October 2025 arXiv papers — page 117

Theo Chinn, Junshu Feng, Stephan Ramon Garcia, Peiting Jiang

We introduce a module-theoretic approach and a linear-programming method to compute the matricial dimension of numerical semigroups. We use these to compute the matricial dimension of every numerical semigroup with Frobenius number at most $10$ or genus at most $6$. Many of these evaluations were beyond the scope of previous techniques.

Daniela Vega, Hannah V. Ceballos, Javier S. Vera, Santiago Rodriguez

Prenatal diagnosis of Congenital Heart Diseases (CHDs) holds great potential for Artificial Intelligence (AI)-driven solutions. However, collecting high-quality diagnostic data remains difficult due to the rarity of these conditions, resulting in imbalanced and low-quality datasets that hinder model performance. Moreover, no public efforts have been made to

Harry Zhang, Luca Carlone

Understanding how humans interact with the surrounding environment, and specifically reasoning about object interactions and affordances, is a critical challenge in computer vision, robotics, and AI. Current approaches often depend on labor-intensive, hand-labeled datasets capturing real-world or simulated human-object interaction (HOI) tasks, which are cost

Umar Farooq, Krishna Upadhyay

Quantum computing education requires students to move beyond classical programming intuitions related to state, determinism, and debugging, and to develop reasoning skills grounded in probability, measurement, and interference. This paper reports on the design and delivery of a combined undergraduate and graduate course at Louisiana State University that emp

Marcelo F. Ciappina

High-order harmonic generation (HHG) in solids has emerged as a versatile platform for exploring ultrafast and quantum-coherent phenomena in condensed matter. Recent advances reveal Berry-phase and topological effects in harmonic emission, strong-field control of excitons and lattice motion, the generation of nonclassical light states driven by quantum and s

David Lima, J. M. Viana Parente Lopes, Jose Matos, Joao Penedones

We use effective string theory (EST) to describe a toroidal 2d domain wall embedded in a 3d torus. In particular, we compute the free energy of the domain wall in an expansion in inverse powers of the area, up to the second non-universal order that involves the Wilson coefficient $\gamma_3$. In order to test our predictions, we simulate the 3d Ising model wi

Shaw Dalen

Prediction markets, such as Polymarket, aggregate dispersed information into tradable probabilities, but they still lack a unifying stochastic kernel comparable to the one options gained from Black-Scholes. As these markets scale with institutional participation, exchange integrations, and higher volumes around elections and macro prints, market makers face

Alexander Stark, Nathan Meier, Jeffrey Hatch, Joshua Kammeraad

Slater basis functions have desirable properties that can improve electronic structure simulations, but improved numerical integration methods are needed. This work builds upon the SlaterGPU library for evaluation of Hamiltonian matrix elements in the resolution-of-the-identity approximation. In particular, a Prolate Spheroidal grid will provide sufficient i

Denis Janiak, Jakub Binkowski, Tomasz Kajdanowicz

Out-of-distribution (OOD) detection is critical for reliable deployment of vision models. Mahalanobis-based detectors remain strong baselines, yet their performance varies widely across modern pretrained representations, and it is unclear which properties of a feature space cause these methods to succeed or fail. We conduct a large-scale study across diverse

Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare, Rishikesh Ranade

Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and time-consuming. This work presents an exploratory comparative study on developing machine learning-based surrogate models for efficient prediction of structural deformation in cras

Fasheng Xu, Xiaoyu Wang, Wei Chen, Karen Xie

The strategic choice of model "openness" has become a defining issue for the foundation model (FM) ecosystem. While this choice is intensely debated, its underlying economic drivers remain underexplored. We construct a two-period game-theoretic model to analyze how openness shapes competition in an AI value chain, featuring an incumbent developer, a downstre

Sayantan Acharya, Abbas Khosravi, Douglas Creighton, Roohallah Alizadehsani

In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightweight dynamic brain connectivity framework based on Time Varying Directed Transfer Function is proposed, where TV DTF features were validated through ML based stress classification

Arielle Sanford, Shuo Sun, Christian B. Mendl

Recent advances in protein structure prediction, such as AlphaFold, have demonstrated the power of deep neural architectures like the Evoformer for capturing complex spatial and evolutionary constraints on protein conformation. However, the depth of the Evoformer, comprising 48 stacked blocks, introduces high computational costs and rigid layerwise discretiz

Yuji Nakatsukasa, Lloyd N. Trefethen

The AAA algorithm for rational approximation is employed to illustrate applications of rational functions all across numerical analysis.

