July 2025 arXiv papers — page 5

Owen Garnier

The purpose of this note is to prove a conjecture of Shvartsman relating a complex projective reflection group with the quotient of a suitable complex braid group by its center. Shvartsman originally proved this result in the case of real projective reflection groups, and we extend it to all complex projective reflection groups. Our study also allows us to c

I. I. Zinchenko, S. V. Salii, A. M. Sobolev, I. A. Zaichikova

We present the results of the further investigation of the Class II methanol maser emission in the $14_1 - 14_0$ A$^{-+}$ transition at 349.1 GHz discovered in 2016 in the remarkable core S255IR-SMA1, harboring a $\sim$20 M$_\odot$ protostar NIRS3, which exhibited a disk-mediated accretion burst in 2015. The present study is based on the observations of this

Elodie Maignant, Xavier Pennec, Alain Trouvé, Anna Calissano

Certain data are naturally modeled by networks or weighted graphs, be they arterial networks or mobility networks. When there is no canonical labeling of the nodes across the dataset, we talk about unlabeled networks. In this paper, we focus on the question of dimensionality reduction for this type of data. More specifically, we address the issue of interpre

Nick Tsipinakis, Panos Parpas

Newton's method has been thoroughly studied for the class of self-concordant functions. However, a local analysis specific to strongly self-concordant functions (a subclass of the former) is missing from the literature. The local quadratic rate of strongly self-concordant functions follows, of course, from the known results for self-concordant functions. How

Alin Bostan, Valentin Féray, Paul Thévenin

We consider a family of infinite sums of products of Catalan numbers, indexed by trees. We show that these sums are polynomials in $1/\pi$ with rational coefficients; the proof is effective and provides an algorithm to explicitly compute these sums. Along the way we introduce parametric liftings of our sums, and show that they are polynomials in the complete

Botao Zhu, Xianbin Wang

Due to the diverse physical attributes of computing resources and tasks, developing effective mechanisms to facilitate task and resource matching in complex connected systems for value-oriented task completion has become increasingly challenging. To address the challenge, this paper proposes a networked physical computing system that integrates the physical

P. Skokowski, M. Matczak, Ł. Frąckowiak, T. Bednarchuk

We present the structural, magnetic, and magnetocaloric properties of thin films with stoichiometry Tb$_{31}$Co$_{69}$ and Dy$_{31}$Co$_{69}$ deposited on naturally oxidized silicon Si (100) substrates. Samples with a thickness $d=50$ nm covered with a protective Au overlayer with a thickness $d_{\rm Au} =5 $ nm were produced using the pulsed laser depositio

Ruoyu Wang, Junda Wu, Yu Xia, Tong Yu

Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks. However, existing works have shown that the effectiveness of ICL is highly sensitive to the choice of demonstrations, with suboptimal examples often leading to unstable or degraded performance. While prior

Luca Bondi, Elena Issoglio, Francesco Russo

This paper focuses on recent works on McKean-Vlasov stochastic differential equations (SDEs) involving singular coefficients. After recalling the classical framework, we review existing recent literature depending on the type of singularities of the coefficients: on the one hand they satisfy some integrability and measurability conditions only, while on the

FASER Collaboration, Roshan Mammen Abraham, Xiaocong Ai, Saul Alonso Monsalve

The muon puzzle -- an excess of muons relative to simulation predictions in ultra-high-energy cosmic-ray air showers -- has been reported by many experiments. This suggests that forward particle production in hadronic interactions is not fully understood. Some of the scenarios proposed to resolve this predict reduced production of forward neutral pions and e

Jozef Klačka, Michal Šturc

Context. Latest papers on the rotation curve of the Milky Way galaxy, i. e. Ou et al. (2024); Jiao et al. (2023); Sylos Labini et al. (2023) suggest a Keplerian decrease in the rotation curve. This behavior is not consistent with other spiral galaxies (Lelli et al. 2016; Mistele et al. 2024). Aims. Show that the prior use of the axisymmetric Jeans equation i

A. Caranti, I. Del Corso, M. Di Matteo, M. Ferrara

Skew braces play a central role in the theory of set-theoretic non-degenerate solutions of the Yang--Baxter equation, since their algebraic properties significantly affect the behaviour of the corresponding solutions (see for example [Ballester-Bolinches et al., Adv. Math. 455 (2024), 109880]). Recently, the study of nilpotency-like conditions for the soluti

Jens Braun, Benjamin Campillo Aveleira, Gudrun Heinrich, Marius Höfer

We present a major update of the one-loop generator GoSam, containing performance improvements as well as new features, in particular functionalities that facilitate calculations beyond the Standard Model in Effective Field Theory frameworks.

