October 2025 arXiv papers — page 10

Nanano Kita

Minimum joins in a graft $(G, T)$, also known as minimum $T$-joins of a graph $G$, are said to be connected if they determine a connected subgraph of $G$. Grafts with a connected minimum join have gained interest ever since Middendorf and Pfeiffer showed that they satisfy Seymour's min-max formula for joins and $T$-cut packings; that is, in such grafts, the

Jean-Philippe Corbeil, Asma Ben Abacha, Jerome Tremblay, Phillip Swazinna

Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-O

M. J. Stift, F. Leone

Aims. Magnetic and abundance maps of chemically peculiar (CP) stars, derived with the help of Zeeman Doppler mapping, have invariably been used as arguments against theories, in particular atomic diffusion theory. We intend to expose the fallacy of these claims. Methods. We have identified in the literature those (5) CP stars for which multiple maps have bee

Yi Zhang, Che Liu, Xiancong Ren, Hanchu Ni

This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multimodal brain model. Its core advantage lie

Josimar J. R. Aguirre, Sarah F. M. Mazzini, Victor G. L. Neumann

Let $\mathbb{F}_{q^n}$ be a finite field with $q^n$ elements. An element $\alpha \in \mathbb{F}_{q^n}$ is called $k$-normal over $\mathbb{F}_q$ if $\alpha$ and its conjugates generate a vector subspace of $\mathbb{F}_{q^n}$ of dimension $n-k$ over $\mathbb{F}_q$. The existence of primitive $k$-normal elements and related properties have been studied througho

Alan T. Tokunaga, Lawrence S. Bernstein, Takashi Onaka

The polycyclic aromatic hydrocarbon (PAH) hypothesis proposes that the aromatic infrared bands (AIBs) observed at 3.3, 6.2, 7.7, 8.6, 11.3, and 12.7 mic originate from gas-phase PAH molecules. These bands exhibit consistent peak wavelengths and profiles in diverse sources, and ISO SWS and JWST spectra show a nearly identical red wing of the 3.3 mic AIB and b

Lívia Dutra, Arthur Lorenzi, Laís Berno, Franciany Campos

We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical

Jackson Bibbens, Cooper Sigrist, Bo Sun, Shahin Kamali

The online bin packing problem and its variants are regularly used to model server allocation problems. Modern concerns surrounding sustainability and overcommitment in cloud computing motivate bin packing models that capture costs associated with highly utilized servers. In this work, we introduce the green bin packing problem, an online variant with a line

Riley Grossman, Michael Smith, Cristian Borcea, Yi Chen

The main goal of this paper is to study how often cookie banners that comply with the General Data Protection Regulation (GDPR) contain aesthetic manipulation, a design tactic to draw users' attention to the button that permits personal data sharing. As a byproduct of this goal, we also evaluate how frequently the banners comply with GDPR and the recommendat

Julian Heckötter, David Janas, Marc Aßmann, Manfred Bayer

Rydberg excitons in the semiconductor Cu$_2$O have been observed in absorption experiments up to a principal quantum number of n = 28 at millikelvin temperatures [1]. Here, we extend the experimental parameter space by variing both temperature and excitation power. In particular, we show that the P excitons close to the band gap react more sensitively to an

Brendan M. Shumberger, T. Daniel Crawford

In this work, we present the first derivation and implementation of analytic gradient methods for the computation of the atomic axial tensors (AATs) required for simulations of vibrational circular dichroism (VCD) spectra using configuration interaction methods including double (CID) and single and double (CISD) excitations. Our new implementation includes t

Mateusz Kapusta

Over the past 30 years, numerous large-scale photometric astronomical surveys have been conducted, including SDSS, Pan-STARRS, Gaia,2MASS, WISE, and others. These surveys provide extensive photometric measurements that can be used to infer a wide range of physical parameters of astronomical objects. Traditionally, Bayesian approaches, such as Markov Chain Mo

Kai Lin, Alan B. Pavan, Amilcar Rabelo de Queiroz, Elcio Abdalla

The article studies the dynamical behavior of axial gravitational perturbations of homogeneous stars in Anti-de Sitter spacetime. Because the radial coordinate $r$ transforms into the tortoise coordinate $y = y(r)$, obeying $y(r=0)=0$ and $y(r\rightarrow\infty)=y_\text{max}<\infty$, the tortoise coordinates domain is finite, and the gravitational waves fail

