November 2025 arXiv papers — page 129

Filipa R. Prudêncio, Mário G. Silveirinha

Time-modulated media offer powerful opportunities for controlling light, yet extending such concepts to optical frequencies has remained challenging. Here we propose a different route to photonic spacetime crystals based on modulation of the anomalous velocity in low-symmetry conductors, particularly Weyl semimetals. We show that when driven by a strong opti

Kiamehr Rezaee, Jose Camacho-Collados, Mohammad Taher Pilehvar

Large Language Models (LLMs) have demonstrated impressive capabilities in solving complex tasks, including those requiring a certain level of reasoning. In this paper, we focus on state tracking, a problem where models need to keep track of the state governing a number of entities. To isolate the state tracking component from other factors, we propose a benc

Ivan Biočić, Bruno Toaldo, Lena Zuspann

Motivated by models of cancer metastasis, this paper introduces a type of (multi-type) branching process that records the positions of particles, representing tumor cells or clusters. Particles may be absorbed (removed from the state space), move, or settle. The process is rigorously constructed, and the Markov property is established via embedding into a mu

Felip Pellicer

Integer factorization is a computational problem of fundamental importance in cybersecurity and secure communications, as its difficulty form the basis of modern public-key cryptography. While Shor's algorithm can solve this problem efficiently on a universal quantum computer, near-term devices require alternative approaches. The Adiabatic Factorization Algo

Sepehr Maleki, Negar Pourmoazemi

We present a concise, self-contained derivation of diffusion-based generative models. Starting from basic properties of Gaussian distributions (densities, quadratic expectations, re-parameterisation, products, and KL divergences), we construct denoising diffusion probabilistic models from first principles. This includes the forward noising process, its close

Baoyi Zeng, Marc-Antoine Martinod, Denis Defrère

Accurate null depth retrieval is critical in nulling interferometry. However, achieving accurate null depth calibration is challenging due to various noise sources, instrumental imperfections, and the complexity of real observational environments. These challenges necessitate advanced calibration techniques that can efficiently handle such uncertainties whil

Isaac Bird, Jordan Williamson

We prove that the homological and Balmer spectra in tensor-triangular geometry are functorial in certain definable functors, thereby providing an alternative perspective on functoriality in tensor-triangular geometry from the viewpoint of purity, and generalising current results in the literature.

Irina Saparina, Mirella Lapata

Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safety risks when that interpretation is wrong. We propose generating a single structured response that enumerates the different ways an ambiguous request can be interpreted, each coupled with a corresponding answer. Our mo

Gonzalo G. de Diego, Georg Stadler

Complex physical systems which exhibit fluid-like behavior are often modeled as non-Newtonian fluids. A crucial element of a non-Newtonian model is the rheology, which relates inner stresses with strain-rates. We propose a framework for inferring rheological models from data that represents the fluid's effective viscosity with a neural network. By writing th

Martín Latorre, Joaquín Barra, Juan Pablo Vera, Joaquín Martinez

Topologically secure spin configurations, such as skyrmions and bimerons, offer a compelling alternative to conventional magnetic domains, potentially enabling high-density, low-power spintronic devices. These pseudo-particles, characterized by their swirling spin textures and nontrivial topological charges, are prevalent and notably influence their electron

Piotr Gorczyca, Hannes Strass

Standpoint logics offer unified modal logic-based formalisms for representing multiple heterogeneous viewpoints. At the same time, many non-monotonic reasoning frameworks can be naturally captured using modal logics, in particular using the modal logic S4F. In this work, we propose a novel formalism called S4F Standpoint Logic, which generalises both S4F and

Lorenzo Pagliara, Violeta Redondo, Enrico Ferrentino, Manuel Ferre

Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to faults. Although robotic solutions have the potential to improve operational safety and effectiveness, current systems still lack reliable autonomy and fault management capabilities. To address this gap, we propos

Louis Martin-Monier, Sehui Chang, Johannes Froech, Zhaoyi Li

Curved and conformal optics offer significant advantages by unlocking additional geometric degrees of freedom for optical design. These capabilities enable enhanced optical performance and are essential for meeting non-optical constraints, such as those imposed by ergonomics, aerodynamics, or wearability. However, existing fabrication techniques such as dire