Rebecca Whittle, Richard D Riley, Lucinda Archer, Gary S Collins

Clinical prediction models enable healthcare professionals to estimate individual outcomes using patient characteristics. Current sample size guidelines for developing or updating models with continuous outcomes aim to minimise overfitting and ensure accurate estimation of population-level parameters, but do not explicitly address the precision of prediction

Anjiao Gu, Shi Jin, Chuwen Ma

The Helmholtz equation is a prototypical model for time-harmonic wave propagation. Numerical solutions become increasingly challenging as the wave number $k$ grows, due to the equation's elliptic yet noncoercive character and the highly oscillatory nature of its solutions, with wavelengths scaling as $1/k$. These features lead to strong indefiniteness and la

Bálint Hartmann, Michelle T. Cirunay

The potential of grid-side flexibility, the latent ability to reconfigure transmission network topology remains under-used partly because of the lack of empirical studies on how real-world grids evolve.

Paroni Roberto, Picchi Scardaoni Marco

We study the $\Gamma$-limit of sequences of variational problems for straight, transversely curved shallow shells, as the width of the planform $\varepsilon$ goes to zero. The energy is of von K\'arm\'an type for shallow shells under suitable boundary conditions. What distinguishes the various regimes is the scaling of the stretching energy $\sim \varepsilon

Ryo Miyoshi, Yuki Okafuji, Takuya Iwamoto, Junya Nakanishi

In recent years, the demand for social robots has grown, requiring them to adapt their behaviors based on users' states. Accurately assessing user experience (UX) in human-robot interaction (HRI) is crucial for achieving this adaptability. UX is a multi-faceted measure encompassing aspects such as sentiment and engagement, yet existing methods often focus on

Gopika Sudhakaran, Hikaru Shindo, Patrick Schramowski, Simone Schaub-Meyer

Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize beyond the relations on which they are trained. While prompt tuning has been used to adapt vision-language models (VLMs) for VRD, it uses handcrafted prompts and struggles with no

Nuno Crokidakis, Lucas Sigaud

In social contexts where individuals consume varying amounts, such as shared meals or bar gatherings, splitting the total bill equally often yields surprisingly fair outcomes. In this work, we develop a statistical physics framework to explain this emergent fairness by modeling individual consumption as stochastic variables drawn from a realistic distributio

Yi Zhang, Erik Leo Haß, Kuo-Yi Chao, Nenad Petrovic

Autonomous driving systems face significant challenges in achieving human-like adaptability, robustness, and interpretability in complex, open-world environments. These challenges stem from fragmented architectures, limited generalization to novel scenarios, and insufficient semantic extraction from perception. To address these limitations, we propose a unif

Piotr Indyk, Michael Kapralov, Kshiteej Sheth, Tal Wagner

Motivated by the problem of fast processing of attention matrices, we study fast algorithms for computing matrix-vector products for asymmetric Gaussian Kernel matrices $K\in \mathbb{R}^{n\times n}$. $K$'s columns are indexed by a set of $n$ keys $k_1,k_2\ldots, k_n\in \mathbb{R}^d$, rows by a set of $n$ queries $q_1,q_2,\ldots,q_n\in \mathbb{R}^d $, and its

Pan Zheng, Yanyan Cai, Bin Xu, Shengcheng Wen

Quantum metrology has recently emerged as a powerful approach for dark matter (DM) searches, particularly using nonclassical bosonic states in microwave cavities that are sensitive to weak signals. Nonclassical cat states - macroscopic superpositions of coherent states featuring sub-Planck interference structures - offer promising advantages for high-precisi