G. G. Luciano, A. Paliathanasis, A. Sheykhi

We explore the cosmological consequences of a modified cosmology inspired by string T-duality. We incorporate the zero-point length correction, $l_0$, into the gravitational potential and derive the modified Friedmann equations via thermodynamic approach at the apparent horizon of a Friedmann-Robertson-Walker (FRW) universe. The resulting framework introduce

Sarah Chehade, Andrea Delgado, Elaine Wong

Non-local games (NLGs) provide a versatile framework for probing quantum correlations and for benchmarking the power of entanglement. In finite dimensions, the standard method for playing several games in parallel requires a tensor product of the local Hilbert spaces, which scales additively in the number of qubits. In this work, we show that this additive c

Felix Kogel, Tatsam Garg, Phillip Groß, Lukas Leczek

Optical Bloch equations and rate equations serve as powerful tools to model light-matter interactions from textbook-like two-level atoms to the complex internal dynamics of molecules. A particular challenge in this context is posed by molecular laser cooling, where many dozens or hundreds of levels need to be taken into account for a comprehensive modeling.

Prasiddha Arunachalam, Phillip Macias, Ryan. J. Foley

We present a new analysis of the JWST infrared spectra of GRB 230307A (AT 2023vfi), a long gamma-ray burst (GRB) with an infrared excess and spectral lines suggestive of significant heavy $r$-process production. The spectra, taken 29 and 61~days after the GRB trigger, have blackbody-like continua with $T_{\rm eff} \approx 550$ K and an emission line near $2.

Kivanc Dogan, Ahmet Orhan

The demand for high efficiency and precise control in electric drive systems has led to the widespread adoption of Interior Permanent Magnet Synchronous Motors (IPMSMs). The performance of these motors is significantly influenced by rotor geometry. Traditionally, rotor shape analysis has been conducted using the finite element method (FEM), which involves hi

S. Kodate, Y. Hayashi, S. Kajita

This study performed tungsten (W) co-deposition experiments using pulsed laser deposition under exposure to helium/argon plasma in a linear plasma device. Micron-sized spherical W particles formed under co-deposition conditions. It was suggested that these particles grew from nanoparticles via the electrostatic collection of W ions in the plasma plume before

A. Rothstein, R. J. Dolleman, L. Klebl, A. Achtermann

We report low-temperature measurements of two adjacent, gate-defined Josephson junctions (JJs) in magic-angle twisted bilayer graphene (MATBG) at a moiré filling factor near $ν= -2$. We show that both junctions exhibit a prominent, gate-tunable Josephson diode effect, which we explain by a combination of large kinetic inductance and non-uniform supercurrent

Jinghao Huang, Yaxiong Chen, Ganchao Liu

With the advancement of drone technology, the volume of video data increases rapidly, creating an urgent need for efficient semantic retrieval. We are the first to systematically propose and study the drone video-text retrieval (DVTR) task. Drone videos feature overhead perspectives, strong structural homogeneity, and diverse semantic expressions of target c

Raghvender Raghvender, Assil Bouzid, Evgenii M. Roginskii, David Hamani

This paper reports on a systematic first-principles molecular dynamics investigation of binary (TlO$_{0.5}$)$_{y}$-(TeO$_2$)$_{1-y}$ and ternary $(TiO$_{2}$)$_{x}$-(TlO$_{0.5}$)$_{y}$-(TeO$_2$)_{1-x-y}$ tellurite glasses. The obtained structural models are validated against available measured X-ray pair distribution functions. In the binary system, increasin

Hyun Tae Jung, Hyungjun Kim, Alejandro L. Garcia, Andrew J. Nonaka

For a gas-solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir adsorption model with compressible fluctuating hydrodynamics. To this end, we derive a thermodynamically consistent mass-

Jeong-Hoon Ju, Susana Lopez-Moreno

In this paper we study the computation of both algebraic and non-algebraic tensor functions under the tensor-tensor multiplication with linear maps. In the case of algebraic tensor functions, we prove that the asymptotic exponent of both the tensor-tensor multiplication and the tensor polynomial evaluation problem under this multiplication is the same as tha