Zecheng Zhang, Hao Liu, Guosheng Fu, Hayden Schaeffer

We propose a finite-element local basis-based operator learning framework for solving partial differential equations (PDEs). Operator learning aims to approximate mappings from input functions to output functions, where the latter are typically represented using basis functions. While non-learnable bases reduce training costs, learnable bases offer greater f

Md. Mehedi Hassan, Shafqat Alam, Shahriar Ahmed Seam, Maruf Ahmed

Automatic segmentation of diverse heterogeneous brain lesions using multi-modal MRI is a challenging problem in clinical neuroimaging, mainly because of the lack of generalizability and high prediction variance of pathology-specific deep learning models. In this work, we propose a unified and adaptive multi-stream framework called SYNAPSE-Net to perform robu

Elizabeth Collins-Woodfin, Han Gia Le

Over the past 50 years, spin glass models have generated a broad range of literature in mathematics, physics, and computer science. There has been much progress in characterizing and proving the limiting free energy of various models, stemming from the original formulas of Parisi. Comparatively less is known about the more detailed topic of free energy fluct

Paul Seurin, Auradha Annaswamy, Linyu Lin

Integrated energy systems (IES) are complex heterogeneous architectures that typically encompass power sources, hydrogen electrolyzers, energy storage, and heat exchangers. This integration is achieved through operating control strategy optimization. However, the lack of physical understanding as to how these systems evolve over time introduces uncertainties

Yichi Zhang, Wenjin Zhao, Zhongdong Han, Kenji Watanabe

Tunable Kondo lattice and heavy fermion physics have been recently reported in moir\'e materials, but most of the studies have focused on the electrical and magnetic properties. Quantitative thermoelectric measurements, which can reveal entropic information of the heavy fermions, have yet to be achieved. Here, we report a comprehensive thermoelectric study o

Qiao Wang, Joseph George

Monitoring household water use in rapidly urbanizing regions is hampered by costly, time-intensive enumeration methods and surveys. We investigate whether publicly available imagery-satellite tiles, Google Street View (GSV) segmentation-and simple geospatial covariates (nightlight intensity, population density) can be utilized to predict household water cons

Joshua L. Pughe-Sanford, Xuehao Ding, Jason J. Moore, Anirvan M. Sengupta

We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact representations of such dynamics. From prior experience, neurons learn coherent sets-regions of stimulus state space whose trajectories evolve cohesively over finite times-and assign membership i

Adam Winchell

The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional

Kris Youakim, Karin Lind

The Milky Way halo has been built-up over cosmic time through the accretion and dissolution of star clusters and dwarf galaxies as well as through their complex interactions with the Galactic disc. Traces of these accreted structures persist to the present day in the chemical and kinematic properties of stars and their orbits and allow for the disentangling

Emil Otis Rosanowski, Jurek Eisinger, Lena Funcke, Ulrich Poschinger

We apply the recently proposed Sample-based Krylov Quantum Diagonalization (SKQD) method to lattice gauge theories, using the Schwinger model with a $\theta$-term as a benchmark. SKQD approximates the ground state of a Hamiltonian, employing a hybrid quantum-classical approach: (i) constructing a Krylov space from bitstrings sampled from time-evolved quantum

Fabio Falcioni, Elena Orlova, Timothy Heightman, Philip Mantrov

In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the energy quality, autocorrelation times, and effective sample size, our findings show that Simulacra's Large Wavefunction Models (LWM) pipeline, paired with state-of-the-art Variationa

Boris Zilber

We recast the well-known axiom system of quantum mechanics used by physicists (the Dirac calculus) in the language of Continuous Logic. For the basic version of the axiomatic system we prove that along with the canonical continuous model the axioms have approximate finite models of large sizes, in fact the continuous model is isomorphic to an ultraproduct of

Yuhao Zhang, Yong Teng, Kenan Song, Xianqiao Wang

Ferrohydrodynamic microfluidics relies on magnetic field gradients to manipulate diamagnetic particles in ferrofluid-filled microenvironments. It has emerged as a promising tool for label-free manipulation of bioparticles, including their separation and phenotyping. This perspective reviews recent progress in the development and applications of ferrofluid-ba