Yu Chen, Guangxi Wu

Background:Chronic kidney disease is one of the most prevalent non-communicable health issues globally, and high body mass index plays a significant role in the onset and progression of chronic kidney disease. Methods: Data on the disease burden attributable to high body mass index were retrieved from the 2021 Global Burden of Disease, Injuries, and Risk Fac

Georg Slamanig, Francesco Corti, Olga Saukh

Parameter-efficient fine-tuning (PEFT) methods reduce the computational costs of updating deep learning models by minimizing the number of additional parameters used to adapt a model to a down- stream task. While extensively researched in large language models (LLMs), their application to smaller models used on edge devices, such as convolutional neural netw

Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu, Dhari Gandhi

As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability appl

Flora Angileri, Andrea Clementi, Emanuele Natale, Michele Salvi

A randomized distributed algorithm called RAES was introduced in [Becchetti et al., SODA 2020] to extract a bounded-degree expander from a dense $n$-vertex expander graph $G = (V, E)$. The algorithm relies on a simple threshold-based procedure. A key assumption in [Becchetti et al., SODA 2020] is that the input graph $G$ is static - i.e., both its vertex set

Arjun Sharma, Ritabrata Thakur, Sharath Jose, Rama Govindarajan

A wide range of natural and engineered fluid flows exhibit spatial or temporal viscosity variations, spanning scales from microbial locomotion to planetary mantle convection. These variations introduce qualitatively new physical mechanisms absent in constant-viscosity flows. This review surveys such phenomena across scales. In low Reynolds number (Stokes) fl

Yuegu Fang, Jiayu Huang, Dong H. Zhang

Accurate quantum mechanical treatment of molecular reactions remains a longstanding challenge, especially for reactions involving deep potential wells and long-lived intermediate complexes. Here, we introduce an interaction region decoupling (IRD) strategy that incorporates structured absorbing potentials to dynamically partition the interaction region into

Alysson Cunha

We prove that the Benjamin Ono equation is globally well-posed in $H^s(\mathbb{R})$ for $s > 1/2$. Our approach does not rely on the global gauge transformation introduced by Tao (arXiv:math/0307289). Instead, we employ a modified version of the standard parabolic regularization method. In particular, this technique also enables us to establish global well-p

Shohini Bhattacharya, Renaud Boussarie, Bernard Pire, Lech Szymanowski

We demonstrate that chiral-odd dimeson generalized distribution amplitudes (CO-GDAs) -- nonperturbative objects encoding the transition of a quark-antiquark pair into two mesons -- can be accessed in high-energy $e^- e^+$ annihilation into two meson pairs, each with a relatively low invariant mass. While chiral-even GDAs contribute to the leading one-photon

Chong Huang, Gaojie Chen, Jing Zhu, Qu Luo

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resourc

Wen-Xuan Long, Shengyu Ye, Marco Moretti, Michele Morelli

The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift com

Gerald Kaushallye Cooray

Cortical activity recorded through EEG and MEG reflects complex dynamics that span multiple temporal and spatial scales. Spectral analyses of these signals consistently reveal power-law behaviour, a hallmark of turbulent systems. In this paper, we derive a kinetic equation for neural field activity based on wave turbulence theory, highlighting how quantities

Lukas Hantzko, Lennart Binkowski

We present a comprehensive classification of one-dimensional coined quantum walks on the infinite line, focusing on the spatial probability distributions they induce. Building on prior results, we identify all initial coin states that lead to symmetric quantum walks for arbitrary coins, and provide a bijective parametrisation of all symmetric quantum walks m

Haodong Huang, Shilong Sun, Yuanpeng Wang, Chiyao Li

Reinforcement learning (RL), driven by data-driven methods, has become an effective solution for robot leg motion control problems. However, the mainstream RL methods for bipedal robot terrain traversal, such as teacher-student policy knowledge distillation, suffer from long training times, which limit development efficiency. To address this issue, this pape

Hongzhe Bi, Lingxuan Wu, Tianwei Lin, Hengkai Tan

Imitation learning for robotic manipulation faces a fundamental challenge: the scarcity of large-scale, high-quality robot demonstration data. Recent robotic foundation models often pre-train on cross-embodiment robot datasets to increase data scale, while they face significant limitations as the diverse morphologies and action spaces across different robot