Jonghun Lee, YongKyung Oh, Sungil Kim, Dong-Young Lim

Neural Differential Equations (NDEs) excel at modeling continuous-time dynamics, effectively handling challenges such as irregular observations, missing values, and noise. Despite their advantages, NDEs face a fundamental challenge in adopting dropout, a cornerstone of deep learning regularization, making them susceptible to overfitting. To address this rese

Paula Gherghinescu, Eugene Vasiliev, Payel Das, Justin Read

Dynamical models are used to study dark matter (DM) in galaxies, how galaxies assemble through mergers, and to test galaxy formation models. Despite its widespread use, there has been no systematic study quantifying how much information can be obtained from just two on-sky positions and line-of-sight velocities, which are typically available for nearby exter

Alessandro Ferreri, Domenico Monaco, Gabriele Peluso

The Fu-Kane-Mele $\mathbb{Z}_2$ index characterizes two-dimensional time-reversal symmetric topological phases of matter. We shed some light on some features of this index by investigating projection-valued maps endowed with a fermionic time-reversal symmetry. Our main contributions are threefold. First, we establish a decomposition theorem, proving that any

Chih-Han Wang, Jie-Yu Yang, Wu Phillip M, Gwo-Tzong Huang

The origin of the four-fold Tc amplification in A_xFe_{2-y}Se_2 (>30 K) compared to FeSe (8 K) remains a central puzzle, complicated by a debate over the true superconducting (SC) parent phase--the I4/m (245) insulating matrix or a supposed I4/mmm metallic phase. Here, we resolve this ambiguity by identifying a novel "stripe-type orthorhombic phase" as the t

Aarush Agarwal, Raymond He, Jan Kieseler, Matteo Cremonesi

We introduce FastGraph, a novel GPU-optimized k-nearest neighbor algorithm specifically designed to accelerate graph construction in low-dimensional spaces (2-10 dimensions), critical for high-performance graph neural networks. Our method employs a GPU-resident, bin-partitioned approach with full gradient-flow support and adaptive parameter tuning, significa

Chunyang Jiang, Paola Merlo

Large language models achieve strong performance on many tasks, but their training makes it hard to see which properties of the input support efficient linguistic rule learning. We ask how three cognitively-inspired principles of input design support sample-efficient linguistic rule induction: analogical structure, contrastive learning, and minimal contextua

Tom Pan, Evan Dramko, Mitchell D. Miller, Anastasios Kyrillidis

Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not incorporate the experimental measurements directly, such as X-ray crystallographic diffraction data. To this end, we explore an approa

Thomas Decker, Volker Tresp, Florian Buettner

Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unknown model behavior under the specific perturbations used. This paper investigates the relationship between uncertainty calibration - the alignment of model confidence with actual ac

Samuel Schneider, Torsten Ueckerdt

Chernyshev, Rauch and Rautenbach [Discrete Math., 2025] introduce forest cuts, i.e., vertex separators that induce a forest. They conjecture that, similar to a result by Chen and Yu [Discrete Math., 2002], every $n$-vertex graph with less than $3n-6$ edges has a forest cut. As an intermediate goal they ask how many edges an $n$-vertex $3$-connected graph mus

P. L. Guillon, G. Dif-Pradalier, Y. Sarazin, D. W. Hughes

Staircase formation and layering is studied in simplified, potential vorticity conserving models of plasmas and geophysical fluids, by investigating turbulent self-organisation and nonlinear saturation with different mechanisms of free energy production -- forcing or linear instability -- and with standard or modified zonal flow responses. To this end, stair

Prosenjit Das, Md Mahadi Rajib, Jayasimha Atulasimha

The performance and reliability of spin-transfer torque magnetic random-access memory (STT-MRAM) can be compromised by anomalous switching behavior, especially during high-speed operations. One such anomaly, known as the "ballooning effect" is characterized by an unexpected non-monotonic increase in the write error rate (WER) with increase in STT current at

Marco Foschini, Marianne Defresne, Emilio Gamba, Bart Bogaerts

Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that derive an assignment to one or more decision variables. However, many candidate explanation steps exist, with different sets of constraints and different decisions they derive. To id

Darsh Pareek, Umesh Kumar, Ruthu Rao, Ravi Janjam

Deep Neural Networks (DNNs) rely on inherent fluctuations in their internal parameters (weights and biases) to effectively navigate the complex optimization landscape and achieve robust performance. While these fluctuations are recognized as crucial for escaping local minima and improving generalization, their precise relationship with fundamental hyperparam