Taylor Schaffner, Benjamin B. Machta

Many protein-protein interaction (PPI) networks take place in the fluid yet structured plasma membrane. Lipid domains, sometimes termed rafts, have been implicated in the functioning of various membrane-bound signaling processes. Here, we present a model and a Monte Carlo simulation framework to investigate how changes in the domain size that arise from pert

Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha

This paper proposes a cooperative integrated estimation-guidance framework for simultaneous interception of a non-maneuvering target using a team of unmanned autonomous vehicles, assuming only a subset of vehicles are equipped with dedicated sensors to measure the target's states. Unlike earlier approaches that focus solely on either estimation or guidance d

Simona Di Stefano, Valentina D'Odorico, Guido Cupani, Dinko Milakovic

The distribution and the abundance of metals in the intergalactic medium (IGM) have strong implications on galaxy formation and evolution models. The ionic transitions of heavy elements in quasar spectra can probe both the mechanisms and the sources of chemical pollution, but high resolution, high signal-to-noise ratio (S/N) spectra are required, as the IGM

Rebeca Beltrán, Chandan Hati, Martin Hirsch, Ana Martín-Galán

Heavy neutral leptons (HNLs) and axion-like particles (ALPs) are both considered well-motivated candidates for beyond the standard model (BSM) physics. If ALPs with sizable couplings to gluons exist, they will be abundantly produced at the LHC. Therefore, HNLs produced via the ALP portal may provide unprecedented sensitivities to HNL parameters. Here, we stu

Michal Outrata, José Pablo Lucero Lorca

We revisit the Hierarchical Poincar\'{e}-Steklov (HPS) method for the Poisson equation using standard Q1 finite elements, building on the original in work on HPS of Martinsson from 2013. While corner degrees of freedom were implicitly handled in that work, subsequent spectral-element implementations have typically avoided them. In Q1-FEM, however, corner cou

Hoa Nguyen, Pongstorn Maidee, Jason Lowe-Power, Alireza Kaviani

In this paper, we introduce Choreographer, a simulation framework that enables a holistic system-level evaluation of fine-grained accelerators designed for latency-sensitive tasks. Unlike existing frameworks, Choreographer captures all hardware and software overheads in core-accelerator and cache-accelerator interactions, integrating a detailed gem5-based ha

Drake Osaben, Jiong Qiu, Dana W. Longcope

A solar flare releases magnetic energy by reconnecting field lines across a current sheet, thereby allowing their relaxation to a lower energy state. The maximum possible energy is released if all field lines relax to a current-free (potential) state. The progress of a flare's reconnection is often measured as the angle-complement between the observed post-r

Asghar Ullah, Hasan Mermer, Melih Özkurt, Igor Lesanovsky

Periodically driven quantum systems can function as highly selective parameter filters. We demonstrate this capability in a finite-size, three-qubit system described by the transverse-field Floquet Ising model. In this system, we identify a period-doubling (PD) dynamical phase that exhibits a stark asymmetry in metrological sensitivity to the magnetic field

Seif Ikbarieh, Maanak Gupta, Elmahedi Mahalal

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation suggestion. Despite advancements, evaluations remain purely qualita

Ashwin Kumar, Hanyu Zhang, David A. Schweidel, William Yeoh

Next location prediction underpins a growing number of mobility, retail, and public-health applications, yet its societal impacts remain largely unexplored. In this paper, we audit state-of-the-art mobility prediction models trained on a large-scale dataset, highlighting hidden disparities based on user demographics. Drawing from aggregate census data, we co

Mihai Prunescu, Joseph M. Shunia

We present several elementary closed-forms that express a non-trivial divisor for every composite integer $n > 1$. Each closed-form consists of a fixed number of elementary arithmetic operations drawn from the set: addition, subtraction, multiplication, integer division, and exponentiation. Two families of closed-forms are developed. First, direct applicatio

Ajinkya Gaikwad, Hitendra Kumar, S. Padmapriya, Praneet Kumar Patra

We study a family of graph modification problems called the F-Vertex Splitting problem. Given a graph G, the task is to determine whether G can be transformed into a graph G-prime belonging to a graph class F through a sequence of at most k vertex splits. We investigate this problem for several target graph classes, namely constellations, cycle graphs, linea

Zimeng Huang, Jinxin Ke, Xiaoxuan Fan, Yufeng Yang

Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matching. In this work, we aim to evaluate a fundamental yet underexplored intelligence: association, a cornerstone of human cognition for creative thinking and knowledge integration.

