December 2025 arXiv papers — page 48

Taiki Matsushita, Youichi Yanase, Takeshi Mizushima, Satoshi Fujimoto

We theoretically investigate the intrinsic (impurity-independent) spin Nernst effect (SNE), a spin current generation perpendicular to temperature gradients, in spin-triplet superconductors. We show that, in these systems, the SNE consists of two distinct contributions: a direct quasiparticle contribution and an indirect supercurrent contribution. The quasip

Surya Jayakumar, Kieran Sullivan, John McLaughlin, Christine O'Meara

This study introduces a novel data-driven framework and the first-ever county-scale application of Spatio-Temporal Graph Neural Networks (STGNN) to forecast composite sustainability indices from herd-level operational records. The methodology employs a novel, end-to-end pipeline utilizing a Variational Autoencoder (VAE) to augment Irish Cattle Breeding Feder

Shanyan Chen, Ali Al-Bayaty, Xiaoyu Song, Marek Perkowski

This paper introduces a new Boolean-based methodology for constructing Segment Display Problems (SDPs) in the quantum domain and solving them using Grover's quantum search algorithm. In the classical domain, the SDPs are typically solved using various techniques, such as human deduction, heuristic search, and methods for solving Boolean satisfiability (SAT)

T. E. Rivera-Thorsen, A. Le Reste, M. J. Hayes, S. Flury

We present an analysis of archival JWST NIRSpec IFS and HST imaging observations of the z = 3 Lyman-Continuum Emitter (LCE) candidate LACES104037. We show that a nearby galaxy, denoted LACES104037-S, has a redshift offset from the main galaxy by only $\sim 450$ km/s. Together with the identification of tidal bridge features between the galaxies, this indicat

Yiming Ma, Hang Liu, Weiwei Zhuang

Stochastic dominance (SD) provides a quantile-based partial ordering of random variables and has broad applications. Its extension to multivariate settings, however, is challenging due to the lack of a canonical ordering in $\mathbb{R}^d$ ($d \ge 2$) and the set-valued character of multivariate quantiles. Based on the multivariate center-outward quantile fun

Mahmudul Hasan, Jingsong Gao, Hao Liang, Yiming Yuan

Time-resolved measurement of Auger-Meitner (AM) decay [Nature 419, 803 (2002)] marked a milestone in the development of attosecond science. To date, the time constants for the AM decay processes obtained from the time-domain experiments were found to be consistent with the values deduced from conventional energy-domain measurements. One of the main factors l

Bruno E. Farias, José Flauzino, Elias P. Duarte

With the exponential growth of the amount of data available on the Internet, optimizing the response time and resource usage for data access becomes essential. Caches are an effective solution that brings data closer to clients, eliminating repetitive requests to servers. This paper presents VNF-Cache, a caching service for geographically remote key-value da

Zar Chi Phyo, Shreya Khisa, Chadi Assi, Sanaa Sharafaddine

This paper investigates a rate-splitting multiple access (RSMA) for uplink pinching antenna system (PASS). Our objective is to maximize the uplink sum rate by jointly optimizing a continuous antenna positioning and user's transmission power. The formulated problem is highly non-convex and difficult to solve directly; to address this challenge, we propose an

Hideki Yanaoka

In magnetohydrodynamic (MHD) flows, incompressibility is assumed for low Mach numbers. However, even at low Mach numbers, the Mach number influences flow and magnetic fields. Therefore, it is necessary to develop a method that can stably analyze low Mach number compressible MHD flows without using the incompressible assumption. This study constructs an energ

Futoshi Minato

This study investigates nuclear responses to two body external fields, interpreted as double phonon excitations, within the subtracted second random phase approximation (SSRPA) for 16O and 40Ca. To clarify the underlying characteristics of these modes, Hartree Fock(HF) and SSRPA with the diagonal approximation are first examined. The resulting strength distr

Luciano Araujo Dourado Filho, Rodrigo Tripodi Calumby

Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained Visual Categorizat

Purva Kulkarni, Aravind Shankara Narayanan

Mesh simplification is the process of reducing the number of vertices, edges and triangles in a three-dimensional (3D) mesh while preserving the overall shape and salient features of the mesh. A popular strategy for this is edge collapse, where an edge connecting two vertices is merged into a single vertex. The edge to collapse is chosen based on a cost func

