December 2025 arXiv papers — page 123

Xi Ou, Longlong Lin, Zeli Wang, Pingpeng Yuan

Bipartite graphs are widely used to model relationships between entities of different types, where nodes are divided into two disjoint sets. Similarity search, a fundamental operation that retrieves nodes similar to a given query node, plays a crucial role in various real-world applications, including machine learning and graph clustering. However, existing

Walter Polilli, Alessio Antonini, Cristiano Platani, Fabio Stagnari

Precise irrigation management requires robust classification of plant water stress. We expanded a morpho-kinematic (MK) framework that derives canopy-movement features from RGB time-lapse imaging evaluating how methodological refinements affect robustness and fine discrimination across four irrigation treatments representing distinct stress histories. The st

Stefano Marini, Costantino Medori, Mauro Nacinovich

We study CR-manifolds of arbitrary CR codimension, mainly focusing on Levi and contact-nondegeneracy and depth. We investigate these and other invariants in the locally homogeneous case, developing a comprehensive theory which establishes correspondences with related properties of the associated CR-algebras and, in the parabolic case, with the combinatorics

Mojtaba Moazen, Amir. M Ahmadian, Musard Balliu

Modern software projects use automated CI/CD pipelines to streamline their development, build, and deployment processes. GitHub Actions is a popular CI/CD platform that enables project maintainers to create custom workflows -- collections of jobs composed of sequential steps -- using reusable components known as actions. Wary of the security risks introduced

Antoine Renard, Michel Rigo

We show how the software Walnut can be used to obtain concise proofs of results concerning variants of the famous Wythoff game, in which blocking maneuvers or terminal positions are added, as discussed respectively by Larsson (2011) and Komak et al. (2025). Our approach provides automatic proofs that both confirm and extend their results, and the same techni

Dario Baum, Arno Förster, Lucas Visscher

A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained o

Niels Langeveld, David Ralston

In this article, we present a binary tree with vertices given by rational functions $p(x)/q(x)$; the root and functional derivation of children are inspired by continued fractions. We prove some special properties of the tree. For example, the zero solutions of the denominators $q(x)$ are all real negative numbers and are dense in $(-\infty,-1]$. For $x>0$ f

Razvan Gabriel Iagar, David Puertas-Centeno, Elio V. Toranzo

In this work we introduce a family of transformations, named \textit{divergence transformations}, interpolating between any pair of probability density functions sharing the same support. We prove the remarkable property that the whole family of Kullback-Leibler and R\'enyi divergences evolves in a monotone way with respect to the transformation parameter. M

Hyungrok Do, Yuyan Wang, Mengling Liu, Myeonggyun Lee

Evaluating the health effects of complex environmental mixtures remains a central challenge in environmental health research. Existing approaches vary in their flexibility, interpretability, scalability, and support for diverse outcome types, often limiting their utility in real-world applications. To address these limitations, we propose a neural network-ba

Graeme E. Johnstone, Catherine F. Higham, Aisha Kanwal, Johannes Herrnsdorf

Fast and accurate eye tracking in a virtual reality or augmented reality headset could lead to better display performance and enable novel methods of user interaction with the system. However, it remains a challenge for a system to combine the required operational speed and accuracy of eye tracking with a technology that has a small enough form factor and we

Niloyendu Roy, Pragya Arora, A K Sood, Rajesh Ganapathy

Single-particle heat engines at atomic and colloidal scales obey the universal thermodynamic bounds on work and efficiency. Here, we translate these principles to the macroscale by building an athermal Stirling engine whose working medium is a millimeter-sized, vibrofluidized granule confined in a time-dependent magnetic trap. By embedding a rattler within t

Jin Lei

I present HPRMAT, a self-contained, high-performance R-matrix solver framework for coupled-channel scattering calculations in nuclear physics. It provides the full R-matrix propagation machinery with the same user-supplied-potential interface as standard R-matrix packages, and is additionally a drop-in replacement for the linear algebra routines of Descouvem

Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam, Juri Papay

Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring dynamic, AI-focused workflows. Rapid evolution in model architectures, scale, datasets, and deployment contexts makes evaluation a moving target. Large language models often memoriz

David Ben-Shlomo, Ronen Berkovich, Eyal Fattal

We investigate the validity of the Markovian assumption in modeling near-wall turbulence by analyzing the detachment of micron-sized particles from the viscous sublayer. By coupling direct numerical simulations with a fractional Ornstein-Uhlenbeck process, we demonstrate that while wall shear stress events follow Poissonian occurrence statistics, their inter