Feng Wang, Tianxiang Chen, Shuyue Wei, Qian Chu

Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in modeling and reconstructing inter-client spatial dependencies while adhering to data locality constraints. Existing methods primarily addres

S. M. Tschopp, H. Vahid, J. M. Brader

Classical density functional theory (DFT) is the primary method for investigations of inhomogeneous fluids in external fields. It requires the excess Helmholtz free energy functional as input to an Euler-Lagrange equation for the one-body density. A variant of this methodology, the force-DFT, uses instead the Yvon-Born-Green equation to generate density prof

Willem Bonnaffé, Yang Hu, Andrea Chatrian, Mengran Fan

Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling that ignores tissue architecture. This introduces irrelevant information and limits interpretability. We introduce histology-informed tiling (HIT), which uses semantic segmentation

Daniele Perlo, Vladimir Despotovic, Selma Boudissa, Sang-Yoon Kim

We introduce a curated video dataset of laboratory rodents for automatic detection of convulsive events. The dataset contains short (10~s) top-down and side-view video clips of individual rodents, labeled at clip level as normal activity or seizure. It includes 10,101 negative samples and 2,952 positive samples collected from 19 subjects. We describe the dat

Yi Zhao, Zhen Yang, Shuaiqi Duan, Wenmeng Yu

Recent advances in vision-language models (VLMs) have expanded their multimodal code generation capabilities, yet their ability to generate executable visualization code from plots, especially for complex 3D, animated, plot-to-plot transformations, or multi-library scenarios, remains underexplored. To address this gap, we introduce PlotGen-Bench, a comprehen

Niclas Boehmer, Maximilian T. Wittmann

Many real-world decision-making problems involve optimizing multiple objectives simultaneously, rendering the selection of the most preferred solution a non-trivial problem: All Pareto optimal solutions are viable candidates, and it is typically up to a decision maker to select one for implementation based on their subjective preferences. To reduce the cogni

Wouter Jongeneel

The notion of an attractor has various definitions in the theory of dynamical systems. Under compactness assumptions, several of those definitions coincide and the theory is rather complete. However, without compactness, the picture becomes blurry. To improve our understanding, we characterize in this work when a closed, not necessarily compact, asymptotical

Ignace Bleukx, Maarten Flippo, Bart Bogaerts, Emir Demirović

In the field of Explainable Constraint Solving, it is common to explain to a user why a problem is unsatisfiable. A recently proposed method for this is to compute a sequence of explanation steps. Such a step-wise explanation shows individual reasoning steps involving constraints from the original specification, that in the end explain a conflict. However, c

Dieu D. Nguyen, Michele Cappellari, Tinh Q. T. Le, Hai N. Ngo

Current spatially resolved kinematic measurements of supermassive black hole (SMBH) masses are largely confined to the local Universe (distances $\lesssim100$ Mpc). We investigate the potential of the Extremely Large Telescope's (ELT) first-light instruments, MICADO and HARMONI, to extend these dynamical measurements to galaxies at redshift $1\lesssim z\less

Max Mowbray, Nilay Shah, Benoît Chachuat

Certifying feasibility in decision-making, critical in many industries, can be framed as a constraint satisfaction problem. This paper focuses on characterising a subset of parameter values from an a priori set that satisfy constraints on a directed acyclic graph of constituent functions. The main assumption is that these functions and constraints may be eva

Susan Ghaderi, Morteza Rahimi, Yves Moreau, Masoud Ahookhosh

This paper addresses the unconstrained minimization of smooth convex functions whose gradients are locally Holder continuous. Building on these results, we analyze the Scaled Gradient Algorithm (SGA) under local smoothness assumptions, proving its global convergence and iteration complexity. Furthermore, under local strong convexity and the Kurdyka-Lojasiewi

Maximiliane Gruber, Jürgen Seiler, André Kaup

Each image acquisition setup leads to its own camera-specific image characteristics degrading the image quality. In learning-based perception algorithms, characteristics occurring during the application phase, but absent in the training data, lead to a domain gap impeding the performance. Previously, pixel-level domain adaptation through unpaired learning of

Jiayang Meng, Tao Huang, Hong Chen, Chen Hou

Federated learning synchronizes models through gradient transmission and aggregation. However, these gradients pose significant privacy risks, as sensitive training data is embedded within them. Existing gradient inversion attacks suffer from significantly degraded reconstruction performance when gradients are perturbed by noise-a common defense mechanism. I