Clayton Shonkwiler, Kandin Theis

We present an algorithm for sampling tightly confined random equilateral closed polygons in three-space which has runtime linear in the number of edges. Using symplectic geometry, sampling such polygons reduces to sampling a moment polytope, and in our confinement model this polytope turns out to be very natural from a combinatorial point of view. This conne

Borna Monazzah Moghaddam, Robin Chhabra

This article presents an extension of the Lagrange-Poincare Equations (LPE) to model the dynamics of spacecraft-manipulator systems operating within a non-inertial orbital reference frame. Building upon prior formulations of LPE for vehicle-manipulator systems, the proposed framework, termed the Lagrange-Poincare-Kepler Equations (LPKE), incorporates the cou

Chao Tang, Arwa Dabbech, Adrian Jackson, Yves Wiaux

The next-generation radio-interferometric (RI) telescopes require imaging algorithms capable of forming high-resolution high-dynamic-range images from large data volumes spanning wide frequency bands. Recently, AIRI, a plug-and-play (PnP) approach taking the forward-backward algorithmic structure (FB), has demonstrated state-of-the-art performance in monochr

Novel Kumar Dey, Yan Wu

In this paper, we investigate the stability of a triangularly coupled triple loop thermosyphon system with momentum and heat exchange at the coupling point as well as the existence of disturbances. The controller consists of a single, local state feedback. From the stability analysis, we obtain explicit bounds on the feedback gains, which depend on the Rayle

Johannes Anschütz, Guido Bosco, Arthur-César Le Bras, Juan Esteban Rodríguez Camargo

We define and initiate the study of analytic de Rham stacks of relative Fargues-Fontaine curves. To this end, we develop a theory of analytic de Rham stacks with sufficiently strong descent and approximation properties. Specializing to the de Rham stack of the Fargues-Fontaine curve attached to $\mathbb{C}_p$, we apply the general theory to obtain a new geom

Nishant Mehrotra, Sandesh Rao Mattu, Robert Calderbank

Zak-OTFS provides a framework for integrated sensing & communication (ISAC) in high delay and Doppler spread environments. Pulse shaping filter design enables joint optimization of sensing and communication performance. For sensing, a localized pulse shaping filter enables input-output (I/O) relation estimates close to the physical scattering channel. For co

Tianchen Zhao, Xuanbai Chen, Zhihua Li, Jun Fang

Recent generative data augmentation methods conditioned on both image and text prompts struggle to balance between fidelity and diversity, as it is challenging to preserve essential image details while aligning with varied text prompts. This challenge arises because representations in the synthesis process often become entangled with non-essential input imag

Abishek Rajan

We consider the space of smooth gradient expanding Ricci soliton structures on $S^1 \times \mathbb{R}^3$ and $S^2 \times \mathbb{R}^2$ which are invariant under the action of $\text{SO}(3) \times \text{SO}(2)$. In the case of each topology, there exists a $2$-parameter family of cohomogeneity one solitons asymptotic to cones over the link $S^2 \times S^1$, a

Junlin Wu, Xianrui Zhong, Jiashuo Sun, Bolian Li

Large language models (LLMs) have demonstrated remarkable advances in reasoning capabilities. However, their performance remains constrained by limited access to explicit and structured domain knowledge. Retrieval-Augmented Generation (RAG) addresses this by incorporating external information as context to augment reasoning. Nevertheless, traditional RAG sys

Oumaima Barhoumi, Ghazal Farhani, Taufiq Rahman, Mohamed H. Zaki

As connected and autonomous vehicles become more widespread, platooning has emerged as a key strategy to improve road capacity, reduce fuel consumption, and enhance traffic flow. However, the benefits of platoons strongly depend on their ability to maintain stability. Instability can lead to unsafe spacing and increased energy usage. In this work, we study p

Xiangyu Chen, Chuhao Zhou, Yuxi Liu, Jianfei Yang

Precise robot manipulation is critical for fine-grained applications such as chemical and biological experiments, where even small errors (e.g., reagent spillage) can invalidate an entire task. Existing approaches often rely on pre-collected expert demonstrations and train policies via imitation learning (IL) or offline reinforcement learning (RL). However,

Ahmed Aly, Essam Mansour, Amr Youssef

Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these logs as connected entities and events, revealing relationships that are missed by linear log representations. Existing systems apply anomaly detection to these graphs but often suffer from high false positive rates