Juuso Luhtala, Vesa Kuikka, Kimmo K. Kaski

Traditional measures of closeness and betweenness centrality in networks rely on the shortest paths between nodes. Many standard metrics fail to accurately reflect the physical or probabilistic characteristics of nodal centrality and network flow, often overlooking processes such as cyclic and recurrent spreading. Here, we present new metrics based on our in

Stefan Tabakov, Asen Popov, Dimitar Dimitrov, S. Ensiye Kiyamousavi

Current vision-language-action (VLA) models generalize poorly, particularly when tasks require new compositions of skills or objects. We introduce Atomic Action Slicing (AAS), a planner-aligned approach that decomposes long-horizon demonstrations into short, typed atomic actions that are easier for planners to use and policies to learn. Using LIBERO demonstr

Sam Gijsen, Marc-Andre Schulz, Kerstin Ritter

The development of foundation models for functional magnetic resonance imaging (fMRI) time series holds significant promise for predicting phenotypes related to disease and cognition. Current models, however, are often trained using a mask-and-reconstruct objective on small brain regions. This focus on low-level information leads to representations that are

Salim B. Ivars, David Artigas, Carlos Mas Arabí, Carles Milián

We present a cardinal solution for the long-standing and fundamental problem associated with the adiabatic, reversible, and controlled excitation of both dark and bright solitons in Kerr micro-resonators with normal group velocity dispersion. Our findings stem from the inclusion of a localised non-Hermitian potential, which we use to drastically reshape the

Abdullah Tokmak, Thomas B. Schön, Dominik Baumann

Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. I

Xinyue Liu, Tao Li

Electron-phonon coupling is believed to be responsible for many spectral anomalies in the cuprate superconductors. In particular, the $B_{1g}$ buckling mode of the oxygen ion in the $CuO_{2}$ plane has been proposed to be responsible for the dramatic peak-dip-hump(PDH) structure in the anti-nodal spectrum. The recent observation of the exceptional flat quasi

Michelena Gabriel, Ernst Christoph, Pablo Bertin

This paper assesses the global employment and trade effects of renewed tariff escalation following the reintroduction of the United States' America First strategy in 2025. Using a multiregional input-output (MRIO) framework integrated with a trade model, the analysis captures endogenous adjustments in bilateral trade shares and final demand in response to ch

Sergei Stepanenko, Emma Nardino, Virgil Marionneau, Dan Frumin

Guarded Interaction Trees are a structure and a fully formalized framework for representing higher-order computations with higher-order effects in Rocq. We present an extension of Guarded Interaction Trees to support formal reasoning about context-dependent effects. That is, effects whose behaviors depend on the evaluation context, e.g., call/cc, shift and r

Dung Le

An improvement of a global Gagliardo-Nienberg inequality with a BMO term is established.

Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper

Seismic processing transforms raw data into subsurface images essential for geophysical applications. Traditional methods face challenges, such as noisy data, and manual parameter tuning, among others. Recently deep learning approaches have proposed alternative solutions to some of these problems. However, important challenges of existing deep learning appro

Valentina Lilova, Toyesh Chakravorty, Julian I. Bibo, Emma Boccaletti

Benchmarking 3D spatial understanding of foundation models is essential for real-world applications such as robotics and autonomous driving. Existing evaluations often rely on downstream fine-tuning with linear heads or task-specific decoders, making it difficult to isolate the intrinsic 3D reasoning ability of pre-trained encoders. In this work, we introduc

Paulius Rauba, Qiyao Wei, Mihaela van der Schaar

We consider the problem of auditing black-box large language models (LLMs) to ensure they behave reliably when deployed in production settings, particularly in high-stakes domains such as legal, medical, and regulatory compliance. Existing approaches for LLM auditing often focus on isolated aspects of model behavior, such as detecting specific biases or eval

Agnes P. C. van Hoof, Andrew J. Levan, Peter G. Jonker, Morgan Fraser

We present James Webb Space Telescope (JWST) and Hubble Space Telescope (HST) observations of the counterpart of EP240801a, at z=1.67, the first fast X-ray transient (FXT) identified as an X-ray flash (XRF) by the Einstein Probe (EP) and Fermi-GBM. Our observations reveal strong photometric and spectroscopic evidence for an associated broad-lined Type Ic sup

