxy groups and clusters detected via their intracluster/intragroup medium (ICM/IGrM) emission. Scaling relations between the intrinsic properties of these systems provid

Vladimir Pastukhov

This paper studies the asymptotic distribution of a constrained lasso-type estimator for denoising signals defined on the nodes of a graph, where the underlying structure encodes relationships between variables. We show that, under suitable assumptions on the penalization parameters, the limiting distribution of the estimator is obtained by applying the corr

Sourav Chakrabarty, Anirvan Chakraborty, Shyamal K. De

We develop algorithms for detecting multiple changepoints in functional data when the number of changepoints is unknown (unsupervised case), when it is specified apriori (supervised case), and when certain bounds are available (semi-supervised case). These algorithms utilize the maximum mean discrepancy (MMD) measure between distributions on Hilbert spaces.

Pablo Peña-Cano, Pablo M. Piaggi

We propose a methodology to generate hybrid machine learning models for the potential energy surface trained simultaneously on data from ab initio electronic structure calculations and on thermodynamic and/or structural observables from experiment. The approach is based on the use of a loss function that includes the mean square error of observables with res

F. Niederhofer, L. Cullinane, D. Massari, N. Bastian

In this paper, we explore the kinematic properties of a sample of 19 young (<1 Gyr) and intermediate-age (1-2.5 Gyr) massive star clusters within the Large Magellanic Cloud (LMC). We analyse the proper motions of the clusters, which have been measured based on multi-epoch Hubble Space Telescope (HST) observations. Additionally, we infer from the HST data hom

Sourav Ballav, Chi-Hsien Tai, Wen-Yu Wen

We extend a four-qubit quantum circuit model of black hole evaporation that enforces semi-causality, a condition that allows information to enter a black hole but strictly forbids any information from escaping from the interior to outside through the horizon. In this work, we introduce a controlled violation of this principle by inserting a parametric contro

Junfu Pu, Teng Wang, Yixiao Ge, Yuying Ge

The proliferation of hour-long videos (e.g., lectures, podcasts, documentaries) has intensified demand for efficient content structuring. However, existing approaches are constrained by small-scale training with annotations that are typical short and coarse, restricting generalization to nuanced transitions in long videos. We introduce ARC-Chapter, the first

Nanxi Chen, Sifan Wang, Rujin Ma, Airong Chen

Physics-informed neural networks (PINNs) represent a new paradigm for solving partial differential equations (PDEs) by integrating physical laws into the learning process of neural networks. However, ensuring that such frameworks fully reflect the physical structure embedded in the governing equations remains an open challenge, particularly for maintaining r

Antonio Politi

Numerical studies of some unidimensional systems suggest that Fourier law is satisfied, where theory predicts a divergence of heat conductivity with the system size. Here, I revisit some such models, finding that in all cases a divergence asymptotically emerges. This includes a variant of the ding-a-ling model, where I find that, contrary to previous claims,

Siyuan Wang, Qing Xia

This paper develops a finite-difference analogue of the boundary integral/element method for the numerical solution of two-dimensional exterior scattering from scatterers of arbitrary shapes. The discrete fundamental solution, known as the lattice Green's function (LGF), for the Helmholtz equation on an infinite lattice is derived and employed to construct b

Gábor Korchmáros, Federico Romaniello, Valentino Smaldore

Algebraic geometry codes on the Hermitian curve have been the subject of several papers, since they happen to have good performances and large automorphism groups. Here, those arising from the Singer cycle of the Hermitian curve are investigated.

Lorenzo Gavassino

It is well known that, at zero wavenumber, the non-hydrodynamic frequencies of uncharged kinetic theory are purely imaginary. On the other hand, it was recently shown that, in resistive magnetohydrodynamics, the interplay between the Israel-Stewart relaxation equation and the Amp\`{e}re-Maxwell law can give rise to a pair of oscillating non-hydrodynamic mode

Yiyi Miao, Taoyu Wu, Ji Jiang, Tong Chen

Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registration methods struggle under real clinical conditions, where cephalograms exhibit projective magnification, geometric distortion, low-contrast dental crowns, and acquisition-depende

Xingwei He, Qianru Zhang, Pengfei Chen, Guanhua Chen

Instruction-following is a critical capability of Large Language Models (LLMs). While existing works primarily focus on assessing how well LLMs adhere to user instructions, they often overlook scenarios where instructions contain conflicting constraints-a common occurrence in complex prompts. The behavior of LLMs under such conditions remains under-explored.