Sergei Shteiner, Pavel Shteyner

We enumerate the row-column-sums of all square tridiagonal $(0,1)$-matrices and prove that their count coincides with OEIS A022026 $-$ the number of acyclic subgraphs of the complete $2\times n$ grid graph. We then extend this correspondence in two independent directions: 1. admitting larger sets of matrix entries, and 2. relaxing the tridiagonal support to

Piyushkumar Patel

Text to video generation has emerged as a critical frontier in generative artificial intelligence, yet existing approaches struggle with maintaining temporal consistency, compositional understanding, and fine grained control over visual narratives. We present MOVAI (Multimodal Original Video AI), a novel hierarchical framework that integrates compositional s

Yunhao Yang, Neel P. Bhatt, Pranay Samineni, Rohan Siva

As AI systems migrate to safety-critical domains, verifying that their actions comply with well-defined rules remains a challenge. Formal methods provide provable guarantees but demand hand-crafted temporal-logic specifications, offering limited expressiveness and accessibility. Deep learning approaches enable evaluation of plans against natural-language con

John Hoffman, Ben Jaye

David and Semmes proved that if all CZOs (of suitable dimension) are bounded with respect to an Ahlfors regular measure, then the measure is uniformly rectifiable. We extend this theorem to the parabolic space and the first Heisenberg group.

Sujit Rajak, Nishkarsh Kumar, Dheeraj Yadav, Suman Mandal

Spin-dependent dispersion and Rashba effect are manifestations of universal spin orbit interaction associated with the breaking of the spatial inversion symmetry in condensed matter and in optical systems. In sharp contrast to this, we report a spin-split dispersion effect of leaky surface plasmons in an inversion-symmetric one dimensional plasmonic grating

ANUBIS Collaboration

Long-lived particles (LLPs), i.e., particles with macroscopic lifetimes $\tau>10$~ns, appear in various extensions of the Standard Model (SM) that address fundamental questions like the particulate nature of dark matter or baryogenesis. The ANUBIS detector will achieve unprecedented sensitivity to such models compared to existing and approved experiments by

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO--Virgo--KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, non-negligible spin--orbit misalignment, and unequal mass ratios between t

Nina Deliu, Brunero Liseo

Conformal prediction has emerged as a cutting-edge methodology in statistics and machine learning, providing prediction intervals with finite-sample frequentist coverage guarantees. Yet, its interplay with Bayesian statistics, often criticised for lacking frequentist guarantees, remains underexplored. Recent work has suggested that conformal prediction can s

Rodrigo A. González, Koen Classens, Cristian R. Rojas, Tom Oomen

Multisine excitations are widely used for identifying multi-input multi-output systems due to their periodicity, data compression properties, and control over the input spectrum. Despite their popularity, the finite sample statistical properties of frequency-domain estimators under multisine excitation, for both nonparametric and parametric settings, remain

Anuj Gupta, Ann Shivers-McNair

In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity,

Marc Benjamin Hahn

Radiation chemistry of model systems irradiated with ultra-high dose-rates (UHDR) is key to obtain a mechanistic understanding of the sparing of healthy tissue, which is called the FLASH effect. It is envisioned to be used for efficient treatment of cancer by FLASH radiotherapy. However, it seems that even the most simple model systems, water irradiated with

Majid Memari, Krista Ruggles

Artificial intelligence (AI) is transforming elementary STEM education, yet evidence remains fragmented. This systematic review synthesizes 258 studies (2020-2025) examining AI applications across eight categories: intelligent tutoring systems (45% of studies), learning analytics (18%), automated assessment (12%), computer vision (8%), educational robotics (