Mohammad Rahbar, Christopher J. Stein

We develop a fluctuation framework to quantify the free energy difference between two equilibrium states connected by nonequilibrium processes under arbitrary dynamics and system-environment coupling. For an open system described by the Hamiltonian of mean force (HMF), we show that the equilibrium free energy difference between two canonical endpoints can be

Edoardo Spezzano, Tomas Jezo, Michael Klasen, Peter Risse

We compute the contributions of heavy quarks to the deep-inelastic scattering structure functions $F_4$ and $F_5$ at next-to-leading order of perturbative QCD in the ACOT scheme. Both analytic results including the details of the calculation as well as numerical results for the neutral and charged current cases are presented. Our study thus lays the groundwo

H. T. J. Bevins

The dark ages 21-cm signal is a promising probe of the currently unobserved infant universe between the formation of the Cosmic Microwave Background around $z \approx 1100$ and the first galaxies around $z\approx 30$. A detection of the signal will help researchers understanding the nature of dark matter and dark energy, the expansion of the universe and any

Mevlüt Bagci, Ali Abusaleh, Daniel Baumartz, Giueseppe Abrami

Social media serves as a critical medium in modern politics because it both reflects politicians' ideologies and facilitates communication with younger generations. We present MultiParTweet, a multilingual tweet corpus from X that connects politicians' social media discourse with German political corpus GerParCor, thereby enabling comparative analyses betwee

Lecheng Liang, Hengyu Li, Shao Ye, Peng Li

The exploration of advanced CO-free catalysts and clarifying the ambiguous kinetic origins and governing factors would undoubtedly open up opportunities to overcome the sluggish kinetics of methanol electrooxidation and promote the development of direct methanol fuel cells. Herein, we constructed a family of Bi-modified Pt3M intermetallic catalysts (Bi-Pt3M/

Yuncheng Lu, Yucen Shi, Aobo Li, Zehao Li

We present an energy-efficient anti-UAV system that integrates frame-based and event-driven object tracking to enable reliable detection of small and fast-moving drones. The system reconstructs binary event frames using run-length encoding, generates region proposals, and adaptively switches between frame mode and event mode based on object size and velocity

Tatsuya Horiguchi

The polynomials $f_{i,j}$ are introduced by Abe-Harada-Horiguchi-Masuda to produce an explicit presentation by generators and relations of the cohomology rings of regular nilpotent Hessenberg varieties. In this paper we quantize the polynomials $f_{i,j}$ by a method of Fomin-Gelfand-Postnikov. Our main result states that their quantizations $F_{i,j}$ are rel

Ziming Li, Joffrey Guilmet, Suzanne Sorli, Hai-Ning Liang

Text entry in Virtual Reality (VR) is challenging, even when accounting for the use of controllers. Prior work has tackled this challenge head-on, improving the efficiency of input methods. These techniques have the advantage of allowing for relatively straightforward text correction. However, text correction without the use of controllers is a topic that ha

Adi Armoni, Ricardo Stuardo, Mark Thomas

We formulate a `master' partition function in three-dimensional $\mathcal{N}=2$ superspace that realises, upon integrating out complementary superfields, both the electric Maxwell--Chern--Simons (MCS) theory and its magnetic $S$-dual: a non-gauge Deser--Jackiw self-dual massive vector times a decoupled level-$k$ Chern--Simons term. The two descriptions share

Snir Meiri, Efi Efrati

Cumulative geometric frustration can drive self-limited assembly and morphology selection through size-dependent energetic costs. However, the slenderness of quasi-one-dimensional systems generally suppresses the formation of long-range longitudinal gradients. We show that the suppression of longitudinal gradients can be overcome by tuning the ratio between

Marcel Dreier, Nora Gourmelon, Dakota Pyles, Fei Wu

The calving fronts of marine-terminating glaciers undergo constant changes. These changes significantly affect the glacier's mass and dynamics, demanding continuous monitoring. To address this need, deep learning models were developed that can automatically delineate the calving front in Synthetic Aperture Radar imagery. However, these models often struggle

Wenni Zheng

We investigate the large-N index analog of the spectrum form factor for ABJM theory in the microcanonical ensemble. In the Cardy-like limit, the most dominant saddle describing the dual black hole decays rapidly at early times. However, the late-time behavior of the spectral form factor is determined by multi-cut saddles, which prevent it from decaying.

Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng

Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning ability for precise diagnosis. To address these limitations, we present DentalGPT, a specialized dental MLLM developed thr

Zhiguo Lu, Jianwen Lou, Mingjun Ma, Hairong Jin

3D teeth segmentation, involving the localization of tooth instances and their semantic categorization in 3D dental models, is a critical yet challenging task in digital dentistry due to the complexity of real-world dentition. In this paper, we propose 3DTeethSAM, an adaptation of the Segment Anything Model 2 (SAM2) for 3D teeth segmentation. SAM2 is a pretr

Stefan Hägele, Adam Misik, Constantin Patsch, Eckehard Steinbach

Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recent work with MIMO mmWave radar has demonstrated its ability to penetrate cardboard packaging for occluded object classification. However, existing models leave room for improvement

A. T. Eberlein, C. N. Pope

The event horizon of the Schwarzschild black hole has been well studied and the singular behavior of the Schwarzschild metric on horizon is understood as a coordinate singularity rather than an essential singularity. One demonstration of this non-singular behavior on horizon was provided by Fronsdal in 1959, by finding a global isometric embedding of the Sch

Chen Zhao, Yu Huang, Tingxuan Chen, Jiaxuan Li

We study the dynamics of capillary filling in tubes of regular polygon cross-section. Using Onsager variational principle, we derive a coupled ordinary differential equation and partial differential equation, which respectively describe time evolution of the bulk flow and the saturation profile of the finger flow. We obtain both numerical solution and self-s

Xue He, Hans Gløckner Giil, Caiqiong Xu, Jicheng Wang

Magnetoresistive effects are typically symmetric under magnetization reversal. However, nonlinear spin transport can give rise to unidirectional magnetoresistance in systems with strong spin-orbit interaction and broken inversion symmetry. Here, we demonstrate that the nonequilibrium magnon accumulation characterized by a finite magnon chemical potential can

Yifan Zhang, Zhiheng Chen, Ye Qiao, Sitao Huang

Aggressively quantized large language models (LLMs), such as BitNet-style 1.58-bit Transformers with ternary weights, make it feasible to deploy generative AI on low-power edge FPGAs. However, as prompts grow to tens of thousands of tokens, edge hardware performance drops sharply with sequence length due to quadratic prefill cost and rapidly increasing KV-ca

Marie S. Breum, Vanessa Didelez, Erin E. Gabriel, Michael C. Sachs

Several frameworks have been proposed for studying causal mediation analysis. What these frameworks have in common is that they all make assumptions for point identifications that can be violated even when treatment is randomized. When a causal effect is not point-identified, one can sometimes derive bounds, i.e. a range of possible values that are consisten

Zhendi Gong, Xin Chen

Despite the success of deep learning based models in medical image segmentation, most state-of-the-art (SOTA) methods perform fully-supervised learning, which commonly rely on large scale annotated training datasets. However, medical image annotation is highly time-consuming, hindering its clinical applications. Semi-supervised learning (SSL) has been emerge

Janaina Mourão-Miranda, Zakria Hussain, Konstantinos Tsirlis, Christophe Phillips

Multiple Kernel Learning (MKL) models combine several kernels in supervised and unsupervised settings to integrate multiple data representations or sources, each represented by a different kernel. MKL seeks an optimal linear combination of base kernels that maximizes a generalized performance measure under a regularization constraint. Various norms have been

Federico Pennino, Maurizio Gabbrielli

The standard paradigm for training deep learning models on sensor data assumes that more data is always better. However, raw sensor streams are often imbalanced and contain significant redundancy, meaning that not all data points contribute equally to model generalization. In this paper, we show that, in some cases, "less is more" when considering datasets.