Mathias Zambach, Miriam Varón, Thomas Veile, Bima N. Sanusi

We here present printable and castable magnetic nanocomposites containing superparamagnetic 11$\pm$3 nm $\gamma$-Fe$_2$O$_3$ particles in an insulating poly-vinyl alcohol polymer matrix. The nanocomposites feature well-dispersed particles with volume fractions between 10 and 45 \%, as confirmed by small-angle neutron scattering. The magnetic volume susceptib

Woo Kyoung Han, Yongjun Lee, Byeonghun Lee, Sang Hyun Park

Despite significant advances in learning-based lossy compression algorithms, standardizing codecs remains a critical challenge. In this paper, we present the JPEG Processing Neural Operator (JPNeO), a next-generation JPEG algorithm that maintains full backward compatibility with the current JPEG format. Our JPNeO improves chroma component preservation and en

Roni Varshavsky

Linear resolutions and the stronger notion of linear quotients are important properties of monomial ideals. In this paper, we fully characterize linear quotients in terms of the lcm-lattice of monomial ideals. We also formulate an analogous characterization for monomial ideals with linear resolutions, making explicit a relationship that is implicit in the ex

Nicholas Tovazzi, Gorka Muñoz-Gil, Michele Caraglio

Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment. By adopting a machine learning approach, we aim to understand how this behavior arises. We consider run-and-tumble agents able to tune the instantaneous probability of switching between the run and the tumble phase. When such agents are navigati

Joel Poncha Lemayian, Hachem Bensalem, Ghyslain Gagnon, Kaiwen Zhang

Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by

Ziye Jia, Sijie He, Qiuming Zhu, Wei Wang

Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we char

Jitka Mrazkova

Studying the production of J/$\psi$ (bound state of charm and anti-charm quark) in proton-proton collisions gives an opportunity to test quantum chromodynamics (QCD) calculations, as the production of J/$\psi$ involves both perturbative and non-perturbative processes. However, theoretical calculations are still unable to fully and simultaneously explain expe

Anaïs Ollagnier, Aline Menin

The rapid growth of publicly available textual resources, such as lexicons and domain-specific corpora, presents challenges in efficiently identifying relevant resources. While repositories are emerging, they often lack advanced search and exploration features. Most search methods rely on keyword queries and metadata filtering, which require prior knowledge

Christian Röken

We present a detailed, mathematically rigorous description of the construction procedure of Penrose diagrams for the example of the maximal analytic extension of the exterior Schwarzschild spacetime. To this end, we first outline the central idea underlying Penrose diagrams, state the general requirements on the spacetimes to be visualized, and give a defini

Mirjana Dimitrievska, Edgardo Saucedo, Stefaan De Wolf, Billy J. Stanbery

The growing demand for photovoltaic (PV) technologies that are lightweight, flexible, and seamlessly integrated into diverse applications has propelled interest in thin-film solar cells. Among these, Cu(In,Ga)(S,Se)2 (CIGS) and metal halide perovskites have garnered significant attention in the past and present, respectively. While CIGS reached commercial re

K. V. Nikolaev, L. I. Goray, P. S. Savchenkov, A. V. Rogachev

This study explores the use of synchrotron measurements as a nanometrology tool for blazed gratings. In grazing incidence geometry, one can measure both the conical diffraction and the diffuse scattering on the grating simultaneously in a single scattering pattern. The sensitivity of scattering patterns to the structure of the blazed gratings is evaluated. T

Saleh Vatan Khah, Savelii Chezhegov, Shahrokh Farahmand, Samuel Horváth

Gradient clipping is a fundamental tool in Deep Learning, improving the high-probability convergence of stochastic first-order methods like SGD, AdaGrad, and Adam under heavy-tailed noise, which is common in training large language models. It is also a crucial component of Differential Privacy (DP) mechanisms. However, existing high-probability convergence a

Yadong Niu, Tianzi Wang, Heinrich Dinkel, Xingwei Sun

While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highly detailed model outputs. To this end, this work introduces