Cameron Cianci, Luchang Jin, Joshua Swaim

We use lattice field theory to study the finite-volume energy spectrum of the $\pi\pi$ system in $SU(2)$ chiral effective field theory (ChEFT) at leading order in the chiral expansion. \hl{This finite-volume spectrum can be directly related to the (infinite-volume) $\pi\pi$ scattering phase shifts by L\"uscher's formula.} We compare our results to the finite

Gurusha Juneja, Jayanth Naga Sai Pasupulati, Alon Albalak, Wenyue Hua

A core challenge for autonomous LLM agents in collaborative settings is balancing robust privacy understanding and preservation alongside task efficacy. Existing privacy benchmarks only focus on simplistic, single-turn interactions where private information can be trivially omitted without affecting task outcomes. In this paper, we introduce MAGPIE (Multi-AG

Chun Wang

In this paper, we investigate new relationships for bilateral series related to two-parameter mock theta functions, which lead to many identities concerning the bilateral mock theta functions. In addition, interesting relations between the classical mock theta functions and the bilateral series are also concluded.

Livia Correa McCormack, Lei Tang, Mathieu Francoeur

The enhancement and attenuation of near-field radiative heat transfer between polaritonic SiC, SiN and SiO2 subwavelength membranes is analyzed. Fluctuational electrodynamics simulations combined with a modal analysis show that all membranes support corner and edge modes, which can induce a large 5.1-fold enhancement for SiC and a 2.1-fold attenuation for Si

Vicente Vergara

We construct a dyadic microlocal partition adapted to a position-dependent fiber metric on phase space and quantify its interaction with Weyl quantization. Under uniform ellipticity, the normalized fiber variable $ζ=T_xξ$ is uniformly comparable with the Euclidean frequency, so the construction does not introduce a new global symbolic order. The essential fe

Chen Yang, Faranak Bahrami, Guangming Cheng, Mayer Feldman

Utilizing tantalum (Ta) in superconducting circuits has led to significant improvements, such as high qubit lifetimes and quality factors in both qubits and resonators, underscoring the importance of material optimization in quantum device performance. In this work, we explore superconducting gap engineering in Ta-based devices as a strategy to expand the ra

Štěpán Marek, Wulf Wulfhekel, Ferdinand Evers, Richard Korytár

The generation of unidirectional motion has been a long-standing challenge in engineering of molecular motors. Here, a mechanism driving the rotation is presented based on electron current through helical orbitals on a $\pi$-bonded carbon chain. Such electron current through helical orbitals has been shown to be circulating around the carbon chain. It is nat

Sophie McKenzie, Jeb Webb, Robin Doss

Educating children and young people to be safe online is essential, especially as the metaverse, a next-generation internet blending immersive technologies, promises to reshape their interactions and amplify their experiences. While virtual reality offers fully immersive, highly interactive, and multi-sensory engagement, it also heightens cyber harm risks fo

Ben Wilks, Michael H. Meylan, Zachary J. Wegert, Vivien J. Challis

This paper considers the problem of water wave scattering by a rectangular anisotropic elastic plate mounted on the ocean surface, with either free, clamped or simply-supported edges. The problem is obtained as an expansion over the dry modes of the elastic plate, which are computed using a Rayleigh--Ritz method. In turn, the component diffraction and radiat

Shuo Sun, Meiling Zhou, Chen Zhao, Joyce H. Keyak

Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such as the DXA T-score and FRAX often lack sensitivity and miss individuals at high risk, particularly those without prior fractures or with osteopenia. To address this limitation, we

Brysen Pfingsten, Jason Hemann

We present a deterministic small-step operational semantics for miniKanren that explicitly represents the evolving search tree during execution. This semantics models interleaving and goal scheduling at fine granularity, allowing each evaluation step-goal activation, suspension, resumption, and success -- to be visualized precisely. Building on this model, w

Conor Rowan

Geodesic problems involve computing trajectories between prescribed initial and final states to minimize a user-defined measure of distance, cost, or energy. They arise throughout physics and engineering -- for instance, in determining optimal paths through complex environments, modeling light propagation in refractive media, and the study of spacetime traje

S. R. Fenley, R. Potrie

We give a complete topological classification of (chain-)transitive partially hyperbolic diffeomorphisms in 3-manifolds in terms of Anosov flows, completing a program proposed by Pujals. In particular, this also allows to give a full answer to the ergodicity conjecture of Hertz-Hertz-Ures for partially hyperbolic diffeomorphisms in dimension 3. This is achie