Carlos E. Cadenas R

These notes aim to provide a classical approach to solving some conformable differential equations based on prior knowledge of how to solve ordinary differential equations. That is, using the methods of separation of variables, homogeneous equations, linear, Bernoulli and exact. Representative examples are presented in all cases. Emphasis is placed on the ne

Christopher Buyalos, Jayden Thadani, Xinbei Wang, Bradley Zykoski

For any $q\in\mathbb{R}$, let $A:=\left(\begin{smallmatrix}1 & 1\\0 & 1\end{smallmatrix}\right), B_q:=\left(\begin{smallmatrix}1 & 0\\q & 1\end{smallmatrix}\right)$ and let $G_q:=\langle A,B_q\rangle\leqslant\operatorname{SL}(2,\mathbb{R})$. Kim and Koberda conjecture that for every $q\in\mathbb{Q}\cap(-4,4)$, the group $G_q$ is not freely generated by these

Alex Gnech, Xilin Zhang, Christian Drischler, R. J. Furnstahl

Nucleon-deuteron ($Nd$) scattering can be used to constrain three-nucleon forces in chiral effective field theory ($\chi$EFT). However, high-fidelity calculations, such as the Hyperspherical Harmonic (HH) method, are computationally expensive, making it difficult or even prohibitive to explore the vast parameter space of $\chi$EFT\xspace. To address this cha

Yasuyuki Matsumura, Chisato Tachibana

We study the long-standing problem of determining the number of principal components in econometric applications from a selective inference perspective. We consider i.i.d. observations from a $p$-dimensional random vector with $p<n$ and define the ``true'' dimensionality as the rank of the population covariance matrix. Building on the sequential testing view

Yaqiao Zhu, Hongkai Wen, Mark Birkin, Man Luo

Large Language Models (LLMs) show remarkable potential for urban computing, from spatial reasoning to predictive analytics. However, evaluating LLMs across diverse urban tasks faces two critical challenges: lack of unified platforms for consistent multi-source data access and fragmented task definitions that hinder fair comparison. To address these challenge

Winfried Hochstättler

The Tic-Tac-Toe matroid is a paving matroid of rank $5$ on 9 elements which is pseudomodular and whose dual is non-algebraic. It has been proposed as a possible example of an algebraic matroid whose dual is not algebraic. We present an infinite family of matroids sharing these properties and generalizing the Tic-Tac-Toe matroid.

Francisco Cunha, Yves Lepage, Miguel Couceiro, Zied Bouraoui

Analogical reasoning is a powerful inductive mechanism, widely used in human cognition and increasingly applied in artificial intelligence. Formal frameworks for analogical inference have been developed for Boolean domains, where inference is provably sound for affine functions and approximately correct for functions close to affine. These results have infor

Andrew J. Long, Bibhushan Shakya, Julia Anabell Ziegler

In standard (symmetry-breaking) first-order phase transitions, the frictional pressure on expanding bubble walls can be dominated by transition radiation -- the emission of a gauge boson with phase-dependent masses as particles present in the thermal plasma pass through bubble walls. This process is enhanced in the soft limit, and is known to produce a signi

ALICE Collaboration

The probability to observe a specific number of strange and multi-strange hadrons ($n_s$), denoted as $P(n_s)$, is measured by ALICE at midrapidity ($|y|<0.5$) in $\sqrt{s} = 5.02$ TeV proton-proton (pp) collisions, dividing events into several multiplicity-density classes. Exploiting a novel technique based on counting the number of strange-particle candida

Alomar Antonia, Rubio Ricardo, Albaiges Gerard, Salort-Benejam Laura

The automatic localization and standardization of anatomical planes in 3D medical imaging remains a challenging problem due to variability in object pose, appearance, and image quality. In 3D ultrasound, these challenges are exacerbated by speckle noise and limited contrast, particularly in fetal imaging. To address these challenges in the context of facial

Benjamin Stoler, Jonathan Francis, Jean Oh

Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To assess robustness under such conditions, we propose new long-tail evaluation settings that repartition datasets to create challenging out-of-distribution (OOD) test sets. We first intr

Sebastián Andrés Cajas Ordóñez, Luis Fernando Torres Torres, Mackenzie J. Meni, Carlos Andrés Duran Paredes

Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantization while ensuring predictable inference latency. We present a curiosity-driven quantized Mixture-of-Experts framework that addresses both through Bayesian epistemic uncertainty-based routing across heterogeneous expert