David A. Kelly, Akchunya Chanchal, Nathan Blake

Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the problem of choosing the right model becomes more and more important. While we can assess a model's classification accuracy statistically, our understanding of the way these models work

Timing Li, Bing Cao, Jiahe Feng, Haifang Cao

Image fusion synthesizes complementary information from multiple sources, mitigating the inherent limitations of unimodal imaging systems. Accurate image registration is essential for effective multi-source data fusion. However, existing registration methods, often based on image translation in Euclidean space, fail to handle cross-modal misalignment effecti

Marco Bussoletti, Mirko Gallo, Amir Jafari, Gregory L. Eyink

It is experimentally well-established that non-equilibrium long-range correlations of concentration fluctuations appear in free diffusion of a solute in a solvent, but it remains unknown how such correlations are established dynamically. We address this problem in a model of Donev, Fai \& Vanden-Eijnden (DFV), obtained from the high-Schmidt limit of the Land

Valéria Carvalho, Márcio Ferreira, Michał Bejger, Constança Providência

We present a simulation-based inference (SBI) framework to constrain the neutron star (NS) equation of state (EoS) from astrophysical observations of masses, radii and tidal deformabilities, using Neural posterior estimation (NPE) with Conditional Normalising Flows (CNF). To ensure that the model conforms with reality, physics-informed constraints are embedd

Maurits Kaptein

We investigate the trade-off between certificate length and verifier runtime. We prove a Verifier Trade-off Theorem showing that reducing the inherent verification time of a language from \(f(n)\) to \(g(n)\), where \(f(n) \ge g(n)\), requires certificates of length at least \(\Omega(\log(f(n) / g(n)))\). This theorem induces a natural hierarchy based on cer

Kyle Gannon, Daniel Max Hoffmann, Krzysztof Krupiński

Initially motivated by Hrushovski's paper on definability patterns, we obtain homeomorphisms between Ellis semigroups related to natural actions of the automorphism groups of first order structures and certain collections of types and Keisler measures. Thus, we can transfer the semigroup operation from these Ellis semigroups to the corresponding collections

Christophe Charlier

We consider the two-dimensional Coulomb gas with a general potential at the determinantal temperature, or equivalently, the eigenvalues of random normal matrices. We prove that the smallest gaps between particles are typically of order $n^{-3/4}$, and that the associated joint point process of gap locations and gap sizes, after rescaling the gaps by $n^{3/4}

Nicklas Werge, Yi-Shan Wu, Manuel Haussmann, Bahareh Tasdighi

Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or task-specific hyperparameters to balance overestimation bias and variance, leaving the challenge of a truly adaptive approach open. We introduce Adaptive Ensemble Aggregation (AEA),

Michal Feldman, Simon Mauras, Divyarthi Mohan, Rebecca Reiffenhäuser

We study online combinatorial allocation problems in the secretary setting, under interdependent values. In the interdependent model, introduced by Milgrom and Weber (1982), each agent possesses a private signal that captures her information about an item for sale, and the value of every agent depends on the signals held by all agents. Mauras, Mohan, and Rei

Piotr Sowinski, Kacper Grzymkowski, Anastasiya Danilenka

Recording data changes in RDF systems is a crucial capability, needed to support auditing, incremental backups, database replication, and event-driven workflows. In large-scale and low-latency RDF applications, the high volume and frequency of updates can cause performance bottlenecks in the serialization and transmission of changes. To alleviate this, we pr

Jason J. Bramburger

The Koopman operator has become a celebrated tool in modern dynamical systems theory for analyzing and interpreting both models and datasets. The linearity of the Koopman operator means that important characteristics about it, and in turn its associated nonlinear system, are captured by its eigenpairs and more generally its spectrum. Many studies point out t

David A Kelly, Hana Chockler

Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other hand, logic-based explanations are formally and rigorously defined but their computability relies on strict assumptions about the model that do not hold on image classifiers. In thi

Sebastian Geissel, Christoph Knochenhauer

We propose a new class of monetary risk measures for assessing financial and ESG risk. The construction is based on classical shortfall risk measures with loss function replaced by a multi-attribute utility function. We present an extensive theoretical analysis of these risk measures, showing specifically how properties of the utility function translate into