Sneha Oommen, Gabby Sanchez, Cassandra T. Britto, Di Wang

This paper presents an evaluation of the AWS Textract in the context of extracting data from receipts. We analyse Textract functionalities using a dataset that includes receipts of varied formats and conditions. Our analysis provided a qualitative view of Textract strengths and limitations. While the receipts totals were consistently detected, we also observ

Luciano Araujo Dourado Filho, Almir Moreira da Silva Neto, Rodrigo Pereira David, Rodrigo Tripodi Calumby

This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set imag

O. G. Turutanov

The Nobel Prize in Physics 2025 has been awarded to John Clarke, John Martinis, and Michel Devoret for "the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit". Their achievements open up possibilities for developing the next generation of quantum technologies, including quantum cryptography, quantum compute

Wu-zhong Guo, Song He, Tao Liu

We develop a unified framework for computing R\'enyi and entanglement entropies of arbitrary spacetime intervals in time-dependent states of $(1+1)$-dimensional conformal field theories. By combining the spacetime density matrix formalism with the replica method, we show that entanglement entropy is well defined for both spacelike and timelike separations. A

Yuechen Yang, Junlin Guo, Yanfan Zhu, Jialin Yue

High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevance of the tissue characteristics at the micro- and macro-environment level is limited by the lack of tools that facilitate the measurement of the spatial interaction of individual

Spencer Rogers, Salman Shahid, Wenchao Ge

The operational resource theory (ORT) measure is a nonclassicality measure for bosonic states, notable for its resource-theoretic properties and connection to metrology. However, it can be difficult to evaluate, being linked to an optimization problem for mixed states. Here, we present ORT measure calculations for mixed states with photon-number coherence. W

Bruce C. Berndt, Örs Rebák

The primary purpose of this paper is to provide a survey of properties, values, identities, and generalizations of the Rogers--Ramanujan continued fraction, which is closely related to the Rogers--Ramanujan identities. Many of these results are found in Ramanujan's first two letters to Hardy, Ramanujan's notebooks, and his lost notebook. Short historical acc

Yufei Zhou

The mod function plays a critical role in numerous data encoding and cryptographic primitives. However, the widely used CKKS homomorphic encryption (HE) scheme supports only arithmetic operations, making it difficult to perform mod computations on encrypted data. Approximating the mod function with polynomials has therefore become an important yet challengin

Heet Bodara, Md Masum Mushfiq, Isma Farah Siddiqui

Large Language Models are increasingly used in conversational systems such as digital personal assistants, shaping how people interact with technology through language. While their responses often sound fluent and natural, they can also carry subtle tone biases such as sounding overly polite, cheerful, or cautious even when neutrality is expected. These tend

Zixuan Huang, Xiang Li, Zhaoyang Lv, James M. Rehg

Videos are continuous 2D projections of 3D worlds. After training on large video data, will global 3D understanding naturally emerge? We study this by quantifying the 3D understanding of existing Video Foundation Models (VidFMs) pretrained on vast video data. We propose the first model-agnostic framework that measures the 3D awareness of various VidFMs by es

Kai Xu

In this paper we construct semiorthogonal decompositions of moduli of principal bundles on a curve into its symmetric powers, for both the moduli stack of all $G$-bundles and the coarse moduli space of semistable $G$-bundles. The essential ingredients in the proof include Borel-Weil-Bott theory for loop groups, highest weight structure of current group repre

Elizabeth Stewart, Suryash Greenwold, Timotius Marselo

Crowd-sourced fact-checking provides social media platforms with a promising method of managing misinformation at scale. However, the success of fact-checking programs like X's Community Notes requires the participation of a critical mass of note-writers who have the time and epistemic resources necessary to write and rate high-quality notes. As X's Communit

Alejandro Cabo-Bizet

Using supersymmetric localization, we show that the partition function of four-dimensional superconformal gauge theories - computed as a trace over BPS states without the insertion of $(-1)^F$ - is perturbatively protected and piecewise independent of the gauge coupling. We derive a matrix-integral representation of this observable at $g_{\text{YM}}=0$ for g

Richard Tawiah, Shu Kay Ng, Geoffrey J. McLachlan

Variable selection naturally arises as a useful subject when faced with data with massive predictor space. In addition to the massive dimensionality, the data may be characterized by intra-subject correlation, and cure fraction, which are ubiquitous in longitudinal studies with recurrent events defining the endpoint of interest. However, variable selection m