Lionel E. Martínez, Ignacio García-Mata, Diego A. Wisniacki

We show that chaos-assisted tunneling (CAT) imposes an intrinsic limit to the protection of Kerr-cat qubits. In the static effective description, tunneling between the quasidegenerate cat states can be exponentially suppressed, ensuring long lifetimes. However, our Floquet analysis reveals that when the nonlinearities increase, chaotic states mediate tunneli

Niclas Göring, Chris Mingard, Yoonsoo Nam, Ard Louis

Feature learning (FL), where neural networks adapt their internal representations during training, remains poorly understood. Using methods from statistical physics, we derive a tractable, self-consistent mean-field (MF) theory for the Bayesian posterior of two-layer non-linear networks trained with stochastic gradient Langevin dynamics (SGLD). At infinite w

Ynes Ineza, Muhammad A. Ullah, Abdul Serwadda, Aurore Munyaneza

Voice interfaces are increasingly used in high-stakes domains such as mobile banking, smart-home security, and hands-free healthcare. Meanwhile, modern generative models have made high-quality voice forgeries inexpensive and easy to create, eroding confidence in voice authentication alone. To strengthen protection against such attacks, we present a second au

Sergei M. Gorbunov

We obtain a Gessel-type expansion in Jack polynomials for the expectations of multiplicative functionals in the circular $\beta$-ensemble. As a consequence, we establish a Szeg\H{o}-type limit theorem for all $H^{1/2}(\mathbb{T})$ functions when $\beta \le 2$, together with an explicit rate of convergence for functions from $H^1(\mathbb{T})$. The estimate is

Kristina D Launey, Grigor H. Sargsyan, Alexis Mercenne, Jutta E. Escher

In this review, we discuss recent applications of the ab initio symmetry-adapted no-core shell-model (SA-NCSM) theory for study and prediction of structure and reactions of stable and unstable nuclei from light to medium mass range. We explore structure properties of neutron-rich He, Mg, and Li isotopes, with a focus on nuclear collectivity, clustering, and

Avik Mahata

We present a second-nearest-neighbor Modified Embedded Atom Method (2NN--MEAM) potential for Scandium (Sc) and Aluminum-Scandium (Al--Sc) alloys that unifies cohesive, thermodynamic, and solidification behavior within a single transferable framework. The Sc component accurately reproduces cohesive energy, lattice constants, defect energetics, and the experim

Arman Behnam, Binghui Wang

Causal representation learning in the anti-causal setting (labels cause features rather than the reverse) presents unique challenges requiring specialized approaches. We propose Anti-Causal Invariant Abstractions (ACIA), a novel measure-theoretic framework for anti-causal representation learning. ACIA employs a two-level design, low-level representations cap

Paul Mammen

Motivated by recent advances in Catalan combinatorics, we study special values of the standard trace on affine Hecke algebras. Starting from a generating function for this trace calculated by Opdam, we use the theory of Szenes and Vergne to obtain residue formulae for the trace. This allows us to derive a product formula for the trace of translation elements

Marc Harary

We present the first uniform XP exact algorithm for unconstrained binary optimization of quadratic, polynomial, fractional, and other objectives under a single parameter, the differentially affine (DA) rank $r$. An objective $f: \{0,1\}^n \to \mathbb{R}$ has DA rank $r$ if there is a feature map $\psi: \{0,1\}^n \to \mathbb{R}^r$ such that each coordinate fl

Nina Cao, Pavan Ravindra, Shubha R. Kharel, Chuntian Cao

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for x-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS

Masih Haseli, Igor Mezić, Jorge Cortés

The Koopman operator, originally defined for dynamical systems without input, has inspired many applications in control. Yet, the theoretical foundations underpinning this progress in control remain underdeveloped. This paper investigates the theoretical structure and connections between two extensions of Koopman theory to control: (i) Koopman operator via i

Md Kamrul Siam, Md Jobair Hossain Faruk, Jerry Q. Cheng, Huanying Gu

This study presents a novel multi-model fusion framework leveraging two state-of-the-art large language models (LLMs), ChatGPT and Claude, to enhance the reliability of chest X-ray interpretation on the CheXpert dataset. From the full CheXpert corpus of 224,316 chest radiographs, we randomly selected 234 radiologist-annotated studies to evaluate unimodal per