Maurits Kaptein

Practitioners making decisions based on causal effects typically ignore structural uncertainty. We analyze when this uncertainty is consequential enough to warrant methodological solutions (Bayesian model averaging over competing causal structures). Focusing on bivariate relationships ($X \rightarrow Y$ vs. $X \leftarrow Y$), we establish that model averagin

Yukun Chen, Zhaofeng Lin, Yanqi Qiu

We determine the exact values of the Fourier dimensions for Gaussian Multiplicative Chaos measures on the $d$-dimensional torus $\mathbb{T}^d$ for all integers $d \ge 1$. This resolves a problem left open in previous works [LQT24,LQT25] for high dimensions $d\ge 3$. The proof relies on a new construction of log-correlated Gaussian fields admitting specific d

Sandip Sahoo, Samriddhi Sankar Ray

Zonal jets manifest themselves as bands with sharp interfaces in the vorticity configuration. We develop an algorithm to track these fluctuating vorticity interfaces and systematically investigate their characteristic spatio-temporal behavior. While the interfacial height fluctuations are typically sub-Gaussian, the corresponding $\textit{fluctuation speeds}

Dominique Geissler, Claire Robertson, Stefan Feuerriegel

Deepfakes images can erode trust in institutions and compromise election outcomes, as people often struggle to discern real images from deepfake images. Improving digital literacy can help address these challenges. Here, we compare the efficacy of five digital literacy interventions to boost people's ability to discern deepfakes: (1) textual guidance on comm

Olga Vershinina, Jacopo Sabbatinelli, Anna Rita Bonfigli, Dalila Colombaretti

Objective. Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies. Research Design and Methods. This study analyzed a cohort of 554 patients (aged 40-87 y

Marcela V. Reale, Gustavo Paccosi, David H. Margarit, Lilia Romanelli

Understanding cancer cell differentiation is essential for advancing its detection, diagnosis, and treatment. Mathematical models significantly contribute to this by providing a theoretical framework to understand the complex interactions between cancer stem cells, differentiated cancer cells, and immune system components. Such models depend on experimental

Vivek Pandey, Nader Motee

Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robust risk framework for analyzing cascading failures in multi-agent rendezvous. To capture the complex interactions between network connectivity, system dynamics, and communication de

Kacper Kadziolka, Saber Salehkaleybar

Causal inference remains a fundamental challenge for large language models. Recent advances in internal reasoning with large language models have sparked interest in whether state-of-the-art reasoning models can robustly perform causal discovery-a task where conventional models often suffer from severe overfitting and near-random performance under data pertu

Jinshan Zhen, Yuanyue Ge, Tianxiao Zhu, Hui Zhao

Accurate mass estimation of table-top grown strawberries under field conditions remains challenging due to frequent occlusions and pose variations. This study proposes a vision-based pipeline integrating RGB-D sensing and deep learning to enable non-destructive, real-time and online mass estimation. The method employed YOLOv8-Seg for instance segmentation, C

Shirui Wang, Zhihui Tang, Huaxia Yang, Qiuhong Gong

Large language models (LLMs) hold promise in clinical decision support but face major challenges in safety evaluation and effectiveness validation. We developed the Clinical Safety-Effectiveness Dual-Track Benchmark (CSEDB), a multidimensional framework built on clinical expert consensus, encompassing 30 criteria covering critical areas like critical illness

A. Canton, L. Fernandez-Jambrina, M. J. Vazquez-Gallo

In this paper we develop the formalism of rational complex Bezier curves. This framework is a simple extension of the CAD paradigm, since it describes arc of curves in terms of control polygons and weights, which are extended to complex values. One of the major advantages of this extension is that we may make use of two different groups of projective transfo

Marco Bauer, Patrick Norman, Andreas Dreuw, Anthony D. Dutoi

The recently proposed excitonic renormalization framework presents an alternative ansatz to elec- tronic structure theory of weakly interacting fragments. It makes use of absolutely localized orbitals and correlated states evaluated on isolated fragments, which are then used to recover the interaction in an ab-initio manner based on a biorthogonal framework.