Haoyi Zhong, Fang-Lue Zhang, Andrew Chalmers, Taehyun Rhee

While instruction-based image editing is emerging, extending it to 360$^\circ$ panoramas introduces additional challenges. Existing methods often produce implausible results in both equirectangular projections (ERP) and perspective views. To address these limitations, we propose SE360, a novel framework for multi-condition guided object editing in 360$^\circ

Leon Ofman, Olga Khabarova, Ryun-Yong Kwon, Yogesh

The Kelvin-Helmholtz instability (KHI) can occur when there is a relative motion between two adjacent fluids. In the case of magnetized plasma, the shear velocity must exceed the local Alfv\'{e}n speed for the instability to develop. The KHI produces nonlinear waves that eventually roll up into vortices and contribute to turbulence and dissipation. In the so

Mozes Jacobs, Thomas Fel, Richard Hakim, Alessandra Brondetta

As Vision Transformers (ViTs) become standard vision backbones, a mechanistic account of their computational phenomenology is essential. Despite architectural cues that hint at dynamical structure, there is no settled framework that interprets Transformer depth as a well-characterized flow. In this work, we introduce the Block-Recurrent Hypothesis (BRH), arg

Shaghayegh Shajarian, Kennedy Marsh, James Benson, Sajad Khorsandroo

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst trust. In this paper, we present ReGAIN, a multi-stage framework t

Rayan Ibrahim, Allison H. Moore

RNA molecules are known to form complex secondary structures including pseudoknots. A systematic framework for the enumeration, classification and prediction of secondary structures is critical to determine the biological significance of the molecular configurations of RNA. Chord diagrams are mathematical objects widely used to represent RNA secondary struct

Thomas M. Henderson, Guo P. Chen, Gustavo E. Scuseria

Seniority is a useful way of organizing Hilbert space for strongly correlated systems. The exact zero-seniority wave function, doubly-occupied configuration interaction (DOCI), provides accurate results (given the right orbitals) for many strongly correlated electronic systems but has a combinatorial computational cost. In many cases, pair coupled cluster do

Eric Yeh, John Cadigan, Ran Chen, Dick Crouch

Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high fidelity to drive simulations to test human behavioral hypotheses, exhibiting more nuance and range than the r

Shalender Singh, Santosh Kumar

Many solid-state quantum platforms do not permit sharp, projective measurements but instead yield continuous voltage or field traces under weak, non-demolition readout. In such systems, standard Bell tests based on dichotomic projective measurements are not directly applicable, raising the question of how quantum nonlocality can be certified from continuous

Miguel Esparza, Vamshi Battal, Ali Mostafavi

As wildfires increasingly evolve into urban conflagrations, traditional risk models that treat structures as isolated assets fail to capture the non-linear contagion dynamics characteristic of the wildland urban interface (WUI). This research bridges the gap between mechanistic physics and data driven learning by establishing a novel dual specialist ensemble

Faramarz Rahmani, Mehdi Sadeghi

We perform a topological classification of the phase structure of a four-dimensional AdS black hole with non-minimal Maxwell coupling. Critical points are treated as topological defects, allowing us to assign a winding number to each black hole branch and compute the global topological invariant W. The system exhibits a duality governed by its Maxwell charge

Mariem Magdy, Juan A. Valiente Kroon

We show how the space spinor formalism for 2-component spinors can be used to construct estimates for spinor fields satisfying first order equations. We discuss the connection of the approach presented in this article with other strategies for the construction of estimates. In addition, we recast several concepts related to the notion of hyperbolicity in the

Xiaoqian Liu, Yifei Guan, Oleg V. Yazyev

Flat bands in graphitic materials emerged as a platform for realizing tunable correlated physics. As a nodal-line semimetal, rhombohedral graphite features flat drumhead surface states in the vicinity of the Dirac points, which carry a nontrivial topological charge. We present a comprehensive study on rhombohedral graphite with twist stacking faults. Using b

Cagatay Isil, Alexander Chen, Yuhang Li, F. Onuralp Ardic

3D image display is essential for next-generation volumetric imaging; however, dense depth multiplexing for 3D image projection remains challenging because diffraction-induced cross-talk rapidly increases as the axial image planes get closer. Here, we introduce a 3D display system comprising a digital encoder and a diffractive optical decoder, which simultan