Zhen-Tao Zhang, Feng Mei

We study an entanglement phase transition in a class of chaotic non-Hermitian spin chains whose spin-spin coupling terms commute with the non-Hermitian contributions. Two representative models are investigated: the transverse-field Ising model with a complex longitudinal field and the non-Hermitian XX model with a transverse field. By analyzing their complex

Kayla Boggess, Sarit Kraus, Lu Feng

Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, most existing explanation methods focus on centralized MARL, failing to address the uncertainty and nondeterminism inherent in decentralized settings. We propose methods to generate

Sahibzada Farhan Amin, Sana Athar, Anja Feldmann, Ha Dao

Internet measurement research is essential for understanding, improving, and securing Internet infrastructure. However, its methods often involve large-scale data collection and user observation, raising complex ethical questions. While recent research has identified ethical challenges in Internet measurement research and laid out best practices, little is k

Jia-Hua Zou, Weizhou Cai, Jia-Qi Wang, Zheng-Xu Zhu

Scaling superconducting quantum processors beyond single dilution refrigerators requires efficient optical interconnects, yet integrating microwave-to-optical (M2O) transducers poses challenges due to frequency mismatches and qubit decoherence. We propose a modular architecture using SNAIL-based parametric coupling to interface Brillouin M2O transducers with

Patrick Cattiaux, Paula Cordero-Encinar, Arnaud Guillin

In this work we study the diffusion annealed Langevin dynamics, a score-based diffusion process recently introduced in the theory of generative models and which is an alternative to the classical overdamped Langevin diffusion. Our goal is to provide a rigorous construction and to study the theoretical efficiency of these models for general base distribution

Mingxing Peng, Ruoyu Yao, Xusen Guo, Jun Ma

Recent advances in closed-loop planning benchmarks have significantly improved the evaluation of autonomous vehicles. However, existing benchmarks still rely on rule-based reactive agents such as the Intelligent Driver Model (IDM), which lack behavioral diversity and fail to capture realistic human interactions, leading to oversimplified traffic dynamics. To

Jeffrey S. Case, Opal Cieslak

We construct a large family of conformally covariant tridifferential operators as tangential operators in the Fefferman--Graham ambient space. Our construction is analogous to the linear and bilinear constructions of Graham--Jenne--Mason--Sparling and Case--Lin--Yuan, respectively. We also show that the symmetrization of our ambient operators are formally se

Cornelia Skaga, Mahdieh S. Sadabadi, Gilbert Bergna-Diaz

As modern power systems continue to evolve into multi-agent, converter-dominated systems that demand reliable, stable, and optimal control architectures within an expandable framework, this paper investigates scalable stability guarantees of a promising nonlinear communication-reliant control framework for DC microgrids. Particularly, relying on nested contr

Lifan Zheng, Jiawei Chen, Qinghong Yin, Jingyuan Zhang

Ensuring the reliability of agent architectures and effectively identifying problematic agents when failures occur are crucial challenges in multi-agent systems (MAS). Advances in large language models (LLMs) have established LLM-based agents as a major branch of MAS, enabling major breakthroughs in complex problem solving and world modeling. However, the re

ALICE Collaboration

Two-particle angular correlations explore particle production mechanisms and underlying event-wide phenomena present in the systems created in hadronic collisions. These correlations are examined as a function of rapidity and azimuthal-angle differences ($\Delta y, \Delta \varphi$) for pairs of like- and unlike-sign pions, kaons, and (anti-)protons produced

Josse Van Delm, Anton Lydike, Joren Dumoulin, Jonas Crols

Contemporary compute platforms increasingly offload compute kernels from CPU to integrated hardware accelerators to reach maximum performance per Watt. Unfortunately, the time the CPU spends on setup control and synchronization has increased with growing accelerator complexity. For systems with complex accelerators, this means that performance can be configu

R. M. Dubrovin, A. I. Brulev, N. R. Vovk, I. A. Eliseyev

Linear and non-linear couplings of magnetic and lattice excitations are at the heart of many fascinating magnetophononic phenomena observed in rare-earth orthoferrites, the distinctive feature of which is the tendency to spin-reorientation transitions. Here we report the results of the experimental study of the spin and lattice dynamics in the Brillouin zone

Yunpeng Zhai, Shuchang Tao, Cheng Chen, Anni Zou

Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they typically require manually constructed task datasets and reinforce