Michael Milford, Tobias Fischer

Place recognition, the ability to identify previously visited locations, is critical for both biological navigation and autonomous systems. This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place. We examine the computational and representational strategies employed ac

Saptak Bhattacharya

Given $n\in\mathbb{N}$ any point on the closed unit disk $\overline{\mathbb{D}}$ can be written as the average of $n$ points on the unit circle $\mathbb{S}^1$. Here we discuss a non-commutative version of this result. We prove that for any Hilbert space $\mathcal{H}$ and a state $\phi:B(\mathcal{H})\to\mathbb{C}$, $\{\phi(U): U\,\mathrm{ unitary}\}=\overline

Folkert de Ronde, Stephan Wong, Sebastian Feld

Advances in quantum algorithms as well as in control hardware designs are continuously being made. These quantum algorithms, expressed as quantum circuits, need to be translated to a set of instructions from a defined quantum instruction-set architecture (ISA), which are executed by the control hardware. These translations can be done by a compiler, targetin

M. V. Suslikov, A. I. Kolbin, N. V. Borisov

The polar V379 Vir is a well-known magnetic cataclysmic variable with a brown dwarf donor. Despite numerous studies of this system across various spectral ranges, a detailed investigation of the orbital variability of its optical spectra has not been carried out. In this work, we present an analysis of spectroscopic observations of V379 Vir obtained with the

Ivo Kraayeveld, Thomas de Jong, Mircea Lazar

Grid-connected power converters encounter significant stability challenges during weak grid faults, when conventional PI-based controllers exhibit an oscillatory response and poor fault-ride-through performance. This paper addresses this problem by replacing the conventional outer PI controllers that regulate DC-link and PCC voltages with an offset-free data

Bohan Zhang, Yiyi Miao, Taoyu Wu, Tong Chen

A structured understanding of intraoral 3D scans is essential for digital orthodontics. However, existing deep-learning approaches rely heavily on modality-specific training, large annotated datasets, and controlled scanning conditions, which limit generalization across devices and hinder deployment in real clinical workflows. Moreover, raw intraoral meshes

Jeryes Danial, Yosi Ben Asher, Itzik Klein

Monocular simultaneous localization and mapping (SLAM) algorithms estimate drone poses and build a 3D map using a single camera. Current algorithms include sparse methods that lack detailed geometry, while learning-driven approaches produce dense maps but are computationally intensive. Monocular SLAM also faces scale ambiguities, which affect its accuracy. T

Alessio Pellegrino, Jacopo Mauro

One of the long-standing goals in optimisation and constraint programming is to describe a problem in natural language and automatically obtain an executable, efficient model. Large language models appear to bring this vision closer, showing impressive results in automatically generating models for classical benchmarks. However, much of this apparent success

Shogo Kusano, Masayuki Uchida

Structural equation modeling (SEM) is a statistical method used to investigate relationships among latent variables. In SEM, the model must be specified in advance. However, in practice, statisticians often have several candidate models and need to select the most appropriate one. Consequently, model selection is a key issue in SEM, and information criteria

Gonçalo Martins, Santiago González-Gaitán, João Duarte, Ana M. Mourão

The persistent 4-6$\sigma$ difference between early- and late-time Hubble constant ($H_{0}$) measurements, known as the "Hubble tension", is a major problem in modern cosmology. We study how differences in colour ($c$), stretch ($x_{1}$), and host galaxy properties-stellar mass ($M$) and specific star formation rate (sSFR)-between calibration and Hubble Flow

A. G. M. Pietrow, C. Kuckein, M. Verma, C. Denker

We present high-resolution spectroscopic observations of the quiet-Sun center-to-limb variations (CLV) of the He I triplet at 10 830 {\AA} and the nearby Si I 10 827 {\AA} line, observed with GREGOR Infrared Spectrograph (GRIS) and the improved High-resolution Fast Imager (HiFI+). The observations cover the interval $\mu = [0.1,\, 1.0]$, where $\mu$ is the c

Yizhen Yin, Yuhua Qi, Dapeng Feng, Hongbo Chen

Active Simultaneous Localization and Mapping (Active SLAM) involves the strategic planning and precise control of a robotic system's movement in order to construct a highly accurate and comprehensive representation of its surrounding environment, which has garnered significant attention within the research community. While the current methods demonstrate eff

Dongchen Han, Tianzhu Ye, Zhuofan Xia, Kaiyi Chen

Scaling up network depth is a fundamental pursuit in neural architecture design, as theory suggests that deeper models offer exponentially greater capability. Benefiting from the residual connections, modern neural networks can scale up to more than one hundred layers and enjoy wide success. However, as networks continue to deepen, current architectures ofte