Emilia Majerz, Witold Dzwinel, Jacek Kitowski

Physics-based machine learning blends traditional science with modern data-driven techniques. Rather than relying exclusively on empirical data or predefined equations, this methodology embeds domain knowledge directly into the learning process, resulting in models that are both more accurate and robust. We leverage this paradigm to accelerate simulations of

Luigi Ruggiero, Carlo Ciacca, Pauline Drexler, Vera Jo Weibel

Superconducting resonators integrated with Ge quantum wells (QWs) offer a promising platform for hybrid quantum devices. Yet, in the most common heterostructure architectures, they have so far been limited by sizable photon losses. Here, we report the fabrication and characterization of microwave resonators patterned in the Al thin film of an in-situ grown s

Kazuaki Tokuyama, Souta Miyamoto, Taichi Masuda, Katsuaki Tanabe

We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures thro

David Santiago Quevedo, Monica Conte, Marjolein Dijkstra, Cristiane Morais Smith

Many active particles are embedded in environments that exhibit viscoelastic properties. An important class of such media lacks a single characteristic relaxation timescale when subjected to a time-dependent stress. Rather, the stress response spans a broad continuum of timescales, a behavior naturally described by a scale-free, fractal-like power-law relaxa

Marcel Krumrein, Julian M. Bopp, Timo Steidl, Wolfgang Knolle

Spin-active colour centres in 4H silicon carbide are promising candidates as building blocks for quantum information applications. To increase the photon count rate of the emitters at low temperatures, the colour centres must be integrated into nanophotonic structures and characterised under cryogenic conditions. Here, we design and fabricate waveguide struc

Fazlollah Hajkarim, Graham White, Yang Xiao

The gravitational wave spectrum arising from using the full velocity profile is well known to differ qualitatively from analytic fits to a broken power law. Former studies have shown that unlike the uncertainties arising from thermal field theory, more diligence in the hydrodynamics can sometimes have limited benefit. However, this was shown in the context o

Xuanchi Zhou, Xiaohui Yao, Shuang Li, Xiaomei Qiao

One focal challenge in engineering low-power and scalable all-oxide spintronic devices lies in exploring ferromagnetic oxide material with perpendicular magnetic anisotropy (PMA) and electronic conductivity while exhibiting tunable spin states. Targeting this need, spinel nickel cobaltite (NiCo2O4, NCO), featured by room-temperature ferrimagnetically metalli

Yuecheng Cai, Jasmin Jelovica

This study presents a computational framework for global local structural analysis of ship hull girders that integrates an equivalent single layer (ESL) model with a graph neural network (GNN). A coarse mesh homogenized ESL model efficiently predicts the global displacement field, from which degrees of freedom (DOFs) along stiffened panel boundaries are extr

Blessing Agyei Kyem, Joshua Kofi Asamoah, Anthony Dontoh, Andrews Danyo

Automated pavement defect detection often struggles to generalize across diverse real-world conditions due to the lack of standardized datasets. Existing datasets differ in annotation styles, distress type definitions, and formats, limiting their integration for unified training. To address this gap, we introduce a comprehensive benchmark dataset that consol

Brendan McBennett, Michael Tanksalvala, Emma E. Nelson, Theodore H. Culman

Dynamic scattering and imaging with coherent, ultrafast, extreme ultraviolet (EUV) light sources can resolve charge, phonon and spin processes on their intrinsic length and time scales. However, full field coherent diffraction imaging requires scanning of the sample combined with computational phase retrieval, making it challenging to quickly acquire a large

Penka Mayster, Assen Tchorbadjieff

Special functions have always played a central role in physics and in mathematics, arising as solutions of nonlinear differential equations, as well as in the theory of branching processes, which extensively uses probability generating functions. The theory of iteration of real functions leads to limit theorems for the discrete-time and real-time Markov bran

E. T. Kokkinakis, I. Komis, K. G. Makris, E. N. Economou

Within the framework of non-Hermitian photonics, we investigate the spectral and dynamical properties of one- and two-dimensional non-Hermitian off-diagonal disordered optical lattices, where randomness is applied to the couplings rather than to the on-site potential terms. We analyze eigenvalue distributions and the localization properties of the eigenmodes

Guocheng Zhen, Yu-Ao Chen, Mingrui Jing, Jingu Xie

Reversing unitary operations is a key task in quantum computing and quantum control. In this work, we introduce and develop the framework of shadow unitary inversion, a relaxed variant of unitary inversion in which the goal is to reproduce the action of the inverse unitary only at the level of the expectation value of a fixed observable. This task captures a