Xin Guo, Zijiu Lyu

This paper studies policy transfer, one of the well-known transfer learning techniques adopted in large language models, for continuous-time reinforcement learning problems. In the case of continuous-time linear-quadratic systems with Shannon's entropy regularization, we fully exploit the Gaussian structure of their optimal policy and the stability of their

Usman Afzaal, Ziyu Su, Usama Sajjad, Hao Lu

Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hyperparameter reporting. To investigate these issues, we trained a CLIP model on the QUILT-1M dataset and systematically evaluated the impact of different hyperparameter settings and

Anna I. Tóth

In cubic metals, a local, magnetic moment with a triplet ground state coupled to $\Gamma_8$ conduction electrons can give rise to various non-Fermi liquid (NFL) quantum critical behaviors. To date, only those exchange couplings have been studied that are spherically symmetric already in the high-temperature, local moment regime. Namely, only the effects of p

Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard

The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filtering towards image-text interleaved document data is under-explored. We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to Filter both high-quality

Paul Vollrath

We prove Papikian's conjecture on the spectrum of the signed up-down walk on the spherical building. Namely, we show that in the spherical building of dimension n-2 and thickness q + 1, the number of distinct eigenvalues is independent of q and for q going to infinity the positive eigenvalues converge to n-1, ... , n-i.

Muhammad Aurangzeb Ahmad

Artificial intelligence surrogates are systems designed to infer preferences when individuals lose decision-making capacity. Fairness in such systems is a domain that has been insufficiently explored. Traditional algorithmic fairness frameworks are insufficient for contexts where decisions are relational, existential, and culturally diverse. This paper explo

Samuel Lewis, Pavel Shlykov

This paper classifies all 4d Nakajima quiver varieties through a combinatorial approach. For each such variety, we describe the symplectic leaves and minimal degenerations between them. Using the resulting Hasse diagrams and secondary hyperplane arrangements, we fully classify the quiver varieties up to isomorphism, a step in the problem of classifying all 4

Kazuki Okigami, Satoru Hayami

We investigate the role of four-spin interactions in stabilizing exotic multiple-$Q$ topological spin textures and demonstrate their ability to realize a skyrmion crystal. While such higher-order interactions are known to be important, their intricate nature makes systematic model construction significantly challenging. To address this issue, we develop a th

Norman Zadeh

In 2011, Friedmann [F 7] claimed to have proved that pathological linear programs existed for which the Simplex method using Zadeh's least-entered rule [Z 14] would take an exponential number of pivots. In 2019, Disser and Hopp [DH 5] argued that there were errors in Friedmann's 2011 construction. In 2020, Disser, Friedmann, and Hopp [DFH 3,4] again contende

Gianluca Calcagni

We adapt the diffusion method employed in fundamentally nonlocal field theories to determine the number of initial conditions for the classicized dynamics of unitary field theories with fakeons, characterized by inverse powers of the d'Alembertian operator $\Box$. We show that this number is two and we recover all the results obtained with a direct calculati

Zitao Hu, Xue-Ning Bai, Xiaochen Sun

We present the formulation, algorithm and numerical tests of the magnetohydrodynamic-particle-in-cell (MHD-PIC) method with particles treated under the guiding center approximation, which we term the MHD-gPIC method, and it is implemented in the Athena++ MHD code. The new MHD-gPIC model consists of thermal (cold) fluid and high-energy particles whose dynamic

Dolores Lara, Christian Rubio-Montiel, Francisco Zaragoza

The pseudo-Grundy index of a graph is the largest number of colors that can be assigned to its edges, such that for every pair of colors $i,j$, if $i < j$ then every edge colored with color $j$ is adjacent to at least one edge colored with color $i$. This index has been widely studied. A geometric graph is a graph drawn in the plane such that its vertices ar

Maryna Kachanovska, Étienne Peillon

We study a limiting absorption principle for the boundary-value problem describing a hybrid plasma resonance, with a regular coefficient in the principal part of the operator that vanishes on a curve inside the domain and changes its sign across this curve. We prove the limiting absorption principle by establishing a priori bounds on the solution in certain

Wenxin Zhang, Yueying Li, Ciamac C. Moallemi, Tianyi Peng

Prompt caching is critical for reducing latency and cost in LLM inference: OpenAI and Anthropic report up to 50-90% cost savings through prompt reuse. Despite its widespread success, little is known about what constitutes an optimal prompt caching policy, particularly when optimizing tail latency, a metric of central importance to practitioners. The widely u