Mutian Xu, Chongjie Ye, Haolin Liu, Yushuang Wu

3D data simulation aims to bridge the gap between simulated and real-captured 3D data, which is a fundamental problem for real-world 3D visual tasks. Most 3D data simulation methods inject predefined physical priors but struggle to capture the full complexity of real data. An optimal approach involves learning an implicit mapping from synthetic to realistic

Donald M. Davis

It is known that, for all n, there exist compact differentiable orientable n-manifolds with dual Stiefel-Whitney class wbar_{n-ahat(n)} nonzero, and this is best possible, but the proof is nonconstructive. Here ahat(n) equals the number of 1's in the binary expansion of n if n equiv 1 mod 4 and exceeds this by 1 otherwise. We find, for all n nonzero mod 4, e

Jorge A. Vila

This piece serves two purposes. Firstly, it aims at elucidating the role of epistasis in shaping, at a molecular level, the evolutionary paths of proteins, as well as the extent to which these epistatic effects are the outcome of an as-yet-unidentified epistatic force. Second, it seeks to ascertain the extent to which the principle of least action will enabl

Donghyun Lee, Dawoon Jeong, Jae W. Lee, Hongil Yoon

Deep neural networks have revolutionized 3D point cloud processing, yet efficiently handling large and irregular point clouds remains challenging. To tackle this problem, we introduce FastPoint, a novel software-based acceleration technique that leverages the predictable distance trend between sampled points during farthest point sampling. By predicting the

Julia Werner, Oliver Bause, Julius Oexle, Maxime Le Floch

Video capsule endoscopy has become increasingly important for investigating the small intestine within the gastrointestinal tract. However, a persistent challenge remains the short battery lifetime of such compact sensor edge devices. Integrating artificial intelligence can help overcome this limitation by enabling intelligent real-time decision-making, ther

Ting Huang, Zeyu Zhang, Hao Tang

Large vision-language models (VLMs) have made significant strides in 2D visual understanding tasks, sparking interest in extending these capabilities to 3D scene understanding. However, current 3D VLMs often struggle with robust reasoning and generalization due to limitations in high-quality spatial data and the static nature of viewpoint assumptions. To add

Shenghao Hua

We introduce a special class of multiple Dirichlet series whose terms are supported on a variety and which admit an Euler product structure.

Panagiotis Dorlis, Nick E. Mavromatos, Sarben Sarkar, Sotirios-Neilos Vlachos

We propose, in (3+1)-dimensional spacetimes, a novel astrophysical source of squeezed graviton states, due to superradiant axionic clouds surrounding rotating (Kerr-type) black holes (BH). The microscopic origin of these axions is diverse, ranging from the Kalb-Ramond (model-independent) axions and compactification axions in string theory, to \cm contorted g

Farah Baracat, Giacomo Indiveri, Elisa Donati

Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike

Bin Xie, Congxuan Zhang, Fagan Wang, Peng Liu

The widespread application of Unmanned Aerial Vehicles (UAVs) has raised serious public safety and privacy concerns, making UAV perception crucial for anti-UAV tasks. However, existing UAV tracking datasets predominantly feature conspicuous objects and lack diversity in scene complexity and attribute representation, limiting their applicability to real-world

Gabriel H. Collin

The estimation of the number of point-sources in the sky is one the oldest problems in astronomy, yet an easy and efficient method for estimating the uncertainty on these counts is still an open problem. Probabilistic cataloging solves the general point-source inference problem, but the trans-dimensional nature of the inference method requires a bespoke appr

Xingle Xu, Yongkang Liu, Dexian Cai, Shi Feng

Multimodal Sentiment Analysis aims to integrate information from various modalities, such as audio, visual, and text, to make complementary predictions. However, it often struggles with irrelevant or misleading visual and auditory information. Most existing approaches typically treat the entire modality information (e.g., a whole image, audio segment, or tex

Michal Svoboda

Jets serve as an important tool to probe QCD both in the vacuum and in the hot and dense medium. The STAR experiment at RHIC plays a key role in studying QCD phenomena across different collision systems ($p$+$p$, $p$+A, A+A), offering access to a kinematic regime that complements that of the LHC. Building on recent jet and event activity studies at STAR, we