Techapon Kampu, Salvatore De Vincenzo

Real solutions of the 1D Klein-Fock-Gordon (KFG) equation automatically cancel out the usual two-vector current density; consequently, the respective continuity equation is trivially satisfied, and a globally conserved quantity cannot be obtained. Additionally, distinguishing between impermeable and permeable boundary conditions (BCs) at a given point is not

Jiayu Ding, Benjamin Seleb, Heather J. Huson, Saad Bhamla

Quadrupedal animals employ diverse galloping strategies to optimize speed, stability, and energy efficiency. However, the biomechanical mechanisms that enable adaptive gait transitions during high-speed locomotion under load remain poorly understood. In this study, we present new empirical and modeling insights into the biomechanics of load-pulling quadruped

Wan Keng Cheong, Ngau Lam

Let $\mathfrak{g}$ denote the classical Lie algebra $\mathfrak{gl}_d$, $\mathfrak{sp}_{2d}$, or $\mathfrak{so}_{2d}$ with a fixed $*$-structure $σ$. Let $M_1, \ldots, M_\ell$ be unitarizable $\mathfrak{g}$-modules (with respect to $σ$), and let ${\bf z}=(z_1, \ldots, z_\ell) \in \mathbb{C}^\ell$. We investigate the action of the Bethe algebra $\mathcal{B}_{\

Gina Wong, Drew Prinster, Suchi Saria, Rama Chellappa

Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets. For complex scenarios involving multiple trials, models, or data sources, conformal prediction sets can be aggregated to create a prediction set that captures the overall uncertainty, often improving precision. However, aggre

Alberto Carta, Iurii Timrov, Sophie Beck, Claude Ederer

The predictive accuracy of popular extensions to density-functional theory (DFT) such as DFT+U and DFT plus dynamical mean-field theory (DFT+DMFT) hinges on using realistic values for the screened Coulomb interaction U. Here, we present a systematic comparison of the two most widely used approaches to compute this parameter, i.e. linear response theory (LRT)

Wei Xie, Viet Ha Hoang, Yin Yang, Yunqing Huang

A recently developed upscaling technique, the multicontinuum homogenization method, has gained significant attention for its effectiveness in modeling complex multiscale systems. This method defines multiple continua based on distinct physical properties and solves a series of constrained cell problems to capture localized information for each continuum. How

Chen Shang, Jiadong Yu, Dinh Thai Hoang

The integration of immersive communication into a human-centric ecosystem has intensified the demand for sophisticated Human Digital Twins (HDTs) driven by multifaceted human data. However, the effective construction of HDTs faces significant challenges due to the heterogeneity of data collection devices, the high energy demands associated with processing in

Andrea Jiménez, Kolja Knauer, Carla Negri Lintzmayer, Martín Matamala

The proper conflict-free chromatic number, $χ_{pcf}(G)$, of a graph $G$ is the least $k$ such that $G$ has a proper $k$-coloring in which for each non-isolated vertex there is a color appearing exactly once among its neighbors. The proper odd chromatic number, $χ_{o}(G)$, of $G$ is the least $k$ such that $G$ has a proper coloring in which for every non-isol

Suguru Shiratori, Elham Kiyani, Khemraj Shukla, George Em Karniadakis

We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are measured, our approach infers unknown closure terms in the governing equations as neural networks. The target to be discovered is the constitutive relations only, while the tempora

Samruddhi Baviskar

Machine learning models used in financial decision systems operate in nonstationary economic environments, yet adversarial robustness is typically evaluated under static assumptions. This work introduces Conditional Adversarial Fragility, a regime dependent phenomenon in which adversarial vulnerability is systematically amplified during periods of macroecono

Wentao Wu, Xiao Wang, Chenglong Li, Jin Tang

Vehicle-centric perception plays a crucial role in many intelligent systems, including large-scale surveillance systems, intelligent transportation, and autonomous driving. Existing approaches lack effective learning of vehicle-related knowledge during pre-training, resulting in poor capability for modeling general vehicle perception representations. To hand