Kartik Tathe, Sayan Ghosh

This paper investigates the martingale characterizations of non-homogeneous counting processes and their fractional generalizations. We show that the weighted sum of non-homogeneous Poisson processes (NPPs) is the non-homogeneous generalized counting process (NGCP). Both the compensated and exponential forms of martingale characterization for NGCP are obtain

Levente Fekésházy, Oliver Schnetz

We present LinApart2, a major update to the LinApart algorithm for univariate partial fraction decomposition. Unlike its predecessor, LinApart2 can handle denominators of arbitrary polynomial degree without explicit factorization, while retaining the efficiency and parallelizability of the Laurent series method. Benchmarks show substantial speedups in both r

Felipe Ballen-Moreno, Pasquale Ferrentino, Milan Amighi, Bram Vanderborght

Understanding the physical interaction with wearable robots is essential to ensure safety and comfort. However, this interaction is complex in two key aspects: (1) the motion involved, and (2) the non-linear behaviour of soft tissues. Multiple approaches have been undertaken to better understand this interaction and to improve the quantitative metrics of phy

Francesco Fontani, Maria Teresa Beltrán, Anton Vasyunin

Growing evidence shows that most stars in the Milky Way, including the Sun, are born in high-mass star-forming regions, but due to both observational and theoretical challenges, our understanding of their chemical evolution is much less clear than that of their low-mass counterparts. Thanks to the capabilities of new generation telescopes and computers, a gr

Martin Gallauer

We define the period as a multiplicative characteristic of stably symmetric monoidal $\infty$-categories, develop its basic properties, and study many examples, with a focus on `ordinary' equivariant and motivic homotopy theory. We apply the findings to isotropic points in motivic tt-geometry. (Includes an appendix by Ivo Dell'Ambrogio on generalized compari

Vytas Zacharovas

We study depoissonization for sequences with entire exponential generating functions. We establish explicit finite-order remainder bounds for Poisson--Charlier approximations, controlled by Poisson-weighted averages of the absolute values of higher-order forward differences of the coefficient sequence. This complements classical analytic depoissonization, wh

Nanjun Li, Ziyue Hao, Quanqiang Wang, Xuanyin Wang

With the rise in sedentary behavior, health problems caused by poor sitting posture have drawn increasing attention. Most existing methods, whether using invasive sensors or computer vision, rely on two-stage pipelines, which result in high intrusiveness, intensive computation, and poor real-time performance on embedded edge devices. Inspired by YOLOv11-Pose

Saidakhmat N. Lakaev, Saidakbar S. Abduvayitov, Shuhrat S. Lakaev

We study a family of lattice Schr\"odinger operators $H_{\mu_1\mu_2}(K)$ describing two identical bosons on the three-dimensional cubic lattice $\mathbb{Z}^3$, where $K \in \mathbb{T}^3$ is the quasi-momentum, and $\mu_1, \mu_2 \in \mathbb{R}$ are coupling constants corresponding to on-site and nearest-neighbour interactions, respectively. We show that the H

Aneesh Barthakur, Luiz F. O. Chamon

As machine learning applications grow increasingly ubiquitous and complex, they face an increasing set of requirements beyond accuracy. The prevalent approach to handle this challenge is to aggregate a weighted combination of requirement violation penalties into the training objective. To be effective, this approach requires careful tuning of these hyperpara

Ignacio Sanchez, Filiberto Fele, Daniel Limon

Robust data-driven controllers typically rely on datasets from previous experiments, which embed information on the variability of the system parameters across past operational conditions. Complementarily, data collected online can contribute to improving the feedback performance relative to the current system's conditions, but are unable to account for the

Marcus Kubsch, Marit Kastaun, Peter Wulff, Nicole Graulich

This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges. The report clarifies key AI concepts to reduce ambig

Yuwen Zhang, Viet Tran, Paul Weng

In clinical machine learning, the coexistence of multiple models with comparable performance (a manifestation of the Rashomon Effect) poses fundamental challenges for trustworthy deployment and evaluation. Small, imbalanced, and noisy datasets, coupled with high-dimensional and weakly identified clinical features, amplify this multiplicity and make conventio

Hua-Lin Huang, Haoran Miao, Yu Ye

The Waring problem of forms concerns the expression of homogeneous multivariate polynomials as sums of powers of linear forms. This paper focuses on complex binary forms, and we solve the Waring problem for them using basic tools in algebra and analysis. In particular, we present elementary treatments of the Apolarity Lemma and Sylvester's 1851 Theorem, whic

Yiyi Miao, Taoyu Wu, Tong Chen, Ji Jiang

Intraoral 3D reconstruction is fundamental to digital orthodontics, yet conventional methods like intraoral scanning are inaccessible for remote tele-orthodontics, which typically relies on sparse smartphone imagery. While 3D Gaussian Splatting (3DGS) shows promise for novel view synthesis, its application to the standard clinical triad of unposed anterior a

Gabdolla Akishev

This article considers the Lipschitz space with mixed logarithmic smoothness of $2\pi$ periodic functions of several variables. We obtain equivalent descriptions of the norm of the Lipschitz space and prove embedding theorems between Besov and Lipschitz spaces.