Bingxuan Liu, Yuxuan Shen, Yuanshunzi Sui

In this article, we investigate the possibility of enhancing the di-jet resonance searches by tagging the final state radiation (FSR) jet, using an event-level deep neural network. It is found that solely relying on the 4-momenta of the leading three jets allows the algorithm to achieve good discriminating power that can identify the hardest FSR jet in signa

Tina Gao, Shimiao Li, Lawrence Pileggi

Advances in leveraging Gaussian processes (GP) have enabled learning and inferring dynamic grid behavior from scarce PMU measurements. However, real measurements can be corrupted by various random and targeted threats, leading to inaccurate and meaningless results. This paper develops robust transient learning to overcome this challenge by exploiting the spa

Xingrui Wang, Jiang Liu, Chao Huang, Xiaodong Yu

Omni-modal large language models (OLLMs) aim to unify audio, vision, and text understanding within a single framework. While existing benchmarks primarily evaluate general cross-modal question-answering ability, it remains unclear whether OLLMs achieve modality-invariant reasoning or exhibit modality-specific biases. We introduce XModBench, a large-scale tri

Aditya Bhosale, Kavitha Chandrasekar, Laxmikant Kale, Sara Kokkila-Schumacher

The last few years have seen an increase in adoption of the cloud for running HPC applications. The pay-as-you-go cost model of these cloud resources has necessitated the development of specialized programming models and schedulers for HPC jobs for efficient utilization of cloud resources. A key aspect of efficient utilization is the ability to rescale appli

Qili Hu, Raymond Lopez-Rios, Zhengdong Gao, Jingwei Ling

Femtosecond laser, owing to their ultrafast time scales and broad frequency bandwidths, have substantially changed fundamental science over the past decades, from chemistry and bio-imaging to quantum physics. Critically, many emerging industrial-scale photonic technologies -- such as optical interconnects, AI accelerators, quantum computing, and LiDAR -- als

Chance Jiajie Li, Zhenze Mo, Yuhan Tang, Ao Qu

Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now approximate human responses at scale, they remain tuned to population-level consensus, often erasing the individuality of reasoning styles and belief trajectories. To advance the vision of more human-like reasoning in

Victor Gitton, Renato Renner

When quantum systems are shared by multiple parties in a network, the measurement outcomes of the parties can exhibit non-classical correlations, i.e., correlations that cannot be obtained if the parties shared classical systems instead. This phenomenon is known as quantum nonlocality and is typically demonstrated in the Bell scenario. However, the Bell scen

Diana Wolfe, Matt Price, Alice Choe, Fergus Kidd

This study investigates whether demographic factors shape adoption and attitudes among employees toward artificial intelligence (AI) technologies at work. Building on an extended Unified Theory of Acceptance and Use of Technology (UTAUT), which reintroduces affective dimensions such as attitude, self-efficacy, and anxiety, we surveyed 2,257 professionals acr

Jai Lalita, Subhashish Banerjee

The interplay of non-classical volume, von Neumann entropy, entropy production, and ergotropy is investigated in various open quantum systems. Two categories of open quantum system models are utilized: spin-spin and spin-boson interaction models. The spin-spin interaction models include the quantum collision and central spin models. On the other hand, the sp

Satyaranjan Sahoo, Dhiren K. Pradhan, Shalini Kumari, Abhisikta Sahu

Lead-free (K0.48Na0.48Li0.04)(Nb1-xTax)O3 (KNLNT-x) ceramics were synthesized to study the effects of Li and Ta substitution on phase transition behavior, microstructure, and ferroelectric, dielectric, and piezoelectric properties. X-ray diffraction and Raman spectroscopy show that compositions with x < 0.10 exhibit a single orthorhombic (Amm2) phase, while

Anthony Bilic, Guangyu Sun, Ming Li, Md Sanzid Bin Hossain

Whole Slide Image (WSI) classification relies on Multiple Instance Learning (MIL) with spatial patch features, yet existing methods struggle to capture global dependencies due to the immense size of WSIs and the local nature of patch embeddings. This limitation hinders the modeling of coarse structures essential for robust diagnostic prediction. We propose F

John Ellis, Tony Gherghetta, Kunio Kaneta, Wenqi Ke

We analyze radiative corrections to the Starobinsky model of inflation arising from self-interactions of the inflaton, and from its Yukawa couplings, $y$, to matter fermions, and dimensionful trilinear couplings, $\kappa$, to scalar fields, which could be responsible for reheating the Universe after inflation. The inflaton self-interactions are found to be o