Sheng Feng, Shuqing Ma, Xiaoqian Zhu

Underwater acoustic target recognition (UATR) is extremely challenging due to the complexity of ship-radiated noise and the variability of ocean environments. Although deep learning (DL) approaches have achieved promising results, most existing models implicitly assume that underwater acoustic data lie in a Euclidean space. This assumption, however, is unsui

Yuan Shen, Xiaojun Wu, Linghua Yu

This study aims to simulate real-world clinical scenarios to systematically evaluate the ability of Large Language Models (LLMs) to extract core medical information from patient chief complaints laden with noise and redundancy, and to verify whether they exhibit a functional decline analogous to Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD

Takafumi Moriya, Masato Mimura, Tomohiro Tanaka, Hiroshi Sato

This paper proposes a unified framework, All-in-One ASR, that allows a single model to support multiple automatic speech recognition (ASR) paradigms, including connectionist temporal classification (CTC), attention-based encoder-decoder (AED), and Transducer, in both offline and streaming modes. While each ASR architecture offers distinct advantages and trad

Hossein Shahabadi, Niki Sepasian, Arash Marioriyad, Ali Sharifi-Zarchi

Achieving compositional alignment between textual descriptions and generated images - covering objects, attributes, and spatial relationships - remains a core challenge for modern text-to-image (T2I) models. Although diffusion-based architectures have been widely studied, the compositional behavior of emerging Visual Autoregressive (VAR) models is still larg

Emmanuel K. Katalay, David O. Dimandja, Jordan F. Masakuna

The performance of machine learning (ML) models often deteriorates when the underlying data distribution changes over time, a phenomenon known as data distribution drift. When this happens, ML models need to be retrained and redeployed. ML Operations (MLOps) is often manual, i.e., humans trigger the process of model retraining and redeployment. In this work,

Yanhong Guo, Bo Hou

The purpose of this paper is to construct infinite-dimensional Poisson bialgebras by the affinization of pre-Poisson algebras. There is a natural Poisson algebra structure on the tensor product of a pre-Poisson algebra and a perm algebra, and the Poisson algebra structure on the tensor product of a pre-Poisson algebra and a special perm algebra characterizes

Néstor Armesto, Miguel Ángel Escobedo, Elena G. Ferreiro, Víctor López-Pardo

The internal structure of the exotic meson X(3872) remains an open question. We investigate its production in heavy-ion collisions under the hypothesis that it is a compact tetraquark. To this end, we derive a coalescence model from the Lindblad equation, assuming that unbound heavy quarks are thermalized within the quark-gluon plasma and that the adiabatic

Stefan Hägele, Adam Gorriahn, Philipp Wolters, Eckehard Steinbach

Millimeter-wave (mmWave) radar is a compact sensing technology that is particularly well suited for perception tasks in situations where vision-based sensors are limited, such as under adverse environmental conditions or occlusion. The complex-valued and nonlinear nature of mmWave radar IQ signals makes complex-valued deep learning a natural choice for extra

Shuhan Zheng, Baoyi Chen, Xiaojian Du, Shuzhe Shi

We present a data-driven analysis within a quantum evolutionary microscopic framework to constrain the in-medium bottomonium potential. In relativistic heavy-ion collisions, bottomonium bound states serve as invaluable probes of the quark-gluon plasma (QGP) owing to their negligible production in the QGP phase. Meanwhile, their non-relativistic nature allows

Licheng Zhang, Yuanqiu Huang, Zhangdong Ouyang

In this note, we prove that every 4-connected optimal 2-planar graph is Hamiltonian-connected. Furthermore, we show that the 4-connectedness condition is sharp by constructing infinitely many 3-connected optimal 2-planar graphs that are non-Hamiltonian.

Joao C. Pinto Barros, Pierpaolo Fontana, Pasquale Sodano, Andrea Trombettoni

In this chapter we review results on the lattice Schwinger model. In par-ticular, we show how the effect of the anomaly is reproduced on the lattice. We connect these results to recent developments in the field of quantum simulation of interacting field theories. Schemes for the quantum simulation of (approximations of) Schwinger models are discussed.

Chong Tang, Hao Dai, Jagmohan Chauhan

The growing demand for real-time DNN applications on edge devices necessitates faster inference of increasingly complex models. Although many devices include specialized accelerators (e.g., mobile GPUs), dynamic control-flow operators and unsupported kernels often fall back to CPU execution. Existing frameworks handle these fallbacks poorly, leaving CPU core

Luis Romero-Ben, Bernat Joseph-Duran, David Sunyer, Gabriela Cembrano

This article presents a data-driven, control-oriented modelling methodology for urban drainage systems (UDS). The proposed framework requires three main key components: input-output data from the element to be modelled, expert knowledge to define the model structure, and data-fitting techniques to obtain optimal parameters. The methodology is evaluated using

Álvaro Leitao, Jonatan Ráfales

In this work, we introduce a machine/deep learning methodology to solve parametric integrals. Besides classical machine learning approaches, we consider a differential learning framework that incorporates derivative information during training, emphasizing its advantageous properties. Our study covers three representative problem classes: statistical functio