Polly Frazer, Emily J. Griffith, David W. Hogg, Amaya Sinha

In the Milky Way disk there is a strong trend linking stellar age to surface element abundances. Here we explore this relationship with a dataset of 8,803 red-giant and red-clump stars with both asteroseismic data from NASA Kepler Mission and surface abundances from the SDSS-V MWM. We find, with a k-nearest-neighbors approach, that the [Mg/H] and [Fe/Mg] abu

Muxin Han

This paper investigates the fundamental issue of triangulation dependence in spinfoam quantum gravity. It introduces a novel framework, named spinfoam stack, to systematically sum spinfoam amplitudes over an infinite class of 2-complexes. These complexes are generated by stacking an arbitrary number of faces upon a simpler root complex. The central result is

Muxin Han

This paper investigates entanglement entropy in 3+1 dimensional Lorentzian covariant Loop Quantum Gravity (LQG). We compute the entanglement entropy for a spatial region from states dynamically generated by a spinfoam path integral that sums over a family of 2-complexes. The resulting entropy exhibits a geometric area law, $S \simeq \beta a$, where the area

Friedrich Lippoth

We provide an example for a smooth and embedded initial state that looses embeddedness in finite time when evolving according to the quasistationary Stefan problem with Gibbs-Thomson correction and kinetic undercooling in 2D.

Yi Luo, Yike Guo, Hamed Hooshangnejad, Kai Ding

Lung nodule detection in chest CT is crucial for early lung cancer diagnosis, yet existing deep learning approaches face challenges when deployed in clinical settings with limited annotated data. While curriculum learning has shown promise in improving model training, traditional static curriculum strategies fail in data-scarce scenarios. We propose Scale Ad

Muxin Han

The non-factorizing nature of the Hilbert space in Loop Quantum Gravity (LQG) due to gauge invariance requires a generalized definition of entanglement entropy. This work employs the framework of von Neumann algebras to investigate the entanglement entropy in LQG. On a graph, the holonomy and flux operators within a region and on the boundary generate a non-

Moonsoo Jeong, Dongbeen Kim, Minseong Kim, Sungkil Lee

We present a Directional Consistency (DC)-driven Adaptive Density Control (ADC) for 3D Gaussian Splatting (DC4GS). Whereas the conventional ADC bases its primitive splitting on the magnitudes of positional gradients, we further incorporate the DC of the gradients into ADC, and realize it through the angular coherence of the gradients. Our DC better captures

J. Clayton Peacock, Vadim Oganesyan, Dries Sels

The Krylov subspace expansion is a workhorse method for sparse numerics that has been increasingly explored as source of physical insight into many-body dynamics in recent years. In this work we revisit the venerable Anderson model of localization in dimensions $d=1, 2, 3, 4$ to construct local integrals of motion (LIOM) in Krylov space. These appear as zero

Andrea Moscatello, Giacomo Ciani, Livia Conti

High-sensitivity optical measurements such as those performed in interferometric gravitational wave detectors are prone to scattered light noise. To minimize it, optical components must meet tight requirements on surface roughness and bulk defects. Nonetheless, the effectiveness of these measures can be undermined by other sources of scattered light. In this

Oindrila Ghosh, Axel Brandenburg, Chiara Caprini, Andrii Neronov

Cosmic voids are magnetized at the level of at least $10^{-17}$ G on Mpc scales, as implied by blazar observations. We show that an electrically conducting plasma is present in the voids, and that, because of the plasma, \emph{diffusion} into the voids of galactic fields generated by a mean-field dynamo is far too slow to explain the present-day void magneti

Pavel Hernández-Amaro, Maria Durban, M. Carmen Aguilera-Morillo

In this work we propose a generalized additive functional regression model for partially observed functional data. Our approach accommodates functional predictors of varying dimensions without requiring imputation of missing observations. Both the functional coefficients and covariates are represented using basis function expansions, with B-splines used in t

Jingchen Wang, Tianqi Hu, Jingjie Yeo

Electrically conducting polymers with mechanical adaptability are essential for flexible electronics, yet most suffer structural degradation under rapid deformation. In this study, multiscale coarse-grained (MSCG) simulations are used to uncover the nanoscale origins of an unusual strain-rate-dependent stiffening in a poly(2-acrylamido-2-methyl-1-propanesulf

Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes

Heterogeneous robot teams operating in realistic settings often must accomplish complex missions requiring collaboration and adaptation to information acquired online. Because robot teams frequently operate in unstructured environments -- uncertain, open-world settings without prior maps -- subtasks must be grounded in robot capabilities and the physical wor

Sam Allen, Enrico Pescara, Johanna Ziegel

Conformal predictive systems are sets of predictive distributions with theoretical out-of-sample calibration guarantees. The calibration guarantees are typically that the set of predictions contains a forecast distribution whose prediction intervals exhibit the correct marginal coverage at all levels. Conformal predictive systems are constructed using confor

Junyi Shen, Noppanat Wadlom, Lingfeng Zhou, Dequan Wang

AI deployment increasingly resembles a pipeline of data transformation, fine-tuning, and agent interactions rather than a monolithic LLM job; recent examples include RLHF/RLAIF training and agentic workflows. To cope with this shift, we propose FlowMesh, a multi-tenant service fabric that executes and optimizes these workloads as one shared service instead o

Hyunji Lee, Wenhao Yu, Hongming Zhang, Kaixin Ma

Hybrid models that combine state space models (SSMs) with attention mechanisms have shown strong performance by leveraging the efficiency of SSMs and the high recall ability of attention. However, the architectural design choices behind these hybrid models remain insufficiently understood. In this work, we analyze hybrid architectures through the lens of mem

Sean Tobin

We introduce a novel method for computing entanglement entropy across surfaces in Loop Quantum Gravity by employing techniques from quantum error correcting codes. In this construction, the redundancy encoded in the gauge invariant subspace is made manifest by embedding it in a larger Hilbert space. The enlarged Hilbert space of a surface does not factorize,

Kshitij Nikhal, Lucas Ackerknecht, Benjamin S. Riggan, Phillip Stahlfeld

The decarbonization of transportation relies on the widespread adoption of electric vehicles (EVs), which requires an accurate understanding of charging behavior to ensure cost-effective, grid-resilient infrastructure. Existing work is constrained by small-scale datasets, simple proximity-based modeling of temporal dependencies, and weak generalization to si

Tim Windecker, Manthan Patel, Moritz Reuss, Richard Schwarzkopf

Vision-language models demonstrate unprecedented performance and generalization across a wide range of tasks and scenarios. Integrating these foundation models into robotic navigation systems opens pathways toward building general-purpose robots. Yet, evaluating these models' navigation capabilities remains constrained by costly real-world trials, overly sim

Mohammad Eskandari, Mojtaba Joodaki

This paper presents an electromagnetic investigation of the crosstalk between two bent microstrip lines (MLs) separated by a perforated planar shield. As an extension of our previous study, the effects of various discontinuities in either the MLs or the shield along the coupling path are analyzed through numerical simulations and validated by measurements. T

Meghraj Prajapat, Alankar Alankar

Electron back-scatter diffraction (EBSD) has traditionally relied upon methods such as the Hough transform and dictionary Indexing to interpret diffraction patterns and extract crystallographic orientation. However, these methods encounter significant limitations, particularly when operating at high scanning speeds, where the exposure time per pattern is dec

Kieran Loehr, Bryan K. Clark

Accurately describing the ground state of strongly correlated systems is essential for understanding their emergent properties. Neural Network Backflow (NNBF) is a powerful variational ansatz that enhances mean-field wave functions by introducing configuration-dependent modifications to single-particle orbitals. Although NNBF is theoretically universal in th

Pedro Antonio Alarcon Granadeno, Arturo Miguel Bernal Russell, Sofia Nelson, Demetrius Hernandez

Cyber-physical systems increasingly rely on foundational models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs) to increase autonomy through enhanced perception, inference, and planning. However, these models also introduce new types of errors, such as hallucinations, over-generalizations, and context misalignments, resulting in incor

Simone Romiti

We present an application of Physics-Informed Neural Networks (PINNs) to the study of $\mathrm{SU}(N_c)$ lattice gauge theories. Our method enables the learning of eigenfunctions and eigenvalues at arbitrary gauge couplings, smoothly moving from the analytically known strong-coupling regime towards weaker couplings. By encoding the Schr\"odinger equation and

Rochak Dhakal, Chen Zhao, Zixin Shi, Joyce H. Keyak

Quantitative computed tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proximal femur. However, deploying automated segmentation models in practice remains difficult because deep networks trained on one dataset often fail when applied to another. This failur