Zhixiang Lu, Xueyuan Deng, Yiran Liu, Yulong Li

Traditional agent-based models (ABMs) of opinion dynamics often fail to capture the psychological heterogeneity driving online polarization due to simplistic homogeneity assumptions. This limitation obscures the critical interplay between individual cognitive biases and information propagation, thereby hindering a mechanistic understanding of how ideological

Chandra Sekhar Kubam

Significant digitalization of financial services in a short period of time has led to an urgent demand to have autonomous, transparent and real-time credit risk decision making systems. The traditional machine learning models are effective in pattern recognition, but do not have the adaptive reasoning, situational awareness, and autonomy needed in modern fin

Imane Jarni, Ayoub Laayoun, Badr Missaoui

In this work, we investigate the multidimensional Skorokhod problem for c\`adl\`ag processes, where the reflection is subject to a minimality condition depending on the law of the solution. We then apply these results to establish existence and uniqueness for multidimensional McKean-Vlasov stochastic differential equations with mean reflection. Finally, we a

Manas Ranjan Sahu, Suraj Thapa Magar, Yadav Prasad Kandel, John M. Nichol

Hybrid quantum devices using surface acoustic waves show promise as key elements of quantum information processors. We report measurements of integrated flip-chip devices consisting of semiconductor quantum dots and surface acoustic wave resonators in lithium niobate. We observed that the pyroelectric effect in lithium niobate inhibited the operation of quan

Artur Apresyan, Shuoxing Wu, Si Xie, Cristián Peña

We present the optimization and performance characterization of the second-generation Fermilab Constant Fraction Discriminator ASIC (FCFD), designed for the readout of AC-coupled low-gain avalanche detector (LGAD) strip-sensors. The FCFD is explicitly engineered to be insensitive to signal-amplitude variations, thereby removing the need for time-walk correct

S. Mazdak Abulnaga, Andrew Hoopes, Malte Hoffmann, Robin Magnet

Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the interior volume. Traditional methods treat volumetric and surface-based registration separately, which often leads to inconsistencies that limit downstream analyses. We propose a

Gnankan Landry Regis N'guessan

Standard neural network architectures employ fixed activation functions (ReLU, tanh, sigmoid) that are poorly suited for approximating functions with singular or fractional power behavior, a structure that arises ubiquitously in physics, including boundary layers, fracture mechanics, and corner singularities. We introduce M\"untz-Sz\'asz Networks (MSN), a no

Alexey Yermakov, Yue Zhao, Marine Denolle, Yiyu Ni

Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variability of source locations, mechanisms, and Earth models (e.g., subsurface structure and topography effects). Addressing these with simulations is hindered by their massive scale, both

Caleb A. Shaw, Dmitriy Y. Anistratov

This paper presents a new Monte Carlo (MC) algorithm for time-dependent particle transport problems with global variance reduction based on automatic weight windows (WWs). The centers of WWs at a time step are defined by the solution of an auxiliary hybrid MC / deterministic problem formed by the low-order second-moment (LOSM) equations. The closures for the

Taiga Kanehira, Peter K. Stansby, Benedict D. Rogers, Mark McAllister

Axisymmetric waves occur across a wide range of scales. This study analyses large-scale gravity-dominated axisymmetric waves, with jet heights of up to 6 m, for which surface-tension effects are negligible. The Bond number is O(10^5) and the Weber number ranges from O(10^4) to O(10^6). Our aim is to clarify the dynamics of highly nonlinear axisymmetric jet f

Jesse Zhang, Aldo Antognini, Marek Bartkowiak, Klaus Kirch

The universality of free fall, a cornerstone of Einstein's theory of gravity, has so far only been tested with neutral composite states of first-generation Standard Model (SM) particles, such as atoms or neutrons, and, most recently, antihydrogen. Extending these gravitational measurements to other sectors of the SM requires the formation of neutral bound st

Ryusuke Hamazaki, Ken Mochizuki, Hisanori Oshima, Yohei Fuji

Thanks to recent experimental advances in simulating and detecting quantum dynamics with high precision and controllability, our understanding of the physics of monitored quantum systems has considerably deepened over the past decades. In this article, we provide an introductory theoretical review on the basic formalisms governing open quantum dynamics under

María Isabel Cortez, Jamal Drewlo, Jaime Gómez, Tobias Jäger

We show a one-to-one correspondence between Toeplitz arrays over residually finite topological groups and model sets obtained via specific cut and project schemes, built from the odometer associated to the Toeplitz array. As an application, we construct irregular Toeplitz arrays which are extensions of maximal rank $k$ over their maximal equicontinuous facto