Ida Mascoloa, Igor Orynyak, Federico Guarracino

The goal of the present study is to offer some accurate analytical formulae for the evaluation of the here defined reversed "von Karman/Brazier's effect", which may appear at first sight anomalous and has previously been noticed and successively explained using both Finite Element analyses and approximate analytical formulations. The proposed analytical form

Xi Wang, Shuo Shi, Chenyu Wu

In the continuously evolving cloud computing and network environment, service function chain (SFC) plays a crucial role in implementing complex services in the network with its flexible deployment capabilities. To address the limitations of existing SFC simulation tools, this paper introduces Mini-SFC, a modular simulation framework that supports both numeri

Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk

Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the forecast, while others are short-lived and should be ignored. Standard forecasting models fail to make this distinction, often either overreacting to noise or missing persistent shifts. We propose Co-TSFA (Contra

Hao Wu, Yuan Gao, Fan Xu, Fan Zhang

High-precision scientific simulation faces a long-standing trade-off between computational efficiency and physical fidelity. To address this challenge, we propose NeuralOGCM, an ocean modeling framework that fuses differentiable programming with deep learning. At the core of NeuralOGCM is a fully differentiable dynamical solver, which leverages physics knowl

Yu-Chi Hou

Let $L$ be a big and semipositive line bundle on a complex projective manifold $X$, and let $\theta\in c_1(L)$ be a smooth semipositive representative. In the adjoint setting $H^0(X,L^k\otimes K_X)$, we prove that Donaldson's quantized Monge--Amp\`ere energy converges to the Monge--Amp\`ere energy for every bounded $\theta$-plurisubharmonic function. This ex

Junheng Peng, Xiaowen Wang, Yingtian Liu, Yong Li

Seismic acoustic impedance inversion is one of the most challenging tasks in geophysical exploration. Many studies have proposed the use of deep learning for processing; however, most of them are limited by factors such as seismic wavelets and low-frequency initial models. Furthermore, self-supervised frameworks constructed entirely using deep learning model

S. A. Khrapak, A. G. Khrapak

Generalized Rosenfeld-Tarazona scaling predicts the power-law dependence of the excess heat capacity of simple liquids on temperature. The two-phase model treats a liquid as a superposition of gas- and solid-like components whose relative abundance is quantified by a liquid rigidity parameter. We demonstrate here that the generalized Rosenfeld-Tarazona scali

Matthew Cannon, Wesley Goar, In-Hee Lee, James Stevenson

Rapid advancements in technology have led to an increased use of artificial intelligence (AI) technologies in medicine and bioinformatics research. In anticipation of this, the National Institutes of Health (NIH) assembled the Bridge to Artificial Intelligence (Bridge2AI) consortium to coordinate development of AI-ready datasets that can be leveraged by AI m

Yosep Dwi Kristanto, Teo Paoletti, Russasmita Sri Padmi, Serli Evidiasari

Previous research has shown that students often struggle to develop an understanding of linear and quadratic relationships. Covariational reasoning has been identified as a way to support this development. This study aims to investigate how covariational reasoning supports students in developing understandings of linear and quadratic relationships within a p

Franco Fagnola, Federico Girotti

We present different characterizations of the notion of irreducibility for Quantum Markov Semigroups (QMSs) and investigate its relationship with other relevant features of the dynamics, such as primitivity, positivity improvement and relaxation; in particular, we show that irreducibility, primitivity and relaxation towards a faithful invariant density are e

Marco S. Bianchi

The perturbative expansion of two-point functions of lowest dimension supersymmetric operators in $\mathcal{N}=4$ SYM and ABJM theory exhibits uniform transcendental weight. Inspired by this, we construct an explicit basis of uniformly transcendental master integrals for these correlators, through four loops in four and three loops in three dimensions. In te

Yi-Hao Zhang, Shao-Zhou Jiang, Ling-Yun Dai

In this study, we analyze the first measurement of the electron-positron invariant mass spectrum in $J/\psi \to \pi^0 e^+e^-$ by BESIII, using the framework of resonance chiral theory. Our results indicate that both strong interaction and electromagnetic transition are essential to accurately describe the data. We obtain the $\pi^0$ transition form factor fo