Sebastian Gürtl, Gloria Schimetta, David Kerschbaumer, Michael Liut

UML and ER diagrams are foundational in computer science education but come with challenges for learners due to the need for abstract thinking, contextual understanding, and mastery of both syntax and semantics. These complexities are difficult to address through traditional teaching methods, which often struggle to provide scalable, personalized feedback, e

Julia Bernatska

In the present paper, a hierarchy of the mKdV equation is integrated by the methods of algebraic geometry. The mKdV hierarchy in question arises on coadjoint orbits in the loop algebra of $\mathfrak{sl}(2)$, and employs a family of hyperelliptic curves as spectral curves. A generic form of the finite-gap solution in any genus is obtained in terms of the $\wp

Sacha Cerf, Clara Wassner, Jack Davis, Francesco Arzani

The Schr\"odinger wavefunction is ubiquitous in quantum mechanics, quantum chemistry, and bosonic quantum information theory. Its zero-set for fermionic systems is well-studied and central for determining chemical properties, yet for bosonic systems the zero-set is less understood, especially in the context of characterizing non-classicality. Here we study t

José Luís da Silva, Mohamed Erraoui

Multiplicative self-decomposable laws describe random variables that can be decomposed into a product of a scaled-down version of themselves and an independent residual term. Shanbhag et al.~(1977) have shown that the gamma distribution is multiplicative self-decomposable, in particular, the exponential distribution. As a result, they established the multipl

Thomas Liege, Perrine Lognone, Matteo Schiavon, Caroline B. Lim

In pursuit of a global quantum key distribution (QKD) network, a service based on untrusted nodes on geostationary satellites could offer wide coverage, continuous operation, and enhanced security compared to the trusted node alternative. Although this scenario has been studied for entanglement-based protocols, such an approach would require large-area teles

Saeed Almheiri, Yerulan Kongrat, Adrian Santosh, Ruslan Tasmukhanov

As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety methods typically assume uniform access and focus on preventing harmful or toxic outputs, without addressing role-specific access constraints. In this work, we investigate whether L

Miriam Rike Ebert, David Christian Ohnmacht, Wolfgang Belzig, Juan Carlos Cuevas

Multiterminal superconducting junctions have revitalized the investigation of the Josephson effect. One of the most interesting aspects of these hybrid systems is the occurrence of multi-Cooper pair tunneling processes that have no analog in two-terminal devices. Such correlated tunneling events are also intimately connected to the Andreev bound states (ABSs

William Cotton, Gourab Giri, P. J. Agnihotri, D. J. Saikia

We present full polarization MeerKAT images of the wide-angle tail, giant radio galaxy J1712$-$2435 at 1.3 GHz with 7.\asec5 resolution and an RMS sensitivity of 8 $\mu$Jy beam$^{-1}$. Due to the angular proximity to the Galactic Center (l=359.6$^\circ$, b=+8.5$^\circ$) the immediate environment is not well understood but there are massive clusters nearby. E

Saito Sakaguchi, Basil Duvernoy, Anders Fridberger, Håkan Olausson

Even simple tactile stimuli can lead to remarkably different perceptions among individuals, both in intensity and pleasantness. To understand the physical factors behind this variation, it is important to investigate how mechanical events are transmitted through the skin. In this study, we visualize the internal skin strains in response to soft brushing stim

Taeheon Lim, Joohyung Lee, Kyungjae Lee, Jungchan Cho

The Federated Learning (FL) approach enables effective learning across distributed systems, while preserving user data privacy. To date, research has primarily focused on addressing statistical heterogeneity and communication efficiency, through which FL has achieved success in classification tasks. However, its application to non-classification tasks, such

Keiichi Shigechi

We study the Fuss--Catalan algebras, which are generalizations of the Temperley--Lieb algebra and act on generalized Dyck paths, through non-crossing partitions. First, the Temperley--Lieb algebra is defined on non-crossing partitions, and a bijection between a Dyck path and a non-crossing partition is shown to be compatible with the Temperley--Lieb algebra

Fan Li, Chang Meng, Jiaqi Fu, Shuchang Liu

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within t