Barry T. Chiang, Frank C. van den Bosch, Hsi-Yu Schive

Dark matter subhalos and satellite galaxies in state-of-the-art cosmological simulations still suffer from the ``overmerging'' problem, where inadequate force and/or mass resolution cause artificially enhanced tidal mass loss and premature disruption. Previous idealized simulations addressing this issue have been restricted to a small subset of the subhalo o

Jahir Argote-Gerald, Genki Miyauchi, Julian Rau, Paul Trodden

Maze-like environments, such as cave and pipe networks, pose unique challenges for multiple robots to coordinate, including communication constraints and congestion. To address these challenges, we propose a distributed multi-agent maze traversal algorithm for environments that can be represented by acyclic graphs. It uses a leader-switching mechanism where

Crystal Su, Kuai Yu, Jingrui Zhang, Mingyuan Shao

This work presents an ontology-integrated large language model (LLM) framework for chemical engineering that unites structured domain knowledge with generative reasoning. The proposed pipeline aligns model training and inference with the COPE ontology through a sequence of data acquisition, semantic preprocessing, information extraction, and ontology mapping

Cristiano Muzzi, Devendra Singh Bhakuni, Marcello Dalmonte, Lenart Zadnik

Symmetries strongly influence transport properties of quantum many-body systems, and can lead to deviations from the generic case of diffusion. In this work, we study the impact of time-reversal symmetry breaking on the transport and its universal aspects in integrable chiral spin ladders. We observe that the infinite-temperature spin transport is superdiffu

Cristina Gheorghe

This paper studies strict fixed point and stability results for multivalued operators which does not satisfy a \'Ciri\'c type contraction condition, but their admissible perturbation does. We focus on the conditions imposed on the admissible perturbation $T_G$ of a Picard operator $T:X\rightarrow P(X)$ such that the strict fixed point and stability results s

Minjeong Song, Hyukjoon Kwon, Valerio Scarani

Channels $\mathcal{N}$ that describe open quantum dynamics are inherently irreversible: it is impossible to undo their effect completely, but one can study partial recovery of the information. The Petz recovery map $\hat{\mathcal{N}}_{\gamma}^{(\texttt{P})}$ is a systematic construction that depends only on $\mathcal{N}$ and on a reference state $\gamma$, wh

Ohidul Alam, Amartya Pal, Paramita Dutta, Arijit Saha

We present a theoretical framework for investigating superconducting proximity effect in altermagnet (AM)-superconductor (SC) heterostructures. In general, AMs, characterized by vanishing net magnetization but spin-split electronic spectra, provide a promising platform for realizing unconventional magnetic phases. We consider a two-dimensional $d$-wave AM pr

Ulaş Özdem

Understanding how quarks are spatially arranged inside exotic pentaquarks remains one of the key open problems in contemporary hadron spectroscopy. The electromagnetic multipole moments of hadrons provide a direct probe of their internal quark--gluon geometry and spatial charge distributions. Motivated by this, we employ QCD light-cone sum rules to compute t

Mahsa Valizadeh, Rui Tuo, James Caverlee

Generative Adversarial Networks (GANs) are proficient at generating synthetic data but continue to suffer from mode collapse, where the generator produces a narrow range of outputs that fool the discriminator but fail to capture the full data distribution. This limitation is particularly problematic, as generative models are increasingly deployed in real-wor

Martin Loebl, Anetta Jedličková, Jakub Černý

We embed buying rights into a (repeated) Arrow-Debreu model to study the long-term effects of regulation through buying rights on arising inequality. Our motivation stems from situations that typically call for regulatory interventions, such as rationing, namely, distribution crises in which demand and supply are persistently misaligned. In such settings, sc

GlueX Collaboration, F. Afzal, M. Albrecht, M. Amaryan

The GlueX experiment at Jefferson Lab has observed $p\bar{p}$ and, for the first time, $\Lambda\bar{\Lambda}$ and $p\bar{\Lambda}$ photoproduction from a proton target at photon energies up to 11.6 GeV. The angular distributions are forward peaked for all produced pairs, consistent with Regge-like $t$-channel exchange. Asymmetric wide-angle anti-baryon distr

Marcus J. Clark, Siddarth K. Joshi

As quantum communication systems and networks are becoming a commercial reality, clarity on their future infrastructure is increasingly important. Based on the inevitable presence of some amount of loss, chromatic dispersion, and timing jitter, we present simulations to show that certain wavelengths and bandwidths have clear advantages.

Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it t

Tiannian Zhu, Zhong Fang, Quansheng Wu, Hongming Weng

Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated "silos". Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a st

Massimiliano Parente, Manuela Bischetti, Umberto Maio, Francesco Salvestrini

Neutral-oxygen [OI] far-infrared emission lines at $63\mu$m and $145\mu$m are powerful probes of the physical conditions in the interstellar medium, although they have not been fully exploited in high-redshift studies. We investigate the connection between [OI] emission lines and key galaxy properties, such as star formation rate (SFR) and H$_2$ content. Our

Devang H. Liya, David J. Rosario, Matthaios Charidis

There is evidence for significant evolution in the gaseous and dust properties of galaxies since the era of cosmic noon ($1\lesssim z\lesssim 2.5$). The well known co-evolution of supermassive black holes with their host galaxies suggests a constant connection between the small-scale (nuclear) and large-scale regions of galaxies. A fundamental component of A

Guillem Domènech

In view of recent interest in the role of "dark" radiation in cosmology, such as cosmic gravitational waves, sterile neutrinos, and dark photons, we clarify the definition of adiabatic initial conditions in the kinetic theory of gases in an expanding universe. Without assuming any form for the phase space distribution function, we identify two possibilities:

Grant C. Weldon, Bradley M. S. Hansen, Smadar Naoz

The existence of giant extrasolar planets on short-period orbits ("hot Jupiters") challenges planet formation theories because such planets are difficult to form close to the star. High-eccentricity migration is a leading explanation, in which giant planets born at large separations are excited to near-unity eccentricities, enabling tidal dissipation at peri

Talia M. O'Shea, Sebastian Heinz, Melinda Soares-Furtado, Zsofi Igo

AGN jets are thought to be vital ingredients in galaxy evolution through the action of kinetic feedback; however, how narrow, relativistic outflows couple to galaxies remains an open question. Jet deceleration, which is often attributed to the entrainment of material, such as stellar winds, is thought to be necessary for efficient coupling. We present a simp

Kevin P. Nuckolls, Nisarga Paul, Alan Chen, Filippo Gaggioli

In the past decade, moir\'e materials have revolutionized how we engineer and control quantum phases of matter. Among incommensurate materials, moir\'e materials are aperiodic composite crystals whose long-wavelength moir\'e superlattices enable tunable properties without chemically modifying their layers. To date, nearly all reports of moir\'e materials hav

João A. Gonçalves

Jet quenching, the modification of jets by the quark-gluon plasma in heavy-ion collisions, provides a sensitive probe of the properties of the medium. A jet-by-jet discrimination study between proton-proton and lead-lead jets using energy flow networks and simple baselines, explicitly retaining medium response and underlying event contamination is presented.

Mikael Chala, Andrii Dashko, Guilherme Guedes

In this work, we study the renormalization-group evolution of parameters in the three-dimensional effective field theory (3D EFT) that describes the thermally driven electroweak phase transition of the Higgs field. We consider tree-level and radiatively generated barriers induced by beyond the Standard Model physics, enabling a first-order phase transition a

Alex M. Garcia, Paul Torrey, Aniket Bhagwat, Xuejian Shen

The distribution of gas-phase metals within galaxies encodes the impact of stellar feedback on galactic evolution. At high-redshift, when galaxies are rapidly assembling, feedback-driven outflows and turbulence can strongly reshape radial metallicity gradients. In this work, we use the FIRE-2, SPICE, Thesan and Thesan Zoom cosmological simulations -- spannin

Giacomo Brunello, Vsevolod Chestnov, Giulio Crisanti, Mathieu Giroux

We present a systematic framework for computing frequency-domain gravitational waveforms from relativistic binary scattering in different asymptotic regimes. The method yields a controlled series expansion that can in principle be extended to arbitrary order in the relevant kinematic parameter. By combining differential-equation techniques with restriction t