Yaru Li, Yanxue Wang, Meng Li, Xinming Li

The health condition of wind turbine (WT) components is crucial for ensuring stable and reliable operation. However, existing fault detection methods are largely limited to visual recognition, producing structured outputs that lack semantic interpretability and fail to support maintenance decision-making. To address these limitations, this study proposes an

Ming Li, Youjin Deng, Jesper Lykke Jacobsen, Jesús Salas

We study the three-point correlation function of the backbone in the two-dimensional $Q$-state Potts model using the Fortuin--Kasteleyn (FK) representation. The backbone is defined as the biconnected skeleton of an FK cluster after removing all dangling ends and bridges. To circumvent the severe critical slowing down in direct Potts simulations for large $Q$

Yixi Chen, Weixuan Liang, Tianrui Liu, Jun-Jie Huang

Kernel power $k$-means (KPKM) leverages a family of means to mitigate local minima issues in kernel $k$-means. However, KPKM faces two key limitations: (1) the computational burden of the full kernel matrix restricts its use on extensive data, and (2) the lack of authentic centroid-sample assignment learning reduces its noise robustness. To overcome these ch

Oussema Dhaouadi, Johannes Meier, Jacques Kaiser, Daniel Cremers

Digital Terrain Models (DTMs) represent the bare-earth elevation and are important in numerous geospatial applications. Such data models cannot be directly measured by sensors and are typically generated from Digital Surface Models (DSMs) derived from LiDAR or photogrammetry. Traditional filtering approaches rely on manually tuned parameters, while learning-

Jiarui Zhang, Yuliang Liu, Zijun Wu, Guosheng Pang

Document parsing is a core task in document intelligence, supporting applications such as information extraction, retrieval-augmented generation, and automated document analysis. However, real-world documents often feature complex layouts with multi-level tables, embedded images or formulas, and cross-page structures, which remain challenging for existing OC

Hui Zheng, Jia-Rui Dong, Si-Hong Zhou

Motivated by extensive new high-precision experimental data, we present an updated analysis of the two-body charm decays $D \to PV$ (with $P =\pi,K, \eta^{(\prime)}$ and $V =\rho, K^*, \omega, \phi$) within the factorization-assisted topological-amplitude (FAT) approach. In the framework, flavor SU(3) symmetry breaking effect is incorporated into the topolog

Yanbing Zhou, Athena Caesura, Corneliu Buda, Xavier Jackson

As quantum hardware advances toward fault-tolerant operation, an intermediate stage known as early fault-tolerant quantum computing (EFTQC) is emerging, where partial error correction enables meaningful computation. In this regime, the ability of quantum processors to scale in size and depth has become a crucial factor shaping their achievable performance. T

Prince Mensah, Pelumi Victor Aderinto, Ibrahim Salihu Yusuf, Arnu Pretorius

Accurate retrieval of vegetation biophysical variables from satellite imagery is crucial for ecosystem monitoring and agricultural management. In this work, we propose a physics-informed Transformer-VAE architecture to invert the PROSAIL radiative transfer model for simultaneous estimation of key canopy parameters from Sentinel-2 data. Unlike previous hybrid

Sagarika Adhikary, Arvin Gopal Subramaniam, Rajesh Singh

Recent studies in the collective behavior of active colloids have shown that a global polar order may emerge due to long-ranged chemo-repulsive interactions between them. Here, we report the role of pinning disorder in the flocking transition for such a system. To this end, we study the problem of chemically interacting active colloids with some fraction of

Hyunjong Lee, Jangho Lee, Jaekoo Lee

Lane detection is an important topic in the future mobility solutions. Real-world environmental challenges such as background clutter, varying illumination, and occlusions pose significant obstacles to effective lane detection, particularly when relying on data-driven approaches that require substantial effort and cost for data collection and annotation. To

Raj Gaurav Maurya, Vaibhav Shukla, Raj Abhijit Dandekar, Rajat Dandekar

Misinformation on social media thrives on surprise, emotion, and identity-driven reasoning, often amplified through human cognitive biases. To investigate these mechanisms, we model large language model (LLM) personas as synthetic agents that mimic user-level biases, ideological alignments, and trust heuristics. Within this setup, we introduce an auditor--no

Nicolas Hoischen, Petar Bevanda, Max Beier, Stefan Sosnowski

Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environment is often unsafe or impractical, motivating offline reinforcement learning from historical data. However, there is limited statistical understanding of the approximation errors in