Maxim Tjøtta, Devashish Shah, Kanishk Modi, Marco Valentini

Extracting the current-phase relationship (CPR) of a single superconducting junction is challenging in practice and traditionally involves embedding the junction in a larger superconducting circuit containing SQUIDs and/or resonators. Applying ac driving to the junction has proven to be a viable and less invasive way to extract information about the few lowe

Chenyang Xu, Siming Li, Hao Wang

Phonocardiogram (PCG) analysis is vital for cardiovascular disease diagnosis, yet the scarcity of labeled pathological data hinders the capability of AI systems. To bridge this, we introduce H-LDM, a Hierarchical Latent Diffusion Model for generating clinically accurate and controllable PCG signals from structured metadata. Our approach features: (1) a multi

Lukas Schroth, Daniel Morton, Amon Lahr, Daniele Gammelli

Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is crucial in MPC, solving the corresponding optimization problem in real time remains challenging when combining long horizons with high-fidelity models that capture both short-term dyn

P. C. Hua, C. H. Li, J. B. Zhang, H. Zhou

A characterization is completed for finite groups acting arc-transitively on maps with square-free Euler characteristic, associated with infinite families of regular maps of square-free Euler characteristic presented. This is based on a classification of finite groups of which each Sylow subgroup has a cyclic or dihedral subgroup of prime index.

Siddiqua Namrah

Enhancing low-light traffic images is crucial for reliable perception in autonomous driving, intelligent transportation, and urban surveillance systems. Nighttime and dimly lit traffic scenes often suffer from poor visibility due to low illumination, noise, motion blur, non-uniform lighting, and glare from vehicle headlights or street lamps, which hinder tas

V. A. Kshirsagar, A. S. Agrawal, S. A. Kadam, Vishwajeet S. Goswami

In this paper, we investigate the accelerating phase of the Universe within the context of $f(R,L_m,T)$ gravity theory, where $R$, $L_m$, and $T$ represent the Ricci scalar, matter Lagrangian, and the trace of the energy-momentum tensor, respectively. We focus on a particular form of modified gravity defined by $f(R,L_m,T) = R - \mu L_m T - \gamma$, with $\m

Wenqing Ai, Hanyu Cheng, Wei Qi

The rapid rise of electric vehicles (EVs) places unprecedented stress on both urban mobility systems and low-voltage power grids. Designing battery swapping and charging networks that are cost-efficient, grid-compatible, and sustainable is therefore a pressing yet complex challenge: service providers must jointly optimize station locations, battery inventory

Marcel Gibier, Nolwenn Celton, Raphaël Duroselle, Pierre Serrano

In this report, we describe our submission to Track 5 of the DCASE 2025 Challenge for the task of Audio Question Answering(AQA). Our system leverages the SSL backbone BEATs to extract frame-level audio features, which are then processed by a classification head to generate segment-level predictions of acoustic events, following the Audioset ontology. These s

Folkert de Ronde, Alexander Knapen, Stephan Wong, Sebastian Feld

As quantum computers continue to improve and support larger, more complex computations, smart control hardware and compilers are needed to efficiently leverage the capabilities of these systems. This paper introduces a novel approach to enhance the execution of quantum algorithms on distributed quantum systems. The proposed method involves the development of

R. K. Paul, J. Vít, P. Levinský, J. Hejtmánek

A series of thin films Fe$_{4-x}$Ge$_x$N (x=0-1) was fabricated onto MgO substrates by magnetron sputtering with the aim of studying the possible enhancement of the anomalous Nernst effect (ANE), envisaged based on Density Functional Theory (DFT) calculations. The Nernst and Hall effects of the series were systematically analyzed, complemented with resistivi

Shalabh Kumar Singh, Shubhashis Sengupta

Artificial intelligence (AI) is emerging as a foundational general-purpose technology, raising new dilemmas of sovereignty in an interconnected world. While governments seek greater control over it, the very foundations of AI--global data pipelines, semiconductor supply chains, open-source ecosystems, and international standards--resist enclosure. This paper