Jiayun Wu, Jiashuo Liu, Zhiyuan Zeng, Tianyang Zhan

LLM deployment in critical domains is currently impeded by persistent hallucinations--generating plausible but factually incorrect assertions. While scaling laws drove significant improvements in general capabilities, theoretical frameworks suggest hallucination is not merely stochastic error but a predictable statistical consequence of training objectives p

José Diogo Da Costa Jesus, Boxi Li, Yuan Gao, Rami Barends

Achieving very fast gates that undercut the natural limits set by decoherence requires going into the strong driving limit. Realizing single-qubit control predicted beyond semi-classical, time-dependent modeling has yet to be experimentally realized on superconducting and most other computing platforms. In this regime, the common model of dynamics within a t

Houston H. Zhang, Tao Zhang, Baoze Lin, Yuanqi Xue

User interface to code (UI2Code) aims to generate executable code that can faithfully reconstruct a given input UI. Prior work focuses largely on web pages and mobile screens, leaving app widgets underexplored. Unlike web or mobile UIs with rich hierarchical context, widgets are compact, context-free micro-interfaces that summarize key information through de

Anastasia Romashkina, Omer Yesilurt, Vahagn Mkhitaryan, Owen Matthiessen

Chiral nanophotonic structures have garnered considerable interest in recent years due to their potential to enhance the efficacy of chirality-sensitive biomolecular detection. Designing metaplatforms to enhance chiroptical signals under linearly polarized excitation is particularly appealing due to the minimal chiral background and the ease of controlling e

Sajid Sekh, Andrzej Ptok, Wojciech Brzezicki, Przemysław Piekarz

The hole-doped NdNiO$_2$ layer deposited on the SrTiO$_{3}$ surface exhibits unconventional superconductivity. Here, we present a systematic study of the electronic and magnetic properties of the NdNiO$_2$ superconductor using the density functional theory (DFT). The strong local Coulomb interactions in the Ni($3d)$ and Nd($4f$) states are included within th

Sujan Warnakulasooriya, Andreas Willig, Xiaobing Wu

Drone applications continue to expand across various domains, with flocking offering enhanced cooperative capabilities but introducing significant challenges during initial formation. Existing flocking algorithms often struggle with efficiency and scalability, particularly when potential collisions force drones into suboptimal trajectories. This paper presen

Matthew Drnevich, Stephen Jiggins, Kyle Cranmer

We consider a generalization of the classifier-based density-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The problem with most loss functions used for this task is that they implicitly define a relationship between the optimal classifier and the target quasiprobabilistic density ratio which is discontinu

Nobuyoshi Komatsu

We phenomenologically derive a cosmological model that includes both a cosmological constant term $\Lambda/3$ and a dissipative driving term $\beta (2 H^{2} + \dot{H})$ by applying both the first law of thermodynamics and an effective entropy (that is proportional to the Bekenstein--Hawking entropy) to matter creation cosmology. Here $H$, $\dot{H}$, and $\be

Maxime Lacour, Pu Ren, Rie Nakata, Nori Nakata

Recent developments in non-ergodic ground-motion models (GMMs) explicitly model systematic spatial variations in source, site, and path effects, reducing standard deviation to 30-40% of ergodic models and enabling more accurate site-specific seismic hazard analysis. Current non-ergodic GMMs rely on Gaussian Process (GP) methods with prescribed correlation fu

Jingyi Qiu, Hong Chen, Zongyi Li

The rise of AI has fueled growing concerns about ``hype'' in machine learning papers, yet a reliable way to quantify rhetorical style independently of substantive content has remained elusive. Because bold language can stem from either strong empirical results or mere rhetorical style, it is often difficult to distinguish between the two. To disentangle rhet

Stefano Grassi

Autonomous multi-agent systems are fundamentally fragile: they struggle to solve the Hayekian Information problem (eliciting dispersed private knowledge) and the Hurwiczian Incentive problem (aligning local actions with global objectives), making coordination computationally intractable. I introduce Mechanism-Based Intelligence (MBI), a paradigm that reconce

Gábor Kasza, János Takátsy, György Wolf

At present, the only experimental access to the properties of cold, dense strongly interacting matter is provided by astrophysical observations. Neutron stars are the only known systems in the Universe that reach densities several times higher than normal nuclear density at nearly zero temperature, making them unique laboratories for studying dense matter. S