Zhen Chen, Yi Zhang, Xiangyu Yin, Chengxuan Qin

Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of facial identity leakage. Recent defense mechanisms like Anti-DreamBooth attempt to mitigate this risk by injecting adversarial perturbations into user photos to prevent successful

Jason Chan, Zhixue Zhao, Robert Gaizauskas

Existing work investigates the reasoning capabilities of large language models (LLMs) to uncover their limitations, human-like biases and underlying processes. Such studies include evaluations of base LLMs (pre-trained on unlabeled corpora only) for this purpose. Our position paper argues that evaluating base LLMs' reasoning capabilities raises inherent meth

Benjamin J. Chang, Keegan J. Moore, Lawrence A. Bergman, Alexander F. Vakakis

Bandwidth is a widely known concept and tool used in structural dynamics to measure an oscillator's capacity to dissipate energy over time, for example when used in half-power damping estimation of structural modes. Root Mean Square (RMS) Bandwidth is a generalization of bandwidth that overcomes some of the limitations encountered with conventional bandwidth

Akiko Morimura, Toyohiko Aiki

We consider an initial-boundary value problem motivated by a mathematical model of moisture transport in porous media. We establish the existence of strong solutions and provide an error estimate for the approximate solutions constructed by the finite volume method. In the proof of the error estimate, the Gagliardo--Nirenberg type inequality for the differen

Yukun Shi, Anshun Zhou, Hao Liu, Jiechen Jiang

The Circular Electron Positron Collider (CEPC) is a next-generation electron$-$positron collider proposed for the precise measurement of the properties of the Higgs boson. To emphasize boson separation and jet reconstruction, the baseline design of the CEPC detector was guided by the particle flow algorithm (PFA) concept. As one of the calorimeter options, t

Xun Huang, Shijia Zhao, Yunxiang Wang, Xin Lu

Embodied navigation is a fundamental capability for robotic agents operating. Real-world deployment requires open vocabulary generalization and low training overhead, motivating zero-shot methods rather than task-specific RL training. However, existing zero-shot methods that build explicit 3D scene graphs often compress rich visual observations into text-onl

Shuyi Liu, Yuming Shang, Xi Zhang

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for enhancing the capabilities of Large Language Models (LLMs) by integrating retrieval-based methods with generative models. As external knowledge repositories continue to expand and the parametric knowledge within models becomes outdated, a critical challenge for RAG systems is resolv

Somashekaracharya G Bhaskaracharya, Aravind Acharya, Bastian Hagedorn, Vinod Grover

Modern deep learning compilers rely on layout abstractions to manage the complex mapping between logical tensor structures and physical memory arrangements. CuTe layouts and Triton linear layouts are widely adopted industry standards. However, these layout systems operate independently with distinct mathematical underpinnings, preventing unified formal analy

Liwei Zhang, Fanli Zhuang, Ning Zhang

This paper investigates a Halpern acceleration of the inexact proximal point method for solving maximal monotone inclusion problems in Hilbert spaces. The proposed Halpern inexact proximal point method (HiPPM) is shown to be globally convergent, and a unified framework is developed to analyze its worst-case convergence behavior. Under mild conditions on the

Shuaiqi Zhang, Zhen-Qing Chen

We propose a novel Black-Scholes model under which the stock price processes are modeled by stochastic differential equations driven by sub-diffusions. The new framework can capture the less financial activity phenomenon during the bear markets while having the classical Black- Scholes model as its special case. The sub-diffusive spot market is arbitrage-fre

Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi, Zuzanna Osika

Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for reliability-aware prediction that enables GFMs to identify and abstain from likely failures. Our approach integrates three complementary signals: geophysical out-of-distribution (

Caterina B. Leimer Saglio, Mattia Corti, Stefano Pagani, Paola F. Antonietti

The association of epileptic activity and Alzheimer's disease (AD) has been increasingly reported in both clinical and experimental studies, suggesting that amyloid-$\beta$ accumulation may directly affect neuronal excitability. Capturing these interactions requires a quantitative description that bridges the molecular alterations of AD with the fast electro

Van Dong Pham, Arpit Jain, Chengye Dong, Li-Syuan Lu

Point defects give rise to sharp modifications in the structures and electronic properties of two-dimensional metals, offering an atomic-level platform for fundamental studies and potential applications. In this work, we investigate atomic-scale defects in a two-dimensional silver monolayer intercalated between epitaxial graphene and SiC using scanning tunne