Oluwasegun Adegoke

We study short-horizon forecasting of weekly terrorism incident counts using the Global Terrorism Database (GTD, 1970--2016). We build a reproducible pipeline with fixed time-based splits and evaluate a Bidirectional LSTM (BiLSTM) against strong classical anchors (seasonal-naive, linear/ARIMA) and a deep LSTM-Attention baseline. On the held-out test set, the

Aidana Beisenova, Wihan Adi, Wenxin Wu, Shovasis K Biswas

High quality-factor (Q) dielectric metasurfaces operating in the visible to near-infrared range usually require sub-200 nm features, limiting their fabrication to expensive, low-throughput electron beam lithography. Here, we demonstrate wafer-scale metasurfaces fabricated using deep ultraviolet lithography (DUVL), a workhorse technology in the semiconductor

Mohammad Heydari Rad, Rezvan Afari, Saeedeh Momtazi

Multiple-choice questions (MCQs) are commonly used in educational testing, as they offer an efficient means of evaluating learners' knowledge. However, generating high-quality MCQs, particularly in low-resource languages such as Persian, remains a significant challenge. This paper introduces FarsiMCQGen, an innovative approach for generating Persian-language

Ynes Ineza, Gerald Jackson, Prince Niyonkuru, Jaden Kevil

File-encrypting ransomware increasingly employs intermittent encryption techniques, encrypting only parts of files to evade classical detection methods.This paper provides a systematic empirical characterization of byte-level statistics under intermittent encryption across common file types, establishing a baseline for how partial encryption reshapes data st

Alex Shtoff

We study nonparametric estimation of a probability mass function (PMF) on a large discrete support, where the PMF is multi-modal and heavy-tailed. The core idea is to treat the empirical PMF as a signal on a line graph and apply a data-dependent low-pass filter. Concretely, we form a symmetric tri-diagonal operator, the path graph Laplacian perturbed with a

Gary Prézeau

This paper reports the first non-zero measurement of a nuclear electric dipole moment using a novel method based on the rate of change of a supercurrent first proposed in 2016~\cite{https://doi.org/10.48550/arxiv.1604.02152} and fleshed out in this current paper. The theory, experimental concept and implementation are described in detail. The non-zero nuclea

Seonguk Yoo, Aljaž Zalar

In this paper, we solve constructively the bivariate truncated moment problem (TMP) of even degree on reducible cubic curves, where the conic part is a hyperbola. According to the classification from our previous work, these represent three out of nine possible canonical forms of reducible cubic curves after applying an affine linear transformation. The TMP

Jesus A. Rodriguez

We investigate the supersymmetric extension of the generalized Kerr-Schild ansatz (gKSA) in Double Field Theory (DFT) including first-order $\alpha'$ corrections. Supersymmetry plays a central role in constraining higher-derivative deformations, and in this work we focus on the structure of the $\mathcal{O}(\alpha')$ Killing Spinor Equations (KSEs). Starting

Lydia Brenner, Carsten Burgard, Vincent Alexander Croft

This paper describes the treatment of systematic uncertainties in a Likelihood formalism. RooUnfold, which includes most of the unfolding methods that are commonly used in particle physics, is used to compare a newly implemented method inside this toolkit to existing methods. The interface with the RooFit statistical software package is used for the treatmen

Marcus A. Thomas

We argue that progress toward AGI is theory limited rather than data or scale limited. Building on the critical rationalism of Popper and Deutsch, we challenge the Platonic Representation Hypothesis. Observationally equivalent worlds can diverge under interventions, so observational adequacy alone cannot guarantee interventional competence. We begin by layin

Margot Bruneaux

In this paper, we study a question of Colliot-Thélène and Iyer concerning the existence of rational sections in families of homogeneous spaces over an abelian variety, after base change by a suitable étale isogeny of the abelian variety. Assuming characteristic zero and that the homogeneous spaces arise from connected reductive groups, the problem is reformu

Alexander Brady, Tunazzina Islam

Social media platforms play a pivotal role in shaping political discourse, but analyzing their vast and rapidly evolving content remains a major challenge. We introduce an end-to-end framework for automatically inducing an interpretable topic taxonomy from unlabeled text corpora. By combining unsupervised clustering with prompt-based inference, our method le