Mazharul Islam Mondal, Issam Mahraj, Milo Sprague, Sabin Regmi

Antiferromagnetic EuM$_{2}$Pn$_{2}$ compounds, where M is a metal element and Pn is a pnictogen element, have been recognized as candidates for realizing a topologically nontrivial electronic structure. In this paper, we focus on EuMg$_2$Bi$_2$, whose topological nature still remains unclear. We present a comprehensive study based on several experimental and

Indranil Halder, Cengiz Pehlevan

Recent developments in large language models have shown advantages in reallocating a notable share of computational resource from training time to inference time. However, the principles behind inference time scaling are not well understood. In this paper, we introduce an analytically tractable model of inference-time scaling: Bayesian linear regression with

Eloïse Rahier, Sebastian Koelling, Guillaume Nadal, Sudarshan Singh

The complexity and richness of phenomena governing alloy crystal growth can be unraveled by examining the three-dimensional atomic-level distribution of elements and impurities incorporated during growth. These species act as atomic fingerprints, revealing the thermodynamic constraints that shape material structure and composition. Herein, we combine transmi

Kirk Vanacore, Rene F. Kizilcec

Large language models (LLMs) are increasingly adopted in educational technologies for a variety of tasks, from generating instructional materials and assisting with assessment design to tutoring. While prior work has investigated how models can be adapted or optimized for specific tasks, far less is known about how well LLMs perform at interpreting authentic

Maélie Caussé, Kieran Bozier, Peter I. C. Cooke, Stefano Racioppi

The discovery of high-temperature superconductors remains a central challenge in materials science. Hydrogen-rich compounds are among the most promising candidates, as they can exhibit phonon-mediated superconductivity at elevated critical temperatures, though their stabilization typically requires extreme pressures. % Here, we report the identification of Y

Tatsuki Oikawa, Kota Ido, Takahiro Misawa, Takashi Koretsune

We present a semi-automated method for obtaining an initial estimate of Wannier functions, designed to facilitate the construction of Wannier functions for describing low-energy effective models of solids, particularly those relevant to strongly correlated electron systems. Our approach automatically determines the hydrogenic projections orbitals and the cen

Paúl Cumba-Armijos, Diego Riofrío-Luzcando, Verónica Rodríguez-Arboleda, Joe Carrión-Jumbo

Recent recollected data suggests that it is possible to automatically detect events that may negatively affect the most vulnerable parts of our society, by using any communication technology like social networks or messaging applications. This research consolidates and prepares a corpus with Spanish bullying expressions taken from Twitter in order to use the

Hanna Noyce, Emily Olejniczak, Vaskar Raychoudhury, Roger O. Smith

Community participation is an important aspect of an individuals physical and mental well-being. This participation is often limited for persons with disabilities, especially those with ambulatory impairments due to the inability to optimally navigate the community. Accessibility is a multi-faceted problem and varies from person to person. Moreover, it depen

Yuan Tian

We investigate the size of the convoy in the speed process in the multi-species asymmetric simple exclusion process (ASEP). Through a coupling argument, we obtain an exact formula for the expected convoy size by relating it to a combinatorial structure. We prove that the asymptotic expected convoy size is universal for all fixed jump rates $q \in [0,1)$. In

E. Vitral, J. A. Hanna, L. Koens

We present a new one-dimensional model for elastic strips based on a nondevelopable ruled surface. An auxiliary field regularizes the Sadowsky narrow-strip model to allow nonzero twist with vanishing curvature. The energy exhibits the scalings derived by Freddi and co-workers, and for a certain choice of parameter, convexifies the Sadowsky energy without pat

Kostas Karagiannis, Aristides Kontogeorgis, Konstantia Manousou Sotiropoulou

This paper investigates the representation-theoretic structure of the Koszul cohomology of a smooth projective variety $X$ over an algebraically closed field $k$, admitting an action of a finite group $G$ of order coprime to ${\rm char}(k)$. Properties of $G$-equivariant functors are employed to show that the associated Koszul complex is a complex of $kG$-mo

Tanja Eisner

We show that, on a standard non-atomic probability space, invertible measure-preserving transformations form a dense $G_\delta$ subset of the space of all measure-preserving transformations endowed with the strong (=weak) operator topology. This implies that all properties which are generic for invertible transformations are also generic for general ones. We