Arnab Bhattacharyya, Davin Choo, Philips George John, Themis Gouleakis

Given i.i.d.~samples from an unknown distribution $P$, the goal of distribution learning is to recover the parameters of a distribution that is close to $P$. When $P$ belongs to the class of product distributions on the Boolean hypercube $\{0,1\}^d$, it is known that $\Omega(d/\varepsilon^2)$ samples are necessary to learn $P$ within total variation (TV) dis

Yilong Zeng, Boyan Tang, Xuanhao Ren, Sherry Zhefang Zhou

This paper introduces the Fractal-Chaotic Oscillation Co-driven (FCOC) framework, a novel paradigm for financial volatility forecasting that systematically resolves the dual challenges of feature fidelity and model responsiveness. FCOC synergizes two core innovations: our novel Fractal Feature Corrector (FFC), engineered to extract high-fidelity fractal sign

Anoop Dhillon, A. Hamed Majedi

Microwave dressed states are found to emerge within the superconducting condensate when coupled to a quantized electromagnetic field due to photon-Cooper pair entanglement. The renormalized energy separation between these states exceeds the prediction of BCS theory, with the enhancement depending on the number of photons and also arising from electromagnetic

Simo Särkkä, Ángel F. García-Fernández

This paper presents an experimental evaluation of parallel-in-time Kalman filters and smoothers using graphics processing units (GPUs). In particular, the paper evaluates different all-prefix-sum algorithms, that is, parallel scan algorithms for temporal parallelization of Kalman filters and smoothers in two ways: by calculating the required number of operat

Rodrigo Mesquita, Bernardo Toninho

Traditionally, in linearly typed languages, consuming a linear resource is synonymous with its syntactic occurrence in the program. However, under the lens of non-strict evaluation, linearity can be further understood semantically, where a syntactic occurrence of a resource does not necessarily entail using that resource when the program is executed. While t

ALICE Collaboration

The centrality dependence of strange ($K_S^0$, $\Lambda + \bar{\Lambda}$) and multi-strange ($\Xi^- + \bar{\Xi^+}$, $\Omega^- + \bar{\Omega}^+$) hadron production is measured by ALICE in the LHC lead-lead (Pb-Pb) collisions at a center-of-mass energy per nucleon pair $\sqrt{s_{\rm NN}} = 5.02$~TeV, using the full data set collected during the LHC Run 2 campa

Oleksiy Klurman, Vlad Matei

We show that for a random polynomial \[ F(X) = \sum_{n=1}^{N} f(n) X^{n-1}, \] where $f(n)$ is a random completely multiplicative function taking values in $\{\pm 1\}$, one has \[ \limsup_{N \to \infty} \mathbb{P}\big[F(X) \text{ is irreducible}\big] = 1. \]

Zhiqiang Wan, Heng Zhang

We study observable sets for Schr\"odinger equations on combinatorial graphs. For one-dimensional lattice Schr\"odinger operators \(H=-\Delta_{\mathrm{disc}}+V\) with \(V(n)\to c\in\mathbb R\) as \(|n|\to\infty\), we prove that a set \(E\subset\mathbb Z\) is observable at some time, equivalently at any time, if and only if it satisfies a local arithmetic con

Zhi-Yuan Li, Xuan-Rui Chen, Bin Wu, Zhen-Ming Xu

Lee-Yang zero theory plays a crucial role in phase transition theory and is widely employed in the critical behavior of statistical thermodynamics. The supercritical regime of black hole thermodynamics remains a relatively unexplored area, and recent applications of this theory to charged anti-de Sitter (AdS) black holes have initiated probes into this regim

Chenyi Li, Wanli Ma, Zichen Wang, Zaiwen Wen

While large language models (LLMs) have shown progress in mathematical reasoning, they still face challenges in formalizing theorems that arise from instantiating abstract structures in concrete settings. With the goal of auto-formalizing mathematical results at the research level, we develop a framework for structure-to-instance theorem autoformalization (S

Y. Tu, B. Wu, E. Martínez-Pañeda

Core-shell electrode particles are a promising morphology control strategy for high-performance lithium-ion batteries. However, experimental observations reveal that these structures remain prone to mechanical failure, with shell fractures and core-shell debonding occurring after a single charge. In this work, we present a novel, comprehensive computational