Meirun Chen, Reza Naserasr

Given a signed bipartite graph $(B, \pi)$ of negative girth $2k$, we present a necessary and sufficient condition for it to have the following property: each signed bipartite graph $(G, \sigma)$ whose negative girth is at least $2k$ and whose underlying graph has treewidth at most $t$ admits a homomorphism to $(B, \pi)$. Applying the result on the signed pro

Lucía Schmidt-Santiago, David Rodríguez-Temporal, Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become a cornerstone technology in clinical microbiology, enabling rapid and accurate microbial identification. However, the development of data-driven diagnostic models remains limited by the lack of sufficiently large, balanced, and standardized spectral dataset

Oscar Cosserat

Hamiltonian Poisson integrators are Poisson integrators that admit a modified Hamiltonian. In this article, we illustrate the importance of the existence of a modified Hamiltonian for Poisson integrators in the context of integrable and non-integrable systems. Examples of Hamiltonian systems are provided by Lotka-Volterra dynamics; in order to investigate st

Sahar Nasirihaghighi, Negin Ghamsarian, Yiping Li, Marcel Breeuwer

Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (FSSL) offers a solution but faces two challenges: pseudo-label reliability depends on the strength of local models, and client devices often require compact or heterogeneous archite

Eric Xue, Ruiyi Zhang, Pengtao Xie

Modern language models remain vulnerable to backdoor attacks via poisoned data, where training inputs containing a trigger are paired with a target output, causing the model to reproduce that behavior whenever the trigger appears at inference time. Recent work has emphasized stealthy attacks that stress-test data-curation defenses using stylized artifacts or

R. A. Battye, S. J. Cotterill, P. Millington

Pseudo-Hermitian (including $\mathcal{PT}$-symmetric) field theories support phenomenology that cannot be replicated in standard Hermitian theories. We describe a concrete example in which the vortex solutions that are realised in a prototypical pseudo-Hermitian field theory exhibit a novel metastability, despite the model parameters residing within the naiv

Xiaochuan Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen

In today's data-driven era, fully automated end-to-end data analytics, particularly insight discovery, is critical for discovering actionable insights that assist organizations in making effective decisions. With the rapid advancement of large language models (LLMs), LLM-driven agents have emerged as a promising paradigm for automating data analysis and insi

Koffi Amezouwui, Brigitte Gelein, Matthieu Marbac, Anthony Sorel

We propose a novel mixture model for football event data that clusters entire possessions to reveal their temporal, sequential, and spatial structure. Each mixture component models possessions as marked spatio-temporal point processes: event types follow a finite Markov chain with an absorbing state for ball loss, event times follow a conditional Gamma proce

Luis Correas-Naranjo, Miguel Camacho-Sánchez, Laëtitia Launet, Milena Zuric

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of

Chinonso Onah, Roman Firt, Kristel Michielsen

We introduce the Constraint-Enhanced Quantum Approximate Optimization Algorithm (CE-QAOA), a shallow, constraint-aware ansatz that operates inside the one-hot product space [n]^m, where m is the number of blocks and each block is initialized in an n-qubit W_n state. We give an ancilla-free, depth-optimal encoder that prepares W_n using n-1 two-qubit rotation

Mohammad Zbeeb, Hasan Abed Al Kader Hammoud, Sina Mukalled, Nadine Rizk

We present AraLingBench: a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language models (LLMs). The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert-designed multiple choice questions that directly assess structural language understanding. Ev

Morgane Dumont, Ahmed Alsaloum, Julian Ernst, Jan Weymeirsch

Spatial dynamic microsimulations probabilistically project geographically referenced units with individual characteristics over time. Like any projection method, their outcomes are inherently uncertain and sensitive to multiple factors. However, such factors are rarely addressed. Applying variance-based sensitivity analysis to both direct and indirect effect

Marcel Gibier, Raphaël Duroselle, Pierre Serrano, Olivier Boeffard

Recent audio-language models have shown impressive performance across a wide range of audio tasks and are increasingly capable of handling long audio inputs. However, the computing costs in these models heavily depend on sequence length, which can become very large given the nature of audio data. In the vision-language domain, token pruning methods have prov

Cyrill Scheidegger, Simon Wandel, Tobias Mütze

Covariate adjustment can enhance precision and power in clinical trials, yet its application to the win odds remains unclear. The win odds is an extension of the win ratio that counts ties as half a win for the treatment and the control group, respectively. In their original form, both the win ratio and the win odds rely on comparing each individual from the

Yuxin Zhang, Ziyu Lu, Hongbo Duan, Keyu Fan

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vital for advancing embodied AI and world models, which depend on diverse, high-quality scenes for learning and evaluation. In this work, we propose Gen3d, a novel method for generati