Martí Roset, Peter Xu

Starting from a topological treatment of the Eisenstein class of a torus bundle, we define log-rigid analytic classes for $\mathrm{SL}_n(\mathbb{Z})$. These are group cohomology classes for $\mathrm{SL}_n(\mathbb{Z})$ valued on log-rigid analytic functions on Drinfeld's $p$-adic symmetric domain. Such classes can be evaluated at points attached to totally re

Yi Zhang, Xiaoran Ye, Ziheng Zhou, Zhaoxin Liang

Quantum droplets (QDs) in weakly interacting ultracold quantum gases are typically characterized by mean-field theories incorporating Lee-Huang-Yang (LHY) quantum fluctuations under simplified zero-range interaction assumptions. However, bridging these models to broader physical regimes like superfluid helium requires precise understanding of short-range int

Patrick D. Manya, Eugene M. Mbuyi, Gothy T. Ngoie, Jordan F. Masakuna

Identifying central nodes using closeness centrality is a critical task in analyzing large-scale complex networks, yet its decentralized computation remains challenging due to high communication overhead. Existing distributed approximation techniques, such as pruning, often fail to fully mitigate the cost of exchanging numerous data packets in large network

Kaijie Wang, Le Yin, Aodi Tian, Zhiqiang Wei

Introduction: The blood-brain barrier (BBB) protects the central nervous system but prevents most neurotherapeutics from reaching effective concentrations in the brain. BBB-penetrating peptides (BBBPs) offer a promising strategy for brain drug delivery; however, the scarcity of positive samples and severe class imbalance hinder the reliable identification of

Hanyue Lou, Jiayi Zhou, Yang Zhang, Boyu Li

Integrating event cameras with Multimodal Large Language Models (MLLMs) promises general scene understanding in challenging visual conditions, yet requires navigating a trade-off between preserving the unique advantages of event data and ensuring compatibility with frame-based models. We address this challenge by using reconstruction as a bridge, proposing a

Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye

Understanding decision-making in multi-AI-agent frameworks is crucial for analyzing strategic interactions in network-effect-driven contexts. This study investigates how AI agents navigate network-effect games, where individual payoffs depend on peer participatio--a context underexplored in multi-agent systems despite its real-world prevalence. We introduce

Mohor Banerjee, Nadya Yuki Wangsajaya, Syed Ali Redha Alsagoff, Min Sen Tan

Large Language Models (LLMs) exhibit remarkable capabilities in natural language understanding and reasoning, but suffer from hallucination: the generation of factually incorrect content. While numerous methods have been developed to reduce hallucinations, their impact on creative generations remains unexplored. This gap is particularly critical for AI-assis

Jelena Bratulić, Sudhanshu Mittal, Thomas Brox, Christian Rupprecht

Feed-forward 3D reconstruction models such as DUSt3R, VGGT, and Depth Anything 3 (DA3) are transformer-based foundation models that infer camera geometry and dense scene structure in a single forward pass. Trained at scale in a supervised fashion, they raise a central question: do these models build upon geometric principles akin to traditional multi-view pi

Mianjie Zheng, Xinquan Yang, Along He, Xuguang Li

Abutment design is a critical step in dental implant restoration. However, manual design involves tedious measurement and fitting, and research on automating this process with AI is limited, due to the unavailability of large annotated datasets. Although self-supervised learning (SSL) can alleviate data scarcity, its need for pre-training and fine-tuning res

Georgios Kaoukis, Ioannis Aris Koufopoulos, Eleni Psaroudaki, Danae Pla Karidi

As AI and web agents become pervasive in decision-making, it is critical to design intelligent systems that not only support sustainability efforts but also guard against misinformation. Greenwashing, i.e., misleading corporate sustainability claims, poses a major challenge to environmental progress. To address this challenge, we introduce EmeraldMind, a fac

Priyam Basu, Yunfeng Zhang, Vipul Raheja

AI-generated text detectors have recently gained adoption in educational and professional contexts. Prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs) however, there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose BAID, a comprehensive

Ferenc Bencs, Chiara Piombi, Guus Regts

In this paper we relate the location of the complex zeros of the reliability polynomial to parameters at which a certain family of rational functions derived from the reliability polynomial exhibits chaotic behaviour. We use this connection to prove new results about the location of reliability zeros. In particular we show that there are zeros with modulus l

Yanan Liu, Jun Liu, Hao Zhang, Dan Xu

Skeleton-based action recognition has garnered significant attention in the computer vision community. Inspired by the recent success of the selective state-space model (SSM) Mamba in modeling 1D temporal sequences, we propose TSkel-Mamba, a hybrid Transformer-Mamba framework that effectively captures both spatial and temporal dynamics. In particular, our ap

Kai Golan Hashiloni, Brenda Kasabe Nokai, Michal Shevach, Esthy Shemesh

We present a new Hebrew medical language model designed to extract structured clinical timelines from electronic health records, enabling the construction of patient journeys. Our model is based on DictaBERT 2.0 and continually pre-trained on over five million de-identified hospital records. To evaluate its effectiveness, we introduce two new datasets -- one

Francesco Flammini Dotti, M. B. N. Kouwenhoven, Kai Wu, Abbas Askar

Context. Comets, asteroids, planetesimals, free-floating planets and brown dwarfs, are continuously injected into the intra-cluster environment after expulsion from their host planetary systems or binary system. The dynamics of large populations of such free-floating comets (ffcs) in a star cluster environment is not yet fully understood. Aims. We investigat

V. M. Rivilla, E. R. Alonso, W. Song, A. Insausti

Understanding the presence and distribution of prebiotic precursors in the interstellar medium (ISM) is key to tracing the chemical origins of life. Among them, 4-oxobutanenitrile (\ch{HCOCH2CH2CN}) has been identified in laboratory simulations as a plausible intermediate in the formation of glutamic acid, a proteinogenic amino acid. Here, we report its gas-

Rafał Potempa, Michał Kordasz, Sundas Naqeeb Khan, Krzysztof Werner

This study aims to introduce the FRQI Pairs method to a wider audience, a novel approach to image classification using Quantum Recurrent Neural Networks (QRNN) with Flexible Representation for Quantum Images (FRQI). The study highlights an innovative approach to use quantum encoded data for an image classification task, suggesting that such quantum-based app

A. Shrestha, E. Kirkinis, M. Olvera de la Cruz

Electrolyte-filled channels with modulated wall charge distribution subjected to an applied DC electric field, form time-independent vortices whose sense of circulation is determined by the field direction [Physical Review Letters $ \mathbf{75}, 755, (1995)$]. In this paper we show that an electrolyte in a channel or cylindrical capillary subjected to an ext

Ilya Safonov, Dmitry Svintsov

Most contemporary architectures of photodetectors based on two-dimensional materials include global gates for carrier density control and local p-n junctions in the channel. We study the dependence of photocurrent in such detectors on the light modulation frequency, fully taking into account the effects of distributed resistance and gate-channel capacitance.

Kumud Lakara, Ruibo Shi, Fran Silavong

Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text; however, their propensity for producing hallucinations poses a significant challenge for their deployment in security-critical domains. In this work, we present TrueBrief, an end-to-end framework specifically designed to enhance the faithfulness of small LLMs

Jelena Losic

The optimal strategy for community detection in complex networks is not universal, but depends critically on the network's underlying structural properties. Although popular graph-theoretic methods, such as Louvain, optimize for modularity, they can overlook nuanced, geometric community structures. Conversely, topological data analysis (TDA) methods such as

Alexander G. Ginsberg, Josefin Ahnström, James T. B. Crawley, Karin Leiderman

Protein S (PS) is a notable anticoagulant implicated in both bleeding and thrombotic disorders, making it a promising drug target. Importantly, PS enhances the anticoagulant function of TFPI$\alpha$, likely circulating in the bloodstream together with TFPI$\alpha$ and a truncated form of factor V (fVshort) in the trimolecular complex, TFPI$\alpha$-fVshort-PS

Gaetano Campi, Andrea Alimenti, Sang-Eon Lee, Luis Balicas

It is known that cuprate artificial high Tc superlattices (AHTS) with period d, composed of quantum wells confining interface space charge in stoichiometric Mott insulator layers (S), with thickness L, at the interface with overdoped normal metallic cuprate layers (N) show a superconducting dome by tuning the geometric L over d ratio of the SNSN superlattice

Vandana Narri, Jonah J. Glunt, Joshua A. Robbins, Jonas Mårtensson

Situational awareness for connected and automated vehicles describes the ability to perceive and predict the behavior of other road-users in the near surroundings. However, pedestrians can become occluded by vehicles or infrastructure, creating significant safety risks due to limited visibility. Vehicle-to-everything communication enables the sharing of perc